pax_global_header00006660000000000000000000000064117454144520014521gustar00rootroot0000000000000052 comment=e094431b6dd4dc803e5e42ec596a7e9899998625 pymvpa-0.4.8/000077500000000000000000000000001174541445200130465ustar00rootroot00000000000000pymvpa-0.4.8/.gitignore000066400000000000000000000010751174541445200150410ustar00rootroot00000000000000*,cover *-stamp *.bak *.kcache *.kcache.* *.prof *.pstats *.py[cod] *.swp *.o *.a *.valgrind.out* *~ .coverage .nfs* /.emacs.local /coverage /logs /temp /trash /debugs \#* \.\#* /build /build-arch* data/haxby8x6 debian/*.debhelper debian/*.substvars debian/files debian/python-mvpa/* mvpa/*/*/*.dylib mvpa/*/*/*.so profile.out svmc_wrap.cpp tests/.noseids tools/codeswarm tools/pdfbook *_flymake.py .ropeproject logs nobackup_* doc/examples/match_distribution_report doc/examples/match_distribution_report.pdf .eric4project doc/source/examples doc/source/generated /datadb pymvpa-0.4.8/3rd/000077500000000000000000000000001174541445200135365ustar00rootroot00000000000000pymvpa-0.4.8/3rd/libsvm/000077500000000000000000000000001174541445200150325ustar00rootroot00000000000000pymvpa-0.4.8/3rd/libsvm/COPYRIGHT000066400000000000000000000027311174541445200163300ustar00rootroot00000000000000 Copyright (c) 2000-2009 Chih-Chung Chang and Chih-Jen Lin All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither name of copyright holders nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. pymvpa-0.4.8/3rd/libsvm/Makefile000066400000000000000000000005431174541445200164740ustar00rootroot00000000000000# Minimalistic makefile for libsvm LIB=libsvm.a MISC=svm.cpp LIBFLAGS=-fPIC CFLAGS=-Wall -O2 TDIR=../../build/libsvm all: prep $(LIB) prep: prep-stamp prep-stamp: mkdir -p $(TDIR) touch $@ .cpp.o: g++ $(CFLAGS) $(LIBFLAGS) -c $^ -o $(TDIR)/$(^:.cpp=.o) $(LIB): $(MISC:.cpp=.o) ar cur $(TDIR)/$@ $(TDIR)/$^ clean: rm -rf $(TDIR) rm -f *-stamp pymvpa-0.4.8/3rd/libsvm/Makefile.win000077500000000000000000000005361174541445200172750ustar00rootroot00000000000000# Minimalistic makefile for libsvm LIB=libsvm.a MISC=svm.cpp LIBFLAGS= CFLAGS=-Wall -O2 TDIR=..\\..\\build\\libsvm all: prep $(LIB) prep: -@mkdir ..\\..\\build -@mkdir ..\\..\\build\\libsvm .cpp.o: g++ $(CFLAGS) $(LIBFLAGS) -c $^ -o $(TDIR)\\$(^:.cpp=.o) $(LIB): $(MISC:.cpp=.o) ar cur $(TDIR)\\$@ $(TDIR)\\$^ clean: -rmdir /S /Q $(TDIR) pymvpa-0.4.8/3rd/libsvm/README000066400000000000000000000002611174541445200157110ustar00rootroot00000000000000This is a copy of LIBSVM 2.89. It is only here to be able to easily build a static lib for linking the LIBSVM python wrapper under windows (when building the binary installer). pymvpa-0.4.8/3rd/libsvm/svm.cpp000066400000000000000000001717211174541445200163540ustar00rootroot00000000000000#include #include #include #include #include #include #include #include "svm.h" int libsvm_version = LIBSVM_VERSION; typedef float Qfloat; typedef signed char schar; #ifndef min template inline T min(T x,T y) { return (x inline T max(T x,T y) { return (x>y)?x:y; } #endif template inline void swap(T& x, T& y) { T t=x; x=y; y=t; } template inline void clone(T*& dst, S* src, int n) { dst = new T[n]; memcpy((void *)dst,(void *)src,sizeof(T)*n); } inline double powi(double base, int times) { double tmp = base, ret = 1.0; for(int t=times; t>0; t/=2) { if(t%2==1) ret*=tmp; tmp = tmp * tmp; } return ret; } #define INF HUGE_VAL #define TAU 1e-12 #define Malloc(type,n) (type *)malloc((n)*sizeof(type)) static void print_string_stdout(const char *s) { fputs(s,stdout); fflush(stdout); } void (*svm_print_string) (const char *) = &print_string_stdout; #if 1 static void info(const char *fmt,...) { char buf[BUFSIZ]; va_list ap; va_start(ap,fmt); vsprintf(buf,fmt,ap); va_end(ap); (*svm_print_string)(buf); } #else static void info(const char *fmt,...) {} #endif // // Kernel Cache // // l is the number of total data items // size is the cache size limit in bytes // class Cache { public: Cache(int l,long int size); ~Cache(); // request data [0,len) // return some position p where [p,len) need to be filled // (p >= len if nothing needs to be filled) int get_data(const int index, Qfloat **data, int len); void swap_index(int i, int j); private: int l; long int size; struct head_t { head_t *prev, *next; // a circular list Qfloat *data; int len; // data[0,len) is cached in this entry }; head_t *head; head_t lru_head; void lru_delete(head_t *h); void lru_insert(head_t *h); }; Cache::Cache(int l_,long int size_):l(l_),size(size_) { head = (head_t *)calloc(l,sizeof(head_t)); // initialized to 0 size /= sizeof(Qfloat); size -= l * sizeof(head_t) / sizeof(Qfloat); size = max(size, 2 * (long int) l); // cache must be large enough for two columns lru_head.next = lru_head.prev = &lru_head; } Cache::~Cache() { for(head_t *h = lru_head.next; h != &lru_head; h=h->next) free(h->data); free(head); } void Cache::lru_delete(head_t *h) { // delete from current location h->prev->next = h->next; h->next->prev = h->prev; } void Cache::lru_insert(head_t *h) { // insert to last position h->next = &lru_head; h->prev = lru_head.prev; h->prev->next = h; h->next->prev = h; } int Cache::get_data(const int index, Qfloat **data, int len) { head_t *h = &head[index]; if(h->len) lru_delete(h); int more = len - h->len; if(more > 0) { // free old space while(size < more) { head_t *old = lru_head.next; lru_delete(old); free(old->data); size += old->len; old->data = 0; old->len = 0; } // allocate new space h->data = (Qfloat *)realloc(h->data,sizeof(Qfloat)*len); size -= more; swap(h->len,len); } lru_insert(h); *data = h->data; return len; } void Cache::swap_index(int i, int j) { if(i==j) return; if(head[i].len) lru_delete(&head[i]); if(head[j].len) lru_delete(&head[j]); swap(head[i].data,head[j].data); swap(head[i].len,head[j].len); if(head[i].len) lru_insert(&head[i]); if(head[j].len) lru_insert(&head[j]); if(i>j) swap(i,j); for(head_t *h = lru_head.next; h!=&lru_head; h=h->next) { if(h->len > i) { if(h->len > j) swap(h->data[i],h->data[j]); else { // give up lru_delete(h); free(h->data); size += h->len; h->data = 0; h->len = 0; } } } } // // Kernel evaluation // // the static method k_function is for doing single kernel evaluation // the constructor of Kernel prepares to calculate the l*l kernel matrix // the member function get_Q is for getting one column from the Q Matrix // class QMatrix { public: virtual Qfloat *get_Q(int column, int len) const = 0; virtual Qfloat *get_QD() const = 0; virtual void swap_index(int i, int j) const = 0; virtual ~QMatrix() {} }; class Kernel: public QMatrix { public: Kernel(int l, svm_node * const * x, const svm_parameter& param); virtual ~Kernel(); static double k_function(const svm_node *x, const svm_node *y, const svm_parameter& param); virtual Qfloat *get_Q(int column, int len) const = 0; virtual Qfloat *get_QD() const = 0; virtual void swap_index(int i, int j) const // no so const... { swap(x[i],x[j]); if(x_square) swap(x_square[i],x_square[j]); } protected: double (Kernel::*kernel_function)(int i, int j) const; private: const svm_node **x; double *x_square; // svm_parameter const int kernel_type; const int degree; const double gamma; const double coef0; static double dot(const svm_node *px, const svm_node *py); double kernel_linear(int i, int j) const { return dot(x[i],x[j]); } double kernel_poly(int i, int j) const { return powi(gamma*dot(x[i],x[j])+coef0,degree); } double kernel_rbf(int i, int j) const { return exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j]))); } double kernel_sigmoid(int i, int j) const { return tanh(gamma*dot(x[i],x[j])+coef0); } double kernel_precomputed(int i, int j) const { return x[i][(int)(x[j][0].value)].value; } }; Kernel::Kernel(int l, svm_node * const * x_, const svm_parameter& param) :kernel_type(param.kernel_type), degree(param.degree), gamma(param.gamma), coef0(param.coef0) { switch(kernel_type) { case LINEAR: kernel_function = &Kernel::kernel_linear; break; case POLY: kernel_function = &Kernel::kernel_poly; break; case RBF: kernel_function = &Kernel::kernel_rbf; break; case SIGMOID: kernel_function = &Kernel::kernel_sigmoid; break; case PRECOMPUTED: kernel_function = &Kernel::kernel_precomputed; break; } clone(x,x_,l); if(kernel_type == RBF) { x_square = new double[l]; for(int i=0;iindex != -1 && py->index != -1) { if(px->index == py->index) { sum += px->value * py->value; ++px; ++py; } else { if(px->index > py->index) ++py; else ++px; } } return sum; } double Kernel::k_function(const svm_node *x, const svm_node *y, const svm_parameter& param) { switch(param.kernel_type) { case LINEAR: return dot(x,y); case POLY: return powi(param.gamma*dot(x,y)+param.coef0,param.degree); case RBF: { double sum = 0; while(x->index != -1 && y->index !=-1) { if(x->index == y->index) { double d = x->value - y->value; sum += d*d; ++x; ++y; } else { if(x->index > y->index) { sum += y->value * y->value; ++y; } else { sum += x->value * x->value; ++x; } } } while(x->index != -1) { sum += x->value * x->value; ++x; } while(y->index != -1) { sum += y->value * y->value; ++y; } return exp(-param.gamma*sum); } case SIGMOID: return tanh(param.gamma*dot(x,y)+param.coef0); case PRECOMPUTED: //x: test (validation), y: SV return x[(int)(y->value)].value; default: return 0; // Unreachable } } // An SMO algorithm in Fan et al., JMLR 6(2005), p. 1889--1918 // Solves: // // min 0.5(\alpha^T Q \alpha) + p^T \alpha // // y^T \alpha = \delta // y_i = +1 or -1 // 0 <= alpha_i <= Cp for y_i = 1 // 0 <= alpha_i <= Cn for y_i = -1 // // Given: // // Q, p, y, Cp, Cn, and an initial feasible point \alpha // l is the size of vectors and matrices // eps is the stopping tolerance // // solution will be put in \alpha, objective value will be put in obj // class Solver { public: Solver() {}; virtual ~Solver() {}; struct SolutionInfo { double obj; double rho; double upper_bound_p; double upper_bound_n; double r; // for Solver_NU }; void Solve(int l, const QMatrix& Q, const double *p_, const schar *y_, double *alpha_, double Cp, double Cn, double eps, SolutionInfo* si, int shrinking); protected: int active_size; schar *y; double *G; // gradient of objective function enum { LOWER_BOUND, UPPER_BOUND, FREE }; char *alpha_status; // LOWER_BOUND, UPPER_BOUND, FREE double *alpha; const QMatrix *Q; const Qfloat *QD; double eps; double Cp,Cn; double *p; int *active_set; double *G_bar; // gradient, if we treat free variables as 0 int l; bool unshrink; // XXX double get_C(int i) { return (y[i] > 0)? Cp : Cn; } void update_alpha_status(int i) { if(alpha[i] >= get_C(i)) alpha_status[i] = UPPER_BOUND; else if(alpha[i] <= 0) alpha_status[i] = LOWER_BOUND; else alpha_status[i] = FREE; } bool is_upper_bound(int i) { return alpha_status[i] == UPPER_BOUND; } bool is_lower_bound(int i) { return alpha_status[i] == LOWER_BOUND; } bool is_free(int i) { return alpha_status[i] == FREE; } void swap_index(int i, int j); void reconstruct_gradient(); virtual int select_working_set(int &i, int &j); virtual double calculate_rho(); virtual void do_shrinking(); private: bool be_shrunk(int i, double Gmax1, double Gmax2); }; void Solver::swap_index(int i, int j) { Q->swap_index(i,j); swap(y[i],y[j]); swap(G[i],G[j]); swap(alpha_status[i],alpha_status[j]); swap(alpha[i],alpha[j]); swap(p[i],p[j]); swap(active_set[i],active_set[j]); swap(G_bar[i],G_bar[j]); } void Solver::reconstruct_gradient() { // reconstruct inactive elements of G from G_bar and free variables if(active_size == l) return; int i,j; int nr_free = 0; for(j=active_size;j 2*active_size*(l-active_size)) { for(i=active_size;iget_Q(i,active_size); for(j=0;jget_Q(i,l); double alpha_i = alpha[i]; for(j=active_size;jl = l; this->Q = &Q; QD=Q.get_QD(); clone(p, p_,l); clone(y, y_,l); clone(alpha,alpha_,l); this->Cp = Cp; this->Cn = Cn; this->eps = eps; unshrink = false; // initialize alpha_status { alpha_status = new char[l]; for(int i=0;i 0) { if(alpha[j] < 0) { alpha[j] = 0; alpha[i] = diff; } } else { if(alpha[i] < 0) { alpha[i] = 0; alpha[j] = -diff; } } if(diff > C_i - C_j) { if(alpha[i] > C_i) { alpha[i] = C_i; alpha[j] = C_i - diff; } } else { if(alpha[j] > C_j) { alpha[j] = C_j; alpha[i] = C_j + diff; } } } else { double quad_coef = Q_i[i]+Q_j[j]-2*Q_i[j]; if (quad_coef <= 0) quad_coef = TAU; double delta = (G[i]-G[j])/quad_coef; double sum = alpha[i] + alpha[j]; alpha[i] -= delta; alpha[j] += delta; if(sum > C_i) { if(alpha[i] > C_i) { alpha[i] = C_i; alpha[j] = sum - C_i; } } else { if(alpha[j] < 0) { alpha[j] = 0; alpha[i] = sum; } } if(sum > C_j) { if(alpha[j] > C_j) { alpha[j] = C_j; alpha[i] = sum - C_j; } } else { if(alpha[i] < 0) { alpha[i] = 0; alpha[j] = sum; } } } // update G double delta_alpha_i = alpha[i] - old_alpha_i; double delta_alpha_j = alpha[j] - old_alpha_j; for(int k=0;krho = calculate_rho(); // calculate objective value { double v = 0; int i; for(i=0;iobj = v/2; } // put back the solution { for(int i=0;iupper_bound_p = Cp; si->upper_bound_n = Cn; info("\noptimization finished, #iter = %d\n",iter); delete[] p; delete[] y; delete[] alpha; delete[] alpha_status; delete[] active_set; delete[] G; delete[] G_bar; } // return 1 if already optimal, return 0 otherwise int Solver::select_working_set(int &out_i, int &out_j) { // return i,j such that // i: maximizes -y_i * grad(f)_i, i in I_up(\alpha) // j: minimizes the decrease of obj value // (if quadratic coefficeint <= 0, replace it with tau) // -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha) double Gmax = -INF; double Gmax2 = -INF; int Gmax_idx = -1; int Gmin_idx = -1; double obj_diff_min = INF; for(int t=0;t= Gmax) { Gmax = -G[t]; Gmax_idx = t; } } else { if(!is_lower_bound(t)) if(G[t] >= Gmax) { Gmax = G[t]; Gmax_idx = t; } } int i = Gmax_idx; const Qfloat *Q_i = NULL; if(i != -1) // NULL Q_i not accessed: Gmax=-INF if i=-1 Q_i = Q->get_Q(i,active_size); for(int j=0;j= Gmax2) Gmax2 = G[j]; if (grad_diff > 0) { double obj_diff; double quad_coef=Q_i[i]+QD[j]-2.0*y[i]*Q_i[j]; if (quad_coef > 0) obj_diff = -(grad_diff*grad_diff)/quad_coef; else obj_diff = -(grad_diff*grad_diff)/TAU; if (obj_diff <= obj_diff_min) { Gmin_idx=j; obj_diff_min = obj_diff; } } } } else { if (!is_upper_bound(j)) { double grad_diff= Gmax-G[j]; if (-G[j] >= Gmax2) Gmax2 = -G[j]; if (grad_diff > 0) { double obj_diff; double quad_coef=Q_i[i]+QD[j]+2.0*y[i]*Q_i[j]; if (quad_coef > 0) obj_diff = -(grad_diff*grad_diff)/quad_coef; else obj_diff = -(grad_diff*grad_diff)/TAU; if (obj_diff <= obj_diff_min) { Gmin_idx=j; obj_diff_min = obj_diff; } } } } } if(Gmax+Gmax2 < eps) return 1; out_i = Gmax_idx; out_j = Gmin_idx; return 0; } bool Solver::be_shrunk(int i, double Gmax1, double Gmax2) { if(is_upper_bound(i)) { if(y[i]==+1) return(-G[i] > Gmax1); else return(-G[i] > Gmax2); } else if(is_lower_bound(i)) { if(y[i]==+1) return(G[i] > Gmax2); else return(G[i] > Gmax1); } else return(false); } void Solver::do_shrinking() { int i; double Gmax1 = -INF; // max { -y_i * grad(f)_i | i in I_up(\alpha) } double Gmax2 = -INF; // max { y_i * grad(f)_i | i in I_low(\alpha) } // find maximal violating pair first for(i=0;i= Gmax1) Gmax1 = -G[i]; } if(!is_lower_bound(i)) { if(G[i] >= Gmax2) Gmax2 = G[i]; } } else { if(!is_upper_bound(i)) { if(-G[i] >= Gmax2) Gmax2 = -G[i]; } if(!is_lower_bound(i)) { if(G[i] >= Gmax1) Gmax1 = G[i]; } } } if(unshrink == false && Gmax1 + Gmax2 <= eps*10) { unshrink = true; reconstruct_gradient(); active_size = l; info("*"); } for(i=0;i i) { if (!be_shrunk(active_size, Gmax1, Gmax2)) { swap_index(i,active_size); break; } active_size--; } } } double Solver::calculate_rho() { double r; int nr_free = 0; double ub = INF, lb = -INF, sum_free = 0; for(int i=0;i0) r = sum_free/nr_free; else r = (ub+lb)/2; return r; } // // Solver for nu-svm classification and regression // // additional constraint: e^T \alpha = constant // class Solver_NU : public Solver { public: Solver_NU() {} void Solve(int l, const QMatrix& Q, const double *p, const schar *y, double *alpha, double Cp, double Cn, double eps, SolutionInfo* si, int shrinking) { this->si = si; Solver::Solve(l,Q,p,y,alpha,Cp,Cn,eps,si,shrinking); } private: SolutionInfo *si; int select_working_set(int &i, int &j); double calculate_rho(); bool be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4); void do_shrinking(); }; // return 1 if already optimal, return 0 otherwise int Solver_NU::select_working_set(int &out_i, int &out_j) { // return i,j such that y_i = y_j and // i: maximizes -y_i * grad(f)_i, i in I_up(\alpha) // j: minimizes the decrease of obj value // (if quadratic coefficeint <= 0, replace it with tau) // -y_j*grad(f)_j < -y_i*grad(f)_i, j in I_low(\alpha) double Gmaxp = -INF; double Gmaxp2 = -INF; int Gmaxp_idx = -1; double Gmaxn = -INF; double Gmaxn2 = -INF; int Gmaxn_idx = -1; int Gmin_idx = -1; double obj_diff_min = INF; for(int t=0;t= Gmaxp) { Gmaxp = -G[t]; Gmaxp_idx = t; } } else { if(!is_lower_bound(t)) if(G[t] >= Gmaxn) { Gmaxn = G[t]; Gmaxn_idx = t; } } int ip = Gmaxp_idx; int in = Gmaxn_idx; const Qfloat *Q_ip = NULL; const Qfloat *Q_in = NULL; if(ip != -1) // NULL Q_ip not accessed: Gmaxp=-INF if ip=-1 Q_ip = Q->get_Q(ip,active_size); if(in != -1) Q_in = Q->get_Q(in,active_size); for(int j=0;j= Gmaxp2) Gmaxp2 = G[j]; if (grad_diff > 0) { double obj_diff; double quad_coef = Q_ip[ip]+QD[j]-2*Q_ip[j]; if (quad_coef > 0) obj_diff = -(grad_diff*grad_diff)/quad_coef; else obj_diff = -(grad_diff*grad_diff)/TAU; if (obj_diff <= obj_diff_min) { Gmin_idx=j; obj_diff_min = obj_diff; } } } } else { if (!is_upper_bound(j)) { double grad_diff=Gmaxn-G[j]; if (-G[j] >= Gmaxn2) Gmaxn2 = -G[j]; if (grad_diff > 0) { double obj_diff; double quad_coef = Q_in[in]+QD[j]-2*Q_in[j]; if (quad_coef > 0) obj_diff = -(grad_diff*grad_diff)/quad_coef; else obj_diff = -(grad_diff*grad_diff)/TAU; if (obj_diff <= obj_diff_min) { Gmin_idx=j; obj_diff_min = obj_diff; } } } } } if(max(Gmaxp+Gmaxp2,Gmaxn+Gmaxn2) < eps) return 1; if (y[Gmin_idx] == +1) out_i = Gmaxp_idx; else out_i = Gmaxn_idx; out_j = Gmin_idx; return 0; } bool Solver_NU::be_shrunk(int i, double Gmax1, double Gmax2, double Gmax3, double Gmax4) { if(is_upper_bound(i)) { if(y[i]==+1) return(-G[i] > Gmax1); else return(-G[i] > Gmax4); } else if(is_lower_bound(i)) { if(y[i]==+1) return(G[i] > Gmax2); else return(G[i] > Gmax3); } else return(false); } void Solver_NU::do_shrinking() { double Gmax1 = -INF; // max { -y_i * grad(f)_i | y_i = +1, i in I_up(\alpha) } double Gmax2 = -INF; // max { y_i * grad(f)_i | y_i = +1, i in I_low(\alpha) } double Gmax3 = -INF; // max { -y_i * grad(f)_i | y_i = -1, i in I_up(\alpha) } double Gmax4 = -INF; // max { y_i * grad(f)_i | y_i = -1, i in I_low(\alpha) } // find maximal violating pair first int i; for(i=0;i Gmax1) Gmax1 = -G[i]; } else if(-G[i] > Gmax4) Gmax4 = -G[i]; } if(!is_lower_bound(i)) { if(y[i]==+1) { if(G[i] > Gmax2) Gmax2 = G[i]; } else if(G[i] > Gmax3) Gmax3 = G[i]; } } if(unshrink == false && max(Gmax1+Gmax2,Gmax3+Gmax4) <= eps*10) { unshrink = true; reconstruct_gradient(); active_size = l; } for(i=0;i i) { if (!be_shrunk(active_size, Gmax1, Gmax2, Gmax3, Gmax4)) { swap_index(i,active_size); break; } active_size--; } } } double Solver_NU::calculate_rho() { int nr_free1 = 0,nr_free2 = 0; double ub1 = INF, ub2 = INF; double lb1 = -INF, lb2 = -INF; double sum_free1 = 0, sum_free2 = 0; for(int i=0;i 0) r1 = sum_free1/nr_free1; else r1 = (ub1+lb1)/2; if(nr_free2 > 0) r2 = sum_free2/nr_free2; else r2 = (ub2+lb2)/2; si->r = (r1+r2)/2; return (r1-r2)/2; } // // Q matrices for various formulations // class SVC_Q: public Kernel { public: SVC_Q(const svm_problem& prob, const svm_parameter& param, const schar *y_) :Kernel(prob.l, prob.x, param) { clone(y,y_,prob.l); cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20))); QD = new Qfloat[prob.l]; for(int i=0;i*kernel_function)(i,i); } Qfloat *get_Q(int i, int len) const { Qfloat *data; int start, j; if((start = cache->get_data(i,&data,len)) < len) { for(j=start;j*kernel_function)(i,j)); } return data; } Qfloat *get_QD() const { return QD; } void swap_index(int i, int j) const { cache->swap_index(i,j); Kernel::swap_index(i,j); swap(y[i],y[j]); swap(QD[i],QD[j]); } ~SVC_Q() { delete[] y; delete cache; delete[] QD; } private: schar *y; Cache *cache; Qfloat *QD; }; class ONE_CLASS_Q: public Kernel { public: ONE_CLASS_Q(const svm_problem& prob, const svm_parameter& param) :Kernel(prob.l, prob.x, param) { cache = new Cache(prob.l,(long int)(param.cache_size*(1<<20))); QD = new Qfloat[prob.l]; for(int i=0;i*kernel_function)(i,i); } Qfloat *get_Q(int i, int len) const { Qfloat *data; int start, j; if((start = cache->get_data(i,&data,len)) < len) { for(j=start;j*kernel_function)(i,j); } return data; } Qfloat *get_QD() const { return QD; } void swap_index(int i, int j) const { cache->swap_index(i,j); Kernel::swap_index(i,j); swap(QD[i],QD[j]); } ~ONE_CLASS_Q() { delete cache; delete[] QD; } private: Cache *cache; Qfloat *QD; }; class SVR_Q: public Kernel { public: SVR_Q(const svm_problem& prob, const svm_parameter& param) :Kernel(prob.l, prob.x, param) { l = prob.l; cache = new Cache(l,(long int)(param.cache_size*(1<<20))); QD = new Qfloat[2*l]; sign = new schar[2*l]; index = new int[2*l]; for(int k=0;k*kernel_function)(k,k); QD[k+l]=QD[k]; } buffer[0] = new Qfloat[2*l]; buffer[1] = new Qfloat[2*l]; next_buffer = 0; } void swap_index(int i, int j) const { swap(sign[i],sign[j]); swap(index[i],index[j]); swap(QD[i],QD[j]); } Qfloat *get_Q(int i, int len) const { Qfloat *data; int j, real_i = index[i]; if(cache->get_data(real_i,&data,l) < l) { for(j=0;j*kernel_function)(real_i,j); } // reorder and copy Qfloat *buf = buffer[next_buffer]; next_buffer = 1 - next_buffer; schar si = sign[i]; for(j=0;jl; double *minus_ones = new double[l]; schar *y = new schar[l]; int i; for(i=0;iy[i] > 0) y[i] = +1; else y[i]=-1; } Solver s; s.Solve(l, SVC_Q(*prob,*param,y), minus_ones, y, alpha, Cp, Cn, param->eps, si, param->shrinking); double sum_alpha=0; for(i=0;il)); for(i=0;il; double nu = param->nu; schar *y = new schar[l]; for(i=0;iy[i]>0) y[i] = +1; else y[i] = -1; double sum_pos = nu*l/2; double sum_neg = nu*l/2; for(i=0;ieps, si, param->shrinking); double r = si->r; info("C = %f\n",1/r); for(i=0;irho /= r; si->obj /= (r*r); si->upper_bound_p = 1/r; si->upper_bound_n = 1/r; delete[] y; delete[] zeros; } static void solve_one_class( const svm_problem *prob, const svm_parameter *param, double *alpha, Solver::SolutionInfo* si) { int l = prob->l; double *zeros = new double[l]; schar *ones = new schar[l]; int i; int n = (int)(param->nu*prob->l); // # of alpha's at upper bound for(i=0;il) alpha[n] = param->nu * prob->l - n; for(i=n+1;ieps, si, param->shrinking); delete[] zeros; delete[] ones; } static void solve_epsilon_svr( const svm_problem *prob, const svm_parameter *param, double *alpha, Solver::SolutionInfo* si) { int l = prob->l; double *alpha2 = new double[2*l]; double *linear_term = new double[2*l]; schar *y = new schar[2*l]; int i; for(i=0;ip - prob->y[i]; y[i] = 1; alpha2[i+l] = 0; linear_term[i+l] = param->p + prob->y[i]; y[i+l] = -1; } Solver s; s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y, alpha2, param->C, param->C, param->eps, si, param->shrinking); double sum_alpha = 0; for(i=0;iC*l)); delete[] alpha2; delete[] linear_term; delete[] y; } static void solve_nu_svr( const svm_problem *prob, const svm_parameter *param, double *alpha, Solver::SolutionInfo* si) { int l = prob->l; double C = param->C; double *alpha2 = new double[2*l]; double *linear_term = new double[2*l]; schar *y = new schar[2*l]; int i; double sum = C * param->nu * l / 2; for(i=0;iy[i]; y[i] = 1; linear_term[i+l] = prob->y[i]; y[i+l] = -1; } Solver_NU s; s.Solve(2*l, SVR_Q(*prob,*param), linear_term, y, alpha2, C, C, param->eps, si, param->shrinking); info("epsilon = %f\n",-si->r); for(i=0;il); Solver::SolutionInfo si; switch(param->svm_type) { case C_SVC: solve_c_svc(prob,param,alpha,&si,Cp,Cn); break; case NU_SVC: solve_nu_svc(prob,param,alpha,&si); break; case ONE_CLASS: solve_one_class(prob,param,alpha,&si); break; case EPSILON_SVR: solve_epsilon_svr(prob,param,alpha,&si); break; case NU_SVR: solve_nu_svr(prob,param,alpha,&si); break; } info("obj = %f, rho = %f\n",si.obj,si.rho); // output SVs int nSV = 0; int nBSV = 0; for(int i=0;il;i++) { if(fabs(alpha[i]) > 0) { ++nSV; if(prob->y[i] > 0) { if(fabs(alpha[i]) >= si.upper_bound_p) ++nBSV; } else { if(fabs(alpha[i]) >= si.upper_bound_n) ++nBSV; } } } info("nSV = %d, nBSV = %d\n",nSV,nBSV); decision_function f; f.alpha = alpha; f.rho = si.rho; return f; } // // svm_model // struct svm_model { svm_parameter param; // parameter int nr_class; // number of classes, = 2 in regression/one class svm int l; // total #SV svm_node **SV; // SVs (SV[l]) double **sv_coef; // coefficients for SVs in decision functions (sv_coef[k-1][l]) double *rho; // constants in decision functions (rho[k*(k-1)/2]) double *probA; // pariwise probability information double *probB; // for classification only int *label; // label of each class (label[k]) int *nSV; // number of SVs for each class (nSV[k]) // nSV[0] + nSV[1] + ... + nSV[k-1] = l // XXX int free_sv; // 1 if svm_model is created by svm_load_model // 0 if svm_model is created by svm_train }; // Platt's binary SVM Probablistic Output: an improvement from Lin et al. void sigmoid_train( int l, const double *dec_values, const double *labels, double& A, double& B) { double prior1=0, prior0 = 0; int i; for (i=0;i 0) prior1+=1; else prior0+=1; int max_iter=100; // Maximal number of iterations double min_step=1e-10; // Minimal step taken in line search double sigma=1e-12; // For numerically strict PD of Hessian double eps=1e-5; double hiTarget=(prior1+1.0)/(prior1+2.0); double loTarget=1/(prior0+2.0); double *t=Malloc(double,l); double fApB,p,q,h11,h22,h21,g1,g2,det,dA,dB,gd,stepsize; double newA,newB,newf,d1,d2; int iter; // Initial Point and Initial Fun Value A=0.0; B=log((prior0+1.0)/(prior1+1.0)); double fval = 0.0; for (i=0;i0) t[i]=hiTarget; else t[i]=loTarget; fApB = dec_values[i]*A+B; if (fApB>=0) fval += t[i]*fApB + log(1+exp(-fApB)); else fval += (t[i] - 1)*fApB +log(1+exp(fApB)); } for (iter=0;iter= 0) { p=exp(-fApB)/(1.0+exp(-fApB)); q=1.0/(1.0+exp(-fApB)); } else { p=1.0/(1.0+exp(fApB)); q=exp(fApB)/(1.0+exp(fApB)); } d2=p*q; h11+=dec_values[i]*dec_values[i]*d2; h22+=d2; h21+=dec_values[i]*d2; d1=t[i]-p; g1+=dec_values[i]*d1; g2+=d1; } // Stopping Criteria if (fabs(g1)= min_step) { newA = A + stepsize * dA; newB = B + stepsize * dB; // New function value newf = 0.0; for (i=0;i= 0) newf += t[i]*fApB + log(1+exp(-fApB)); else newf += (t[i] - 1)*fApB +log(1+exp(fApB)); } // Check sufficient decrease if (newf=max_iter) info("Reaching maximal iterations in two-class probability estimates\n"); free(t); } double sigmoid_predict(double decision_value, double A, double B) { double fApB = decision_value*A+B; if (fApB >= 0) return exp(-fApB)/(1.0+exp(-fApB)); else return 1.0/(1+exp(fApB)) ; } // Method 2 from the multiclass_prob paper by Wu, Lin, and Weng void multiclass_probability(int k, double **r, double *p) { int t,j; int iter = 0, max_iter=max(100,k); double **Q=Malloc(double *,k); double *Qp=Malloc(double,k); double pQp, eps=0.005/k; for (t=0;tmax_error) max_error=error; } if (max_error=max_iter) info("Exceeds max_iter in multiclass_prob\n"); for(t=0;tl); double *dec_values = Malloc(double,prob->l); // random shuffle for(i=0;il;i++) perm[i]=i; for(i=0;il;i++) { int j = i+rand()%(prob->l-i); swap(perm[i],perm[j]); } for(i=0;il/nr_fold; int end = (i+1)*prob->l/nr_fold; int j,k; struct svm_problem subprob; subprob.l = prob->l-(end-begin); subprob.x = Malloc(struct svm_node*,subprob.l); subprob.y = Malloc(double,subprob.l); k=0; for(j=0;jx[perm[j]]; subprob.y[k] = prob->y[perm[j]]; ++k; } for(j=end;jl;j++) { subprob.x[k] = prob->x[perm[j]]; subprob.y[k] = prob->y[perm[j]]; ++k; } int p_count=0,n_count=0; for(j=0;j0) p_count++; else n_count++; if(p_count==0 && n_count==0) for(j=begin;j 0 && n_count == 0) for(j=begin;j 0) for(j=begin;jx[perm[j]],&(dec_values[perm[j]])); // ensure +1 -1 order; reason not using CV subroutine dec_values[perm[j]] *= submodel->label[0]; } svm_destroy_model(submodel); svm_destroy_param(&subparam); } free(subprob.x); free(subprob.y); } sigmoid_train(prob->l,dec_values,prob->y,probA,probB); free(dec_values); free(perm); } // Return parameter of a Laplace distribution double svm_svr_probability( const svm_problem *prob, const svm_parameter *param) { int i; int nr_fold = 5; double *ymv = Malloc(double,prob->l); double mae = 0; svm_parameter newparam = *param; newparam.probability = 0; svm_cross_validation(prob,&newparam,nr_fold,ymv); for(i=0;il;i++) { ymv[i]=prob->y[i]-ymv[i]; mae += fabs(ymv[i]); } mae /= prob->l; double std=sqrt(2*mae*mae); int count=0; mae=0; for(i=0;il;i++) if (fabs(ymv[i]) > 5*std) count=count+1; else mae+=fabs(ymv[i]); mae /= (prob->l-count); info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma= %g\n",mae); free(ymv); return mae; } // label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data // perm, length l, must be allocated before calling this subroutine void svm_group_classes(const svm_problem *prob, int *nr_class_ret, int **label_ret, int **start_ret, int **count_ret, int *perm) { int l = prob->l; int max_nr_class = 16; int nr_class = 0; int *label = Malloc(int,max_nr_class); int *count = Malloc(int,max_nr_class); int *data_label = Malloc(int,l); int i; for(i=0;iy[i]; int j; for(j=0;jparam = *param; model->free_sv = 0; // XXX if(param->svm_type == ONE_CLASS || param->svm_type == EPSILON_SVR || param->svm_type == NU_SVR) { // regression or one-class-svm model->nr_class = 2; model->label = NULL; model->nSV = NULL; model->probA = NULL; model->probB = NULL; model->sv_coef = Malloc(double *,1); if(param->probability && (param->svm_type == EPSILON_SVR || param->svm_type == NU_SVR)) { model->probA = Malloc(double,1); model->probA[0] = svm_svr_probability(prob,param); } decision_function f = svm_train_one(prob,param,0,0); model->rho = Malloc(double,1); model->rho[0] = f.rho; int nSV = 0; int i; for(i=0;il;i++) if(fabs(f.alpha[i]) > 0) ++nSV; model->l = nSV; model->SV = Malloc(svm_node *,nSV); model->sv_coef[0] = Malloc(double,nSV); int j = 0; for(i=0;il;i++) if(fabs(f.alpha[i]) > 0) { model->SV[j] = prob->x[i]; model->sv_coef[0][j] = f.alpha[i]; ++j; } free(f.alpha); } else { // classification int l = prob->l; int nr_class; int *label = NULL; int *start = NULL; int *count = NULL; int *perm = Malloc(int,l); // group training data of the same class svm_group_classes(prob,&nr_class,&label,&start,&count,perm); svm_node **x = Malloc(svm_node *,l); int i; for(i=0;ix[perm[i]]; // calculate weighted C double *weighted_C = Malloc(double, nr_class); for(i=0;iC; for(i=0;inr_weight;i++) { int j; for(j=0;jweight_label[i] == label[j]) break; if(j == nr_class) fprintf(stderr,"warning: class label %d specified in weight is not found\n", param->weight_label[i]); else weighted_C[j] *= param->weight[i]; } // train k*(k-1)/2 models bool *nonzero = Malloc(bool,l); for(i=0;iprobability) { probA=Malloc(double,nr_class*(nr_class-1)/2); probB=Malloc(double,nr_class*(nr_class-1)/2); } int p = 0; for(i=0;iprobability) svm_binary_svc_probability(&sub_prob,param,weighted_C[i],weighted_C[j],probA[p],probB[p]); f[p] = svm_train_one(&sub_prob,param,weighted_C[i],weighted_C[j]); for(k=0;k 0) nonzero[si+k] = true; for(k=0;k 0) nonzero[sj+k] = true; free(sub_prob.x); free(sub_prob.y); ++p; } // build output model->nr_class = nr_class; model->label = Malloc(int,nr_class); for(i=0;ilabel[i] = label[i]; model->rho = Malloc(double,nr_class*(nr_class-1)/2); for(i=0;irho[i] = f[i].rho; if(param->probability) { model->probA = Malloc(double,nr_class*(nr_class-1)/2); model->probB = Malloc(double,nr_class*(nr_class-1)/2); for(i=0;iprobA[i] = probA[i]; model->probB[i] = probB[i]; } } else { model->probA=NULL; model->probB=NULL; } int total_sv = 0; int *nz_count = Malloc(int,nr_class); model->nSV = Malloc(int,nr_class); for(i=0;inSV[i] = nSV; nz_count[i] = nSV; } info("Total nSV = %d\n",total_sv); model->l = total_sv; model->SV = Malloc(svm_node *,total_sv); p = 0; for(i=0;iSV[p++] = x[i]; int *nz_start = Malloc(int,nr_class); nz_start[0] = 0; for(i=1;isv_coef = Malloc(double *,nr_class-1); for(i=0;isv_coef[i] = Malloc(double,total_sv); p = 0; for(i=0;isv_coef[j-1][q++] = f[p].alpha[k]; q = nz_start[j]; for(k=0;ksv_coef[i][q++] = f[p].alpha[ci+k]; ++p; } free(label); free(probA); free(probB); free(count); free(perm); free(start); free(x); free(weighted_C); free(nonzero); for(i=0;il; int *perm = Malloc(int,l); int nr_class; // stratified cv may not give leave-one-out rate // Each class to l folds -> some folds may have zero elements if((param->svm_type == C_SVC || param->svm_type == NU_SVC) && nr_fold < l) { int *start = NULL; int *label = NULL; int *count = NULL; svm_group_classes(prob,&nr_class,&label,&start,&count,perm); // random shuffle and then data grouped by fold using the array perm int *fold_count = Malloc(int,nr_fold); int c; int *index = Malloc(int,l); for(i=0;ix[perm[j]]; subprob.y[k] = prob->y[perm[j]]; ++k; } for(j=end;jx[perm[j]]; subprob.y[k] = prob->y[perm[j]]; ++k; } struct svm_model *submodel = svm_train(&subprob,param); if(param->probability && (param->svm_type == C_SVC || param->svm_type == NU_SVC)) { double *prob_estimates=Malloc(double,svm_get_nr_class(submodel)); for(j=begin;jx[perm[j]],prob_estimates); free(prob_estimates); } else for(j=begin;jx[perm[j]]); svm_destroy_model(submodel); free(subprob.x); free(subprob.y); } free(fold_start); free(perm); } int svm_get_svm_type(const svm_model *model) { return model->param.svm_type; } int svm_get_nr_class(const svm_model *model) { return model->nr_class; } void svm_get_labels(const svm_model *model, int* label) { if (model->label != NULL) for(int i=0;inr_class;i++) label[i] = model->label[i]; } double svm_get_svr_probability(const svm_model *model) { if ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) && model->probA!=NULL) return model->probA[0]; else { fprintf(stderr,"Model doesn't contain information for SVR probability inference\n"); return 0; } } void svm_predict_values(const svm_model *model, const svm_node *x, double* dec_values) { if(model->param.svm_type == ONE_CLASS || model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) { double *sv_coef = model->sv_coef[0]; double sum = 0; for(int i=0;il;i++) sum += sv_coef[i] * Kernel::k_function(x,model->SV[i],model->param); sum -= model->rho[0]; *dec_values = sum; } else { int i; int nr_class = model->nr_class; int l = model->l; double *kvalue = Malloc(double,l); for(i=0;iSV[i],model->param); int *start = Malloc(int,nr_class); start[0] = 0; for(i=1;inSV[i-1]; int p=0; for(i=0;inSV[i]; int cj = model->nSV[j]; int k; double *coef1 = model->sv_coef[j-1]; double *coef2 = model->sv_coef[i]; for(k=0;krho[p]; dec_values[p] = sum; p++; } free(kvalue); free(start); } } double svm_predict(const svm_model *model, const svm_node *x) { if(model->param.svm_type == ONE_CLASS || model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) { double res; svm_predict_values(model, x, &res); if(model->param.svm_type == ONE_CLASS) return (res>0)?1:-1; else return res; } else { int i; int nr_class = model->nr_class; double *dec_values = Malloc(double, nr_class*(nr_class-1)/2); svm_predict_values(model, x, dec_values); int *vote = Malloc(int,nr_class); for(i=0;i 0) ++vote[i]; else ++vote[j]; } int vote_max_idx = 0; for(i=1;i vote[vote_max_idx]) vote_max_idx = i; free(vote); free(dec_values); return model->label[vote_max_idx]; } } double svm_predict_probability( const svm_model *model, const svm_node *x, double *prob_estimates) { if ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) && model->probA!=NULL && model->probB!=NULL) { int i; int nr_class = model->nr_class; double *dec_values = Malloc(double, nr_class*(nr_class-1)/2); svm_predict_values(model, x, dec_values); double min_prob=1e-7; double **pairwise_prob=Malloc(double *,nr_class); for(i=0;iprobA[k],model->probB[k]),min_prob),1-min_prob); pairwise_prob[j][i]=1-pairwise_prob[i][j]; k++; } multiclass_probability(nr_class,pairwise_prob,prob_estimates); int prob_max_idx = 0; for(i=1;i prob_estimates[prob_max_idx]) prob_max_idx = i; for(i=0;ilabel[prob_max_idx]; } else return svm_predict(model, x); } const char *svm_type_table[] = { "c_svc","nu_svc","one_class","epsilon_svr","nu_svr",NULL }; const char *kernel_type_table[]= { "linear","polynomial","rbf","sigmoid","precomputed",NULL }; int svm_save_model(const char *model_file_name, const svm_model *model) { FILE *fp = fopen(model_file_name,"w"); if(fp==NULL) return -1; const svm_parameter& param = model->param; fprintf(fp,"svm_type %s\n", svm_type_table[param.svm_type]); fprintf(fp,"kernel_type %s\n", kernel_type_table[param.kernel_type]); if(param.kernel_type == POLY) fprintf(fp,"degree %d\n", param.degree); if(param.kernel_type == POLY || param.kernel_type == RBF || param.kernel_type == SIGMOID) fprintf(fp,"gamma %g\n", param.gamma); if(param.kernel_type == POLY || param.kernel_type == SIGMOID) fprintf(fp,"coef0 %g\n", param.coef0); int nr_class = model->nr_class; int l = model->l; fprintf(fp, "nr_class %d\n", nr_class); fprintf(fp, "total_sv %d\n",l); { fprintf(fp, "rho"); for(int i=0;irho[i]); fprintf(fp, "\n"); } if(model->label) { fprintf(fp, "label"); for(int i=0;ilabel[i]); fprintf(fp, "\n"); } if(model->probA) // regression has probA only { fprintf(fp, "probA"); for(int i=0;iprobA[i]); fprintf(fp, "\n"); } if(model->probB) { fprintf(fp, "probB"); for(int i=0;iprobB[i]); fprintf(fp, "\n"); } if(model->nSV) { fprintf(fp, "nr_sv"); for(int i=0;inSV[i]); fprintf(fp, "\n"); } fprintf(fp, "SV\n"); const double * const *sv_coef = model->sv_coef; const svm_node * const *SV = model->SV; for(int i=0;ivalue)); else while(p->index != -1) { fprintf(fp,"%d:%.8g ",p->index,p->value); p++; } fprintf(fp, "\n"); } if (ferror(fp) != 0 || fclose(fp) != 0) return -1; else return 0; } static char *line = NULL; static int max_line_len; static char* readline(FILE *input) { int len; if(fgets(line,max_line_len,input) == NULL) return NULL; while(strrchr(line,'\n') == NULL) { max_line_len *= 2; line = (char *) realloc(line,max_line_len); len = (int) strlen(line); if(fgets(line+len,max_line_len-len,input) == NULL) break; } return line; } svm_model *svm_load_model(const char *model_file_name) { FILE *fp = fopen(model_file_name,"rb"); if(fp==NULL) return NULL; // read parameters svm_model *model = Malloc(svm_model,1); svm_parameter& param = model->param; model->rho = NULL; model->probA = NULL; model->probB = NULL; model->label = NULL; model->nSV = NULL; char cmd[81]; while(1) { fscanf(fp,"%80s",cmd); if(strcmp(cmd,"svm_type")==0) { fscanf(fp,"%80s",cmd); int i; for(i=0;svm_type_table[i];i++) { if(strcmp(svm_type_table[i],cmd)==0) { param.svm_type=i; break; } } if(svm_type_table[i] == NULL) { fprintf(stderr,"unknown svm type.\n"); free(model->rho); free(model->label); free(model->nSV); free(model); return NULL; } } else if(strcmp(cmd,"kernel_type")==0) { fscanf(fp,"%80s",cmd); int i; for(i=0;kernel_type_table[i];i++) { if(strcmp(kernel_type_table[i],cmd)==0) { param.kernel_type=i; break; } } if(kernel_type_table[i] == NULL) { fprintf(stderr,"unknown kernel function.\n"); free(model->rho); free(model->label); free(model->nSV); free(model); return NULL; } } else if(strcmp(cmd,"degree")==0) fscanf(fp,"%d",¶m.degree); else if(strcmp(cmd,"gamma")==0) fscanf(fp,"%lf",¶m.gamma); else if(strcmp(cmd,"coef0")==0) fscanf(fp,"%lf",¶m.coef0); else if(strcmp(cmd,"nr_class")==0) fscanf(fp,"%d",&model->nr_class); else if(strcmp(cmd,"total_sv")==0) fscanf(fp,"%d",&model->l); else if(strcmp(cmd,"rho")==0) { int n = model->nr_class * (model->nr_class-1)/2; model->rho = Malloc(double,n); for(int i=0;irho[i]); } else if(strcmp(cmd,"label")==0) { int n = model->nr_class; model->label = Malloc(int,n); for(int i=0;ilabel[i]); } else if(strcmp(cmd,"probA")==0) { int n = model->nr_class * (model->nr_class-1)/2; model->probA = Malloc(double,n); for(int i=0;iprobA[i]); } else if(strcmp(cmd,"probB")==0) { int n = model->nr_class * (model->nr_class-1)/2; model->probB = Malloc(double,n); for(int i=0;iprobB[i]); } else if(strcmp(cmd,"nr_sv")==0) { int n = model->nr_class; model->nSV = Malloc(int,n); for(int i=0;inSV[i]); } else if(strcmp(cmd,"SV")==0) { while(1) { int c = getc(fp); if(c==EOF || c=='\n') break; } break; } else { fprintf(stderr,"unknown text in model file: [%s]\n",cmd); free(model->rho); free(model->label); free(model->nSV); free(model); return NULL; } } // read sv_coef and SV int elements = 0; long pos = ftell(fp); max_line_len = 1024; line = Malloc(char,max_line_len); char *p,*endptr,*idx,*val; while(readline(fp)!=NULL) { p = strtok(line,":"); while(1) { p = strtok(NULL,":"); if(p == NULL) break; ++elements; } } elements += model->l; fseek(fp,pos,SEEK_SET); int m = model->nr_class - 1; int l = model->l; model->sv_coef = Malloc(double *,m); int i; for(i=0;isv_coef[i] = Malloc(double,l); model->SV = Malloc(svm_node*,l); svm_node *x_space = NULL; if(l>0) x_space = Malloc(svm_node,elements); int j=0; for(i=0;iSV[i] = &x_space[j]; p = strtok(line, " \t"); model->sv_coef[0][i] = strtod(p,&endptr); for(int k=1;ksv_coef[k][i] = strtod(p,&endptr); } while(1) { idx = strtok(NULL, ":"); val = strtok(NULL, " \t"); if(val == NULL) break; x_space[j].index = (int) strtol(idx,&endptr,10); x_space[j].value = strtod(val,&endptr); ++j; } x_space[j++].index = -1; } free(line); if (ferror(fp) != 0 || fclose(fp) != 0) return NULL; model->free_sv = 1; // XXX return model; } void svm_destroy_model(svm_model* model) { if(model->free_sv && model->l > 0) free((void *)(model->SV[0])); for(int i=0;inr_class-1;i++) free(model->sv_coef[i]); free(model->SV); free(model->sv_coef); free(model->rho); free(model->label); free(model->probA); free(model->probB); free(model->nSV); free(model); } void svm_destroy_param(svm_parameter* param) { free(param->weight_label); free(param->weight); } const char *svm_check_parameter(const svm_problem *prob, const svm_parameter *param) { // svm_type int svm_type = param->svm_type; if(svm_type != C_SVC && svm_type != NU_SVC && svm_type != ONE_CLASS && svm_type != EPSILON_SVR && svm_type != NU_SVR) return "unknown svm type"; // kernel_type, degree int kernel_type = param->kernel_type; if(kernel_type != LINEAR && kernel_type != POLY && kernel_type != RBF && kernel_type != SIGMOID && kernel_type != PRECOMPUTED) return "unknown kernel type"; if(param->degree < 0) return "degree of polynomial kernel < 0"; // cache_size,eps,C,nu,p,shrinking if(param->cache_size <= 0) return "cache_size <= 0"; if(param->eps <= 0) return "eps <= 0"; if(svm_type == C_SVC || svm_type == EPSILON_SVR || svm_type == NU_SVR) if(param->C <= 0) return "C <= 0"; if(svm_type == NU_SVC || svm_type == ONE_CLASS || svm_type == NU_SVR) if(param->nu <= 0 || param->nu > 1) return "nu <= 0 or nu > 1"; if(svm_type == EPSILON_SVR) if(param->p < 0) return "p < 0"; if(param->shrinking != 0 && param->shrinking != 1) return "shrinking != 0 and shrinking != 1"; if(param->probability != 0 && param->probability != 1) return "probability != 0 and probability != 1"; if(param->probability == 1 && svm_type == ONE_CLASS) return "one-class SVM probability output not supported yet"; // check whether nu-svc is feasible if(svm_type == NU_SVC) { int l = prob->l; int max_nr_class = 16; int nr_class = 0; int *label = Malloc(int,max_nr_class); int *count = Malloc(int,max_nr_class); int i; for(i=0;iy[i]; int j; for(j=0;jnu*(n1+n2)/2 > min(n1,n2)) { free(label); free(count); return "specified nu is infeasible"; } } } free(label); free(count); } return NULL; } int svm_check_probability_model(const svm_model *model) { return ((model->param.svm_type == C_SVC || model->param.svm_type == NU_SVC) && model->probA!=NULL && model->probB!=NULL) || ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) && model->probA!=NULL); } pymvpa-0.4.8/3rd/libsvm/svm.h000066400000000000000000000043251174541445200160140ustar00rootroot00000000000000#ifndef _LIBSVM_H #define _LIBSVM_H #define LIBSVM_VERSION 289 #ifdef __cplusplus extern "C" { #endif extern int libsvm_version; struct svm_node { int index; double value; }; struct svm_problem { int l; double *y; struct svm_node **x; }; enum { C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR }; /* svm_type */ enum { LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED }; /* kernel_type */ struct svm_parameter { int svm_type; int kernel_type; int degree; /* for poly */ double gamma; /* for poly/rbf/sigmoid */ double coef0; /* for poly/sigmoid */ /* these are for training only */ double cache_size; /* in MB */ double eps; /* stopping criteria */ double C; /* for C_SVC, EPSILON_SVR and NU_SVR */ int nr_weight; /* for C_SVC */ int *weight_label; /* for C_SVC */ double* weight; /* for C_SVC */ double nu; /* for NU_SVC, ONE_CLASS, and NU_SVR */ double p; /* for EPSILON_SVR */ int shrinking; /* use the shrinking heuristics */ int probability; /* do probability estimates */ }; struct svm_model *svm_train(const struct svm_problem *prob, const struct svm_parameter *param); void svm_cross_validation(const struct svm_problem *prob, const struct svm_parameter *param, int nr_fold, double *target); int svm_save_model(const char *model_file_name, const struct svm_model *model); struct svm_model *svm_load_model(const char *model_file_name); int svm_get_svm_type(const struct svm_model *model); int svm_get_nr_class(const struct svm_model *model); void svm_get_labels(const struct svm_model *model, int *label); double svm_get_svr_probability(const struct svm_model *model); void svm_predict_values(const struct svm_model *model, const struct svm_node *x, double* dec_values); double svm_predict(const struct svm_model *model, const struct svm_node *x); double svm_predict_probability(const struct svm_model *model, const struct svm_node *x, double* prob_estimates); void svm_destroy_model(struct svm_model *model); void svm_destroy_param(struct svm_parameter *param); const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param); int svm_check_probability_model(const struct svm_model *model); extern void (*svm_print_string) (const char *); #ifdef __cplusplus } #endif #endif /* _LIBSVM_H */ pymvpa-0.4.8/AUTHOR000066400000000000000000000001761174541445200137770ustar00rootroot00000000000000Michael Hanke Yaroslav O. Halchenko Per B. Sederberg pymvpa-0.4.8/COPYING000066400000000000000000000025671174541445200141130ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: .. _chap_license: ******* License ******* The PyMVPA package, including all examples, code snippets and attached documentation is covered by the MIT license. :: The MIT License Copyright (c) 2006-2009 Michael Hanke 2007-2009 Yaroslav Halchenko Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. pymvpa-0.4.8/Changelog000066400000000000000000000521511174541445200146640ustar00rootroot00000000000000.. -*- mode: rst -*- .. _chap_changelog: .. index:: changelog **************************** PyMVPA Development Changelog **************************** This changelog only lists rather macroscopic changes to PyMVPA. The full VCS changelog for 0.4.x series of PyMVPA is available here: https://github.com/PyMVPA/PyMVPA/commits/maint%2F0.4 In addition there is also a somewhat unconventional visual changelog: http://www.pymvpa.org/history.html 'Closes' statement IDs refer to the Debian bug tracking system and can be queried by visiting the URL:: http://bugs.debian.org/ Unreleased changes Changes described here are not yet released, but available from VCS repository. * Many, many, many Releases ======== * 0.4.8 (Tue, Apr 23 2012) (Total: 14 commits) A bugfix release * Fixed - Compatibility with libsvm 3.10, shogun >= 1.0 (Closes: #655643) - import ma directly from numpy - :class:`GPRLinearWeights` -- correct access to weights - :class:`FslEV3` -- gzip import and getNEVs - :func:`read_fsl_design` -- correct splitting of the fields - :func:`score` -- assure std to be an array during application * RF - extensions are built inplace * 0.4.7 (Tue, Mar 07 2011) (Total: 12 commits) A bugfix release * Fixed - Addressed the issue with input NIfTI files having scl_* fields set: it could result in incorrect analyses and map2nifti-produced NIfTI files. Now input files account for scaling/offset if scl_ fields direct to do so. Moreover upon map2nifti, those fields get reset. - :file:`doc/examples/searchlight_minimal.py` - best error is the minimal one * Enhancements - :class:`~mvpa.clfs.gnb.GNB` can now tolerate training datasets with a single label - :class:`~mvpa.clfs.meta.TreeClassifier` can have trailing nodes with no classifier assigned * 0.4.6 (Tue, Feb 01 2011) (Total: 20 commits) A bugfix release * Fixed (few BF commits): - Compatibility with numpy 1.5.1 (histogram) and scipy 0.8.0 (workaround for a regression in legendre) - Compatibility with libsvm 3.0 - :class:`~mvpa.clfs.plr.PLR` robustification * Enhancements - Enforce suppression of numpy warnings while running unittests. Also setting verbosity >= 3 enables all warnings (Python, NumPy, and PyMVPA) - :file:`doc/examples/nested_cv.py` example (adopted from 0.5) - Introduced base class :class:`~mvpa.clfs.base.LearnerError` for classifiers' exceptions (adopted from 0.5) - Adjusted example data to live upto nibabel's warranty of NIfTI standard-compliance - More robust operation of MC iterations -- skip iterations where classifier experienced difficulties and raise an exception (e.g. due to degenerate data) * 0.4.5 (Fri, Oct 01 2010) (Total: 27 commits) A bugfix release * Fixed (13 BF commits): - Compatible with LIBSVM >= 2.91 (Closes: #583018) - No string exceptions raised (Python 2.6 compatibility) - Setting of shrinking parameter in :mod:`~mvpa.clfs.sg` interface - Deducing number of SVs for SVR (LIBSVM) - Correction of significance in the tails of non-parametric tests * Miscellaneous: - Development repository moved to http://github.com/PyMVPA/PyMVPA * 0.4.4 (Mon, Feb 2 2010) (Total: 144 commits) Primarily a bugfix release, probably the last in 0.4 series since development for 0.5 release is leaping forward. * New functionality (19 NF commits): - :class:`~mvpa.clfs.gnb.GNB` implements Gaussian Naïve Bayes Classifier. - :func:`~mvpa.misc.fsl.base.read_fsl_design` to read FSL FEAT design.fsf files (Contributed by Russell A. Poldrack). - :class:`~mvpa.datasets.miscfx.SequenceStats` to provide basic statistics on labels sequence (counter-balancing, autocorrelation). - New exceptions :class:`~mvpa.clfs.base.DegenerateInputError` and :class:`~mvpa.clfs.base.FailedToTrainError` to be thrown by classifiers primarily during training/testing. - Debug target `STATMC` to report on progress of Monte-Carlo sampling (during permutation testing). * Refactored (15 RF commits): - To get users prepared to 0.5 release, internally and in some examples/documentation, access to states and parameters is done via corresponding collections, not from the top level object (e.g. `clf.states.predictions` instead of soon-to-be-deprecated `clf.predictions`). That should lead also to improved performance. - Adopted copy.py from python2.6 (support Ellipsis as well). * Fixed (38 BF commits): - GLM output does not depend on the enabled states any more. - Variety of docstrings fixed and/or improved. - Do not derive NaN scaling for SVM's C whenever data is degenerate (lead to never finishing SVM training). - :mod:`~mvpa.clfs.sg` : + KRR is optional now -- avoids crashing if KRR is not available. + tolerance to absent `set_precompute_matrix` in svmlight in recent shogun versions. + support for recent (present in 0.9.1) API change in exposing debug levels. - Python 2.4 compatibility issues: :class:`~mvpa.clfs.knn.kNN` and :class:`~mvpa.featsel.ifs.IFS` * 0.4.3 (Sat, 5 Sep 2009) (Total: 165 commits) * Online documentation editor is no longer available due to low demand -- please submit changes via email. * Performance (Contributed by Valentin Haenel) (3 OPT commits): - Further optimized LIBSVM bindings. - Copy-if-sorted in :class:`~mvpa.datasets.base.Dataset.selectFeatures`. * New functionality (25 NF commits): - :class:`~mvpa.mappers.procrustean.ProcrusteanMapper` with orthogonal and oblique transformations. - Ability to generate simple reports using :mod:`reportlab`. See/run :file:`examples/match_distribution.py` for example. - :class:`~mvpa.clfs.meta.TreeClassifier` -- construct simple hierarchies of classifiers. - :func:`~mvpa.base.info.wtf` to report information about the system/PyMVPA to be included in the bug reports. - Parameter 'reverse' to swap training/testing splits in :class:`~mvpa.datasets.splitters.Splitter` . - Example code for the analysis of event-related dataset using :class:`~mvpa.datasets.nifti.ERNiftiDataset`. - :meth:`~mvpa.misc.io.base.SampleAttributes.toEvents` to create lists of :class:`~mvpa.misc.support.Event`. - :file:`mvpa-prep-fmri` was extended with plotting of motion correction parameters. - :class:`~mvpa.misc.io.base.ColumnData` can be explicitly told either file contains a header. - In :class:`~mvpa.atlases.base.XMLBasedAtlas` (e.g. :mod:`~mvpa.atlases.fsl` atlases) it is now possible to provide custom 'image_file' to get maps or indexes for the areas given an atlas's volume registered into subject space. - Updated included LIBSVM version to 2.89 and provided support for its "silencing". * Refactored (27 RF commits): - Dataset's :meth:`~mvpa.datasets.base.Dataset.copy` with deep=False allows for shallow copying the dataset. - :class:`~mvpa.clfs.meta.FeatureSelectionClassifier` s in :mod:`~mvpa.clfs.warehouse` not to reuse the same classifiers, but to use clones. * Fixed (70 BF commits): - :class:`~mvpa.measures.anova.OneWayAnova`: previously degrees of freedom were not considered while computing F-scores. - Majority voting strategy in :class:`~mvpa.clfs.knn.kNN`: it was not working. - Various fixes to ensure cross-platform building (:mod:`numpy` header locations, etc). - Stability fixes in :class:`~mvpa.clfs.transerror.ConfusionMatrix`. - :meth:`~mvpa.datasets.base.Dataset.idsonboundaries`: samples at the end of the sequence were not handled properly. - Proper "untraining" of :class:`~mvpa.clfs.meta.FeatureSelectionClassifier` s classifiers which use sensitivities: it could lead to various unpleasant side-effects if the same slave classifier was used simultaneously by multiple :class:`MetaClassifiers` (like :class:`~mvpa.clfs.meta.TreeClassifier`). * Documentation (25 DOC commits): citations, spelling corrections, etc. * 0.4.2 (Mon, 25 May 2009) * New correlation stability measure (:class:`~mvpa.measures.corrstability.CorrStability`). * New elastic net classifier (:class:`~mvpa.clfs.enet.ENET`). * New GLM-Net regression/classifier (:class:`~mvpa.clfs.glmnet.GLMNET`). * New measure :class:`~mvpa.measures.anova.CompoundOneWayAnova`. * New measure :class:`~mvpa.measures.ds.DSMDatasetMeasure`. * New meta-measure :class:`~mvpa.measures.splitmeasure.TScoredFeaturewiseMeasure`. * New basic :class:`~mvpa.measures.glm.GLM` implementation. * New examples for Gaussian process regression. * New example showing a searchlight analysis employing a dissimilarity matrix based measure. * New :class:`~mvpa.mappers.zscore.ZScoreMapper`. * New import helper for FSL design matrices (:class:`~mvpa.misc.fsl.base.FslGLMDesign`). * New implementation of a mapper using a self-organizing map (:class:`~mvpa.mappers.som.SimpleSOMMapper`) and a corresponding example. * Matplotlib backend is now configurable via :envvar:`MVPA_MATPLOTLIB_BACKEND`. * PyMVPA version is now avialable from :data:`mvpa.__version__`. * Renamed `mvpa.misc.plot.errLinePLot` to :func:`~mvpa.misc.plot.plotErrLine` for consistency. * Fixed :class:`~mvpa.datasets.splitters.NFoldSplitter` to support N-3 and larger splits. * Improved speed of LIBSVM backend. Thanks to Valentin Haenel and Tiziano Zito. * Updated included LIBSVM version to 2.89. * Adjust LIBSVM Python interface for recent NumPy API and latest LIBSVM release 2.89. * Refactored examples parser into a standalone tool to turn PyMVPA examples into restructured text sources. * 0.4.1 (Sat, 24 Jan 2009) * Unit tests and example data are now also installed. In conjunction with :func:`mvpa.test`, this allow to easily run unittests from within Python. * :class:`~mvpa.datasets.nifti.NiftiDataset` capable to handle files with less than 4 dimensions, which can, optionally, be provided as a list of filenames or :class:`~nifti.NiftiImage` objects. That makes it easy to load data from a sequence of files. * Changes (code refactorings) which *might impact* any user who imports from :mod:`~mvpa.suite`: - Pre-populated warehouses of classifiers and regressions are renamed from clfs and regrs into :data:`~mvpa.clfs.warehouse.clfswh` and :data:`~mvpa.clfs.warehouse.regrswh` respectively. - :class:`~mvpa.misc.io.hamster.Hamster` is not derived from :class:`dict` any longer -- just from a basic :class:`object` class. API includes methods 'dump', 'asdict' and a property 'registered'. * Changes (code refactorings) which *should not impact* any user who imports from :mod:`~mvpa.suite`: - Meta classifiers definitions moved from :mod:`~mvpa.clfs.base` into :mod:`~mvpa.clfs.meta`. - Splitters definitions moved from :mod:`~mvpa.datasets.splitter` into :mod:`~mvpa.datasets.splitters` * 0.4.0 (Sat, 15 Nov 2008) * Add :class:`~mvpa.misc.io.hamster.Hamster`, as a simple facility to easily store any serializable objects in a compressed file and later on resurrect all of them with a single line of code. * SVM backend is now configurable via :envvar:`MVPA_SVM_BACKEND` (libsvm or shogun). * Non-deterministic tests in the unittest battery are now configurable via :envvar:`MVPA_TESTS_LABILE`. * New helper to determine and plot the best matching distribution(s) for the data (matchDistribution, plotDistributionMatches). It is WiP thus API can change in the upcoming release. * Simplifies API of mappers. * Splitters can now limit the number of splits automatically. * New :class:`~mvpa.mappers.base.CombinedMapper` to map between multiple, independent dataspace and a common feature space. * New :class:`~mvpa.mappers.base.ChainMapper` to create chains of mappers of abitrary lenght (e.g. to build preprocessing pipelines). * New :class:`~mvpa.datasets.event.EventDataset` to rapidly extract boxcar-shaped samples from data array using a simple list of :class:`~mvpa.misc.support.Event` definitions. * Removed obsolete MetricMapper class. :class:`~mvpa.mappers.base.Mapper` itself provides the facilities for dealing with metrics. * :class:`~mvpa.mappers.boxcar.BoxcarMapper` can now handle data with more than four dimensions/axis and also performs reverse mapping of single boxcar samples. * :class:`~mvpa.misc.fsl.base.FslEV3` can now convert EV3 files into a list of :class:`~mvpa.misc.support.Event` instances. * Results of tests for external dependencies are now stored in PyMVPA's config manager (`mvpa.cfg`) and can be stored to a file (not done automatically at the moment). This will significantly decrease the time needed to import the `mvpa` module, as it prevents the repeated and lengthy tests for working externals. * Initial support for ROC computing and AUC as an accuracy measure. * Weights of LARS are now available via :class:`~mvpa.clfs.lars.LARSWeights`. * Added an initial list of MVPA-related references to the manual, tagged with keywords and comments as well is DOI or similar URL reference to the original document. * Added initial glossary to the manual. * New 'Module reference', as a middle-ground between manual and API reference. * New manual section about meta-classifiers (contributed by James M. Hughes). * New minimal example for a 'getting started' section in the manual. * Former :envvar:`MVPA_QUICKTEST` was renamed to :envvar:`MVPA_TESTS_QUICK`. * Update installation instructions for RPM-based distributions to make use of the OpenSUSE Build Service. * Updated install instructions for several RPM-based GNU/Linux distributions. * Switch from distutils to numpy.distutils (no change in dependencies). * Depend on PyNIfTI >= 0.20081017.1 and gain a smaller memory footprint when accessing NIfTI files via all datasets with NIfTI support. * Added workaround to make PyMVPA work with older Shogun releases and those from 0.6.4 on, which introduced backward-incompatible API changes. * 0.3.1 (Sun, 14 Sep 2008) * New manual section about feature selection with a focus on RFE. Contributed by James M. Hughes. * New dataset type :class:`~mvpa.datasets.channel.ChannelDataset` for data structured in channels. Might be useful for data modalities like EEG and MEG. This dataset includes support for common preprocessing steps like resampling and baseline signal substraction. * Plotting of topographies on heads. Thanks to Ingo Fründ for contributing this code. Additionally, a new example shows how to do such plots. * New general purpose function for generating barplots and candlestick plots with error bars (:func:`~mvpa.misc.plot.base.plotBars`). * Dataset supports mapping of string labels onto numerical labels, removing the need to perform this mapping manually in user code. 'clfs_examples.py' is adjusted accordingly to demonstrate the new feature. * New :meth:`mvpa.clfs.base.Classifier.summary` method to dump classifier settings. * Improved and more flexible :func:`~mvpa.misc.plot.erp.plotERPs`. * New :class:`~mvpa.measures.irelief.IterativeRelief` sensitivity analyzer. * Added visualization of confusion matrices via :meth:`mvpa.clfs.transerror.ConfusionMatrix.plot` inspired by Ingo Fründ. * The PyMVPA version is now globally available in :data:`mvpa.pymvpa_version`. * BugFix: :class:`~mvpa.misc.io.meg.TuebingenMEG` reader failed in some cases. * Several improvements (docs and implementation) for building PyMVPA on MacOS X. * New convenience accessor methods (:meth:`~mvpa.datasets.base.Dataset.select`, :meth:`~mvpa.datasets.base.Dataset.where` and :meth:`~mvpa.datasets.base.Dataset.__getitem__`) for :class`~mvpa.datasets.base.Dataset`. * New :func:`mvpa.seed()` function to configure the random number generators from user code. * Added reader for a MEG sensor locations format (:class:`~mvpa.misc.io.meg.TuebingenMEGSensorLocations`). * Initial model selection support for GRP (using openopt). * And tons of minor bugfixes, additional tests and improved documentation. * 0.3.0 (Mon, 18 Aug 2008) * Import of binary EEP files (used by EEProbe) and EEPDataset class. * Initial version of a meta dataset class (MetaDataset). This is a container for multiple datasets, which behaves like a dataset itself. * Regression performance is summarized now within RegressionStatistics. * Error functions: CorrErrorPFx, RelativeRMSErrorFx. * Measures: CorrCoef. * Data generators: chirp, wr1996 * Few more examples: curvefitting, kerneldemo, smellit, projections * Updated kNN classifier. kNN is now able to use custom distance function to determine that nearest neighbors. It also (re)gained the ability to do simple majority or weighted voting. * Some initial convenience functions for plotting typical results and data exploration. * Unified configuration handling with support for user-specific and analysis-specific config files, as well as the ability to override all config settings via environment variables. The configuration handling is used for PyMVPA internal settings, but can also be easily used for custom (user-)settings. * Improved modularity, e.g. SciPy is not required anymore, but still very useful. * Initial implementations of ICA and PCA mapper using functionality provided by MDP. These mappers are more or less untested and should be used with great care. * Further improved docstrings of some classes, but still a long way to go. * New 'boxcar' mapper, which is the similar to the already present transformWithBoxCar() function, but implemented as a mapper. * New SampleGroupMapper that can be used for e.g. block averaging of samples. See new FAQ item. * Stripped redundant suffixes from module names, e.g. mvpa.datasets.niftidataset -> mvpa.datasets.nifti * mvpa.misc.cmdline variables opt* and opts* were groupped within opt and optss class instances. Also names of the options were changed to match 'dest' of the options. Use tools/refactor.py to quickly fix your custom code. * Change all references to PyMVPA website to www.pymvpa.org. * Make website stylesheet compatible with sphinx 0.4. * Several minor improvements of the compatibility with MacOS. * Extended FAQ section of the manual. * Bugfix: doubleGammaHRF() ignoring K2 argument. * 0.2.2 (Tue, 17 Jun 2008) * Extended build instructions: Added section on OpenSUSE. * Replaced ugly PYMVPA_LIBSVM environment variable to trigger compiling the LIBSVM wrapper with a proper '--with-libsvm' switch in setup.py. Additionally, setup.py now detects if included LIBSVM has been built and enables LIBSVM wrapper automatically in this case. * Added proper Makefiles for LIBSVM copy, with configurable compiler flags. * Added 'setup.cfg' to remove the need to manually specify swig-opts (Windows specific configuration is in 'setup.cfg.win'). * 0.2.1 (Sun, 15 Jun 2008) * Several improvements to make building PyMVPA on Windows systems easy (e.g. added dedicated Makefile.win to build a binary installer). * Improved and extended documentation for building and installing PyMVPA. * Include a minimal copy of the required (patched) LIBSVM library (currently version 2.85.0) for convenience. This copy is automatically compiled and used for the LIBSVM wrapper when PyMVPA built using the `Make` approach. * 0.2.0 (Wed, 29 May 2008) * New Splitter class (HalfSplitter) to split into first and second half. * New Splitter class (CustomSplitter) to allow for splits with an arbitrary number of datasets per split and the ability to specify the association of samples with any of those datasets (not just the validation set). * New sparse multinomial logistic regression (SMLR) classifier and associated sensitivity analyzer. * New least angle regression classifier (LARS). * New Gaussian process regression classifier (GPR). * Initial documentation on extending PyMVPA. * Switch to Sphinx for documentation handling. * New example comparing the performance of all classifiers on some artificial datasets. * New data mapper performing singular value decomposition (SVDMapper) and an example showing its usage. * More sophisticated data preprocessing: removal of non-linear trends and other arbitrary confounding regressors. * New `Harvester` class to feed data from arbitrary generators into multiple objects and store results of returned values and arbitrary properties. * Added documentation about how to build patched libsvm version with sane debug output. * libsvm bindings are not build by default anymore. Instructions on how to reenable them are available in the manual. * New wrapper from SVM implementation of the Shogun toolbox. * Important bugfix in RFE, which reported incorrect feature ids in some cases. * Added ability to compute stats/probabilities for all measures and transfer errors. * 0.1.0 (Wed, 20 Feb 2008) * First public release. pymvpa-0.4.8/MANIFEST.in000066400000000000000000000002261174541445200146040ustar00rootroot00000000000000include AUTHOR COPYING MANIFEST.in setup.* include Changelog TODO Makefile* recursive-include doc * recursive-include tools * recursive-include 3rd * pymvpa-0.4.8/Makefile000066400000000000000000000372131174541445200145140ustar00rootroot00000000000000PROFILE_FILE=$(CURDIR)/$(BUILDDIR)/main.pstats COVERAGE_REPORT=$(CURDIR)/$(BUILDDIR)/coverage BUILDDIR=$(CURDIR)/build HTML_DIR=$(BUILDDIR)/html DOCSRC_DIR=$(BUILDDIR)/doc MAN_DIR=$(BUILDDIR)/man APIDOC_DIR=$(HTML_DIR)/api PDF_DIR=$(BUILDDIR)/pdf LATEX_DIR=$(BUILDDIR)/latex WWW_DIR=$(BUILDDIR)/website SWARM_DIR=$(BUILDDIR)/swarm WWW_UPLOAD_URI=www.pymvpa.org:/home/www/v04.pymvpa.org/pymvpa DATA_URI=apsy.gse.uni-magdeburg.de:/home/hanke/public_html/software/pymvpa/data SWARMTOOL_DIR=tools/codeswarm SWARMTOOL_DIRFULL=$(CURDIR)/$(SWARMTOOL_DIR) RSYNC_OPTS=-az -H --no-perms --no-owner --verbose --progress --no-g # # Conditional depends regulated from outside # ifdef PYMVPA_NO_3RD build_depends := else build_depends := endif # # Details on the Python/system # PYVER := $(shell python -V 2>&1 | cut -d ' ' -f 2,2 | cut -d '.' -f 1,2) DISTUTILS_PLATFORM := $(shell python -c "import distutils.util; print distutils.util.get_platform()") # # Little helpers # mkdir-%: if [ ! -d $($*) ]; then mkdir -p $($*); fi # # Building # all: build # build included 3rd party pieces (if present) 3rd: 3rd-stamp 3rd-stamp: find 3rd -mindepth 1 -maxdepth 1 -type d | \ while read d; do \ [ -f "$$d/Makefile" ] && $(MAKE) -C "$$d" || :; \ done touch $@ debian-build: # reuse is better than duplication (yoh) debian/rules build build: build-stamp build-stamp: $(build_depends) python setup.py config --noisy python setup.py build_ext --inplace # # Cleaning # # this target is used to clean things for a fresh build clean: # clean 3rd party pieces find 3rd -mindepth 1 -maxdepth 1 -type d | \ while read d; do \ [ -f "$$d/Makefile" ] && $(MAKE) -C "$$d" clean || : ; \ done # clean tools $(MAKE) -C tools clean # clean all bits and pieces -@rm -f MANIFEST -@rm -f mvpa/clfs/lib*/*.so \ mvpa/clfs/lib*/*.dylib \ mvpa/clfs/lib*/*_wrap.* \ mvpa/clfs/lib*/*c.py \ mvpa/tests/*.{prof,pstats,kcache} @find . -name '*.py[co]' \ -o -name '*,cover' \ -o -name '.coverage' \ -o -name 'iterate.dat' \ -o -iname '*~' \ -o -iname '*.kcache' \ -o -iname '*.gch' \ -o -iname '*_flymake.*' \ -o -iname '#*#' | xargs -L 10 rm -f -@rm -rf build -@rm -rf dist *report -@rm -f *-stamp *_report.pdf *_report.log pymvpa.cfg # this target should put the source tree into shape for building the source # distribution distclean: clean # if we are on debian system - we might have left-overs from build -@$(MAKE) debian-clean -@rm -rf tools/codeswarm debian-clean: # remove stamps for builds since state is not really built any longer -fakeroot debian/rules clean # # Documentation # doc: website manpages manpages: mkdir-MAN_DIR PYTHONPATH=.:$(PYTHONPATH) help2man -N -n 'preprocess fMRI data for PyMVPA' \ bin/mvpa-prep-fmri > $(MAN_DIR)/mvpa-prep-fmri.1 PYTHONPATH=. help2man -N -n 'query stereotaxic atlases' \ bin/atlaslabeler > $(MAN_DIR)/atlaslabeler.1 prepare-docsrc: mkdir-BUILDDIR rsync --copy-unsafe-links -rvuhp doc/ $(BUILDDIR)/doc rsync --copy-unsafe-links -rvhup doc/pics/ $(DOCSRC_DIR)/examples/pics references: tools/bib2rst_ref.py htmldoc: modref-templates examples2rst build cd $(DOCSRC_DIR) && MVPA_EXTERNALS_RAISE_EXCEPTION=off PYTHONPATH=$(CURDIR):$(PYTHONPATH) $(MAKE) html BUILDROOT=$(BUILDDIR) cd $(HTML_DIR)/modref && ln -sf ../_static cd $(HTML_DIR)/examples && ln -sf ../_static cp $(DOCSRC_DIR)/pics/history_splash.png $(HTML_DIR)/_images/ pdfdoc: modref-templates examples2rst build pdfdoc-stamp pdfdoc-stamp: cd $(DOCSRC_DIR) && MVPA_EXTERNALS_RAISE_EXCEPTION=off PYTHONPATH=../..:$(PYTHONPATH) $(MAKE) latex BUILDROOT=$(BUILDDIR) cd $(LATEX_DIR) && $(MAKE) all-pdf touch $@ # Create a handy .pdf of the manual to be printed as a book handbook: pdfdoc cd tools && $(MAKE) pdfbook build/tools/pdfbook -2 \ $(LATEX_DIR)/PyMVPA-Manual.pdf $(LATEX_DIR)/PyMVPA-Manual-Handbook.pdf modref-templates: prepare-docsrc modref-templates-stamp modref-templates-stamp: PYTHONPATH=.:$(PYTHONPATH) tools/build_modref_templates.py touch $@ examples2rst: prepare-docsrc examples2rst-stamp examples2rst-stamp: tools/ex2rst \ --project PyMVPA \ --outdir $(DOCSRC_DIR)/examples \ --exclude doc/examples/searchlight.py \ doc/examples touch $@ apidoc: apidoc-stamp apidoc-stamp: build # Disabled profiling for now, it consumes huge amounts of memory, so I doubt # that all buildds can do it. In theory it would only be done on a single # developer machine, because it is only necessary for the arch-all package, # but e.g. dpkg-buildpackage runs the indep target anyway -- not sure about # the buildds, though. #apidoc-stamp: profile mkdir -p $(HTML_DIR)/api LC_ALL=C MVPA_EPYDOC_WARNINGS=once tools/epydoc --config doc/api/epydoc.conf touch $@ # this takes some minutes !! profile: build mvpa/tests/main.py @PYTHONPATH=.:$(PYTHONPATH) tools/profile -K -O $(PROFILE_FILE) mvpa/tests/main.py # # Website # website: website-stamp website-stamp: mkdir-WWW_DIR apidoc htmldoc pdfdoc cp -r $(HTML_DIR)/* $(WWW_DIR) cp $(LATEX_DIR)/*.pdf $(WWW_DIR) tools/sitemap.sh > $(WWW_DIR)/sitemap.xml # main icon of the website cp $(DOCSRC_DIR)/pics/favicon.png $(WWW_DIR)/_images/ # for those who do not care about and just trying to download it cp $(DOCSRC_DIR)/pics/favicon.png $(WWW_DIR)/favicon.ico # provide robots.txt to minimize unnecessary traffic cp $(DOCSRC_DIR)/_static/robots.txt $(WWW_DIR)/ # provide promised pylintrc mkdir -p $(WWW_DIR)/misc && cp $(DOCSRC_DIR)/misc/pylintrc $(WWW_DIR)/misc touch $@ upload-website: website chmod a+rX -R $(WWW_DIR) rsync -rzlhvp --delete --chmod=Dg+s,g+rw $(WWW_DIR)/* $(WWW_UPLOAD_URI)/ upload-htmldoc: htmldoc rsync -rzlhvp --delete --chmod=Dg+s,g+rw $(HTML_DIR)/* $(WWW_UPLOAD_URI)/ # # Tests (unittests, docs, examples) # ut-%: build @PYTHONPATH=.:$(PYTHONPATH) nosetests --nocapture mvpa/tests/test_$*.py unittest: build @echo "I: Running unittests (without optimization nor debug output)" PYTHONPATH=.:$(PYTHONPATH) python mvpa/tests/main.py # test if PyMVPA is working if optional externals are missing unittest-badexternals: build @echo "I: Running unittests under assumption of missing optional externals." @PYTHONPATH=mvpa/tests/badexternals:.:$(PYTHONPATH) python mvpa/tests/main.py 2>&1 \ | grep -v -e 'WARNING: Known dependency' -e 'Please note: w' \ -e 'WARNING:.*SMLR.* implementation' # only non-labile tests unittest-nonlabile: build @echo "I: Running only non labile unittests. None of them should ever fail." @PYTHONPATH=.:$(PYTHONPATH) MVPA_TESTS_LABILE=no python mvpa/tests/main.py # test if no errors would result if we force enabling of all states unittest-states: build @echo "I: Running unittests with all states enabled." @PYTHONPATH=.:$(PYTHONPATH) MVPA_DEBUG=ENFORCE_STATES_ENABLED python mvpa/tests/main.py # Run unittests with optimization on -- helps to catch unconditional # debug calls unittest-optimization: build @echo "I: Running unittests with python -O." @PYTHONPATH=.:$(PYTHONPATH) python -O mvpa/tests/main.py # Run unittests with all debug ids and some metrics (crossplatform ones) on. # That does: # additional checking, # debug() calls validation, etc unittest-debug: build @echo "I: Running unittests with debug output. No progress output." @PYTHONPATH=.:$(PYTHONPATH) MVPA_DEBUG=.* MVPA_DEBUG_METRICS=ALL \ python mvpa/tests/main.py 2>&1 \ | sed -n -e '/^[=-]\{60,\}$$/,/^\(MVPA_SEED=\|OK\)/p' # Run all unittests # Run with 'make -k' if you like to sweep through all of them, so # failure in one of them does not stop the full sweep unittests: unittest-nonlabile unittest unittest-badexternals \ unittest-optimization unittest-states unittest-debug te-%: build @echo -n "I: Testing example $*: " @MVPA_EXAMPLES_INTERACTIVE=no PYTHONPATH=.:$(PYTHONPATH) MVPA_MATPLOTLIB_BACKEND=agg \ python doc/examples/$*.py >| temp-$@.log 2>&1 \ && echo "passed" || { echo "failed:"; cat temp-$@.log; } @rm -f temp-$@.log testexamples: te-svdclf te-smlr te-searchlight_2d te-sensanas te-pylab_2d \ te-curvefitting te-projections te-kerneldemo te-clfs_examples \ te-erp_plot te-match_distribution te-permutation_test \ te-searchlight_minimal te-smlr te-start_easy te-topo_plot \ te-gpr te-gpr_model_selection0 tm-%: build PYTHONPATH=.:$(PYTHONPATH) nosetests --with-doctest --doctest-extension .rst \ --doctest-tests doc/$*.rst testmanual: build @echo "I: Testing code samples found in documentation" @PYTHONPATH=.:$(PYTHONPATH) MVPA_MATPLOTLIB_BACKEND=agg \ nosetests --with-doctest --doctest-extension .rst --doctest-tests doc/ # Check if everything (with few exclusions) is imported in unitests is # known to the mvpa.suite() testsuite: @echo "I: Running full testsuite" @tfile=`mktemp -u testsuiteXXXXXXX`; \ git grep -h '^\W*from mvpa.*import' mvpa/tests | \ grep -v '^\W*#' | \ sed -e 's/^.*from *\(mvpa[^ ]*\) im.*/from \1 import/g' | \ sort | uniq | \ grep -v -e 'mvpa\.base\.dochelpers' \ -e 'mvpa\.\(tests\|support\)' \ -e 'mvpa\.misc\.args' \ -e 'mvpa\.clfs\.\(libsvmc\|sg\)' \ | while read i; do \ grep -q "^ *$$i" mvpa/suite.py || \ { echo "E: '$$i' is missing from mvpa.suite()"; touch "$$tfile"; }; \ done; \ [ -f "$$tfile" ] && { rm -f "$$tfile"; exit 1; } || : # Check if links to api/ within documentation are broken. testapiref: apidoc @for tf in doc/*.rst; do \ out=$$(for f in `grep api/mvpa $$tf | sed -e 's|.*\(api/mvpa.*html\).*|\1|g' `; do \ ff=build/html/$$f; [ ! -f $$ff ] && echo "E: $$f missing!"; done; ); \ [ "x$$out" == "x" ] || echo -e "$$tf:\n$$out"; done # Check if there is no WARNINGs from sphinx testsphinx: htmldoc { grep -A1 system-message build/html/modref/*html && exit 1 || exit 0 ; } # Check if stored cfg after whole suite is imported is safe to be # reloaded testcfg: build @echo "I: Running test to check that stored configuration is acceptable." -@rm -f pymvpa.cfg @PYTHONPATH=.:$(PYTHONPATH) python -c 'from mvpa.suite import *; cfg.save("pymvpa.cfg");' @PYTHONPATH=.:$(PYTHONPATH) python -c 'from mvpa.suite import *;' @echo "+I: Run non-labile testing to verify safety of stored configuration" @PYTHONPATH=.:$(PYTHONPATH) MVPA_TESTS_LABILE=no python mvpa/tests/main.py @echo "+I: Check all known dependencies and store them" @PYTHONPATH=.:$(PYTHONPATH) python -c \ 'from mvpa.suite import *; mvpa.base.externals.testAllDependencies(force=False); cfg.save("pymvpa.cfg");' @echo "+I: Run non-labile testing to verify safety of stored configuration" @PYTHONPATH=.:$(PYTHONPATH) MVPA_TESTS_LABILE=no python mvpa/tests/main.py -@rm -f pymvpa.cfg test: unittests testmanual testsuite testapiref testexamples testcfg # Target to be called after some major refactoring # It skips some flavors of unittests testrefactor: unittest testmanual testsuite testapiref testexamples coverage: $(COVERAGE_REPORT) $(COVERAGE_REPORT): build @echo "I: Generating coverage data and report. Takes awhile. No progress output." @{ \ export PYTHONPATH=.:$(PYTHONPATH) MVPA_DEBUG=.* MVPA_DEBUG_METRICS=ALL; \ python-coverage -x mvpa/tests/main.py >/dev/null 2>&1; \ python-coverage -r -i -o /usr,/var >| $(COVERAGE_REPORT); \ grep -v '100%$$' $(COVERAGE_REPORT); \ python-coverage -a -i -o /usr,/var ; } # # Sources # pylint: pylint -e --rcfile doc/misc/pylintrc mvpa # # Generate new source distribution # (not to be run by users, depends on debian environment) orig-src: distclean debian-clean # clean existing dist dir first to have a single source tarball to process -rm -rf dist if [ -f debian/changelog ]; then \ if [ ! "$$(dpkg-parsechangelog | egrep ^Version | cut -d ' ' -f 2,2 | cut -d '-' -f 1,1)" == "$$(python setup.py -V)" ]; then \ printf "WARNING: Changelog version does not match tarball version!\n" ;\ exit 1; \ fi \ fi # let python create the source tarball # enable libsvm to get all sources! python setup.py sdist --formats=gztar # rename to proper Debian orig source tarball and move upwards # to keep it out of the Debian diff file=$$(ls -1 dist); ver=$${file%*.tar.gz}; ver=$${ver#pymvpa-*}; mv dist/$$file ../pymvpa_$$ver.orig.tar.gz # clean leftover rm MANIFEST # make Debian source package # # DO NOT depend on orig-src here as it would generate a source tarball in a # Debian branch and might miss patches! debsrc: cd .. && dpkg-source -i'\.(gbp.conf|git\.*)' -b $(CURDIR) bdist_rpm: 3rd python setup.py bdist_rpm \ --doc-files "doc data" \ --packager "PyMVPA Authors " \ --vendor "PyMVPA Authors " # build MacOS installer -- depends on patched bdist_mpkg for Leopard bdist_mpkg: 3rd python tools/mpkg_wrapper.py setup.py build_ext python tools/mpkg_wrapper.py setup.py install # # Data # fetch-data: rsync $(RSYNC_OPTS) $(DATA_URI) . # Various other data which might be sensitive and not distribu fetch-data-nonfree: fetch-data-nonfree-stamp fetch-data-nonfree-stamp: @mkdir -p temp # clean up previous location to make sure we don't have it @rm -f data/nonfree/audio/Peter_Nalitch-Guitar.mp3 # remove directories which should be bogus now @rmdir data/nonfree/audio data/nonfree 2>/dev/null || : rsync $(RSYNC_OPTS) dev.pymvpa.org:/home/data/nonfree temp/ && touch $@ # # Various sugarings (e.g. swarm) # AUDIO_TRACK=temp/nonfree/audio/Peter_Nalitch-Guitar.mp3 # With permission of the author, we can use Gitar for our visual history $(AUDIO_TRACK): fetch-data-nonfree # Nice visual git log # Requires: sun-java5-jdk, ffmpeg, ant codeswarm: $(SWARM_DIR)/pymvpa-codeswarm.flv $(SWARM_DIR)/frames: $(SWARMTOOL_DIR) $(SWARM_DIR)/git.xml @echo "I: Visualizing git history using codeswarm" @mkdir -p $(SWARM_DIR)/frames cd $(SWARMTOOL_DIR) && ./run.sh ../../doc/misc/codeswarm.config $(SWARM_DIR)/pymvpa-codeswarm.flv: $(SWARM_DIR)/frames $(AUDIO_TRACK) @echo "I: Generating codeswarm video" @cd $(SWARM_DIR) && \ ffmpeg -r $$(echo "scale=2; $$(ls -1 frames/ |wc -l) / 154" | bc) -f image2 \ -i frames/code_swarm-%05d.png -r 15 -b 250k \ -i ../../$(AUDIO_TRACK) -ar 22050 -ab 128k -acodec libmp3lame \ -y -ac 2 pymvpa-codeswarm.flv $(SWARM_DIR)/git.log: Makefile @echo "I: Dumping git log in codeswarm preferred format" @mkdir -p $(SWARM_DIR) @git log --name-status --branches \ --pretty=format:'%n------------------------------------------------------------------------%nr%h | %ae | %ai (%aD) | x lines%nChanged paths:' | \ sed -e 's,[a-z]*@onerussian.com,Yarik,g' \ -e 's,\(michael\.*hanke@\(gmail.com\|mvpa1.dartmouth.edu\)\|neukom-data@neukom-data-desktop\.(none)\),Michael,g' \ -e 's,\(per@parsec.Princeton.EDU\|per@sync.(none)\|psederberg@gmail.com\),Per,g' \ -e 's,emanuele@relativita.com,Emanuele,g' \ -e 's,jhughes@austin.cs.dartmouth.edu,James,g' \ -e 's,valentin.haenel@gmx.de,Valentin,g' \ -e 's,gorlins@mit.edu,Scott,g' \ -e 's,Ingo.Fruend@gmail.com,Ingo,g' >| $@ $(SWARM_DIR)/git.xml: $(SWARMTOOL_DIR)/run.sh $(SWARM_DIR)/git.log @python $(SWARMTOOL_DIR)/convert_logs/convert_logs.py \ -g $(SWARM_DIR)/git.log -o $(SWARM_DIR)/git.xml $(SWARMTOOL_DIR)/run.sh: @echo "I: Checking out codeswarm tool source code" @svn checkout http://codeswarm.googlecode.com/svn/trunk/ $(SWARMTOOL_DIR) upload-codeswarm: codeswarm rsync -rzhvp --delete --chmod=Dg+s,g+rw,o+r $(SWARM_DIR)/*.flv $(WWW_UPLOAD_URI)/files/ # # Trailer # .PHONY: fetch-data debsrc orig-src pylint apidoc pdfdoc htmldoc doc manual \ all profile website fetch-data-misc upload-website \ test testsuite testmanual testapiref testexamples testrefactor \ unittest unittest-debug unittest-optimization unittest-nonlabile \ unittest-badexternals unittests \ distclean debian-clean \ handbook codeswarm upload-codeswarm coverage pymvpa-0.4.8/Makefile.win000066400000000000000000000021751174541445200153070ustar00rootroot00000000000000# Makefile to build PyMVPA under Windows using a standard Python # distribution and MinGW # # Adjust this path to match the version and location of your Python # installation PYTHON_VERSION=2.6 PYTHON_PATH=C:\\Python26 # # Building # all: build configure-inplace-use # build included 3rd party pieces (if present) 3rd: 3rd-stamp 3rd-stamp: cd 3rd\libsvm & $(MAKE) -f Makefile.win build: 3rd # build pymvpa extensions including libsvm set PYTHON_INCLUDE="$(PYTHON_PATH)\\include" \ & python setup.py build_ext installer: 3rd build # now build the installer python setup.py bdist_wininst --bitmap doc\pics\logo.bmp configure-inplace-use: copy build\\lib.win32-$(PYTHON_VERSION)\\mvpa\\clfs\\libsmlrc\\smlrc.pyd \ mvpa\\clfs\\libsmlrc copy build\\lib.win32-$(PYTHON_VERSION)\\mvpa\\clfs\\libsvmc\\_svmc.pyd \ mvpa\\clfs\\libsvmc # # Cleaning # clean: -rmdir /S /Q build -del /S *.a *.o *.gch *.pyd # # Testing # ut-%: build configure-inplace-use @set PYTHONPATH=$(CURDIR) & cd tests & python test_$*.py unittest: build configure-inplace-use @set PYTHONPATH=$(CURDIR) & cd tests & python main.py # # Trailer # .PHONY: all pymvpa-0.4.8/README000066400000000000000000000003651174541445200137320ustar00rootroot00000000000000This is PyMVPA -- Multivariate Pattern Analysis in Python. For information how to install PyMVPA please see doc/installation.rst. Further information and access to binary packages is available from the project website at http://www.pymvpa.org pymvpa-0.4.8/TODO000066400000000000000000000014461174541445200135430ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 79 -*- .. ex: set tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. _chap_todo: .. index:: todo **** TODO **** * OptimizedClassifier: to automatically select the model in an easy and non-biased(i.e. non-cheating) way, so it could become a part of any more advanced pipeline as a regular classifier * Add ability to add/modify custom attributes to a dataset instance * IPython mode * Possibly make NiftiDataset default to float32 when it sees that the data are ints * Along with ICA mapper, we should add a PLS mapper pymvpa-0.4.8/bin/000077500000000000000000000000001174541445200136165ustar00rootroot00000000000000pymvpa-0.4.8/bin/atlaslabeler000077500000000000000000000652631174541445200162130ustar00rootroot00000000000000#!/usr/bin/python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Script to provide anatomical labels for the voxels, or their statistics """ import re, sys import mvpa from mvpa.misc.cmdline import parser, opts, opt from mvpa.base import verbose, warning, externals if externals.exists('nifti', raiseException=True): from nifti import NiftiImage if __debug__: from mvpa.base import debug # from rumba.tools.misc import * from mvpa.atlases.transformation import * from mvpa.atlases import Atlas, ReferencesAtlas, FSLProbabilisticAtlas, \ KNOWN_ATLASES, KNOWN_ATLAS_FAMILIES, XMLAtlasException from optparse import OptionParser, Option import numpy as N #import numpy.linalg as la # to read in transformation matrix try: import psyco psyco.profile() except: pass def selectVoxelsFromVolumeIteratorNumPY(volFileName, lt=None, ut=None): """ Generator which returns value + coordinates with values of non-0 entries from the `volFileName` :Returns: tuple with 0th entry value, the others are voxel coordinates More effective than previous loopy iteration since uses numpy's where function, but for now is limited only to non-0 voxels selection """ try: volFile = NiftiImage(volFileName) except: raise IOError("Cannot open image file %s" % volFileName) volData = volFile.data voxdim = volFile.voxdim if lt is None and ut is None: mask = volData != 0.0 elif lt is None and ut is not None: mask = volData <= ut elif lt is not None and ut is None: mask = volData >= lt else: mask = N.logical_and(volData >= lt, volData <= ut) matchingVoxels = N.where(mask) # qf = volFile.qform # qfi = la.inv(qf) for ivoxel in xrange(len(matchingVoxels[0])): # in reverse order since numpy struct has order t, z, y, x voxel = ( matchingVoxels[2][ivoxel], matchingVoxels[1][ivoxel], matchingVoxels[0][ivoxel] ) value = volData[ voxel[-1], voxel[-2], voxel[-3] ] yield (value, voxel[0], voxel[1], voxel[2], 0) #voxel[-4]) def parsedCoordinatesIterator( parseString="^\s*(?P\S+)[ \t,](?P\S+)[ \t,](?P\S+)\s*$", inputStream=sys.stdin): """Iterator to provide coordinates/values parsed from the string stream, most often from the stdin """ parser = re.compile(parseString) for line in inputStream.readlines(): line = line.strip() match = parser.match(line) if not match: if __debug__: debug('ATL', "Line '%s' did not match '%s'" % (line, parseString)) else: r = match.groupdict() if r.has_key('v'): v = float(r['v']) else: v = 0.0 if r.has_key('t'): t = float(r['t']) else: t = 0.0 yield (v, float(r['x']), float(r['y']), float(r['z']), t) # XXX helper to process labels... move me def presentLabels(labels): if isinstance(labels, list): res = [] for label in labels: # XXX warning -- some inconsistencies in atlas.py # need refactoring s = label['label'] #.text if label.has_key('prob') and not options.createSummary: s += "(%d%%%%)" % label['prob'] res += [s] if res == []: res = ['None'] return '/'.join(res) else: if options.abbreviatedLabels: return labels['label'].abbr else: return labels['label'].text #def processCmdLine(): parser.usage = "%s [OPTIONS] [input_file.nii.gz]" % sys.argv[0] + """ Examples: %s -s -A talairach-dist -d 10 -R Closest\ Gray -l Structure,Brodmann\ area -cC mask.nii.gz produces a summary per each structure and brodmann area, for each voxel looking within 10mm radius for the closest gray matter voxel. """ % (sys.argv[0], ) parser.version = "%prog " + mvpa.__version__ # can't use due to conflict with -d (debug and distance) #parser.option_groups = [opts.common] parser.add_option(opt.verbose) parser.add_option(opt.help) parser.add_option("-a", "--atlas-file", action="store", type="string", dest="atlasFile", default=None, help="Atlas file to use. Overrides --atlas-path and --atlas") parser.add_option("--atlas-path", action="store", type="string", dest="atlasPath", default=None, help=r"Path to the atlas files. '%(name)s' will be replaced" " with the atlas name. See -A. Defaults depend on the" " atlas family.") parser.add_option("-A", "--atlas", action="store", type="choice", dest="atlasName", default="talairach", choices=KNOWN_ATLASES.keys(), help="Atlas to use. Choices: %s" % ', '.join(KNOWN_ATLASES.keys())) parser.add_option("-i", "--input-coordinates-file", action="store", type="string", dest="inputCoordFile", default=None, help="Fetch coordinates from ASCII file") parser.add_option("-o", "--output-file", action="store", type="string", dest="outputFile", default=None, help="Output file. Otherwise standard output") parser.add_option("-d", "--max-distance", action="store", type="float", dest="maxDistance", default=0, help="When working with reference/distance atlases, what" " maximal distance to use to look for the voxel of interest") parser.add_option("-T", "--transformation-file", type="string", dest="transformationFile", help="First transformation to apply to the data. Usually"+ " should be subject -> standard(MNI) transformation") parser.add_option("-s", "--summary", action="count", dest="createSummary", default=0, help="Either to create a summary instead of dumping voxels." " Use multiple -s for greater verbose summary") parser.add_option("--ss", "--sort-summary-by", type="choice", dest="sortSummaryBy", default="name", choices=['name', 'count', 'a-p'], help="How to sort summary entries. " " a-p sorts anterior-posterior order") parser.add_option("--dumpmap-file", action="store", dest="dumpmapFile", default=None, help="If original data is given as image file, dump indexes" " per each treholded voxels into provided here output file") parser.add_option("-l", "--levels", type="string", dest="levels", default=None, help="Indexes of levels which to print, or based on which " "to create a summary (for a summary levels=4 is default). " "To get listing of known for the atlas levels, use '-l list'") parser.add_option("--mni2tal", type="choice", choices=["matthewbrett", "lancaster07fsl", "lancaster07pooled", "meyerlindenberg98"], dest="MNI2TalTransformation", default="matthewbrett", help="Choose between available transformations from mni " "2 talairach space") parser.add_option("--thr", "--lthr", "--lower-threshold", action="store", type="float", dest="lowerThreshold", default=None, help="Lower threshold for voxels to output") parser.add_option("--uthr", "--upper-threshold", action="store", type="float", dest="upperThreshold", default=None, help="Upper threshold for voxels to output") parser.add_option("--abbr", "--abbreviated-labels", action="store_true", dest="abbreviatedLabels", help="Manipulate with abbreviations for labels instead of" " full names, if the atlas has such") # Parameters to be inline with older talairachlabel parser.add_option("-c", "--tc", "--show-target-coord", action="store_true", dest="showTargetCoordinates", help="Show target coordinates") parser.add_option("--tv", "--show-target-voxel", action="store_true", dest="showTargetVoxel", help="Show target coordinates") parser.add_option("--rc", "--show-referenced-coord", action="store_true", dest="showReferencedCoordinates", help="Show referenced coordinates/distance in case if we are" " working with reference atlas") parser.add_option("-C", "--oc", "--show-orig-coord", action="store_true", dest="showOriginalCoordinates", help="Show original coordinates") parser.add_option("-V", "--show-values", action="store_true", dest="showValues", help="Show values") parser.add_option("-I", "--input-space", action="store", type="string", dest="inputSpace", default="MNI", help="Space in which input volume/coordinates provided in. For instance Talairach/MNI") parser.add_option("-F", "--forbid-direct-mapping", action="store_true", dest="forbidDirectMapping", default=False, help="If volume is provided it first tries to do direct " "mapping voxel-2-voxel if there is no transformation file " "given. This option forbids such behavior and does " "coordinates mapping anyway.") parser.add_option("-t", "--talairach", action="store_true", dest="coordInTalairachSpace", default=False, help="Coordinates are in talairach space (1x1x1mm)," + " otherwise assumes in mni space (2x2x2mm)." " Shortcut for '-I Talairach'") parser.add_option("-H", "--half-voxel-correction", action="store_false", dest="halfVoxelCorrection", default=True, help="Adjust coord by 0.5mm after transformation to " + \ "Tal space. Please use -H to turn such adjustment off") parser.add_option("-r", "--relative-to-origin", action="store_true", dest="coordRelativeToOrigin", help="Coords are relative to the origin standard form" + " ie in spatial units (mm), otherwise the default assumes" + " raw voxel dimensions") parser.add_option("--input-line-format", action="store", type="string", dest="inputLineFormat", default=r"^\s*(?P\S+)[ \t,]+(?P\S+)[ \t,]+(?P\S+)\s*$", help="Format of the input lines (if ASCII input is provided)") # Specific atlas options # TODO : group into options groups # Reference atlas parser.add_option("-R", "--reference", action="store", type="string", dest="referenceLevel", default="Closest Gray", help="Which level to reference in the case of reference" " atlas") # Probabilistic atlases parser.add_option("--prob-thr", action="store", type="float", dest="probThr", default=25.0, help="At what probability (in %) to threshold in " "probabilistic atlases (e.g. FSL)") parser.add_option("--prob-strategy", action="store", type="choice", dest="probStrategy", choices=['all', 'max'], default='max', help="What strategy to use for reporting. 'max' would report" " single area (above threshold) with maximal probabilitity") (options, infiles) = parser.parse_args() #atlas.relativeToOrigin = options.coordRelativeToOrigin if len(infiles)>1: print "We cannot handle multiple input files at once" sys.exit(1) fileIn = None coordT = None niftiInput = None # Setup coordinates read-in # # compatibility with older talairachlabel if options.inputCoordFile: fileIn = file(options.inputCoordFile) coordsIterator = parsedCoordinatesIterator(options.inputLineFormat, fileIn) # input is stdin elif len(infiles)==0: coordsIterator = parsedCoordinatesIterator(options.inputLineFormat) else: if len(infiles)>1: print "Just a single file should be provided at the command line" sys.exit(1) infile = infiles[0] # got a volume/file to process try: if __debug__: debug('ATL', "Testing if 0th element in the list a volume") niftiInput = NiftiImage(infile) if __debug__: debug('ATL', "Yes it is") # if we got here -- it is a proper volume # XXX ask Michael to remove nasty warning message coordsIterator = selectVoxelsFromVolumeIteratorNumPY( infile, options.lowerThreshold, options.upperThreshold) assert(coordT is None) coordT = Linear(niftiInput.qform) # previous iterator returns space coordinates options.coordRelativeToOrigin = True except: if __debug__: debug('ATL', "No it is not") fileIn = file(infile) coordsIterator = parsedCoordinatesIterator( options.inputLineFormat, fileIn) # Open and initialize atlas lookup if options.atlasFile is None: if options.atlasPath is None: options.atlasPath = KNOWN_ATLASES[options.atlasName] options.atlasFile = options.atlasPath % ( {'name': options.atlasName} ) if not options.forbidDirectMapping \ and niftiInput is not None and not options.transformationFile: akwargs = { 'resolution': niftiInput.pixdim[0], 'query_voxel': True } verbose(1, "Will attempt direct mapping from input voxels into atlas " "voxels at resolution %.2f" % akwargs['resolution']) atlas = Atlas(options.atlasFile, **akwargs) # verify that we got the same qforms in atlas and in the data file if atlas.space != options.inputSpace: verbose(0, "Cannot do direct mapping between input image in %s space and" " atlas in %s space. Use -I switch to override input space if" " it misspecified, or use -T to provide transformation. Trying" " to proceed" %(options.inputSpace, atlas.space), 1) atlas.query_voxel = False elif not (niftiInput.qform == atlas._image.qform).all(): warning( "Cannot do direct mapping between files with different qforms." " Please provide original transformation (-T)." "\n Input qform:\n%s\n Atlas qform: \n%s" %(niftiInput.qform, atlas._image.qform), 1) # reset variables atlas.query_voxel = False else: coordT = None else: atlas = Atlas(options.atlasFile) if isinstance(atlas, ReferencesAtlas): options.referenceLevel = options.referenceLevel.replace('/', ' ') atlas.setReferenceLevel(options.referenceLevel) atlas.distance = options.maxDistance else: options.showReferencedCoordinates = False if isinstance(atlas, FSLProbabilisticAtlas): atlas.strategy = options.probStrategy atlas.thr = options.probThr ## If not in Talairach -- in MNI with voxel size 2x2x2 # Original talairachlabel assumed that if respective to origin -- voxels were # scaled already. #if options.coordInTalairachSpace: # voxelSizeOriginal = N.array([1, 1, 1]) #else: # voxelSizeOriginal = N.array([2, 2, 2]) if options.coordInTalairachSpace: options.inputSpace = "Talairach" if not (options.inputSpace == atlas.space or (options.inputSpace in ["MNI", "Talairach"] and atlas.space == "Talairach")): raise XMLAtlasException("Unknown space '%s' which is not the same as atlas" "space '%s' either" % ( inputSpace, atlas.space )) if atlas.query_voxel: # we do direct mapping coordT = None else: verbose(2, "Chaining needed transformations") # by default -- no transformation if options.transformationFile: externals.exists('scipy', raiseException=True) from scipy.io import read_array transfMatrix = read_array(options.transformationFile) coordT = Linear(transfMatrix, previous=coordT) verbose(2, "coordT got linear transformation from file %s" % options.transformationFile) voxelOriginOriginal = None voxelSizeOriginal = None if not options.coordRelativeToOrigin: if options.inputSpace == "Talairach": # assume that atlas is in Talairach space already voxelOriginOriginal = atlas.origin voxelSizeOriginal = N.array([1, 1, 1]) elif options.inputSpace == "MNI": # need to adjust for MNI origin as it was thought to be at # in terms of voxels #voxelOriginOriginal = N.array([46, 64, 37]) voxelOriginOriginal = N.array([45, 63, 36]) voxelSizeOriginal = N.array([2.0, 2.0, 2.0]) warning("Assuming elderly sizes for MNI volumes with" " origin %s and sizes %s" %\ ( `voxelOriginOriginal`, `voxelSizeOriginal`)) if not (voxelOriginOriginal is None and voxelSizeOriginal is None): verbose(2, "Assigning origin adjusting transformation with"+\ " origin=%s and voxelSize=%s" %\ ( `voxelOriginOriginal`, `voxelSizeOriginal`)) coordT = SpaceTransformation(origin=voxelOriginOriginal, voxelSize=voxelSizeOriginal, toRealSpace=True, previous=coordT) # besides adjusting for different origin we need to transform into # Talairach space if options.inputSpace == "MNI" and atlas.space == "Talairach": verbose(2, "Assigning transformation %s" % options.MNI2TalTransformation) # What transformation to use coordT = {"matthewbrett": MNI2Tal_MatthewBrett, "lancaster07fsl": MNI2Tal_Lancaster07FSL, "lancaster07pooled": MNI2Tal_Lancaster07pooled, "meyerlindenberg98": MNI2Tal_MeyerLindenberg98, "yohflirt": MNI2Tal_YOHflirt }\ [options.MNI2TalTransformation](previous=coordT) if options.inputSpace == "MNI" and options.halfVoxelCorrection: originCorrection = N.array([0.5, 0.5, 0.5]) else: # perform transformation any way to convert to voxel space (integers) originCorrection = None # To be closer to what original talairachlabel did -- add 0.5 to each coord coordT = SpaceTransformation(origin=originCorrection, voxelSize=None, toRealSpace=False, previous = coordT) if options.createSummary: summary = {} if options.levels is None: options.levels = str(min(4, atlas.Nlevels-1)) if options.levels is None: options.levels = range(atlas.Nlevels) elif isinstance(options.levels, basestring): if options.levels == 'list': print "Known levels and their indicies:\n" + atlas.levelsListing() sys.exit(0) slevels = options.levels.split(',') options.levels = [] for level in slevels: try: int_level = int(level) except: if atlas.levels_dict.has_key(level): int_level = atlas.levels_dict[level].index else: raise RuntimeError( "Unknown level '%s'. " % level + "Known levels and their indicies:\n" + atlas.levelsListing()) options.levels += [int_level] else: raise ValueError("Don't know how to handle list of levels %s." "Example is '1,2,3'" % (options.levels,)) verbose(3, "Operating on following levels: %s" % options.levels) # assign levels to the atlas atlas.levels = options.levels if options.outputFile: output = open(options.outputFile, 'w') else: output = sys.stdout # validity check if options.dumpmapFile: if niftiInput is None: raise RuntimeError, "You asked to dump indexes into the volume, " \ "but input wasn't a volume" sys.exit(1) ni_dump = NiftiImage(infile) ni_dump_data = N.zeros((len(options.levels),) + ni_dump.data.shape[:3]) # Read coordinates numVoxels = 0 for c in coordsIterator: value, coord_orig, t = c[0], c[1:4], c[4] if __debug__: debug('ATL', "Obtained coord_orig=%s with value %s" % (repr(coord_orig), value)) lt, ut = options.lowerThreshold, options.upperThreshold if lt is not None and value < lt: verbose(5, "Value %s is less than lower threshold %s, thus voxel " "is skipped" % (value, options.lowerThreshold)) continue if ut is not None and value > ut: verbose(5, "Value %s is greater than upper threshold %s, thus voxel " "is skipped" % (value, options.upperThreshold)) continue numVoxels += 1 # Apply necessary transformations coord = coord_orig = N.array(coord_orig) if coordT: coord = coordT[ coord_orig ] # Query label voxel = atlas[ coord ] voxel['coord_orig'] = coord_orig voxel['value'] = value voxel['t'] = t if options.createSummary: summaryIndex = "" voxel_labels = voxel["labels"] for i,ind in enumerate(options.levels): voxel_label = voxel_labels[i] text = presentLabels(voxel_label) #if len(voxel_label): # assert(voxel_label['index'] == ind) summaryIndex += text + " / " if not summary.has_key(summaryIndex): summary[summaryIndex] = {'values':[], 'max':value, 'maxcoord':coord_orig} if voxel.has_key('voxel_referenced'): summary[summaryIndex]['distances'] = [] summary_ = summary[summaryIndex] summary_['values'].append(value) if summary_['max'] < value: summary_['max'] = value summary_['maxcoord'] = coord_orig if voxel.has_key('voxel_referenced'): if voxel['voxel_referenced'] and voxel['distance']>=1e-3: verbose(5, 'Appending distance %e for voxel at %s' % (voxel['distance'], voxel['coord_orig'])) summary_['distances'].append(voxel['distance']) else: # Display while reading/processing first, out = True, "" if options.showValues: out += "%(value)5.2f " if options.showOriginalCoordinates: out += "%(coord_orig)s ->" if options.showReferencedCoordinates: out += " %(voxel_referenced)s=>%(distance).2f=>%(voxel_queried)s ->" if options.showTargetCoordinates: out += " %(coord_queried)s: " #out += "(%d,%d,%d): " % tuple(map(lambda x:int(round(x)),coord)) if options.showTargetVoxel: out += " %(voxel_queried)s ->" if options.levels is None: options.levels = range(len(voxel['labels'])) labels = [presentLabels(voxel['labels'][i]) for i in options.levels] out += ','.join(labels) #if options.abbreviatedLabels: # out += ','.join([l.abbr for l in labels]) #else: # out += ','.join([l.text for l in labels]) #try: output.write(out % voxel + "\n") #except: # import pydb # pydb.debugger() if options.dumpmapFile: try: ni_dump_data[:, coord_orig[-1], coord_orig[-2], coord_orig[-3]] = \ [voxel['labels'][i]['label'].index for i,ind in enumerate(options.levels)] except Exception, e: import pydb pydb.debugger() # if we opened any file -- close it if fileIn: fileIn.close() if options.dumpmapFile: ni_dump = NiftiImage(ni_dump_data, ni_dump.header) ni_dump.save(options.dumpmapFile) def statistics(values): N_ = len(values) if N_==0: return 0, None, None, None, None, "" mean = N.mean(values) std = N.std(values) minv = N.min(values) maxv = N.max(values) ssummary = "[%3.2f : %3.2f] %3.2f+-%3.2f" % (minv, maxv, mean, std) return N_, mean, std, minv, maxv, ssummary def generateSummary(summary, output): """Output the summary """ # Sort either by the name (then ascending) or by the number of # elements (then descending) sort_keys = [(k, len(v['values']), v['maxcoord'][1]) for k,v in summary.iteritems()] sort_index, sort_reverse = { 'name' : (0, False), 'count': (1, True), 'a-p': (2, True)}[options.sortSummaryBy] sort_keys.sort(cmp=lambda x,y: cmp(x[sort_index], y[sort_index]), reverse=sort_reverse) # and here are the keys keys = [x[0] for x in sort_keys] maxkeylength = max (map(len, keys)) # may be I should have simply made a counter ;-) total = sum(map(lambda x:len(x['values']), summary.values())) for index in keys: summary_ = summary[index] values = summary_['values'] N, mean, std, minv, maxv, ssummary = statistics(values) # print "N=", N msg = "%%%ds:" % maxkeylength output.write(msg % index) output.write("%4d/%4.1f%% items" \ % (N, 100.0*N/total )) if options.createSummary>1: output.write(" %s" % ssummary) if options.createSummary>2: output.write(" max at %s" % summary_['maxcoord']) if options.createSummary>3 and summary_.has_key('distances'): # if we got statistics over referenced voxels Nr, mean, std, minv, maxv, ssummary = \ statistics(summary_['distances']) Nr = len(summary_['distances']) # print "N=", N, " Nr=", Nr output.write(" Referenced: %d/%d%% Distances: %s" \ % (Nr, int(Nr*100.0 / N), \ ssummary)) output.write("\n") # output might fail to flush, like in the case with broken pipe # -- imho that is not a big deal, ie not worth scaring the user try: output.flush() except IOError: pass output.write("-----\n") output.write("TOTAL: %d items\n" % total) if options.createSummary: if numVoxels == 0: verbose(1, "No matching voxels were found.") else: generateSummary(summary, output) if options.outputFile: output.close() pymvpa-0.4.8/bin/mvpa-prep-fmri000077500000000000000000000161061174541445200164120ustar00rootroot00000000000000#!/usr/bin/python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Tiny tool to prepare a directory for a typical analysis of fMRI data with PyMVPA. Tools from the FSL suite will be used for preprocessing. It takes a 4D fMRI timeseries as input and performs the following steps: - extract an example volume - perform motion correction using the example volume as reference - conservative skull-stripping and brain mask generation - masking of the motion-corrected timeseries with the brain mask All results will be stored either in the current directory, or in a subdirectory with the subject ID (if specified).""" import sys import os from subprocess import call import numpy as N from mvpa.misc.cmdline import parser, opt from mvpa.base import verbose, externals, error import mvpa if __debug__: from mvpa.base import debug _EXFUNC_CONV_DICT = {'last' : lambda x: x-1, 'first': lambda x: 0, 'middle': lambda x: int(x/2)} """Dictionary to get exemplar volume given a literal string""" def prepParser(parser): # use module docstring for help output parser.usage = "%s [OPTIONS] \n\n" % sys.argv[0] + __doc__ parser.version = "%prog " + mvpa.__version__ parser.add_option(opt.verbose) parser.add_option(opt.help) parser.add_option("-s", "--subject-id", action="store", type="string", dest="subj", default=None, help="Subject ID used as output path") parser.add_option("-e", "--example-func-vol", action="store", type="string", dest="exfunc", default='middle', help="Volume (numeric ID or 'last', 'first', 'middle') " "to be used as an example functional image. " "Default: 10") parser.add_option("-m", "--mcflirt-options", action="store", type="string", dest="mcflirt_opts", default='', help="Options for MCFLIRT. '-plots' is auto-added ") parser.add_option("-p", "--mcflirt-plots", action="store_true", dest="mcflirt_plots", help="Create a .pdf with plots of motion parameters") parser.add_option("-b", "--bet-options", action="store", type="string", dest="bet_opts", default='-f 0.3', help="Options for BET. '-m' is auto-added. " "Default: '-f 0.3' for a safe guess of the brain " "outline") def main(): """ """ prepParser(parser) (options, infiles) = parser.parse_args() # late import of pynifti to be able to get help output without a big # external dep. externals.exists('nifti', raiseException=True) from nifti import NiftiImage if len(infiles) > 1 or not len(infiles): error("%s needs exactly one input fMRI image as argument. " "Got %s" % (sys.argv[0], str(infiles))) func_fname = infiles[0] # compressed or uncompressed? decide by input image # XXX maybe add override option if func_fname.lower().endswith('nii.gz'): nii_ext = '.nii.gz' verbose(2, "Output files will be compressed NIfTI images") else: nii_ext = '.nii' verbose(2, "Output files will be uncompressed NIfTI images") # determine output path if not options.subj is None: opath = options.subj else: opath = os.path.curdir if not os.path.exists(opath): verbose(1, "Create output directory '%s'" % opath) os.makedirs(opath) else: verbose(2, "Using output path '%s'" % opath) verbose(2, "Load image file from '%s'" % func_fname) func_nim = NiftiImage(func_fname, load=True) # process exfunc option exfunc = options.exfunc.lower() timepoints = func_nim.timepoints if exfunc in _EXFUNC_CONV_DICT.keys(): exfuncid = _EXFUNC_CONV_DICT[exfunc](timepoints) else: try: exfuncid = int(exfunc) except ValueError, e: error("Failed to convert '%s' into numerical id of " "volume." % (exfunc)) if exfuncid >= timepoints or exfuncid < 0: error("Example functional volume id must be in the " "range 0 .. %d. Got %d." % (timepoints-1, exfuncid)) verbose(2, "Extract volume %i as example volume" % exfuncid) ef_nim = NiftiImage(func_nim.data[exfuncid], func_nim.header) ef_nim.save(os.path.join(opath, 'example_func' + nii_ext)) # close input file -- will operate on motion-corrected one later on del func_nim mcflirt_call = \ ' '.join( ['mcflirt', '-in ' + func_fname, '-out ' + os.path.join(opath, 'func_mc'), '-reffile ' + os.path.join(opath, 'example_func'), '-verbose 0', '-plots', options.mcflirt_opts]).strip() verbose(2, "Perform motion correction ('%s')" % mcflirt_call) # run MCFLIRT (silence stderr; 5 being some random file descriptor) if call(mcflirt_call, shell=True, stderr=None): error("MCFLIRT failed to perform the motion correction.") if options.mcflirt_plots: verbose(2, "Plot motion parameters estimates") externals.exists('pylab', raiseException=True) mc = McFlirtParams(os.path.join(opath, 'func_mc.par')) for k, (title, fields, ylabel) in enumerate( (('Translation', ('x', 'y', 'z'), 'mm'), ('Rotation', ('rot1', 'rot2', 'rot3'), 'radians'))): P.subplot(211+k) P.title(title) P.plot([0, timepoints], [0, 0], '0.6') for dim in fields: P.plot(mc[dim], label=dim) P.legend() P.axis('tight') P.ylabel(ylabel) P.gcf().savefig(os.path.join(opath, 'func_mc.pdf')) bet_call = \ ' '.join( ['bet', os.path.join(opath, 'example_func'), os.path.join(opath, 'example_func_brain'), '-m', options.bet_opts]).strip() verbose(2, "Determine brain mask in functional space ('%s')" % bet_call) # run BET (silence stderr; 5 being some random file descriptor) if call(bet_call, shell=True, stderr=None): error("BET failed to perform the skull stripping.") verbose(2, "Threshold image background using brain mask") mask_nim = NiftiImage(os.path.join(opath, 'example_func_brain_mask')) func_nim = NiftiImage(os.path.join(opath, 'func_mc')) # special case: single slice mask if len(mask_nim.extent) < 3: func_nim.data[:, N.asarray([mask_nim.data]) == 0] = 0 else: func_nim.data[:, mask_nim.data == 0] = 0 func_nim.save() if __name__ == '__main__': main() pymvpa-0.4.8/doc/000077500000000000000000000000001174541445200136135ustar00rootroot00000000000000pymvpa-0.4.8/doc/Makefile000066400000000000000000000047471174541445200152670ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. 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    This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.
    {{ super() }} {% endblock %} pymvpa-0.4.8/doc/api/000077500000000000000000000000001174541445200143645ustar00rootroot00000000000000pymvpa-0.4.8/doc/api/TREE000066400000000000000000000025451174541445200150540ustar00rootroot00000000000000XXX Dataset might need to get labelweights parameter which could be used by some classifiers Mapper Dataset (S1) | \- appendtohistory(item) | \-ICAMapper \ | MaskMapper \------- MappedDataset (S1, Mapper) \ forward(ndarray) | MaskedDataset (S1, mask) | NiftiDataset Splitter ErrorFunction | | NFoldSplitter(S2) CrossValidation \ call(dataset) Clf (LinearSVM) train \- weights predict | BoostedClassifier Clf <- optimizeClassifier(optimizer, clf, parameter_to_optimize) default combiner = average OptimizerAlgorithm | LineSearchOptimizer GridSearch FeatureStrip(SensitivityAnalyzer) after train return ErrorFunction, sensitivity, Optimizer | ParameterOptimizer(optimizeralgorithm, parameter, classifier) BoostedOptimizer(splitter, optimizer) # NOTES Sensitivity -> RankList (N.argsort) sens <- Combine dataset <- selectImportantFeature RFE(dataset, sensAnalyzer): do sens <- sensAnalyzer(dataset) dataset <- selectImportantFeature(dataset, sens) until the world collapses into pymvpa-0.4.8/doc/api/epydoc.conf000066400000000000000000000105311174541445200165160ustar00rootroot00000000000000[epydoc] # Epydoc section marker (required by ConfigParser) # The list of objects to document. Objects can be named using # dotted names, module filenames, or package directory names. # Alases for this option include "objects" and "values". modules: mvpa # The type of output that should be generated. Should be one # of: html, text, latex, dvi, ps, pdf. output: html # The path to the output directory. May be relative or absolute. target: build/html/api # An integer indicating how verbose epydoc should be. The default # value is 0; negative values will supress warnings and errors; # positive values will give more verbose output. verbosity: 0 # A boolean value indicating that Epydoc should show a tracaback # in case of unexpected error. By default don't show tracebacks debug: 0 # If True, don't try to use colors or cursor control when doing # textual output. The default False assumes a rich text prompt simple-term: 0 ### Generation options # The default markup language for docstrings, for modules that do # not define __docformat__. Defaults to epytext. docformat: restructuredtext # Whether or not parsing should be used to examine objects. parse: yes # Whether or not introspection should be used to examine objects. introspect: no # Don't examine in any way the modules whose dotted name match this # regular expression pattern. #exclude # Don't perform introspection on the modules whose dotted name match this # regular expression pattern. #exclude-introspect # Don't perform parsing on the modules whose dotted name match this # regular expression pattern. #exclude-parse # The format for showing inheritance objects. # It should be one of: 'grouped', 'listed', 'included'. inheritance: listed # Whether or not to inclue private variables. (Even if included, # private variables will be hidden by default.) private: yes # Whether or not to list each module's imports. imports: yes # Whether or not to include syntax highlighted source code in # the output (HTML only). sourcecode: yes # Whether or not to includea a page with Epydoc log, containing # effective option at the time of generation and the reported logs. include-log: no ### Output options # The documented project's name. name: PyMVPA: Python MultiVariate Pattern Analysis # The CSS stylesheet for HTML output. Can be the name of a builtin # stylesheet, or the name of a file. css: white # The documented project's URL. url: http://v04.pymvpa.org # HTML code for the project link in the navigation bar. If left # unspecified, the project link will be generated based on the # project's name and URL. #link: My Cool Project # The "top" page for the documentation. Can be a URL, the name # of a module or class, or one of the special names "trees.html", # "indices.html", or "help.html" #top: os.path # An alternative help file. The named file should contain the # body of an HTML file; navigation bars will be added to it. #help: my_helpfile.html # Whether or not to include a frames-based table of contents. frames: yes # Whether each class should be listed in its own section when # generating LaTeX or PDF output. separate-classes: no ### API linking options # Define a new API document. A new interpreted text role # will be created #external-api: epydoc # Use the records in this file to resolve objects in the API named NAME. #external-api-file: epydoc:api-objects.txt # Use this URL prefix to configure the string returned for external API. #external-api-root: epydoc:http://epydoc.sourceforge.net/api ### Graph options # The list of graph types that should be automatically included # in the output. Graphs are generated using the Graphviz "dot" # executable. Graph types include: "classtree", "callgraph", # "umlclass". Use "all" to include all graph types graph: all # The path to the Graphviz "dot" executable, used to generate # graphs. dotpath: /usr/bin/dot # The name of one or more pstat files (generated by the profile # or hotshot module). These are used to generate call graphs. pstat: build/main.pstats # Specify the font used to generate Graphviz graphs. # (e.g., helvetica or times). graph-font: Helvetica # Specify the font size used to generate Graphviz graphs. graph-font-size: 10 ### Return value options # The condition upon which Epydoc should exit with a non-zero # exit status. Possible values are error, warning, docstring_warning #fail-on: error pymvpa-0.4.8/doc/authors.rst000066400000000000000000000025041174541445200160330ustar00rootroot00000000000000.. -*- mode: rst -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: The PyMVPA developers team currently consists of: * `Michael Hanke`_, Dartmouth College, USA * `Yaroslav O. Halchenko`_, Dartmouth College, USA * `Per B. Sederberg`_, Princeton University, USA * `Emanuele Olivetti`_, Fondazione Bruno Kessler, Italy .. _Michael Hanke: http://apsy.gse.uni-magdeburg.de/hanke .. _Yaroslav O. Halchenko: http://www.onerussian.com .. _Per B. Sederberg: http://www.princeton.edu/~persed/ .. _Emanuele Olivetti: http://sra.fbk.eu/people/olivetti/ We are very grateful to the following people, who have contributed valuable advice, code or documentation to PyMVPA: * `Greg Detre`_, Princeton University, USA * `Ingo Fründ`_, TU Berlin, Germany * `Scott Gorlin`_, MIT, USA * `Valentin Haenel`_, TU Berlin, Germany * `James M. Hughes`_, Dartmouth College, USA * `James Kyle`_, UCLA, USA * `Tiziano Zito`_, BCCN, Germany .. _Greg Detre: http://www.princeton.edu/~gdetre/ .. _James M. Hughes: http://www.cs.dartmouth.edu/~hughes/index.html .. _Ingo Fründ: http://www.cognition.tu-berlin.de/menue/members/ingo_fruend/ .. _James Kyle: http://www.ccn.ucla.edu/users/jkyle .. _Scott Gorlin: http://www.scottgorlin.com .. _Valentin Haenel: http://www.cognition.tu-berlin.de/menue/members/valentin_haenel/ .. _Tiziano Zito: http://itb.biologie.hu-berlin.de/~zito/ pymvpa-0.4.8/doc/changelog.rst000077700000000000000000000000001174541445200203142../Changelogustar00rootroot00000000000000pymvpa-0.4.8/doc/classifiers.rst000066400000000000000000000624631174541445200166670ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. _chap_classifiers: .. index:: classifier *********** Classifiers *********** PyMVPA includes a number of ready-to-use classifiers, which are described in the following sections. All classifiers implement the same, very simple interface. Each classifier object takes all relevant parameters as arguments to its constructor. Once instantiated, the classifier object's :meth:`~mvpa.clfs.base.Classifier.train` method can be called with some dataset. This trains the classifier using *all* samples in the respective dataset. The major task for a classifier is to make predictions. Predictions are made by calling the classifier's :meth:`~mvpa.clfs.base.Classifier.predict` method with one or multiple data samples. :meth:`~mvpa.clfs.base.Classifier.predict` operates on pure sample data and not datasets, as in some cases the true label for a sample might be totally unknown. This examples demonstrates the typical daily life of a classifier. >>> import numpy as N >>> from mvpa.clfs.knn import kNN >>> from mvpa.datasets import Dataset >>> training = Dataset(samples=N.array( ... N.arange(100),ndmin=2, dtype='float').T, ... labels=[0] * 50 + [1] * 50) >>> rand100 = N.random.rand(10)*100 >>> validation = Dataset(samples=N.array(rand100, ndmin=2, dtype='float').T, ... labels=[ int(i>50) for i in rand100 ]) >>> clf = kNN(k=10) >>> clf.train(training) >>> N.mean(clf.predict(training.samples) == training.labels) 1.0 >>> N.mean(clf.predict(validation.samples) == validation.labels) 1.0 Two datasets with 100 and 10 samples each are generated. Both datasets only have one feature and the associated label is 0 if the feature value is below 50 or 1 otherwise. The larger dataset contains all integers in the interval (0,100) and is used to train the classifier. The smaller is used as a validation dataset, to check whether the classifier learned something that generalizes well across samples not included in the training dataset. In this case the validation dataset consists of 10 random floating point values in the interval (0,100). The classifier in this example is a :class:`~mvpa.clfs.knn.kNN` (k-Nearest-Neighbour) classifier that makes use of the 10 nearest neighbours of a data sample to make its predictions (k=10). One can see that after the training the classifier performs optimally on the training dataset as well as on the validation data samples. The choice of the classifier in the above example is more or less arbitrary. Any classifier in PyMVPA could be used in place of kNN. This demonstrates another useful feature of PyMVPA's classifiers. Due to the high-level abstraction and the simple interface, almost all classifiers can be combined with most algorithms in PyMVPA. This makes it very easy to test different classifiers on some dataset (see Fig. 1). .. image:: pics/classifier_comparison_plot.png :align: center :alt: Classifier comparison A comparison of the behavior of different classifiers (k-Nearest-Neighbour, linear SVM, logistic regression, ridge regression and SVM with radial basis function kernel) on a simple classification problem. The code to generate these figure can be found in the `pylab_2d.py` example in the :ref:`example_pylab_2d` section. .. index:: states Stateful objects ================ Before looking at the different classifiers in more detail, it is important to mention another feature common to all of them. While their interface is simple, classifiers are in no way limited to report only predictions. All classifiers implement an additional interface: Objects of any class that are derived from :class:`~mvpa.misc.state.ClassWithCollections` have attributes (we refer to such attributes as state variables), which are conditionally computed and stored by PyMVPA. Such conditional storage and access is handy if a variable of interest might consume a lot of memory or needs intensive computation, and not needed in most (or in some) of the use cases. For instance, the :class:`~mvpa.clfs.base.Classifier` class defines the `trained_labels` state variable, which just stores the unique labels for which the classifier was trained. Since `trained_labels` stores meaningful information only for a trained classifier, attempt to access 'clf.trained_labels' before training would result in an error, >>> from mvpa.misc.exceptions import UnknownStateError >>> try: ... untrained_clf = kNN() ... labels = untrained_clf.trained_labels ... except UnknownStateError: ... "Does not work" 'Does not work' since the classifier has not seen the data yet and, thus, does not know the labels. In other words, it is not yet in the state to know anything about the labels. Any state variable can be enabled or disabled on per instance basis at any time of the execution (see :class:`~mvpa.misc.state.ClassWithCollections`). To continue the last example, each classifier, or more precisely every stateful object, can be asked to report existing state-related attributes: >>> list_with_verbose_explanations = clf.states.listing 'clf.states' is an instance of :class:`~mvpa.misc.state.StateCollection` class which is a container for all state variables of the given class. Although values can be queried or set (if state is enabled) operating directly on the stateful object >>> clf.trained_labels array([0, 1]) any other operation on the state (e.g. enabling, disabling) has to be carried out through the `states` attribute. >>> print clf.states states{trained_dataset predicting_time*+ training_confusion predictions*+...} >>> clf.states.enable('values') >>> print clf.states states{trained_dataset predicting_time*+ training_confusion predictions*+...} >>> clf.states.disable('values') A string representation of the state collection mentioned above lists all state variables present accompanied with 2 markers: '+' for an enabled state variable, and '*' for a variable that stores some value (but might have been disabled already and, therefore, would have no '+' and attempts to reassign it would result in no action). .. TODO: Refactor By default all classifiers provide state variables `values`, `predictions`. The latter is simply the set of predictions that was returned by the last call to the objects :meth:`~mvpa.clfs.base.Classifier.predict` method. The former is heavily classifier-specific. By convention the `values` key provides access to the raw values that a classifier prediction is based on. Depending on the classifier, this information might required significant resources when stored. Therefore all states can be disabled or enabled (`states.disable()`, `states.enable()`) and their current status can be queried like this: >>> clf.states.isActive('predictions') True >>> clf.states.isActive('values') False States can be enabled or disabled during stateful object construction, if `enable_states` or `disable_states` (or both) arguments, which store the list of desired state variables names, passed to the object constructor. Keyword 'all' can be used to select all known states for that stateful object. .. index:: error, classifier error, transfer error .. _transfer_error: Error Calculation ================= The :class:`~mvpa.clfs.transerror.TransferError` class provides a convenient way to determine the transfer error of a trained classifier on some validation dataset, i.e. the accuracy of the classifier's predictions on a novel, independent dataset. A :class:`~mvpa.clfs.transerror.TransferError` object is instanciated by passing a classifier object to the constructor. Optionally a custom error function can be specified (see `errorfx` argument). To compute the transfer error simply call the object with a validation dataset. The computed error value is returned. :class:`~mvpa.clfs.transerror.TransferError` also supports a state variable `confusion` that contains the full confusion matrix of the predictions made on the validation dataset. The confusion matrix is disabled by default. If the :class:`~mvpa.clfs.transerror.TransferError` object is called with an optional training dataset, the contained classifier is first training using this dataset before predictions on the validation dataset are made. >>> from mvpa.clfs.transerror import TransferError >>> clf = kNN(k=10) >>> terr = TransferError(clf) >>> terr(validation, training ) 0.0 .. index:: cross-validation .. _cross-validation: Cross-validated Transfer Error ------------------------------ Often one is not only interested in a single transfer error on one validation or test dataset, but on a cross-validated estimate of the transfer error. A popular method is the so-called leave-one-out cross-validation. The :class:`~mvpa.algorithms.cvtranserror.CrossValidatedTransferError` class provides a simple way to compute such measure. It utilizes a :class:`~mvpa.clfs.transerror.TransferError` object and a :class:`~mvpa.datasets.splitters.Splitter`. When called with a :class:`~mvpa.datasets.base.Dataset` the splitter generates splits of the Dataset and the transfer error for all splits is computed by training on one of the splitted datasets and making predictions on the other. By default the mean of transfer errors is returned (but the actual `combiner` function is customizable). The following example shows the minimal code for a leave-one-out cross-validation reusing the transfer error object from the previous example and some :class:`~mvpa.datasets.base.Dataset` `data`. >>> # create some dataset >>> from mvpa.misc.data_generators import normalFeatureDataset >>> data = normalFeatureDataset(perlabel=50, nlabels=2, ... nfeatures=20, nonbogus_features=[3, 7], ... snr=3.0) >>> # now cross-validation >>> from mvpa.algorithms.cvtranserror import CrossValidatedTransferError >>> from mvpa.datasets.splitters import NFoldSplitter >>> cvterr = CrossValidatedTransferError(terr, ... NFoldSplitter(cvtype=1), ... enable_states=['confusion']) >>> error = cvterr(data) Error Reporting =============== PyMVPA is equipped with easy ways to have a glance overview over the generalization performance of a cross-validated classifier. Such summary is provided by instances of a :class:`~mvpa.clfs.transerror.ConfusionMatrix` class, and is accompanied by various performance metrics. For example, the 8-fold cross-validation of the dataset with 8 labels with the SMLR classifier produced the following confusion matrix:: >>> # Simple 'print cvterr.confusion' provides the same output >>> # without the description of abbreviations >>> print cvterr.confusion.asstring(description=True) \ ... # doctest: +SKIP --------. 3kHz 7kHz 12kHz 20kHz 30kHz song1 song2 song3 song4 song5 predict.\targets 38 39 40 41 42 43 44 45 46 47 `------ ---- ----- ----- ----- ----- ----- ----- ----- ----- ----- P' N' FP FN PPV NPV TPR SPC FDR MCC 3kHz / 38 84 42 27 4 4 2 1 0 15 19 198 1351 114 90 0.42 0.93 0.48 0.92 0.58 0.36 7kHz / 39 43 94 16 0 1 1 1 2 1 24 183 1331 89 80 0.51 0.94 0.54 0.93 0.49 0.43 12kHz / 40 21 16 103 5 2 2 0 0 6 13 168 1312 65 70 0.61 0.95 0.6 0.95 0.39 0.51 20kHz / 41 1 2 13 158 1 0 0 1 3 1 180 1202 22 15 0.88 0.99 0.91 0.98 0.12 0.77 30kHz / 42 3 0 2 3 162 0 0 0 0 0 170 1194 8 11 0.95 0.99 0.94 0.99 0.05 0.82 song1 / 43 3 1 1 0 1 160 0 0 2 5 173 1199 13 14 0.92 0.99 0.92 0.99 0.08 0.8 song2 / 44 1 1 0 0 0 0 171 0 0 0 173 1176 2 2 0.99 1 0.99 1 0.01 0.86 song3 / 45 1 1 1 0 0 0 0 170 2 0 175 1179 5 4 0.97 1 0.98 1 0.03 0.84 song4 / 46 7 3 3 2 2 2 0 0 139 7 165 1240 26 34 0.84 0.97 0.8 0.98 0.16 0.71 song5 / 47 10 14 7 1 0 7 0 1 5 104 149 1310 45 69 0.7 0.95 0.6 0.97 0.3 0.55 Per target: ---- ----- ----- ----- ----- ----- ----- ----- ----- ----- P 174 174 173 173 173 174 173 174 173 173 N 1560 1560 1561 1561 1561 1560 1561 1560 1561 1561 TP 84 94 103 158 162 160 171 170 139 104 TN 1261 1251 1242 1187 1183 1185 1174 1175 1206 1241 Summary\Means: ---- ----- ----- ----- ----- ----- ----- ----- ----- ----- 173 1249 38 39 0.78 0.97 0.78 0.97 0.22 0.66 ACC 0.78 ACC% 77.57 # of sets 8 Statistics computed in 1-vs-rest fashion per each target. Abbreviations (for details see http://en.wikipedia.org/wiki/ROC_curve): TP : true positive (AKA hit) TN : true negative (AKA correct rejection) FP : false positive (AKA false alarm, Type I error) FN : false negative (AKA miss, Type II error) TPR: true positive rate (AKA hit rate, recall, sensitivity) TPR = TP / P = TP / (TP + FN) FPR: false positive rate (AKA false alarm rate, fall-out) FPR = FP / N = FP / (FP + TN) ACC: accuracy ACC = (TP + TN) / (P + N) SPC: specificity SPC = TN / (FP + TN) = 1 - FPR PPV: positive predictive value (AKA precision) PPV = TP / (TP + FP) NPV: negative predictive value NPV = TN / (TN + FN) FDR: false discovery rate FDR = FP / (FP + TP) MCC: Matthews Correlation Coefficient MCC = (TP*TN - FP*FN)/sqrt(P N P' N') # of sets: number of target/prediction sets which were provided In addition to the abusively informative textual representation, there is an alternative graphical representation of the confusion matrix via the :meth:`~mvpa.clfs.transerror.ConfusionMatrix.plot` method of a :class:`~mvpa.clfs.transerror.ConfusionMatrix`:: >>> import pylab as P >>> cvterr.confusion.plot() \ ... # doctest: +SKIP >>> P.show() \ ... # doctest: +SKIP .. image:: pics/confusion_matrix.* :align: center :alt: Classification confusion matrix Basic Supervised Learning Methods ================================= PyMVPA provides a number of learning methods (i.e. classifiers or regression algorithms) that can be plug into the various algorithms that are also part of the framework. Most importantly they all can be combined or enhanced with :ref:`metaclassifiers`. .. index:: Gaussian process regression, GPR Gaussian Process Regression --------------------------- :class:`~mvpa.clfs.gpr.GPR` (`Wikipedia entry about Gaussian process regression`_). .. _Wikipedia entry about Gaussian process regression: http://en.wikipedia.org/wiki/Gaussian_process_regression .. index:: k-nearest-neighbour, kNN k-Nearest-Neighbour ------------------- The :class:`~mvpa.clfs.knn.kNN` classifier makes predictions based on the labels of nearby samples. It currently uses Euclidean distance to determine the nearest neighbours, but future enhancements may include support for other kernels. .. index:: least angle regression, LARS Least Angle Regression ---------------------- :class:`~mvpa.clfs.lars.LARS` :ref:`Efron et al. (2004) ` .. index:: logistic regression, penalized logistic regression Penalized Logistic Regression ----------------------------- The penalized logistic regression (:class:`~mvpa.clfs.plr.PLR`) is similar to the ridge in that it has a penalty term, however, it is trained to predict a binary outcome by means of the logistic function (`Wikipedia entry about logistic regression`_). .. _Wikipedia entry about logistic regression: http://en.wikipedia.org/wiki/Logistic_regression .. index:: ridge regression Ridge Regression ---------------- Ridge regression (aka Tikhonov regularization) is a variant of a linear regression (`Wikipedia entry about ridge regression`_). The ridge regression classifier (:class:`~mvpa.clfs.ridge.RidgeReg`) performs a simple linear regression with a penalty parameter to help avoid over-fitting. The regression inserts an intercept term so that you do not have to center your data. .. _Wikipedia entry about ridge regression: http://en.wikipedia.org/wiki/Ridge_regression .. index:: sparse multinomial logistic regression, SMLR Sparse Multinomial Logistic Regression -------------------------------------- Sparse Multinomial Logistic Regression (:class:`~mvpa.clfs.smlr.SMLR`; :ref:`Krishnapuram et al., 2005 `) is a fast multi-class classifier that can easily deal with high-dimensional problems (`research paper about SMLR`_). PyMVPA includes two implementations: one in pure Python and a faster one that makes use of a C extension for the performance critical pieces of the code. .. _research paper about SMLR: http://www.cs.duke.edu/~amink/publications/manuscripts/hartemink05.pami.pdf .. index:: support vector machine, SVM Support Vector Machines ----------------------- Support vector machine (:ref:`Vapnik, 1995 `) classifiers (and regressions) are popular since they can deal with very high dimensional problems (`Wikipedia entry about SVM`_), while maintaining reasonable generalization performance. The support vector machine classes provide a family of classifiers by wrapping LIBSVM_ and Shogun_ libraries, with corresponding base classes :class:`~mvpa.clfs.svm.libsvm.SVM` and :class:`~mvpa.clfs.svm.sg.SVM` accordingly. By default SVM class is bound to LIBSVM's implementation if such is available (shogun otherwise). While any SVM class provides a complete interface, the others child classes make it easy to run some subset of standard classifiers, such as linear SVM, with a default set of parameters (see :class:`~mvpa.clfs.svm.LinearCSVMC`, :class:`~mvpa.clfs.svm.LinearNuSVMC`, :class:`~mvpa.clfs.svm.RbfNuSVMC` and :class:`~mvpa.clfs.svm.RbfCSVMC`). .. _LIBSVM: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ .. _Shogun: http://www.shogun-toolbox.org .. _Wikipedia entry about SVM: http://en.wikipedia.org/wiki/Support_Vector_Machine .. _metaclassifiers: Meta-Classifiers ================ *This section has been contributed by James M. Hughes.* A meta-classifier is essentially a blanket term used to describe all classes that appear functionally equivalent to a regular :class:`~mvpa.clfs.base.Classifier`, but which in reality provide some extra amount of functionality on top of a normal classifier. Furthermore, they generally do not implement a :class:`~mvpa.clfs.base.Classifier` *per se*, but rather take a :class:`~mvpa.clfs.base.Classifier` as input. The methods then typically called on a classifier (e.g., `train` or `predict`) can be called on the meta-classifier, but will call the input classifier's routines, before or after some other function that the meta-classifier provides. Examples of Meta-Classifiers ---------------------------- At present, there are two primary meta-classifiers implemented in the PyMVPA package, beneath which there are several specific options: :class:`~mvpa.clfs.meta.BoostedClassifier` typically uses multiple classifiers internally :class:`~mvpa.clfs.meta.ProxyClassifier` typically performs some action on the data/labels before classification is performed Within these more general categories, specific classifiers are implemented. For example, there are several :class:`~mvpa.clfs.meta.BoostedClassifier` subclasses: :class:`~mvpa.clfs.meta.CombinedClassifier` combines predictions using a :class:`~mvpa.clfs.meta.PredictionsCombiner` functor :class:`~mvpa.clfs.meta.MulticlassClassifier` performs multi-class classification by means of a list of :class:`~mvpa.clfs.meta.BinaryClassifier` instances. Typical use-case is to wrap a binary classifier to give it ability to operate on multiple classes via voting over classifiers for all possible pairs of the categories :class:`~mvpa.clfs.meta.SplitClassifier` combines a :class:`~mvpa.clfs.base.Classifier` and an arbitrary :class:`~mvpa.datasets.splitters.Splitter` Furthermore, there are also several :class:`~mvpa.clfs.meta.ProxyClassifier` subclasses: :class:`~mvpa.clfs.meta.BinaryClassifier` maps a set of labels into two categories (+1 and -1) :class:`~mvpa.clfs.meta.MappedClassifier` uses a mapper on input data prior to training/testing :class:`~mvpa.clfs.meta.FeatureSelectionClassifier` performs some kind of :class:`~mvpa.featsel.base.FeatureSelection` prior to training/testing Implementation Examples ----------------------- Classifiers such as the :class:`~mvpa.clfs.meta.FeatureSelectionClassifier` are particularly useful because they simplify the process of selecting features and then using only that subset of features to classify novel exemplars (the `predict` stage). They become even more powerful when combined with :class:`~mvpa.clfs.meta.SplitClassifier`, so that even the task of withholding certain data points to create statistically valid training and testing datasets is abstracted and wrapped up within a single object (and, ultimately, very few method calls). Consider the following code, which can be found in `mvpa/clfs/warehouse.py`_: .. _mvpa/clfs/warehouse.py: api/mvpa.clfs.warehouse-pysrc.html >>> from mvpa.clfs.meta import SplitClassifier, FeatureSelectionClassifier >>> from mvpa.clfs.svm import LinearCSVMC >>> from mvpa.clfs.transerror import ConfusionBasedError >>> from mvpa.featsel.rfe import RFE >>> from mvpa.featsel.helpers import FractionTailSelector >>> >>> rfesvm_split = SplitClassifier(LinearCSVMC()) >>> clf = \ ... FeatureSelectionClassifier( ... clf = LinearCSVMC(), ... # on features selected via RFE ... feature_selection = RFE( ... # based on sensitivity of a clf which does ... # splitting internally ... sensitivity_analyzer=rfesvm_split.getSensitivityAnalyzer(), ... transfer_error=ConfusionBasedError( ... rfesvm_split, ... confusion_state="confusion"), ... # and whose internal error we use ... feature_selector=FractionTailSelector( ... 0.2, mode='discard', tail='lower'), ... # remove 20% of features at each step ... update_sensitivity=True), ... # update sensitivity at each step ... descr='LinSVM+RFE(splits_avg)' ) This analysis combines the :class:`~mvpa.clfs.meta.FeatureSelectionClassifier` and the :class:`~mvpa.clfs.meta.SplitClassifier` to perform internal splitting of the data and then perform FeatureSelection based on those splits. Such analyses can be easily created due to the straightforward way that classifier and meta-classifiers can be combined. Please refer to the relevant documentation sections for more information about the specifics of each meta-classifier. Retrainable Classifiers ======================= Some classifiers have ability to provide quick (i.e in terms of performance) re-training if they were previously trained, and only part of their specification got changed. For instance, for kernel-based classifier (e.g. GPR) it makes no sense to recompute kernel matrix, if only a classifier (not kernel) parameter (e.g. ``sigma_noise``) was changed. Another similar usecase: for :ref:`null-hypothesis statistical testing ` it might be needed to train classifier multiple times on a randomized set of labels. Only classifiers which have ``retrainable`` in their ``_clf_internals`` are capable of efficient retraining. To enable retraining, just provide ``retrainable=True`` to the constructor of the classifier. Internally retrainable classifiers will try to deduce what was changed in the specification of the classifier (e.g. training/testing datasets, parameters) and act accordingly. To reduce training/prediction time even more, classifier might directly be instructed with what aspects were changed. It must be previously trained / predicted, so later on :meth:`~mvpa.clfs.base.Classifier.retrain` and :meth:`~mvpa.clfs.base.Classifier.repredict` methods could be called. :meth:`~mvpa.clfs.base.Classifier.repredict` can be called only with the same data, for which it was earlier predicted. See API doc for more information. Implementation of efficient retraining is not straightforward, thus it is strongly advised to * enable ``CHECK_RETRAIN`` debug target while developing the code for analysis. That might guard you against obvious misuses of retraining feature, as well as to spot bugs in the code * validate on a simple dataset that analysis code provides the same results if classifier was created retrainable or not Classifiers "Warehouse" ======================= To facilitate easy trial of different classifiers for any specific task, :class:`~mvpa.clfs.warehouse.Warehouse` of classifiers clfs.warehouse.clfs was defined to create a sample collection of some commonly used parameterizations of the classifiers present in PyMVPA. Such collection can be queried by any set of known keywords/tags with tags prefixed with ``!`` being excluded:: >>> from mvpa.clfs.warehouse import clfswh >>> tryme = clfswh['multiclass', '!svm'] to simply sweep through classifiers which are capable of multiclass classification and are not SVM based. pymvpa-0.4.8/doc/conf.py000066400000000000000000000303461174541445200151200ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # PyMVPA documentation build configuration file, created by # sphinx-quickstart on Sun May 4 09:06:06 2008. # # This file is execfile()d with the current directory set to its containing dir. # # The contents of this file are pickled, so don't put values in the namespace # that aren't pickleable (module imports are okay, they're removed automatically). # # All configuration values have a default value; values that are commented out # serve to show the default value. import sys, os, re import numpy as N import mvpa try: import matplotlib matplotlib.use('svg') except: pass ################################################## # Config settings are at the bottom of the file! # ################################################## # If your extensions are in another directory, add it here. If the directory # is relative to the documentation root, use os.path.abspath to make it # absolute, like shown here. #sys.path.append(os.path.abspath('some/directory')) def extractItemListBlock(blocktypes, lines): """Extract a number of lines belonging to an indented block. The block is defined by a minimum indentation level, in turn defined by a line starting with any string given by the 'blocktypes' sequence. It returns the lines matching the block and the start and endline index wrt the original line sequence. WARNING: It may explode if there is more than one block with the same identifier. """ param = None in_block = False indent = None start_line = None end_line = None for i, line in enumerate(lines): # ignore empty lines if line.isspace() or not len(line.strip()): continue # strip leading whitespace sline = line.lstrip() # look for block start if N.any([sline.startswith(bt) for bt in blocktypes]): in_block = True indent = len(line) - len(sline) start_line = i continue # check if end is reached if in_block and len(line) - len(sline) <= indent: end_line = i return param, start_line, end_line # store param block line if in_block: if not param: param = [] param.append(line) # when nothing follows param block if start_line: end_line = len(lines) - 1 return param, start_line, end_line def smoothName(s): """Handle all kinds of voodoo cases, that might disturb RsT """ s = s.strip() s = re.sub('\*', '\*', s) return s def segmentItemList(lines, name): """Parse the lines of a block into segment items of the format used in PyMVPA:: name[: type] (multiline) description """ # assumes no empty lines left! items = [] last_item = None # determine indentation level indent = len(lines[0]) - len(lines[0].lstrip()) for line in lines: # if top level indent, we have a parameter def if indent == len(line) - len(line.lstrip()): # rescue previous one if last_item is not None: items.append(last_item) last_item = None last_item = {'name': None, 'type': None, 'descr': []} # try splitting param def l = line.split(':') if len(l) >= 2: last_item['name'] = smoothName(l[0]) last_item['type'] = u':'.join(l[1:]).strip() elif len(l) == 1: last_item['name'] = smoothName(line) else: print line raise RuntimeError, \ 'Should not have happend, inspect %s' % name else: # it must belong to last_item and be its description if last_item is None: print line raise ValueError, \ 'Parameter description, without parameter in %s' % name last_item['descr'].append(line.strip()) if last_item is not None: items.append(last_item) return items def reformatParameterBlock(lines, name): """Format a proper parameters block from the lines of a docstring version of this block. """ params = segmentItemList(lines, name) out = [] # collection is done, now pretty print for p in params: out.append(':param ' + p['name'] + ': ') if len(p['descr']): # append first description line to previous one out[-1] += p['descr'][0] for l in p['descr'][1:]: out.append(' ' + l) if p['type']: out.append(':type ' + p['name'] + ': ' + p['type']) # safety line out.append(u'') return out def reformatReturnsBlock(lines, name): """Format a proper returns block from the lines of a docstring version of this block. """ ret = segmentItemList(lines, name) if not len(ret) == 1: raise ValueError, \ '%s docstring specifies more than one return value' % name ret = ret[0] out = [] out.append(':rtype: ' + ret['name']) if len(ret['descr']): out.append(':returns:') for l in ret['descr']: out.append(' ' + l) # safety line out.append(u'') return out def reformatExampleBlock(lines, name): """Turn an example block into a verbatim text. """ out = [u'::', u''] out += lines # safety line out.append(u'') return out # demo function to access docstrings for processing def dumpit(app, what, name, obj, options, lines): """ For each docstring this function is called with the following set of arguments: app the Sphinx application object what the type of the object which the docstring belongs to (one of "module", "class", "exception", "function", "method", "attribute") name the fully qualified name of the object obj the object itself options the options given to the directive: an object with attributes inherited_members, undoc_members, show_inheritance and noindex that are true if the flag option of same name was given to the auto directive lines the lines of the docstring (as a list) """ param, pstart, pend = extractItemListBlock([':Parameters:', ':Parameter:'], lines) if param: # make it beautiful param = reformatParameterBlock(param, name) # replace old block with new one lines[pstart:pend] = param returns, rstart, rend = extractItemListBlock([':Returns:'], lines) if returns: returns = reformatReturnsBlock(returns, name) lines[rstart:rend] = returns examples, exstart, exend = extractItemListBlock([':Examples:', ':Example:'], lines) if examples: print 'WARNING: Example in %s should become a proper snippet' % name examples = reformatExampleBlock(examples, name) lines[exstart:exend] = examples # kill things that sphinx does not know ls, lstart, lend = extractItemListBlock(['.. packagetree::'], lines) if ls: del(lines[lstart:lend]) # add empty line at begining of class docs to separate base class list from # class docs (should actually be done by sphinx IMHO) if what == 'class': lines.insert(0, u'') # make this file a sphinx extension itself, to be able to do docstring # post-processing def setup(app): app.connect('autodoc-process-docstring', dumpit) # General configuration # --------------------- # If your extensions are in another directory, add it here. If the directory # is relative to the documentation root, use os.path.abspath to make it # absolute, like shown here. sys.path.append(os.path.abspath('sphinxext')) # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'inheritance_diagram'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. source_encoding = 'utf-8' # The master toctree document. master_doc = 'contents' # General substitutions. project = 'PyMVPA' copyright = '2006-2009, Michael Hanke, Yaroslav Halchenko, Per B. Sederberg' # The default replacements for |version| and |release|, also used in various # other places throughout the built documents. # # The short X.Y version. version = mvpa.__version__ # The full version, including alpha/beta/rc tags. release = mvpa.__version__ # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. today_fmt = '%B %d, %Y' # List of documents that shouldn't be included in the build. unused_docs = [] # what to put into API doc (just class doc, just init, or both autoclass_content = 'both' # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # Options for HTML output # ----------------------- # The style sheet to use for HTML and HTML Help pages. A file of that name # must exist either in Sphinx' static/ path, or in one of the custom paths # given in html_static_path. html_style = 'pymvpa.css' # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". html_title = 'PyMVPA Home' # The name of an image file (within the static path) to place at the top of # the sidebar. #html_logo = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. html_sidebars = {'index': 'indexsidebar.html'} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {'index': 'index.html'} # If false, no module index is generated. html_use_modindex = False # If true, the reST sources are included in the HTML build as _sources/. html_copy_source = False # If true, links to the reST sources are added to the pages. html_show_sourcelink = False # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. #html_use_opensearch = False # Output file base name for HTML help builder. htmlhelp_basename = 'PyMVPAdoc' # Options for LaTeX output # ------------------------ # The paper size ('letter' or 'a4'). latex_paper_size = 'a4' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, document class [howto/manual]). latex_documents = [ ('manual', 'PyMVPA-Manual.tex', 'PyMVPA Manual', 'Michael~Hanke, Yaroslav~O.~Halchenko, Per~B.~Sederberg, ' 'James M. Hughes', 'manual'), ('devguide', 'PyMVPA-DevGuide.tex', 'PyMVPA Developer Guidelines', 'Michael~Hanke, Yaroslav~O.~Halchenko, Per~B.~Sederberg', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. latex_logo = os.path.join('_static', 'logo.pdf') # Additional stuff for the LaTeX preamble. latex_preamble = """ \usepackage{enumitem} \setdescription{style=nextline,font=\\normalfont} """ # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_use_modindex = True pymvpa-0.4.8/doc/contents.rst000066400000000000000000000005011174541445200161760ustar00rootroot00000000000000.. _contents: ***************************** PyMVPA Documentation Contents ***************************** .. toctree:: intro installation overview datasets classifiers measures featsel misc examples matlab faq glossary references legal changelog history todo modref pymvpa-0.4.8/doc/datasets.rst000066400000000000000000000401551174541445200161620ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. index:: dataset, sample attribute, dataset attribute .. _chap_datasets: ******** Datasets ******** The first step of any analysis in PyMVPA involves reading the data and putting it into the necessary shape for the intended analysis. But even after the initial setup, many algorithms have to modify datasets, e.g. by selecting a subset of it, or simple transformations of the data (e.g. z-scoring), or more complex things like projections into alternative representations/spaces. This section introduces the basic concepts of a dataset in PyMVPA and shows useful operations typically performed on datasets. The Basic Concepts ================== A minimal dataset in PyMVPA consists of a number of :term:`sample`\ s, where each individual sample is nothing more than a vector of values. Each sample is associated with a :term:`label`, which defines the category the respective sample belongs to, or in more general terms, defines the model that should be learned by a classifier. Moreover, samples can be grouped into so-called :term:`chunk`\ s, where each chunk is assumed to be statistically independent from all other data chunks. The foundation of PyMVPA's data handling is the :class:`~mvpa.datasets.base.Dataset` class. Basically, this class stores data samples, sample attributes and dataset attributes. By definition, sample attributes assign a value to each data sample (e.g. labels, or chunks) and dataset attributes are additional information or functionality that apply to the whole dataset. Most likely the :class:`~mvpa.datasets.base.Dataset` class will not be used directly, but through one of the derived classes. However, it is perfectly possible to use it directly. In the simplest case a dataset can be constructed by specifying some data samples and the corresponding class labels. >>> import numpy as N >>> from mvpa.datasets import Dataset >>> data = Dataset(samples=N.random.normal(size=(10,5)), labels=1) >>> data .. index:: chunks, labels, feature, sample The above example creates a dataset with 10 samples and 5 features each. The values of all features stem from normally distributed random noise. The class label '1' is assigned to all samples. Instead of a single scalar value `labels` can also be a sequence with individual labels for each data sample. In this case the length of this sequence has to match the number of samples. Interestingly, the dataset object tells us about 10 `chunks`. In PyMVPA chunks are used to group subsets of data samples. However, if no grouping information is provided all data samples are assumed to be in their own group, hence no sample grouping is performed. Both `labels` and `chunks` are so called *sample attributes*. All sample attributes are stored in sequence-type containers consisting of one value per sample. These containers can be accessed by properties with the same as the attribute: >>> data.labels array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) >>> data.chunks array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) The *data samples* themselves are stored as a two-dimensional matrix where each row vector is a `sample` and each column vector contains the values of a `feature` across all `samples`. The :class:`~mvpa.datasets.base.Dataset` class provides access to the samples matrix via the `samples` property. >>> data.samples.shape (10, 5) The :class:`~mvpa.datasets.base.Dataset` class itself can only deal with 2d sample matrices. However, PyMVPA provides a very easy way to deal with data where each data sample is more than a 1d vector: `Data Mapping`_ .. index:: mapper, sample, feature Data Mapping ============ It was already mentioned that the :class:`~mvpa.datasets.base.Dataset` class cannot deal with data samples that are more than simple vectors. This could be a problem in cases where the data has a higher dimensionality, e.g. functional brain-imaging data where each data sample is typically a three-dimensional volume. One approach to deal with this situation would be to concatenate the whole volume into a 1d vector. While this would work in certain cases there is definitely information lost. Especially for brain-imaging data one would most likely want keep information about neighborhood and distances between data sample elements. In PyMVPA this is done by mappers that transform data samples from their original *dataspace* into the so-called *features space*. In the above neuro-imaging example the *dataspace* is three-dimensional and the *feature space* always refers to the 2d `samples x features` representation that is required by the :class:`~mvpa.datasets.base.Dataset` class. In the context of mappers the dataspace is sometimes also referred to as *in-space* (i.e. the initial data that goes into the mapper) while the feature space is labeled as *out-space* (i.e. the mapper output when doing forward mapping). The task of a mapper, besides transforming samples into 1d vectors, is to retain as much information of the dataspace as possible. Some mappers provide information about dataspace metrics and feature neighbourhood, but all mappers are able to do reverse mapping from feature space into the original dataspace. Usually one does not have to deal with mappers directly. PyMVPA provides some convenience subclasses of :class:`~mvpa.datasets.base.Dataset` that automatically perform the necessary mapping operations internally. .. index:: MaskedDataset For an introduction into to concept of a dataset with mapping capabilities we can take a look at the :class:`~mvpa.datasets.masked.MaskedDataset` class. This dataset class works almost exactly like the basic :class:`~mvpa.datasets.base.Dataset` class, except that it provides some additional methods and is more flexible with respect to the format of the sample data. A masked dataset can be created just like a normal dataset. >>> from mvpa.datasets.masked import MaskedDataset >>> mdata = MaskedDataset(samples=N.random.normal(size=(5,3,4)), ... labels=[1,2,3,4,5]) >>> mdata However, unlike :class:`~mvpa.datasets.base.Dataset` the :class:`~mvpa.datasets.masked.MaskedDataset` class can deal with sample data arrays with more than two dimensions. More precisely it handles arrays of any dimensionality. The only assumption that is made is that the first axis of a sample array separates the sample data points. In the above example we therefore have 5 samples, where each sample is a 3x4 plane. .. index:: forward mapping, reverse mapping If we look at the self-description of the created dataset we can see that it doesn't tell us about 3x4 plane, but simply 12 features. That is because internally the sample array is automatically reshaped into the aforementioned 2d matrix representation of the :class:`~mvpa.datasets.base.Dataset` class. However, the information about the original dataspace is not lost, but kept inside the mapper used by :class:`~mvpa.datasets.masked.MaskedDataset`. Two useful methods of :class:`~mvpa.datasets.masked.MaskedDataset` make use of the mapper: `mapForward()` and `mapReverse()`. The former can be used to transform additional data from dataspace into the feature space and the latter performs the same in the opposite direction. >>> mdata.mapForward(N.arange(12).reshape(3,4)).shape (12,) >>> mdata.mapReverse(N.array([1]*mdata.nfeatures)).shape (3, 4) Especially reverse mapping can be very useful when visualizing classification results and information maps on the original dataspace. Another feature of mapped datasets is that valid mapping information is maintained even when the feature space changes. When running some feature selection algorithm (see :ref:`chap_featsel`) some features of the original features set will be removed, but after feature selection one will most likely want to know where the selected (or removed) features are in the original dataspace. To make use of the neuro-imaging example again: The most convenient way to access this kind of information would be a map of the selected features that can be overlayed over some anatomical image. This is trivial with PyMVPA, because the mapping is automatically updated upon feature selection. >>> mdata.mapReverse(N.arange(1,mdata.nfeatures+1)) array([[ 1, 2, 3, 4], [ 5, 6, 7, 8], [ 9, 10, 11, 12]]) >>> sdata = mdata.selectFeatures([2,7,9,10]) >>> sdata >>> sdata.mapReverse(N.arange(1,sdata.nfeatures+1)) array([[0, 0, 1, 0], [0, 0, 0, 2], [0, 3, 4, 0]]) .. index:: feature selection The above example selects four features from the set of the 12 original ones, by passing their ids to the `selectFeatures()` method. The method returns a new dataset only containing the four selected features. Resultant dataset contains a copy of the corresponding features of the original dataset. All other information like class labels and chunks are maintained. By calling `mapReverse()` on the new dataset one can see that the remaining four features are precisely mapped back onto their original locations in the data space. .. index:: syntactic sugaring .. _data_sugaring: Data Access Sugaring ==================== Complementary to self-descriptive attribute names (e.g. `labels`, `samples`) datasets have a few concise shortcuts to get quick access to some attributes or perform some common action ================ ============ ================ Attribute Abbreviation Definition class ---------------- ------------ ---------------- samples S :class:`~mvpa.datasets.base.Dataset` labels L :class:`~mvpa.datasets.base.Dataset` uniquelabels UL :class:`~mvpa.datasets.base.Dataset` chunks C :class:`~mvpa.datasets.base.Dataset` uniquechunks UC :class:`~mvpa.datasets.base.Dataset` origids I :class:`~mvpa.datasets.base.Dataset` samples_original O :class:`~mvpa.datasets.mapped.MappedDataset` ================ ============ ================ .. index:: data formats .. _data_formats: Data Formats ============ The concept of mappers in conjunction with the functionality provided by the :class:`~mvpa.datasets.base.Dataset` class, makes it very easy to create new dataset types with support for specialized data types and formats. The following is a non-exhaustive list of data formats currently supported by PyMVPA (for additional formats take a look at the subclasses of :class:`~mvpa.datasets.base.Dataset`): * NumPy arrays PyMVPA builds its dataset facilities on NumPy arrays. Basically, anything that can be converted into a NumPy array can also be converted into a dataset. Together with the corresponding labels, NumPy arrays can simply be passed to the :class:`~mvpa.datasets.base.Dataset` constructor to create a dataset. With arrays it is possible to use the classes :class:`~mvpa.datasets.base.Dataset`, :class:`~mvpa.datasets.mapped.MappedDataset` (to combine the samples with any custom mapping algorithm) or :class:`~mvpa.datasets.masked.MaskedDataset` (readily provides a :class:`~mvpa.mappers.array.DenseArrayMapper`). * Plain text Using the NumPy function `fromfile()` a variety of text file formats (e.g. CSV) can be read and converted into NumPy arrays. * NIfTI/Analyze images PyMVPA provides a specialized dataset for MRI data in the NIfTI format. :class:`~mvpa.datasets.nifti.NiftiDataset` uses PyNIfTI_ to read the data and automatically configures an appropriate :class:`~mvpa.mappers.array.DenseArrayMapper` with metric information read from the NIfTI file header. * EEP binary files Another special dataset type is :class:`~mvpa.datasets.eep.EEPDataset`. It reads data from binary EEP file (written by eeprobe_) .. _PyNIfTI: http://niftilib.sf.net/pynifti .. _eeprobe: http://www.ant-neuro.com/products/eeprobe .. index:: data splitting, splitter, leave-one-out .. _data_splitter: Data Splitting ============== In many cases some algorithm should not run on a complete dataset, but just some parts of it. One well-known example is leave-one-out cross-validation, where a dataset is typically split into a number of training and validation datasets. A classifier is trained on the training set and its generalization performance is tested using the validation set. It is important to strictly separate training and validation datasets as otherwise no valid statement can be made whether a classifier really generated an appropriate model of the training data. Violating this requirement spuriously elevates the classification performance, often termed 'peeking' in the literature. However, they provide no relevant information because they are based on cheating or peeking and do not describe signal similarities between training and validation datasets. .. this point about 'peeking' is a critical one and maybe deserves emphasis. i was just looking at how we deal with it in our documentation, and we need to improve ours too! With the splitter classes derived from the base :class:`~mvpa.datasets.splitters.Splitter`, PyMVPA makes dataset splitting easy. All dataset splitters in PyMVPA are implemented as Python generators, meaning that when called with a dataset once, they return one dataset split per iteration and an appropriate Exception when they are done. This is exactly the same behavior as of e.g. the Python `xrange()` function. .. index:: working data, validation data To perform data splitting for the already mentioned cross-validation, PyMVPA provides the :class:`~mvpa.datasets.splitters.NFoldSplitter` class. It implements a method to generate arbitrary N-M splits, where N is the number of different chunks in a dataset and M is any non-negative integer smaller than N. Doing a leave-one-out split of our example dataset looks like this: >>> from mvpa.datasets.splitters import NFoldSplitter >>> splitter = NFoldSplitter(cvtype=1) # Do N-1 >>> for wdata, vdata in splitter(data): ... pass where `wdata` is the *working dataset* and `vdata` is the *validation dataset*. If we have a look a those datasets we can see that the splitter did what we intended: >>> split = [ i for i in splitter(data)][0] >>> for s in split: ... print s Dataset / float64 9 x 5 uniq: 1 labels 9 chunks Dataset / float64 1 x 5 uniq: 1 labels 1 chunks >>> split[0].uniquechunks array([1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> split[1].uniquechunks array([0]) In the first split, the working dataset contains nine chunks of the original dataset and the validation set contains the remaining chunk. Behavior of the splitters can be heavily customized by additional arguments to the constructor (see :class:`~mvpa.datasets.splitters.Splitter` for extended help on the arguments). For instance, in the analysis in fMRI data it might be important to assure that samples in the training and testing parts of the split are not neighboring samples (unless it is otherwise assured by the presence of baseline condition on the boundaries between chunks, samples of which are discarded prior the statistical learning analysis). Providing argument `discard_boundary=1` to the splitter, would remove from both training and testing parts a single sample, which lie on the boundary between chunks. Providing `discard_boundary=(2,0)` would remove 2 samples only from training part of the split (which is desired strategy for `NFoldSplitter` where training part contains majority of the data). .. index:: processing object The usage of the splitter, creating a splitter object and calling it with a dataset, is a very common design pattern in the PyMVPA package. Like splitters, there are many more so called *processing objects*. These classes or objects are instantiated by passing all relevant parameters to the constructor. Processing objects can then be called multiple times with different datasets to perform their algorithm on the respective dataset. This design applies to the majority of the algorithms implemented in PyMVPA. pymvpa-0.4.8/doc/devguide.rst000066400000000000000000000673621174541445200161570ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 79 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. _chap_devguide: *************************** PyMVPA Developer Guidelines *************************** Documentation ============= Documentation of the code and supplementary material (such as this file) should be done in reST_ (reStructuredText) light markup language. See `Demo `__ or a `Cheatsheet `__ for a quick demo. Code Documentation ------------------ Code must be documented in accordance to `epydoc + reST usage guidelines `__ However, the main focus should be put on a properly rendering *Module reference*. The module reference is also generated from the docstrings and there very similar to the API docs generated by epydoc. The main difference is that epydoc generates a much richer set of information (e.g. inheritance graphs), which might not be useful, but even counter-productive in a user-centered documentation. The module reference tries to limit the amount of information to a reasonable extent. it embeds technical docs into the user manual and therefore allows for easy (and automatic) cross-references between manual and module reference. A bunch of functions in `doc/conf.py` take care of converting the docstrings into the proper format to be processed by Sphinx. This is done to ensure both *human-readable* plain text docs (e.g. when using within IPython, and at the same time, an extensive use of Sphinx's markup capabilities. Parameter lists should be written as definition lists and not bulleted lists. For an example how to do it right, please see mvpa/datasets/dataset.py. Basically, it should look like this:: :Parameters: : : optional multiline description The textwidth of all docstrings should not exceed **72** characters, to ensure nicely looking docs on a 80 characters terminal in IPython. Examples -------- Examples should be complete and stand-alone scripts located in `doc/examples`. If an example involves any kind of interactive step, it should honor the :envvar:`MVPA_EXAMPLES_INTERACTIVE` setting, to allow for automatic testing of all examples. In case of a matplotlib-based visualization such snippet should be sufficient:: from mvpa import cfg if cfg.getboolean('examples', 'interactive', True): P.show() All examples are automatically converted into RsT documents for inclusion in the manual. Each of them is preprocessed in the following way: * Any header till the first docstring is stripped. * Each top-level (non-assigned) docstring is taken as a text block in the generated RsT source file. Such a docstring might appear anywhere in the example, not just at the beginning. In this case, the code snippet is properly split and the text block is inserted at the corresponding location. * All remaining lines are treated as code and inserted in the RsT source with appropriate markup. The first docstring in each example must have a proper section heading (with '=' markup). Finally, each example should be added to the appropriate `toctree` in `doc/examples.rst`. Code Formatting =============== pylint Code should be conformant with Pylint_ driven by config located at `doc/misc/pylintrc `__. It assumes camelback notation (classes start with capitals, functions with lowercase) and indentation using 4 spaces (i.e. no tabs) Variables are low-case and can have up to 2 _s. To engage, use 1 of 3 methods: - place it in *~/.pylintrc* for user-wide installation - use within a call to pylint:: pylint --rcfile=$PWD/doc/misc/pylintrc - export environment variable from mvpa sources top directory:: export PYLINTRC=$PWD/doc/misc/pylintrc 2 empty lines According to original python style guidelines: single empty line to separate methods within class, and 2 empty lines between classes **BUT** we do 2 empty between methods, 3 empty between classes module docstring Each module should start with a docstring describing the module (which is not inside the hashed-comment of each file) look at mapper or neighbor for tentative organization if copyright/license has to be present in each file. header Each file should contain a header from `doc/misc/header.py `__. notes Use following keywords will be caught by pylint to provide a summary of what yet to be done in the given file FIXME something which needs fixing (sooner than later) TODO future plan (i.e. later than sooner) XXX some concern/question YYY comment/answer to above mentioned XXX concern WiP Work in Progress: API and functionality might rapidly change Coding Conventions ================== __repr__ most of the classes should provide meaningful and concise summary over their identity (name + parameters + some summary over results if any) Naming Conventions ================== Function Arguments ------------------ dataset vs data Ones which are supposed to be derived from :class:`~mvpa.datasets.base.Dataset` class should have suffix (or whole name) ``dataset``. In contrast, if argument is expected to be simply a NumPy_ array, suffix should be ``data``. For example:: class Classifier(ClassWithCollections): ... def train(self, dataset): ... def predict(self, data): class FeatureSelection(ClassWithCollections): ... def __call__(self, dataset, testdataset): Such convention should be enforced in all ``*train``, ``*predict`` functions of classifiers. .. _NumPy: http://numpy.scipy.org/ Tests ===== * Every more or less "interesting" bugfix should be accompanied by a unittest which might help to prevent it in the future refactoring * Every new feature should have a unittest * Unit tests that might be non-deterministic (e.g. depending on classifier performance, which is turn is randomly initialized) should be made conditional like this: >>> from mvpa import cfg >>> if cfg.getboolean('tests', 'labile', default='yes'): ... pass Extending PyMVPA ================ This section shall provide a developer with the necessary pieces of information for writing extensions to PyMVPA. The guidelines given here, must be obeyed to ensure a maximum of compatibilty and inter-operability. As a consequence, all modifications that introduce changes to the basic interfaces outlined below have to be documented here and also should be announced in the changelog. Adding an External Dependency ----------------------------- Introducing new external dependencies should be done in a completely optional fashion. This includes both build-dependencies and runtime dependencies. With `mvpa.base.externals` PyMVPA provides a simple framework to test the availability of certain external components and publish the results of the tests throughout PyMVPA. Adding a new Dataset type ------------------------- * Required interface for Mapper. * only new subclasses of MappedDataset + new Mappers (all other as improvements into the Dataset base class)? go into `mvpa/datasets/` Adding a new Classifier ----------------------- To add a new classifier implementation it is sufficient to create a new sub-class of :class:`~mvpa.clfs.base.Classifier` and add implementations of the following methods: `__init__(**kwargs)` Additional arguments and keyword arguments may be added, but the base-class contructor has to be called with `**kwargs`! `_train(dataset)` Has to train the classifier when it is called with a :class:`~mvpa.datasets.base.Dataset`. Successive calls to this methods always have to train the classifier on the respective datasets. An eventually existing prior training status has to be cleared automatically. Nothing is returned. `_predict(data)` Unlike `_train()` the method is not called with a :class:`~mvpa.datasets.base.Dataset` instance, but with any sequence of data samples (e.g. arrays). It has to return a sequence of predictions, one for each data sample. With this minimal implementation the classifier provides some useful functionality, by automatically storing some relevant information upon request in state variables. .. IncludeStates: clfs.base Classifier Supported states: ================== ============================================== ========= State Name Description Default ------------------ ---------------------------------------------- --------- feature_ids Feature IDS which were used for the actual Disabled training. predicting_time Time (in seconds) which took classifier to Enabled predict. predictions Most recent set of predictions. Enabled trained_dataset The dataset it has been trained on. Disabled trained_labels Set of unique labels it has been trained on. Enabled training_confusion Confusion matrix of learning performance. Disabled training_time Time (in seconds) which took classifier to Enabled train. values Internal classifier values the most recent Disabled predictions are based on. ================== ============================================== ========= If any intended functionality cannot be realized be implementing above methods. The :class:`~mvpa.clfs.base.Classifier` class offers some additionals methods that might be overriden by sub-classes. For all methods described below it is strongly recommended to call the base-class methods at the end of the implementation in the sub-class to preserve the full functionality. `_pretrain(dataset)` Called with the :class:`~mvpa.datasets.base.Dataset` instance that shall be trained with, but before the actual training is performed. `_posttrain(dataset)` Called with the :class:`~mvpa.datasets.base.Dataset` instance the classifier was trained on, just after training was performed. `_prepredict(data)` Called with the data samples the classifier should do a prediction with, just before the actual `_prediction()` call. `_postpredict(data, result)` Called with the data sample for which predictions were made and the resulting predictions themselves. Source code files of all classifier implementations go into `mvpa/clfs/`. Outstanding Questions: * when states and when properties? Adding a new DatasetMeasure --------------------------- There are few possible base-classes for new measures (former sensitivity analyzers). First, :class:`~mvpa.measures.base.DatasetMeasure` can directly be sub-classed. It is a base class for any measure to be computed on a :class:`~mvpa.datasets.base.Dataset`. This is the more generic approach. In the most of the cases, measures are to be reported per each feature, thus :class:`~mvpa.measures.base.FeaturewiseDatasetMeasure` should serve as a base class in those cases. Furthermore, for measures that make use of some classifier and extract the sensitivities from it, :class:`~mvpa.measures.base.Sensitivity` (derived from :class:`~mvpa.measures.base.FeaturewiseDatasetMeasure`) is a more appropriate base-class, as it provides some additional useful functionality for this use case (e.g. training a classifier if needed). All measures (actually all objects based on :class:`~mvpa.measures.base.DatasetMeasure`) support a `transformer` keyword argument to their constructor. The functor passed as its value is called with the to be returned results and its outcome is returned as the final results. By default no transformation is performed. If a :class:`~mvpa.measures.base.DatasetMeasure` computes a characteristic, were both large positive and large negative values indicate high relevance, it should nevertheless *not* return absolute sensitivities, but set a default transformer instead that takes the absolute (e.g. plain `N.absolute` or a convinience wrapper Absolute_). To add a new measure implementation it is sufficient to create a new sub-class of :class:`~mvpa.measures.base.DatasetMeasure` (or :class:`~mvpa.measures.base.FeaturewiseDatasetMeasure`, or :class:`~mvpa.measures.base.Sensitivity`) and add an implementation of the `_call(dataset)` method. It will be called with an instance of :class:`~mvpa.datasets.base.Dataset`. :class:`~mvpa.measures.base.FeaturewiseDatasetMeasure` (e.g. :class:`~mvpa.measures.base.Sensitivity` as well) has to return a vector of featurewise sensitivity scores. .. IncludeStates: measures.base DatasetMeasure Supported states: ================== ============================================== ========= State Name Description Default ------------------ ---------------------------------------------- --------- null_prob State variable. Enabled raw_results Computed results before applying any Disabled transformation algorithm. ================== ============================================== ========= Source code files of all sensitivity analyzer implementations go into `mvpa/measures/`. Classifier-independent Sensitivity Analyzers ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Nothing special. Classifier-based Sensitivity Analyzers ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A :class:`~mvpa.measures.base.Sensitivity` behaves exactly like its classifier-independent sibling, but additionally provides support for embedding the necessary classifier and handles its training upon request (boolean `force_training` keyword argument of the constructor). Access to the embedded classifier object is provided via the `clf` property. .. IncludeStates: measures.base Sensitivity Supported states: ================== ============================================== ========= State Name Description Default ------------------ ---------------------------------------------- --------- base_sensitivities Stores basic sensitivities if the sensitivity Disabled relies on combining multiple ones. null_prob State variable. Enabled raw_results Computed results before applying any Disabled transformation algorithm. ================== ============================================== ========= Outstanding Questions: * What is a :class:`mvpa.measures.base.ProxyClassifierSensitivityAnalyzer` useful for? * Shouldn't there be a `sensitivities` state? .. _Absolute: api/mvpa.misc.transformers-module.html#Absolute Adding a new Algorithm ---------------------- go into `mvpa/algorithms/` Git Repository ============== Layout ------ The repository is structured by a number of branches. Each developer should prefix his/her branches with a unique string plus '/' (maybe initials or similar). Currently there are: :mh: Michael Hanke :per: Per B. Sederberg :yoh: Yaroslav Halchenko Each developer can have an infinite number of branches. If the number of branches causes gitk output to exceed a usual 19" screen, the respective developer has to spend some bucks (or euros) on new screens for all others ;-) The main release branch is called *master*. This is a merge-only branch. Features finished or updated by some developer are merged from the corresponding branch into *master*. At a certain point the current state of *master* is tagged -- a release is done. Only usable feature should end-up in *master*. Ideally *master* should be releasable at all times. Something must not be merged into master if *any* unit test fails. Additionally, there are packaging branches. They are labeled after the package target (e.g. *debian* for a Debian package). Releases are merged into the packaging branches, packaging get updated if necessary and the branch gets tagged when a package version is released. Maintenance (as well as backport) releases should be gone under *maint/codename.flavor* (e.g. *maint/lenny*, *maint/lenny.security*, *maint/sarge.bpo*). Commits ------- Please prefix all commit summaries with one (or more) of the following labels. This should help others to easily classify the commits into meaningful categories: * *BF* : bug fix * *RF* : refactoring * *NF* : new feature * *BW* : addresses backward-compatibility * *OPT* : optimization * *BK* : breaks something and/or tests fail * *PL* : making pylint happier * *DOC*: for all kinds of documentation related commits .. _reST: http://docutils.sourceforge.net/docs/ref/rst/restructuredtext.html .. _EmacsreST: http://docutils.sourceforge.net/docs/user/emacs.html .. _Pylint: http://packages.debian.org/unstable/python/pylint Merges ------ For easy tracking of what changes were absorbed during merge, we advice to enable merge summary within git: git-config merge.summary true Changelog ========= The PyMVPA changelog is located in the toplevel directory of the source tree in the `Changelog` file. The content of this file should be formated as restructured text to make it easy to put it into manual appendix and on the website. This changelog should neither replicate the VCS commit log nor the distribution packaging changelogs (e.g. debian/changelog). It should be focused on the user perspective and is intended to list rather macroscopic and/or important changes to the module, like feature additions or bugfixes in the algorithms with implications to the performance or validity of results. It may list references to 3rd party bugtrackers, in case the reported bugs match the criteria listed above. Changelog entries should be tagged with the name of the developer(s) (mainly) involved in the modification -- initials are sufficient for people contributing regularly. Changelog entries should be added whenever something is ready to be merged into the master branch, not necessarily with a release already approaching. Developer-TODO ============== Things to implement for the next release (Release goals) -------------------------------------------------------- * A part of below restructuring TODO but is separate due to it importance: come up with cleaner hierarchy and tagging of classifiers and regressions -- now they are all `Classifier` * Unify parameter naming across all classifiers and come up with a labeling guideline for future classifier implementations and wrappers:: Numeric parameters can be part of .params Collection now, so they are joined together. * Provide sufficient documentation about internal variable naming to make Harvester/Harvesting functionality usable. Currently the user is supposed to know, how a particular *local* variable is called to be able to harvest e.g. `feature_ids` of classifiers over cross-validation folds:: class.HARVESTABLE={'blah' : ' some description'} Add information on HARVESTABLE and StateVariable Collectable -> Attribute base.attributes * Restructure code base (incl. renaming and moving pieces) Let's use the following list to come up with a nice structure for all logical components we have: * Datasets * Sensitivity analyzers (maybe: featurewise measures) * Classifier sensitivities (SVM, SMLR) -> respective classifiers * ANOVA -> mvpa.measures.anova * Noise perturbation -> -> mvpa.measures.noisepertrubation * meta-algorithms (splitting) -> mvpa.measures DatasetMeasure -> Measure (transformers) FeaturewiseDatasetMeasure? combiners to be absorbed withing transformers? and then gone? {Classifier?}Sensitivity? * Mappers:: mvpa.mappers (AKA mvpa.projections mvpa.transformers) * Along with PCA/ICA mappers, we should add a PLS mapper:: PCA.train(learningdataset) .forward, .backward Package pychem for Debian, see how to use from PyMVPA! ;-) Same for MDP (i.e. use from pymvpa) * Feature selection algorithms * Simple thresholding * RFE * IFS * .mapper state variable mvpa.featsel (NB no featsel.featsel.featsel more than 4 times!) mvpa.featsel.rfe mvpa.featsel.ifs * several base classes with framework infrastructure (Harvester, ClassWithCollections, virtual properties, ...) * Transfer error calculation * Cross-validation support * Monte-Carlo-based significance testing * Dataset splitter * Metrics and distance functions * Functions operating on dataset for preprocessing or transformations * Commandline interface support * Functions to generate artificial datasets * Error functions (i.e. for TransferError) * Custom exception types * Python 2.5 copy() aka external code shipped with PyMVPA * Several helpers for data IO * Left-over from the last attempt to establish a generic parameter interface * Detrending (operating on Datasets) * Result 'Transformers' to be used with 'transformer=' kwarg * Debugging and verbosity infrastructure * plus additional helpers, ranging from simple to complex scattered all over the place * Resultant hierarchy: - mvpa + datasets + clfs + measures + featsel * Add ability to add/modify custom attributes to a dataset. * Possibly make NiftiDataset default to float32 when it sees that the data are ints. * Add kernel methods as option to all classifiers, not just SVMs. For example, you should be able to run a predefined or custom kernel on the samples going into SMLR. * TransferError needs to know what type of data to send to any specific ErrorFX. Right now there is only support for predictions and labels, but the area under the ROC and the correlation-based error functions expect to receive the "values" or "probabilities" from a classifier. Just to make this harder, every classifier is different. For example, a ridge regression's predictions are continuous values, whereas for a SVM you need to pass in the probabilities. For binary: 1 value multiclass: 1 value, or N values * In a related issue, the predictions and values states of the classifiers need to have a consitent format. Currently, SVM returns a list of dictionaries for values and SMLR returns a numpy ndarray. Long and medium term TODOs (aka stuff that has been here forever) ----------------------------------------------------------------- * Agree upon sensitivities returned by the classifiers. Now SMLR/libsvm.SVM returns (nfeatures x X), (where X is either just 1 for binary problems, or nclasses in full multiclass in SMLR, or nclasses-1 for libsvm(?) or not-full SMLR). In case of sg.SVM and GPR (I believe) it is just (nfeatures,). MaskMapper puked on reverse in the first specification... think about combiner -- should it or should not be there... etc * selected_ids -> implement via MaskMapper? yoh: it might be preferable to manipulate/expose MaskMapper instead of plain list of selected_ids within FeatureSelection classes * unify naming of working/testing * transerror.py for instance uses testdata/trainingdata * rfe.py dataset, testdataset * implement proper cloning of classifiers. untrain() doesn't work in some cases, since we can create somewhat convolved object definitions so it is hard, if not impossible, to get to all used classifiers. See for instance clfswh['SVM/Multiclass+RFE']. We can't get all the way into classifier-based sensitivity analyzer. Thus instead of tracking all the way down in hierarchy, we should finally create proper 'parametrization' handling of classifiers, so we could easily clone basic ones (which might have active SWIG bindings), and top-level ones should implement .clone() themselves. or may be some other way, but things should be done. Or may be via proper implementation of __reduce__ etc * mvpa.misc.warning may be should use stock python warnings module instead of custom one? * ConfusionBasedError -> InternalError ? * Think about how to deal with Transformers to serve them with basic_analyzers... May be transformer can be a an argument for any analyzer! Ha! Indeed... may be later * Renaming of the modules transerror.py -> errors.py * SVM: getSV and getSVCoef return very 'packed' presentation whenever classifier is multiclass. Thus they have to be unpacked before proper use (unless it is simply a binary classifier). * Regression tests: for instance using sample dataset which we have already, run doc/examples/searchlight.py and store output to validate against. Probably the best would be to create a regression test suite within unit tests which would load the dataset and run various algorithms on it a verify the results against previousely obtained (and dumped to the disk) * Agree on how to describe parameters to functions. Describe in NOTES.coding. * feature_selector -- may be we should return a tuple (selected_ids, discarded_ids)? Michael: Is there any use case for that? ElementSelector can 'select' and 'discard' already. DO we need both simultaneously? * Basic documentation: Examples (more is better) describing various use cases (everything in the cncre should be done in examples) * Non-linear SVM RFE * ParameterOptimizer (might be also OptimizedClassifier which uses parameterOptimizer internally but as the result there is a classifier which automatically optimizes its parameters. It is close in idea to classifier based on RFE) * provide for Dataset -- Dataset.__featattr which has attributes for features similar to __dsattr way. in --> data -> dataShape out --> features -> Building a binary installer on MacOS X 10.5 =========================================== A simple way to build a binary installer for Mac OS is bdist_mpkg_. This is a setuptools extension that uses the proper native parts of MacOS to build the installer. However, for PyMVPA there are two problems with bdist_mpkg_: 1. PyMVPA uses distutils not setuptools and 2. current bdist_mpkg_ 0.4.3 does not work for MacOS X 10.5 (Leopard). But both can be solved. Per 1) A simple wrapper script in `tools/mpkg_wrapper.py` will enable the use of setuptools on top of distutils, while keeping the distutils part in a usable state. Per 2) The following patch (against 0.4.3.) makes bdist_mpkg_ compatible with MacOS 10.5. It basically changes the way bdist_mpkg_ determined the GID of the admin group. 10.5 removed the `nidump` command:: diff -rNu bdist_mpkg-0.4.3/bdist_mpkg/tools.py bdist_mpkg-0.4.3.leopard/bdist_mpkg/tools.py --- bdist_mpkg-0.4.3/bdist_mpkg/tools.py 2006-07-09 00:39:00.000000000 -0400 +++ bdist_mpkg-0.4.3.leopard/bdist_mpkg/tools.py 2008-08-21 07:43:35.000000000 -0400 @@ -79,15 +79,12 @@ yield os.path.join(root, fn) def get_gid(name, _cache={}): - if not _cache: - for line in os.popen('/usr/bin/nidump group .'): - fields = line.split(':') - if len(fields) >= 3: - _cache[fields[0]] = int(fields[2]) - try: - return _cache[name] - except KeyError: - raise ValueError('group %s not found' % (name,)) + for line in os.popen("dscl . -read /Groups/" + name + " PrimaryGroupID"): + fields = [f.strip() for f in line.split(':')] + if fields[0] == "PrimaryGroupID": + return fields[1] + + raise ValueError('group %s not found' % (name,)) def find_root(path, base='/'): """ .. _bdist_mpkg: http://undefined.org/python/#bdist_mpkg pymvpa-0.4.8/doc/examples.rst000066400000000000000000000033051174541445200161640ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. index:: example .. _chap_examples: ************* Full Examples ************* Each of the examples in this section is a stand-alone script containing all necessary code to run some analysis. All examples are shipped with PyMVPA and can be found in the `doc/examples/` directory in the source package. This directory might include some more special-interest examples which are not listed here. Some examples need to access a sample dataset available in the `data/` directory within the root of the PyMVPA hierarchy, and thus have to be invoked directly from PyMVPA root (e.g. `doc/examples/searchlight_2d.py`). Alternatively, one can download a full example dataset, which is explained in the next section. .. include:: misc/exampledata.readme Preprocessing ============= .. toctree:: examples/projections examples/smellit Analysis ======== .. toctree:: examples/start_easy examples/smlr examples/clfs_examples examples/gpr examples/searchlight_minimal examples/searchlight_2d examples/searchlight_dsm examples/sensanas examples/svdclf examples/permutation_test examples/match_distribution examples/eventrelated Visualization ============= .. toctree:: examples/erp_plot examples/pylab_2d examples/topo_plot examples/som Miscellaneous ============= .. toctree:: examples/kerneldemo examples/curvefitting pymvpa-0.4.8/doc/examples/000077500000000000000000000000001174541445200154315ustar00rootroot00000000000000pymvpa-0.4.8/doc/examples/clfs_examples.py000077500000000000000000000074311174541445200206400ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Classifier Sweep ================ This examples shows a test of various classifiers on different datasets. """ from mvpa.suite import * # no MVPA warnings during whole testsuite warning.handlers = [] def main(): # fix seed or set to None for new each time N.random.seed(44) # Load Haxby dataset example attrs = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes_literal.txt')) haxby8 = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'), labels=attrs.labels, labels_map=True, chunks=attrs.chunks, mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz'), dtype=N.float32) # preprocess slightly rest_label = haxby8.labels_map['rest'] detrend(haxby8, perchunk=True, model='linear') zscore(haxby8, perchunk=True, baselinelabels=[rest_label], targetdtype='float32') haxby8_no0 = haxby8.selectSamples(haxby8.labels != rest_label) dummy2 = normalFeatureDataset(perlabel=30, nlabels=2, nfeatures=100, nchunks=6, nonbogus_features=[11, 10], snr=3.0) for (dataset, datasetdescr), clfs_ in \ [ ((dummy2, "Dummy 2-class univariate with 2 useful features out of 100"), clfswh[:]), ((pureMultivariateSignal(8, 3), "Dummy XOR-pattern"), clfswh['non-linear']), ((haxby8_no0, "Haxby 8-cat subject 1"), clfswh['multiclass']), ]: print "%s\n %s" % (datasetdescr, dataset.summary(idhash=False)) print " Classifier " \ "%corr #features\t train predict full" for clf in clfs_: print " %-40s: " % clf.descr, # Lets do splits/train/predict explicitely so we could track # timing otherwise could be just #cv = CrossValidatedTransferError( # TransferError(clf), # NFoldSplitter(), # enable_states=['confusion']) #error = cv(dataset) #print cv.confusion # to report transfer error confusion = ConfusionMatrix(labels_map=dataset.labels_map) times = [] nf = [] t0 = time.time() clf.states.enable('feature_ids') for nfold, (training_ds, validation_ds) in \ enumerate(NFoldSplitter()(dataset)): clf.train(training_ds) nf.append(len(clf.feature_ids)) if nf[-1] == 0: break predictions = clf.predict(validation_ds.samples) confusion.add(validation_ds.labels, predictions) times.append([clf.training_time, clf.predicting_time]) if nf[-1] == 0: print "no features were selected. skipped" continue tfull = time.time() - t0 times = N.mean(times, axis=0) nf = N.mean(nf) # print "\n", confusion print "%5.1f%% %-4d\t %.2fs %.2fs %.2fs" % \ (confusion.percentCorrect, nf, times[0], times[1], tfull) if __name__ == "__main__": main() pymvpa-0.4.8/doc/examples/curvefitting.py000077500000000000000000000034521174541445200205230ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Curve-Fitting ============= An example showing how to fit an HRF model to noisy peristimulus time-series data. First, importing the necessary pieces: """ import numpy as N import pylab as P from mvpa.misc.plot import plotErrLine from mvpa.misc.fx import singleGammaHRF, leastSqFit from mvpa import cfg """ Now, we generate some noisy "trial time courses" from a simple gamma function (40 samples, 6s time-to-peak, 7s FWHM and no additional scaling: """ a = N.asarray([singleGammaHRF(N.arange(20), A=6, W=7, K=1)] * 40) # get closer to reality with noise a += N.random.normal(size=a.shape) """ Fitting a gamma function to this data is easy (using resonable seeds for the parameter search (5s time-to-peak, 5s FWHM, and no scaling): """ fpar, succ = leastSqFit(singleGammaHRF, [5,5,1], a) """ Generate high-resultion curves for the 'true' time course and the fitted one for visualization and plot them together with the data: """ x = N.linspace(0,20) curves = [(x, singleGammaHRF(x, 6, 7, 1)), (x, singleGammaHRF(x, *fpar))] # plot data (with error bars) and both curves plotErrLine(a, curves=curves, linestyle='-') # add legend to plot P.legend(('original', 'fit')) if cfg.getboolean('examples', 'interactive', True): # show the cool figure P.show() """ The ouput of the provided example should look like .. image:: ../pics/ex_curvefitting.* :align: center :alt: Curve fitting example """ pymvpa-0.4.8/doc/examples/erp_plot.py000077500000000000000000000051671174541445200176430ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ ERP/ERF-Plots ============= Example demonstrating an ERP-style plots. Actually, this code can be used to plot various time-locked data types. This example uses MEG data and therefore generates an ERF-plot. """ from mvpa.suite import * # load data meg = TuebingenMEG(os.path.join(pymvpa_dataroot, 'tueb_meg.dat.gz')) # Define plots for easy feeding into plotERP plots = [] colors = ['r', 'b', 'g'] # figure out pre-stimulus onset interval t0 = -meg.timepoints[0] plots = [ {'label' : meg.channelids[i], 'color' : colors[i], 'data' : meg.data[:, i, :]} for i in xrange(len(meg.channelids)) ] # Common arguments for all plots cargs = { 'SR' : meg.samplingrate, 'pre_onset' : t0, # Plot only 50ms before and 100ms after the onset since we have # just few trials 'pre' : 0.05, 'post' : 0.1, # Plot all 'errors' in different degrees of shadings 'errtype' : ['ste', 'ci95', 'std'], # Set to None if legend manages to obscure the plot 'legend' : 'best', 'alinewidth' : 1 # assume that we like thin lines } # Create a new figure fig = P.figure(figsize=(12, 8)) # Following plots are plotted inverted (negative up) for the # demonstration of this capability and elderly convention for ERP # plots. That is controlled with ymult (negative gives negative up) # Plot MEG sensors # frame_on=False guarantees abent outside rectangular axis with # labels. plotERP recreates its own axes centered at (0,0) ax = fig.add_subplot(2, 1, 1, frame_on=False) plotERPs(plots[:2], ylabel='$pT$', ymult=-1e12, ax=ax, **cargs) # Plot EEG sensor ax = fig.add_subplot(2, 1, 2, frame_on=False) plotERPs(plots[2:3], ax=ax, ymult=-1e6, **cargs) # Additional example: plotting a single ERP on an existing plot # without drawing axis: # # plotERP(data=meg.data[:, 0, :], SR=meg.samplingrate, pre=pre, # pre_mean=pre, errtype=errtype, ymult=-1.0) if cfg.getboolean('examples', 'interactive', True): # show all the cool figures P.show() """ The ouput of the provided example is presented below. It is not a very fascinating one due to the limited number of samples provided in the dataset shipped within the toolbox. .. image:: ../pics/ex_erp_plot.* :align: center :alt: ERP plot example """ pymvpa-0.4.8/doc/examples/eventrelated.py000077500000000000000000000146211174541445200204740ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Spatio-temporal Analysis of event-related fMRI data =================================================== .. index:: event-related fMRI The purpose of this example is to show how to use spatio-temporal samples in an event-related fMRI data analysis. We start as usual by loading the PyMVPA suite. The tiny fMRI dataset, included in the sources will server as an example dataset. Although the original paradigm of this dataset is a block-design experiment, we'll analyze it in an event-related fashion, where each block will be considered as an individual event. """ from mvpa.suite import * # filename of the source fMRI timeseries image fmri_src = os.path.join(pymvpa_dataroot, 'bold.nii.gz') mask = NiftiImage(os.path.join(pymvpa_dataroot, 'mask.nii.gz')) # load the samples attributes as usual and preserve the # literal labels attr = SampleAttributes( os.path.join(pymvpa_dataroot, 'attributes_literal.txt'), literallabels=True) """ For an event-related analysis most of the processing is done on data samples that are somehow derived from a set of events. The rest of the data could be considered irrelevant. However, some e.g. preprocessing is only meaningful when performed on the full timeseries and not the segmented event samples. An example is the detrending that typically needs to be done on the original, continuous timeseries. Therefore we are going to load the data twice: first as a simple volume-based dataset for an initial preprocessing, and second to extract the events of interest. """ verbose(1, "Load data for preprocessing") pre_ds = NiftiImage(fmri_src) # actual labels are not important here, could be 'labels=1' pre_ds = NiftiDataset(samples=fmri_src, labels=attr.labels, chunks=attr.chunks, mask=mask) # convert to floats pre_ds.setSamplesDType('float') # detrend on full timeseries detrend(pre_ds, perchunk=True, model='linear') """ After the detrending, we can now segment the timeseries into a set of events. To achieve this we have to compile a list of event definitions first. In this example we will simply convert the block-design setup defined by the samples attributes into events, so that each block become an event with an associated onset and duration. The necessary chunk settings are taken from these attributes as well. Finally, we are only interested in `face` or `house` blocks. """ evs = [ev for ev in attr.toEvents() if ev['label'] in ['face', 'house']] """ Since we might want to take a look at the sensitivity profile ranging from just before until a little after each block, we are slightly moving the event onsets (one volume prior the actual event) and request to extract a set of twelve consecutive volume a as sample for each event. """ for ev in evs: ev['onset'] -= 1 ev['duration'] = 12 """ A :class:`~mvpa.datasets.nifti.ERNiftiDataset` can now be used to segment the timeseries and automatically extract boxcar-shaped multi-volume samples. It is also capable of applying a volume mask. """ # could use evconv... verbose(1, "Segmenting timeseries into events") ds = ERNiftiDataset(samples=pre_ds.map2Nifti(), events=evs, mask=mask, labels_map={'face': 1, 'house': 2}) """ For demonstration purposes we copy the pristine dataset before any further processing is done. """ # preserve orig_ds = deepcopy(ds) """ The rest is pretty much standard. A dataset with spatio-temporal fMRI samples behaves just as any other dataset type. We perform normalization by Z-scoring the data and settle on a linear SVM classifier to perform a cross-validated sensitivity analysis. """ # using rest as baseline zscore(ds, perchunk=True) clf = LinearCSVMC() sclf = SplitClassifier(clf, NFoldSplitter(), enable_states=['confusion', 'training_confusion']) # Compute sensitivity, which in turn trains the sclf sensitivities = \ sclf.getSensitivityAnalyzer(combiner=None, slave_combiner=None)(ds) """ Before looking at the sensitivity profile we first have to inspect the classifier performance in the cross-validation, since only for a model with reasonable generalization performance it would make sense to interpret the model parameters, i.e. classifier weights. If this is done we could dump the spatio-temporal sensitivity profile, which covers all voxels in the dataset for the full duration of the events, into a NIfTI file. """ print sclf.confusion #ds.map2Nifti(N.mean(sensitivities, axis=0)).save('fs_sens.nii.gz') """ However, we are going to plot it for some target voxel right away, and compare it to the actual signal timecourse prior and after normalization. We can use the dataset's mapper to convert the sensitivity vector for each CV-fold back into a 4D snippet. """ # reverse map sensitivities -> fold x volumes x Z x Y x X smaps = N.array([ds.mapReverse(s) for s in sensitivities]) # extract sensitivity profile for target voxel ijk(33,10,0) v = (0, 3, 15) smap = smaps[:,:,v[0],v[1],v[2]] """ Now, we plot the orginal signal after initial detrending, """ P.subplot(311) P.title('Voxel zyx%s\nblock-onset@1, block-offset@8' % `v`) for l in ds.uniquelabels: P.plot( ds.mapReverse( orig_ds.samples[ds.labels==l].mean(axis=0) )[:,v[0],v[1],v[2]]) P.ylabel('Signal after detrending') P.axhline(linestyle='--', color='0.6') """ the peristimulus timecourse after Z-scoring, """ P.subplot(312) for l in ds.uniquelabels: P.plot( ds.mapReverse( ds.samples[ds.labels==l].mean(axis=0) )[:,v[0],v[1],v[2]]) P.ylabel('Signal after normalization') P.axhline(linestyle='--', color='0.6') """ and finally the associated SVM weight profile for each peristimulus timepoint of the voxel. """ P.subplot(313) plotErrLine(smap) P.ylabel('Sensitivity') P.xlabel('Peristimulus volumes') P.axhline(linestyle='--', color='0.6') if cfg.getboolean('examples', 'interactive', True): # show all the cool figures P.show() pymvpa-0.4.8/doc/examples/gpr.py000077500000000000000000000102721174541445200166000ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # Copyright (c) 2008 Emanuele Olivetti # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ The effect of different hyperparameters in GPR ============================================== .. index:: GPR The following example runs Gaussian Process Regression (GPR) on a simple 1D dataset using squared exponential (i.e., Gaussian or RBF) kernel and different hyperparameters. The resulting classifier solutions are finally visualized in a single figure. As usual we start by importing all of PyMVPA: """ # Lets use LaTeX for proper rendering of greek from matplotlib import rc rc('text', usetex=True) from mvpa.suite import * """ The next lines build two datasets using one of PyMVPA's data generators. """ # Generate dataset for training: train_size = 40 F = 1 dataset = data_generators.sinModulated(train_size, F) # Generate dataset for testing: test_size = 100 dataset_test = data_generators.sinModulated(test_size, F, flat=True) """ The last configuration step is the definition of four sets of hyperparameters to be used for GPR. """ # Hyperparameters. Each row is [sigma_f, length_scale, sigma_noise] hyperparameters = N.array([[1.0, 0.2, 0.4], [1.0, 0.1, 0.1], [1.0, 1.0, 0.1], [1.0, 0.1, 1.0]]) """ The plotting of the final figure and the actually GPR runs are performed in a single loop. """ rows = 2 columns = 2 P.figure(figsize=(12, 12)) for i in range(rows*columns): P.subplot(rows, columns, i+1) regression = True logml = True data_train = dataset.samples label_train = dataset.labels data_test = dataset_test.samples label_test = dataset_test.labels """ The next lines configure a squared exponential kernel with the set of hyperparameters for the current subplot and assign the kernel to the GPR instance. """ sigma_f, length_scale, sigma_noise = hyperparameters[i, :] kse = KernelSquaredExponential(length_scale=length_scale, sigma_f=sigma_f) g = GPR(kse, sigma_noise=sigma_noise, regression=regression) print g if regression: g.states.enable("predicted_variances") if logml: g.states.enable("log_marginal_likelihood") """ After training GPR the predictions are queried by passing the test dataset samples and accuracy measures are computed. """ g.train(dataset) prediction = g.predict(data_test) # print label_test # print prediction accuracy = None if regression: accuracy = N.sqrt(((prediction-label_test)**2).sum()/prediction.size) print "RMSE:", accuracy else: accuracy = (prediction.astype('l')==label_test.astype('l')).sum() \ / float(prediction.size) print "accuracy:", accuracy """ The remaining code simply plots both training and test datasets, as well as the GPR solutions. """ if F == 1: P.title(r"$\sigma_f=%0.2f$, $length_s=%0.2f$, $\sigma_n=%0.2f$" \ % (sigma_f,length_scale,sigma_noise)) P.plot(data_train, label_train, "ro", label="train") P.plot(data_test, prediction, "b-", label="prediction") P.plot(data_test, label_test, "g+", label="test") if regression: P.plot(data_test, prediction-N.sqrt(g.predicted_variances), "b--", label=None) P.plot(data_test, prediction+N.sqrt(g.predicted_variances), "b--", label=None) P.text(0.5, -0.8, "$RMSE=%.3f$" %(accuracy)) P.text(0.5, -0.95, "$LML=%.3f$" %(g.log_marginal_likelihood)) else: P.text(0.5, -0.8, "$accuracy=%s" % accuracy) P.legend(loc='lower right') print "LML:", g.log_marginal_likelihood if cfg.getboolean('examples', 'interactive', True): # show all the cool figures P.show() pymvpa-0.4.8/doc/examples/gpr_model_selection0.py000077500000000000000000000057101174541445200221060ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # Copyright (c) 2008 Emanuele Olivetti # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Run simple model selection (grid search over hyperparameters' space) of Gaussian Process Regression (GPR) on a simple 1D example. """ __docformat__ = 'restructuredtext' import numpy as N from mvpa.suite import * import pylab as P # Generate train and test dataset: train_size = 40 test_size = 100 F = 1 dataset = data_generators.sinModulated(train_size, F) dataset_test = data_generators.sinModulated(test_size, F, flat=True) print "Looking for better hyperparameters: grid search" # definition of the search grid: sigma_noise_steps = N.linspace(0.1, 0.5, num=20) length_scale_steps = N.linspace(0.05, 0.6, num=20) # Evaluation of log maringal likelohood spanning the hyperparameters' grid: lml = N.zeros((len(sigma_noise_steps), len(length_scale_steps))) lml_best = -N.inf length_scale_best = 0.0 sigma_noise_best = 0.0 i = 0 for x in sigma_noise_steps: j = 0 for y in length_scale_steps: kse = KernelSquaredExponential(length_scale=y) g = GPR(kse, sigma_noise=x, regression=True) g.states.enable("log_marginal_likelihood") g.train(dataset) lml[i, j] = g.log_marginal_likelihood if lml[i, j] > lml_best: lml_best = lml[i, j] length_scale_best = y sigma_noise_best = x # print x,y,lml_best pass j += 1 pass i += 1 pass # Log marginal likelihood contour plot: P.figure() X = N.repeat(sigma_noise_steps[:, N.newaxis], sigma_noise_steps.size, axis=1) Y = N.repeat(length_scale_steps[N.newaxis, :], length_scale_steps.size, axis=0) step = (lml.max()-lml.min())/30 P.contour(X, Y, lml, N.arange(lml.min(), lml.max()+step, step), colors='k') P.plot([sigma_noise_best], [length_scale_best], "k+", markeredgewidth=2, markersize=8) P.xlabel("noise standard deviation") P.ylabel("characteristic length_scale") P.title("log marginal likelihood") P.axis("tight") print "lml_best", lml_best print "sigma_noise_best", sigma_noise_best print "length_scale_best", length_scale_best print "number of expected upcrossing on the unitary intervale:", \ 1.0/(2*N.pi*length_scale_best) # TODO: reincarnate by providing a function within gpr.py # # Plot predicted values using best hyperparameters: # P.figure() # compute_prediction(1.0, length_scale_best, sigma_noise_best, True, dataset, # dataset_test.samples, dataset_test.labels, F, True) if cfg.getboolean('examples', 'interactive', True): # show all the cool figures P.show() pymvpa-0.4.8/doc/examples/kerneldemo.py000077500000000000000000000037751174541445200201470ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Kernel-Demo =========== This is an example demonstrating various kernel implementation in PyMVPA. """ from mvpa.suite import * from mvpa.clfs.kernel import * import pylab as P # N.random.seed(1) data = N.random.rand(4, 2) for kernel_class, kernel_args in ( (KernelConstant, {'sigma_0':1.0}), (KernelConstant, {'sigma_0':1.0}), (KernelLinear, {'Sigma_p':N.eye(data.shape[1])}), (KernelLinear, {'Sigma_p':N.ones(data.shape[1])}), (KernelLinear, {'Sigma_p':2.0}), (KernelLinear, {}), (KernelExponential, {}), (KernelSquaredExponential, {}), (KernelMatern_3_2, {}), (KernelMatern_5_2, {}), (KernelRationalQuadratic, {}), ): kernel = kernel_class(**kernel_args) print kernel result = kernel.compute(data) # In the following we draw some 2D functions at random from the # distribution N(O,kernel) defined by each available kernel and # plot them. These plots shows the flexibility of a given kernel # (with default parameters) when doing interpolation. The choice # of a kernel defines a prior probability over the function space # used for regression/classfication with GPR/GPC. count = 1 for k in kernel_dictionary.keys(): P.subplot(3,4,count) # X = N.random.rand(size)*12.0-6.0 # X.sort() X = N.arange(-1,1,.02) X = X[:,N.newaxis] ker = kernel_dictionary[k]() K = ker.compute(X,X) for i in range(10): f = N.random.multivariate_normal(N.zeros(X.shape[0]),K) P.plot(X[:,0],f,"b-") P.title(k) P.axis('tight') count += 1 if cfg.getboolean('examples', 'interactive', True): # show all the cool figures P.show() pymvpa-0.4.8/doc/examples/match_distribution.py000077500000000000000000000065061174541445200217100ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Determine the Distribution of some Variable =========================================== This is an example demonstrating discovery of the distribution facility. """ from mvpa.suite import * verbose.level = 2 if __debug__: # report useful debug information for the example debug.active += ['STAT', 'STAT_'] report = Report(name='match_distribution_report', title='PyMVPA Example: match_distribution.py') verbose.handlers += [report] # Lets add verbose output to the report. # Similar action could be done to 'debug' # # Figure for just normal distribution # # generate random signal from normal distribution verbose(1, "Random signal with normal distribution") data = N.random.normal(size=(1000, 1)) # find matching distributions # NOTE: since kstest is broken in older versions of scipy # p-roc testing is done here, which aims to minimize # false positives/negatives while doing H0-testing test = 'p-roc' figsize = (15, 10) verbose(1, "Find matching datasets") matches = matchDistribution(data, test=test, p=0.05) P.figure(figsize=figsize) P.subplot(2, 1, 1) plotDistributionMatches(data, matches, legend=1, nbest=5) P.title('Normal: 5 best distributions') P.subplot(2, 1, 2) plotDistributionMatches(data, matches, nbest=5, p=0.05, tail='any', legend=4) P.title('Accept regions for two-tailed test') # we are done with the figure -- add it to report report.figure() # # Figure for fMRI data sample we have # verbose(1, "Load sample fMRI dataset") attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt')) dataset = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'), labels=attr.labels, chunks=attr.chunks, mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz')) # select random voxel dataset = dataset.selectFeatures( [int(N.random.uniform()*dataset.nfeatures)]) verbose(2, "Minimal preprocessing to remove the bias per each voxel") detrend(dataset, perchunk=True, model='linear') zscore(dataset, perchunk=True, baselinelabels=[0], targetdtype='float32') # on all voxels at once, just for the sake of visualization data = dataset.samples.ravel() verbose(2, "Find matching distribution") matches = matchDistribution(data, test=test, p=0.05) P.figure(figsize=figsize) P.subplot(2, 1, 1) plotDistributionMatches(data, matches, legend=1, nbest=5) P.title('Random voxel: 5 best distributions') P.subplot(2, 1, 2) plotDistributionMatches(data, matches, nbest=5, p=0.05, tail='any', legend=4) P.title('Accept regions for two-tailed test') report.figure() if cfg.getboolean('examples', 'interactive', True): # store the report report.save() # show the cool figure P.show() """ Example output for a random voxel is .. image:: ../pics/ex_match_distribution.* :align: center :alt: Matching distributions for a random voxel """ pymvpa-0.4.8/doc/examples/mri_plot.py000077500000000000000000000043011174541445200176310ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Basic (f)MRI plotting ===================== .. index:: plotting Estimate basic univariate sensitivity (ANOVA) an plot it overlayed on top of the anatomical. We start with basic steps: loading PyMVPA and the example fMRI dataset, basic preprocessing, estimation of the ANOVA scores and plotting. """ from mvpa.suite import * # load PyMVPA example dataset attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes_literal.txt')) dataset = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'), labels=attr.labels, labels_map=True, chunks=attr.chunks, mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz')) # since we don't have a proper anatomical -- lets overlay on BOLD nianat = NiftiImage(dataset.O[0], header=dataset.niftihdr) # do chunkswise linear detrending on dataset detrend(dataset, perchunk=True, model='linear') # define sensitivity analyzer sensana = OneWayAnova(transformer=N.abs) sens = sensana(dataset) """ It might be convinient to pre-define common arguments for multiple calls to plotMRI """ mri_args = { 'background' : nianat, # could be a filename 'background_mask' : os.path.join(pymvpa_dataroot, 'mask.nii.gz'), 'overlay_mask' : os.path.join(pymvpa_dataroot, 'mask.nii.gz'), 'do_stretch_colors' : False, 'cmap_bg' : 'gray', 'cmap_overlay' : 'autumn', # YlOrRd_r # P.cm.autumn 'fig' : None, # create new figure 'interactive' : cfg.getboolean('examples', 'interactive', True), } fig = plotMRI(overlay=dataset.map2Nifti(sens), vlim=(0.5, None), #vlim_type="symneg_z", **mri_args) """ Output of the example analysis: .. image:: ../pics/ex_plotMRI.* :align: center :alt: Simple plotting facility for (f)MRI """ pymvpa-0.4.8/doc/examples/nested_cv.py000077500000000000000000000117161174541445200177660ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Nested Cross-Validation ======================= .. index:: model selection, cross-validation Often it is desired to explore multiple models (classifiers, parameterizations) but it becomes an easy trap for introducing an optimistic bias into generalization estimate. The easiest but computationally intensive solution to overcome such a bias is to carry model selection by estimating the same (or different) performance characteristic while operating only on training data. If such performance is a cross-validation, then it leads to the so called "nested cross-validation" procedure. This example will demonstrate on how to implement such nested cross-validation while selecting the best performing classifier from the warehouse of available within PyMVPA. """ from mvpa.suite import * # increase verbosity a bit for now verbose.level = 3 # pre-seed RNG if you want to investigate the effects, thus # needing reproducible results #mvpa.seed(3) # To minimize divergence from code for >= 0.5 np = N """ For this simple example lets generate some fresh random data with 2 relevant features and low SNR. """ dataset = normalFeatureDataset(perlabel=24, nlabels=2, nchunks=3, nonbogus_features=[0, 1], nfeatures=100, snr=3.0) """ For the demonstration of model selection benefit, lets first compute cross-validated error using simple and popular kNN. """ clf_sample = kNN() cv_sample = CrossValidatedTransferError( TransferError(clf_sample), NFoldSplitter()) verbose(1, "Estimating error using a sample classifier") error_sample = np.mean(cv_sample(dataset)) """ For the convenience lets define a helpful function which we will use twice -- once within cross-validation, and once on the whole dataset """ def select_best_clf(dataset_, clfs): """Select best model according to CVTE Helper function which we will use twice -- once for proper nested cross-validation, and once to see how big an optimistic bias due to model selection could be if we simply provide an entire dataset. Parameters ---------- dataset_ : Dataset clfs : list of Classifiers Which classifiers to explore Returns ------- best_clf, best_error """ best_error = None for clf in clfs: cv = CrossValidatedTransferError(TransferError(clf), NFoldSplitter()) # unfortunately we don't have ability to reassign clf atm # cv.transerror.clf = clf try: error = np.mean(cv(dataset_)) except LearnerError, e: # skip the classifier if data was not appropriate and it # failed to learn/predict at all continue if best_error is None or error < best_error: best_clf = clf best_error = error verbose(4, "Classifier %s cv error=%.2f" % (clf.descr, error)) verbose(3, "Selected the best out of %i classifiers %s with error %.2f" % (len(clfs), best_clf.descr, best_error)) return best_clf, best_error """ First lets select a classifier within cross-validation, thus eliminating model-selection bias """ errors = [] best_clfs = {} confusion = ConfusionMatrix() verbose(1, "Estimating error using nested CV for model selection") for isplit, (dstrain, dstest) in enumerate(NFoldSplitter()(dataset)): verbose(2, "Processing split #%i" % isplit) best_clf, best_error = select_best_clf(dstrain, clfswh['!gnpp']) best_clfs[best_clf.descr] = best_clfs.get(best_clf.descr, 0) + 1 # now that we have the best classifier, lets assess its transfer # to the testing dataset while training on entire training te = TransferError(best_clf, enable_states=['confusion']) errors.append(te(dstest, dstrain)) confusion += te.states.confusion """ And for comparison, lets assess what would be the best performance if we simply explore all available classifiers, providing all the data at once """ verbose(1, "Estimating error via fishing expedition (best clf on entire dataset)") cheating_clf, cheating_error = select_best_clf(dataset, clfswh['!gnpp']) print """Errors: sample classifier (kNN): %.2f model selection within cross-validation: %.2f model selection via fishing expedition: %.2f with %s """ % (error_sample, np.mean(errors), cheating_error, cheating_clf.descr) print "# of times following classifiers were selected within " \ "nested cross-validation:" for c, count in sorted(best_clfs.items(), key=lambda x:x[1], reverse=True): print " %i times %s" % (count, c) print "\nConfusion table for the nested cross-validation results:" print confusion pymvpa-0.4.8/doc/examples/permutation_test.py000077500000000000000000000113151174541445200214150ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Monte-Carlo testing of Classifier-based Analyses ================================================ .. index:: statistical testing, monte-carlo, permutation It is often desirable to be able to make statements like *"Performance is significantly above chance-level"*. PyMVPA supports *NULL* (aka *H0*) hypothesis testing for :ref:`transfer errors ` and all :ref:`dataset measures `. In both cases the object computing the measure or transfer error takes an optional constructor argument `null_dist`. The value of this argument is an instance of some :class:`~mvpa.clfs.stats.NullDist` estimator. If *NULL* distribution is luckily a-priori known, it is possible to reuse any distribution specified in `scipy.stats` module. If the parameters of the distribution are known, such distribution instance can be used to initialize FixedNullDist_ instance to be specified in `null_dist` parameter. However, as with other applications of statistics in classifier-based analyses there is the problem that we do not know the distribution of a variable like error or performance under the *NULL* hypothesis to assign the adored p-values, i.e. the probability of a result given that there is no signal. Even worse, the chance-level or guess probability of a classifier depends on the content of a validation dataset, e.g. balanced or unbalanced number of samples per label and total number of labels). One approach to deal with this situation is to estimate the *NULL* distribution. A generic way to do this are permutation tests (aka *Monte Carlo*, :ref:`Nichols et al. (2006) `). Then *NULL* distribution is estimated by computing some measure multiple times using datasets with no relevant signal in them. These datasets are generated by permuting the labels of all samples in the training dataset each time the measure is computed, and therefore randomizing/removing any possible relevant information. Given the measures computed using the permuted datasets one can now determine the probability of the empirical measure (i.e. the one computed from the original training dataset) under the *no signal* condition. This is simply the fraction of measures from the permutation runs that is larger or smaller than the emprical (depending on whether on is looking at performances or errors). If the family of the distribution is known (e.g. Gaussian/Normal) and provided in `dist_class` parameter of MCNullDist, then permutation tests done by MCNullDist_ allow to determine the distribution parameters. Under strong assumption of Gaussian distribution, 20-30 permutations should be sufficient to get sensible estimates of the distribution parameters. If no distribution family can be assumed, with a larger number of permutations, derivation of CDF out of population is possible with Nonparametric_ probability function (which is the default value of `dist_class` for MCNullDist_). If `null_dist` is provided, the respective :class:`~mvpa.clfs.transerror.TransferError` or :class:`~mvpa.measures.base.DatasetMeasure` instance will automatically use it to estimate the *NULL* distribution and store the associated *p*-values in a state variable named `null_prob`. .. _Distribution: api/mvpa.clfs.stats.NullDist-class.html .. _Nonparametric: api/mvpa.clfs.stats.Nonparametric-class.html .. _MCNullDist: api/mvpa.clfs.stats.MCNullDist-class.html .. _FixedNullDist: api/mvpa.clfs.stats.FixedNullDist-class.html """ # lazy import from mvpa.suite import * # enable progress output for MC estimation if __debug__: debug.active += ["STATMC"] # some example data with signal train = normalFeatureDataset(perlabel=50, nlabels=2, nfeatures=3, nonbogus_features=[0,1], snr=0.3, nchunks=1) # define class to estimate NULL distribution of errors # use left tail of the distribution since we use MeanMatchFx as error # function and lower is better # in a real analysis the number of permutations should be larger # to get stable estimates terr = TransferError(clf=SMLR(), null_dist=MCNullDist(permutations=100, tail='left')) # compute classifier error on training dataset (should be low :) err = terr(train, train) print 'Error on training set:', err # check that the result is highly significant since we know that the # data has signal print 'Corresponding p-value: ', terr.null_prob pymvpa-0.4.8/doc/examples/projections.py000077500000000000000000000040771174541445200203550ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Visualization of Data Projection Methods ======================================== """ from mvpa.misc.data_generators import noisy_2d_fx from mvpa.mappers.pca import PCAMapper from mvpa.mappers.svd import SVDMapper from mvpa.mappers.ica import ICAMapper from mvpa import cfg import pylab as P import numpy as N center = [10, 20] axis_range = 7 def plotProjDir(mproj): p = mproj + N.array(center).T P.plot([center[0], p[0,0]], [center[1], p[0,1]], hold=True) P.plot([center[0], p[1,0]], [center[1], p[1,1]], hold=True) mappers = { 'PCA': PCAMapper(), 'SVD': SVDMapper(), 'ICA': ICAMapper(), } datasets = [ noisy_2d_fx(100, lambda x: x, [lambda x: x], center, noise_std=.5), noisy_2d_fx(50, lambda x: x, [lambda x: x, lambda x: -x], center, noise_std=.5), noisy_2d_fx(50, lambda x: x, [lambda x: x, lambda x: 0], center, noise_std=.5), ] ndatasets = len(datasets) nmappers = len(mappers.keys()) P.figure(figsize=(8,8)) fig = 1 for ds in datasets: for mname, mapper in mappers.iteritems(): mapper.train(ds) dproj = mapper.forward(ds.samples) mproj = mapper.proj print mproj P.subplot(ndatasets, nmappers, fig) if fig <= 3: P.title(mname) P.axis('equal') P.scatter(ds.samples[:, 0], ds.samples[:, 1], s=30, c=(ds.labels) * 200) plotProjDir(mproj) fig += 1 if cfg.getboolean('examples', 'interactive', True): P.show() """ Output of the example: .. image:: ../pics/ex_projections.* :align: center :alt: SVD/ICA/PCA projections """ pymvpa-0.4.8/doc/examples/pylab_2d.py000077500000000000000000000121311174541445200175000ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Simple Plotting of Classifier Behavior ====================================== .. index:: plotting example This example runs a number of classifiers on a simple 2D dataset and plots the decision surface of each classifier. First compose some sample data -- no PyMVPA involved. """ import numpy as N # set up the labeled data # two skewed 2-D distributions num_dat = 200 dist = 4 # Absolute max value allowed. Just to assure proper plots xyamax = 10 feat_pos=N.random.randn(2, num_dat) feat_pos[0, :] *= 2. feat_pos[1, :] *= .5 feat_pos[0, :] += dist feat_pos = feat_pos.clip(-xyamax, xyamax) feat_neg=N.random.randn(2, num_dat) feat_neg[0, :] *= .5 feat_neg[1, :] *= 2. feat_neg[0, :] -= dist feat_neg = feat_neg.clip(-xyamax, xyamax) # set up the testing features npoints = 101 x1 = N.linspace(-xyamax, xyamax, npoints) x2 = N.linspace(-xyamax, xyamax, npoints) x,y = N.meshgrid(x1, x2); feat_test = N.array((N.ravel(x), N.ravel(y))) """Now load PyMVPA and convert the data into a proper :class:`~mvpa.datasets.base.Dataset`.""" from mvpa.suite import * # create the pymvpa dataset from the labeled features patternsPos = Dataset(samples=feat_pos.T, labels=1) patternsNeg = Dataset(samples=feat_neg.T, labels=0) ds_lin = patternsPos + patternsNeg """Let's add another dataset: XOR. This problem is not linear separable and therefore need a non-linear classifier to be solved. The dataset is provided by the PyMVPA dataset warehouse. """ # 30 samples per condition, SNR 3 ds_nl = pureMultivariateSignal(30,3) datasets = {'linear': ds_lin, 'non-linear': ds_nl} """This demo utilizes a number of classifiers. The instantiation of a classifier involves almost no runtime costs, so it is easily possible compile a long list, if necessary.""" # set up classifiers to try out clfs = {'Ridge Regression': RidgeReg(), 'Linear SVM': LinearNuSVMC(probability=1, enable_states=['probabilities']), 'RBF SVM': RbfNuSVMC(probability=1, enable_states=['probabilities']), 'SMLR': SMLR(lm=0.01), 'Logistic Regression': PLR(criterion=0.00001), 'k-Nearest-Neighbour': kNN(k=10), 'GNB': GNB(common_variance=True), 'GNB(common_variance=False)': GNB(common_variance=False), } """Now we are ready to run the classifiers. The following loop trains and queries each classifier to finally generate a nice plot showing the decision surface of each individual classifier, both for the linear and the non-linear dataset.""" for id, ds in datasets.iteritems(): # loop over classifiers and show how they do fig = 0 # make a new figure P.figure(figsize=(9, 9)) print "Processing %s problem..." % id for c in clfs: # tell which one we are doing print "Running %s classifier..." % (c) # make a new subplot for each classifier fig += 1 P.subplot(3, 3, fig) # plot the training points P.plot(ds.samples[ds.labels == 1, 0], ds.samples[ds.labels == 1, 1], "r.") P.plot(ds.samples[ds.labels == 0, 0], ds.samples[ds.labels == 0, 1], "b.") # select the clasifier clf = clfs[c] # enable saving of the values used for the prediction clf.states.enable('values') # train with the known points clf.train(ds) # run the predictions on the test values pre = clf.predict(feat_test.T) # if ridge, use the prediction, otherwise use the values if c == 'Ridge Regression' or c.startswith('k-Nearest'): # use the prediction res = N.asarray(pre) elif c == 'Logistic Regression': # get out the values used for the prediction res = N.asarray(clf.values) elif c in ['SMLR']: res = N.asarray(clf.values[:, 1]) elif c.startswith('GNB'): # Since probabilities are raw: for visualization lets # operate on logprobs and in comparison one to another res = clf.values[:, 1] - clf.values[:, 0] # Scale and position around 0.5 res = 0.5 + res/max(N.abs(res)) else: # get the probabilities from the svm res = N.asarray([(q[1][1] - q[1][0] + 1) / 2 for q in clf.probabilities]) # reshape the results z = N.asarray(res).reshape((npoints, npoints)) # plot the predictions P.pcolor(x, y, z, shading='interp') P.clim(0, 1) P.colorbar() P.contour(x, y, z, linewidths=1, colors='black', hold=True) P.axis('tight') # add the title P.title(c) if cfg.getboolean('examples', 'interactive', True): # show all the cool figures P.show() pymvpa-0.4.8/doc/examples/pymvpa.cfg000066400000000000000000000050631174541445200174320ustar00rootroot00000000000000### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # Example configuration file to be used with PyMVPA # # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # This is a comprehensive list of all settings currently recognized by PyMVPA. # Users can add arbitrary additional settings, both in new and already existing # sections. [general] #debug = #verbose = #seed = 12345 [verbose] # comma-separated list of handlers, e.g. stdout #output = [error] #output = [warnings] # integer #bt = # integer #count = # comma-separated list of handlers, e.g. stdout #output = # Boolean (former: MVPA_NO_WARNINGS) suppress = no [debug] # comma-separated list of handlers, e.g. stdout #output = #metrics = # either to use custom (improved) exception handler to report # information about pymvpa useful during bug reporting #wtf = no [examples] interactive = yes [svm] # which SVM implementation to use by default: libsvm or shogun backend = libsvm [matplotlib] # override the default matplotlib's backend # backend = pdf [rpy] # to prevent stalled exectution of PyMVPA upon problems in R # session of R is always responding '1' whenever R asks for input. # 1 corresponds to "abort (with core dump, if enabled)". # Unfortunately such callback does not work reliably, thus disabled # by default interactive = yes [externals] # whether to really raise an exception when an externals test fails _and_ # raising an exception was requested raise exception = True # whether to issue warning when an externals test fails _and_ # issuing a warning was requested issue warning = True # whether to retest the availability of an external dependency, depite an # already present (but possibly outdated) test result retest = no # options starting with 'have ' indicate the presence or absence of external # dependencies #have scipy = no [tests] # whether to perform tests where the outcome is not deterministic labile = yes # if enabled, the unit tests will not run multiple classifiers on the same # test, which reduces the time to run a full test significantly. quick = no # if enabled, unit tests consuming lots of memory will not automatically run # as part of the main unittest battery lowmem = no # verbosity level of the unittest runner verbosity = 1 # scale SNR of simulated data more than 1 to reduce failures of labile tests snr scale = 1.0 [doc] # whether to enhance the docstrings with base class and state information pimp docstrings = yes pymvpa-0.4.8/doc/examples/searchlight.py000077500000000000000000000075031174541445200203100ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Searchlight Analysis on an fMRI Dataset ======================================= Example demonstrating a searchlight analysis on an fMRI dataset. """ from mvpa.suite import * def main(): """ Wrapped into a function call for easy profiling later on """ parser.usage = """\ %s [options] [] where labels+blocks is a text file that lists the class label and the associated block of each data sample/volume as a tuple of two integer values (separated by a single space). -- one tuple per line.""" \ % sys.argv[0] parser.option_groups = [opts.SVM, opts.KNN, opts.general, opts.common] # Set a set of available classifiers for this example opt.clf.choices=['knn', 'lin_nu_svmc', 'rbf_nu_svmc'] opt.clf.default='lin_nu_svmc' parser.add_options([opt.clf, opt.zscore]) (options, files) = parser.parse_args() if not len(files) in [3, 4]: parser.error("Please provide 3 or 4 files in the command line") sys.exit(1) verbose(1, "Loading data") # data filename dfile = files[0] # text file with labels and block definitions (chunks) cfile = files[1] # mask volume filename mfile = files[2] ofile = None if len(files)>=4: # outfile name ofile = files[3] # read conditions into an array (assumed to be two columns of integers) # TODO: We need some generic helper to read conditions stored in some # common formats verbose(2, "Reading conditions from file %s" % cfile) attrs = SampleAttributes(cfile) verbose(2, "Loading volume file %s" % dfile) data = NiftiDataset(samples=dfile, labels=attrs.labels, chunks=attrs.chunks, mask=mfile, dtype=N.float32) # do not try to classify baseline condition # XXX this is only valid for our haxby8 example dataset and should # probably be turned into a proper --baselinelabel option that can # be used for zscoring as well. data = data.selectSamples(data.labels != 0) if options.zscore: verbose(1, "Zscoring data samples") zscore(data, perchunk=True) if options.clf == 'knn': clf = kNN(k=options.knearestdegree) elif options.clf == 'lin_nu_svmc': clf = LinearNuSVMC(nu=options.svm_nu) elif options.clf == 'rbf_nu_svmc': clf = RbfNuSVMC(nu=options.svm_nu) else: raise ValueError, 'Unknown classifier type: %s' % `options.clf` verbose(3, "Using '%s' classifier" % options.clf) verbose(1, "Computing") verbose(3, "Assigning a measure to be CrossValidation") # compute N-1 cross-validation with the selected classifier in each sphere cv = CrossValidatedTransferError(TransferError(clf), NFoldSplitter(cvtype=options.crossfolddegree)) verbose(3, "Generating Searchlight instance") # contruct searchlight with 5mm radius # this assumes that the spatial pixdim values in the source NIfTI file # are specified in mm sl = Searchlight(cv, radius=options.radius) # run searchlight verbose(3, "Running searchlight on loaded data") results = sl(data) if not ofile is None: # map the result vector back into a nifti image rimg = data.map2Nifti(results) # save to file rimg.save(ofile) else: print results if __name__ == "__main__": main() pymvpa-0.4.8/doc/examples/searchlight_2d.py000077500000000000000000000103121174541445200206650ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Searchlight on fMRI data ======================== .. index:: searchlight, NIfTI The example shows how to run a searchlight analysis on the example fMRI dataset that is shipped with PyMVPA. As always, we first have to import PyMVPA. """ from mvpa.suite import * """As searchlight analyses are usually quite expensive in term of computational ressources, we are going to enable some progress output, to entertain us while we are waiting.""" # enable debug output for searchlight call if __debug__: debug.active += ["SLC"] """The next section simply loads the example dataset and performs some standard preprocessing steps on it.""" # # load PyMVPA example dataset # attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt')) dataset = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'), labels=attr.labels, chunks=attr.chunks, mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz')) # # preprocessing # # do chunkswise linear detrending on dataset detrend(dataset, perchunk=True, model='linear') # only use 'rest', 'house' and 'scrambled' samples from dataset dataset = dataset.selectSamples( N.array([ l in [0,2,6] for l in dataset.labels], dtype='bool')) # zscore dataset relative to baseline ('rest') mean zscore(dataset, perchunk=True, baselinelabels=[0], targetdtype='float32') # remove baseline samples from dataset for final analysis dataset = dataset.selectSamples(N.array([l != 0 for l in dataset.labels], dtype='bool')) """But now for the interesting part: Next we define the measure that shall be computed for each sphere. Theoretically, this can be anything, but here we choose to compute a full leave-one-out cross-validation using a linear Nu-SVM classifier.""" # # Run Searchlight # # choose classifier clf = LinearNuSVMC() # setup measure to be computed by Searchlight # cross-validated mean transfer using an N-fold dataset splitter cv = CrossValidatedTransferError(TransferError(clf), NFoldSplitter()) """Finally, we run the searchlight analysis for three different radii, each time computing an error for each sphere. To achieve this, we simply use the :class:`~mvpa.measures.searchlight.Searchlight` class, which takes any :term:`processing object` and a radius as arguments. The :term:`processing object` has to compute the intended measure, when called with a dataset. The :class:`~mvpa.measures.searchlight.Searchlight` object will do nothing more than generating small datasets for each sphere, feeding it to the processing object and storing its result. After the errors are computed for all spheres, the resulting vector is then mapped back into the original fMRI dataspace and plotted.""" # setup plotting fig = 0 P.figure(figsize=(12,4)) for radius in [1,5,10]: # tell which one we are doing print "Running searchlight with radius: %i ..." % (radius) # setup Searchlight with a custom radius # radius has to be in the same unit as the nifti file's pixdim # property. sl = Searchlight(cv, radius=radius) # run searchlight on example dataset and retrieve error map sl_map = sl(dataset) # map sensitivity map into original dataspace orig_sl_map = dataset.mapReverse(N.array(sl_map)) masked_orig_sl_map = N.ma.masked_array(orig_sl_map, mask=orig_sl_map == 0) # make a new subplot for each classifier fig += 1 P.subplot(1,3,fig) P.title('Radius %i' % radius) P.imshow(masked_orig_sl_map[0], interpolation='nearest', aspect=1.25, cmap=P.cm.autumn) P.clim(0.5, 0.65) P.colorbar(shrink=0.6) if cfg.getboolean('examples', 'interactive', True): # show all the cool figures P.show() pymvpa-0.4.8/doc/examples/searchlight_dsm.py000077500000000000000000000050551174541445200211530ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ A searchlight computing a dissimilarity matrix measure ====================================================== .. index:: searchlight, cross-validation, dissimilarity matrix This example extends the minimal Searchlight example to use a dissimilarity matrix-based DatasetMetric to compute Searchlight-center significance. This is based on representational similarity analysis (RSA) as presented in :ref:`Kriegeskorte et al. (2008) `. First import all necessary parts of PyMVPA. """ from mvpa.suite import * """Create a small artificial dataset.""" # overcomplicated way to generate an example dataset ds = normalFeatureDataset(perlabel=10, nlabels=2, nchunks=2, nfeatures=10, nonbogus_features=[3, 7], snr=5.0) dataset = MaskedDataset(samples=ds.samples, labels=ds.labels, chunks=ds.chunks) """Create a dissimilarity matrix based on the labels of the data points in our test dataset. This will allow us to see if there is a correlation between any given searchlight sphere and the experimental conditions.""" # create dissimilarity matrix using the 'confusion' distance # metric dsm = DSMatrix(dataset.labels, 'confusion') """Now it only takes three lines for a searchlight analysis.""" # setup measure to be computed in each sphere (correlation # distance between dissimilarity matrix and the dissimilarities # of a particular searchlight sphere across experimental # conditions), N.B. in this example between-condition # dissimilarity is also pearson's r (i.e., correlation distance) dsmetric = DSMDatasetMeasure(dsm, 'pearson', 'pearson') # setup searchlight with 5 mm radius and measure configured above sl = Searchlight(dsmetric, radius=5) # run searchlight on dataset sl_map = sl(dataset) print 'Best performing sphere error:', max(sl_map) """ If this analysis is done on a fMRI dataset using `NiftiDataset` the resulting searchlight map (`sl_map`) can be mapped back into the original dataspace and viewed as a brain overlay. :ref:`Another example ` shows a typical application of this algorithm. .. Mention the fact that it also is a special `SensitivityAnalyzer` """ pymvpa-0.4.8/doc/examples/searchlight_minimal.py000077500000000000000000000054001174541445200220100ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Minimal Searchlight Example =========================== .. index:: searchlight, cross-validation The term :class:`~mvpa.measures.searchlight.Searchlight` refers to an algorithm that runs a scalar :class:`~mvpa.measures.base.DatasetMeasure` on all possible spheres of a certain size within a dataset (that provides information about distances between feature locations). The measure typically computed is a cross-validated transfer error (see :ref:`CrossValidatedTransferError `). The idea to use a searchlight as a sensitivity analyzer on fMRI datasets stems from :ref:`Kriegeskorte et al. (2006) `. A searchlight analysis is can be easily performed. This examples shows a minimal draft of a complete analysis. First import a necessary pieces of PyMVPA -- this time each bit individually. """ from mvpa.datasets.masked import MaskedDataset from mvpa.datasets.splitters import OddEvenSplitter from mvpa.clfs.svm import LinearCSVMC from mvpa.clfs.transerror import TransferError from mvpa.algorithms.cvtranserror import CrossValidatedTransferError from mvpa.measures.searchlight import Searchlight from mvpa.misc.data_generators import normalFeatureDataset """For the sake of simplicity, let's use a small artificial dataset.""" # overcomplicated way to generate an example dataset ds = normalFeatureDataset(perlabel=10, nlabels=2, nchunks=2, nfeatures=10, nonbogus_features=[3, 7], snr=5.0) dataset = MaskedDataset(samples=ds.samples, labels=ds.labels, chunks=ds.chunks) """Now it only takes three lines for a searchlight analysis.""" # setup measure to be computed in each sphere (cross-validated # generalization error on odd/even splits) cv = CrossValidatedTransferError( TransferError(LinearCSVMC()), OddEvenSplitter()) # setup searchlight with 5 mm radius and measure configured above sl = Searchlight(cv, radius=5) # run searchlight on dataset sl_map = sl(dataset) print 'Best performing sphere error:', min(sl_map) """ If this analysis is done on a fMRI dataset using `NiftiDataset` the resulting searchlight map (`sl_map`) can be mapped back into the original dataspace and viewed as a brain overlay. :ref:`Another example ` shows a typical application of this algorithm. .. Mention the fact that it also is a special `SensitivityAnalyzer` """ pymvpa-0.4.8/doc/examples/sensanas.py000077500000000000000000000111041174541445200176160ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Sensitivity Measure =================== .. index:: sensitivity Run some basic and meta sensitivity measures on the example fMRI dataset that comes with PyMVPA and plot the computed featurewise measures for each. The generated figure shows sensitivity maps computed by six sensitivity analyzers. We start by loading PyMVPA and the example fMRI dataset. """ from mvpa.suite import * # load PyMVPA example dataset attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt')) dataset = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'), labels=attr.labels, chunks=attr.chunks, mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz')) """As with classifiers it is easy to define a bunch of sensitivity analyzers. It is usually possible to simply call `getSensitivityAnalyzer()` on any classifier to get an instance of an appropriate sensitivity analyzer that uses this particular classifier to compute and extract sensitivity scores. """ # define sensitivity analyzer sensanas = { 'a) ANOVA': OneWayAnova(transformer=N.abs), 'b) Linear SVM weights': LinearNuSVMC().getSensitivityAnalyzer( transformer=N.abs), 'c) I-RELIEF': IterativeRelief(transformer=N.abs), 'd) Splitting ANOVA (odd-even)': SplitFeaturewiseMeasure(OneWayAnova(transformer=N.abs), OddEvenSplitter()), 'e) Splitting SVM (odd-even)': SplitFeaturewiseMeasure( LinearNuSVMC().getSensitivityAnalyzer(transformer=N.abs), OddEvenSplitter()), 'f) I-RELIEF Online': IterativeReliefOnline(transformer=N.abs), 'g) Splitting ANOVA (nfold)': SplitFeaturewiseMeasure(OneWayAnova(transformer=N.abs), NFoldSplitter()), 'h) Splitting SVM (nfold)': SplitFeaturewiseMeasure( LinearNuSVMC().getSensitivityAnalyzer(transformer=N.abs), NFoldSplitter()), } """Now, we are performing some a more or less standard preprocessing steps: detrending, selecting a subset of the experimental conditions, normalization of each feature to a standard mean and variance.""" # do chunkswise linear detrending on dataset detrend(dataset, perchunk=True, model='linear') # only use 'rest', 'shoe' and 'bottle' samples from dataset dataset = dataset.selectSamples( N.array([ l in [0,3,7] for l in dataset.labels], dtype='bool')) # zscore dataset relative to baseline ('rest') mean zscore(dataset, perchunk=True, baselinelabels=[0], targetdtype='float32') # remove baseline samples from dataset for final analysis dataset = dataset.selectSamples(N.array([l != 0 for l in dataset.labels], dtype='bool')) """Finally, we will loop over all defined analyzers and let them compute the sensitivity scores. The resulting vectors are then mapped back into the dataspace of the original fMRI samples, which are then plotted.""" fig = 0 P.figure(figsize=(14, 8)) keys = sensanas.keys() keys.sort() for s in keys: # tell which one we are doing print "Running %s ..." % (s) # compute sensitivies # I-RELIEF assigns zeros, which corrupts voxel masking for pylab's # imshow, so adding some epsilon :) smap = sensanas[s](dataset)+0.00001 # map sensitivity map into original dataspace orig_smap = dataset.mapReverse(smap) masked_orig_smap = N.ma.masked_array(orig_smap, mask=orig_smap == 0) # make a new subplot for each classifier fig += 1 P.subplot(3, 3, fig) P.title(s) P.imshow(masked_orig_smap[0], interpolation='nearest', aspect=1.25, cmap=P.cm.autumn) # uniform scaling per base sensitivity analyzer if s.count('ANOVA'): P.clim(0, 30) elif s.count('SVM'): P.clim(0, 0.055) else: pass P.colorbar(shrink=0.6) if cfg.getboolean('examples', 'interactive', True): # show all the cool figures P.show() """ Output of the example analysis: .. image:: ../pics/ex_sensanas.* :align: center :alt: Various sensitivity analysis results """ pymvpa-0.4.8/doc/examples/smellit.py000077500000000000000000000057251174541445200174700ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Simple Data-Exploration ======================= Example showing some possibilities of data exploration (i.e. to 'smell' data). """ import numpy as N import pylab as P import os from mvpa import pymvpa_dataroot from mvpa.misc.plot import plotFeatureHist, plotSamplesDistance from mvpa import cfg from mvpa.datasets.nifti import NiftiDataset from mvpa.misc.io import SampleAttributes from mvpa.datasets.miscfx import zscore, detrend # load example fmri dataset attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt')) ds = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'), labels=attr.labels, chunks=attr.chunks, mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz')) # only use the first 5 chunks to save some cpu-cycles ds = ds.selectSamples(ds.chunks < 5) # take a look at the distribution of the feature values in all # sample categories and chunks plotFeatureHist(ds, perchunk=True, bins=20, normed=True, xlim=(0, ds.samples.max())) if cfg.getboolean('examples', 'interactive', True): P.show() # next only works with floating point data ds.setSamplesDType('float') # look at sample similiarity # Note, the decreasing similarity with increasing temporal distance # of the samples P.subplot(121) plotSamplesDistance(ds, sortbyattr='chunks') P.title('Sample distances (sorted by chunks)') # similar distance plot, but now samples sorted by their # respective labels, i.e. samples with same labels are plotted # in adjacent columns/rows. # Note, that the first and largest group corresponds to the # 'rest' condition in the dataset P.subplot(122) plotSamplesDistance(ds, sortbyattr='labels') P.title('Sample distances (sorted by labels)') if cfg.getboolean('examples', 'interactive', True): P.show() # z-score features individually per chunk print 'Detrending data' detrend(ds, perchunk=True, model='regress', polyord=2) print 'Z-Scoring data' zscore(ds) P.subplot(121) plotSamplesDistance(ds, sortbyattr='chunks') P.title('Distances: z-scored, detrended (sorted by chunks)') P.subplot(122) plotSamplesDistance(ds, sortbyattr='labels') P.title('Distances: z-scored, detrended (sorted by labels)') if cfg.getboolean('examples', 'interactive', True): P.show() # XXX add some more, maybe show effect of preprocessing """ Outputs of the example script. Data prior to preprocessing .. image:: ../pics/ex_smellit2.* :align: center :alt: Data prior preprocessing Data after minimal preprocessing .. image:: ../pics/ex_smellit3.* :align: center :alt: Data after z-scoring and detrending """ pymvpa-0.4.8/doc/examples/smlr.py000077500000000000000000000064121174541445200167660ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Compare SMLR to Linear SVM Classifier ===================================== .. index:: SMLR, SVM Runs both classifiers on the the same dataset and compare their performance. This example also shows an example usage of confusion matrices and how two classifers can be combined. """ from mvpa.suite import * if __debug__: debug.active.append('SMLR_') # features of sample data print "Generating samples..." nfeat = 10000 nsamp = 100 ntrain = 90 goodfeat = 10 offset = .5 # create the sample datasets samp1 = N.random.randn(nsamp,nfeat) samp1[:,:goodfeat] += offset samp2 = N.random.randn(nsamp,nfeat) samp2[:,:goodfeat] -= offset # create the pymvpa training dataset from the labeled features patternsPos = Dataset(samples=samp1[:ntrain,:], labels=1) patternsNeg = Dataset(samples=samp2[:ntrain,:], labels=0) trainpat = patternsPos + patternsNeg # create patters for the testing dataset patternsPos = Dataset(samples=samp1[ntrain:,:], labels=1) patternsNeg = Dataset(samples=samp2[ntrain:,:], labels=0) testpat = patternsPos + patternsNeg # set up the SMLR classifier print "Evaluating SMLR classifier..." smlr = SMLR(fit_all_weights=True) # enable saving of the values used for the prediction smlr.states.enable('values') # train with the known points smlr.train(trainpat) # run the predictions on the test values pre = smlr.predict(testpat.samples) # calculate the confusion matrix smlr_confusion = ConfusionMatrix( labels=trainpat.uniquelabels, targets=testpat.labels, predictions=pre) # now do the same for a linear SVM print "Evaluating Linear SVM classifier..." lsvm = LinearNuSVMC(probability=1) # enable saving of the values used for the prediction lsvm.states.enable('values') # train with the known points lsvm.train(trainpat) # run the predictions on the test values pre = lsvm.predict(testpat.samples) # calculate the confusion matrix lsvm_confusion = ConfusionMatrix( labels=trainpat.uniquelabels, targets=testpat.labels, predictions=pre) # now train SVM with selected features print "Evaluating Linear SVM classifier with SMLR's features..." keepInd = (N.abs(smlr.weights).mean(axis=1)!=0) newtrainpat = trainpat.selectFeatures(keepInd, sort=False) newtestpat = testpat.selectFeatures(keepInd, sort=False) # train with the known points lsvm.train(newtrainpat) # run the predictions on the test values pre = lsvm.predict(newtestpat.samples) # calculate the confusion matrix lsvm_confusion_sparse = ConfusionMatrix( labels=newtrainpat.uniquelabels, targets=newtestpat.labels, predictions=pre) print "SMLR Percent Correct:\t%g%% (Retained %d/%d features)" % \ (smlr_confusion.percentCorrect, (smlr.weights!=0).sum(), N.prod(smlr.weights.shape)) print "linear-SVM Percent Correct:\t%g%%" % \ (lsvm_confusion.percentCorrect) print "linear-SVM Percent Correct (with %d features from SMLR):\t%g%%" % \ (keepInd.sum(), lsvm_confusion_sparse.percentCorrect) pymvpa-0.4.8/doc/examples/som.py000077500000000000000000000102551174541445200166070ustar00rootroot00000000000000#!/usr/bin/env python #emacs: -*- mode: python-mode; py-indent-offset: 4; indent-tabs-mode: nil -*- #ex: set sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Self-organizing Maps ==================== .. index:: mapper, self-organizing map, SOM, SimpleSOMMapper This is a demonstration of how a self-organizing map (SOM), also known as a Kohonen network, can be used to map high-dimensional data into a two-dimensional representation. For the sake of an easy visualization 'high-dimensional' in this case is 3D. In general, SOMs might be useful for visualizing high-dimensional data in terms of its similarity structure. Especially large SOMs (i.e. with large number of Kohonen units) are known to perform mappings that preserve the topology of the original data, i.e. neighboring data points in input space will also be represented in adjacent locations on the SOM. The following code shows the 'classic' color mapping example, i.e. the SOM will map a number of colors into a rectangular area. """ from mvpa.suite import * """ First, we define some colors as RGB values from the interval (0,1), i.e. with white being (1, 1, 1) and black being (0, 0, 0). Please note, that a substantial proportion of the defined colors represent variations of 'blue', which are supposed to be represented in more detail in the SOM. """ colors = [[0., 0., 0.], [0., 0., 1.], [0., 0., 0.5], [0.125, 0.529, 1.0], [0.33, 0.4, 0.67], [0.6, 0.5, 1.0], [0., 1., 0.], [1., 0., 0.], [0., 1., 1.], [1., 0., 1.], [1., 1., 0.], [1., 1., 1.], [.33, .33, .33], [.5, .5, .5], [.66, .66, .66]] # store the names of the colors for visualization later on color_names = \ ['black', 'blue', 'darkblue', 'skyblue', 'greyblue', 'lilac', 'green', 'red', 'cyan', 'violet', 'yellow', 'white', 'darkgrey', 'mediumgrey', 'lightgrey'] """ Since we are going to use a mapper, we will put the color vectors into a dataset. To be able to do this, we will assign an arbitrary label, although it will not be used at all, since this SOM mapper uses an unsupervised training algorithm. """ ds = Dataset(samples=colors, labels=1) """ Now we can instantiate the mapper. It will internally use a so-called Kohonen layer to map the data onto. We tell the mapper to use a rectangular layer with 20 x 30 units. This will be the output space of the mapper. Additionally, we tell it to train the network using 400 iterations and to use custom learning rate. """ som = SimpleSOMMapper((20, 30), 400, learning_rate=0.05) """ Finally, we train the mapper with the previously defined 'color' dataset. """ som.train(ds) """ Each unit in the Kohonen layer can be treated as a pointer into the high-dimensional input space, that can be queried to inspect which input subspaces the SOM maps onto certain sections of its 2D output space. The color-mapping generated by this example's SOM can be shown with a single matplotlib call: """ P.imshow(som.K, origin='lower') """ And now, let's take a look onto which coordinates the initial training prototypes were mapped to. The get those coordinates we can simply feed the training data to the mapper and plot the output. """ mapped = som(colors) P.title('Color SOM') # SOM's kshape is (rows x columns), while matplotlib wants (X x Y) for i, m in enumerate(mapped): P.text(m[1], m[0], color_names[i], ha='center', va='center', bbox=dict(facecolor='white', alpha=0.5, lw=0)) """ The text labels of the original training colors will appear at the 'mapped' locations in the SOM -- and should match with the underlying color. """ # show the figure if cfg.getboolean('examples', 'interactive', True): P.show() """ The following figure shows an exemplary solution of the SOM mapping of the 3D color-space onto the 2D SOM node layer: .. image:: ../pics/ex_som.* :align: center :alt: Color-space mapping by a self-organizing map. """ pymvpa-0.4.8/doc/examples/start_easy.py000077500000000000000000000033701174541445200201670ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Tiny Example of a Full Cross-Validation ======================================= Very, very simple example showing a complete cross-validation procedure with no fancy additions whatsoever. """ # get PyMVPA running from mvpa.suite import * # load PyMVPA example dataset attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt')) dataset = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'), labels=attr.labels, chunks=attr.chunks, mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz')) # do chunkswise linear detrending on dataset detrend(dataset, perchunk=True, model='linear') # zscore dataset relative to baseline ('rest') mean zscore(dataset, perchunk=True, baselinelabels=[0], targetdtype='float32') # select class 1 and 2 for this demo analysis # would work with full datasets (just a little slower) dataset = dataset.selectSamples( N.array([l in [1, 2] for l in dataset.labels], dtype='bool')) # setup cross validation procedure, using SMLR classifier cv = CrossValidatedTransferError( TransferError(SMLR()), OddEvenSplitter()) # and run it error = cv(dataset) print "Error for %i-fold cross-validation on %i-class problem: %f" \ % (len(dataset.uniquechunks), len(dataset.uniquelabels), error) pymvpa-0.4.8/doc/examples/svdclf.py000077500000000000000000000063541174541445200172770ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Classification of SVD-mapped Datasets ===================================== .. index:: mapper, SVD, MappedClassifier Demonstrate the usage of a dataset mapper performing data projection onto singular value components within a cross-validation -- for *any* clasifier. """ from mvpa.suite import * if __debug__: debug.active += ["CROSSC"] # # load PyMVPA example dataset # attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt')) dataset = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'), labels=attr.labels, chunks=attr.chunks, mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz')) # # preprocessing # # do chunkswise linear detrending on dataset detrend(dataset, perchunk=True, model='linear') # only use 'rest', 'cats' and 'scissors' samples from dataset dataset = dataset.selectSamples( N.array([ l in [0,4,5] for l in dataset.labels], dtype='bool')) # zscore dataset relative to baseline ('rest') mean zscore(dataset, perchunk=True, baselinelabels=[0], targetdtype='float32') # remove baseline samples from dataset for final analysis dataset = dataset.selectSamples(N.array([l != 0 for l in dataset.labels], dtype='bool')) print dataset # Specify the base classifier to be used # To parametrize the classifier to be used # Clf = lambda *args:LinearCSVMC(C=-10, *args) # Just to assign a particular classifier class Clf = LinearCSVMC # define some classifiers: a simple one and several classifiers with # built-in SVDs clfs = [('All orig.\nfeatures (%i)' % dataset.nfeatures, Clf()), ('All Comps\n(%i)' % (dataset.nsamples \ - (dataset.nsamples / len(dataset.uniquechunks)),), MappedClassifier(Clf(), SVDMapper())), ('First 5\nComp.', MappedClassifier(Clf(), SVDMapper(selector=range(5)))), ('First 30\nComp.', MappedClassifier(Clf(), SVDMapper(selector=range(30)))), ('Comp.\n6-30', MappedClassifier(Clf(), SVDMapper(selector=range(5,30))))] # run and visualize in barplot results = [] labels = [] for desc, clf in clfs: print desc cv = CrossValidatedTransferError( TransferError(clf), NFoldSplitter(), enable_states=['results']) cv(dataset) results.append(cv.results) labels.append(desc) plotBars(results, labels=labels, title='Linear C-SVM classification (cats vs. scissors)', ylabel='Mean classification error (N-1 cross-validation, 12-fold)', distance=0.5) if cfg.getboolean('examples', 'interactive', True): P.show() """ Output of the example analysis: .. image:: ../pics/ex_svdclf.* :align: center :alt: Generalization performance on the selected PCs. """ pymvpa-0.4.8/doc/examples/topo_plot.py000077500000000000000000000033641174541445200200330ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """ Generating Topography plots =========================== Example demonstrating a topography plot.""" from mvpa.suite import * # Sanity check if we have griddata available externals.exists("griddata", raiseException=True) # EEG example splot P.subplot(1, 2, 1) # load the sensor information from their definition file. # This file has sensor names, as well as their 3D coordinates sensors=XAVRSensorLocations(os.path.join(pymvpa_dataroot, 'xavr1010.dat')) # make up some artifical topography # 'enable' to channels, all others set to off ;-) topo = N.zeros(len(sensors.names)) topo[sensors.names.index('O1')] = 1 topo[sensors.names.index('F4')] = 1 # plot with sensor locations shown plotHeadTopography(topo, sensors.locations(), plotsensors=True) # MEG example plot P.subplot(1, 2, 2) # load MEG sensor locations sensors=TuebingenMEGSensorLocations( os.path.join(pymvpa_dataroot, 'tueb_meg_coord.xyz')) # random values this time topo = N.random.randn(len(sensors.names)) # plot without additional interpolation plotHeadTopography(topo, sensors.locations(), interpolation='nearest') if cfg.getboolean('examples', 'interactive', True): # show all the cool figures P.show() """ The ouput of the provided example should look like .. image:: ../pics/ex_topo_plot.* :align: center :alt: Topography plot of MEG data """ pymvpa-0.4.8/doc/faq.rst000066400000000000000000000341011174541445200151130ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. _chap_faq: ************************** Frequently Asked Questions ************************** General ======= .. index:: optimization It is sloooooow. What can I do? ------------------------------- Have you tried running the Python interpreter with `-O`? PyMVPA provides lots of debug messages with information that is computed in addition to the work that really has to be done. However, if Python is running in *optimized* mode, PyMVPA will not waste time on this and really tries to be fast. If you are already running it optimized, then maybe you are doing something really demanding... I am tired of writing these endless import blocks. Any alternative? ------------------------------------------------------------------- Sure. Instead of individually importing all pieces that are required by a script, you can import them all at once. A simple: >>> import mvpa.suite as mvpa makes everything directly accessible through the mvpa namespace, e.g. `mvpa.datasets.base.Dataset` becomes `mvpa.Dataset`. Really lazy people can even do: >>> from mvpa.suite import * However, as always there is a price to pay for this convenience. In contrast to the individual imports there is some initial performance and memory cost. In the worst case you'll get all external dependencies loaded (e.g. a full R session), just because you have them installed. Therefore, it might be better to limit this use to case where individual key presses matter and use individual imports for production scripts. I feel like I want to contribute something, do you mind? -------------------------------------------------------- Not at all! If you think there is something that is not well explained in the documentation, send us an improvement. If you implemented a new algorithm using PyMVPA that you want to share, please share. If you have an idea for some other improvement (e.g. speed, functionality), but you have no time/cannot/do not want to implement it yourself, please post your idea to the PyMVPA mailing list. .. index:: Git, development I want to develop a new feature for PyMVPA. How can I do it efficiently? ------------------------------------------------------------------------ The best way is to use Git for both, getting the latest code from the repository and preparing the patch. Here is a quick sketch of the workflow. First get the latest code:: git clone git://github.com/PyMVPA/PyMVPA.git This will create a new `PyMVPA` subdirectory, that contains the complete repository. Enter this directory and run `gitk --all` to browse the full history and *all* branches that have ever been published. You can run:: git fetch origin in this directory at any time to get the latest changes from the main repository. Next, you have to decide what you want to base your new feature on. In the simplest case this is the `master` branch (the one that contains the code that will become the next release). Creating a local branch based on the (remote) `master` branch is:: git checkout -b my_hack origin/master Now you are ready to start hacking. You are free to use all powers of Git (and yours, of course). You can do multiple commits, fetch new stuff from the repository, and merge it into your local branch, ... To get a feeling what can be done, take a look `very short description of Git`_ or `a more comprehensive Git tutorial`_. .. _very short description of Git: http://sysmonblog.co.uk/misc/git_by_example/ .. _a more comprehensive Git tutorial: http://www-cs-students.stanford.edu/~blynn/gitmagic/ When you are done with the new feature, you can prepare the patch for inclusion into PyMVPA. If you have done multiple commits you might want to squash them into a single patch containing the new feature. You can do this with `git-rebase`. In recent version `git-rebase` has an option `--interactive`, which allows you to easily pick, squash or even further edit any of the previous commits you have made. Rebase your local branch against the remote branch you started hacking on (`origin/master` in this example):: git rebase --interactive origin/master When you are done, you can generate the final patch file:: git-format-patch origin/master Above command will generate a file for each commit in you local branch that is not yet part of `origin/master`. The patch files can then be easily emailed. The manual is quite insufficient. When will you improve it? ----------------------------------------------------------- Writing a manual can be a tricky task if you already know the details and have to imagine what might be the most interesting information for someone who is just starting. If you feel that something is missing which has cost you some time to figure out, please drop us a note and we will add it as soon as possible. If you have developed some code snippets to demonstrate some feature or non-trivial behavior (maybe even trivial ones, which are not as obvious as they should be), please consider sharing this snippet with us and we will put it into the example collection or the manual. Thanks! Data import, export and storage =============================== What file formats are understood by PyMVPA? ------------------------------------------- Please see the :ref:`data_formats` section. What if there is no special file format for some particular datatype? --------------------------------------------------------------------- With the :class:`~mvpa.misc.io.hamster.Hamster` class, PyMVPA supports storing *any* kind of serializable data into a (compressed) file (see the class documentation for a trivial usage example). The facility is particularly useful for storing any number of intermediate analysis results, e.g. for post-processing. Data preprocessing ================== .. index:: invariant features Is there an easy way to remove invariant features from a dataset? ----------------------------------------------------------------- You might have to deal with invariant features in case like an fMRI dataset, where the *brain mask* is slightly larger than the thresholded fMRI timeseries image. Such invariant features (i.e. features with zero variance) are sometime a problem, e.g. they will lead to numerical difficulties when z-scoring the features of a dataset (i.e. division by zero). The `mvpa.datasets.miscfx` module provides a convenience function `removeInvariantFeatures()` that strips such features from a dataset. .. index:: block-averaging How can I do :term:`block-averaging` of my block-design fMRI dataset? --------------------------------------------------------------------- The easiest way is to use a mapper to transform/average the respective samples. Suppose you have a dataset: >>> dataset = normalFeatureDataset() >>> dataset Averaging all samples with the same label in each chunk individually is done by applying a samples mapper to the dataset. >>> from mvpa.mappers.samplegroup import SampleGroupMapper >>> from mvpa.misc.transformers import FirstAxisMean >>> >>> m = SampleGroupMapper(fx=FirstAxisMean) >>> mapped_dataset = dataset.applyMapper(samplesmapper=m) >>> mapped_dataset `SampleGroupMapper` applies a function to every group of samples in each chunk individually. Using `FirstAxisMean` as function, therefore yields one sample of each label per chunk. Data analysis ============= .. index:: feature selection, feature_ids How do I know which features were finally selected by a classifier doing feature selection? ------------------------------------------------------------------------------------------- All classifier possess a state variable `feature_ids`. When enable, the classifier stores the ids of all features that were finally used to train the classifier. >>> clf = FeatureSelectionClassifier( ... kNN(k=5), ... SensitivityBasedFeatureSelection( ... SMLRWeights(SMLR(lm=1.0), transformer=Absolute), ... FixedNElementTailSelector(1, tail='upper', mode='select')), ... enable_states = ['feature_ids']) >>> clf.train(dataset) >>> final_dataset = dataset.selectFeatures(clf.feature_ids) >>> final_dataset In the above code snippet a kNN classifier is defined, that performs a feature selection step prior training. Features are selected according to the absolute magnitude of the weights of a SMLR classifier trained on the data (same training data that will also go into kNN). Absolute SMLR weights are used for feature selection as large negative values also indicate important information. Finally, the classifier is configured to select the single most important feature (given the SMLR weights). After enabling the `feature_ids` state, the classifier provides the desired information, that can e.g. be applied to generate a stripped dataset for an analysis of the similarity structure. .. index:: sensitivity, cross-validation How do I extract sensitivities from a classifier used within a cross-validation? -------------------------------------------------------------------------------- .. The answer depends on size of the classification problem and the used classifier. If you can afford to keep a copy of the trained classifier for each data split, the most elegant solution is probably a :class:`~mvpa.clfs.meta.SplitClassifier`... ...BUT no yet :class:`~mvpa.algorithms.cvtranserror.CrossValidatedTransferError` provides an interface to access any classifier-related information: `harvest_attribs`. Harvesting the sensitivities computed by all classifiers (without recomputing them again) looks like this: >>> cv = CrossValidatedTransferError( ... TransferError(SMLR()), ... OddEvenSplitter(), ... harvest_attribs=\ ... ['transerror.clf.getSensitivityAnalyzer(force_training=False)()']) >>> merror = cv(dataset) >>> sensitivities = cv.harvested.values()[0] >>> N.array(sensitivities).shape == (2, dataset.nfeatures) True First, we define an instance of :class:`~mvpa.algorithms.cvtranserror.CrossValidatedTransferError` that uses an SMLR_ classifier to perform the cross-validation on odd-even splits of a dataset. The important piece is the definition of the `harvest_attribs`. It takes a list of code snippets that will be executed in the local context of the cross-validation function. The :class:`~mvpa.clfs.transerror.TransferError` instance used to train and test the classifier on each split is available via `transerror`. The rest is easy: :class:`~mvpa.clfs.transerror.TransferError` provides access to its classifier and any classifier can in turn generate an appropriate :class:`~mvpa.measures.base.Sensitivity` instance via `getSensitivityAnalyzer()`. This generator method takes additional arguments to the constructor of the :class:`mvpa.measures.base.Sensitivity` class. In this case we want to prevent retraining the classifiers, as they will be trained anyway by the :class:`~mvpa.clfs.transerror.TransferError` instance they belong to. The return values of all code snippets defined in `harvest_attribs` are available in the `harvested` state variable. `harvested` is a dictionary where the keys are the code snippets used to compute the value. As the key in this case is pretty long, we simply take the first (and only) value from the dictionary. The value is actually a list of sensitivity vectors, one per split. .. _SMLR : api/mvpa.clfs.smlr.SMLR-class.html .. _faq_literal_labels: Can PyMVPA deal with literal class labels? ------------------------------------------ Yes and no. In general the classifiers wrapped or implemented in PyMVPA are not capable of handling literal labels, some even might require binary labels. However, PyMVPA datasets provide functionality to map any set of literal labels to a corresponding set of numerical labels. Let's take a look: >>> # invent some samples (arbitrary in this example) >>> samples = N.random.randn(3).reshape(3,1) First we will construct a Dataset the usual way (3 samples with unique numerical labels, all in one chunk: >>> Dataset(samples=samples, labels=range(3), chunks=1) Now, we are trying to create the same dataset using literal labels: >>> # now create the same dataset using literal labels >>> ds = Dataset(samples=samples, ... labels=['one', 'two', 'three'], ... chunks=1) >>> ds.labels[0] 'one' This approach simply stored the literal labels in the dataset and will most likely lead to unpredictable behavior of classifiers that cannot handle them. A more flexible approach is to let the dataset map the literal labels to numerical ones: >>> ds = Dataset(samples=samples, ... labels=['one', 'two', 'three'], ... chunks=1, ... labels_map=True) >>> ds >>> ds.labels[0] 0 >>> for k in sorted(ds.labels_map.keys()): ... print k, ds.labels_map[k] one 0 three 1 two 2 With this approach the labels stored in the dataset are now numerical. However, the mapping between literal and numerical labels is somewhat arbitrary. If a fixed mapping is possible or intended (e.g. same mapping for multiple dataset), the mapping can be set explicitly: >>> ds = Dataset(samples=samples, ... labels=['one', 'two', 'three'], ... chunks=1, ... labels_map={'one': 1, 'two': 2, 'three': 3}) >>> for k in sorted(ds.labels_map.keys()): ... print k, ds.labels_map[k] one 1 three 3 two 2 PyMVPA will use the labels mapping to display literal instead of numerical labels e.g. in confusion matrices. pymvpa-0.4.8/doc/featsel.rst000066400000000000000000000242261174541445200157760ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. index:: feature selection .. _chap_featsel: ***************** Feature Selection ***************** *This section has been contributed by James M. Hughes.* It is often the case in machine learning problems that we wish to reduce a feature space of high dimensionality into something more manageable by selecting only those features that contribute most to classification performance. Feature selection methods attempt to achieve this goal in an algorithmic fashion. .. index:: FeatureSelectionClassifier PyMVPA's flexible framework allows various feature selection methods to take place within a small block of code. :class:`~mvpa.clfs.meta.FeatureSelectionClassifier` extends the basic classifier framework to allow for the use of arbitrary methods of feature selection according to whatever ranking metric, feature selection criteria, and stopping criterion the user chooses for a given application. Examples of the code/classification algorithms presented here can be found in `mvpa/clfs/warehouse.py`_. More formally, a :class:`~mvpa.clfs.meta.FeatureSelectionClassifier` is a meta-classifier. That is, it is not a classifier itself -- it can take any *slave* :class:`~mvpa.clfs.base.Classifier`, perform some feature selection in advance, select those features, and then train the provided *slave* :class:`~mvpa.clfs.base.Classifier` on those features. Externally, however, it looks like a :class:`~mvpa.clfs.base.Classifier`, in that it fulfills the specialization of the Classifier base class. The following are the relevant arguments to the constructor of such a :class:`~mvpa.clfs.base.Classifier`: `clf`: :class:`~mvpa.clfs.base.Classifier` classifier based on which mask classifiers is created `feature_selection`: FeatureSelection_ whatever feature selection is considered best `testdataset`: :class:`~mvpa.datasets.base.Dataset` (optional) dataset which would be given on call to feature_selection .. index:: FeatureSelection Let us turn out attention to the second argument, FeatureSelection_. As noted above, this feature selection can be arbitrary and should be chosen appropriately for the task at hand. For example, we could perform a one-way ANOVA statistic to select features, then keep only the most important 5% of them. It is crucial to note that, in PyMVPA, the way in which features are selected (in this example by keeping only 5% of them) is wholly independent of the way features are ranked (in this example, by using a one-way ANOVA). Feature selection using this method could be accomplished using the following code (from `mvpa/clfs/warehouse.py`_): >>> from mvpa.suite import * >>> FeatureSelection = SensitivityBasedFeatureSelection( ... OneWayAnova(), ... FractionTailSelector(0.05, mode='select', tail='upper')) A more interesting analysis is one in which we use the weights (hyperplane coefficients) to rank features. This allows us to use the same classifier to train the selected features as we used to select them: .. here we'll put the warehouse.py example of linear svm weights from yarik's email >>> sample_linear_svm = clfswh['linear', 'svm'][0] >>> clf = \ ... FeatureSelectionClassifier( ... sample_linear_svm, ... SensitivityBasedFeatureSelection( ... sample_linear_svm.getSensitivityAnalyzer(transformer=Absolute), ... FractionTailSelector(0.05, mode='select', tail='upper')), ... descr="LinSVM on 5%(SVM)") It bears mentioning at this point that caution must be exercised when selecting features. The process of feature selection must be performed on an independent training dataset: it is not possible to select features using the entire dataset, re-train a classifier on a subset of the original data (but using only the selected features) and then test on a held-out testing dataset. This results in an obvious positive bias in classification performance. PyMVPA allows for easy dataset splitting, however, so creating independent training and testing datasets is easily accomplished, for instance using an :class:`~mvpa.datasets.splitters.NFoldSplitter`, :class:`~mvpa.datasets.splitters.OddEvenSplitter`, etc. .. fill in end of last paragraph with suggestions for how to take in an entire original dataset and split it: should we just do a cross-validated outer loop that uses multiple training/testing splits and does RFE on each of these splits? .. index:: recursive feature selection, RFE .. _recursive_feature_elimination: Recursive Feature Elimination ============================= Recursive feature elimination (RFE_, applied to fMRI data in (:ref:`Hanson et al., 2008 `)) is a technique that falls under the larger umbrella of feature selection. Recursive feature elimination specifically attempts to reduce the number of selected features used for classification in the following way: * A classifier is trained on a subset of the data and features are ranked according to an arbitrary metric. * Some amount of those features is either selected or discarded according to a pre-selected rule. * The classifier is retrained and features are once again ranked; this process continues until some criterion determined \textit{a priori} (such as classification error) is reached. * One or more classifiers trained only on the final set of selected features are used on a generalization dataset and performance is calculated. PyMVPA's flexible framework allows each of these steps to take place within a small block of code. To actually perform recursive feature elimination, we consider two separate analysis scenarios that deal with a pre-selected training dataset: * We split the training dataset into an arbitrary number of independent datasets and perform RFE on each of these; the sensitivity analysis of features is performed independently for each split and features are selected based on those independent measures. * We split the training dataset into an arbitrary number of independent datasets (as before), but we average the feature sensitivities and select which features to prune/select based on that one average measure. .. index:: SplitClassifier We will concentrate on the second approach. The following code can be used to perform such an analysis: >>> rfesvm_split = SplitClassifier(LinearCSVMC()) >>> clf = \ ... FeatureSelectionClassifier( ... clf = LinearCSVMC(), ... # on features selected via RFE ... feature_selection = RFE( ... # based on sensitivity of a clf which does splitting internally ... sensitivity_analyzer=rfesvm_split.getSensitivityAnalyzer(), ... transfer_error=ConfusionBasedError( ... rfesvm_split, ... confusion_state="confusion"), ... # and whose internal error we use ... feature_selector=FractionTailSelector( ... 0.2, mode='discard', tail='lower'), ... # remove 20% of features at each step ... update_sensitivity=True), ... # update sensitivity at each step ... descr='LinSVM+RFE(splits_avg)' ) The code above introduces the :class:`~mvpa.clfs.meta.SplitClassifier`, which in this case is yet another *meta-classifier* that takes in a :class:`~mvpa.clfs.base.Classifier` (in this case a LinearCSVMC_) and an arbitrary :class:`~mvpa.datasets.splitters.Splitter` object, so that the dataset can be split in whatever way the user desires. Prior to training, the :class:`~mvpa.clfs.meta.SplitClassifier` splits the training dataset, dedicates a separate classifier to each split, trains each on the training part of the split, and then computes transfer error on the testing part of the split. If a :class:`~mvpa.clfs.meta.SplitClassifier` instance is later on asked to *predict* some new data, it uses (by default) the MaximalVote_ strategy to derive an answer. A summary about the performance of a :class:`~mvpa.clfs.meta.SplitClassifier` internally on each split of the training dataset is available by accessing the `confusion` state variable. To summarize somewhat, RFE_ is just one method of feature selection, so we use a :class:`~mvpa.clfs.meta.FeatureSelectionClassifier` to facilitate this. To parameterize the RFE process, we refer above to the following: `sensitivity_analyzer` in this case just the default from a linear C-SVM (the SVM weights), taken as an average over all splits (in accordance with scenario 2 as above) `transfer_error` confusion-based error that relies on the confusion matrices computed during splitting of the dataset by the :class:`~mvpa.clfs.meta.SplitClassifier`; this is used to provide a value that can be compared against a stopping criterion to stop eliminating features `feature_selector` in this example we simply discard the 20% of features deemed least important `update_sensitivity` true to retrain the classifiers each time we eliminate features; should be false if a non-classifier-based sensitivity measure (such as one-way ANOVA) is used As has been shown, recursive feature elimination is an easy-to-implement, flexible, and powerful tool within the PyMVPA framework. Various ranking methods for selecting features have been discussed. Additionally, several analysis scenarios have been presented, along with enough requisite knowledge that the user can plug in whatever classifiers, error metrics, or sensitivity measures are most appropriate for the task at hand. .. _RFE: api/mvpa.featsel.rfe.RFE-class.html .. _MaximalVote: api/mvpa.clfs.meta.MaximalVote-class.html .. _FeatureSelection: api/mvpa.featsel.base.FeatureSelection-class.html .. _LinearCSVMC: api/mvpa.clfs.svm.LinearCSVMC-class.html .. _mvpa/clfs/warehouse.py: api/mvpa.clfs.warehouse-pysrc.html .. index:: incremental feature search, IFS .. _incremental_feature_search: Incremental Feature Search ========================== IFS_ (to be written) .. _IFS: api/mvpa.featsel.ifs.IFS-class.html .. What are the practical differences (besides speed) between RFE and IFS? pymvpa-0.4.8/doc/glossary.rst000066400000000000000000000123261174541445200162140ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. _chap_glossary: ******** Glossary ******** The literature concerning the application of multivariate pattern analysis procedures to neuro-scientific datasets contains a lot of specific terms to refer to procedures or types of data, that are of particular importance. Unfortunately, sometimes various terms refer to the same construct and even worse these terms do not necessarily match the terminology used in the machine learning literature. The following glossary is an attempt to map the various terms found in the literature to the terminology used in this manual. .. glossary:: Block-averaging Averaging all samples recorded during a block of continuous stimulation in a block-design fMRI experiment. The rationale behind this technique is, that a averaging might lead to an improved signal-to-noise ratio. However, averaging further decreases the number of samples in a dataset, which is already very low in typical fMRI datasets, especially in comparison to the number of features/voxels. Block-averaging might nevertheless improve the classifier performance, *if* it indeed improves signal-to-noise *and* the respective classifier benefits more from few high-quality samples than from a larger set of lower-quality samples. Chunk A chunk is a group of samples. In PyMVPA chunks define *independent* groups of samples (note: the groups are independent from each other, not the samples in each particular group). This information is important in the context of a cross-validation procedure, as it is required to measure the classifier performance on independent test datasets to be able to compute unbiased generalization estimates. This is of particular importance in the case of fMRI data, where two successively recorded volumes cannot be considered as independent measurements. This is due to the significant temporal forward contamination of the hemodynamic response whose correlate is measured by the MR scanner. Dataset In PyMVPA a dataset is the combination of samples, their ... Decoding This term is usually used to refer to the application of machine learning or pattern recognition techniques to brainimaging datasets, and therefore is another term for :term:`MVPA`. Sometimes also 'brain-reading' is used as another alternative. Epoch Sometimes used to refer to a group of successively acquired samples, and, thus, related to a :term:`chunk`. Example Another term for :term:`sample`. Feature This is a name for a variable in the :term:`dataset`. fMRI This abbrevation stands for *functional magnetic resonance imaging*. Label A label associates each :term:`sample` in the :term:`dataset` with a certain category, experimental condition or, in case of a regression problem, with some metric variable. The label therefore defines the model that a classifier has to learn. The labels also provide the "true" model value when computing classifier errors. MVPA This term originally stems from the authors of the Matlab MVPA toolbox, and in that context stands for *multi-voxel pattern analysis* (see :ref:`Norman et al., 2006 `). PyMVPA obviously adopted this acronym. However, as PyMVPA is explicitly designed to operate on non-fMRI data as well, the 'voxel' term is not appropriate and therefore MVPA in this context stands for the more general term *multivariate pattern analysis*. Processing object Most objects dealing with data are implemented as processing objects. Such objects are instantiated *once*, with all appropriate parameters configured as desired. When created, they can be used multiple time by simply calling them with new data. Sample A sample a vector with observations for all :term:`feature` variables. Sensitivity The sensitivity is a score assigned to a particular :term:`feature` with respect to its impact on a classifier's decision. The sensitivity is often available from a classifier's :term:`weight vector`. There are some more scores which are similar to a sensitivity in terms of indicating the "importance" of a particular feature -- examples are a univariate :ref:`anova` score or a :ref:`noise_perturbation` measure. Sensitivity Map A vector of several sensitivity scores -- one for each feature in a dataset. Spatial Discrimination Map (SDM) This is another term for a :term:`sensitivity map`, used in e.g. :ref:`Wang et al. (2007) `. Statistical Discrimination Map (SDM) This is another term for a :term:`sensitivity map`, used in e.g. :ref:`Sato et al. (2008) `, where instead of raw sensitivity significance testing result is assigned. Time-compression This usually refers to the :term:`block-averaging` of samples from a block-design fMRI dataset. Weight Vector See :term:`sensitivity`. pymvpa-0.4.8/doc/history.rst000066400000000000000000000034541174541445200160540ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. _chap_code_swarm: .. index:: code swarm ****************** PyMVPA - The Movie ****************** If you are interested in the evolution of PyMVPA you might also be interested in the following movie. It is a visualization of the activity in the PyMVPA source repository since it first has been published in May 2007. The movie is generated with tools from the `code_swarm project`_. .. _code_swarm project: http://vis.cs.ucdavis.edu/~ogawa/codeswarm/ In the movie, each file in the PyMVPA sources that is changed appears as a particle on the screen centered around the name of the developer that contributed this change. The colors indicate which part of PyMVPA the respective file belongs to (see the legend on the left). The animation is regenerated every couple of weeks to include the more recent commits. .. raw:: html Play video Music by `Peter Nalitch`_. Many thanks for his special permission to use his song *Gitar* (© Peter Nalitch) for our project history. Please be sure to also watch the `outstanding video clip`_! .. _Peter Nalitch: http://peternalitch.ru .. _outstanding video clip: http://www.youtube.com/watch?v=AOzkN8dHnjk pymvpa-0.4.8/doc/index.rst000066400000000000000000000235201174541445200154560ustar00rootroot00000000000000.. -*- mode: rst -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: PyMVPA is a Python_ module intended to ease pattern classification analyses of large datasets. In the neuroimaging contexts such analysis techniques are also known as :term:`decoding` or :term:`MVPA` analysis. PyMVPA provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. .. _Python: http://www.python.org PyMVPA stands for **M**\ ulti\ **V**\ ariate **P**\ attern **A**\ nalysis (:term:`MVPA`) in **Py**\ thon. PyMVPA is developed inside the `Debian Experimental Psychology Project`_. This website, the source code repository and download services are hosted on Alioth_, a service that is kindly provided by the `Debian project`_. .. _Debian Experimental Psychology Project: http://pkg-exppsy.alioth.debian.org .. _Alioth: http://alioth.debian.org .. _Debian project: http://www.debian.org News ==== PyMVPA 0.4.8 is out [24 Apr 2012] This release brings a set of bug fixes, including compatibility with recent shogun (>=1.0) and libsvm >= 3.10. See the :ref:`changelog ` for details. .. raw:: html
    PyMVPA Extravaganza 2009 at Dartmouth College [30th Nov -- 4th Dec] :ref:`Read more ` about the topics and achievements. First publication from outside the PyMVPA team employing PyMVPA [19 Jul 2009] :ref:`Sun et al. (2009) `: *Elucidating an MRI-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms.* .. _pydocweb: https://code.launchpad.net/~pauli-virtanen/scipy/pydocweb Documentation ============= For users --------- * :ref:`User Documentation ` [PDF-manual_] (**the** documentation). * :ref:`Installation Instructions ` * :ref:`FAQ ` (short answers to common problems) * :ref:`Module Reference ` (user-oriented reference) * :ref:`Bibliography ` (references to interesting literature) * :ref:`Development Changelog ` [:ref:`Movie version `] (see what has changed) .. _PDF-manual: PyMVPA-Manual.pdf * :ref:`exampledata` (single subject dataset from :ref:`Haxby et al., 2001 `) .. comment to separate the two lists * :ref:`genindex` (access by keywords) * :ref:`search` (online and offline full-text search) For developers -------------- * :ref:`Developer Guidelines ` [PDF-guide_] (information for people contributing code) * `API Reference`_ (comprehensive and up-to-date information about the details of the implementation) .. _PDF-guide: PyMVPA-DevGuide.pdf .. _API Reference: api/index.html License ======= PyMVPA is free-software (beer and speech) and covered by the `MIT License`_. This applies to all source code, documentation, examples and snippets inside the source distribution (including this website). Please see the :ref:`appendix of the manual ` for the copyright statement and the full text of the license. .. _MIT License: http://www.opensource.org/licenses/mit-license.php .. _appendix of the manual: manual.html#license Download ======== Binary packages --------------- Binary packages are available for: * Debian and Ubuntu (:ref:`installation instructions `) PyMVPA is an `official Debian package`_ (`python-mvpa`). Additionally, backports for some Debian and Ubuntu releases are also available. Please read the `package repository instructions`_ to learn about how to obtain them. * RPM-based GNU/Linux distributions (:ref:`installation instructions `) RPM packages are provided through the `OpenSUSE Build Service`_. The currently supported distributions include: CentOS 5, Fedora 9-12, RedHat Enterprise Linux 5, OpenSUSE 11.0 up to 11.2 (but also OpenSUSE Factory). The build service supports RPM-package repositories (`SUSE-related`_ and `Fedora, Redhat and CentOS-related`_) and `1-click-installations`_. * MacOS X (:ref:`installation instructions `) PyMVPA is available from the MacPorts_ framework. * Windows (:ref:`installation instructions `) An installer for Python 2.5 is available from the `download area`_. If there are no binary packages for your particular operating system or platform, you need to compile your own. The manual contains :ref:`instructions ` to build PyMVPA in various environments. .. _MacPorts: http://www.macports.org .. _official Debian package: http://packages.debian.org/python-mvpa .. _package repository instructions: http://neuro.debian.net/#how-to-use-this-repository .. _SUSE-related: http://download.opensuse.org/repositories/home:/hankem:/suse/ .. _Fedora, Redhat and CentOS-related: http://download.opensuse.org/repositories/home:/hankem:/rh5/ .. _1-click-installations: http://software.opensuse.org/search?baseproject=ALL&p=1&q=python-mvpa .. _OpenSUSE Build Service: https://build.opensuse.org/ Source code ----------- Source code tarballs of PyMVPA releases are available from the `download area`_. Alternatively, one can also download a tarball of the latest development snapshot_ (i.e. the current state of the *master* branch of the PyMVPA source code repository). To get access to both the full PyMVPA history and the latest development code, the PyMVPA Git_ repository is publicly available. To view the repository, please point your webbrowser to gitweb: http://github.com/PyMVPA/PyMVPA To clone (aka checkout) the PyMVPA repository simply do: :: git clone http://github.com/PyMVPA/PyMVPA After a short while you will have a `PyMVPA` directory below your current working directory, that contains the PyMVPA repository. More detailed instructions on :ref:`installation requirements ` and on how to :ref:`build PyMVPA from source ` are provided in the manual. .. _download area: http://alioth.debian.org/frs/?group_id=30954 .. _Git: http://git.or.cz/ .. _snapshot: http://github.com/PyMVPA/PyMVPA/archives/master Support ======= If you have problems installing the software or questions about usage, documentation or something else related to PyMVPA, you can post to the PyMVPA mailing list (preferred) or contact the authors on IRC: :Mailing list: pkg-exppsy-pymvpa@lists.alioth.debian.org [subscription_, archive_] :IRC: #exppsy on OTFC/Freenode All users should subscribe to the mailing list. PyMVPA is still a young project that is under heavy development. Significant modifications (hopefully improvements) are very likely to happen frequently. The mailing list is the preferred way to announce such changes. The mailing list archive can also be searched using the *mailing list archive search* located in the sidebar of the PyMVPA home page. .. _subscription: http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa .. _archive: http://lists.alioth.debian.org/pipermail/pkg-exppsy-pymvpa/ Publications ============ .. include:: publications.rst Authors & Contributors ====================== .. include:: authors.rst Similar or Related Projects =========================== .. in alphanumerical order There are a number other projects with -- in comparison to PyMVPA -- partially overlapping features or a similar purpose. Some of their functionality is already available through and within the PyMVPA framework. *Only* free software projects are listed here. * 3dsvm_: AFNI_ plugin to apply support vector machine classifiers to fMRI data. * Elefant_: Efficient Learning, Large-scale Inference, and Optimization Toolkit. Multi-purpose open source library for machine learning. * MDP_: Python data processing framework. MDP_ provides various algorithms. *PyMVPA makes use of MDP's PCA and ICA implementations.* * `MVPA Toolbox`_: Matlab-based toolbox to facilitate multi-voxel pattern analysis of fMRI neuroimaging data. * NiPy_: Project with growing functionality to analyze brain imaging data. NiPy_ is heavily connected to SciPy and lots of functionality developed within NiPy becomes part of SciPy. * OpenMEEG_: Software package for low-frequency bio-electromagnetism solving forward problems in the field of EEG and MEG. OpenMEEG includes Python bindings. * Orange_: Powerful general-purpose data mining software. Orange also has Python bindings. * `PyMGH/PyFSIO`_: Python IO library to for FreeSurfer's `.mgh` data format. * PyML_: Interactive object oriented framework for machine learning written in Python. PyML focuses on SVMs and other kernel methods. * PyNIfTI_: Read and write NIfTI images from within Python. *PyMVPA uses PyNIfTI to access MRI datasets.* * `scikits.learn`_: Python module integrating classic machine learning algorithms in the tightly-knit world of scientific Python packages. * Shogun_: Comprehensive machine learning toolbox with bindings to various programming languages. *PyMVPA can optionally use implementations of Support Vector Machines from Shogun.* .. _3dsvm: http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dsvm.html .. _AFNI: http://afni.nimh.nih.gov/ .. _Elefant: http://elefant.developer.nicta.com.au .. _Shogun: http://www.fml.tuebingen.mpg.de/raetsch/projects/shogun .. _Orange: http://magix.fri.uni-lj.si/orange .. _PyML: http://pyml.sourceforge.net .. _MDP: http://mdp-toolkit.sourceforge.net .. _MVPA Toolbox: http://www.csbmb.princeton.edu/mvpa/ .. _NiPy: http://neuroimaging.scipy.org .. _PyMGH/PyFSIO: http://code.google.com/p/pyfsio .. _PyNIfTI: http://niftilib.sourceforge.net/pynifti .. _OpenMEEG: http://openmeeg.gforge.inria.fr .. _scikits.learn: http://scikit-learn.sourceforge.net pymvpa-0.4.8/doc/installation.rst000066400000000000000000000702431174541445200170540ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. _chap_installation: ************ Installation ************ This section covers the necessary steps to install and run PyMVPA. It contains a comprehensive list of software dependencies, as well as recommendation for additional software packages that further enhance the functionality provided by PyMVPA. .. _requirements: Dependencies ============ PyMVPA is designed to be able to easily interface with various libraries and computing environments. However, most of these external software packages only enhance functionality built into PyMVPA or add a different flavor of some algorithm (e.g. yet another classifier). In fact, the framework itself has only two mandatory dependencies (see below), which are known to be very portable. It is therefore possible to run PyMVPA on a wide variety of platforms and operating systems, ranging from computing mainframes, to regular desktop machines. It even runs on a cell phone. .. image:: pics/pymvpa_on_phone.jpg :alt: Picture showing PyMVPA running on an OpenMoko This picture shows PyMVPA on an OpenMoko_ cell phone --- running the `pylab_2d.py` example in an IPython_ session. .. note:: In general a phone might not be the optimal environment for data analysis with PyMVPA, but PyMVPA itself does not restrict the user's choice of the platform to the usual suspects. (A `highres image`_ is available, if you want to double check. ;-) .. _OpenMoko: http://www.openmoko.com .. _highres image: http://www.onerussian.com/php/album.php?page=Photos/Geek/20081015FR/&image=img_1107.jpg .. index:: required software, NumPy Must Have --------- The following software packages are required or PyMVPA will not work at all. Python_ 2.4 with ctypes_ 1.0.1 or a later Python 2.X release With some modifications PyMVPA could probably work with Python 2.3, but as it is quite old already and Python 2.4 is widely available there should be no need to do this. NumPy_ PyMVPA makes extensive use of NumPy to store and handle data. There is no way around it. .. _Python: http://www.python.org .. _NumPy: http://numpy.scipy.org/ .. _ctypes: http://python.net/crew/theller/ctypes/ .. index:: recommended software, SciPy, PyNIfTI, Shogun, R, RPy Strong Recommendations ---------------------- While most parts of PyMVPA will work without any additional software, some functionality makes use (or can optionally make use) of external software packages. It is strongly recommended to install these packages as well, if they are available on a particular target platform. SciPy_: linear algebra, standard distributions, signal processing, data IO SciPy_ is mainly used by the statistical testing and the logistic regression classifier code. However, the SciPy package provides a lot of functionality that might be relevant in the context of PyMVPA, e.g. IO support for Matlab .mat files. PyNIfTI_ (>= 0.20081017.1): access to NIfTI files PyMVPA provides a convenient wrapper for datasets stored in the NIfTI format, that internally uses PyNIfTI. If you don't need that, PyNIfTI is not necessary, but otherwise it makes it really easy to read from and write to NIfTI images. All dataset types dealing with NIfTI data will not be available without a functional PyNIfTI installation. Since PyMVPA 0.4.0 at least PyNIfTI version 0.20081017.1 (or later) is required. .. _SciPy: http://www.scipy.org/ .. _PyNIfTI: http://niftilib.sourceforge.net/pynifti/ .. index:: suggested software, IPython, FSL, AFNI, LIBSVM, matplotlib, hlcuster Suggestions ----------- The following list of software is again not required by PyMVPA, but these packages add additional functionality (e.g. classifiers implemented in external libraries) and might make life a lot easier by leading to more efficiency when using PyMVPA. IPython_: frontend If you want to use PyMVPA interactively it is strongly recommend to use IPython_. If you think: *"Oh no, not another one, I already have to learn about PyMVPA."* please invest a tiny bit of time to watch the `Five Minutes with IPython`_ screencasts at showmedo.com_, so at least you know what you are missing. In the context of cluster computing IPython_ is also the way to go. FSL_: preprocessing and analysis of (f)MRI data PyMVPA provides some simple bindings to FSL output and filetypes (e.g. EV files, estimated motion correct parameters and MELODIC output directories). This makes it fairly easy to e.g. use FSL's implementation of ICA for data reduction and proceed with analyzing the estimated ICs in PyMVPA. AFNI_: preprocessing and analysis of (f)MRI data Similar to FSL, AFNI is a free package for processing (f)MRI data. Though its primary data file format is BRIK files, it has the ability to read and write NIFTI files, which easily integrate with PyMVPA. Shogun_: various classifiers PyMVPA currently can make use of several SVM implementations of the Shogun_ toolbox. It requires the modular python interface of Shogun to be installed. Any version from 0.6 on should work. LIBSVM_: fast SVM classifier Only the C library is required and none of the Python bindings that are available on the upstream website. PyMVPA provides its own Python wrapper for LIBSVM which is a fork based on the one included in the LIBSVM package. Additionally the upstream LIBSVM distribution causes flooding of the console with a huge amount of debugging messages. Please see the `Building from Source`_ section for information on how to build an alternative version that does not have this problem. Since version 0.2.2, PyMVPA contains a minimal copy of LIBSVM in its source distribution. R_ and RPy_: more classifiers Currently PyMVPA provides wrappers around LARS, ElasticNet, and GLMNet R libraries available from CRAN_. On Debian-based machines you might like to install r-cran-* packages from cran2deb_ repository. matplotlib_: Matlab-style plotting library for Python This is a very powerful plotting library that allows you to export into a large variety of raster and vector formats (e.g. SVG), and thus, is ideal to produce publication quality figures. The examples shipped with PyMVPA show a number of possibilities how to use matplotlib for data visualization. hcluster_: generating, visualizing, and analyzing hierarchical clusters This module is a nice addition to SciPy_ and can be used to perform cluster analyses and plot dendrograms of their results. .. _Shogun: http://www.shogun-toolbox.org .. _LIBSVM: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ .. _hcluster: http://code.google.com/p/scipy-cluster/ .. _matplotlib: http://matplotlib.sourceforge.net/ .. _IPython: http://ipython.scipy.org .. _Five Minutes with IPython: http://showmedo.com/videos/series?name=CnluURUTV .. _showmedo.com: http://showmedo.com .. _FSL: http://www.fmrib.ox.ac.uk/fsl/ .. _AFNI: http://afni.nimh.nih.gov/afni/ .. _RPy: http://rpy.sourceforge.net .. _R: http://www.r-project.org .. _cran2deb: http://debian.cran.r-project.org .. _CRAN: http://cran.r-project.org .. index:: installation, binary packages .. _obtaining: Installing Binary Packages ========================== .. Don't forget to mention that the only reasonable way to use this piece of software (like every other piece) is under Debian! Also mention that Ubuntu is no excuse ;-) The easiest way to obtain PyMVPA is to use pre-built binary packages. Currently we provide such packages or installers for the Debian/Ubuntu family, several RPM-based GNU/Linux distributions, MacOS X and 32-bit Windows (see below). If there are no binary packages for your operating system or platform yet, you can build PyMVPA from source. Please refer to `Building from Source`_ for more information. .. note:: If you have difficulties deploying PyMVPA itself or third-party modules, such as Shogun, on non Debian-based systems, we would advise you to give a try to `NeuroDebian virtual machine`_ which would allow you to benefit from Debian packaging of PyMVPA and its dependencies by running Debian in a virtualized environment. .. _NeuroDebian virtual machine: http://neuro.debian.net/vm.html .. index:: binary packages .. index:: Debian .. _install_debian: Debian ------ PyMVPA is available as an `official Debian package`_ (`python-mvpa`; since *lenny*). The documentation is provided by the optional `python-mvpa-doc` package. To install PyMVPA simply do:: sudo aptitude install python-mvpa .. _official Debian package: http://packages.debian.org/python-mvpa .. index:: backports, Debian, Ubuntu .. _install_debianbackports: Debian backports and inofficial Ubuntu packages ----------------------------------------------- Backports for the current Debian stable release and binary packages for recent Ubuntu releases are available from a `Debian Neuroscience Repository`_. Please read the `package repository instructions`_ to learn about how to obtain them. Otherwise install as you would do with any other Debian package. .. _Debian Neuroscience Repository: http://neuro.debian.net .. _package repository instructions: http://neuro.debian.net/#how-to-use-this-repository .. index:: Windows, Windows installer .. _install_win: Windows ------- There are a few Python distributions for Windows. In theory all of them should work equally well. However, we only tested the standard Python distribution from www.python.org (with version 2.5.2). First you need to download and install Python. Use the Python installer for this job. Yo do not need to install the Python test suite and utility scripts. From now on we will assume that Python was installed in `C:\\Python25` and that this directory has been added to the `PATH` environment variable. For a minimal installation of PyMVPA the only thing you need in addition is NumPy_. Download a matching NumPy windows installer for your Python version (in this case 2.5) from the `SciPy download page`_ and install it. Now, you can use the PyMVPA windows installer to install PyMVPA on your system. If done, verify that everything went fine by opening a command prompt and start Python by typing `python` and hit enter. Now you should see the Python prompt. Import the mvpa module, which should cause no error messages. >>> import mvpa >>> Although you have a working installation already, most likely you want to install some additional software. First and foremost install SciPy_ -- download from the same page where you also got the NumPy installer. If you want to use PyMVPA to analyze fMRI datasets, you probably also want to install PyNIfTI_. Download the corresponding installer from the website of the `NIfTI libraries`_ and install it. PyNIfTI does not come with the required `zlib` library, so you also need to download and install it. A binary installer is available from the `GnuWin32 project`_. Install it in some arbitrary folder (just the binaries nothing else), find the `zlib1.dll` file in the `bin` subdirectory and move it in the Windows `system32` (or `system` on 64-bit Windows 7) directory. Verify that it works by importing the `nifti` module in Python. >>> import nifti >>> Another piece of software you might want to install is matplotlib_. The project website offers a binary installer for Windows. If you are using the standard Python distribution and matplotlib complains about a missing `msvcp71.dll`, be sure to obey the installation instructions for Windows on the matplotlib website. With this set of packages you should be able to run most of the PyMVPA examples which are shipped with the source code in the `doc/examples` directory. .. _SciPy download page: http://scipy.org/Download .. _NIfTI libraries: http://niftilib.sourceforge.net/ .. _GnuWin32 project: http://gnuwin32.sourceforge.net/ .. index:: MacOS X .. _install_macos: MacOS X ------- The easiest installation method for OSX is via MacPorts_. MacPorts is a package management system for MacOS, which is in some respects very similiar to RPM or APT which are used in most GNU/Linux distributions. However, rather than installing binary packages, it compiles software from source on the target machine. *The MacPort of PyMVPA is kindly maintained by James Kyle .* .. note:: MacPorts_ needs `XCode developer tools`_ to be installed first, as the operating system does not come with a compiler by default. .. _XCode developer tools: http://developer.apple.com/tools/xcode/ .. _MacPorts: http://www.macports.org In the context of PyMVPA MacPorts is much easier to handle than the previously available PyMVPA installer for Macs (which was discontinued with PyMVPA 0.4.1). Although the initial overhead to setup MacPorts on a machine is higher than simply installing PyMVPA using the former installer, MacPorts saves the user a significant amount of time (in the long run). This is due to the fact that this framework will not only take care of updating a PyMVPA installation automatically whenever a new release is available. It will also provide many of the optional dependencies of PyMVPA (e.g. NumPy_, SciPy_, matplotlib_, IPython_, Shogun_, and pywt_) in the same environment and therefore abolishes the need to manually check dozens of websites for updates and deal with an unbelievable number of different installation methods. .. _Shogun: http://www.shogun-toolbox.org .. _pywt: http://www.pybytes.com/pywavelets MacPorts provides a universal binary package installer that is downloadable at http://www.macports.org/install.php After downloading, simply mount the dmg image and double click `MacPorts.pkg`. By default, MacPorts installs to `/opt/local`. After the installation is completed, you must ensure that your paths are set up correctly in order to access the programs and utilities installed by MacPorts. For exhaustive details on editing shell paths please see: http://www.debian.org/doc/manuals/reference/ch01.en.html#_customizing_bash A typical `.bash_profile` set up for MacPorts might look like:: > export PATH=/opt/local/bin:/opt/local/sbin:$PATH Be sure to source your .bash_profile or close Terminal.app and reopen it for these changes to take effect. Once MacPorts is installed and your environment is properly configured, PyMVPA is installed using a single command:: > $ sudo port install py25-pymvpa +scipy +pynifti +hcluster +libsvm > +matplotlib +pywavelet The `+foo` arguments add support within PyMVPA for these packages. For a full list of available 3rd party packages please see:: > $ port variants py25-pymvpa If this is your first time using MacPorts Python 2.5 will be automatically installed for you. However, an additional step is needed:: $ sudo port install python_select $ sudo python_select python25 MacPorts has the ability of installing several Python versions at a time, the `python_select` utility ensures that the default Python (located at `/opt/local/bin/python`) points to your preferred version. Upon success, open a terminal window and start Python by typing `python` and hit return. Now try to import the PyMVPA module by doing: >>> import mvpa >>> If no error messages appear, you have succesfully installed PyMVPA. .. index:: OpenSUSE .. _install_rpm: RPM-based GNU/Linux Distributions --------------------------------- To install one of the RPM packages provided through the `OpenSUSE Build Service`_, first download it from the `OpenSUSE software website`_. .. note:: This site does not only offer OpenSUSE packages, but also binaries for other distributions, including: CentOS 5, Fedora 9-12, RedHat Enterprise Linux 5, OpenSUSE 11.0 up to 11.2. Once downloaded, open a console and invoke (the example command refers to PyMVPA 0.4.4):: rpm -i python-mvpa-0.4.4-1.1.i386.rpm The OpenSUSE website also offers `1-click-installations`_ for distributions supporting it. A more convenient way to install PyMVPA and automatically receive software updates is to included one of the RPM-package repositories in the system's package management configuration. For e.g. OpenSUSE 11.0, simply use Yast to add another repository, using the following URL: http://download.opensuse.org/repositories/home:/hankem:/suse/openSUSE_11.0/ For other distributions use the respective package managers (e.g. Yum) to setup the repository URL. The repositories include all core dependencies of PyMVPA (usually Numpy and PyNIfTI), if they are not available from other repositories of the respective distribution. There are two different repository groups, one for `SUSE-related packages`_ and another one for `Fedora, Redhat and CentOS-related packages`_. Please note that on Redhat and CentOS systems you will also have to enable the `Extra Packages for Enterprise Linux (EPEL)`_ repository. .. _Extra Packages for Enterprise Linux (EPEL): http://fedoraproject.org/wiki/EPEL .. _SUSE-related packages: http://download.opensuse.org/repositories/home:/hankem:/suse/ .. _Fedora, Redhat and CentOS-related packages: http://download.opensuse.org/repositories/home://hankem://rh5/ .. _1-click-installations: http://software.opensuse.org/search?baseproject=ALL&p=1&q=python-mvpa .. _OpenSUSE software website: http://software.opensuse.org/search?baseproject=ALL&p=1&q=python-mvpa .. _OpenSUSE Build Service: https://build.opensuse.org/ .. _buildfromsource: .. index:: building from source, source package, MacOS X Building from Source ==================== If a binary package for your platform and operating system is provided, you do not have to build the packages on your own -- use the corresponding pre-build packages instead. However, if there are no binary packages for your system, or you want to try a new (unreleased) version of PyMVPA, you can easily build PyMVPA on your own. Any recent GNU/Linux distribution should be capable of doing it (e.g. RedHat). Additionally, building PyMVPA also works on Mac OS X and Windows systems. .. _PyMVPA project website: http://www.pymvpa.org .. index:: releases, development snapshot Three Ways to Obtain the Sources -------------------------------- The first step is obtaining the sources. The source code tarballs of all PyMVPA releases are available from the `PyMVPA project website`_. Alternatively, one can also download a tarball of the latest development snapshot_ (i.e. the current state of the *master* branch of the PyMVPA source code repository). .. _snapshot: http://github.com/PyMVPA/PyMVPA/archives/master .. index:: Git, Git repository If you want to have access to both, the full PyMVPA history and the latest development code, you can use the PyMVPA Git_ repository, which is publicly available. To view the repository, please point your web browser to gitweb: http://github.com/PyMVPA/PyMVPA The gitweb browser also allows to download arbitrary development snapshots of PyMVPA. For a full clone (aka checkout) of the PyMVPA repository simply do: :command:`git clone git://github.com/PyMVPA/PyMVPA.git` After a short while you will have a `PyMVPA` directory below your current working directory, that contains the PyMVPA repository. .. _Git: http://git.or.cz/ .. index:: build instructions Build it (General instructions) ------------------------------- In general you can build PyMVPA like any other Python module (using the Python *distutils*). This general method will be outline first. However, in some situations or on some platforms alternative ways of building PyMVPA might be more convenient -- alternative approaches are listed at the end of this section. To build PyMVPA from source simply enter the root of the source tree (obtained by either extracting the source package or cloning the repository) and run: :command:`python setup.py build_ext` If you are using a Python version older than 2.5, you need to have python-ctypes (>= 1.0.1) installed to be able to do this. Now, you are ready to install the package. Do this by invoking: :command:`python setup.py install` Most likely you need superuser privileges for this step. If you want to install in a non-standard location, please take a look at the :command:`--prefix` option. You also might want to consider :command:`--optimize`. Now you should be ready to use PyMVPA on your system. .. index:: LIBSVM, SWIG Build with enabled LIBSVM bindings ---------------------------------- From the 0.2 release of PyMVPA on, the LIBSVM_ classifier extension is not build by default anymore. However, it is still shipped with PyMVPA and can be enabled at build time. To be able to do this you need to have SWIG_ installed on your system. PyMVPA needs a patched LIBSVM version, as the original distribution generates a huge amount of debugging messages and therefore makes the console and PyMVPA output almost unusable. Debian (since lenny: 2.84.0-1) and Ubuntu (since gutsy) already include the patched version. For all other systems a minimal copy of the patched sources is included in the PyMVPA source package (`3rd/libsvm`). If you do not have a proper LIBSVM_ package, you can build the library from the copy of the code that is shipped with PyMVPA. To do this, simply invoke:: make 3rd Now build PyMVPA as described above. The build script will automatically detect that LIBSVM_ is available and builds the LIBSVM wrapper module for you. If your system provides an appropriate LIBSVM_ version, you need to have the development files (headers and library) installed. Depending on where you installed them, it might be necessary to specify the full path to that location with the `--include-dirs`, `--library-dirs` and `--swig` options. Now add the '--with-libsvm' flag when building PyMVPA:: python setup.py build_ext --with-libsvm \ [ -I -L ] The installation procedure is equivalent to the build setup without LIBSVM_, except that the '--with--libsvm' flag also has to be set when installing:: python setup.py install --with-libsvm .. _SWIG: http://www.swig.org/ .. index:: alternative build procedure Alternative build procedure --------------------------- Alternatively, if you are doing development in PyMVPA or if you simply do not want (or do not have sufficient permissions to do so) to install PyMVPA system wide, you can simply call `make` (same `make build`) in the top-level directory of the source tree to build PyMVPA. Then extend or define your environment variable `PYTHONPATH` to point to the root of PyMVPA sources (i.e. where you invoked all previous commands from): export PYTHONPATH=$PWD .. note:: This procedure also always builds the LIBSVM_ extension and therefore also requires the patched LIBSVM version and SWIG to be available. .. index:: building on Windows .. _build_win: Windows ------- On Windows the whole situation is a little more tricky, as the system doesn't come with a compiler by default. Nevertheless, it is easily possible to build PyMVPA from source. One could use the Microsoft compiler that comes with Visual Studio to do it, but as this is commercial software and not everybody has access to it, we will outline a way that exclusively involves free and open source software. First one needs to install the packages required to run PyMVPA as explained :ref:`above `. Next we need to obtain and install the MinGW compiler collection. Download the *Automated MinGW Installer* from the `MinGW project website`_. Now, run it and choose to install the `current` package. You will need the *MinGW base tools*, *g++* compiler and *MinGW Make*. For the remaining parts of the section, we will assume that MinGW got installed in `C:\\MinGW` and the directory `C:\\MinGW\\bin` has been added to the `PATH` environment variable, to be able to easily access all MinGW tools. .. note:: It is not necessary to install `MSYS`_ to build PyMVPA, but it might handy to have it. If you want to build the LIBSVM wrapper for PyMVPA, you also need to download SWIG_ (actually *swigwin*, the distribution for Windows). SWIG does not have to be installed, just unzip the file you downloaded and add the root directory of the extracted sources to the `PATH` environment variable (make sure that this directory contains `swig.exe`, if not, you haven't downloaded `swigwin`). PyMVPA comes with a specific build setup configuration for Windows -- `setup.cfg.win` in the root of the source tarball. Please rename this file to `setup.cfg`. This is only necessary, if you have *not* configured your Python distutils installation to always use MinGW instead of the Microsoft compilers. Now, we are ready to build PyMVPA. The easiest way to do this, is to make use of the `Makefile.win` that is shipped with PyMVPA to build a binary installer package (`.exe`). Make sure, that the settings at the top of `Makefile.win` (the file is located in the root directory of the source distribution) correspond to your Python installation -- if not, first adjust them accordingly before your proceed. When everything is set, do:: mingw32-make -f Makefile.win installer Upon success you can find the installer in the `dist` subdirectory. Install it as described :ref:`above `. .. _MinGW project website: http://www.mingw.org/ .. _MSYS: http://www.mingw.org/msys.shtml .. index:: OpenSUSE .. _build_suse: OpenSUSE -------- Building PyMVPA on OpenSUSE involves the following steps (tested with 10.3): First add the OpenSUSE science repository, that contains most of the required packages (e.g. NumPy, SciPy, matplotlib), to the Yast configuration. The URL for OpenSUSE 10.3 is:: http://download.opensuse.org/repositories/science/openSUSE_10.3/ Now, install the following required packages: * a recent C and C++ compiler (e.g. GCC 4.1) * `python-devel` (Python development package) * `python-numpy` (NumPy) * `swig` (SWIG is only necessary, if you want to make use of LIBSVM) Now you can simply compile and install PyMVPA, as outlined above, in the general build instructions (or alternatively using the method with LIBSVM). If you have problems compiling the NIfTI libraries and PyNIfTI on OpenSUSE, try the following: Download the `nifticlib` source tarball, extract it and run `make` in the top-level source directory. Be sure to install the `zlib-devel` package before. Now, download the `pynifti` source tarball extract it, and edit `setup.py`. Change the line:: libraries = [ 'niftiio' ], to:: libraries = [ 'niftiio', 'znz', 'z' ], as mentioned in the PyNIfTI installation instructions. This is necessary, as the above approach does only generate static NIfTI libraries which are not properly linked with all dependencies. Now, compile PyNIfTI with:: python setup.py build_ext -I /include \ -L /lib --swig-opts="-I/include" where `` is the directory that contains the extracted `nifticlibs` sources. Finally, install PyNIfTI with:: sudo python setup.py install If you want to run the PyMVPA examples including the ones that make use of the plotting capabilities of `matplotlib` you need to install of few more packages (mostly due to broken dependencies in the corresponding OpenSUSE packages): * `python-scipy` * `python-gobject2` * `python-gtk` .. index:: Fedora .. _build_fedora: Fedora ------ On Fedora (tested with Fedora 9) you first have to install a few required packages, that are not installed by default. Simply do:: yum install numpy gcc gcc-c++ python-devel swig You might also want to consider installing some more packages, that will make your life significantly easier:: yum install scipy ipython python-matplotlib Now, you are ready to compile and install PyMVPA as describe in the :ref:`general build instructions `. .. index:: MacOS X .. _build_macos: MacOS X ------- Since the MacPorts_ system basically compiles from source there should be no need to perform this step manually. However, if one intends to compile without MacPorts_ the `XCode developer tools`_, have to be installed first, as the operating system does not come with a compiler by default. If you want to use or even work on the latest development code, you should also install Git_. There is a `MacOS installer for Git`_, that make this step very easy. .. _MacOS installer for Git: http://code.google.com/p/git-osx-installer/ Otherwise follow the :ref:`general build instructions `. pymvpa-0.4.8/doc/intro.rst000066400000000000000000000211601174541445200155000ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. _chap_intro: ************ Introduction ************ .. index:: MVPA PyMVPA is a Python_ module intended to ease pattern classification analysis of large datasets. It provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms. While it is not limited to neuroimaging data it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free software to run. Theoretically PyMVPA should run on anything that can run a Python_ interpreter, although the proof is yet to come. PyMVPA stands for *Multivariate Pattern Analysis* in Python_. .. _Python: http://www.python.org What this Manual is NOT ======================= .. index:: textbook, review, API reference, examples This manual does not make an attempt to be a comprehensive introduction into machine learning *theory*. There is a wealth of high-quality text books about this field available. Two very good examples are: `Pattern Recognition and Machine Learning`_ by `Christopher M. Bishop`_, and `The Elements of Statistical Learning: Data Mining, Inference, and Prediction`_ by `Trevor Hastie`_, `Robert Tibshirani`_, and `Jerome Friedman`_ (PDF was generously made available online_ free of charge). There is a growing number of introductory papers about the application of machine learning algorithms to (f)MRI data. A very high-level overview about the basic principles is available in :ref:`Mur et al. (2009) `. A more detailed tutorial covering a wide variety of aspects is provided in :ref:`Pereira et al. (in press) `. Two reviews by :ref:`Norman et al. (2006) ` and :ref:`Haynes and Rees (2006) ` give a broad overview about the literature. This manual also does not describe every technical bit and piece of the PyMVPA package, but is instead focused on the user perspective. Developers should have a look at the `API documentation`_, which is a detailed, comprehensive and up-to-date description of the whole package. Users looking for an overview of the public programming interface of the framework are referred to the :ref:`chap_modref`. The :ref:`chap_modref` is similar to the API reference, but hides overly technical information, which are only relevant for people intending to extend the framework by adding more functionality. More examples and usage patterns extending the ones described here can be taken from the examples shipped with the PyMVPA source distribution (`doc/examples/`; some of them are also available in the :ref:`chap_examples` chapter of this manual) or even the unit test battery, also part of the source distribution (in the `tests/` directory). .. _API Documentation: api/index.html .. _Christopher M. Bishop: http://research.microsoft.com/~cmbishop/ .. _Pattern Recognition and Machine Learning: http://research.microsoft.com/~cmbishop/PRML .. _online: .. _The Elements of Statistical Learning\: Data Mining, Inference, and Prediction: http://www-stat.stanford.edu/~tibs/ElemStatLearn/ .. _Trevor Hastie: http://www-stat.stanford.edu/~hastie/ .. _Robert Tibshirani: http://www-stat.stanford.edu/~tibs/ .. _Jerome Friedman: http://www-stat.stanford.edu/~jhf/ .. _history: .. index:: history, MVPA toolbox for Matlab, license, free software A bit of History ================ The roots of PyMVPA date back to early 2005. At that time it was a C++ library (no Python_ yet) developed by Michael Hanke and Sebastian Krüger, intended to make it easy to apply artificial neural networks to pattern recognition problems. During a visit to `Princeton University`_ in spring 2005, Michael Hanke was introduced to the `MVPA toolbox`_ for `Matlab `_, which had several advantages over a C++ library. Most importantly it was easier to use. While a user of a C++ library is forced to write a significant amount of front-end code, users of the MVPA toolbox could simply load their data and start analyzing it, providing a common interface to functions drawn from a variety of libraries. .. _Princeton University: http://www.princeton.edu .. _MVPA toolbox: http://www.csbmb.princeton.edu/mvpa/ However, there are some disadvantages when writing a toolbox in Matlab. While users in general benefit from the powers of Matlab, they are at the same time bound to the goodwill of a commercial company. That this is indeed a problem becomes obvious when one considers the time when the vendor of Matlab was not willing to support the Mac platform. Therefore even if the MVPA toolbox is `GPL-licensed`_ it cannot fully benefit from the enormous advantages of the free software development model environment (free as in free speech, not only free beer). .. _GPL-licensed: http://www.gnu.org/copyleft/gpl.html For these reasons, Michael thought that a successor to the C++ library should remain truly free software, remain fully object-oriented (in contrast to the MVPA toolbox), but should be at least as easy to use and extensible as the MVPA toolbox. After evaluating some possibilities Michael decided that `Python`_ is the most promising candidate that was fully capable of fulfilling the intended development goal. Python is a very powerful language that magically combines the possibility to write really fast code and a simplicity that allows one to learn the basic concepts within a few days. .. index:: RPy, PyMatlab One of the major advantages of Python is the availability of a huge amount of so called *modules*. Modules can include extensions written in a hardcore language like C (or even FORTRAN) and therefore allow one to incorporate high-performance code without having to leave the Python environment. Additionally some Python modules even provide links to other toolkits. For example `RPy`_ allows to use the full functionality of R_ from inside Python. Even Matlab can be used via some Python modules (see PyMatlab_ for an example). .. _RPy: http://rpy.sourceforge.net/ .. _R: http://www.r-project.org .. _PyMatlab: http://code.google.com/p/pymatlab/ After the decision for Python was made, Michael started development with a simple k-Nearest-Neighbor classifier and a cross-validation class. Using the mighty NumPy_ package made it easy to support data of any dimensionality. Therefore PyMVPA can easily be used with 4d fMRI dataset, but equally well with EEG/MEG data (3d) or even non-neuroimaging datasets. .. _NumPy: http://numpy.scipy.org/ .. index:: NIfTI By September 2007 PyMVPA included support for reading and writing datasets from and to the `NIfTI format`_, kNN and Support Vector Machine classifiers, as well as several analysis algorithms (e.g. searchlight and incremental feature search). .. _NIfTI format: http://nifti.nimh.nih.gov/ During another visit in Princeton in October 2007 Michael met with `Yaroslav Halchenko`_ and `Per B. Sederberg`_. That incident and the following discussions and hacking sessions of Michael and Yaroslav lead to a major refactoring of the PyMVPA codebase, making it much more flexible/extensible, faster and easier than it has ever been before. .. _Yaroslav Halchenko: http://www.onerussian.com/ .. _Per B. Sederberg: http://www.princeton.edu/~persed/ .. index:: citation, PyMVPA poster Authors & Contributors ====================== .. include:: authors.rst How to cite PyMVPA ================== .. include:: publications.rst Acknowledgements ================ We are greatful to the developers and contributers of NumPy_, SciPy_ and IPython_ for providing an excellent Python-based computing environment. Additionally, as PyMVPA makes use of a lot of external software packages (e.g. classifier implementations), we want to acknowledge the authors of the respective tools and libraries (e.g. LIBSVM_ or Shogun_) and thank them for developing their packages as free and open source software. Finally, we would like to express our acknowledgements to the `Debian project`_ for providing us with hosting facilities for mailing lists and source code repositories. But most of all for developing the *universal operating system*. .. Please add some notes when you think that you should give credits to someone that enables or motivates you to work on PyMVPA ;-) .. _Debian project: http://www.debian.org .. _SciPy: http://www.scipy.org/ .. _Shogun: http://www.shogun-toolbox.org .. _IPython: http://ipython.scipy.org .. _LIBSVM: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ pymvpa-0.4.8/doc/legal.rst000077700000000000000000000000001174541445200166722../COPYINGustar00rootroot00000000000000pymvpa-0.4.8/doc/manual.rst000066400000000000000000000043471174541445200156320ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ********************************** PDF version of the PyMVPA Manual ********************************** The PDF version of the manual is available for download_. .. _download: PyMVPA-Manual.pdf .. toctree:: intro installation overview datasets classifiers measures featsel misc examples matlab faq glossary references legal changelog modref .. high-level comments .. incorporate a standalone section on file formats and interoperability. clearly, Nifti is one, but i'm still unclear about what else PyMVPA can/can't import .. for us (Matlab MVPA), the tutorial_easy quickstart was an enormous success. i strongly recommend having some similar quick, hands-on guide. feel free to borrow/steal/adapt anything from tutorial_easy for your needs if you like it (though you should probably check with jim before re-distributing the sample data). .. you dive straight into the nitty-gritty of the different kinds of datasets, attributes and other data structures. having a high-level summary of the most important points might make it easier for a new reader to get the big picture, and makes it more likely that people who don't like documentation will at least read the most important points .. use more examples .. i know that i would personally benefit from a 'PyMVPA for Matlab MVPA users' section. perhaps this is something that per and i will end up hammering out over the next few months .. i'm a big fan of Howtos... it sounds like you're creating a collection of snippets, but maybe consider embedding them into the manual with a little description of what they're doing, alternatives etc. .. maybe a glossary might help. i'm starting to see how you're using 'samples' vs 'datasets' etc. but it would be nice to have a quick reference .. this is a really, really good start for a 0.1 release. good job! pymvpa-0.4.8/doc/math/000077500000000000000000000000001174541445200145445ustar00rootroot00000000000000pymvpa-0.4.8/doc/math/kernels.tex000066400000000000000000000374561174541445200167500ustar00rootroot00000000000000\documentclass[a4paper,11pt]{article} \usepackage[latin1]{inputenc} \usepackage[english]{babel} \usepackage{epsfig} \usepackage{amsmath} \usepackage{amsfonts} \newcommand\R{{\mathbb R}} \newcommand\x{{\mathbf x}} \newcommand\X{{\mathbf X}} \newcommand\K{{\mathbf K}} \newcommand\J{{\mathbf J}} \newcommand\LL{{\mathbf L}} \newcommand\ELL{{\Ivec \ell}} %%\newcommand\L{{\mathbf L}} %%\DeclareMathOperator*{\argmax}{arg\,max} %% \DeclareMathOperator*{\argmax}{argmax} %% (argmax wihtouth mid space) \DeclareMathOperator*{\argmin}{arg\,min} \DeclareMathOperator*{\var}{var} \DeclareMathOperator*{\dm}{dm} \newcommand{\Rvec}[1]{{\bf #1}} \newcommand{\Ivec}[1]{\mbox{\boldmath $#1$}} \title{Kernels} \author{Emanuele Olivetti} \begin{document} \maketitle \section{Introduction} This document gives a detailed description of kernels implemented in PyMVPA together with derivation of their gradients. Gradients are useful when trying to maximize the marginal likelihood of a Gaussian, i.e., during model selection. The following notation and definitions are used: \begin{itemize} \item $\x \in \R^D$ : a $D$-dimensional column vector, $\x = (x_1,\ldots,x_D)$. \item $\X = (\x_1^{\top},\ldots,\x_N^{\top})$ : a $N \times D$ matrix where each row is a $D$-dimensional vector. $\X$ is also called set of \emph{samples}. $\X_{* i}$ indicates the $i$-th column of $\X$ and is a column vector. $\X_{j *}$ indicates the $j$-th row of $\X$ and is a row vector. \item $k: \R^D \times \R^D \rightarrow \R$ : a covariance (or kernel) function. \item $\K(\X,\X')$ : the matrix extension of $k$, i.e., $\K_{pq} = k(\X_{*p},\X'_{*q})$. If $\X$ is a $N \times D$ matrix and $\X'$ is $N' \times D$ then $\K(\X,\X')$ is a $N \times N'$ matrix. \item $\J_{n,m}$ : the $n \times m$ matrix of ones, i.e., a matrix where each element is 1. \item $\|\mathbf{z}\|_p$ : the $p$-norm of vector $\mathbf{z}$ defined as $\|\mathbf{z}\|_p = (\sum_{i=1}^D \|z_i\|^p)^{\frac{1}{p}}$. Euclidean norm is $p=2$, then $\|\mathbf{z}\|_2 = \sqrt{\mathbf{z}^{\top}\mathbf{z}} = \sqrt{\sum_{i=1}^D z_i^2}$. \item $\|\mathbf{z},\mathbf{w}\|_p$ : the \emph{weighted} $p$-norm of vector $\mathbf{z}$ defined as $\|\mathbf{z},\mathbf{w}\|_p = (\sum_{i=1}^D w_i|z_i|^p)^{\frac{1}{p}}$. Euclidean norm is $p=2$, then $\|\mathbf{z},\mathbf{w}\|_2 = \sqrt{\mathbf{z}^{\top} \mathbf{W}^{-1} \mathbf{z}} = \sqrt{\sum_{i=1}^D w_i z_i^2}$, where $\mathbf{W} = diag(\mathbf{w})$. \item $\dm(\X,\X')$ : the \emph{Euclidean distance matrix} between $\X$ and $\X'$ defined element by element as $\dm(\X,\X')_{pq} = \|\X_{p *} - \X'_{q *}\|_2 = \sqrt{\sum_{i=1}^D (\X_{p i} - \X'_{q i})^2}$. If $\X$ is a $N \times D$ matrix and $\X'$ is $N' \times D$ then $\dm(\X,\X1)$ is a $N \times N'$ matrix. Note that $\dm(\X,X')$ is the square root of what it is usually called ``distance matrix''. \item $\dm(\X,\X',\mathbf{w})$ : the \emph{weighted} Euclidean distance matrix between $\X$ and $\X'$ defined element by element as $\dm(\X,\X',\mathbf{w})_{pq} = \|(\X_{p *} - \X'_{q *})^{\top}, \mathbf{w}\|_2 = \sqrt{\sum_{i=1}^D w_i(\X_{p i} - \X'_{q i})^2}$ through the weight vector $\mathbf{w} \in \R^D$. It is straightforward that $\dm(\X,\X') = \dm(\X,\X',\mathbf{J}_{D,1})$. \item $\X \bullet \mathbf{Y}$ : the Hadamard (or Schur) matrix product, i.e. the entrywise product between matrices of the same size. Let $\mathbf{Z} = \X \bullet \mathbf{Y}$, then $z_{ij} = x_{ij} y_{ij}$. \item $\X^{\alpha}$ : $(\X^{\alpha})_{ij} = (X_{ij})^{\alpha}$. \end{itemize} \section{Constant kernel} $$k(\x,\x') = \sigma_0^2$$ where $\sigma_0 \ge 0$ is the standard deviation of the Gaussian prior probability $\mathcal{N}(0,\sigma_0^2)$ of the value of the constant. $$\K(\X,\X') = \sigma_0^2 \J_{N,N'}$$ $$\mathbf{\Theta} = \{\sigma_0\}$$ $$\frac{\partial k}{\partial \sigma_0}(\x,\x') = 2\sigma_0$$ $$\frac{\partial \K}{\partial \sigma_0} = 2\sigma_0 \J_{N,N'}$$ $$A = \sigma_0^2$$ $$A \ge 0$$ $$\mathbf{\Theta}^* = \{A\}$$ $$k(\x,\x') = A$$ $$\K(\X,\X') = A \J_{N,N'}$$ $$\frac{\partial k}{\partial A} = 1$$ $$\nabla_A \K = \frac{\partial \K}{\partial A} = \J_{N,N'}$$ Note that using $A$ as hyperparameter the gradient becomes constant. \section{Linear kernel} Let $\Ivec{\Sigma}_p$ be the $D \times D$ covariance matrix of the Gaussian prior probability $\mathcal{N}(\Ivec{0},\Ivec{\Sigma}_p)$ of the weights of the Bayesian linear regression. $$k(\x,\x') = \x^{\top} \Ivec{\Sigma}_p \x'$$ $$\K(\X,\X') = \X \Ivec{\Sigma}_p \X'^{\top}$$ In order to simplify formulas we assume $\Ivec{\Sigma}_p$ is diagonal, i.e., $\Ivec{\Sigma}_p = diag(\Ivec{\sigma}^2_p)$ where $\Ivec{\sigma}^2_p = ({\sigma^2_p}_1,\ldots,{\sigma^2_p}_D)$: $$k(\x,\x') = \sum_{i=1}^D {\sigma^2_p}_i x_i x'_i$$ $$\mathbf{\Theta} = \{{\sigma_p}_1,\ldots,{\sigma_p}_D\}$$ $$\frac{\partial k}{\partial {\sigma_p}_i} = 2 {\sigma_p}_i x_i x'_i$$ $$A_i = {\sigma_p^2}_i$$ $$A_i \ge 0$$ $$\mathbf{A} = (A_1,\ldots,A_D)^{\top}$$ $$\mathbf{\Theta}^* = \{ \mathbf{A} \}$$ $$k(\x,\x') = \x^{\top} diag(\mathbf{A}) \x'$$ $$\K(\X,\X') = \X diag(\mathbf{A}) \X'^{\top}$$ $$\frac{\partial k}{\partial A_i} = x_i x'_i$$ $$\frac{\partial \K}{\partial A_i} = \X_{* i} {\X'_{* i}}^{\top}$$ $$\nabla_{\mathbf{A}} \K = ( \X_{* 1} {\X'_{* 1}}^{\top}, \ldots, \X_{* D} {\X'_{* D}}^{\top})$$ As expected the gradient is independent of the hyperparameters values and can be computed once for all at the beginning. \section{Polynomial kernel} $$k(\x,\x') = (\sigma_0^2 + \x^{\top} \Ivec{\Sigma}_p \x')^p = (\sigma_0^2 + \sum_{i=1}^D {\sigma^2_p}_i x_i x'_i)^p$$ $$\K(\X,\X') = (\sigma_0^2 \mathbf{J}_{N,N'} + \X \Ivec{\Sigma}_p \X'^{\top})^p$$ In order to simplify formulas we assume $\Ivec{\Sigma}_p$ is diagonal, i.e., $\Ivec{\Sigma}_p = diag(\Ivec{\sigma}^2_p)$ where $\Ivec{\sigma}^2_p = ({\sigma^2_p}_1,\ldots,{\sigma^2_p}_D)$. $$\sigma_0 \ge 0$$ $$\Ivec{\sigma}_p = ({\sigma_p}_1,\ldots,{\sigma_p}_D)$$ $${\sigma_p}_i \ge 0$$ $$\mathbf{\Theta} = \{\sigma_0,\Ivec{\sigma}_p, p\}$$ $$A = \sigma_0^2$$ $$B_i = {\sigma^2_p}_i$$ $$\mathbf{B} = (B_1,\ldots,B_D)$$ $$\mathbf{\Theta}^* = \{A,\mathbf{B}, p\}$$ $$k(\x,\x') = (A + \x^{\top} diag(\mathbf{B}) \x')^p = (A + \sum_{i=1}^D B_i x_i x'_i)^p$$ $$\frac{\partial k}{\partial A} = p(A + \sum_{i=1}^D B_i x_i x'_i)^{p-1}$$ $$\frac{\partial \K}{\partial A} = p(A\mathbf{J}_{N,N'} + \sum_{i=1}^D B_i \X_{*i} {\X'_{*i}}^{\top})^{p-1}$$ $$\frac{\partial k}{\partial B_i} = p(A + \sum_{i=1}^D B_i x_i x'_i)^{p-1} x_i x'_i = \frac{\partial k}{\partial A} x_i x'_i$$ $$\frac{\partial \K}{\partial B_i} = \frac{\partial \K}{\partial A} \X_{*i} {\X'_{*i}}^{\top}$$ $$\frac{\partial k}{\partial p} = k(\x,\x') \ln(A + \sum_{i=1}^D B_i x_i x'_i)$$ $$\frac{\partial \K}{\partial p} = \K(\X,\X') \bullet \ln(A \mathbf{J}_{N,N'}+ \sum_{i=1}^D B_i \X_{*i} \X'_{*i})$$ $$\mathbf{M} = A \mathbf{J}_{N,N'}+ \sum_{i=1}^D B_i \X_{*i} \X'_{*i}$$ $$\nabla_{A,\mathbf{B},p} \K = \left(p\mathbf{M}^{p-1},\left\{p\mathbf{M}^{p-1} \X_{*i} {\X'_{*i}}^{\top} \right\}_{i=1,\ldots,D}, \K(\X,\X') \bullet \ln(\mathbf{M}) \right)$$ \section{Exponential kernel} \subsection{Scalar Lengthscale $\ell$} $$k(\x,\x') = \sigma_f^2 e^{-\frac{\|\x-\x'\|_2}{\ell}}$$ $$\ell > 0$$ $$\sigma_f \ge 0$$ $$\mathbf{\Theta} = \{ \sigma_f, \ell \}$$ $$\K(\X,\X') = \sigma_f^2 e^{-\frac{1}{\ell}\dm(\X,\X')}$$ $$A = \sigma_f^2$$ $$A \ge 0$$ $$B = -\frac{1}{\ell}$$ $$B < 0$$ $$\mathbf{\Theta}^* = \{ A, B \}$$ $$k(\x,\x') = A e^{B\|\x-\x'\|_2}$$ $$\K(\X,\X') = A e^{B \dm(\X-\X')}$$ $$\frac{\partial k}{\partial A} = e^{B\|\x-\x'\|_2} = \frac{1}{A}k(\x,\x')$$ $$\frac{\partial \K}{\partial A} = e^{B \dm(\X,\X')} = \frac{1}{A} \K(\X,\X')$$ $$\frac{\partial k}{\partial B} = A e^{B\|\x-\x'\|_2} \|\x-\x'\|_2 = k(\x,\x') \|\x-\x'\|_2$$ $$\frac{\partial \K}{\partial B} = \K(\X,\X') \bullet \dm(\X-\X')$$ $$\nabla_{A,B} \K = (\frac{1}{A} \K(\X,\X'), \K(\X,\X') \bullet \dm(\X-\X'))$$ Note that if $\K(\X,\X')$ is precomputed, then the gradient consists in just two element-by-element products, the second being against a constant matrix independent of the hyperparameters. \subsection{Vector of Lengthscales $\Ivec{\ell}$} Given $\Ivec{\ell} = (\ell_1,\ldots,\ell_D)$, $\ell_i \ge 0$ and $\ELL^{-1} = (1/\ell_1,\ldots,1/\ell_D)$ $$k(\x,\x') = \sigma_f^2 e^{-\|\x-\x',\ELL^{-2}\|_2}= \sigma_f^2 e^{-\sqrt{\sum_{i=1}^D \left(\frac{x_i - x'_i}{\ell_i}\right)^2}}$$ $$K(\X,\X') = \sigma_f^2 e^{-\dm(\X,\X',\ELL^{-2})}$$ $$\mathbf{\Theta} = \{ \sigma_f, \ELL\}$$ $$\sigma_f \ge 0$$ $$\ell_i > 0$$ $$A = \sigma_f^2$$ $$\mathbf{B} = \ELL^{-2}$$ $$A \ge 0$$ $$B_i > 0$$ $$\mathbf{\Theta}^* = \{ A, \mathbf{B}\}$$ $$k(\x,\x') = A e^{-\|\x-\x',\mathbf{B}\|_2} = A e^{-\sqrt{\sum_{i=1}^D B_i(x_i - x'_i)^2}}$$ $$K(\X,\X') = A e^{-\dm(\X,\X',\mathbf{B})}$$ $$\frac{\partial k}{\partial A} = e^{-\sqrt{\sum_{i=1}^D B_i(x_i - x'_i)^2}} = \frac{k(\x,\x')}{A}$$ $$\frac{\partial \K}{\partial A} = e^{-\dm(\X,\X',\mathbf{B})} = \frac{1}{A}\K(\X,\X')$$ $$\frac{\partial k}{\partial B_i} = A e^{-\|\x-\x',\mathbf{B}\|_2} \left( -\frac{1}{2} \|\x-\x',\mathbf{B}\|_2^3 \right) (x_i - x'_i)^2 = -\frac{1}{2} k(\x,\x') \|\x-\x',\mathbf{B}\|_2^3 (x_i - x'_i)^2 $$ $$\frac{\partial \K}{\partial B_i} = -\frac{1}{2} K(\X,\X') \bullet \dm(\X-\X',\mathbf{B})^3 \bullet (\X_{*i}J_{1,N'}- (\X'_{*i}J_{1,N})^{\top})^2$$ $$\nabla_{A,\mathbf{B}} \K = \left(\frac{1}{A}\K(\X,\X'), \left\{-\frac{1}{2} K(\X,\X') \bullet \dm(\X-\X',\mathbf{B})^3 \bullet (\X_{*i}J_{1,N'}- (\X'_{*i}J_{1,N})^{\top})^2 \right\}_{i=1 \ldots D} \right)$$ Note that $\frac{\partial \K}{\partial A}$ requires just the multiplication of a constant by the kernel matrix $\K(\X,\X')$ whose values usually already available. Instead $\frac{\partial \K}{\partial B_i}$ is a entrywise product of 3 matrices: $\K(\X,\X')$ (usually already available), $\dm(\X-\X',\mathbf{B})^3$ (which is, apart the cube, part of the computation of $\K(\X,\X')$ so it can be stored in advance), and $(\X_{*i}J_{1,N'}- (\X'_{*i}J_{1,N})^{\top})^2$ which does not depend upon $A$ and $\mathbf{B}$ so it can be computed once for all. Note that in NumPy $(\X_{*i}J_{1,N'}- (\X'_{*i}J_{1,N})^{\top})^2$ can be computed as {\ttfamily numpy.subtract.outer($X_{*i},X'_{*i}$)**2}. \section{Squared Exponential kernel} \subsection{Scalar Lengthscale $\ell$} $$k(\x,\x') = \sigma_f^2 e^{-\frac{1}{2\ell^2} (\x-\x')^{\top}(\x-\x')} = \sigma_f^2 e^{-\frac{1}{2\ell^2} \sum_{i=1}^D (x_i - x'_i)^2} = \sigma_f^2 e^{-\frac{1}{2\ell^2} \|\x-\x'\|_2^2}$$ $$\K(\X,\X') = \sigma_f^2 e^{-\frac{1}{2\ell^2} \dm(\X,\X')^2}$$ $$\sigma_f \ge 0$$ $$\ell > 0$$ $$\mathbf{\Theta} = \{ \sigma_f, \ell \}$$ $$\frac{\partial k}{\partial \sigma_f} = \frac{2}{\sigma_f}k$$ $$\frac{\partial \K}{\partial \sigma_f} = \frac{2}{\sigma_f}\K$$ $$\frac{\partial k}{\partial \ell} = \ell^{-3} k \|\x-\x'\|_2^2 $$ $$\frac{\partial \K}{\partial \ell} = \ell^{-3} \K \bullet \dm(\X,\X')^2$$ $$\nabla_{\sigma_f,\ell} \K = \left(\frac{2}{\sigma_f}K(\X,\X'), \ell^{-3} \K \bullet \dm(\X-\X')^2 \right)$$ Logscale: $$A = \ln{\sigma_f}$$ $$\sigma_f = e^A$$ $$\frac{\partial k}{\partial A} = 2k$$ $$\frac{\partial \K}{\partial \sigma_f} = 2\K$$ $$B = \ln{\ell}$$ $$\ell = e^B$$ $$\frac{\partial k}{\partial B} = \ell^{-2} k \|\x-\x'\|_2^2$$ $$\frac{\partial \K}{\partial B} = \ell^{-2} \K \bullet \dm(\X,\X')^2 = \K \bullet \dm(\X,\X',\ell^{-2}\J_N)^2$$ $$\nabla_{A,B} \K = \left( 2\K(\X,\X'), \ell^{-2} \K \bullet \dm(\X-\X')^2 \right)$$ Another mapping: $$A = \sigma_f^2$$ $$B = -\frac{1}{\ell^2}$$ $$A \ge 0$$ $$B < 0$$ $$\mathbf{\Theta}^* = \{ A, B\}$$ $$k(\x,\x') = A e^{B (\x-\x')^{\top}(\x-\x')}$$ $$\K(\X,\X') = A e^{B \dm(\X,\X')^2}$$ $$\frac{\partial k}{\partial A} = \frac{k(\x,\x')}{A}$$ $$\frac{\partial \K}{\partial A} = \frac{1}{A}\K(\X,\X')$$ $$\frac{\partial k}{\partial B} = k(\x,\x') \|\x-\x'\|_2^2$$ $$\frac{\partial \K}{\partial B} = K(\X,\X') \bullet \dm(\X,\X')^2$$ $$\nabla_{A,B} \K = (\frac{1}{A} \K(\X,\X'), \dm(\X-\X')^2 \bullet \K(\X,\X'))$$ Note that $\nabla_{A,B} \K$ is similar to that of the exponential kernel so almost all comments made before applies here as well. \subsection{Vector of Lengthscales $\ELL$} Let $\mathbf{L} = diag(\ELL)$: $$k(\x,\x') = \sigma_f^2 e^{-\frac{1}{2}(\x-\x')^{\top} \LL^{-2} (\x-\x')} = \sigma_f e^{-\frac{1}{2}\sum_{i=1}^D \frac{(x_i - x'_i)^2}{\ell_i^2}} = \sigma_f^2 e^{-\frac{1}{2}\|\x-\x',\ELL^{-2}\|_2^2}$$ $$\K(\X,\X') = \sigma_f^2 e^{-\frac{1}{2} \dm(\X,\X',\ELL^{-2})^2}$$ $$\sigma_f \ge 0$$ $$\ell_i > 0$$ $$\mathbf{\Theta} = \{ \sigma_f, \ELL\}$$ $$\frac{\partial k}{\partial \sigma_f} = \frac{2}{\sigma_f}k$$ $$\frac{\partial \K}{\partial \sigma_f} = \frac{2}{\sigma_f}\K$$ $$\frac{\partial k}{\partial \ell_i} = \ell_i^{-3} k \|\x_i-\x_i'\|_2^2 $$ $$\frac{\partial \K}{\partial \ell_i} = \ell_i^{-3} \K \bullet (\X_{*i}J_{1,N'}- (\X'_{*i}J_{1,N})^{\top})^2$$ $$\nabla_{\sigma_f,\ell} \K = \left(\frac{2}{\sigma_f}K(\X,\X'), \ell_i^{-3} \K \bullet (\X_{*i}J_{1,N'}- (\X'_{*i}J_{1,N})^{\top})^2 \right)$$ Logscale: $$A = \ln{\sigma_f}$$ $$\sigma_f = e^A$$ $$\frac{\partial k}{\partial A} = 2k$$ $$\frac{\partial \K}{\partial \sigma_f} = 2\K$$ $$B_i = \ln{\ell_i}$$ $$\ell_i = e^B_i$$ $$\frac{\partial k}{\partial B_i} = \ell_i^{-2} k \|\x_i-\x'_i\|_2^2$$ $$\frac{\partial \K}{\partial B_i} = \ell^{-2} \K \bullet \dm(\X,\X')^2 = \ell_i^{-2} \K \bullet (\X_{*i}J_{1,N'}- (\X'_{*i}J_{1,N})^{\top})^2$$ $$\nabla_{A,\mathbf{B}} \K = \left( 2\K(\X,\X'), \left\{ \ell_i^{-2} \K \bullet (\X_{*i}J_{1,N'}- (\X'_{*i}J_{1,N})^{\top})^2 \right\}_{i=1 \ldots D} \right)$$ Another mapping: $$A = \sigma_f$$ $$\mathbf{B} = -\frac{1}{2}\ELL^{-2} = \left(-\frac{1}{2\ell_1^2},\ldots,-\frac{1}{2\ell_D^2} \right)$$ $$A \ge 0$$ $$B_i < 0$$ $$\mathbf{\Theta}^* = \{ A, \mathbf{B}\}$$ $$k(\x,\x') = A e^{(\x-\x')^{\top} diag(\mathbf{B}) (\x-\x')} = A e^{\sum_{i=1}^D B_i (x_i - x'_i)^2}$$ $$\K(\X,\X') = A e^{\dm(\X,\X',\mathbf{B})}$$ $$\frac{\partial k}{\partial A} = \frac{k(\x,x')}{A}$$ $$\frac{\partial \K}{\partial A} = \frac{1}{A}\K(\X,X')$$ $$\frac{\partial k}{\partial B_i} = k(\x,\x') (x_i -x'_i)^2$$ $$\frac{\partial \K}{\partial B_i} = \K(\X,\X') \bullet (\X_{*i}J_{1,N'}- (\X'_{*i}J_{1,N})^{\top})^2$$ $$\nabla_{A,\mathbf{B}} \K = \left(\frac{\K(\X,\X')}{A}, \left\{ K(\X,\X') \bullet (\X_{*i}J_{1,N'}- (\X'_{*i}J_{1,N})^{\top})^2 \right\}_{i=1 \ldots D} \right)$$ Note that $\nabla_{A,\mathbf{B}} \K$ requires to compute $K(\X,\X')$ (which is usually already available), and its entrywise product with $(\X_{*i}J_{1,N'}- (\X'_{*i}J_{1,N})^{\top})^2$ which is independent of the value of the hyperparameters and can be precomputed once for all. \section{$\gamma$-Exponential kernels} $$k(\x,\x') = \sigma_f^2 e^{-(\frac{\x-\x'}{\ell})^\gamma}$$ $$k(\x,\x') = \sigma_f^2 e^{-(\frac{\x-\x'}{\ELL})^\gamma}$$ \section{Mat\'ern kernels} %% $$k_{\mbox{Mat\'ern}}(\x,\x') = \frac{2^{1-\nu}}{\Gamma(\nu)} \left(\sqrt{2\nu} \frac{\|\x-\x'\|_2}{\ell}\right)^{\nu} \K_{\nu}\left(\sqrt{2}\nu \frac{\|\x-\x'\|_2}{\ell} \right)$$ %% $$\ell > 0$$ %% $$\nu > 0$$ %% where $K_{\nu}$ is a modified Bessel function (REMOVE?). Let $\nu$ be half integer, i.e., $\nu = p + 1/2$ ($p \in \mathbb{Z}^+$), then $$k_{\nu=p+1/2}(\x,\x') = e^{-\sqrt{2\nu}\frac{\|\x-\x'\|}{\ell}} \frac{\Gamma(p+1)}{\Gamma(2p+1)} \sum_{i=0}^p \frac{(p+i)!}{i!(p-i)!}\left(\sqrt{8\nu}\frac{\|\x-\x'\|}{\ell}\right)^{p-i}$$ is the class of Mat\'ern covariance functions with half-integer $\nu$. $$\nu > 0$$ $$\ell > 0$$ When $\nu \rightarrow \infty$ we obtain the squared exponential covariance function. Most popular cases of the Mat\'ern functions are $p=0$ (exponential kernel), $p=1$ and $p=2$: $$k_{\nu=1/2}(\x,\x') = e^{-\frac{\|\x-\x'\|_2}{\ell}}$$ $$k_{\nu=3/2}(\x,\x') = \left(1+\sqrt{3}\frac{\|\x-\x'\|_2}{\ell} \right) e^{-\sqrt{3}\frac{\|\x-\x'\|_2}{\ell}}$$ $$k_{\nu=5/2}(\x,\x') = \left(1+\sqrt{5}\frac{\|\x-\x'\|_2}{\ell} + \frac{5\|\x-\x'\|_2^2}{3\ell^2}\right) e^{-\sqrt{5}\frac{\|\x-\x'\|_2}{\ell}}$$ \section{Rational Quadratic kernels} $$k_{RQ} = \left(1+\frac{\|\x-\x'\|_2^2}{2\alpha\ell^2} \right)^{-\alpha}$$ $$\alpha > 0$$ $$\ell > 0$$ \end{document} pymvpa-0.4.8/doc/matlab.rst000066400000000000000000000027211174541445200156070ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. _chap_matlab: .. index:: Matlab *********************** PyMVPA for Matlab Users *********************** If you are coming from Matlab, you will soon notice a lot of similarities between Matlab and Python (besides the huge advantages of Python over Matlab). For an easy transition you might want to have a look at a `basic comparison of Matlab and NumPy`_. .. _basic comparison of Matlab and NumPy: http://www.scipy.org/NumPy_for_Matlab_Users .. index:: MVPA toolbox for Matlab It would be nice to have some guidelines on how to use PyMVPA for users who are already familiar with the `Matlab MVPA toolbox`_. If you are using both packages and could compile a few tips, your contribution would be most welcome. .. _Matlab MVPA toolbox: http://www.csbmb.princeton.edu/mvpa/ A recent paper by :ref:`Jurica and van Leeuwen (2009) ` describes an open-source MATLAB®-to-Python compiler which might be a very useful tool to migrate a substantial amount of Matlab-based source code to Python and therefore also aids the migration of developers from Matlab to the new *"general open-source lingua franca for scientific computation"*. pymvpa-0.4.8/doc/measures.rst000066400000000000000000000201351174541445200161720ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. index:: measure, sensitivity .. _chap_measures: ******** Measures ******** PyMVPA provides a number of useful measures. The vast majority of them are dedicated to feature selection. To increase analysis flexibility, PyMVPA distinguishes two parts of a feature selection procedure. First, the impact of each individual feature on a classification has to be determined. The resulting map reflects the sensitivities of all features with respect to a certain decision and, therefore, algorithms generating these maps are summarized as :class:`~mvpa.measures.base.Sensitivity` in PyMVPA. .. index:: feature selection Second, once the feature sensitivities are known, they can be used as criteria for feature selection. However, possible selection strategies range from very simple *Go with the 10% best features* to more complicated algorithms like :ref:`recursive_feature_elimination`. Because :ref:`sensitivity_measures` and selections strategies can be arbitrarily combined, PyMVPA offers a quite flexible framework for feature selection. .. index:: processing object Similar to dataset splitters, all PyMVPA algorithms are implemented and behave like :term:`processing object`\ s. To recap, this means that they are instantiated by passing all relevant arguments to the constructor. Once created, they can be used multiple times by calling them with different datasets. .. Again general overview first. What is a `SensitivityAnalyzer`, what is the difference between a `FeatureSelection` and an `ElementSelector`. Finally more detailed note and references for each larger algorithm. .. index:: sensitivity .. _sensitivity_measures: Sensitivity Measures ==================== It was already mentioned that a :class:`~mvpa.measures.base.Sensitivity` computes a featurewise score that indicates how much interesting signal each feature contains -- hoping that this score somehow correlates with the impact of the features on a classifier's decision for a certain problem. Every sensitivity analyzer object computes a one-dimensional array with the respective score for every feature, when called with a :class:`~mvpa.datasets.base.Dataset`. Due to this common behavior all :class:`~mvpa.measures.base.Sensitivity` types are interchangeable and can be combined with any other algorithm requiring a sensitivity analyzer. By convention higher sensitivity values indicate more interesting features. There are two types of sensitivity analyzers in PyMVPA. Basic sensitivity analyzers directly compute a score from a Dataset. Meta sensitivity analyzers on the other hand utilize another sensitivity analyzer to compute their sensitivity maps. Basic Sensitivity (and related Measures) ---------------------------------------- .. index:: anova, F-score, univariate, measure .. _anova: ANOVA ^^^^^ The :class:`~mvpa.measures.anova.OneWayAnova` class provides a simple (and fast) univariate measure, that can be used for feature selection, although it is not a proper sensitivity measure. For each feature an individual F-score is computed as the fraction of between and within group variances. Groups are defined by samples with unique labels. Higher F-scores indicate higher sensitivities, as with all other sensitivity analyzers. .. index:: classifier weights, weights, SVM, measure Linear SVM Weights ^^^^^^^^^^^^^^^^^^ The featurewise weights of a trained support vector machine are another possible sensitivity measure. The :class:`mvpa.clfs.libsvmc.sens.LinearSVMWeights` and :class:`mvpa.clfs.sg.sens.LinearSVMWeights` classes can internally train all types of *linear* support vector machines and report those weights. In contrast to the F-scores computed by an ANOVA, the weights can be positive or negative, with both extremes indicating higher sensitivities. To deal with this property all subclasses of :class:`~mvpa.measures.base.DatasetMeasure` support a `transformer` arguments in the constructor. A transformer is a functor that is finally called with the computed sensitivity map. PyMVPA already comes with some convenience functors which can be used for this purpose (see :mod:`~mvpa.misc.transformers`). >>> from mvpa.misc.data_generators import normalFeatureDataset >>> from mvpa.clfs.svm import LinearCSVMC >>> from mvpa.misc.transformers import Absolute >>> >>> ds = normalFeatureDataset() >>> ds >>> >>> clf = LinearCSVMC() >>> sensana = clf.getSensitivityAnalyzer() >>> sens = sensana(ds) >>> sens.shape (4,) >>> (sens < 0).any() True >>> sensana_abs = clf.getSensitivityAnalyzer(transformer=Absolute) >>> (sensana_abs(ds) < 0).any() False Above example shows how to use an existing classifier instance to report sensitivity values (a linear SVM in this case). The computed sensitivity vector contains one element for each feature in the dataset. :mod:`~mvpa.misc.transformers` can be used to post-process the sensitivity scores, e.g. reporting absolute values for feature selection purposes, instead of raw sensitivities. .. note:: The `SVMWeights` classes *cannot* extract reasonable weights from non-linear SVMs (e.g. with RBF kernels). Other linear Classifier Weights ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Any linear classifier in PyMVPA can report its weights. The procedure is identical for all of them. As outlined in the example using linear SVM weights, simply call :meth:`~mvpa.clfs.base.Classifier.getSensitivityAnalyzer` on a classifier instance and you'll get an appropriate :class:`~mvpa.measures.base.Sensitivity` object. Additionally, it is possible to force (re)training of the underlying classifier or simply report the weights computed during a previous training run. Examples of other classifier-based linear sensitivity analyzers are: :class:`~mvpa.clfs.smlr.SMLRWeights` and :class:`~mvpa.clfs.gpr.GPRLinearWeights`. .. index:: noise perturbation, measure .. _noise_perturbation: Noise Perturbation ^^^^^^^^^^^^^^^^^^ Noise perturbation is a generic approach to determine feature sensitivity. The sensitivity analyzer :class:`~mvpa.measures.noiseperturbation.NoisePerturbationSensitivity`) computes a scalar :class:`~mvpa.measures.base.DatasetMeasure` using the original dataset. Afterwards, for each single feature a noise pattern is added to the respective feature and the dataset measure is recomputed. The sensitivity of each feature is the difference between the dataset measure of the original dataset and the one with added noise. The reasoning behind this algorithm is that adding noise to *important* features will impair a dataset measure like cross-validated classifier transfer error. However, adding noise to a feature that already only contains noise, will not change such a measure. Depending on the used scalar :class:`~mvpa.measures.base.DatasetMeasure` using the sensitivity analyzer might be really CPU-intensive! Also depending on the measure, it might be necessary to use appropriate :mod:`~mvpa.misc.transformers` (see :mod:`~mvpa.misc.transformers` constructor arguments) to ensure that higher values represent higher sensitivities. .. index:: meta measures Meta Sensitivity Measures ------------------------- Meta Sensitivity Measures are FeaturewiseDatasetMeasures that internally use one of the `Basic Sensitivity (and related Measures)`_ to compute their sensitivity scores. .. index:: splitting measures, measure Splitting Measures ^^^^^^^^^^^^^^^^^^ The SplittingFeaturewiseMeasure uses a :class:`~mvpa.datasets.splitters.Splitter` to generate dataset splits. A FeaturewiseDatasetMeasure is then used to compute sensitivity maps for all these dataset splits. At the end a `combiner` function is called with all sensitivity maps to produce the final sensitivity map. By default the mean sensitivity maps across all splits is computed. .. _SplitFeaturewiseMeasure: api/mvpa.measures.splitmeasure.SplitFeaturewiseMeasure-class.html pymvpa-0.4.8/doc/misc.rst000066400000000000000000000346301174541445200153060ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. _chap_misc: .. index:: misc ************* Miscellaneous ************* .. index:: settings, configuration, cfg Managing (Custom) Configurations ================================ PyMVPA provides a facility to handle arbitrary configuration settings. This facility can be used to control some aspects of the behavior of PyMVPA itself, as well as to store and query custom configuration items, e.g. to control one's own analysis scripts. An instance of this configuration manager is loaded whenever the `mvpa` module is imported. It can be used from any script like this: >>> from mvpa import cfg By default the config manager reads settings from two config files (if any of them exists). The first is a file named `.pymvpa.cfg` and located in the user's home directory. The second is `pymvpa.cfg` in the current directory. Please note, that settings found in the second file override the ones in the first. The syntax of both files is the one also known from the Windows INI files. Basically, `Python's ConfigParser`_ is used to read those file and the config supports whatever this parser can read. A minimal example config file might look like this:: [general] verbose = 1 It consists of a section `general` containing a single setting `verbose`, which is set to `1`. PyMVPA recognizes a number of such sections and configuration variables. A full list is shown at the end of this section and is also available in the source package (`doc/examples/pymvpa.cfg`). .. _Python's ConfigParser: http://docs.python.org/lib/module-ConfigParser.html In addition to configuration files, the config manager also looks for special environment variables to read settings from. Names of such variables have to start with `MVPA_` following by the an optional section name and the variable name itself (with `_` as delimiter). If no section name is provided, the variables will be associated with section `general`. Some examples:: MVPA_VERBOSE=1 will become:: [general] verbose = 1 However, :envvar:`MVPA_VERBOSE_OUTPUT` `= stdout` becomes:: [verbose] output = stdout Any lenght of variable name is allowed, e.g. `MVPA_SEC1_LONG_VARIABLE_NAME=1` becomes:: [sec1] long variable name = 1 Settings read from environment variables have the highest priority and override settings found in the config files. Therefore environment variables can be used to quickly adjust some setting without having to edit the config files. The config manager can easily be queried from inside scripts. In addition to the interface of `Python's ConfigParser`_ it has a few convenience functions mostly to allow for a default value in case no setting was found. For example: >>> cfg.getboolean('warnings', 'suppress', default=False) False queries the config manager whether warnings should be suppressed (i.e. if there is a variable `suppress` in section `warnings`). In case, there is now such setting, i.e. neither config files nor environment variables defined it, the `default` values is returned. Please see the documentation of `ConfigManager`_ for its full functionality. .. _ConfigManager: api/mvpa.base.config.ConfigManager-class.html .. index:: config file The source tarballs includes an example configuration file (`doc/examples/pymvpa.cfg`) with the comprehensive list of settings recognized by PyMVPA itself: .. literalinclude:: examples/pymvpa.cfg :language: ini .. index:: progress tracking, verbosity, debug, warning Progress Tracking ================= .. some parts should migrate into developer reference I guess There are 3 types of messages PyMVPA can produce: verbose_ regular informative messages about generic actions being performed debug_ messages about the progress of computation, manipulation on data structures warning_ messages which are reported by mvpa if something goes a little unexpected but not critical .. _verbose: api/mvpa.misc-module.html#verbose .. _debug: api/mvpa.misc-module.html#debug .. _warning: api/mvpa.misc-module.html#warning .. index:: redirecting output Redirecting Output ------------------ By default, all types of messages are printed by PyMVPA to the standard output. It is possible to redirect them to standard error, or a file, or a list of multiple such targets, by using environment variable ``MVPA_?_OUTPUT``, where X is either ``VERBOSE``, ``DEBUG``, or ``WARNING`` correspondingly. E.g.:: export MVPA_VERBOSE_OUTPUT=stdout,/tmp/1 MVPA_WARNING_OUTPUT=/tmp/3 MVPA_DEBUG_OUTPUT=stderr,/tmp/2 would direct verbose messages to standard output as well as to ``/tmp/1`` file, warnings will be stored only in ``/tmp/3``, and debug output would appear on standard error output, as well as in the file ``/tmp/2``. PyMVPA output redirection though has no effect on external libraries debug output if corresponding debug_ target is enabled shogun debug output (if any of internal ``SG_`` debug_ targets is enabled) appears on standard output SMLR debug output (if ``SMLR_`` debug_ target is enabled) appears on standard output LIBSVM debug output (if ``LIBSVM`` debug_ target is enabled) appears on standard error One of the possible redirections is Python's ``StringIO`` class. Instance of such class can be added to the ``handlers`` and queried later on for the information to be dumped to a file later on. It is useful if output path is specified at run time, thus it is impossible to redirect verbose or debug from the start of the program: >>> import sys >>> from mvpa.base import verbose >>> from StringIO import StringIO >>> stringout = StringIO() >>> verbose.handlers = [sys.stdout, stringout] >>> verbose.level = 3 >>> >>> verbose(1, 'msg1') msg1 >>> out_prefix='/tmp/' >>> >>> verbose(2, 'msg2') msg2 >>> # open('%sverbose.log' % out_prefix, 'w').write(stringout.getvalue()) >>> print stringout.getvalue(), msg1 msg2 >>> .. index:: verbosity Verbose Messages ---------------- Primarily for a user of PyMVPA to provide information about the progress of their scripts. Such messages are printed out if their level specified as the first parameter to verbose_ function call is less than specified. There are two easy ways to specify verbosity level: * command line: you can use opt.verbose_ for precrafted command line option for to give facility to change it from your script (see examples) * environment variable :envvar:`MVPA_VERBOSE` * code: verbose.level property The following verbosity levels are supported: :0: nothing besides errors :1: high level stuff -- top level operation or file operations :2: cmdline handling :3: n.a. :4: computation/algorithm relevant thing .. index:: warning Warning Messages ---------------- Reported by PyMVPA if something goes a little unexpected but not critical. By default they are printed just once per occasion, i.e. once per piece of code where it is called. Following environment variables control the behavior of warnings: * :envvar:`MVPA_WARNINGS_COUNT` `=` controls for how many invocations of specific warning it gets printed (default behavior is 1 for once). Specification of negative count results in all invocations being printed, and value of 0 obviously suppresses the warnings * :envvar:`MVPA_WARNINGS_SUPPRESS` analogous to :envvar:`MVPA_WARNINGS_COUNT` `=0` it resultant behavior * :envvar:`MVPA_WARNINGS_BT` `=` controls up to how many lines of traceback is printed for the warnings In python code, invocation of warning with argument ``bt = True`` enforces printout of traceback whenever warning tracebacks are disabled by default. .. index:: debug Debug Messages -------------- Debug messages are used to track progress of any computation inside PyMVPA while the code run by python without optimization (i.e. without ``-O`` switch to python). They are specified not by the level but by some id usually specific for a particular PyMVPA routine. For example ``RFEC`` id causes debugging information about `Recursive Feature Elimination call`_ to be printed (See `base module sources`_ for the list of all ids, or print ``debug.registered`` property). Analogous to verbosity level there are two easy ways to specify set of ids to be enabled (reported): * command line: you can use optDebug_ for precrafted command line option to provide it from your script (see examples). If in command line if optDebug_ is used, ``-d list`` is given, PyMVPA will print out list of known ids. * environment: variable :envvar:`MVPA_DEBUG` can contain comma-separated list of ids or python regular expressions to match multiple ids. Thus specifying :envvar:`MVPA_DEBUG` `=CLF.*` would enable all ids which start with ``CLF``, and :envvar:`MVPA_DEBUG` `=.*` would enable all known ids. * code: debug.active property (e.g. ``debug.active = [ 'RFEC', 'CLF' ]``) Besides printing debug messages, it is also possible to print some metric. You can define new metrics or select predefined ones: vmem (Linux specific): amount of virtual memory consumed by the task pid (Linux specific): PID of the process reltime How many seconds passed since previous debug printout asctime Time stamp tb Traceback (``module1:line_number1[,line_number2...]>module2:line_number..``) where this debug statement was requested tbc Concise traceback printout -- prefix common with the previous invocation is replaced with ``...`` To enable list of metrics you can use :envvar:`MVPA_DEBUG_METRICS` environment variable to list desired metric names comma-separated. If ``ALL`` is provided, it enables all the metrics. As it was mentioned earlier, debug messages are printed only in non-optimized python invocation. That was done to eliminate any slowdown introduced by such 'debugging' output, which might appear at some computational bottleneck places in the code. Some of the debug ids are defined to facilitate additional checking of the validity of the analysis. Their debug ids a prefixed by ``CHECK_``. E.g. ``CHECK_RETRAIN`` id would cause additional checking of the data in retraining phase. Such additional testing might spot out some bugs in the internal logic, thus enabled when full test suite is ran. .. TODO: Unify loggers behind verbose and debug. imho debug should have also way to specify the level for the message so we could provide more debugging information if desired. .. _opt.verbose: api/mvpa.misc.cmdline-module.html#opt.verbose .. _optDebug: api/mvpa.misc.cmdline-module.html#optDebug .. _base module sources: api/mvpa.base-pysrc.html .. _Recursive Feature Elimination call: api/mvpa.featsel.rfe.RFE-class.html#__call__ PyMVPA Status Summary --------------------- While reporting found bugs, it is advised to provide information about the operating system/environment and availability of PyMVPA externals. Please use :func:`~mvpa.base.info.wtf` to collect such useful information to be included with the bug reports. Alternatively, same printout can be obtained upon not handled exception automagically, if environment variable :envvar:`MVPA_DEBUG_WTF` is set. Additional Little Helpers ========================= .. index:: random number generation, RNG Random Number Generation ------------------------ To facilitate reproducible troubleshooting, a seed value of random generator of NumPy can be provided in debug mode (python is called without ``-O``) via environment variable :envvar:`MVPA_SEED` `=`. Otherwise it gets seeded with random integer which can be displayed with debug id ``RANDOM`` e.g.:: > MVPA_SEED=123 MVPA_DEBUG=RANDOM python test_clf.py [RANDOM] DBG: Seeding RNG with 123 ... > MVPA_DEBUG=RANDOM python test_clf.py [RANDOM] DBG: Seeding RNG with 1447286079 ... Unittests at a Grasp -------------------- .. index:: unittests If it is needed to just quickly grasp through all unittests without making them to test multiple classifiers (implemented with sweeparg), define environmental variable :envvar:`MVPA_TESTS_QUICK` e.g.:: > MVPA_WARNINGS_SUPPRESS=no MVPA_TESTS_QUICK=yes python test_clf.py ............... ---------------------------------------------------------------------- Ran 15 tests in 0.845s Some tests are not 100% deterministic as they operate on random data (e.g. the performance of a randomly initialized classifier). Therefore, in some cases, specific unit tests might fail when running the full test battery. To exclude these test cases (and only those where non-deterministic behavior immanent) one can use the :envvar:`MVPA_TESTS_LABILE` configuration and set it to 'off'. Others ------ (to be written) .. put information about IO helpers, external bindings, etc here .. index:: FSL, detrending, motion correction FSL Bindings ============ PyMVPA contains a few little helpers to make interfacing with FSL_ easier. The purpose of these helpers is to increase the efficiency when doing an analysis by (re)using useful information that is already available from some FSL output. FSL usually stores most interesting information in the NIfTI format. Therefore it can be easily imported into PyMVPA using PyNIfTI. However, some information is stored in text files, e.g. estimated motion correction parameters and *FEAT's three-column custom EV* files. PyMVPA provides import and export helpers for both of them (among other stuff like a *MELODIC* results import helper). .. _motion-aware_detrending: Here is an example how the *McFlirt* parameter output can be used to perform motion-aware data detrending: >>> from os import path >>> import numpy as N >>> >>> # some dummy dataset >>> from mvpa.datasets import Dataset >>> ds = Dataset(samples=N.random.normal(size=(19, 3)), labels=1) >>> >>> # load motion correction output >>> from mvpa.misc.fsl.base import McFlirtParams >>> mc = McFlirtParams(path.join('mvpa', 'data', 'bold_mc.par')) >>> >>> # simple plot using pylab (use pylab.show() or pylab.savefig() >>> # afterwards) >>> mc.plot() >>> >>> # detrend some dataset with mc params as additonal regressors >>> from mvpa.datasets.miscfx import detrend >>> res = detrend(ds, model='regress', opt_reg=mc.toarray()) >>> # 'res' contains all regressors and their associated weights All FSL bindings are located in the `mvpa.misc.fsl`_ module. .. _FSL: http://www.fmrib.ox.ac.uk .. _mvpa.misc.fsl: api/mvpa.misc.fsl-module.html pymvpa-0.4.8/doc/misc/000077500000000000000000000000001174541445200145465ustar00rootroot00000000000000pymvpa-0.4.8/doc/misc/codeswarm.config000066400000000000000000000051031174541445200177200ustar00rootroot00000000000000# This is a sample configuration file for code_swarm # Frame width Width=480 # Frame height Height=360 # Input file InputFile=../../build/swarm/git.xml # Particle sprite file ParticleSpriteFile=src/particle.png #Font Settings Font=SansSerif FontSize=10 BoldFontSize=12 # Project time per frame MillisecondsPerFrame=21600000 # Maximum number of Background processes MaxThreads=4 # Optional Method instead of MillisecondsPerFrame #FramesPerDay=4 # Background in R,G,B Background=0,0,0 # Color assignment rules # Keep in order, do not skip numbers. Numbers start # at 1. # # Pattern: "Label", "regex", R,G,B, R,G,B # Label is optional. If it is omitted, the regex # will be used. # ColorAssign1="Examples","doc/examples/.*", 0,255,255, 0,255,255 ColorAssign2="Docs","doc/.*\.(rst,txt)", 0,0,255, 0,0,255 ColorAssign3="Tests",".*test_.*\.py", 0,255,0, 0,255,0 ColorAssign4="Datasets","mvpa/dataset.*", 255,0,0, 255,0,0 ColorAssign5="Classifiers","mvpa/clf.*", 255,160,65, 255,160,65 ColorAssign6="Mappers","mvpa/.*mapper.*\.py", 255,255,0, 255,255,0 ColorAssign7="MVPA Generic",".*", 255,0,255, 255,0,255 # Save each frame to an image? TakeSnapshots=True # Where to save each frame SnapshotLocation=../../build/swarm/frames/code_swarm-#####.png # Draw names (combinatory) : # Draw sharp names? DrawNamesSharp=true # And draw a glow around names? (Runs slower) DrawNamesHalos=true # Draw files (combinatory) : # Draw sharp files DrawFilesSharp=false # Draw fuzzy files DrawFilesFuzzy=true # Draw jelly files DrawFilesJelly=false # Show the Legend at start ShowLegend=true # Show the History at start ShowHistory=true # Show the Date at start ShowDate=true # Show edges between authors and files, mostly for debug purpose ShowEdges=false # Turn on Debug counts. ShowDebug=false # Natural distance of files to people EdgeLength=35 # Amount of life to decrement EdgeDecrement=-2 FileDecrement=-2 PersonDecrement=-1 #Speeds. #Optional: NodeSpeed=7.0, If used, FileSpeed and PersonSpeed need not be set. # FileSpeed=7.0 PersonSpeed=2.0 #Masses FileMass=1.0 PersonMass=10.0 # Life of an Edge EdgeLife=250 # Life of a File FileLife=200 # Life of a Person PersonLife=255 # Highlight percent. # This is the amount of time that the person or # file will be highlighted. HighlightPct=5 ## Physics engine selection and configuration # Directory physics engine config files reside in. PhysicsEngineConfigDir=physics_engine # Force calculation algorithms ("PhysicsEngineLegacy", "PhysicsEngineSimple"...) : PhysicsEngineSelection=PhysicsEngineLegacy # OpenGL is experimental. Use at your own risk. UseOpenGL=false pymvpa-0.4.8/doc/misc/emacs000066400000000000000000000122101174541445200155550ustar00rootroot00000000000000;; emacs: -*- mode: emacs-lisp; indent-tabs-mode: nil -*- ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;; ; ; See COPYING file distributed along with the PyMVPA package for the ; copyright and license terms. ; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;;; ;; ;; ;; This file is to help PyMVPA users who use emacs for their needs. ;; It enables suggested modes (if available), and sets up environment ;; variables needed by Python and pylint ;; ;; Recommended usage: ;; ;; * symlink this file as .emacs.local into the root of PyMVPA project: ;; ln -s doc/misc/emacs .emacs.local ;; ;; * add following snippet into your .emacs to enable loading of local ;; emacs configuration: ;; (push "." load-path) ;add current path ;; (load ".emacs.local" t) ;; (pop load-path) ;clean up ;; * for flymake to work correctly, you would need to have epylint script ;; installed anywhere in the PATH. You can obtain the script from ;; ;; http://git.onerussian.com/?p=etc/emacs.git;a=blob;f=.emacs.d/bin/epylint;hb=HEAD ;; ;; Now, whenever you start emacs in the root directory of PyMVPA project, ;; it should load .emacs.local and setup suggested Emacs environment. ;; ;; Disclaimer: this config file is not extensively tested and was ripped away ;; from Yaroslav's .emacs configuration available from ;; http://git.onerussian.com/?p=etc/emacs.git;a=summary (setenv "PYTHONPATH" (expand-file-name default-directory)) (setenv "PYLINTRC" (concat (expand-file-name default-directory) "doc/misc/pylintrc")) ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;; Python ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;; Set IPython to be the python command and give it arguments (when (locate-library "python-mode") (when (locate-library "ipython") (require 'ipython) (when (locate-library "ansi-color") (add-hook 'py-shell-hook 'my-activate-ansi-colors))) ;; We want pylint call (add-hook 'python-mode-hook '(lambda () (when (and (stringp (buffer-file-name)) (not (string-match ".*/tmp/python-.*" (buffer-file-name)))) (when (locate-library "pylint") (load-library "pylint") (local-set-key "\C-xc" 'pylint) ) (when (locate-library "pymacs") (load-library "pymacs") (when (pymacs-load "ropemacs" "rope-" t) (ropemacs-mode t) (set ropemacs-guess-project t))) (when (not indent-tabs-mode) (when (locate-library "show-wspace") (when (not show-ws-highlight-tabs-p) (show-ws-toggle-show-tabs)))) (when (locate-library "outline") (defun py-outline-level () "This is so that `current-column` DTRT in otherwise-hidden text" ;; from ada-mode.el (let (buffer-invisibility-spec) (save-excursion (beginning-of-line) (skip-chars-forward "\t ") (/ (current-column) py-indent-offset)))) ;; this fragment originally came from the web somewhere, but the outline-regexp ;; was horribly broken and is broken in all instances of this code floating ;; around. Finally fixed by Charl P. Botha ;; enable our level computation (setq outline-level 'py-outline-level) ;;(setq outline-regexp "[^ tn]|[ t]*(def[ t]+|class[ t]+)") ;;(setq outline-regexp "\\([ \t]*\n\\)?[ \t]*\\(if\\|for\\|def\\|class\\)[ \t]+.*[:\\\][ \t]*\\(#.*\\)?$") ;; (setq outline-regexp "\\(^[ \t]*\n\\)?[ \t]*\\(if\\|for\\|def\\|class\\|else\\|elif\\|try\\|except\\|finally\\)") ;; (setq outline-regexp "\\(^[ \t]*\n\\)?[ \t]*\\(def\\|class\\|@\\)") (setq outline-regexp "\\([ \t]*\\(def\\|class\\|@\\)\\|^#\\)") ;; without explicit keywords: ;;(setq outline-regexp "^[ \t\n]*\\([^ \t]+\\)[ \t]+.*[:\\\][ \t]*\\(#.*\\)?$") ;; custom shortcuts ;;(outline-shortcuts) ;; turn on outline mode (outline-minor-mode t) ;; initially hide all but the headers (hide-body) ) (show-paren-mode 1) (flymake-mode 1) ))) ;hook ) ;python-mode ;; Lets enable flymake + pylint tandem (when (load "flymake" t) (defun flymake-pylint-init () (let* ((temp-file (flymake-init-create-temp-buffer-copy 'flymake-create-temp-inplace)) (local-file (file-relative-name temp-file (file-name-directory buffer-file-name)))) (list "epylint" (list local-file)))) (add-to-list 'flymake-allowed-file-name-masks '("\\.py\\'" flymake-pylint-init)) ;; helper to put pylint errors into the status line ;; borrowed from ;; http://plope.com/Members/chrism/flymake-mode (load "flymake-cursor" t) (add-hook 'find-file-hook 'flymake-find-file-hook) ) (setq enable-local-variables t enable-local-eval t search-highlight t ;highlight found matches query-replace-highlight t ;highlight found matches tab-width 4 show-trailing-whitespace t ;show trailing spaces by default inhibit-startup-message t ;ok I've seen the copyleft &c ) (custom-set-variables '(safe-local-variable-values (quote ((py-indent-offset . 4))))) pymvpa-0.4.8/doc/misc/exampledata.readme000066400000000000000000000104451174541445200202160ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. index:: example fMRI dataset .. _exampledata: Example fMRI Dataset ==================== For an easy start with PyMVPA an `example fMRI dataset`_ is provided. This is a single subject from a study published by :ref:`Haxby et al. (2001) `. This dataset has already been repeatedly reanalyzed since its first publication (e.g. :ref:`Hanson et al (2004) ` and :ref:`O'Toole et al. (2005) < OJA+05`). .. note:: The orginal authors of :ref:`Haxby et al. (2001) ` hold the copyright of this dataset and made it available under the terms of the `Creative Commons Attribution-Share Alike 3.0`_ license. .. _Creative Commons Attribution-Share Alike 3.0: http://creativecommons.org/licenses/by-sa/3.0/ The subset of the dataset that is available here has been converted into the NIfTI dataformat and is preprocessed to a degree that should allow people without prior fMRI experience to perform meaningful analyses. Moreover, it should not require further preprocessing with external tools. All preprocessing has been performed using tools from FSL_. Specifically, the 4D fMRI timeseries has been skull-stripped and thresholded to zero-out non-brain voxels (using a brain outline estimate significantly larger than the brain, to prevent removal of edge voxels actually covering brain tissue). The corresponding commandline call to BET was:: bet bold bold_brain -F -f 0.5 -g 0 Afterwards the timeseries has been motion-corrected using MCFLIRT:: mcflirt -in bold_brain -out bold_mc -plots The following files are available in the `example fMRI dataset`_ download (approx. 100 MB): .. _example FMRI dataset: http://www.pymvpa.org/files/pymvpa_exampledata.tar.bz2 bold.nii.gz The motion-corrected and skull-stripped 4D timeseries (1452 volumes with 40 x 64 x 64 voxels, corresponding to a voxel size of 3.5 x 3.75 x 3.75 mm and a volume repetition time of 2.5 seconds). The timeseries contains all 12 runs of the original experiment, concatenated in a single file. Please note, that the timeseries signal is *not* detrended. bold_mc.par The motion correction parameter output. This is a 6-column textfile with three rotation and three translation parameters respectively. This information can be used e.g. as additional regressors for :ref:`motion-aware timeseries detrending `. mask.nii.gz A binary mask with a conservative brain outline estimate, i.e. including some non-brain voxels to prevent the exclusion of brain tissue. attributes_literal.txt A two-column text file with the stimulation condition and the corresponding experimental run for each volume in the timeseries image. The labels are given in literal form (e.g. 'face'). attributes.txt Similar to `attributes_literal.txt`, but with the condition labels encoded as integers. This file is only provided for earlier PyMVPA version, that could not handle :ref:`literal labels `. Once downloaded and extracted (e.g. into a folder `data/`), the dataset can be easily loaded like this: >>> from mvpa.misc.io.base import SampleAttributes >>> from mvpa.datasets.nifti import NiftiDataset >>> attrs = SampleAttributes('data/attributes_literal.txt', ... literallabels=True) >>> ds = NiftiDataset(samples='data/bold.nii.gz', ... labels=attrs.labels, ... chunks=attrs.chunks, ... labels_map=True, ... mask='data/mask.nii.gz') Note, that instead of specific import statements, it is usually more convinient, but slower, to import all functionality from PyMVPA at once with `from mvpa.suite import *` statement. .. note:: The dataset used in the :ref:`examples ` shipped with PyMVPA is actually a minimal version (posterior half of a single brain slice) of this full dataset. After appropriately adjusting the path, it is possible to run several of the examples on this full dataset. .. _FSL: http://www.fmrib.ox.ac.uk/fsl pymvpa-0.4.8/doc/misc/header.py000066400000000000000000000006501174541445200163510ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """""" __docformat__ = 'restructuredtext' pymvpa-0.4.8/doc/misc/ipy_profile_pymvpa.py000066400000000000000000000021331174541445200210340ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """PyMVPA mode for IPython. """ __docformat__ = 'restructuredtext' from IPython import ipapi # The import below effectively obsoletes your old-style ipythonrc[.ini], # so consider yourself warned! import ipy_defaults import mvpa def main(): ip = ipapi.get() # PyMVPA specific ip.ex('import mvpa') # and now the whole suite # but no, since ipython segfaults (tested with version 0.8.4) # the whole things seems to be related to RPy and friends # running the same command after IPython startup is completed # is no problem, though. #ip.ex('from mvpa.suite import *') print """ ########################### # Welcome to PyMVPA %s # ########################### """ % mvpa.__version__ main() pymvpa-0.4.8/doc/misc/pylintrc000066400000000000000000000055341174541445200163440ustar00rootroot00000000000000# PyLint configuration file for the project pymvpa. # # Agreed formatting (per yoh+michael voice dialog) is camel. # # This pylintrc file will use the default settings except for the # naming conventions, which will allow for camel case naming as found # in Java code or several libraries such as PyQt, etc. # # At some moment it was modified by yoh from the original one # which can be found on debian systems at # /usr/share/doc/pylint/examples/pylintrc_camelcase # # Just place it in ~/.pylintrc for user-wide installation or simply # use within a call to pylint or export environment variable # export PYLINTRC=$PWD/doc/misc/pylintrc [BASIC] # Regular expression which should only match correct module names module-rgx=(([a-z][a-z0-9_]*)|([A-Z][a-zA-Z0-9_]+))$ attr-rgx=[a-z_][a-zA-Z0-9_]{2,30} # Regular expression which should only match correct class names class-rgx=[A-Z_]+[a-zA-Z0-9]+$ # Regular expression which should only match correct function names function-rgx=[a-z_]+[a-z_][a-zA-Z0-9]*$ # Regular expression which should only match correct method names method-rgx=([a-z_]|__)[a-zA-Z0-9]*(__)?$ # Regular expression which should only match correct argument names argument-rgx=[a-z][a-zA-Z0-9]*_*[a-zA-Z0-9]*_*[a-zA-Z0-9]*_?$ # Regular expression which should only match correct variable names variable-rgx=([a-z_]+[a-zA-Z0-9]*_*[a-zA-Z0-9]*_*[a-zA-Z0-9]*_?||(__.*__))$||[A-Z] # Regular expression which should only match correct module level names # Default: (([A-Z_][A-Z1-9_]*)|(__.*__))$ const-rgx=([a-z_]+[a-zA-Z0-9]*_*[a-zA-Z0-9]*_*[a-zA-Z0-9]*_?|__.*__)$||[A-Z] [FORMAT] indent-string=' ' [DESIGN] # We are capable to follow that many, yes! max-branchs = 20 # some base class constructors have quite a few arguments max-args = 14 # and due to ClassWithCollections and state variables classes by default have lots # of attributes max-attributes = 14 # some sci computation can't be handled efficiently without having #lots of locals max-locals = 35 [MESSAGES CONTROL] # Disable the following PyLint messages: # R0903 - Not enough public methods # W0105 - String statement has no effect # often used for after-line doc # W0142 - Used * or ** magic # W0232 - Class has no __init__ method # W0212 - Access to a protected member ... of a client class # W0613 - Unused argument # E1101 - Has no member (countless false-positives) disable-msg=R0903,W0142,W0105,W0212,W0613,E1101 [REPORTS] # set the output format. Available formats are text, parseable, colorized and # html output-format=colorized # Include message's id in output include-ids=yes # Tells wether to display a full report or only the messages reports=no [MISCELLANEOUS] # List of note tags to take in consideration, separated by a comma. # FIXME -- something which needs fixing # TODO -- future plan # XXX -- some concern # YYY -- comment/answer to above mentioned concern notes=FIXME,TODO,XXX,YYY pymvpa-0.4.8/doc/misc/references.bib000066400000000000000000000377501174541445200173610ustar00rootroot00000000000000@Comment{x-kbibtex-encoding=utf-8} @Comment{ This file is used to autogenerate doc/references.rst using tools/bib2rst\_ref.py . Due to external dependency on pybliographer (which is discontinued project), automatic regeneration is not enabled, thus you are required to run make references to regenerate doc/references.rst if you modified this file. } @Article{ HGF+01, Author = "James V. Haxby and M. I. Gobbini and M. L. Furey and A. Ishai and J. L. Schouten and P. Pietrini", Title = "Distributed and overlapping representations of faces and objects in ventral temporal cortex.", Journal = "Science", Volume = "293", Pages = "2425–2430", year = 2001, doi = "10.1126/science.1063736", pymvpa-keywords = "split-correlation classifier" } @Article{ CPL+06, Author = "X. Chen and F. Pereira and W. Lee and Stephen Strother and Tom Mitchell", Title = "Exploring predictive and reproducible modeling with the single-subject {FIAC} dataset.", Journal = "Human Brain Mapping", Volume = "27", Pages = "452–461", url = "http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=16565951", year = 2006, doi = "10.1002/hbm.20243", pymvpa-keywords = "feature selection stability", pymvpa-summary = "This paper illustrates the necessity to consider the stability or reproducibility of a classifier's feature selection as at least equally important to it's generalization performance." } @Article{ LSC+05, issn = "1053-8119", volume = "26", year = "2005", journal = "Neuroimage", title = "Support vector machines for temporal classification of block design fMRI data.", pages = "317–329", affiliation = "Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, 30322, USA.", author = "Stephen LaConte and Stephen Strother and Vladimir Cherkassky and Jon Anderson and Xiaoping Hu", doi = "10.1016/j.neuroimage.2005.01.048", pymvpa-summary = "Comprehensive evaluation of preprocessing options with respect to SVM-classifier (and others) performance on block-design fMRI data.", pymvpa-keywords = "SVM" } @Article{ KGB06, issn = "0027-8424", volume = "103", year = "2006", journal = "Proceedings of the National Academy of Sciences of the USA", title = "Information-based functional brain mapping.", pages = "3863–3868", author = "Nikolaus Kriegeskorte and Rainer Goebel and Peter A. Bandettini", doi = "10.1073/pnas.0600244103", pymvpa-keywords = "searchlight", pymvpa-summary = "Paper introducing the searchlight algorithm.", affiliation = "Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Building 10, Room 1D80B, 10 Center Drive MSC 1148, Bethesda, MD 20892-1148, USA. niko@nih.gov" } @Article{ HR06, issn = "1471-003X", volume = "7", year = "2006", journal = "Nature Reviews Neuroscience", title = "Decoding mental states from brain activity in humans.", pages = "523–534", author = "John-Dylan Haynes and Geraint Rees", doi = "10.1038/nrn1931", pymvpa-summary = "Review of decoding studies, emphasizing the importance of ethical issues concerning the privacy of personal thought." } @Book{ Vap95, title = "The Nature of Statistical Learning Theory", author = "Vladimir Vapnik", publisher = "Springer", address = "New York", isbn = "0-387-94559-8", year = "1995", pymvpa-keywords = "support vector machine, SVM" } @Article{ KCF+05, Author = "B. Krishnapuram and L. Carin and M. A. Figueiredo and A. J. Hartemink", Title = "Sparse multinomial logistic regression: fast algorithms and generalization bounds.", Journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence", Volume = "27", Pages = "957–968", url = "http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?cmd=prlinks&dbfrom=pubmed&retmode=ref&id=15943426", year = 2005, pymvpa-keywords = "sparse multinomial logistic regression, SMLR", doi = "10.1109/TPAMI.2005.127" } @Article{ EHJ+04, title = "Least Angle Regression", author = "Bradley Efron and Hastie. Trevor and Iain Johnstone and Robert Tibshirani", journal = "Annals of Statistics", pages = "407–499", volume = "32", year = "2004", doi = "10.1214/009053604000000067", pymvpa-keywords = "least angle regression, LARS" } @Article{ HH08, issn = "0899-7667", volume = "20", year = "2008", journal = "Neural Computation", title = "Brain reading using full brain support vector machines for object recognition: there is no ``face'' identification area.", pages = "486–503", author = "Stephen José Hanson and Yaroslav O. Halchenko", doi = "10.1162/neco.2007.09-06-340", pymvpa-keywords = "support vector machine, SVM, recursive feature elimination, RFE", affiliation = "Rutgers Mind/Brain Analysis Laboratories, Psychology Department, Rutgers University, Newark, NJ 07102, U.S.A. jose@tractatus.rutgers.edu." } @Article{ NPD+06, issn = "1364-6613", volume = "10", year = "2006", journal = "Trends in Cognitive Science", title = "Beyond mind-reading: multi-voxel pattern analysis of fMRI data.", pages = "424–430", author = "Kenneth A. Norman and Sean M. Polyn and Greg J. Detre and James V. Haxby", doi = "10.1016/j.tics.2006.07.005" } @Article{ Dem06, author = "Janez DemÅ¡ar", title = "Statistical Comparisons of Classifiers over Multiple Data Sets", journal = "Journal of Machine Learning Research", volume = "7", year = "2006", issn = "1533-7928", pages = "1–30", publisher = "MIT Press", address = "Cambridge, MA, USA", url = "http://portal.acm.org/citation.cfm?id=1248548", pymvpa-summary = "This is a review of several classifier benchmark procedures." } @Article{ NH02, issn = "1065-9471", volume = "15", number = "1", year = "2002", Journal = "Human Brain Mapping", title = "Nonparametric permutation tests for functional neuroimaging: a primer with examples.", pages = "1–25", author = "Thomas E Nichols and Andrew P Holmes", doi = "10.1002/hbm.1058", affiliation = "Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.", pymvpa-summary = "Overview of standard nonparametric randomization and permutation testing applied to neuroimaging data (e.g. fMRI)" } @Article{ SMM+08, volume = "172", number = "1", year = "2008", journal = "Journal of Neuroscience Methods", title = "The impact of functional connectivity changes on support vector machines mapping of fMRI data.", pages = "94–104", doi = "10.1016/j.jneumeth.2008.04.008", author = "João Ricardo Sato and Janaina Mourão-Miranda and Maria da Graça {Morais Martin} and Edson Amaro and Pedro Alberto Morettin and Michael John Brammer", pymvpa-summary = "Discussion of possible scenarios where univariate and multivariate (SVM) sensitivity maps derived from the same dataset could differ. Including the case were univariate methods would assign a substantially larger score to some features.", pymvpa-keywords = "support vector machine, SVM, sensitivity" } @Article{ WCW+07, issn = "1053-8119", volume = "36", number = "4", year = "2007", journal = "Neuroimage", title = "Support vector machine learning-based fMRI data group analysis.", pages = "1139–51", author = "Ze Wang and Anna R. Childress and Jiongjiong Wang and John A. Detre", doi = "10.1016/j.neuroimage.2007.03.072", pymvpa-keywords = "support vector machine, SVM, group analysis" } @Article{ OJA+05, title = "Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex ", author = "A. J. O'Toole and F. Jiang and H. Abdi and James V. Haxby", journal = "Journal of Cognitive Neuroscience", pages = "580–590", volume = "17", year = "2005", doi = "10.1162/0898929053467550" } @Article{ OJA+07, Author = "A. J. O'Toole and F. Jiang and H. Abdi and N. Penard and J. P. Dunlop and M. A. Parent", Title = "Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data.", Journal = "Journal of Cognitive Neuroscience", Volume = "19", Pages = "1735–1752", doi = "10.1162/jocn.2007.19.11.1735", year = 2007 } @Article{ GE03, author = "I. Guyon and A. Elisseeff", title = "An Introduction to Variable and Feature Selection", volume = "3", year = "2003", pages = "1157–1182", journal = "Journal of Machine Learning", url = "http://www.jmlr.org/papers/v3/guyon03a.html" } @Article{ HMH04, Author = "Stephen José Hanson and T. Matsuka and James V. Haxby", Title = "Combinatorial codes in ventral temporal lobe for object recognition: {H}axby (2001) revisited: is there a ``face'' area?", Journal = "Neuroimage", Volume = "23", Pages = "156–166", year = 2004, doi = "10.1016/j.neuroimage.2004.05.020" } @Article{ ZH05, title = "Regularization and variable selection via the elastic net", author = "H. Zou and T. Hastie", journal = "Journal of the Royal Statistical Society Series B", volume = "67", number = "2", pages = "301–320", year = "2005", publisher = "Blackwell Synergy", keywords = "Feature Selection, Machine Learning", url = "http://www-stat.stanford.edu/%7Ehastie/Papers/B67.2%20(2005)%20301-320%20Zou%20%26%20Hastie.pdf" } @Article{ MHN+04, title = "Learning to Decode Cognitive States from Brain Images", author = "Tom Mitchell and Rebecca Hutchinson and Radu S. Niculescu and Francisco Pereira and Xuerui Wang and Marcel Just and Sharlene Newman", doi = "10.1023/B:MACH.0000035475.85309.1b", journal = "Machine Learning", volume = "57", pages = "145–175", year = "2004" } @Article{ PP07, issn = "1047-3211", volume = "17", year = "2007", journal = "Cerebral Cortex", title = "Decoding near-threshold perception of fear from distributed single-trial brain activation.", pages = "691–701", author = "Luiz Pessoa and Srikanth Padmala", pymvpa-summary = "Analysis of slow event-related fMRI data using patter classification techniques.", doi = "10.1093/cercor/bhk020" } @Article{ KT05, issn = "1097-6256", volume = "8", year = "2005", journal = "Nature Neuroscience", title = "Decoding the visual and subjective contents of the human brain.", pages = "679–685", author = "Yukiyasu Kamitani and Frank Tong", pymvpa-summary = "One of the two studies showing the possibility to read out orientation information from visual cortex.", doi = "10.1038/nn1444" } @Manual{ HHS+latest, title = "The PyMVPA Manual", author = "Michael Hanke and Yaroslav O. Halchenko and Per B. Sederberg and James M. Hughes", address = "Available online at http://www.pymvpa.org/PyMVPA-Manual.pdf" } @Article{ HHS+09a, title = "PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data", author = "Michael Hanke and Yaroslav O. Halchenko and Per B. Sederberg and Stephen José Hanson and James V. Haxby and Stefan Pollmann", journal = "Neuroinformatics", year = "2009", pymvpa-summary = "Introduction into the analysis of fMRI data using PyMVPA.", pages = "37–53", volume = "7", number = "1", doi = "10.1007/s12021-008-9041-y", pymvpa-keywords = "PyMVPA, fMRI" } @Article{ PMB+IP, title = "Machine learning classifiers and fMRI: A tutorial overview", author = "Francisco Pereira and Tom Mitchell and Matthew Botvinick", journal = "Neuroimage", year = "in press", doi = "10.1016/j.neuroimage.2008.11.007" } @Article{ HHS+09b, issn = "1662-5196", volume = "3", year = "2009", journal = "Frontiers in Neuroinformatics", title = "PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data.", pages = "3", author = "Michael Hanke and Yaroslav O. Halchenko and Per B. Sederberg and Emanuele Olivetti and Ingo Fründ and Jochem W. Rieger and Christoph S. Herrmann and James V. Haxby and Stephen José Hanson and Stefan Pollmann", doi = "10.3389/neuro.11.003.2009", pymvpa-keywords = "PyMVPA, fMRI, EEG, MEG, extracellular recordings", pymvpa-summary = "Demonstration of PyMVPA capabilities concerning multi-modal or modality-agnostic data analysis." } @Article{ MBK09, year = "2009", journal = "Social Cognitive and Affective Neuroscience", title = "Revealing representational content with pattern-information fMRI–an introductory guide.", author = "Marieke Mur and Peter A. Bandettini and Nikolaus Kriegeskorte", doi = "10.1093/scan/nsn044" } @Article{ JL09, title = "OMPC: an open-source MATLAB-to-Python compiler.", author = "Peter Jurica and Cees {van Leeuwen}", journal = "Frontiers in Neuroinformatics", pages = "5", volume = "3", year = "2009", doi = "10.3389/neuro.11.005.2009" } @Article{ KFS+09, title = "Center-surround patterns emerge as optimal predictors for human saccade targets", author = "Wolf Kienzle and Matthias O. Franz and Bernhard Schölkopf and Felix A. Wichmann", journal = "Journal of Vision", year = "in press", pymvpa-summary = "This paper offers an approach to make sense out of feature sensitivities of non-linear classifiers." } @Article{ KMB08, volume = "2", year = "2008", journal = "Frontiers in Systems Neuroscience", title = "Representational similarity analysis - connecting the branches of systems neuroscience.", pages = "4", author = "Nikolaus Kriegeskorte and Marieke Mur and Peter A. Bandettini", doi = "10.3389/neuro.06.004.2008" } @Article{ SET+09, title = "Elucidating an MRI-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms", author = "Daqiang Sun and Theo G.M. {van Erp} and Paul M. Thompson and Carrie E. Bearden and Melita Daley and Leila Kushan and Molly E. Hardt and Keith H. Nuechterlein and Arthur W. Toga and Tyrone D. Cannon", journal = "Biological Psychiatry", year = "2009", doi = "10.1016/j.biopsych.2009.07.019", pymvpa-keywords = "PyMVPA, psychosis, MRI", pymvpa-summary = "First published study employing PyMVPA for MRI-based analysis of Psychosis." } @Article{ JSW09, title = "Does Cognitive Science Need Kernels?", volume = "13", url = "http://www.sciencedirect.com/science/article/B6VH9-4X4R9BC-1/2/e2e90008d0a8887878c72777462335fd", author = "Frank Jäkel and Bernhard Schölkopf and Felix A. Wichmann", journal = "Trends in Cognitive Sciences", pages = "381–388", year = "2009", doi = "10.1016/j.tics.2009.06.002", pymvpa-summary = "A summary of the relationship of machine learning and cognitive science. Moreover it also points out the role of kernel-based methods in this context.", pymvpa-keywords = "kernel, similarity" } @Article{ HHH+10, title = "Statistical learning analysis in neuroscience: aiming for transparency.", author = "Michael Hanke and Yaroslav O. Halchenko and James V. Haxby and Stefan Pollmann", journal = "Frontiers in Neuroscience", year = "accepted", pymvpa-summary = "Focused review article emphasizing the role of transparency to facilitate adoption and evaluation of statistical learning techniques in neuroimaging research." } @Article{ MHH10, title = "Implicit memory for object locations depends on reactivation of encoding-related brain regions", author = "Anna Manelis and Catherine Hanson and Stephen José Hanson", journal = "Human Brain Mapping", number = "(In press)", year = "2010", pymvpa-keywords = "PyMVPA, implicit memory, MRI" } @Book{ HTF09, title = "The Elements of Statistical Learning: Data Mining, Inference, and Prediction", author = "Trevor Hastie and Robert Tibshirani and Jerome H. Friedman", publisher = "Springer", address = "New York", edition = "2", year = "2009", isbn = "978-0-387-84857-0", url = "http://www-stat.stanford.edu/~tibs/ElemStatLearn/", doi = "10.1007/b94608", pymvpa-summary = "Excellent summary of virtually all techniques relevant to the field. A free PDF version of this book is available from the authors' website at http://www-stat.stanford.edu/~tibs/ElemStatLearn/" } @Article{ LBB+98, title = "Gradient-based learning applied to document recognition", author = "Y. Lecun and L. Bottou and Y. Bengio and P. Haffner", journal = "Proceedings of the IEEE", pages = "2278–2324", volume = "86", number = "11", month = "Nov", year = 1998, issn = "0018-9219", doi = "10.1109/5.726791", pymvpa-keywords = "handwritten character recognition, multilayer neural networks, MNIST", pymvpa-summary = "Paper introducing Modified NIST (MNIST) dataset for performance comparisons of character recognition performance across a variety of classifiers." } pymvpa-0.4.8/doc/misc/references.in000066400000000000000000000012751174541445200172240ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. index:: references .. _chap_references: ********** References ********** This list aims to be a collection of literature, that is of particular interest in the context of multivariate pattern analysis. It includes all references cited throughout this manual, but also a number of additional manuscripts containing descriptions of interesting analysis methods or fruitful experiments. pymvpa-0.4.8/doc/misc/replace_header.sh000066400000000000000000000026251174541445200200320ustar00rootroot00000000000000#!/bin/bash # emacs: -*- mode: shell-script; c-basic-offset: 4; tab-width: 4; indent-tabs-mode: t -*- # vi: set ft=sh sts=4 ts=4 sw=4 noet: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## #echo "./tests/crossval.py" | \ #echo "tests/test_algorithms.py" | \ #echo "mvpa/datasets/mapper.py" | \ find -iname '*.py' | \ while read fname; do grep -q 'This package is distributed in the hope' $fname || continue descr="`grep '[#"]* *PyMVPA: ' "$fname" | head -1 | sed -e 's/^.*PyMVPA: //g' -e 's/"""//g'`" [ "$descr" == "" ] && \ descr="`sed -n -e '/###/,/^[^#]/p' "$fname" | grep '"""' | head -1 | sed -e 's/^"""\(PyMVPA: *\)*\(.*\)"""/\2/g'`" [ "$descr" == "" ] && \ descr="`sed -n -e '/###/,/^[^#]/p' $fname | sed -n -e '3s/^# *//gp'`" echo "$fname:$descr" cat $fname \ | sed -e '0,/### ###/d' -e '0,/### ###/d' \ | sed -e '1rdoc/misc/header.py' \ | sed -e 's/\t/ /g' \ | sed -n -e "s/\(\"PyMVPA: \)\"/\1${descr}\"/g" -e '2,$p' \ | sed -e 's/ -- loosely implemented//g' \ | sponge $fname done exit 0 problematic: mvpa/misc/fsl/__init__.py -- removed header completely for some reason... tests/test_algorithms.py os gone! mvpa/datasets/mapper.py is gone pymvpa-0.4.8/doc/modref.rst000066400000000000000000000032201174541445200156160ustar00rootroot00000000000000.. -*- mode: rst -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: .. _chap_modref: **************** Module Reference **************** This module reference extends the manual with a comprehensive overview of the currently available functionality, that is built into PyMVPA. However, instead of a full list including every single line of the PyMVPA code base, this reference limits itself to the relevant pieces of the application programming interface (API) that are of particular interest to users of this framework. Each module in the package is documented by a general summary of its purpose and the list of classes and functions it provides. For developers, more detailed (technical) information is available in the `API reference`_. .. _API Reference: api/index.html Global Facilities ================= .. toctree:: modref/mvpa Datasets: Input, Output, Storage and Preprocessing ================================================== .. toctree:: :glob: modref/mvpa.datasets* Mappers: Data Transformations ============================= .. toctree:: :glob: modref/mvpa.mappers* Classifiers and Errors ====================== .. toctree:: :glob: modref/mvpa.clfs* Measures: Searchlights and Sensitivties ======================================= .. toctree:: :glob: modref/mvpa.measures* Feature Selection ================= .. toctree:: :glob: modref/mvpa.featsel* Additional Algorithms ===================== .. toctree:: :glob: modref/mvpa.algorithms* Common Facilities ================= .. toctree:: :glob: modref/mvpa.base* Miscellaneous ============= .. toctree:: :glob: modref/mvpa.misc* modref/mvpa.atlases* pymvpa-0.4.8/doc/overview.rst000066400000000000000000000077651174541445200162320ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### *************** Getting Started *************** For the Impatient ================= If you only have five minutes to decide whether you want to use PyMVPA, take the first minute to look at the following example of a cross-validation procedure on an fMRI dataset (the full source code!). It is not heavily commented, but should simply give you an idea how PyMVPA feels like. First import the whole PyMVPA module: >>> from mvpa.suite import * Now, load the dataset from a NIfTI file. An additional 2-column textfile has the label and associated experimental run of each volume in the dataset (one volume per line). Finally, a mask is loaded to exclude non-brain voxels. >>> attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt')) >>> dataset = NiftiDataset( ... samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'), ... labels=attr.labels, ... chunks=attr.chunks, ... mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz')) Perform linear detrending and afterwards zscore the timeseries of each voxel using the mean and standard deviation determined from *rest* volumes (all done for each experimental run individually). >>> detrend(dataset, perchunk=True, model='linear') >>> zscore(dataset, perchunk=True, baselinelabels=[0], ... targetdtype='float32') Select a subset of two stimulation conditions from the whole dataset. >>> dataset = dataset['labels', [1,2]] Finally, setup the cross-validation procedure using an odd-even split of the dataset and a *SMLR* classifier -- and run it. >>> cv = CrossValidatedTransferError( ... TransferError(SMLR()), ... OddEvenSplitter()) >>> error = cv(dataset) Done. The mean error of classifier predictions on the test dataset across dataset splits is stored in `error`. If you think that is a good start, take the remaining four minutes to take a look at the examples shipped in the source distribution of PyMVPA (`doc/examples/`; some of them are also listed in :ref:`chap_examples` section of this manual). The examples provide a coarse overview of a substantial portion of the functionality provided by PyMVPA, ranging from basic classifier usage, over more sophisticated analysis strategies to simple visualization demos. All examples are executable scripts that are meant to be run from to toplevel directory of the extracted source tarball, e.g.:: $ doc/examples/start_easy.py which would run the example shown in the first part of this section. However, once you found something interesting in the examples you should consider skipping through this manual, as it contains a lot of information that is complementary to the API reference and the examples. And now for the details ... .. index:: introduction, modular architecture Module Overview =============== The PyMVPA package consists of three major parts: :ref:`Data handling `, :ref:`Classifiers ` and various algorithms and measures that operate on datasets and classifiers. In the following sections the basic concept of all three parts will be described and examples using certain parts of the PyMVPA package will be given. .. image:: pics/design.* :alt: PyMVPA architecture The manual does not cover all bits and pieces of PyMVPA. Detailed information about the module layout and additional documentation about all included functionality is available from the :ref:`Module Reference ` -- or the `API Reference`_ if you are interested in a more technical document. The main purpose of the manual is to give an idea how the individual parts of PyMVPA can be combined to perform complex analyses -- easily. .. _API Reference: api/index.html pymvpa-0.4.8/doc/pics/000077500000000000000000000000001174541445200145515ustar00rootroot00000000000000pymvpa-0.4.8/doc/pics/classifier_comparison_plot.png000066400000000000000000004163151174541445200227050ustar00rootroot00000000000000‰PNG  IHDRô›Û8sRGB®ÎébKGDÿÿÿ ½§“ pHYsaa¨?§itIMEØ 8½ú] IDATxÚìyxUöþ?UÕ{wö=@XE@öˆl "8¨ŒëFGÑaÐq\TÔqTEÆu”aæ ¢¢*²#$@VH:½WÕïªÞ’@¤ƒ÷}žz’îyï{î¹çHº®ë4P3räHžþyÎ<óÌ£ž«iß}÷;vì`È!´k×.öÚ'Ÿ|B§NèÚµkJý>¯×ËþóÎ?ÿ|\.×ÏÛºu+_ý5 à´ÓN‹=¿fͬV+¬ö³¾ûî;n¿ýv^}õU:uêtÔs#‘3gÎÄjµò׿þµÊsþò—¿°wï^^xá$I7ë †,š@@@àT@MCUU^ýu®½öZæÍ›‡¦i±×|ðAV¬X‘r¿+??Ÿ3fpèС£ž·bÅ &OžÌÿøGB¡Pìù_|‘×_½FŸÕ±cGþüç?Ó¼ysqC5@XDœJ,Y²„-Z0jÔ(d¹²néܹ3K—.å†nHR³•ñªU«øùçŸÉÌÌäì³Ï¦C‡”••ñõ×_³eË4M£S§NŒ=«Õ À—_~Iii)ééé¬_¿žîÝ»3räHöíÛÇÊ•+),,$;;›sÎ9‡ôôt8ÀòåË9xð ‡>}úpúé§óî»ïRZZʳÏ>Kzz:çw}ûö­ò;ggg³uëVÞ{ï=.½ôÒ*'9‘H„Õ«W³qãF¬V+ƒ¦wïÞ„B! 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If you use PyMVPA in your research please cite the one that matches best. In addition there is also a list of studies done by other groups employing PyMVPA somewhere in the analysis. Peer-reviewed publications -------------------------- Hanke, M., Halchenko, Y. O., Haxby, J. V., and Pollmann, S. (accepted) *Statistical learning analysis in neuroscience: aiming for transparency*. Frontiers in Neuroscience. Focused review article emphasizing the role of transparency to facilitate adoption and evaluation of statistical learning techniques in neuroimaging research. Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. V., Hanson, S. J. and Pollmann, S. (2009) `PyMVPA\: a unifying approach to the analysis of neuroscientific data`_. Frontiers in Neuroinformatics, 3:3. Demonstration of PyMVPA capabilities concerning multi-modal or modality-agnostic data analysis. .. _PyMVPA\: a unifying approach to the analysis of neuroscientific data: http://dx.doi.org/10.3389/neuro.11.003.2009 Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2009). `PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data`_. Neuroinformatics, 7, 37-53. First paper introducing fMRI data analysis with PyMVPA. .. _PyMVPA\: A Python toolbox for multivariate pattern analysis of fMRI data: http://dx.doi.org/10.1007/s12021-008-9041-y Posters ------- Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2008). `PyMVPA: A Python toolbox for machine-learning based data analysis.`_ Poster emphasizing PyMVPA's capabilities concerning multi-modal data analysis at the annual meeting of the Society for Neuroscience, Washington, 2008. Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2008). `PyMVPA: A Python toolbox for classifier-based data analysis.`_ First presentation of PyMVPA at the conference *Psychologie und Gehirn* [Psychology and Brain], Magdeburg_, 2008. This poster received the poster prize of the *German Society for Psychophysiology and its Application*. .. _PyMVPA\: A Python toolbox for classifier-based data analysis.: http://www.pymvpa.org/files/PyMVPA_PuG2008.pdf .. _PyMVPA\: A Python toolbox for machine-learning based data analysis.: http://www.pymvpa.org/files/PyMVPA_SfN2008.pdf .. _Magdeburg: http://www.magdeburg.de/ Studies employing PyMVPA ------------------------ * :ref:`Sun et al. (2009) `: *Elucidating an MRI-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms.* * :ref:`Manelis et al. (2010) `: *Implicit memory for object locations depends on reactivation of encoding-related brain regions* pymvpa-0.4.8/doc/references.rst000066400000000000000000000271761174541445200165030ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- # # THIS IS A GENERATED FILE -- DO NOT EDIT! # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. index:: references .. _chap_references: ********** References ********** This list aims to be a collection of literature, that is of particular interest in the context of multivariate pattern analysis. It includes all references cited throughout this manual, but also a number of additional manuscripts containing descriptions of interesting analysis methods or fruitful experiments. .. _CPL+06: **Chen, X., Pereira, F., Lee, W., Strother, S. & Mitchell, T.** (2006). Exploring predictive and reproducible modeling with the single-subject FIAC dataset. *Human Brain Mapping*, *27*, 452–461. *This paper illustrates the necessity to consider the stability or reproducibility of a classifier's feature selection as at least equally important to it's generalization performance.* Keywords: :keyword:`feature selection stability` DOI: http://dx.doi.org/10.1002/hbm.20243 .. _Dem06: **DemÅ¡ar, J.** (2006). Statistical Comparisons of Classifiers over Multiple Data Sets. *Journal of Machine Learning Research*, *7*, 1–30. *This is a review of several classifier benchmark procedures.* URL: http://portal.acm.org/citation.cfm?id=1248548 .. _EHJ+04: **Efron, B., Trevor, H., Johnstone, I. & Tibshirani, R.** (2004). Least Angle Regression. *Annals of Statistics*, *32*, 407–499. Keywords: :keyword:`least angle regression`, :keyword:`LARS` DOI: http://dx.doi.org/10.1214/009053604000000067 .. _GE03: **Guyon, I. & Elisseeff, A.** (2003). An Introduction to Variable and Feature Selection. *Journal of Machine Learning*, *3*, 1157–1182. URL: http://www.jmlr.org/papers/v3/guyon03a.html .. _HHH+10: **Hanke, M., Halchenko, Y. O., Haxby, J. V. & Pollmann, S.** (accepted). Statistical learning analysis in neuroscience: aiming for transparency. *Frontiers in Neuroscience*. *Focused review article emphasizing the role of transparency to facilitate adoption and evaluation of statistical learning techniques in neuroimaging research.* .. _HHS+latest: **Hanke, M., Halchenko, Y. O., Sederberg, P. B. & Hughes, J. M.** The PyMVPA Manual. Available online at http://www.pymvpa.org/PyMVPA-Manual.pdf. .. _HHS+09a: **Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S.** (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. *Neuroinformatics*, *7*, 37–53. *Introduction into the analysis of fMRI data using PyMVPA.* Keywords: :keyword:`PyMVPA`, :keyword:`fMRI` DOI: http://dx.doi.org/10.1007/s12021-008-9041-y .. _HHS+09b: **Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. V., Hanson, S. J. & Pollmann, S.** (2009). PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data. *Frontiers in Neuroinformatics*, *3*, 3. *Demonstration of PyMVPA capabilities concerning multi-modal or modality-agnostic data analysis.* Keywords: :keyword:`PyMVPA`, :keyword:`fMRI`, :keyword:`EEG`, :keyword:`MEG`, :keyword:`extracellular recordings` DOI: http://dx.doi.org/10.3389/neuro.11.003.2009 .. _HH08: **Hanson, S. J. & Halchenko, Y. O.** (2008). Brain reading using full brain support vector machines for object recognition: there is no "face" identification area. *Neural Computation*, *20*, 486–503. Keywords: :keyword:`support vector machine`, :keyword:`SVM`, :keyword:`recursive feature elimination`, :keyword:`RFE` DOI: http://dx.doi.org/10.1162/neco.2007.09-06-340 .. _HMH04: **Hanson, S. J., Matsuka, T. & Haxby, J. V.** (2004). Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: is there a "face" area?. *Neuroimage*, *23*, 156–166. DOI: http://dx.doi.org/10.1016/j.neuroimage.2004.05.020 .. _HGF+01: **Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L. & Pietrini, P.** (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. *Science*, *293*, 2425–2430. Keywords: :keyword:`split-correlation classifier` DOI: http://dx.doi.org/10.1126/science.1063736 .. _HR06: **Haynes, J. & Rees, G.** (2006). Decoding mental states from brain activity in humans. *Nature Reviews Neuroscience*, *7*, 523–534. *Review of decoding studies, emphasizing the importance of ethical issues concerning the privacy of personal thought.* DOI: http://dx.doi.org/10.1038/nrn1931 .. _JL09: **Jurica, P. & van Leeuwen, C.** (2009). OMPC: an open-source MATLAB-to-Python compiler. *Frontiers in Neuroinformatics*, *3*, 5. DOI: http://dx.doi.org/10.3389/neuro.11.005.2009 .. _JSW09: **Jäkel, F., Schölkopf, B. & Wichmann, F. A.** (2009). Does Cognitive Science Need Kernels?. *Trends in Cognitive Sciences*, *13*, 381–388. *A summary of the relationship of machine learning and cognitive science. Moreover it also points out the role of kernel-based methods in this context.* Keywords: :keyword:`kernel`, :keyword:`similarity` DOI: http://dx.doi.org/10.1016/j.tics.2009.06.002 .. _KT05: **Kamitani, Y. & Tong, F.** (2005). Decoding the visual and subjective contents of the human brain. *Nature Neuroscience*, *8*, 679–685. *One of the two studies showing the possibility to read out orientation information from visual cortex.* DOI: http://dx.doi.org/10.1038/nn1444 .. _KFS+09: **Kienzle, W., Franz, M. O., Schölkopf, B. & Wichmann, F. A.** (in press). Center-surround patterns emerge as optimal predictors for human saccade targets. *Journal of Vision*. *This paper offers an approach to make sense out of feature sensitivities of non-linear classifiers.* .. _KGB06: **Kriegeskorte, N., Goebel, R. & Bandettini, P. A.** (2006). Information-based functional brain mapping. *Proceedings of the National Academy of Sciences of the USA*, *103*, 3863–3868. *Paper introducing the searchlight algorithm.* Keywords: :keyword:`searchlight` DOI: http://dx.doi.org/10.1073/pnas.0600244103 .. _KMB08: **Kriegeskorte, N., Mur, M. & Bandettini, P. A.** (2008). Representational similarity analysis - connecting the branches of systems neuroscience. *Frontiers in Systems Neuroscience*, *2*, 4. DOI: http://dx.doi.org/10.3389/neuro.06.004.2008 .. _KCF+05: **Krishnapuram, B., Carin, L., Figueiredo, M. A. & Hartemink, A. J.** (2005). Sparse multinomial logistic regression: fast algorithms and generalization bounds. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, *27*, 957–968. Keywords: :keyword:`sparse multinomial logistic regression`, :keyword:`SMLR` DOI: http://dx.doi.org/10.1109/TPAMI.2005.127 .. _LSC+05: **LaConte, S., Strother, S., Cherkassky, V., Anderson, J. & Hu, X.** (2005). Support vector machines for temporal classification of block design fMRI data. *Neuroimage*, *26*, 317–329. *Comprehensive evaluation of preprocessing options with respect to SVM-classifier (and others) performance on block-design fMRI data.* Keywords: :keyword:`SVM` DOI: http://dx.doi.org/10.1016/j.neuroimage.2005.01.048 .. _MHH10: **Manelis, A., Hanson, C. & Hanson, S. J.** (2010). Implicit memory for object locations depends on reactivation of encoding-related brain regions. *Human Brain Mapping*. Keywords: :keyword:`PyMVPA`, :keyword:`implicit memory`, :keyword:`MRI` .. _MHN+04: **Mitchell, T., Hutchinson, R., Niculescu, R. S., Pereira, F., Wang, X., Just, M. & Newman, S.** (2004). Learning to Decode Cognitive States from Brain Images. *Machine Learning*, *57*, 145–175. DOI: http://dx.doi.org/10.1023/B:MACH.0000035475.85309.1b .. _MBK09: **Mur, M., Bandettini, P. A. & Kriegeskorte, N.** (2009). Revealing representational content with pattern-information fMRI–an introductory guide. *Social Cognitive and Affective Neuroscience*. DOI: http://dx.doi.org/10.1093/scan/nsn044 .. _NH02: **Nichols, T. E. & Holmes, A. P.** (2002). Nonparametric permutation tests for functional neuroimaging: a primer with examples. *Human Brain Mapping*, *15*, 1–25. *Overview of standard nonparametric randomization and permutation testing applied to neuroimaging data (e.g. fMRI)* DOI: http://dx.doi.org/10.1002/hbm.1058 .. _NPD+06: **Norman, K. A., Polyn, S. M., Detre, G. J. & Haxby, J. V.** (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. *Trends in Cognitive Science*, *10*, 424–430. DOI: http://dx.doi.org/10.1016/j.tics.2006.07.005 .. _OJA+05: **O'Toole, A. J., Jiang, F., Abdi, H. & Haxby, J. V.** (2005). Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex . *Journal of Cognitive Neuroscience*, *17*, 580–590. DOI: http://dx.doi.org/10.1162/0898929053467550 .. _OJA+07: **O'Toole, A. J., Jiang, F., Abdi, H., Penard, N., Dunlop, J. P. & Parent, M. A.** (2007). Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. *Journal of Cognitive Neuroscience*, *19*, 1735–1752. DOI: http://dx.doi.org/10.1162/jocn.2007.19.11.1735 .. _PMB+IP: **Pereira, F., Mitchell, T. & Botvinick, M.** (in press). Machine learning classifiers and fMRI: A tutorial overview. *Neuroimage*. DOI: http://dx.doi.org/10.1016/j.neuroimage.2008.11.007 .. _PP07: **Pessoa, L. & Padmala, S.** (2007). Decoding near-threshold perception of fear from distributed single-trial brain activation. *Cerebral Cortex*, *17*, 691–701. *Analysis of slow event-related fMRI data using patter classification techniques.* DOI: http://dx.doi.org/10.1093/cercor/bhk020 .. _SMM+08: **Sato, J. R., Mourão-Miranda, J., Martin, M. d. G. M., Amaro, E., Morettin, P. A. & Brammer, M. J.** (2008). The impact of functional connectivity changes on support vector machines mapping of fMRI data. *Journal of Neuroscience Methods*, *172*, 94–104. *Discussion of possible scenarios where univariate and multivariate (SVM) sensitivity maps derived from the same dataset could differ. Including the case were univariate methods would assign a substantially larger score to some features.* Keywords: :keyword:`support vector machine`, :keyword:`SVM`, :keyword:`sensitivity` DOI: http://dx.doi.org/10.1016/j.jneumeth.2008.04.008 .. _SET+09: **Sun, D., van Erp, T. G., Thompson, P. M., Bearden, C. E., Daley, M., Kushan, L., Hardt, M. E., Nuechterlein, K. H., Toga, A. W. & Cannon, T. D.** (2009). Elucidating an MRI-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms. *Biological Psychiatry*. *First published study employing PyMVPA for MRI-based analysis of Psychosis.* Keywords: :keyword:`PyMVPA`, :keyword:`psychosis`, :keyword:`MRI` DOI: http://dx.doi.org/10.1016/j.biopsych.2009.07.019 .. _Vap95: **Vapnik, V.** (1995). The Nature of Statistical Learning Theory. Springer: New York. Keywords: :keyword:`support vector machine`, :keyword:`SVM` .. _WCW+07: **Wang, Z., Childress, A. R., Wang, J. & Detre, J. A.** (2007). Support vector machine learning-based fMRI data group analysis. *Neuroimage*, *36*, 1139–51. Keywords: :keyword:`support vector machine`, :keyword:`SVM`, :keyword:`group analysis` DOI: http://dx.doi.org/10.1016/j.neuroimage.2007.03.072 .. _ZH05: **Zou, H. & Hastie, T.** (2005). Regularization and variable selection via the elastic net. *Journal of the Royal Statistical Society Series B*, *67*, 301–320. URL: http://www-stat.stanford.edu/%7Ehastie/Papers/B67.2%20(2005)%20301-320%20Zou%20%26%20Hastie.pdf pymvpa-0.4.8/doc/sphinxext/000077500000000000000000000000001174541445200156455ustar00rootroot00000000000000pymvpa-0.4.8/doc/sphinxext/inheritance_diagram.py000066400000000000000000000324771174541445200222110ustar00rootroot00000000000000""" Defines a docutils directive for inserting inheritance diagrams. Provide the directive with one or more classes or modules (separated by whitespace). For modules, all of the classes in that module will be used. Example:: Given the following classes: class A: pass class B(A): pass class C(A): pass class D(B, C): pass class E(B): pass .. inheritance-diagram: D E Produces a graph like the following: A / \ B C / \ / E D The graph is inserted as a PNG+image map into HTML and a PDF in LaTeX. """ import inspect import os import re import subprocess try: from hashlib import md5 except ImportError: from md5 import md5 from docutils.nodes import Body, Element from docutils.parsers.rst import directives from sphinx.roles import xfileref_role def my_import(name): """Module importer - taken from the python documentation. This function allows importing names with dots in them.""" mod = __import__(name) components = name.split('.') for comp in components[1:]: mod = getattr(mod, comp) return mod class DotException(Exception): pass class InheritanceGraph(object): """ Given a list of classes, determines the set of classes that they inherit from all the way to the root "object", and then is able to generate a graphviz dot graph from them. """ def __init__(self, class_names, show_builtins=False): """ *class_names* is a list of child classes to show bases from. If *show_builtins* is True, then Python builtins will be shown in the graph. """ self.class_names = class_names self.classes = self._import_classes(class_names) self.all_classes = self._all_classes(self.classes) if len(self.all_classes) == 0: raise ValueError("No classes found for inheritance diagram") self.show_builtins = show_builtins py_sig_re = re.compile(r'''^([\w.]*\.)? # class names (\w+) \s* $ # optionally arguments ''', re.VERBOSE) def _import_class_or_module(self, name): """ Import a class using its fully-qualified *name*. """ try: path, base = self.py_sig_re.match(name).groups() except: raise ValueError( "Invalid class or module '%s' specified for inheritance diagram" % name) fullname = (path or '') + base path = (path and path.rstrip('.')) if not path: path = base try: module = __import__(path, None, None, []) # We must do an import of the fully qualified name. Otherwise if a # subpackage 'a.b' is requested where 'import a' does NOT provide # 'a.b' automatically, then 'a.b' will not be found below. This # second call will force the equivalent of 'import a.b' to happen # after the top-level import above. my_import(fullname) except ImportError: raise ValueError( "Could not import class or module '%s' specified for inheritance diagram" % name) try: todoc = module for comp in fullname.split('.')[1:]: todoc = getattr(todoc, comp) except AttributeError: raise ValueError( "Could not find class or module '%s' specified for inheritance diagram" % name) # If a class, just return it if inspect.isclass(todoc): return [todoc] elif inspect.ismodule(todoc): classes = [] for cls in todoc.__dict__.values(): if inspect.isclass(cls) and cls.__module__ == todoc.__name__: classes.append(cls) return classes raise ValueError( "'%s' does not resolve to a class or module" % name) def _import_classes(self, class_names): """ Import a list of classes. """ classes = [] for name in class_names: classes.extend(self._import_class_or_module(name)) return classes def _all_classes(self, classes): """ Return a list of all classes that are ancestors of *classes*. """ all_classes = {} def recurse(cls): all_classes[cls] = None for c in cls.__bases__: if c not in all_classes: recurse(c) for cls in classes: recurse(cls) return all_classes.keys() def class_name(self, cls, parts=0): """ Given a class object, return a fully-qualified name. This works for things I've tested in matplotlib so far, but may not be completely general. """ module = cls.__module__ if module == '__builtin__': fullname = cls.__name__ else: fullname = "%s.%s" % (module, cls.__name__) if parts == 0: return fullname name_parts = fullname.split('.') return '.'.join(name_parts[-parts:]) def get_all_class_names(self): """ Get all of the class names involved in the graph. """ return [self.class_name(x) for x in self.all_classes] # These are the default options for graphviz default_graph_options = { "rankdir": "UD", "size": '"8.0, 8.0"' } default_node_options = { "shape": "box", "fontsize": 10, "height": 0.25, "fontname": "Vera Sans, DejaVu Sans, Liberation Sans, Arial, Helvetica, sans", "style": '"setlinewidth(0.5)"' } default_edge_options = { "arrowsize": 0.5, "style": '"setlinewidth(0.5)"' } def _format_node_options(self, options): return ','.join(["%s=%s" % x for x in options.items()]) def _format_graph_options(self, options): return ''.join(["%s=%s;\n" % x for x in options.items()]) def generate_dot(self, fd, name, parts=0, urls={}, graph_options={}, node_options={}, edge_options={}): """ Generate a graphviz dot graph from the classes that were passed in to __init__. *fd* is a Python file-like object to write to. *name* is the name of the graph *urls* is a dictionary mapping class names to http urls *graph_options*, *node_options*, *edge_options* are dictionaries containing key/value pairs to pass on as graphviz properties. """ g_options = self.default_graph_options.copy() g_options.update(graph_options) n_options = self.default_node_options.copy() n_options.update(node_options) e_options = self.default_edge_options.copy() e_options.update(edge_options) fd.write('digraph %s {\n' % name) fd.write(self._format_graph_options(g_options)) for cls in self.all_classes: if not self.show_builtins and cls in __builtins__.values(): continue name = self.class_name(cls, parts) # Write the node this_node_options = n_options.copy() url = urls.get(self.class_name(cls)) if url is not None: this_node_options['URL'] = '"%s"' % url fd.write(' "%s" [%s];\n' % (name, self._format_node_options(this_node_options))) # Write the edges for base in cls.__bases__: if not self.show_builtins and base in __builtins__.values(): continue base_name = self.class_name(base, parts) fd.write(' "%s" -> "%s" [%s];\n' % (base_name, name, self._format_node_options(e_options))) fd.write('}\n') def run_dot(self, args, name, parts=0, urls={}, graph_options={}, node_options={}, edge_options={}): """ Run graphviz 'dot' over this graph, returning whatever 'dot' writes to stdout. *args* will be passed along as commandline arguments. *name* is the name of the graph *urls* is a dictionary mapping class names to http urls Raises DotException for any of the many os and installation-related errors that may occur. """ try: dot = subprocess.Popen(['dot'] + list(args), stdin=subprocess.PIPE, stdout=subprocess.PIPE, close_fds=True) except OSError: raise DotException("Could not execute 'dot'. Are you sure you have 'graphviz' installed?") except ValueError: raise DotException("'dot' called with invalid arguments") except: raise DotException("Unexpected error calling 'dot'") self.generate_dot(dot.stdin, name, parts, urls, graph_options, node_options, edge_options) dot.stdin.close() result = dot.stdout.read() returncode = dot.wait() if returncode != 0: raise DotException("'dot' returned the errorcode %d" % returncode) return result class inheritance_diagram(Body, Element): """ A docutils node to use as a placeholder for the inheritance diagram. """ pass def inheritance_diagram_directive(name, arguments, options, content, lineno, content_offset, block_text, state, state_machine): """ Run when the inheritance_diagram directive is first encountered. """ node = inheritance_diagram() class_names = arguments # Create a graph starting with the list of classes graph = InheritanceGraph(class_names) # Create xref nodes for each target of the graph's image map and # add them to the doc tree so that Sphinx can resolve the # references to real URLs later. These nodes will eventually be # removed from the doctree after we're done with them. for name in graph.get_all_class_names(): refnodes, x = xfileref_role( 'class', ':class:`%s`' % name, name, 0, state) node.extend(refnodes) # Store the graph object so we can use it to generate the # dot file later node['graph'] = graph # Store the original content for use as a hash node['parts'] = options.get('parts', 0) node['content'] = " ".join(class_names) return [node] def get_graph_hash(node): return md5(node['content'] + str(node['parts'])).hexdigest()[-10:] def html_output_graph(self, node): """ Output the graph for HTML. This will insert a PNG with clickable image map. """ graph = node['graph'] parts = node['parts'] graph_hash = get_graph_hash(node) name = "inheritance%s" % graph_hash path = '_images' dest_path = os.path.join(setup.app.builder.outdir, path) if not os.path.exists(dest_path): os.makedirs(dest_path) png_path = os.path.join(dest_path, name + ".png") path = setup.app.builder.imgpath # Create a mapping from fully-qualified class names to URLs. urls = {} for child in node: if child.get('refuri') is not None: urls[child['reftitle']] = child.get('refuri') elif child.get('refid') is not None: urls[child['reftitle']] = '#' + child.get('refid') # These arguments to dot will save a PNG file to disk and write # an HTML image map to stdout. image_map = graph.run_dot(['-Tpng', '-o%s' % png_path, '-Tcmapx'], name, parts, urls) return ('%s' % (path, name, name, image_map)) def latex_output_graph(self, node): """ Output the graph for LaTeX. This will insert a PDF. """ graph = node['graph'] parts = node['parts'] graph_hash = get_graph_hash(node) name = "inheritance%s" % graph_hash dest_path = os.path.abspath(os.path.join(setup.app.builder.outdir, '_images')) if not os.path.exists(dest_path): os.makedirs(dest_path) pdf_path = os.path.abspath(os.path.join(dest_path, name + ".pdf")) graph.run_dot(['-Tpdf', '-o%s' % pdf_path], name, parts, graph_options={'size': '"6.0,6.0"'}) return '\n\\includegraphics{%s}\n\n' % pdf_path def visit_inheritance_diagram(inner_func): """ This is just a wrapper around html/latex_output_graph to make it easier to handle errors and insert warnings. """ def visitor(self, node): try: content = inner_func(self, node) except DotException, e: # Insert the exception as a warning in the document warning = self.document.reporter.warning(str(e), line=node.line) warning.parent = node node.children = [warning] else: source = self.document.attributes['source'] self.body.append(content) node.children = [] return visitor def do_nothing(self, node): pass def setup(app): setup.app = app setup.confdir = app.confdir app.add_node( inheritance_diagram, latex=(visit_inheritance_diagram(latex_output_graph), do_nothing), html=(visit_inheritance_diagram(html_output_graph), do_nothing)) app.add_directive( 'inheritance-diagram', inheritance_diagram_directive, False, (1, 100, 0), parts = directives.nonnegative_int) pymvpa-0.4.8/doc/todo.rst000077700000000000000000000000001174541445200162102../TODOustar00rootroot00000000000000pymvpa-0.4.8/doc/workshops/000077500000000000000000000000001174541445200156525ustar00rootroot00000000000000pymvpa-0.4.8/doc/workshops/2009-fall.rst000066400000000000000000000325211174541445200177150ustar00rootroot00000000000000.. -*- mode: rst; fill-column: 78 -*- .. ex: set sts=4 ts=4 sw=4 et tw=79: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### .. _chap_workshop_2009fall: ******************************** PyMVPA Extravaganza -- Fall 2009 ******************************** This development workshop took take place at Dartmouth College, Nov 30 -- Dec 4, 2009. Goals ===== The primary purpose of this first PyMVPA workshop was to gather all people involved in (or related to) the development of PyMVPA. Participants introduced their projects and discussed their integration into, or interoperation with the PyMVPA main line. In addition, we discussed changes scheduled for the upcoming 0.5 release of PyMVPA that are supposed to improve shortcomings of the original design, or missing features that have been identified over the past two years. These include: * More flexible data storage: A new dataset implementation. * Better integration of PyMVPA and MDP_. * Establishing an optimization framework within PyMVPA. * Various performance improvements, e.g. kernel-caching, parallelization, potential of CUDA. .. _MDP: http://mdp-toolkit.sourceforge.net/ Participants ============ * Satrajit Ghosh, MIT, USA (for the kick-off talks) * Scott Gorlin, MIT, USA * Valentin Haenel, BCCN, Germany * Yaroslav O. Halchenko, Dartmouth College, USA * Michael Hanke, Dartmouth College, USA * Emanuele Olivetti, Fondazione Bruno Kessler, Italy * Per B. Sederberg, Princeton University, USA (virtual) * Tiziano Zito, BCCN, Germany Kick-off Talks ============== The workshop started on *Monday Nov 30th at 9:30am* with a series of talks covering the various aspects of the workshop (abstracts below). PyMVPA: Where we are now, and where we are going ------------------------------------------------ Yaroslav O. Halchenko, Michael Hanke This talk will give a brief summary of our original concept of PyMVPA that we had in mind when designing it two years ago, and how the project evolved since then. We will touch upon several issues we had to face concerning development, quality assurance, and deployment. While the latest PyMVPA release offers a wide array of tools and algorithms, we also identified a number of problems that limit further integration of novel techniques into the framework. The talk will conclude with an outline how we believe these issues can be resolved and introduces a number of improvements that will become available with the next milestone release: PyMVPA **0.5**. MDP inside out -------------- Tiziano Zito MDP is a Python collection of machine learning algorithms and a framework for implementing new algorithms and combining them into data processing workflows. MDP has been designed around two main ideas: expose a simple API, to allow scientific users to use it as a standalone library, and organize the internal structure of the objects to encourage developers to extend it and embed it in other libraries such as PyMVPA. In my talk, I will use MDP as a starting point to hash over some basic principles of scientific software design. I will discuss the criteria that inform the design of MDP and their specific implementation, and examine their advantages, limitations and possible alternatives. I will conclude with a summary of the current status and future plans for MDP development. Nipype - A Python framework for neuroimaging -------------------------------------------- Satrajit Ghosh Nipype is a project under the umbrella of Nipy, an effort to develop open-source, community-developed neuroimaging tools in Python. The goals of Nipype are two-fold: 1) to provide a uniform interface to existing neuroimaging software packages; and 2) to provide a pipelined environment for efficient batch-processing that can tie together different neuroimaging data analysis algorithms. By exposing a consistent interface to the external packages, researchers are able to explore a wide range of imaging algorithms and configure their own analysis pipeline which best fits their data and research objectives, and perform their analysis in a highly structured environment. The nipype framework is accessible to the wide range of programming expertise often found in neuroimaging, allowing for both easy-to-use high-level scripting and low-level algorithm development for unlimited customization. Profiling PyMVPA ---------------- Valentin Haenel In this talk I will present the work we did to compare the PyMVPA and Matlab implementations of the searchlight algorithm. This will include a description of how we iteratively discovered various bottlenecks and the steps taken to eliminate these. In particular, I will first present modifications of the source code and then show the resulting change in profiler output. I may conclude with some ideas for future work and some additional remarks about optimization in general. Supervised Tract Segmentation ----------------------------- Emanuele Olivetti Automatic segmentation of tractography data into pathways/tracts is a problem traditionally addressed by means of unsupervised techniques, i.e., clustering streamlines. The core of this work is to adopt instead a supervised approach, learning from the segmentation made by an expert neuroanatomist in order to predict tracts in new brains. In this talk a novel set of supervised approaches to the tract segmentation problem will be illustrated. The proposed solutions are based on machine learning topics like "supervised clustering", "learning with similarity functions" and "transduction". These solutions allow to exploit both diffusion and functional MRI data, to avoid co-registration between different subjects and to predict tracts in hemispheres different from the training example. Preliminary results support these claims. An intended goal of this talk is to open a discussion on how to map the building blocks of the proposed methods into the PyMVPA framework in order to support tractography data analysis natively and, more in general, to provide novel machine learning approaches to the users. Caching kernels --------------- Scott Gorlin A major bottleneck in a standard classification analysis relies on calculating the dot product between vectors in high-dimensional space. This is especially time consuming when there are few samples but the number of dimensions is high, such as the case of fMRI data. In fact, many common analysis techniques such as cross validation, bootstrapping, and model selection require that the kernel be recalculated for each permutation, even if that exact calculation has been done before. This presentation analyzes the problem inherent in a high-level library such as PyMVPA and illustrates one example of how to cache and reuse kernels, greatly simplifying the underlying computations and accelerating many analytical technique implementations by several orders of magnitude. Workshop Results ================ The workshop has been a huge success. We worked on further integrating PyMVPA with other Python-based software packages, both to make use of them inside PyMVPA, but also to better expose PyMVPA's functionality to other packages. The kick-off talks were followed by four days of intensive coding. During these days we were able to integrate virtually all outstanding patches that have been offered over the last year, but could not be merged yet due to required changes in the codebase. Below is a list of projects that we have been working on during the workshop. Moreover, we were able to continue the transition towards the new dataset implementation that had been started prior to the workshop. A significant number of additional unittest has been ported to the new code -- as usual identifying and fixing a number of bugs. .. figure:: ../pics/extravaganza-dc09.jpg Workshop participants (from left to right and top to bottom): Emanuele Olivetti, Scott Gorlin, Michael Hanke, Tiziano Zito, Yaroslav O. Halchenko, Valentin Haenel Grand Kernel Unification ------------------------ Scott Gorlin, Yaroslav O. Halchenko, and Emanuele Olivetti Many core MVPA algorithms rely on expensive kernel computations. However, most of these algorithms have their own naming standards and backend implementations which are not interchangeable, meaning that new advances in kernel logic or software implementations are not generally beneficial to PyMVPA as a whole. To solve this, we have implemented a new class hierarchy which not only specifies a standard kernel interface, but also allows the automatic translation of kernels from one software backend to another. Specifically, it is now possible to specify new kernel classes in pure Python (or any method which can expose a Numpy array, such as PyCUDA or custom C) and automatically convert these back and forth to Shogun kernels. This has the immediate advantage of allowing custom kernels for any Shogun-based classifier (e.g. SVM), using Shogun kernels for fast computation in any other solver (e.g., GPR), or the automatic exchange of kernels for any implementation in the future. Cached Kernel Optimization -------------------------- Scott Gorlin, Yaroslav O. Halchenko The main benefit of the kernel unification work is that new kernel classes can be specified in pure Python. A new optimized kernel class we have implemented is a CachedKernel which can automatically cache and reuse kernel matrices from any other NumpyKernel (or any kernel which is convertible to Numpy, such as a Shogun kernel). This class will prove extremely useful for techniques such as cross-validation, bootstrapping, etc, where the kernel product is normally recalculated every time it is computed - e.g., every time SVM.train(...) is called. Caching the kernel will avoid these expensive computations and greatly speed up this type of analysis by several orders of magnitude. Flexible, straightforward adaptor for arbitrary MDP nodes and flows ------------------------------------------------------------------- Michael Hanke, Tiziano Zito Although previously PyMVPA used MDP to provide a subset of its functionality through Mappers, this was limited to single nodes (e.g. PCA, ICA) and was not meant to be extended by users (except for subclassing and writing a new node wrapper by hand). Now, PyMVPA included flexible adaptors for arbitrary MDP nodes, or whole MDP flows. Besides incremental training, these adaptors offer access to the full functionality of the underlying node or flow. Straightforward (single-line of code) wrapping allows to seamlessly blend MDP into PyMVPA. The benefits are two-fold: PyMVPA users have now access to the full functionality of MDP without having to develop custom mappers. This includes algorithms, such as PCA, ICA, factor analysis, discriminant analysis, slow feature analysis, or restricted Boltzmann machines, and many more. MDP users can now use PyMVPA to perform convenient cross-validation of classification procedures with arbitrary mixes of PyMVPA classifiers and measure and MDP nodes, and flows. Non-matrix Dataset and prototype mapper for tractography data (and more!) ------------------------------------------------------------------------- Emanuele Olivetti, Michael Hanke The vast majority of algorithms available (and desirable) in PyMVPA requires data in a 2D matrix format. For this reason, until now, PyMVPA accepted only 2D matrices as samples in a Dataset. However, sometimes this causes problems, for example, with tractography data. That consists of a set of streamlines, a streamline being a polyline made of a non-constant number of points. In PyMVPA terms it means that the number of features in the corresponding dataset of streamlines would be different across instances. The purpose of a set of patches made during the workshop is twofold: first to allow PyMVPA to accept also row-wise iterable collections as a Dataset independently of the content of each row and second to provide a mapper to transform these every kind of Dataset into 2D matrix Dataset. The mapper is prototype-based which means that each instance within the Dataset (e.g., each streamline) is mapped into a fixed size M-dimensional vector. The M values are computed by specifying a similarity (or kernel, or distance) function which evaluates the distance of that instance against a given set of other M instances (e.g., other M streamlines) called *prototypes*. An example application is supervised tract segmentation from tractography data which now can be mapped into a standard binary classification problem over the usual 2D matrix class-labeled dataset. This approach to adress varying features-space sizes is flexible, and not limited to the tractography domain. Optimization and Generalization of Searchlight-analyses ------------------------------------------------------- Valentin Haenel, Michael Hanke The searchlight analysis code has been ported to the new dataset/mapper framework, taking into account the result of a profiling analysis done by Valentin Haenel during the last year. The new code avoids significant look-up penalties of the previous implementation. Moreover, it has been generalized to support arbitrary look-up algorithms (e.g. kd-tree_) and is no longer limited to sphere-based spatial searchlights. .. _kd-tree: http://en.wikipedia.org/wiki/Kd-tree Acknowledgements ================ We are grateful to Prof. James Haxby for sponsoring this workshop and hosting it in his lab. pymvpa-0.4.8/mvpa/000077500000000000000000000000001174541445200140115ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/__init__.py000066400000000000000000000075731174541445200161360ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """MultiVariate Pattern Analysis Package Organization ==================== The mvpa package contains the following subpackages and modules: .. packagetree:: :style: UML :group Algorithms: algorithms :group Anatomical Atlases: atlases :group Basic Data Structures: datasets :group Classifiers (supervised learners): clfs :group Feature Selections: featsel :group Mappers (usually unsupervised learners): mappers :group Measures: measures :group Miscellaneous: base misc support :group Unittests: tests :author: `Michael Hanke `__, `Yaroslav Halchenko `__, `Per B. Sederberg `__ :requires: Python 2.4+ :version: 0.4.8 :see: `The PyMVPA webpage `__ :see: `GIT Repository Browser `__ :license: The MIT License :copyright: |copy| 2006-2010 Michael Hanke :copyright: |copy| 2007-2010 Yaroslav O. Halchenko :newfield contributor: Contributor, Contributors (Alphabetical Order) :contributor: `Emanuele Olivetti `__ :contributor: `Per B. Sederberg `__ .. |copy| unicode:: 0xA9 .. copyright sign """ __docformat__ = 'restructuredtext' # canonical PyMVPA version string __version__ = '0.4.8' import os import random import numpy as N from mvpa.base import cfg from mvpa.base import externals from mvpa.base.info import wtf # locate data root -- data might not be installed, but if it is, it should be at # this location pymvpa_dataroot = os.path.join(os.path.dirname(__file__), 'data') if not __debug__: try: import psyco psyco.profile() except ImportError: from mvpa.base import verbose verbose(2, "Psyco online compilation is not enabled") else: # Controllable seeding of random number generator from mvpa.base import debug debug('INIT', 'mvpa') if cfg.has_option('general', 'seed'): _random_seed = cfg.getint('general', 'seed') else: _random_seed = int(N.random.uniform()*(2**31-1)) def seed(random_seed): """Uniform and combined seeding of all relevant random number generators. """ N.random.seed(random_seed) random.seed(random_seed) seed(_random_seed) # import the main unittest interface from mvpa.tests import run as test # PyMVPA is useless without numpy # Also, this check enforcing population of externals.versions # for possible later version checks, hence don't remove externals.exists('numpy', force=True, raiseException=True) # We might need to suppress the warnings so enforcing check here, # it is ok if it would fail externals.exists('scipy', force=True, raiseException=False) if __debug__: debug('RANDOM', 'Seeding RNG with %d' % _random_seed) debug('INIT', 'mvpa end') # Attach custom top-level exception handler if cfg.getboolean('debug', 'wtf', default=False): import sys _sys_excepthook = sys.excepthook def _pymvpa_excepthook(*args): """Custom exception handler to report also pymvpa's wtf Calls original handler, and then collects WTF and spits it out """ ret = _sys_excepthook(*args) sys.stdout.write("PyMVPA's WTF: collecting information... hold on...") sys.stdout.flush() wtfs = wtf() sys.stdout.write("\rPyMVPA's WTF: \n") sys.stdout.write(str(wtfs)) return ret sys.excepthook = _pymvpa_excepthook pymvpa-0.4.8/mvpa/algorithms/000077500000000000000000000000001174541445200161625ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/algorithms/__init__.py000066400000000000000000000010631174541445200202730ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Import helper for PyMVPA algorithms.""" if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.algorithms') if __debug__: debug('INIT', 'mvpa.algorithms end') pymvpa-0.4.8/mvpa/algorithms/cvtranserror.py000066400000000000000000000234111174541445200212670ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Cross-validate a classifier on a dataset""" __docformat__ = 'restructuredtext' from mvpa.support.copy import deepcopy from mvpa.measures.base import DatasetMeasure from mvpa.datasets.splitters import NoneSplitter from mvpa.base import warning from mvpa.misc.state import StateVariable, Harvestable from mvpa.misc.transformers import GrandMean if __debug__: from mvpa.base import debug class CrossValidatedTransferError(DatasetMeasure, Harvestable): """Classifier cross-validation. This class provides a simple interface to cross-validate a classifier on datasets generated by a splitter from a single source dataset. Arbitrary performance/error values can be computed by specifying an error function (used to compute an error value for each cross-validation fold) and a combiner function that aggregates all computed error values across cross-validation folds. """ results = StateVariable(enabled=False, doc= """Store individual results in the state""") splits = StateVariable(enabled=False, doc= """Store the actual splits of the data. Can be memory expensive""") transerrors = StateVariable(enabled=False, doc= """Store copies of transerrors at each step. If enabled - operates on clones of transerror, but for the last split original transerror is used""") confusion = StateVariable(enabled=False, doc= """Store total confusion matrix (if available)""") training_confusion = StateVariable(enabled=False, doc= """Store total training confusion matrix (if available)""") samples_error = StateVariable(enabled=False, doc="Per sample errors.") def __init__(self, transerror, splitter=None, combiner='mean', expose_testdataset=False, harvest_attribs=None, copy_attribs='copy', **kwargs): """ :Parameters: transerror: TransferError instance Provides the classifier used for cross-validation. splitter: Splitter | None Used to split the dataset for cross-validation folds. By convention the first dataset in the tuple returned by the splitter is used to train the provided classifier. If the first element is 'None' no training is performed. The second dataset is used to generate predictions with the (trained) classifier. If `None` (default) an instance of :class:`~mvpa.datasets.splitters.NoneSplitter` is used. combiner: Functor | 'mean' Used to aggregate the error values of all cross-validation folds. If 'mean' (default) the grand mean of the transfer errors is computed. expose_testdataset: bool In the proper pipeline, classifier must not know anything about testing data, but in some cases it might lead only to marginal harm, thus migth wanted to be enabled (provide testdataset for RFE to determine stopping point). harvest_attribs: list of basestr What attributes of call to store and return within harvested state variable copy_attribs: None | basestr Force copying values of attributes on harvesting **kwargs: All additional arguments are passed to the :class:`~mvpa.measures.base.DatasetMeasure` base class. """ DatasetMeasure.__init__(self, **kwargs) Harvestable.__init__(self, harvest_attribs, copy_attribs) if splitter is None: self.__splitter = NoneSplitter() else: self.__splitter = splitter if combiner == 'mean': self.__combiner = GrandMean else: self.__combiner = combiner self.__transerror = transerror self.__expose_testdataset = expose_testdataset # TODO: put back in ASAP # def __repr__(self): # """String summary over the object # """ # return """CrossValidatedTransferError / # splitter: %s # classifier: %s # errorfx: %s # combiner: %s""" % (indentDoc(self.__splitter), indentDoc(self.__clf), # indentDoc(self.__errorfx), indentDoc(self.__combiner)) def _call(self, dataset): """Perform cross-validation on a dataset. 'dataset' is passed to the splitter instance and serves as the source dataset to generate split for the single cross-validation folds. """ # store the results of the splitprocessor results = [] self.states.splits = [] # local bindings states = self.states clf = self.__transerror.clf expose_testdataset = self.__expose_testdataset # what states to enable in terr terr_enable = [] for state_var in ['confusion', 'training_confusion', 'samples_error']: if states.isEnabled(state_var): terr_enable += [state_var] # charge states with initial values summaryClass = clf._summaryClass clf_hastestdataset = hasattr(clf, 'testdataset') self.states.confusion = summaryClass() self.states.training_confusion = summaryClass() self.states.transerrors = [] self.states.samples_error = dict([(id, []) for id in dataset.origids]) # enable requested states in child TransferError instance (restored # again below) if len(terr_enable): self.__transerror.states._changeTemporarily( enable_states=terr_enable) # We better ensure that underlying classifier is not trained if we # are going to deepcopy transerror if states.isEnabled("transerrors"): self.__transerror.untrain() # splitter for split in self.__splitter(dataset): # only train classifier if splitter provides something in first # element of tuple -- the is the behavior of TransferError if states.isEnabled("splits"): self.states.splits.append(split) if states.isEnabled("transerrors"): # copy first and then train, as some classifiers cannot be copied # when already trained, e.g. SWIG'ed stuff lastsplit = None for ds in split: if ds is not None: lastsplit = ds._dsattr['lastsplit'] break if lastsplit: # only if we could deduce that it was last split # use the 'mother' transerror transerror = self.__transerror else: # otherwise -- deep copy transerror = deepcopy(self.__transerror) else: transerror = self.__transerror # assign testing dataset if given classifier can digest it if clf_hastestdataset and expose_testdataset: transerror.clf.testdataset = split[1] # run the beast result = transerror(split[1], split[0]) # unbind the testdataset from the classifier if clf_hastestdataset and expose_testdataset: transerror.clf.testdataset = None # next line is important for 'self._harvest' call self._harvest(locals()) # XXX Look below -- may be we should have not auto added .? # then transerrors also could be deprecated if states.isEnabled("transerrors"): self.states.transerrors.append(transerror) # XXX: could be merged with next for loop using a utility class # that can add dict elements into a list if states.isEnabled("samples_error"): for k, v in \ transerror.states.samples_error.iteritems(): self.states.samples_error[k].append(v) # pull in child states for state_var in ['confusion', 'training_confusion']: if states.isEnabled(state_var): states[state_var].value.__iadd__( transerror.states[state_var].value) if __debug__: debug("CROSSC", "Split #%d: result %s" \ % (len(results), `result`)) results.append(result) # Since we could have operated with a copy -- bind the last used one back self.__transerror = transerror # put states of child TransferError back into original config if len(terr_enable): self.__transerror.states._resetEnabledTemporarily() self.states.results = results """Store state variable if it is enabled""" # Provide those labels_map if appropriate try: if states.isEnabled("confusion"): states.confusion.labels_map = dataset.labels_map if states.isEnabled("training_confusion"): states.training_confusion.labels_map = dataset.labels_map except: pass return self.__combiner(results) splitter = property(fget=lambda self:self.__splitter, doc="Access to the Splitter instance.") transerror = property(fget=lambda self:self.__transerror, doc="Access to the TransferError instance.") combiner = property(fget=lambda self:self.__combiner, doc="Access to the configured combiner.") pymvpa-0.4.8/mvpa/algorithms/hyperalignment.py000066400000000000000000000057211174541445200215670ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Hyperalignment of functional data to the common space References: TODO... see SMLR code for example on how to embed the reference so in future it gets properly referenced... """ __docformat__ = 'restructuredtext' from mvpa.support.copy import deepcopy from mvpa.base import warning from mvpa.misc.state import StateVariable, ClassWithCollections from mvpa.misc.param import Parameter from mvpa.misc.transformers import GrandMean if __debug__: from mvpa.base import debug class Hyperalignment(ClassWithCollections): """ ... Given a set of datasets (may be just data) provide mapping of features into a common space """ # May be something we might store optionally upon user request who_knows_maybe_something_to_store_optionally = \ StateVariable(enabled=False, doc= """....""") # Lets use built-in facilities to specify parameters which # constructor should accept alignment = Parameter(None, # might provide allowedtype later on doc="""... XXX If `None` (default) an instance of :class:`~mvpa.mappers.procrustean.ProcrusteanMapper` is used.""") levels = Parameter(3, allowedtype='int', min=1, doc="Number of levels ....XXX ") combiner1 = Parameter('mean', # doc="XXX ") combiner2 = Parameter('mean', # doc="XXX ") def __init__(self, alignment=None, levels=3, combiner1='mean', combiner2='mean', **kwargs): ClassWithCollections.__init__(self, **kwargs) if self.params.alignment == None: self.params.alignment = ProcrusteanMapper() raise NotImlementedError, "WiP! Use development branch of version >= 0.5.0" def __call__(self, data): """Estimate mappers for each data(set) Parameters ---------- data : list or tuple of dataset of data XXX Returns ------- A list of trained Mappers ... of length equal to len(data) """ params = self.params # for quicker access ;) nelements = len(data) # might prefer some other way to initialize... later result = [deepcopy(params.alignment) for i in xrange(nelements)] # Level 1 commonspace = data[0] for m, d in zip(mappers[1:], data[1:]): # XXX For now lets just call this way: m.train(d, commonspace) commonspace = mean(m.forward(d), commonspace)# here yarik stopped ;) # Level 2 to params.levels return result pymvpa-0.4.8/mvpa/atlases/000077500000000000000000000000001174541445200154455ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/atlases/__init__.py000066400000000000000000000020261174541445200175560ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Import helper for PyMVPA anatomical atlases Module Organization =================== mvpa.atlases module contains support for various atlases .. packagetree:: :style: UML :group Base Implementations: base :group Atlases from FSL: fsl :group Helpers: warehouse transformation """ __docformat__ = 'restructuredtext' if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.atlases') from mvpa.atlases.base import LabelsAtlas, ReferencesAtlas, XMLAtlasException from mvpa.atlases.fsl import FSLProbabilisticAtlas from mvpa.atlases.warehouse import Atlas, KNOWN_ATLASES, KNOWN_ATLAS_FAMILIES if __debug__: debug('INIT', 'mvpa.atlases end') pymvpa-0.4.8/mvpa/atlases/base.py000066400000000000000000000650601174541445200167400ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Base classes for Anatomy atlases support TODOs: ====== * major optimization. Now code is sloppy and slow -- plenty of checks etc Module Organization =================== mvpa.atlases.base module contains support for various atlases .. packagetree:: :style: UML :group Base: BaseAtlas XMLBasedAtlas Label Level LabelsLevel :group Talairach: PyMVPAAtlas LabelsAtlas ReferencesAtlas :group Exceptions: XMLAtlasException """ from mvpa.base import externals if externals.exists('lxml', raiseException=True): from lxml import etree, objectify from mvpa.base.dochelpers import enhancedDocString import os, re import numpy as N from numpy.linalg import norm from mvpa.atlases.transformation import SpaceTransformation, Linear from mvpa.misc.support import reuseAbsolutePath if externals.exists('nifti', raiseException=True): from nifti import NiftiImage from mvpa.base import warning if __debug__: from mvpa.base import debug def checkRange(coord, range): """ Check if coordinates are within range (0,0,0) - (range) Return True on success """ # TODO: optimize if len(coord) != len(range): raise ValueError("Provided coordinate %s and given range %s" % \ (`coord`, `range`) + \ " have different dimensionality" ) for c,r in zip(coord, range): if c<0 or c>=r: return False return True class BaseAtlas(object): """Base class for the atlases. """ def __init__ (self): """ Create an atlas object based on the... XXX """ self.__name = "blank" # XXX use or remove class XMLAtlasException(Exception): """ Exception to be thrown if smth goes wrong dealing with XML based atlas """ def __init__(self, msg=""): self.__msg = msg def __repr__(self): return self.__msg class XMLBasedAtlas(BaseAtlas): def __init__(self, filename=None, resolution=None, image_file=None, query_voxel=False, coordT=None, levels=None): """ :Parameters: filename : string Filename for the xml definition of the atlas resolution : None or float Some atlases link to multiple images at different resolutions. if None -- best resolution is selected using 0th dimension resolution image_file : None or str If None, overrides filename for the used imagefile, so it could load a custom (re-registered) atlas maps query_voxel : bool By default [x,y,z] assumes coordinates in space, but if query_voxel is True, they are assumed to be voxel coordinates coordT Optional transformation to apply first levels : None or slice or list of int What levels by default to operate on """ BaseAtlas.__init__(self) self.__version = None self.__type = None # XXX use or remove self._image_file = None self.__atlas = None self._filename = filename self._resolution = resolution self._force_image_file = image_file self.query_voxel = query_voxel self.levels = levels if filename: self.loadAtlas(filename) # common sanity checks if not self._checkVersion(self.version): raise IOError("Version %s is not recognized to be native to class %s" % \ (self.version, self.__name__)) if not set(['header', 'data']) == set([i.tag for i in self.getchildren()]): raise IOError("No header or data were defined in %s" % filename) header = self.header headerChildrenTags = XMLBasedAtlas._children_tags(header) if not ('images' in headerChildrenTags) or \ not ('imagefile' in XMLBasedAtlas._children_tags(header.images)): raise XMLAtlasException("Atlas requires image/imagefile header fields") # Load and post-process images self._image = None self._loadImages() if self._image is not None: self._extent = N.abs(N.asanyarray(self._image.extent[0:3])) self._voxdim = N.asanyarray(self._image.voxdim) self.relativeToOrigin = True # Assign transformation to get into voxel coordinates, # spaceT will be set accordingly self.setCoordT(coordT) self._loadData() def _checkRange(self, c): """ check and adjust the voxel coordinates""" # check range # list(c) for consistent appearance... some times c might be ndarray if __debug__: debug('ATL__', "Querying for voxel %s" % `list(c)`) if not checkRange(c, self.extent): msg = "Coordinates %s are not within the extent %s." \ "Reset to (0,0,0)" % ( `c`, `self.extent` ) if __debug__: debug('ATL_', msg) # assume that voxel [0,0,0] is blank c = [0]*3; return c @staticmethod def _checkVersion(version): """To be overriden in the derived classes. By default anything is good""" return True def _loadImages(self): """To be overriden in the derived classes. By default does nothing""" pass def _loadData(self): """To be overriden in the derived classes. By default does nothing""" pass def loadAtlas(self, filename): if __debug__: debug('ATL_', "Loading atlas definition xml file " + filename) # Create objectify parser first parser = etree.XMLParser(remove_blank_text=True) lookup = objectify.ObjectifyElementClassLookup() parser.setElementClassLookup(lookup) try: self.__atlas = etree.parse(filename, parser).getroot() except IOError: raise XMLAtlasException("Failed to load XML file %s" % filename) @property def version(self): if not self.__atlas is None \ and ("version" in self.__atlas.attrib.keys()): return self.__atlas.get("version") else: return None @staticmethod def _compare_lists(checkitems, neededitems): raise RuntimeError, "DEPRECATED _compare_lists" checkitems.sort() neededitems.sort() return (checkitems == neededitems) @staticmethod def _children_tags(root): return [i.tag for i in root.getchildren()] def __getattr__(self, attr): """ Lazy way to provide access to the definitions in the atlas """ if not self.__atlas is None: return getattr(self.__atlas, attr) else: raise XMLAtlasException("Atlas in " + self.__name__ + " was not read yet") def setCoordT(self, coordT): """Set coordT transformation. spaceT needs to be adjusted since we glob those two transformations together """ self._coordT = coordT # lets store for debugging etc if self._image is not None: # Combine with the image's qform coordT = Linear(N.linalg.inv(self._image.qform), previous=coordT) self._spaceT = SpaceTransformation( previous=coordT, toRealSpace=False ) def labelPoint(self, coord, levels=None): """Return labels for the given spatial point at specified levels Function takes care about first transforming the point into the voxel space :Parameters: coord : tuple Coordinates of the point (xyz) levels : None or list of int At what levels to return the results """ coord_ = N.asarray(coord) # or we would alter what should be constant #if not isinstance(coord, N.numpy): #c = self.getVolumeCoordinate(coord) #c = self.spaceT.toVoxelSpace(coord_) #if self.coordT: # coord_t = self.coordT[coord_] #else: # coord_t = coord_ c = self.spaceT(coord_) result = self.labelVoxel(c, levels) result['coord_queried'] = coord #result['coord_trans'] = coord_t result['voxel_atlas'] = c return result def levelsListing(self): lkeys = range(self.Nlevels) return '\n'.join(['%d: ' % k + str(self._levels_dict[k]) for k in lkeys]) def _getLevels(self, levels=None): """Helper to provide list of levels to operate on Depends on given `levels` as well as self.levels """ if levels is None: levels = [ i for i in xrange(self.Nlevels) ] elif (isinstance(levels, slice)): # levels are given as a range if levels.step: step = levels.step else: step = 1 if levels.start: start = levels.start else: start = 0 if levels.stop: stop = levels.stop else: stop = self.Nlevels levels = [ i for i in xrange(start, stop, step) ] elif isinstance(levels, list) or isinstance(levels, tuple): # levels given as list levels = list(levels) elif isinstance(levels, int): levels = [ levels ] else: raise TypeError('Given levels "%s" are of unsupported type' % `levels`) # test given values levels_dict = self.levels_dict for level in levels: if not level in levels_dict: raise ValueError, \ "Levels %s is not known (out of range?). Known levels are:\n%s" \ % (level, self.levelsListing()) return levels def __getitem__(self, index): """ Accessing the elements via simple indexing. Examples: print atlas[ 0, -7, 20, [1,2,3] ] print atlas[ (0, -7, 20), 1:2 ] print atlas[ (0, -7, 20) ] print atlas[ (0, -7, 20), : ] """ if len(index) in [2, 4]: levels_slice = index[-1] else: if self.levels is None: levels_slice = slice(None,None,None) else: levels_slice = self.levels levels = self._getLevels(levels=levels_slice) if len(index) in [3, 4]: # we got coordinates 1 by 1 + may be a level coord = index[0:3] elif len(index) in [1, 2]: coord = index[0] if isinstance(coord, list) or isinstance(coord, tuple): if len(coord) != 3: raise TypeError("Given coordinates must be in 3D") else: raise TypeError("Given coordinates must be a list or a tuple") else: raise TypeError("Unknown shape of parameters `%s`" % `index`) if self.query_voxel: return self.labelVoxel(coord, levels) else: return self.labelPoint(coord, levels) # REDO in some sane fashion so referenceatlas returns levels for the base def _getLevelsDict(self): return self._getLevelsDict_virtual() def _getLevelsDict_virtual(self): return self._levels_dict levels_dict = property(fget=_getLevelsDict) origin = property(fget=lambda self:self._origin) extent = property(fget=lambda self:self._extent) voxdim = property(fget=lambda self:self._voxdim) spaceT = property(fget=lambda self:self._spaceT) coordT = property(fget=lambda self:self._spaceT, fset=setCoordT) class Label(object): """Represents a label. Just to bring all relevant information together """ def __init__ (self, text, abbr=None, coord=(None, None,None), count=0, index=0): """ :Parameters: text : basestring fullname of the label abbr : basestring abbreviated name (optional) coord : tuple of float coordinates (optional) count : int count of those labels in the atlas (optional) """ self.__text = text.strip() if abbr is not None: abbr = abbr.strip() self.__abbr = abbr self.__coord = coord self.__count = count self.__index = int(index) @property def index(self): return self.__index def __repr__(self): return "Label(%s%s, coord=(%s, %s, %s), count=%s, index=%s)" % \ ((self.__text, (', abbr=%s' % repr(self.__abbr), '')[int(self.__abbr is None)]) + tuple(self.__coord) + (self.__count, self.__index)) def __str__(self): return self.__text @staticmethod def generateFromXML(Elabel): kwargs = {} if Elabel.attrib.has_key('x'): kwargs['coord'] = ( Elabel.attrib.get('x'), Elabel.attrib.get('y'), Elabel.attrib.get('z') ) for l in ('count', 'abbr', 'index'): if Elabel.attrib.has_key(l): kwargs[l] = Elabel.attrib.get(l) return Label(Elabel.text.strip(), **kwargs) @property def count(self): return self.__count @property def coord(self): return self.__coord @property def text(self): return self.__text @property def abbr(self): """Returns abbreviated version if such is available """ if self.__abbr in [None, ""]: return self.__text else: return self.__abbr class Level(object): """Represents a level. Just to bring all relevant information together """ def __init__ (self, description): self.description = description self._type = "Base" def __repr__(self): return "%s Level: %s" % \ (self.levelType, self.description) def __str__(self): return self.description @staticmethod def generateFromXML(Elevel, levelType=None): """ Simple factory of levels """ if levelType is None: if not Elevel.attrib.has_key("type"): raise XMLAtlasException("Level must have type specified. Level: " + `Elevel`) levelType = Elevel.get("type") levelTypes = { 'label': LabelsLevel, 'reference': ReferencesLevel } if levelTypes.has_key(levelType): return levelTypes[levelType].generateFromXML(Elevel) else: raise XMLAtlasException("Unknown level type " + levelType) levelType = property(lambda self: self._type) class LabelsLevel(Level): """Level of labels. XXX extend """ def __init__ (self, description, index=None, labels=[]): Level.__init__(self, description) self.__index = index self.__labels = labels self._type = "Labels" def __repr__(self): return Level.__repr__(self) + " [%d] " % \ (self.__index) @staticmethod def generateFromXML(Elevel, levelIndex=[0]): # XXX this is just for label type of level. For distance we need to ... # we need to assure the right indexing index = 0 if Elevel.attrib.has_key("index"): index = int(Elevel.get("index")) maxindex = max([int(i.get('index')) \ for i in Elevel.label[:]]) labels = [ None for i in xrange(maxindex+1) ] for label in Elevel.label[:]: labels[ int(label.get('index')) ] = Label.generateFromXML(label) levelIndex[0] = max(levelIndex[0], index) + 1 # assign next one return LabelsLevel(Elevel.get('description'), index, labels) @property def index(self): return self.__index @property def labels(self): return self.__labels def __getitem__(self, index): return self.__labels[index] def find(self, target, unique=True): """Return labels descr of which matches the string :Parameters: target : str or re._pattern_type Substring in abbreviation to be searched for, or compiled regular expression to be searched or matched if anchored. unique : bool If True, raise exception if none or more than 1 was found. Return just a single item if found (not list). """ if isinstance(target, re._pattern_type): res = [l for l in self.__labels if target.search(l.abbr)] else: res = [l for l in self.__labels if target in l.abbr] if unique: if len(res) != 1: raise ValueError, "Got %d matches whenever just 1 was " \ "looked for (target was %s)." % (len(res), target) return res[0] else: return res class ReferencesLevel(Level): """Level which carries reference points """ def __init__ (self, description, indexes=[]): Level.__init__(self, description) self.__indexes = indexes self._type = "References" @staticmethod def generateFromXML(Elevel): # XXX should probably do the same for the others? requiredAttrs = ['x', 'y', 'z', 'type', 'description'] if not set(requiredAttrs) == set(Elevel.attrib.keys()): raise XMLAtlasException("ReferencesLevel has to have " + "following attributes defined " + `requiredAttrs`) indexes = ( int(Elevel.get("x")), int(Elevel.get("y")), int(Elevel.get("z")) ) return ReferencesLevel(Elevel.get('description'), indexes) @property def indexes(self): return self.__indexes class PyMVPAAtlas(XMLBasedAtlas): """Base class for PyMVPA atlases, such as LabelsAtlas and ReferenceAtlas """ source = 'PyMVPA' def __init__(self, *args, **kwargs): XMLBasedAtlas.__init__(self, *args, **kwargs) # sanity checks header = self.header headerChildrenTags = XMLBasedAtlas._children_tags(header) if not ('space' in headerChildrenTags) or \ not ('space-flavor' in headerChildrenTags): raise XMLAtlasException("PyMVPA Atlas requires specification of" + " the space in which atlas resides") self.__space = header.space.text self.__spaceFlavor = header['space-flavor'].text __doc__ = enhancedDocString('PyMVPAAtlas', locals(), XMLBasedAtlas) def _loadImages(self): # shortcut imagefile = self.header.images.imagefile #self.Nlevels = len(self._levels_by_id) # Set offset if defined in XML file # XXX: should just take one from the qoffset... now that one is # defined... this origin might be misleading actually self._origin = N.array( (0, 0, 0) ) if imagefile.attrib.has_key('offset'): self._origin = N.array( [int(x) for x in imagefile.get('offset').split(',')] ) # Load the image file which has labels if self._force_image_file is not None: imagefilename = self._force_image_file else: imagefilename = imagefile.text imagefilename = reuseAbsolutePath(self._filename, imagefilename) try: self._image = NiftiImage(imagefilename) except RuntimeError, e: raise RuntimeError, " Cannot open file %s due to %s" % (imagefilename, e) self._data = self._image.data # remove bogus dimensions on top of 4th if len(self._data.shape[0:-4]) > 0: bogus_dims = self._data.shape[0:-4] if max(bogus_dims)>1: raise RuntimeError, "Atlas %s has more than 4 of non-singular" \ "dimensions" % imagefilename new_shape = self._data.shape[-4:] self._data.reshape(new_shape) #if self._image.extent[3] != self.Nlevels: # raise XMLAtlasException("Atlas %s has %d levels defined whenever %s has %d volumes" % \ # ( filename, self.Nlevels, imagefilename, self._image.extent[3] )) def _loadData(self): # Load levels self._levels_dict = {} # preprocess labels for different levels self._Nlevels = 0 index_incr = 0 for index, child in enumerate(self.data.getchildren()): if child.tag == 'level': level = Level.generateFromXML(child) self._levels_dict[level.description] = level if hasattr(level, 'index'): index = level.index else: # to avoid collision if some levels do # have indexes while index_incr in self._levels_dict: index_incr += 1 index, index_incr = index_incr, index_incr+1 self._levels_dict[index] = level else: raise XMLAtlasException( "Unknown child '%s' within data" % child.tag) self._Nlevels += 1 def _getNLevelsVirtual(self): return self._Nlevels def _getNLevels(self): return self._getNLevelsVirtual() @staticmethod def _checkVersion(version): # For compatibility lets support "RUMBA" atlases return version.startswith("pymvpa-") or version.startswith("rumba-") space = property(fget=lambda self:self.__space) spaceFlavor = property(fget=lambda self:self.__spaceFlavor) Nlevels = property(fget=_getNLevels) class LabelsAtlas(PyMVPAAtlas): """ Atlas which provides labels for the given coordinate """ def labelVoxel(self, c, levels=None): """ Return labels for the given voxel at specified levels specified by index """ levels = self._getLevels(levels=levels) result = {'voxel_queried' : c} # check range c = self._checkRange(c) resultLevels = [] for level in levels: if self._levels_dict.has_key(level): level_ = self._levels_dict[ level ] else: raise IndexError( "Unknown index or description for level %d" % level) resultIndex = int(self._data[ level_.index, \ c[2], c[1], c[0] ]) resultLevels += [ {'index': level_.index, 'id': level_.description, 'label' : level_[ resultIndex ]} ] result['labels'] = resultLevels return result __doc__ = enhancedDocString('LabelsAtlas', locals(), PyMVPAAtlas) class ReferencesAtlas(PyMVPAAtlas): """ Atlas which provides references to the other atlases. Example: the atlas which has references to the closest points (closest Gray, etc) in another atlas. """ def __init__(self, distance=0, *args, **kwargs): """Initialize `ReferencesAtlas` """ PyMVPAAtlas.__init__(self, *args, **kwargs) # sanity checks if not ('reference-atlas' in XMLBasedAtlas._children_tags(self.header)): raise XMLAtlasException( "ReferencesAtlas must refer to a some other atlas") referenceAtlasName = self.header["reference-atlas"].text # uff -- another evil import but we better use the factory method from mvpa.atlases.warehouse import Atlas self.__referenceAtlas = Atlas(filename=reuseAbsolutePath( self._filename, referenceAtlasName)) if self.__referenceAtlas.space != self.space or \ self.__referenceAtlas.spaceFlavor != self.spaceFlavor: raise XMLAtlasException( "Reference and original atlases should be in the same space") self.__referenceLevel = None self.setDistance(distance) __doc__ = enhancedDocString('ReferencesAtlas', locals(), PyMVPAAtlas) # number of levels must be of the referenced atlas due to # handling of that in __getitem__ #Nlevels = property(fget=lambda self:self.__referenceAtlas.Nlevels) def _getNLevelsVirtual(self): return self.__referenceAtlas.Nlevels def setReferenceLevel(self, level): """ Set the level which will be queried """ if self._levels_dict.has_key(level): self.__referenceLevel = self._levels_dict[level] else: raise IndexError("Unknown reference level " + `level` + ". Known are " + `self._levels_dict.keys()`) def labelVoxel(self, c, levels = None): if self.__referenceLevel is None: warning("You did not provide what level to use " "for reference. Assigning 0th level -- '%s'" % (self._levels_dict[0],)) self.setReferenceLevel(0) # return self.__referenceAtlas.labelVoxel(c, levels) c = self._checkRange(c) # obtain coordinates of the closest voxel cref = self._data[ self.__referenceLevel.indexes, c[2], c[1], c[0] ] dist = norm( (cref - c) * self.voxdim ) if __debug__: debug('ATL__', "Closest referenced point for %s is " "%s at distance %3.2f" % (`c`, `cref`, dist)) if (self.distance - dist) >= 1e-3: # neglect everything smaller result = self.__referenceAtlas.labelVoxel(cref, levels) result['voxel_referenced'] = c result['distance'] = dist else: result = self.__referenceAtlas.labelVoxel(c, levels) if __debug__: debug('ATL__', "Closest referenced point is " "further than desired distance %.2f" % self.distance) result['voxel_referenced'] = None result['distance'] = 0 return result def levelsListing(self): return self.__referenceAtlas.levelsListing() def _getLevelsDict_virtual(self): return self.__referenceAtlas.levels_dict def setDistance(self, distance): """ Set desired maximal distance for the reference """ if distance < 0: raise ValueError("Distance should not be negative. " " Thus '%f' is not a legal value" % distance) if __debug__: debug('ATL__', "Setting maximal distance for queries to be %d" % distance) self.__distance = distance distance = property(fget=lambda self:self.__distance, fset=setDistance) pymvpa-0.4.8/mvpa/atlases/fsl.py000066400000000000000000000200101174541445200165740ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """FSL atlases interfaces """ from mvpa.base import warning, externals if externals.exists('nifti', raiseException=True): from nifti import NiftiImage import os, re import numpy as N from mvpa.misc.support import reuseAbsolutePath from mvpa.base.dochelpers import enhancedDocString from mvpa.atlases.base import XMLBasedAtlas, LabelsLevel if __debug__: from mvpa.base import debug # # Atlases from FSL # class FSLAtlas(XMLBasedAtlas): """Base class for FSL atlases """ source = 'FSL' def __init__(self, *args, **kwargs): """ """ XMLBasedAtlas.__init__(self, *args, **kwargs) self.space = 'MNI' __doc__ = enhancedDocString('FSLAtlas', locals(), XMLBasedAtlas) def _loadImages(self): resolution = self._resolution header = self.header images = header.images # Load present images # XXX might be refactored to avoid duplication of # effort with PyMVPAAtlas ni_image = None resolutions = [] if self._force_image_file is None: imagefile_candidates = [ reuseAbsolutePath(self._filename, i.imagefile.text, force=True) for i in images] else: imagefile_candidates = [self._force_image_file] for imagefilename in imagefile_candidates: try: ni_image_ = NiftiImage(imagefilename, load=False) except RuntimeError, e: raise RuntimeError, " Cannot open file " + imagefilename resolution_ = ni_image_.pixdim[0] if resolution is None: # select this one if the best if ni_image is None or \ resolution_ < ni_image.pixdim[0]: ni_image = ni_image_ self._image_file = imagefilename else: if resolution_ == resolution: ni_image = ni_image_ self._image_file = imagefilename break else: resolutions += [resolution_] # TODO: also make use of summaryimagefile may be? if ni_image is None: msg = "Could not find an appropriate atlas among %d atlases." if resolution is not None: msg += " Atlases had resolutions %s" % \ (resolutions,) raise RuntimeError, msg if __debug__: debug('ATL__', "Loading atlas data from %s" % self._image_file) self._image = ni_image self._resolution = ni_image.pixdim[0] self._origin = N.abs(ni_image.header['qoffset']) * 1.0 # XXX self._data = self._image.data def _loadData(self): """ """ # Load levels self._levels_dict = {} # preprocess labels for different levels self.Nlevels = 1 #level = Level.generateFromXML(self.data, levelType='label') level = LabelsLevel.generateFromXML(self.data)#, levelType='label') level.description = self.header.name.text self._levels_dict = {0: level} #for index, child in enumerate(self.data.getchildren()): # if child.tag == 'level': # level = Level.generateFromXML(child) # self._levels_dict[level.description] = level # try: # self._levels_dict[level.index] = level # except: # pass # else: # raise XMLAtlasException("Unknown child '%s' within data" % child.tag) # self.Nlevels += 1 #pass @staticmethod def _checkVersion(version): return re.search('^[0-9]+\.[0-9]', version) is not None class FSLProbabilisticAtlas(FSLAtlas): """Probabilistic FSL atlases """ def __init__(self, thr=0.0, strategy='all', sort=True, *args, **kwargs): """ :Parameters: thr : float Value to threshold at strategy : basestring Possible values all - all entries above thr max - entry with maximal value sort : bool Either to sort entries for 'all' strategy according to probability """ FSLAtlas.__init__(self, *args, **kwargs) self.thr = thr self.strategy = strategy self.sort = sort __doc__ = enhancedDocString('FSLProbabilisticAtlas', locals(), FSLAtlas) def labelVoxel(self, c, levels=None): """Return labels for the voxel :Parameters: - c : tuple of coordinates (xyz) - levels : just for API consistency (heh heh). Must be 0 for FSL atlases """ if levels is not None and not (levels in [0, [0], (0,)]): raise ValueError, \ "I guess we don't support levels other than 0 in FSL atlas" # check range c = self._checkRange(c) # XXX think -- may be we should better assign each map to a # different level level = 0 resultLabels = [] for index, area in enumerate(self._levels_dict[level]): prob = int(self._data[index, c[2], c[1], c[0]]) if prob > self.thr: resultLabels += [dict(index=index, #id= label=area.text, prob=prob)] if self.sort or self.strategy == 'max': resultLabels.sort(cmp=lambda x,y: cmp(x['prob'], y['prob']), reverse=True) if self.strategy == 'max': resultLabels = resultLabels[:1] elif self.strategy == 'all': pass else: raise ValueError, 'Unknown strategy %s' % self.strategy result = {'voxel_queried' : c, # in the list since we have only single level but # with multiple entries 'labels': [resultLabels]} return result def find(self, *args, **kwargs): """Just a shortcut to the only level. See :class:`~mvpa.atlases.base.Level.find` for more info """ return self.levels_dict[0].find(*args, **kwargs) def getMap(self, target, strategy='unique'): """Return a probability map :Parameters: target : int or str or re._pattern_type If int, map for given index is returned. Otherwise, .find is called with unique=True to find matching area strategy : str in ('unique', 'max') If 'unique', then if multiple areas match, exception would be raised. In case of 'max', each voxel would get maximal value of probabilities from all matching areas """ if isinstance(target, int): return self._data[target] else: lev = self.levels_dict[0] # we have just 1 here if strategy == 'unique': return self.getMap(lev.find(target, unique=True).index) else: maps = N.array(self.getMaps(target)) return N.max(maps, axis=0) def getMaps(self, target): """Return a list of probability maps for the target :Parameters: target : str or re._pattern_type .find is called with a target and unique=False to find all matches """ lev = self.levels_dict[0] # we have just 1 here return [self.getMap(l.index) for l in lev.find(target, unique=False)] class FSLLabelsAtlas(XMLBasedAtlas): """Not sure what this one was for""" def __init__(self, *args, **kwargs): """not implemented""" FSLAtlas.__init__(self, *args, **kwargs) raise NotImplementedError pymvpa-0.4.8/mvpa/atlases/transformation.py000066400000000000000000000221501174541445200210650ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Coordinate transformations""" import numpy as N if __debug__: from mvpa.base import debug class TypeProxy: """ Simple class to convert from and then back to original type working with list, tuple, ndarray and having XXX Obsolete functionality ?? """ def __init__(self, value, toType=N.array): if isinstance(value, list): self.__type = list elif isinstance(value, tuple): self.__type = tuple elif isinstance(value, N.ndarray): self.__type = N.array else: raise IndexError("Not understood format of coordinates '%s' for the transformation" % `coord`) def __call__(self, value): return self.__type(value) # def __getitem__(self, value): return self.__type(value) class TransformationBase: """ Basic class to describe a transformation. Pretty much an interface """ def __init__(self, previous=None): self.previous = previous def __getitem__(self, icoord): """ Obtain coordinates, apply the transformation and spit out in the same format (list, tuple, numpy.array) """ # remember original type #speed origType = TypeProxy(coord) # just in case it is not an ndarray, and to provide a copy to manipulate with coord = N.array(icoord) # apply previous transformation if such defined if self.previous: # if __debug__: debug('ATL__', "Applying previous transformation on `%s`" % `coord`) coord = self.previous[coord] #speed if __debug__: debug('ATL__', "Applying main transformation on `%s`" % `coord`) # apply transformation coord_out = self.apply(coord) #speed if __debug__: debug('ATL__', "Applied and got `%s`" % `coord_out`) #speed return origType(coord_out) return coord_out def __call__(self, coord): return self[coord] def apply(self, coord): return coord class SpaceTransformation(TransformationBase): """ To perform transformation from Voxel into Real Space. Simple one -- would subtract the origin and multiply by voxelSize. if toRealSpace is True then on call/getitem converts to RealSpace """ def __init__(self, voxelSize=None, origin=None, toRealSpace=True, *args, **kwargs): TransformationBase.__init__(self, *args, **kwargs) if not voxelSize is None: self.voxelSize = N.asarray(voxelSize) else: self.voxelSize = 1 if not origin is None: self.origin = N.asarray(origin) else: self.origin = 0 if toRealSpace: self.apply = self.toRealSpace else: self.apply = self.toVoxelSpace def toRealSpace(self, coord): #speed if not self.origin is None: coord -= self.origin #speed if not self.voxelSize is None: coord *= self.voxelSize return coord def toVoxelSpace(self, coord): #speed if not self.voxelSize is None: coord /= self.voxelSize #speed if not self.origin is None: coord += self.origin return map(lambda x:int(round(x)), coord) class Linear(TransformationBase): """ Simple linear transformation defined by a matrix """ def __init__(self, transf=N.eye(4), **kwargs): transf = N.asarray(transf) # assure that we have arrays not matrices prev = kwargs.get('previous', None) if prev is not None and isinstance(prev, Linear): if prev.N == transf.shape[0] -1: if __debug__: debug('ATL__', "Colliding 2 linear transformations into 1") transf = N.dot(transf, prev.M) # reassign previous transformation to the current one kwargs['previous'] = prev.previous TransformationBase.__init__(self, **kwargs) self.M = transf self.N = self.M.shape[0] - 1 def apply(self, coord): #speed if len(coord) != self.__N: #speed raise ValueError("Transformation operates on %dD coordinates" \ #speed % self.__N ) #speed if __debug__: debug('ATL__', "Applying linear coord transformation + %s" % self.__M) # Might better come up with a linear transformation coord_ = N.r_[coord, [1.0]] result = N.dot(self.M, coord_) return result[0:-1] class MNI2Tal_MatthewBrett(TransformationBase): """ Transformation to bring MNI coordinates into MNI space Apparently it is due to Matthew Brett http://imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach """ def __init__(self, *args, **kwargs): TransformationBase.__init__(self, *args, **kwargs) self.__upper = Linear( N.array([ [0.9900, 0, 0, 0 ], [0, 0.9688, 0.0460, 0 ], [0,-0.0485, 0.9189, 0 ], [0, 0, 0, 1.0000] ] ) ) self.__lower = Linear(N.array( [ [0.9900, 0, 0, 0 ], [0, 0.9688, 0.0420, 0 ], [0,-0.0485, 0.8390, 0 ], [0, 0, 0, 1.0000] ] ) ) def apply(self, coord): return {True: self.__upper, False: self.__lower}[coord[2]>=0][coord] def MNI2Tal_MeyerLindenberg98 (*args, **kwargs): """ Due to Andreas Meyer-Lindenberg Taken from http://imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach """ return Linear( N.array([ [ 0.88, 0, 0, -0.8], [ 0, 0.97, 0, -3.32], [ 0, 0.05, 0.88, -0.44], [ 0.00000, 0.00000, 0.00000, 1.00000] ]), *args, **kwargs ) def MNI2Tal_YOHflirt (*args, **kwargs): """Transformations obtained using flirt from Talairach to Standard Transformations were obtained by registration of grey/white matter image from talairach atlas to FSL's standard volume. Following sequence of commands was used: fslroi /usr/share/rumba/atlases/data/talairach_atlas.nii.gz talairach_graywhite.nii.gz 3 1 flirt -in talairach_graywhite.nii.gz \ -ref /usr/apps/fsl.4.1/data/standard/MNI152_T1_1mm_brain.nii.gz \ -out talairach2mni.nii.gz -omat talairach2mni.mat \ -searchrx -20 20 -searchry -20 20 -searchrz -20 20 -coarsesearch 10 -finesearch 6 -v flirt -datatype float -in talairach_graywhite.nii.gz -init talairach2mni.mat \ -ref /usr/apps/fsl.4.1/data/standard/MNI152_T1_1mm_brain.nii.gz \ -out talairach2mni_fine1.nii.gz -omat talairach2mni_fine1.mat \ -searchrx -10 10 -searchry -10 10 -searchrz -10 10 -coarsesearch 5 -finesearch 1 -v convert_xfm -inverse -omat mni2talairach.mat talairach2mni_fine1.mat """ return Linear( t=N.array([ [ 1.00448, -0.00629625, 0.00741359, 0.70565, ], [ 0.0130797, 0.978238, 0.0731315, -3.8354, ], [ 0.000248407, -0.0374777, 0.838311, 18.6202, ], [ 0, 0, 0, 1, ], ]) , *args, **kwargs ) def Tal2MNI_YOHflirt (*args, **kwargs): """See MNI2Tal_YOHflirt doc """ return Linear( N.array([ [ 1.00452, 0.00441281, -0.011011, -0.943886], [ -0.0141149, 1.00867, -0.169177, 14.7016], [ 0.00250222, 0.0920984, 1.18656, -33.922], [ 0.00000, 0.00000, 0.00000, 1.00000] ]), *args, **kwargs ) def MNI2Tal_Lancaster07FSL (*args, **kwargs): return Linear( N.array([ [ 0.9464, 0.0034, -0.0026, -1.0680], [ -0.0083, 0.9479, -0.0580, -1.0239], [ 0.0053, 0.0617, 0.9010, 3.1883], [ 0.0000, 0.0000, 0.0000, 1.0000] ]), *args, **kwargs ) def Tal2MNI_Lancaster07FSL (*args, **kwargs): return Linear( N.array([ [ 1.056585, -0.003972, 0.002793, 1.115461], [ 0.008834, 1.050528, 0.067651, 0.869379], [-0.00682 , -0.071916, 1.105229, -3.60472 ], [ 0. , 0. , 0. , 1. ]]), *args, **kwargs ) def MNI2Tal_Lancaster07pooled (*args, **kwargs): return Linear( N.array([ [ 0.93570, 0.00290, -0.00720, -1.04230], [ -0.00650, 0.93960, -0.07260, -1.39400], [ 0.01030, 0.07520, 0.89670, 3.64750], [ 0.00000, 0.00000, 0.00000, 1.00000] ]), *args, **kwargs ) def Tal2MNI_Lancaster07pooled (*args, **kwargs): return Linear( N.array([ [ 1.06860, -0.00396, 0.00826, 1.07816], [ 0.00640, 1.05741, 0.08566, 1.16824], [ -0.01281, -0.08863, 1.10792, -4.17805], [ 0.00000, 0.00000, 0.00000, 1.00000] ]), *args, **kwargs ) if __name__ == '__main__': #t = Tal2Mni tl = Tal2MNI_Lancaster07FSL() tli = MNI2Tal_Lancaster07FSL() tml = MNI2Tal_MeyerLindenberg98() #print t[1,3,2] print tl[(1,3,2)] print tli[[1,3,2]] print tml[[1,3,2]] # print t[(1,3,2,2)] pymvpa-0.4.8/mvpa/atlases/warehouse.py000066400000000000000000000072241174541445200200260ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Collection of the known atlases""" import os from mvpa.atlases.base import * from mvpa.atlases.fsl import * KNOWN_ATLAS_FAMILIES = { 'pymvpa': (["talairach", "talairach-dist"], r"/usr/share/rumba/atlases/data/%(name)s_atlas.xml"), 'fsl': (["HarvardOxford-Cortical", "HarvardOxford-Subcortical", "JHU-tracts", "Juelich", "MNI", "Thalamus"], r"/usr/share/fsl/data/atlases/%(name)s.xml") # XXX make use of FSLDIR } # map to go from the name to the path KNOWN_ATLASES = dict(reduce(lambda x,y:x+[(yy,y[1]) for yy in y[0]], KNOWN_ATLAS_FAMILIES.values(), [])) def Atlas(filename=None, name=None, *args, **kwargs): """A convinience factory for the atlases """ if filename is None: if name is None: raise ValueError, \ "Please provide either path or name of the atlas to be used" atlaspath = KNOWN_ATLASES[name] filename = atlaspath % ( {'name': name} ) if not os.path.exists(filename): raise IOError, \ "File %s for atlas %s was not found" % (filename, name) else: if name is not None: raise ValueError, "Provide only filename or name" try: tempAtlas = XMLBasedAtlas(filename=filename, *args, **kwargs) version = tempAtlas.version atlas_source = None for cls in [PyMVPAAtlas, FSLAtlas]: if cls._checkVersion(version): atlas_source = cls.source break if atlas_source is None: if __debug__: debug('ATL_', "Unknown atlas " + filename) return tempAtlas atlasTypes = { 'PyMVPA': {"Label" : LabelsAtlas, "Reference": ReferencesAtlas}, 'FSL': {"Label" : FSLLabelsAtlas, "Probabalistic": FSLProbabilisticAtlas} }[atlas_source] atlasType = tempAtlas.header.type.text if atlasTypes.has_key(atlasType): if __debug__: debug('ATL_', "Creating %s Atlas" % atlasType) return atlasTypes[atlasType](filename=filename, *args, **kwargs) #return ReferencesAtlas(filename) else: printdebug("Unknown %s type '%s' of atlas in %s." " Known are %s" % (atlas_source, atlasType, filename, atlasTypes.keys()), 2) return tempAtlas except XMLAtlasException, e: print "File %s is not a valid XML based atlas due to %s" \ % (filename, `e`) raise e if __name__ == '__main__': from mvpa.base import verbose verbose.level = 10 for name in [ #'data/talairach_atlas.xml', '/usr/share/fsl/data/atlases/HarvardOxford-Cortical.xml', '/usr/share/fsl/data/atlases/HarvardOxford-Subcortical.xml' ]: atlas = Atlas(name) #print isinstance(atlas.atlas, objectify.ObjectifiedElement) #print atlas.header.images.imagefile.get('offset') #print atlas.labelVoxel( (0, -7, 20) ) #print atlas[ 0, 0, 0 ] print atlas[ -63, -12, 22 ] #print atlas[ 0, -7, 20, [1,2,3] ] #print atlas[ (0, -7, 20), 1:2 ] #print atlas[ (0, -7, 20) ] #print atlas[ (0, -7, 20), : ] # print atlas.getLabels(0) pymvpa-0.4.8/mvpa/base/000077500000000000000000000000001174541445200147235ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/base/__init__.py000066400000000000000000000271071174541445200170430ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Base functionality of PyMVPA Module Organization =================== mvpa.base module contains various modules which are used through out PyMVPA code, and are generic building blocks .. packagetree:: :style: UML :group Basic: externals, config, verbosity, dochelpers """ __docformat__ = 'restructuredtext' import sys from mvpa.base.config import ConfigManager from mvpa.base.verbosity import LevelLogger, OnceLogger # # Setup verbose and debug outputs # class _SingletonType(type): """Simple singleton implementation adjusted from http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/412551 """ def __init__(mcs, *args): type.__init__(mcs, *args) mcs._instances = {} def __call__(mcs, sid, instance, *args): if not sid in mcs._instances: mcs._instances[sid] = instance return mcs._instances[sid] class __Singleton: """To ensure single instance of a class instantiation (object) """ __metaclass__ = _SingletonType def __init__(self, *args): pass # Provided __call__ just to make silly pylint happy def __call__(self): raise NotImplementedError # # As the very first step: Setup configuration registry instance and # read all configuration settings from files and env variables # cfg = __Singleton('cfg', ConfigManager()) verbose = __Singleton("verbose", LevelLogger( handlers = cfg.get('verbose', 'output', default='stdout').split(','))) # Not supported/explained/used by now since verbose(0, is to print errors #error = __Singleton("error", LevelLogger( # handlers=environ.get('MVPA_ERROR_OUTPUT', 'stderr').split(','))) # Levels for verbose # 0 -- nothing besides errors # 1 -- high level stuff -- top level operation or file operations # 2 -- cmdline handling # 3 -- # 4 -- computation/algorithm relevant thingies # Helper for errors printing def error(msg, critical=True): """Helper function to output errors in a consistent way. :Parameters: msg : string Actual error message (will be prefixed with ERROR:) critical : bool If critical error -- exit with """ verbose(0, "ERROR: " + msg) if critical: raise sys.exit(1) # Lets check if environment can tell us smth if cfg.has_option('general', 'verbose'): verbose.level = cfg.getint('general', 'verbose') class WarningLog(OnceLogger): """Logging class of messsages to be printed just once per each message """ def __init__(self, btlevels=10, btdefault=False, maxcount=1, *args, **kwargs): """Define Warning logger. It is defined by btlevels : int how many levels of backtrack to print to give a hint on WTF btdefault : bool if to print backtrace for all warnings at all maxcount : int how many times to print each warning """ OnceLogger.__init__(self, *args, **kwargs) self.__btlevels = btlevels self.__btdefault = btdefault self.__maxcount = maxcount self.__explanation_seen = False def __call__(self, msg, bt=None): import traceback if bt is None: bt = self.__btdefault tb = traceback.extract_stack(limit=2) msgid = repr(tb[-2]) # take parent as the source of ID fullmsg = "WARNING: %s" % msg if not self.__explanation_seen: self.__explanation_seen = True fullmsg += "\n * Please note: warnings are " + \ "printed only once, but underlying problem might " + \ "occur many times *" if bt and self.__btlevels > 0: fullmsg += "Top-most backtrace:\n" fullmsg += reduce(lambda x, y: x + "\t%s:%d in %s where '%s'\n" % \ y, traceback.extract_stack(limit=self.__btlevels), "") OnceLogger.__call__(self, msgid, fullmsg, self.__maxcount) def _setMaxCount(self, value): """Set maxcount for the warning""" self.__maxcount = value maxcount = property(fget=lambda x:x.__maxcount, fset=_setMaxCount) # XXX what is 'bt'? Maybe more verbose name? if cfg.has_option('warnings', 'bt'): warnings_btlevels = cfg.getint('warnings', 'bt') warnings_bt = True else: warnings_btlevels = 10 warnings_bt = False if cfg.has_option('warnings', 'count'): warnings_maxcount = cfg.getint('warnings', 'count') else: warnings_maxcount = 1 warning = WarningLog( handlers={ False: cfg.get('warnings', 'output', default='stdout').split(','), True: []}[cfg.getboolean('warnings', 'suppress', default=False)], btlevels=warnings_btlevels, btdefault=warnings_bt, maxcount=warnings_maxcount ) if __debug__: from mvpa.base.verbosity import DebugLogger # NOTE: all calls to debug must be preconditioned with # if __debug__: debug = __Singleton("debug", DebugLogger( handlers=cfg.get('debug', 'output', default='stdout').split(','))) # set some debugging matricses to report # debug.registerMetric('vmem') # List agreed sets for debug debug.register('PY', "No suppression of various warnings (numpy, scipy) etc.") debug.register('DBG', "Debug output itself") debug.register('DOCH', "Doc helpers") debug.register('INIT', "Just sequence of inits") debug.register('RANDOM', "Random number generation") debug.register('EXT', "External dependencies") debug.register('EXT_', "External dependencies (verbose)") debug.register('TEST', "Debug unittests") debug.register('MODULE_IN_REPR', "Include module path in __repr__") debug.register('ID_IN_REPR', "Include id in __repr__") debug.register('CMDLINE', "Handling of command line parameters") debug.register('DG', "Data generators") debug.register('LAZY', "Miscelaneous 'lazy' evaluations") debug.register('LOOP', "Support's loop construct") debug.register('PLR', "PLR call") debug.register('SLC', "Searchlight call") debug.register('SA', "Sensitivity analyzers") debug.register('SOM', "Self-organizing-maps (SOM)") debug.register('IRELIEF', "Various I-RELIEFs") debug.register('SA_', "Sensitivity analyzers (verbose)") debug.register('PSA', "Perturbation analyzer call") debug.register('RFEC', "Recursive Feature Elimination call") debug.register('RFEC_', "Recursive Feature Elimination call (verbose)") debug.register('IFSC', "Incremental Feature Search call") debug.register('DS', "*Dataset") debug.register('DS_NIFTI', "NiftiDataset(s)") debug.register('DS_', "*Dataset (verbose)") debug.register('DS_ID', "ID Datasets") debug.register('DS_STATS',"Datasets statistics") debug.register('SPL', "*Splitter") debug.register('TRAN', "Transformers") debug.register('TRAN_', "Transformers (verbose)") # CHECKs debug.register('CHECK_DS_SELECT', "Check in dataset.select() for sorted and unique indexes") debug.register('CHECK_DS_SORTED', "Check in datasets for sorted") debug.register('CHECK_IDS_SORTED', "Check for ids being sorted in mappers") debug.register('CHECK_TRAINED', "Checking in checking if clf was trained on given dataset") debug.register('CHECK_RETRAIN', "Checking in retraining/retesting") debug.register('CHECK_STABILITY', "Checking for numerical stability") debug.register('ENFORCE_STATES_ENABLED', "Forcing all states to be enabled") debug.register('MAP', "*Mapper") debug.register('MAP_', "*Mapper (verbose)") debug.register('COL', "Generic Collectable") debug.register('UATTR', "Attributes with unique") debug.register('ST', "State") debug.register('STV', "State Variable") debug.register('COLR', "Collector for states and classifier parameters") debug.register('ES', "Element selectors") debug.register('CLF', "Base Classifiers") debug.register('CLF_', "Base Classifiers (verbose)") #debug.register('CLF_TB', # "Report traceback in train/predict. Helps to resolve WTF calls it") debug.register('CLFBST', "BoostClassifier") #debug.register('CLFBST_TB', "BoostClassifier traceback") debug.register('CLFBIN', "BinaryClassifier") debug.register('CLFTREE', "TreeClassifier") debug.register('CLFMC', "MulticlassClassifier") debug.register('CLFSPL', "SplitClassifier") debug.register('CLFSPL_',"SplitClassifier (verbose)") debug.register('CLFFS', "FeatureSelectionClassifier") debug.register('CLFFS_', "FeatureSelectionClassifier (verbose)") debug.register('STAT', "Statistics estimates") debug.register('STAT_', "Statistics estimates (verbose)") debug.register('STAT__', "Statistics estimates (very verbose)") debug.register('STATMC', "Progress in Monte-Carlo estimation") debug.register('FS', "FeatureSelections") debug.register('FS_', "FeatureSelections (verbose)") debug.register('FSPL', "FeatureSelectionPipeline") debug.register('SVM', "SVM") debug.register('SVM_', "SVM (verbose)") debug.register('LIBSVM', "Internal libsvm output") debug.register('SMLR', "SMLR") debug.register('SMLR_', "SMLR verbose") debug.register('LARS', "LARS") debug.register('LARS_', "LARS (verbose)") debug.register('ENET', "ENET") debug.register('ENET_', "ENET (verbose)") debug.register('GLMNET', "GLMNET") debug.register('GLMNET_', "GLMNET (verbose)") debug.register('GNB', "GNB - Gaussian Naive Bayes") debug.register('GPR', "GPR") debug.register('GPR_WEIGHTS', "Track progress of GPRWeights computation") debug.register('KERNEL', "Kernels module") debug.register('MOD_SEL', "Model Selector (also makes openopt's iprint=0)") debug.register('OPENOPT', "OpenOpt toolbox verbose (iprint=1)") debug.register('SG', "PyMVPA SG wrapping") debug.register('SG_', "PyMVPA SG wrapping verbose") debug.register('SG__', "PyMVPA SG wrapping debug") debug.register('SG_SVM', "Internal shogun debug output for SVM itself") debug.register('SG_FEATURES', "Internal shogun debug output for features") debug.register('SG_LABELS', "Internal shogun debug output for labels") debug.register('SG_KERNELS', "Internal shogun debug output for kernels") debug.register('SG_PROGRESS', "Internal shogun progress bar during computation") debug.register('IOH', "IO Helpers") debug.register('IO_HAM', "Hamster") debug.register('CM', "Confusion matrix computation") debug.register('ROC', "ROC analysis") debug.register('CROSSC', "Cross-validation call") debug.register('CERR', "Various ClassifierErrors") debug.register('ATL', "Atlases") debug.register('ATL_', "Atlases (verbose)") debug.register('ATL__', "Atlases (very verbose)") debug.register('REP', "Reports") debug.register('REP_', "Reports (verbose)") # Lets check if environment can tell us smth if cfg.has_option('general', 'debug'): debug.setActiveFromString(cfg.get('general', 'debug')) # Lets check if environment can tell us smth if cfg.has_option('debug', 'metrics'): debug.registerMetric(cfg.get('debug', 'metrics').split(",")) if __debug__: debug('INIT', 'mvpa.base end') pymvpa-0.4.8/mvpa/base/config.py000066400000000000000000000153241174541445200165470ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Registry-like monster""" __docformat__ = 'restructuredtext' from ConfigParser import SafeConfigParser import os.path class ConfigManager(SafeConfigParser): """Central configuration registry for PyMVPA. The purpose of this class is to collect all configurable settings used by various parts of PyMVPA. It is fairly simple and does only little more than the standard Python ConfigParser. Like ConfigParser it is blind to the data that it stores, i.e. not type checking is performed. Configuration files (INI syntax) in multiple location are passed when the class is instanciated or whenever `Config.reload()` is called later on. By default it looks for a config file named `pymvpa.cfg` in the current directory and `.pymvpa.cfg` in the user's home directory. Morever, the constructor takes an optional argument with a list of additional file names to parse. In addition to configuration files, this class also looks for special environment variables to read settings from. Names of such variables have to start with `MVPA_` following by the an optional section name and the variable name itself ('_' as delimiter). If no section name is provided, the variables will be associated with section `general`. Some examples:: MVPA_VERBOSE=1 will become:: [general] verbose = 1 However, `MVPA_VERBOSE_OUTPUT=stdout` becomes:: [verbose] output = stdout Any lenght of variable name as allowed, e.g. MVPA_SEC1_LONG_VARIABLE_NAME=1 becomes:: [sec1] long variable name = 1 Settings from custom configuration files (specified by the constructor argument) have the highest priority and override settings found in the current directory. They in turn override user-specific settings and finally the content of any `MVPA_*` environment variables overrides all settings read from any file. """ # things we want to count on to be available _DEFAULTS = {'general': { 'verbose': '1', } } def __init__(self, filenames=None): """Initialization reads settings from config files and env. variables. :Parameters: filenames: list of filenames """ SafeConfigParser.__init__(self) # store additional config file names if not filenames is None: self.__cfg_filenames = filenames else: self.__cfg_filenames = [] # set critical defaults for sec, vars in ConfigManager._DEFAULTS.iteritems(): self.add_section(sec) for key, value in vars.iteritems(): self.set(sec, key, value) # now get the setting self.reload() def reload(self): """Re-read settings from all configured locations. """ # listof filenames to parse (custom plus some standard ones) filenames = self.__cfg_filenames \ + ['pymvpa.cfg', os.path.join(os.path.expanduser('~'), '.pymvpa.cfg')] # read local and user-specific config files = self.read(filenames) # no look for variables in the environment for var in [v for v in os.environ.keys() if v.startswith('MVPA_')]: # strip leading 'MVPA_' and lower case entries svar = var[5:].lower() # section is next element in name (or 'general' if simple name) if not svar.count('_'): sec = 'general' else: cut = svar.find('_') sec = svar[:cut] svar = svar[cut + 1:].replace('_', ' ') # check if section is already known and add it if not if not self.has_section(sec): self.add_section(sec) # set value self.set(sec, svar, os.environ[var]) def __repr__(self): """Generate INI file content with current configuration. """ # make adaptor to use str as file-like (needed for ConfigParser.write() class file2str(object): def __init__(self): self.__s = '' def write(self, val): self.__s += val def str(self): return self.__s r = file2str() self.write(r) return r.str() def save(self, filename): """Write current configuration to a file. """ f = open(filename, 'w') self.write(f) f.close() def get(self, section, option, default=None, **kwargs): """Wrapper around SafeConfigParser.get() with a custom default value. This method simply wraps the base class method, but adds a `default` keyword argument. The value of `default` is returned whenever the config parser does not have the requested option and/or section. """ if not self.has_option(section, option): return default return SafeConfigParser.get(self, section, option, **kwargs) def getboolean(self, section, option, default=None): """Wrapper around SafeConfigParser.getboolean() with a custom default. This method simply wraps the base class method, but adds a `default` keyword argument. The value of `default` is returned whenever the config parser does not have the requested option and/or section. """ if not self.has_option(section, option): if isinstance(default, bool): return default else: if default.lower() not in self._boolean_states: raise ValueError, 'Not a boolean: %s' % default return self._boolean_states[default.lower()] return SafeConfigParser.getboolean(self, section, option) def getAsDType(self, section, option, dtype, default=None): """Convenience method to query options with a custom default and type This method simply wraps the base class method, but adds a `default` keyword argument. The value of `default` is returned whenever the config parser does not have the requested option and/or section. In addition, the returned value is converted into the specified `dtype`. """ if not self.has_option(section, option): return default return SafeConfigParser._get(self, section, dtype, option) pymvpa-0.4.8/mvpa/base/dochelpers.py000066400000000000000000000314311174541445200174270ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Various helpers to improve docstrings and textual output""" __docformat__ = 'restructuredtext' import re, textwrap # for table2string import numpy as N from math import ceil from StringIO import StringIO from mvpa import cfg from mvpa.base import externals if __debug__: from mvpa.base import debug __add_init2doc = False __in_ipython = externals.exists('running ipython env') # if ran within IPython -- might need to add doc to init if __in_ipython: __rst_mode = 0 # either to do ReST links at all _rst_sep = "" _rst_sep2 = "" from IPython import Release # XXX figure out exact version when init doc started to be added to class # description if Release.version <= '0.8.1': __add_init2doc = True else: __rst_mode = 1 _rst_sep = "`" _rst_sep2 = ":" def _rst(s, snotrst=''): """Produce s only in __rst mode""" if __rst_mode: return s else: return snotrst def rstUnderline(text, markup): """Add and underline RsT string matching the length of the given string. """ return text + '\n' + markup * len(text) def singleOrPlural(single, plural, n): """Little helper to spit out single or plural version of a word. """ ni = int(n) if ni > 1 or ni == 0: # 1 forest, 2 forests, 0 forests return plural else: return single def handleDocString(text, polite=True): """Take care of empty and non existing doc strings.""" if text == None or not len(text): if polite: return 'No documentation found. Sorry!' else: return '' else: # Problem is that first line might often have no offset, so might # need to be ignored from dedent call if not text.startswith(' '): lines = text.split('\n') text2 = '\n'.join(lines[1:]) return lines[0] + "\n" + textwrap.dedent(text2) else: return textwrap.dedent(text) def _indent(text, istr=' '): """Simple indenter """ return '\n'.join(istr + s for s in text.split('\n')) __parameters_str_re = re.compile("[\n^]\s*:?Parameters?:?\s*\n") """regexp to match :Parameter: and :Parameters: stand alone in a line""" def _splitOutParametersStr(initdoc): """Split documentation into (header, parameters, suffix) :Parameters: initdoc : string The documentation string """ # TODO: bind it to the only word in the line p_res = __parameters_str_re.search(initdoc) if p_res is None: result = initdoc, "", "" else: # Could have been accomplished also via re.match # where new line is after :Parameters: # parameters header index ph_i = p_res.start() # parameters body index pb_i = p_res.end() # end of parameters try: pe_i = initdoc.index('\n\n', pb_i) except ValueError: pe_i = len(initdoc) result = initdoc[:ph_i].rstrip('\n '), \ initdoc[pb_i:pe_i], initdoc[pe_i:] # XXX a bit of duplication of effort since handleDocString might # do splitting internally return [handleDocString(x, polite=False).strip('\n') for x in result] __re_params = re.compile('(?:\n\S.*?)+$') __re_spliter1 = re.compile('(?:\n|\A)(?=\S)') __re_spliter2 = re.compile('[\n:]') def _parseParameters(paramdoc): """Parse parameters and return list of (name, full_doc_string) It is needed to remove multiple entries for the same parameter like it could be with adding parameters from the parent class It assumes that previousely parameters were unwrapped, so their documentation starts at the begining of the string, like what should it be after _splitOutParametersStr """ entries = __re_spliter1.split(paramdoc) result = [(__re_spliter2.split(e)[0].strip(), e) for e in entries if e != ''] if __debug__: debug('DOCH', 'parseParameters: Given "%s", we split into %s' % (paramdoc, result)) return result def enhancedDocString(item, *args, **kwargs): """Generate enhanced doc strings for various items. :Parameters: item : basestring or class What object requires enhancing of documentation *args : list Includes base classes to look for parameters, as well, first item must be a dictionary of locals if item is given by a string force_extend : bool Either to force looking for the documentation in the parents. By default force_extend = False, and lookup happens only if kwargs is one of the arguments to the respective function (e.g. item.__init__) skip_params : list of basestring List of parameters (in addition to [kwargs]) which should not be added to the documentation of the class. It is to be used from a collector, ie whenever class is already created """ # Handling of arguments if len(kwargs): if set(kwargs.keys()).issubset(set(['force_extend'])): raise ValueError, "Got unknown keyword arguments (smth among %s)" \ " in enhancedDocString." % kwargs force_extend = kwargs.get('force_extend', False) skip_params = kwargs.get('skip_params', []) # XXX make it work also not only with classes but with methods as well if isinstance(item, basestring): if len(args)<1 or not isinstance(args[0], dict): raise ValueError, \ "Please provide locals for enhancedDocString of %s" % item name = item lcl = args[0] args = args[1:] elif hasattr(item, "im_class"): # bound method raise NotImplementedError, \ "enhancedDocString is not yet implemented for methods" elif hasattr(item, "__name__"): name = item.__name__ lcl = item.__dict__ else: raise ValueError, "Don't know how to extend docstring for %s" % item # check whether docstring magic is requested or not if not cfg.getboolean('doc', 'pimp docstrings', True): return lcl['__doc__'] #return lcl['__doc__'] rst_lvlmarkup = ["=", "-", "_"] # would then be called for any child... ok - ad hoc for SVM??? if hasattr(item, '_customizeDoc') and name=='SVM': item._customizeDoc() initdoc = "" if lcl.has_key('__init__'): func = lcl['__init__'] initdoc = func.__doc__ # either to extend arguments # do only if kwargs is one of the arguments # in python 2.5 args are no longer in co_names but in varnames extend_args = force_extend or \ 'kwargs' in (func.func_code.co_names + func.func_code.co_varnames) if __debug__ and not extend_args: debug('DOCH', 'Not extending parameters for %s' % name) if initdoc is None: initdoc = "Initialize instance of %s" % name initdoc, params, suffix = _splitOutParametersStr(initdoc) if lcl.has_key('_paramsdoc'): params += '\n' + handleDocString(lcl['_paramsdoc']) params_list = _parseParameters(params) known_params = set([i[0] for i in params_list]) # no need for placeholders skip_params = set(skip_params + ['kwargs', '**kwargs']) # XXX we do evil check here, refactor code to separate # regressions out of the classifiers, and making # retrainable flag not available for those classes which # can't actually do retraining. Although it is not # actually that obvious for Meta Classifiers if hasattr(item, '_clf_internals'): clf_internals = item._clf_internals skip_params.update([i for i in ('regression', 'retrainable') if not (i in clf_internals)]) known_params.update(skip_params) if extend_args: # go through all the parents and obtain their init parameters parent_params_list = [] for i in args: if hasattr(i, '__init__'): # XXX just assign within a class to don't redo without need initdoc_ = i.__init__.__doc__ if initdoc_ is None: continue splits_ = _splitOutParametersStr(initdoc_) params_ = splits_[1] parent_params_list += _parseParameters(params_.lstrip()) # extend with ones which are not known to current init for i, v in parent_params_list: if not (i in known_params): params_list += [(i, v)] known_params.update([i]) # if there are parameters -- populate the list if len(params_list): params_ = '\n'.join([i[1].rstrip() for i in params_list if not i[0] in skip_params]) initdoc += "\n\n%sParameters%s\n" % ( (_rst_sep2,)*2 ) \ + _indent(params_) if suffix != "": initdoc += "\n\n" + suffix initdoc = handleDocString(initdoc) # Finally assign generated doc to the constructor lcl['__init__'].__doc__ = initdoc docs = [ handleDocString(lcl['__doc__']) ] # Optionally populate the class documentation with it if __add_init2doc and initdoc != "": docs += [ rstUnderline('Constructor information for `%s` class' % name, rst_lvlmarkup[2]), initdoc ] # Add information about the states if available if lcl.has_key('_statesdoc'): # no indent is necessary since states list must be already indented docs += [_rst('.. note::\n ') + 'Available state variables:', handleDocString(item._statesdoc)] if len(args): bc_intro = _rst(' ') + 'Please refer to the documentation of the ' \ 'base %s for more information:' \ % (singleOrPlural('class', 'classes', len(args))) docs += [_rst('\n.. seealso::'), bc_intro, ' ' + ',\n '.join(['%s%s.%s%s' % (_rst(':class:`~'), i.__module__, i.__name__, _rst_sep) for i in args]) ] itemdoc = '\n\n'.join(docs) # remove some bogus new lines -- never 3 empty lines in doc are useful result = re.sub("\s*\n\s*\n\s*\n", "\n\n", itemdoc) return result def table2string(table, out=None): """Given list of lists figure out their common widths and print to out :Parameters: table : list of lists of strings What is aimed to be printed out : None or stream Where to print. If None -- will print and return string :Returns: string if out was None """ print2string = out is None if print2string: out = StringIO() # equalize number of elements in each row Nelements_max = max(len(x) for x in table) for i, table_ in enumerate(table): table[i] += [''] * (Nelements_max - len(table_)) # figure out lengths within each column atable = N.asarray(table) markup_strip = re.compile('^@[lrc]') col_width = [ max( [len(markup_strip.sub('', x)) for x in column] ) for column in atable.T ] string = "" for i, table_ in enumerate(table): string_ = "" for j, item in enumerate(table_): item = str(item) if item.startswith('@'): align = item[1] item = item[2:] if not align in ['l', 'r', 'c']: raise ValueError, 'Unknown alignment %s. Known are l,r,c' else: align = 'c' NspacesL = ceil((col_width[j] - len(item))/2.0) NspacesR = col_width[j] - NspacesL - len(item) if align == 'c': pass elif align == 'l': NspacesL, NspacesR = 0, NspacesL + NspacesR elif align == 'r': NspacesL, NspacesR = NspacesL + NspacesR, 0 else: raise RuntimeError, 'Should not get here with align=%s' % align string_ += "%%%ds%%s%%%ds " \ % (NspacesL, NspacesR) % ('', item, '') string += string_.rstrip() + '\n' out.write(string) if print2string: value = out.getvalue() out.close() return value pymvpa-0.4.8/mvpa/base/externals.py000066400000000000000000000446661174541445200173220ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Helper to verify presence of external libraries and modules """ __docformat__ = 'restructuredtext' import os from mvpa.base import warning from mvpa import cfg from mvpa.misc.support import SmartVersion if __debug__: from mvpa.base import debug class _VersionsChecker(dict): """Helper class to check the versions of the available externals """ def __getitem__(self, key): if not self.has_key(key): exists(key, force=True, raiseException=True) return super(_VersionsChecker, self).__getitem__(key) versions = _VersionsChecker() """Versions of available externals, as tuples """ def __check_scipy(): """Check if scipy is present an if it is -- store its version """ import warnings exists('numpy', raiseException='always') # To don't allow any crappy warning to sneak in warnings.simplefilter('ignore', DeprecationWarning) try: import scipy as sp except: warnings.simplefilter('default', DeprecationWarning) raise warnings.simplefilter('default', DeprecationWarning) # Infiltrate warnings if necessary numpy_ver = versions['numpy'] scipy_ver = versions['scipy'] = SmartVersion(sp.__version__) # There is way too much deprecation warnings spit out onto the # user. Lets assume that they should be fixed by scipy 0.7.0 time if scipy_ver >= "0.6.0" and scipy_ver < "0.7.0" \ and numpy_ver > "1.1.0": import warnings if not __debug__ or (__debug__ and not 'PY' in debug.active): if __debug__: debug('EXT', "Setting up filters for numpy DeprecationWarnings") filter_lines = [ ('NumpyTest will be removed in the next release.*', DeprecationWarning), ('PyArray_FromDims: use PyArray_SimpleNew.', DeprecationWarning), ('PyArray_FromDimsAndDataAndDescr: use PyArray_NewFromDescr.', DeprecationWarning), # Trick re.match, since in warnings absent re.DOTALL in re.compile ('[\na-z \t0-9]*The original semantics of histogram is scheduled to be.*' '[\na-z \t0-9]*', Warning) ] for f, w in filter_lines: warnings.filterwarnings('ignore', f, w) def __check_numpy(): """Check if numpy is present (it must be) an if it is -- store its version """ import numpy as N versions['numpy'] = SmartVersion(N.__version__) def __check_pywt(features=None): """Check for available functionality within pywt :Parameters: features : list of basestring List of known features to check such as 'wp reconstruct', 'wp reconstruct fixed' """ import pywt import numpy as N data = N.array([ 0.57316901, 0.65292526, 0.75266733, 0.67020084, 0.46505364, 0.76478331, 0.33034164, 0.49165547, 0.32979941, 0.09696717, 0.72552711, 0.4138999 , 0.54460628, 0.786351 , 0.50096306, 0.72436454, 0.2193098 , -0.0135051 , 0.34283984, 0.65596245, 0.49598417, 0.39935064, 0.26370727, 0.05572373, 0.40194438, 0.47004551, 0.60327258, 0.25628266, 0.32964893, 0.24009889,]) mode = 'per' wp = pywt.WaveletPacket(data, 'sym2', mode) wp2 = pywt.WaveletPacket(data=None, wavelet='sym2', mode=mode) try: for node in wp.get_level(2): wp2[node.path] = node.data except: raise ImportError, \ "Failed to reconstruct WP by specifying data in the layer" if 'wp reconstruct fixed' in features: rec = wp2.reconstruct() if N.linalg.norm(rec[:len(data)] - data) > 1e-3: raise ImportError, \ "Failed to reconstruct WP correctly" return True def __check_libsvm_verbosity_control(): """Check for available verbose control functionality """ import mvpa.clfs.libsvmc._svmc as _svmc try: _svmc.svm_set_verbosity(0) except: raise ImportError, "Provided version of libsvm has no way to control " \ "its level of verbosity" def __check_shogun(bottom_version, custom_versions=[]): """Check if version of shogun is high enough (or custom known) to be enabled in the testsuite :Parameters: bottom_version : int Bottom version which must be satisfied custom_versions : list of int Arbitrary list of versions which could got patched for a specific issue """ import shogun.Classifier as __sc ver = __sc.Version_get_version_revision() if (ver in custom_versions) or (ver >= bottom_version): return True else: raise ImportError, 'Version %s is smaller than needed %s' % \ (ver, bottom_version) def __check_weave(): """Apparently presence of scipy is not sufficient since some versions experience problems. E.g. in Sep,Oct 2008 lenny's weave failed to work. May be some other converter could work (? See http://lists.debian.org/debian-devel/2008/08/msg00730.html for a similar report. Following simple snippet checks compilation of the basic code using weave """ from scipy import weave from scipy.weave import converters, build_tools import numpy as N # to shut weave up import sys # we can't rely on weave at all at the restoring argv. On etch box # restore_sys_argv() is apparently is insufficient oargv = sys.argv[:] ostdout = sys.stdout if not( __debug__ and 'EXT_' in debug.active): from StringIO import StringIO sys.stdout = StringIO() # *nix specific solution to shut weave up. # Some users must complain and someone # needs to fix this to become more generic. cargs = [">/dev/null", "2>&1"] else: cargs = [] fmsg = None try: data = N.array([1,2,3]) counter = weave.inline("data[0]=fabs(-1);", ['data'], type_converters=converters.blitz, verbose=0, extra_compile_args=cargs, compiler = 'gcc') except Exception, e: fmsg = "Failed to build simple weave sample." \ " Exception was %s" % str(e) sys.stdout = ostdout # needed to fix sweave which might "forget" to restore sysv # build_tools.restore_sys_argv() sys.argv = oargv if fmsg is not None: raise ImportError, fmsg else: return "Everything is cool" def __check_atlas_family(family): # XXX I guess pylint will dislike it a lot from mvpa.atlases.warehouse import KNOWN_ATLAS_FAMILIES names, pathpattern = KNOWN_ATLAS_FAMILIES[family] filename = pathpattern % {'name':names[0]} if not os.path.exists(filename): raise ImportError, "Cannot find file %s for atlas family %s" \ % (filename, family) pass def __check_stablerdist(): import scipy.stats import numpy as N ## Unfortunately 0.7.0 hasn't fixed the issue so no chance but to do ## a proper numerical test here try: scipy.stats.rdist(1.32, 0, 1).cdf(-1.0 + N.finfo(float).eps) # Actually previous test is insufficient for 0.6, so enabling # elderly test on top # ATM all known implementations which implement custom cdf for # rdist are misbehaving, so there should be no _cdf if '_cdf' in scipy.stats.distributions.rdist_gen.__dict__.keys(): raise ImportError, \ "scipy.stats carries misbehaving rdist distribution" except ZeroDivisionError: raise RuntimeError, "RDist in scipy is still unstable on the boundaries" def __check_rv_discrete_ppf(): """Unfortunately 0.6.0-12 of scipy pukes on simple ppf """ import scipy.stats try: bdist = scipy.stats.binom(100, 0.5) bdist.ppf(0.9) except TypeError: raise RuntimeError, "pmf is broken in discrete dists of scipy.stats" def __check_in_ipython(): # figure out if ran within IPython if '__IPYTHON__' in globals()['__builtins__']: return raise RuntimeError, "Not running in IPython session" def __check_openopt(): try: import openopt as _ return except ImportError: pass import scikits.openopt as _ return def __check_matplotlib(): """Check for presence of matplotlib and set backend if requested.""" import matplotlib backend = cfg.get('matplotlib', 'backend') if backend: matplotlib.use(backend) def __check_pylab(): """Check if matplotlib is there and then pylab""" exists('matplotlib', raiseException='always') import pylab as P def __check_pylab_plottable(): """Simple check either we can plot anything using pylab. Primary use in unittests """ try: exists('pylab', raiseException='always') import pylab as P fig = P.figure() P.plot([1,2], [1,2]) P.close(fig) except: raise RuntimeError, "Cannot plot in pylab" return True def __check_griddata(): """griddata might be independent module or part of mlab """ try: from griddata import griddata as __ return True except ImportError: if __debug__: debug('EXT_', 'No python-griddata available') from matplotlib.mlab import griddata as __ return True def __check_reportlab(): import reportlab as rl versions['reportlab'] = SmartVersion(rl.Version) def __check_rpy(): """Check either rpy is available and also set it for the sane execution """ #import rpy_options #rpy_options.set_options(VERBOSE=False, SETUP_READ_CONSOLE=False) # SETUP_WRITE_CONSOLE=False) #rpy_options.set_options(VERBOSE=False, SETUP_WRITE_CONSOLE=False) # SETUP_WRITE_CONSOLE=False) # if not cfg.get('rpy', 'read_console', default=False): # print "no read" # rpy_options.set_options(SETUP_READ_CONSOLE=False) # if not cfg.get('rpy', 'write_console', default=False): # print "no write" # rpy_options.set_options(SETUP_WRITE_CONSOLE=False) import rpy if not cfg.getboolean('rpy', 'interactive', default=True) \ and (rpy.get_rpy_input() is rpy.rpy_io.rpy_input): if __debug__: debug('EXT_', "RPy: providing dummy callback for input to return '1'") def input1(*args): return "1" # which is "1: abort (with core dump, if enabled)" rpy.set_rpy_input(input1) # contains list of available (optional) external classifier extensions _KNOWN = {'libsvm':'import mvpa.clfs.libsvmc._svm as __; x=__.convert2SVMNode', 'libsvm verbosity control':'__check_libsvm_verbosity_control();', 'nifti':'from nifti import NiftiImage as __', 'nifti ge 0.20090205.1': 'from nifti.clib import detachDataFromImage as __', 'ctypes':'import ctypes as __', 'shogun':'import shogun as __', 'shogun.krr': 'import shogun.Regression as __; x=__.KRR', 'shogun.mpd': 'import shogun.Classifier as __; x=__.MPDSVM', 'shogun.lightsvm': 'import shogun.Classifier as __; x=__.SVMLight', 'shogun.svrlight': 'from shogun.Regression import SVRLight as __', 'numpy': "__check_numpy()", 'scipy': "__check_scipy()", 'good scipy.stats.rdist': "__check_stablerdist()", 'good scipy.stats.rv_discrete.ppf': "__check_rv_discrete_ppf()", 'weave': "__check_weave()", 'pywt': "import pywt as __", 'pywt wp reconstruct': "__check_pywt(['wp reconstruct'])", 'pywt wp reconstruct fixed': "__check_pywt(['wp reconstruct fixed'])", 'rpy': "__check_rpy()", 'lars': "exists('rpy', raiseException='always'); import rpy; rpy.r.library('lars')", 'elasticnet': "exists('rpy', raiseException='always'); import rpy; rpy.r.library('elasticnet')", 'glmnet': "exists('rpy', raiseException='always'); import rpy; rpy.r.library('glmnet')", 'matplotlib': "__check_matplotlib()", 'pylab': "__check_pylab()", 'pylab plottable': "__check_pylab_plottable()", 'openopt': "__check_openopt()", 'mdp': "import mdp as __", 'mdp ge 2.4': "from mdp.nodes import LLENode as __", 'sg_fixedcachesize': "__check_shogun(3043, [2456])", # 3318 corresponds to release 0.6.4 'sg ge 0.6.4': "__check_shogun(3318)", 'hcluster': "import hcluster as __", 'griddata': "__check_griddata()", 'cPickle': "import cPickle as __", 'gzip': "import gzip as __", 'lxml': "from lxml import objectify as __", 'atlas_pymvpa': "__check_atlas_family('pymvpa')", 'atlas_fsl': "__check_atlas_family('fsl')", 'running ipython env': "__check_in_ipython()", 'reportlab': "__check_reportlab()", 'nose': "import nose as __", } def exists(dep, force=False, raiseException=False, issueWarning=None): """ Test whether a known dependency is installed on the system. This method allows us to test for individual dependencies without testing all known dependencies. It also ensures that we only test for a dependency once. :Parameters: dep : string or list of string The dependency key(s) to test. force : boolean Whether to force the test even if it has already been performed. raiseException : boolean or 'always' Whether to raise RuntimeError if dependency is missing. If True, it is still conditioned on the global setting MVPA_EXTERNALS_RAISE_EXCEPTION, while would raise exception if missing despite the configuration if 'always'. issueWarning : string or None or True If string, warning with given message would be thrown. If True, standard message would be used for the warning text. """ # if we are provided with a list of deps - go through all of them if isinstance(dep, list) or isinstance(dep, tuple): results = [ exists(dep_, force, raiseException) for dep_ in dep ] return bool(reduce(lambda x,y: x and y, results, True)) # where to look in cfg cfgid = 'have ' + dep # pre-handle raiseException according to the global settings and local argument if isinstance(raiseException, str): if raiseException.lower() == 'always': raiseException = True else: raise ValueError("Unknown value of raiseException=%s. " "Must be bool or 'always'" % raiseException) else: # must be bool conditioned on the global settings raiseException = raiseException \ and cfg.getboolean('externals', 'raise exception', True) # prevent unnecessarry testing if cfg.has_option('externals', cfgid) \ and not cfg.getboolean('externals', 'retest', default='no') \ and not force: if __debug__: debug('EXT', "Skip retesting for '%s'." % dep) # check whether an exception should be raised, even though the external # was already tested previously if not cfg.getboolean('externals', cfgid) and raiseException: raise RuntimeError, "Required external '%s' was not found" % dep return cfg.getboolean('externals', cfgid) # determine availability of external (non-cached) # default to 'not found' result = False if not _KNOWN.has_key(dep): raise ValueError, "%s is not a known dependency key." % (dep) else: # try and load the specific dependency if __debug__: debug('EXT', "Checking for the presence of %s" % dep) # Exceptions which are silently caught while running tests for externals _caught_exceptions = [ImportError, AttributeError, RuntimeError] # check whether RPy is involved and catch its excpetions as well. # however, try to determine whether this is really necessary, as # importing RPy also involved starting a full-blown R session, which can # take seconds and therefore is quite nasty... if dep.count('rpy') or _KNOWN[dep].count('rpy'): try: if dep == 'rpy': __check_rpy() # needed to be run to adjust options first else: if exists('rpy'): # otherwise no need to add anything -- test # would fail since rpy isn't available from rpy import RException _caught_exceptions += [RException] except: pass estr = '' try: exec _KNOWN[dep] result = True except tuple(_caught_exceptions), e: estr = ". Caught exception was: " + str(e) if __debug__: debug('EXT', "Presence of %s is%s verified%s" % (dep, {True:'', False:' NOT'}[result], estr)) if not result: if raiseException: raise RuntimeError, "Required external '%s' was not found" % dep if issueWarning is not None \ and cfg.getboolean('externals', 'issue warning', True): if issueWarning is True: warning("Required external '%s' was not found" % dep) else: warning(issueWarning) # store result in config manager if not cfg.has_section('externals'): cfg.add_section('externals') if result: cfg.set('externals', 'have ' + dep, 'yes') else: cfg.set('externals', 'have ' + dep, 'no') return result def testAllDependencies(force=False): """ Test for all known dependencies. :Parameters: force : boolean Whether to force the test even if it has already been performed. """ # loop over all known dependencies for dep in _KNOWN: if not exists(dep, force): warning("%s is not available." % dep) if __debug__: debug('EXT', 'The following optional externals are present: %s' \ % [ k[5:] for k in cfg.options('externals') if k.startswith('have') \ and cfg.getboolean('externals', k) == True ]) pymvpa-0.4.8/mvpa/base/info.py000066400000000000000000000144441174541445200162370ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Provide system and PyMVPA information useful while reporting bugs """ __docformat__ = 'restructuredtext' import time, sys, os, subprocess import platform as pl from tempfile import mkstemp from StringIO import StringIO import mvpa from mvpa.base import externals, cfg def _t2s(t): res = [] for e in t: if isinstance(e, tuple): es = _t2s(e) if es != '': res += ['(%s)' % es] elif e != '': res += [e] return '/'.join(res) __all__ = ['wtf'] class WTF(object): """Convenience class to contain information about PyMVPA and OS TODO: refactor to actually not contain just string representation but rather a dictionary (of dictionaries) """ def __init__(self): self._info = '' self._acquire() def _acquire(self): """ TODO: refactor and redo ;) """ out = StringIO() out.write("Current date: %s\n" % time.strftime("%Y-%m-%d %H:%M")) out.write("PyMVPA:\n") out.write(" Version: %s\n" % mvpa.__version__) out.write(" Path: %s\n" % mvpa.__file__) # Try to obtain git information if available out.write(" Version control (GIT):\n") try: gitpath = os.path.join(os.path.dirname(mvpa.__file__), os.path.pardir) gitpathgit = os.path.join(gitpath, '.git') if os.path.exists(gitpathgit): for scmd, cmd in [ ('Status', ['status']), ('Reference', 'show-ref -h HEAD'.split(' ')), ('Difference from last release %s' % mvpa.__version__, ['diff', '--shortstat', 'upstream/%s...' % mvpa.__version__])]: try: (tmpd, tmpn) = mkstemp('mvpa', 'git') retcode = subprocess.call(['git', '--git-dir=%s' % gitpathgit, '--work-tree=%s' % gitpath] + cmd, stdout=tmpd, stderr=subprocess.STDOUT) finally: outlines = open(tmpn, 'r').readlines() if len(outlines): out.write(' %s:\n %s' % (scmd, ' '.join(outlines))) os.remove(tmpn) #except Exception, e: # pass else: raise RuntimeError, "%s is not under GIT" % gitpath except Exception, e: out.write(' GIT information could not be obtained due "%s"\n' % e) out.write('SYSTEM:\n') out.write(' OS: %s\n' % ' '.join([os.name, pl.system(), pl.release(), pl.version()]).rstrip()) out.write(' Distribution: %s\n' % ' '.join([_t2s(pl.dist()), _t2s(pl.mac_ver()), _t2s(pl.win32_ver())]).rstrip()) # Test and list all dependencies: sdeps = {True: [], False: []} for dep in sorted(externals._KNOWN): sdeps[externals.exists(dep, force=False)] += [dep] out.write('EXTERNALS:\n') out.write(' Present: %s\n' % ', '.join(sdeps[True])) out.write(' Absent: %s\n' % ', '.join(sdeps[False])) SV = ('.__version__', ) # standard versioning out.write(' Versions of critical externals:\n') for e, mname, fs in ( ('ctypes', None, SV), ('matplotlib', None, SV), ('lxml', None, ('.etree.__version__',)), ('nifti', None, SV), ('numpy', None, SV), ('openopt', 'openopt', SV), ('openopt', 'scikits.openopt', ('.openopt.__version__',)), ('pywt', None, SV), ('rpy', None, ('.rpy_version',)), ('scipy', None, SV), ('shogun', None, ('.Classifier.Version_get_version_release()',)), ): try: if not externals.exists(e): continue #sver = 'not present' else: if mname is None: mname = e m = __import__(mname) svers = [eval('m%s' % (f,)) for f in fs] sver = ' '.join(svers) except Exception, exc: sver = 'failed to query due to "%s"' % str(exc) out.write(' %-12s: %s\n' % (e, sver)) if externals.exists('matplotlib'): import matplotlib out.write(' Matplotlib backend: %s\n' % matplotlib.get_backend()) out.write("RUNTIME:\n") out.write(" PyMVPA Environment Variables:\n") out.write(' '.join([' %-20s: "%s"\n' % (str(k), str(v)) for k, v in os.environ.iteritems() if (k.startswith('MVPA') or k.startswith('PYTHON'))])) out.write(" PyMVPA Runtime Configuration:\n") out.write(' ' + str(cfg).replace('\n', '\n ').rstrip() + '\n') try: procstat = open('/proc/%d/status' % os.getpid()).readlines() out.write(' Process Information:\n') out.write(' ' + ' '.join(procstat)) except: pass self._info = out.getvalue() def __repr__(self): if self._info is None: self._acquire() return self._info __str__ = __repr__ def wtf(filename=None): """Report summary about PyMVPA and the system :Keywords: filename : None or string If provided, information will be stored in a file, not printed to the screen """ info = WTF() if filename is not None: out = file(filename, 'w').write(str(info)) else: return info if __name__ == '__main__': print wtf() pymvpa-0.4.8/mvpa/base/report.py000066400000000000000000000245361174541445200166220ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Creating simple PDF reports using reportlab """ __docformat__ = 'restructuredtext' import os from datetime import datetime import mvpa from mvpa.base import externals, verbose if __debug__: from mvpa.base import debug if externals.exists('reportlab', raiseException=True): import reportlab as rl from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Image from reportlab.lib.styles import getSampleStyleSheet from reportlab.lib.units import inch # Actually current reportlab's Image can't deal directly with .pdf images # Lets use png for now if externals.versions['reportlab'] >= '1112.2': _fig_ext_default = 'pdf' else: _fig_ext_default = 'png' __all__ = [ 'rl', 'Report', 'escapeXML' ] def escapeXML(s): s = s.replace('&', '&') s = s.replace('<', '<') s = s.replace('>', '>') return s class Report(object): """Simple PDF reports using reportlab Named report 'report' generates 'report.pdf' and directory 'report/' with images which were requested to be included in the report You can attach report to the existing 'verbose' with report = Report() verbose.handlers += [report] and then all verbose messages present on the screen will also be recorded in the report. Use report.text("string") to add arbitrary text report.xml("

    skajdsf

    ") to add XML snippet or report.figure() to add the current figure to the report. report.figures() to add existing figures to the report, but they might not be properly interleaved with verbose messages if there were any between the creations of the figures. Inspired by Andy Connolly """ def __init__(self, name='report', title=None, path=None, author=None, style="Normal", fig_ext=None, font='Helvetica', pagesize=None): """Initialize report :Parameters: name : string Name of the report title : string or None Title to start the report, if None, name will be used path : string Top directory where named report will be stored. Has to be set now to have correct path for storing image renderings. Default: current directory author : string or None Optional author identity to be printed style : string Default Paragraph to be used. Must be the one of the known to reportlab styles, e.g. Normal fig_ext : string What extension to use for figures by default. If None, a default will be used. Since versions prior 2.2 of reportlab might do not support pdf, 'png' is default for those, 'pdf' otherwise font : string Name of the font to use pagesize : tuple of floats Optional page size if not to be default """ if pagesize is None: pagesize = rl.rl_config.defaultPageSize self.pagesize = pagesize self.name = name self.author = author self.font = font self.title = title if fig_ext is None: self.fig_ext = _fig_ext_default else: self.fig_ext = fig_ext if path is None: self._filename = name else: self._filename = os.path.join(path, name) self.__nfigures = 0 try: styles = getSampleStyleSheet() self.style = styles.byName[style] except KeyError: raise ValueError, \ "Style %s is not know to reportlab. Known are %s" \ % (styles.keys()) self._story = [] @property def __preamble(self): """Compose the beginning of the report """ date = datetime.today().isoformat(' ') owner = 'PyMVPA v. %s' % mvpa.__version__ if self.author is not None: owner += ' Author: %s' % self.author return [ Spacer(1, 0.8*inch), Paragraph("Generated on " + date, self.style), Paragraph(owner, self.style)] + self.__flowbreak def clear(self): """Clear the report """ self._story = [] def xml(self, line, style=None): """Adding XML string to the report """ if __debug__ and not self in debug.handlers: debug("REP", "Adding xml '%s'" % line.strip()) if style is None: style = self.style self._story.append(Paragraph(line, style=style)) def text(self, line, **kwargs): """Add a text string to the report """ if __debug__ and not self in debug.handlers: debug("REP_", "Adding text '%s'" % line.strip()) # we need to convert some of the characters to make it # legal XML line = escapeXML(line) self.xml(line, **kwargs) write = text """Just an alias for .text, so we could simply provide report as a handler for verbose """ def figure(self, fig=None, name=None, savefig_kwargs={}, **kwargs): """Add a figure to the report :Parameters: fig : None or string or `figure.Figure` Figure to place into report string : treat as a filename Figure : stores it into a file under directory and embedds into the report None : takes the current figure savefig_kwargs : dict Additional keyword arguments to provide savefig with (e.g. dpi) **kwargs Passed to :class:`reportlab.platypus.Image` constructor """ if externals.exists('pylab', raiseException=True): import pylab as P figure = P.matplotlib.figure if fig is None: fig = P.gcf() if isinstance(fig, figure.Figure): # Create directory if needed if not (os.path.exists(self._filename) and os.path.isdir(self._filename)): os.makedirs(self._filename) # Figure out the name for image self.__nfigures += 1 if name is None: name = 'Figure#' name = name.replace('#', str(self.__nfigures)) # Save image fig_filename = os.path.join(self._filename, '%s.%s' % (name, self.fig_ext)) if __debug__ and not self in debug.handlers: debug("REP_", "Saving figure '%s' into %s" % (fig, fig_filename)) fig.savefig(fig_filename, **savefig_kwargs) # adjust fig to the one to be included fig = fig_filename if __debug__ and not self in debug.handlers: debug("REP", "Adding figure '%s'" % fig) im = Image(fig, **kwargs) # If the inherent or provided width/height are too large -- shrink down imsize = (im.drawWidth, im.drawHeight) # Reduce the size if necessary so reportlab does not puke later on r = [float(d)/m for d,m in zip(imsize, self.pagesize)] maxr = max(r) if maxr > 1.0: if __debug__ and not self in debug.handlers: debug("REP_", "Shrinking figure by %.3g" % maxr) im.drawWidth /= maxr im.drawHeight /= maxr self._story.append(im) def figures(self, *args, **kwargs): """Adds all present figures at once If called twice, it might add the same figure multiple times, so make sure to close all previous figures if you use figures() multiple times """ if externals.exists('pylab', raiseException=True): import pylab as P figs = P.matplotlib._pylab_helpers.Gcf.figs if __debug__ and not self in debug.handlers: debug('REP', "Saving all %d present figures" % len(figs)) for fid, f in figs.iteritems(): self.figure(f.canvas.figure, *args, **kwargs) @property def __flowbreak(self): return [Spacer(1, 0.2*inch), Paragraph("-" * 150, self.style), Spacer(1, 0.2*inch)] def flowbreak(self): """Just a marker for the break of the flow """ if __debug__ and not self in debug.handlers: debug("REP", "Adding flowbreak") self._story.append(self.__flowbreak) ## def __del__(self): ## """Store report upon deletion ## """ ## if __debug__ and not self in debug.handlers: ## debug("REP", "Report is being deleted. Storing") ## self.save() def save(self, add_preamble=True): """Saves PDF :Parameters: add_preamble : bool Either to add preamble containing title/date/author information """ if self.title is None: title = self.name + " report" else: title = self.title pageinfo = self.name + " data" def myFirstPage(canvas, doc): canvas.saveState() canvas.setFont(self.font, 16) canvas.drawCentredString(self.pagesize[0]/2.0, self.pagesize[1]-108, title) canvas.setFont(self.font, 9) canvas.drawString(inch, 0.75 * inch, "First Page / %s" % pageinfo) canvas.restoreState() def myLaterPages(canvas, doc): canvas.saveState() canvas.setFont(self.font, 9) canvas.drawString(inch, 0.75 * inch, "Page %d %s" % (doc.page, pageinfo)) canvas.restoreState() filename = self._filename + ".pdf" doc = SimpleDocTemplate(filename) story = self._story if add_preamble: story = self.__preamble + story if __debug__ and not self in debug.handlers: debug("REP", "Saving the report into %s" % filename) doc.build(story, onFirstPage=myFirstPage, onLaterPages=myLaterPages) pymvpa-0.4.8/mvpa/base/report_dummy.py000066400000000000000000000021211174541445200200170ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Dummy report class, to just be there in case if reportlab is not available. """ __docformat__ = 'restructuredtext' from mvpa.base import warning if __debug__: from mvpa.base import debug def _dummy(*args, **kwargs): pass class Report(object): """Dummy report class which does nothing but complains if used """ def __init__(self, *args, **kwargs): """Initialize dummy report """ warning("You are using DummyReport - no action will be taken. " "Please install reportlab to enjoy reporting facility " "within PyMVPA") def __getattribute__(self, index): """ """ # returns a dummy function return _dummy pymvpa-0.4.8/mvpa/base/verbosity.py000066400000000000000000000435561174541445200173400ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Verbose output and debugging facility Examples: from verbosity import verbose, debug; debug.active = [1,2,3]; debug(1, "blah") """ __docformat__ = 'restructuredtext' from sys import stdout, stderr # GOALS # any logger should be able # to log into a file or stdout/stderr # provide ability to log with/without a new line at the end # # debug logger should be able # to log sets of debug statements # add/remove debug setid items # give verbose description about registered debugset items class Logger(object): """ Base class to provide logging """ def __init__(self, handlers=None): """Initialize the logger with a set of handlers to use for output Each hanlder must have write() method implemented """ if handlers == None: handlers = [stdout] self.__close_handlers = [] self.__handlers = [] # pylint friendliness self._setHandlers(handlers) self.__lfprev = True self.__crprev = 0 # number of symbols in previous cr-ed def __del__(self): self._closeOpenedHandlers() def _setHandlers(self, handlers): """Set list of handlers for the log. A handler can be opened files, stdout, stderr, or a string, which will be considered a filename to be opened for writing """ handlers_ = [] self._closeOpenedHandlers() for handler in handlers: if isinstance(handler, basestring): try: handler = {'stdout' : stdout, 'stderr' : stderr}[handler.lower()] except: try: handler = open(handler, 'w') self.__close_handlers.append(handler) except: raise RuntimeError, \ "Cannot open file %s for writing by the logger" \ % handler handlers_.append(handler) self.__handlers = handlers_ def _closeOpenedHandlers(self): """Close opened handlers (such as opened logfiles """ for handler in self.__close_handlers: handler.close() def _getHandlers(self): """Return active handlers """ return self.__handlers def __call__(self, msg, lf=True, cr=False, *args, **kwargs): """ Write msg to each of the handlers. It can append a newline (lf = Line Feed) or return to the beginning before output and to take care about cleaning previous message if present it appends a newline (lf = Line Feed) since most commonly each call is a separate message """ if kwargs.has_key('msgargs'): msg = msg % kwargs['msgargs'] if cr: msg_ = "" if self.__crprev > 0: # wipe out older line to make sure to see no ghosts msg_ = "\r%s" % (" "*self.__crprev) msg_ += "\r" + msg self.__crprev = len(msg) msg = msg_ # since it makes no sense this days for cr and lf, # override lf lf = False else: self.__crprev += len(msg) if lf: msg = msg + "\n" self.__crprev = 0 # nothing to clear for handler in self.__handlers: try: handler.write(msg) except: print "Failed writing on handler %s" % handler raise try: handler.flush() except: # it might be not implemented.. pass self.__lfprev = lf handlers = property(fget=_getHandlers, fset=_setHandlers) lfprev = property(fget=lambda self:self.__lfprev) class LevelLogger(Logger): """ Logger which prints based on level -- ie everything which is smaller than specified level """ def __init__(self, level=0, indent=" ", *args, **kwargs): """Define level logger. It is defined by `level`, int: to which messages are reported `indent`, string: symbol used to indent """ Logger.__init__(self, *args, **kwargs) self.__level = level # damn pylint ;-) self.__indent = indent self._setLevel(level) self._setIndent(indent) def _setLevel(self, level): """Set logging level """ ilevel = int(level) if ilevel < 0: raise ValueError, \ "Negative verbosity levels (got %d) are not supported" \ % ilevel self.__level = ilevel def _setIndent(self, indent): """Either to indent the lines based on message log level""" self.__indent = "%s" % indent def __call__(self, level, msg, *args, **kwargs): """ Write msg and indent using self.indent it if it was requested it appends a newline since most commonly each call is a separate message """ if level <= self.level: if self.lfprev and self.indent: # indent if previous line ended with newline msg = self.indent*level + msg Logger.__call__(self, msg, *args, **kwargs) level = property(fget=lambda self: self.__level, fset=_setLevel) indent = property(fget=lambda self: self.__indent, fset=_setIndent) class OnceLogger(Logger): """ Logger which prints a message for a given ID just once. It could be used for one-time warning to don't overfill the output with useless repeatative messages """ def __init__(self, *args, **kwargs): """Define once logger. """ Logger.__init__(self, *args, **kwargs) self._known = {} def __call__(self, ident, msg, count=1, *args, **kwargs): """ Write `msg` if `ident` occured less than `count` times by now. """ if not self._known.has_key(ident): self._known[ident] = 0 if count < 0 or self._known[ident] < count: self._known[ident] += 1 Logger.__call__(self, msg, *args, **kwargs) class SetLogger(Logger): """ Logger which prints based on defined sets identified by Id. """ def __init__(self, register=None, active=None, printsetid=True, *args, **kwargs): if register == None: register = {} if active == None: active = [] Logger.__init__(self, *args, **kwargs) self.__printsetid = printsetid self.__registered = register # all "registered" sets descriptions # which to output... pointless since __registered self._setActive(active) self._setPrintsetid(printsetid) def _setActive(self, active): """Set active logging set """ # just unique entries... we could have simply stored Set I guess, # but then smth like debug.active += ["BLAH"] would not work from mvpa.base import verbose self.__active = [] registered_keys = self.__registered.keys() for item in list(set(active)): if item == '': continue if isinstance(item, basestring): if item in ['?', 'list', 'help']: self.print_registered(detailed=(item != '?')) raise SystemExit(0) if item.upper() == "ALL": verbose(2, "Enabling all registered debug handlers") self.__active = registered_keys break # try to match item as it is regexp regexp_str = "^%s$" % item try: regexp = re.compile(regexp_str) except: raise ValueError, \ "Unable to create regular expression out of %s" % item matching_keys = filter(regexp.match, registered_keys) toactivate = matching_keys if len(toactivate) == 0: ids = self.registered.keys() ids.sort() raise ValueError, \ "Unknown debug ID '%s' was asked to become active," \ " or regular expression '%s' did not get any match" \ " among known ids: %s" \ % (item, regexp_str, ids) else: toactivate = [item] # Lets check if asked items are known for item_ in toactivate: if not (item_ in registered_keys): raise ValueError, \ "Unknown debug ID %s was asked to become active" \ % item_ self.__active += toactivate self.__active = list(set(self.__active)) # select just unique ones self.__maxstrlength = max([len(str(x)) for x in self.__active] + [0]) if len(self.__active): verbose(2, "Enabling debug handlers: %s" % `self.__active`) def _setPrintsetid(self, printsetid): """Either to print set Id at each line""" self.__printsetid = printsetid def __call__(self, setid, msg, *args, **kwargs): """ Write msg It appends a newline since most commonly each call is a separate message """ if setid in self.__active: if len(msg)>0 and self.__printsetid: msg = "[%%-%ds] " % self.__maxstrlength % (setid) + msg Logger.__call__(self, msg, *args, **kwargs) def register(self, setid, description): """ "Register" a new setid with a given description for easy finding """ if self.__registered.has_key(setid): raise ValueError, \ "Setid %s is already known with description '%s'" % \ ( `setid`, self.__registered[setid] ) self.__registered[setid] = description def setActiveFromString(self, value): """Given a string listing registered(?) setids, make then active """ # somewhat evil but works since verbose must be initiated # by now self.active = value.split(",") def print_registered(self, detailed=True): print "Registered debug entries: ", kd = self.registered rks = sorted(kd.keys()) maxl = max([len(k) for k in rks]) if not detailed: # short list print ', '.join(rks) else: print for k in rks: print '%%%ds %%s' % maxl % (k, kd[k]) printsetid = property(fget=lambda self: self.__printsetid, \ fset=_setPrintsetid) active = property(fget=lambda self: self.__active, fset=_setActive) registered = property(fget=lambda self: self.__registered) if __debug__: import os, re import traceback import time from os import getpid from os.path import basename, dirname def parseStatus(field='VmSize'): """Return stat information on current process. Usually it is needed to know where the memory is gone, that is why VmSize is the default for the field to spit out TODO: Spit out multiple fields. Use some better way than parsing proc """ fd = open('/proc/%d/status' % getpid()) lines = fd.readlines() fd.close() match = filter(lambda x:re.match('^%s:'%field, x), lines)[0].strip() match = re.sub('[ \t]+', ' ', match) return match def mbasename(s): """Custom function to include directory name if filename is too common Also strip .py at the end """ base = basename(s).rstrip('py').rstrip('.') if base in ['base', '__init__']: base = basename(dirname(s)) + '.' + base return base class TraceBack(object): def __init__(self, collide=False): """Initialize TrackBack metric :Parameters: collide : bool if True then prefix common with previous invocation gets replaced with ... """ self.__prev = "" self.__collide = collide def __call__(self): ftb = traceback.extract_stack(limit=100)[:-2] entries = [[mbasename(x[0]), str(x[1])] for x in ftb] entries = filter(lambda x:x[0] != 'unittest', entries) # lets make it more consize entries_out = [entries[0]] for entry in entries[1:]: if entry[0] == entries_out[-1][0]: entries_out[-1][1] += ',%s' % entry[1] else: entries_out.append(entry) sftb = '>'.join(['%s:%s' % (mbasename(x[0]), x[1]) for x in entries_out]) if self.__collide: # lets remove part which is common with previous invocation prev_next = sftb common_prefix = os.path.commonprefix((self.__prev, sftb)) common_prefix2 = re.sub('>[^>]*$', '', common_prefix) if common_prefix2 != "": sftb = '...' + sftb[len(common_prefix2):] self.__prev = prev_next return sftb class RelativeTime(object): """Simple helper class to provide relative time it took from previous invocation""" def __init__(self, format="%3.3f sec"): self.__prev = None self.__format = format def __call__(self): dt = 0.0 ct = time.time() if not self.__prev is None: dt = ct - self.__prev self.__prev = ct return self.__format % dt class DebugLogger(SetLogger): """ Logger for debugging purposes. Expands SetLogger with ability to print some interesting information (named Metric... XXX) about current process at each debug printout """ _known_metrics = { 'vmem' : lambda : parseStatus(field='VmSize'), 'pid' : lambda : parseStatus(field='Pid'), 'asctime' : time.asctime, 'tb' : TraceBack(), 'tbc' : TraceBack(collide=True), } def __init__(self, metrics=None, offsetbydepth=True, *args, **kwargs): if metrics == None: metrics = [] SetLogger.__init__(self, *args, **kwargs) self.__metrics = [] self._offsetbydepth = offsetbydepth self._reltimer = RelativeTime() self._known_metrics = DebugLogger._known_metrics self._known_metrics['reltime'] = self._reltimer for metric in metrics: self._registerMetric(metric) def registerMetric(self, func): """Register some metric to report func can be either a function call or a string which should correspond to known metrics """ if isinstance(func, basestring): if func in ['all', 'ALL']: func = self._known_metrics.keys() if isinstance(func, basestring): if DebugLogger._known_metrics.has_key(func): func = DebugLogger._known_metrics[func] else: if func in ['?', 'list', 'help']: print 'Known debug metrics: ', \ ', '.join(DebugLogger._known_metrics.keys()) raise SystemExit(0) else: raise ValueError, \ "Unknown name %s for metric in DebugLogger" % \ func + " Known metrics are " + \ `DebugLogger._known_metrics.keys()` elif isinstance(func, list): self.__metrics = [] # reset for item in func: self.registerMetric(item) return if not func in self.__metrics: try: from mvpa.base import debug debug("DBG", "Registering metric %s" % func) self.__metrics.append(func) except: pass def __call__(self, setid, msg, *args, **kwargs): if not self.registered.has_key(setid): raise ValueError, "Not registered debug ID %s" % setid if not setid in self.active: # don't even compute the metrics, since they might # be statefull as RelativeTime return if len(msg) > 0: msg_ = ' / '.join([str(x()) for x in self.__metrics]) if len(msg_)>0: msg_ = "{%s}" % msg_ # determine blank offset using backstacktrace if self._offsetbydepth: level = len(traceback.extract_stack())-2 else: level = 1 if len(msg)>250 and 'DBG' in self.active and not setid.endswith('_TB'): tb = traceback.extract_stack(limit=2) msg += " !!!2LONG!!!. From %s" % str(tb[0]) msg = "DBG%s:%s%s" % (msg_, " "*level, msg) SetLogger.__call__(self, setid, msg, *args, **kwargs) def _setOffsetByDepth(self, b): self._offsetbydepth = b offsetbydepth = property(fget=lambda x:x._offsetbydepth, fset=_setOffsetByDepth) metrics = property(fget=lambda x:x.__metrics, fset=registerMetric) pymvpa-0.4.8/mvpa/clfs/000077500000000000000000000000001174541445200147405ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/clfs/__init__.py000066400000000000000000000017411174541445200170540ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Import helper for PyMVPA classifiers Module Organization =================== mvpa.clfs module contains various classifiers .. packagetree:: :style: UML :group Base: base :group Meta Classifiers: meta :group Specific Implementations: knn svm _svmbase plr ridge smlr libsmlrc gpr blr :group External Interfaces: lars libsvmc sg :group Utilities: transerror model_selector stats kernel distance :group Warehouse of Classifiers: warehouse """ __docformat__ = 'restructuredtext' if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.clfs') if __debug__: debug('INIT', 'mvpa.clfs end') pymvpa-0.4.8/mvpa/clfs/_svmbase.py000066400000000000000000000340701174541445200171150ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Common to all SVM implementations functionality. For internal use only""" __docformat__ = 'restructuredtext' import numpy as N import textwrap from mvpa.support.copy import deepcopy from mvpa.base import warning from mvpa.base.dochelpers import handleDocString, _rst, _rst_sep2 from mvpa.clfs.base import Classifier from mvpa.misc.param import Parameter from mvpa.misc.transformers import SecondAxisSumOfAbs if __debug__: from mvpa.base import debug class _SVM(Classifier): """Support Vector Machine Classifier. Base class for all external SVM implementations. """ """ Derived classes should define: * _KERNELS: map(dict) should define assignment to a tuple containing implementation kernel type, list of parameters adherent to the kernel, and sensitivity analyzer e.g.:: _KERNELS = { 'linear': (shogun.Kernel.LinearKernel, (), LinearSVMWeights), 'rbf' : (shogun.Kernel.GaussianKernel, ('gamma',), None), ... } * _KNOWN_IMPLEMENTATIONS: map(dict) should define assignment to a tuple containing implementation of the SVM, list of parameters adherent to the implementation, additional internals, and description e.g.:: _KNOWN_IMPLEMENTATIONS = { 'C_SVC' : (svm.svmc.C_SVC, ('C',), ('binary', 'multiclass'), 'C-SVM classification'), ... } """ _ATTRIBUTE_COLLECTIONS = ['params', 'kernel_params'] # enforce presence of params collections _SVM_PARAMS = { 'C' : Parameter(-1.0, doc='Trade-off parameter between width of the ' 'margin and number of support vectors. Higher C -- ' 'more rigid margin SVM. In linear kernel, negative ' 'values provide automatic scaling of their value ' 'according to the norm of the data'), 'nu' : Parameter(0.5, min=0.0, max=1.0, doc='Fraction of datapoints within the margin'), 'cache_size': Parameter(100, doc='Size of the kernel cache, specified in megabytes'), 'coef0': Parameter(0.5, doc='Offset coefficient in polynomial and sigmoid kernels'), 'degree': Parameter(3, doc='Degree of polynomial kernel'), # init the parameter interface 'tube_epsilon': Parameter(0.01, doc='Epsilon in epsilon-insensitive loss function of ' 'epsilon-SVM regression (SVR)'), 'gamma': Parameter(0, doc='Scaling (width in RBF) within non-linear kernels'), 'tau': Parameter(1e-6, doc='TAU parameter of KRR regression in shogun'), 'max_shift': Parameter(10, min=0.0, doc='Maximal shift for SGs GaussianShiftKernel'), 'shift_step': Parameter(1, min=0.0, doc='Shift step for SGs GaussianShiftKernel'), 'probability': Parameter(0, doc='Flag to signal either probability estimate is obtained ' 'within LIBSVM'), 'scale': Parameter(1.0, doc='Scale factor for linear kernel. ' '(0 triggers automagic rescaling by SG'), 'shrinking': Parameter(1, doc='Either shrinking is to be conducted'), 'weight_label': Parameter([], allowedtype='[int]', doc='To be used in conjunction with weight for custom ' 'per-label weight'), # TODO : merge them into a single dictionary 'weight': Parameter([], allowedtype='[double]', doc='Custom weights per label'), # For some reason setting up epsilon to 1e-5 slowed things down a bit # in comparison to how it was before (in yoh/master) by up to 20%... not clear why # may be related to 1e-3 default within _svm.py? 'epsilon': Parameter(5e-5, min=1e-10, doc='Tolerance of termination criteria. (For nu-SVM default is 0.001)') } _clf_internals = [ 'svm', 'kernel-based' ] def __init__(self, kernel_type='linear', **kwargs): """Init base class of SVMs. *Not to be publicly used* :Parameters: kernel_type : basestr String must be a valid key for cls._KERNELS TODO: handling of parameters might migrate to be generic for all classifiers. SVMs are chosen to be testbase for that functionality to see how well it would fit. """ # Check if requested implementation is known svm_impl = kwargs.get('svm_impl', None) if not svm_impl in self._KNOWN_IMPLEMENTATIONS: raise ValueError, \ "Unknown SVM implementation '%s' is requested for %s." \ "Known are: %s" % (svm_impl, self.__class__, self._KNOWN_IMPLEMENTATIONS.keys()) self._svm_impl = svm_impl # Check the kernel kernel_type = kernel_type.lower() if not kernel_type in self._KERNELS: raise ValueError, "Unknown kernel " + kernel_type self._kernel_type_literal = kernel_type impl, add_params, add_internals, descr = \ self._KNOWN_IMPLEMENTATIONS[svm_impl] # Add corresponding parameters to 'known' depending on the # implementation chosen if add_params is not None: self._KNOWN_PARAMS = \ self._KNOWN_PARAMS[:] + list(add_params) # Add corresponding kernel parameters to 'known' depending on what # kernel chosen if self._KERNELS[kernel_type][1] is not None: self._KNOWN_KERNEL_PARAMS = \ self._KNOWN_KERNEL_PARAMS[:] + list(self._KERNELS[kernel_type][1]) # Assign per-instance _clf_internals self._clf_internals = self._clf_internals[:] # Add corresponding internals if add_internals is not None: self._clf_internals += list(add_internals) self._clf_internals.append(svm_impl) if kernel_type == 'linear': self._clf_internals += [ 'linear', 'has_sensitivity' ] else: self._clf_internals += [ 'non-linear' ] # pop out all args from **kwargs which are known to be SVM parameters _args = {} for param in self._KNOWN_KERNEL_PARAMS + self._KNOWN_PARAMS + ['svm_impl']: if param in kwargs: _args[param] = kwargs.pop(param) try: Classifier.__init__(self, **kwargs) except TypeError, e: if "__init__() got an unexpected keyword argument " in e.args[0]: # TODO: make it even more specific -- if that argument is listed # within _SVM_PARAMS e.args = tuple( [e.args[0] + "\n Given SVM instance of class %s knows following parameters: %s" % (self.__class__, self._KNOWN_PARAMS) + ", and kernel parameters: %s" % self._KNOWN_KERNEL_PARAMS] + list(e.args)[1:]) raise e # populate collections and add values from arguments for paramfamily, paramset in ( (self._KNOWN_PARAMS, self.params), (self._KNOWN_KERNEL_PARAMS, self.kernel_params)): for paramname in paramfamily: if not (paramname in self._SVM_PARAMS): raise ValueError, "Unknown parameter %s" % paramname + \ ". Known SVM params are: %s" % self._SVM_PARAMS.keys() param = deepcopy(self._SVM_PARAMS[paramname]) param._setName(paramname) if paramname in _args: param.value = _args[paramname] # XXX might want to set default to it -- not just value paramset.add(param) # tune up C if it has one and non-linear classifier is used if self.params.isKnown('C') and kernel_type != "linear" \ and self.params['C'].isDefault: if __debug__: debug("SVM_", "Assigning default C value to be 1.0 for SVM " "%s with non-linear kernel" % self) self.params['C'].default = 1.0 # Some postchecks if self.params.isKnown('weight') and self.params.isKnown('weight_label'): if not len(self.weight_label) == len(self.weight): raise ValueError, "Lenghts of 'weight' and 'weight_label' lists " \ "must be equal." self._kernel_type = self._KERNELS[kernel_type][0] if __debug__: debug("SVM", "Initialized %s with kernel %s:%s" % (self, kernel_type, self._kernel_type)) def __repr__(self): """Definition of the object summary over the object """ res = "%s(kernel_type='%s', svm_impl='%s'" % \ (self.__class__.__name__, self._kernel_type_literal, self._svm_impl) sep = ", " for col in [self.params, self.kernel_params]: for k in col.names: # list only params with not default values if col[k].isDefault: continue res += "%s%s=%s" % (sep, k, col[k].value) #sep = ', ' for name, invert in ( ('enable', False), ('disable', True) ): states = self.states._getEnabled(nondefault=False, invert=invert) if len(states): res += sep + "%s_states=%s" % (name, str(states)) res += ")" return res def _getDefaultC(self, data): """Compute default C TODO: for non-linear SVMs """ if self._kernel_type_literal == 'linear': datasetnorm = N.mean(N.sqrt(N.sum(data*data, axis=1))) if datasetnorm == 0: warning("Obtained degenerate data with zero norm for training " "of %s. Scaling of C cannot be done." % self) return 1.0 value = 1.0/(datasetnorm**2) if __debug__: debug("SVM", "Default C computed to be %f" % value) else: warning("TODO: Computation of default C is not yet implemented" + " for non-linear SVMs. Assigning 1.0") value = 1.0 return value def _getDefaultGamma(self, dataset): """Compute default Gamma TODO: unify bloody libsvm interface so it makes use of this function. Now it is computed within SVMModel.__init__ """ if self.kernel_params.isKnown('gamma'): value = 1.0 / len(dataset.uniquelabels) if __debug__: debug("SVM", "Default Gamma is computed to be %f" % value) else: raise RuntimeError, "Shouldn't ask for default Gamma here" return value def getSensitivityAnalyzer(self, **kwargs): """Returns an appropriate SensitivityAnalyzer.""" sana = self._KERNELS[self._kernel_type_literal][2] if sana is not None: kwargs.setdefault('combiner', SecondAxisSumOfAbs) return sana(self, **kwargs) else: raise NotImplementedError, \ "Sensitivity analyzers for kernel %s is TODO" % \ self._kernel_type_literal @classmethod def _customizeDoc(cls): #cdoc_old = cls.__doc__ # Need to append documentation to __init__ method idoc_old = cls.__init__.__doc__ idoc = """ SVM/SVR definition is dependent on specifying kernel, implementation type, and parameters for each of them which vary depending on the choices made. Desired implementation is specified in `svm_impl` argument. Here is the list if implementations known to this class, along with specific to them parameters (described below among the rest of parameters), and what tasks it is capable to deal with (e.g. regression, binary and/or multiclass classification). %sImplementations%s""" % (_rst_sep2, _rst_sep2) class NOSClass(object): """Helper -- NothingOrSomething ;) If list is not empty -- return its entries within string s """ def __init__(self): self.seen = [] def __call__(self, l, s, empty=''): if l is None or not len(l): return empty else: lsorted = list(l) lsorted.sort() self.seen += lsorted return s % (', '.join(lsorted)) NOS = NOSClass() # Describe implementations idoc += ''.join( ['\n %s : %s' % (k, v[3]) + NOS(v[1], "\n Parameters: %s") + NOS(v[2], "\n%s Capabilities: %%s" % _rst(('','\n')[int(len(v[1])>0)], '')) for k,v in cls._KNOWN_IMPLEMENTATIONS.iteritems()]) # Describe kernels idoc += """ Kernel choice is specified as a string argument `kernel_type` and it can be specialized with additional arguments to this constructor function. Some kernels might allow computation of per feature sensitivity. %sKernels%s""" % (_rst_sep2, _rst_sep2) idoc += ''.join( ['\n %s' % k + ('', ' : provides sensitivity')[int(v[2] is not None)] + '\n ' + NOS(v[1], '%s', 'No parameters') for k,v in cls._KERNELS.iteritems()]) # Finally parameters NOS.seen += cls._KNOWN_PARAMS + cls._KNOWN_KERNEL_PARAMS idoc += '\n:Parameters:\n' + '\n'.join( [v.doc(indent=' ') for k,v in cls._SVM_PARAMS.iteritems() if k in NOS.seen]) cls.__dict__['__init__'].__doc__ = handleDocString(idoc_old) + idoc # populate names in parameters for k,v in _SVM._SVM_PARAMS.iteritems(): v._setName(k) pymvpa-0.4.8/mvpa/clfs/base.py000066400000000000000000001016411174541445200162270ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Base class for all classifiers. At the moment, regressions are treated just as a special case of classifier (or vise verse), so the same base class `Classifier` is utilized for both kinds. """ __docformat__ = 'restructuredtext' import numpy as N from mvpa.support.copy import deepcopy import time from mvpa.misc.support import idhash from mvpa.misc.state import StateVariable, ClassWithCollections from mvpa.misc.param import Parameter from mvpa.clfs.transerror import ConfusionMatrix, RegressionStatistics from mvpa.base import warning if __debug__: from mvpa.base import debug class LearnerError(Exception): """Base class for exceptions thrown by the learners (classifiers, regressions)""" pass class DegenerateInputError(LearnerError): """Exception to be thrown by learners if input data is bogus, i.e. no features or samples""" pass class FailedToTrainError(LearnerError): """Exception to be thrown whenever classifier fails to learn for some reason""" pass class FailedToPredictError(LearnerError): """Exception to be thrown whenever classifier fails to provide predictions. Usually happens if it was trained on degenerate data but without any complaints. """ pass class Classifier(ClassWithCollections): """Abstract classifier class to be inherited by all classifiers """ # Kept separate from doc to don't pollute help(clf), especially if # we including help for the parent class _DEV__doc__ = """ Required behavior: For every classifier is has to be possible to be instantiated without having to specify the training pattern. Repeated calls to the train() method with different training data have to result in a valid classifier, trained for the particular dataset. It must be possible to specify all classifier parameters as keyword arguments to the constructor. Recommended behavior: Derived classifiers should provide access to *values* -- i.e. that information that is finally used to determine the predicted class label. Michael: Maybe it works well if each classifier provides a 'values' state member. This variable is a list as long as and in same order as Dataset.uniquelabels (training data). Each item in the list corresponds to the likelyhood of a sample to belong to the respective class. However the semantics might differ between classifiers, e.g. kNN would probably store distances to class- neighbors, where PLR would store the raw function value of the logistic function. So in the case of kNN low is predictive and for PLR high is predictive. Don't know if there is the need to unify that. As the storage and/or computation of this information might be demanding its collection should be switchable and off be default. Nomenclature * predictions : corresponds to the quantized labels if classifier spits out labels by .predict() * values : might be different from predictions if a classifier's predict() makes a decision based on some internal value such as probability or a distance. """ # Dict that contains the parameters of a classifier. # This shall provide an interface to plug generic parameter optimizer # on all classifiers (e.g. grid- or line-search optimizer) # A dictionary is used because Michael thinks that access by name is nicer. # Additionally Michael thinks ATM that additional information might be # necessary in some situations (e.g. reasonably predefined parameter range, # minimal iteration stepsize, ...), therefore the value to each key should # also be a dict or we should use mvpa.misc.param.Parameter'... trained_labels = StateVariable(enabled=True, doc="Set of unique labels it has been trained on") trained_nsamples = StateVariable(enabled=True, doc="Number of samples it has been trained on") trained_dataset = StateVariable(enabled=False, doc="The dataset it has been trained on") training_confusion = StateVariable(enabled=False, doc="Confusion matrix of learning performance") predictions = StateVariable(enabled=True, doc="Most recent set of predictions") values = StateVariable(enabled=True, doc="Internal classifier values the most recent " + "predictions are based on") training_time = StateVariable(enabled=True, doc="Time (in seconds) which took classifier to train") predicting_time = StateVariable(enabled=True, doc="Time (in seconds) which took classifier to predict") feature_ids = StateVariable(enabled=False, doc="Feature IDS which were used for the actual training.") _clf_internals = [] """Describes some specifics about the classifier -- is that it is doing regression for instance....""" regression = Parameter(False, allowedtype='bool', doc="""Either to use 'regression' as regression. By default any Classifier-derived class serves as a classifier, so regression does binary classification.""", index=1001) # TODO: make it available only for actually retrainable classifiers retrainable = Parameter(False, allowedtype='bool', doc="""Either to enable retraining for 'retrainable' classifier.""", index=1002) def __init__(self, **kwargs): """Cheap initialization. """ ClassWithCollections.__init__(self, **kwargs) self.__trainednfeatures = None """Stores number of features for which classifier was trained. If None -- it wasn't trained at all""" self._setRetrainable(self.params.retrainable, force=True) if self.params.regression: for statevar in [ "trained_labels"]: #, "training_confusion" ]: if self.states.isEnabled(statevar): if __debug__: debug("CLF", "Disabling state %s since doing regression, " % statevar + "not classification") self.states.disable(statevar) self._summaryClass = RegressionStatistics else: self._summaryClass = ConfusionMatrix clf_internals = self._clf_internals if 'regression' in clf_internals and not ('binary' in clf_internals): # regressions are used as binary classifiers if not # asked to perform regression explicitly # We need a copy of the list, so we don't override class-wide self._clf_internals = clf_internals + ['binary'] # deprecate #self.__trainedidhash = None #"""Stores id of the dataset on which it was trained to signal #in trained() if it was trained already on the same dataset""" def __str__(self): if __debug__ and 'CLF_' in debug.active: return "%s / %s" % (repr(self), super(Classifier, self).__str__()) else: return repr(self) def __repr__(self, prefixes=[]): return super(Classifier, self).__repr__(prefixes=prefixes) def _pretrain(self, dataset): """Functionality prior to training """ # So we reset all state variables and may be free up some memory # explicitly params = self.params if not params.retrainable: self.untrain() else: # just reset the states, do not untrain self.states.reset() if not self.__changedData_isset: self.__resetChangedData() _changedData = self._changedData __idhashes = self.__idhashes __invalidatedChangedData = self.__invalidatedChangedData # if we don't know what was changed we need to figure # them out if __debug__: debug('CLF_', "IDHashes are %s" % (__idhashes)) # Look at the data if any was changed for key, data_ in (('traindata', dataset.samples), ('labels', dataset.labels)): _changedData[key] = self.__wasDataChanged(key, data_) # if those idhashes were invalidated by retraining # we need to adjust _changedData accordingly if __invalidatedChangedData.get(key, False): if __debug__ and not _changedData[key]: debug('CLF_', 'Found that idhash for %s was ' 'invalidated by retraining' % key) _changedData[key] = True # Look at the parameters for col in self._paramscols: changedParams = self._collections[col].whichSet() if len(changedParams): _changedData[col] = changedParams self.__invalidatedChangedData = {} # reset it on training if __debug__: debug('CLF_', "Obtained _changedData is %s" % (self._changedData)) if not params.regression and 'regression' in self._clf_internals \ and not self.states.isEnabled('trained_labels'): # if classifier internally does regression we need to have # labels it was trained on if __debug__: debug("CLF", "Enabling trained_labels state since it is needed") self.states.enable('trained_labels') def _posttrain(self, dataset): """Functionality post training For instance -- computing confusion matrix :Parameters: dataset : Dataset Data which was used for training """ if self.states.isEnabled('trained_labels'): self.trained_labels = dataset.uniquelabels self.trained_dataset = dataset self.trained_nsamples = dataset.nsamples # needs to be assigned first since below we use predict self.__trainednfeatures = dataset.nfeatures if __debug__ and 'CHECK_TRAINED' in debug.active: self.__trainedidhash = dataset.idhash if self.states.isEnabled('training_confusion') and \ not self.states.isSet('training_confusion'): # we should not store predictions for training data, # it is confusing imho (yoh) self.states._changeTemporarily( disable_states=["predictions"]) if self.params.retrainable: # we would need to recheck if data is the same, # XXX think if there is a way to make this all # efficient. For now, probably, retrainable # classifiers have no chance but not to use # training_confusion... sad self.__changedData_isset = False predictions = self.predict(dataset.samples) self.states._resetEnabledTemporarily() self.training_confusion = self._summaryClass( targets=dataset.labels, predictions=predictions) try: self.training_confusion.labels_map = dataset.labels_map except: pass if self.states.isEnabled('feature_ids'): self.feature_ids = self._getFeatureIds() def _getFeatureIds(self): """Virtual method to return feature_ids used while training Is not intended to be called anywhere but from _posttrain, thus classifier is assumed to be trained at this point """ # By default all features are used return range(self.__trainednfeatures) def summary(self): """Providing summary over the classifier""" s = "Classifier %s" % self states = self.states states_enabled = states.enabled if self.trained: s += "\n trained" if states.isSet('training_time'): s += ' in %.3g sec' % states.training_time s += ' on data with' if states.isSet('trained_labels'): s += ' labels:%s' % list(states.trained_labels) nsamples, nchunks = None, None if states.isSet('trained_nsamples'): nsamples = states.trained_nsamples if states.isSet('trained_dataset'): td = states.trained_dataset nsamples, nchunks = td.nsamples, len(td.uniquechunks) if nsamples is not None: s += ' #samples:%d' % nsamples if nchunks is not None: s += ' #chunks:%d' % nchunks s += " #features:%d" % self.__trainednfeatures if states.isSet('feature_ids'): s += ", used #features:%d" % len(states.feature_ids) if states.isSet('training_confusion'): s += ", training error:%.3g" % states.training_confusion.error else: s += "\n not yet trained" if len(states_enabled): s += "\n enabled states:%s" % ', '.join([str(states[x]) for x in states_enabled]) return s def clone(self): """Create full copy of the classifier. It might require classifier to be untrained first due to present SWIG bindings. TODO: think about proper re-implementation, without enrollment of deepcopy """ if __debug__: debug("CLF", "Cloning %s#%s" % (self, id(self))) try: return deepcopy(self) except: self.untrain() return deepcopy(self) def _train(self, dataset): """Function to be actually overridden in derived classes """ raise NotImplementedError def train(self, dataset): """Train classifier on a dataset Shouldn't be overridden in subclasses unless explicitly needed to do so """ if dataset.nfeatures == 0 or dataset.nsamples == 0: raise DegenerateInputError( "Cannot train classifier %s on degenerate data %s" % (self, dataset)) if __debug__: debug("CLF", "Training classifier %(clf)s on dataset %(dataset)s", msgargs={'clf':self, 'dataset':dataset}) self._pretrain(dataset) # remember the time when started training t0 = time.time() if dataset.nfeatures > 0: result = self._train(dataset) else: warning("Trying to train on dataset with no features present") if __debug__: debug("CLF", "No features present for training, no actual training " \ "is called") result = None self.training_time = time.time() - t0 self._posttrain(dataset) return result def _prepredict(self, data): """Functionality prior prediction """ if not ('notrain2predict' in self._clf_internals): # check if classifier was trained if that is needed if not self.trained: raise ValueError, \ "Classifier %s wasn't yet trained, therefore can't " \ "predict" % self nfeatures = data.shape[1] # check if number of features is the same as in the data # it was trained on if nfeatures != self.__trainednfeatures: raise ValueError, \ "Classifier %s was trained on data with %d features, " % \ (self, self.__trainednfeatures) + \ "thus can't predict for %d features" % nfeatures if self.params.retrainable: if not self.__changedData_isset: self.__resetChangedData() _changedData = self._changedData _changedData['testdata'] = \ self.__wasDataChanged('testdata', data) if __debug__: debug('CLF_', "prepredict: Obtained _changedData is %s" % (_changedData)) def _postpredict(self, data, result): """Functionality after prediction is computed """ self.predictions = result if self.params.retrainable: self.__changedData_isset = False def _predict(self, data): """Actual prediction """ raise NotImplementedError def predict(self, data): """Predict classifier on data Shouldn't be overridden in subclasses unless explicitly needed to do so. Also subclasses trying to call super class's predict should call _predict if within _predict instead of predict() since otherwise it would loop """ data = N.asarray(data) if __debug__: debug("CLF", "Predicting classifier %(clf)s on data %(data)s", msgargs={'clf':self, 'data':data.shape}) # remember the time when started computing predictions t0 = time.time() states = self.states # to assure that those are reset (could be set due to testing # post-training) states.reset(['values', 'predictions']) self._prepredict(data) if self.__trainednfeatures > 0 \ or 'notrain2predict' in self._clf_internals: result = self._predict(data) else: warning("Trying to predict using classifier trained on no features") if __debug__: debug("CLF", "No features were present for training, prediction is " \ "bogus") result = [None]*data.shape[0] states.predicting_time = time.time() - t0 if 'regression' in self._clf_internals and not self.params.regression: # We need to convert regression values into labels # XXX unify may be labels -> internal_labels conversion. #if len(self.trained_labels) != 2: # raise RuntimeError, "Ask developer to implement for " \ # "multiclass mapping from regression into classification" # must be N.array so we copy it to assign labels directly # into labels, or should we just recreate "result"??? result_ = N.array(result) if states.isEnabled('values'): # values could be set by now so assigning 'result' would # be misleading if not states.isSet('values'): states.values = result_.copy() else: # it might be the values are pointing to result at # the moment, so lets assure this silly way that # they do not overlap states.values = N.array(states.values, copy=True) trained_labels = self.trained_labels for i, value in enumerate(result): dists = N.abs(value - trained_labels) result[i] = trained_labels[N.argmin(dists)] if __debug__: debug("CLF_", "Converted regression result %(result_)s " "into labels %(result)s for %(self_)s", msgargs={'result_':result_, 'result':result, 'self_': self}) self._postpredict(data, result) return result # deprecate ??? def isTrained(self, dataset=None): """Either classifier was already trained. MUST BE USED WITH CARE IF EVER""" if dataset is None: # simply return if it was trained on anything return not self.__trainednfeatures is None else: res = (self.__trainednfeatures == dataset.nfeatures) if __debug__ and 'CHECK_TRAINED' in debug.active: res2 = (self.__trainedidhash == dataset.idhash) if res2 != res: raise RuntimeError, \ "isTrained is weak and shouldn't be relied upon. " \ "Got result %b although comparing of idhash says %b" \ % (res, res2) return res def _regressionIsBogus(self): """Some classifiers like BinaryClassifier can't be used for regression""" if self.params.regression: raise ValueError, "Regression mode is meaningless for %s" % \ self.__class__.__name__ + " thus don't enable it" @property def trained(self): """Either classifier was already trained""" return self.isTrained() def untrain(self): """Reset trained state""" self.__trainednfeatures = None # probably not needed... retrainable shouldn't be fully untrained # or should be??? #if self.params.retrainable: # # ??? don't duplicate the code ;-) # self.__idhashes = {'traindata': None, 'labels': None, # 'testdata': None, 'testtraindata': None} super(Classifier, self).reset() def getSensitivityAnalyzer(self, **kwargs): """Factory method to return an appropriate sensitivity analyzer for the respective classifier.""" raise NotImplementedError # # Methods which are needed for retrainable classifiers # def _setRetrainable(self, value, force=False): """Assign value of retrainable parameter If retrainable flag is to be changed, classifier has to be untrained. Also internal attributes such as _changedData, __changedData_isset, and __idhashes should be initialized if it becomes retrainable """ pretrainable = self.params['retrainable'] if (force or value != pretrainable.value) \ and 'retrainable' in self._clf_internals: if __debug__: debug("CLF_", "Setting retrainable to %s" % value) if 'meta' in self._clf_internals: warning("Retrainability is not yet crafted/tested for " "meta classifiers. Unpredictable behavior might occur") # assure that we don't drag anything behind if self.trained: self.untrain() states = self.states if not value and states.isKnown('retrained'): states.remove('retrained') states.remove('repredicted') if value: if not 'retrainable' in self._clf_internals: warning("Setting of flag retrainable for %s has no effect" " since classifier has no such capability. It would" " just lead to resources consumption and slowdown" % self) states.add(StateVariable(enabled=True, name='retrained', doc="Either retrainable classifier was retrained")) states.add(StateVariable(enabled=True, name='repredicted', doc="Either retrainable classifier was repredicted")) pretrainable.value = value # if retrainable we need to keep track of things if value: self.__idhashes = {'traindata': None, 'labels': None, 'testdata': None} #, 'testtraindata': None} if __debug__ and 'CHECK_RETRAIN' in debug.active: # ??? it is not clear though if idhash is faster than # simple comparison of (dataset != __traineddataset).any(), # but if we like to get rid of __traineddataset then we # should use idhash anyways self.__trained = self.__idhashes.copy() # just same Nones self.__resetChangedData() self.__invalidatedChangedData = {} elif 'retrainable' in self._clf_internals: #self.__resetChangedData() self.__changedData_isset = False self._changedData = None self.__idhashes = None if __debug__ and 'CHECK_RETRAIN' in debug.active: self.__trained = None def __resetChangedData(self): """For retrainable classifier we keep track of what was changed This function resets that dictionary """ if __debug__: debug('CLF_', 'Retrainable: resetting flags on either data was changed') keys = self.__idhashes.keys() + self._paramscols # we might like to just reinit values to False??? #_changedData = self._changedData #if isinstance(_changedData, dict): # for key in _changedData.keys(): # _changedData[key] = False self._changedData = dict(zip(keys, [False]*len(keys))) self.__changedData_isset = False def __wasDataChanged(self, key, entry, update=True): """Check if given entry was changed from what known prior. If so -- store only the ones needed for retrainable beastie """ idhash_ = idhash(entry) __idhashes = self.__idhashes changed = __idhashes[key] != idhash_ if __debug__ and 'CHECK_RETRAIN' in debug.active: __trained = self.__trained changed2 = entry != __trained[key] if isinstance(changed2, N.ndarray): changed2 = changed2.any() if changed != changed2 and not changed: raise RuntimeError, \ 'idhash found to be weak for %s. Though hashid %s!=%s %s, '\ 'values %s!=%s %s' % \ (key, idhash_, __idhashes[key], changed, entry, __trained[key], changed2) if update: __trained[key] = entry if __debug__ and changed: debug('CLF_', "Changed %s from %s to %s.%s" % (key, __idhashes[key], idhash_, ('','updated')[int(update)])) if update: __idhashes[key] = idhash_ return changed # def __updateHashIds(self, key, data): # """Is twofold operation: updates hashid if was said that it changed. # # or if it wasn't said that data changed, but CHECK_RETRAIN and it found # to be changed -- raise Exception # """ # # check_retrain = __debug__ and 'CHECK_RETRAIN' in debug.active # chd = self._changedData # # # we need to updated idhashes # if chd[key] or check_retrain: # keychanged = self.__wasDataChanged(key, data) # if check_retrain and keychanged and not chd[key]: # raise RuntimeError, \ # "Data %s found changed although wasn't " \ # "labeled as such" % key # # Additional API which is specific only for retrainable classifiers. # For now it would just puke if asked from not retrainable one. # # Might come useful and efficient for statistics testing, so if just # labels of dataset changed, then # self.retrain(dataset, labels=True) # would cause efficient retraining (no kernels recomputed etc) # and subsequent self.repredict(data) should be also quite fase ;-) def retrain(self, dataset, **kwargs): """Helper to avoid check if data was changed actually changed Useful if just some aspects of classifier were changed since its previous training. For instance if dataset wasn't changed but only classifier parameters, then kernel matrix does not have to be computed. Words of caution: classifier must be previously trained, results always should first be compared to the results on not 'retrainable' classifier (without calling retrain). Some additional checks are enabled if debug id 'CHECK_RETRAIN' is enabled, to guard against obvious mistakes. :Parameters: kwargs that is what _changedData gets updated with. So, smth like ``(params=['C'], labels=True)`` if parameter C and labels got changed """ # Note that it also demolishes anything for repredicting, # which should be ok in most of the cases if __debug__: if not self.params.retrainable: raise RuntimeError, \ "Do not use re(train,predict) on non-retrainable %s" % \ self if kwargs.has_key('params') or kwargs.has_key('kernel_params'): raise ValueError, \ "Retraining for changed params not working yet" self.__resetChangedData() # local bindings chd = self._changedData ichd = self.__invalidatedChangedData chd.update(kwargs) # mark for future 'train()' items which are explicitely # mentioned as changed for key, value in kwargs.iteritems(): if value: ichd[key] = True self.__changedData_isset = True # To check if we are not fooled if __debug__ and 'CHECK_RETRAIN' in debug.active: for key, data_ in (('traindata', dataset.samples), ('labels', dataset.labels)): # so it wasn't told to be invalid if not chd[key] and not ichd.get(key, False): if self.__wasDataChanged(key, data_, update=False): raise RuntimeError, \ "Data %s found changed although wasn't " \ "labeled as such" % key # TODO: parameters of classifiers... for now there is explicit # 'forbidance' above # Below check should be superseeded by check above, thus never occur. # remove later on ??? if __debug__ and 'CHECK_RETRAIN' in debug.active and self.trained \ and not self._changedData['traindata'] \ and self.__trained['traindata'].shape != dataset.samples.shape: raise ValueError, "In retrain got dataset with %s size, " \ "whenever previousely was trained on %s size" \ % (dataset.samples.shape, self.__trained['traindata'].shape) self.train(dataset) def repredict(self, data, **kwargs): """Helper to avoid check if data was changed actually changed Useful if classifier was (re)trained but with the same data (so just parameters were changed), so that it could be repredicted easily (on the same data as before) without recomputing for instance train/test kernel matrix. Should be used with caution and always compared to the results on not 'retrainable' classifier. Some additional checks are enabled if debug id 'CHECK_RETRAIN' is enabled, to guard against obvious mistakes. :Parameters: data data which is conventionally given to predict kwargs that is what _changedData gets updated with. So, smth like ``(params=['C'], labels=True)`` if parameter C and labels got changed """ if len(kwargs)>0: raise RuntimeError, \ "repredict for now should be used without params since " \ "it makes little sense to repredict if anything got changed" if __debug__ and not self.params.retrainable: raise RuntimeError, \ "Do not use retrain/repredict on non-retrainable classifiers" self.__resetChangedData() chd = self._changedData chd.update(**kwargs) self.__changedData_isset = True # check if we are attempted to perform on the same data if __debug__ and 'CHECK_RETRAIN' in debug.active: for key, data_ in (('testdata', data),): # so it wasn't told to be invalid #if not chd[key]:# and not ichd.get(key, False): if self.__wasDataChanged(key, data_, update=False): raise RuntimeError, \ "Data %s found changed although wasn't " \ "labeled as such" % key # Should be superseded by above # remove in future??? if __debug__ and 'CHECK_RETRAIN' in debug.active \ and not self._changedData['testdata'] \ and self.__trained['testdata'].shape != data.shape: raise ValueError, "In repredict got dataset with %s size, " \ "whenever previously was trained on %s size" \ % (data.shape, self.__trained['testdata'].shape) return self.predict(data) # TODO: callback into retrainable parameter #retrainable = property(fget=_getRetrainable, fset=_setRetrainable, # doc="Specifies either classifier should be retrainable") pymvpa-0.4.8/mvpa/clfs/blr.py000066400000000000000000000137421174541445200161000ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # Copyright (c) 2008 Emanuele Olivetti # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Bayesian Linear Regression (BLR).""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.misc.state import StateVariable from mvpa.clfs.base import Classifier if __debug__: from mvpa.misc import debug class BLR(Classifier): """Bayesian Linear Regression (BLR). """ predicted_variances = StateVariable(enabled=False, doc="Variance per each predicted value") log_marginal_likelihood = StateVariable(enabled=False, doc="Log Marginal Likelihood") _clf_internals = [ 'blr', 'regression', 'linear' ] def __init__(self, sigma_p = None, sigma_noise=1.0, **kwargs): """Initialize a BLR regression analysis. :Parameters: sigma_noise : float the standard deviation of the gaussian noise. (Defaults to 0.1) """ # init base class first Classifier.__init__(self, **kwargs) # pylint happiness self.w = None # It does not make sense to calculate a confusion matrix for a # BLR: self.states.enable('training_confusion', False) # set the prior on w: N(0,sigma_p) , specifying the covariance # sigma_p on w: self.sigma_p = sigma_p # set noise level: self.sigma_noise = sigma_noise self.predicted_variances = None self.log_marginal_likelihood = None self.labels = None pass def __repr__(self): """String summary of the object """ return """BLR(w=%s, sigma_p=%s, sigma_noise=%f, enable_states=%s)""" % \ (self.w, self.sigma_p, self.sigma_noise, str(self.states.enabled)) def compute_log_marginal_likelihood(self): """ Compute log marginal likelihood using self.train_fv and self.labels. """ # log_marginal_likelihood = None # return log_marginal_likelihood raise NotImplementedError def _train(self, data): """Train regression using `data` (`Dataset`). """ # provide a basic (i.e. identity matrix) and correct prior # sigma_p, if not provided before or not compliant to 'data': if self.sigma_p == None: # case: not provided self.sigma_p = N.eye(data.samples.shape[1]+1) elif self.sigma_p.shape[1] != (data.samples.shape[1]+1): # case: wrong dimensions self.sigma_p = N.eye(data.samples.shape[1]+1) else: # ...then everything is OK :) pass # add one fake column of '1.0' to model the intercept: self.samples_train = N.hstack([data.samples,N.ones((data.samples.shape[0],1))]) if type(self.sigma_p)!=type(self.samples_train): # if sigma_p is a number... self.sigma_p = N.eye(self.samples_train.shape[1])*self.sigma_p # convert in matrix pass self.A_inv = N.linalg.inv(1.0/(self.sigma_noise**2) * N.dot(self.samples_train.T, self.samples_train) + N.linalg.inv(self.sigma_p)) self.w = 1.0/(self.sigma_noise**2) * N.dot(self.A_inv, N.dot(self.samples_train.T, data.labels)) pass def _predict(self, data): """ Predict the output for the provided data. """ data = N.hstack([data,N.ones((data.shape[0],1),dtype=data.dtype)]) predictions = N.dot(data,self.w) if self.states.isEnabled('predicted_variances'): # do computation only if state variable was enabled self.predicted_variances = N.dot(data, N.dot(self.A_inv, data.T)).diagonal()[:,N.newaxis] return predictions def set_hyperparameters(self,*args): """ Set hyperparameters' values. Note that this is a list so the order of the values is important. """ args=args[0] self.sigma_noise = args[0] if len(args)>1: self.sigma_p = N.array(args[1:]) # XXX check if this is ok pass return pass if __name__ == "__main__": import pylab pylab.close("all") pylab.ion() from mvpa.misc.data_generators import linear_awgn train_size = 10 test_size = 100 F = 1 # dimensions of the dataset # N.random.seed(1) slope = N.random.rand(F) intercept = N.random.rand(1) print "True slope:",slope print "True intercept:",intercept dataset_train = linear_awgn(train_size, intercept=intercept, slope=slope) # print dataset.labels dataset_test = linear_awgn(test_size, intercept=intercept, slope=slope, flat=True) regression = True logml = False b = BLR(sigma_p=N.eye(F+1), sigma_noise=0.1, regression=True) b.states.enable("predicted_variances") b.train(dataset_train) predictions = b.predict(dataset_test.samples) print "Predicted slope and intercept:",b.w if F==1: pylab.plot(dataset_train.samples,dataset_train.labels,"ro",label="train") pylab.plot(dataset_test.samples,predictions,"b-",label="prediction") pylab.plot(dataset_test.samples,predictions+N.sqrt(b.predicted_variances),"b--",label="pred(+/-)std") pylab.plot(dataset_test.samples,predictions-N.sqrt(b.predicted_variances),"b--",label=None) # pylab.plot(dataset_test.samples,dataset_test.labels,"go") pylab.legend() pylab.xlabel("samples") pylab.ylabel("labels") pylab.title("Bayesian Linear Regression on dataset 'linear_AWGN'") pass pymvpa-0.4.8/mvpa/clfs/distance.py000066400000000000000000000346071174541445200171160ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Distance functions to be used in kernels and elsewhere """ __docformat__ = 'restructuredtext' # TODO: Make all distance functions accept 2D matrices samples x features # and compute the distance matrix between all samples. They would # need to be capable of dealing with unequal number of rows! # If we would have that, we could make use of them in kNN. import numpy as N from mvpa.base import externals if __debug__: from mvpa.base import debug, warning def cartesianDistance(a, b): """Return Cartesian distance between a and b """ return N.linalg.norm(a-b) def absminDistance(a, b): """Returns dinstance max(\|a-b\|) XXX There must be better name! XXX Actually, why is it absmin not absmax? Useful to select a whole cube of a given "radius" """ return max(abs(a-b)) def manhattenDistance(a, b): """Return Manhatten distance between a and b """ return sum(abs(a-b)) def mahalanobisDistance(x, y=None, w=None): """Calculate Mahalanobis distance of the pairs of points. :Parameters: `x` first list of points. Rows are samples, columns are features. `y` second list of points (optional) `w` : N.ndarray optional inverse covariance matrix between the points. It is computed if not given Inverse covariance matrix can be calculated with the following w = N.linalg.solve(N.cov(x.T), N.identity(x.shape[1])) or w = N.linalg.inv(N.cov(x.T)) """ # see if pairwise between two matrices or just within a single matrix if y is None: # pairwise distances of single matrix # calculate the inverse correlation matrix if necessary if w is None: w = N.linalg.inv(N.cov(x.T)) # get some shapes of the data mx, nx = x.shape #mw, nw = w.shape # allocate for the matrix to fill d = N.zeros((mx, mx), dtype=N.float32) for i in range(mx-1): # get the current row to compare xi = x[i, :] # replicate the row xi = xi[N.newaxis, :].repeat(mx-i-1, axis=0) # take the distance between all the matrices dc = x[i+1:mx, :] - xi # scale the distance by the correlation d[i+1:mx, i] = N.real(N.sum((N.inner(dc, w) * N.conj(dc)), 1)) # fill the other direction of the matrix d[i, i+1:mx] = d[i+1:mx, i].T else: # is between two matrixes # calculate the inverse correlation matrix if necessary if w is None: # calculate over all points w = N.linalg.inv(N.cov(N.concatenate((x, y)).T)) # get some shapes of the data mx, nx = x.shape my, ny = y.shape # allocate for the matrix to fill d = N.zeros((mx, my), dtype=N.float32) # loop over shorter of two dimensions if mx <= my: # loop over the x patterns for i in range(mx): # get the current row to compare xi = x[i, :] # replicate the row xi = xi[N.newaxis, :].repeat(my, axis=0) # take the distance between all the matrices dc = xi - y # scale the distance by the correlation d[i, :] = N.real(N.sum((N.inner(dc, w) * N.conj(dc)), 1)) else: # loop over the y patterns for j in range(my): # get the current row to compare yj = y[j, :] # replicate the row yj = yj[N.newaxis, :].repeat(mx, axis=0) # take the distance between all the matrices dc = x - yj # scale the distance by the correlation d[:, j] = N.real(N.sum((N.inner(dc, w) * N.conj(dc)), 1)) # return the dist return N.sqrt(d) def squared_euclidean_distance(data1, data2=None, weight=None): """Compute weighted euclidean distance matrix between two datasets. :Parameters: data1 : N.ndarray first dataset data2 : N.ndarray second dataset. If None, compute the euclidean distance between the first dataset versus itself. (Defaults to None) weight : N.ndarray vector of weights, each one associated to each dimension of the dataset (Defaults to None) """ if __debug__: # check if both datasets are floating point if not N.issubdtype(data1.dtype, 'f') \ or (data2 is not None and not N.issubdtype(data2.dtype, 'f')): warning('Computing euclidean distance on integer data ' \ 'is not supported.') # removed for efficiency (see below) #if weight is None: # weight = N.ones(data1.shape[1], 'd') # unitary weight # In the following you can find faster implementations of this # basic code: # # squared_euclidean_distance_matrix = \ # N.zeros((data1.shape[0], data2.shape[0]), 'd') # for i in range(size1): # for j in range(size2): # squared_euclidean_distance_matrix[i, j] = \ # ((data1[i, :]-data2[j, :])**2*weight).sum() # pass # pass # Fast computation of distance matrix in Python+NumPy, # adapted from Bill Baxter's post on [numpy-discussion]. # Basically: (x-y)**2*w = x*w*x - 2*x*w*y + y*y*w # based on value of weight and data2 we might save on computation # and resources if weight is None: data1w = data1 if data2 is None: data2, data2w = data1, data1w else: data2w = data2 else: data1w = data1 * weight if data2 is None: data2, data2w = data1, data1w else: data2w = data2 * weight squared_euclidean_distance_matrix = \ (data1w * data1).sum(1)[:, None] \ -2 * N.dot(data1w, data2.T) \ + (data2 * data2w).sum(1) # correction to some possible numerical instabilities: less0 = squared_euclidean_distance_matrix < 0 if __debug__ and 'CHECK_STABILITY' in debug.active: less0num = N.sum(less0) if less0num > 0: norm0 = N.linalg.norm(squared_euclidean_distance_matrix[less0]) totalnorm = N.linalg.norm(squared_euclidean_distance_matrix) if totalnorm != 0 and norm0 / totalnorm > 1e-8: warning("Found %d elements out of %d unstable (<0) in " \ "computation of squared_euclidean_distance_matrix. " \ "Their norm is %s when total norm is %s" % \ (less0num, N.sum(less0.shape), norm0, totalnorm)) squared_euclidean_distance_matrix[less0] = 0 return squared_euclidean_distance_matrix def oneMinusCorrelation(X, Y): """Return one minus the correlation matrix between the rows of two matrices. This functions computes a matrix of correlations between all pairs of rows of two matrices. Unlike NumPy's corrcoef() this function will only considers pairs across matrices and not within, e.g. both elements of a pair never have the same source matrix as origin. Both arrays need to have the same number of columns. :Parameters: X: 2D-array Y: 2D-array Example: >>> X = N.random.rand(20,80) >>> Y = N.random.rand(5,80) >>> C = oneMinusCorrelation(X, Y) >>> print C.shape (20, 5) """ # check if matrices have same number of columns if __debug__: if not X.shape[1] == Y.shape[1]: raise ValueError, 'correlation() requires to matrices with the ' \ 'same #columns (Got: %s and %s)' \ % (X.shape, Y.shape) # zscore each sample/row Zx = X - N.c_[X.mean(axis=1)] Zx /= N.c_[X.std(axis=1)] Zy = Y - N.c_[Y.mean(axis=1)] Zy /= N.c_[Y.std(axis=1)] C = ((N.matrix(Zx) * N.matrix(Zy).T) / Zx.shape[1]).A # let it behave like a distance, i.e. smaller is closer C -= 1.0 return N.abs(C) def pnorm_w_python(data1, data2=None, weight=None, p=2, heuristic='auto', use_sq_euclidean=True): """Weighted p-norm between two datasets (pure Python implementation) ||x - x'||_w = (\sum_{i=1...N} (w_i*|x_i - x'_i|)**p)**(1/p) :Parameters: data1 : N.ndarray First dataset data2 : N.ndarray or None Optional second dataset weight : N.ndarray or None Optional weights per 2nd dimension (features) p Power heuristic : basestring Which heuristic to use: * 'samples' -- python sweep over 0th dim * 'features' -- python sweep over 1st dim * 'auto' decides automatically. If # of features (shape[1]) is much larger than # of samples (shape[0]) -- use 'samples', and use 'features' otherwise use_sq_euclidean : bool Either to use squared_euclidean_distance_matrix for computation if p==2 """ if weight == None: weight = N.ones(data1.shape[1], 'd') pass if p == 2 and use_sq_euclidean: return N.sqrt(squared_euclidean_distance(data1=data1, data2=data2, weight=weight**2)) if data2 == None: data2 = data1 pass S1,F1 = data1.shape[:2] S2,F2 = data2.shape[:2] # sanity check if not (F1==F2==weight.size): raise ValueError, \ "Datasets should have same #columns == #weights. Got " \ "%d %d %d" % (F1, F2, weight.size) d = N.zeros((S1, S2), 'd') # Adjust local functions for specific p values # pf - power function # af - after function if p == 1: pf = lambda x:x af = lambda x:x else: pf = lambda x:x ** p af = lambda x:x ** (1.0/p) # heuristic 'auto' might need to be adjusted if heuristic == 'auto': heuristic = {False: 'samples', True: 'features'}[(F1/S1) < 500] if heuristic == 'features': # Efficient implementation if the feature size is little. for NF in range(F1): d += pf(N.abs(N.subtract.outer(data1[:,NF], data2[:,NF]))*weight[NF]) pass elif heuristic == 'samples': # Efficient implementation if the feature size is much larger # than number of samples for NS in xrange(S1): dfw = pf(N.abs(data1[NS] - data2) * weight) d[NS] = N.sum(dfw, axis=1) pass else: raise ValueError, "Unknown heuristic '%s'. Need one of " \ "'auto', 'samples', 'features'" % heuristic return af(d) if externals.exists('weave'): from scipy import weave from scipy.weave import converters def pnorm_w(data1, data2=None, weight=None, p=2): """Weighted p-norm between two datasets (scipy.weave implementation) ||x - x'||_w = (\sum_{i=1...N} (w_i*|x_i - x'_i|)**p)**(1/p) :Parameters: data1 : N.ndarray First dataset data2 : N.ndarray or None Optional second dataset weight : N.ndarray or None Optional weights per 2nd dimension (features) p Power """ if weight == None: weight = N.ones(data1.shape[1], 'd') pass S1, F1 = data1.shape[:2] code = "" if data2 == None or id(data1)==id(data2): if not (F1==weight.size): raise ValueError, \ "Dataset should have same #columns == #weights. Got " \ "%d %d" % (F1, weight.size) F = F1 d = N.zeros((S1, S1), 'd') try: code_peritem = \ {1.0 : "tmp = tmp+weight(t)*fabs(data1(i,t)-data1(j,t))", 2.0 : "tmp2 = weight(t)*(data1(i,t)-data1(j,t));" \ " tmp = tmp + tmp2*tmp2"}[p] except KeyError: code_peritem = "tmp = tmp+pow(weight(t)*fabs(data1(i,t)-data1(j,t)),p)" code = """ int i,j,t; double tmp, tmp2; for (i=0; i` 'Regularization and Variable Selection via the Elastic Net' Journal of the Royal Statistical Society, Series B, 67, 301-320. Similar to SMLR, it performs a feature selection while performing classification, but instead of starting with all features, it starts with none and adds them in, which is similar to boosting. Unlike LARS it has both L1 and L2 regularization (instead of just L1). This means that while it tries to sparsify the features it also tries to keep redundant features, which may be very very good for fMRI classification. In the true nature of the PyMVPA framework, this algorithm was actually implemented in R by Zou and Hastie and wrapped via RPy. To make use of ENET, you must have R and RPy installed as well as both the lars and elasticnet contributed package. You can install the R and RPy with the following command on Debian-based machines: sudo aptitude install python-rpy python-rpy-doc r-base-dev You can then install the lars and elasticnet package by running R as root and calling: install.packages() """ _clf_internals = [ 'enet', 'regression', 'linear', 'has_sensitivity', 'does_feature_selection' ] def __init__(self, lm=1.0, trace=False, normalize=True, intercept=True, max_steps=None, **kwargs): """ Initialize ENET. See the help in R for further details on the following parameters: :Parameters: lm : float Penalty parameter. 0 will perform LARS with no ridge regression. Default is 1.0. trace : boolean Whether to print progress in R as it works. normalize : boolean Whether to normalize the L2 Norm. intercept : boolean Whether to add a non-penalized intercept to the model. max_steps : None or int If not None, specify the total number of iterations to run. Each iteration adds a feature, but leaving it none will add until convergence. """ # init base class first Classifier.__init__(self, **kwargs) # set up the params self.__lm = lm self.__normalize = normalize self.__intercept = intercept self.__trace = trace self.__max_steps = max_steps # pylint friendly initializations self.__weights = None """The beta weights for each feature.""" self.__trained_model = None """The model object after training that will be used for predictions.""" # It does not make sense to calculate a confusion matrix for a # regression self.states.enable('training_confusion', False) def __repr__(self): """String summary of the object """ return """ENET(lm=%s, normalize=%s, intercept=%s, trace=%s, max_steps=%s, enable_states=%s)""" % \ (self.__lm, self.__normalize, self.__intercept, self.__trace, self.__max_steps, str(self.states.enabled)) def _train(self, data): """Train the classifier using `data` (`Dataset`). """ if self.__max_steps is None: # train without specifying max_steps self.__trained_model = rpy.r.enet(data.samples, data.labels[:,N.newaxis], self.__lm, normalize=self.__normalize, intercept=self.__intercept, trace=self.__trace) else: # train with specifying max_steps self.__trained_model = rpy.r.enet(data.samples, data.labels[:,N.newaxis], self.__lm, normalize=self.__normalize, intercept=self.__intercept, trace=self.__trace, max_steps=self.__max_steps) # find the step with the lowest Cp (risk) # it is often the last step if you set a max_steps # must first convert dictionary to array # Cp_vals = N.asarray([self.__trained_model['Cp'][str(x)] # for x in range(len(self.__trained_model['Cp']))]) # self.__lowest_Cp_step = Cp_vals.argmin() # set the weights to the last step self.__weights = N.zeros(data.nfeatures,dtype=self.__trained_model['beta.pure'].dtype) ind = N.asarray(self.__trained_model['allset'])-1 self.__weights[ind] = self.__trained_model['beta.pure'][-1,:] # # set the weights to the final state # self.__weights = self.__trained_model['beta'][-1,:] def _predict(self, data): """Predict the output for the provided data. """ # predict with the final state (i.e., the last step) res = rpy.r.predict_enet(self.__trained_model, data, mode='step', type='fit', s=self.__trained_model['beta.pure'].shape[0]) #s=self.__lowest_Cp_step) fit = N.asarray(res['fit']) if len(fit.shape) == 0: # if we just got 1 sample with a scalar fit = fit.reshape( (1,) ) return fit def _getFeatureIds(self): """Return ids of the used features """ return N.where(N.abs(self.__weights)>0)[0] def getSensitivityAnalyzer(self, **kwargs): """Returns a sensitivity analyzer for ENET.""" return ENETWeights(self, **kwargs) weights = property(lambda self: self.__weights) class ENETWeights(Sensitivity): """`SensitivityAnalyzer` that reports the weights ENET trained on a given `Dataset`. """ _LEGAL_CLFS = [ ENET ] def _call(self, dataset=None): """Extract weights from ENET classifier. ENET always has weights available, so nothing has to be computed here. """ clf = self.clf weights = clf.weights if __debug__: debug('ENET', "Extracting weights for ENET - "+ "Result: min=%f max=%f" %\ (N.min(weights), N.max(weights))) return weights pymvpa-0.4.8/mvpa/clfs/glmnet.py000066400000000000000000000311421174541445200166010ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """GLM-Net (GLMNET) regression classifier.""" __docformat__ = 'restructuredtext' # system imports import numpy as N import mvpa.base.externals as externals # do conditional to be able to build module reference if externals.exists('rpy', raiseException=True) and \ externals.exists('glmnet', raiseException=True): import rpy rpy.r.library('glmnet') # local imports from mvpa.clfs.base import Classifier from mvpa.measures.base import Sensitivity from mvpa.misc.param import Parameter if __debug__: from mvpa.base import debug def _label2indlist(labels, ulabels): """Convert labels to list of unique label indicies starting at 1. """ # allocate for the new one-of-M labels new_labels = N.zeros(len(labels), dtype=N.int) # loop and convert to one-of-M for i, c in enumerate(ulabels): new_labels[labels == c] = i+1 return [str(l) for l in new_labels.tolist()] class _GLMNET(Classifier): """GLM-Net regression (GLMNET) `Classifier`. GLM-Net is the model selection algorithm from: Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent. http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf To make use of GLMNET, you must have R and RPy installed as well as both the glmnet contributed package. You can install the R and RPy with the following command on Debian-based machines: sudo aptitude install python-rpy python-rpy-doc r-base-dev You can then install the glmnet package by running R as root and calling: install.packages() """ _clf_internals = [ 'glmnet', 'linear', 'has_sensitivity', 'does_feature_selection' ] family = Parameter('gaussian', allowedtype='basestring', choices=["gaussian", "multinomial"], doc="""Response type of your labels (either 'gaussian' for regression or 'multinomial' for classification).""") alpha = Parameter(1.0, min=0.01, max=1.0, allowedtype='float', doc="""The elastic net mixing parameter. Larger values will give rise to less L2 regularization, with alpha=1.0 as a true LASSO penalty.""") nlambda = Parameter(100, allowedtype='int', min=1, doc="""Maximum number of lambdas to calculate before stopping if not converged.""") standardize = Parameter(True, allowedtype='bool', doc="""Whether to standardize the variables prior to fitting.""") thresh = Parameter(1e-4, min=1e-10, max=1.0, allowedtype='float', doc="""Convergence threshold for coordinate descent.""") pmax = Parameter(None, min=1, allowedtype='None or int', doc="""Limit the maximum number of variables ever to be nonzero.""") maxit = Parameter(100, min=10, allowedtype='int', doc="""Maximum number of outer-loop iterations for 'multinomial' families.""") model_type = Parameter('covariance', allowedtype='basestring', choices=["covariance", "naive"], doc="""'covariance' saves all inner-products ever computed and can be much faster than 'naive'. The latter can be more efficient for nfeatures>>nsamples situations.""") def __init__(self, **kwargs): """ Initialize GLM-Net. See the help in R for further details on the parameters """ # init base class first Classifier.__init__(self, **kwargs) # pylint friendly initializations self.__weights = None """The beta weights for each feature.""" self.__trained_model = None """The model object after training that will be used for predictions.""" self.__trained_model_dict = None """The model object in dict form after training that will be used for predictions.""" # It does not make sense to calculate a confusion matrix for a # regression # YOH: sorry for not clear semantics... pyvmpa is evolving, # regressions will store RegressionStatistics within the # confusion, so it is ok to have training_confusion # enabled, but .regression parameter needs to be set to true, # therefor above conditioning and tuneup of kwargs in _R #if self.params.family == 'gaussian': # self.states.enable('training_confusion', False) # def __repr__(self): # """String summary of the object # """ # return """ENET(lm=%s, normalize=%s, intercept=%s, trace=%s, max_steps=%s, enable_states=%s)""" % \ # (self.__lm, # self.__normalize, # self.__intercept, # self.__trace, # self.__max_steps, # str(self.states.enabled)) def _train(self, dataset): """Train the classifier using `data` (`Dataset`). """ # process the labels based on the model family if self.params.family == 'gaussian': # do nothing, just save the labels as a list labels = dataset.labels.tolist() pass elif self.params.family == 'multinomial': # turn lables into list of range values starting at 1 labels = _label2indlist(dataset.labels, dataset.uniquelabels) self.__ulabels = dataset.uniquelabels.copy() # process the pmax if self.params.pmax is None: # set it to the num features pmax = dataset.nfeatures else: # use the value pmax = self.params.pmax # train with specifying max_steps # must not convert trained model to dict or we'll get segfault rpy.set_default_mode(rpy.NO_CONVERSION) self.__trained_model = rpy.r.glmnet(dataset.samples, labels, family=self.params.family, alpha=self.params.alpha, nlambda=self.params.nlambda, standardize=self.params.standardize, thresh=self.params.thresh, pmax=pmax, maxit=self.params.maxit, type=self.params.model_type) rpy.set_default_mode(rpy.NO_DEFAULT) # get a dict version of the model self.__trained_model_dict = rpy.r.as_list(self.__trained_model) # save the lambda of last step self.__last_lambda = self.__trained_model_dict['lambda'][-1] # set the weights to the last step weights = rpy.r.coef(self.__trained_model, s=self.__last_lambda) if self.params.family == 'multinomial': self.__weights = N.hstack([rpy.r.as_matrix(weights[str(i)])[1:] for i in range(1,len(self.__ulabels)+1)]) elif self.params.family == 'gaussian': self.__weights = rpy.r.as_matrix(weights)[1:] def _predict(self, data): """ Predict the output for the provided data. """ # predict with standard method values = rpy.r.predict(self.__trained_model, newx=data, type='link', s=self.__last_lambda) # predict with the final state (i.e., the last step) classes = None if self.params.family == 'multinomial': # remove last dimension of values values = values[:,:,0] # get the classes too (they are 1-indexed) rpy.set_default_mode(rpy.NO_CONVERSION) class_ind = rpy.r.predict(self.__trained_model, newx=data, type='class', s=self.__last_lambda) rpy.set_default_mode(rpy.NO_DEFAULT) class_ind = rpy.r.as_vector(class_ind) # convert the strings to ints and subtract 1 class_ind = N.array([int(float(c))-1 for c in class_ind]) # convert to actual labels classes = self.__ulabels[class_ind] else: # is gaussian, so just remove last dim of values values = values[:,0] # values need to be set anyways if values state is enabled self.values = values if classes is not None: # set the values and return none return classes else: # return the values as predictions return values def _getFeatureIds(self): """Return ids of the used features """ return N.where(N.abs(self.__weights)>0)[0] def getSensitivityAnalyzer(self, **kwargs): """Returns a sensitivity analyzer for GLMNET.""" return GLMNETWeights(self, **kwargs) weights = property(lambda self: self.__weights) class GLMNETWeights(Sensitivity): """`SensitivityAnalyzer` that reports the weights GLMNET trained on a given `Dataset`. """ _LEGAL_CLFS = [ _GLMNET ] def _call(self, dataset=None): """Extract weights from GLMNET classifier. GLMNET always has weights available, so nothing has to be computed here. """ clf = self.clf weights = clf.weights if __debug__: debug('GLMNET', "Extracting weights for GLMNET - "+ "Result: min=%f max=%f" %\ (N.min(weights), N.max(weights))) return weights class GLMNET_R(_GLMNET): """ GLM-NET Gaussian Regression Classifier. This is the GLM-NET algorithm from Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent. http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf parameterized to be a regression. See GLMNET_C for the multinomial classifier version. """ _clf_internals = _GLMNET._clf_internals + ['regression'] def __init__(self, **kwargs): """ Initialize GLM-Net. See the help in R for further details on the parameters """ # make sure they didn't specify incompatible model regr_family = 'gaussian' family = kwargs.pop('family', regr_family).lower() if family != regr_family: warning('You specified the parameter family=%s, but we ' 'force this to be "%s" for regression.' % (family, regr_family)) family = regr_family regression = kwargs.pop('regression', None) if regression is None: # enforce regression by default, but regression might be used as # a binary classifier as well, so leave it as is if it was # explicitly specified regression = True # init base class first, forcing regression _GLMNET.__init__(self, family=family, regression=regression, **kwargs) class GLMNET_C(_GLMNET): """ GLM-NET Multinomial Classifier. This is the GLM-NET algorithm from Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent. http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf parameterized to be a multinomial classifier. See GLMNET_Class for the gaussian regression version. """ _clf_internals = _GLMNET._clf_internals + ['multiclass', 'binary'] def __init__(self, **kwargs): """ Initialize GLM-Net multinomial classifier. See the help in R for further details on the parameters """ # make sure they didn't specify regression if not kwargs.pop('family', None) is None: warning('You specified the "family" parameter, but we ' 'force this to be "multinomial".') # init base class first, forcing regression _GLMNET.__init__(self, family='multinomial', **kwargs) pymvpa-0.4.8/mvpa/clfs/gnb.py000066400000000000000000000264651174541445200160750ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Gaussian Naive Bayes Classifier EXPERIMENTAL ;) Basic implementation of Gaussian Naive Bayes classifier. """ __docformat__ = 'restructuredtext' import numpy as N from numpy import ones, zeros, sum, abs, isfinite, dot from mvpa.base import warning, externals from mvpa.clfs.base import Classifier from mvpa.misc.param import Parameter from mvpa.misc.state import StateVariable #from mvpa.measures.base import Sensitivity #from mvpa.misc.transformers import SecondAxisMaxOfAbs # XXX ? if __debug__: from mvpa.base import debug class GNB(Classifier): """Gaussian Naive Bayes `Classifier`. GNB is a probabilistic classifier relying on Bayes rule to estimate posterior probabilities of labels given the data. Naive assumption in it is an independence of the features, which allows to combine per-feature likelihoods by a simple product across likelihoods of"independent" features. See http://en.wikipedia.org/wiki/Naive_bayes for more information. Provided here implementation is "naive" on its own -- various aspects could be improved, but has its own advantages: - implementation is simple and straightforward - no data copying while considering samples of specific class - provides alternative ways to assess prior distribution of the classes in the case of unbalanced sets of samples (see parameter `prior`) - makes use of NumPy broadcasting mechanism, so should be relatively efficient - should work for any dimensionality of samples GNB is listed both as linear and non-linear classifier, since specifics of separating boundary depends on the data and/or parameters: linear separation is achieved whenever samples are balanced (or prior='uniform') and features have the same variance across different classes (i.e. if common_variance=True to enforce this). Whenever decisions are made based on log-probabilities (parameter logprob=True, which is the default), then state variable `values` if enabled would also contain log-probabilities. Also mention that normalization by the evidence (P(data)) is disabled by default since it has no impact per se on classification decision. You might like set parameter normalize to True if you want to access properly scaled probabilities in `values` state variable. """ # XXX decide when should we set corresponding internal, # since it depends actually on the data -- no clear way, # so set both linear and non-linear _clf_internals = [ 'gnb', 'linear', 'non-linear', 'binary', 'multiclass' ] common_variance = Parameter(False, allowedtype='bool', doc="""Use the same variance across all classes.""") prior = Parameter('laplacian_smoothing', allowedtype='basestring', choices=["laplacian_smoothing", "uniform", "ratio"], doc="""How to compute prior distribution.""") logprob = Parameter(True, allowedtype='bool', doc="""Operate on log probabilities. Preferable to avoid unneeded exponentiation and loose precision. If set, logprobs are stored in `values`""") normalize = Parameter(False, allowedtype='bool', doc="""Normalize (log)prob by P(data). Requires probabilities thus for `logprob` case would require exponentiation of 'logprob's, thus disabled by default since does not impact classification output. """) def __init__(self, **kwargs): """Initialize an GNB classifier. """ # init base class first Classifier.__init__(self, **kwargs) # pylint friendly initializations self.means = None """Means of features per class""" self.variances = None """Variances per class, but "vars" is taken ;)""" self.ulabels = None """Labels classifier was trained on""" self.priors = None """Class probabilities""" # Define internal state of classifier self._norm_weight = None def _train(self, dataset): """Train the classifier using `dataset` (`Dataset`). """ params = self.params # get the dataset information into easy vars X = dataset.samples labels = dataset.labels self.ulabels = ulabels = dataset.uniquelabels nlabels = len(ulabels) #params = self.params # for quicker access label2index = dict((l, il) for il, l in enumerate(ulabels)) # set the feature dimensions nsamples = len(X) s_shape = X.shape[1:] # shape of a single sample self.means = means = \ N.zeros((nlabels, ) + s_shape) self.variances = variances = \ N.zeros((nlabels, ) + s_shape) # degenerate dimension are added for easy broadcasting later on nsamples_per_class = N.zeros((nlabels,) + (1,)*len(s_shape)) # Estimate means and number of samples per each label for s, l in zip(X, labels): il = label2index[l] # index of the label nsamples_per_class[il] += 1 means[il] += s # helped function - squash all dimensions but 1 squash = lambda x: N.atleast_1d(x.squeeze()) ## Actually compute the means non0labels = (squash(nsamples_per_class) != 0) means[non0labels] /= nsamples_per_class[non0labels] # Estimate variances # better loop than repmat! ;) for s, l in zip(X, labels): il = label2index[l] # index of the label variances[il] += (s - means[il])**2 ## Actually compute the variances if params.common_variance: # we need to get global std cvar = N.sum(variances, axis=0)/nsamples # sum across labels # broadcast the same variance across labels variances[:] = cvar else: variances[non0labels] /= nsamples_per_class[non0labels] # Store prior probabilities prior = params.prior if prior == 'uniform': self.priors = N.ones((nlabels,))/nlabels elif prior == 'laplacian_smoothing': self.priors = (1+squash(nsamples_per_class)) \ / (float(nsamples) + nlabels) elif prior == 'ratio': self.priors = squash(nsamples_per_class) / float(nsamples) else: raise ValueError( "No idea on how to handle '%s' way to compute priors" % params.prior) # Precompute and store weighting coefficient for Gaussian if params.logprob: # it would be added to exponent self._norm_weight = -0.5 * N.log(2*N.pi*variances) else: self._norm_weight = 1.0/N.sqrt(2*N.pi*variances) if __debug__ and 'GNB' in debug.active: debug('GNB', "training finished on data.shape=%s " % (X.shape, ) + "min:max(data)=%f:%f" % (N.min(X), N.max(X))) def untrain(self): """Untrain classifier and reset all learnt params """ self.means = None self.variances = None self.ulabels = None self.priors = None super(GNB, self).untrain() def _predict(self, data): """Predict the output for the provided data. """ params = self.params # argument of exponentiation scaled_distances = \ -0.5 * (((data - self.means[:, N.newaxis, ...])**2) \ / self.variances[:, N.newaxis, ...]) if params.logprob: # if self.params.common_variance: # XXX YOH: # For decision there is no need to actually compute # properly scaled p, ie 1/sqrt(2pi * sigma_i) could be # simply discarded since it is common across features AND # classes # For completeness -- computing everything now even in logprob lprob_csfs = self._norm_weight[:, N.newaxis, ...] + scaled_distances # XXX for now just cut/paste with different operators, but # could just bind them and reuse in the same equations # Naive part -- just a product of probabilities across features ## First we need to reshape to get class x samples x features lprob_csf = lprob_csfs.reshape( lprob_csfs.shape[:2] + (-1,)) ## Now -- sum across features lprob_cs = lprob_csf.sum(axis=2) # Incorporate class probabilities: prob_cs_cp = lprob_cs + N.log(self.priors[:, N.newaxis]) else: # Just a regular Normal distribution with per # feature/class mean and variances prob_csfs = \ self._norm_weight[:, N.newaxis, ...] * N.exp(scaled_distances) # Naive part -- just a product of probabilities across features ## First we need to reshape to get class x samples x features prob_csf = prob_csfs.reshape( prob_csfs.shape[:2] + (-1,)) ## Now -- product across features prob_cs = prob_csf.prod(axis=2) # Incorporate class probabilities: prob_cs_cp = prob_cs * self.priors[:, N.newaxis] # Normalize by evidence P(data) if params.normalize: if params.logprob: prob_cs_cp_real = N.exp(prob_cs_cp) else: prob_cs_cp_real = prob_cs_cp prob_s_cp_marginals = N.sum(prob_cs_cp_real, axis=0) if params.logprob: prob_cs_cp -= N.log(prob_s_cp_marginals) else: prob_cs_cp /= prob_s_cp_marginals # Take the class with maximal (log)probability winners = prob_cs_cp.argmax(axis=0) predictions = [self.ulabels[c] for c in winners] self.values = prob_cs_cp.T # set to the probabilities per class if __debug__ and 'GNB' in debug.active: debug('GNB', "predict on data.shape=%s min:max(data)=%f:%f " % (data.shape, N.min(data), N.max(data))) return predictions # XXX Later come up with some # could be a simple t-test maps using distributions # per each class #def getSensitivityAnalyzer(self, **kwargs): # """Returns a sensitivity analyzer for GNB.""" # return GNBWeights(self, **kwargs) # XXX Is there any reason to use properties? #means = property(lambda self: self.__biases) #variances = property(lambda self: self.__weights) ## class GNBWeights(Sensitivity): ## """`SensitivityAnalyzer` that reports the weights GNB trained ## on a given `Dataset`. ## """ ## _LEGAL_CLFS = [ GNB ] ## def _call(self, dataset=None): ## """Extract weights from GNB classifier. ## GNB always has weights available, so nothing has to be computed here. ## """ ## clf = self.clf ## means = clf.means ## XXX we can do something better ;) ## return means pymvpa-0.4.8/mvpa/clfs/gpr.py000066400000000000000000000517141174541445200161120ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # Copyright (c) 2008 Emanuele Olivetti # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Gaussian Process Regression (GPR).""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base import externals from mvpa.misc.state import StateVariable from mvpa.clfs.base import Classifier from mvpa.misc.param import Parameter from mvpa.clfs.kernel import KernelSquaredExponential, KernelLinear from mvpa.measures.base import Sensitivity from mvpa.misc.exceptions import InvalidHyperparameterError from mvpa.datasets import Dataset if externals.exists("scipy", raiseException=True): from scipy.linalg import cho_solve as SLcho_solve from scipy.linalg import cholesky as SLcholesky import scipy.linalg as SL # Some local binding for bits of speed up SLAError = SL.basic.LinAlgError if __debug__: from mvpa.base import debug # Some local bindings for bits of speed up Nlog = N.log Ndot = N.dot Ndiag = N.diag NLAcholesky = N.linalg.cholesky NLAsolve = N.linalg.solve NLAError = N.linalg.linalg.LinAlgError eps64 = N.finfo(N.float64).eps # Some precomputed items. log is relatively expensive _halflog2pi = 0.5 * Nlog(2 * N.pi) class GPR(Classifier): """Gaussian Process Regression (GPR). """ predicted_variances = StateVariable(enabled=False, doc="Variance per each predicted value") log_marginal_likelihood = StateVariable(enabled=False, doc="Log Marginal Likelihood") log_marginal_likelihood_gradient = StateVariable(enabled=False, doc="Log Marginal Likelihood Gradient") _clf_internals = [ 'gpr', 'regression', 'retrainable' ] # NOTE XXX Parameters of the classifier. Values available as # clf.parameter or clf.params.parameter, or as # clf.params['parameter'] (as the full Parameter object) # # __doc__ and __repr__ for class is conviniently adjusted to # reflect values of those params # Kernel machines/classifiers should be refactored also to behave # the same and define kernel parameter appropriately... TODO, but SVMs # already kinda do it nicely ;-) sigma_noise = Parameter(0.001, allowedtype='float', min=1e-10, doc="the standard deviation of the gaussian noise.") # XXX For now I don't introduce kernel parameter since yet to unify # kernel machines #kernel = Parameter(None, allowedtype='Kernel', # doc="Kernel object defining the covariance between instances. " # "(Defaults to KernelSquaredExponential if None in arguments)") lm = Parameter(0.0, min=0.0, allowedtype='float', doc="""The regularization term lambda. Increase this when the kernel matrix is not positive, definite.""") def __init__(self, kernel=None, **kwargs): """Initialize a GPR regression analysis. :Parameters: kernel : Kernel a kernel object defining the covariance between instances. (Defaults to KernelSquaredExponential if None in arguments) """ # init base class first Classifier.__init__(self, **kwargs) # It does not make sense to calculate a confusion matrix for a GPR # XXX it does ;) it will be a RegressionStatistics actually ;-) # So if someone desires -- let him have it # self.states.enable('training_confusion', False) # set kernel: if kernel is None: kernel = KernelSquaredExponential() self.__kernel = kernel # append proper clf_internal depending on the kernel # TODO: unify finally all kernel-based machines. # make SMLR to use kernels if isinstance(kernel, KernelLinear): self._clf_internals += ['linear'] else: self._clf_internals += ['non-linear'] if externals.exists('openopt') \ and not 'has_sensitivity' in self._clf_internals: self._clf_internals += ['has_sensitivity'] # No need to initialize state variables. Unless they got set # they would raise an exception self.predicted_variances = # None self.log_marginal_likelihood = None self._init_internals() pass def _init_internals(self): """Reset some internal variables to None. To be used in constructor and untrain() """ self._train_fv = None self._labels = None self._km_train_train = None self._train_labels = None self._alpha = None self._L = None self._LL = None self.__kernel.reset() pass def __repr__(self): """String summary of the object """ return super(GPR, self).__repr__( prefixes=['kernel=%s' % self.__kernel]) def compute_log_marginal_likelihood(self): """ Compute log marginal likelihood using self.train_fv and self.labels. """ if __debug__: debug("GPR", "Computing log_marginal_likelihood") self.log_marginal_likelihood = \ -0.5*Ndot(self._train_labels, self._alpha) - \ Nlog(self._L.diagonal()).sum() - \ self._km_train_train.shape[0] * _halflog2pi return self.log_marginal_likelihood def compute_gradient_log_marginal_likelihood(self): """Compute gradient of the log marginal likelihood. This version use a more compact formula provided by Williams and Rasmussen book. """ # XXX EO: check whether the precomputed self.alpha self.Kinv # are actually the ones corresponding to the hyperparameters # used to compute this gradient! # YYY EO: currently this is verified outside gpr.py but it is # not an efficient solution. # XXX EO: Do some memoizing since it could happen that some # hyperparameters are kept constant by user request, so we # don't need (somtimes) to recompute the corresponding # gradient again. # self.Kinv = N.linalg.inv(self._C) # Faster: Kinv = SLcho_solve(self._LL, N.eye(self._L.shape[0])) alphalphaT = N.dot(self._alpha[:,None], self._alpha[None,:]) tmp = alphalphaT - Kinv # Pass tmp to __kernel and let it compute its gradient terms. # This scales up to huge number of hyperparameters: grad_LML_hypers = self.__kernel.compute_lml_gradient( tmp, self._train_fv) grad_K_sigma_n = 2.0*self.sigma_noise*N.eye(tmp.shape[0]) # Add the term related to sigma_noise: # grad_LML_sigma_n = 0.5 * N.trace(N.dot(tmp,grad_K_sigma_n)) # Faster formula: tr(AB) = (A*B.T).sum() grad_LML_sigma_n = 0.5 * (tmp * (grad_K_sigma_n).T).sum() lml_gradient = N.hstack([grad_LML_sigma_n, grad_LML_hypers]) self.log_marginal_likelihood_gradient = lml_gradient return lml_gradient def compute_gradient_log_marginal_likelihood_logscale(self): """Compute gradient of the log marginal likelihood when hyperparameters are in logscale. This version use a more compact formula provided by Williams and Rasmussen book. """ # Kinv = N.linalg.inv(self._C) # Faster: Kinv = SLcho_solve(self._LL, N.eye(self._L.shape[0])) alphalphaT = N.dot(self._alpha[:,None], self._alpha[None,:]) tmp = alphalphaT - Kinv grad_LML_log_hypers = \ self.__kernel.compute_lml_gradient_logscale(tmp, self._train_fv) grad_K_log_sigma_n = 2.0 * self.sigma_noise ** 2 * N.eye(Kinv.shape[0]) # Add the term related to sigma_noise: # grad_LML_log_sigma_n = 0.5 * N.trace(N.dot(tmp, grad_K_log_sigma_n)) # Faster formula: tr(AB) = (A * B.T).sum() grad_LML_log_sigma_n = 0.5 * (tmp * (grad_K_log_sigma_n).T).sum() lml_gradient = N.hstack([grad_LML_log_sigma_n, grad_LML_log_hypers]) self.log_marginal_likelihood_gradient = lml_gradient return lml_gradient def getSensitivityAnalyzer(self, flavor='auto', **kwargs): """Returns a sensitivity analyzer for GPR. :Parameters: flavor : basestring What sensitivity to provide. Valid values are 'linear', 'model_select', 'auto'. In case of 'auto' selects 'linear' for linear kernel and 'model_select' for the rest. 'linear' corresponds to GPRLinearWeights and 'model_select' to GRPWeights """ # XXX The following two lines does not work since # self.__kernel is instance of kernel.KernelLinear and not # just KernelLinear. How to fix? # YYY yoh is not sure what is the problem... KernelLinear is actually # kernel.KernelLinear so everything shoudl be ok if flavor == 'auto': flavor = ('model_select', 'linear')\ [int(isinstance(self.__kernel, KernelLinear))] if __debug__: debug("GPR", "Returning '%s' sensitivity analyzer" % flavor) # Return proper sensitivity if flavor == 'linear': return GPRLinearWeights(self, **kwargs) elif flavor == 'model_select': # sanity check if not ('has_sensitivity' in self._clf_internals): raise ValueError, \ "model_select flavor is not available probably " \ "due to not available 'openopt' module" return GPRWeights(self, **kwargs) else: raise ValueError, "Flavor %s is not recognized" % flavor def _train(self, data): """Train the classifier using `data` (`Dataset`). """ # local bindings for faster lookup retrainable = self.params.retrainable if retrainable: newkernel = False newL = False _changedData = self._changedData self._train_fv = train_fv = data.samples self._train_labels = train_labels = data.labels if not retrainable or _changedData['traindata'] \ or _changedData.get('kernel_params', False): if __debug__: debug("GPR", "Computing train train kernel matrix") self._km_train_train = km_train_train = self.__kernel.compute(train_fv) newkernel = True if retrainable: self._km_train_test = None # reset to facilitate recomputation else: if __debug__: debug("GPR", "Not recomputing kernel since retrainable and " "nothing has changed") km_train_train = self._km_train_train # reuse if not retrainable or newkernel or _changedData['params']: if __debug__: debug("GPR", "Computing L. sigma_noise=%g" % self.sigma_noise) # XXX it seems that we do not need binding to object, but may be # commented out code would return? self._C = km_train_train + \ self.sigma_noise**2 * N.identity(km_train_train.shape[0], 'd') # The following decomposition could raise # N.linalg.linalg.LinAlgError because of numerical # reasons, due to the too rapid decay of 'self._C' # eigenvalues. In that case we try adding a small constant # to self._C, e.g. epsilon=1.0e-20. It should be a form of # Tikhonov regularization. This is equivalent to adding # little white gaussian noise to data. # # XXX EO: how to choose epsilon? # # Cholesky decomposition is provided by three different # NumPy/SciPy routines (fastest first): # 1) self._LL = scipy.linalg.cho_factor(self._C, lower=True) # self._L = L = N.tril(self._LL[0]) # 2) self._L = scipy.linalg.cholesky(self._C, lower=True) # 3) self._L = numpy.linalg.cholesky(self._C) # Even though 1 is the fastest we choose 2 since 1 does # not return a clean lower-triangular matrix (see docstring). # PBS: I just made it so the KernelMatrix is regularized # all the time. I figured that if ever you were going to # use regularization, you would want to set it yourself # and use the same value for all folds of your data. try: # apply regularization epsilon = self.params.lm * N.eye(self._C.shape[0]) self._L = SLcholesky(self._C + epsilon, lower=True) self._LL = (self._L, True) except SLAError: raise SLAError("Kernel matrix is not positive, definite. " + \ "Try increasing the lm parameter.") pass newL = True else: if __debug__: debug("GPR", "Not computing L since kernel, data and params " "stayed the same") L = self._L # reuse # XXX we leave _alpha being recomputed, although we could check # if newL or _changedData['labels'] # if __debug__: debug("GPR", "Computing alpha") # self._alpha = NLAsolve(L.transpose(), # NLAsolve(L, train_labels)) # Faster: self._alpha = SLcho_solve(self._LL, train_labels) # compute only if the state is enabled if self.states.isEnabled('log_marginal_likelihood'): self.compute_log_marginal_likelihood() pass if retrainable: # we must assign it only if it is retrainable self.states.retrained = not newkernel or not newL if __debug__: debug("GPR", "Done training") pass def _predict(self, data): """ Predict the output for the provided data. """ retrainable = self.params.retrainable if not retrainable or self._changedData['testdata'] \ or self._km_train_test is None: if __debug__: debug('GPR', "Computing train test kernel matrix") km_train_test = self.__kernel.compute(self._train_fv, data) if retrainable: self._km_train_test = km_train_test self.states.repredicted = False else: if __debug__: debug('GPR', "Not recomputing train test kernel matrix") km_train_test = self._km_train_test self.states.repredicted = True predictions = Ndot(km_train_test.transpose(), self._alpha) if self.states.isEnabled('predicted_variances'): # do computation only if state variable was enabled if not retrainable or self._km_test_test is None \ or self._changedData['testdata']: if __debug__: debug('GPR', "Computing test test kernel matrix") km_test_test = self.__kernel.compute(data) if retrainable: self._km_test_test = km_test_test else: if __debug__: debug('GPR', "Not recomputing test test kernel matrix") km_test_test = self._km_test_test if __debug__: debug("GPR", "Computing predicted variances") L = self._L # v = NLAsolve(L, km_train_test) # Faster: piv = N.arange(L.shape[0]) v = SL.lu_solve((L.T, piv), km_train_test, trans=1) # self.predicted_variances = \ # Ndiag(km_test_test - Ndot(v.T, v)) \ # + self.sigma_noise**2 # Faster formula: N.diag(Ndot(v.T, v)) = (v**2).sum(0): self.predicted_variances = Ndiag(km_test_test) - (v ** 2).sum(0) \ + self.sigma_noise ** 2 pass if __debug__: debug("GPR", "Done predicting") return predictions def _setRetrainable(self, value, force=False): """Internal function : need to set _km_test_test """ super(GPR, self)._setRetrainable(value, force) if force or (value and value != self.params.retrainable): self._km_test_test = None def untrain(self): super(GPR, self).untrain() # XXX might need to take special care for retrainable. later self._init_internals() pass def set_hyperparameters(self, hyperparameter): """ Set hyperparameters' values. Note that 'hyperparameter' is a sequence so the order of its values is important. First value must be sigma_noise, then other kernel's hyperparameters values follow in the exact order the kernel expect them to be. """ if hyperparameter[0] < self.params['sigma_noise'].min: raise InvalidHyperparameterError() self.sigma_noise = hyperparameter[0] if hyperparameter.size > 1: self.__kernel.set_hyperparameters(hyperparameter[1:]) pass return kernel = property(fget=lambda self:self.__kernel) pass class GPRLinearWeights(Sensitivity): """`SensitivityAnalyzer` that reports the weights GPR trained on a given `Dataset`. In case of KernelLinear compute explicitly the coefficients of the linear regression, together with their variances (if requested). Note that the intercept is not computed. """ variances = StateVariable(enabled=False, doc="Variances of the weights (for KernelLinear)") _LEGAL_CLFS = [ GPR ] def _call(self, dataset): """Extract weights from GPR """ clf = self.clf kernel = clf.kernel train_fv = clf._train_fv weights = Ndot(kernel.Sigma_p, Ndot(train_fv.T, clf._alpha)) if self.states.isEnabled('variances'): # super ugly formulas that can be quite surely improved: tmp = N.linalg.inv(clf._L) Kyinv = Ndot(tmp.T, tmp) # XXX in such lengthy matrix manipulations you might better off # using N.matrix where * is a matrix product self.states.variances = Ndiag( kernel.Sigma_p - Ndot(kernel.Sigma_p, Ndot(train_fv.T, Ndot(Kyinv, Ndot(train_fv, kernel.Sigma_p))))) return weights if externals.exists('openopt'): from mvpa.clfs.model_selector import ModelSelector class GPRWeights(Sensitivity): """`SensitivityAnalyzer` that reports the weights GPR trained on a given `Dataset`. """ _LEGAL_CLFS = [ GPR ] def _call(self, dataset): """Extract weights from GPR """ clf = self.clf # normalize data: clf._train_labels = (clf._train_labels - clf._train_labels.mean()) \ / clf._train_labels.std() # clf._train_fv = (clf._train_fv-clf._train_fv.mean(0)) \ # /clf._train_fv.std(0) dataset = Dataset(samples=clf._train_fv, labels=clf._train_labels) clf.states.enable("log_marginal_likelihood") ms = ModelSelector(clf, dataset) # Note that some kernels does not have gradient yet! # XXX Make it initialize to clf's current hyperparameter values # or may be add ability to specify starting points in the constructor sigma_noise_initial = 1.0e-5 sigma_f_initial = 1.0 length_scale_initial = N.ones(dataset.nfeatures)*1.0e4 # length_scale_initial = N.random.rand(dataset.nfeatures)*1.0e4 hyp_initial_guess = N.hstack([sigma_noise_initial, sigma_f_initial, length_scale_initial]) fixedHypers = N.array([0]*hyp_initial_guess.size, dtype=bool) fixedHypers = None problem = ms.max_log_marginal_likelihood( hyp_initial_guess=hyp_initial_guess, optimization_algorithm="scipy_lbfgsb", ftol=1.0e-3, fixedHypers=fixedHypers, use_gradient=True, logscale=True) if __debug__ and 'GPR_WEIGHTS' in debug.active: problem.iprint = 1 lml = ms.solve() weights = 1.0/ms.hyperparameters_best[2:] # weight = 1/length_scale if __debug__: debug("GPR", "%s, train: shape %s, labels %s, min:max %g:%g, " "sigma_noise %g, sigma_f %g" % (clf, clf._train_fv.shape, N.unique(clf._train_labels), clf._train_fv.min(), clf._train_fv.max(), ms.hyperparameters_best[0], ms.hyperparameters_best[1])) return weights pymvpa-0.4.8/mvpa/clfs/kernel.py000066400000000000000000000614511174541445200166010ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # Copyright (c) 2008 Emanuele Olivetti # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Kernels for Gaussian Process Regression and Classification.""" _DEV__DOC__ = """ Make use of Parameter Collections to keep parameters of the kernels. Then we would get a uniform .reset() functionality. Now reset is provided just for parts which are failing in the unittests, but there is many more places where they are not reset properly if classifier gets trained on some new data of different dimensionality """ __docformat__ = 'restructuredtext' import numpy as N from mvpa.misc.exceptions import InvalidHyperparameterError from mvpa.clfs.distance import squared_euclidean_distance if __debug__: from mvpa.base import debug, warning class Kernel(object): """Kernel function base class. """ def __init__(self): pass def __repr__(self): return "Kernel()" def compute(self, data1, data2=None): raise NotImplementedError def reset(self): """Resets the kernel dropping internal variables to the original values""" pass def compute_gradient(self,alphaalphaTK): raise NotImplementedError def compute_lml_gradient(self,alphaalphaT_Kinv,data): raise NotImplementedError def compute_lml_gradient_logscale(self,alphaalphaT_Kinv,data): raise NotImplementedError pass class KernelConstant(Kernel): """The constant kernel class. """ def __init__(self, sigma_0=1.0, **kwargs): """Initialize the constant kernel instance. :Parameters: sigma_0 : float standard deviation of the Gaussian prior probability N(0,sigma_0**2) of the intercept of the constant regression. (Defaults to 1.0) """ # init base class first Kernel.__init__(self, **kwargs) self.sigma_0 = sigma_0 self.kernel_matrix = None def __repr__(self): return "%s(sigma_0=%s)" % (self.__class__.__name__, str(self.sigma_0)) def compute(self, data1, data2=None): """Compute kernel matrix. :Parameters: data1 : numpy.ndarray data data2 : numpy.ndarray data (Defaults to None) """ if data2 is None: data2 = data1 pass self.kernel_matrix = \ (self.sigma_0 ** 2) * N.ones((data1.shape[0], data2.shape[0])) return self.kernel_matrix def set_hyperparameters(self, hyperparameter): if hyperparameter < 0: raise InvalidHyperparameterError() self.sigma_0 = hyperparameter return def compute_lml_gradient(self,alphaalphaT_Kinv,data): K_grad_sigma_0 = 2*self.sigma_0 # self.lml_gradient = 0.5*(N.trace(N.dot(alphaalphaT_Kinv,K_grad_sigma_0*N.ones(alphaalphaT_Kinv.shape))) # Faster formula: N.trace(N.dot(A,B)) = (A*(B.T)).sum() # Fastest when B is a constant: B*A.sum() self.lml_gradient = 0.5*N.array(K_grad_sigma_0*alphaalphaT_Kinv.sum()) return self.lml_gradient def compute_lml_gradient_logscale(self,alphaalphaT_Kinv,data): K_grad_sigma_0 = 2*self.sigma_0**2 self.lml_gradient = 0.5*N.array(K_grad_sigma_0*alphaalphaT_Kinv.sum()) return self.lml_gradient pass class KernelLinear(Kernel): """The linear kernel class. """ def __init__(self, Sigma_p=None, sigma_0=1.0, **kwargs): """Initialize the linear kernel instance. :Parameters: Sigma_p : numpy.ndarray Covariance matrix of the Gaussian prior probability N(0,Sigma_p) on the weights of the linear regression. (Defaults to None) sigma_0 : float the standard deviation of the Gaussian prior N(0,sigma_0**2) of the intercept of the linear regression. (Deafults to 1.0) """ # init base class first Kernel.__init__(self, **kwargs) # TODO: figure out cleaner way... probably by using KernelParameters ;-) self.Sigma_p = Sigma_p self.sigma_0 = sigma_0 self.kernel_matrix = None def __repr__(self): return "%s(Sigma_p=%s, sigma_0=%s)" \ % (self.__class__.__name__, str(self.Sigma_p), str(self.sigma_0)) def reset(self): super(KernelLinear, self).reset() self._Sigma_p = self._Sigma_p_orig def compute(self, data1, data2=None): """Compute kernel matrix. Set Sigma_p to correct dimensions and default value if necessary. :Parameters: data1 : numpy.ndarray data data2 : numpy.ndarray data (Defaults to None) """ if data2 is None: data2 = data1 pass # it is better to use separate lines of computation, to don't # incure computation cost without need (otherwise # N.dot(self.Sigma_p, data2.T) can take forever for relatively # large number of features) Sigma_p = self.Sigma_p # local binding #if scalar - scale second term appropriately if N.isscalar(Sigma_p): if Sigma_p == 1.0: data2_sc = data2.T else: data2_sc = Sigma_p * data2.T # if vector use it as diagonal matrix -- ie scale each row by # the given value elif len(Sigma_p.shape) == 1 and \ Sigma_p.shape[0] == data1.shape[1]: # which due to numpy broadcasting is the same as product # with scalar above data2_sc = (Sigma_p * data1).T # if it is a full matrix -- full-featured and lengthy # matrix product else: data2_sc = N.dot(Sigma_p, data2.T) pass # XXX if Sigma_p is changed a warning should be issued! # XXX other cases of incorrect Sigma_p could be catched self.kernel_matrix = N.dot(data1, data2_sc) + self.sigma_0 ** 2 return self.kernel_matrix def set_hyperparameters(self, hyperparameter): # XXX in the next line we assume that the values we want to # assign to Sigma_p are a constant or a vector (the diagonal # of Sigma_p actually). This is a limitation since these # values could be in general an hermitian matrix (i.e., a # covariance matrix)... but how to tell ModelSelector/OpenOpt # to proved just "hermitian" set of values? So for now we skip # the general case, which seems not to useful indeed. if N.any(hyperparameter < 0): raise InvalidHyperparameterError() self.sigma_0 = N.array(hyperparameter[0]) self._Sigma_p = N.diagflat(hyperparameter[1:]) return def compute_lml_gradient(self,alphaalphaT_Kinv,data): def lml_grad(K_grad_i): # return N.trace(N.dot(alphaalphaT_Kinv,K_grad_i)) # Faster formula: N.trace(N.dot(A,B)) = (A*(B.T)).sum() return (alphaalphaT_Kinv*(K_grad_i.T)).sum() lml_gradient = [] lml_gradient.append(2*self.sigma_0*alphaalphaT_Kinv.sum()) for i in range(self.Sigma_p.shape[0]): # Note that Sigma_p is not squared in compute() so it # disappears in the partial derivative: K_grad_i = N.multiply.outer(data[:,i],data[:,i]) lml_gradient.append(lml_grad(K_grad_i)) pass self.lml_gradient = lml_gradient = 0.5*N.array(lml_gradient) return lml_gradient def compute_lml_gradient_logscale(self,alphaalphaT_Kinv,data): def lml_grad(K_grad_i): # return N.trace(N.dot(alphaalphaT_Kinv,K_grad_i)) # Faster formula: N.trace(N.dot(A,B)) = (A*(B.T)).sum() return (alphaalphaT_Kinv*(K_grad_i.T)).sum() lml_gradient = [] lml_gradient.append(2*self.sigma_0**2*alphaalphaT_Kinv.sum()) Sigma_p = self.Sigma_p # local binding for i in range(Sigma_p.shape[0]): # Note that Sigma_p is not squared in compute() so it # disappears in the partial derivative: K_grad_log_i = Sigma_p[i,i]*N.multiply.outer(data[:,i],data[:,i]) lml_gradient.append(lml_grad(K_grad_log_i)) pass self.lml_gradient = lml_gradient = 0.5*N.array(lml_gradient) return lml_gradient def _setSigma_p(self, v): """Set Sigma_p value and store _orig for reset """ if (v is None): # if Sigma_p is not set use a scalar 1.0 v = 1.0 self._Sigma_p_orig = self._Sigma_p = v Sigma_p = property(fget=lambda x:x._Sigma_p, fset=_setSigma_p) pass class KernelExponential(Kernel): """The Exponential kernel class. Note that it can handle a length scale for each dimension for Automtic Relevance Determination. """ def __init__(self, length_scale=1.0, sigma_f = 1.0, **kwargs): """Initialize an Exponential kernel instance. :Parameters: length_scale : float OR numpy.ndarray the characteristic length-scale (or length-scales) of the phenomenon under investigation. (Defaults to 1.0) sigma_f : float Signal standard deviation. (Defaults to 1.0) """ # init base class first Kernel.__init__(self, **kwargs) self.length_scale = length_scale self.sigma_f = sigma_f self.kernel_matrix = None def __repr__(self): return "%s(length_scale=%s, sigma_f=%s)" \ % (self.__class__.__name__, str(self.length_scale), str(self.sigma_f)) def compute(self, data1, data2=None): """Compute kernel matrix. :Parameters: data1 : numpy.ndarray data data2 : numpy.ndarray data (Defaults to None) """ # XXX the following computation can be (maybe) made more # efficient since length_scale is squared and then # square-rooted uselessly. # Weighted euclidean distance matrix: self.wdm = N.sqrt(squared_euclidean_distance( data1, data2, weight=(self.length_scale**-2))) self.kernel_matrix = \ self.sigma_f**2 * N.exp(-self.wdm) return self.kernel_matrix def gradient(self, data1, data2): """Compute gradient of the kernel matrix. A must for fast model selection with high-dimensional data. """ raise NotImplementedError def set_hyperparameters(self, hyperparameter): """Set hyperaparmeters from a vector. Used by model selection. """ if N.any(hyperparameter < 0): raise InvalidHyperparameterError() self.sigma_f = hyperparameter[0] self.length_scale = hyperparameter[1:] return def compute_lml_gradient(self,alphaalphaT_Kinv,data): """Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel. Shorter formula. Allows vector of lengthscales (ARD) BUT THIS LAST OPTION SEEMS NOT TO WORK FOR (CURRENTLY) UNKNOWN REASONS. """ self.lml_gradient = [] def lml_grad(K_grad_i): # return N.trace(N.dot(alphaalphaT_Kinv,K_grad_i)) # Faster formula: N.trace(N.dot(A,B)) = (A*(B.T)).sum() return (alphaalphaT_Kinv*(K_grad_i.T)).sum() grad_sigma_f = 2.0/self.sigma_f*self.kernel_matrix self.lml_gradient.append(lml_grad(grad_sigma_f)) if N.isscalar(self.length_scale) or self.length_scale.size==1: # use the same length_scale for all dimensions: K_grad_l = self.wdm*self.kernel_matrix*(self.length_scale**-1) self.lml_gradient.append(lml_grad(K_grad_l)) else: # use one length_scale for each dimension: for i in range(self.length_scale.size): K_grad_i = (self.length_scale[i]**-3)*(self.wdm**-1)*self.kernel_matrix*N.subtract.outer(data[:,i],data[:,i])**2 self.lml_gradient.append(lml_grad(K_grad_i)) pass pass self.lml_gradient = 0.5*N.array(self.lml_gradient) return self.lml_gradient def compute_lml_gradient_logscale(self,alphaalphaT_Kinv,data): """Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel. Shorter formula. Allows vector of lengthscales (ARD). BUT THIS LAST OPTION SEEMS NOT TO WORK FOR (CURRENTLY) UNKNOWN REASONS. """ self.lml_gradient = [] def lml_grad(K_grad_i): # return N.trace(N.dot(alphaalphaT_Kinv,K_grad_i)) # Faster formula: N.trace(N.dot(A,B)) = (A*(B.T)).sum() return (alphaalphaT_Kinv*(K_grad_i.T)).sum() grad_log_sigma_f = 2.0*self.kernel_matrix self.lml_gradient.append(lml_grad(grad_log_sigma_f)) if N.isscalar(self.length_scale) or self.length_scale.size==1: # use the same length_scale for all dimensions: K_grad_l = self.wdm*self.kernel_matrix self.lml_gradient.append(lml_grad(K_grad_l)) else: # use one length_scale for each dimension: for i in range(self.length_scale.size): K_grad_i = (self.length_scale[i]**-2)*(self.wdm**-1)*self.kernel_matrix*N.subtract.outer(data[:,i],data[:,i])**2 self.lml_gradient.append(lml_grad(K_grad_i)) pass pass self.lml_gradient = 0.5*N.array(self.lml_gradient) return self.lml_gradient pass class KernelSquaredExponential(Kernel): """The Squared Exponential kernel class. Note that it can handle a length scale for each dimension for Automtic Relevance Determination. """ def __init__(self, length_scale=1.0, sigma_f=1.0, **kwargs): """Initialize a Squared Exponential kernel instance. :Parameters: length_scale : float OR numpy.ndarray the characteristic length-scale (or length-scales) of the phenomenon under investigation. (Defaults to 1.0) sigma_f : float Signal standard deviation. (Defaults to 1.0) """ # init base class first Kernel.__init__(self, **kwargs) self.length_scale = length_scale self.sigma_f = sigma_f self.kernel_matrix = None def reset(self): super(KernelSquaredExponential, self).reset() self._length_scale = self._length_scale_orig def __repr__(self): return "%s(length_scale=%s, sigma_f=%s)" \ % (self.__class__.__name__, str(self.length_scale), str(self.sigma_f)) def compute(self, data1, data2=None): """Compute kernel matrix. :Parameters: data1 : numpy.ndarray data data2 : numpy.ndarray data (Defaults to None) """ # weighted squared euclidean distance matrix: self.wdm2 = squared_euclidean_distance(data1, data2, weight=(self.length_scale**-2)) self.kernel_matrix = self.sigma_f**2 * N.exp(-0.5*self.wdm2) # XXX EO: old implementation: # self.kernel_matrix = \ # self.sigma_f * N.exp(-squared_euclidean_distance( # data1, data2, weight=0.5 / (self.length_scale ** 2))) return self.kernel_matrix def set_hyperparameters(self, hyperparameter): """Set hyperaparmeters from a vector. Used by model selection. """ if N.any(hyperparameter < 0): raise InvalidHyperparameterError() self.sigma_f = hyperparameter[0] self._length_scale = hyperparameter[1:] return def compute_lml_gradient(self,alphaalphaT_Kinv,data): """Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel. Shorter formula. Allows vector of lengthscales (ARD). """ self.lml_gradient = [] def lml_grad(K_grad_i): # return N.trace(N.dot(alphaalphaT_Kinv,K_grad_i)) # Faster formula: N.trace(N.dot(A,B)) = (A*(B.T)).sum() return (alphaalphaT_Kinv*(K_grad_i.T)).sum() grad_sigma_f = 2.0/self.sigma_f*self.kernel_matrix self.lml_gradient.append(lml_grad(grad_sigma_f)) if N.isscalar(self.length_scale) or self.length_scale.size==1: # use the same length_scale for all dimensions: K_grad_l = self.wdm2*self.kernel_matrix*(1.0/self.length_scale) self.lml_gradient.append(lml_grad(K_grad_l)) else: # use one length_scale for each dimension: for i in range(self.length_scale.size): K_grad_i = 1.0/(self.length_scale[i]**3)*self.kernel_matrix*N.subtract.outer(data[:,i],data[:,i])**2 self.lml_gradient.append(lml_grad(K_grad_i)) pass pass self.lml_gradient = 0.5*N.array(self.lml_gradient) return self.lml_gradient def compute_lml_gradient_logscale(self,alphaalphaT_Kinv,data): """Compute grandient of the kernel and return the portion of log marginal likelihood gradient due to the kernel. Hyperparameters are in log scale which is sometimes more stable. Shorter formula. Allows vector of lengthscales (ARD). """ self.lml_gradient = [] def lml_grad(K_grad_i): # return N.trace(N.dot(alphaalphaT_Kinv,K_grad_i)) # Faster formula: N.trace(N.dot(A,B)) = (A*(B.T)).sum() return (alphaalphaT_Kinv*(K_grad_i.T)).sum() K_grad_log_sigma_f = 2.0*self.kernel_matrix self.lml_gradient.append(lml_grad(K_grad_log_sigma_f)) if N.isscalar(self.length_scale) or self.length_scale.size==1: # use the same length_scale for all dimensions: K_grad_log_l = self.wdm2*self.kernel_matrix self.lml_gradient.append(lml_grad(K_grad_log_l)) else: # use one length_scale for each dimension: for i in range(self.length_scale.size): K_grad_log_l_i = 1.0/(self.length_scale[i]**2)*self.kernel_matrix*N.subtract.outer(data[:,i],data[:,i])**2 self.lml_gradient.append(lml_grad(K_grad_log_l_i)) pass pass self.lml_gradient = 0.5*N.array(self.lml_gradient) return self.lml_gradient def _setlength_scale(self, v): """Set value of length_scale and its _orig """ self._length_scale = self._length_scale_orig = v length_scale = property(fget=lambda x:x._length_scale, fset=_setlength_scale) pass class KernelMatern_3_2(Kernel): """The Matern kernel class for the case ni=3/2 or ni=5/2. Note that it can handle a length scale for each dimension for Automtic Relevance Determination. """ def __init__(self, length_scale=1.0, sigma_f=1.0, numerator=3.0, **kwargs): """Initialize a Squared Exponential kernel instance. :Parameters: length_scale : float OR numpy.ndarray the characteristic length-scale (or length-scales) of the phenomenon under investigation. (Defaults to 1.0) sigma_f : float Signal standard deviation. (Defaults to 1.0) numerator: float the numerator of parameter ni of Matern covariance functions. Currently only numerator=3.0 and numerator=5.0 are implemented. (Defaults to 3.0) """ # init base class first Kernel.__init__(self, **kwargs) self.length_scale = length_scale self.sigma_f = sigma_f self.kernel_matrix = None if numerator == 3.0 or numerator == 5.0: self.numerator = numerator else: raise NotImplementedError def __repr__(self): return "%s(length_scale=%s, ni=%d/2)" \ % (self.__class__.__name__, str(self.length_scale), self.numerator) def compute(self, data1, data2=None): """Compute kernel matrix. :Parameters: data1 : numpy.ndarray data data2 : numpy.ndarray data (Defaults to None) """ tmp = squared_euclidean_distance( data1, data2, weight=0.5 / (self.length_scale ** 2)) if self.numerator == 3.0: tmp = N.sqrt(tmp) self.kernel_matrix = \ self.sigma_f**2 * (1.0 + N.sqrt(3.0) * tmp) \ * N.exp(-N.sqrt(3.0) * tmp) elif self.numerator == 5.0: tmp2 = N.sqrt(tmp) self.kernel_matrix = \ self.sigma_f**2 * (1.0 + N.sqrt(5.0) * tmp2 + 5.0 / 3.0 * tmp) \ * N.exp(-N.sqrt(5.0) * tmp2) return self.kernel_matrix def gradient(self, data1, data2): """Compute gradient of the kernel matrix. A must for fast model selection with high-dimensional data. """ # TODO SOON # grad = ... # return grad raise NotImplementedError def set_hyperparameters(self, hyperparameter): """Set hyperaparmeters from a vector. Used by model selection. Note: 'numerator' is not considered as an hyperparameter. """ if N.any(hyperparameter < 0): raise InvalidHyperparameterError() self.sigma_f = hyperparameter[0] self.length_scale = hyperparameter[1:] return pass class KernelMatern_5_2(KernelMatern_3_2): """The Matern kernel class for the case ni=5/2. This kernel is just KernelMatern_3_2(numerator=5.0). """ def __init__(self, **kwargs): """Initialize a Squared Exponential kernel instance. :Parameters: length_scale : float OR numpy.ndarray the characteristic length-scale (or length-scales) of the phenomenon under investigation. (Defaults to 1.0) """ KernelMatern_3_2.__init__(self, numerator=5.0, **kwargs) pass class KernelRationalQuadratic(Kernel): """The Rational Quadratic (RQ) kernel class. Note that it can handle a length scale for each dimension for Automtic Relevance Determination. """ def __init__(self, length_scale=1.0, sigma_f=1.0, alpha=0.5, **kwargs): """Initialize a Squared Exponential kernel instance. :Parameters: length_scale : float OR numpy.ndarray the characteristic length-scale (or length-scales) of the phenomenon under investigation. (Defaults to 1.0) sigma_f : float Signal standard deviation. (Defaults to 1.0) alpha: float The parameter of the RQ functions family. (Defaults to 2.0) """ # init base class first Kernel.__init__(self, **kwargs) self.length_scale = length_scale self.sigma_f = sigma_f self.kernel_matrix = None self.alpha = alpha def __repr__(self): return "%s(length_scale=%s, alpha=%f)" \ % (self.__class__.__name__, str(self.length_scale), self.alpha) def compute(self, data1, data2=None): """Compute kernel matrix. :Parameters: data1 : numpy.ndarray data data2 : numpy.ndarray data (Defaults to None) """ tmp = squared_euclidean_distance( data1, data2, weight=1.0 / (self.length_scale ** 2)) self.kernel_matrix = \ self.sigma_f**2 * (1.0 + tmp / (2.0 * self.alpha)) ** -self.alpha return self.kernel_matrix def gradient(self, data1, data2): """Compute gradient of the kernel matrix. A must for fast model selection with high-dimensional data. """ # TODO SOON # grad = ... # return grad raise NotImplementedError def set_hyperparameters(self, hyperparameter): """Set hyperaparmeters from a vector. Used by model selection. Note: 'alpha' is not considered as an hyperparameter. """ if N.any(hyperparameter < 0): raise InvalidHyperparameterError() self.sigma_f = hyperparameter[0] self.length_scale = hyperparameter[1:] return pass # dictionary of avalable kernels with names as keys: kernel_dictionary = {'constant': KernelConstant, 'linear': KernelLinear, 'exponential': KernelExponential, 'squared exponential': KernelSquaredExponential, 'Matern ni=3/2': KernelMatern_3_2, 'Matern ni=5/2': KernelMatern_5_2, 'rational quadratic': KernelRationalQuadratic} pymvpa-0.4.8/mvpa/clfs/knn.py000066400000000000000000000202611174541445200161010ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """k-Nearest-Neighbour classifier.""" __docformat__ = 'restructuredtext' import sys # not worthy of externals checking _dict_has_key = sys.version_info >= (2, 5) import numpy as N from mvpa.base import warning from mvpa.misc.support import indentDoc from mvpa.clfs.base import Classifier from mvpa.base.dochelpers import enhancedDocString from mvpa.clfs.distance import squared_euclidean_distance if __debug__: from mvpa.base import debug class kNN(Classifier): """ k-Nearest-Neighbour classifier. This is a simple classifier that bases its decision on the distances between the training dataset samples and the test sample(s). Distances are computed using a customizable distance function. A certain number (`k`)of nearest neighbors is selected based on the smallest distances and the labels of this neighboring samples are fed into a voting function to determine the labels of the test sample. Training a kNN classifier is extremely quick, as no actuall training is performed as the training dataset is simply stored in the classifier. All computations are done during classifier prediction. .. note:: If enabled, kNN stores the votes per class in the 'values' state after calling predict(). """ _clf_internals = ['knn', 'non-linear', 'binary', 'multiclass', 'notrain2predict' ] def __init__(self, k=2, dfx=squared_euclidean_distance, voting='weighted', **kwargs): """ :Parameters: k: unsigned integer Number of nearest neighbours to be used for voting. dfx: functor Function to compute the distances between training and test samples. Default: squared euclidean distance voting: str Voting method used to derive predictions from the nearest neighbors. Possible values are 'majority' (simple majority of classes determines vote) and 'weighted' (votes are weighted according to the relative frequencies of each class in the training data). **kwargs: Additonal arguments are passed to the base class. """ # init base class first Classifier.__init__(self, **kwargs) self.__k = k self.__dfx = dfx self.__voting = voting self.__data = None def __repr__(self, prefixes=[]): """Representation of the object """ return super(kNN, self).__repr__( ["k=%d" % self.__k, "dfx=%s" % self.__dfx, "voting=%s" % repr(self.__voting)] + prefixes) def __str__(self): return "%s\n data: %s" % \ (Classifier.__str__(self), indentDoc(self.__data)) def _train(self, data): """Train the classifier. For kNN it is degenerate -- just stores the data. """ self.__data = data if __debug__: if str(data.samples.dtype).startswith('uint') \ or str(data.samples.dtype).startswith('int'): warning("kNN: input data is in integers. " + \ "Overflow on arithmetic operations might result in"+\ " errors. Please convert dataset's samples into" +\ " floating datatype if any error is reported.") self.__weights = None # create dictionary with an item for each condition uniquelabels = data.uniquelabels self.__votes_init = dict(zip(uniquelabels, [0] * len(uniquelabels))) def _predict(self, data): """Predict the class labels for the provided data. Returns a list of class labels (one for each data sample). """ # make sure we're talking about arrays data = N.asarray(data) # checks only in debug mode if __debug__: if not data.ndim == 2: raise ValueError, "Data array must be two-dimensional." if not data.shape[1] == self.__data.nfeatures: raise ValueError, "Length of data samples (features) does " \ "not match the classifier." # compute the distance matrix between training and test data with # distances stored row-wise, ie. distances between test sample [0] # and all training samples will end up in row 0 dists = self.__dfx(self.__data.samples, data).T # determine the k nearest neighbors per test sample knns = dists.argsort(axis=1)[:, :self.__k] # predicted class labels will go here predicted = [] if self.__voting == 'majority': vfx = self.getMajorityVote elif self.__voting == 'weighted': vfx = self.getWeightedVote else: raise ValueError, "kNN told to perform unknown voting '%s'." \ % self.__voting # perform voting results = [vfx(knn) for knn in knns] # extract predictions predicted = [r[0] for r in results] # store the predictions in the state. Relies on State._setitem to do # nothing if the relevant state member is not enabled self.predictions = predicted self.values = [r[1] for r in results] return predicted def getMajorityVote(self, knn_ids): """Simple voting by choosing the majority of class neighbors. """ # local bindings _data = self.__data labels = _data.labels # number of occerences for each unique class in kNNs votes = self.__votes_init.copy() for nn in knn_ids: votes[labels[nn]] += 1 # find the class with most votes # return votes as well to store them in the state if _dict_has_key: # approx 5% faster implementation than below maxvotes = max(votes.iteritems(), key=lambda x:x[1])[0] else: # no key keyword for max in elderly versions maxvotes = max([(v, k) for k, v in votes.iteritems()])[1] return maxvotes, \ [votes[ul] for ul in _data.uniquelabels] # transform into lists def getWeightedVote(self, knn_ids): """Vote with classes weighted by the number of samples per class. """ # local bindings _data = self.__data uniquelabels = _data.uniquelabels # Lazy evaluation if self.__weights is None: # # It seemed to Yarik that this has to be evaluated just once per # training dataset. # self.__labels = labels = self.__data.labels Nlabels = len(labels) Nuniquelabels = len(uniquelabels) # TODO: To get proper speed up for the next line only, # histogram should be computed # via sorting + counting "same" elements while reducing. # Guaranteed complexity is NlogN whenever now it is N^2 # compute the relative proportion of samples belonging to each # class (do it in one loop to improve speed and reduce readability self.__weights = \ [ 1.0 - ((labels == label).sum() / Nlabels) \ for label in uniquelabels ] self.__weights = dict(zip(uniquelabels, self.__weights)) labels = self.__labels # number of occerences for each unique class in kNNs votes = self.__votes_init.copy() for nn in knn_ids: votes[labels[nn]] += 1 # weight votes votes = [ self.__weights[ul] * votes[ul] for ul in uniquelabels] # find the class with most votes # return votes as well to store them in the state return uniquelabels[N.asarray(votes).argmax()], \ votes def untrain(self): """Reset trained state""" self.__data = None super(kNN, self).untrain() pymvpa-0.4.8/mvpa/clfs/lars.py000066400000000000000000000220671174541445200162620ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Least angle regression (LARS) classifier.""" __docformat__ = 'restructuredtext' # system imports import numpy as N import mvpa.base.externals as externals # do conditional to be able to build module reference if externals.exists('rpy', raiseException=True) and \ externals.exists('lars', raiseException=True): import rpy rpy.r.library('lars') # local imports from mvpa.clfs.base import Classifier, FailedToTrainError from mvpa.measures.base import Sensitivity from mvpa.base import warning if __debug__: from mvpa.base import debug known_models = ('lasso', 'stepwise', 'lar', 'forward.stagewise') class LARS(Classifier): """Least angle regression (LARS) `Classifier`. LARS is the model selection algorithm from: Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani, Least Angle Regression Annals of Statistics (with discussion) (2004) 32(2), 407-499. A new method for variable subset selection, with the lasso and 'epsilon' forward stagewise methods as special cases. Similar to SMLR, it performs a feature selection while performing classification, but instead of starting with all features, it starts with none and adds them in, which is similar to boosting. This classifier behaves more like a ridge regression in that it returns prediction values and it treats the training labels as continuous. In the true nature of the PyMVPA framework, this algorithm is actually implemented in R by Trevor Hastie and wrapped via RPy. To make use of LARS, you must have R and RPy installed as well as the LARS contributed package. You can install the R and RPy with the following command on Debian-based machines: sudo aptitude install python-rpy python-rpy-doc r-base-dev You can then install the LARS package by running R as root and calling: install.packages() """ # XXX from yoh: it is linear, isn't it? _clf_internals = [ 'lars', 'regression', 'linear', 'has_sensitivity', 'does_feature_selection', ] def __init__(self, model_type="lasso", trace=False, normalize=True, intercept=True, max_steps=None, use_Gram=False, **kwargs): """ Initialize LARS. See the help in R for further details on the following parameters: :Parameters: model_type : string Type of LARS to run. Can be one of ('lasso', 'lar', 'forward.stagewise', 'stepwise'). trace : boolean Whether to print progress in R as it works. normalize : boolean Whether to normalize the L2 Norm. intercept : boolean Whether to add a non-penalized intercept to the model. max_steps : None or int If not None, specify the total number of iterations to run. Each iteration adds a feature, but leaving it none will add until convergence. use_Gram : boolean Whether to compute the Gram matrix (this should be false if you have more features than samples.) """ # init base class first Classifier.__init__(self, **kwargs) if not model_type in known_models: raise ValueError('Unknown model %s for LARS is specified. Known' % model_type + 'are %s' % `known_models`) # set up the params self.__type = model_type self.__normalize = normalize self.__intercept = intercept self.__trace = trace self.__max_steps = max_steps self.__use_Gram = use_Gram # pylint friendly initializations self.__lowest_Cp_step = None self.__weights = None """The beta weights for each feature.""" self.__trained_model = None """The model object after training that will be used for predictions.""" # It does not make sense to calculate a confusion matrix for a # regression # YOH: we do have summary statistics for regressions #self.states.enable('training_confusion', False) def __repr__(self): """String summary of the object """ return "LARS(type='%s', normalize=%s, intercept=%s, trace=%s, " \ "max_steps=%s, use_Gram=%s, regression=%s, " \ "enable_states=%s)" % \ (self.__type, self.__normalize, self.__intercept, self.__trace, self.__max_steps, self.__use_Gram, self.regression, str(self.states.enabled)) def _train(self, data): """Train the classifier using `data` (`Dataset`). """ if self.__max_steps is None: # train without specifying max_steps trained_model = rpy.r.lars(data.samples, data.labels[:,N.newaxis], type=self.__type, normalize=self.__normalize, intercept=self.__intercept, trace=self.__trace, use_Gram=self.__use_Gram) else: # train with specifying max_steps trained_model = rpy.r.lars(data.samples, data.labels[:,N.newaxis], type=self.__type, normalize=self.__normalize, intercept=self.__intercept, trace=self.__trace, use_Gram=self.__use_Gram, max_steps=self.__max_steps) # find the step with the lowest Cp (risk) # it is often the last step if you set a max_steps # must first convert dictionary to array try: Cp = trained_model['Cp'] if '0' in Cp: # If there was any Cp_vals = N.asarray([Cp[str(x)] for x in range(len(Cp))]) else: Cp_vals = None except TypeError, e: raise FailedToTrainError, \ "Failed to train %s on %s. Got '%s' while trying to access " \ "trained model %s" % (self, data, e, trained_model) if Cp_vals is None: # if there were no any -- just choose 0th lowest_Cp_step = 0 elif N.isnan(Cp_vals[0]): # sometimes may come back nan, so just pick the last one lowest_Cp_step = len(Cp_vals)-1 else: # determine the lowest lowest_Cp_step = Cp_vals.argmin() self.__lowest_Cp_step = lowest_Cp_step # set the weights to the lowest Cp step self.__weights = trained_model['beta'][lowest_Cp_step, :] self.__trained_model = trained_model # bind to an instance # # set the weights to the final state # self.__weights = self.__trained_model['beta'][-1,:] def _predict(self, data): """ Predict the output for the provided data. """ # predict with the final state (i.e., the last step) # predict with the lowest Cp step try: res = rpy.r.predict_lars(self.__trained_model, data, mode='step', s=self.__lowest_Cp_step) #s=self.__trained_model['beta'].shape[0]) fit = N.atleast_1d(res['fit']) except rpy.RPyRException, e: warning("Failed to obtain predictions using %s on %s." "Re-raising exception." % (self, data)) raise self.values = fit return fit def _getFeatureIds(self): """Return ids of the used features """ return N.where(N.abs(self.__weights)>0)[0] def getSensitivityAnalyzer(self, **kwargs): """Returns a sensitivity analyzer for LARS.""" return LARSWeights(self, **kwargs) weights = property(lambda self: self.__weights) class LARSWeights(Sensitivity): """`SensitivityAnalyzer` that reports the weights LARS trained on a given `Dataset`. """ _LEGAL_CLFS = [ LARS ] def _call(self, dataset=None): """Extract weights from LARS classifier. LARS always has weights available, so nothing has to be computed here. """ clf = self.clf weights = clf.weights if __debug__: debug('LARS', "Extracting weights for LARS - "+ "Result: min=%f max=%f" %\ (N.min(weights), N.max(weights))) return weights pymvpa-0.4.8/mvpa/clfs/libsmlrc/000077500000000000000000000000001174541445200165475ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/clfs/libsmlrc/__init__.py000066400000000000000000000037231174541445200206650ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Wraper for the stepwise_regression function for SMLR.""" if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.clfs.libsmlrc') import numpy as N import ctypes as C import os import sys from mvpa.clfs.libsmlrc.ctypes_helper import extend_args, c_darray # connect to library that's in this directory if sys.platform == 'win32': # on windows things get tricky as we compile this lib as an extension # so it get a .pyd name suffix instead of .dll smlrlib = C.cdll[os.path.join(os.path.dirname(__file__), 'smlrc.pyd')] elif sys.platform == 'darwin': # look for .so extension on Mac (not .dylib this time) smlrlib = C.cdll[os.path.join(os.path.dirname(__file__), 'smlrc.so')] else: smlrlib = N.ctypeslib.load_library('smlrc', os.path.dirname(__file__)) # wrap the stepwise function def stepwise_regression(*args): func = smlrlib.stepwise_regression func.argtypes = [C.c_int, C.c_int, c_darray, C.c_int, C.c_int, c_darray, C.c_int, C.c_int, c_darray, C.c_int, C.c_int, c_darray, C.c_int, C.c_int, c_darray, C.c_int, c_darray, C.c_int, c_darray, C.c_int, c_darray, C.c_int, C.c_int, C.c_double, C.c_float, C.c_float, C.c_int64] func.restype = C.c_long # get the new arglist arglist = extend_args(*args) return func(*arglist) if __debug__: debug('INIT', 'mvpa.clfs.libsmlrc end') pymvpa-0.4.8/mvpa/clfs/libsmlrc/ctypes_helper.py000066400000000000000000000045421174541445200217740ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Helpers for wrapping C libraries with ctypes.""" import numpy as N import ctypes as C # define an array type to help with wrapping c_darray = N.ctypeslib.ndpointer(dtype=N.float64, flags='aligned,contiguous') c_larray = N.ctypeslib.ndpointer(dtype=N.int64, flags='aligned,contiguous') c_farray = N.ctypeslib.ndpointer(dtype=N.float32, flags='aligned,contiguous') c_iarray = N.ctypeslib.ndpointer(dtype=N.int32, flags='aligned,contiguous') def extend_args(*args): """Turn ndarray arguments into dims and arrays.""" arglist = [] for arg in args: if isinstance(arg, N.ndarray): # add the dimensions arglist.extend(arg.shape) # just append the arg arglist.append(arg) return arglist ############################################################# # I'm not sure the rest is helpful, but I'll keep it for now. ############################################################# # incomplete type conversion typemap = { N.float64: C.c_double, N.float32: C.c_float, N.int64: C.c_int64, N.int32: C.c_int32} def process_args(*args): """Turn ndarray arguments into dims and array pointers for calling a ctypes-wrapped function.""" arglist = [] for arg in args: if isinstance(arg, N.ndarray): # add the dimensions arglist.extend(arg.shape) # add the pointer to the ndarray arglist.append(arg.ctypes.data_as( C.POINTER(typemap[arg.dtype.type]))) else: # just append the arg arglist.append(arg) return arglist def get_argtypes(*args): argtypes = [] for arg in args: if isinstance(arg, N.ndarray): # add the dimensions argtypes.extend([C.c_int]*len(arg.shape)) # add the pointer to the ndarray argtypes.append(N.ctypeslib.ndpointer(dtype=arg.dtype)) else: # try and figure out the type argtypes.append(arg) return argtypes pymvpa-0.4.8/mvpa/clfs/libsmlrc/smlr.c000066400000000000000000000121041174541445200176660ustar00rootroot00000000000000/*emacs: -*- mode: c-mode; tab-width: 8; c-basic-offset: 2; indent-tabs-mode: t -*- ex: set sts=4 ts=8 sw=4 noet: */ #include #include #include #include #include #include DL_EXPORT(int) stepwise_regression(int w_rows, int w_cols, double w[w_rows][w_cols], int X_rows, int X_cols, double X[X_rows][X_cols], int XY_rows, int XY_cols, double XY[XY_rows][XY_cols], int Xw_rows, int Xw_cols, double Xw[Xw_rows][Xw_cols], int E_rows, int E_cols, double E[E_rows][E_cols], int ac_rows, double ac[ac_rows], int lm_2_ac_rows, double lm_2_ac[lm_2_ac_rows], int S_rows, double S[S_rows], int M, int maxiter, double convergence_tol, float resamp_decay, float min_resamp, int verbose, long long int seed) { // initialize the iterative optimization double incr = DBL_MAX; long non_zero = 0; long wasted_basis = 0; long needed_basis = 0; int changed = 0; // for calculating stepwise changes double w_old; double w_new; double w_diff; double grad; double XdotP; double E_new_m; double sum2_w_diff; double sum2_w_old; // get the num features and num classes int nd = w_rows; int ns = E_rows; // loop indexes int i = 0; // prob of resample each weight // allocate everything in heap -- not on stack float** p_resamp = (float **)calloc(w_rows, sizeof(float*)); for (i=0; i lm_2_ac[basis]) { // more towards bounds, but keep it w_new -= lm_2_ac[basis]; changed = 1; // umark from being zero if necessary if (w_old == 0.0) { non_zero += 1; // reset the p_resample p_resamp[basis][m] = 1.0; // we needed the basis needed_basis += 1; } } else if (w_new < -lm_2_ac[basis]) { // more towards bounds, but keep it w_new += lm_2_ac[basis]; changed = 1; // umark from being zero if necessary if (w_old == 0.0) { non_zero += 1; // reset the p_resample p_resamp[basis][m] = 1.0; // we needed the basis needed_basis += 1; } } else { // gonna zero it out w_new = 0.0; // decrease the p_resamp p_resamp[basis][m] -= (p_resamp[basis][m] - min_resamp) * resamp_decay; // set the number of non-zero if (w_old == 0.0) { // we didn't change changed = 0; // and wasted a basis wasted_basis += 1; } else { // we changed changed = 1; // must update num non_zero non_zero -= 1; } } // process changes if necessary if (changed == 1) { // update the expected values w_diff = w_new - w_old; for (i=0; i 0): self.maxlen = max(self.maxlen, max(x[i].keys())) else: self.maxlen = max(self.maxlen, len(x[i])) svmc.svm_problem_l_set(prob, size) svmc.svm_problem_y_set(prob, y_array) svmc.svm_problem_x_set(prob, x_matrix) def __repr__(self): return "" % (self.size) def __del__(self): if __debug__: debug('CLF_', 'Destroying libsvm.SVMProblem %s' % `self`) svmc.delete_svm_problem(self.prob) svmc.delete_double(self.y_array) for i in range(self.size): svmc.svm_node_array_destroy(self.data[i]) svmc.svm_node_matrix_destroy(self.x_matrix) class SVMModel: def __init__(self, arg1, arg2=None): if arg2 == None: # create model from file filename = arg1 self.model = svmc.svm_load_model(filename) else: # create model from problem and parameter prob, param = arg1, arg2 self.prob = prob if param.gamma == 0: param.gamma = 1.0/prob.maxlen msg = svmc.svm_check_parameter(prob.prob, param.param) if msg: raise ValueError, msg self.model = svmc.svm_train(prob.prob, param.param) #setup some classwide variables self.nr_class = svmc.svm_get_nr_class(self.model) self.svm_type = svmc.svm_get_svm_type(self.model) #create labels(classes) intarr = svmc.new_int(self.nr_class) svmc.svm_get_labels(self.model, intarr) self.labels = intArray2List(intarr, self.nr_class) svmc.delete_int(intarr) #check if valid probability model self.probability = svmc.svm_check_probability_model(self.model) def __repr__(self): """ Print string representation of the model or easier comprehension and some statistics """ ret = '' def predict(self, x): data = convert2SVMNode(x) ret = svmc.svm_predict(self.model, data) svmc.svm_node_array_destroy(data) return ret def getNRClass(self): return self.nr_class def getLabels(self): if self.svm_type == NU_SVR \ or self.svm_type == EPSILON_SVR \ or self.svm_type == ONE_CLASS: raise TypeError, "Unable to get label from a SVR/ONE_CLASS model" return self.labels #def getParam(self): # return SVMParameter( # svmc_parameter=svmc.svm_model_param_get(self.model)) def predictValuesRaw(self, x): #convert x into SVMNode, allocate a double array for return n = self.nr_class*(self.nr_class-1)//2 data = convert2SVMNode(x) dblarr = svmc.new_double(n) svmc.svm_predict_values(self.model, data, dblarr) ret = doubleArray2List(dblarr, n) svmc.delete_double(dblarr) svmc.svm_node_array_destroy(data) return ret def predictValues(self, x): v = self.predictValuesRaw(x) if self.svm_type == NU_SVR \ or self.svm_type == EPSILON_SVR \ or self.svm_type == ONE_CLASS: return v[0] else: #self.svm_type == C_SVC or self.svm_type == NU_SVC count = 0 d = {} for i in range(len(self.labels)): for j in range(i+1, len(self.labels)): d[self.labels[i], self.labels[j]] = v[count] d[self.labels[j], self.labels[i]] = -v[count] count += 1 return d def predictProbability(self, x): #c code will do nothing on wrong type, so we have to check ourself if self.svm_type == NU_SVR or self.svm_type == EPSILON_SVR: raise TypeError, "call get_svr_probability or get_svr_pdf " \ "for probability output of regression" elif self.svm_type == ONE_CLASS: raise TypeError, "probability not supported yet for one-class " \ "problem" #only C_SVC, NU_SVC goes in if not self.probability: raise TypeError, "model does not support probabiliy estimates" #convert x into SVMNode, alloc a double array to receive probabilities data = convert2SVMNode(x) dblarr = svmc.new_double(self.nr_class) pred = svmc.svm_predict_probability(self.model, data, dblarr) pv = doubleArray2List(dblarr, self.nr_class) svmc.delete_double(dblarr) svmc.svm_node_array_destroy(data) p = {} for i in range(len(self.labels)): p[self.labels[i]] = pv[i] return pred, p def getSVRProbability(self): #leave the Error checking to svm.cpp code ret = svmc.svm_get_svr_probability(self.model) if ret == 0: raise TypeError, "not a regression model or probability " \ "information not available" return ret def getSVRPdf(self): #get_svr_probability will handle error checking sigma = self.getSVRProbability() return lambda z: exp(-fabs(z)/sigma)/(2*sigma) def save(self, filename): svmc.svm_save_model(filename, self.model) def __del__(self): if __debug__: debug('CLF_', 'Destroying libsvm.SVMModel %s' % (`self`)) try: if svmc.__version__ < 300: svmc.svm_destroy_model(self.model) else: svmc.svm_destroy_model_helper(self.model) except: # blind way to overcome problem of already deleted model and # "SVMModel instance has no attribute 'model'" in ignored pass def getTotalNSV(self): return svmc.svm_model_l_get(self.model) def getNSV(self): """Returns a list with the number of support vectors per class. """ return [ svmc.int_getitem(svmc.svm_model_nSV_get( self.model ), i) for i in range( self.nr_class ) ] def getSV(self): """Returns an array with the all support vectors. array( nSV x ) """ return svmc.svm_node_matrix2numpy_array( svmc.svm_model_SV_get(self.model), self.getTotalNSV(), self.prob.maxlen) def getSVCoef(self): """Return coefficients for SVs... Needs to be used directly with caution! Summary on what is happening in libsvm internals with sv_coef svm_model's sv_coef (especially) are "cleverly" packed into a matrix nr_class - 1 x #SVs_total which stores coefficients for nr_class x (nr_class-1) / 2 binary classifiers' SV coefficients. For classifier i-vs-j General packing rule can be described as: i-th row contains sv_coefficients for SVs of class i it took in all i-vs-j or j-vs-i classifiers. Another useful excerpt from svm.cpp is // classifier (i,j): coefficients with // i are in sv_coef[j-1][nz_start[i]...], // j are in sv_coef[i][nz_start[j]...] It can also be described as j-th column lists coefficients for SV # j which belongs to some class C, which it took (if it was an SV, ie != 0) in classifiers i vs C (iff iC) This way no byte of storage is wasted but imho such setup is quite convolved """ return svmc.doubleppcarray2numpy_array( svmc.svm_model_sv_coef_get(self.model), self.nr_class - 1, self.getTotalNSV()) def getRho(self): """Return constant(s) in decision function(s) (if multi-class)""" return doubleArray2List(svmc.svm_model_rho_get(self.model), self.nr_class * (self.nr_class-1)/2) pymvpa-0.4.8/mvpa/clfs/libsvmc/sens.py000066400000000000000000000105251174541445200177240ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Provide sensitivity measures for libsvm's SVM.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base import warning from mvpa.misc.state import StateVariable from mvpa.misc.param import Parameter from mvpa.measures.base import Sensitivity if __debug__: from mvpa.base import debug class LinearSVMWeights(Sensitivity): """`SensitivityAnalyzer` for the LIBSVM implementation of a linear SVM. """ _ATTRIBUTE_COLLECTIONS = ['params'] biases = StateVariable(enabled=True, doc="Offsets of separating hyperplanes") split_weights = Parameter(False, allowedtype='bool', doc="If binary classification either to sum SVs per each " "class separately") def __init__(self, clf, **kwargs): """Initialize the analyzer with the classifier it shall use. :Parameters: clf: LinearSVM classifier to use. Only classifiers sub-classed from `LinearSVM` may be used. """ # init base classes first Sensitivity.__init__(self, clf, **kwargs) def _call(self, dataset, callables=[]): # local bindings clf = self.clf model = clf.model if clf.params.regression: nr_class = None else: nr_class = model.nr_class if not nr_class in [None, 2]: warning("You are estimating sensitivity for SVM %s trained on %d" % (str(self.clf), self.clf.model.nr_class) + " classes. Make sure that it is what you intended to do" ) svcoef = N.matrix(model.getSVCoef()) svs = N.matrix(model.getSV()) rhos = N.asarray(model.getRho()) self.biases = rhos if self.split_weights: if nr_class != 2: raise NotImplementedError, \ "Cannot compute per-class weights for" \ " non-binary classification task" # libsvm might have different idea on the ordering # of labels, so we would need to map them back explicitely svm_labels = model.getLabels() # labels as assigned by libsvm ds_labels = list(dataset.uniquelabels) # labels in the dataset senses = [None for i in ds_labels] # first label is given positive value for i, (c, l) in enumerate( [(svcoef > 0, lambda x: x), (svcoef < 0, lambda x: x*-1)] ): # convert to array, and just take the meaningful dimension c_ = c.A[0] senses[ds_labels.index(svm_labels[i])] = \ (l(svcoef[:, c_] * svs[c_, :])).A[0] weights = N.array(senses) else: # XXX yoh: .mean() is effectively # averages across "sensitivities" of all paired classifiers (I # think). See more info on this topic in svm.py on how sv_coefs # are stored # # First multiply SV coefficients with the actual SVs to get # weighted impact of SVs on decision, then for each feature # take mean across SVs to get a single weight value # per feature weights = svcoef * svs if __debug__ and 'SVM' in debug.active: if clf.params.regression: nsvs = model.getTotalNSV() else: nsvs = model.getNSV() if clf.regression: svm_type = clf._svm_impl # type of regression else: svm_type = '%d-class SVM(%s)' % (nr_class, clf._svm_impl) debug('SVM', "Extracting weights for %s: #SVs=%s, " % \ (svm_type, nsvs) + \ " SVcoefshape=%s SVs.shape=%s Rhos=%s." % \ (svcoef.shape, svs.shape, rhos) + \ " Result: min=%f max=%f" % (N.min(weights), N.max(weights))) return N.asarray(weights.T) _customizeDocInherit = True pymvpa-0.4.8/mvpa/clfs/libsvmc/svm.py000066400000000000000000000315251174541445200175640ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Wrap the libsvm package into a very simple class interface.""" __docformat__ = 'restructuredtext' import numpy as N import operator from mvpa.base import warning from mvpa.misc.state import StateVariable from mvpa.clfs._svmbase import _SVM from mvpa.clfs.libsvmc import _svm as svm from sens import LinearSVMWeights if __debug__: from mvpa.base import debug # we better expose those since they are mentioned in docstrings # although pylint would not be happy from mvpa.clfs.libsvmc._svmc import \ C_SVC, NU_SVC, EPSILON_SVR, \ NU_SVR, LINEAR, POLY, RBF, SIGMOID, \ PRECOMPUTED, ONE_CLASS class SVM(_SVM): """Support Vector Machine Classifier. This is a simple interface to the libSVM package. """ # Since this is internal feature of LibSVM, this state variable is present # here probabilities = StateVariable(enabled=False, doc="Estimates of samples probabilities as provided by LibSVM") _KERNELS = { "linear": (svm.svmc.LINEAR, None, LinearSVMWeights), "rbf" : (svm.svmc.RBF, ('gamma',), None), "poly": (svm.svmc.POLY, ('gamma', 'degree', 'coef0'), None), "sigmoid": (svm.svmc.SIGMOID, ('gamma', 'coef0'), None), } # TODO: Complete the list ;-) # TODO p is specific for SVR _KNOWN_PARAMS = [ 'epsilon', 'probability', 'shrinking', 'weight_label', 'weight'] _KNOWN_KERNEL_PARAMS = [ 'cache_size' ] _KNOWN_IMPLEMENTATIONS = { 'C_SVC' : (svm.svmc.C_SVC, ('C',), ('binary', 'multiclass'), 'C-SVM classification'), 'NU_SVC' : (svm.svmc.NU_SVC, ('nu',), ('binary', 'multiclass'), 'nu-SVM classification'), 'ONE_CLASS' : (svm.svmc.ONE_CLASS, (), ('oneclass',), 'one-class-SVM'), 'EPSILON_SVR' : (svm.svmc.EPSILON_SVR, ('C', 'tube_epsilon'), ('regression',), 'epsilon-SVM regression'), 'NU_SVR' : (svm.svmc.NU_SVR, ('nu', 'tube_epsilon'), ('regression',), 'nu-SVM regression') } _clf_internals = _SVM._clf_internals + [ 'libsvm' ] def __init__(self, kernel_type='linear', **kwargs): # XXX Determine which parameters depend on each other and implement # safety/simplifying logic around them # already done for: nr_weight # thought: weight and weight_label should be a dict """Interface class to LIBSVM classifiers and regressions. Default implementation (C/nu/epsilon SVM) is chosen depending on the given parameters (C/nu/tube_epsilon). """ svm_impl = kwargs.get('svm_impl', None) # Depending on given arguments, figure out desired SVM # implementation if svm_impl is None: for arg, impl in [ ('tube_epsilon', 'EPSILON_SVR'), ('C', 'C_SVC'), ('nu', 'NU_SVC') ]: if kwargs.has_key(arg): svm_impl = impl if __debug__: debug('SVM', 'No implementation was specified. Since ' '%s is given among arguments, assume %s' % (arg, impl)) break if svm_impl is None: svm_impl = 'C_SVC' if __debug__: debug('SVM', 'Assign C_SVC "by default"') kwargs['svm_impl'] = svm_impl # init base class _SVM.__init__(self, kernel_type, **kwargs) self._svm_type = self._KNOWN_IMPLEMENTATIONS[svm_impl][0] if 'nu' in self._KNOWN_PARAMS and 'epsilon' in self._KNOWN_PARAMS: # overwrite eps param with new default value (information # taken from libSVM docs self.params['epsilon'].setDefault(0.001) self.__model = None """Holds the trained SVM.""" def _train(self, dataset): """Train SVM """ # libsvm needs doubles if dataset.samples.dtype == 'float64': src = dataset.samples else: src = dataset.samples.astype('double') svmprob = svm.SVMProblem( dataset.labels.tolist(), src ) # Translate few params TRANSLATEDICT = {'epsilon': 'eps', 'tube_epsilon': 'p'} args = [] for paramname, param in self.params.items.items() \ + self.kernel_params.items.items(): if paramname in TRANSLATEDICT: argname = TRANSLATEDICT[paramname] elif paramname in svm.SVMParameter.default_parameters: argname = paramname else: if __debug__: debug("SVM_", "Skipping parameter %s since it is not known" "to libsvm" % paramname) continue args.append( (argname, param.value) ) # ??? All those parameters should be fetched if present from # **kwargs and create appropriate parameters within .params or # .kernel_params libsvm_param = svm.SVMParameter( kernel_type=self._kernel_type, svm_type=self._svm_type, **dict(args)) """Store SVM parameters in libSVM compatible format.""" if self.params.isKnown('C'):#svm_type in [svm.svmc.C_SVC]: C = self.params.C if not operator.isSequenceType(C): # we were not given a tuple for balancing between classes C = [C] Cs = list(C[:]) # copy for i in xrange(len(Cs)): if Cs[i] < 0: Cs[i] = self._getDefaultC(dataset.samples)*abs(Cs[i]) if __debug__: debug("SVM", "Default C for %s was computed to be %s" % (C[i], Cs[i])) libsvm_param._setParameter('C', Cs[0]) if len(Cs)>1: C0 = abs(C[0]) scale = 1.0/(C0)#*N.sqrt(C0)) # so we got 1 C per label if len(Cs) != len(dataset.uniquelabels): raise ValueError, "SVM was parametrized with %d Cs but " \ "there are %d labels in the dataset" % \ (len(Cs), len(dataset.uniquelabels)) weight = [ c*scale for c in Cs ] libsvm_param._setParameter('weight', weight) self.__model = svm.SVMModel(svmprob, libsvm_param) def _predict(self, data): """Predict values for the data """ # libsvm needs doubles if data.dtype == 'float64': src = data else: src = data.astype('double') states = self.states predictions = [ self.model.predict(p) for p in src ] if states.isEnabled("values"): if self.regression: values = [ self.model.predictValuesRaw(p)[0] for p in src ] else: trained_labels = self.trained_labels nlabels = len(trained_labels) # XXX We do duplicate work. model.predict calls # predictValuesRaw internally and then does voting or # thresholding. So if speed becomes a factor we might # want to move out logic from libsvm over here to base # predictions on obtined values, or adjust libsvm to # spit out values from predict() as well if nlabels == 2: # Apperently libsvm reorders labels so we need to # track (1,0) values instead of (0,1) thus just # lets take negative reverse values = [ self.model.predictValues(p)[(trained_labels[1], trained_labels[0])] for p in src ] if len(values) > 0: if __debug__: debug("SVM", "Forcing values to be ndarray and reshaping" " them into 1D vector") values = N.asarray(values).reshape(len(values)) else: # In multiclass we return dictionary for all pairs # of labels, since libsvm does 1-vs-1 pairs values = [ self.model.predictValues(p) for p in src ] states.values = values if states.isEnabled("probabilities"): # XXX Is this really necesssary? yoh don't think so since # assignment to states is doing the same #self.probabilities = [ self.model.predictProbability(p) # for p in src ] try: states.probabilities = [ self.model.predictProbability(p) for p in src ] except TypeError: warning("Current SVM %s doesn't support probability " % self + " estimation.") return predictions def summary(self): """Provide quick summary over the SVM classifier""" s = super(SVM, self).summary() if self.trained: s += '\n # of SVs: %d' % self.__model.getTotalNSV() try: prm = svm.svmc.svm_model_param_get(self.__model.model) C = svm.svmc.svm_parameter_C_get(prm) # extract information of how many SVs sit inside the margin, # i.e. so called 'bounded SVs' inside_margin = N.sum( # take 0.99 to avoid rounding issues N.abs(self.__model.getSVCoef()) >= 0.99*svm.svmc.svm_parameter_C_get(prm)) s += ' #bounded SVs:%d' % inside_margin s += ' used C:%5g' % C except: pass return s def untrain(self): """Untrain libsvm's SVM: forget the model """ if __debug__: debug("SVM", "Untraining %s and destroying libsvm model" % self) super(SVM, self).untrain() del self.__model self.__model = None model = property(fget=lambda self: self.__model) """Access to the SVM model.""" #class LinearSVM(SVM): # """Base class of all linear SVM classifiers that make use of the libSVM # package. Still not meant to be used directly. # """ # # def __init__(self, svm_impl, **kwargs): # """The constructor arguments are virtually identical to the ones of # the SVM class, except that 'kernel_type' is set to LINEAR. # """ # # init base class # SVM.__init__(self, kernel_type='linear', # svm_impl=svm_impl, **kwargs) # # # def getSensitivityAnalyzer(self, **kwargs): # """Returns an appropriate SensitivityAnalyzer.""" # return LibSVMLinearSVMWeights(self, **kwargs) # # #class LinearNuSVMC(LinearSVM): # """Classifier for linear Nu-SVM classification. # """ # # def __init__(self, **kwargs): # """ # """ # # init base class # LinearSVM.__init__(self, svm_impl='NU_SVC', **kwargs) # # #class LinearCSVMC(LinearSVM): # """Classifier for linear C-SVM classification. # """ # # def __init__(self, **kwargs): # """ # """ # # init base class # LinearSVM.__init__(self, svm_impl='C_SVC', **kwargs) # # # #class RbfNuSVMC(SVM): # """Nu-SVM classifier using a radial basis function kernel. # """ # # def __init__(self, **kwargs): # """ # """ # # init base class # SVM.__init__(self, kernel_type='rbf', # svm_impl='NU_SVC', **kwargs) # # #class RbfCSVMC(SVM): # """C-SVM classifier using a radial basis function kernel. # """ # # def __init__(self, **kwargs): # """ # """ # # init base class # SVM.__init__(self, kernel_type='rbf', # svm_impl='C_SVC', **kwargs) # # try to configure libsvm 'noise reduction'. Due to circular imports, # we can't check externals here since it would not work. try: # if externals.exists('libsvm verbosity control'): if __debug__ and "LIBSVM" in debug.active: debug("LIBSVM", "Setting verbosity for libsvm to 255") svm.svmc.svm_set_verbosity(255) else: svm.svmc.svm_set_verbosity(0) except AttributeError: warning("Available LIBSVM has no way to control verbosity of the output") # Assign SVM class to limited set of LinearSVMWeights LinearSVMWeights._LEGAL_CLFS = [SVM] pymvpa-0.4.8/mvpa/clfs/libsvmc/svmc.i000066400000000000000000000167671174541445200175420ustar00rootroot00000000000000//-*-c++-*- /*emacs: -*- mode: c++; tab-width: 4; c-basic-offset: 4; indent-tabs-mode: t -*- ex: set sts=4 ts=4 sw=4 noet: */ %module svmc %{ #include "svm.h" #include #include // Just to assure its availability even on older versions of libsvm // where no such preprocessor variable was available (when was it? ;)) #ifndef LIBSVM_VERSION # define LIBSVM_VERSION 0 #endif enum { __version__ = LIBSVM_VERSION }; #if LIBSVM_VERSION < 300 struct svm_model { svm_parameter param;// parameter int nr_class; // number of classes, = 2 in regression/one class svm int l; // total #SV svm_node **SV; // SVs (SV[l]) double **sv_coef; // coefficients for SVs in decision functions (sv_coef[k-1][l]) double *rho; // constants in decision functions (rho[k*(k-1)/2]) double *probA; // pariwise probability information double *probB; // for classification only int *label; // label of each class (label[k]) int *nSV; // number of SVs for each class (nSV[k]) // nSV[0] + nSV[1] + ... + nSV[k-1] = l // XXX int free_sv; // 1 if svm_model is created by svm_load_model // 0 if svm_model is created by svm_train }; #endif /* convert node matrix into a numpy array */ static PyObject* svm_node_matrix2numpy_array(struct svm_node** matrix, int rows, int cols) { npy_intp dims[2] = {rows,cols}; PyObject* array = 0; array = PyArray_SimpleNew ( 2, dims, NPY_DOUBLE ); /* array subscription is [row][column] */ PyArrayObject* a = (PyArrayObject*) array; double* data = (double *)a->data; int i,j; for (i = 0; idata; int i,j; for (i = 0; i= 2.89 */ #if LIBSVM_VERSION >= 289 /* borrowed from original libsvm code */ static void print_null(const char *s) {} static void print_string_stdout(const char *s) { fputs(s,stdout); fflush(stdout); } /* provide convenience wrapper */ void svm_set_verbosity(int verbosity_flag){ if (verbosity_flag) # if LIBSVM_VERSION < 291 svm_print_string = &print_string_stdout; else svm_print_string = &print_null; # else svm_set_print_string_function(&print_string_stdout); else svm_set_print_string_function(&print_null); # endif } #endif %} %init %{ import_array(); %} enum { __version__ = LIBSVM_VERSION }; enum { C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR }; /* svm_type */ enum { LINEAR, POLY, RBF, SIGMOID, PRECOMPUTED }; /* kernel_type */ struct svm_parameter { int svm_type; int kernel_type; int degree; // for poly double gamma; // for poly/rbf/sigmoid double coef0; // for poly/sigmoid // these are for training only double cache_size; // in MB double eps; // stopping criteria double C; // for C_SVC, EPSILON_SVR and NU_SVR int nr_weight; // for C_SVC int *weight_label; // for C_SVC double* weight; // for C_SVC double nu; // for NU_SVC, ONE_CLASS, and NU_SVR double p; // for EPSILON_SVR int shrinking; // use the shrinking heuristics int probability; }; struct svm_problem { int l; double *y; struct svm_node **x; }; // // svm_model // struct svm_model { svm_parameter param; // parameter int nr_class; // number of classes, = 2 in regression/one class svm int l; // total #SV svm_node **SV; // SVs (SV[l]) double **sv_coef; // coefficients for SVs in decision functions (sv_coef[k-1][l]) double *rho; // constants in decision functions (rho[k*(k-1)/2]) double *probA; // pariwise probability information double *probB; // for classification only int *label; // label of each class (label[k]) int *nSV; // number of SVs for each class (nSV[k]) // nSV[0] + nSV[1] + ... + nSV[k-1] = l // XXX int free_sv; // 1 if svm_model is created by svm_load_model // 0 if svm_model is created by svm_train }; /* one really wants to configure verbosity within python! */ void svm_set_verbosity(int verbosity_flag); struct svm_model *svm_train(const struct svm_problem *prob, const struct svm_parameter *param); void svm_cross_validation(const struct svm_problem *prob, const struct svm_parameter *param, int nr_fold, double *target); int svm_save_model(const char *model_file_name, const struct svm_model *model); struct svm_model *svm_load_model(const char *model_file_name); int svm_get_svm_type(const struct svm_model *model); int svm_get_nr_class(const struct svm_model *model); void svm_get_labels(const struct svm_model *model, int *label); double svm_get_svr_probability(const struct svm_model *model); void svm_predict_values(const struct svm_model *model, const struct svm_node *x, double* decvalue); double svm_predict(const struct svm_model *model, const struct svm_node *x); double svm_predict_probability(const struct svm_model *model, const struct svm_node *x, double* prob_estimates); %inline %{ /* Just for bloody compatibility with deprecated method, which would not be used any ways, but declaring it for newer versions should allow to build across different versions without patching */ #if LIBSVM_VERSION >= 310 void svm_destroy_model(struct svm_model *model); #endif %} /* Not necessary: the weight vector is (de)allocated at python-part void svm_destroy_param(struct svm_parameter *param); */ const char *svm_check_parameter(const struct svm_problem *prob, const struct svm_parameter *param); int svm_check_probability_model(const struct svm_model *model); static PyObject* svm_node_matrix2numpy_array(struct svm_node** matrix, int rows, int cols); static PyObject* doubleppcarray2numpy_array(double** data, int rows, int cols); %include carrays.i %array_functions(int,int) %array_functions(double,double) %inline %{ struct svm_node *svm_node_array(int size) { return (struct svm_node *)malloc(sizeof(struct svm_node)*size); } void svm_node_array_set(struct svm_node *array, int i, int index, double value) { array[i].index = index; array[i].value = value; } void svm_node_array_set(struct svm_node *array, PyObject *indices, PyObject *values) { int length = PyList_Size(indices); int i; for (i = 0; i< length; i++){ array[i].index = (int)PyInt_AS_LONG(PyList_GetItem(indices, i)); PyObject* obj = PyArray_GETITEM(values, PyArray_GETPTR1(values, i)); array[i].value = (double)PyFloat_AS_DOUBLE(obj); Py_DECREF(obj); } } void svm_node_array_destroy(struct svm_node *array) { free(array); } struct svm_node **svm_node_matrix(int size) { return (struct svm_node **)malloc(sizeof(struct svm_node *)*size); } void svm_node_matrix_set(struct svm_node **matrix, int i, struct svm_node* array) { matrix[i] = array; } void svm_node_matrix_destroy(struct svm_node **matrix) { free(matrix); } #if LIBSVM_VERSION >= 300 void svm_destroy_model_helper(svm_model* model_ptr) { // yoh: Silence the blurber // fprintf(stderr,"warning: svm_destroy_model is deprecated and should not be used. Please use svm_free_and_destroy_model(svm_model **model_ptr_ptr)\n"); svm_free_and_destroy_model(&model_ptr); } #endif %} pymvpa-0.4.8/mvpa/clfs/meta.py000066400000000000000000001421001174541445200162360ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Classes for meta classifiers -- classifiers which use other classifiers Meta Classifiers can be grouped according to their function as :group BoostedClassifiers: CombinedClassifier MulticlassClassifier SplitClassifier :group ProxyClassifiers: ProxyClassifier BinaryClassifier MappedClassifier FeatureSelectionClassifier :group PredictionsCombiners for CombinedClassifier: PredictionsCombiner MaximalVote MeanPrediction """ __docformat__ = 'restructuredtext' import operator import numpy as N from mvpa.misc.args import group_kwargs from mvpa.mappers.mask import MaskMapper from mvpa.datasets.splitters import NFoldSplitter from mvpa.misc.state import StateVariable, ClassWithCollections, Harvestable from mvpa.clfs.base import Classifier from mvpa.misc.transformers import FirstAxisMean from mvpa.measures.base import \ BoostedClassifierSensitivityAnalyzer, ProxyClassifierSensitivityAnalyzer, \ MappedClassifierSensitivityAnalyzer, \ FeatureSelectionClassifierSensitivityAnalyzer from mvpa.base import warning if __debug__: from mvpa.base import debug class BoostedClassifier(Classifier, Harvestable): """Classifier containing the farm of other classifiers. Should rarely be used directly. Use one of its childs instead """ # should not be needed if we have prediction_values upstairs # raw_predictions should be handled as Harvestable??? raw_predictions = StateVariable(enabled=False, doc="Predictions obtained from each classifier") raw_values = StateVariable(enabled=False, doc="Values obtained from each classifier") def __init__(self, clfs=None, propagate_states=True, harvest_attribs=None, copy_attribs='copy', **kwargs): """Initialize the instance. :Parameters: clfs : list list of classifier instances to use (slave classifiers) propagate_states : bool either to propagate enabled states into slave classifiers. It is in effect only when slaves get assigned - so if state is enabled not during construction, it would not necessarily propagate into slaves kwargs : dict dict of keyworded arguments which might get used by State or Classifier """ if clfs == None: clfs = [] Classifier.__init__(self, **kwargs) Harvestable.__init__(self, harvest_attribs, copy_attribs) self.__clfs = None """Pylint friendly definition of __clfs""" self.__propagate_states = propagate_states """Enable current enabled states in slave classifiers""" self._setClassifiers(clfs) """Store the list of classifiers""" def __repr__(self, prefixes=[]): if self.__clfs is None or len(self.__clfs)==0: #prefix_ = "clfs=%s" % repr(self.__clfs) prefix_ = [] else: prefix_ = ["clfs=[%s,...]" % repr(self.__clfs[0])] return super(BoostedClassifier, self).__repr__(prefix_ + prefixes) def _train(self, dataset): """Train `BoostedClassifier` """ for clf in self.__clfs: clf.train(dataset) def _posttrain(self, dataset): """Custom posttrain of `BoostedClassifier` Harvest over the trained classifiers if it was asked to so """ Classifier._posttrain(self, dataset) if self.states.isEnabled('harvested'): for clf in self.__clfs: self._harvest(locals()) if self.params.retrainable: self.__changedData_isset = False def _getFeatureIds(self): """Custom _getFeatureIds for `BoostedClassifier` """ # return union of all used features by slave classifiers feature_ids = set([]) for clf in self.__clfs: feature_ids = feature_ids.union(set(clf.feature_ids)) return list(feature_ids) def _predict(self, data): """Predict using `BoostedClassifier` """ raw_predictions = [ clf.predict(data) for clf in self.__clfs ] self.raw_predictions = raw_predictions assert(len(self.__clfs)>0) if self.states.isEnabled("values"): if N.array([x.states.isEnabled("values") for x in self.__clfs]).all(): values = [ clf.values for clf in self.__clfs ] self.raw_values = values else: warning("One or more classifiers in %s has no 'values' state" % self + "enabled, thus BoostedClassifier can't have" + " 'raw_values' state variable defined") return raw_predictions def _setClassifiers(self, clfs): """Set the classifiers used by the boosted classifier We have to allow to set list of classifiers after the object was actually created. It will be used by MulticlassClassifier """ self.__clfs = clfs """Classifiers to use""" if len(clfs): for flag in ['regression']: values = N.array([clf.params[flag].value for clf in clfs]) value = values.any() if __debug__: debug("CLFBST", "Setting %(flag)s=%(value)s for classifiers " "%(clfs)s with %(values)s", msgargs={'flag' : flag, 'value' : value, 'clfs' : clfs, 'values' : values}) # set flag if it needs to be trained before predicting self.params[flag].value = value # enable corresponding states in the slave-classifiers if self.__propagate_states: for clf in self.__clfs: clf.states.enable(self.states.enabled, missingok=True) # adhere to their capabilities + 'multiclass' # XXX do intersection across all classifiers! # TODO: this seems to be wrong since it can be regression etc self._clf_internals = [ 'binary', 'multiclass', 'meta' ] if len(clfs)>0: self._clf_internals += self.__clfs[0]._clf_internals def untrain(self): """Untrain `BoostedClassifier` Has to untrain any known classifier """ if not self.trained: return for clf in self.clfs: clf.untrain() super(BoostedClassifier, self).untrain() def getSensitivityAnalyzer(self, **kwargs): """Return an appropriate SensitivityAnalyzer""" return BoostedClassifierSensitivityAnalyzer( self, **kwargs) clfs = property(fget=lambda x:x.__clfs, fset=_setClassifiers, doc="Used classifiers") class ProxyClassifier(Classifier): """Classifier which decorates another classifier Possible uses: - modify data somehow prior training/testing: * normalization * feature selection * modification - optimized classifier? """ def __init__(self, clf, **kwargs): """Initialize the instance :Parameters: clf : Classifier classifier based on which mask classifiers is created """ Classifier.__init__(self, regression=clf.regression, **kwargs) self.__clf = clf """Store the classifier to use.""" # adhere to slave classifier capabilities # TODO: unittest self._clf_internals = self._clf_internals[:] + ['meta'] if clf is not None: self._clf_internals += clf._clf_internals def __repr__(self, prefixes=[]): return super(ProxyClassifier, self).__repr__( ["clf=%s" % repr(self.__clf)] + prefixes) def summary(self): s = super(ProxyClassifier, self).summary() if self.trained: s += "\n Slave classifier summary:" + \ '\n + %s' % \ (self.__clf.summary().replace('\n', '\n |')) return s def _train(self, dataset): """Train `ProxyClassifier` """ # base class does nothing much -- just proxies requests to underlying # classifier self.__clf.train(dataset) # for the ease of access # TODO: if to copy we should exclude some states which are defined in # base Classifier (such as training_time, predicting_time) # YOH: for now _copy_states_ would copy only set states variables. If # anything needs to be overriden in the parent's class, it is # welcome to do so #self.states._copy_states_(self.__clf, deep=False) def _predict(self, data): """Predict using `ProxyClassifier` """ clf = self.__clf if self.states.isEnabled('values'): clf.states.enable(['values']) result = clf.predict(data) # for the ease of access self.states._copy_states_(self.__clf, ['values'], deep=False) return result def untrain(self): """Untrain ProxyClassifier """ if not self.__clf is None: self.__clf.untrain() super(ProxyClassifier, self).untrain() @group_kwargs(prefixes=['slave_'], passthrough=True) def getSensitivityAnalyzer(self, slave_kwargs, **kwargs): """Return an appropriate SensitivityAnalyzer""" return ProxyClassifierSensitivityAnalyzer( self, analyzer=self.__clf.getSensitivityAnalyzer(**slave_kwargs), **kwargs) clf = property(lambda x:x.__clf, doc="Used `Classifier`") # # Various combiners for CombinedClassifier # class PredictionsCombiner(ClassWithCollections): """Base class for combining decisions of multiple classifiers""" def train(self, clfs, dataset): """PredictionsCombiner might need to be trained :Parameters: clfs : list of Classifier List of classifiers to combine. Has to be classifiers (not pure predictions), since combiner might use some other state variables (value's) instead of pure prediction's dataset : Dataset training data in this case """ pass def __call__(self, clfs, dataset): """Call function :Parameters: clfs : list of Classifier List of classifiers to combine. Has to be classifiers (not pure predictions), since combiner might use some other state variables (value's) instead of pure prediction's """ raise NotImplementedError class MaximalVote(PredictionsCombiner): """Provides a decision using maximal vote rule""" predictions = StateVariable(enabled=True, doc="Voted predictions") all_label_counts = StateVariable(enabled=False, doc="Counts across classifiers for each label/sample") def __init__(self): """XXX Might get a parameter to use raw decision values if voting is not unambigous (ie two classes have equal number of votes """ PredictionsCombiner.__init__(self) def __call__(self, clfs, dataset): """Actuall callable - perform voting Extended functionality which might not be needed actually: Since `BinaryClassifier` might return a list of possible predictions (not just a single one), we should consider all of those MaximalVote doesn't care about dataset itself """ if len(clfs)==0: return [] # to don't even bother all_label_counts = None for clf in clfs: # Lets check first if necessary state variable is enabled if not clf.states.isEnabled("predictions"): raise ValueError, "MaximalVote needs classifiers (such as " + \ "%s) with state 'predictions' enabled" % clf predictions = clf.predictions if all_label_counts is None: all_label_counts = [ {} for i in xrange(len(predictions)) ] # for every sample for i in xrange(len(predictions)): prediction = predictions[i] if not operator.isSequenceType(prediction): prediction = (prediction,) for label in prediction: # for every label # XXX we might have multiple labels assigned # but might not -- don't remember now if not all_label_counts[i].has_key(label): all_label_counts[i][label] = 0 all_label_counts[i][label] += 1 predictions = [] # select maximal vote now for each sample for i in xrange(len(all_label_counts)): label_counts = all_label_counts[i] # lets do explicit search for max so we know # if it is unique maxk = [] # labels of elements with max vote maxv = -1 for k, v in label_counts.iteritems(): if v > maxv: maxk = [k] maxv = v elif v == maxv: maxk.append(k) assert len(maxk) >= 1, \ "We should have obtained at least a single key of max label" if len(maxk) > 1: warning("We got multiple labels %s which have the " % maxk + "same maximal vote %d. XXX disambiguate" % maxv) predictions.append(maxk[0]) self.all_label_counts = all_label_counts self.predictions = predictions return predictions class MeanPrediction(PredictionsCombiner): """Provides a decision by taking mean of the results """ predictions = StateVariable(enabled=True, doc="Mean predictions") def __call__(self, clfs, dataset): """Actuall callable - perform meaning """ if len(clfs)==0: return [] # to don't even bother all_predictions = [] for clf in clfs: # Lets check first if necessary state variable is enabled if not clf.states.isEnabled("predictions"): raise ValueError, "MeanPrediction needs classifiers (such " \ " as %s) with state 'predictions' enabled" % clf all_predictions.append(clf.predictions) # compute mean predictions = N.mean(N.asarray(all_predictions), axis=0) self.predictions = predictions return predictions class ClassifierCombiner(PredictionsCombiner): """Provides a decision using training a classifier on predictions/values TODO: implement """ predictions = StateVariable(enabled=True, doc="Trained predictions") def __init__(self, clf, variables=None): """Initialize `ClassifierCombiner` :Parameters: clf : Classifier Classifier to train on the predictions variables : list of basestring List of state variables stored in 'combined' classifiers, which to use as features for training this classifier """ PredictionsCombiner.__init__(self) self.__clf = clf """Classifier to train on `variables` states of provided classifiers""" if variables == None: variables = ['predictions'] self.__variables = variables """What state variables of the classifiers to use""" def untrain(self): """It might be needed to untrain used classifier""" if self.__clf: self.__clf.untrain() def __call__(self, clfs, dataset): """ """ if len(clfs)==0: return [] # to don't even bother raise NotImplementedError class CombinedClassifier(BoostedClassifier): """`BoostedClassifier` which combines predictions using some `PredictionsCombiner` functor. """ def __init__(self, clfs=None, combiner=None, **kwargs): """Initialize the instance. :Parameters: clfs : list of Classifier list of classifier instances to use combiner : PredictionsCombiner callable which takes care about combining multiple results into a single one (e.g. maximal vote for classification, MeanPrediction for regression)) kwargs : dict dict of keyworded arguments which might get used by State or Classifier NB: `combiner` might need to operate not on 'predictions' descrete labels but rather on raw 'class' values classifiers estimate (which is pretty much what is stored under `values` """ if clfs == None: clfs = [] BoostedClassifier.__init__(self, clfs, **kwargs) # assign default combiner if combiner is None: combiner = (MaximalVote, MeanPrediction)[int(self.regression)]() self.__combiner = combiner """Functor destined to combine results of multiple classifiers""" def __repr__(self, prefixes=[]): """Literal representation of `CombinedClassifier`. """ return super(CombinedClassifier, self).__repr__( ["combiner=%s" % repr(self.__combiner)] + prefixes) def summary(self): """Provide summary for the `CombinedClassifier`. """ s = super(CombinedClassifier, self).summary() if self.trained: s += "\n Slave classifiers summaries:" for i, clf in enumerate(self.clfs): s += '\n + %d clf: %s' % \ (i, clf.summary().replace('\n', '\n |')) return s def untrain(self): """Untrain `CombinedClassifier` """ try: self.__combiner.untrain() except: pass super(CombinedClassifier, self).untrain() def _train(self, dataset): """Train `CombinedClassifier` """ BoostedClassifier._train(self, dataset) # combiner might need to train as well self.__combiner.train(self.clfs, dataset) def _predict(self, data): """Predict using `CombinedClassifier` """ BoostedClassifier._predict(self, data) # combiner will make use of state variables instead of only predictions # returned from _predict predictions = self.__combiner(self.clfs, data) self.predictions = predictions if self.states.isEnabled("values"): if self.__combiner.states.isActive("values"): # XXX or may be we could leave simply up to accessing .combiner? self.values = self.__combiner.values else: if __debug__: warning("Boosted classifier %s has 'values' state enabled," " but combiner doesn't have 'values' active, thus " " .values cannot be provided directly, access .clfs" % self) return predictions combiner = property(fget=lambda x:x.__combiner, doc="Used combiner to derive a single result") class TreeClassifier(ProxyClassifier): """`TreeClassifier` which allows to create hierarchy of classifiers Functions by grouping some labels into a single "meta-label" and training classifier first to separate between meta-labels. Then each group further proceeds with classification within each group. Possible scenarios:: TreeClassifier(SVM(), {'animate': ((1,2,3,4), TreeClassifier(SVM(), {'human': (('male', 'female'), SVM()), 'animals': (('monkey', 'dog'), SMLR())})), 'inanimate': ((5,6,7,8), SMLR())}) would create classifier which would first do binary classification to separate animate from inanimate, then for animate result it would separate to classify human vs animal and so on:: SVM / \ animate inanimate / \ SVM SMLR / \ / | \ \ human animal 5 6 7 8 | | SVM SVM / \ / \ male female monkey dog 1 2 3 4 If it is desired to have a trailing node with a single label and thus without any classification, such as in SVM / \ g1 g2 / \ 1 SVM / \ 2 3 then just specify None as the classifier to use:: TreeClassifier(SVM(), {'g1': ((1,), None), 'g2': ((1,2,3,4), SVM())}) """ _DEV__doc = """ Questions: * how to collect confusion matrices at a particular layer if such classifier is given to SplitClassifier or CVTE * What additional states to add, something like clf_labels -- store remapped labels for the dataset clf_values ... * What do we store into values ? just values from the clfs[] for corresponding samples, or top level clf values as well? * what should be SensitivityAnalyzer? by default it would just use top slave classifier (i.e. animate/inanimate) Problems? * .clf is not actually "proxied" per se, so not sure what things should be taken care of yet... TODO: * Allow a group to be just a single category, so no further classifier is needed, it just should stay separate from the other groups Possible TODO: * Add ability to provide results of clf.values as features into input of clfs[]. This way we could provide additional 'similarity' information to the "other" branch """ def __init__(self, clf, groups, **kwargs): """Initialize TreeClassifier :Parameters: clf : Classifier Classifier to separate between the groups groups : dict of meta-label: tuple of (tuple of labels, classifier) Defines the groups of labels and their classifiers. See :class:`~mvpa.clfs.meta.TreeClassifier` for example """ # Basic initialization ProxyClassifier.__init__(self, clf, **kwargs) self._regressionIsBogus() # XXX RF: probably create internal structure with dictionary, # not just a tuple, and store all information in there # accordingly self._groups = groups self._index2group = groups.keys() # All processing of groups needs to be handled within _train # since labels_map is not available here and definition # is allowed to carry both symbolic and numeric values for # labels # We can only assign respective classifiers self.clfs = dict([(gk, c) for gk, (ls, c) in groups.iteritems()]) """Dictionary of classifiers used by the groups""" def __repr__(self, prefixes=[]): """String representation of TreeClassifier """ prefix = "groups=%s" % repr(self._groups) return super(TreeClassifier, self).__repr__([prefix] + prefixes) def summary(self): """Provide summary for the `TreeClassifier`. """ s = super(TreeClassifier, self).summary() if self.trained: s += "\n Node classifiers summaries:" for i, (clfname, clf) in enumerate(self.clfs.iteritems()): s += '\n + %d %s clf: %s' % \ (i, clfname, clf.summary().replace('\n', '\n |')) return s def _train(self, dataset): """Train TreeClassifier First train .clf on groupped samples, then train each of .clfs on a corresponding subset of samples. """ # Local bindings clf, clfs, index2group = self.clf, self.clfs, self._index2group # Handle groups of labels groups = self._groups labels_map = dataset.labels_map # just for convenience if labels_map is None: labels_map = {} groups_labels = {} # just groups with numeric indexes label2index = {} # how to map old labels to new known = set() for gi, gk in enumerate(index2group): ls = groups[gk][0] # if mapping exists -- map ls_ = [labels_map.get(l, l) for l in ls] known_already = known.intersection(ls_) if len(known_already): raise ValueError, "Grouping of labels is not appropriate. " \ "Got labels %s already among known in %s. " \ "Used labelsmap %s" % (known_already, known, labels_map) groups_labels[gk] = ls_ # needed? XXX for l in ls_: label2index[l] = gi known = known.union(ls_) # TODO: check if different literal labels weren't mapped into # same numerical but here asked to belong to different groups # yoh: actually above should catch it # Check if none of the labels is missing from known groups dsul = set(dataset.uniquelabels) if known.intersection(dsul) != dsul: raise ValueError, \ "Dataset %s had some labels not defined in groups: %s. " \ "Known are %s" % \ (dataset, dsul.difference(known), known) # We can operate on the same dataset here # Nope: doesn't work nicely with the classifier like kNN # which links to the dataset used in the training, # so whenever if we simply restore labels back, we # would get kNN confused in _predict() # Therefore we need to create a shallow copy of # dataset and provide it with new labels ds_group = dataset.copy(deep=False) # assign new labels group samples into groups of labels ds_group.labels = [label2index[l] for l in dataset.labels] # train primary classifier if __debug__: debug('CLFTREE', "Training primary %(clf)s on %(ds)s", msgargs=dict(clf=clf, ds=ds_group)) clf.train(ds_group) # ??? should we obtain values for anything? # may be we could training values of .clfs to be added # as features to the next level -- i.e. .clfs # Proceed with next 'layer' and train all .clfs on corresponding # selection of samples # ??? should we may be allow additional 'the other' category, to # signal contain all the other categories data? probably not # since then it would lead to undetermined prediction (which # might be not a bad thing altogether...) for gk in groups.iterkeys(): clf = clfs[gk] group_labels = groups_labels[gk] if clf is None: # Trailing node if len(group_labels) != 1: raise ValueError( "Trailing nodes with no classifier assigned must have " "only a single label associated. Got %s defined in " "group %r of %s" % (group_labels, gk, self)) else: # select samples per each group ids = dataset.idsbylabels(group_labels) ds_group = dataset.selectSamples(ids) if __debug__: debug('CLFTREE', "Training %(clf)s for group %(gk)s on %(ds)s", msgargs=dict(clf=clfs[gk], gk=gk, ds=ds_group)) # and train corresponding slave clf clf.train(ds_group) def untrain(self): """Untrain TreeClassifier """ super(TreeClassifier, self).untrain() for clf in self.clfs.values(): if clf is not None: clf.untrain() def _predict(self, data): """ """ # Local bindings clfs, index2group, groups = self.clfs, self._index2group, self._groups clf_predictions = N.asanyarray(ProxyClassifier._predict(self, data)) # assure that predictions are indexes, ie int clf_predictions = clf_predictions.astype(int) # now for predictions pointing to specific groups go into # corresponding one predictions = N.array([N.nan]*len(data)) for pred_group in set(clf_predictions): gk = index2group[pred_group] clf_ = clfs[gk] group_indexes = (clf_predictions == pred_group) if __debug__: debug('CLFTREE', 'Predicting for group %s using %s on %d samples' % (gk, clf_, N.sum(group_indexes))) if clf_ is None: predictions[group_indexes] = groups[gk][0] # our only label else: predictions[group_indexes] = clf_.predict(data[group_indexes]) return predictions class BinaryClassifier(ProxyClassifier): """`ProxyClassifier` which maps set of two labels into +1 and -1 """ def __init__(self, clf, poslabels, neglabels, **kwargs): """ :Parameters: clf : Classifier classifier to use poslabels : list list of labels which are treated as +1 category neglabels : list list of labels which are treated as -1 category """ ProxyClassifier.__init__(self, clf, **kwargs) self._regressionIsBogus() # Handle labels sposlabels = set(poslabels) # so to remove duplicates sneglabels = set(neglabels) # so to remove duplicates # check if there is no overlap overlap = sposlabels.intersection(sneglabels) if len(overlap)>0: raise ValueError("Sets of positive and negative labels for " + "BinaryClassifier must not overlap. Got overlap " % overlap) self.__poslabels = list(sposlabels) self.__neglabels = list(sneglabels) # define what values will be returned by predict: if there is # a single label - return just it alone, otherwise - whole # list # Such approach might come useful if we use some classifiers # over different subsets of data with some voting later on # (1-vs-therest?) if len(self.__poslabels) > 1: self.__predictpos = self.__poslabels else: self.__predictpos = self.__poslabels[0] if len(self.__neglabels) > 1: self.__predictneg = self.__neglabels else: self.__predictneg = self.__neglabels[0] def __repr__(self, prefixes=[]): prefix = "poslabels=%s, neglabels=%s" % ( repr(self.__poslabels), repr(self.__neglabels)) return super(BinaryClassifier, self).__repr__([prefix] + prefixes) def _train(self, dataset): """Train `BinaryClassifier` """ idlabels = [(x, +1) for x in dataset.idsbylabels(self.__poslabels)] + \ [(x, -1) for x in dataset.idsbylabels(self.__neglabels)] # XXX we have to sort ids since at the moment Dataset.selectSamples # doesn't take care about order idlabels.sort() # select the samples orig_labels = None # If we need all samples, why simply not perform on original # data, an just store/restore labels. But it really should be done # within Dataset.selectSamples if len(idlabels) == dataset.nsamples \ and [x[0] for x in idlabels] == range(dataset.nsamples): # the last condition is not even necessary... just overly # cautious datasetselected = dataset # no selection is needed orig_labels = dataset.labels # but we would need to restore labels if __debug__: debug('CLFBIN', "Assigned all %d samples for binary " % (dataset.nsamples) + " classification among labels %s/+1 and %s/-1" % (self.__poslabels, self.__neglabels)) else: datasetselected = dataset.selectSamples([ x[0] for x in idlabels ]) if __debug__: debug('CLFBIN', "Selected %d samples out of %d samples for binary " % (len(idlabels), dataset.nsamples) + " classification among labels %s/+1 and %s/-1" % (self.__poslabels, self.__neglabels) + ". Selected %s" % datasetselected) # adjust the labels datasetselected.labels = [ x[1] for x in idlabels ] # now we got a dataset with only 2 labels if __debug__: assert((datasetselected.uniquelabels == [-1, 1]).all()) self.clf.train(datasetselected) if not orig_labels is None: dataset.labels = orig_labels def _predict(self, data): """Predict the labels for a given `data` Predicts using binary classifier and spits out list (for each sample) where with either poslabels or neglabels as the "label" for the sample. If there was just a single label within pos or neg labels then it would return not a list but just that single label. """ binary_predictions = ProxyClassifier._predict(self, data) self.values = binary_predictions predictions = [ {-1: self.__predictneg, +1: self.__predictpos}[x] for x in binary_predictions] self.predictions = predictions return predictions class MulticlassClassifier(CombinedClassifier): """`CombinedClassifier` to perform multiclass using a list of `BinaryClassifier`. such as 1-vs-1 (ie in pairs like libsvm doesn) or 1-vs-all (which is yet to think about) """ def __init__(self, clf, bclf_type="1-vs-1", **kwargs): """Initialize the instance :Parameters: clf : Classifier classifier based on which multiple classifiers are created for multiclass bclf_type "1-vs-1" or "1-vs-all", determines the way to generate binary classifiers """ CombinedClassifier.__init__(self, **kwargs) self._regressionIsBogus() if not clf is None: clf._regressionIsBogus() self.__clf = clf """Store sample instance of basic classifier""" # Some checks on known ways to do multiclass if bclf_type == "1-vs-1": pass elif bclf_type == "1-vs-all": # TODO raise NotImplementedError else: raise ValueError, \ "Unknown type of classifier %s for " % bclf_type + \ "BoostedMulticlassClassifier" self.__bclf_type = bclf_type # XXX fix it up a bit... it seems that MulticlassClassifier should # be actually ProxyClassifier and use BoostedClassifier internally def __repr__(self, prefixes=[]): prefix = "bclf_type=%s, clf=%s" % (repr(self.__bclf_type), repr(self.__clf)) return super(MulticlassClassifier, self).__repr__([prefix] + prefixes) def _train(self, dataset): """Train classifier """ # construct binary classifiers ulabels = dataset.uniquelabels if self.__bclf_type == "1-vs-1": # generate pairs and corresponding classifiers biclfs = [] for i in xrange(len(ulabels)): for j in xrange(i+1, len(ulabels)): clf = self.__clf.clone() biclfs.append( BinaryClassifier( clf, poslabels=[ulabels[i]], neglabels=[ulabels[j]])) if __debug__: debug("CLFMC", "Created %d binary classifiers for %d labels" % (len(biclfs), len(ulabels))) self.clfs = biclfs elif self.__bclf_type == "1-vs-all": raise NotImplementedError # perform actual training CombinedClassifier._train(self, dataset) class SplitClassifier(CombinedClassifier): """`BoostedClassifier` to work on splits of the data """ """ TODO: SplitClassifier and MulticlassClassifier have too much in common -- need to refactor: just need a splitter which would split dataset in pairs of class labels. MulticlassClassifier does just a tiny bit more which might be not necessary at all: map sets of labels into 2 categories... """ # TODO: unify with CrossValidatedTransferError which now uses # harvest_attribs to expose gathered attributes confusion = StateVariable(enabled=False, doc="Resultant confusion whenever classifier trained " + "on 1 part and tested on 2nd part of each split") splits = StateVariable(enabled=False, doc= """Store the actual splits of the data. Can be memory expensive""") # ??? couldn't be training_confusion since it has other meaning # here, BUT it is named so within CrossValidatedTransferError # -- unify # decided to go with overriding semantics tiny bit. For split # classifier training_confusion would correspond to summary # over training errors across all splits. Later on if need comes # we might want to implement global_training_confusion which would # correspond to overall confusion on full training dataset as it is # done in base Classifier #global_training_confusion = StateVariable(enabled=False, # doc="Summary over training confusions acquired at each split") def __init__(self, clf, splitter=NFoldSplitter(cvtype=1), **kwargs): """Initialize the instance :Parameters: clf : Classifier classifier based on which multiple classifiers are created for multiclass splitter : Splitter `Splitter` to use to split the dataset prior training """ CombinedClassifier.__init__(self, regression=clf.regression, **kwargs) self.__clf = clf """Store sample instance of basic classifier""" if isinstance(splitter, type): raise ValueError, \ "Please provide an instance of a splitter, not a type." \ " Got %s" % splitter self.__splitter = splitter def _train(self, dataset): """Train `SplitClassifier` """ # generate pairs and corresponding classifiers bclfs = [] # local binding states = self.states clf_template = self.__clf if states.isEnabled('confusion'): states.confusion = clf_template._summaryClass() if states.isEnabled('training_confusion'): clf_template.states.enable(['training_confusion']) states.training_confusion = clf_template._summaryClass() clf_hastestdataset = hasattr(clf_template, 'testdataset') # for proper and easier debugging - first define classifiers and then # train them for split in self.__splitter.splitcfg(dataset): if __debug__: debug("CLFSPL_", "Deepcopying %(clf)s for %(sclf)s", msgargs={'clf':clf_template, 'sclf':self}) clf = clf_template.clone() bclfs.append(clf) self.clfs = bclfs self.splits = [] for i, split in enumerate(self.__splitter(dataset)): if __debug__: debug("CLFSPL", "Training classifier for split %d" % (i)) if states.isEnabled("splits"): self.splits.append(split) clf = self.clfs[i] # assign testing dataset if given classifier can digest it if clf_hastestdataset: clf.testdataset = split[1] clf.train(split[0]) # unbind the testdataset from the classifier if clf_hastestdataset: clf.testdataset = None if states.isEnabled("confusion"): predictions = clf.predict(split[1].samples) self.confusion.add(split[1].labels, predictions, clf.states.get('values', None)) if __debug__: dact = debug.active if 'CLFSPL_' in dact: debug('CLFSPL_', 'Split %d:\n%s' % (i, self.confusion)) elif 'CLFSPL' in dact: debug('CLFSPL', 'Split %d error %.2f%%' % (i, self.confusion.summaries[-1].error)) if states.isEnabled("training_confusion"): states.training_confusion += \ clf.states.training_confusion # hackish way -- so it should work only for ConfusionMatrix??? try: if states.isEnabled("confusion"): states.confusion.labels_map = dataset.labels_map if states.isEnabled("training_confusion"): states.training_confusion.labels_map = dataset.labels_map except: pass @group_kwargs(prefixes=['slave_'], passthrough=True) def getSensitivityAnalyzer(self, slave_kwargs, **kwargs): """Return an appropriate SensitivityAnalyzer for `SplitClassifier` :Parameters: combiner If not provided, FirstAxisMean is assumed """ kwargs.setdefault('combiner', FirstAxisMean) return BoostedClassifierSensitivityAnalyzer( self, analyzer=self.__clf.getSensitivityAnalyzer(**slave_kwargs), **kwargs) splitter = property(fget=lambda x:x.__splitter, doc="Splitter user by SplitClassifier") class MappedClassifier(ProxyClassifier): """`ProxyClassifier` which uses some mapper prior training/testing. `MaskMapper` can be used just a subset of features to train/classify. Having such classifier we can easily create a set of classifiers for BoostedClassifier, where each classifier operates on some set of features, e.g. set of best spheres from SearchLight, set of ROIs selected elsewhere. It would be different from simply applying whole mask over the dataset, since here initial decision is made by each classifier and then later on they vote for the final decision across the set of classifiers. """ def __init__(self, clf, mapper, **kwargs): """Initialize the instance :Parameters: clf : Classifier classifier based on which mask classifiers is created mapper whatever `Mapper` comes handy """ ProxyClassifier.__init__(self, clf, **kwargs) self.__mapper = mapper """mapper to help us our with prepping data to training/classification""" def _train(self, dataset): """Train `MappedClassifier` """ # first train the mapper # XXX: should training be done using whole dataset or just samples # YYY: in some cases labels might be needed, thus better full dataset self.__mapper.train(dataset) # for train() we have to provide dataset -- not just samples to train! wdataset = dataset.applyMapper(featuresmapper = self.__mapper) ProxyClassifier._train(self, wdataset) def _predict(self, data): """Predict using `MappedClassifier` """ return ProxyClassifier._predict(self, self.__mapper.forward(data)) @group_kwargs(prefixes=['slave_'], passthrough=True) def getSensitivityAnalyzer(self, slave_kwargs, **kwargs): """Return an appropriate SensitivityAnalyzer""" return MappedClassifierSensitivityAnalyzer( self, analyzer=self.clf.getSensitivityAnalyzer(**slave_kwargs), **kwargs) mapper = property(lambda x:x.__mapper, doc="Used mapper") class FeatureSelectionClassifier(ProxyClassifier): """`ProxyClassifier` which uses some `FeatureSelection` prior training. `FeatureSelection` is used first to select features for the classifier to use for prediction. Internally it would rely on MappedClassifier which would use created MaskMapper. TODO: think about removing overhead of retraining the same classifier if feature selection was carried out with the same classifier already. It has been addressed by adding .trained property to classifier, but now we should expclitely use isTrained here if we want... need to think more """ _clf_internals = [ 'does_feature_selection', 'meta' ] def __init__(self, clf, feature_selection, testdataset=None, **kwargs): """Initialize the instance :Parameters: clf : Classifier classifier based on which mask classifiers is created feature_selection : FeatureSelection whatever `FeatureSelection` comes handy testdataset : Dataset optional dataset which would be given on call to feature_selection """ ProxyClassifier.__init__(self, clf, **kwargs) self.__maskclf = None """Should become `MappedClassifier`(mapper=`MaskMapper`) later on.""" self.__feature_selection = feature_selection """`FeatureSelection` to select the features prior training""" self.__testdataset = testdataset """`FeatureSelection` might like to use testdataset""" def untrain(self): """Untrain `FeatureSelectionClassifier` Has to untrain any known classifier """ if self.__feature_selection is not None: self.__feature_selection.untrain() if not self.trained: return if not self.__maskclf is None: self.__maskclf.untrain() super(FeatureSelectionClassifier, self).untrain() def _train(self, dataset): """Train `FeatureSelectionClassifier` """ # temporarily enable selected_ids self.__feature_selection.states._changeTemporarily( enable_states=["selected_ids"]) if __debug__: debug("CLFFS", "Performing feature selection using %s" % self.__feature_selection + " on %s" % dataset) (wdataset, tdataset) = self.__feature_selection(dataset, self.__testdataset) if __debug__: add_ = "" if "CLFFS_" in debug.active: add_ = " Selected features: %s" % \ self.__feature_selection.selected_ids debug("CLFFS", "%(fs)s selected %(nfeat)d out of " + "%(dsnfeat)d features.%(app)s", msgargs={'fs':self.__feature_selection, 'nfeat':wdataset.nfeatures, 'dsnfeat':dataset.nfeatures, 'app':add_}) # create a mask to devise a mapper # TODO -- think about making selected_ids a MaskMapper mappermask = N.zeros(dataset.nfeatures) mappermask[self.__feature_selection.selected_ids] = 1 mapper = MaskMapper(mappermask) self.__feature_selection.states._resetEnabledTemporarily() # create and assign `MappedClassifier` self.__maskclf = MappedClassifier(self.clf, mapper) # we could have called self.__clf.train(dataset), but it would # cause unnecessary masking self.__maskclf.clf.train(wdataset) # for the ease of access # TODO see for ProxyClassifier #self.states._copy_states_(self.__maskclf, deep=False) def _getFeatureIds(self): """Return used feature ids for `FeatureSelectionClassifier` """ return self.__feature_selection.selected_ids def _predict(self, data): """Predict using `FeatureSelectionClassifier` """ clf = self.__maskclf if self.states.isEnabled('values'): clf.states.enable(['values']) result = clf._predict(data) # for the ease of access self.states._copy_states_(clf, ['values'], deep=False) return result def setTestDataset(self, testdataset): """Set testing dataset to be used for feature selection """ self.__testdataset = testdataset maskclf = property(lambda x:x.__maskclf, doc="Used `MappedClassifier`") feature_selection = property(lambda x:x.__feature_selection, doc="Used `FeatureSelection`") @group_kwargs(prefixes=['slave_'], passthrough=True) def getSensitivityAnalyzer(self, slave_kwargs, **kwargs): """Return an appropriate SensitivityAnalyzer had to clone from mapped classifier??? """ return FeatureSelectionClassifierSensitivityAnalyzer( self, analyzer=self.clf.getSensitivityAnalyzer(**slave_kwargs), **kwargs) testdataset = property(fget=lambda x:x.__testdataset, fset=setTestDataset) pymvpa-0.4.8/mvpa/clfs/model_selector.py000066400000000000000000000273341174541445200203230ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # Copyright (c) 2008 Emanuele Olivetti # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Model selction.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base import externals from mvpa.misc.exceptions import InvalidHyperparameterError if externals.exists("scipy", raiseException=True): import scipy.linalg as SL # no sense to import this module if openopt is not available if externals.exists("openopt", raiseException=True): try: from openopt import NLP except ImportError: from scikits.openopt import NLP if __debug__: from mvpa.base import debug def _openopt_debug(): # shut up or make verbose OpenOpt # (-1 = no logs, 0 = small log, 1 = verbose) if __debug__: da = debug.active if 'OPENOPT' in da: return 1 elif 'MOD_SEL' in da: return 0 return -1 class ModelSelector(object): """Model selection facility. Select a model among multiple models (i.e., a parametric model, parametrized by a set of hyperparamenters). """ def __init__(self, parametric_model, dataset): self.parametric_model = parametric_model self.dataset = dataset self.hyperparameters_best = None self.log_marginal_likelihood_best = None self.problem = None pass def max_log_marginal_likelihood(self, hyp_initial_guess, maxiter=1, optimization_algorithm="scipy_cg", ftol=1.0e-3, fixedHypers=None, use_gradient=False, logscale=False): """ Set up the optimization problem in order to maximize the log_marginal_likelihood. :Parameters: parametric_model : Classifier the actual parameteric model to be optimized. hyp_initial_guess : numpy.ndarray set of hyperparameters' initial values where to start optimization. optimization_algorithm : string actual name of the optimization algorithm. See http://scipy.org/scipy/scikits/wiki/NLP for a comprehensive/updated list of available NLP solvers. (Defaults to 'ralg') ftol : float threshold for the stopping criterion of the solver, which is mapped in OpenOpt NLP.ftol (Defaults to 1.0e-3) fixedHypers : numpy.ndarray (boolean array) boolean vector of the same size of hyp_initial_guess; 'False' means that the corresponding hyperparameter must be kept fixed (so not optimized). (Defaults to None, which during means all True) NOTE: the maximization of log_marginal_likelihood is a non-linear optimization problem (NLP). This fact is confirmed by Dmitrey, author of OpenOpt. """ self.problem = None self.use_gradient = use_gradient self.logscale = logscale # use log-scale on hyperparameters to enhance numerical stability self.optimization_algorithm = optimization_algorithm self.hyp_initial_guess = N.array(hyp_initial_guess) self.hyp_initial_guess_log = N.log(self.hyp_initial_guess) if fixedHypers is None: fixedHypers = N.zeros(self.hyp_initial_guess.shape[0],dtype=bool) pass self.freeHypers = -fixedHypers if self.logscale: self.hyp_running_guess = self.hyp_initial_guess_log.copy() else: self.hyp_running_guess = self.hyp_initial_guess.copy() pass self.f_last_x = None def f(x): """ Wrapper to the log_marginal_likelihood to be maximized. """ # XXX EO: since some OpenOpt NLP solvers does not # implement lower bounds the hyperparameters bounds are # implemented inside PyMVPA: (see dmitrey's post on # [SciPy-user] 20080628). # # XXX EO: OpenOpt does not implement optimization of a # subset of the hyperparameters so it is implemented here. # # XXX EO: OpenOpt does not implement logrithmic scale of # the hyperparameters (to enhance numerical stability), so # it is implemented here. self.f_last_x = x.copy() self.hyp_running_guess[self.freeHypers] = x # REMOVE print "guess:",self.hyp_running_guess,x try: if self.logscale: self.parametric_model.set_hyperparameters(N.exp(self.hyp_running_guess)) else: self.parametric_model.set_hyperparameters(self.hyp_running_guess) pass except InvalidHyperparameterError: if __debug__: debug("MOD_SEL","WARNING: invalid hyperparameters!") return -N.inf try: self.parametric_model.train(self.dataset) except (N.linalg.linalg.LinAlgError, SL.basic.LinAlgError, ValueError): # Note that ValueError could be raised when Cholesky gets Inf or Nan. if __debug__: debug("MOD_SEL", "WARNING: Cholesky failed! Invalid hyperparameters!") return -N.inf log_marginal_likelihood = self.parametric_model.compute_log_marginal_likelihood() # REMOVE print log_marginal_likelihood return log_marginal_likelihood def df(x): """ Proxy to the log_marginal_likelihood first derivative. Necessary for OpenOpt when using derivatives. """ self.hyp_running_guess[self.freeHypers] = x # REMOVE print "df guess:",self.hyp_running_guess,x # XXX EO: Most of the following lines can be skipped if # df() is computed just after f() with the same # hyperparameters. The partial results obtained during f() # are what is needed for df(). For now, in order to avoid # bugs difficult to trace, we keep this redunundancy. A # deep check with how OpenOpt works or using memoization # should solve this issue. try: if self.logscale: self.parametric_model.set_hyperparameters(N.exp(self.hyp_running_guess)) else: self.parametric_model.set_hyperparameters(self.hyp_running_guess) pass except InvalidHyperparameterError: if __debug__: debug("MOD_SEL", "WARNING: invalid hyperparameters!") return -N.inf # Check if it is possible to avoid useless computations # already done in f(). According to tests and information # collected from OpenOpt people, it is sufficiently # unexpected that the following test succeed: if N.any(x!=self.f_last_x): if __debug__: debug("MOD_SEL","UNEXPECTED: recomputing train+log_marginal_likelihood.") try: self.parametric_model.train(self.dataset) except (N.linalg.linalg.LinAlgError, SL.basic.LinAlgError, ValueError): if __debug__: debug("MOD_SEL", "WARNING: Cholesky failed! Invalid hyperparameters!") # XXX EO: which value for the gradient to return to # OpenOpt when hyperparameters are wrong? return N.zeros(x.size) log_marginal_likelihood = self.parametric_model.compute_log_marginal_likelihood() # recompute what's needed (to be safe) REMOVE IN FUTURE! pass if self.logscale: gradient_log_marginal_likelihood = self.parametric_model.compute_gradient_log_marginal_likelihood_logscale() else: gradient_log_marginal_likelihood = self.parametric_model.compute_gradient_log_marginal_likelihood() pass # REMOVE print "grad:",gradient_log_marginal_likelihood return gradient_log_marginal_likelihood[self.freeHypers] if self.logscale: # vector of hyperparameters' values where to start the search x0 = self.hyp_initial_guess_log[self.freeHypers] else: x0 = self.hyp_initial_guess[self.freeHypers] pass self.contol = 1.0e-20 # Constraint tolerance level # XXX EO: is it necessary to use contol when self.logscale is # True and there is no lb? Ask dmitrey. if self.use_gradient: # actual instance of the OpenOpt non-linear problem self.problem = NLP(f, x0, df=df, contol=self.contol, goal='maximum') else: self.problem = NLP(f, x0, contol=self.contol, goal='maximum') pass self.problem.name = "Max LogMargLikelihood" if not self.logscale: # set lower bound for hyperparameters: avoid negative # hyperparameters. Note: problem.n is the size of # hyperparameters' vector self.problem.lb = N.zeros(self.problem.n)+self.contol pass # max number of iterations for the optimizer. self.problem.maxiter = maxiter # check whether the derivative of log_marginal_likelihood converged to # zero before ending optimization self.problem.checkdf = True # set increment of log_marginal_likelihood under which the optimizer stops self.problem.ftol = ftol self.problem.iprint = _openopt_debug() return self.problem def solve(self, problem=None): """Solve the maximization problem, check outcome and collect results. """ # XXX: this method can be made more abstract in future in the # sense that it could work not only for # log_marginal_likelihood but other measures as well # (e.g. cross-valideted error). if N.all(self.freeHypers==False): # no optimization needed self.hyperparameters_best = self.hyp_initial_guess.copy() try: self.parametric_model.set_hyperparameters(self.hyperparameters_best) except InvalidHyperparameterError: if __debug__: debug("MOD_SEL", "WARNING: invalid hyperparameters!") self.log_marginal_likelihood_best = -N.inf return self.log_marginal_likelihood_best self.parametric_model.train(self.dataset) self.log_marginal_likelihood_best = self.parametric_model.compute_log_marginal_likelihood() return self.log_marginal_likelihood_best result = self.problem.solve(self.optimization_algorithm) # perform optimization! if result.stopcase == -1: # XXX: should we use debug() for the following messages? # If so, how can we track the missing convergence to a # solution? print "Unable to find a maximum to log_marginal_likelihood" elif result.stopcase == 0: print "Limits exceeded" elif result.stopcase == 1: self.hyperparameters_best = self.hyp_initial_guess.copy() if self.logscale: self.hyperparameters_best[self.freeHypers] = N.exp(result.xf) # best hyperparameters found # NOTE is it better to return a copy? else: self.hyperparameters_best[self.freeHypers] = result.xf pass self.log_marginal_likelihood_best = result.ff # actual best vuale of log_marginal_likelihood pass self.stopcase = result.stopcase return self.log_marginal_likelihood_best pass pymvpa-0.4.8/mvpa/clfs/plr.py000066400000000000000000000126361174541445200161170ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Penalized logistic regression classifier.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.misc.exceptions import ConvergenceError from mvpa.clfs.base import Classifier, FailedToTrainError if __debug__: from mvpa.base import debug class PLR(Classifier): """Penalized logistic regression `Classifier`. """ _clf_internals = [ 'plr', 'binary', 'linear' ] def __init__(self, lm=1, criterion=1, reduced=0.0, maxiter=20, **kwargs): """ Initialize a penalized logistic regression analysis :Parameters: lm : int the penalty term lambda. criterion : int the criterion applied to judge convergence. reduced : float if not 0, the rank of the data is reduced before performing the calculations. In that case, reduce is taken as the fraction of the first singular value, at which a dimension is not considered significant anymore. A reasonable criterion is reduced=0.01 maxiter : int maximum number of iterations. If no convergence occurs after this number of iterations, an exception is raised. """ # init base class first Classifier.__init__(self, **kwargs) self.__lm = lm self.__criterion = criterion self.__reduced = reduced self.__maxiter = maxiter def __repr__(self): """String summary over the object """ return """PLR(lm=%f, criterion=%d, reduced=%s, maxiter=%d, enable_states=%s)""" % \ (self.__lm, self.__criterion, self.__reduced, self.__maxiter, str(self.states.enabled)) def _train(self, data): """Train the classifier using `data` (`Dataset`). """ # Set up the environment for fitting the data X = data.samples.T d = data.labels if set(d) != set([0, 1]): raise ValueError, \ "Regressors for logistic regression should be [0,1]. Got %s" \ %(set(d),) if self.__reduced != 0 : # Data have reduced rank from scipy.linalg import svd # Compensate for reduced rank: # Select only the n largest eigenvectors U, S, V = svd(X.T) if S[0] == 0: raise FailedToTrainError( "Data provided to PLR seems to be degenerate -- " "0-th singular value is 0") S /= S[0] V = N.matrix(V[:, :N.max(N.where(S > self.__reduced)) + 1]) # Map Data to the subspace spanned by the eigenvectors X = (X.T * V).T nfeatures, npatterns = X.shape # Weighting vector w = N.matrix(N.zeros( (nfeatures + 1, 1), 'd')) # Error for convergence criterion dw = N.matrix(N.ones( (nfeatures + 1, 1), 'd')) # Patterns of interest in the columns X = N.matrix( \ N.concatenate((X, N.ones((1, npatterns), 'd')), 0) \ ) p = N.matrix(N.zeros((1, npatterns), 'd')) # Matrix implementation of penalty term Lambda = self.__lm * N.identity(nfeatures + 1, 'd') Lambda[nfeatures, nfeatures] = 0 # Gradient g = N.matrix(N.zeros((nfeatures + 1, 1), 'd')) # Fisher information matrix H = N.matrix(N.identity(nfeatures + 1, 'd')) # Optimize k = 0 while N.sum(N.ravel(dw.A ** 2)) > self.__criterion: p[:, :] = self.__f(w.T * X) g[:, :] = X * (d - p).T - Lambda * w H[:, :] = X * N.diag(p.A1 * (1 - p.A1)) * X.T + Lambda dw[:, :] = H.I * g w += dw k += 1 if k > self.__maxiter: raise ConvergenceError, \ "More than %d Iterations without convergence" % \ (self.__maxiter) if __debug__: debug("PLR", \ "PLR converged after %d steps. Error: %g" % \ (k, N.sum(N.ravel(dw.A ** 2)))) if self.__reduced: # We have computed in rank reduced space -> # Project to original space self.w = V * w[:-1] self.offset = w[-1] else: self.w = w[:-1] self.offset = w[-1] def __f(self, y): """This is the logistic function f, that is used for determination of the vector w""" return 1. / (1 + N.exp(-y)) def _predict(self, data): """ Predict the class labels for the provided data Returns a list of class labels """ # make sure the data are in matrix form data = N.matrix(N.asarray(data)) # get the values and then predictions values = N.ravel(self.__f(self.offset + data * self.w)) predictions = values > 0.5 # save the state if desired, relying on State._setitem_ to # decide if we will actually save the values self.predictions = predictions self.values = values return predictions pymvpa-0.4.8/mvpa/clfs/ridge.py000066400000000000000000000062011174541445200164030ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Ridge regression classifier.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base import externals if externals.exists("scipy", raiseException=True): from scipy.linalg import lstsq from mvpa.clfs.base import Classifier class RidgeReg(Classifier): """Ridge regression `Classifier`. This ridge regression adds an intercept term so your labels do not have to be zero-centered. """ _clf_internals = ['ridge', 'regression', 'linear'] def __init__(self, lm=None, **kwargs): """ Initialize a ridge regression analysis. :Parameters: lm : float the penalty term lambda. (Defaults to .05*nFeatures) """ # init base class first Classifier.__init__(self, **kwargs) # pylint happiness self.w = None # It does not make sense to calculate a confusion matrix for a # ridge regression self.states.enable('training_confusion', False) # verify that they specified lambda self.__lm = lm # store train method config self.__implementation = 'direct' def __repr__(self): """String summary of the object """ if self.__lm is None: return """Ridge(lm=.05*nfeatures, enable_states=%s)""" % \ (str(self.states.enabled)) else: return """Ridge(lm=%f, enable_states=%s)""" % \ (self.__lm, str(self.states.enabled)) def _train(self, data): """Train the classifier using `data` (`Dataset`). """ if self.__implementation == "direct": # create matrices to solve with additional penalty term # determine the lambda matrix if self.__lm is None: # Not specified, so calculate based on .05*nfeatures Lambda = .05*data.nfeatures*N.eye(data.nfeatures) else: # use the provided penalty Lambda = self.__lm*N.eye(data.nfeatures) # add the penalty term a = N.concatenate( \ (N.concatenate((data.samples, N.ones((data.nsamples, 1))), 1), N.concatenate((Lambda, N.zeros((data.nfeatures, 1))), 1))) b = N.concatenate((data.labels, N.zeros(data.nfeatures))) # perform the least sq regression and save the weights self.w = lstsq(a, b)[0] else: raise ValueError, "Unknown implementation '%s'" \ % self.__implementation def _predict(self, data): """ Predict the output for the provided data. """ # predict using the trained weights return N.dot(N.concatenate((data, N.ones((len(data), 1))), 1), self.w) pymvpa-0.4.8/mvpa/clfs/sg/000077500000000000000000000000001174541445200153515ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/clfs/sg/__init__.py000066400000000000000000000011711174541445200174620ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Classifiers provied by shogun (sg) library""" __docformat__ = 'restructuredtext' if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.clfs.sg') from mvpa.clfs.sg.svm import SVM if __debug__: debug('INIT', 'mvpa.clfs.sg end') pymvpa-0.4.8/mvpa/clfs/sg/sens.py000066400000000000000000000042131174541445200166730ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Provide sensitivity measures for sg's SVM.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base import externals if externals.exists('shogun', raiseException=True): import shogun.Classifier from mvpa.misc.state import StateVariable from mvpa.measures.base import Sensitivity if __debug__: from mvpa.base import debug class LinearSVMWeights(Sensitivity): """`Sensitivity` that reports the weights of a linear SVM trained on a given `Dataset`. """ biases = StateVariable(enabled=True, doc="Offsets of separating hyperplanes") def __init__(self, clf, **kwargs): """Initialize the analyzer with the classifier it shall use. :Parameters: clf: LinearSVM classifier to use. Only classifiers sub-classed from `LinearSVM` may be used. """ # init base classes first Sensitivity.__init__(self, clf, **kwargs) def __sg_helper(self, svm): """Helper function to compute sensitivity for a single given SVM""" self.offsets = svm.get_bias() svcoef = N.matrix(svm.get_alphas()) svnums = svm.get_support_vectors() svs = self.clf.traindataset.samples[svnums,:] res = (svcoef * svs).mean(axis=0).A1 return res def _call(self, dataset): # XXX Hm... it might make sense to unify access functions # naming across our swig libsvm wrapper and sg access # functions for svm svm = self.clf.svm if isinstance(svm, shogun.Classifier.MultiClassSVM): sens = [] for i in xrange(svm.get_num_svms()): sens.append(self.__sg_helper(svm.get_svm(i))) else: sens = self.__sg_helper(svm) return N.asarray(sens) pymvpa-0.4.8/mvpa/clfs/sg/svm.py000066400000000000000000000656441174541445200165470ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Wrap the libsvm package into a very simple class interface.""" __docformat__ = 'restructuredtext' _DEV__doc__ = """ TODOs: * dual-license under GPL for use of SG? * for recent versions add ability to specify/parametrize normalization scheme for the kernel, and reuse 'scale' now for the normalizer * Add support for simplified linear classifiers (which do not require storing all training SVs/samples to make classification in predict()) """ import numpy as N from mvpa import _random_seed # Rely on SG from mvpa.base import externals, warning if externals.exists('shogun', raiseException=True): import shogun.Features import shogun.Classifier import shogun.Regression import shogun.Kernel import shogun.Library # Figure out debug IDs once and for all if hasattr(shogun.Kernel, 'M_DEBUG'): _M_DEBUG = shogun.Kernel.M_DEBUG _M_ERROR = shogun.Kernel.M_ERROR elif hasattr(shogun.Kernel, 'MSG_DEBUG'): _M_DEBUG = shogun.Kernel.MSG_DEBUG _M_ERROR = shogun.Kernel.MSG_ERROR else: _M_DEBUG, _M_ERROR = None, None warning("Could not figure out debug IDs within shogun. " "No control over shogun verbosity would be provided") try: # reuse the same seed for shogun shogun.Library.Math_init_random(_random_seed) # and do it twice to stick ;) for some reason is necessary # atm shogun.Library.Math_init_random(_random_seed) except Exception, e: warning('Shogun cannot be seeded due to %s' % (e,)) import operator from mvpa.misc.param import Parameter from mvpa.base import warning from mvpa.clfs.base import FailedToTrainError from mvpa.clfs.meta import MulticlassClassifier from mvpa.clfs._svmbase import _SVM from mvpa.misc.state import StateVariable from mvpa.measures.base import Sensitivity from sens import * if __debug__: from mvpa.base import debug def _setdebug(obj, partname): """Helper to set level of debugging output for SG :Parameters: obj In SG debug output seems to be set per every object partname : basestring For what kind of object we are talking about... could be automated later on (TODO) """ if _M_DEBUG is None: return debugname = "SG_%s" % partname.upper() switch = {True: (_M_DEBUG, 'M_DEBUG', "enable"), False: (_M_ERROR, 'M_ERROR', "disable")} key = __debug__ and debugname in debug.active sglevel, slevel, progressfunc = switch[key] if __debug__: debug("SG_", "Setting verbosity for shogun.%s instance: %s to %s" % (partname, `obj`, slevel)) obj.io.set_loglevel(sglevel) try: exec "obj.io.%s_progress()" % progressfunc except: warning("Shogun version installed has no way to enable progress" + " reports") def _tosg(data): """Draft helper function to convert data we have into SG suitable format TODO: Support different datatypes """ if __debug__: debug("SG_", "Converting data for shogun into RealFeatures") features = shogun.Features.RealFeatures(data.astype('double').T) if __debug__: debug("SG__", "Done converting data for shogun into RealFeatures") _setdebug(features, 'Features') return features class SVM(_SVM): """Support Vector Machine Classifier(s) based on Shogun This is a simple base interface """ num_threads = Parameter(1, min=1, doc='Number of threads to utilize') # NOTE: gamma is width in SG notation for RBF(Gaussian) _KERNELS = {} if externals.exists('shogun', raiseException=True): _KERNELS = { "linear": (shogun.Kernel.LinearKernel, ('scale',), LinearSVMWeights), "rbf" : (shogun.Kernel.GaussianKernel, ('gamma',), None), "rbfshift": (shogun.Kernel.GaussianShiftKernel, ('gamma', 'max_shift', 'shift_step'), None), "sigmoid": (shogun.Kernel.SigmoidKernel, ('cache_size', 'gamma', 'coef0'), None), } _KNOWN_PARAMS = [ 'epsilon' ] _KNOWN_KERNEL_PARAMS = [ ] _clf_internals = _SVM._clf_internals + [ 'sg', 'retrainable' ] if externals.exists('sg ge 0.6.4'): _KERNELS['linear'] = (shogun.Kernel.LinearKernel, (), LinearSVMWeights) # Some words of wisdom from shogun author: # XXX remove after proper comments added to implementations """ If you'd like to train linear SVMs use SGD or OCAS. These are (I am serious) the fastest linear SVM-solvers to date. (OCAS cannot do SVMs with standard additive bias, but will L2 reqularize it - though it should not matter much in practice (although it will give slightly different solutions)). Note that SGD has no stopping criterion (you simply have to specify the number of iterations) and that OCAS has a different stopping condition than svmlight for example which may be more tight and more loose depending on the problem - I sugeest 1e-2 or 1e-3 for epsilon. If you would like to train kernel SVMs use libsvm/gpdt/svmlight - depending on the problem one is faster than the other (hard to say when, I *think* when your dataset is very unbalanced chunking methods like svmlight/gpdt are better), for smaller problems definitely libsvm. If you use string kernels then gpdt/svmlight have a special 'linadd' speedup for this (requires sg 0.6.2 - there was some inefficiency in the code for python-modular before that). This is effective for big datasets and (I trained on 10 million strings based on this). And yes currently we only implemented parallel training for svmlight, however all SVMs can be evaluated in parallel. """ _KNOWN_IMPLEMENTATIONS = {} if externals.exists('shogun', raiseException=True): _KNOWN_IMPLEMENTATIONS = { "libsvm" : (shogun.Classifier.LibSVM, ('C',), ('multiclass', 'binary'), "LIBSVM's C-SVM (L2 soft-margin SVM)"), "gmnp" : (shogun.Classifier.GMNPSVM, ('C',), ('multiclass', 'binary'), "Generalized Nearest Point Problem SVM"), # XXX should have been GPDT, shogun has it fixed since some version "gpbt" : (shogun.Classifier.GPBTSVM, ('C',), ('binary',), "Gradient Projection Decomposition Technique for " \ "large-scale SVM problems"), "gnpp" : (shogun.Classifier.GNPPSVM, ('C',), ('binary',), "Generalized Nearest Point Problem SVM"), ## TODO: Needs sparse features... # "svmlin" : (shogun.Classifier.SVMLin, ''), # "liblinear" : (shogun.Classifier.LibLinear, ''), # "subgradient" : (shogun.Classifier.SubGradientSVM, ''), ## good 2-class linear SVMs # "ocas" : (shogun.Classifier.SVMOcas, ''), # "sgd" : ( shogun.Classifier.SVMSGD, ''), # regressions "libsvr": (shogun.Regression.LibSVR, ('C', 'tube_epsilon',), ('regression',), "LIBSVM's epsilon-SVR"), } def __init__(self, kernel_type='linear', **kwargs): """Interface class to Shogun's classifiers and regressions. Default implementation is 'libsvm'. """ svm_impl = kwargs.get('svm_impl', 'libsvm').lower() kwargs['svm_impl'] = svm_impl # init base class _SVM.__init__(self, kernel_type=kernel_type, **kwargs) self.__svm = None """Holds the trained svm.""" self.__svm_apply = None """Compatibility convenience to bind to the classify/apply method of __svm""" # Need to store original data... # TODO: keep 1 of them -- just __traindata or __traindataset # For now it is needed for computing sensitivities self.__traindataset = None # internal SG swig proxies self.__traindata = None self.__kernel = None self.__kernel_test = None self.__testdata = None def __condition_kernel(self, kernel): # XXX I thought that it is needed only for retrainable classifier, # but then krr gets confused, and svrlight needs it to provide # meaningful results even without 'retraining' if self._svm_impl in ['svrlight', 'lightsvm']: try: kernel.set_precompute_matrix(True, True) except Exception, e: # N/A in shogun 0.9.1... TODO: RF if __debug__: debug('SG_', "Failed call to set_precompute_matrix for %s: %s" % (self, e)) def _train(self, dataset): """Train SVM """ # XXX watchout # self.untrain() newkernel, newsvm = False, False # local bindings for faster lookup retrainable = self.params.retrainable if retrainable: _changedData = self._changedData # LABELS ul = None self.__traindataset = dataset # OK -- we have to map labels since # binary ones expect -1/+1 # Multiclass expect labels starting with 0, otherwise they puke # when ran from ipython... yikes if __debug__: debug("SG_", "Creating labels instance") if 'regression' in self._clf_internals: labels_ = N.asarray(dataset.labels, dtype='double') else: ul = dataset.uniquelabels ul.sort() if len(ul) == 2: # assure that we have -1/+1 _labels_dict = {ul[0]:-1.0, ul[1]:+1.0} elif len(ul) < 2: raise FailedToTrainError, \ "We do not have 1-class SVM brought into SG yet" else: # can't use plain enumerate since we need them swapped _labels_dict = dict([ (ul[i], i) for i in range(len(ul))]) # reverse labels dict for back mapping in _predict _labels_dict_rev = dict([(x[1], x[0]) for x in _labels_dict.items()]) # bind to instance as well self._labels_dict = _labels_dict self._labels_dict_rev = _labels_dict_rev # Map labels # # TODO: top level classifier should take care about labels # mapping if that is needed if __debug__: debug("SG__", "Mapping labels using dict %s" % _labels_dict) labels_ = N.asarray([ _labels_dict[x] for x in dataset.labels ], dtype='double') labels = shogun.Features.Labels(labels_) _setdebug(labels, 'Labels') # KERNEL if not retrainable or _changedData['traindata'] or _changedData['kernel_params']: # If needed compute or just collect arguments for SVM and for # the kernel kargs = [] for arg in self._KERNELS[self._kernel_type_literal][1]: value = self.kernel_params[arg].value # XXX Unify damn automagic gamma value if arg == 'gamma' and value == 0.0: value = self._getDefaultGamma(dataset) kargs += [value] if retrainable and __debug__: if _changedData['traindata']: debug("SG", "Re-Creating kernel since training data has changed") if _changedData['kernel_params']: debug("SG", "Re-Creating kernel since params %s has changed" % _changedData['kernel_params']) # create training data if __debug__: debug("SG_", "Converting input data for shogun") self.__traindata = _tosg(dataset.samples) if __debug__: debug("SG", "Creating kernel instance of %s giving arguments %s" % (`self._kernel_type`, kargs)) self.__kernel = kernel = \ self._kernel_type(self.__traindata, self.__traindata, *kargs) if externals.exists('sg ge 0.6.4'): kernel.set_normalizer(shogun.Kernel.IdentityKernelNormalizer()) newkernel = True self.kernel_params.reset() # mark them as not-changed _setdebug(kernel, 'Kernels') self.__condition_kernel(kernel) if retrainable: if __debug__: debug("SG_", "Resetting test kernel for retrainable SVM") self.__kernel_test = None self.__kernel_args = kargs # TODO -- handle _changedData['params'] correctly, ie without recreating # whole SVM Cs = None if not retrainable or self.__svm is None or _changedData['params']: # SVM if self.params.isKnown('C'): C = self.params.C if not operator.isSequenceType(C): # we were not given a tuple for balancing between classes C = [C] Cs = list(C[:]) # copy for i in xrange(len(Cs)): if Cs[i]<0: Cs[i] = self._getDefaultC(dataset.samples)*abs(Cs[i]) if __debug__: debug("SG_", "Default C for %s was computed to be %s" % (C[i], Cs[i])) # XXX do not jump over the head and leave it up to the user # ie do not rescale automagically by the number of samples #if len(Cs) == 2 and not ('regression' in self._clf_internals) and len(ul) == 2: # # we were given two Cs # if N.max(C) < 0 and N.min(C) < 0: # # and both are requested to be 'scaled' TODO : # # provide proper 'features' to the parameters, # # so we could specify explicitely if to scale # # them by the number of samples here # nl = [N.sum(labels_ == _labels_dict[l]) for l in ul] # ratio = N.sqrt(float(nl[1]) / nl[0]) # #ratio = (float(nl[1]) / nl[0]) # Cs[0] *= ratio # Cs[1] /= ratio # if __debug__: # debug("SG_", "Rescaled Cs to %s to accomodate the " # "difference in number of training samples" % # Cs) # Choose appropriate implementation svm_impl_class = self.__get_implementation(ul) if __debug__: debug("SG", "Creating SVM instance of %s" % `svm_impl_class`) if self._svm_impl in ['libsvr', 'svrlight']: # for regressions constructor a bit different self.__svm = svm_impl_class(Cs[0], self.params.epsilon, self.__kernel, labels) elif self._svm_impl in ['krr']: self.__svm = svm_impl_class(self.params.tau, self.__kernel, labels) else: self.__svm = svm_impl_class(Cs[0], self.__kernel, labels) self.__svm.set_epsilon(self.params.epsilon) # To stay compatible with versions across API changes in sg 1.0.0 self.__svm_apply = hasattr(self.__svm, 'apply') \ and self.__svm.apply \ or self.__svm.classify # the last one for old API # Set shrinking if self.params.isKnown('shrinking'): shrinking = self.params.shrinking if __debug__: debug("SG_", "Setting shrinking to %s" % shrinking) self.__svm.set_shrinking_enabled(shrinking) if Cs is not None and len(Cs) == 2: if __debug__: debug("SG_", "Since multiple Cs are provided: %s, assign them" % Cs) self.__svm.set_C(Cs[0], Cs[1]) self.params.reset() # mark them as not-changed newsvm = True _setdebug(self.__svm, 'SVM') # Set optimization parameters if self.params.isKnown('tube_epsilon') and \ hasattr(self.__svm, 'set_tube_epsilon'): self.__svm.set_tube_epsilon(self.params.tube_epsilon) self.__svm.parallel.set_num_threads(self.params.num_threads) else: if __debug__: debug("SG_", "SVM instance is not re-created") if _changedData['labels']: # labels were changed if __debug__: debug("SG__", "Assigning new labels") self.__svm.set_labels(labels) if newkernel: # kernel was replaced if __debug__: debug("SG__", "Assigning new kernel") self.__svm.set_kernel(self.__kernel) assert(_changedData['params'] is False) # we should never get here if retrainable: # we must assign it only if it is retrainable self.states.retrained = not newsvm or not newkernel # Train if __debug__ and 'SG' in debug.active: if not self.regression: lstr = " with labels %s" % dataset.uniquelabels else: lstr = "" debug("SG", "%sTraining %s on data%s" % (("","Re-")[retrainable and self.states.retrained], self, lstr)) self.__svm.train() if __debug__: debug("SG_", "Done training SG_SVM %s" % self._kernel_type) # Report on training if (__debug__ and 'SG__' in debug.active) or \ self.states.isEnabled('training_confusion'): trained_labels = self.__svm_apply().get_labels() else: trained_labels = None if __debug__ and "SG__" in debug.active: debug("SG__", "Original labels: %s, Trained labels: %s" % (dataset.labels, trained_labels)) # Assign training confusion right away here since we are ready # to do so. # XXX TODO use some other state variable like 'trained_labels' and # use it within base Classifier._posttrain to assign predictions # instead of duplicating code here # XXX For now it can be done only for regressions since labels need to # be remapped and that becomes even worse if we use regression # as a classifier so mapping happens upstairs if self.regression and self.states.isEnabled('training_confusion'): self.states.training_confusion = self._summaryClass( targets=dataset.labels, predictions=trained_labels) def _predict(self, data): """Predict values for the data """ retrainable = self.params.retrainable if retrainable: changed_testdata = self._changedData['testdata'] or \ self.__kernel_test is None if not retrainable or changed_testdata: testdata = _tosg(data) if not retrainable: if __debug__: debug("SG__", "Initializing SVMs kernel of %s with training/testing samples" % self) # We can just reuse kernel used for training self.__kernel.init(self.__traindata, testdata) self.__condition_kernel(self.__kernel) else: if changed_testdata: if __debug__: debug("SG__", "Re-creating testing kernel of %s giving " "arguments %s" % (`self._kernel_type`, self.__kernel_args)) kernel_test = self._kernel_type(self.__traindata, testdata, *self.__kernel_args) _setdebug(kernel_test, 'Kernels') custk_args = ([self.__traindata, testdata], [])[ int(externals.exists('sg ge 0.6.4'))] if __debug__: debug("SG__", "Re-creating custom testing kernel giving " "arguments %s" % (str(custk_args))) kernel_test_custom = shogun.Kernel.CustomKernel(*custk_args) _setdebug(kernel_test_custom, 'Kernels') self.__kernel_test = kernel_test_custom self.__kernel_test.set_full_kernel_matrix_from_full( kernel_test.get_kernel_matrix()) elif __debug__: debug("SG__", "Re-using testing kernel") assert(self.__kernel_test is not None) self.__svm.set_kernel(self.__kernel_test) if __debug__: debug("SG_", "Classifying testing data") # doesn't do any good imho although on unittests helps tiny bit... hm #self.__svm.init_kernel_optimization() values_ = self.__svm_apply() if values_ is None: raise RuntimeError, "We got empty list of values from %s" % self values = values_.get_labels() if retrainable: # we must assign it only if it is retrainable self.states.repredicted = repredicted = not changed_testdata if __debug__: debug("SG__", "Re-assigning learing kernel. Repredicted is %s" % repredicted) # return back original kernel self.__svm.set_kernel(self.__kernel) if __debug__: debug("SG__", "Got values %s" % values) if ('regression' in self._clf_internals): predictions = values else: # local bindings _labels_dict = self._labels_dict _labels_dict_rev = self._labels_dict_rev if len(_labels_dict) == 2: predictions = 1.0 - 2*N.signbit(values) else: predictions = values # assure that we have the same type label_type = type(_labels_dict.values()[0]) # remap labels back adjusting their type predictions = [_labels_dict_rev[label_type(x)] for x in predictions] if __debug__: debug("SG__", "Tuned predictions %s" % predictions) # store state variable # TODO: extract values properly for multiclass SVMs -- # ie 1 value per label or pairs for all 1-vs-1 classifications self.values = values ## to avoid leaks with not yet properly fixed shogun if not retrainable: try: testdata.free_features() except: pass return predictions def untrain(self): super(SVM, self).untrain() if not self.params.retrainable: if __debug__: debug("SG__", "Untraining %(clf)s and destroying sg's SVM", msgargs={'clf':self}) # to avoid leaks with not yet properly fixed shogun # XXX make it nice... now it is just stable ;-) if True: # not self.__traindata is None: if True: # try: if self.__kernel is not None: del self.__kernel self.__kernel = None if self.__kernel_test is not None: del self.__kernel_test self.__kernel_test = None if self.__svm is not None: del self.__svm self.__svm = None self.__svm_apply = None if self.__traindata is not None: # Let in for easy demonstration of the memory leak in shogun #for i in xrange(10): # debug("SG__", "cachesize pre free features %s" % # (self.__svm.get_kernel().get_cache_size())) self.__traindata.free_features() del self.__traindata self.__traindata = None self.__traindataset = None #except: # pass if __debug__: debug("SG__", "Done untraining %(self)s and destroying sg's SVM", msgargs=locals()) elif __debug__: debug("SG__", "Not untraining %(self)s since it is retrainable", msgargs=locals()) def __get_implementation(self, ul): if 'regression' in self._clf_internals or len(ul) == 2: svm_impl_class = SVM._KNOWN_IMPLEMENTATIONS[self._svm_impl][0] else: if self._svm_impl == 'libsvm': svm_impl_class = shogun.Classifier.LibSVMMultiClass elif self._svm_impl == 'gmnp': svm_impl_class = shogun.Classifier.GMNPSVM else: raise RuntimeError, \ "Shogun: Implementation %s doesn't handle multiclass " \ "data. Got labels %s. Use some other classifier" % \ (self._svm_impl, self.__traindataset.uniquelabels) if __debug__: debug("SG_", "Using %s for multiclass data of %s" % (svm_impl_class, self._svm_impl)) return svm_impl_class svm = property(fget=lambda self: self.__svm) """Access to the SVM model.""" traindataset = property(fget=lambda self: self.__traindataset) """Dataset which was used for training TODO -- might better become state variable I guess""" # Conditionally make some of the implementations available if they are # present in the present shogun for name, item, params, descr in \ [('mpd', "shogun.Classifier.MPDSVM", "('C',), ('binary',)", "MPD classifier from shogun"), ('lightsvm', "shogun.Classifier.SVMLight", "('C',), ('binary',)", "SVMLight classification http://svmlight.joachims.org/"), ('svrlight', "shogun.Regression.SVRLight", "('C','tube_epsilon',), ('regression',)", "SVMLight regression http://svmlight.joachims.org/"), ('krr', "shogun.Regression.KRR", "('tau',), ('regression',)", "Kernel Ridge Regression"), ]: if externals.exists('shogun.%s' % name): exec "SVM._KNOWN_IMPLEMENTATIONS[\"%s\"] = (%s, %s, \"%s\")" % (name, item, params, descr) # Assign SVM class to limited set of LinearSVMWeights LinearSVMWeights._LEGAL_CLFS = [SVM] pymvpa-0.4.8/mvpa/clfs/smlr.py000066400000000000000000000511371174541445200162760ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Sparse Multinomial Logistic Regression classifier.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base import warning, externals from mvpa.clfs.base import Classifier from mvpa.measures.base import Sensitivity from mvpa.misc.exceptions import ConvergenceError from mvpa.misc.param import Parameter from mvpa.misc.state import StateVariable from mvpa.misc.transformers import SecondAxisMaxOfAbs _DEFAULT_IMPLEMENTATION = "Python" if externals.exists('ctypes'): # Uber-fast C-version of the stepwise regression from mvpa.clfs.libsmlrc import stepwise_regression as _cStepwiseRegression _DEFAULT_IMPLEMENTATION = "C" else: _cStepwiseRegression = None warning("SMLR implementation without ctypes is overwhelmingly slow." " You are strongly advised to install python-ctypes") if __debug__: from mvpa.base import debug def _label2oneofm(labels, ulabels): """Convert labels to one-of-M form. TODO: Might be useful elsewhere so could migrate into misc/ """ # allocate for the new one-of-M labels new_labels = N.zeros((len(labels), len(ulabels))) # loop and convert to one-of-M for i, c in enumerate(ulabels): new_labels[labels == c, i] = 1 return new_labels class SMLR(Classifier): """Sparse Multinomial Logistic Regression `Classifier`. This is an implementation of the SMLR algorithm published in :ref:`Krishnapuram et al., 2005 ` (2005, IEEE Transactions on Pattern Analysis and Machine Intelligence). Be sure to cite that article if you use this classifier for your work. """ _clf_internals = [ 'smlr', 'linear', 'has_sensitivity', 'binary', 'multiclass', 'does_feature_selection' ] # XXX: later 'kernel-based'? lm = Parameter(.1, min=1e-10, allowedtype='float', doc="""The penalty term lambda. Larger values will give rise to more sparsification.""") convergence_tol = Parameter(1e-3, min=1e-10, max=1.0, allowedtype='float', doc="""When the weight change for each cycle drops below this value the regression is considered converged. Smaller values lead to tighter convergence.""") resamp_decay = Parameter(0.5, allowedtype='float', min=0.0, max=1.0, doc="""Decay rate in the probability of resampling a zero weight. 1.0 will immediately decrease to the min_resamp from 1.0, 0.0 will never decrease from 1.0.""") min_resamp = Parameter(0.001, allowedtype='float', min=1e-10, max=1.0, doc="Minimum resampling probability for zeroed weights") maxiter = Parameter(10000, allowedtype='int', min=1, doc="""Maximum number of iterations before stopping if not converged.""") has_bias = Parameter(True, allowedtype='bool', doc="""Whether to add a bias term to allow fits to data not through zero""") fit_all_weights = Parameter(True, allowedtype='bool', doc="""Whether to fit weights for all classes or to the number of classes minus one. Both should give nearly identical results, but if you set fit_all_weights to True it will take a little longer and yield weights that are fully analyzable for each class. Also, note that the convergence rate may be different, but convergence point is the same.""") implementation = Parameter(_DEFAULT_IMPLEMENTATION, allowedtype='basestring', choices=["C", "Python"], doc="""Use C or Python as the implementation of stepwise_regression. C version brings significant speedup thus is the default one.""") seed = Parameter(None, allowedtype='None or int', doc="""Seed to be used to initialize random generator, might be used to replicate the run""") unsparsify = Parameter(False, allowedtype='bool', doc="""***EXPERIMENTAL*** Whether to unsparsify the weights via regression. Note that it likely leads to worse classifier performance, but more interpretable weights.""") std_to_keep = Parameter(2.0, allowedtype='float', doc="""Standard deviation threshold of weights to keep when unsparsifying.""") def __init__(self, **kwargs): """Initialize an SMLR classifier. """ """ TODO: # Add in likelihood calculation # Add kernels, not just direct methods. """ # init base class first Classifier.__init__(self, **kwargs) if _cStepwiseRegression is None and self.implementation == 'C': warning('SMLR: C implementation is not available.' ' Using pure Python one') self.implementation = 'Python' # pylint friendly initializations self.__ulabels = None """Unigue labels from the training set.""" self.__weights_all = None """Contains all weights including bias values""" self.__weights = None """Just the weights, without the biases""" self.__biases = None """The biases, will remain none if has_bias is False""" def _pythonStepwiseRegression(self, w, X, XY, Xw, E, auto_corr, lambda_over_2_auto_corr, S, M, maxiter, convergence_tol, resamp_decay, min_resamp, verbose, seed = None): """The (much slower) python version of the stepwise regression. I'm keeping this around for now so that we can compare results.""" # get the data information into easy vars ns, nd = X.shape # initialize the iterative optimization converged = False incr = N.finfo(N.float).max non_zero, basis, m, wasted_basis, cycles = 0, 0, 0, 0, 0 sum2_w_diff, sum2_w_old, w_diff = 0.0, 0.0, 0.0 p_resamp = N.ones(w.shape, dtype=N.float) if seed is not None: # set the random seed N.random.seed(seed) if __debug__: debug("SMLR_", "random seed=%s" % seed) # perform the optimization while not converged and cycles < maxiter: # get the starting weight w_old = w[basis, m] # see if we're gonna update if (w_old != 0) or N.random.rand() < p_resamp[basis, m]: # let's do it # get the probability P = E[:, m]/S # set the gradient grad = XY[basis, m] - N.dot(X[:, basis], P) # calculate the new weight with the Laplacian prior w_new = w_old + grad/auto_corr[basis] # keep weights within bounds if w_new > lambda_over_2_auto_corr[basis]: w_new -= lambda_over_2_auto_corr[basis] changed = True # unmark from being zero if necessary if w_old == 0: non_zero += 1 # reset the prob of resampling p_resamp[basis, m] = 1.0 elif w_new < -lambda_over_2_auto_corr[basis]: w_new += lambda_over_2_auto_corr[basis] changed = True # unmark from being zero if necessary if w_old == 0: non_zero += 1 # reset the prob of resampling p_resamp[basis, m] = 1.0 else: # gonna zero it out w_new = 0.0 # decrease the p_resamp p_resamp[basis, m] -= (p_resamp[basis, m] - \ min_resamp) * resamp_decay # set number of non-zero if w_old == 0: changed = False wasted_basis += 1 else: changed = True non_zero -= 1 # process any changes if changed: #print "w[%d, %d] = %g" % (basis, m, w_new) # update the expected values w_diff = w_new - w_old Xw[:, m] = Xw[:, m] + X[:, basis]*w_diff E_new_m = N.exp(Xw[:, m]) S += E_new_m - E[:, m] E[:, m] = E_new_m # update the weight w[basis, m] = w_new # keep track of the sqrt sum squared diffs sum2_w_diff += w_diff*w_diff # add to the old no matter what sum2_w_old += w_old*w_old # update the class and basis m = N.mod(m+1, w.shape[1]) if m == 0: # we completed a cycle of labels basis = N.mod(basis+1, nd) if basis == 0: # we completed a cycle of features cycles += 1 # assess convergence incr = N.sqrt(sum2_w_diff) / \ (N.sqrt(sum2_w_old)+N.finfo(N.float).eps) # save the new weights converged = incr < convergence_tol if __debug__: debug("SMLR_", \ "cycle=%d ; incr=%g ; non_zero=%d ; " % (cycles, incr, non_zero) + "wasted_basis=%d ; " % (wasted_basis) + "sum2_w_old=%g ; sum2_w_diff=%g" % (sum2_w_old, sum2_w_diff)) # reset the sum diffs and wasted_basis sum2_w_diff = 0.0 sum2_w_old = 0.0 wasted_basis = 0 if not converged: raise ConvergenceError, \ "More than %d Iterations without convergence" % \ (maxiter) # calcualte the log likelihoods and posteriors for the training data #log_likelihood = x return cycles def _train(self, dataset): """Train the classifier using `dataset` (`Dataset`). """ # Process the labels to turn into 1 of N encoding labels = _label2oneofm(dataset.labels, dataset.uniquelabels) self.__ulabels = dataset.uniquelabels.copy() Y = labels M = len(self.__ulabels) # get the dataset information into easy vars X = dataset.samples # see if we are adding a bias term if self.params.has_bias: if __debug__: debug("SMLR_", "hstacking 1s for bias") # append the bias term to the features X = N.hstack((X, N.ones((X.shape[0], 1), dtype=X.dtype))) if self.params.implementation.upper() == 'C': _stepwise_regression = _cStepwiseRegression # # TODO: avoid copying to non-contig arrays, use strides in ctypes? if not (X.flags['C_CONTIGUOUS'] and X.flags['ALIGNED']): if __debug__: debug("SMLR_", "Copying data to get it C_CONTIGUOUS/ALIGNED") X = N.array(X, copy=True, dtype=N.double, order='C') # currently must be double for the C code if X.dtype != N.double: if __debug__: debug("SMLR_", "Converting data to double") # must cast to double X = X.astype(N.double) # set the feature dimensions elif self.params.implementation.upper() == 'PYTHON': _stepwise_regression = self._pythonStepwiseRegression else: raise ValueError, \ "Unknown implementation %s of stepwise_regression" % \ self.params.implementation # set the feature dimensions ns, nd = X.shape # decide the size of weights based on num classes estimated if self.params.fit_all_weights: c_to_fit = M else: c_to_fit = M-1 # Precompute what we can auto_corr = ((M-1.)/(2.*M))*(N.sum(X*X, 0)) XY = N.dot(X.T, Y[:, :c_to_fit]) lambda_over_2_auto_corr = (self.params.lm/2.)/auto_corr # set starting values w = N.zeros((nd, c_to_fit), dtype=N.double) Xw = N.zeros((ns, c_to_fit), dtype=N.double) E = N.ones((ns, c_to_fit), dtype=N.double) S = M*N.ones(ns, dtype=N.double) # set verbosity if __debug__: verbosity = int( "SMLR_" in debug.active ) debug('SMLR_', 'Calling stepwise_regression. Seed %s' % self.params.seed) else: verbosity = 0 # call the chosen version of stepwise_regression cycles = _stepwise_regression(w, X, XY, Xw, E, auto_corr, lambda_over_2_auto_corr, S, M, self.params.maxiter, self.params.convergence_tol, self.params.resamp_decay, self.params.min_resamp, verbosity, self.params.seed) if cycles >= self.params.maxiter: # did not converge raise ConvergenceError, \ "More than %d Iterations without convergence" % \ (self.params.maxiter) # see if unsparsify the weights if self.params.unsparsify: # unsparsify w = self._unsparsify_weights(X, w) # save the weights self.__weights_all = w self.__weights = w[:dataset.nfeatures, :] if self.states.isEnabled('feature_ids'): self.feature_ids = N.where(N.max(N.abs(w[:dataset.nfeatures, :]), axis=1)>0)[0] # and a bias if self.params.has_bias: self.__biases = w[-1, :] if __debug__: debug('SMLR', "train finished in %d cycles on data.shape=%s " % (cycles, X.shape) + "min:max(data)=%f:%f, got min:max(w)=%f:%f" % (N.min(X), N.max(X), N.min(w), N.max(w))) def _unsparsify_weights(self, samples, weights): """Unsparsify weights via least squares regression.""" # allocate for the new weights new_weights = N.zeros(weights.shape, dtype=N.double) # get the sample data we're predicting and the sum squared # total variance b = samples sst = N.power(b - b.mean(0),2).sum(0) # loop over each column for i in range(weights.shape[1]): w = weights[:,i] # get the nonzero ind ind = w!=0 # get the features with non-zero weights a = b[:,ind] # predict all the data with the non-zero features betas = N.linalg.lstsq(a,b)[0] # determine the R^2 for each feature based on the sum # squared prediction error f = N.dot(a,betas) sse = N.power((b-f),2).sum(0) rsquare = N.zeros(sse.shape,dtype=sse.dtype) gind = sst>0 rsquare[gind] = 1-(sse[gind]/sst[gind]) # derrive new weights by combining the betas and weights # scaled by the rsquare new_weights[:,i] = N.dot(w[ind],betas)*rsquare # take the tails tozero = N.abs(new_weights) < self.params.std_to_keep*N.std(new_weights) orig_zero = weights==0.0 if orig_zero.sum() < tozero.sum(): # should not end up with fewer than start tozero = orig_zero new_weights[tozero] = 0.0 debug('SMLR_', "Start nonzero: %d; Finish nonzero: %d" % \ ((weights!=0).sum(), (new_weights!=0).sum())) return new_weights def _getFeatureIds(self): """Return ids of the used features """ return N.where(N.max(N.abs(self.__weights), axis=1)>0)[0] def _predict(self, data): """Predict the output for the provided data. """ # see if we are adding a bias term if self.params.has_bias: # append the bias term to the features data = N.hstack((data, N.ones((data.shape[0], 1), dtype=data.dtype))) # append the zeros column to the weights if necessary if self.params.fit_all_weights: w = self.__weights_all else: w = N.hstack((self.__weights_all, N.zeros((self.__weights_all.shape[0], 1)))) # determine the probability values for making the prediction dot_prod = N.dot(data, w) E = N.exp(dot_prod) S = N.sum(E, 1) if __debug__: debug('SMLR', "predict on data.shape=%s min:max(data)=%f:%f " % (`data.shape`, N.min(data), N.max(data)) + "min:max(w)=%f:%f min:max(dot_prod)=%f:%f min:max(E)=%f:%f" % (N.min(w), N.max(w), N.min(dot_prod), N.max(dot_prod), N.min(E), N.max(E))) values = E / S[:, N.newaxis].repeat(E.shape[1], axis=1) self.values = values # generate predictions predictions = N.asarray([self.__ulabels[N.argmax(vals)] for vals in values]) # no need to assign state variable here -- would be done # in Classifier._postpredict anyway #self.predictions = predictions return predictions def getSensitivityAnalyzer(self, **kwargs): """Returns a sensitivity analyzer for SMLR.""" kwargs.setdefault('combiner', SecondAxisMaxOfAbs) return SMLRWeights(self, **kwargs) biases = property(lambda self: self.__biases) weights = property(lambda self: self.__weights) class SMLRWeights(Sensitivity): """`SensitivityAnalyzer` that reports the weights SMLR trained on a given `Dataset`. By default SMLR provides multiple weights per feature (one per label in training dataset). By default, all weights are combined into a single sensitivity value. Please, see the `FeaturewiseDatasetMeasure` constructor arguments how to custmize this behavior. """ biases = StateVariable(enabled=True, doc="A 1-d ndarray of biases") _LEGAL_CLFS = [ SMLR ] def _call(self, dataset=None): """Extract weights from SMLR classifier. SMLR always has weights available, so nothing has to be computed here. """ clf = self.clf weights = clf.weights # XXX: MH: The following warning is inappropriate. In almost all cases # SMLR will return more than one weight per feature. Even in the case of # binary problem it will fit both weights by default. So unless you # specify fit_all_weights=False manually this warning is always there. # To much annoyance IMHO. I moved this information into the docstring, # as there is no technical problem here, as FeaturewiseDatasetMeasure # by default applies a combiner -- just that people should know... # PLEASE ACKNOWLEDGE AND REMOVE #if weights.shape[1] != 1: # warning("You are estimating sensitivity for SMLR %s with multiple" # " sensitivities available %s. Make sure that it is what you" # " intended to do" % (self, weights.shape) ) if clf.has_bias: self.biases = clf.biases if __debug__: debug('SMLR', "Extracting weights for %d-class SMLR" % (weights.shape[1]+1) + "Result: min=%f max=%f" %\ (N.min(weights), N.max(weights))) return weights pymvpa-0.4.8/mvpa/clfs/stats.py000066400000000000000000001130201174541445200164450ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Estimator for classifier error distributions.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base import externals, warning from mvpa.misc.state import ClassWithCollections, StateVariable if __debug__: from mvpa.base import debug class Nonparametric(object): """Non-parametric 1d distribution -- derives cdf based on stored values. Introduced to complement parametric distributions present in scipy.stats. """ def __init__(self, dist_samples, correction='clip'): """ :Parameters: dist_samples : ndarray Samples to be used to assess the distribution. correction : {'clip'} or None, optional Determines the behavior when .cdf is queried. If None -- no correction is made. If 'clip' -- values are clipped to lie in the range [1/(N+2), (N+1)/(N+2)] (simply because non-parametric assessment lacks the power to resolve with higher precision in the tails, so 'imagery' samples are placed in each of the two tails). """ self._dist_samples = N.ravel(dist_samples) self._correction = correction def __repr__(self): return '%s(%r%s)' % ( self.__class__.__name__, self._dist_samples, ('', ', correction=%r' % self._correction) [int(self._correction != 'clip')]) @staticmethod def fit(dist_samples): return [dist_samples] def cdf(self, x): """Returns the cdf value at `x`. """ dist_samples = self._dist_samples res = N.vectorize(lambda v:(dist_samples <= v).mean())(x) if self._correction == 'clip': nsamples = len(dist_samples) N.clip(res, 1.0/(nsamples+2), (nsamples+1.0)/(nsamples+2), res) elif self._correction is None: pass else: raise ValueError, \ '%r is incorrect value for correction parameter of %s' \ % (self._correction, self.__class__.__name__) return res def _pvalue(x, cdf_func, tail, return_tails=False, name=None): """Helper function to return p-value(x) given cdf and tail :Parameters: cdf_func : callable Function to be used to derive cdf values for x tail : str ('left', 'right', 'any', 'both') Which tail of the distribution to report. For 'any' and 'both' it chooses the tail it belongs to based on the comparison to p=0.5. In the case of 'any' significance is taken like in a one-tailed test. return_tails : bool If True, a tuple return (pvalues, tails), where tails contain 1s if value was from the right tail, and 0 if the value was from the left tail. """ is_scalar = N.isscalar(x) if is_scalar: x = [x] cdf = cdf_func(x) if __debug__ and 'CHECK_STABILITY' in debug.active: cdf_min, cdf_max = N.min(cdf), N.max(cdf) if cdf_min < 0 or cdf_max > 1.0: s = ('', ' for %s' % name)[int(name is not None)] warning('Stability check of cdf %s failed%s. Min=%s, max=%s' % \ (cdf_func, s, cdf_min, cdf_max)) # no escape but to assure that CDF is in the right range. Some # distributions from scipy tend to jump away from [0,1] cdf = N.clip(cdf, 0, 1.0) if tail == 'left': if return_tails: right_tail = N.zeros(cdf.shape, dtype=bool) elif tail == 'right': cdf = 1 - cdf if return_tails: right_tail = N.ones(cdf.shape, dtype=bool) elif tail in ('any', 'both'): right_tail = (cdf >= 0.5) cdf[right_tail] = 1.0 - cdf[right_tail] if tail == 'both': # we need to half the signficance cdf *= 2 # Assure that NaNs didn't get significant value cdf[N.isnan(x)] = 1.0 if is_scalar: res = cdf[0] else: res = cdf if return_tails: return (res, right_tail) else: return res class NullDist(ClassWithCollections): """Base class for null-hypothesis testing. """ # Although base class is not benefiting from states, derived # classes do (MCNullDist). For the sake of avoiding multiple # inheritance and associated headache -- let them all be ClassWithCollections, # performance hit should be negligible in most of the scenarios _ATTRIBUTE_COLLECTIONS = ['states'] def __init__(self, tail='both', **kwargs): """Cheap initialization. :Parameter: tail: str ('left', 'right', 'any', 'both') Which tail of the distribution to report. For 'any' and 'both' it chooses the tail it belongs to based on the comparison to p=0.5. In the case of 'any' significance is taken like in a one-tailed test. """ ClassWithCollections.__init__(self, **kwargs) self._setTail(tail) def __repr__(self, prefixes=[]): return super(NullDist, self).__repr__( prefixes=["tail=%s" % `self.__tail`] + prefixes) def _setTail(self, tail): # sanity check if tail not in ('left', 'right', 'any', 'both'): raise ValueError, 'Unknown value "%s" to `tail` argument.' \ % tail self.__tail = tail def fit(self, measure, wdata, vdata=None): """Implement to fit the distribution to the data.""" raise NotImplementedError def cdf(self, x): """Implementations return the value of the cumulative distribution function (left or right tail dpending on the setting). """ raise NotImplementedError def p(self, x, **kwargs): """Returns the p-value for values of `x`. Returned values are determined left, right, or from any tail depending on the constructor setting. In case a `FeaturewiseDatasetMeasure` was used to estimate the distribution the method returns an array. In that case `x` can be a scalar value or an array of a matching shape. """ return _pvalue(x, self.cdf, self.__tail, **kwargs) tail = property(fget=lambda x:x.__tail, fset=_setTail) class MCNullDist(NullDist): """Null-hypothesis distribution is estimated from randomly permuted data labels. The distribution is estimated by calling fit() with an appropriate `DatasetMeasure` or `TransferError` instance and a training and a validation dataset (in case of a `TransferError`). For a customizable amount of cycles the training data labels are permuted and the corresponding measure computed. In case of a `TransferError` this is the error when predicting the *correct* labels of the validation dataset. The distribution can be queried using the `cdf()` method, which can be configured to report probabilities/frequencies from `left` or `right` tail, i.e. fraction of the distribution that is lower or larger than some critical value. This class also supports `FeaturewiseDatasetMeasure`. In that case `cdf()` returns an array of featurewise probabilities/frequencies. """ _DEV_DOC = """ TODO automagically decide on the number of samples/permutations needed Caution should be paid though since resultant distributions might be quite far from some conventional ones (e.g. Normal) -- it is expected to them to be bimodal (or actually multimodal) in many scenarios. """ dist_samples = StateVariable(enabled=False, doc='Samples obtained for each permutation') def __init__(self, dist_class=Nonparametric, permutations=100, **kwargs): """Initialize Monte-Carlo Permutation Null-hypothesis testing :Parameters: dist_class: class This can be any class which provides parameters estimate using `fit()` method to initialize the instance, and provides `cdf(x)` method for estimating value of x in CDF. All distributions from SciPy's 'stats' module can be used. permutations: int This many permutations of label will be performed to determine the distribution under the null hypothesis. """ NullDist.__init__(self, **kwargs) self._dist_class = dist_class self._dist = [] # actual distributions self.__permutations = permutations """Number of permutations to compute the estimate the null distribution.""" def __repr__(self, prefixes=[]): prefixes_ = ["permutations=%s" % self.__permutations] if self._dist_class != Nonparametric: prefixes_.insert(0, 'dist_class=%s' % `self._dist_class`) return super(MCNullDist, self).__repr__( prefixes=prefixes_ + prefixes) def fit(self, measure, wdata, vdata=None): """Fit the distribution by performing multiple cycles which repeatedly permuted labels in the training dataset. :Parameters: measure: (`Featurewise`)`DatasetMeasure` | `TransferError` TransferError instance used to compute all errors. wdata: `Dataset` which gets permuted and used to compute the measure/transfer error multiple times. vdata: `Dataset` used for validation. If provided measure is assumed to be a `TransferError` and working and validation dataset are passed onto it. """ dist_samples = [] """Holds the values for randomized labels.""" # Needs to be imported here upon demand due to circular imports # TODO: place MC into a separate module from mvpa.clfs.base import DegenerateInputError, FailedToTrainError # decide on the arguments to measure if not vdata is None: measure_args = [vdata, wdata] else: measure_args = [wdata] # estimate null-distribution skipped = 0 # # of skipped permutations for p in xrange(self.__permutations): # new permutation all the time # but only permute the training data and keep the testdata constant # if __debug__: debug('STATMC', "Doing %i permutations: %i" \ % (self.__permutations, p+1), cr=True) # TODO this really needs to be more clever! If data samples are # shuffled within a class it really makes no difference for the # classifier, hence the number of permutations to estimate the # null-distribution of transfer errors can be reduced dramatically # when the *right* permutations (the ones that matter) are done. wdata.permuteLabels(True, perchunk=False) # compute and store the measure of this permutation # assume it has `TransferError` interface try: dist_samples.append(measure(*measure_args)) except (DegenerateInputError, FailedToTrainError), e: if __debug__: debug('STATMC', " skipped", cr=True) warning("Failed to estimate %s on %s, due to %s. " "Permutation %d skipped." % (measure, measure_args, e, p)) skipped += 1 continue if __debug__: debug('STATMC', ' Skipped: %d permutations' % skipped) # restore original labels wdata.permuteLabels(False, perchunk=False) # store samples self.dist_samples = dist_samples = N.asarray(dist_samples) # fit distribution per each element # to decide either it was done on scalars or vectors shape = dist_samples.shape nshape = len(shape) # if just 1 dim, original data was scalar, just create an # artif dimension for it if nshape == 1: dist_samples = dist_samples[:, N.newaxis] # fit per each element. # XXX could be more elegant? may be use N.vectorize? dist_samples_rs = dist_samples.reshape((shape[0], -1)) dist = [] for samples in dist_samples_rs.T: params = self._dist_class.fit(samples) if __debug__ and 'STAT' in debug.active: debug('STAT', 'Estimated parameters for the %s are %s' % (self._dist_class, str(params))) dist.append(self._dist_class(*params)) self._dist = dist def cdf(self, x): """Return value of the cumulative distribution function at `x`. """ if self._dist is None: # XXX We might not want to descriminate that way since # usually generators also have .cdf where they rely on the # default parameters. But then what about Nonparametric raise RuntimeError, "Distribution has to be fit first" is_scalar = N.isscalar(x) if is_scalar: x = [x] x = N.asanyarray(x) xshape = x.shape # assure x is a 1D array now x = x.reshape((-1,)) if len(self._dist) != len(x): raise ValueError, 'Distribution was fit for structure with %d' \ ' elements, whenever now queried with %d elements' \ % (len(self._dist), len(x)) # extract cdf values per each element cdfs = [ dist.cdf(v) for v, dist in zip(x, self._dist) ] return N.array(cdfs).reshape(xshape) def clean(self): """Clean stored distributions Storing all of the distributions might be too expensive (e.g. in case of Nonparametric), and the scope of the object might be too broad to wait for it to be destroyed. Clean would bind dist_samples to empty list to let gc revoke the memory. """ self._dist = [] class FixedNullDist(NullDist): """Proxy/Adaptor class for SciPy distributions. All distributions from SciPy's 'stats' module can be used with this class. >>> import numpy as N >>> from scipy import stats >>> from mvpa.clfs.stats import FixedNullDist >>> >>> dist = FixedNullDist(stats.norm(loc=2, scale=4)) >>> dist.p(2) 0.5 >>> >>> dist.cdf(N.arange(5)) array([ 0.30853754, 0.40129367, 0.5 , 0.59870633, 0.69146246]) >>> >>> dist = FixedNullDist(stats.norm(loc=2, scale=4), tail='right') >>> dist.p(N.arange(5)) array([ 0.69146246, 0.59870633, 0.5 , 0.40129367, 0.30853754]) """ def __init__(self, dist, **kwargs): """ :Parameter: dist: distribution object This can be any object the has a `cdf()` method to report the cumulative distribition function values. """ NullDist.__init__(self, **kwargs) self._dist = dist def fit(self, measure, wdata, vdata=None): """Does nothing since the distribution is already fixed.""" pass def cdf(self, x): """Return value of the cumulative distribution function at `x`. """ return self._dist.cdf(x) def __repr__(self, prefixes=[]): prefixes_ = ["dist=%s" % `self._dist`] return super(FixedNullDist, self).__repr__( prefixes=prefixes_ + prefixes) class AdaptiveNullDist(FixedNullDist): """Adaptive distribution which adjusts parameters according to the data WiP: internal implementation might change """ def fit(self, measure, wdata, vdata=None): """Cares about dimensionality of the feature space in measure """ try: nfeatures = len(measure.feature_ids) except ValueError: # XXX nfeatures = N.prod(wdata.shape[1:]) dist_gen = self._dist if not hasattr(dist_gen, 'fit'): # frozen already dist_gen = dist_gen.dist # rv_frozen at least has it ;) args, kwargs = self._adapt(nfeatures, measure, wdata, vdata) if __debug__: debug('STAT', 'Adapted parameters for %s to be %s, %s' % (dist_gen, args, kwargs)) self._dist = dist_gen(*args, **kwargs) def _adapt(self, nfeatures, measure, wdata, vdata=None): raise NotImplementedError class AdaptiveRDist(AdaptiveNullDist): """Adaptive rdist: params are (nfeatures-1, 0, 1) """ def _adapt(self, nfeatures, measure, wdata, vdata=None): return (nfeatures-1, 0, 1), {} # XXX: RDist has stability issue, just run # python -c "import scipy.stats; print scipy.stats.rdist(541,0,1).cdf(0.72)" # to get some improbable value, so we need to take care about that manually # here def cdf(self, x): cdf_ = self._dist.cdf(x) bad_values = N.where(N.abs(cdf_)>1) # XXX there might be better implementation (faster/elegant) using N.clip, # the only problem is that instability results might flip the sign # arbitrarily if len(bad_values[0]): # in this distribution we have mean at 0, so we can take that easily # into account cdf_bad = cdf_[bad_values] x_bad = x[bad_values] cdf_bad[x_bad<0] = 0.0 cdf_bad[x_bad>=0] = 1.0 cdf_[bad_values] = cdf_bad return cdf_ class AdaptiveNormal(AdaptiveNullDist): """Adaptive Normal Distribution: params are (0, sqrt(1/nfeatures)) """ def _adapt(self, nfeatures, measure, wdata, vdata=None): return (0, 1.0/N.sqrt(nfeatures)), {} if externals.exists('scipy'): from mvpa.support.stats import scipy from scipy.stats import kstest """ Thoughts: So we can use `scipy.stats.kstest` (Kolmogorov-Smirnov test) to check/reject H0 that samples come from a given distribution. But since it is based on a full range of data, we might better of with some ad-hoc checking by the detection power of the values in the tail of a tentative distribution. """ # We need a way to fixate estimation of some parameters # (e.g. mean) so lets create a simple proxy, or may be class # generator later on, which would take care about punishing change # from the 'right' arguments import scipy class rv_semifrozen(object): """Helper proxy-class to fit distribution when some parameters are known It is an ugly hack with snippets of code taken from scipy, which is Copyright (c) 2001, 2002 Enthought, Inc. and is distributed under BSD license http://www.scipy.org/License_Compatibility """ def __init__(self, dist, loc=None, scale=None, args=None): self._dist = dist # loc and scale theta = (loc, scale) # args Narg_ = dist.numargs if args is not None: Narg = len(args) if Narg > Narg_: raise ValueError, \ 'Distribution %s has only %d arguments. Got %d' \ % (dist, Narg_, Narg) args += (None,) * (Narg_ - Narg) else: args = (None,) * Narg_ args_i = [i for i,v in enumerate(args) if v is None] self._fargs = (list(args+theta), args_i) """Arguments which should get some fixed value""" def __call__(self, *args, **kwargs): """Upon call mimic call to get actual rv_frozen distribution """ return self._dist(*args, **kwargs) def nnlf(self, theta, x): # - sum (log pdf(x, theta),axis=0) # where theta are the parameters (including loc and scale) # fargs, fargs_i = self._fargs try: i=-1 if fargs[-1] is not None: scale = fargs[-1] else: scale = theta[i] i -= 1 if fargs[-2] is not None: loc = fargs[-2] else: loc = theta[i] i -= 1 args = theta[:i+1] # adjust args if there were fixed for i,a in zip(fargs_i, args): fargs[i] = a args = fargs[:-2] except IndexError: raise ValueError, "Not enough input arguments." if not self._argcheck(*args) or scale <= 0: return N.inf x = N.asarray((x-loc) / scale) cond0 = (x <= self.a) | (x >= self.b) if (N.any(cond0)): return N.inf else: return self._nnlf(x, *args) + len(x)*N.log(scale) def fit(self, data, *args, **kwds): loc0, scale0 = map(kwds.get, ['loc', 'scale'], [0.0, 1.0]) fargs, fargs_i = self._fargs Narg = len(args) Narg_ = self.numargs if Narg != Narg_: if Narg > Narg_: raise ValueError, "Too many input arguments." else: args += (1.0,)*(self.numargs-Narg) # Provide only those args which are not fixed, and # append location and scale (if not fixed) at the end if len(fargs_i) != Narg_: x0 = tuple([args[i] for i in fargs_i]) else: x0 = args if fargs[-2] is None: x0 = x0 + (loc0,) if fargs[-1] is None: x0 = x0 + (scale0,) opt_x = scipy.optimize.fmin(self.nnlf, x0, args=(N.ravel(data),), disp=0) # reconstruct back i = 0 loc, scale = fargs[-2:] if fargs[-1] is None: i -= 1 scale = opt_x[i] if fargs[-2] is None: i -= 1 loc = opt_x[i] # assign those which weren't fixed for i in fargs_i: fargs[i] = opt_x[i] #raise ValueError opt_x = N.hstack((fargs[:-2], (loc, scale))) return opt_x def __setattr__(self, a, v): if not a in ['_dist', '_fargs', 'fit', 'nnlf']: self._dist.__setattr__(a, v) else: object.__setattr__(self, a, v) def __getattribute__(self, a): """We need to redirect all queries correspondingly """ if not a in ['_dist', '_fargs', 'fit', 'nnlf']: return getattr(self._dist, a) else: return object.__getattribute__(self, a) def matchDistribution(data, nsamples=None, loc=None, scale=None, args=None, test='kstest', distributions=None, **kwargs): """Determine best matching distribution. Can be used for 'smelling' the data, as well to choose a parametric distribution for data obtained from non-parametric testing (e.g. `MCNullDist`). WiP: use with caution, API might change :Parameters: data : N.ndarray Array of the data for which to deduce the distribution. It has to be sufficiently large to make a reliable conclusion nsamples : int or None If None -- use all samples in data to estimate parametric distribution. Otherwise use only specified number randomly selected from data. loc : float or None Loc for the distribution (if known) scale : float or None Scale for the distribution (if known) test : basestring What kind of testing to do. Choices: 'p-roc' : detection power for a given ROC. Needs two parameters: `p=0.05` and `tail='both'` 'kstest' : 'full-body' distribution comparison. The best choice is made by minimal reported distance after estimating parameters of the distribution. Parameter `p=0.05` sets threshold to reject null-hypothesis that distribution is the same. WARNING: older versions (e.g. 0.5.2 in etch) of scipy have incorrect kstest implementation and do not function properly distributions : None or list of basestring or tuple(basestring, dict) Distributions to check. If None, all known in scipy.stats are tested. If distribution is specified as a tuple, then it must contain name and additional parameters (name, loc, scale, args) in the dictionary. Entry 'scipy' adds all known in scipy.stats. **kwargs Additional arguments which are needed for each particular test (see above) :Example: data = N.random.normal(size=(1000,1)); matches = matchDistribution( data, distributions=['rdist', ('rdist', {'name':'rdist_fixed', 'loc': 0.0, 'args': (10,)})], nsamples=30, test='p-roc', p=0.05) """ # Handle parameters _KNOWN_TESTS = ['p-roc', 'kstest'] if not test in _KNOWN_TESTS: raise ValueError, 'Unknown kind of test %s. Known are %s' \ % (test, _KNOWN_TESTS) data = N.ravel(data) # data sampled if nsamples is not None: if __debug__: debug('STAT', 'Sampling %d samples from data for the ' \ 'estimation of the distributions parameters' % nsamples) indexes_selected = (N.random.sample(nsamples)*len(data)).astype(int) data_selected = data[indexes_selected] else: indexes_selected = N.arange(len(data)) data_selected = data p_thr = kwargs.get('p', 0.05) if test == 'p-roc': tail = kwargs.get('tail', 'both') data_p = _pvalue(data, Nonparametric(data).cdf, tail) data_p_thr = N.abs(data_p) <= p_thr true_positives = N.sum(data_p_thr) if true_positives == 0: raise ValueError, "Provided data has no elements in non-" \ "parametric distribution under p<=%.3f. Please " \ "increase the size of data or value of p" % p if __debug__: debug('STAT_', 'Number of positives in non-parametric ' 'distribution is %d' % true_positives) if distributions is None: distributions = ['scipy'] # lets see if 'scipy' entry was in there try: scipy_ind = distributions.index('scipy') distributions.pop(scipy_ind) sp_dists = scipy.stats.distributions.__all__ sp_version = externals.versions['scipy'] if sp_version >= '0.9.0': for d_ in ['ncf']: if d_ in sp_dists: warning("Not considering %s distribution because of " "known issues in scipy %s" % (d_, sp_version)) _ = sp_dists.pop(sp_dists.index(d_)) distributions += sp_dists except ValueError: pass results = [] for d in distributions: dist_gen, loc_, scale_, args_ = None, loc, scale, args if isinstance(d, basestring): dist_gen = d dist_name = d elif isinstance(d, tuple): if not (len(d)==2 and isinstance(d[1], dict)): raise ValueError,\ "Tuple specification of distribution must be " \ "(d, {params}). Got %s" % (d,) dist_gen = d[0] loc_ = d[1].get('loc', loc) scale_ = d[1].get('scale', scale) args_ = d[1].get('args', args) dist_name = d[1].get('name', str(dist_gen)) else: dist_gen = d dist_name = str(d) # perform actions which might puke for some distributions try: dist_gen_ = getattr(scipy.stats, dist_gen) # specify distribution 'optimizer'. If loc or scale was provided, # use home-brewed rv_semifrozen if args_ is not None or loc_ is not None or scale_ is not None: dist_opt = rv_semifrozen(dist_gen_, loc=loc_, scale=scale_, args=args_) else: dist_opt = dist_gen_ dist_params = dist_opt.fit(data_selected) if __debug__: debug('STAT__', 'Got distribution parameters %s for %s' % (dist_params, dist_name)) if test == 'p-roc': cdf_func = lambda x: dist_gen_.cdf(x, *dist_params) # We need to compare detection under given p cdf_p = N.abs(_pvalue(data, cdf_func, tail, name=dist_gen)) cdf_p_thr = cdf_p <= p_thr D, p = N.sum(N.abs(data_p_thr - cdf_p_thr))*1.0/true_positives, 1 if __debug__: res_sum = 'D=%.2f' % D elif test == 'kstest': D, p = kstest(data, d, args=dist_params) if __debug__: res_sum = 'D=%.3f p=%.3f' % (D, p) except (TypeError, ValueError, AttributeError, NotImplementedError), e:#Exception, e: if __debug__: debug('STAT__', 'Testing for %s distribution failed due to %s' % (d, str(e))) continue if p > p_thr and not N.isnan(D): results += [ (D, dist_gen, dist_name, dist_params) ] if __debug__: debug('STAT_', 'Testing for %s dist.: %s' % (dist_name, res_sum)) else: if __debug__: debug('STAT__', 'Cannot consider %s dist. with %s' % (d, res_sum)) continue # sort in ascending order, so smaller is better results.sort() if __debug__ and 'STAT' in debug.active: # find the best and report it nresults = len(results) sresult = lambda r:'%s(%s)=%.2f' % (r[1], ', '.join(map(str, r[3])), r[0]) if nresults>0: nnextbest = min(2, nresults-1) snextbest = ', '.join(map(sresult, results[1:1+nnextbest])) debug('STAT', 'Best distribution %s. Next best: %s' % (sresult(results[0]), snextbest)) else: debug('STAT', 'Could not find suitable distribution') # return all the results return results if externals.exists('pylab'): import pylab as P def plotDistributionMatches(data, matches, nbins=31, nbest=5, expand_tails=8, legend=2, plot_cdf=True, p=None, tail='both'): """Plot best matching distributions :Parameters: data : N.ndarray Data which was used to obtain the matches matches : list of tuples Sorted matches as provided by matchDistribution nbins : int Number of bins in the histogram nbest : int Number of top matches to plot expand_tails : int How many bins away to add to parametrized distributions plots legend : int Either to provide legend and statistics in the legend. 1 -- just lists distributions. 2 -- adds distance measure 3 -- tp/fp/fn in the case if p is provided plot_cdf : bool Either to plot cdf for data using non-parametric distribution p : float or None If not None, visualize null-hypothesis testing (given p). Bars in the histogram which fall under given p are colored in red. False positives and false negatives are marked as triangle up and down symbols correspondingly tail : ('left', 'right', 'any', 'both') If p is not None, the choise of tail for null-hypothesis testing :Returns: tuple(histogram, list of lines) """ hist = P.hist(data, nbins, normed=1, align='center') data_range = [N.min(data), N.max(data)] # x's x = hist[1] dx = x[expand_tails] - x[0] # how much to expand tails by x = N.hstack((x[:expand_tails] - dx, x, x[-expand_tails:] + dx)) nonparam = Nonparametric(data) # plot cdf if plot_cdf: P.plot(x, nonparam.cdf(x), 'k--', linewidth=1) p_thr = p data_p = _pvalue(data, nonparam.cdf, tail) data_p_thr = (data_p <= p_thr).ravel() x_p = _pvalue(x, Nonparametric(data).cdf, tail) x_p_thr = N.abs(x_p) <= p_thr # color bars which pass thresholding in red for thr, bar in zip(x_p_thr[expand_tails:], hist[2]): bar.set_facecolor(('w','r')[int(thr)]) if not len(matches): # no matches were provided warning("No matching distributions were provided -- nothing to plot") return (hist, ) lines = [] labels = [] for i in xrange(min(nbest, len(matches))): D, dist_gen, dist_name, params = matches[i] dist = getattr(scipy.stats, dist_gen)(*params) label = '%s' % (dist_name) if legend > 1: label += '(D=%.2f)' % (D) xcdf_p = N.abs(_pvalue(x, dist.cdf, tail)) xcdf_p_thr = (xcdf_p <= p_thr).ravel() if p is not None and legend > 2: # We need to compare detection under given p data_cdf_p = N.abs(_pvalue(data, dist.cdf, tail)) data_cdf_p_thr = (data_cdf_p <= p_thr).ravel() # true positives tp = N.logical_and(data_cdf_p_thr, data_p_thr) # false positives fp = N.logical_and(data_cdf_p_thr, ~data_p_thr) # false negatives fn = N.logical_and(~data_cdf_p_thr, data_p_thr) label += ' tp/fp/fn=%d/%d/%d)' % \ tuple(map(N.sum, [tp,fp,fn])) pdf = dist.pdf(x) line = P.plot(x, pdf, '-', linewidth=2, label=label) color = line[0].get_color() if plot_cdf: cdf = dist.cdf(x) P.plot(x, cdf, ':', linewidth=1, color=color, label=label) # TODO: decide on tp/fp/fn by not centers of the bins but # by the values in data in the ranges covered by # those bins. Then it would correspond to the values # mentioned in the legend if p is not None: # true positives xtp = N.logical_and(xcdf_p_thr, x_p_thr) # false positives xfp = N.logical_and(xcdf_p_thr, ~x_p_thr) # false negatives xfn = N.logical_and(~xcdf_p_thr, x_p_thr) # no need to plot tp explicitely -- marked by color of the bar # P.plot(x[xtp], pdf[xtp], 'o', color=color) P.plot(x[xfp], pdf[xfp], '^', color=color) P.plot(x[xfn], pdf[xfn], 'v', color=color) lines.append(line) labels.append(label) if legend: P.legend(lines, labels) return (hist, lines) #if True: # data = N.random.normal(size=(1000,1)); # matches = matchDistribution( # data, # distributions=['scipy', # ('norm', {'name':'norm_known', # 'scale': 1.0, # 'loc': 0.0})], # nsamples=30, test='p-roc', p=0.05) # P.figure(); plotDistributionMatches(data, matches, nbins=101, # p=0.05, legend=4, nbest=5) def autoNullDist(dist): """Cheater for human beings -- wraps `dist` if needed with some NullDist tail and other arguments are assumed to be default as in NullDist/MCNullDist """ if dist is None or isinstance(dist, NullDist): return dist elif hasattr(dist, 'fit'): if __debug__: debug('STAT', 'Wrapping %s into MCNullDist' % dist) return MCNullDist(dist) else: if __debug__: debug('STAT', 'Wrapping %s into FixedNullDist' % dist) return FixedNullDist(dist) # if no scipy, we need nanmean def _chk_asarray(a, axis): if axis is None: a = N.ravel(a) outaxis = 0 else: a = N.asarray(a) outaxis = axis return a, outaxis def nanmean(x, axis=0): """Compute the mean over the given axis ignoring nans. :Parameters: x : ndarray input array axis : int axis along which the mean is computed. :Results: m : float the mean.""" x, axis = _chk_asarray(x,axis) x = x.copy() Norig = x.shape[axis] factor = 1.0-N.sum(N.isnan(x),axis)*1.0/Norig x[N.isnan(x)] = 0 return N.mean(x,axis)/factor pymvpa-0.4.8/mvpa/clfs/svm.py000066400000000000000000000100171174541445200161160ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Importer for the available SVM and SVR machines. Multiple external libraries implementing Support Vector Machines (Classification) and Regressions are available: LIBSVM, and shogun. This module is just a helper to provide default implementation for SVM depending on the availability of external libraries. By default LIBSVM implementation is choosen by default, but in any case both libraries are available through importing from this module: > from mvpa.clfs.svm import sg, libsvm > help(sg.SVM) > help(libsvm.SVM) Please refer to particular interface for more documentation about parametrization and available kernels and implementations. """ __docformat__ = 'restructuredtext' # take care of conditional import of external classifiers from mvpa.base import warning, cfg, externals from _svmbase import _SVM if __debug__: from mvpa.base import debug # default SVM implementation SVM = None _NuSVM = None # TODO: handle choices within cfg _VALID_BACKENDS = ('libsvm', 'shogun', 'sg') default_backend = cfg.get('svm', 'backend', default='libsvm').lower() if default_backend == 'shogun': default_backend = 'sg' if not default_backend in _VALID_BACKENDS: raise ValueError, 'Configuration option svm.backend got invalid value %s.' \ ' Valid choices are %s' % (default_backend, _VALID_BACKENDS) if __debug__: debug('SVM', 'Default SVM backend is %s' % default_backend) if externals.exists('shogun'): from mvpa.clfs import sg SVM = sg.SVM #if not 'LinearCSVMC' in locals(): # from mvpa.clfs.sg.svm import * if externals.exists('libsvm'): # By default for now we want simply to import all SVMs from libsvm from mvpa.clfs import libsvmc as libsvm _NuSVM = libsvm.SVM if default_backend == 'libsvm' or SVM is None: if __debug__ and default_backend != 'libsvm' and SVM is None: debug('SVM', 'Default SVM backend %s was not found, so using libsvm' % default_backend) SVM = libsvm.SVM #from mvpa.clfs.libsvm.svm import * if SVM is None: warning("None of SVM implementions libraries was found") else: _defaultC = _SVM._SVM_PARAMS['C'].default _defaultNu = _SVM._SVM_PARAMS['nu'].default # Define some convinience classes class LinearCSVMC(SVM): """C-SVM classifier using linear kernel. See help for %s for more details """ % SVM.__class__.__name__ def __init__(self, C=_defaultC, **kwargs): """ """ # init base class SVM.__init__(self, C=C, kernel_type='linear', **kwargs) class RbfCSVMC(SVM): """C-SVM classifier using a radial basis function kernel. See help for %s for more details """ % SVM.__class__.__name__ def __init__(self, C=_defaultC, **kwargs): """ """ # init base class SVM.__init__(self, C=C, kernel_type='RBF', **kwargs) if _NuSVM is not None: class LinearNuSVMC(_NuSVM): """Nu-SVM classifier using linear kernel. See help for %s for more details """ % _NuSVM.__class__.__name__ def __init__(self, nu=_defaultNu, **kwargs): """ """ # init base class _NuSVM.__init__(self, nu=nu, kernel_type='linear', **kwargs) class RbfNuSVMC(SVM): """Nu-SVM classifier using a radial basis function kernel. See help for %s for more details """ % SVM.__class__.__name__ def __init__(self, nu=_defaultNu, **kwargs): # init base class SVM.__init__(self, nu=nu, kernel_type='RBF', **kwargs) pymvpa-0.4.8/mvpa/clfs/transerror.py000066400000000000000000001532741174541445200175270ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Utility class to compute the transfer error of classifiers.""" __docformat__ = 'restructuredtext' import mvpa.support.copy as copy import numpy as N from StringIO import StringIO from math import log10, ceil from mvpa.base import externals from mvpa.misc.errorfx import meanPowerFx, rootMeanPowerFx, RMSErrorFx, \ CorrErrorFx, CorrErrorPFx, RelativeRMSErrorFx, MeanMismatchErrorFx, \ AUCErrorFx from mvpa.base import warning from mvpa.misc.state import StateVariable, ClassWithCollections from mvpa.base.dochelpers import enhancedDocString, table2string from mvpa.clfs.stats import autoNullDist if __debug__: from mvpa.base import debug if externals.exists('scipy'): from scipy.stats.stats import nanmean else: from mvpa.clfs.stats import nanmean def _p2(x, prec=2): """Helper to print depending on the type nicely. For some reason %.2g for 100 prints exponential form which is ugly """ if isinstance(x, int): return "%d" % x elif isinstance(x, float): s = ("%%.%df" % prec % x).rstrip('0').rstrip('.').lstrip() if s == '': s = '0' return s else: return "%s" % x class SummaryStatistics(object): """Basic class to collect targets/predictions and report summary statistics It takes care about collecting the sets, which are just tuples (targets, predictions, values). While 'computing' the matrix, all sets are considered together. Children of the class are responsible for computation and display. """ _STATS_DESCRIPTION = ( ('# of sets', 'number of target/prediction sets which were provided', None), ) def __init__(self, targets=None, predictions=None, values=None, sets=None): """Initialize SummaryStatistics targets or predictions cannot be provided alone (ie targets without predictions) :Parameters: targets Optional set of targets predictions Optional set of predictions values Optional set of values (which served for prediction) sets Optional list of sets """ self._computed = False """Flag either it was computed for a given set of data""" self.__sets = (sets, [])[int(sets is None)] """Datasets (target, prediction) to compute confusion matrix on""" self._stats = {} """Dictionary to keep statistics. Initialized here to please pylint""" if not targets is None or not predictions is None: if not targets is None and not predictions is None: self.add(targets=targets, predictions=predictions, values=values) else: raise ValueError, \ "Please provide none or both targets and predictions" def add(self, targets, predictions, values=None): """Add new results to the set of known results""" if len(targets) != len(predictions): raise ValueError, \ "Targets[%d] and predictions[%d]" % (len(targets), len(predictions)) + \ " have different number of samples" if values is not None and len(targets) != len(values): raise ValueError, \ "Targets[%d] and values[%d]" % (len(targets), len(values)) + \ " have different number of samples" # enforce labels in predictions to be of the same datatype as in # targets, since otherwise we are getting doubles for unknown at a # given moment labels nonetype = type(None) for i in xrange(len(targets)): t1, t2 = type(targets[i]), type(predictions[i]) # if there were no prediction made - leave None, otherwise # convert to appropriate type if t1 != t2 and t2 != nonetype: #warning("Obtained target %s and prediction %s are of " % # (t1, t2) + "different datatypes.") if isinstance(predictions, tuple): predictions = list(predictions) predictions[i] = t1(predictions[i]) if values is not None: # assure that we have a copy, or otherwise further in-place # modifications might screw things up (some classifiers share # values and spit out results) values = copy.deepcopy(values) self.__sets.append( (targets, predictions, values) ) self._computed = False def asstring(self, short=False, header=True, summary=True, description=False): """'Pretty print' the matrix :Parameters: short : bool if True, ignores the rest of the parameters and provides consise 1 line summary header : bool print header of the table summary : bool print summary (accuracy) description : bool print verbose description of presented statistics """ raise NotImplementedError def __str__(self): """String summary over the `SummaryStatistics` It would print description of the summary statistics if 'CM' debug target is active """ if __debug__: description = ('CM' in debug.active) else: description = False return self.asstring(short=False, header=True, summary=True, description=description) def __iadd__(self, other): """Add the sets from `other` s `SummaryStatistics` to current one """ #print "adding ", other, " to ", self # need to do shallow copy, or otherwise smth like "cm += cm" # would loop forever and exhaust memory eventually othersets = copy.copy(other.__sets) for set in othersets: self.add(*set)#[0], set[1]) return self def __add__(self, other): """Add two `SummaryStatistics`s """ result = copy.copy(self) result += other return result def compute(self): """Actually compute the confusion matrix based on all the sets""" if self._computed: return self._compute() self._computed = True def _compute(self): """Compute basic statistics """ self._stats = {'# of sets' : len(self.sets)} @property def summaries(self): """Return a list of separate summaries per each stored set""" return [ self.__class__(sets=[x]) for x in self.sets ] @property def error(self): raise NotImplementedError @property def stats(self): self.compute() return self._stats def reset(self): """Cleans summary -- all data/sets are wiped out """ self.__sets = [] self._computed = False sets = property(lambda self:self.__sets) class ROCCurve(object): """Generic class for ROC curve computation and plotting """ def __init__(self, labels, sets=None): """ :Parameters: labels : list labels which were used (in order of values if multiclass, or 1 per class for binary problems (e.g. in SMLR)) sets : list of tuples list of sets for the analysis """ self._labels = labels self._sets = sets self.__computed = False def _compute(self): """Lazy computation if needed """ if self.__computed: return # local bindings labels = self._labels Nlabels = len(labels) sets = self._sets # Handle degenerate cases politely if Nlabels < 2: warning("ROC was asked to be evaluated on data with %i" " labels which is a degenerate case.") self._ROCs = [] self._aucs = [] return # take sets which have values in the shape we can handle def _checkValues(set_): """Check if values are 'acceptable'""" if len(set_)<3: return False x = set_[2] # TODO: OPT: need optimization if (x is None) or len(x) == 0: return False # undefined for v in x: try: if Nlabels <= 2 and N.isscalar(v): continue if (isinstance(v, dict) or # not dict for pairs ((Nlabels>=2) and len(v)!=Nlabels) # 1 per each label for multiclass ): return False except Exception, e: # Something else which is not supported, like # in shogun interface we don't yet extract values per each label or # in pairs in the case of built-in multiclass if __debug__: debug('ROC', "Exception %s while checking " "either %s are valid labels" % (str(e), x)) return False return True sets_wv = filter(_checkValues, sets) # check if all had values, if not -- complain Nsets_wv = len(sets_wv) if Nsets_wv > 0 and len(sets) != Nsets_wv: warning("Only %d sets have values assigned from %d sets. " "ROC estimates might be incorrect." % (Nsets_wv, len(sets))) # bring all values to the same 'shape': # 1 value per each label. In binary classifier, if only a single # value is provided, add '0' for 0th label 'value'... it should # work taking drunk Yarik logic ;-) # yoh: apparently it caused problems whenever we had just a single # unique label in the sets. Introduced handling for # NLabels == 1 for iset,s in enumerate(sets_wv): # we will do inplace modification, thus go by index values = s[2] # we would need it to be a list to reassign element with a list if isinstance(values, N.ndarray) and len(values.shape)==1: # XXX ??? so we are going away from inplace modifications? values = list(values) rangev = None for i in xrange(len(values)): v = values[i] if N.isscalar(v): if Nlabels == 1: # ensure the right dimensionality values[i] = N.array(v, ndmin=2) elif Nlabels == 2: def last_el(x): """Helper function. Returns x if x is scalar, and last element if x is not (ie list/tuple)""" if N.isscalar(x): return x else: return x[-1] if rangev is None: # we need to figure out min/max values # to invert for the 0th label values_ = [last_el(x) for x in values] rangev = N.min(values_) + N.max(values_) values[i] = [rangev - v, v] else: raise ValueError, \ "Cannot have a single 'value' for multiclass" \ " classification. Got %s" % (v) elif len(v) != Nlabels: raise ValueError, \ "Got %d values whenever there is %d labels" % \ (len(v), Nlabels) # reassign possibly adjusted values sets_wv[iset] = (s[0], s[1], N.asarray(values)) # we need to estimate ROC per each label # XXX order of labels might not correspond to the one among 'values' # which were used to make a decision... check ROCs, aucs = [], [] # 1 per label for i,label in enumerate(labels): aucs_pl = [] ROCs_pl = [] for s in sets_wv: targets_pl = (N.asanyarray(s[0]) == label).astype(int) # XXX we might unify naming between AUC/ROC ROC = AUCErrorFx() aucs_pl += [ROC([N.asanyarray(x)[i] for x in s[2]], targets_pl)] ROCs_pl.append(ROC) if len(aucs_pl)>0: ROCs += [ROCs_pl] aucs += [nanmean(aucs_pl)] #aucs += [N.mean(aucs_pl)] # store results within the object self._ROCs = ROCs self._aucs = aucs self.__computed = True @property def aucs(self): """Compute and return set of AUC values 1 per label """ self._compute() return self._aucs @property def ROCs(self): self._compute() return self._ROCs def plot(self, label_index=0): """ TODO: make it friendly to labels given by values? should we also treat labels_map? """ externals.exists("pylab", raiseException=True) import pylab as P self._compute() labels = self._labels # select only ROCs for the given label ROCs = self.ROCs[label_index] fig = P.gcf() ax = P.gca() P.plot([0, 1], [0, 1], 'k:') for ROC in ROCs: P.plot(ROC.fp, ROC.tp, linewidth=1) P.axis((0.0, 1.0, 0.0, 1.0)) P.axis('scaled') P.title('Label %s. Mean AUC=%.2f' % (label_index, self.aucs[label_index])) P.xlabel('False positive rate') P.ylabel('True positive rate') class ConfusionMatrix(SummaryStatistics): """Class to contain information and display confusion matrix. Implementation of the `SummaryStatistics` in the case of classification problem. Actual computation of confusion matrix is delayed until all data is acquired (to figure out complete set of labels). If testing data doesn't have a complete set of labels, but you like to include all labels, provide them as a parameter to the constructor. Confusion matrix provides a set of performance statistics (use asstring(description=True) for the description of abbreviations), as well ROC curve (http://en.wikipedia.org/wiki/ROC_curve) plotting and analysis (AUC) in the limited set of problems: binary, multiclass 1-vs-all. """ _STATS_DESCRIPTION = ( ('TP', 'true positive (AKA hit)', None), ('TN', 'true negative (AKA correct rejection)', None), ('FP', 'false positive (AKA false alarm, Type I error)', None), ('FN', 'false negative (AKA miss, Type II error)', None), ('TPR', 'true positive rate (AKA hit rate, recall, sensitivity)', 'TPR = TP / P = TP / (TP + FN)'), ('FPR', 'false positive rate (AKA false alarm rate, fall-out)', 'FPR = FP / N = FP / (FP + TN)'), ('ACC', 'accuracy', 'ACC = (TP + TN) / (P + N)'), ('SPC', 'specificity', 'SPC = TN / (FP + TN) = 1 - FPR'), ('PPV', 'positive predictive value (AKA precision)', 'PPV = TP / (TP + FP)'), ('NPV', 'negative predictive value', 'NPV = TN / (TN + FN)'), ('FDR', 'false discovery rate', 'FDR = FP / (FP + TP)'), ('MCC', "Matthews Correlation Coefficient", "MCC = (TP*TN - FP*FN)/sqrt(P N P' N')"), ('AUC', "Area under (AUC) curve", None), ) + SummaryStatistics._STATS_DESCRIPTION def __init__(self, labels=None, labels_map=None, **kwargs): """Initialize ConfusionMatrix with optional list of `labels` :Parameters: labels : list Optional set of labels to include in the matrix labels_map : None or dict Dictionary from original dataset to show mapping into numerical labels targets Optional set of targets predictions Optional set of predictions """ SummaryStatistics.__init__(self, **kwargs) if labels == None: labels = [] self.__labels = labels """List of known labels""" self.__labels_map = labels_map """Mapping from original into given labels""" self.__matrix = None """Resultant confusion matrix""" # XXX might want to remove since summaries does the same, just without # supplying labels @property def matrices(self): """Return a list of separate confusion matrix per each stored set""" return [ self.__class__(labels=self.labels, labels_map=self.labels_map, sets=[x]) for x in self.sets] def _compute(self): """Actually compute the confusion matrix based on all the sets""" super(ConfusionMatrix, self)._compute() if __debug__: if not self.__matrix is None: debug("LAZY", "Have to recompute %s#%s" \ % (self.__class__.__name__, id(self))) # TODO: BinaryClassifier might spit out a list of predictions for each # value need to handle it... for now just keep original labels try: # figure out what labels we have labels = \ list(reduce(lambda x, y: x.union(set(y[0]).union(set(y[1]))), self.sets, set(self.__labels))) except: labels = self.__labels # Check labels_map if it was provided if it covers all the labels labels_map = self.__labels_map if labels_map is not None: labels_set = set(labels) map_labels_set = set(labels_map.values()) if not map_labels_set.issuperset(labels_set): warning("Provided labels_map %s is not coherent with labels " "provided to ConfusionMatrix. No reverse mapping " "will be provided" % labels_map) labels_map = None # Create reverse map labels_map_rev = None if labels_map is not None: labels_map_rev = {} for k,v in labels_map.iteritems(): v_mapping = labels_map_rev.get(v, []) v_mapping.append(k) labels_map_rev[v] = v_mapping self.__labels_map_rev = labels_map_rev labels.sort() self.__labels = labels # store the recomputed labels Nlabels, Nsets = len(labels), len(self.sets) if __debug__: debug("CM", "Got labels %s" % labels) # Create a matrix for all votes mat_all = N.zeros( (Nsets, Nlabels, Nlabels), dtype=int ) # create total number of samples of each label counts # just for convinience I guess since it can always be # computed from mat_all counts_all = N.zeros( (Nsets, Nlabels) ) # reverse mapping from label into index in the list of labels rev_map = dict([ (x[1], x[0]) for x in enumerate(labels)]) for iset, set_ in enumerate(self.sets): for t,p in zip(*set_[:2]): mat_all[iset, rev_map[p], rev_map[t]] += 1 # for now simply compute a sum of votes across different sets # we might do something more sophisticated later on, and this setup # should easily allow it self.__matrix = N.sum(mat_all, axis=0) self.__Nsamples = N.sum(self.__matrix, axis=0) self.__Ncorrect = sum(N.diag(self.__matrix)) TP = N.diag(self.__matrix) offdiag = self.__matrix - N.diag(TP) stats = { '# of labels' : Nlabels, 'TP' : TP, 'FP' : N.sum(offdiag, axis=1), 'FN' : N.sum(offdiag, axis=0)} stats['CORR'] = N.sum(TP) stats['TN'] = stats['CORR'] - stats['TP'] stats['P'] = stats['TP'] + stats['FN'] stats['N'] = N.sum(stats['P']) - stats['P'] stats["P'"] = stats['TP'] + stats['FP'] stats["N'"] = stats['TN'] + stats['FN'] stats['TPR'] = stats['TP'] / (1.0*stats['P']) # reset nans in TPRs to 0s whenever there is no entries # for those labels among the targets stats['TPR'][stats['P'] == 0] = 0 stats['PPV'] = stats['TP'] / (1.0*stats["P'"]) stats['NPV'] = stats['TN'] / (1.0*stats["N'"]) stats['FDR'] = stats['FP'] / (1.0*stats["P'"]) stats['SPC'] = (stats['TN']) / (1.0*stats['FP'] + stats['TN']) MCC_denom = N.sqrt(1.0*stats['P']*stats['N']*stats["P'"]*stats["N'"]) nz = MCC_denom!=0.0 stats['MCC'] = N.zeros(stats['TP'].shape) stats['MCC'][nz] = \ (stats['TP'] * stats['TN'] - stats['FP'] * stats['FN'])[nz] \ / MCC_denom[nz] stats['ACC'] = N.sum(TP)/(1.0*N.sum(stats['P'])) stats['ACC%'] = stats['ACC'] * 100.0 # # ROC computation if available ROC = ROCCurve(labels=labels, sets=self.sets) aucs = ROC.aucs if len(aucs)>0: stats['AUC'] = aucs if len(aucs) != Nlabels: raise RuntimeError, \ "We must got a AUC per label. Got %d instead of %d" % \ (len(aucs), Nlabels) self.ROC = ROC else: # we don't want to provide ROC if it is bogus stats['AUC'] = [N.nan] * Nlabels self.ROC = None # compute mean stats for k,v in stats.items(): stats['mean(%s)' % k] = N.mean(v) self._stats.update(stats) def asstring(self, short=False, header=True, summary=True, description=False): """'Pretty print' the matrix :Parameters: short : bool if True, ignores the rest of the parameters and provides consise 1 line summary header : bool print header of the table summary : bool print summary (accuracy) description : bool print verbose description of presented statistics """ if len(self.sets) == 0: return "Empty" self.compute() # some shortcuts labels = self.__labels labels_map_rev = self.__labels_map_rev matrix = self.__matrix labels_rev = [] if labels_map_rev is not None: labels_rev = [','.join([str(x) for x in labels_map_rev[l]]) for l in labels] out = StringIO() # numbers of different entries Nlabels = len(labels) Nsamples = self.__Nsamples.astype(int) stats = self._stats if short: return "%(# of sets)d sets %(# of labels)d labels " \ " ACC:%(ACC).2f" \ % stats Ndigitsmax = int(ceil(log10(max(Nsamples)))) Nlabelsmax = max( [len(str(x)) for x in labels] ) # length of a single label/value L = max(Ndigitsmax+2, Nlabelsmax) #, len("100.00%")) res = "" stats_perpredict = ["P'", "N'", 'FP', 'FN', 'PPV', 'NPV', 'TPR', 'SPC', 'FDR', 'MCC'] # print AUC only if ROC was computed if self.ROC is not None: stats_perpredict += [ 'AUC' ] stats_pertarget = ['P', 'N', 'TP', 'TN'] stats_summary = ['ACC', 'ACC%', '# of sets'] #prefixlen = Nlabelsmax + 2 + Ndigitsmax + 1 prefixlen = Nlabelsmax + 1 pref = ' '*(prefixlen) # empty prefix if matrix.shape != (Nlabels, Nlabels): raise ValueError, \ "Number of labels %d doesn't correspond the size" + \ " of a confusion matrix %s" % (Nlabels, matrix.shape) # list of lists of what is printed printed = [] underscores = [" %s" % ("-" * L)] * Nlabels if header: # labels printed.append(['@l----------. '] + labels_rev) printed.append(['@lpredictions\\targets'] + labels) # underscores printed.append(['@l `------'] \ + underscores + stats_perpredict) # matrix itself for i, line in enumerate(matrix): l = labels[i] if labels_rev != []: l = '@r%10s / %s' % (labels_rev[i], l) printed.append( [l] + [ str(x) for x in line ] + [ _p2(stats[x][i]) for x in stats_perpredict]) if summary: ## Various alternative schemes ;-) # printed.append([''] + underscores) # printed.append(['@lPer target \ Means:'] + underscores + \ # [_p2(x) for x in mean_stats]) # printed.append(['Means:'] + [''] * len(labels) # + [_p2(x) for x in mean_stats]) printed.append(['@lPer target:'] + underscores) for stat in stats_pertarget: printed.append([stat] + [ _p2(stats[stat][i]) for i in xrange(Nlabels)]) # compute mean stats # XXX refactor to expose them in stats as well, as # mean(FCC) mean_stats = N.mean(N.array([stats[k] for k in stats_perpredict]), axis=1) printed.append(['@lSummary \ Means:'] + underscores + [_p2(stats['mean(%s)' % x]) for x in stats_perpredict]) for stat in stats_summary: printed.append([stat] + [_p2(stats[stat])]) table2string(printed, out) if description: out.write("\nStatistics computed in 1-vs-rest fashion per each " \ "target.\n") out.write("Abbreviations (for details see " \ "http://en.wikipedia.org/wiki/ROC_curve):\n") for d, val, eq in self._STATS_DESCRIPTION: out.write(" %-3s: %s\n" % (d, val)) if eq is not None: out.write(" " + eq + "\n") #out.write("%s" % printed) result = out.getvalue() out.close() return result def plot(self, labels=None, numbers=False, origin='upper', numbers_alpha=None, xlabels_vertical=True, numbers_kwargs={}, **kwargs): """Provide presentation of confusion matrix in image :Parameters: labels : list of int or basestring Optionally provided labels guarantee the order of presentation. Also value of None places empty column/row, thus provides visual groupping of labels (Thanks Ingo) numbers : bool Place values inside of confusion matrix elements numbers_alpha : None or float Controls textual output of numbers. If None -- all numbers are plotted in the same intensity. If some float -- it controls alpha level -- higher value would give higher contrast. (good value is 2) origin : basestring Which left corner diagonal should start xlabels_vertical : bool Either to plot xlabels vertical (benefitial if number of labels is large) numbers_kwargs : dict Additional keyword parameters to be added to numbers (if numbers is True) **kwargs Additional arguments given to imshow (\eg me cmap) :Returns: (fig, im, cb) -- figure, imshow, colorbar """ externals.exists("pylab", raiseException=True) import pylab as P self.compute() labels_order = labels # some shortcuts labels = self.__labels labels_map = self.__labels_map labels_map_rev = self.__labels_map_rev matrix = self.__matrix # craft original mapping from a label into index in the matrix labels_indexes = dict([(x,i) for i,x in enumerate(labels)]) labels_rev = [] if labels_map_rev is not None: labels_rev = [','.join([str(x) for x in labels_map_rev[l]]) for l in labels] labels_map_full = dict(zip(labels_rev, labels)) if labels_order is not None: labels_order_filtered = filter(lambda x:x is not None, labels_order) labels_order_filtered_set = set(labels_order_filtered) # Verify if all labels provided in labels if set(labels) == labels_order_filtered_set: # We were provided numerical (most probably) set labels_plot = labels_order elif len(labels_rev) \ and set(labels_rev) == labels_order_filtered_set: # not clear if right whenever there were multiple labels # mapped into the same labels_plot = [] for l in labels_order: v = None if l is not None: v = labels_map_full[l] labels_plot += [v] else: raise ValueError, \ "Provided labels %s do not match set of known " \ "original labels (%s) or mapped labels (%s)" % \ (labels_order, labels, labels_rev) else: labels_plot = labels # where we have Nones? isempty = N.array([l is None for l in labels_plot]) non_empty = N.where(N.logical_not(isempty))[0] # numbers of different entries NlabelsNN = len(non_empty) Nlabels = len(labels_plot) if matrix.shape != (NlabelsNN, NlabelsNN): raise ValueError, \ "Number of labels %d doesn't correspond the size" + \ " of a confusion matrix %s" % (NlabelsNN, matrix.shape) confusionmatrix = N.zeros((Nlabels, Nlabels)) mask = confusionmatrix.copy() ticks = [] tick_labels = [] # populate in a silly way reordered_indexes = [labels_indexes[i] for i in labels_plot if i is not None] for i, l in enumerate(labels_plot): if l is not None: j = labels_indexes[l] confusionmatrix[i, non_empty] = matrix[j, reordered_indexes] confusionmatrix[non_empty, i] = matrix[reordered_indexes, j] ticks += [i + 0.5] if labels_map_rev is not None: tick_labels += ['/'.join(labels_map_rev[l])] else: tick_labels += [str(l)] else: mask[i, :] = mask[:, i] = 1 confusionmatrix = N.ma.MaskedArray(confusionmatrix, mask=mask) # turn off automatic update if interactive if P.matplotlib.get_backend() == 'TkAgg': P.ioff() fig = P.gcf() ax = P.gca() ax.axis('off') # some customization depending on the origin xticks_position, yticks, ybottom = { 'upper': ('top', [Nlabels-x for x in ticks], 0.1), 'lower': ('bottom', ticks, 0.2) }[origin] # Plot axi = fig.add_axes([0.15, ybottom, 0.7, 0.7]) im = axi.imshow(confusionmatrix, interpolation="nearest", origin=origin, aspect='equal', extent=(0, Nlabels, 0, Nlabels), **kwargs) # plot numbers if numbers: numbers_kwargs_ = {'fontsize': 10, 'horizontalalignment': 'center', 'verticalalignment': 'center'} maxv = float(N.max(confusionmatrix)) colors = [im.to_rgba(0), im.to_rgba(maxv)] for i,j in zip(*N.logical_not(mask).nonzero()): v = confusionmatrix[j, i] # scale alpha non-linearly if numbers_alpha is None: alpha = 1.0 else: # scale according to value alpha = 1 - N.array(1 - v / maxv) ** numbers_alpha y = {'lower':j, 'upper':Nlabels-j-1}[origin] numbers_kwargs_['color'] = colors[int(v1: P.legend(lines[:2], ('Target', 'Prediction')) if plot_stats: P.title(self.asstring(short='very')) if splot: nplot += 1 sps.append(P.subplot(nplots, 1, nplot)) for s in self.sets: P.plot(s[0], s[1], 'o', markeredgewidth=0.2, markersize=2) P.gca().set_aspect('equal') if P.matplotlib.get_backend() == 'TkAgg': P.ion() P.draw() return fig, sps def asstring(self, short=False, header=True, summary=True, description=False): """'Pretty print' the statistics""" if len(self.sets) == 0: return "Empty" self.compute() stats = self.stats if short: if short == 'very': # " RMSE/RMP_t:%(RMSE/RMP_t).2f" \ return "%(# of sets)d sets CCe=%(CCe).2f p=%(CCp).2g" \ " RMSE:%(RMSE).2f" \ " Summary (stacked data): " \ "CCe=%(Summary CCe).2f p=%(Summary CCp).2g" \ % stats else: return "%(# of sets)d sets CCe=%(CCe).2f+-%(CCe_std).3f" \ " RMSE=%(RMSE).2f+-%(RMSE_std).3f" \ " RMSE/RMP_t=%(RMSE/RMP_t).2f+-%(RMSE/RMP_t_std).3f" \ % stats stats_data = ['RMP_t', 'STD_t', 'RMP_p', 'STD_p'] # CCp needs tune up of format so excluded stats_ = ['CCe', 'RMSE', 'RMSE/RMP_t'] stats_summary = ['# of sets'] out = StringIO() printed = [] if header: # labels printed.append(['Statistics', 'Mean', 'Std', 'Min', 'Max']) # underscores printed.append(['----------', '-----', '-----', '-----', '-----']) def print_stats(printed, stats_): # Statistics itself for stat in stats_: s = [stat] for suffix in ['', '_std', '_min', '_max']: s += [ _p2(stats[stat+suffix], 3) ] printed.append(s) printed.append(["Data: "]) print_stats(printed, stats_data) printed.append(["Results: "]) print_stats(printed, stats_) printed.append(["Summary: "]) printed.append(["CCe", _p2(stats['Summary CCe']), "", "p=", '%g' % stats['Summary CCp']]) printed.append(["RMSE", _p2(stats['Summary RMSE'])]) printed.append(["RMSE/RMP_t", _p2(stats['Summary RMSE/RMP_t'])]) if summary: for stat in stats_summary: printed.append([stat] + [_p2(stats[stat])]) table2string(printed, out) if description: out.write("\nDescription of printed statistics.\n" " Suffixes: _t - targets, _p - predictions\n") for d, val, eq in self._STATS_DESCRIPTION: out.write(" %-3s: %s\n" % (d, val)) if eq is not None: out.write(" " + eq + "\n") result = out.getvalue() out.close() return result @property def error(self): self.compute() return self.stats['RMSE'] class ClassifierError(ClassWithCollections): """Compute (or return) some error of a (trained) classifier on a dataset. """ confusion = StateVariable(enabled=False) """TODO Think that labels might be also symbolic thus can't directly be indicies of the array """ training_confusion = StateVariable(enabled=False, doc="Proxy training_confusion from underlying classifier.") def __init__(self, clf, labels=None, train=True, **kwargs): """Initialization. :Parameters: clf : Classifier Either trained or untrained classifier labels : list if provided, should be a set of labels to add on top of the ones present in testdata train : bool unless train=False, classifier gets trained if trainingdata provided to __call__ """ ClassWithCollections.__init__(self, **kwargs) self.__clf = clf self._labels = labels """Labels to add on top to existing in testing data""" self.__train = train """Either to train classifier if trainingdata is provided""" __doc__ = enhancedDocString('ClassifierError', locals(), ClassWithCollections) def __copy__(self): """TODO: think... may be we need to copy self.clf""" out = ClassifierError.__new__(TransferError) ClassifierError.__init__(out, self.clf) return out def _precall(self, testdataset, trainingdataset=None): """Generic part which trains the classifier if necessary """ if not trainingdataset is None: if self.__train: # XXX can be pretty annoying if triggered inside an algorithm # where it cannot be switched of, but retraining might be # intended or at least not avoidable. # Additonally isTrained docs say: # MUST BE USED WITH CARE IF EVER # # switching it off for now #if self.__clf.isTrained(trainingdataset): # warning('It seems that classifier %s was already trained' % # self.__clf + ' on dataset %s. Please inspect' \ # % trainingdataset) if self.states.isEnabled('training_confusion'): self.__clf.states._changeTemporarily( enable_states=['training_confusion']) self.__clf.train(trainingdataset) if self.states.isEnabled('training_confusion'): self.training_confusion = self.__clf.training_confusion self.__clf.states._resetEnabledTemporarily() if self.__clf.states.isEnabled('trained_labels') and \ not testdataset is None: newlabels = set(testdataset.uniquelabels) \ - set(self.__clf.trained_labels) if len(newlabels)>0: warning("Classifier %s wasn't trained to classify labels %s" % (`self.__clf`, `newlabels`) + " present in testing dataset. Make sure that you have" + " not mixed order/names of the arguments anywhere") ### Here checking for if it was trained... might be a cause of trouble # XXX disabled since it is unreliable.. just rely on explicit # self.__train # if not self.__clf.isTrained(trainingdataset): # self.__clf.train(trainingdataset) # elif __debug__: # debug('CERR', # 'Not training classifier %s since it was ' % `self.__clf` # + ' already trained on dataset %s' % `trainingdataset`) def _call(self, testdataset, trainingdataset=None): raise NotImplementedError def _postcall(self, testdataset, trainingdataset=None, error=None): pass def __call__(self, testdataset, trainingdataset=None): """Compute the transfer error for a certain test dataset. If `trainingdataset` is not `None` the classifier is trained using the provided dataset before computing the transfer error. Otherwise the classifier is used in it's current state to make the predictions on the test dataset. Returns a scalar value of the transfer error. """ self._precall(testdataset, trainingdataset) error = self._call(testdataset, trainingdataset) self._postcall(testdataset, trainingdataset, error) if __debug__: debug('CERR', 'Classifier error on %s: %.2f' % (testdataset, error)) return error def untrain(self): """Untrain the *Error which relies on the classifier """ self.clf.untrain() @property def clf(self): return self.__clf @property def labels(self): return self._labels class TransferError(ClassifierError): """Compute the transfer error of a (trained) classifier on a dataset. The actual error value is computed using a customizable error function. Optionally the classifier can be trained by passing an additional training dataset to the __call__() method. """ null_prob = StateVariable(enabled=True, doc="Stores the probability of an error result under " "the NULL hypothesis") samples_error = StateVariable(enabled=False, doc="Per sample errors computed by invoking the " "error function for each sample individually. " "Errors are available in a dictionary with each " "samples origid as key.") def __init__(self, clf, errorfx=MeanMismatchErrorFx(), labels=None, null_dist=None, **kwargs): """Initialization. :Parameters: clf : Classifier Either trained or untrained classifier errorfx Functor that computes a scalar error value from the vectors of desired and predicted values (e.g. subclass of `ErrorFunction`) labels : list if provided, should be a set of labels to add on top of the ones present in testdata null_dist : instance of distribution estimator """ ClassifierError.__init__(self, clf, labels, **kwargs) self.__errorfx = errorfx self.__null_dist = autoNullDist(null_dist) __doc__ = enhancedDocString('TransferError', locals(), ClassifierError) def __copy__(self): """Performs deepcopying of the classifier.""" # TODO -- use ClassifierError.__copy__ out = TransferError.__new__(TransferError) TransferError.__init__(out, self.clf.clone(), self.errorfx, self._labels) return out # XXX: TODO: unify naming? test/train or with ing both def _call(self, testdataset, trainingdataset=None): """Compute the transfer error for a certain test dataset. If `trainingdataset` is not `None` the classifier is trained using the provided dataset before computing the transfer error. Otherwise the classifier is used in it's current state to make the predictions on the test dataset. Returns a scalar value of the transfer error. """ # OPT: local binding clf = self.clf if testdataset is None: # We cannot do anythin, but we can try to figure out WTF and # warn the user accordingly in some usecases import traceback as tb filenames = [x[0] for x in tb.extract_stack(limit=100)] rfe_matches = [f for f in filenames if f.endswith('/rfe.py')] cv_matches = [f for f in filenames if f.endswith('cvtranserror.py')] msg = "" if len(rfe_matches) > 0 and len(cv_matches): msg = " It is possible that you used RFE with stopping " \ "criterion based on the TransferError and directly" \ " from CrossValidatedTransferError, such approach" \ " would require exposing testing dataset " \ " to the classifier which might heavily bias " \ " generalization performance estimate. If you are " \ " sure to use it that way, create CVTE with " \ " parameter expose_testdataset=True" raise ValueError, "Transfer error call obtained None " \ "as a dataset for testing.%s" % msg predictions = clf.predict(testdataset.samples) # compute confusion matrix # Should it migrate into ClassifierError.__postcall? # -> Probably not because other childs could estimate it # not from test/train datasets explicitely, see # `ConfusionBasedError`, where confusion is simply # bound to classifiers confusion matrix states = self.states if states.isEnabled('confusion'): confusion = clf._summaryClass( #labels=self.labels, targets=testdataset.labels, predictions=predictions, values=clf.states.get('values', None)) try: confusion.labels_map = testdataset.labels_map except: pass states.confusion = confusion if states.isEnabled('samples_error'): samples_error = [] for i, p in enumerate(predictions): samples_error.append(self.__errorfx([p], testdataset.labels[i:i+1])) states.samples_error = dict(zip(testdataset.origids, samples_error)) # compute error from desired and predicted values error = self.__errorfx(predictions, testdataset.labels) return error def _postcall(self, vdata, wdata=None, error=None): """ """ # estimate the NULL distribution when functor and training data is # given if not self.__null_dist is None and not wdata is None: # we need a matching transfer error instances (e.g. same error # function), but we have to disable the estimation of the null # distribution in that child to prevent infinite looping. null_terr = copy.copy(self) null_terr.__null_dist = None self.__null_dist.fit(null_terr, wdata, vdata) # get probability of error under NULL hypothesis if available if not error is None and not self.__null_dist is None: self.null_prob = self.__null_dist.p(error) @property def errorfx(self): return self.__errorfx @property def null_dist(self): return self.__null_dist class ConfusionBasedError(ClassifierError): """For a given classifier report an error based on internally computed error measure (given by some `ConfusionMatrix` stored in some state variable of `Classifier`). This way we can perform feature selection taking as the error criterion either learning error, or transfer to splits error in the case of SplitClassifier """ def __init__(self, clf, labels=None, confusion_state="training_confusion", **kwargs): """Initialization. :Parameters: clf : Classifier Either trained or untrained classifier confusion_state Id of the state variable which stores `ConfusionMatrix` labels : list if provided, should be a set of labels to add on top of the ones present in testdata """ ClassifierError.__init__(self, clf, labels, **kwargs) self.__confusion_state = confusion_state """What state to extract from""" if not clf.states.isKnown(confusion_state): raise ValueError, \ "State variable %s is not defined for classifier %s" % \ (confusion_state, `clf`) if not clf.states.isEnabled(confusion_state): if __debug__: debug('CERR', "Forcing state %s to be enabled for %s" % (confusion_state, `clf`)) clf.states.enable(confusion_state) __doc__ = enhancedDocString('ConfusionBasedError', locals(), ClassifierError) def _call(self, testdata, trainingdata=None): """Extract transfer error. Nor testdata, neither trainingdata is used """ confusion = self.clf.states[self.__confusion_state].value self.confusion = confusion return confusion.error pymvpa-0.4.8/mvpa/clfs/warehouse.py000066400000000000000000000463761174541445200173340ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Collection of classifiers to ease the exploration. """ __docformat__ = 'restructuredtext' import operator # Define sets of classifiers from mvpa.clfs.meta import FeatureSelectionClassifier, SplitClassifier, \ MulticlassClassifier from mvpa.clfs.smlr import SMLR from mvpa.clfs.knn import kNN from mvpa.clfs.gnb import GNB from mvpa.clfs.kernel import KernelLinear, KernelSquaredExponential # Helpers from mvpa.base import externals, cfg from mvpa.measures.anova import OneWayAnova from mvpa.misc.transformers import Absolute from mvpa.clfs.smlr import SMLRWeights from mvpa.featsel.helpers import FractionTailSelector, \ FixedNElementTailSelector, RangeElementSelector from mvpa.featsel.base import SensitivityBasedFeatureSelection _KNOWN_INTERNALS = [ 'knn', 'binary', 'svm', 'linear', 'smlr', 'does_feature_selection', 'has_sensitivity', 'multiclass', 'non-linear', 'kernel-based', 'lars', 'regression', 'libsvm', 'sg', 'meta', 'retrainable', 'gpr', 'notrain2predict', 'ridge', 'blr', 'gnpp', 'enet', 'glmnet', 'gnb', 'plr'] class Warehouse(object): """Class to keep known instantiated classifiers Should provide easy ways to select classifiers of needed kind: clfswh['linear', 'svm'] should return all linear SVMs clfswh['linear', 'multiclass'] should return all linear classifiers capable of doing multiclass classification """ def __init__(self, known_tags=None, matches=None): """Initialize warehouse :Parameters: known_tags : list of basestring List of known tags matches : dict Optional dictionary of additional matches. E.g. since any regression can be used as a binary classifier, matches={'binary':['regression']}, would allow to provide regressions also if 'binary' was requested """ self._known_tags = set(known_tags) self.__items = [] self.__keys = set() if matches is None: matches = {} self.__matches = matches def __getitem__(self, *args): if isinstance(args[0], tuple): args = args[0] # so we explicitely handle [:] if args == (slice(None),): args = [] # lets remove optional modifier '!' dargs = set([str(x).lstrip('!') for x in args]).difference( self._known_tags) if len(dargs)>0: raise ValueError, "Unknown internals %s requested. Known are %s" % \ (list(dargs), list(self._known_tags)) # dummy implementation for now result = [] # check every known item for item in self.__items: good = True # by default each one counts for arg in args: # check for rejection first if arg.startswith('!'): if (arg[1:] in item._clf_internals): good = False break else: continue # check for inclusion found = False for arg in [arg] + self.__matches.get(arg, []): if (arg in item._clf_internals): found = True break good = found if not good: break if good: result.append(item) return result def __iadd__(self, item): if operator.isSequenceType(item): for item_ in item: self.__iadd__(item_) else: if not hasattr(item, '_clf_internals'): raise ValueError, "Cannot register %s " % item + \ "which has no _clf_internals defined" if len(item._clf_internals) == 0: raise ValueError, "Cannot register %s " % item + \ "which has empty _clf_internals" clf_internals = set(item._clf_internals) if clf_internals.issubset(self._known_tags): self.__items.append(item) self.__keys |= clf_internals else: raise ValueError, 'Unknown clf internal(s) %s' % \ clf_internals.difference(self._known_tags) return self @property def internals(self): """Known internal tags of the classifiers """ return self.__keys def listing(self): """Listing (description + internals) of registered items """ return [(x.descr, x._clf_internals) for x in self.__items] @property def items(self): """Registered items """ return self.__items clfswh = Warehouse(known_tags=_KNOWN_INTERNALS) # classifiers regrswh = Warehouse(known_tags=_KNOWN_INTERNALS) # regressions # NB: # - Nu-classifiers are turned off since for haxby DS default nu # is an 'infisible' one # - Python's SMLR is turned off for the duration of development # since it is slow and results should be the same as of C version # clfswh += [ SMLR(lm=0.1, implementation="C", descr="SMLR(lm=0.1)"), SMLR(lm=1.0, implementation="C", descr="SMLR(lm=1.0)"), #SMLR(lm=10.0, implementation="C", descr="SMLR(lm=10.0)"), #SMLR(lm=100.0, implementation="C", descr="SMLR(lm=100.0)"), #SMLR(implementation="Python", descr="SMLR(Python)") ] clfswh += \ [ MulticlassClassifier(clfswh['smlr'][0], descr='Pairs+maxvote multiclass on ' + \ clfswh['smlr'][0].descr) ] if externals.exists('libsvm'): from mvpa.clfs import libsvmc as libsvm clfswh._known_tags.update(libsvm.SVM._KNOWN_IMPLEMENTATIONS.keys()) clfswh += [libsvm.SVM(descr="libsvm.LinSVM(C=def)", probability=1), libsvm.SVM( C=-10.0, descr="libsvm.LinSVM(C=10*def)", probability=1), libsvm.SVM( C=1.0, descr="libsvm.LinSVM(C=1)", probability=1), libsvm.SVM(svm_impl='NU_SVC', descr="libsvm.LinNuSVM(nu=def)", probability=1) ] clfswh += [libsvm.SVM(kernel_type='RBF', descr="libsvm.RbfSVM()"), libsvm.SVM(kernel_type='RBF', svm_impl='NU_SVC', descr="libsvm.RbfNuSVM(nu=def)"), libsvm.SVM(kernel_type='poly', descr='libsvm.PolySVM()', probability=1), #libsvm.svm.SVM(kernel_type='sigmoid', # svm_impl='C_SVC', # descr='libsvm.SigmoidSVM()'), ] # regressions regrswh._known_tags.update(['EPSILON_SVR', 'NU_SVR']) regrswh += [libsvm.SVM(svm_impl='EPSILON_SVR', descr='libsvm epsilon-SVR', regression=True), libsvm.SVM(svm_impl='NU_SVR', descr='libsvm nu-SVR', regression=True)] if externals.exists('shogun'): from mvpa.clfs import sg clfswh._known_tags.update(sg.SVM._KNOWN_IMPLEMENTATIONS) # some classifiers are not yet ready to be used out-of-the-box in # PyMVPA, thus we don't populate warehouse with their instances bad_classifiers = [ 'mpd', # was segfault, now non-training on testcases, and XOR. # and was described as "for educational purposes", thus # shouldn't be used for real data ;-) # Should be a drop-in replacement for lightsvm 'gpbt', # fails to train for testAnalyzerWithSplitClassifier # also 'retraining' doesn't work -- fails to generalize 'gmnp', # would fail with 'assertion Cache_Size > 2' # if shogun < 0.6.3, also refuses to train 'svrlight', # fails to 'generalize' as a binary classifier # after 'binning' 'krr', # fails to generalize ] if not externals.exists('sg_fixedcachesize'): # would fail with 'assertion Cache_Size > 2' if shogun < 0.6.3 bad_classifiers.append('gnpp') for impl in sg.SVM._KNOWN_IMPLEMENTATIONS: # Uncomment the ones to disable if impl in bad_classifiers: continue clfswh += [ sg.SVM( descr="sg.LinSVM(C=def)/%s" % impl, svm_impl=impl), sg.SVM( C=-10.0, descr="sg.LinSVM(C=10*def)/%s" % impl, svm_impl=impl), sg.SVM( C=1.0, descr="sg.LinSVM(C=1)/%s" % impl, svm_impl=impl), ] clfswh += [ sg.SVM(kernel_type='RBF', descr="sg.RbfSVM()/%s" % impl, svm_impl=impl), # sg.SVM(kernel_type='RBF', # descr="sg.RbfSVM(gamma=0.1)/%s" # % impl, svm_impl=impl, gamma=0.1), # sg.SVM(descr="sg.SigmoidSVM()/%s" # % impl, svm_impl=impl, kernel_type="sigmoid"), ] _optional_regressions = [] if externals.exists('shogun.krr'): _optional_regressions += ['krr'] for impl in ['libsvr'] + _optional_regressions:# \ # XXX svrlight sucks in SG -- dont' have time to figure it out #+ ([], ['svrlight'])['svrlight' in sg.SVM._KNOWN_IMPLEMENTATIONS]: regrswh._known_tags.update([impl]) regrswh += [ sg.SVM(svm_impl=impl, descr='sg.LinSVMR()/%s' % impl, regression=True), #sg.SVM(svm_impl=impl, kernel_type='RBF', # descr='sg.RBFSVMR()/%s' % impl, # regression=True), ] if len(clfswh['svm', 'linear']) > 0: # if any SVM implementation is known, import default ones from mvpa.clfs.svm import * # lars from R via RPy if externals.exists('lars'): import mvpa.clfs.lars as lars from mvpa.clfs.lars import LARS for model in lars.known_models: # XXX create proper repository of classifiers! lars_clf = LARS(descr="LARS(%s)" % model, model_type=model) clfswh += lars_clf # is a regression, too lars_regr = LARS(descr="_LARS(%s, regression=True)" % model, regression=True, model_type=model) regrswh += lars_regr # clfswh += MulticlassClassifier(lars, # descr='Multiclass %s' % lars.descr) ## PBS: enet has some weird issue that causes it to fail. GLMNET is ## better anyway, so just use that instead ## # enet from R via RPy ## if externals.exists('elasticnet'): ## from mvpa.clfs.enet import ENET ## clfswh += ENET(descr="ENET()") ## regrswh += ENET(descr="ENET(regression=True)", regression=True) # glmnet from R via RPy if externals.exists('glmnet'): from mvpa.clfs.glmnet import GLMNET_C, GLMNET_R clfswh += GLMNET_C(descr="GLMNET_C()") regrswh += GLMNET_R(descr="GLMNET_R()") # kNN clfswh += kNN(k=5, descr="kNN(k=5)") clfswh += kNN(k=5, voting='majority', descr="kNN(k=5, voting='majority')") clfswh += \ FeatureSelectionClassifier( kNN(), SensitivityBasedFeatureSelection( SMLRWeights(SMLR(lm=1.0, implementation="C")), RangeElementSelector(mode='select')), descr="kNN on SMLR(lm=1) non-0") clfswh += \ FeatureSelectionClassifier( kNN(), SensitivityBasedFeatureSelection( OneWayAnova(), FractionTailSelector(0.05, mode='select', tail='upper')), descr="kNN on 5%(ANOVA)") clfswh += \ FeatureSelectionClassifier( kNN(), SensitivityBasedFeatureSelection( OneWayAnova(), FixedNElementTailSelector(50, mode='select', tail='upper')), descr="kNN on 50(ANOVA)") # GNB clfswh += GNB(descr="GNB()") clfswh += GNB(common_variance=True, descr="GNB(common_variance=True)") clfswh += GNB(prior='uniform', descr="GNB(prior='uniform')") clfswh += \ FeatureSelectionClassifier( GNB(), SensitivityBasedFeatureSelection( OneWayAnova(), FractionTailSelector(0.05, mode='select', tail='upper')), descr="GNB on 5%(ANOVA)") # GPR if externals.exists('scipy'): from mvpa.clfs.gpr import GPR clfswh += GPR(kernel=KernelLinear(), descr="GPR(kernel='linear')") clfswh += GPR(kernel=KernelSquaredExponential(), descr="GPR(kernel='sqexp')") # BLR from mvpa.clfs.blr import BLR clfswh += BLR(descr="BLR()") #PLR from mvpa.clfs.plr import PLR clfswh += PLR(descr="PLR()") if externals.exists('scipy'): clfswh += PLR(reduced=0.05, descr="PLR(reduced=0.01)") # SVM stuff if len(clfswh['linear', 'svm']) > 0: linearSVMC = clfswh['linear', 'svm', cfg.get('svm', 'backend', default='libsvm').lower() ][0] # "Interesting" classifiers clfswh += \ FeatureSelectionClassifier( linearSVMC.clone(), SensitivityBasedFeatureSelection( SMLRWeights(SMLR(lm=0.1, implementation="C")), RangeElementSelector(mode='select')), descr="LinSVM on SMLR(lm=0.1) non-0") clfswh += \ FeatureSelectionClassifier( linearSVMC.clone(), SensitivityBasedFeatureSelection( SMLRWeights(SMLR(lm=1.0, implementation="C")), RangeElementSelector(mode='select')), descr="LinSVM on SMLR(lm=1) non-0") # "Interesting" classifiers clfswh += \ FeatureSelectionClassifier( RbfCSVMC(), SensitivityBasedFeatureSelection( SMLRWeights(SMLR(lm=1.0, implementation="C")), RangeElementSelector(mode='select')), descr="RbfSVM on SMLR(lm=1) non-0") clfswh += \ FeatureSelectionClassifier( linearSVMC.clone(), SensitivityBasedFeatureSelection( OneWayAnova(), FractionTailSelector(0.05, mode='select', tail='upper')), descr="LinSVM on 5%(ANOVA)") clfswh += \ FeatureSelectionClassifier( linearSVMC.clone(), SensitivityBasedFeatureSelection( OneWayAnova(), FixedNElementTailSelector(50, mode='select', tail='upper')), descr="LinSVM on 50(ANOVA)") clfswh += \ FeatureSelectionClassifier( linearSVMC.clone(), SensitivityBasedFeatureSelection( linearSVMC.getSensitivityAnalyzer(transformer=Absolute), FractionTailSelector(0.05, mode='select', tail='upper')), descr="LinSVM on 5%(SVM)") clfswh += \ FeatureSelectionClassifier( linearSVMC.clone(), SensitivityBasedFeatureSelection( linearSVMC.getSensitivityAnalyzer(transformer=Absolute), FixedNElementTailSelector(50, mode='select', tail='upper')), descr="LinSVM on 50(SVM)") ### Imports which are specific to RFEs # from mvpa.datasets.splitters import OddEvenSplitter # from mvpa.clfs.transerror import TransferError # from mvpa.featsel.rfe import RFE # from mvpa.featsel.helpers import FixedErrorThresholdStopCrit # from mvpa.clfs.transerror import ConfusionBasedError # SVM with unbiased RFE -- transfer-error to another splits, or in # other terms leave-1-out error on the same dataset # Has to be bound outside of the RFE definition since both analyzer and # error should use the same instance. rfesvm_split = SplitClassifier(linearSVMC)#clfswh['LinearSVMC'][0]) # "Almost" classical RFE. If this works it would differ only that # our transfer_error is based on internal splitting and classifier used # within RFE is a split classifier and its sensitivities per split will get # averaged # #clfswh += \ # FeatureSelectionClassifier( # clf = LinearCSVMC(), #clfswh['LinearSVMC'][0], # we train LinearSVM # feature_selection = RFE( # on features selected via RFE # # based on sensitivity of a clf which does splitting internally # sensitivity_analyzer=rfesvm_split.getSensitivityAnalyzer(), # transfer_error=ConfusionBasedError( # rfesvm_split, # confusion_state="confusion"), # # and whose internal error we use # feature_selector=FractionTailSelector( # 0.2, mode='discard', tail='lower'), # # remove 20% of features at each step # update_sensitivity=True), # # update sensitivity at each step # descr='LinSVM+RFE(splits_avg)' ) # #clfswh += \ # FeatureSelectionClassifier( # clf = LinearCSVMC(), # we train LinearSVM # feature_selection = RFE( # on features selected via RFE # # based on sensitivity of a clf which does splitting internally # sensitivity_analyzer=rfesvm_split.getSensitivityAnalyzer(), # transfer_error=ConfusionBasedError( # rfesvm_split, # confusion_state="confusion"), # # and whose internal error we use # feature_selector=FractionTailSelector( # 0.2, mode='discard', tail='lower'), # # remove 20% of features at each step # update_sensitivity=False), # # update sensitivity at each step # descr='LinSVM+RFE(splits_avg,static)' ) rfesvm = LinearCSVMC() # This classifier will do RFE while taking transfer error to testing # set of that split. Resultant classifier is voted classifier on top # of all splits, let see what that would do ;-) #clfswh += \ # SplitClassifier( # which does splitting internally # FeatureSelectionClassifier( # clf = LinearCSVMC(), # feature_selection = RFE( # on features selected via RFE # sensitivity_analyzer=\ # rfesvm.getSensitivityAnalyzer(transformer=Absolute), # transfer_error=TransferError(rfesvm), # stopping_criterion=FixedErrorThresholdStopCrit(0.05), # feature_selector=FractionTailSelector( # 0.2, mode='discard', tail='lower'), # # remove 20% of features at each step # update_sensitivity=True)), # # update sensitivity at each step # descr='LinSVM+RFE(N-Fold)') # # #clfswh += \ # SplitClassifier( # which does splitting internally # FeatureSelectionClassifier( # clf = LinearCSVMC(), # feature_selection = RFE( # on features selected via RFE # sensitivity_analyzer=\ # rfesvm.getSensitivityAnalyzer(transformer=Absolute), # transfer_error=TransferError(rfesvm), # stopping_criterion=FixedErrorThresholdStopCrit(0.05), # feature_selector=FractionTailSelector( # 0.2, mode='discard', tail='lower'), # # remove 20% of features at each step # update_sensitivity=True)), # # update sensitivity at each step # splitter = OddEvenSplitter(), # descr='LinSVM+RFE(OddEven)') pymvpa-0.4.8/mvpa/data/000077500000000000000000000000001174541445200147225ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/data/README000066400000000000000000000004371174541445200156060ustar00rootroot00000000000000PyMVPA Example Dataset ---------------------- Posterior half of a single axial slice. Numerical sample labels: * 0 - rest * 1 - face * 2 - house * 3 - shoe * 4 - cat * 5 - scissors * 6 - scrambledpix * 7 - bottle * 8 - chair 12 independent chunks of data samples. pymvpa-0.4.8/mvpa/data/attributes.txt000066400000000000000000000136421174541445200176570ustar00rootroot000000000000000 0 0 0 0 0 0 0 0 0 0 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 0 0 0 0 0 0 0 0 0 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 6 0 6 0 6 0 6 0 6 0 6 0 6 0 6 0 0 0 0 0 0 0 0 0 0 0 7 0 7 0 7 0 7 0 7 0 7 0 7 0 7 0 7 0 0 0 0 0 0 0 0 0 0 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 0 1 0 1 0 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 0 1 0 1 0 1 0 1 0 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 0 1 0 1 0 1 0 1 0 1 8 1 8 1 8 1 8 1 8 1 8 1 8 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3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 3 0 3 0 3 0 3 0 3 0 3 0 3 6 3 6 3 6 3 6 3 6 3 6 3 6 3 6 3 6 3 0 3 0 3 0 3 0 3 0 3 7 3 7 3 7 3 7 3 7 3 7 3 7 3 7 3 7 3 0 3 0 3 0 3 0 3 0 3 5 3 5 3 5 3 5 3 5 3 5 3 5 3 5 3 5 3 0 3 0 3 0 3 0 3 0 3 0 3 0 4 0 4 0 4 0 4 0 4 0 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 0 4 0 4 0 4 0 4 0 4 0 4 5 4 5 4 5 4 5 4 5 4 5 4 5 4 5 4 5 4 0 4 0 4 0 4 0 4 0 4 7 4 7 4 7 4 7 4 7 4 7 4 7 4 7 4 7 4 0 4 0 4 0 4 0 4 0 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 1 4 0 4 0 4 0 4 0 4 0 4 8 4 8 4 8 4 8 4 8 4 8 4 8 4 8 4 8 4 0 4 0 4 0 4 0 4 0 4 0 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 0 4 0 4 0 4 0 4 0 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0 4 0 4 0 4 0 4 0 4 6 4 6 4 6 4 6 4 6 4 6 4 6 4 6 4 6 4 0 4 0 4 0 4 0 4 0 4 0 4 0 5 0 5 0 5 0 5 0 5 0 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 2 5 0 5 0 5 0 5 0 5 0 5 0 5 6 5 6 5 6 5 6 5 6 5 6 5 6 5 6 5 6 5 0 5 0 5 0 5 0 5 0 5 1 5 1 5 1 5 1 5 1 5 1 5 1 5 1 5 1 5 0 5 0 5 0 5 0 5 0 5 3 5 3 5 3 5 3 5 3 5 3 5 3 5 3 5 3 5 0 5 0 5 0 5 0 5 0 5 8 5 8 5 8 5 8 5 8 5 8 5 8 5 8 5 8 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-9.443078e-03 1.131936e-04 -1.954743e-03 -8.408776e-03 -7.947395e-03 3.652411e-04 -9.304592e-03 1.714332e-04 -8.614636e-03 -5.082897e-03 -7.550162e-03 3.865422e-04 -9.132657e-03 1.908393e-04 -1.214401e-02 -2.580862e-03 -7.131855e-03 4.082540e-04 -8.955261e-03 1.935190e-04 -1.380374e-02 -1.645126e-03 -6.692078e-03 4.194510e-04 pymvpa-0.4.8/mvpa/data/mask.nii.gz000066400000000000000000000003051174541445200167730ustar00rootroot00000000000000‹@"Med_.nii‹ad 103h0ˆ00Â!*`aÓ ûÓÒÜ  Xˆìxƒ3„.†Ê®Øls ö1Ñ3 Æ]”„? n:s¦Ð‰¡ïHè0u`q0ù $ VÀ$Ö€nvž¶!ÕÝK @lqEšIÈ1Ž)B¾iØ µÍ#ÎDâL"ÞDêšGši„M$Ý<Üf’g.iØÌ|æ1ó¨aÖH4Trð“ÉÕ pymvpa-0.4.8/mvpa/data/sample_design.fsf000066400000000000000000000531631174541445200202440ustar00rootroot00000000000000# Automatically generated by mk_fsf.py # FEAT version number set fmri(version) 5.98 # Are we in MELODIC? set fmri(inmelodic) 0 # Analysis level # 1 : First-level analysis # 2 : Higher-level analysis set fmri(level) 1 # Which stages to run # 0 : No first-level analysis (registration and/or group stats only) # 7 : Full first-level analysis # 1 : Pre-Stats # 3 : Pre-Stats + Stats # 2 : Stats # 6 : Stats + Post-stats # 4 : Post-stats set fmri(analysis) 7 # Use relative filenames set fmri(relative_yn) 0 # Balloon help set fmri(help_yn) 0 # Run Featwatcher set fmri(featwatcher_yn) 0 # Cleanup first-level standard-space images set fmri(sscleanup_yn) 0 # Perfusion tag/control order set fmri(tagfirst) 1 # Number of first-level analyses set fmri(multiple) 1 # Higher-level input type # 1 : Inputs are lower-level FEAT directories # 2 : Inputs are cope images from FEAT directories set fmri(inputtype) 1 # Carry out pre-stats processing? set fmri(filtering_yn) 1 # Brain/background threshold, % set fmri(brain_thresh) 10 # Critical z for design efficiency calculation set fmri(critical_z) 5.3 # Noise level set fmri(noise) 0.66 # Noise AR(1) set fmri(noisear) 0.34 # Post-stats-only directory copying # 0 : Overwrite original post-stats results # 1 : Copy original FEAT directory for new Contrasts, Thresholding, Rendering set fmri(newdir_yn) 0 # Motion correction # 0 : None # 1 : MCFLIRT set fmri(mc) 0 # Spin-history (currently obsolete) set fmri(sh_yn) 0 # B0 fieldmap unwarping? set fmri(regunwarp_yn) 0 # EPI dwell time (ms) set fmri(dwell) 0.7 # EPI TE (ms) set fmri(te) 35 # % Signal loss threshold set fmri(signallossthresh) 10 # Unwarp direction set fmri(unwarp_dir) y- # Slice timing correction # 0 : None # 1 : Regular up (0, 1, 2, 3, ...) # 2 : Regular down # 3 : Use slice order file # 4 : Use slice timings file # 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... ) set fmri(st) 0 # Slice timings file set fmri(st_file) "" # BET brain extraction set fmri(bet_yn) 0 # Intensity normalization set fmri(norm_yn) 0 # Perfusion subtraction set fmri(perfsub_yn) 0 # Highpass temporal filtering set fmri(temphp_yn) 1 # Lowpass temporal filtering set fmri(templp_yn) 0 # MELODIC ICA data exploration set fmri(melodic_yn) 0 # Carry out main stats? set fmri(stats_yn) 1 # Carry out prewhitening? set fmri(prewhiten_yn) 1 # Add motion parameters to model # 0 : No # 1 : Yes set fmri(motionevs) 0 # Robust outlier detection in FLAME? set fmri(robust_yn) 0 # Higher-level modelling # 3 : Fixed effects # 0 : Mixed Effects: Simple OLS # 2 : Mixed Effects: FLAME 1 # 1 : Mixed Effects: FLAME 1+2 set fmri(mixed_yn) 2 # Number of EVs set fmri(evs_vox) 0 # Number of contrasts # Number of F-tests set fmri(nftests_orig) 0 set fmri(nftests_real) 0 # Add constant column to design matrix? (obsolete) set fmri(constcol) 0 # Carry out post-stats steps? set fmri(poststats_yn) 1 # Pre-threshold masking? set fmri(threshmask) "" # Thresholding # 0 : None # 1 : Uncorrected # 2 : Voxel # 3 : Cluster set fmri(thresh) 0 # P threshold set fmri(prob_thresh) 0.05 # Z threshold set fmri(z_thresh) 2.3 # Z min/max for colour rendering # 0 : Use actual Z min/max # 1 : Use preset Z min/max set fmri(zdisplay) 0 # Z min in colour rendering set fmri(zmin) 2 # Z max in colour rendering set fmri(zmax) 8 # Colour rendering type # 0 : Solid blobs # 1 : Transparent blobs set fmri(rendertype) 1 # Background image for higher-level stats overlays # 1 : Mean highres # 2 : First highres # 3 : Mean functional # 4 : First functional # 5 : Standard space template set fmri(bgimage) 1 # Create time series plots set fmri(tsplot_yn) 0 # Registration? set fmri(reg_yn) 1 # Registration to initial structural #set fmri(reginitial_highres_yn) 1 # Search space for registration to initial structural # 0 : No search # 90 : Normal search # 180 : Full search set fmri(reginitial_highres_search) 90 # Degrees of Freedom for registration to initial structural set fmri(reginitial_highres_dof) 7 # Registration to main structural set fmri(reghighres_yn) 1 # Search space for registration to main structural # 0 : No search # 90 : Normal search # 180 : Full search set fmri(reghighres_search) 90 # Degrees of Freedom for registration to main structural set fmri(reghighres_dof) 6 # Registration to standard image? set fmri(regstandard_yn) 1 # Standard image set fmri(regstandard) "/corral/utexas/poldracklab/software/fsl/data/standard/MNI152_T1_2mm_brain" # Search space for registration to standard space # 0 : No search # 90 : Normal search # 180 : Full search set fmri(regstandard_search) 90 # Degrees of Freedom for registration to standard space set fmri(regstandard_dof) 12 # Do nonlinear registration from structural to standard space? # Control nonlinear warp field resolution set fmri(regstandard_nonlinear_warpres) 10 # High pass filter cutoff set fmri(paradigm_hp) 100 # Number of lower-level copes feeding into higher-level analysis set fmri(ncopeinputs) 0 # Add confound EVs text file set fmri(confoundevs) 0 # Contrast & F-tests mode # real : control real EVs # orig : control original EVs set fmri(con_mode_old) orig set fmri(con_mode) orig # Contrast masking - use >0 instead of thresholding? set fmri(conmask_zerothresh_yn) 0 # Do contrast masking at all? set fmri(conmask1_1) 0 ########################################################## # Now options that don't appear in the GUI # Alternative example_func image (not derived from input 4D dataset) set fmri(alternative_example_func) "" # Alternative (to BETting) mask image set fmri(alternative_mask) "" # Initial structural space registration initialisation transform set fmri(init_initial_highres) "" # Structural space registration initialisation transform set fmri(init_highres) "" # Standard space registration initialisation transform set fmri(init_standard) "" # For full FEAT analysis: overwrite existing .feat output dir? set fmri(overwrite_yn) 0 ### AUTOMATICALLY GENERATED PART### set fmri(regstandard_nonlinear_yn) 1 set fmri(ndelete) 0 set fmri(outputdir) "/openfmri/staged//ds018/sub001/model/task002_run001.feat" set feat_files(1) "/openfmri/staged//ds018/sub001/BOLD/task002_run001/bold_mcf_brain" set fmri(reginitial_highres_yn) 0 set highres_files(1) "/openfmri/staged//ds018/sub001/anatomy/highres001_brain" set fmri(npts) 300 set fmri(tr) 2.00 set fmri(evs_orig) 13 set fmri(evs_real) 26 set fmri(smooth) 6 set fmri(ncon_orig) 8 set fmri(ncon_real) 8 set fmri(evtitle1) "task" set fmri(shape1) 3 set fmri(custom1) "/openfmri/staged//ds018/sub001/behav/task002_run001/cond001.txt" set fmri(convolve1) 3 set fmri(convolve_phase1) 0 set fmri(tempfilt_yn1) 1 set fmri(deriv_yn1) 1 set fmri(ortho1.0) 0 set fmri(ortho1.1) 0 set fmri(ortho1.2) 0 set fmri(ortho1.3) 0 set fmri(ortho1.4) 0 set fmri(ortho1.5) 0 set fmri(ortho1.6) 0 set fmri(ortho1.7) 0 set fmri(ortho1.8) 0 set fmri(ortho1.9) 0 set fmri(ortho1.10) 0 set fmri(ortho1.11) 0 set fmri(ortho1.12) 0 set fmri(ortho1.13) 0 set fmri(conpic_real.1) 1 set fmri(conname_real.1) "task" set fmri(conname_orig.1) "task" set fmri(con_real1.1) 1 set fmri(con_real1.2) 0 set fmri(con_real1.3) 0 set fmri(con_real1.4) 0 set fmri(con_real1.5) 0 set fmri(con_real1.6) 0 set fmri(con_real1.7) 0 set fmri(con_real1.8) 0 set fmri(con_real1.9) 0 set fmri(con_real1.10) 0 set fmri(con_real1.11) 0 set fmri(con_real1.12) 0 set fmri(con_real1.13) 0 set fmri(con_real1.14) 0 set fmri(con_real1.15) 0 set fmri(con_real1.16) 0 set fmri(con_real1.17) 0 set fmri(con_real1.18) 0 set fmri(con_real1.19) 0 set fmri(con_real1.20) 0 set fmri(con_real1.21) 0 set fmri(con_real1.22) 0 set fmri(con_real1.23) 0 set fmri(con_real1.24) 0 set fmri(con_real1.25) 0 set fmri(con_real1.26) 0 set fmri(con_orig1.1) 1 set fmri(con_orig1.2) 0 set fmri(con_orig1.3) 0 set fmri(con_orig1.4) 0 set fmri(con_orig1.5) 0 set fmri(con_orig1.6) 0 set fmri(con_orig1.7) 0 set fmri(con_orig1.8) 0 set fmri(con_orig1.9) 0 set fmri(con_orig1.10) 0 set fmri(con_orig1.11) 0 set fmri(con_orig1.12) 0 set fmri(con_orig1.13) 0 set fmri(evtitle2) "gains parametric" set fmri(shape2) 3 set fmri(custom2) "/openfmri/staged//ds018/sub001/behav/task002_run001/cond002.txt" set fmri(convolve2) 3 set fmri(convolve_phase2) 0 set fmri(tempfilt_yn2) 1 set fmri(deriv_yn2) 1 set fmri(ortho2.0) 0 set fmri(ortho2.1) 0 set fmri(ortho2.2) 0 set fmri(ortho2.3) 0 set fmri(ortho2.4) 0 set fmri(ortho2.5) 0 set fmri(ortho2.6) 0 set fmri(ortho2.7) 0 set fmri(ortho2.8) 0 set fmri(ortho2.9) 0 set fmri(ortho2.10) 0 set fmri(ortho2.11) 0 set fmri(ortho2.12) 0 set fmri(ortho2.13) 0 set fmri(conpic_real.2) 1 set fmri(conname_real.2) "gains parametric" set fmri(conname_orig.2) "gains parametric" set fmri(con_real2.1) 0 set fmri(con_real2.2) 0 set fmri(con_real2.3) 1 set fmri(con_real2.4) 0 set fmri(con_real2.5) 0 set fmri(con_real2.6) 0 set fmri(con_real2.7) 0 set fmri(con_real2.8) 0 set fmri(con_real2.9) 0 set fmri(con_real2.10) 0 set fmri(con_real2.11) 0 set fmri(con_real2.12) 0 set fmri(con_real2.13) 0 set fmri(con_real2.14) 0 set fmri(con_real2.15) 0 set fmri(con_real2.16) 0 set fmri(con_real2.17) 0 set fmri(con_real2.18) 0 set fmri(con_real2.19) 0 set fmri(con_real2.20) 0 set fmri(con_real2.21) 0 set fmri(con_real2.22) 0 set fmri(con_real2.23) 0 set fmri(con_real2.24) 0 set fmri(con_real2.25) 0 set fmri(con_real2.26) 0 set fmri(con_orig2.1) 0 set fmri(con_orig2.2) 1 set fmri(con_orig2.3) 0 set fmri(con_orig2.4) 0 set fmri(con_orig2.5) 0 set fmri(con_orig2.6) 0 set fmri(con_orig2.7) 0 set fmri(con_orig2.8) 0 set fmri(con_orig2.9) 0 set fmri(con_orig2.10) 0 set fmri(con_orig2.11) 0 set fmri(con_orig2.12) 0 set fmri(con_orig2.13) 0 set fmri(evtitle3) "loss parametric" set fmri(shape3) 3 set fmri(custom3) "/openfmri/staged//ds018/sub001/behav/task002_run001/cond003.txt" set fmri(convolve3) 3 set fmri(convolve_phase3) 0 set fmri(tempfilt_yn3) 1 set fmri(deriv_yn3) 1 set fmri(ortho3.0) 0 set fmri(ortho3.1) 0 set fmri(ortho3.2) 0 set fmri(ortho3.3) 0 set fmri(ortho3.4) 0 set fmri(ortho3.5) 0 set fmri(ortho3.6) 0 set fmri(ortho3.7) 0 set fmri(ortho3.8) 0 set fmri(ortho3.9) 0 set fmri(ortho3.10) 0 set fmri(ortho3.11) 0 set fmri(ortho3.12) 0 set fmri(ortho3.13) 0 set fmri(conpic_real.3) 1 set fmri(conname_real.3) "loss parametric" set fmri(conname_orig.3) "loss parametric" set fmri(con_real3.1) 0 set fmri(con_real3.2) 0 set fmri(con_real3.3) 0 set fmri(con_real3.4) 0 set fmri(con_real3.5) 1 set fmri(con_real3.6) 0 set fmri(con_real3.7) 0 set fmri(con_real3.8) 0 set fmri(con_real3.9) 0 set fmri(con_real3.10) 0 set fmri(con_real3.11) 0 set fmri(con_real3.12) 0 set fmri(con_real3.13) 0 set fmri(con_real3.14) 0 set fmri(con_real3.15) 0 set fmri(con_real3.16) 0 set fmri(con_real3.17) 0 set fmri(con_real3.18) 0 set fmri(con_real3.19) 0 set fmri(con_real3.20) 0 set fmri(con_real3.21) 0 set fmri(con_real3.22) 0 set fmri(con_real3.23) 0 set fmri(con_real3.24) 0 set fmri(con_real3.25) 0 set fmri(con_real3.26) 0 set fmri(con_orig3.1) 0 set fmri(con_orig3.2) 0 set fmri(con_orig3.3) 1 set fmri(con_orig3.4) 0 set fmri(con_orig3.5) 0 set fmri(con_orig3.6) 0 set fmri(con_orig3.7) 0 set fmri(con_orig3.8) 0 set fmri(con_orig3.9) 0 set fmri(con_orig3.10) 0 set fmri(con_orig3.11) 0 set fmri(con_orig3.12) 0 set fmri(con_orig3.13) 0 set fmri(evtitle4) "outcome gain trials" set fmri(shape4) 3 set fmri(custom4) "/openfmri/staged//ds018/sub001/behav/task002_run001/cond004.txt" set fmri(convolve4) 3 set fmri(convolve_phase4) 0 set fmri(tempfilt_yn4) 1 set fmri(deriv_yn4) 1 set fmri(ortho4.0) 0 set fmri(ortho4.1) 0 set fmri(ortho4.2) 0 set fmri(ortho4.3) 0 set fmri(ortho4.4) 0 set fmri(ortho4.5) 0 set fmri(ortho4.6) 0 set fmri(ortho4.7) 0 set fmri(ortho4.8) 0 set fmri(ortho4.9) 0 set fmri(ortho4.10) 0 set fmri(ortho4.11) 0 set fmri(ortho4.12) 0 set fmri(ortho4.13) 0 set fmri(conpic_real.4) 1 set fmri(conname_real.4) "outcome gain trials" set fmri(conname_orig.4) "outcome gain trials" set fmri(con_real4.1) 0 set fmri(con_real4.2) 0 set fmri(con_real4.3) 0 set fmri(con_real4.4) 0 set fmri(con_real4.5) 0 set fmri(con_real4.6) 0 set fmri(con_real4.7) 1 set fmri(con_real4.8) 0 set fmri(con_real4.9) 0 set fmri(con_real4.10) 0 set fmri(con_real4.11) 0 set fmri(con_real4.12) 0 set fmri(con_real4.13) 0 set fmri(con_real4.14) 0 set fmri(con_real4.15) 0 set fmri(con_real4.16) 0 set fmri(con_real4.17) 0 set fmri(con_real4.18) 0 set fmri(con_real4.19) 0 set fmri(con_real4.20) 0 set fmri(con_real4.21) 0 set fmri(con_real4.22) 0 set fmri(con_real4.23) 0 set fmri(con_real4.24) 0 set fmri(con_real4.25) 0 set fmri(con_real4.26) 0 set fmri(con_orig4.1) 0 set fmri(con_orig4.2) 0 set fmri(con_orig4.3) 0 set fmri(con_orig4.4) 1 set fmri(con_orig4.5) 0 set fmri(con_orig4.6) 0 set fmri(con_orig4.7) 0 set fmri(con_orig4.8) 0 set fmri(con_orig4.9) 0 set fmri(con_orig4.10) 0 set fmri(con_orig4.11) 0 set fmri(con_orig4.12) 0 set fmri(con_orig4.13) 0 set fmri(evtitle5) "outcome loss trials" set fmri(shape5) 3 set fmri(custom5) "/openfmri/staged//ds018/sub001/behav/task002_run001/cond005.txt" set fmri(convolve5) 3 set fmri(convolve_phase5) 0 set fmri(tempfilt_yn5) 1 set fmri(deriv_yn5) 1 set fmri(ortho5.0) 0 set fmri(ortho5.1) 0 set fmri(ortho5.2) 0 set fmri(ortho5.3) 0 set fmri(ortho5.4) 0 set fmri(ortho5.5) 0 set fmri(ortho5.6) 0 set fmri(ortho5.7) 0 set fmri(ortho5.8) 0 set fmri(ortho5.9) 0 set fmri(ortho5.10) 0 set fmri(ortho5.11) 0 set fmri(ortho5.12) 0 set fmri(ortho5.13) 0 set fmri(conpic_real.5) 1 set fmri(conname_real.5) "outcome loss trials" set fmri(conname_orig.5) "outcome loss trials" set fmri(con_real5.1) 0 set fmri(con_real5.2) 0 set fmri(con_real5.3) 0 set fmri(con_real5.4) 0 set fmri(con_real5.5) 0 set fmri(con_real5.6) 0 set fmri(con_real5.7) 0 set fmri(con_real5.8) 0 set fmri(con_real5.9) 1 set fmri(con_real5.10) 0 set fmri(con_real5.11) 0 set fmri(con_real5.12) 0 set fmri(con_real5.13) 0 set fmri(con_real5.14) 0 set fmri(con_real5.15) 0 set fmri(con_real5.16) 0 set fmri(con_real5.17) 0 set fmri(con_real5.18) 0 set fmri(con_real5.19) 0 set fmri(con_real5.20) 0 set fmri(con_real5.21) 0 set fmri(con_real5.22) 0 set fmri(con_real5.23) 0 set fmri(con_real5.24) 0 set fmri(con_real5.25) 0 set fmri(con_real5.26) 0 set fmri(con_orig5.1) 0 set fmri(con_orig5.2) 0 set fmri(con_orig5.3) 0 set fmri(con_orig5.4) 0 set fmri(con_orig5.5) 1 set fmri(con_orig5.6) 0 set fmri(con_orig5.7) 0 set fmri(con_orig5.8) 0 set fmri(con_orig5.9) 0 set fmri(con_orig5.10) 0 set fmri(con_orig5.11) 0 set fmri(con_orig5.12) 0 set fmri(con_orig5.13) 0 set fmri(evtitle6) "outcome gain parametric" set fmri(shape6) 3 set fmri(custom6) "/openfmri/staged//ds018/sub001/behav/task002_run001/cond006.txt" set fmri(convolve6) 3 set fmri(convolve_phase6) 0 set fmri(tempfilt_yn6) 1 set fmri(deriv_yn6) 1 set fmri(ortho6.0) 0 set fmri(ortho6.1) 0 set fmri(ortho6.2) 0 set fmri(ortho6.3) 0 set fmri(ortho6.4) 0 set fmri(ortho6.5) 0 set fmri(ortho6.6) 0 set fmri(ortho6.7) 0 set fmri(ortho6.8) 0 set fmri(ortho6.9) 0 set fmri(ortho6.10) 0 set fmri(ortho6.11) 0 set fmri(ortho6.12) 0 set fmri(ortho6.13) 0 set fmri(conpic_real.6) 1 set fmri(conname_real.6) "outcome gain parametric" set fmri(conname_orig.6) "outcome gain parametric" set fmri(con_real6.1) 0 set fmri(con_real6.2) 0 set fmri(con_real6.3) 0 set fmri(con_real6.4) 0 set fmri(con_real6.5) 0 set fmri(con_real6.6) 0 set fmri(con_real6.7) 0 set fmri(con_real6.8) 0 set fmri(con_real6.9) 0 set fmri(con_real6.10) 0 set fmri(con_real6.11) 1 set fmri(con_real6.12) 0 set fmri(con_real6.13) 0 set fmri(con_real6.14) 0 set fmri(con_real6.15) 0 set fmri(con_real6.16) 0 set fmri(con_real6.17) 0 set fmri(con_real6.18) 0 set fmri(con_real6.19) 0 set fmri(con_real6.20) 0 set fmri(con_real6.21) 0 set fmri(con_real6.22) 0 set fmri(con_real6.23) 0 set fmri(con_real6.24) 0 set fmri(con_real6.25) 0 set fmri(con_real6.26) 0 set fmri(con_orig6.1) 0 set fmri(con_orig6.2) 0 set fmri(con_orig6.3) 0 set fmri(con_orig6.4) 0 set fmri(con_orig6.5) 0 set fmri(con_orig6.6) 1 set fmri(con_orig6.7) 0 set fmri(con_orig6.8) 0 set fmri(con_orig6.9) 0 set fmri(con_orig6.10) 0 set fmri(con_orig6.11) 0 set fmri(con_orig6.12) 0 set fmri(con_orig6.13) 0 set fmri(evtitle7) "outcome loss parametric" set fmri(shape7) 3 set fmri(custom7) "/openfmri/staged//ds018/sub001/behav/task002_run001/cond007.txt" set fmri(convolve7) 3 set fmri(convolve_phase7) 0 set fmri(tempfilt_yn7) 1 set fmri(deriv_yn7) 1 set fmri(ortho7.0) 0 set fmri(ortho7.1) 0 set fmri(ortho7.2) 0 set fmri(ortho7.3) 0 set fmri(ortho7.4) 0 set fmri(ortho7.5) 0 set fmri(ortho7.6) 0 set fmri(ortho7.7) 0 set fmri(ortho7.8) 0 set fmri(ortho7.9) 0 set fmri(ortho7.10) 0 set fmri(ortho7.11) 0 set fmri(ortho7.12) 0 set fmri(ortho7.13) 0 set fmri(conpic_real.7) 1 set fmri(conname_real.7) "outcome loss parametric" set fmri(conname_orig.7) "outcome loss parametric" set fmri(con_real7.1) 0 set fmri(con_real7.2) 0 set fmri(con_real7.3) 0 set fmri(con_real7.4) 0 set fmri(con_real7.5) 0 set fmri(con_real7.6) 0 set fmri(con_real7.7) 0 set fmri(con_real7.8) 0 set fmri(con_real7.9) 0 set fmri(con_real7.10) 0 set fmri(con_real7.11) 0 set fmri(con_real7.12) 0 set fmri(con_real7.13) 1 set fmri(con_real7.14) 0 set fmri(con_real7.15) 0 set fmri(con_real7.16) 0 set fmri(con_real7.17) 0 set fmri(con_real7.18) 0 set fmri(con_real7.19) 0 set fmri(con_real7.20) 0 set fmri(con_real7.21) 0 set fmri(con_real7.22) 0 set fmri(con_real7.23) 0 set fmri(con_real7.24) 0 set fmri(con_real7.25) 0 set fmri(con_real7.26) 0 set fmri(con_orig7.1) 0 set fmri(con_orig7.2) 0 set fmri(con_orig7.3) 0 set fmri(con_orig7.4) 0 set fmri(con_orig7.5) 0 set fmri(con_orig7.6) 0 set fmri(con_orig7.7) 1 set fmri(con_orig7.8) 0 set fmri(con_orig7.9) 0 set fmri(con_orig7.10) 0 set fmri(con_orig7.11) 0 set fmri(con_orig7.12) 0 set fmri(con_orig7.13) 0 set fmri(conpic_real.8) 1 set fmri(conname_real.8) "all" set fmri(conname_orig.8) "all" set fmri(con_real8.1) 1 set fmri(con_real8.2) 0 set fmri(con_real8.3) 1 set fmri(con_real8.4) 0 set fmri(con_real8.5) 1 set fmri(con_real8.6) 0 set fmri(con_real8.7) 1 set fmri(con_real8.8) 0 set fmri(con_real8.9) 1 set fmri(con_real8.10) 0 set fmri(con_real8.11) 1 set fmri(con_real8.12) 0 set fmri(con_real8.13) 1 set fmri(con_real8.14) 0 set fmri(con_real8.15) 0 set fmri(con_real8.16) 0 set fmri(con_real8.17) 0 set fmri(con_real8.18) 0 set fmri(con_real8.19) 0 set fmri(con_real8.20) 0 set fmri(con_real8.21) 0 set fmri(con_real8.22) 0 set fmri(con_real8.23) 0 set fmri(con_real8.24) 0 set fmri(con_real8.25) 0 set fmri(con_real8.26) 0 set fmri(con_orig8.1) 1 set fmri(con_orig8.2) 1 set fmri(con_orig8.3) 1 set fmri(con_orig8.4) 1 set fmri(con_orig8.5) 1 set fmri(con_orig8.6) 1 set fmri(con_orig8.7) 1 set fmri(con_orig8.8) 0 set fmri(con_orig8.9) 0 set fmri(con_orig8.10) 0 set fmri(con_orig8.11) 0 set fmri(con_orig8.12) 0 set fmri(con_orig8.13) 0 set fmri(evtitle8) "motpar1" set fmri(shape8) 2 set fmri(convolve8) 0 set fmri(convolve_phase8) 0 set fmri(tempfilt_yn8) 1 set fmri(deriv_yn8) 1 set fmri(custom8) "/openfmri/staged//ds018/sub001/BOLD/task002_run001/bold_mcf.par.1" set fmri(ortho8.0) 0 set fmri(ortho8.1) 0 set fmri(ortho8.2) 0 set fmri(ortho8.3) 0 set fmri(ortho8.4) 0 set fmri(ortho8.5) 0 set fmri(ortho8.6) 0 set fmri(ortho8.7) 0 set fmri(ortho8.8) 0 set fmri(ortho8.9) 0 set fmri(ortho8.10) 0 set fmri(ortho8.11) 0 set fmri(ortho8.12) 0 set fmri(ortho8.13) 0 set fmri(evtitle9) "motpar2" set fmri(shape9) 2 set fmri(convolve9) 0 set fmri(convolve_phase9) 0 set fmri(tempfilt_yn9) 1 set fmri(deriv_yn9) 1 set fmri(custom9) "/openfmri/staged//ds018/sub001/BOLD/task002_run001/bold_mcf.par.2" set fmri(ortho9.0) 0 set fmri(ortho9.1) 0 set fmri(ortho9.2) 0 set fmri(ortho9.3) 0 set fmri(ortho9.4) 0 set fmri(ortho9.5) 0 set fmri(ortho9.6) 0 set fmri(ortho9.7) 0 set fmri(ortho9.8) 0 set fmri(ortho9.9) 0 set fmri(ortho9.10) 0 set fmri(ortho9.11) 0 set fmri(ortho9.12) 0 set fmri(ortho9.13) 0 set fmri(evtitle10) "motpar3" set fmri(shape10) 2 set fmri(convolve10) 0 set fmri(convolve_phase10) 0 set fmri(tempfilt_yn10) 1 set fmri(deriv_yn10) 1 set fmri(custom10) "/openfmri/staged//ds018/sub001/BOLD/task002_run001/bold_mcf.par.3" set fmri(ortho10.0) 0 set fmri(ortho10.1) 0 set fmri(ortho10.2) 0 set fmri(ortho10.3) 0 set fmri(ortho10.4) 0 set fmri(ortho10.5) 0 set fmri(ortho10.6) 0 set fmri(ortho10.7) 0 set fmri(ortho10.8) 0 set fmri(ortho10.9) 0 set fmri(ortho10.10) 0 set fmri(ortho10.11) 0 set fmri(ortho10.12) 0 set fmri(ortho10.13) 0 set fmri(evtitle11) "motpar4" set fmri(shape11) 2 set fmri(convolve11) 0 set fmri(convolve_phase11) 0 set fmri(tempfilt_yn11) 1 set fmri(deriv_yn11) 1 set fmri(custom11) "/openfmri/staged//ds018/sub001/BOLD/task002_run001/bold_mcf.par.4" set fmri(ortho11.0) 0 set fmri(ortho11.1) 0 set fmri(ortho11.2) 0 set fmri(ortho11.3) 0 set fmri(ortho11.4) 0 set fmri(ortho11.5) 0 set fmri(ortho11.6) 0 set fmri(ortho11.7) 0 set fmri(ortho11.8) 0 set fmri(ortho11.9) 0 set fmri(ortho11.10) 0 set fmri(ortho11.11) 0 set fmri(ortho11.12) 0 set fmri(ortho11.13) 0 set fmri(evtitle12) "motpar5" set fmri(shape12) 2 set fmri(convolve12) 0 set fmri(convolve_phase12) 0 set fmri(tempfilt_yn12) 1 set fmri(deriv_yn12) 1 set fmri(custom12) "/openfmri/staged//ds018/sub001/BOLD/task002_run001/bold_mcf.par.5" set fmri(ortho12.0) 0 set fmri(ortho12.1) 0 set fmri(ortho12.2) 0 set fmri(ortho12.3) 0 set fmri(ortho12.4) 0 set fmri(ortho12.5) 0 set fmri(ortho12.6) 0 set fmri(ortho12.7) 0 set fmri(ortho12.8) 0 set fmri(ortho12.9) 0 set fmri(ortho12.10) 0 set fmri(ortho12.11) 0 set fmri(ortho12.12) 0 set fmri(ortho12.13) 0 set fmri(evtitle13) "motpar6" set fmri(shape13) 2 set fmri(convolve13) 0 set fmri(convolve_phase13) 0 set fmri(tempfilt_yn13) 1 set fmri(deriv_yn13) 1 set fmri(custom13) "/openfmri/staged//ds018/sub001/BOLD/task002_run001/bold_mcf.par.6" set fmri(ortho13.0) 0 set fmri(ortho13.1) 0 set fmri(ortho13.2) 0 set fmri(ortho13.3) 0 set fmri(ortho13.4) 0 set fmri(ortho13.5) 0 set fmri(ortho13.6) 0 set fmri(ortho13.7) 0 set fmri(ortho13.8) 0 set fmri(ortho13.9) 0 set fmri(ortho13.10) 0 set fmri(ortho13.11) 0 set fmri(ortho13.12) 0 set fmri(ortho13.13) 0 pymvpa-0.4.8/mvpa/data/smpl_attr.txt000066400000000000000000000000571174541445200174720ustar00rootroot00000000000000# to be ignored 2 4 0.4 1 3 1 # also ignored pymvpa-0.4.8/mvpa/data/tueb_meg.dat.gz000066400000000000000000001405561174541445200176350ustar00rootroot00000000000000‹S1¬Hvp02cf-3ffasu.datíýÍŽ.»²5 µ‹«àj)ýoÓ!:@n¤Õ@ !qÿŒa;ÂéˆÜgÓA¢QŸ¾w¯Ss>ó©L‡ãĈÿÓÿåÿñÿü¿ÿû¿ü?þ¿ÿÿ×ÿ_ÿ«Ÿç'üÄŸô“ÊOýi?ýgüüaø ñ'¤ŸBù õ'´ŸÐÂø‰ÏOÄ¿‰?1ýÄüËO¬?±ýÄþÇOz~RøIøÊô“òO*?©þ¤ö“úO?ùùÉá'ÇŸŒß˜rùÉõ'·ŸÜòø)ÏO ?%þ”ôSð@å§ÔŸÒ~Jÿ)ã§>?5üÔøSÓOÍ?Ï[jû©ý§ŽŸöü´ðÓâOK?-ÿ´òÓð:í§õŸ6~úóÓÃO?=ýôüÓËO¯?oÛúøÏÏ?#þŒô3òÏ(?£þŒö3p< ǃóxp NäÁ‘<8“‡òàT˃ÏÍcÃçxp<9ÏŽ‡ÇÓãñáü0Dž/>‡3 8Ä€S 8Æ€s 8È€“ 8Ê€³ 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16.6135 MZO01 -11.3049 0.892652 4.24027 -16.2624 1.26524 4.78533 MZO02 -11.6667 1.16014 -0.982373 -16.644 1.51473 -0.645077 MZP01 -6.47856 0.137594 13.504 -9.59148 0.23432 17.4172 MZP02 -9.84413 0.586053 9.37474 -14.2462 0.789525 11.7397 pymvpa-0.4.8/mvpa/data/xavr1010.dat000066400000000000000000000030041174541445200166730ustar00rootroot00000000000000 Fp1 69 -0.307 0.946 0.099 Fp2 69 0.307 0.946 0.099 Fz 69 -0.000 0.672 0.741 F3 69 -0.516 0.637 0.574 F4 69 0.516 0.637 0.574 F7 69 -0.805 0.585 0.099 F8 69 0.805 0.585 0.099 FC1 69 -0.313 0.343 0.835 FC2 69 0.313 0.343 0.835 FC6 69 0.853 0.327 0.407 FC5 69 -0.853 0.327 0.407 FT9 69 -0.883 0.321 -0.342 FT10 69 0.883 0.321 -0.342 Cz 69 0.000 0.000 1.000 C3 69 -0.672 -0.000 0.741 C4 69 0.672 0.000 0.741 T7 69 -0.995 -0.000 0.099 T8 69 0.995 0.000 0.099 CP1 69 -0.313 -0.345 0.835 CP2 69 0.313 -0.345 0.835 CP5 69 -0.853 -0.327 0.407 CP6 69 0.853 -0.327 0.407 TP9 69 -0.883 -0.321 -0.342 TP10 69 0.883 -0.321 -0.342 Pz 69 -0.000 -0.672 0.741 P3 69 -0.516 -0.637 0.574 P4 69 0.516 -0.637 0.574 P7 69 -0.805 -0.585 0.099 P8 69 0.805 -0.585 0.099 O1 69 -0.307 -0.946 0.099 O2 69 0.307 -0.946 0.099 pymvpa-0.4.8/mvpa/datasets/000077500000000000000000000000001174541445200156215ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/datasets/__init__.py000066400000000000000000000022371174541445200177360ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """PyMVPA datasets and helper classes such as splitters Module Description ================== `Dataset` and derived classes are dedicated to contain the data and associated information (such as labels, chunk(session) identifiers. Module Organization =================== The mvpa.datasets module contains the following modules: .. packagetree:: :style: UML :group Generic Datasets: base mapped masked meta :group Specialized Datasets: nifti channel eep event :group Splitters: splitter :group Miscellaneous: miscfx miscfx_sp """ __docformat__ = 'restructuredtext' if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.datasets') # nothing in here that works without the base class from mvpa.datasets.base import Dataset if __debug__: debug('INIT', 'mvpa.datasets end') pymvpa-0.4.8/mvpa/datasets/base.py000066400000000000000000002126251174541445200171150ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Dataset container""" __docformat__ = 'restructuredtext' import operator import random import mvpa.support.copy as copy import numpy as N # Sooner or later Dataset would become ClassWithCollections as well, but for # now just an object -- thus commenting out tentative changes # #XXX from mvpa.misc.state import ClassWithCollections, SampleAttribute from mvpa.misc.exceptions import DatasetError from mvpa.misc.support import idhash as idhash_ from mvpa.base.dochelpers import enhancedDocString, table2string from mvpa.base import warning if __debug__: from mvpa.base import debug def _validate_indexes_uniq_sorted(seq, fname, item): """Helper function to validate that seq contains unique sorted values """ if operator.isSequenceType(seq): seq_unique = N.unique(seq) if len(seq) != len(seq_unique): warning("%s() operates only with indexes for %s without" " repetitions. Repetitions were removed." % (fname, item)) if N.any(N.sort(seq) != seq_unique): warning("%s() does not guarantee the original order" " of selected %ss. Use selectSamples() and " " selectFeatures(sort=False) instead" % (fname, item)) #XXX class Dataset(ClassWithCollections): class Dataset(object): """*The* Dataset. This class provides a container to store all necessary data to perform MVPA analyses. These are the data samples, as well as the labels associated with the samples. Additionally, samples can be grouped into chunks. :Groups: - `Creators`: `__init__`, `selectFeatures`, `selectSamples`, `applyMapper` - `Mutators`: `permuteLabels` Important: labels assumed to be immutable, i.e. no one should modify them externally by accessing indexed items, ie something like ``dataset.labels[1] += 100`` should not be used. If a label has to be modified, full copy of labels should be obtained, operated on, and assigned back to the dataset, otherwise dataset.uniquelabels would not work. The same applies to any other attribute which has corresponding unique* access property. """ # XXX Notes about migration to use Collections to store data and # attributes for samples, features, and dataset itself: # changes: # _data -> s_attr collection (samples attributes) # _dsattr -> ds_attr collection # f_attr collection (features attributes) # static definition to track which unique attributes # have to be reset/recomputed whenever anything relevant # changes # unique{labels,chunks} become a part of dsattr _uniqueattributes = [] """Unique attributes associated with the data""" _registeredattributes = [] """Registered attributes (stored in _data)""" _requiredattributes = ['samples', 'labels'] """Attributes which have to be provided to __init__, or otherwise no default values would be assumed and construction of the instance would fail""" #XXX _ATTRIBUTE_COLLECTIONS = [ 's_attr', 'f_attr', 'ds_attr' ] #XXX """Assure those 3 collections to be present in all datasets""" #XXX #XXX samples__ = SampleAttribute(doc="Samples data. 0th index is time", hasunique=False) # XXX #XXX labels__ = SampleAttribute(doc="Labels for the samples", hasunique=True) #XXX chunks__ = SampleAttribute(doc="Chunk identities for the samples", hasunique=True) #XXX # samples ids (already unique by definition) #XXX origids__ = SampleAttribute(doc="Chunk identities for the samples", hasunique=False) def __init__(self, # for copy constructor data=None, dsattr=None, # automatic dtype conversion dtype=None, # new instances samples=None, labels=None, labels_map=None, chunks=None, origids=None, # flags check_data=True, copy_samples=False, copy_data=True, copy_dsattr=True): """Initialize dataset instance There are basically two different way to create a dataset: 1. Create a new dataset from samples and sample attributes. In this mode a two-dimensional `ndarray` has to be passed to the `samples` keyword argument and the corresponding samples attributes are provided via the `labels` and `chunks` arguments. 2. Copy contructor mode The second way is used internally to perform quick coyping of datasets, e.g. when performing feature selection. In this mode and the two dictionaries (`data` and `dsattr`) are required. For performance reasons this mode bypasses most of the sanity check performed by the previous mode, as for internal operations data integrity is assumed. :Parameters: data : dict Dictionary with an arbitrary number of entries. The value for each key in the dict has to be an ndarray with the same length as the number of rows in the samples array. A special entry in this dictionary is 'samples', a 2d array (samples x features). A shallow copy is stored in the object. dsattr : dict Dictionary of dataset attributes. An arbitrary number of arbitrarily named and typed objects can be stored here. A shallow copy of the dictionary is stored in the object. dtype: type | None If None -- do not change data type if samples is an ndarray. Otherwise convert samples to dtype. :Keywords: samples : ndarray 2d array (samples x features) labels An array or scalar value defining labels for each samples. Generally `labels` should be numeric, unless `labels_map` is used labels_map : None or bool or dict Map original labels into numeric labels. If True, the mapping is computed if labels are literal. If is False, no mapping is computed. If dict instance -- provided mapping is verified and applied. If you want to have labels_map just be present given already numeric labels, just assign labels_map dictionary to existing dataset instance chunks An array or scalar value defining chunks for each sample Each of the Keywords arguments overwrites what is/might be already in the `data` container. """ #XXX ClassWithCollections.__init__(self) # see if data and dsattr are none, if so, make them empty dicts if data is None: data = {} if dsattr is None: dsattr = {} # initialize containers; default values are empty dicts # always make a shallow copy of what comes in, otherwise total chaos # is likely to happen soon if copy_data: # deep copy (cannot use copy.deepcopy, because samples is an # exception # but shallow copy first to get a shared version of the data in # any case lcl_data = data.copy() for k, v in data.iteritems(): # skip copying samples if requested if k == 'samples' and not copy_samples: continue lcl_data[k] = v.copy() else: # shallow copy # XXX? yoh: it might be better speed wise just assign dictionary # without any shallow .copy lcl_data = data.copy() if copy_dsattr and len(dsattr)>0: # deep copy if __debug__: debug('DS', "Deep copying dsattr %s" % `dsattr`) lcl_dsattr = copy.deepcopy(dsattr) else: # shallow copy lcl_dsattr = copy.copy(dsattr) # has to be not private since otherwise derived methods # would have problem accessing it and _registerAttribute # would fail on lambda getters self._data = lcl_data """What makes a dataset.""" self._dsattr = lcl_dsattr """Dataset attriibutes.""" # store samples (and possibly transform/reshape/retype them) if not samples is None: if __debug__: if lcl_data.has_key('samples'): debug('DS', "`Data` dict has `samples` (%s) but there is also" \ " __init__ parameter `samples` which overrides " \ " stored in `data`" % (`lcl_data['samples'].shape`)) lcl_data['samples'] = self._shapeSamples(samples, dtype, copy_samples) # TODO? we might want to have the same logic for chunks and labels # ie if no labels present -- assign arange # MH: don't think this is necessary -- or is there a use case? # labels if not labels is None: if __debug__: if lcl_data.has_key('labels'): debug('DS', "`Data` dict has `labels` (%s) but there is also" + " __init__ parameter `labels` which overrides " + " stored in `data`" % (`lcl_data['labels']`)) if lcl_data.has_key('samples'): lcl_data['labels'] = \ self._expandSampleAttribute(labels, 'labels') # check if we got all required attributes for attr in self._requiredattributes: if not lcl_data.has_key(attr): raise DatasetError, \ "Attribute %s is required to initialize dataset" % \ attr nsamples = self.nsamples # chunks if not chunks == None: lcl_data['chunks'] = \ self._expandSampleAttribute(chunks, 'chunks') elif not lcl_data.has_key('chunks'): # if no chunk information is given assume that every pattern # is its own chunk lcl_data['chunks'] = N.arange(nsamples) # samples origids if not origids is None: # simply assign if provided lcl_data['origids'] = origids elif not lcl_data.has_key('origids'): # otherwise contruct unqiue ones lcl_data['origids'] = N.arange(len(lcl_data['labels'])) else: # assume origids have been specified already (copy constructor # mode) leave them as they are, e.g. to make origids survive # selectSamples() pass # Initialize attributes which are registered but were not setup for attr in self._registeredattributes: if not lcl_data.has_key(attr): if __debug__: debug("DS", "Initializing attribute %s" % attr) lcl_data[attr] = N.zeros(nsamples) # labels_map labels_ = N.asarray(lcl_data['labels']) labels_map_known = lcl_dsattr.has_key('labels_map') if labels_map is True: # need to compose labels_map if labels_.dtype.char == 'S': # or not labels_map_known: # Create mapping ulabels = list(set(labels_)) ulabels.sort() labels_map = dict([ (x[1], x[0]) for x in enumerate(ulabels) ]) if __debug__: debug('DS', 'Mapping for the labels computed to be %s' % labels_map) else: if __debug__: debug('DS', 'Mapping of labels was requested but labels ' 'are not strings. Skipped') labels_map = None pass elif labels_map is False: labels_map = None if isinstance(labels_map, dict): if labels_map_known: if __debug__: debug('DS', "`dsattr` dict has `labels_map` (%s) but there is also" \ " __init__ parameter `labels_map` (%s) which overrides " \ " stored in `dsattr`" % (lcl_dsattr['labels_map'], labels_map)) lcl_dsattr['labels_map'] = labels_map # map labels if needed (if strings or was explicitely requested) if labels_.dtype.char == 'S' or not labels_map_known: if __debug__: debug('DS_', "Remapping labels using mapping %s" % labels_map) # need to remap # !!! N.array is important here try: lcl_data['labels'] = N.array( [labels_map[x] for x in lcl_data['labels']]) except KeyError, e: raise ValueError, "Provided labels_map %s is insufficient " \ "to map all the labels. Mapping for label %s is " \ "missing" % (labels_map, e) elif not lcl_dsattr.has_key('labels_map'): lcl_dsattr['labels_map'] = labels_map elif __debug__: debug('DS_', 'Not overriding labels_map in dsattr since it has one') if check_data: self._checkData() # lazy computation of unique members #self._resetallunique('_dsattr', self._dsattr) # Michael: we cannot do this conditional here. When selectSamples() # removes a whole data chunk the uniquechunks values will be invalid. # Same applies to labels of course. if not labels is None or not chunks is None: # for a speed up to don't go through all uniqueattributes # when no need lcl_dsattr['__uniquereseted'] = False self._resetallunique(force=True) __doc__ = enhancedDocString('Dataset', locals()) @property def idhash(self): """To verify if dataset is in the same state as when smth else was done Like if classifier was trained on the same dataset as in question""" _data = self._data res = idhash_(_data) # we cannot count on the order the values in the dict will show up # with `self._data.value()` and since idhash will be order-dependent # we have to make it deterministic keys = _data.keys() keys.sort() for k in keys: res += idhash_(_data[k]) return res def _resetallunique(self, force=False): """Set to None all unique* attributes of corresponding dictionary """ _dsattr = self._dsattr if not force and _dsattr['__uniquereseted']: return _uniqueattributes = self._uniqueattributes if __debug__ and "DS_" in debug.active: debug("DS_", "Reseting all attributes %s for dataset %s" % (_uniqueattributes, self.summary(uniq=False, idhash=False, stats=False, lstats=False))) # I guess we better checked if dictname is known but... for k in _uniqueattributes: _dsattr[k] = None _dsattr['__uniquereseted'] = True def _getuniqueattr(self, attrib, dict_): """Provide common facility to return unique attributes XXX `dict_` can be simply replaced now with self._dsattr """ # local bindings _dsattr = self._dsattr if not _dsattr.has_key(attrib) or _dsattr[attrib] is None: if __debug__ and 'DS_' in debug.active: debug("DS_", "Recomputing unique set for attrib %s within %s" % (attrib, self.summary(uniq=False, stats=False, lstats=False))) # uff... might come up with better strategy to keep relevant # attribute name _dsattr[attrib] = N.unique( N.asanyarray(dict_[attrib[6:]]) ) assert(not _dsattr[attrib] is None) _dsattr['__uniquereseted'] = False return _dsattr[attrib] def _setdataattr(self, attrib, value): """Provide common facility to set attributes """ if len(value) != self.nsamples: raise ValueError, \ "Provided %s have %d entries while there is %d samples" % \ (attrib, len(value), self.nsamples) self._data[attrib] = N.asarray(value) uniqueattr = "unique" + attrib _dsattr = self._dsattr if _dsattr.has_key(uniqueattr): _dsattr[uniqueattr] = None def _getNSamplesPerAttr( self, attrib='labels' ): """Returns the number of samples per unique label. """ # local bindings _data = self._data # XXX hardcoded dict_=self._data.... might be in self._dsattr uniqueattr = self._getuniqueattr(attrib="unique" + attrib, dict_=_data) # use dictionary to cope with arbitrary labels result = dict(zip(uniqueattr, [ 0 ] * len(uniqueattr))) for l in _data[attrib]: result[l] += 1 # XXX only return values to mimic the old interface but we might want # to return the full dict instead # return result return result def _getSampleIdsByAttr(self, values, attrib="labels", sort=True): """Return indecies of samples given a list of attributes """ if not operator.isSequenceType(values) \ or isinstance(values, basestring): values = [ values ] # TODO: compare to plain for loop through the labels # on a real data example sel = N.array([], dtype=N.int16) _data = self._data for value in values: sel = N.concatenate(( sel, N.where(_data[attrib]==value)[0])) if sort: # place samples in the right order sel.sort() return sel def idsonboundaries(self, prior=0, post=0, attributes_to_track=['labels', 'chunks'], affected_labels=None, revert=False): """Find samples which are on the boundaries of the blocks Such samples might need to be removed. By default (with prior=0, post=0) ids of the first samples in a 'block' are reported :Parameters: prior : int how many samples prior to transition sample to include post : int how many samples post the transition sample to include attributes_to_track : list of basestring which attributes to track to decide on the boundary condition affected_labels : list of basestring for which labels to perform selection. If None - for all revert : bool either to revert the meaning and provide ids of samples which are found to not to be boundary samples """ # local bindings _data = self._data labels = self.labels nsamples = self.nsamples lastseen = none = [None for attr in attributes_to_track] transitions = [] for i in xrange(nsamples+1): if i < nsamples: current = [_data[attr][i] for attr in attributes_to_track] else: current = none if lastseen != current: # transition point new_transitions = range(max(0, i-prior), min(nsamples-1, i+post)+1) if affected_labels is not None: new_transitions = [labels[i] for i in new_transitions if i in affected_labels] transitions += new_transitions lastseen = current transitions = set(transitions) if revert: transitions = set(range(nsamples)).difference(transitions) # postprocess transitions = N.array(list(transitions)) transitions.sort() return list(transitions) def _shapeSamples(self, samples, dtype, copy): """Adapt different kinds of samples Handle all possible input value for 'samples' and tranform them into a 2d (samples x feature) representation. """ # put samples array into correct shape # 1d arrays or simple sequences are assumed to be a single pattern if (not isinstance(samples, N.ndarray)): # it is safe to provide dtype which defaults to None, # when N would choose appropriate dtype automagically samples = N.array(samples, ndmin=2, dtype=dtype, copy=copy) else: if samples.ndim < 2 \ or (not dtype is None and dtype != samples.dtype): if dtype is None: dtype = samples.dtype samples = N.array(samples, ndmin=2, dtype=dtype, copy=copy) elif copy: samples = samples.copy() # only samples x features matrices are supported if len(samples.shape) > 2: raise DatasetError, "Only (samples x features) -> 2d sample " \ + "are supported (got %s shape of samples)." \ % (`samples.shape`) \ +" Consider MappedDataset if applicable." return samples def _checkData(self): """Checks `_data` members to have the same # of samples. """ # # XXX: Maybe just run this under __debug__ and remove the `check_data` # from the constructor, which is too complicated anyway? # # local bindings nsamples = self.nsamples _data = self._data for k, v in _data.iteritems(): if not len(v) == nsamples: raise DatasetError, \ "Length of sample attribute '%s' [%i] does not " \ "match the number of samples in the dataset [%i]." \ % (k, len(v), nsamples) # check for unique origids uniques = N.unique(_data['origids']) uniques.sort() # need to copy to prevent sorting the original array sorted_ids = _data['origids'].copy() sorted_ids.sort() if not (uniques == sorted_ids).all(): raise DatasetError, "Samples IDs are not unique." # Check if labels as not literal if N.asanyarray(_data['labels'].dtype.char == 'S'): warning('Labels for dataset %s are literal, should be numeric. ' 'You might like to use labels_map argument.' % self) def _expandSampleAttribute(self, attr, attr_name): """If a sample attribute is given as a scalar expand/repeat it to a length matching the number of samples in the dataset. """ try: # if we are initializing with a single string -- we should # treat it as a single label if isinstance(attr, basestring): raise TypeError if len(attr) != self.nsamples: raise DatasetError, \ "Length of sample attribute '%s' [%d]" \ % (attr_name, len(attr)) \ + " has to match the number of samples" \ + " [%d]." % self.nsamples # store the sequence as array return N.array(attr) except TypeError: # make sequence of identical value matching the number of # samples return N.repeat(attr, self.nsamples) @classmethod def _registerAttribute(cls, key, dictname="_data", abbr=None, hasunique=False): """Register an attribute for any Dataset class. Creates property assigning getters/setters depending on the availability of corresponding _get, _set functions. """ classdict = cls.__dict__ if not classdict.has_key(key): if __debug__: debug("DS", "Registering new attribute %s" % key) # define get function and use corresponding # _getATTR if such defined getter = '_get%s' % key if classdict.has_key(getter): getter = '%s.%s' % (cls.__name__, getter) else: getter = "lambda x: x.%s['%s']" % (dictname, key) # define set function and use corresponding # _setATTR if such defined setter = '_set%s' % key if classdict.has_key(setter): setter = '%s.%s' % (cls.__name__, setter) elif dictname=="_data": setter = "lambda self,x: self._setdataattr" + \ "(attrib='%s', value=x)" % (key) else: setter = None if __debug__: debug("DS", "Registering new property %s.%s" % (cls.__name__, key)) exec "%s.%s = property(fget=%s, fset=%s)" % \ (cls.__name__, key, getter, setter) if abbr is not None: exec "%s.%s = property(fget=%s, fset=%s)" % \ (cls.__name__, abbr, getter, setter) if hasunique: uniquekey = "unique%s" % key getter = '_get%s' % uniquekey if classdict.has_key(getter): getter = '%s.%s' % (cls.__name__, getter) else: getter = "lambda x: x._getuniqueattr" + \ "(attrib='%s', dict_=x.%s)" % (uniquekey, dictname) if __debug__: debug("DS", "Registering new property %s.%s" % (cls.__name__, uniquekey)) exec "%s.%s = property(fget=%s)" % \ (cls.__name__, uniquekey, getter) if abbr is not None: exec "%s.U%s = property(fget=%s)" % \ (cls.__name__, abbr, getter) # create samplesper properties sampleskey = "samplesper%s" % key[:-1] # remove ending 's' XXX if __debug__: debug("DS", "Registering new property %s.%s" % (cls.__name__, sampleskey)) exec "%s.%s = property(fget=%s)" % \ (cls.__name__, sampleskey, "lambda x: x._getNSamplesPerAttr(attrib='%s')" % key) cls._uniqueattributes.append(uniquekey) # create idsby properties sampleskey = "idsby%s" % key # remove ending 's' XXX if __debug__: debug("DS", "Registering new property %s.%s" % (cls.__name__, sampleskey)) exec "%s.%s = %s" % (cls.__name__, sampleskey, "lambda self, x: " + "self._getSampleIdsByAttr(x,attrib='%s')" % key) cls._uniqueattributes.append(uniquekey) cls._registeredattributes.append(key) elif __debug__: warning('Trying to reregister attribute `%s`. For now ' % key + 'such capability is not present') def __str__(self): """String summary over the object """ try: ssummary = self.summary(uniq=True, idhash=__debug__ and ('DS_ID' in debug.active), stats=__debug__ and ('DS_STATS' in debug.active), lstats=__debug__ and ('DS_STATS' in debug.active), ) except (AttributeError, KeyError), e: # __str__ or __repr__ could have been requested before actual # instance is populated, e.g. by tracebacks of pdb/pydb. # ??? this case might be generic enough to allow for common # decorator around plentiful of __str__ and __repr__s ssummary = str(e) return ssummary def __repr__(self): return "<%s>" % str(self) def summary(self, uniq=True, stats=True, idhash=False, lstats=True, maxc=30, maxl=20): """String summary over the object :Parameters: uniq : bool Include summary over data attributes which have unique idhash : bool Include idhash value for dataset and samples stats : bool Include some basic statistics (mean, std, var) over dataset samples lstats : bool Include statistics on chunks/labels maxc : int Maximal number of chunks when provide details on labels/chunks maxl : int Maximal number of labels when provide details on labels/chunks """ # local bindings samples = self.samples _data = self._data _dsattr = self._dsattr if idhash: idhash_ds = "{%s}" % self.idhash idhash_samples = "{%s}" % idhash_(samples) else: idhash_ds = "" idhash_samples = "" s = """Dataset %s/ %s %d%s x %d""" % \ (idhash_ds, samples.dtype, self.nsamples, idhash_samples, self.nfeatures) ssep = (' ', '\n')[lstats] if uniq: s += "%suniq:" % ssep for uattr in _dsattr.keys(): if not uattr.startswith("unique"): continue attr = uattr[6:] try: value = self._getuniqueattr(attrib=uattr, dict_=_data) s += " %d %s" % (len(value), attr) except: pass if isinstance(self.labels_map, dict): s += ' labels_mapped' if stats: # TODO -- avg per chunk? # XXX We might like to use scipy.stats.describe to get # quick summary statistics (mean/range/skewness/kurtosis) if self.nfeatures: s += "%sstats: mean=%g std=%g var=%g min=%g max=%g\n" % \ (ssep, N.mean(samples), N.std(samples), N.var(samples), N.min(samples), N.max(samples)) else: s += "%sstats: dataset has no features\n" % ssep if lstats: s += self.summary_labels(maxc=maxc, maxl=maxl) return s def summary_labels(self, maxc=30, maxl=20): """Provide summary statistics over the labels and chunks :Parameters: maxc : int Maximal number of chunks when provide details maxl : int Maximal number of labels when provide details """ # We better avoid bound function since if people only # imported Dataset without miscfx it would fail from mvpa.datasets.miscfx import getSamplesPerChunkLabel spcl = getSamplesPerChunkLabel(self) # XXX couldn't they be unordered? ul = self.uniquelabels.tolist() uc = self.uniquechunks.tolist() s = "" if len(ul) < maxl and len(uc) < maxc: s += "\nCounts of labels in each chunk:" # only in a resonable case do printing table = [[' chunks\labels'] + ul] table += [[''] + ['---'] * len(ul)] for c, counts in zip(uc, spcl): table.append([ str(c) ] + counts.tolist()) s += '\n' + table2string(table) else: s += "No details due to large number of labels or chunks. " \ "Increase maxc and maxl if desired" labels_map = self.labels_map if isinstance(labels_map, dict): s += "\nOriginal labels were mapped using following mapping:" s += '\n\t'+'\n\t'.join([':\t'.join(map(str, x)) for x in labels_map.items()]) + '\n' def cl_stats(axis, u, name1, name2): """ Compute statistics per label """ stats = {'min': N.min(spcl, axis=axis), 'max': N.max(spcl, axis=axis), 'mean': N.mean(spcl, axis=axis), 'std': N.std(spcl, axis=axis), '#%ss' % name2: N.sum(spcl>0, axis=axis)} entries = [' ' + name1, 'mean', 'std', 'min', 'max', '#%ss' % name2] table = [ entries ] for i, l in enumerate(u): d = {' ' + name1 : l} d.update(dict([ (k, stats[k][i]) for k in stats.keys()])) table.append( [ ('%.3g', '%s')[isinstance(d[e], basestring)] % d[e] for e in entries] ) return '\nSummary per %s across %ss\n' % (name1, name2) \ + table2string(table) if len(ul) < maxl: s += cl_stats(0, ul, 'label', 'chunk') if len(uc) < maxc: s += cl_stats(1, uc, 'chunk', 'label') return s def __iadd__(self, other): """Merge the samples of one Dataset object to another (in-place). No dataset attributes, besides labels_map, will be merged! Additionally, a new set of unique `origids` will be generated. """ # local bindings _data = self._data other_data = other._data if not self.nfeatures == other.nfeatures: raise DatasetError, "Cannot add Dataset, because the number of " \ "feature do not match." # take care about labels_map and labels slm = self.labels_map olm = other.labels_map if N.logical_xor(slm is None, olm is None): raise ValueError, "Cannot add datasets where only one of them " \ "has labels map assigned. If needed -- implement it" # concatenate all sample attributes for k,v in _data.iteritems(): if k == 'origids': # special case samples origids: for now just regenerate unique # ones could also check if concatenation is unique, but it # would be costly performance-wise _data[k] = N.arange(len(v) + len(other_data[k])) elif k == 'labels' and slm is not None: # special care about labels if mapping was in effect, # we need to append 2nd map to the first one and # relabel 2nd dataset nlm = slm.copy() # figure out maximal numerical label used now nextid = N.sort(nlm.values())[-1] + 1 olabels = other.labels olabels_remap = {} for ol, olnum in olm.iteritems(): if not nlm.has_key(ol): # check if we can preserve old numberic label # if not -- assign some new one not yet present # in any dataset if olnum in nlm.values(): nextid = N.sort(nlm.values() + olm.values())[-1] + 1 else: nextid = olnum olabels_remap[olnum] = nextid nlm[ol] = nextid nextid += 1 else: olabels_remap[olnum] = nlm[ol] olabels = [olabels_remap[x] for x in olabels] # finally compose new labels _data['labels'] = N.concatenate((v, olabels), axis=0) # and reassign new mapping self._dsattr['labels_map'] = nlm if __debug__: # check if we are not dealing with colliding # mapping, since it is problematic and might lead # to various complications if (len(set(slm.keys())) != len(set(slm.values()))) or \ (len(set(olm.keys())) != len(set(olm.values()))): warning("Adding datasets where multiple labels " "mapped to the same ID is not recommended. " "Please check the outcome. Original mappings " "were %s and %s. Resultant is %s" % (slm, olm, nlm)) else: _data[k] = N.concatenate((v, other_data[k]), axis=0) # might be more sophisticated but for now just reset -- it is safer ;) self._resetallunique() return self def __add__( self, other ): """Merge the samples two Dataset objects. All data of both datasets is copied, concatenated and a new Dataset is returned. NOTE: This can be a costly operation (both memory and time). If performance is important consider the '+=' operator. """ # create a new object of the same type it is now and NOT only Dataset out = super(Dataset, self).__new__(self.__class__) # now init it: to make it work all Dataset contructors have to accept # Class(data=Dict, dsattr=Dict) out.__init__(data=self._data, dsattr=self._dsattr, copy_samples=True, copy_data=True, copy_dsattr=True) out += other return out def copy(self, deep=True): """Create a copy (clone) of the dataset, by fully copying current one :Keywords: deep : bool deep flag is provided to __init__ for copy_{samples,data,dsattr}. By default full copy is done. """ # create a new object of the same type it is now and NOT only Dataset out = super(Dataset, self).__new__(self.__class__) # now init it: to make it work all Dataset contructors have to accept # Class(data=Dict, dsattr=Dict) out.__init__(data=self._data, dsattr=self._dsattr, copy_samples=True, copy_data=True, copy_dsattr=True) return out def selectFeatures(self, ids=None, sort=True, groups=None): """Select a number of features from the current set. :Parameters: ids iterable container to select ids sort : bool if to sort Ids. Order matters and `selectFeatures` assumes incremental order. If not such, in non-optimized code selectFeatures would verify the order and sort Returns a new Dataset object with a copy of corresponding features from the original samples array. WARNING: The order of ids determines the order of features in the returned dataset. This might be useful sometimes, but can also cause major headaches! Order would is verified when running in non-optimized code (if __debug__) """ if ids is None and groups is None: raise ValueError, "No feature selection specified." # start with empty list if no ids where specified (so just groups) if ids is None: ids = [] if not groups is None: if not self._dsattr.has_key('featuregroups'): raise RuntimeError, \ "Dataset has no feature grouping information." for g in groups: ids += (self._dsattr['featuregroups'] == g).nonzero()[0].tolist() # XXX set sort default to True, now sorting has to be explicitely # disabled and warning is not necessary anymore if sort: ids = copy.deepcopy(ids) ids.sort() elif __debug__ and 'CHECK_DS_SORTED' in debug.active: from mvpa.misc.support import isSorted if not isSorted(ids): warning("IDs for selectFeatures must be provided " + "in sorted order, otherwise major headache might occur") # shallow-copy all stuff from current data dict new_data = self._data.copy() # assign the selected features -- data is still shared with # current dataset new_data['samples'] = self._data['samples'][:, ids] # apply selection to feature groups as well if self._dsattr.has_key('featuregroups'): new_dsattr = self._dsattr.copy() new_dsattr['featuregroups'] = self._dsattr['featuregroups'][ids] else: new_dsattr = self._dsattr # create a new object of the same type it is now and NOT only Dataset dataset = super(Dataset, self).__new__(self.__class__) # now init it: to make it work all Dataset contructors have to accept # Class(data=Dict, dsattr=Dict) dataset.__init__(data=new_data, dsattr=new_dsattr, check_data=False, copy_samples=False, copy_data=False, copy_dsattr=False ) return dataset def applyMapper(self, featuresmapper=None, samplesmapper=None, train=True): """Obtain new dataset by applying mappers over features and/or samples. While featuresmappers leave the sample attributes information unchanged, as the number of samples in the dataset is invariant, samplesmappers are also applied to the samples attributes themselves! Applying a featuresmapper will destroy any feature grouping information. :Parameters: featuresmapper : Mapper `Mapper` to somehow transform each sample's features samplesmapper : Mapper `Mapper` to transform each feature across samples train : bool Flag whether to train the mapper with this dataset before applying it. TODO: selectFeatures is pretty much applyMapper(featuresmapper=MaskMapper(...)) """ # shallow-copy all stuff from current data dict new_data = self._data.copy() # apply mappers if samplesmapper: if __debug__: debug("DS", "Training samplesmapper %s" % `samplesmapper`) samplesmapper.train(self) if __debug__: debug("DS", "Applying samplesmapper %s" % `samplesmapper` + " to samples of dataset `%s`" % `self`) # get rid of existing 'origids' as they are not valid anymore and # applying a mapper to them is not really meaningful if new_data.has_key('origids'): del(new_data['origids']) # apply mapper to all sample-wise data in dataset for k in new_data.keys(): new_data[k] = samplesmapper.forward(self._data[k]) # feature mapping might affect dataset attributes # XXX: might be obsolete when proper feature attributes are implemented new_dsattr = self._dsattr if featuresmapper: if __debug__: debug("DS", "Training featuresmapper %s" % `featuresmapper`) featuresmapper.train(self) if __debug__: debug("DS", "Applying featuresmapper %s" % `featuresmapper` + " to samples of dataset `%s`" % `self`) new_data['samples'] = featuresmapper.forward(self._data['samples']) # remove feature grouping, who knows what the mapper did to the # features if self._dsattr.has_key('featuregroups'): new_dsattr = self._dsattr.copy() del(new_dsattr['featuregroups']) else: new_dsattr = self._dsattr # create a new object of the same type it is now and NOT only Dataset dataset = super(Dataset, self).__new__(self.__class__) # now init it: to make it work all Dataset contructors have to accept # Class(data=Dict, dsattr=Dict) dataset.__init__(data=new_data, dsattr=new_dsattr, check_data=False, copy_samples=False, copy_data=False, copy_dsattr=False ) # samples attributes might have changed after applying samplesmapper if samplesmapper: dataset._resetallunique(force=True) return dataset def selectSamples(self, ids): """Choose a subset of samples defined by samples IDs. Returns a new dataset object containing the selected sample subset. TODO: yoh, we might need to sort the mask if the mask is a list of ids and is not ordered. Clarify with Michael what is our intent here! """ # without having a sequence a index the masked sample array would # loose its 2d layout if not operator.isSequenceType( ids ): ids = [ids] # TODO: Reconsider crafting a slice if it can be done to don't copy # the data #try: # minmask = min(mask) # maxmask = max(mask) #except: # minmask = min(map(int,mask)) # maxmask = max(map(int,mask)) # lets see if we could get it done with cheap view/slice #(minmask, maxmask) != (0, 1) and \ #if len(mask) > 2 and \ # N.array([N.arange(minmask, maxmask+1) == N.array(mask)]).all(): # slice_ = slice(minmask, maxmask+1) # if __debug__: # debug("DS", "We can and do convert mask %s into splice %s" % # (mask, slice_)) # mask = slice_ # mask all sample attributes data = {} for k, v in self._data.iteritems(): data[k] = v[ids, ] # create a new object of the same type it is now and NOT onyl Dataset dataset = super(Dataset, self).__new__(self.__class__) # now init it: to make it work all Dataset contructors have to accept # Class(data=Dict, dsattr=Dict) dataset.__init__(data=data, dsattr=self._dsattr, check_data=False, copy_samples=False, copy_data=False, copy_dsattr=False) dataset._resetallunique(force=True) return dataset def index(self, *args, **kwargs): """Universal indexer to obtain indexes of interesting samples/features. See .select() for more information :Return: tuple of (samples indexes, features indexes). Each item could be also None, if no selection on samples or features was requested (to discriminate between no selected items, and no selections) """ s_indx = [] # selections for samples f_indx = [] # selections for features return_dataset = kwargs.pop('return_dataset', False) largs = len(args) args = list(args) # so we could override # Figure out number of positional largs_nonstring = 0 # need to go with index since we might need to override internally for i in xrange(largs): l = args[i] if isinstance(l, basestring): if l.lower() == 'all': # override with a slice args[i] = slice(None) else: break largs_nonstring += 1 if largs_nonstring >= 1: s_indx.append(args[0]) if __debug__ and 'CHECK_DS_SELECT' in debug.active: _validate_indexes_uniq_sorted(args[0], 'select', 'samples') if largs_nonstring == 2: f_indx.append(args[1]) if __debug__ and 'CHECK_DS_SELECT' in debug.active: _validate_indexes_uniq_sorted(args[1], 'select', 'features') elif largs_nonstring > 2: raise ValueError, "Only two positional arguments are allowed" \ ". 1st for samples, 2nd for features" # process left positional arguments which must encode selections like # ('labels', [1,2,3]) if (largs - largs_nonstring) % 2 != 0: raise ValueError, "Positional selections must come in pairs:" \ " e.g. ('labels', [1,2,3])" for i in xrange(largs_nonstring, largs, 2): k, v = args[i:i+2] kwargs[k] = v # process keyword parameters data_ = self._data for k, v in kwargs.iteritems(): if k == 'samples': s_indx.append(v) elif k == 'features': f_indx.append(v) elif data_.has_key(k): # so it is an attribute for samples # XXX may be do it not only if __debug__ if __debug__: # and 'CHECK_DS_SELECT' in debug.active: if not N.any([isinstance(v, cls) for cls in [list, tuple, slice, int]]): raise ValueError, "Trying to specify selection for %s " \ "based on unsupported '%s'" % (k, v) s_indx.append(self._getSampleIdsByAttr(v, attrib=k, sort=False)) else: raise ValueError, 'Keyword "%s" is not known, thus' \ 'select() failed' % k def combine_indexes(indx, nelements): """Helper function: intersect selections given in indx :Parameters: indxs : list of lists or slices selections of elements nelements : int number of elements total for deriving indexes from slices """ indx_sel = None # pure list of ids for selection for s in indx: if isinstance(s, slice) or \ isinstance(s, N.ndarray) and s.dtype==bool: # XXX there might be a better way than reconstructing the full # index list. Also we are loosing ability to do simlpe slicing, # ie w.o making a copy of the selected data all_indexes = N.arange(nelements) s = all_indexes[s] elif not operator.isSequenceType(s): s = [ s ] if indx_sel is None: indx_sel = set(s) else: # To be consistent #if not isinstance(indx_sel, set): # indx_sel = set(indx_sel) indx_sel = indx_sel.intersection(s) # if we got set -- convert if isinstance(indx_sel, set): indx_sel = list(indx_sel) # sort for the sake of sanity indx_sel.sort() return indx_sel # Select samples if len(s_indx) == 1 and isinstance(s_indx[0], slice) \ and s_indx[0] == slice(None): # so no actual selection -- full slice s_indx = s_indx[0] else: # else - get indexes if len(s_indx) == 0: s_indx = None else: s_indx = combine_indexes(s_indx, self.nsamples) # Select features if len(f_indx): f_indx = combine_indexes(f_indx, self.nfeatures) else: f_indx = None return s_indx, f_indx def select(self, *args, **kwargs): """Universal selector WARNING: if you need to select duplicate samples (e.g. samples=[5,5]) or order of selected samples of features is important and has to be not ordered (e.g. samples=[3,2,1]), please use selectFeatures or selectSamples functions directly Examples: Mimique plain selectSamples:: dataset.select([1,2,3]) dataset[[1,2,3]] Mimique plain selectFeatures:: dataset.select(slice(None), [1,2,3]) dataset.select('all', [1,2,3]) dataset[:, [1,2,3]] Mixed (select features and samples):: dataset.select([1,2,3], [1, 2]) dataset[[1,2,3], [1, 2]] Select samples matching some attributes:: dataset.select(labels=[1,2], chunks=[2,4]) dataset.select('labels', [1,2], 'chunks', [2,4]) dataset['labels', [1,2], 'chunks', [2,4]] Mixed -- out of first 100 samples, select only those with labels 1 or 2 and belonging to chunks 2 or 4, and select features 2 and 3:: dataset.select(slice(0,100), [2,3], labels=[1,2], chunks=[2,4]) dataset[:100, [2,3], 'labels', [1,2], 'chunks', [2,4]] """ s_indx, f_indx = self.index(*args, **kwargs) # Select samples if s_indx == slice(None): # so no actual selection was requested among samples. # thus proceed with original dataset if __debug__: debug('DS', 'in select() not selecting samples') ds = self else: # else do selection if __debug__: debug('DS', 'in select() selecting samples given selections' + str(s_indx)) ds = self.selectSamples(s_indx) # Select features if f_indx is not None: if __debug__: debug('DS', 'in select() selecting features given selections' + str(f_indx)) ds = ds.selectFeatures(f_indx) return ds def where(self, *args, **kwargs): """Obtain indexes of interesting samples/features. See select() for more information XXX somewhat obsoletes idsby... """ s_indx, f_indx = self.index(*args, **kwargs) if s_indx is not None and f_indx is not None: return s_indx, f_indx elif s_indx is not None: return s_indx else: return f_indx def __getitem__(self, *args): """Convinience dataset parts selection See select for more information """ # for cases like ['labels', 1] if len(args) == 1 and isinstance(args[0], tuple): args = args[0] args_, args = args, () for a in args_: if isinstance(a, slice) and \ isinstance(a.start, basestring): # for the constructs like ['labels':[1,2]] if a.stop is None or a.step is not None: raise ValueError, \ "Selection must look like ['chunks':[2,3]]" args += (a.start, a.stop) else: args += (a,) return self.select(*args) def permuteLabels(self, status, perchunk=True, assure_permute=False): """Permute the labels. TODO: rename status into something closer in semantics. :Parameters: status : bool Calling this method with set to True, the labels are permuted among all samples. If 'status' is False the original labels are restored. perchunk : bool If True permutation is limited to samples sharing the same chunk value. Therefore only the association of a certain sample with a label is permuted while keeping the absolute number of occurences of each label value within a certain chunk constant. assure_permute : bool If True, assures that labels are permutted, ie any one is different from the original one """ # local bindings _data = self._data if len(self.uniquelabels)<2: raise RuntimeError, \ "Call to permuteLabels is bogus since there is insuficient" \ " number of labels: %s" % self.uniquelabels if not status: # restore originals if _data.get('origlabels', None) is None: raise RuntimeError, 'Cannot restore labels. ' \ 'permuteLabels() has never been ' \ 'called with status == True.' self.labels = _data['origlabels'] _data.pop('origlabels') else: # store orig labels, but only if not yet done, otherwise multiple # calls with status == True will destroy the original labels if not _data.has_key('origlabels') \ or _data['origlabels'] == None: # bind old labels to origlabels _data['origlabels'] = _data['labels'] # copy labels _data['labels'] = copy.copy(_data['labels']) labels = _data['labels'] # now scramble if perchunk: for o in self.uniquechunks: labels[self.chunks == o] = \ N.random.permutation(labels[self.chunks == o]) else: labels = N.random.permutation(labels) self.labels = labels if assure_permute: if not (_data['labels'] != _data['origlabels']).any(): if not (assure_permute is True): if assure_permute == 1: raise RuntimeError, \ "Cannot assure permutation of labels %s for " \ "some reason with chunks %s and while " \ "perchunk=%s . Should not happen" % \ (self.labels, self.chunks, perchunk) else: assure_permute = 11 # make 10 attempts if __debug__: debug("DS", "Recalling permute to assure different labels") self.permuteLabels(status, perchunk=perchunk, assure_permute=assure_permute-1) def getRandomSamples(self, nperlabel): """Select a random set of samples. If 'nperlabel' is an integer value, the specified number of samples is randomly choosen from the group of samples sharing a unique label value ( total number of selected samples: nperlabel x len(uniquelabels). If 'nperlabel' is a list which's length has to match the number of unique label values. In this case 'nperlabel' specifies the number of samples that shall be selected from the samples with the corresponding label. The method returns a Dataset object containing the selected samples. """ # if interger is given take this value for all classes if isinstance(nperlabel, int): nperlabel = [ nperlabel for i in self.uniquelabels ] sample = [] # for each available class labels = self.labels for i, r in enumerate(self.uniquelabels): # get the list of pattern ids for this class sample += random.sample( (labels == r).nonzero()[0], nperlabel[i] ) return self.selectSamples( sample ) # def _setchunks(self, chunks): # """Sets chunks and recomputes uniquechunks # """ # self._data['chunks'] = N.array(chunks) # self._dsattr['uniquechunks'] = None # None!since we might not need them def getNSamples( self ): """Currently available number of patterns. """ return self._data['samples'].shape[0] def getNFeatures( self ): """Number of features per pattern. """ return self._data['samples'].shape[1] def getLabelsMap(self): """Stored labels map (if any) """ return self._dsattr.get('labels_map', None) def setLabelsMap(self, lm): """Set labels map. Checks for the validity of the mapping -- values should cover all existing labels in the dataset """ values = set(lm.values()) labels = set(self.uniquelabels) if not values.issuperset(labels): raise ValueError, \ "Provided mapping %s has some existing labels (out of %s) " \ "missing from mapping" % (list(values), list(labels)) self._dsattr['labels_map'] = lm def setSamplesDType(self, dtype): """Set the data type of the samples array. """ # local bindings _data = self._data if _data['samples'].dtype != dtype: _data['samples'] = _data['samples'].astype(dtype) def defineFeatureGroups(self, definition): """Assign `definition` to featuregroups XXX Feature-groups was not finished to be useful """ if not len(definition) == self.nfeatures: raise ValueError, \ "Length of feature group definition %i " \ "does not match the number of features %i " \ % (len(definition), self.nfeatures) self._dsattr['featuregroups'] = N.array(definition) def convertFeatureIds2FeatureMask(self, ids): """Returns a boolean mask with all features in `ids` selected. :Parameters: ids: list or 1d array To be selected features ids. :Returns: ndarray: dtype='bool' All selected features are set to True; False otherwise. """ fmask = N.repeat(False, self.nfeatures) fmask[ids] = True return fmask def convertFeatureMask2FeatureIds(self, mask): """Returns feature ids corresponding to non-zero elements in the mask. :Parameters: mask: 1d ndarray Feature mask. :Returns: ndarray: integer Ids of non-zero (non-False) mask elements. """ return mask.nonzero()[0] @staticmethod def _checkCopyConstructorArgs(**kwargs): """Common sanity check for Dataset copy constructor calls.""" # check if we have samples (somwhere) samples = None if kwargs.has_key('samples'): samples = kwargs['samples'] if samples is None and kwargs.has_key('data') \ and kwargs['data'].has_key('samples'): samples = kwargs['data']['samples'] if samples is None: raise DatasetError, \ "`samples` must be provided to copy constructor call." if not len(samples.shape) == 2: raise DatasetError, \ "samples must be in 2D shape in copy constructor call." # read-only class properties nsamples = property( fget=getNSamples ) nfeatures = property( fget=getNFeatures ) labels_map = property( fget=getLabelsMap, fset=setLabelsMap ) def datasetmethod(func): """Decorator to easily bind functions to a Dataset class """ if __debug__: debug("DS_", "Binding function %s to Dataset class" % func.func_name) # Bind the function setattr(Dataset, func.func_name, func) # return the original one return func # Following attributes adherent to the basic dataset Dataset._registerAttribute("samples", "_data", abbr='S', hasunique=False) Dataset._registerAttribute("labels", "_data", abbr='L', hasunique=True) Dataset._registerAttribute("chunks", "_data", abbr='C', hasunique=True) # samples ids (already unique by definition) Dataset._registerAttribute("origids", "_data", abbr='I', hasunique=False) # XXX This is the place to redo the Dataset base class in a more powerful, yet # simpler way. The basic goal is to allow for all kinds of attributes: # # 1) Samples attributes (per-sample full) # 2) Features attributes (per-feature stuff) # # 3) Dataset attributes (per-dataset stuff) # # Use cases: # # 1) labels and chunks -- goal: it should be possible to have multivariate # labels, e.g. to specify targets for a neural network output layer # # 2) feature binding/grouping -- goal: easily define ROIs in datasets, or # group/mark various types of feature so they could be selected or # discarded all together # # 3) Mappers, or chains of them (this should be possible already, but could # be better integrated to make applyMapper() obsolete). # # # Perform distortion correction on __init__(). The copy contructor # implementation should move into a separate classmethod. # # Think about implementing the current 'clever' attributes in terms of one-time # properties as suggested by Fernando on nipy-devel. # ... from mvpa.misc.state import ClassWithCollections, Collection from mvpa.misc.attributes import SampleAttribute, FeatureAttribute, \ DatasetAttribute # Remaining public interface of Dataset class _Dataset(ClassWithCollections): """The successor of Dataset. """ # placeholder for all three basic collections of a Dataset # put here to be able to check whether the AttributesCollector already # instanciated a particular collection # XXX maybe it should not do this at all for Dataset sa = None fa = None dsa = None # storage of samples in a plain NumPy array for fast access samples = None def __init__(self, samples, sa=None, fa=None, dsa=None): """ This is the generic internal constructor. Its main task is to allow for a maximum level of customization during dataset construction, including fast copy construction. Parameters ---------- samples : ndarray Data samples. sa : Collection Samples attributes collection. fa : Collection Features attributes collection. dsa : Collection Dataset attributes collection. """ # init base class ClassWithCollections.__init__(self) # Internal constructor -- users focus on init* Methods # Every dataset needs data (aka samples), completely data-driven # analyses might not even need labels, so this is the only mandatory # argument # XXX add checks self.samples = samples # Everything else in a dataset (except for samples) is organized in # collections # copy attributes from source collections (scol) into target # collections (tcol) for scol, tcol in ((sa, self.sa), (fa, self.fa), (dsa, self.dsa)): # make sure we have the target collection if tcol is None: # XXX maybe use different classes for the collections # but currently no reason to do so tcol = Collection(owner=self) # transfer the attributes if not scol is None: for name, attr in scol.items.iteritems(): # this will also update the owner of the attribute # XXX discuss the implications of always copying tcol.add(copy.copy(attr)) @classmethod def initSimple(klass, samples, labels, chunks): # use Numpy convention """ One line summary. Long description. Parameters ---------- samples : ndarray The two-dimensional samples matrix. labels : ndarray chunks : ndarray Returns ------- blah blah Notes ----- blah blah See Also -------- blah blah Examples -------- blah blah """ # Demo user contructor # compile the necessary samples attributes collection labels_ = SampleAttribute(name='labels') labels_.value = labels chunks_ = SampleAttribute(name='chunks') chunks_.value = chunks # feels strange that one has to give the name again # XXX why does items have to be a dict when each samples # attr already knows its name sa = Collection(items={'labels': labels_, 'chunks': chunks_}) # common checks should go into __init__ return klass(samples, sa=sa) def getNSamples( self ): """Currently available number of patterns. """ return self.samples.shape[0] def getNFeatures( self ): """Number of features per pattern. """ return self.samples.shape[1] # # @property # def idhash(self): # pass # # # def idsonboundaries(self, prior=0, post=0, # attributes_to_track=['labels', 'chunks'], # affected_labels=None, # revert=False): # pass # # # def summary(self, uniq=True, stats=True, idhash=False, lstats=True, # maxc=30, maxl=20): # pass # # # def summary_labels(self, maxc=30, maxl=20): # pass # # # def __iadd__(self, other): # pass # # # def __add__( self, other ): # pass # # # def copy(self): # pass # # # def selectFeatures(self, ids=None, sort=True, groups=None): # pass # # # def applyMapper(self, featuresmapper=None, samplesmapper=None, # train=True): # pass # # # def selectSamples(self, ids): # pass # # # def index(self, *args, **kwargs): # pass # # # def select(self, *args, **kwargs): # pass # # # def where(self, *args, **kwargs): # pass # # # def __getitem__(self, *args): # pass # # # def permuteLabels(self, status, perchunk=True, assure_permute=False): # pass # # # def getRandomSamples(self, nperlabel): # pass # # # def getLabelsMap(self): # pass # # # def setLabelsMap(self, lm): # pass # # # def setSamplesDType(self, dtype): # pass # # # def defineFeatureGroups(self, definition): # pass # # # def convertFeatureIds2FeatureMask(self, ids): # pass # # # def convertFeatureMask2FeatureIds(self, mask): # pass pymvpa-0.4.8/mvpa/datasets/channel.py000066400000000000000000000207321174541445200176070ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Dataset handling data structured in channels.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.datasets.mapped import MappedDataset from mvpa.mappers.mask import MaskMapper from mvpa.base.dochelpers import enhancedDocString import mvpa.support.copy as copy from mvpa.base import externals if externals.exists('scipy'): from scipy import signal class ChannelDataset(MappedDataset): """Dataset handling data structured into channels. Channels are assumes to contain several timepoints, thus this Dataset stores some additional properties (reference time `t0`, temporal distance of two timepoints `dt` and `channelids` (names)). """ def __init__(self, samples=None, dsattr=None, t0=None, dt=None, channelids=None, **kwargs): """Initialize ChannelDataset. :Parameters: samples: ndarray Three-dimensional array: (samples x channels x timepoints). t0: float Reference time of the first timepoint. Can be used to preserve information about the onset of some stimulation. Preferably in seconds. dt: float Temporal distance between two timepoints. Has to be given in seconds. Otherwise `samplingrate` property will not return `Hz`. channelids: list List of channel names. """ # if dsattr is none, set it to an empty dict if dsattr is None: dsattr = {} # check samples if not samples is None and len(samples.shape) != 3: raise ValueError, \ "ChannelDataset takes 3D array as samples." # charge dataset properties # but only if some value if (not dt is None) or not dsattr.has_key('ch_dt'): dsattr['ch_dt'] = dt if (not channelids is None) or not dsattr.has_key('ch_ids'): dsattr['ch_ids'] = channelids if (not t0 is None) or not dsattr.has_key('ch_t0'): dsattr['ch_t0'] = t0 # come up with mapper if fresh samples were provided if not samples is None: mapper = MaskMapper(N.ones(samples.shape[1:], dtype='bool')) else: # Doesn't make difference at the moment, but might come 'handy'? mapper = dsattr.get('mapper', None) # init dataset MappedDataset.__init__(self, samples=samples, mapper=mapper, dsattr=dsattr, **(kwargs)) __doc__ = enhancedDocString('ChannelDataset', locals(), MappedDataset) def substractBaseline(self, t=None): """Substract mean baseline signal from the each timepoint. The baseline is determined by computing the mean over all timepoints specified by `t`. The samples of the dataset are modified in-place and nothing is returned. :Parameter: t: int | float | None If an integer, `t` denotes the number of timepoints in the from the start of each sample to be used to compute the baseline signal. If a floating point value, `t` is the duration of the baseline window from the start of each sample in whatever unit corresponding to the datasets `samplingrate`. Finally, if `None` the `t0` property of the dataset is used to determine `t` as it would have been specified as duration. """ # if no baseline length is given, use t0 if t is None: t = N.abs(self.t0) # determine length of baseline in samples if isinstance(t, float): t = N.round(t * self.samplingrate) # get original data data = self.O # compute baseline # XXX: shouldn't this be done per chunk? baseline = N.mean(data[:, :, :t], axis=2) # remove baseline data -= baseline[..., N.newaxis] # put data back into dataset self.samples[:] = self.mapForward(data) if externals.exists('scipy'): def resample(self, nt=None, sr=None, dt=None, window='ham', inplace=True, **kwargs): """Convenience method to resample data sample channel-wise. Resampling target can be specified by number of timepoint or temporal distance or sampling rate. Please note that this method only operates on `ChannelDataset` and always returns such. :Parameters: nt: int Number of timepoints to resample to. dt: float Temporal distance of samples after resampling. sr: float Target sampling rate. inplace : bool If inplace=False, it would create and return a new dataset with new samples **kwargs: All additional arguments are passed to resample() from scipy.signal :Return: ChannelDataset """ if nt is None and sr is None and dt is None: raise ValueError, \ "Required argument missing. Either needs ntimepoints, sr or dt." # get data in original shape orig_data = self.O if len(orig_data.shape) != 3: raise ValueError, "resample() only works with data from ChannelDataset." orig_nt = orig_data.shape[2] orig_length = self.dt * orig_nt if nt is None: # translate dt or sr into nt if not dt is None: nt = orig_nt * float(self.dt) / dt elif not sr is None: nt = orig_nt * float(sr) / self.samplingrate else: raise RuntimeError, 'This should not happen!' else: raise RuntimeError, 'This should not happen!' nt = N.round(nt) # downsample data data = signal.resample(orig_data, nt, axis=2, window=window, **kwargs) new_dt = float(orig_length) / nt dsattr = self._dsattr # would be needed for not inplace generation if inplace: dsattr['ch_dt'] = new_dt # XXX We could have resampled range(nsamples) and # rounded it. and adjust then mapper's mask # accordingly instead of creating a new one. # It would give us opportunity to assess what # resampling did... mapper = MaskMapper(N.ones(data.shape[1:], dtype='bool')) # reassign a new mapper. dsattr['mapper'] = mapper self.samples = mapper.forward(data) return self else: # we have to pass dsattr inside to don't loose # some additional attributes such as # labels_map dsattr = copy.deepcopy(dsattr) return ChannelDataset(data=self._data, dsattr=dsattr, samples=data, t0=self.t0, dt=new_dt, channelids=self.channelids, copy_data=True, copy_dsattr=False) channelids = property(fget=lambda self: self._dsattr['ch_ids'], doc='List of channel IDs') t0 = property(fget=lambda self: self._dsattr['ch_t0'], doc='Temporal position of first sample in the ' \ 'timeseries (in seconds) -- possibly relative ' \ 'to stimulus onset.') dt = property(fget=lambda self: self._dsattr['ch_dt'], doc='Time difference between two samples ' \ '(in seconds).') samplingrate = property(fget=lambda self: 1.0 / self._dsattr['ch_dt'], doc='Yeah, sampling rate.') pymvpa-0.4.8/mvpa/datasets/eep.py000066400000000000000000000046351174541445200167540ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Dataset that gets its samples from an EEP binary file""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.datasets.channel import ChannelDataset from mvpa.misc.io.eepbin import EEPBin from mvpa.base.dochelpers import enhancedDocString from mvpa.base import warning class EEPDataset(ChannelDataset): """Dataset using a EEP binary file as source. EEP files are used by *eeprobe* a software for analysing even-related potentials (ERP), which was developed at the Max-Planck Institute for Cognitive Neuroscience in Leipzig, Germany. http://www.ant-neuro.com/products/eeprobe """ def __init__(self, samples=None, **kwargs): """Initialize EEPDataset. :Parameters: samples: Filename (string) of a EEP binary file or an `EEPBin` object """ # dataset props defaults dt = t0 = channelids = None # default way to use the constructor: with filename if not samples is None: if isinstance(samples, str): # open the eep file try: eb = EEPBin(samples) except RuntimeError, e: warning("ERROR: EEPDatasets: Cannot open samples file %s" \ % samples) # should we make also error? raise e elif isinstance(samples, EEPBin): # nothing special eb = samples else: raise ValueError, \ "EEPDataset constructor takes the filename of an " \ "EEP file or a EEPBin object as 'samples' argument." samples = eb.data dt = eb.dt channelids = eb.channels t0 = eb.t0 # init dataset ChannelDataset.__init__(self, samples=samples, dt=dt, t0=t0, channelids=channelids, **(kwargs)) __doc__ = enhancedDocString('EEPDataset', locals(), ChannelDataset) pymvpa-0.4.8/mvpa/datasets/event.py000066400000000000000000000174131174541445200173220ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Event-based dataset type""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.mappers.array import DenseArrayMapper from mvpa.mappers.boxcar import BoxcarMapper from mvpa.mappers.mask import MaskMapper from mvpa.datasets.base import Dataset from mvpa.datasets.mapped import MappedDataset from mvpa.mappers.base import ChainMapper, CombinedMapper from mvpa.base import warning class EventDataset(MappedDataset): """Event-based dataset This dataset type can be used to segment 'raw' data input into meaningful boxcar-shaped samples, by simply defining a list of events (see :class:`~mvpa.misc.support.Event`). Additionally, it can be used to add arbitrary information (as features) to each event-sample (extracted from the event list itself). An appropriate mapper is automatically constructed, that merges original samples and additional features into a common feature space and also separates them again during reverse-mapping. Otherwise, this dataset type is a regular dataset (in contrast to `MetaDataset`). The properties of an :class:`~mvpa.misc.support.Event` supported/required by this class are: `onset` An integer indicating the startpoint of an event as the sample index in the input data. `duration` How many input data samples following the onset sample should be considered for an event. The embedded :class:`~mvpa.mappers.boxcar.BoxcarMapper` will use the maximum boxlength (i.e., `duration`) of all defined events to create a regular-shaped data array. `label` The corresponding label of that event (numeric or literal). `chunk` An optional chunk id. `features` A list with an arbitrary number of features values (floats), that will be added to the feature vector of the corresponding sample. """ def __init__(self, samples=None, events=None, mask=None, bcshape=None, dametric=None, **kwargs): """ :Parameters: samples: ndarray 'Raw' input data from which boxcar-shaped samples will be extracted. events: sequence of `Event` instances Both an events `onset` and `duration` are assumed to be provided as #samples. The boxlength will be determined by the maximum duration of all events. mask: boolean array Only features corresponding to non-zero mask elements will be considered for the final dataset. The mask shape either has to match the shape of the generated boxcar-samples, or the shape of the 'raw' input samples. In the latter case, the mask is automatically expanded to cover the whole boxcar. If no mask is provided, a full mask will be constructed automatically. bcshape: tuple Shape of the boxcar samples generated by the embedded boxcar mapper. If not provided this is determined automatically. However, this required an extra mapping step. dametric: Metric Custom metric to be used by the embedded DenseArrayMapper. **kwargs All additional arguments are passed to the base class. """ # check if we are in copy constructor mode if events is None: MappedDataset.__init__(self, samples=samples, **kwargs) return # # otherwise we really want to freshly prepare a dataset # # loop over events and extract all meaningful information to charge # a boxcar mapper startpoints = [e['onset'] for e in events] try: durations = [e['duration'] for e in events] except KeyError: raise ValueError, "Each event must have a `duration`!" # we need a regular array, so all events must have a common # boxlength boxlength = max(durations) if __debug__: if not max(durations) == min(durations): warning('Boxcar mapper will use maximum boxlength (%i) of all ' 'provided Events.'% boxlength) # now look for stuff we need for the dataset itself try: labels = [e['label'] for e in events] except KeyError: raise ValueError, "Each event must have a `label`!" # chunks are optional chunks = [e['chunk'] for e in events if e.has_key('chunk')] if not len(chunks): chunks = None # optional stuff # extract additional features for each event extrafeatures = [e['features'] for e in events if e.has_key('features')] # sanity check for extra features if len(extrafeatures): if len(extrafeatures) == len(startpoints): try: # will fail if varying number of features per event extrafeatures = N.asanyarray(extrafeatures) except ValueError: raise ValueError, \ 'Unequal number of extra features per event' else: raise ValueError, \ 'Each event has to provide to same number of extra ' \ 'features.' else: extrafeatures = None # now build the mapper # we know the properties of the boxcar mapper, so now use it # to determine its output size unless it is already provided bcmapper = BoxcarMapper(startpoints, boxlength) # determine array mapper input shape, as a fail-safe procedure # in case no mask provided, and to check the mask sanity if we have one if bcshape is None: # map the data and look at the shape of the first sample # to determine the properties of the array mapper bcshape = bcmapper(samples)[0].shape # expand the mask if necessary (ie. if provided in raw sample space and # not in boxcar space if not mask is None: if len(mask.shape) < len(bcshape)-1: # complement needed dimensions mshape = mask.shape missing_dims = len(bcshape) - 1 - len(mshape) mask = mask.reshape((1,)*missing_dims + mshape) if len(mask.shape) == len(bcshape) - 1: # replicate per each boxcar elemenet mask = N.array([mask] * bcshape[0]) # now we can build the array mapper, using the optionally provided # custom metric amapper = DenseArrayMapper(mask=mask, shape=bcshape, metric=dametric) # now compose the full mapper for the main samples mapper = ChainMapper([bcmapper, amapper]) # if we have extra features, we need to combine them with the rest if not extrafeatures is None: # first half for main samples, second half simple mask mapper # for unstructured additional features mapper = CombinedMapper( (mapper, MaskMapper(mask=N.ones(extrafeatures.shape[1])))) # add extra features to the samples samples = (samples, extrafeatures) # finally init baseclass MappedDataset.__init__(self, samples=samples, labels=labels, chunks=chunks, mapper=mapper, **kwargs) pymvpa-0.4.8/mvpa/datasets/mapped.py000066400000000000000000000132651174541445200174500ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Mapped dataset""" __docformat__ = 'restructuredtext' import mvpa.support.copy as copy from mvpa.datasets import Dataset from mvpa.base.dochelpers import enhancedDocString from mvpa.misc.exceptions import DatasetError class MappedDataset(Dataset): """A `Dataset` which is created by applying a `Mapper` to the data. Upon contruction `MappedDataset` uses a `Mapper` to transform the samples from their original into the two-dimensional matrix representation that is required by the `Dataset` class. This class enhanced the `Dataset` interface with two additional methods: `mapForward()` and `mapReverse()`. Both take arbitrary data arrays (with matching shape) and transform them using the embedded mapper from the original dataspace into a one- or two-dimensional representation (for arrays corresponding to the shape of a single or multiple samples respectively) or vice versa. Most likely, this class will not be used directly, but rather indirectly through one of its subclasses (e.g. `MaskedDataset`). """ def __init__(self, samples=None, mapper=None, dsattr=None, **kwargs): """ If `samples` and `mapper` arguments are not `None` the mapper is used to forward-map the samples array and the result is passed to the `Dataset` constructor. :Parameters: mapper: Instance of `Mapper` This mapper will be embedded in the dataset and is used and updated, by all subsequent mapping or feature selection procedures. **kwargs: All other arguments are simply passed to and handled by the constructor of `Dataset`. """ # there are basically two mode for the constructor: # 1. internal mode - only data and dsattr dict # 2. user mode - samples != None # and mapper != None # see if dsattr is none, if so, set to empty dict if dsattr is None: dsattr = {} # if a mapper was passed, store it in dsattr dict that gets passed # to base Dataset if not mapper is None: # TODO: check mapper for compliance with dimensionality within _data # may be only within __debug__ dsattr['mapper'] = mapper # if the samples are passed to the special arg, use the mapper to # transform them. if not samples is None: if not dsattr.has_key('mapper') or dsattr['mapper'] is None: raise DatasetError, \ "Constructor of MappedDataset requires a mapper " \ "if unmapped samples are provided." Dataset.__init__(self, samples=mapper.forward(samples), dsattr=dsattr, **(kwargs)) else: Dataset._checkCopyConstructorArgs(samples=samples, dsattr=dsattr, **kwargs) Dataset.__init__(self, dsattr=dsattr, **(kwargs)) __doc__ = enhancedDocString('MappedDataset', locals(), Dataset) def mapForward(self, data): """Map data from the original dataspace into featurespace. """ return self.mapper.forward(data) def mapReverse(self, data): """Reverse map data from featurespace into the original dataspace. """ return self.mapper.reverse(data) def mapSelfReverse(self): """Reverse samples from featurespace into the original dataspace. """ return self.mapper.reverse(self.samples) def selectFeatures(self, ids, plain=False, sort=False): """Select features given their ids. The methods behaves similar to Dataset.selectFeatures(), but additionally takes care of adjusting the embedded mapper appropriately. :Parameters: ids: sequence Iterable container to select ids plain: boolean Flag whether to return MappedDataset (or just Dataset) sort: boolean Flag whether to sort Ids. Order matters and selectFeatures assumes incremental order. If not such, in non-optimized code selectFeatures would verify the order and sort """ # call base method to get selected feature subset if plain: sdata = Dataset(self._data, self._dsattr, check_data=False, copy_samples=False, copy_data=False, copy_dsattr=False) return sdata.selectFeatures(ids=ids, sort=sort) else: sdata = Dataset.selectFeatures(self, ids=ids, sort=sort) # since we have new DataSet we better have a new mapper sdata._dsattr['mapper'] = copy.deepcopy(sdata._dsattr['mapper']) if sort: sdata._dsattr['mapper'].selectOut(sorted(ids)) else: sdata._dsattr['mapper'].selectOut(ids) return sdata # read-only class properties mapper = property(fget=lambda self: self._dsattr['mapper']) samples_original = property(fget=mapSelfReverse, doc="Return samples in the original shape") # syntactic sugarings O = property(fget=mapSelfReverse, doc="Return samples in the original shape") pymvpa-0.4.8/mvpa/datasets/masked.py000066400000000000000000000061411174541445200174410ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Dataset with applied mask""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.datasets.mapped import MappedDataset from mvpa.mappers.array import DenseArrayMapper if __debug__: from mvpa.base import debug class MaskedDataset(MappedDataset): """Helper class which is `MappedDataset` with using `MaskMapper`. TODO: since what it does is simply some checkes/data_mangling in the constructor, it might be absorbed inside generic `MappedDataset` """ def __init__(self, samples=None, mask=None, **kwargs): """ :Parameters: mask: ndarray the chosen features equal the non-zero mask elements. """ # might contain the default mapper mapper = None # need if clause here as N.array(None) != None if not samples is None: # XXX should be asanyarray? but then smth segfaults on unittests samples = N.asarray(samples) mapper = DenseArrayMapper(mask=mask, shape=samples.shape[1:]) if not mapper is None: if samples is None: raise ValueError, \ "Constructor of MaskedDataset requires both a samples " \ "array and a mask if one of both is provided." # init base class -- MappedDataset takes care of all the forward # mapping stuff MappedDataset.__init__( self, samples=samples, mapper=mapper, **(kwargs)) else: MappedDataset.__init__(self, **(kwargs)) def selectFeaturesByMask(self, mask, plain=False): """Use a mask array to select features from the current set. :Parameters: mask : ndarray input mask plain : bool `True` directs to return a simple `Dataset`, `False` -- a new `MaskedDataset` object Returns a new MaskedDataset object with a view of the original pattern array (no copying is performed). The final selection mask only contains features that are present in the current feature mask AND the selection mask passed to this method. """ # AND new and old mask to get the common features comb_mask = N.logical_and(mask != 0, self.mapper.getMask(copy=False) != 0) if __debug__: debug('DS', "VERY SUBOPTIMAL - do not rely on performance") # transform mask into feature space fmask = self.mapper.forward( comb_mask != 0 ) #TODO all this will be gone soon anyway -- need proper selectIn within # a mapper return self.selectFeatures(fmask.nonzero()[0], plain=plain) pymvpa-0.4.8/mvpa/datasets/meta.py000066400000000000000000000154751174541445200171350ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Dataset container""" __docformat__ = 'restructuredtext' import numpy as N import random from mvpa.datasets.mapped import MappedDataset if __debug__: from mvpa.base import debug, warning class MetaDataset(object): """Dataset container The class is useful to combine several Datasets with different origin and type and bind them together. Such a combined dataset can then by used to e.g. pass it to a classifier. MetaDataset does not permanently duplicate data stored in the dataset it contains. The combined samples matrix is build on demand and samples attribute access is redirected to the first dataset in the container. Currently operations other than samples or feature selection are not fully supported, e.g. passing a MetaDataset to detrend() will initially result in a detrended MetaDataset, but the combined and detrended samples matrix will be lost after the next call to selectSamples() or selectFeatures(), which freshly pulls samples from all datasets in the container. """ # This class is intentionally _not_ implemented as a subclass of Dataset. # IMHO Dataset contains to much logic unecessary logic. # XXX implement MappedMetaDataset along with a MetaMapper that simply calls # the mappers in the datasets in the container; or maybe just add flag to # MetaDataset to behave like a MappedDataset def __init__(self, datasets): """Initialize dataset instance :Parameters: datasets : list """ # XXX Maybe add checks that all datasets have identical samples # attributes self.__datasets = datasets # contains the combine samples matrix for caching self.__samples = None def rebuildSamples(self): """Update the combined samples matrix from all underlying datasets. """ # note, that hstack will do a copy of _all_ data self.__samples = N.hstack([ds.samples for ds in self.__datasets]) def __getattr__(self, name): """Implemented to redirect access to underlying datasets. """ if name == 'samples': # do something to combine (and cache) samples arrays if self.__samples is None: self.rebuildSamples() return self.__samples else: # redirect all other to first dataset # ??? maybe limit to some specific supported ones return self.__datasets[0].__getattribute__(name) def selectFeatures(self, ids, sort=True): """Do feature selection on all underlying datasets at once. """ # determine which features belong to what dataset # and call its selectFeatures() accordingly ids = N.asanyarray(ids) result = [] fsum = 0 for ds in self.__datasets: # bool which meta feature ids belongs to this dataset selector = N.logical_and(ids < fsum + ds.nfeatures, ids >= fsum) # make feature ids relative to this dataset selected = ids[selector] - fsum # do feature selection on underlying dataset # XXX not sure if we should keep empty datasets? (probably) result.append(ds.selectFeatures(selected)) fsum += ds.nfeatures return MetaDataset(result) def applyMapper(self, *args, **kwargs): """Apply a mapper on all underlying datasets. """ return MetaDataset([ds.applyMapper(*args, **kwargs) \ for ds in self.__datasets]) def selectSamples(self, *args, **kwargs): """Select samples from all underlying datasets at once. """ return MetaDataset([ds.selectSamples(*args, **kwargs) \ for ds in self.__datasets]) def permuteLabels(self, *args, **kwargs): """Toggle label permutation. """ # permute on first self.__datasets[0].permuteLabels(*args, **kwargs) # and apply to all others for ds in self.__datasets[1:]: ds.samples[:] = self.__datasets[0].samples def getRandomSamples(self, nperlabel): """Return a MetaDataset with a random subset of samples. """ # if interger is given take this value for all classes if isinstance(nperlabel, int): nperlabel = [ nperlabel for i in self.__datasets[0].uniquelabels ] sample = [] # for each available class for i, r in enumerate(self.__datasets[0].uniquelabels): # get the list of pattern ids for this class sample += \ random.sample((self.__datasets[0].labels == r).nonzero()[0], nperlabel[i] ) return MetaDataset([ds.selectSamples(sample) \ for ds in self.__datasets]) def getNSamples( self ): """Currently available number of samples. """ return self.__datasets[0].nsamples def getNFeatures( self ): """Number of features per sample. """ return N.sum([ds.nfeatures for ds in self.__datasets]) def setSamplesDType(self, dtype): """Set the data type of the samples array. """ # reset samples self.__samples = None for ds in self.__datasets: if ds.samples.dtype != dtype: ds.samples = ds.samples.astype(dtype) def mapReverse(self, val): """Perform reverse mapping :Return: List of results per each used mapper and the corresponding part of the provided `val`. """ # assure array and transpose for easy slicing # i.e. transpose of 1D does nothing, but of 2D puts features # along first dimension val = N.asanyarray(val).T # do we have multiple or just one mflag = len(val.shape) > 1 result = [] fsum = 0 for ds in self.__datasets: # calculate upper border fsum_new = fsum + ds.nfeatures # now map back if mapper is present, otherwise just store # need to pass transposed!! if isinstance(ds, MappedDataset): result.append(ds.mapReverse(val[fsum:fsum_new].T)) else: result.append(val[fsum:fsum_new].T) fsum = fsum_new return result # read-only class properties nsamples = property(fget=getNSamples) nfeatures = property(fget=getNFeatures) datasets = property(fget=lambda self: self.__datasets) pymvpa-0.4.8/mvpa/datasets/miscfx.py000066400000000000000000000310061174541445200174640ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Misc function performing operations on datasets. All the functions defined in this module must accept dataset as the first argument since they are bound to Dataset class in the trailer. """ __docformat__ = 'restructuredtext' from operator import isSequenceType import numpy as N from mvpa.datasets.base import Dataset, datasetmethod from mvpa.base.dochelpers import table2string from mvpa.misc.support import getBreakPoints from mvpa.base import externals, warning if __debug__: from mvpa.base import debug if externals.exists('scipy'): from mvpa.datasets.miscfx_sp import detrend @datasetmethod def zscore(dataset, mean=None, std=None, perchunk=True, baselinelabels=None, pervoxel=True, targetdtype='float64'): """Z-Score the samples of a `Dataset` (in-place). `mean` and `std` can be used to pass custom values to the z-scoring. Both may be scalars or arrays. All computations are done *in place*. Data upcasting is done automatically if necessary into `targetdtype` If `baselinelabels` provided, and `mean` or `std` aren't provided, it would compute the corresponding measure based only on labels in `baselinelabels` If `perchunk` is True samples within the same chunk are z-scored independent of samples from other chunks, e.i. mean and standard deviation are calculated individually. """ if __debug__ and perchunk \ and N.array(dataset.samplesperchunk.values()).min() <= 2: warning("Z-scoring chunk-wise and one chunk with less than three " "samples will set features in these samples to either zero " "(with 1 sample in a chunk) " "or -1/+1 (with 2 samples in a chunk).") # cast to floating point datatype if necessary if str(dataset.samples.dtype).startswith('uint') \ or str(dataset.samples.dtype).startswith('int'): dataset.setSamplesDType(targetdtype) def doit(samples, mean, std, statsamples=None): """Internal method.""" if statsamples is None: # if nothing provided -- mean/std on all samples statsamples = samples if pervoxel: axisarg = {'axis':0} else: axisarg = {} # calculate mean if necessary if mean is None: mean = statsamples.mean(**axisarg) # de-mean samples -= mean # calculate std-deviation if necessary # XXX YOH: would that be actually what we want? # may be we want actually estimate of deviation from the mean, # which per se might be not statsamples.mean (see above)? # if logic to be changed -- adjust ZScoreMapper as well if std is None: std = statsamples.std(**axisarg) # do the z-scoring if pervoxel: # Assure std being an array if N.isscalar(std): std = N.ones(samples.shape[1]) else: # and so we don't perform list comparison to 0 std = N.asanyarray(std) samples[:, std != 0] /= std[std != 0] else: samples /= std return samples if baselinelabels is None: statids = None else: statids = set(dataset.idsbylabels(baselinelabels)) # for the sake of speed yoh didn't simply create a list # [True]*dataset.nsamples to provide easy selection of everything if perchunk: for c in dataset.uniquechunks: slicer = N.where(dataset.chunks == c)[0] if not statids is None: statslicer = list(statids.intersection(set(slicer))) dataset.samples[slicer] = doit(dataset.samples[slicer], mean, std, dataset.samples[statslicer]) else: slicedsamples = dataset.samples[slicer] dataset.samples[slicer] = doit(slicedsamples, mean, std, slicedsamples) elif statids is None: doit(dataset.samples, mean, std, dataset.samples) else: doit(dataset.samples, mean, std, dataset.samples[list(statids)]) @datasetmethod def aggregateFeatures(dataset, fx=N.mean): """Apply a function to each row of the samples matrix of a dataset. The functor given as `fx` has to honour an `axis` keyword argument in the way that NumPy used it (e.g. NumPy.mean, var). :Returns: a new `Dataset` object with the aggregated feature(s). """ agg = fx(dataset.samples, axis=1) return Dataset(samples=N.array(agg, ndmin=2).T, labels=dataset.labels, chunks=dataset.chunks) @datasetmethod def removeInvariantFeatures(dataset): """Returns a new dataset with all invariant features removed. """ return dataset.selectFeatures(dataset.samples.std(axis=0).nonzero()[0]) @datasetmethod def coarsenChunks(source, nchunks=4): """Change chunking of the dataset Group chunks into groups to match desired number of chunks. Makes sense if originally there were no strong groupping into chunks or each sample was independent, thus belonged to its own chunk :Parameters: source : Dataset or list of chunk ids dataset or list of chunk ids to operate on. If Dataset, then its chunks get modified nchunks : int desired number of chunks """ if isinstance(source, Dataset): chunks = source.chunks else: chunks = source chunks_unique = N.unique(chunks) nchunks_orig = len(chunks_unique) if nchunks_orig < nchunks: raise ValueError, \ "Original number of chunks is %d. Cannot coarse them " \ "to get %d chunks" % (nchunks_orig, nchunks) # figure out number of samples per each chunk counts = dict(zip(chunks_unique, [ 0 ] * len(chunks_unique))) for c in chunks: counts[c] += 1 # now we need to group chunks to get more or less equalized number # of samples per chunk. No sophistication is done -- just # consecutively group to get close to desired number of samples # per chunk avg_chunk_size = N.sum(counts.values())*1.0/nchunks chunks_groups = [] cur_chunk = [] nchunks = 0 cur_chunk_nsamples = 0 samples_counted = 0 for i, c in enumerate(chunks_unique): cc = counts[c] cur_chunk += [c] cur_chunk_nsamples += cc # time to get a new chunk? if (samples_counted + cur_chunk_nsamples >= (nchunks+1)*avg_chunk_size) or i==nchunks_orig-1: chunks_groups.append(cur_chunk) samples_counted += cur_chunk_nsamples cur_chunk_nsamples = 0 cur_chunk = [] nchunks += 1 if len(chunks_groups) != nchunks: warning("Apparently logic in coarseChunks is wrong. " "It was desired to get %d chunks, got %d" % (nchunks, len(chunks_groups))) # remap using groups # create dictionary chunks_map = {} for i, group in enumerate(chunks_groups): for c in group: chunks_map[c] = i chunks_new = [chunks_map[x] for x in chunks] if __debug__: debug("DS_", "Using dictionary %s to remap old chunks %s into new %s" % (chunks_map, chunks, chunks_new)) if isinstance(source, Dataset): if __debug__: debug("DS", "Coarsing %d chunks into %d chunks for %s" %(nchunks_orig, len(chunks_new), source)) source.chunks = chunks_new return else: return chunks_new @datasetmethod def getSamplesPerChunkLabel(dataset): """Returns an array with the number of samples per label in each chunk. Array shape is (chunks x labels). :Parameters: dataset: Dataset Source dataset. """ ul = dataset.uniquelabels uc = dataset.uniquechunks count = N.zeros((len(uc), len(ul)), dtype='uint') for cc, c in enumerate(uc): for lc, l in enumerate(ul): count[cc, lc] = N.sum(N.logical_and(dataset.labels == l, dataset.chunks == c)) return count class SequenceStats(dict): """Simple helper to provide representation of sequence statistics Matlab analog: http://cfn.upenn.edu/aguirre/code/matlablib/mseq/mtest.m WARNING: Experimental -- API might change without warning! Current implementation is ugly! """ def __init__(self, seq, order=2):#, chunks=None, perchunk=False): """Initialize SequenceStats :Parameters: seq : list or ndarray Actual sequence of labels :Keywords: order : int Maximal order of counter-balancing check. For perfect counterbalancing all matrices should be identical """ """ chunks : None or list or ndarray Chunks to use if `perchunk`=True perchunk .... TODO """ dict.__init__(self) self.order = order self._seq = seq self.stats = None self._str_stats = None self.__compute() def __repr__(self): """Representation of SequenceStats """ return "SequenceStats(%s, order=%d)" % (repr(self._seq), self.order) def __str__(self): return self._str_stats def __compute(self): """Compute stats and string representation """ # Do actual computation order = self.order seq = list(self._seq) # assure list nsamples = len(seq) # # of samples/labels ulabels = sorted(list(set(seq))) # unique labels nlabels = len(ulabels) # # of labels # mapping for labels labels_map = dict([(l, i) for i,l in enumerate(ulabels)]) # map sequence first seqm = [labels_map[i] for i in seq] nperlabel = N.bincount(seqm) res = dict(ulabels=ulabels) # Estimate counter-balance cbcounts = N.zeros((order, nlabels, nlabels), dtype=int) for cb in xrange(order): for i,j in zip(seqm[:-(cb+1)], seqm[cb+1:]): cbcounts[cb, i, j] += 1 res['cbcounts'] = cbcounts """ Lets compute relative counter-balancing Ideally, nperlabel[i]/nlabels should precede each label """ # Autocorrelation corr = [] # for all possible shifts: for shift in xrange(1, nsamples): shifted = seqm[shift:] + seqm[:shift] # ??? User pearsonsr with p may be? corr += [N.corrcoef(seqm, shifted)[0, 1]] # ??? report high (anti)correlations? res['corrcoef'] = corr = N.array(corr) res['sumabscorr'] = sumabscorr = N.sum(N.abs(corr)) self.update(res) # Assign textual summary # XXX move into a helper function and do on demand t = [ [""] * (1 + self.order*(nlabels+1)) for i in xrange(nlabels+1) ] t[0][0] = "Labels/Order" for i, l in enumerate(ulabels): t[i+1][0] = '%s:' % l for cb in xrange(order): t[0][1+cb*(nlabels+1)] = "O%d" % (cb+1) for i in xrange(nlabels+1): t[i][(cb+1)*(nlabels+1)] = " | " m = cbcounts[cb] # ??? there should be better way to get indexes ind = N.where(~N.isnan(m)) for i, j in zip(*ind): t[1+i][1+cb*(nlabels+1)+j] = '%d' % m[i, j] sout = "Original sequence had %d entries from set %s\n" \ % (len(seq), ulabels) + \ "Counter-balance table for orders up to %d:\n" % order \ + table2string(t) sout += "Correlations: min=%.2g max=%.2g mean=%.2g sum(abs)=%.2g" \ % (min(corr), max(corr), N.mean(corr), sumabscorr) self._str_stats = sout def plot(self): """Plot correlation coefficients """ externals.exists('pylab', raiseException=True) import pylab as P P.plot(self['corrcoef']) P.title('Auto-correlation of the sequence') P.xlabel('Offset') P.ylabel('Correlation Coefficient') P.show() pymvpa-0.4.8/mvpa/datasets/miscfx_sp.py000066400000000000000000000167271174541445200202030ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Misc function performing operations on datasets which are based on scipy """ __docformat__ = 'restructuredtext' from mvpa.base import externals import numpy as N np = N # just for easy merging of changes into >=0.5 from operator import isSequenceType from mvpa.datasets.base import Dataset, datasetmethod from mvpa.misc.support import getBreakPoints if externals.exists('scipy', raiseException=True): from scipy import signal from scipy.linalg import lstsq from scipy.special import legendre def legendre_(n, x): # Scipy 0.8.0 (and possibly later) has regression of reporting # 'inf's for negative boundary. Lets guard against it for now leg = legendre(n) r = leg(x) infs = np.isinf(r) if np.any(infs): r[infs] = leg(x[infs] + 1e-10) # offset to try to overcome problems return r @datasetmethod def detrend(dataset, perchunk=False, model='linear', polyord=None, opt_reg=None): """ Given a dataset, detrend the data inplace either entirely or per each chunk :Parameters: dataset : Dataset dataset to operate on perchunk : bool either to operate on whole dataset at once or on each chunk separately model Type of detrending model to run. If 'linear' or 'constant', scipy.signal.detrend is used to perform a linear or demeaning detrend. Polynomial detrending is activated when 'regress' is used or when polyord or opt_reg are specified. polyord : int or list Order of the Legendre polynomial to remove from the data. This will remove every polynomial up to and including the provided value. For example, 3 will remove 0th, 1st, 2nd, and 3rd order polynomials from the data. N.B.: The 0th polynomial is the baseline shift, the 1st is the linear trend. If you specify a single int and perchunk is True, then this value is used for each chunk. You can also specify a different polyord value for each chunk by providing a list or ndarray of polyord values the length of the number of chunks. opt_reg : ndarray Optional ndarray of additional information to regress out from the dataset. One example would be to regress out motion parameters. As with the data, time is on the first axis. """ if polyord is not None or opt_reg is not None: model='regress' if model in ['linear', 'constant']: # perform scipy detrend bp = 0 # no break points by default if perchunk: try: bp = getBreakPoints(dataset.chunks) except ValueError, e: raise ValueError, \ "Failed to assess break points between chunks. Often " \ "that is due to discontinuities within a chunk, which " \ "ruins idea of per-chunk detrending. Original " \ "exception was: %s" % str(e) dataset.samples[:] = signal.detrend(dataset.samples, axis=0, type=model, bp=bp) elif model in ['regress']: # perform regression-based detrend return __detrend_regress(dataset, perchunk=perchunk, polyord=polyord, opt_reg=opt_reg) else: # raise exception because not found raise ValueError('Specified model type (%s) is unknown.' % (model)) def __detrend_regress(dataset, perchunk=True, polyord=None, opt_reg=None): """ Given a dataset, perform a detrend inplace, regressing out polynomial terms as well as optional regressors, such as motion parameters. :Parameters: dataset : Dataset Dataset to operate on perchunk : bool Either to operate on whole dataset at once or on each chunk separately. If perchunk is True, all the samples within a chunk should be contiguous and the chunks should be sorted in order from low to high. polyord : int Order of the Legendre polynomial to remove from the data. This will remove every polynomial up to and including the provided value. For example, 3 will remove 0th, 1st, 2nd, and 3rd order polynomials from the data. N.B.: The 0th polynomial is the baseline shift, the 1st is the linear trend. If you specify a single int and perchunk is True, then this value is used for each chunk. You can also specify a different polyord value for each chunk by providing a list or ndarray of polyord values the length of the number of chunks. opt_reg : ndarray Optional ndarray of additional information to regress out from the dataset. One example would be to regress out motion parameters. As with the data, time is on the first axis. """ # Process the polyord to be a list with length of the number of chunks if not polyord is None: if not isSequenceType(polyord): # repeat to be proper length polyord = [polyord]*len(dataset.uniquechunks) elif perchunk and len(polyord) != len(dataset.uniquechunks): raise ValueError("If you specify a sequence of polyord values " + \ "they sequence length must match the " + \ "number of unique chunks in the dataset.") # loop over chunks if necessary if perchunk: # get the unique chunks uchunks = dataset.uniquechunks # loop over each chunk reg = [] for n, chunk in enumerate(uchunks): # get the indices for that chunk cinds = dataset.chunks == chunk # see if add in polyord values if not polyord is None: # create the timespan x = N.linspace(-1, 1, cinds.sum()) # create each polyord with the value for that chunk for n in range(polyord[n] + 1): newreg = N.zeros((dataset.nsamples, 1)) newreg[cinds, 0] = legendre_(n, x) reg.append(newreg) else: # take out mean over entire dataset reg = [] # see if add in polyord values if not polyord is None: # create the timespan x = N.linspace(-1, 1, dataset.nsamples) for n in range(polyord[0] + 1): reg.append(legendre_(n, x)[:, N.newaxis]) # see if add in optional regs if not opt_reg is None: # add in the optional regressors, too reg.append(opt_reg) # combine the regs if len(reg) > 0: if len(reg) > 1: regs = N.hstack(reg) else: regs = reg[0] else: # no regs to remove raise ValueError("You must specify at least one " + \ "regressor to regress out.") # perform the regression res = lstsq(regs, dataset.samples) # remove all but the residuals yhat = N.dot(regs, res[0]) dataset.samples -= yhat # return the results return res, regs pymvpa-0.4.8/mvpa/datasets/nifti.py000066400000000000000000000471601174541445200173140ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Dataset that gets its samples from a NIfTI file""" __docformat__ = 'restructuredtext' from mvpa.base import externals import sys import numpy as N from mvpa.support.copy import deepcopy if __debug__: from mvpa.base import debug if externals.exists('nifti', raiseException=True): if sys.version_info[:2] >= (2, 5): # enforce absolute import NiftiImage = __import__('nifti', globals(), locals(), [], 0).NiftiImage else: # little trick to be able to import 'nifti' package (which has same # name) oldname = __name__ # crazy name with close to zero possibility to cause whatever __name__ = 'iaugf9zrkjsbdv89' from nifti import NiftiImage # restore old settings __name__ = oldname from mvpa.datasets.base import Dataset from mvpa.datasets.mapped import MappedDataset from mvpa.datasets.event import EventDataset from mvpa.mappers.base import CombinedMapper from mvpa.mappers.metric import DescreteMetric, cartesianDistance from mvpa.mappers.array import DenseArrayMapper from mvpa.base import warning def getNiftiFromAnySource(src, ensure=False, enforce_dim=None, scale_data=True): """Load/access NIfTI data from files or instances. :Parameters: src: str | NiftiImage Filename of a NIfTI image or a `NiftiImage` instance. ensure : bool If True, through ValueError exception if cannot be loaded. enforce_dim : int or None If not None, it is the dimensionality of the data to be enforced, commonly 4D for the data, and 3D for the mask in case of fMRI. scale_data : bool NIfTI header specifies scl_slope and scl_inter for scaling and offsetting the data. By default those will get applied to the data (change in behavior post 0.4.6). :Returns: NiftiImage | None If the source is not supported None is returned. """ nifti = None # figure out what type if isinstance(src, str): # open the nifti file try: nifti = NiftiImage(src) except RuntimeError, e: warning("ERROR: NiftiDatasets: Cannot open NIfTI file %s" \ % src) raise e elif isinstance(src, NiftiImage): # nothing special nifti = src elif (isinstance(src, list) or isinstance(src, tuple)) \ and len(src)>0 \ and (isinstance(src[0], str) or isinstance(src[0], NiftiImage)): # load from a list of given entries if enforce_dim is not None: enforce_dim_ = enforce_dim - 1 else: enforce_dim_ = None if __debug__: debug('DS_NIFTI', 'Loading from a sequence of sources: %s' % (src,)) srcs = [getNiftiFromAnySource(s, ensure=ensure, enforce_dim=enforce_dim_) for s in src] if __debug__: # lets check if they all have the same dimensionality shapes = [s.data.shape for s in srcs] if not N.all([s == shapes[0] for s in shapes]): raise ValueError, \ "Input volumes contain variable number of dimensions:" \ " %s" % (shapes,) # Combine them all into a single beast # And since they all could have varying scl_* - reset those hdr = srcs[0].header hdr['scl_slope'] = 1. hdr['scl_inter'] = 0. nifti = NiftiImage(N.array([s.asarray() for s in srcs]), hdr) elif ensure: raise ValueError, "Cannot load NIfTI from %s" % (src,) if nifti is not None and enforce_dim is not None: shape, new_shape = nifti.data.shape, None lshape = len(shape) # check if we need to tune up shape if lshape < enforce_dim: # if we are missing required dimension(s) new_shape = (1,)*(enforce_dim-lshape) + shape elif lshape > enforce_dim: # if there are bogus dimensions at the beginning bogus_dims = lshape - enforce_dim if shape[:bogus_dims] != (1,)*bogus_dims: raise ValueError, \ "Cannot enforce %dD on data with shape %s" \ % (enforce_dim, shape) new_shape = shape[bogus_dims:] # tune up shape if needed if new_shape is not None: if __debug__: debug('DS_NIFTI', 'Enforcing shape %s for %s data from %s' % (new_shape, shape, src)) nifti.data.shape = new_shape if nifti is not None and scale_data: if nifti.slope and not (nifti.slope == 1.0 and nifti.intercept == 0.0): if __debug__: debug('DS_NIFTI', 'Scaling the data from %s' % (src,)) nifti.data = nifti.getScaledData() else: # Do nothing -- just debug message if __debug__: debug('DS_NIFTI', 'Although scaling was requested, data from %s' ' has no scaling parameters set -- thus no scaling' % (src,)) return nifti def getNiftiData(nim): """Convenience function to extract the data array from a NiftiImage This function will make use of advanced features of PyNIfTI to prevent unnecessary copying if a sufficent version is available. """ if externals.exists('nifti ge 0.20090205.1'): return nim.data else: return nim.asarray() def _get_safe_header(nids): """Given *NiftiDataset, returns a copy of NIfTI header with reset scl_ fields """ hdr = nids.niftihdr.copy() hdr['scl_slope'] = 1. hdr['scl_inter'] = 0. return hdr class NiftiDataset(MappedDataset): """Dataset loading its samples from a NIfTI image or file. Samples can be loaded from a NiftiImage instance or directly from a NIfTI file. This class stores all relevant information from the NIfTI file header and provides information about the metrics and neighborhood information of all voxels. Most importantly it allows to map data back into the original data space and format via :meth:`~mvpa.datasets.nifti.NiftiDataset.map2Nifti`. This class allows for convenient pre-selection of features by providing a mask to the constructor. Only non-zero elements from this mask will be considered as features. NIfTI files are accessed via PyNIfTI. See http://niftilib.sourceforge.net/pynifti/ for more information about pynifti. """ # XXX: Every dataset should really have an example of howto instantiate # it (necessary parameters). def __init__(self, samples=None, mask=None, dsattr=None, enforce_dim=4, scale_data=True, **kwargs): """ :Parameters: samples: str | NiftiImage Filename of a NIfTI image or a `NiftiImage` instance. mask: str | NiftiImage | ndarray Filename of a NIfTI image or a `NiftiImage` instance or an ndarray of appropriate shape. enforce_dim : int or None If not None, it is the dimensionality of the data to be enforced, commonly 4D for the data, and 3D for the mask in case of fMRI. scale_data : bool NIfTI header specifies scl_slope and scl_inter for scaling and offsetting the data. By default those will get applied to the data (change in behavior post 0.4.6). """ # if in copy constructor mode if not dsattr is None and dsattr.has_key('mapper'): MappedDataset.__init__(self, samples=samples, dsattr=dsattr, **kwargs) return # # the following code only deals with contructing fresh datasets from # scratch # # load the samples niftisamples = getNiftiFromAnySource(samples, ensure=True, enforce_dim=enforce_dim, scale_data=scale_data) samples = niftisamples.data # do not put the whole NiftiImage in the dict as this will most # likely be deepcopy'ed at some point and ensuring data integrity # of the complex Python-C-Swig hybrid might be a tricky task. # Only storing the header dict should achieve the same and is more # memory efficient and even simpler dsattr = {'niftihdr': niftisamples.header} # figure out what the mask is, but onyl handle known cases, the rest # goes directly into the mapper which maybe knows more niftimask = getNiftiFromAnySource(mask, scale_data=scale_data) if niftimask is None: pass elif isinstance(niftimask, N.ndarray): mask = niftimask else: mask = getNiftiData(niftimask) # build an appropriate mapper that knows about the metrics of the NIfTI # data # NiftiDataset uses a DescreteMetric with cartesian # distance and element size from the NIfTI header # 'voxdim' is (x,y,z) while 'samples' are (t,z,y,x) elementsize = [i for i in reversed(niftisamples.voxdim)] mapper = DenseArrayMapper(mask=mask, shape=samples.shape[1:], metric=DescreteMetric(elementsize=elementsize, distance_function=cartesianDistance)) MappedDataset.__init__(self, samples=samples, mapper=mapper, dsattr=dsattr, **kwargs) def map2Nifti(self, data=None): """Maps a data vector into the dataspace and wraps it with a NiftiImage. The header data of this object is used to initialize the new NiftiImage (scl_slope and scl_inter are reset to 1.0 and 0.0 accordingly). :Parameters: data : ndarray or Dataset The data to be wrapped into NiftiImage. If None (default), it would wrap samples of the current dataset. If it is a Dataset instance -- takes its samples for mapping """ if data is None: data = self.samples elif isinstance(data, Dataset): # ease users life data = data.samples dsarray = self.mapper.reverse(data) return NiftiImage(dsarray, _get_safe_header(self)) def getDt(self): """Return the temporal distance of two samples/volumes. This method tries to be clever and always returns `dt` in seconds, by using unit information from the NIfTI header. If such information is not present the assumed unit will also be `seconds`. """ # plain value hdr = self.niftihdr TR = hdr['pixdim'][4] # by default assume seconds as unit and do not scale scale = 1.0 # figure out units, if available if hdr.has_key('time_unit'): unit_code = hdr['time_unit'] / 8 elif hdr.has_key('xyzt_unit'): unit_code = int(hdr['xyzt_unit']) / 8 else: warning("No information on time units is available. Assuming " "seconds") unit_code = 0 # handle known units # XXX should be refactored to use actual unit labels from pynifti # when version 0.20090205 or later is assumed to be available on all # machines if unit_code in [0, 1, 2, 3]: if unit_code == 0: warning("Time units were not specified in NiftiImage. " "Assuming seconds.") scale = [ 1.0, 1.0, 1e-3, 1e-6 ][unit_code] else: warning("Time units are incorrectly coded: value %d whenever " "allowed are 8 (sec), 16 (millisec), 24 (microsec). " "Assuming seconds." % (unit_code * 8,) ) return TR * scale niftihdr = property(fget=lambda self: self._dsattr['niftihdr'], doc='Access to the NIfTI header dictionary.') dt = property(fget=getDt, doc='Time difference between two samples (in seconds). ' 'AKA TR in fMRI world.') samplingrate = property(fget=lambda self: 1.0 / self.dt, doc='Sampling rate (based on .dt).') class ERNiftiDataset(EventDataset): """Dataset with event-defined samples from a NIfTI timeseries image. This is a convenience dataset to facilitate the analysis of event-related fMRI datasets. Boxcar-shaped samples are automatically extracted from the full timeseries using :class:`~mvpa.misc.support.Event` definition lists. For each event all volumes covering that particular event in time (including partial coverage) are used to form the corresponding sample. The class supports the conversion of events defined in 'realtime' into the descrete temporal space defined by the NIfTI image. Moreover, potentially varying offsets between true event onset and timepoint of the first selected volume can be stored as an additional feature in the dataset. Additionally, the dataset supports masking. This is done similar to the masking capabilities of :class:`~mvpa.datasets.nifti.NiftiDataset`. However, the mask can either be of the same shape as a single NIfTI volume, or can be of the same shape as the generated boxcar samples, i.e. a samples consisting of three volumes with 24 slices and 64x64 inplane resolution needs a mask with shape (3, 24, 64, 64). In the former case the mask volume is automatically expanded to be identical in a volumes of the boxcar. """ def __init__(self, samples=None, events=None, mask=None, evconv=False, storeoffset=False, tr=None, enforce_dim=4, scale_data=True, **kwargs): """ :Parameters: mask: str | NiftiImage | ndarray Filename of a NIfTI image or a `NiftiImage` instance or an ndarray of appropriate shape. evconv: bool Convert event definitions using `onset` and `duration` in some temporal unit into #sample notation. storeoffset: bool Whether to store temproal offset information when converting Events into descrete time. Only considered when evconv == True. tr: float Temporal distance of two adjacent NIfTI volumes. This can be used to override the corresponding value in the NIfTI header. enforce_dim : int or None If not None, it is the dimensionality of the data to be enforced, commonly 4D for the data, and 3D for the mask in case of fMRI. scale_data : bool NIfTI header specifies scl_slope and scl_inter for scaling and offsetting the data. By default those will get applied to the data (change in behavior post 0.4.6). """ # check if we are in copy constructor mode if events is None: EventDataset.__init__(self, samples=samples, events=events, mask=mask, **kwargs) return nifti = getNiftiFromAnySource(samples, ensure=True, enforce_dim=enforce_dim, scale_data=scale_data) # no copying samples = nifti.data # do not put the whole NiftiImage in the dict as this will most # likely be deepcopy'ed at some point and ensuring data integrity # of the complex Python-C-Swig hybrid might be a tricky task. # Only storing the header dict should achieve the same and is more # memory efficient and even simpler dsattr = {'niftihdr': nifti.header} # determine TR, take from NIfTI header by default dt = nifti.rtime # override if necessary if not tr is None: dt = tr # NiftiDataset uses a DescreteMetric with cartesian # distance and element size from the NIfTI header # 'voxdim' is (x,y,z) while 'samples' are (t,z,y,x) elementsize = [dt] + [i for i in reversed(nifti.voxdim)] # XXX metric might be inappropriate if boxcar has length 1 # might move metric setup after baseclass init and check what has # really happened metric = DescreteMetric(elementsize=elementsize, distance_function=cartesianDistance) # convert EVs if necessary -- not altering original if evconv: if dt == 0: raise ValueError, "'dt' cannot be zero when converting Events" events = [ev.asDescreteTime(dt, storeoffset) for ev in events] else: # do not touch the original events = deepcopy(events) # forcefully convert onset and duration into integers, as expected # by the baseclass for ev in events: oldonset = ev['onset'] oldduration = ev['duration'] ev['onset'] = int(ev['onset']) ev['duration'] = int(ev['duration']) if not oldonset == ev['onset'] \ or not oldduration == ev['duration']: warning("Loosing information during automatic integer " "conversion of EVs. Consider an explicit conversion" " by setting `evconv` in ERNiftiDataset().") # pull mask array from NIfTI (if present) if mask is None: pass elif isinstance(mask, N.ndarray): # plain array can be passed on to base class pass else: mask_nim = getNiftiFromAnySource(mask, scale_data=scale_data) if not mask_nim is None: mask = getNiftiData(mask_nim) else: raise ValueError, "Cannot load mask from '%s'" % mask # finally init baseclass EventDataset.__init__(self, samples=samples, events=events, mask=mask, dametric=metric, dsattr=dsattr, **kwargs) def map2Nifti(self, data=None): """Maps a data vector into the dataspace and wraps it with a NiftiImage. The header data of this object is used to initialize the new NiftiImage (scl_slope and scl_inter are reset to 1.0 and 0.0 accordingly). .. note:: Only the features corresponding to voxels are mapped back -- not any additional features passed via the Event definitions. :Parameters: data : ndarray or Dataset The data to be wrapped into NiftiImage. If None (default), it would wrap samples of the current dataset. If it is a Dataset instance -- takes its samples for mapping """ if data is None: data = self.samples elif isinstance(data, Dataset): # ease users life data = data.samples mr = self.mapper.reverse(data) # trying to determine which part should go into NiftiImage if isinstance(self.mapper, CombinedMapper): # we have additional feature in the dataset -- ignore them mr = mr[0] else: pass return NiftiImage(mr, _get_safe_header(self)) niftihdr = property(fget=lambda self: self._dsattr['niftihdr'], doc='Access to the NIfTI header dictionary.') pymvpa-0.4.8/mvpa/datasets/splitters.py000066400000000000000000000572121174541445200202330ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Collection of dataset splitters. Module Description ================== Splitters are destined to split the provided dataset varous ways to simplify cross-validation analysis, implement boosting of the estimates, or sample null-space via permutation testing. Most of the splitters at the moment split 2-ways -- conventionally first part is used for training, and 2nd part for testing by `CrossValidatedTransferError` and `SplitClassifier`. Brief Description of Available Splitters ======================================== * `NoneSplitter` - just return full dataset as the desired part (training/testing) * `OddEvenSplitter` - 2 splits: (odd samples,even samples) and (even, odd) * `HalfSplitter` - 2 splits: (first half, second half) and (second, first) * `NFoldSplitter` - splits for N-Fold cross validation. Module Organization =================== .. packagetree:: :style: UML """ __docformat__ = 'restructuredtext' import operator import numpy as N import mvpa.misc.support as support from mvpa.base.dochelpers import enhancedDocString from mvpa.datasets.miscfx import coarsenChunks if __debug__: from mvpa.base import debug class Splitter(object): """Base class of dataset splitters. Each splitter should be initialized with all its necessary parameters. The final splitting is done running the splitter object on a certain Dataset via __call__(). This method has to be implemented like a generator, i.e. it has to return every possible split with a yield() call. Each split has to be returned as a sequence of Datasets. The properties of the splitted dataset may vary between implementations. It is possible to declare a sequence element as 'None'. Please note, that even if there is only one Dataset returned it has to be an element in a sequence and not just the Dataset object! """ _STRATEGIES = ('first', 'random', 'equidistant') _NPERLABEL_STR = ['equal', 'all'] def __init__(self, nperlabel='all', nrunspersplit=1, permute=False, count=None, strategy='equidistant', discard_boundary=None, attr='chunks', reverse=False): """Initialize splitter base. :Parameters: nperlabel : int or str (or list of them) or float Number of dataset samples per label to be included in each split. If given as a float, it must be in [0,1] range and would mean the ratio of selected samples per each label. Two special strings are recognized: 'all' uses all available samples (default) and 'equal' uses the maximum number of samples the can be provided by all of the classes. This value might be provided as a sequence whos length matches the number of datasets per split and indicates the configuration for the respective dataset in each split. nrunspersplit: int Number of times samples for each split are chosen. This is mostly useful if a subset of the available samples is used in each split and the subset is randomly selected for each run (see the `nperlabel` argument). permute : bool If set to `True`, the labels of each generated dataset will be permuted on a per-chunk basis. count : None or int Desired number of splits to be output. It is limited by the number of splits possible for a given splitter (e.g. `OddEvenSplitter` can have only up to 2 splits). If None, all splits are output (default). strategy : str If `count` is not None, possible strategies are possible: first First `count` splits are chosen random Random (without replacement) `count` splits are chosen equidistant Splits which are equidistant from each other discard_boundary : None or int or sequence of int If not `None`, how many samples on the boundaries between parts of the split to discard in the training part. If int, then discarded in all parts. If a sequence, numbers to discard are given per part of the split. E.g. if splitter splits only into (training, testing) parts, then `discard_boundary`=(2,0) would instruct to discard 2 samples from training which are on the boundary with testing. attr : str Sample attribute used to determine splits. reverse : bool If True, the order of datasets in the split is reversed, e.g. instead of (training, testing), (training, testing) will be spit out """ # pylint happyness block self.__nperlabel = None self.__runspersplit = nrunspersplit self.__permute = permute self.__splitattr = attr self._reverse = reverse self.discard_boundary = discard_boundary # we don't check it, thus no reason to make it private. # someone might find it useful to change post creation # TODO utilize such (or similar) policy through out the code self.count = count """Number (max) of splits to output on call""" self._setStrategy(strategy) # pattern sampling status vars self.setNPerLabel(nperlabel) __doc__ = enhancedDocString('Splitter', locals()) def _setStrategy(self, strategy): """Set strategy to select splits out from available """ strategy = strategy.lower() if not strategy in self._STRATEGIES: raise ValueError, "strategy is not known. Known are %s" \ % str(self._STRATEGIES) self.__strategy = strategy def setNPerLabel(self, value): """Set the number of samples per label in the split datasets. 'equal' sets sample size to highest possible number of samples that can be provided by each class. 'all' uses all available samples (default). """ if isinstance(value, basestring): if not value in self._NPERLABEL_STR: raise ValueError, "Unsupported value '%s' for nperlabel." \ " Supported ones are %s or float or int" % (value, self._NPERLABEL_STR) self.__nperlabel = value def _getSplitConfig(self, uniqueattr): """Each subclass has to implement this method. It gets a sequence with the unique attribte ids of a dataset and has to return a list of lists containing attribute ids to split into the second dataset. """ raise NotImplementedError def __call__(self, dataset): """Splits the dataset. This method behaves like a generator. """ # local bindings to methods to gain some speedup ds_class = dataset.__class__ DS_permuteLabels = ds_class.permuteLabels try: DS_getNSamplesPerLabel = ds_class._getNSamplesPerAttr except AttributeError: # Some "not-real" datasets e.g. MetaDataset, might not # have it pass DS_getRandomSamples = ds_class.getRandomSamples # for each split cfgs = self.splitcfg(dataset) # Select just some splits if desired count, Ncfgs = self.count, len(cfgs) # further makes sense only iff count < Ncfgs, # otherwise all strategies are equivalent if count is not None and count < Ncfgs: if count < 1: # we can only wish a good luck return strategy = self.strategy if strategy == 'first': cfgs = cfgs[:count] elif strategy in ['equidistant', 'random']: if strategy == 'equidistant': # figure out what step is needed to # acommodate the `count` number step = float(Ncfgs) / count assert(step >= 1.0) indexes = [int(round(step * i)) for i in xrange(count)] elif strategy == 'random': indexes = N.random.permutation(range(Ncfgs))[:count] # doesn't matter much but lets keep them in the original # order at least indexes.sort() else: # who said that I am paranoid? raise RuntimeError, "Really should not happen" if __debug__: debug("SPL", "For %s strategy selected %s splits " "from %d total" % (strategy, indexes, Ncfgs)) cfgs = [cfgs[i] for i in indexes] # update Ncfgs Ncfgs = len(cfgs) # Finally split the data for isplit, split in enumerate(cfgs): # determine sample sizes if not operator.isSequenceType(self.__nperlabel) \ or isinstance(self.__nperlabel, str): nperlabelsplit = [self.__nperlabel] * len(split) else: nperlabelsplit = self.__nperlabel # get splitted datasets split_ds = self.splitDataset(dataset, split) # do multiple post-processing runs for this split for run in xrange(self.__runspersplit): # post-process all datasets finalized_datasets = [] for ds, nperlabel in zip(split_ds, nperlabelsplit): # Set flag of dataset either this was the last split # ??? per our discussion this might be the best # solution which would scale if we care about # thread-safety etc if ds is not None: ds._dsattr['lastsplit'] = (isplit == Ncfgs-1) # permute the labels if self.__permute: DS_permuteLabels(ds, True, perchunk=True) # select subset of samples if requested if nperlabel == 'all' or ds is None: finalized_datasets.append(ds) else: # We need to select a subset of samples # TODO: move all this logic within getRandomSamples # go for maximum possible number of samples provided # by each label in this dataset if nperlabel == 'equal': # determine the min number of samples per class npl = N.array(DS_getNSamplesPerLabel( ds, attrib='labels').values()).min() elif isinstance(nperlabel, float) or ( operator.isSequenceType(nperlabel) and len(nperlabel) > 0 and isinstance(nperlabel[0], float)): # determine number of samples per class and take # a ratio counts = N.array(DS_getNSamplesPerLabel( ds, attrib='labels').values()) npl = (counts * nperlabel).round().astype(int) else: npl = nperlabel # finally select the patterns finalized_datasets.append( DS_getRandomSamples(ds, npl)) if self._reverse: yield finalized_datasets[::-1] else: yield finalized_datasets def splitDataset(self, dataset, specs): """Split a dataset by separating the samples where the configured sample attribute matches an element of `specs`. :Parameters: dataset : Dataset This is this source dataset. specs : sequence of sequences Contains ids of a sample attribute that shall be split into the another dataset. :Returns: Tuple of splitted datasets. """ # collect the sample ids for each resulting dataset filters = [] none_specs = 0 cum_filter = None # Prepare discard_boundary discard_boundary = self.discard_boundary if isinstance(discard_boundary, int): if discard_boundary != 0: discard_boundary = (discard_boundary,) * len(specs) else: discard_boundary = None splitattr_data = eval('dataset.' + self.__splitattr) for spec in specs: if spec is None: filters.append(None) none_specs += 1 else: filter_ = N.array([ i in spec \ for i in splitattr_data]) filters.append(filter_) if cum_filter is None: cum_filter = filter_ else: cum_filter = N.logical_and(cum_filter, filter_) # need to turn possible Nones into proper ids sequences if none_specs > 1: raise ValueError, "Splitter cannot handle more than one `None` " \ "split definition." for i, filter_ in enumerate(filters): if filter_ is None: filters[i] = N.logical_not(cum_filter) # If it was told to discard samples on the boundary to the # other parts of the split if discard_boundary is not None: ndiscard = discard_boundary[i] if ndiscard != 0: # XXX sloppy implementation for now. It still # should not be the main reason for a slow-down of # the whole analysis ;) f, lenf = filters[i], len(filters[i]) f_pad = N.concatenate(([True]*ndiscard, f, [True]*ndiscard)) for d in xrange(2*ndiscard+1): f = N.logical_and(f, f_pad[d:d+lenf]) filters[i] = f[:] # split data: return None if no samples are left # XXX: Maybe it should simply return an empty dataset instead, but # keeping it this way for now, to maintain current behavior split_datasets = [] # local bindings dataset_selectSamples = dataset.selectSamples for filter_ in filters: if (filter_ == False).all(): split_datasets.append(None) else: split_datasets.append(dataset_selectSamples(filter_)) return split_datasets def __str__(self): """String summary over the object """ return \ "SplitterConfig: nperlabel:%s runs-per-split:%d permute:%s" \ % (self.__nperlabel, self.__runspersplit, self.__permute) def splitcfg(self, dataset): """Return splitcfg for a given dataset""" return self._getSplitConfig(eval('dataset.unique' + self.__splitattr)) strategy = property(fget=lambda self:self.__strategy, fset=_setStrategy) class NoneSplitter(Splitter): """This is a dataset splitter that does **not** split. It simply returns the full dataset that it is called with. The passed dataset is returned as the second element of the 2-tuple. The first element of that tuple will always be 'None'. """ _known_modes = ['first', 'second'] def __init__(self, mode='second', **kwargs): """Cheap init -- nothing special :Parameters: mode Either 'first' or 'second' (default) -- which output dataset would actually contain the samples """ Splitter.__init__(self, **(kwargs)) if not mode in NoneSplitter._known_modes: raise ValueError, "Unknown mode %s for NoneSplitter" % mode self.__mode = mode __doc__ = enhancedDocString('NoneSplitter', locals(), Splitter) def _getSplitConfig(self, uniqueattrs): """Return just one full split: no first or second dataset. """ if self.__mode == 'second': return [([], None)] else: return [(None, [])] def __str__(self): """String summary over the object """ return \ "NoneSplitter / " + Splitter.__str__(self) class OddEvenSplitter(Splitter): """Split a dataset into odd and even values of the sample attribute. The splitter yields to splits: first (odd, even) and second (even, odd). """ def __init__(self, usevalues=False, **kwargs): """Cheap init. :Parameters: usevalues: bool If True the values of the attribute used for splitting will be used to determine odd and even samples. If False odd and even chunks are defined by the order of attribute values, i.e. first unique attribute is odd, second is even, despite the corresponding values might indicate the opposite (e.g. in case of [2,3]. """ Splitter.__init__(self, **(kwargs)) self.__usevalues = usevalues __doc__ = enhancedDocString('OddEvenSplitter', locals(), Splitter) def _getSplitConfig(self, uniqueattrs): """Huka chaka! YOH: LOL XXX """ if self.__usevalues: return [(None, uniqueattrs[(uniqueattrs % 2) == True]), (None, uniqueattrs[(uniqueattrs % 2) == False])] else: return [(None, uniqueattrs[N.arange(len(uniqueattrs)) %2 == True]), (None, uniqueattrs[N.arange(len(uniqueattrs)) %2 == False])] def __str__(self): """String summary over the object """ return \ "OddEvenSplitter / " + Splitter.__str__(self) class HalfSplitter(Splitter): """Split a dataset into two halves of the sample attribute. The splitter yields to splits: first (1st half, 2nd half) and second (2nd half, 1st half). """ def __init__(self, **kwargs): """Cheap init. """ Splitter.__init__(self, **(kwargs)) __doc__ = enhancedDocString('HalfSplitter', locals(), Splitter) def _getSplitConfig(self, uniqueattrs): """Huka chaka! """ return [(None, uniqueattrs[:len(uniqueattrs)/2]), (None, uniqueattrs[len(uniqueattrs)/2:])] def __str__(self): """String summary over the object """ return \ "HalfSplitter / " + Splitter.__str__(self) class NGroupSplitter(Splitter): """Split a dataset into N-groups of the sample attribute. For example, NGroupSplitter(2) is the same as the HalfSplitter and yields to splits: first (1st half, 2nd half) and second (2nd half, 1st half). """ def __init__(self, ngroups=4, **kwargs): """Initialize the N-group splitter. :Parameters: ngroups: int Number of groups to split the attribute into. kwargs Additional parameters are passed to the `Splitter` base class. """ Splitter.__init__(self, **(kwargs)) self.__ngroups = ngroups __doc__ = enhancedDocString('NGroupSplitter', locals(), Splitter) def _getSplitConfig(self, uniqueattrs): """Huka chaka, wuka waka! """ # make sure there are more of attributes than desired groups if len(uniqueattrs) < self.__ngroups: raise ValueError, "Number of groups (%d) " % (self.__ngroups) + \ "must be less than " + \ "or equal to the number of unique attributes (%d)" % \ (len(uniqueattrs)) # use coarsenChunks to get the split indices split_ind = coarsenChunks(uniqueattrs, nchunks=self.__ngroups) split_ind = N.asarray(split_ind) # loop and create splits split_list = [(None, uniqueattrs[split_ind==i]) for i in range(self.__ngroups)] return split_list def __str__(self): """String summary over the object """ return \ "N-%d-GroupSplitter / " % self.__ngroup + Splitter.__str__(self) class NFoldSplitter(Splitter): """Generic N-fold data splitter. Provide folding splitting. Given a dataset with N chunks, with cvtype=1 (which is default), it would generate N splits, where each chunk sequentially is taken out (with replacement) for cross-validation. Example, if there is 4 chunks, splits for cvtype=1 are: [[1, 2, 3], [0]] [[0, 2, 3], [1]] [[0, 1, 3], [2]] [[0, 1, 2], [3]] If cvtype>1, then all possible combinations of cvtype number of chunks are taken out for testing, so for cvtype=2 in previous example: [[2, 3], [0, 1]] [[1, 3], [0, 2]] [[1, 2], [0, 3]] [[0, 3], [1, 2]] [[0, 2], [1, 3]] [[0, 1], [2, 3]] """ def __init__(self, cvtype = 1, **kwargs): """Initialize the N-fold splitter. :Parameters: cvtype: int Type of cross-validation: N-(cvtype) kwargs Additional parameters are passed to the `Splitter` base class. """ Splitter.__init__(self, **(kwargs)) # pylint happiness block self.__cvtype = cvtype __doc__ = enhancedDocString('NFoldSplitter', locals(), Splitter) def __str__(self): """String summary over the object """ return \ "N-%d-FoldSplitter / " % self.__cvtype + Splitter.__str__(self) def _getSplitConfig(self, uniqueattrs): """Returns proper split configuration for N-M fold split. """ return [(None, i) for i in \ support.getUniqueLengthNCombinations(uniqueattrs, self.__cvtype)] class CustomSplitter(Splitter): """Split a dataset using an arbitrary custom rule. The splitter is configured by passing a custom spitting rule (`splitrule`) to its constructor. Such a rule is basically a sequence of split definitions. Every single element in this sequence results in excatly one split generated by the Splitter. Each element is another sequence for sequences of sample ids for each dataset that shall be generated in the split. Example: * Generate two splits. In the first split the *second* dataset contains all samples with sample attributes corresponding to either 0, 1 or 2. The *first* dataset of the first split contains all samples which are not split into the second dataset. The second split yields three datasets. The first with all samples corresponding to sample attributes 1 and 2, the second dataset contains only samples with attrbiute 3 and the last dataset contains the samples with attribute 5 and 6. CustomSplitter([(None, [0, 1, 2]), ([1,2], [3], [5, 6])]) """ def __init__(self, splitrule, **kwargs): """Cheap init. """ Splitter.__init__(self, **(kwargs)) self.__splitrule = splitrule __doc__ = enhancedDocString('CustomSplitter', locals(), Splitter) def _getSplitConfig(self, uniqueattrs): """Huka chaka! """ return self.__splitrule def __str__(self): """String summary over the object """ return "CustomSplitter / " + Splitter.__str__(self) pymvpa-0.4.8/mvpa/featsel/000077500000000000000000000000001174541445200154345ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/featsel/__init__.py000066400000000000000000000015421174541445200175470ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Feature selection methods. Brief Description of Available Methods ====================================== * `SensitivityBasedFeatureSelection` - generic class to provide feature selection given some sensitivity measure * `RFE` - recursive feature elimination (RFE) * `IFS` - incremental feature selection """ __docformat__ = 'restructuredtext' if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.featsel') if __debug__: debug('INIT', 'mvpa.featsel end') pymvpa-0.4.8/mvpa/featsel/base.py000066400000000000000000000243671174541445200167340ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Feature selection base class and related stuff base classes and helpers.""" __docformat__ = 'restructuredtext' from mvpa.featsel.helpers import FractionTailSelector from mvpa.misc.state import StateVariable, ClassWithCollections if __debug__: from mvpa.base import debug class FeatureSelection(ClassWithCollections): """Base class for any feature selection Base class for Functors which implement feature selection on the datasets. """ selected_ids = StateVariable(enabled=False) def __init__(self, **kwargs): # base init first ClassWithCollections.__init__(self, **kwargs) def __call__(self, dataset, testdataset=None): """Invocation of the feature selection :Parameters: dataset : Dataset dataset used to select features testdataset : Dataset dataset the might be used to compute a stopping criterion Returns a tuple with the dataset containing the selected features. If present the tuple also contains the selected features of the test dataset. Derived classes must provide interface to access other relevant to the feature selection process information (e.g. mask, elimination step (in RFE), etc) """ raise NotImplementedError def untrain(self): """ 'Untrain' feature selection Necessary for full 'untraining' of the classifiers. By default does nothing, needs to be overridden in corresponding feature selections to pass to the sensitivities """ pass class SensitivityBasedFeatureSelection(FeatureSelection): """Feature elimination. A `FeaturewiseDatasetMeasure` is used to compute sensitivity maps given a certain dataset. These sensitivity maps are in turn used to discard unimportant features. """ sensitivity = StateVariable(enabled=False) def __init__(self, sensitivity_analyzer, feature_selector=FractionTailSelector(0.05), **kwargs ): """Initialize feature selection :Parameters: sensitivity_analyzer : FeaturewiseDatasetMeasure sensitivity analyzer to come up with sensitivity feature_selector : Functor Given a sensitivity map it has to return the ids of those features that should be kept. """ # base init first FeatureSelection.__init__(self, **kwargs) self.__sensitivity_analyzer = sensitivity_analyzer """Sensitivity analyzer to use once""" self.__feature_selector = feature_selector """Functor which takes care about removing some features.""" def untrain(self): if __debug__: debug("FS_", "Untraining sensitivity-based FS: %s" % self) self.__sensitivity_analyzer.untrain() def __call__(self, dataset, testdataset=None): """Select the most important features :Parameters: dataset : Dataset used to compute sensitivity maps testdataset: Dataset optional dataset to select features on Returns a tuple of two new datasets with selected feature subset of `dataset`. """ sensitivity = self.__sensitivity_analyzer(dataset) """Compute the sensitivity map.""" self.sensitivity = sensitivity # Select features to preserve selected_ids = self.__feature_selector(sensitivity) if __debug__: debug("FS_", "Sensitivity: %s Selected ids: %s" % (sensitivity, selected_ids)) # Create a dataset only with selected features wdataset = dataset.selectFeatures(selected_ids) if not testdataset is None: wtestdataset = testdataset.selectFeatures(selected_ids) else: wtestdataset = None # Differ from the order in RFE when actually error reported is for results = (wdataset, wtestdataset) # WARNING: THIS MUST BE THE LAST THING TO DO ON selected_ids selected_ids.sort() self.selected_ids = selected_ids # dataset with selected features is returned return results # make it accessible from outside sensitivity_analyzer = property(fget=lambda self:self.__sensitivity_analyzer, doc="Measure which was used to do selection") class FeatureSelectionPipeline(FeatureSelection): """Feature elimination through the list of FeatureSelection's. Given as list of FeatureSelections it applies them in turn. """ nfeatures = StateVariable( doc="Number of features before each step in pipeline") # TODO: may be we should also append resultant number of features? def __init__(self, feature_selections, **kwargs ): """Initialize feature selection pipeline :Parameters: feature_selections : lisf of FeatureSelection selections which to use. Order matters """ # base init first FeatureSelection.__init__(self, **kwargs) self.__feature_selections = feature_selections """Selectors to use in turn""" def untrain(self): if __debug__: debug("FS_", "Untraining FS pipeline: %s" % self) for fs in self.__feature_selections: fs.untrain() def __call__(self, dataset, testdataset=None, **kwargs): """Invocation of the feature selection """ wdataset = dataset wtestdataset = testdataset self.selected_ids = None self.nfeatures = [] """Number of features at each step (before running selection)""" for fs in self.__feature_selections: # enable selected_ids state if it was requested from this class fs.states._changeTemporarily( enable_states=["selected_ids"], other=self) if self.states.isEnabled("nfeatures"): self.nfeatures.append(wdataset.nfeatures) if __debug__: debug('FSPL', 'Invoking %s on (%s, %s)' % (fs, wdataset, wtestdataset)) wdataset, wtestdataset = fs(wdataset, wtestdataset, **kwargs) if self.states.isEnabled("selected_ids"): if self.selected_ids == None: self.selected_ids = fs.selected_ids else: self.selected_ids = self.selected_ids[fs.selected_ids] fs.states._resetEnabledTemporarily() return (wdataset, wtestdataset) feature_selections = property(fget=lambda self:self.__feature_selections, doc="List of `FeatureSelections`") class CombinedFeatureSelection(FeatureSelection): """Meta feature selection utilizing several embedded selection methods. Each embedded feature selection method is computed individually. Afterwards all feature sets are combined by either taking the union or intersection of all sets. The individual feature sets of all embedded methods are optionally avialable from the `selections_ids` state variable. """ selections_ids = StateVariable( doc="List of feature id sets for each performed method.") def __init__(self, feature_selections, combiner, **kwargs): """ :Parameters: feature_selections: list FeatureSelection instances to run. Order is not important. combiner: 'union', 'intersection' which method to be used to combine the feature selection set of all computed methods. """ FeatureSelection.__init__(self, **kwargs) self.__feature_selections = feature_selections self.__combiner = combiner def untrain(self): if __debug__: debug("FS_", "Untraining combined FS: %s" % self) for fs in self.__feature_selections: fs.untrain() def __call__(self, dataset, testdataset=None): """Really run it. """ # to hold the union selected_ids = None # to hold the individuals self.selections_ids = [] for fs in self.__feature_selections: # we need the feature ids that were selection by each method, # so enable them temporarily fs.states._changeTemporarily( enable_states=["selected_ids"], other=self) # compute feature selection, but ignore return datasets fs(dataset, testdataset) # retrieve feature ids and determined union of all selections if selected_ids == None: selected_ids = set(fs.selected_ids) else: if self.__combiner == 'union': selected_ids.update(fs.selected_ids) elif self.__combiner == 'intersection': selected_ids.intersection_update(fs.selected_ids) else: raise ValueError, "Unknown combiner '%s'" % self.__combiner # store individual set in state self.selections_ids.append(fs.selected_ids) # restore states to previous settings fs.states._resetEnabledTemporarily() # finally apply feature set union selection to original datasets selected_ids = sorted(list(selected_ids)) # take care of optional second dataset td_sel = None if not testdataset is None: td_sel = testdataset.selectFeatures(self.selected_ids) # and main dataset d_sel = dataset.selectFeatures(selected_ids) # finally store ids in state self.selected_ids = selected_ids return (d_sel, td_sel) feature_selections = property(fget=lambda self:self.__feature_selections, doc="List of `FeatureSelections`") combiner = property(fget=lambda self:self.__combiner, doc="Selection set combination method.") pymvpa-0.4.8/mvpa/featsel/helpers.py000066400000000000000000000354041174541445200174560ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """""" __docformat__ = 'restructuredtext' from math import floor import numpy as N from mvpa.misc.state import ClassWithCollections, StateVariable if __debug__: from mvpa.base import debug # # Functors to be used for FeatureSelection # class BestDetector(object): """Determine whether the last value in a sequence is the best one given some criterion. """ def __init__(self, func=min, lastminimum=False): """Initialize with number of steps :Parameters: fun : functor Functor to select the best results. Defaults to min lastminimum : bool Toggle whether the latest or the earliest minimum is used as optimal value to determine the stopping criterion. """ self.__func = func self.__lastminimum = lastminimum self.__bestindex = None """Stores the index of the last detected best value.""" def __call__(self, errors): """Returns True if the last value in `errors` is the best or False otherwise. """ isbest = False # just to prevent ValueError if len(errors)==0: return isbest minerror = self.__func(errors) if self.__lastminimum: # make sure it is an array errors = N.array(errors) # to find out the location of the minimum but starting from the # end! minindex = N.array((errors == minerror).nonzero()).max() else: minindex = errors.index(minerror) self.__bestindex = minindex # if minimal is the last one reported -- it is the best if minindex == len(errors)-1: isbest = True return isbest bestindex = property(fget=lambda self:self.__bestindex) class StoppingCriterion(object): """Base class for all functors to decide when to stop RFE (or may be general optimization... so it probably will be moved out into some other module """ def __call__(self, errors): """Instruct when to stop. Every implementation should return `False` when an empty list is passed as argument. Returns tuple `stop`. """ raise NotImplementedError class MultiStopCrit(StoppingCriterion): """Stop computation if the latest error drops below a certain threshold. """ def __init__(self, crits, mode='or'): """ :Parameters: crits : list of StoppingCriterion instances For each call to MultiStopCrit all of these criterions will be evaluated. mode : any of ('and', 'or') Logical function to determine the multi criterion from the set of base criteria. """ if not mode in ('and', 'or'): raise ValueError, \ "A mode '%s' is not supported." % `mode` self.__mode = mode self.__crits = crits def __call__(self, errors): """Evaluate all criteria to determine the value of the multi criterion. """ # evaluate all crits crits = [ c(errors) for c in self.__crits ] if self.__mode == 'and': return N.all(crits) else: return N.any(crits) class FixedErrorThresholdStopCrit(StoppingCriterion): """Stop computation if the latest error drops below a certain threshold. """ def __init__(self, threshold): """Initialize with threshold. :Parameters: threshold : float [0,1] Error threshold. """ StoppingCriterion.__init__(self) if threshold > 1.0 or threshold < 0.0: raise ValueError, \ "Threshold %f is out of a reasonable range [0,1]." \ % `threshold` self.__threshold = threshold def __call__(self, errors): """Nothing special.""" if len(errors)==0: return False if errors[-1] < self.__threshold: return True else: return False threshold = property(fget=lambda x:x.__threshold) class NStepsStopCrit(StoppingCriterion): """Stop computation after a certain number of steps. """ def __init__(self, steps): """Initialize with number of steps. :Parameters: steps : int Number of steps after which to stop. """ StoppingCriterion.__init__(self) if steps < 0: raise ValueError, \ "Number of steps %i is out of a reasonable range." \ % `steps` self.__steps = steps def __call__(self, errors): """Nothing special.""" if len(errors) >= self.__steps: return True else: return False steps = property(fget=lambda x:x.__steps) class NBackHistoryStopCrit(StoppingCriterion): """Stop computation if for a number of steps error was increasing """ def __init__(self, bestdetector=BestDetector(), steps=10): """Initialize with number of steps :Parameters: bestdetector : BestDetector instance used to determine where the best error is located. steps : int How many steps to check after optimal value. """ StoppingCriterion.__init__(self) if steps < 0: raise ValueError, \ "Number of steps (got %d) should be non-negative" % steps self.__bestdetector = bestdetector self.__steps = steps def __call__(self, errors): stop = False # just to prevent ValueError if len(errors)==0: return stop # charge best detector self.__bestdetector(errors) # if number of elements after the min >= len -- stop if len(errors) - self.__bestdetector.bestindex > self.__steps: stop = True return stop steps = property(fget=lambda x:x.__steps) class ElementSelector(ClassWithCollections): """Base class to implement functors to select some elements based on a sequence of values. """ ndiscarded = StateVariable(True, doc="Store number of discarded elements.") def __init__(self, mode='discard', **kwargs): """Cheap initialization. :Parameters: mode : ['discard', 'select'] Decides whether to `select` or to `discard` features. """ ClassWithCollections.__init__(self, **kwargs) self._setMode(mode) """Flag whether to select or to discard elements.""" def _setMode(self, mode): """Choose `select` or `discard` mode.""" if not mode in ['discard', 'select']: raise ValueError, "Unkown selection mode [%s]. Can only be one " \ "of 'select' or 'discard'." % mode self.__mode = mode def __call__(self, seq): """Implementations in derived classed have to return a list of selected element IDs based on the given sequence. """ raise NotImplementedError mode = property(fget=lambda self:self.__mode, fset=_setMode) class RangeElementSelector(ElementSelector): """Select elements based on specified range of values""" def __init__(self, lower=None, upper=None, inclusive=False, mode='select', **kwargs): """Initialization `RangeElementSelector` :Parameters: lower If not None -- select elements which are above of specified value upper If not None -- select elements which are lower of specified value inclusive Either to include end points mode overrides parent's default to be 'select' since it is more native for RangeElementSelector XXX TODO -- unify?? `upper` could be lower than `lower` -- then selection is done on values <= lower or >=upper (ie tails). This would produce the same result if called with flipped values for mode and inclusive. If no upper no lower is set, assuming upper,lower=0, thus outputing non-0 elements """ if lower is None and upper is None: lower, upper = 0, 0 """Lets better return non-0 values if none of bounds is set""" # init State before registering anything ElementSelector.__init__(self, mode=mode, **kwargs) self.__range = (lower, upper) """Values on which to base selection""" self.__inclusive = inclusive def __call__(self, seq): """Returns selected IDs. """ lower, upper = self.__range len_seq = len(seq) if not lower is None: if self.__inclusive: selected = seq >= lower else: selected = seq > lower else: selected = N.ones( (len_seq), dtype=N.bool ) if not upper is None: if self.__inclusive: selected_upper = seq <= upper else: selected_upper = seq < upper if not lower is None: if lower < upper: # regular range selected = N.logical_and(selected, selected_upper) else: # outside, though that would be similar to exclude selected = N.logical_or(selected, selected_upper) else: selected = selected_upper if self.mode == 'discard': selected = N.logical_not(selected) result = N.where(selected)[0] if __debug__: debug("ES", "Selected %d out of %d elements" % (len(result), len_seq)) return result class TailSelector(ElementSelector): """Select elements from a tail of a distribution. The default behaviour is to discard the lower tail of a given distribution. """ # TODO: 'both' to select from both tails def __init__(self, tail='lower', sort=True, **kwargs): """Initialize TailSelector :Parameters: tail : ['lower', 'upper'] Choose the tail to be processed. sort : bool Flag whether selected IDs will be sorted. Disable if not necessary to save some CPU cycles. """ # init State before registering anything ElementSelector.__init__(self, **kwargs) self._setTail(tail) """Know which tail to select.""" self.__sort = sort def _setTail(self, tail): """Set the tail to be processed.""" if not tail in ['lower', 'upper']: raise ValueError, "Unkown tail argument [%s]. Can only be one " \ "of 'lower' or 'upper'." % tail self.__tail = tail def _getNElements(self, seq): """In derived classes has to return the number of elements to be processed given a sequence values forming the distribution. """ raise NotImplementedError def __call__(self, seq): """Returns selected IDs. """ # TODO: Think about selecting features which have equal values but # some are selected and some are not len_seq = len(seq) # how many to select (cannot select more than available) nelements = min(self._getNElements(seq), len_seq) # make sure that data is ndarray and compute a sequence rank matrix # lowest value is first seqrank = N.array(seq).argsort() if self.mode == 'discard' and self.__tail == 'upper': good_ids = seqrank[:-1*nelements] self.ndiscarded = nelements elif self.mode == 'discard' and self.__tail == 'lower': good_ids = seqrank[nelements:] self.ndiscarded = nelements elif self.mode == 'select' and self.__tail == 'upper': good_ids = seqrank[-1*nelements:] self.ndiscarded = len_seq - nelements else: # select lower tail good_ids = seqrank[:nelements] self.ndiscarded = len_seq - nelements # sort ids to keep order # XXX should we do here are leave to other place if self.__sort: good_ids.sort() return good_ids class FixedNElementTailSelector(TailSelector): """Given a sequence, provide set of IDs for a fixed number of to be selected elements. """ def __init__(self, nelements, **kwargs): """Cheap initialization. :Parameters: nelements : int Number of elements to select/discard. """ TailSelector.__init__(self, **kwargs) self.__nelements = None self._setNElements(nelements) def __repr__(self): return "%s number=%f" % ( TailSelector.__repr__(self), self.nelements) def _getNElements(self, seq): return self.__nelements def _setNElements(self, nelements): if __debug__: if nelements <= 0: raise ValueError, "Number of elements less or equal to zero " \ "does not make sense." self.__nelements = nelements nelements = property(fget=lambda x:x.__nelements, fset=_setNElements) class FractionTailSelector(TailSelector): """Given a sequence, provide Ids for a fraction of elements """ def __init__(self, felements, **kwargs): """Cheap initialization. :Parameters: felements : float (0,1.0] Fraction of elements to select/discard. Note: Even when 0.0 is specified at least one element will be selected. """ TailSelector.__init__(self, **kwargs) self._setFElements(felements) def __repr__(self): return "%s fraction=%f" % ( TailSelector.__repr__(self), self.__felements) def _getNElements(self, seq): num = int(floor(self.__felements * len(seq))) num = max(1, num) # remove at least 1 # no need for checks as base class will do anyway #return min(num, nselect) return num def _setFElements(self, felements): """What fraction to discard""" if felements > 1.0 or felements < 0.0: raise ValueError, \ "Fraction (%f) cannot be outside of [0.0,1.0]" \ % felements self.__felements = felements felements = property(fget=lambda x:x.__felements, fset=_setFElements) pymvpa-0.4.8/mvpa/featsel/ifs.py000066400000000000000000000155721174541445200166010ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Incremental feature search (IFS). Very similar to Recursive feature elimination (RFE), but instead of begining with all features and stripping some sequentially, start with an empty feature set and include important features successively. """ __docformat__ = 'restructuredtext' import numpy as N from mvpa.support.copy import copy from mvpa.featsel.base import FeatureSelection from mvpa.featsel.helpers import NBackHistoryStopCrit, \ FixedNElementTailSelector, \ BestDetector from mvpa.misc.state import StateVariable if __debug__: from mvpa.base import debug class IFS(FeatureSelection): """Incremental feature search. A scalar `DatasetMeasure` is computed multiple times on variations of a certain dataset. These measures are in turn used to incrementally select important features. Starting with an empty feature set the dataset measure is first computed for each single feature. A number of features is selected based on the resulting data measure map (using an `ElementSelector`). Next the dataset measure is computed again using each feature in addition to the already selected feature set. Again the `ElementSelector` is used to select more features. For each feature selection the transfer error on some testdatset is computed. This procedure is repeated until a given `StoppingCriterion` is reached. """ errors = StateVariable() def __init__(self, data_measure, transfer_error, bestdetector=BestDetector(), stopping_criterion=NBackHistoryStopCrit(BestDetector()), feature_selector=FixedNElementTailSelector(1, tail='upper', mode='select'), **kwargs ): """Initialize incremental feature search :Parameters: data_measure : DatasetMeasure Computed for each candidate feature selection. transfer_error : TransferError Compute against a test dataset for each incremental feature set. bestdetector : Functor Given a list of error values it has to return a boolean that signals whether the latest error value is the total minimum. stopping_criterion : Functor Given a list of error values it has to return whether the criterion is fulfilled. """ # bases init first FeatureSelection.__init__(self, **kwargs) self.__data_measure = data_measure self.__transfer_error = transfer_error self.__feature_selector = feature_selector self.__bestdetector = bestdetector self.__stopping_criterion = stopping_criterion def __call__(self, dataset, testdataset): """Proceed and select the features recursively eliminating less important ones. :Parameters: `dataset`: `Dataset` used to select features and train classifiers to determine the transfer error. `testdataset`: `Dataset` used to test the trained classifer on a certain feature set to determine the transfer error. Returns a tuple with the dataset containing the feature subset of `dataset` that had the lowest transfer error of all tested sets until the stopping criterion was reached. The tuple also contains a dataset with the corrsponding features from the `testdataset`. """ errors = [] """Computed error for each tested features set.""" # feature candidate are all features in the pattern object candidates = range( dataset.nfeatures ) # initially empty list of selected features selected = [] # results in here please results = None # as long as there are candidates left # the loop will most likely get broken earlier if the stopping # criterion is reached while len( candidates ): # measures for all candidates measures = [] # for all possible candidates for i, candidate in enumerate(candidates): if __debug__: debug('IFSC', "Tested %i" % i, cr=True) # take the new candidate and all already selected features # select a new temporay feature subset from the dataset # XXX assume MappedDataset and issue plain=True ?? tmp_dataset = \ dataset.selectFeatures(selected + [candidate]) # compute data measure on this feature set measures.append(self.__data_measure(tmp_dataset)) measures = [N.asscalar(m) for m in measures] # Select promissing feature candidates (staging) # IDs are only applicable to the current set of feature candidates tmp_staging_ids = self.__feature_selector(measures) # translate into real candidate ids staging_ids = [ candidates[i] for i in tmp_staging_ids ] # mark them as selected and remove from candidates selected += staging_ids for i in staging_ids: candidates.remove(i) # compute transfer error for the new set # XXX assume MappedDataset and issue plain=True ?? error = self.__transfer_error(testdataset.selectFeatures(selected), dataset.selectFeatures(selected)) errors.append(error) # Check if it is time to stop and if we got # the best result stop = self.__stopping_criterion(errors) isthebest = self.__bestdetector(errors) if __debug__: debug('IFSC', "nselected %i; error: %.4f " \ "best/stop=%d/%d\n" \ % (len(selected), errors[-1], isthebest, stop), cr=True, lf=True) if isthebest: # do copy to survive later selections results = copy(selected) # leave the loop when the criterion is reached if stop: break # charge state self.errors = errors # best dataset ever is returned return dataset.selectFeatures(results), \ testdataset.selectFeatures(results) pymvpa-0.4.8/mvpa/featsel/rfe.py000066400000000000000000000273241174541445200165720ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Recursive feature elimination.""" __docformat__ = 'restructuredtext' from mvpa.clfs.transerror import ClassifierError from mvpa.measures.base import Sensitivity from mvpa.featsel.base import FeatureSelection from mvpa.featsel.helpers import BestDetector, \ NBackHistoryStopCrit, \ FractionTailSelector from numpy import arange from mvpa.misc.state import StateVariable if __debug__: from mvpa.base import debug # TODO: Abs value of sensitivity should be able to rule RFE # Often it is what abs value of the sensitivity is what matters. # So we should either provide a simple decorator around arbitrary # FeatureSelector to convert sensitivities to abs values before calling # actual selector, or a decorator around SensitivityEstimators class RFE(FeatureSelection): """Recursive feature elimination. A `FeaturewiseDatasetMeasure` is used to compute sensitivity maps given a certain dataset. These sensitivity maps are in turn used to discard unimportant features. For each feature selection the transfer error on some testdatset is computed. This procedure is repeated until a given `StoppingCriterion` is reached. Such strategy after Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Mach. Learn., 46(1-3), 389--422. was applied to SVM-based analysis of fMRI data in Hanson, S. J. & Halchenko, Y. O. (2008). Brain reading using full brain support vector machines for object recognition: there is no "face identification area". Neural Computation, 20, 486--503. """ # TODO: remove # doesn't work nicely -- if FeatureSelection defines its states via # _register_states, they would simply be ignored #_register_states = {'errors':True, # 'nfeatures':True, # 'history':True} errors = StateVariable() nfeatures = StateVariable() history = StateVariable() sensitivities = StateVariable(enabled=False) def __init__(self, sensitivity_analyzer, transfer_error, feature_selector=FractionTailSelector(0.05), bestdetector=BestDetector(), stopping_criterion=NBackHistoryStopCrit(BestDetector()), train_clf=None, update_sensitivity=True, **kargs ): # XXX Allow for multiple stopping criterions, e.g. error not decreasing # anymore OR number of features less than threshold """Initialize recursive feature elimination :Parameters: sensitivity_analyzer : FeaturewiseDatasetMeasure object transfer_error : TransferError object used to compute the transfer error of a classifier based on a certain feature set on the test dataset. NOTE: If sensitivity analyzer is based on the same classifier as transfer_error is using, make sure you initialize transfer_error with train=False, otherwise it would train classifier twice without any necessity. feature_selector : Functor Given a sensitivity map it has to return the ids of those features that should be kept. bestdetector : Functor Given a list of error values it has to return a boolean that signals whether the latest error value is the total minimum. stopping_criterion : Functor Given a list of error values it has to return whether the criterion is fulfilled. train_clf : bool Flag whether the classifier in `transfer_error` should be trained before computing the error. In general this is required, but if the `sensitivity_analyzer` and `transfer_error` share and make use of the same classifier it can be switched off to save CPU cycles. Default `None` checks if sensitivity_analyzer is based on a classifier and doesn't train if so. update_sensitivity : bool If False the sensitivity map is only computed once and reused for each iteration. Otherwise the senstitivities are recomputed at each selection step. """ # base init first FeatureSelection.__init__(self, **kargs) self.__sensitivity_analyzer = sensitivity_analyzer """Sensitivity analyzer used to call at each step.""" self.__transfer_error = transfer_error """Compute transfer error for each feature set.""" self.__feature_selector = feature_selector """Functor which takes care about removing some features.""" self.__stopping_criterion = stopping_criterion self.__bestdetector = bestdetector if train_clf is None: self.__train_clf = isinstance(sensitivity_analyzer, Sensitivity) else: self.__train_clf = train_clf """Flag whether training classifier is required.""" self.__update_sensitivity = update_sensitivity """Flag whether sensitivity map is recomputed for each step.""" # force clf training when sensitivities are not updated as otherwise # shared classifiers are not retrained if not self.__update_sensitivity \ and isinstance(self.__transfer_error, ClassifierError) \ and not self.__train_clf: if __debug__: debug("RFEC", "Forcing training of classifier since " + "sensitivities aren't updated at each step") self.__train_clf = True def __call__(self, dataset, testdataset): """Proceed and select the features recursively eliminating less important ones. :Parameters: dataset : Dataset used to compute sensitivity maps and train a classifier to determine the transfer error testdataset : Dataset used to test the trained classifer to determine the transfer error Returns a tuple of two new datasets with the feature subset of `dataset` that had the lowest transfer error of all tested sets until the stopping criterion was reached. The first dataset is the feature subset of the training data and the second the selection of the test dataset. """ errors = [] """Computed error for each tested features set.""" self.nfeatures = [] """Number of features at each step. Since it is not used by the algorithm it is stored directly in the state variable""" self.history = arange(dataset.nfeatures) """Store the last step # when the feature was still present """ self.sensitivities = [] stop = False """Flag when RFE should be stopped.""" results = None """Will hold the best feature set ever.""" wdataset = dataset """Operate on working dataset initially identical.""" wtestdataset = testdataset """Same feature selection has to be performs on test dataset as well. This will hold the current testdataset.""" step = 0 """Counter how many selection step where done.""" orig_feature_ids = arange(dataset.nfeatures) """List of feature Ids as per original dataset remaining at any given step""" sensitivity = None """Contains the latest sensitivity map.""" result_selected_ids = orig_feature_ids """Resultant ids of selected features. Since the best is not necessarily is the last - we better keep this one around. By default -- all features are there""" selected_ids = result_selected_ids while wdataset.nfeatures > 0: if __debug__: debug('RFEC', "Step %d: nfeatures=%d" % (step, wdataset.nfeatures)) # mark the features which are present at this step # if it brings anyb mentionable computational burden in the future, # only mark on removed features at each step self.history[orig_feature_ids] = step # Compute sensitivity map if self.__update_sensitivity or sensitivity == None: sensitivity = self.__sensitivity_analyzer(wdataset) if self.states.isEnabled("sensitivities"): self.sensitivities.append(sensitivity) # do not retrain clf if not necessary if self.__train_clf: error = self.__transfer_error(wtestdataset, wdataset) else: error = self.__transfer_error(wtestdataset, None) # Record the error errors.append(error) # Check if it is time to stop and if we got # the best result stop = self.__stopping_criterion(errors) isthebest = self.__bestdetector(errors) nfeatures = wdataset.nfeatures if self.states.isEnabled("nfeatures"): self.nfeatures.append(wdataset.nfeatures) # store result if isthebest: results = (wdataset, wtestdataset) result_selected_ids = orig_feature_ids if __debug__: debug('RFEC', "Step %d: nfeatures=%d error=%.4f best/stop=%d/%d " % (step, nfeatures, error, isthebest, stop)) # stop if it is time to finish if nfeatures == 1 or stop: break # Select features to preserve selected_ids = self.__feature_selector(sensitivity) if __debug__: debug('RFEC_', "Sensitivity: %s, nfeatures_selected=%d, selected_ids: %s" % (sensitivity, len(selected_ids), selected_ids)) # Create a dataset only with selected features wdataset = wdataset.selectFeatures(selected_ids) # select corresponding sensitivity values if they are not # recomputed if not self.__update_sensitivity: sensitivity = sensitivity[selected_ids] # need to update the test dataset as well # XXX why should it ever become None? # yoh: because we can have __transfer_error computed # using wdataset. See xia-generalization estimate # in lightsvm. Or for god's sake leave-one-out # on a wdataset # TODO: document these cases in this class if not testdataset is None: wtestdataset = wtestdataset.selectFeatures(selected_ids) step += 1 # WARNING: THIS MUST BE THE LAST THING TO DO ON selected_ids selected_ids.sort() if self.states.isEnabled("history") or self.states.isEnabled('selected_ids'): orig_feature_ids = orig_feature_ids[selected_ids] if hasattr(self.__transfer_error, "clf"): self.__transfer_error.clf.untrain() # charge state variables self.errors = errors self.selected_ids = result_selected_ids # best dataset ever is returned return results pymvpa-0.4.8/mvpa/mappers/000077500000000000000000000000001174541445200154605ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/mappers/__init__.py000066400000000000000000000026301174541445200175720ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """PyMVPA mappers. Module Description ================== Various space transformations which are intended to map between two spaces, most of the time both ways, and optionally requiring training. Classifiers from the mvpa.clfs module could be considered mappers as well, but they all are supervised, and only provide ND->1D mapping, most of the time without reverse transformation. Module Organization =================== The mvpa.mappers module contains the following modules: .. packagetree:: :style: UML :group Base: base mask metric :group Specialized: wavelet boxcar svd ica pca samplegroup """ __docformat__ = 'restructuredtext' if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.mappers') # do not pull them all -- we have mvpa.suite for that #from mvpa.mappers.mask import MaskMapper #from mvpa.mappers.pca import PCAMapper #from mvpa.mappers.svd import SVDMapper #from mvpa.mappers.boxcar import BoxcarMapper #from mvpa.mappers.array import DenseArrayMapper if __debug__: debug('INIT', 'mvpa.mappers end') pymvpa-0.4.8/mvpa/mappers/array.py000066400000000000000000000120711174541445200171510ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Data mapper""" __docformat__ = 'restructuredtext' import numpy as N from operator import isSequenceType from mvpa.mappers.mask import MaskMapper from mvpa.mappers.metric import DescreteMetric, cartesianDistance from mvpa.base.dochelpers import enhancedDocString if __debug__: from mvpa.base import warning from mvpa.misc.support import isSorted class DenseArrayMapper(MaskMapper): """Mapper for equally spaced dense arrays.""" """TODO: yoh thinks we should move that 'metric' assignment into MaskMapper, based on the fact if distance_function is given either as an argument or may be class variable. That would pretty much remove the need for a separate class of DenseArrayMapper and it could become just a sugaring helper function which would initiate MaskMapper (or some other mapper may be with appropriate distance_function and/or mapper Otherwise it is again -- orthogonality -- will we need to device NonmaskedArrayMapper which has no mask assigned but might be a good cartesian cube on its own or smth like that? """ def __init__(self, mask=None, metric=None, distance_function=cartesianDistance, elementsize=None, shape=None, **kwargs): """Initialize DenseArrayMapper :Parameters: mask : array an array in the original dataspace and its nonzero elements are used to define the features included in the dataset. alternatively, the `shape` argument can be used to define the array dimensions. metric : Metric Corresponding metric for the space. No attempt is made to determine whether a certain metric is reasonable for this mapper. If `metric` is None -- `DescreteMetric` is constructed that assumes an equal (1) spacing of all mask elements with a `distance_function` given as a parameter listed below. distance_function : functor Distance function to use as the parameter to `DescreteMetric` if `metric` is not specified, elementsize : list or scalar Determines spacing within `DescreteMetric`. If it is given as a scalar, corresponding value is assigned to all dimensions, which are found within `mask` shape: tuple The shape of the array to be mapped. If `shape` is provided instead of `mask`, a full mask (all True) of the desired shape is constructed. If `shape` is specified in addition to `mask`, the provided mask is extended to have the same number of dimensions. :Note: parameters `elementsize` and `distance_function` are relevant only if `metric` is None """ if mask is None: if shape is None: raise ValueError, \ "Either `shape` or `mask` have to be specified." else: # make full dataspace mask if nothing else is provided mask = N.ones(shape, dtype='bool') else: if not shape is None: # expand mask to span all dimensions but first one # necessary e.g. if only one slice from timeseries of volumes is # requested. mask = N.array(mask, ndmin=len(shape)) # check for compatibility if not shape == mask.shape: raise ValueError, \ "The mask dataspace shape %s is not " \ "compatible with the provided shape %s." \ % (mask.shape, shape) # configure the baseclass with the processed mask MaskMapper.__init__(self, mask, metric=metric, **kwargs) # We must have metric assigned if self.metric == None: if elementsize is None: elementsize = [1]*len(mask.shape) else: if isSequenceType(elementsize): if len(elementsize) != len(mask.shape): raise ValueError, \ "Number of elements in elementsize [%d]" % \ len(elementsize) + " doesn't match shape " + \ "of the mask [%s]" % (`mask.shape`) else: elementsize = [ elementsize ] * len(mask.shape) self.metric = DescreteMetric(elementsize=[1]*len(mask.shape), distance_function=distance_function) __doc__ = enhancedDocString('DenseArrayMapper', locals(), MaskMapper) def __str__(self): return "DenseArrayMapper: %d -> %d" \ % (self.getInSize(), self.getOutSize()) pymvpa-0.4.8/mvpa/mappers/base.py000066400000000000000000000565201174541445200167540ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Data mapper""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.mappers.metric import Metric from mvpa.datasets import Dataset from mvpa.misc.vproperty import VProperty from mvpa.base.dochelpers import enhancedDocString if __debug__: from mvpa.base import warning from mvpa.base import debug class Mapper(object): """Interface to provide mapping between two spaces: IN and OUT. Methods are prefixed correspondingly. forward/reverse operate on the entire dataset. get(In|Out)Id[s] operate per element:: forward ---------> IN OUT <--------/ reverse """ def __init__(self, metric=None): """ :Parameters: metric : Metric Optional metric """ self.__metric = None """Pylint happiness""" self.setMetric(metric) """Actually assign the metric""" # # The following methods are abstract and merely define the intended # interface of a mapper and have to be implemented in derived classes. See # the docstrings of the respective methods for details about what they # should do. # def forward(self, data): """Map data from the IN dataspace into OUT space. """ raise NotImplementedError def reverse(self, data): """Reverse map data from OUT space into the IN space. """ raise NotImplementedError def getInSize(self): """Returns the size of the entity in input space""" raise NotImplementedError def getOutSize(self): """Returns the size of the entity in output space""" raise NotImplementedError def selectOut(self, outIds): """Limit the OUT space to a certain set of features. :Parameters: outIds: sequence Subset of ids of the current feature in OUT space to keep. """ raise NotImplementedError def getInId(self, outId): """Translate a feature id into a coordinate/index in input space. Such a translation might not be meaningful or even possible for a particular mapping algorithm and therefore cannot be relied upon. """ raise NotImplementedError # # The following methods are candidates for reimplementation in derived # classes, in cases where the provided default behavior is not appropriate. # def isValidOutId(self, outId): """Validate feature id in OUT space. Override if OUT space is not simly a 1D vector """ return(outId >= 0 and outId < self.getOutSize()) def isValidInId(self, inId): """Validate id in IN space. Override if IN space is not simly a 1D vector """ return(inId >= 0 and inId < self.getInSize()) def train(self, dataset): """Perform training of the mapper. This method is called to put the mapper in a state that allows it to perform to intended mapping. :Parameter: dataset: Dataset or subclass .. note:: The default behavior of this method is to do nothing. """ pass def getNeighbor(self, outId, *args, **kwargs): """Get feature neighbors in input space, given an id in output space. This method has to be reimplemented whenever a derived class does not provide an implementation for :meth:`~mvpa.mappers.base.Mapper.getInId`. """ if self.metric is None: raise RuntimeError, "No metric was assigned to %s, thus no " \ "neighboring information is present" % self if self.isValidOutId(outId): inId = self.getInId(outId) for inId in self.getNeighborIn(inId, *args, **kwargs): yield self.getOutId(inId) # # The following methods provide common functionality for all mappers # and there should be no immediate need to reimplement them # def getNeighborIn(self, inId, *args, **kwargs): """Return the list of coordinates for the neighbors. :Parameters: inId id (index) of an element in input dataspace. *args, **kwargs Any additional arguments are passed to the embedded metric of the mapper. XXX See TODO below: what to return -- list of arrays or list of tuples? """ if self.metric is None: raise RuntimeError, "No metric was assigned to %s, thus no " \ "neighboring information is present" % self isValidInId = self.isValidInId if isValidInId(inId): for neighbor in self.metric.getNeighbor(inId, *args, **kwargs): if isValidInId(neighbor): yield neighbor def getNeighbors(self, outId, *args, **kwargs): """Return the list of coordinates for the neighbors. By default it simply constructs the list based on the generator returned by getNeighbor() """ return [ x for x in self.getNeighbor(outId, *args, **kwargs) ] def __repr__(self): if self.__metric is not None: s = "metric=%s" % repr(self.__metric) else: s = '' return "%s(%s)" % (self.__class__.__name__, s) def __call__(self, data): """Calls the mappers forward() method. """ return self.forward(data) def getMetric(self): """To make pylint happy""" return self.__metric def setMetric(self, metric): """To make pylint happy""" if metric is not None and not isinstance(metric, Metric): raise ValueError, "metric for Mapper must be an " \ "instance of a Metric class . Got %s" \ % `type(metric)` self.__metric = metric metric = property(fget=getMetric, fset=setMetric) nfeatures = VProperty(fget=getOutSize) class ProjectionMapper(Mapper): """Linear mapping between multidimensional spaces. This class cannot be used directly. Sub-classes have to implement the `_train()` method, which has to compute the projection matrix `_proj` and optionally offset vectors `_offset_in` and `_offset_out` (if initialized with demean=True, which is default) given a dataset (see `_train()` docstring for more information). Once the projection matrix is available, this class provides functionality to perform forward and backwards linear mapping of data, the latter by default using pseudo-inverse (but could be altered in subclasses, like hermitian (conjugate) transpose in case of SVD). Additionally, `ProjectionMapper` supports optional selection of arbitrary component (i.e. columns of the projection matrix) of the projection. Forward and back-projection matrices (a.k.a. *projection* and *reconstruction*) are available via the `proj` and `recon` properties. """ _DEV__doc__ = """Think about renaming `demean`, may be `translation`?""" def __init__(self, selector=None, demean=True): """Initialize the ProjectionMapper :Parameters: selector: None | list Which components (i.e. columns of the projection matrix) should be used for mapping. If `selector` is `None` all components are used. If a list is provided, all list elements are treated as component ids and the respective components are selected (all others are discarded). demean: bool Either data should be demeaned while computing projections and applied back while doing reverse() """ Mapper.__init__(self) self._selector = selector self._proj = None """Forward projection matrix.""" self._recon = None """Reverse projection (reconstruction) matrix.""" self._demean = demean """Flag whether to demean the to be projected data, prior to projection. """ self._offset_in = None """Offset (most often just mean) in the input space""" self._offset_out = None """Offset (most often just mean) in the output space""" __doc__ = enhancedDocString('ProjectionMapper', locals(), Mapper) def train(self, dataset, *args, **kwargs): """Determine the projection matrix. :Parameters: dataset : Dataset Dataset to operate on *args Optional positional arguments to pass to _train of subclass **kwargs Optional keyword arguments to pass to _train of subclass """ # store the feature wise mean if isinstance(dataset, Dataset): samples = dataset.samples else: samples = dataset self._offset_in = samples.mean(axis=0) # ??? Setting of _offset_out is to be done in a child # class # compute projection matrix with subclass logic self._train(dataset, *args, **kwargs) # perform component selection if self._selector is not None: self.selectOut(self._selector) def _demeanData(self, data): """Helper which optionally demeans """ if self._demean: # demean the training data data = data - self._offset_in if __debug__ and "MAP_" in debug.active: debug("MAP_", "%s: Mean of data in input space %s was subtracted" % (self.__class__.__name__, self._offset_in)) return data def _train(self, dataset): """Worker method. Needs to be implemented by subclass. This method has to train the mapper and store the resulting transformation matrix in `self._proj`. """ raise NotImplementedError def forward(self, data, demean=None): """Perform forward projection. :Parameters: data: ndarray Data array to map demean: boolean | None Override demean setting for this method call. :Returns: NumPy array """ # let arg overwrite instance flag if demean is None: demean = self._demean if self._proj is None: raise RuntimeError, "Mapper needs to be train before used." d = N.asmatrix(data) # Remove input offset if present if demean and self._offset_in is not None: d = d - self._offset_in # Do forward projection res = (d * self._proj).A # Add output offset if present if demean and self._offset_out is not None: res += self._offset_out return res def reverse(self, data): """Reproject (reconstruct) data into the original feature space. :Returns: NumPy array """ if self._proj is None: raise RuntimeError, "Mapper needs to be trained before used." d = N.asmatrix(data) # Remove offset if present in output space if self._demean and self._offset_out is not None: d = d - self._offset_out # Do reverse projection res = (d * self.recon).A # Add offset in input space if self._demean and self._offset_in is not None: res += self._offset_in return res def _computeRecon(self): """Given that a projection is present -- compute reconstruction matrix. By default -- pseudoinverse of projection matrix. Might be overridden in derived classes for efficiency. """ return N.linalg.pinv(self._proj) def _getRecon(self): """Compute (if necessary) and return reconstruction matrix """ # (re)build reconstruction matrix recon = self._recon if recon is None: self._recon = recon = self._computeRecon() return recon def getInSize(self): """Returns the number of original features.""" return self._proj.shape[0] def getOutSize(self): """Returns the number of components to project on.""" return self._proj.shape[1] def selectOut(self, outIds): """Choose a subset of components (and remove all others).""" self._proj = self._proj[:, outIds] if self._offset_out is not None: self._offset_out = self._offset_out[outIds] # invalidate reconstruction matrix self._recon = None proj = property(fget=lambda self: self._proj, doc="Projection matrix") recon = property(fget=_getRecon, doc="Backprojection matrix") class CombinedMapper(Mapper): """Meta mapper that combines several embedded mappers. This mapper can be used the map from several input dataspaces into a common output dataspace. When :meth:`~mvpa.mappers.base.CombinedMapper.forward` is called with a sequence of data, each element in that sequence is passed to the corresponding mapper, which in turned forward-maps the data. The output of all mappers is finally stacked (horizontally or column or feature-wise) into a single large 2D matrix (nsamples x nfeatures). .. note:: This mapper can only embbed mappers that transform data into a 2D (nsamples x nfeatures) representation. For mappers not supporting this transformation, consider wrapping them in a :class:`~mvpa.mappers.base.ChainMapper` with an appropriate post-processing mapper. CombinedMapper fully supports forward and backward mapping, training, runtime selection of a feature subset (in output dataspace) and retrieval of neighborhood information. """ def __init__(self, mappers, **kwargs): """ :Parameters: mappers: list of Mapper instances The order of the mappers in the list is important, as it will define the order in which data snippets have to be passed to :meth:`~mvpa.mappers.base.CombinedMapper.forward`. **kwargs All additional arguments are passed to the base-class constructor. """ Mapper.__init__(self, **kwargs) if not len(mappers): raise ValueError, \ 'CombinedMapper needs at least one embedded mapper.' self._mappers = mappers def forward(self, data): """Map data from the IN spaces into to common OUT space. :Parameter: data: sequence Each element in the `data` sequence is passed to the corresponding embedded mapper and is mapped individually by it. The number of elements in `data` has to match the number of embedded mappers. Each element is `data` has to provide the same number of samples (first dimension). :Returns: array: nsamples x nfeatures Horizontally stacked array of all embedded mapper outputs. """ if not len(data) == len(self._mappers): raise ValueError, \ "CombinedMapper needs a sequence with data for each " \ "Mapper" # return a big array for the result of the forward mapped data # of each embedded mapper try: return N.hstack( [self._mappers[i].forward(d) for i, d in enumerate(data)]) except ValueError: raise ValueError, \ "Embedded mappers do not generate same number of samples. " \ "Check input data." def reverse(self, data): """Reverse map data from OUT space into the IN spaces. :Parameter: data: array Single data array to be reverse mapped into a sequence of data snippets in their individual IN spaces. :Returns: list """ # assure array and transpose # i.e. transpose of 1D does nothing, but of 2D puts features # along first dimension data = N.asanyarray(data).T if not len(data) == self.getOutSize(): raise ValueError, \ "Data shape does match mapper reverse mapping properties." result = [] fsum = 0 for m in self._mappers: # calculate upper border fsum_new = fsum + m.getOutSize() result.append(m.reverse(data[fsum:fsum_new].T)) fsum = fsum_new return result def train(self, dataset): """Trains all embedded mappers. The provided training dataset is splitted appropriately and the corresponding pieces are passed to the :meth:`~mvpa.mappers.base.Mapper.train` method of each embedded mapper. :Parameter: dataset: :class:`~mvpa.datasets.base.Dataset` or subclass A dataset with the number of features matching the `outSize` of the `CombinedMapper`. """ if dataset.nfeatures != self.getOutSize(): raise ValueError, "Training dataset does not match the mapper " \ "properties." fsum = 0 for m in self._mappers: # need to split the dataset fsum_new = fsum + m.getOutSize() m.train(dataset.selectFeatures(range(fsum, fsum_new))) fsum = fsum_new def getInSize(self): """Returns the size of the entity in input space""" return N.sum(m.getInSize() for m in self._mappers) def getOutSize(self): """Returns the size of the entity in output space""" return N.sum(m.getOutSize() for m in self._mappers) def selectOut(self, outIds): """Remove some elements and leave only ids in 'out'/feature space. .. note:: The subset selection is done inplace :Parameter: outIds: sequence All output feature ids to be selected/kept. """ # determine which features belong to what mapper # and call its selectOut() accordingly ids = N.asanyarray(outIds) fsum = 0 for m in self._mappers: # bool which meta feature ids belongs to this mapper selector = N.logical_and(ids < fsum + m.getOutSize(), ids >= fsum) # make feature ids relative to this dataset selected = ids[selector] - fsum fsum += m.getOutSize() # finally apply to mapper m.selectOut(selected) def getNeighbor(self, outId, *args, **kwargs): """Get the ids of the neighbors of a single feature in output dataspace. :Parameters: outId: int Single id of a feature in output space, whos neighbors should be determined. *args, **kwargs Additional arguments are passed to the metric of the embedded mapper, that is responsible for the corresponding feature. Returns a list of outIds """ fsum = 0 for m in self._mappers: fsum_new = fsum + m.getOutSize() if outId >= fsum and outId < fsum_new: return m.getNeighbor(outId - fsum, *args, **kwargs) fsum = fsum_new raise ValueError, "Invalid outId passed to CombinedMapper.getNeighbor()" def __repr__(self): s = Mapper.__repr__(self).rstrip(' )') # beautify if not s[-1] == '(': s += ' ' s += 'mappers=[%s])' % ', '.join([m.__repr__() for m in self._mappers]) return s class ChainMapper(Mapper): """Meta mapper that embedded a chain of other mappers. Each mapper in the chain is called successively to perform forward or reverse mapping. .. note:: In its current implementation the `ChainMapper` treats all but the last mapper as simple pre-processing (in forward()) or post-processing (in reverse()) steps. All other capabilities, e.g. training and neighbor metrics are provided by or affect *only the last mapper in the chain*. With respect to neighbor metrics this means that they are determined based on the input space of the *last mapper* in the chain and *not* on the input dataspace of the `ChainMapper` as a whole """ def __init__(self, mappers, **kwargs): """ :Parameters: mappers: list of Mapper instances **kwargs All additional arguments are passed to the base-class constructor. """ Mapper.__init__(self, **kwargs) if not len(mappers): raise ValueError, 'ChainMapper needs at least one embedded mapper.' self._mappers = mappers def forward(self, data): """Calls all mappers in the chain successively. :Parameter: data data to be chain-mapped. """ mp = data for m in self._mappers: mp = m.forward(mp) return mp def reverse(self, data): """Calls all mappers in the chain successively, in reversed order. :Parameter: data: array data array to be reverse mapped into the orginal dataspace. """ mp = data for m in reversed(self._mappers): mp = m.reverse(mp) return mp def train(self, dataset): """Trains the *last* mapper in the chain. :Parameter: dataset: :class:`~mvpa.datasets.base.Dataset` or subclass A dataset with the number of features matching the `outSize` of the last mapper in the chain (which is identical to the one of the `ChainMapper` itself). """ if dataset.nfeatures != self.getOutSize(): raise ValueError, "Training dataset does not match the mapper " \ "properties." self._mappers[-1].train(dataset) def getInSize(self): """Returns the size of the entity in input space""" return self._mappers[0].getInSize() def getOutSize(self): """Returns the size of the entity in output space""" return self._mappers[-1].getOutSize() def selectOut(self, outIds): """Remove some elements from the *last* mapper in the chain. :Parameter: outIds: sequence All output feature ids to be selected/kept. """ self._mappers[-1].selectOut(outIds) def getNeighbor(self, outId, *args, **kwargs): """Get the ids of the neighbors of a single feature in output dataspace. .. note:: The neighbors are determined based on the input space of the *last mapper* in the chain and *not* on the input dataspace of the `ChainMapper` as a whole! :Parameters: outId: int Single id of a feature in output space, whos neighbors should be determined. *args, **kwargs Additional arguments are passed to the metric of the embedded mapper, that is responsible for the corresponding feature. Returns a list of outIds """ return self._mappers[-1].getNeighbor(outId, *args, **kwargs) def __repr__(self): s = Mapper.__repr__(self).rstrip(' )') # beautify if not s[-1] == '(': s += ' ' s += 'mappers=[%s])' % ', '.join([m.__repr__() for m in self._mappers]) return s pymvpa-0.4.8/mvpa/mappers/boxcar.py000066400000000000000000000163541174541445200173210ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Data mapper""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base.dochelpers import enhancedDocString from mvpa.mappers.base import Mapper from mvpa.misc.support import isInVolume if __debug__: from mvpa.base import debug class BoxcarMapper(Mapper): """Mapper to combine multiple samples into a single sample. .. note:: This mapper is somewhat unconventional since it doesn't preserve number of samples (ie the size of 0-th dimension). """ _COLLISION_RESOLUTIONS = ['mean'] def __init__(self, startpoints, boxlength, offset=0, collision_resolution='mean'): """ :Parameters: startpoints: sequence Index values along the first axis of 'data'. boxlength: int The number of elements after 'startpoint' along the first axis of 'data' to be considered for the boxcar. offset: int The offset between the provided starting point and the actual start of the boxcar. collision_resolution : 'mean' if a sample belonged to multiple output samples, then on reverse, how to resolve the value """ Mapper.__init__(self) startpoints = N.asanyarray(startpoints) if N.issubdtype(startpoints.dtype, 'i'): self.startpoints = startpoints else: if __debug__: debug('MAP', "Boxcar: obtained startpoints are not of int type." " Rounding and changing dtype") self.startpoints = N.asanyarray(N.round(startpoints), dtype='i') # Sanity checks if boxlength < 1: raise ValueError, "Boxlength lower than 1 makes no sense." if boxlength - int(boxlength) != 0: raise ValueError, "boxlength must be an integer value." self.boxlength = int(boxlength) self.offset = offset self.__selectors = None if not collision_resolution in self._COLLISION_RESOLUTIONS: raise ValueError, "Unknown method to resolve the collision." \ " Valid are %s" % self._COLLISION_RESOLUTIONS self.__collision_resolution = collision_resolution __doc__ = enhancedDocString('BoxcarMapper', locals(), Mapper) def __repr__(self): s = super(BoxcarMapper, self).__repr__() return s.replace("(", "(boxlength=%d, offset=%d, startpoints=%s, " "collision_resolution='%s'" % (self.boxlength, self.offset, str(self.startpoints), str(self.__collision_resolution)), 1) def forward(self, data): """Project an ND matrix into N+1D matrix This method also handles the special of forward mapping a single 'raw' sample. Such a sample is extended (by concatenating clones of itself) to cover a full boxcar. This functionality is only availably after a full data array has been forward mapped once. :Returns: array: (#startpoint, ...) """ # in case the mapper is already charged if not self.__selectors is None: # if we have a single 'raw' sample (not a boxcar) # extend it to cover the full box -- useful if one # wants to forward map a mask in raw dataspace (e.g. # fMRI ROI or channel map) into an appropriate mask vector if data.shape == self._outshape[2:]: return N.asarray([data] * self.boxlength) self._inshape = data.shape startpoints = self.startpoints offset = self.offset boxlength = self.boxlength # check for illegal boxes for sp in self.startpoints: if ( sp + offset + boxlength - 1 > len(data)-1 ) \ or ( sp + offset < 0 ): raise ValueError, \ 'Illegal box: start: %i, offset: %i, length: %i' \ % (sp, offset, boxlength) # build a list of list where each sublist contains the indexes of to be # averaged data elements self.__selectors = [ N.arange(i + offset, i + offset + boxlength) \ for i in startpoints ] selected = N.asarray([ data[ box ] for box in self.__selectors ]) self._outshape = selected.shape return selected def reverse(self, data): """Uncombine features back into original space. Samples which were not touched by forward will get value 0 assigned """ if data.shape == self._outshape: # reconstruct to full input space from the provided data # done below pass elif data.shape == self._outshape[1:]: # single sample was given, simple return it again. # this is done because other mappers also work with 'single' # samples return data else: raise ValueError, "BoxcarMapper operates either on single samples" \ " %s or on the full dataset in 'reverse()' which must have " \ "shape %s. Got data of shape %s" \ % (self._outshape[1:], self._outshape, data.shape) # the rest of this method deals with reconstructing the full input # space from the boxcar samples assert(data.shape[0] == len(self.__selectors)) # am I right? :) output = N.zeros(self._inshape, dtype=data.dtype) output_counts = N.zeros((self._inshape[0],), dtype=int) for i, selector in enumerate(self.__selectors): output[selector, ...] += data[i, ...] output_counts[selector] += 1 # scale output if self.__collision_resolution == 'mean': # which samples how multiple sources? g1 = output_counts > 1 # average them # doing complicated transposing to be able to process array with # nd > 2 output_ = output[g1].T output_ /= output_counts[g1] output[g1] = output_.T return output def getInSize(self): """Returns the number of original samples which were combined. """ return self._inshape[0] def isValidOutId(self, outId): """Validate if OutId is valid """ try: return isInVolume(outId, self._outshape[1:]) except: return False def isValidInId(self, inId): """Validate if InId is valid """ try: return isInVolume(inId, self._inshape[1:]) except: return False def getOutSize(self): """Returns the number of output samples. """ return N.prod(self._outshape[1:]) def selectOut(self, outIds): """Just complain for now""" raise NotImplementedError, \ "For feature selection use MaskMapper on output of the %s mapper" \ % self.__class__.__name__ pymvpa-0.4.8/mvpa/mappers/ica.py000066400000000000000000000044421174541445200165720ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Data mapper""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base.dochelpers import enhancedDocString from mvpa.mappers.base import ProjectionMapper import mvpa.base.externals as externals if externals.exists('mdp', raiseException=True): from mdp.nodes import FastICANode, CuBICANode class ICAMapper(ProjectionMapper): """Mapper to project data onto ICA components estimated from some dataset. After the mapper has been instantiated, it has to be train first. The ICA mapper only handles 2D data matrices. """ def __init__(self, algorithm='cubica', transpose=False, **kwargs): ProjectionMapper.__init__(self, **kwargs) self._algorithm = algorithm self._transpose = transpose __doc__ = enhancedDocString('ICAMapper', locals(), ProjectionMapper) def _train(self, dataset): """Determine the projection matrix onto the components from a 2D samples x feature data matrix. """ white_param = {} # more features than samples? -> rank deficiancy # if not tranposing the data, MDP has to do SVD prior to ICA if dataset.samples.shape[1] > dataset.samples.shape[0] \ and not self._transpose: white_param['svd'] = True if self._algorithm == 'fastica': node = FastICANode(white_parm=white_param, dtype=dataset.samples.dtype) elif self._algorithm == 'cubica': node = CuBICANode(white_parm=white_param, dtype=dataset.samples.dtype) else: raise NotImplementedError # node.train(dataset.samples.T) # self._proj = dataset.samples.T * N.asmatrix(node.get_projmatrix()) # print self._proj.shape # else: node.train(dataset.samples) self._proj = N.asmatrix(node.get_projmatrix()) self._recon = N.asmatrix(node.get_recmatrix()) pymvpa-0.4.8/mvpa/mappers/lle.py000066400000000000000000000100371174541445200166070ustar00rootroot00000000000000#emacs: -*- mode: python-mode; py-indent-offset: 4; indent-tabs-mode: nil -*- #ex: set sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Local Linear Embedding Data mapper. This is a wrapper class around the corresponding MDP nodes LLE and HLLE (since MDP 2.4). """ __docformat__ = 'restructuredtext' from mvpa.base import externals import numpy as N from mvpa.mappers.base import Mapper if externals.exists('mdp ge 2.4', raiseException=True): from mdp.nodes import LLENode, HLLENode class LLEMapper(Mapper): """Locally linear embbeding Mapper. This mapper performs dimensionality reduction. It wraps two algorithms provided by the Modular Data Processing (MDP) framework. Locally linear embedding (LLE) approximates the input data with a low-dimensional surface and reduces its dimensionality by learning a mapping to the surface. This wrapper class provides access to two different LLE algorithms (i.e. the corresponding MDP processing nodes). 1) An algorithm outlined in *An Introduction to Locally Linear Embedding* by L. Saul and S. Roweis, using improvements suggested in *Locally Linear Embedding for Classification* by D. deRidder and R.P.W. Duin (aka `LLENode`) and 2) Hessian Locally Linear Embedding analysis based on algorithm outlined in *Hessian Eigenmaps: new locally linear embedding techniques for high-dimensional data* by C. Grimes and D. Donoho, 2003. .. note:: This mapper only provides forward-mapping functionality -- no reverse mapping is available. .. seealso:: http://mdp-toolkit.sourceforge.net """ def __init__(self, k, algorithm='lle', **kwargs): """ :Parameters: k: int Number of nearest neighbor to be used by the algorithm. algorithm: 'lle' | 'hlle' Either use the standard LLE algorithm or Hessian Linear Local Embedding (HLLE). **kwargs: Additional arguments are passed to the underlying MDP node. Most importantly this is the `output_dim` argument, that determines the number of dimensions to mapper is using as output space. """ # no meaningful metric Mapper.__init__(self, metric=None) self._algorithm = algorithm self._node_kwargs = kwargs self._k = k self._node = None def train(self, ds): """Train the mapper. """ if self._algorithm == 'lle': self._node = LLENode(self._k, dtype=ds.samples.dtype, **self._node_kwargs) elif self._algorithm == 'hlle': self._node = HLLENode(self._k, dtype=ds.samples.dtype, **self._node_kwargs) else: raise NotImplementedError self._node.train(ds.samples) self._node.stop_training() def forward(self, data): """Map data from the IN dataspace into OUT space. """ # experience the beauty of MDP -- just call the beast and be done ;-) return self.node(data) def reverse(self, data): """Reverse map data from OUT space into the IN space. """ raise NotImplementedError def getInSize(self): """Returns the size of the entity in input space""" return self.node.input_dim def getOutSize(self): """Returns the size of the entity in output space""" return self.node.output_dim def _accessNode(self): """Provide access to the underlying MDP processing node. With some care. """ if self._node is None: raise RuntimeError, \ 'The LLEMapper needs to be trained before access to the ' \ 'processing node is possible.' return self._node node = property(fget=_accessNode) pymvpa-0.4.8/mvpa/mappers/mask.py000066400000000000000000000331501174541445200167670ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Data mapper which applies mask to the data""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.mappers.base import Mapper from mvpa.base.dochelpers import enhancedDocString from mvpa.misc.support import isInVolume if __debug__: from mvpa.base import debug, warning from mvpa.misc.support import isSorted class MaskMapper(Mapper): """Mapper which uses a binary mask to select "Features" """ def __init__(self, mask, **kwargs): """Initialize MaskMapper :Parameters: mask : array an array in the original dataspace and its nonzero elements are used to define the features included in the dataset """ Mapper.__init__(self, **kwargs) self.__mask = self.__maskdim = self.__masksize = \ self.__masknonzerosize = self.__forwardmap = \ self.__masknonzero = None # to make pylint happy self._initMask(mask) __doc__ = enhancedDocString('MaskMapper', locals(), Mapper) def __str__(self): return "MaskMapper: %d -> %d" \ % (self.__masksize, self.__masknonzerosize) def __repr__(self): s = super(MaskMapper, self).__repr__() return s.replace("(", "(mask=%s," % self.__mask, 1) # XXX # XXX HAS TO TAKE CARE OF SUBCLASSES!!! # XXX # # def __deepcopy__(self, memo=None): # # XXX memo does not seem to be used # if memo is None: # memo = {} # from mvpa.support.copy import deepcopy # out = MaskMapper.__new__(MaskMapper) # Mapper.__init__(out) # out.__mask = self.__mask.copy() # out.__maskdim = self.__maskdim # out.__masksize = self.__masksize # out.__masknonzero = deepcopy(self.__masknonzero) # out.__masknonzerosize = self.__masknonzerosize # out.__forwardmap = self.__forwardmap.copy() # # return out def _initMask(self, mask): """Initialize internal state with mask-derived information It is needed to initialize structures for the fast and reverse lookup to don't impose performance hit on any future operation """ # NOTE: If any new class member are added here __deepcopy__() has to # be adjusted accordingly! self.__mask = (mask != 0) self.__maskdim = len(mask.shape) self.__masksize = N.prod(mask.shape) # Following introduces space penalty but are needed # for efficient processing. # Store all coordinates for backward mapping self.__masknonzero = mask.nonzero() self.__masknonzerosize = len(self.__masknonzero[0]) #from IPython.Shell import IPShellEmbed #ipshell = IPShellEmbed() #ipshell() #import pydb; pydb.debugger() # Store forward mapping (ie from coord into outId) # TODO to save space might take appropriate int type # depending on masknonzerosize # it could be done with a dictionary, but since mask # might be relatively big, it is better to simply use # a chunk of RAM ;-) self.__forwardmap = N.zeros(mask.shape, dtype=N.int64) # under assumption that we +1 values in forwardmap so that # 0 can be used to signal outside of mask self.__forwardmap[self.__masknonzero] = \ N.arange(self.__masknonzerosize) def forward(self, data): """Map data from the original dataspace into featurespace. """ data = N.asanyarray(data) # assure it is an array datadim = len(data.shape) datashape = data.shape[(-1)*self.__maskdim:] if not datashape == self.__mask.shape: raise ValueError, \ "The shape of data to be mapped %s " % `datashape` \ + " does not match the mapper's mask shape %s" \ % `self.__mask.shape` if self.__maskdim == datadim: # we had to select by __masknonzero if we didn't sort # Ids and wanted to preserve the order #return data[ self.__masknonzero ] return data[ self.__mask ] elif self.__maskdim+1 == datadim: # XXX XXX XXX below line should be accomodated also # to make use of self.__masknonzero instead of # plain mask if we want to preserve the (re)order return data[ :, self.__mask ] else: raise ValueError, \ "Shape of the to be mapped data, does not match the " \ "mapper mask. Only one (optional) additional dimension " \ "exceeding the mask shape is supported." def reverse(self, data): """Reverse map data from featurespace into the original dataspace. """ data = N.asanyarray(data) datadim = len(data.shape) if not datadim in [1, 2]: raise ValueError, \ "Only 2d or 1d data can be reverse mapped. "\ "Got data of shape %s" % (data.shape,) if datadim == 1: # Verify that we are trying to reverse data of proper dimension. # In 1D case numpy would not complain and will broadcast # the values if __debug__ and self.nfeatures != len(data): raise ValueError, \ "Cannot reverse map data with %d elements, whenever " \ "mask knows only %d" % (len(data), self.nfeatures) mapped = N.zeros(self.__mask.shape, dtype=data.dtype) mapped[self.__mask] = data elif datadim == 2: # Verify that we are trying to reverse data of proper dimension. # In 2D case numpy we should have matching # of features if __debug__ and self.nfeatures != data.shape[1]: raise ValueError, \ "Cannot reverse map data of shape %s, whenever " \ "mask knows only %d features" \ % (data.shape, self.nfeatures) mapped = N.zeros(data.shape[:1] + self.__mask.shape, dtype=data.dtype) mapped[:, self.__mask] = data return mapped def getInSize(self): """InShape is a shape of original mask""" return self.__masksize def getOutSize(self): """OutSize is a number of non-0 elements in the mask""" return self.__masknonzerosize def getMask(self, copy = True): """By default returns a copy of the current mask. If 'copy' is set to False a reference to the mask is returned instead. This shared mask must not be modified! """ if copy: return self.__mask.copy() else: return self.__mask def getInId(self, outId): """Returns a features coordinate in the original data space for a given feature id. If this method is called with a list of feature ids it returns a 2d-array where the first axis corresponds the dimensions in 'In' dataspace and along the second axis are the coordinates of the features on this dimension (like the output of NumPy.array.nonzero()). XXX it might become __get_item__ access method """ # XXX Might be improved by storing also transpose of # __masknonzero return N.array([self.__masknonzero[i][outId] for i in xrange(self.__maskdim)]) def getInIds(self): """Returns a 2d array where each row contains the coordinate of the feature with the corresponding id. """ return N.transpose(self.__masknonzero) def isValidInId(self, inId): mask = self.mask return (isInVolume(inId, mask.shape) and mask[tuple(inId)] != 0) def getOutId(self, coord): """Translate a feature mask coordinate into a feature ID. """ # FIXME Since lists/arrays accept negative indexes to go from # the end -- we need to check coordinates explicitely. Otherwise # we would get warping effect try: tcoord = tuple(coord) if self.__mask[tcoord] == 0: raise ValueError, \ "The point %s didn't belong to the mask" % (`coord`) return self.__forwardmap[tcoord] except TypeError: raise ValueError, \ "Coordinates %s are of incorrect dimension. " % `coord` + \ "The mask has %d dimensions." % self.__maskdim except IndexError: raise ValueError, \ "Coordinates %s are out of mask boundary. " % `coord` + \ "The mask is of %s shape." % `self.__mask.shape` def selectOut(self, outIds): """Only listed outIds would remain. *Function assumes that outIds are sorted*. In __debug__ mode selectOut would check if obtained IDs are sorted and would warn the user if they are not. .. note:: If you feel strongly that you need to remap features internally (ie to allow Ids with mixed order) please contact developers of mvpa to discuss your use case. The function used to accept a matrix-mask as the input but now it really has to be a list of IDs Feature/Bug: * Negative outIds would not raise exception - just would be treated 'from the tail' """ if __debug__ and 'CHECK_SORTEDIDS' in debug.active: # per short conversation with Michael -- we should not # allow reordering since we saw no viable use case for # it. Thus -- warn user is outIds are not in sorted order # and no sorting was requested may be due to performance # considerations if not isSorted(outIds): warning("IDs for selectOut must be provided " + "in sorted order, otherwise .forward() would fail"+ " on the data with multiple samples") # adjust mask and forwardmap discarded = N.array([ True ] * self.nfeatures) discarded[outIds] = False # create a map of discarded Ids discardedin = tuple(self.getInId(discarded)) self.__mask[discardedin] = False self.__masknonzerosize = len(outIds) self.__masknonzero = [ x[outIds] for x in self.__masknonzero ] # adjust/remap not discarded in forwardmap # since we merged _tent/maskmapper-init-noloop it is not necessary # to zero-out discarded entries since we anyway would check with mask # in getOutId(s) self.__forwardmap[self.__masknonzero] = \ N.arange(self.__masknonzerosize) def discardOut(self, outIds): """Listed outIds would be discarded """ # adjust mask and forwardmap discardedin = tuple(self.getInId(outIds)) self.__mask[discardedin] = False # since we merged _tent/maskmapper-init-noloop it is not necessary # to zero-out discarded entries since we anyway would check with mask # in getOutId(s) # self.__forwardmap[discardedin] = 0 self.__masknonzerosize -= len(outIds) self.__masknonzero = [ N.delete(x, outIds) for x in self.__masknonzero ] # adjust/remap not discarded in forwardmap self.__forwardmap[self.__masknonzero] = \ N.arange(self.__masknonzerosize) # OPT: we can adjust __forwardmap only for ids which are higher than # the smallest outId among discarded. Similar strategy could be done # for selectOut but such index has to be figured out first there # .... # comment out for now... introduce when needed # def getInEmpty(self): # """Returns empty instance of input object""" # raise NotImplementedError # # # def getOutEmpty(self): # """Returns empty instance of output object""" # raise NotImplementedError def convertOutIds2OutMask(self, outIds): """Returns a boolean mask with all features in `outIds` selected. :Parameters: outIds: list or 1d array To be selected features ids in out-space. :Returns: ndarray: dtype='bool' All selected features are set to True; False otherwise. """ fmask = N.repeat(False, self.nfeatures) fmask[outIds] = True return fmask def convertOutIds2InMask(self, outIds): """Returns a boolean mask with all features in `ouIds` selected. This method works exactly like Mapper.convertOutIds2OutMask(), but the feature mask is finally (reverse) mapped into in-space. :Parameters: outIds: list or 1d array To be selected features ids in out-space. :Returns: ndarray: dtype='bool' All selected features are set to True; False otherwise. """ return self.reverse(self.convertOutIds2OutMask(outIds)) # Read-only props mask = property(fget=lambda self:self.getMask(False)) # TODO Unify tuple/array conversion of coordinates. tuples are needed # for easy reference, arrays are needed when doing computation on # coordinates: for some reason numpy doesn't handle casting into # array from tuples while performing arithm operations... pymvpa-0.4.8/mvpa/mappers/metric.py000066400000000000000000000225171174541445200173240ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Classes and functions to provide sense of distances between sample points""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.clfs.distance import cartesianDistance class Metric(object): """Abstract class for any metric. Subclasses abstract a metric of a dataspace with certain properties and can be queried for structural information. Currently, this is limited to neighborhood information, i.e. identifying the surround a some coordinate in the respective dataspace. At least one of the methods (getNeighbors, getNeighbor) has to be overriden in every derived class. NOTE: derived #2 from derived class #1 has to override all methods which were overrident in class #1 """ def getNeighbors(self, *args, **kwargs): """Return the list of coordinates for the neighbors. By default it simply constracts the list based on the generator getNeighbor """ return [ x for x in self.getNeighbor(*args, **kwargs) ] def getNeighbor(self, *args, **kwargs): """Generator to return coordinate of the neighbor. Base class contains the simplest implementation, assuming that getNeighbors returns iterative structure to spit out neighbors 1-by-1 """ for neighbor in self.getNeighbors(*args, **kwargs): yield neighbor class DescreteMetric(Metric): """Find neighboring points in descretized space If input space is descretized and all points fill in N-dimensional cube, this finder returns list of neighboring points for a given distance. For all `origin` coordinates this class exclusively operates on discretized values, not absolute coordinates (which are e.g. in mm). Additionally, this metric has the notion of compatible and incompatible dataspace metrics, i.e. the descrete space might contain dimensions for which computing an overall distance is not meaningful. This could, for example, be a combined spatio-temporal space (three spatial dimension, plus the temporal one). This metric allows to define a boolean mask (`compatmask`) which dimensions share the same dataspace metrics and for which the distance function should be evaluated. If a `compatmask` is provided, all cordinates are projected into the subspace of the non-zero dimensions and distances are computed within that space. However, by using a per dimension radius argument for the getNeighbor methods, it is nevertheless possible to define a neighborhood along all dimension. For all non-compatible axes the respective radius is treated as a one-dimensional distance along the respective axis. """ def __init__(self, elementsize=1, distance_function=cartesianDistance, compatmask=None): """ :Parameters: elementsize: float | sequence The extent of a dataspace element along all dimensions. distance_function: functor The distance measure used to determine distances between dataspace elements. compatmask: 1D bool array | None A mask where all non-zero elements indicate dimensions with compatible spacemetrics. If None (default) all dimensions are assumed to have compatible spacemetrics. """ Metric.__init__(self) self.__filter_radius = None self.__filter_coord = None self.__distance_function = distance_function self.__elementsize = N.array(elementsize, ndmin=1) self.__Ndims = len(self.__elementsize) self.compatmask = compatmask def _expandRadius(self, radius): # expand radius to be equal along all dimensions if just scalar # is provided if N.isscalar(radius): radius = N.array([radius] * len(self.__elementsize), dtype='float') else: radius = N.array(radius, dtype='float') return radius def _computeFilter(self, radius): """ (Re)compute filter_coord based on given radius """ if not N.all(radius[self.__compatmask][0] == radius[self.__compatmask]): raise ValueError, \ "Currently only neighborhood spheres are supported, " \ "not ellipsoids." # store radius in compatible space compat_radius = radius[self.__compatmask][0] # compute radius in units of elementsize per axis elementradius_per_axis = radius / self.__elementsize # build prototype search space filter_radiuses = N.ceil(N.abs(elementradius_per_axis)).astype('int') filter_center = filter_radiuses comp_center = filter_center[self.__compatmask] \ * self.__elementsize[self.__compatmask] filter_mask = N.ones((filter_radiuses * 2) + 1, dtype='bool') # get coordinates of all elements f_coords = N.transpose(filter_mask.nonzero()) # but start with empty mask filter_mask[:] = False # check all filter element for coord in f_coords: dist = self.__distance_function( coord[self.__compatmask] * self.__elementsize[self.__compatmask], comp_center) # compare with radius if dist <= compat_radius: # zero too distant filter_mask[N.array(coord, ndmin=2).T.tolist()] = True self.__filter_coord = N.array( filter_mask.nonzero() ).T \ - filter_center self.__filter_radius = radius def getNeighbors(self, origin, radius=0): """Returns coordinates of the neighbors which are within distance from coord. :Parameters: origin: 1D array The center coordinate of the neighborhood. radius: scalar | sequence If a scalar, the radius is treated as identical along all dimensions of the dataspace. If a sequence, it defines a per dimension radius, thus has to have the same number of elements as dimensions. Currently, only spherical neighborhoods are supported. Therefore, the radius has to be equal along all dimensions flagged as having compatible dataspace metrics. It is, however, possible to define variant radii for all other dimensions. """ if len(origin) != self.__Ndims: raise ValueError("Obtained coordinates [%r] which have different " "number of dimensions (%d) from known " "elementsize" % (origin, self.__Ndims)) # take care of postprocessing the radius the ensure validity of the next # conditional radius = self._expandRadius(radius) if N.any(radius != self.__filter_radius): self._computeFilter(radius) # for the ease of future references, it is better to transform # coordinates into tuples return origin + self.__filter_coord def _setFilter(self, filter_coord): """Lets allow to specify some custom filter to use """ self.__filter_coord = filter_coord def _getFilter(self): """Lets allow to specify some custom filter to use """ return self.__filter_coord def _setElementSize(self, v): # reset filter radius _elementsize = N.array(v, ndmin=1) # assure that it is read-only and it gets reassigned # only as a whole to trigger this house-keeping _elementsize.flags.writeable = False self.__elementsize = _elementsize self.__Ndims = len(_elementsize) self.__filter_radius = None def _getCompatMask(self): """Return compatmask .. note:: Don't modify in place since it would need to require to reset __filter_radius whenever changed """ return self.__compatmask def _setCompatMask(self, compatmask): """Set new value of compatmask """ if compatmask is None: self.__compatmask = N.ones(self.__elementsize.shape, dtype='bool') else: self.__compatmask = N.array(compatmask, dtype='bool') if not self.__elementsize.shape == self.__compatmask.shape: raise ValueError, '`compatmask` is of incompatible shape ' \ '(need %s, got %s)' % (`self.__elementsize.shape`, `self.__compatmask.shape`) self.__filter_radius = None # reset so filter gets recomputed filter_coord = property(fget=_getFilter, fset=_setFilter) elementsize = property(fget=lambda self: self.__elementsize, fset=_setElementSize) compatmask = property(_getCompatMask, _setCompatMask) # Template for future classes # # class MeshMetric(Metric): # """Return list of neighboring points on a mesh # """ # def getNeighbors(self, origin, distance=0): # """Return neighbors""" # raise NotImplementedError pymvpa-0.4.8/mvpa/mappers/pca.py000066400000000000000000000040231174541445200165740ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Data mapper""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base import warning from mvpa.base.dochelpers import enhancedDocString from mvpa.mappers.base import ProjectionMapper import mvpa.base.externals as externals if externals.exists('mdp', raiseException=True): from mdp.nodes import NIPALSNode class PCAMapper(ProjectionMapper): """Mapper to project data onto PCA components estimated from some dataset. After the mapper has been instantiated, it has to be train first. The PCA mapper only handles 2D data matrices. """ def __init__(self, transpose=False, **kwargs): ProjectionMapper.__init__(self, **kwargs) self._var = None __doc__ = enhancedDocString('PCAMapper', locals(), ProjectionMapper) def _train(self, dataset): """Determine the projection matrix onto the components from a 2D samples x feature data matrix. """ samples = dataset.samples dtype = samples.dtype if str(dtype).startswith('uint') \ or str(dtype).startswith('int'): warning("PCA: input data is in integers. " + \ "MDP's NIPALSNode operates only on floats, thus "+\ "coercing to double") dtype = N.double samples = samples.astype(N.double) node = NIPALSNode(dtype=dtype) node.train(samples) self._proj = N.asmatrix(node.get_projmatrix()) self._recon = N.asmatrix(node.get_recmatrix()) # store variance per PCA component self._var = node.d var = property(fget=lambda self: self._var, doc='Variances per component') pymvpa-0.4.8/mvpa/mappers/procrustean.py000066400000000000000000000173101174541445200204010ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Procrustean rotation mapper""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base.dochelpers import enhancedDocString from mvpa.mappers.base import ProjectionMapper from mvpa.datasets import Dataset from mvpa.featsel.helpers import ElementSelector if __debug__: from mvpa.base import debug class ProcrusteanMapper(ProjectionMapper): """Mapper to project from one space to another using Procrustean transformation (shift + scaling + rotation) """ _DEV__doc__ = """Possibly revert back to inherit from ProjectionMapper""" def __init__(self, scaling=True, reflection=True, reduction=True, oblique=False, oblique_rcond=-1, **kwargs): """Initialize the ProcrusteanMapper :Parameters: scaling: bool Scale data for the transformation (no longer rigid body transformation) reflection: bool Allow for the data to be reflected (so it might not be a rotation). Effective only for non-oblique transformations reduction: bool If true, it is allowed to map into lower-dimensional space. Forward transformation might be suboptimal then and reverse transformation might not recover all original variance oblique: bool Either to allow non-orthogonal transformation -- might heavily overfit the data if there is less samples than dimensions. Use `oblique_rcond`. oblique_rcond: float Cutoff for 'small' singular values to regularize the inverse. See :class:`~numpy.linalg.lstsq` for more information. """ ProjectionMapper.__init__(self, **kwargs) self._scaling = scaling """Either to determine the scaling factor""" self._reduction = reduction self._reflection = reflection self._oblique = oblique self._oblique_rcond = oblique_rcond self._scale = None """Estimated scale""" __doc__ = enhancedDocString('ProcrusteanMapper', locals(), ProjectionMapper) # XXX we should just use beautiful ClassWithCollections everywhere... makes # life so easier... for now -- manual def __repr__(self): s = ProjectionMapper.__repr__(self).rstrip(' )') if not s[-1] == '(': s += ', ' s += "scaling=%d, reflection=%d, reduction=%d, " \ "oblique=%s, oblique_rcond=%g)" % \ (self._scaling, self._reflection, self._reduction, self._oblique, self._oblique_rcond) return s # XXX we have to override train since now we have multiple datasets # alternative way is to assign target to the labels of the source # dataset def _train(self, source, target=None): """Train Procrustean transformation :Parameters: source : dataset or ndarray Source space for determining the transformation. If target is None, then labels of 'source' dataset are taken as the target target : dataset or ndarray or Null Target space for determining the transformation """ # Since it is unsupervised, we don't care about labels datas = () odatas = () means = () shapes = () assess_residuals = __debug__ and 'MAP_' in debug.active if target is None: target = source.labels for i, ds in enumerate((source, target)): if isinstance(ds, Dataset): data = N.asarray(ds.samples) else: data = ds if assess_residuals: odatas += (data,) if i == 0: mean = self._offset_in else: mean = data.mean(axis=0) data = data - mean means += (mean,) datas += (data,) shapes += (data.shape,) # shortcuts for sizes sn, sm = shapes[0] tn, tm = shapes[1] # Check the sizes if sn != tn: raise ValueError, "Data for both spaces should have the same " \ "number of samples. Got %d in source and %d in target space" \ % (sn, tn) # Sums of squares ssqs = [N.sum(d**2, axis=0) for d in datas] # XXX check for being invariant? # needs to be tuned up properly and not raise but handle for i in xrange(2): if N.all(ssqs[i] <= N.abs((N.finfo(datas[i].dtype).eps * sn * means[i] )**2)): raise ValueError, "For now do not handle invariant in time datasets" norms = [ N.sqrt(N.sum(ssq)) for ssq in ssqs ] normed = [ data/norm for (data, norm) in zip(datas, norms) ] # add new blank dimensions to source space if needed if sm < tm: normed[0] = N.hstack( (normed[0], N.zeros((sn, tm-sm))) ) if sm > tm: if self._reduction: normed[1] = N.hstack( (normed[1], N.zeros((sn, sm-tm))) ) else: raise ValueError, "reduction=False, so mapping from " \ "higher dimensionality " \ "source space is not supported. Source space had %d " \ "while target %d dimensions (features)" % (sm, tm) source, target = normed if self._oblique: # Just do silly linear system of equations ;) or naive # inverse problem if sn == sm and tm == 1: T = N.linalg.solve(source, target) else: T = N.linalg.lstsq(source, target, rcond=self._oblique_rcond)[0] ss = 1.0 else: # Orthogonal transformation # figure out optimal rotation U, s, Vh = N.linalg.svd(N.dot(target.T, source), full_matrices=False) T = N.dot(Vh.T, U.T) if not self._reflection: # then we need to assure that it is only rotation # "recipe" from # http://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem # for more and info and original references, see # http://dx.doi.org/10.1007%2FBF02289451 nsv = len(s) s[:-1] = 1 s[-1] = N.linalg.det(T) T = N.dot(U[:, :nsv] * s, Vh) # figure out scale and final translation # XXX with reflection False -- not sure if here or there or anywhere... ss = sum(s) # if we were to collect standardized distance # std_d = 1 - sD**2 # select out only relevant dimensions if sm != tm: T = T[:sm, :tm] self._scale = scale = ss * norms[1] / norms[0] # Assign projection if self._scaling: proj = scale * T else: proj = T self._proj = proj if self._demean: self._offset_out = means[1] if __debug__ and 'MAP_' in debug.active: # compute the residuals res_f = self.forward(odatas[0]) d_f = N.linalg.norm(odatas[1] - res_f)/N.linalg.norm(odatas[1]) res_r = self.reverse(odatas[1]) d_r = N.linalg.norm(odatas[0] - res_r)/N.linalg.norm(odatas[0]) debug('MAP_', "%s, residuals are forward: %g," " reverse: %g" % (repr(self), d_f, d_r)) pymvpa-0.4.8/mvpa/mappers/samplegroup.py000066400000000000000000000066111174541445200203740ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Data mapper""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base.dochelpers import enhancedDocString from mvpa.mappers.base import Mapper from mvpa.misc.transformers import FirstAxisMean if __debug__: from mvpa.base import debug class SampleGroupMapper(Mapper): # name is ugly, please help! """Mapper to apply a mapping function to samples of the same type. A customimzable function is applied individually to all samples with the same unique label from the same chunk. This mapper is somewhat unconventional since it doesn't preserve number of samples (ie the size of 0-th dimension...) """ def __init__(self, fx=FirstAxisMean): """Initialize the PCAMapper Parameters: startpoints: A sequence of index value along the first axis of 'data'. boxlength: The number of elements after 'startpoint' along the first axis of 'data' to be considered for averaging. offset: The offset between the starting point and the averaging window (boxcar). collision_resolution : string if a sample belonged to multiple output samples, then on reverse, how to resolve the value (choices: 'mean') """ Mapper.__init__(self) self.__fx = fx self.__uniquechunks = None self.__uniquelabels = None self.__chunks = None self.__labels = None self.__datashape = None __doc__ = enhancedDocString('SampleGroupMapper', locals(), Mapper) def train(self, dataset): """ """ # just store the relevant information self.__uniquechunks = dataset.uniquechunks self.__uniquelabels = dataset.uniquelabels self.__chunks = dataset.chunks self.__labels = dataset.labels self.__datashape = (dataset.nfeatures, ) def forward(self, data): """ """ if self.__datashape is None: raise RuntimeError, \ "SampleGroupMapper needs to be trained before it can be used" mdata = [] # for each label in each chunk for c in self.__uniquechunks: for l in self.__uniquelabels: mdata.append(self.__fx(data[N.logical_and(self.__labels == l, self.__chunks == c)])) return N.array(mdata) def reverse(self, data): """This is not implemented.""" raise NotImplementedError def getInSize(self): """Returns the number of original samples which were combined. """ return self.__datashape[0] def getOutSize(self): """Returns the number of output samples. """ return self.__datashape[0] def selectOut(self, outIds): """Just complain for now""" raise NotImplementedError, \ "For feature selection use MaskMapper on output of the %s mapper" \ % self.__class__.__name__ pymvpa-0.4.8/mvpa/mappers/som.py000066400000000000000000000210671174541445200166360ustar00rootroot00000000000000#emacs: -*- mode: python-mode; py-indent-offset: 4; indent-tabs-mode: nil -*- #ex: set sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Self-organizing map (SOM) mapper.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.mappers.base import Mapper if __debug__: from mvpa.base import debug class SimpleSOMMapper(Mapper): """Mapper using a self-organizing map (SOM) for dimensionality reduction. This mapper provides a simple, but pretty fast implementation of a self-organizing map using an unsupervised training algorithm. It performs a ND -> 2D mapping, which can for, example, be used for visualization of high-dimensional data. This SOM implementation uses squared Euclidean distance to determine the best matching Kohonen unit and a Gaussian neighborhood influence kernel. """ def __init__(self, kshape, niter, learning_rate=0.005, iradius=None): """ :Parameters: kshape: (int, int) Shape of the internal Kohonen layer. Currently, only 2D Kohonen layers are supported, although the length of an axis might be set to 1. niter: int Number of iteration during network training. learning_rate: float Initial learning rate, which will continuously decreased during network training. iradius: float | None Initial radius of the Gaussian neighborhood kernel radius, which will continuously decreased during network training. If `None` (default) the radius is set equal to the longest edge of the Kohonen layer. """ # init base class Mapper.__init__(self) self.kshape = N.array(kshape, dtype='int') if iradius is None: self.radius = self.kshape.max() else: self.radius = iradius # learning rate self.lrate = learning_rate # number of training iterations self.niter = niter # precompute whatever can be done # scalar for decay of learning rate and radius across all iterations self.iter_scale = self.niter / N.log(self.radius) # the internal kohonen layer self._K = None def train(self, ds): """Perform network training. :Parameter: ds: Dataset All samples in the dataset will be used for unsupervised training of the SOM. """ # XXX initialize with clever default, e.g. plain of first two PCA # components self._K = N.random.standard_normal(tuple(self.kshape) + (ds.nfeatures,)) # units weight vector deltas for batch training # (height x width x #features) unit_deltas = N.zeros(self._K.shape, dtype='float') # precompute distance kernel between elements in the Kohonen layer # that will remain constant throughout the training # (just compute one quadrant, as the distances are symmetric) # XXX maybe do other than squared Euclidean? dqd = N.fromfunction(lambda x, y: (x**2 + y**2)**0.5, self.kshape, dtype='float') # for all iterations for it in xrange(1, self.niter + 1): # compute the neighborhood impact kernel for this iteration # has to be recomputed since kernel shrinks over time k = self._computeInfluenceKernel(it, dqd) # for all training vectors for s in ds.samples: # determine closest unit (as element coordinate) b = self._getBMU(s) # train all units at once by unfolding the kernel (from the # single quadrant that is precomputed), cutting it to the # right shape and simply multiply it to the difference of target # and all unit weights.... infl = N.vstack(( N.hstack(( # upper left k[b[0]:0:-1, b[1]:0:-1], # upper right k[b[0]:0:-1, :self.kshape[1] - b[1]])), N.hstack(( # lower left k[:self.kshape[0] - b[0], b[1]:0:-1], # lower right k[:self.kshape[0] - b[0], :self.kshape[1] - b[1]])) )) unit_deltas += infl[:,:,N.newaxis] * (s - self._K) # apply cumulative unit deltas self._K += unit_deltas if __debug__: debug("SOM", "Iteration %d/%d done: ||unit_deltas||=%g" % (it, self.niter, N.sqrt(N.sum(unit_deltas **2)))) # reset unit deltas unit_deltas.fill(0.) def _computeInfluenceKernel(self, iter, dqd): """Compute the neighborhood kernel for some iteration. :Parameters: iter: int The iteration for which to compute the kernel. dqd: array (nrows x ncolumns) This is one quadrant of Euclidean distances between Kohonen unit locations. """ # compute radius decay for this iteration curr_max_radius = self.radius * N.exp(-1.0 * iter / self.iter_scale) # same for learning rate curr_lrate = self.lrate * N.exp(-1.0 * iter / self.iter_scale) # compute Gaussian influence kernel infl = N.exp((-1.0 * dqd) / (2 * curr_max_radius * iter)) infl *= curr_lrate # hard-limit kernel to max radius # XXX is this really necessary? infl[dqd > curr_max_radius] = 0. return infl def _getBMU(self, sample): """Returns the ID of the best matching unit. 'best' is determined as minimal squared Euclidean distance between any units weight vector and some given target `sample` :Parameters: sample: array Target sample. :Returns: tuple: (row, column) """ # TODO expose distance function as parameter loc = N.argmin(((self.K - sample) ** 2).sum(axis=2)) # assumes 2D Kohonen layer return (N.divide(loc, self.kshape[1]), loc % self.kshape[1]) def forward(self, data): """Map data from the IN dataspace into OUT space. Mapping is performs by simple determining the best matching Kohonen unit for each data sample. """ return N.array([self._getBMU(d) for d in data]) def reverse(self, data): """Reverse map data from OUT space into the IN space. """ # simple transform into appropriate array slicing and # return the associated Kohonen unit weights return self.K[tuple(N.transpose(data))] def getInSize(self): """Returns the size of the entity in input space""" return self.K.shape[-1] def getOutSize(self): """Returns the size of the entity in output space""" return self.K.shape[:-1] def selectOut(self, outIds): """Limit the OUT space to a certain set of features. This is currently not implemented. Moreover, although it is technically possible to implement this functionality, it is unsure whether it is meaningful in the context of SOMs. """ raise NotImplementedError def getInId(self, outId): """Translate a feature id into a coordinate/index in input space. This is not meaningful in the context of SOMs. """ raise NotImplementedError def isValidOutId(self, outId): """Validate feature id in OUT space. """ return (outId >= 0).all() and (outId < self.kshape).all() def __repr__(self): s = Mapper.__repr__(self).rstrip(' )') # beautify if not s[-1] == '(': s += ' ' s += 'kshape=%s, niter=%i, learning_rate=%f, iradius=%f)' \ % (str(tuple(self.kshape)), self.niter, self.lrate, self.radius) return s def _accessKohonen(self): """Provide access to the Kohonen layer. With some care. """ if self._K is None: raise RuntimeError, \ 'The SOM needs to be trained before access to the Kohonen ' \ 'layer is possible.' return self._K K = property(fget=_accessKohonen) pymvpa-0.4.8/mvpa/mappers/svd.py000066400000000000000000000065601174541445200166350ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Singular-value decomposition mapper""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base.dochelpers import enhancedDocString from mvpa.mappers.base import ProjectionMapper from mvpa.featsel.helpers import ElementSelector if __debug__: from mvpa.base import debug class SVDMapper(ProjectionMapper): """Mapper to project data onto SVD components estimated from some dataset. """ def __init__(self, **kwargs): """Initialize the SVDMapper :Parameters: **kwargs: All keyword arguments are passed to the ProjectionMapper constructor. Note, that for the 'selector' argument this class also supports passing a `ElementSelector` instance, which will be used to determine the to be selected features, based on the singular values of each component. """ ProjectionMapper.__init__(self, **kwargs) self._sv = None """Singular values of the training matrix.""" __doc__ = enhancedDocString('SVDMapper', locals(), ProjectionMapper) def _train(self, dataset): """Determine the projection matrix onto the SVD components from a 2D samples x feature data matrix. """ X = N.asmatrix(dataset.samples) X = self._demeanData(X) # singular value decomposition U, SV, Vh = N.linalg.svd(X, full_matrices=0) # store the final matrix with the new basis vectors to project the # features onto the SVD components. And store its .H right away to # avoid computing it in forward() self._proj = Vh.H # also store singular values of all components self._sv = SV if __debug__: debug("MAP", "SVD was done on %s and obtained %d SVs " % (dataset, len(SV)) + " (%d non-0, max=%f)" % (len(SV.nonzero()), SV[0])) # .norm might be somewhat expensive to compute if "MAP_" in debug.active: debug("MAP_", "Mixing matrix has %s shape and norm=%f" % (self._proj.shape, N.linalg.norm(self._proj))) def selectOut(self, outIds): """Choose a subset of SVD components (and remove all others).""" # handle ElementSelector operating on SV (base class has no idea about) # XXX think about more generic interface, where some 'measure' is assigned # per each projection dimension, like in _sv in case of SVD. # May be selector could be parametrized with an instance + attribute as literal # so later on it could extract necessary values? if isinstance(self._selector, ElementSelector): ProjectionMapper.selectOut(self, self._selector(self._sv)) else: ProjectionMapper.selectOut(self, outIds) def _computeRecon(self): """Since singular vectors are orthonormal, sufficient to take hermitian """ return self._proj.H sv = property(fget=lambda self: self._sv, doc="Singular values") pymvpa-0.4.8/mvpa/mappers/wavelet.py000066400000000000000000000323651174541445200175120ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Wavelet mappers""" from mvpa.base import externals if externals.exists('pywt', raiseException=True): # import conditional to be able to import the whole module while building # the docs even if pywt is not installed import pywt import numpy as N from mvpa.base import warning from mvpa.mappers.base import Mapper from mvpa.base.dochelpers import enhancedDocString if __debug__: from mvpa.base import debug # WaveletPacket and WaveletTransformation mappers share lots of common # functionality at the moment class _WaveletMapper(Mapper): """Generic class for Wavelet mappers (decomposition and packet) """ def __init__(self, dim=1, wavelet='sym4', mode='per', maxlevel=None): """Initialize _WaveletMapper mapper :Parameters: dim : int or tuple of int dimensions to work across (for now just scalar value, ie 1D transformation) is supported wavelet : basestring one from the families available withing pywt package mode : basestring periodization mode maxlevel : int or None number of levels to use. If None - automatically selected by pywt """ Mapper.__init__(self) self._dim = dim """Dimension to work along""" self._maxlevel = maxlevel """Maximal level of decomposition. None for automatic""" if not wavelet in pywt.wavelist(): raise ValueError, \ "Unknown family of wavelets '%s'. Please use one " \ "available from the list %s" % (wavelet, pywt.wavelist()) self._wavelet = wavelet """Wavelet family to use""" if not mode in pywt.MODES.modes: raise ValueError, \ "Unknown periodization mode '%s'. Please use one " \ "available from the list %s" % (mode, pywt.MODES.modes) self._mode = mode """Periodization mode""" def forward(self, data): data = N.asanyarray(data) self._inshape = data.shape self._intimepoints = data.shape[self._dim] res = self._forward(data) self._outshape = res.shape return res def reverse(self, data): data = N.asanyarray(data) return self._reverse(data) def _forward(self, *args): raise NotImplementedError def _reverse(self, *args): raise NotImplementedError def getInSize(self): """Returns the number of original features.""" return self._inshape[1:] def getOutSize(self): """Returns the number of wavelet components.""" return self._outshape[1:] def selectOut(self, outIds): """Choose a subset of components... just use MaskMapper on top?""" raise NotImplementedError, "Please use in conjunction with MaskMapper" __doc__ = enhancedDocString('_WaveletMapper', locals(), Mapper) def _getIndexes(shape, dim): """Generator for coordinate tuples providing slice for all in `dim` XXX Somewhat sloppy implementation... but works... """ if len(shape) < dim: raise ValueError, "Dimension %d is incorrect for a shape %s" % \ (dim, shape) n = len(shape) curindexes = [0] * n curindexes[dim] = Ellipsis#slice(None) # all elements for dimension dim while True: yield tuple(curindexes) for i in xrange(n): if i == dim and dim == n-1: return # we reached it -- thus time to go if curindexes[i] == shape[i] - 1: if i == n-1: return curindexes[i] = 0 else: if i != dim: curindexes[i] += 1 break class WaveletPacketMapper(_WaveletMapper): """Convert signal into an overcomplete representaion using Wavelet packet """ def __init__(self, level=None, **kwargs): """Initialize WaveletPacketMapper mapper :Parameters: level : int or None What level to decompose at. If 'None' data for all levels is provided, but due to different sizes, they are placed in 1D row. """ _WaveletMapper.__init__(self,**kwargs) self.__level = level # XXX too much of duplications between such methods -- it begs # refactoring def __forwardSingleLevel(self, data): if __debug__: debug('MAP', "Converting signal using DWP (single level)") wp = None level = self.__level wavelet = self._wavelet mode = self._mode dim = self._dim level_paths = None for indexes in _getIndexes(data.shape, self._dim): if __debug__: debug('MAP_', " %s" % (indexes,), lf=False, cr=True) WP = pywt.WaveletPacket( data[indexes], wavelet=wavelet, mode=mode, maxlevel=level) level_nodes = WP.get_level(level) if level_paths is None: # Needed for reconstruction self.__level_paths = N.array([node.path for node in level_nodes]) level_datas = N.array([node.data for node in level_nodes]) if wp is None: newdim = data.shape newdim = newdim[:dim] + level_datas.shape + newdim[dim+1:] if __debug__: debug('MAP_', "Initializing storage of size %s for single " "level (%d) mapping of data of size %s" % (newdim, level, data.shape)) wp = N.empty( tuple(newdim) ) wp[indexes] = level_datas return wp def __forwardMultipleLevels(self, data): wp = None levels_length = None # total length at each level levels_lengths = None # list of lengths per each level for indexes in _getIndexes(data.shape, self._dim): if __debug__: debug('MAP_', " %s" % (indexes,), lf=False, cr=True) WP = pywt.WaveletPacket( data[indexes], wavelet=self._wavelet, mode=self._mode, maxlevel=self._maxlevel) if levels_length is None: levels_length = [None] * WP.maxlevel levels_lengths = [None] * WP.maxlevel levels_datas = [] for level in xrange(WP.maxlevel): level_nodes = WP.get_level(level+1) level_datas = [node.data for node in level_nodes] level_lengths = [len(x) for x in level_datas] level_length = N.sum(level_lengths) if levels_lengths[level] is None: levels_lengths[level] = level_lengths elif levels_lengths[level] != level_lengths: raise RuntimeError, \ "ADs of same level of different samples should have same number of elements." \ " Got %s, was %s" % (level_lengths, levels_lengths[level]) if levels_length[level] is None: levels_length[level] = level_length elif levels_length[level] != level_length: raise RuntimeError, \ "Levels of different samples should have same number of elements." \ " Got %d, was %d" % (level_length, levels_length[level]) level_data = N.hstack(level_datas) levels_datas.append(level_data) # assert(len(data) == levels_length) # assert(len(data) >= Ntimepoints) if wp is None: newdim = list(data.shape) newdim[self._dim] = N.sum(levels_length) wp = N.empty( tuple(newdim) ) wp[indexes] = N.hstack(levels_datas) self.levels_lengths, self.levels_length = levels_lengths, levels_length if __debug__: debug('MAP_', "") debug('MAP', "Done convertion into wp. Total size %s" % str(wp.shape)) return wp def _forward(self, data): if __debug__: debug('MAP', "Converting signal using DWP") if self.__level is None: return self.__forwardMultipleLevels(data) else: return self.__forwardSingleLevel(data) # # Reverse mapping # def __reverseSingleLevel(self, wp): # local bindings level_paths = self.__level_paths # define wavelet packet to use WP = pywt.WaveletPacket( data=None, wavelet=self._wavelet, mode=self._mode, maxlevel=self.__level) # prepare storage signal_shape = wp.shape[:1] + self.getInSize() signal = N.zeros(signal_shape) Ntime_points = self._intimepoints for indexes in _getIndexes(signal_shape, self._dim): if __debug__: debug('MAP_', " %s" % (indexes,), lf=False, cr=True) for path, level_data in zip(level_paths, wp[indexes]): WP[path] = level_data signal[indexes] = WP.reconstruct(True)[:Ntime_points] return signal def _reverse(self, data): if __debug__: debug('MAP', "Converting signal back using DWP") if self.__level is None: raise NotImplementedError else: if not externals.exists('pywt wp reconstruct'): raise NotImplementedError, \ "Reconstruction for a single level for versions of " \ "pywt < 0.1.7 (revision 103) is not supported" if not externals.exists('pywt wp reconstruct fixed'): warning("Reconstruction using available version of pywt might " "result in incorrect data in the tails of the signal") return self.__reverseSingleLevel(data) class WaveletTransformationMapper(_WaveletMapper): """Convert signal into wavelet representaion """ def _forward(self, data): """Decompose signal into wavelets's coefficients via dwt """ if __debug__: debug('MAP', "Converting signal using DWT") wd = None coeff_lengths = None for indexes in _getIndexes(data.shape, self._dim): if __debug__: debug('MAP_', " %s" % (indexes,), lf=False, cr=True) coeffs = pywt.wavedec( data[indexes], wavelet=self._wavelet, mode=self._mode, level=self._maxlevel) # Silly Yarik embedds extraction of statistics right in place #stats = [] #for coeff in coeffs: # stats_ = [N.std(coeff), # N.sqrt(N.dot(coeff, coeff)), # ]# + list(N.histogram(coeff, normed=True)[0])) # stats__ = list(coeff) + stats_[:] # stats__ += list(N.log(stats_)) # stats__ += list(N.sqrt(stats_)) # stats__ += list(N.array(stats_)**2) # stats__ += [ N.median(coeff), N.mean(coeff), scipy.stats.kurtosis(coeff) ] # stats.append(stats__) #coeffs = stats coeff_lengths_ = N.array([len(x) for x in coeffs]) if coeff_lengths is None: coeff_lengths = coeff_lengths_ assert((coeff_lengths == coeff_lengths_).all()) if wd is None: newdim = list(data.shape) newdim[self._dim] = N.sum(coeff_lengths) wd = N.empty( tuple(newdim) ) coeff = N.hstack(coeffs) wd[indexes] = coeff if __debug__: debug('MAP_', "") debug('MAP', "Done DWT. Total size %s" % str(wd.shape)) self.lengths = coeff_lengths return wd def _reverse(self, wd): if __debug__: debug('MAP', "Performing iDWT") signal = None wd_offsets = [0] + list(N.cumsum(self.lengths)) Nlevels = len(self.lengths) Ntime_points = self._intimepoints #len(time_points) # unfortunately sometimes due to padding iDWT would return longer # sequences, thus we just limit to the right ones for indexes in _getIndexes(wd.shape, self._dim): if __debug__: debug('MAP_', " %s" % (indexes,), lf=False, cr=True) wd_sample = wd[indexes] wd_coeffs = [wd_sample[wd_offsets[i]:wd_offsets[i+1]] for i in xrange(Nlevels)] # need to compose original list time_points = pywt.waverec( wd_coeffs, wavelet=self._wavelet, mode=self._mode) if signal is None: newdim = list(wd.shape) newdim[self._dim] = Ntime_points signal = N.empty(newdim) signal[indexes] = time_points[:Ntime_points] if __debug__: debug('MAP_', "") debug('MAP', "Done iDWT. Total size %s" % (signal.shape, )) return signal pymvpa-0.4.8/mvpa/mappers/zscore.py000066400000000000000000000064621174541445200173470ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Simple mapper to perform zscoring""" __docformat__ = 'restructuredtext' from mvpa.base import warning, externals import numpy as N from mvpa.base.dochelpers import enhancedDocString from mvpa.mappers.base import ProjectionMapper if externals.exists('scipy', raiseException=True): import scipy.sparse if externals.versions['scipy'] >= (0, 7, 0): _identity = scipy.sparse.identity else: _identity = scipy.sparse.spidentity __all__ = [ 'ZScoreMapper' ] # just to don't leak all the evil ;) class ZScoreMapper(ProjectionMapper): """Mapper to project data into standardized values (z-scores). After the mapper has been instantiated, it has to be train first. Since it tries to reuse ProjectionMapper, invariant features will simply be assigned a std == 1, which would be equivalent to not standardizing them at all. This is similar to not touching them at all, so similar to what zscore function currently does """ def __init__(self, baselinelabels=None, **kwargs): """Initialize ZScoreMapper :Parameters: baselinelabels : None or list of int or string Used to compute mean and variance TODO: not in effect now and need to be adherent to all `ProjectionMapper`s """ ProjectionMapper.__init__(self, **kwargs) if baselinelabels is not None: raise NotImplementedError, "Support for baseline labels " \ "is not yet implemented in ZScoreMapper" self.baselinelabels = baselinelabels #self._var = None __doc__ = enhancedDocString('ZScoreMapper', locals(), ProjectionMapper) def _train(self, dataset): """Determine the diagonal matrix with coefficients for standartization """ samples = dataset.samples X = self._demeanData(samples) std = X.std(axis=0) # ??? equivalent to not touching values at all, but we don't # have such ability in ProjectionMapper atm afaik std[std == 0] = 1.0 n = len(std) # scipy or numpy manages to screw up: # or YOH is too tired?: # (Pydb) zsm._proj # <1x1 sparse matrix of type '' # with 1 stored elements (space for 1) # in Compressed Sparse Column format> # *(Pydb) (N.asmatrix(ds1.samples) - zsm._mean).shape # (120, 1) # *(Pydb) (N.asmatrix(ds1.samples) - zsm._mean) * zsm._proj # matrix([[-0.13047326]]) # # so we will handle case with n = 1 with regular non-sparse # matrices if n > 1: proj = _identity(n) proj.setdiag(1.0/std) recon = _identity(n) recon.setdiag(std) self._proj = proj self._recon = recon else: self._proj = N.matrix([[1.0/std[0]]]) self._recon = N.matrix([std]) pymvpa-0.4.8/mvpa/measures/000077500000000000000000000000001174541445200156355ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/measures/__init__.py000066400000000000000000000016401174541445200177470ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """PyMVPA measures. Module Description ================== Provide some measures given a dataset. Most of the time, derivatives of `FeaturewiseDatasetMeasure` are used, such as * `OneWayAnova` * `CorrCoef` * `IterativeRelief` * `NoisePerturbationSensitivity` Also many classifiers natively provide sensitivity estimators via the call to `getSensitivityAnalyzer` method """ __docformat__ = 'restructuredtext' if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.measures') if __debug__: debug('INIT', 'mvpa.measures end') pymvpa-0.4.8/mvpa/measures/anova.py000066400000000000000000000071551174541445200173230ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """FeaturewiseDatasetMeasure performing a univariate ANOVA.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.measures.base import FeaturewiseDatasetMeasure # TODO: Extend with access to functionality from scipy.stats? # For binary: # 2-sample kolmogorov-smirnof might be interesting # (scipy.stats.ks_2samp) to judge if two conditions are derived # from different distributions (take it as 'activity' vs 'rest'), # # For binary+multiclass: # kruskal-wallis H-test (scipy.stats.kruskal) # # and may be some others class OneWayAnova(FeaturewiseDatasetMeasure): """`FeaturewiseDatasetMeasure` that performs a univariate ANOVA. F-scores are computed for each feature as the standard fraction of between and within group variances. Groups are defined by samples with unique labels. No statistical testing is performed, but raw F-scores are returned as a sensitivity map. As usual F-scores have a range of [0,inf] with greater values indicating higher sensitivity. """ def _call(self, dataset, labels=None): # This code is based on SciPy's stats.f_oneway() # Copyright (c) Gary Strangman. All rights reserved # License: BSD # # However, it got tweaked and optimized to better fit into PyMVPA. # number of groups if labels is None: labels = dataset.labels ul = N.unique(labels) na = len(ul) bign = float(dataset.nsamples) alldata = dataset.samples # total squares of sums sostot = N.sum(alldata, axis=0) sostot *= sostot sostot /= bign # total sum of squares sstot = N.sum(alldata * alldata, axis=0) - sostot # between group sum of squares ssbn = 0 for l in ul: # all samples for the respective label d = alldata[labels == l] sos = N.sum(d, axis=0) sos *= sos ssbn += sos / float(len(d)) ssbn -= sostot # within sswn = sstot - ssbn # degrees of freedom dfbn = na-1 dfwn = bign - na # mean sums of squares msb = ssbn / float(dfbn) msw = sswn / float(dfwn) f = msb / msw # assure no NaNs -- otherwise it leads instead of # sane unittest failure (check of NaNs) to crazy # File "mtrand.pyx", line 1661, in mtrand.shuffle # TypeError: object of type 'numpy.int64' has no len() # without any sane backtrace f[N.isnan(f)] = 0 return f # XXX maybe also compute p-values? #prob = scipy.stats.fprob(dfbn, dfwn, f) #return prob class CompoundOneWayAnova(OneWayAnova): """Compound comparisons via univariate ANOVA. Provides F-scores per each label if compared to the other labels. """ def _call(self, dataset): """Computes featurewise f-scores using compound comparisons.""" orig_labels = dataset.labels labels = orig_labels.copy() results = [] for ul in dataset.uniquelabels: labels[orig_labels == ul] = 1 labels[orig_labels != ul] = 2 results.append(OneWayAnova._call(self, dataset, labels)) # features x labels return N.array(results).T pymvpa-0.4.8/mvpa/measures/base.py000066400000000000000000000725321174541445200171320ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Base class for data measures: algorithms that quantify properties of datasets. Besides the `DatasetMeasure` base class this module also provides the (abstract) `FeaturewiseDatasetMeasure` class. The difference between a general measure and the output of the `FeaturewiseDatasetMeasure` is that the latter returns a 1d map (one value per feature in the dataset). In contrast there are no restrictions on the returned value of `DatasetMeasure` except for that it has to be in some iterable container. """ __docformat__ = 'restructuredtext' import numpy as N import mvpa.support.copy as copy from mvpa.misc.state import StateVariable, ClassWithCollections from mvpa.misc.args import group_kwargs from mvpa.misc.transformers import FirstAxisMean, SecondAxisSumOfAbs from mvpa.base.dochelpers import enhancedDocString from mvpa.base import externals, warning from mvpa.clfs.stats import autoNullDist if __debug__: from mvpa.base import debug class DatasetMeasure(ClassWithCollections): """A measure computed from a `Dataset` All dataset measures support arbitrary transformation of the measure after it has been computed. Transformation are done by processing the measure with a functor that is specified via the `transformer` keyword argument of the constructor. Upon request, the raw measure (before transformations are applied) is stored in the `raw_results` state variable. Additionally all dataset measures support the estimation of the probabilit(y,ies) of a measure under some distribution. Typically this will be the NULL distribution (no signal), that can be estimated with permutation tests. If a distribution estimator instance is passed to the `null_dist` keyword argument of the constructor the respective probabilities are automatically computed and stored in the `null_prob` state variable. .. note:: For developers: All subclasses shall get all necessary parameters via their constructor, so it is possible to get the same type of measure for multiple datasets by passing them to the __call__() method successively. """ raw_results = StateVariable(enabled=False, doc="Computed results before applying any " + "transformation algorithm") null_prob = StateVariable(enabled=True) """Stores the probability of a measure under the NULL hypothesis""" null_t = StateVariable(enabled=False) """Stores the t-score corresponding to null_prob under assumption of Normal distribution""" def __init__(self, transformer=None, null_dist=None, **kwargs): """Does nothing special. :Parameters: transformer: Functor This functor is called in `__call__()` to perform a final processing step on the to be returned dataset measure. If None, nothing is called null_dist: instance of distribution estimator The estimated distribution is used to assign a probability for a certain value of the computed measure. """ ClassWithCollections.__init__(self, **kwargs) self.__transformer = transformer """Functor to be called in return statement of all subclass __call__() methods.""" null_dist_ = autoNullDist(null_dist) if __debug__: debug('SA', 'Assigning null_dist %s whenever original given was %s' % (null_dist_, null_dist)) self.__null_dist = null_dist_ __doc__ = enhancedDocString('DatasetMeasure', locals(), ClassWithCollections) def __call__(self, dataset): """Compute measure on a given `Dataset`. Each implementation has to handle a single arguments: the source dataset. Returns the computed measure in some iterable (list-like) container applying transformer if such is defined """ result = self._call(dataset) result = self._postcall(dataset, result) return result def _call(self, dataset): """Actually compute measure on a given `Dataset`. Each implementation has to handle a single arguments: the source dataset. Returns the computed measure in some iterable (list-like) container. """ raise NotImplemented def _postcall(self, dataset, result): """Some postprocessing on the result """ self.states.raw_results = result if not self.__transformer is None: if __debug__: debug("SA_", "Applying transformer %s" % self.__transformer) result = self.__transformer(result) # estimate the NULL distribution when functor is given if not self.__null_dist is None: if __debug__: debug("SA_", "Estimating NULL distribution using %s" % self.__null_dist) # we need a matching datameasure instance, but we have to disable # the estimation of the null distribution in that child to prevent # infinite looping. measure = copy.copy(self) measure.__null_dist = None self.__null_dist.fit(measure, dataset) if self.states.isEnabled('null_t'): # get probability under NULL hyp, but also request # either it belong to the right tail null_prob, null_right_tail = \ self.__null_dist.p(result, return_tails=True) self.states.null_prob = null_prob externals.exists('scipy', raiseException=True) from scipy.stats import norm # TODO: following logic should appear in NullDist, # not here tail = self.null_dist.tail if tail == 'left': acdf = N.abs(null_prob) elif tail == 'right': acdf = 1.0 - N.abs(null_prob) elif tail in ['any', 'both']: acdf = 1.0 - N.clip(N.abs(null_prob), 0, 0.5) else: raise RuntimeError, 'Unhandled tail %s' % tail # We need to clip to avoid non-informative inf's ;-) # that happens due to lack of precision in mantissa # which is 11 bits in double. We could clip values # around 0 at as low as 1e-100 (correspond to z~=21), # but for consistency lets clip at 1e-16 which leads # to distinguishable value around p=1 and max z=8.2. # Should be sufficient range of z-values ;-) clip = 1e-16 null_t = norm.ppf(N.clip(acdf, clip, 1.0 - clip)) # assure that we deal with arrays: null_t = N.array(null_t, ndmin=1, copy=False) null_t[~null_right_tail] *= -1.0 # revert sign for negatives self.states.null_t = null_t # store else: # get probability of result under NULL hypothesis if available # and don't request tail information self.null_prob = self.__null_dist.p(result) return result def __repr__(self, prefixes=[]): """String representation of DatasetMeasure Includes only arguments which differ from default ones """ prefixes = prefixes[:] if self.__transformer is not None: prefixes.append("transformer=%s" % self.__transformer) if self.__null_dist is not None: prefixes.append("null_dist=%s" % self.__null_dist) return super(DatasetMeasure, self).__repr__(prefixes=prefixes) def untrain(self): """'Untraining' Measure Some derived classes might used classifiers, so we need to untrain those """ pass @property def null_dist(self): """Return Null Distribution estimator""" return self.__null_dist @property def transformer(self): """Return transformer""" return self.__transformer class FeaturewiseDatasetMeasure(DatasetMeasure): """A per-feature-measure computed from a `Dataset` (base class). Should behave like a DatasetMeasure. """ base_sensitivities = StateVariable(enabled=False, doc="Stores basic sensitivities if the sensitivity " + "relies on combining multiple ones") # XXX should we may be default to combiner=None to avoid # unexpected results? Also rethink if we need combiner here at # all... May be combiners should be 'adjoint' with transformer # YYY in comparison to CombinedSensitivityAnalyzer here default # value for combiner is worse than anywhere. From now on, # default combiners should be provided "in place", ie # in SMLR it makes sense to have SecondAxisMaxOfAbs, # in SVM (pair-wise) only for not-binary should be # SecondAxisSumOfAbs, though could be Max as well... uff # YOH: started to do so, but still have issues... thus # reverting back for now # MH: Full ack -- voting for no default combiners! def __init__(self, combiner=SecondAxisSumOfAbs, **kwargs): # SecondAxisSumOfAbs """Initialize :Parameters: combiner : Functor The combiner is only applied if the computed featurewise dataset measure is more than one-dimensional. This is different from a `transformer`, which is always applied. By default, the sum of absolute values along the second axis is computed. """ DatasetMeasure.__init__(self, **kwargs) self.__combiner = combiner def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] if self.__combiner != SecondAxisSumOfAbs: prefixes.append("combiner=%s" % self.__combiner) return \ super(FeaturewiseDatasetMeasure, self).__repr__(prefixes=prefixes) def _call(self, dataset): """Computes a per-feature-measure on a given `Dataset`. Behaves like a `DatasetMeasure`, but computes and returns a 1d ndarray with one value per feature. """ raise NotImplementedError def _postcall(self, dataset, result): """Adjusts per-feature-measure for computed `result` TODO: overlaps in what it does heavily with CombinedSensitivityAnalyzer, thus this one might make use of CombinedSensitivityAnalyzer yoh thinks, and here base_sensitivities doesn't sound appropriate. MH: There is indeed some overlap, but also significant differences. This one operates on a single sensana and combines over second axis, CombinedFeaturewiseDatasetMeasure uses first axis. Additionally, 'Sensitivity' base class is FeaturewiseDatasetMeasures which would have to be changed to CombinedFeaturewiseDatasetMeasure to deal with stuff like SMLRWeights that return multiple sensitivity values by default. Not sure if unification of both (and/or removal of functionality here does not lead to an overall more complicated situation, without any real gain -- after all this one works ;-) """ # !!! This is not stupid -- it is intended -- some times we might get # scalars as input. result = N.atleast_1d(result) result_sq = result.squeeze() # Assure that we have some iterable (could be a scalar if it # was just a single value in 1D array) result_sq = N.atleast_1d(result_sq) if len(result_sq.shape)>1: n_base = result.shape[1] """Number of base sensitivities""" if self.states.isEnabled('base_sensitivities'): b_sensitivities = [] if not self.states.isKnown('biases'): biases = None else: biases = self.states.biases if len(self.states.biases) != n_base: warning("Number of biases %d differs from number " "of base sensitivities %d which could happen " "when measure is collided across labels." % (len(self.states.biases), n_base)) for i in xrange(n_base): if not biases is None: if n_base > 1 and len(biases) == 1: # The same bias for all bases bias = biases[0] else: bias = biases[i] else: bias = None b_sensitivities = StaticDatasetMeasure( measure = result[:,i], bias = bias) self.states.base_sensitivities = b_sensitivities # After we stored each sensitivity separately, # we can apply combiner if self.__combiner is not None: result = self.__combiner(result) else: # remove bogus dimensions # XXX we might need to come up with smth better. May be some naive # combiner? :-) result = result_sq # call base class postcall result = DatasetMeasure._postcall(self, dataset, result) return result @property def combiner(self): """Return combiner""" return self.__combiner class StaticDatasetMeasure(DatasetMeasure): """A static (assigned) sensitivity measure. Since implementation is generic it might be per feature or per whole dataset """ def __init__(self, measure=None, bias=None, *args, **kwargs): """Initialize. :Parameters: measure actual sensitivity to be returned bias optionally available bias """ DatasetMeasure.__init__(self, *args, **kwargs) if measure is None: raise ValueError, "Sensitivity measure has to be provided" self.__measure = measure self.__bias = bias def _call(self, dataset): """Returns assigned sensitivity """ return self.__measure #XXX Might need to move into StateVariable? bias = property(fget=lambda self:self.__bias) # # Flavored implementations of FeaturewiseDatasetMeasures class Sensitivity(FeaturewiseDatasetMeasure): _LEGAL_CLFS = [] """If Sensitivity is classifier specific, classes of classifiers should be listed in the list """ def __init__(self, clf, force_training=True, **kwargs): """Initialize the analyzer with the classifier it shall use. :Parameters: clf : :class:`Classifier` classifier to use. force_training : Bool if classifier was already trained -- do not retrain """ """Does nothing special.""" FeaturewiseDatasetMeasure.__init__(self, **kwargs) _LEGAL_CLFS = self._LEGAL_CLFS if len(_LEGAL_CLFS) > 0: found = False for clf_class in _LEGAL_CLFS: if isinstance(clf, clf_class): found = True break if not found: raise ValueError, \ "Classifier %s has to be of allowed class (%s), but is %s" \ % (clf, _LEGAL_CLFS, `type(clf)`) self.__clf = clf """Classifier used to computed sensitivity""" self._force_training = force_training """Either to force it to train""" def __repr__(self, prefixes=None): if prefixes is None: prefixes = [] prefixes.append("clf=%s" % repr(self.clf)) if not self._force_training: prefixes.append("force_training=%s" % self._force_training) return super(Sensitivity, self).__repr__(prefixes=prefixes) def __call__(self, dataset=None): """Train classifier on `dataset` and then compute actual sensitivity. If the classifier is already trained it is possible to extract the sensitivities without passing a dataset. """ # local bindings clf = self.__clf if not clf.trained or self._force_training: if dataset is None: raise ValueError, \ "Training classifier to compute sensitivities requires " \ "a dataset." if __debug__: debug("SA", "Training classifier %s %s" % (`clf`, {False: "since it wasn't yet trained", True: "although it was trained previousely"} [clf.trained])) clf.train(dataset) return FeaturewiseDatasetMeasure.__call__(self, dataset) def _setClassifier(self, clf): self.__clf = clf def untrain(self): """Untrain corresponding classifier for Sensitivity """ if self.__clf is not None: self.__clf.untrain() @property def feature_ids(self): """Return feature_ids used by the underlying classifier """ return self.__clf._getFeatureIds() clf = property(fget=lambda self:self.__clf, fset=_setClassifier) class CombinedFeaturewiseDatasetMeasure(FeaturewiseDatasetMeasure): """Set sensitivity analyzers to be merged into a single output""" sensitivities = StateVariable(enabled=False, doc="Sensitivities produced by each analyzer") # XXX think again about combiners... now we have it in here and as # well as in the parent -- FeaturewiseDatasetMeasure # YYY because we don't use parent's _call. Needs RF def __init__(self, analyzers=None, # XXX should become actually 'measures' combiner=None, #FirstAxisMean, **kwargs): """Initialize CombinedFeaturewiseDatasetMeasure :Parameters: analyzers : list or None List of analyzers to be used. There is no logic to populate such a list in __call__, so it must be either provided to the constructor or assigned to .analyzers prior calling """ if analyzers is None: analyzers = [] FeaturewiseDatasetMeasure.__init__(self, **kwargs) self.__analyzers = analyzers """List of analyzers to use""" self.__combiner = combiner """Which functor to use to combine all sensitivities""" def _call(self, dataset): sensitivities = [] for ind,analyzer in enumerate(self.__analyzers): if __debug__: debug("SA", "Computing sensitivity for SA#%d:%s" % (ind, analyzer)) sensitivity = analyzer(dataset) sensitivities.append(sensitivity) self.states.sensitivities = sensitivities if __debug__: debug("SA", "Returning combined using %s sensitivity across %d items" % (self.__combiner, len(sensitivities))) if self.__combiner is not None: sensitivities = self.__combiner(sensitivities) else: # assure that we have an ndarray on output sensitivities = N.asarray(sensitivities) return sensitivities def untrain(self): """Untrain CombinedFDM """ if self.__analyzers is not None: for anal in self.__analyzers: anal.untrain() def _setAnalyzers(self, analyzers): """Set the analyzers """ self.__analyzers = analyzers """Analyzers to use""" analyzers = property(fget=lambda x:x.__analyzers, fset=_setAnalyzers, doc="Used analyzers") # XXX Why did we come to name everything analyzer? inputs of regular # things like CombinedFeaturewiseDatasetMeasure can be simple # measures.... class SplitFeaturewiseDatasetMeasure(FeaturewiseDatasetMeasure): """Compute measures across splits for a specific analyzer""" # XXX This beast is created based on code of # CombinedFeaturewiseDatasetMeasure, thus another reason to refactor sensitivities = StateVariable(enabled=False, doc="Sensitivities produced for each split") splits = StateVariable(enabled=False, doc= """Store the actual splits of the data. Can be memory expensive""") def __init__(self, splitter, analyzer, insplit_index=0, combiner=None, **kwargs): """Initialize SplitFeaturewiseDatasetMeasure :Parameters: splitter : Splitter Splitter to use to split the dataset analyzer : DatasetMeasure Measure to be used. Could be analyzer as well (XXX) insplit_index : int splitter generates tuples of dataset on each iteration (usually 0th for training, 1st for testing). On what split index in that tuple to operate. """ # XXX might want to extend insplit_index to handle 'all', so we store # sensitivities for all parts of the splits... not sure if it is needed # XXX We really think through whole transformer/combiners pipelining # Here we provide combiner None since if needs to be combined # within each sensitivity, it better be done within analyzer FeaturewiseDatasetMeasure.__init__(self, combiner=None, **kwargs) self.__analyzer = analyzer """Analyzer to use per split""" self.__combiner = combiner """Which functor to use to combine all sensitivities""" self.__splitter = splitter """Splitter to be used on the dataset""" self.__insplit_index = insplit_index def untrain(self): """Untrain SplitFeaturewiseDatasetMeasure """ if self.__analyzer is not None: self.__analyzer.untrain() def _call(self, dataset): # local bindings analyzer = self.__analyzer insplit_index = self.__insplit_index sensitivities = [] self.states.splits = splits = [] store_splits = self.states.isEnabled("splits") for ind,split in enumerate(self.__splitter(dataset)): ds = split[insplit_index] if __debug__ and "SA" in debug.active: debug("SA", "Computing sensitivity for split %d on " "dataset %s using %s" % (ind, ds, analyzer)) sensitivity = analyzer(ds) sensitivities.append(sensitivity) if store_splits: splits.append(split) self.states.sensitivities = sensitivities if __debug__: debug("SA", "Returning sensitivities combined using %s across %d items " "generated by splitter %s" % (self.__combiner, len(sensitivities), self.__splitter)) if self.__combiner is not None: sensitivities = self.__combiner(sensitivities) else: # assure that we have an ndarray on output sensitivities = N.asarray(sensitivities) return sensitivities class BoostedClassifierSensitivityAnalyzer(Sensitivity): """Set sensitivity analyzers to be merged into a single output""" # XXX we might like to pass parameters also for combined_analyzer @group_kwargs(prefixes=['slave_'], assign=True) def __init__(self, clf, analyzer=None, combined_analyzer=None, slave_kwargs={}, **kwargs): """Initialize Sensitivity Analyzer for `BoostedClassifier` :Parameters: clf : `BoostedClassifier` Classifier to be used analyzer : analyzer Is used to populate combined_analyzer slave_* Arguments to pass to created analyzer if analyzer is None """ Sensitivity.__init__(self, clf, **kwargs) if combined_analyzer is None: # sanitarize kwargs kwargs.pop('force_training', None) combined_analyzer = CombinedFeaturewiseDatasetMeasure(**kwargs) self.__combined_analyzer = combined_analyzer """Combined analyzer to use""" if analyzer is not None and len(self._slave_kwargs): raise ValueError, \ "Provide either analyzer of slave_* arguments, not both" self.__analyzer = analyzer """Analyzer to use for basic classifiers within boosted classifier""" def untrain(self): """Untrain BoostedClassifierSensitivityAnalyzer """ if self.__analyzer is not None: self.__analyzer.untrain() self.__combined_analyzer.untrain() def _call(self, dataset): analyzers = [] # create analyzers for clf in self.clf.clfs: if self.__analyzer is None: analyzer = clf.getSensitivityAnalyzer(**(self._slave_kwargs)) if analyzer is None: raise ValueError, \ "Wasn't able to figure basic analyzer for clf %s" % \ `clf` if __debug__: debug("SA", "Selected analyzer %s for clf %s" % \ (`analyzer`, `clf`)) else: # XXX shallow copy should be enough... analyzer = copy.copy(self.__analyzer) # assign corresponding classifier analyzer.clf = clf # if clf was trained already - don't train again if clf.trained: analyzer._force_training = False analyzers.append(analyzer) self.__combined_analyzer.analyzers = analyzers # XXX not sure if we don't want to call directly ._call(dataset) to avoid # double application of transformers/combiners, after all we are just # 'proxying' here to combined_analyzer... # YOH: decided -- lets call ._call return self.__combined_analyzer._call(dataset) combined_analyzer = property(fget=lambda x:x.__combined_analyzer) class ProxyClassifierSensitivityAnalyzer(Sensitivity): """Set sensitivity analyzer output just to pass through""" clf_sensitivities = StateVariable(enabled=False, doc="Stores sensitivities of the proxied classifier") @group_kwargs(prefixes=['slave_'], assign=True) def __init__(self, clf, analyzer=None, **kwargs): """Initialize Sensitivity Analyzer for `BoostedClassifier` """ Sensitivity.__init__(self, clf, **kwargs) if analyzer is not None and len(self._slave_kwargs): raise ValueError, \ "Provide either analyzer of slave_* arguments, not both" self.__analyzer = analyzer """Analyzer to use for basic classifiers within boosted classifier""" def untrain(self): super(ProxyClassifierSensitivityAnalyzer, self).untrain() if self.__analyzer is not None: self.__analyzer.untrain() def _call(self, dataset): # OPT: local bindings clfclf = self.clf.clf analyzer = self.__analyzer if analyzer is None: analyzer = clfclf.getSensitivityAnalyzer( **(self._slave_kwargs)) if analyzer is None: raise ValueError, \ "Wasn't able to figure basic analyzer for clf %s" % \ `clfclf` if __debug__: debug("SA", "Selected analyzer %s for clf %s" % \ (analyzer, clfclf)) # bind to the instance finally self.__analyzer = analyzer # TODO "remove" unnecessary things below on each call... # assign corresponding classifier analyzer.clf = clfclf # if clf was trained already - don't train again if clfclf.trained: analyzer._force_training = False result = analyzer._call(dataset) self.states.clf_sensitivities = result return result analyzer = property(fget=lambda x:x.__analyzer) class MappedClassifierSensitivityAnalyzer(ProxyClassifierSensitivityAnalyzer): """Set sensitivity analyzer output be reverse mapped using mapper of the slave classifier""" def _call(self, dataset): sens = super(MappedClassifierSensitivityAnalyzer, self)._call(dataset) # So we have here the case that some sensitivities are given # as nfeatures x nclasses, thus we need to take .T for the # mapper and revert back afterwards # devguide's TODO lists this point to 'disguss' sens_mapped = self.clf.mapper.reverse(sens.T) return sens_mapped.T class FeatureSelectionClassifierSensitivityAnalyzer(ProxyClassifierSensitivityAnalyzer): """Set sensitivity analyzer output be reverse mapped using mapper of the slave classifier""" def _call(self, dataset): sens = super(FeatureSelectionClassifierSensitivityAnalyzer, self)._call(dataset) # So we have here the case that some sensitivities are given # as nfeatures x nclasses, thus we need to take .T for the # mapper and revert back afterwards # devguide's TODO lists this point to 'disguss' sens_mapped = self.clf.maskclf.mapper.reverse(sens.T) return sens_mapped.T pymvpa-0.4.8/mvpa/measures/corrcoef.py000066400000000000000000000045321174541445200200150ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """FeaturewiseDatasetMeasure of correlation with the labels.""" __docformat__ = 'restructuredtext' from mvpa.base import externals import numpy as N if externals.exists('scipy', raiseException=True): # TODO: implement corrcoef optionally without scipy, e.g. N.corrcoef from scipy.stats import pearsonr from mvpa.measures.base import FeaturewiseDatasetMeasure class CorrCoef(FeaturewiseDatasetMeasure): """`FeaturewiseDatasetMeasure` that performs correlation with labels XXX: Explain me! """ def __init__(self, pvalue=False, attr='labels', **kwargs): """Initialize :Parameters: pvalue : bool Either to report p-value of pearsons correlation coefficient instead of pure correlation coefficient attr : basestring What attribut to correlate with """ # init base classes first FeaturewiseDatasetMeasure.__init__(self, **kwargs) self.__pvalue = int(pvalue) self.__attr = attr def _call(self, dataset): """Computes featurewise scores.""" attrdata = eval('dataset.' + self.__attr) samples = dataset.samples pvalue_index = self.__pvalue result = N.empty((dataset.nfeatures,), dtype=float) for ifeature in xrange(dataset.nfeatures): samples_ = samples[:, ifeature] corr = pearsonr(samples_, attrdata) corrv = corr[pvalue_index] # Should be safe to assume 0 corr_coef (or 1 pvalue) if value # is actually NaN, although it might not be the case (covar of # 2 constants would be NaN although should be 1) if N.isnan(corrv): if N.var(samples_) == 0.0 and N.var(attrdata) == 0.0 \ and len(samples_): # constant terms corrv = 1.0 - pvalue_index else: corrv = pvalue_index result[ifeature] = corrv return result pymvpa-0.4.8/mvpa/measures/corrstability.py000066400000000000000000000063161174541445200211070ustar00rootroot00000000000000#emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- #ex: set sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """FeaturewiseDatasetMeasure of stability of labels across chunks based on correlation.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.measures.base import FeaturewiseDatasetMeasure class CorrStability(FeaturewiseDatasetMeasure): """`FeaturewiseDatasetMeasure` that assesses feature stability across runs for each unique label by correlating label activity for pairwise combinations of the chunks. If there are multiple samples with the same label in a single chunk (as is typically the case) this algorithm will take the featurewise average of the sample activations to get a single value per label, per chunk. """ def __init__(self, attr='labels', **kwargs): """Initialize :Parameters: attr : basestring Attribute to correlate across chunks. """ # init base classes first FeaturewiseDatasetMeasure.__init__(self, **kwargs) self.__attr = attr def _call(self, dataset): """Computes featurewise scores.""" # get the attributes (usally the labels) and the samples attrdata = eval('dataset.' + self.__attr) samples = dataset.samples # take mean within chunks dat = [] labels = [] chunks = [] for c in dataset.uniquechunks: for l in N.unique(attrdata): ind = (dataset.chunks==c)&(attrdata==l) if ind.sum() == 0: # no instances, so skip continue # append the mean, and the label/chunk info dat.append(samples[ind,:].mean(0)) labels.append(l) chunks.append(c) # convert to arrays dat = N.asarray(dat) labels = N.asarray(labels) chunks = N.asarray(chunks) # get indices for correlation (all pairwise values across # chunks) ind1 = [] ind2 = [] for i,c1 in enumerate(N.unique(chunks)[:-1]): for c2 in N.unique(chunks)[i+1:]: for l in N.unique(labels): v1 = N.where((chunks==c1)&(labels==l))[0] v2 = N.where((chunks==c2)&(labels==l))[0] if labels[v1] == labels[v2]: # the labels match, so add them ind1.extend(v1) ind2.extend(v2) # convert the indices to arrays ind1 = N.asarray(ind1) ind2 = N.asarray(ind2) # remove the mean from the datasets dat1 = dat[ind1,:] - dat[ind1,:].mean(0)[N.newaxis,:].repeat(dat[ind1,:].shape[0],0) dat2 = dat[ind2,:] - dat[ind2,:].mean(0)[N.newaxis,:].repeat(dat[ind2,:].shape[0],0) # calculate the correlation from the covariance and std covar = (dat1*dat2).mean(0) / dat1.std(0) * dat2.std(0) return covar pymvpa-0.4.8/mvpa/measures/ds.py000066400000000000000000000030211174541445200166110ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Dissimilarity measure. """ __docformat__ = 'restructuredtext' import numpy as N from mvpa.measures.base import DatasetMeasure from mvpa.misc.stats import DSMatrix class DSMDatasetMeasure(DatasetMeasure): """DSMDatasetMeasure creates a DatasetMeasure object where metric can be one of 'euclidean', 'spearman', 'pearson' or 'confusion'""" def __init__(self, dsmatrix, dset_metric, output_metric='spearman'): DatasetMeasure.__init__(self) self.dsmatrix = dsmatrix self.dset_metric = dset_metric self.output_metric = output_metric self.dset_dsm = [] def __call__(self, dataset): # create the dissimilarity matrix for the data in the input dataset self.dset_dsm = DSMatrix(dataset.samples, self.dset_metric) in_vec = self.dsmatrix.getVectorForm() dset_vec = self.dset_dsm.getVectorForm() # concatenate the two vectors, send to dissimlarity function test_mat = N.asarray([in_vec, dset_vec]) test_dsmatrix = DSMatrix(test_mat, self.output_metric) # return correct dissimilarity value return test_dsmatrix.getFullMatrix()[0, 1] pymvpa-0.4.8/mvpa/measures/glm.py000066400000000000000000000100471174541445200167700ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """The general linear model (GLM).""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.measures.base import FeaturewiseDatasetMeasure from mvpa.misc.state import StateVariable class GLM(FeaturewiseDatasetMeasure): """General linear model (GLM). Regressors can be defined in a design matrix and a linear fit of the data is computed univariately (i.e. indepently for each feature). This measure can report 'raw' parameter estimates (i.e. beta weights) of the linear model, as well as standardized parameters (z-stat) using an ordinary least squares (aka fixed-effects) approach to estimate the parameter estimate. The measure is reported in a (nfeatures x nregressors)-shaped array. """ pe = StateVariable(enabled=False, doc="Parameter estimates (nfeatures x nparameters).") zstat = StateVariable(enabled=False, doc="Standardized parameter estimates (nfeatures x nparameters).") def __init__(self, design, voi='pe', **kwargs): """ :Parameters: design: array(nsamples x nregressors) GLM design matrix. voi: 'pe' | 'zstat' Variable of interest that should be reported as feature-wise measure. 'beta' are the parameter estimates and 'zstat' returns standardized parameter estimates. """ FeaturewiseDatasetMeasure.__init__(self, **kwargs) # store the design matrix as a such (no copying if already array) self._design = N.asmatrix(design) # what should be computed ('variable of interest') if not voi in ['pe', 'zstat']: raise ValueError, \ "Unknown variable of interest '%s'" % str(voi) self._voi = voi # will store the precomputed Moore-Penrose pseudo-inverse of the # design matrix (lazy calculation) self._inv_design = None # also store the inverse of the inner product for beta variance # estimation self._inv_ip = None def _call(self, dataset): # just for the beauty of it X = self._design # precompute transformation is not yet done if self._inv_design is None: self._inv_ip = (X.T * X).I self._inv_design = self._inv_ip * X.T # get parameter estimations for all features at once # (betas x features) betas = self._inv_design * dataset.samples # charge state self.states.pe = pe = betas.T.A # if betas and no z-stats are desired return them right away if self._voi == 'pe' and not self.states.isEnabled('zstat'): # return as (feature x beta) return pe # compute residuals residuals = X * betas residuals -= dataset.samples # estimates of the parameter variance and compute zstats # assumption of mean(E) == 0 and equal variance # XXX next lines ignore off-diagonal elements and hence covariance # between regressors. The humble being writing these lines asks the # god of statistics for forgives, because it knows not what it does diag_ip = N.diag(self._inv_ip) # (features x betas) beta_vars = N.array([ r.var() * diag_ip for r in residuals.T ]) # (parameter x feature) zstat = pe / N.sqrt(beta_vars) # charge state self.states.zstat = zstat if self._voi == 'pe': # return as (feature x beta) return pe elif self._voi == 'zstat': # return as (feature x zstat) return zstat # we shall never get to this point raise ValueError, \ "Unknown variable of interest '%s'" % str(self._voi) pymvpa-0.4.8/mvpa/measures/irelief.py000066400000000000000000000410131174541445200176250ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # Copyright (c) 2008 Emanuele Olivetti # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """FeaturewiseDatasetMeasure performing multivariate Iterative RELIEF (I-RELIEF) algorithm. See : Y. Sun, Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 29, no. 6, pp. 1035-1051, June 2007.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.measures.base import FeaturewiseDatasetMeasure from mvpa.clfs.kernel import KernelSquaredExponential, KernelExponential, \ KernelMatern_3_2, KernelMatern_5_2 from mvpa.clfs.distance import pnorm_w if __debug__: from mvpa.base import debug class IterativeRelief_Devel(FeaturewiseDatasetMeasure): """`FeaturewiseDatasetMeasure` that performs multivariate I-RELIEF algorithm. Batch version allowing various kernels. UNDER DEVELOPEMNT. Batch I-RELIEF-2 feature weighting algorithm. Works for binary or multiclass class-labels. Batch version with complexity O(T*N^2*I), where T is the number of iterations, N the number of instances, I the number of features. See: Y. Sun, Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 29, no. 6, pp. 1035-1051, June 2007. http://plaza.ufl.edu/sunyijun/Paper/PAMI_1.pdf Note that current implementation allows to use only exponential-like kernels. Support for linear kernel will be added later. """ def __init__(self, threshold = 1.0e-2, kernel = None, kernel_width = 1.0, w_guess = None, **kwargs): """Constructor of the IRELIEF class. """ # init base classes first FeaturewiseDatasetMeasure.__init__(self, **kwargs) # Threshold in W changes (stopping criterion for irelief) self.threshold = threshold if kernel == None: self.kernel = KernelExponential else: self.kernel = kernel self.w_guess = w_guess self.w = None self.kernel_width = kernel_width def compute_M_H(self, label): """Compute hit/miss dictionaries. For each instance compute the set of indices having the same class label and different class label. Note that this computation is independent of the number of features. """ M = {} H = {} for i in range(label.size): M[i] = N.where(label != label[i])[0] tmp = (N.where(label == label[i])[0]).tolist() tmp.remove(i) # There must be at least two exampls for class label[i] assert(tmp != []) H[i] = N.array(tmp) return M, H def _call(self, dataset): """Computes featurewise I-RELIEF weights.""" samples = dataset.samples NS, NF = samples.shape[:2] if self.w_guess == None: self.w = N.ones(NF, 'd') # do normalization in all cases to be safe :) self.w = self.w/(self.w**2).sum() M, H = self.compute_M_H(dataset.labels) while True: self.k = self.kernel(length_scale = self.kernel_width/self.w) d_w_k = self.k.compute(samples) # set d_w_k to zero where distance=0 (i.e. kernel == # 1.0), otherwise I-RELIEF could not converge. # XXX Note that kernel==1 for distance=0 only for # exponential kernels!! IMPROVE d_w_k[N.abs(d_w_k-1.0) < 1.0e-15] = 0.0 ni = N.zeros(NF, 'd') for n in range(NS): # d_w_k[n,n] could be omitted since == 0.0 gamma_n = 1.0 - N.nan_to_num(d_w_k[n, M[n]].sum() \ / (d_w_k[n, :].sum()-d_w_k[n, n])) alpha_n = N.nan_to_num(d_w_k[n, M[n]]/(d_w_k[n, M[n]].sum())) beta_n = N.nan_to_num(d_w_k[n, H[n]]/(d_w_k[n, H[n]].sum())) m_n = (N.abs(samples[n, :] - samples[M[n], :]) \ * alpha_n[:, None]).sum(0) h_n = (N.abs(samples[n, :] - samples[H[n], :]) \ * beta_n[:, None]).sum(0) ni += gamma_n*(m_n-h_n) ni = ni/NS ni_plus = N.clip(ni, 0.0, N.inf) # set all negative elements to zero w_new = N.nan_to_num(ni_plus/(N.sqrt((ni_plus**2).sum()))) change = N.abs(w_new-self.w).sum() if __debug__ and 'IRELIEF' in debug.active: debug('IRELIEF', "change=%.4f max=%f min=%.4f mean=%.4f std=%.4f #nan=%d" % (change, w_new.max(), w_new.min(), w_new.mean(), w_new.std(), N.isnan(w_new).sum())) # update weights: self.w = w_new if change < self.threshold: break return self.w class IterativeReliefOnline_Devel(IterativeRelief_Devel): """`FeaturewiseDatasetMeasure` that performs multivariate I-RELIEF algorithm. Online version. UNDER DEVELOPMENT Online version with complexity O(T*N*I), where N is the number of instances and I the number of features. See: Y. Sun, Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 29, no. 6, pp. 1035-1051, June 2007. http://plaza.ufl.edu/sunyijun/Paper/PAMI_1.pdf Note that this implementation is not fully online, since hit and miss dictionaries (H,M) are computed once at the beginning using full access to all labels. This can be easily corrected to a full online implementation. But this is not mandatory now since the major goal of this current online implementation is reduction of computational complexity. """ def __init__(self, a=5.0, permute=True, max_iter=3, **kwargs): """Constructor of the IRELIEF class. """ # init base classes first IterativeRelief_Devel.__init__(self, **kwargs) self.a = a # parameter of the learning rate self.permute = permute # shuffle data when running I-RELIEF self.max_iter = max_iter # maximum number of iterations def _call(self, dataset): """Computes featurewise I-RELIEF-2 weights. Online version.""" NS = dataset.samples.shape[0] NF = dataset.samples.shape[1] if self.w_guess == None: self.w = N.ones(NF, 'd') # do normalization in all cases to be safe :) self.w = self.w/(self.w**2).sum() M, H = self.compute_M_H(dataset.labels) ni = N.zeros(NF, 'd') pi = N.zeros(NF, 'd') if self.permute: # indices to go through samples in random order random_sequence = N.random.permutation(NS) else: random_sequence = N.arange(NS) change = self.threshold + 1.0 iteration = 0 counter = 0.0 while change > self.threshold and iteration < self.max_iter: if __debug__: debug('IRELIEF', "Iteration %d" % iteration) for t in range(NS): counter += 1.0 n = random_sequence[t] self.k = self.kernel(length_scale = self.kernel_width/self.w) d_w_k_xn_Mn = self.k.compute(dataset.samples[None, n, :], dataset.samples[M[n], :]).squeeze() d_w_k_xn_Mn_sum = d_w_k_xn_Mn.sum() d_w_k_xn_x = self.k.compute(dataset.samples[None, n, :], dataset.samples).squeeze() gamma_n = 1.0 - d_w_k_xn_Mn_sum / d_w_k_xn_x.sum() alpha_n = d_w_k_xn_Mn / d_w_k_xn_Mn_sum d_w_k_xn_Hn = self.k.compute(dataset.samples[None, n, :], dataset.samples[H[n], :]).squeeze() beta_n = d_w_k_xn_Hn / d_w_k_xn_Hn.sum() m_n = (N.abs(dataset.samples[n, :] - dataset.samples[M[n], :]) \ * alpha_n[:, N.newaxis]).sum(0) h_n = (N.abs(dataset.samples[n, :] - dataset.samples[H[n], :]) \ * beta_n[:, N.newaxis]).sum(0) pi = gamma_n * (m_n-h_n) learning_rate = 1.0 / (counter * self.a + 1.0) ni_new = ni + learning_rate * (pi - ni) ni = ni_new # set all negative elements to zero ni_plus = N.clip(ni, 0.0, N.inf) w_new = N.nan_to_num(ni_plus / (N.sqrt((ni_plus ** 2).sum()))) change = N.abs(w_new - self.w).sum() if t % 10 == 0 and __debug__ and 'IRELIEF' in debug.active: debug('IRELIEF', "t=%d change=%.4f max=%f min=%.4f mean=%.4f std=%.4f" " #nan=%d" % (t, change, w_new.max(), w_new.min(), w_new.mean(), w_new.std(), N.isnan(w_new).sum())) self.w = w_new if change < self.threshold and iteration > 0: break iteration += 1 return self.w class IterativeRelief(FeaturewiseDatasetMeasure): """`FeaturewiseDatasetMeasure` that performs multivariate I-RELIEF algorithm. Batch version. Batch I-RELIEF-2 feature weighting algorithm. Works for binary or multiclass class-labels. Batch version with complexity O(T*N^2*I), where T is the number of iterations, N the number of instances, I the number of features. See: Y. Sun, Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 29, no. 6, pp. 1035-1051, June 2007. http://plaza.ufl.edu/sunyijun/Paper/PAMI_1.pdf Note that current implementation allows to use only exponential-like kernels. Support for linear kernel will be added later. """ def __init__(self, threshold=1.0e-2, kernel_width=1.0, w_guess=None, **kwargs): """Constructor of the IRELIEF class. """ # init base classes first FeaturewiseDatasetMeasure.__init__(self, **kwargs) # Threshold in W changes (stopping criterion for irelief). self.threshold = threshold self.w_guess = w_guess self.w = None self.kernel_width = kernel_width def compute_M_H(self, label): """Compute hit/miss dictionaries. For each instance compute the set of indices having the same class label and different class label. Note that this computation is independent of the number of features. XXX should it be some generic function since it doesn't use self """ M = {} H = {} for i in range(label.size): M[i] = N.where(label != label[i])[0] tmp = (N.where(label == label[i])[0]).tolist() tmp.remove(i) # There must be least two exampls for class label[i] assert(tmp != []) H[i] = N.array(tmp) return M, H def k(self, distances): """Exponential kernel.""" kd = N.exp(-distances/self.kernel_width) # set kd to zero where distance=0 otherwise I-RELIEF could not converge. kd[N.abs(distances) < 1.0e-15] = 0.0 return kd def _call(self, dataset): """Computes featurewise I-RELIEF weights.""" samples = dataset.samples NS, NF = samples.shape[:2] if self.w_guess == None: w = N.ones(NF, 'd') w /= (w ** 2).sum() # do normalization in all cases to be safe :) M, H = self.compute_M_H(dataset.labels) while True: d_w_k = self.k(pnorm_w(data1=samples, weight=w, p=1)) ni = N.zeros(NF, 'd') for n in range(NS): # d_w_k[n, n] could be omitted since == 0.0 gamma_n = 1.0 - N.nan_to_num(d_w_k[n, M[n]].sum() \ / (d_w_k[n, :].sum() - d_w_k[n, n])) alpha_n = N.nan_to_num(d_w_k[n, M[n]] / (d_w_k[n, M[n]].sum())) beta_n = N.nan_to_num(d_w_k[n, H[n]] / (d_w_k[n, H[n]].sum())) m_n = (N.abs(samples[n, :] - samples[M[n], :]) \ * alpha_n[:, None]).sum(0) h_n = (N.abs(samples[n, :] - samples[H[n], :]) \ * beta_n[:, None]).sum(0) ni += gamma_n*(m_n - h_n) ni = ni / NS ni_plus = N.clip(ni, 0.0, N.inf) # set all negative elements to zero w_new = N.nan_to_num(ni_plus / (N.sqrt((ni_plus**2).sum()))) change = N.abs(w_new - w).sum() if __debug__ and 'IRELIEF' in debug.active: debug('IRELIEF', "change=%.4f max=%f min=%.4f mean=%.4f std=%.4f #nan=%d" \ % (change, w_new.max(), w_new.min(), w_new.mean(), w_new.std(), N.isnan(w_new).sum())) # update weights: w = w_new if change < self.threshold: break self.w = w return w class IterativeReliefOnline(IterativeRelief): """`FeaturewiseDatasetMeasure` that performs multivariate I-RELIEF algorithm. Online version. This algorithm is exactly the one in the referenced paper (I-RELIEF-2 online), using weighted 1-norm and Exponential Kernel. """ def __init__(self, a=10.0, permute=True, max_iter=3, **kwargs): """Constructor of the IRELIEF class. """ # init base classes first IterativeRelief.__init__(self, **kwargs) self.a = a # parameter of the learning rate self.permute = permute # shuffle data when running I-RELIEF self.max_iter = max_iter # maximum number of iterations def _call(self, dataset): """Computes featurewise I-RELIEF-2 weights. Online version.""" # local bindings samples = dataset.samples NS, NF = samples.shape[:2] threshold = self.threshold a = self.a if self.w_guess == None: w = N.ones(NF, 'd') # do normalization in all cases to be safe :) w /= (w ** 2).sum() M, H = self.compute_M_H(dataset.labels) ni = N.zeros(NF, 'd') pi = N.zeros(NF, 'd') if self.permute: # indices to go through x in random order random_sequence = N.random.permutation(NS) else: random_sequence = N.arange(NS) change = threshold + 1.0 iteration = 0 counter = 0.0 while change > threshold and iteration < self.max_iter: if __debug__: debug('IRELIEF', "Iteration %d" % iteration) for t in range(NS): counter += 1.0 n = random_sequence[t] d_xn_x = N.abs(samples[n, :] - samples) d_w_k_xn_x = self.k((d_xn_x * w).sum(1)) d_w_k_xn_Mn = d_w_k_xn_x[M[n]] d_w_k_xn_Mn_sum = d_w_k_xn_Mn.sum() gamma_n = 1.0 - d_w_k_xn_Mn_sum / d_w_k_xn_x.sum() alpha_n = d_w_k_xn_Mn / d_w_k_xn_Mn_sum d_w_k_xn_Hn = d_w_k_xn_x[H[n]] beta_n = d_w_k_xn_Hn / d_w_k_xn_Hn.sum() m_n = (d_xn_x[M[n], :] * alpha_n[:, None]).sum(0) h_n = (d_xn_x[H[n], :] * beta_n[:, None]).sum(0) pi = gamma_n * (m_n - h_n) learning_rate = 1.0 / (counter * a + 1.0) ni_new = ni + learning_rate * (pi - ni) ni = ni_new # set all negative elements to zero ni_plus = N.clip(ni, 0.0, N.inf) w_new = N.nan_to_num(ni_plus / (N.sqrt((ni_plus ** 2).sum()))) change = N.abs(w_new - w).sum() if t % 10 == 0 and __debug__ and 'IRELIEF' in debug.active: debug('IRELIEF', "t=%d change=%.4f max=%f min=%.4f mean=%.4f std=%.4f" " #nan=%d" % (t, change, w_new.max(), w_new.min(), w_new.mean(), w_new.std(), N.isnan(w_new).sum())) w = w_new if change < threshold and iteration > 0: break iteration += 1 self.w = w return w pymvpa-0.4.8/mvpa/measures/noiseperturbation.py000066400000000000000000000073121174541445200217660ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """This is a `FeaturewiseDatasetMeasure` that uses a scalar `DatasetMeasure` and selective noise perturbation to compute a sensitivity map. """ __docformat__ = 'restructuredtext' if __debug__: from mvpa.base import debug from mvpa.support.copy import deepcopy import numpy as N from mvpa.measures.base import FeaturewiseDatasetMeasure class NoisePerturbationSensitivity(FeaturewiseDatasetMeasure): """This is a `FeaturewiseDatasetMeasure` that uses a scalar `DatasetMeasure` and selective noise perturbation to compute a sensitivity map. First the scalar `DatasetMeasure` computed using the original dataset. Next the data measure is computed multiple times each with a single feature in the dataset perturbed by noise. The resulting difference in the scalar `DatasetMeasure` is used as the sensitivity for the respective perturbed feature. Large differences are treated as an indicator of a feature having great impact on the scalar `DatasetMeasure`. The computed sensitivity map might have positive and negative values! """ def __init__(self, datameasure, noise=N.random.normal): """Cheap initialization. :Parameters: datameasure: `Datameasure` that is used to quantify the effect of noise perturbation. noise: Functor to generate noise. The noise generator has to return an 1d array of n values when called the `size=n` keyword argument. This is the default interface of the random number generators in NumPy's `random` module. """ # init base classes first FeaturewiseDatasetMeasure.__init__(self) self.__datameasure = datameasure self.__noise = noise def _call(self, dataset): """Compute the sensitivity map. Returns a 1d array of sensitivities for all features in `dataset`. """ # first cast to floating point dtype, because noise is most likely # floating point as well and '+=' on int would not do the right thing # XXX should we already deepcopy here to keep orig dtype? if not N.issubdtype(dataset.samples.dtype, N.float): dataset.setSamplesDType('float32') if __debug__: nfeatures = dataset.nfeatures sens_map = [] # compute the datameasure on the original dataset # this is used as a baseline orig_measure = self.__datameasure(dataset) # do for every _single_ feature in the dataset for feature in xrange(dataset.nfeatures): if __debug__: debug('PSA', "Analyzing %i features: %i [%i%%]" \ % (nfeatures, feature+1, float(feature+1)/nfeatures*100,), cr=True) # make a copy of the dataset to preserve data integrity wdata = deepcopy(dataset) # add noise to current feature wdata.samples[:, feature] += self.__noise(size=wdata.nsamples) # compute the datameasure on the perturbed dataset perturbed_measure = self.__datameasure(wdata) # difference from original datameasure is sensitivity sens_map.append(perturbed_measure - orig_measure) if __debug__: debug('PSA', '') return N.array(sens_map) pymvpa-0.4.8/mvpa/measures/pls.py000066400000000000000000000030651174541445200170110ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## __docformat__ = 'restructuredtext' import numpy as N from mvpa.measures.base import FeaturewiseDatasetMeasure if __debug__: from mvpa.base import debug class PLS(FeaturewiseDatasetMeasure): def __init__(self, num_permutations=200, num_bootstraps=100, **kwargs): # init base classes first FeaturewiseDatasetMeasure.__init__(self, **kwargs) # save the args for the analysis self.num_permutations = num_permutations self.num_bootstraps = num_bootstraps def _calc_pls(self,mat,labels): # take mean within condition(label) and concat to make a # condition by features matrix X = [] for ul in N.unique(labels): X.append(mat[labels==ul].mean(axis=0)) X = N.asarray(X) # center each condition by subtracting the grand mean X -= X.mean(axis=1)[:,N.newaxis].repeat(X.shape[1],axis=1) # run SVD (checking to transpose if necessary) U,s,Vh = N.linalg.svd(X, full_matrices=0) # run procrust to reorder if necessary def _procrust(): pass def _call(self,dataset): # pass class TaskPLS(PLS): pass pymvpa-0.4.8/mvpa/measures/searchlight.py000066400000000000000000000167571174541445200205240ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Implementation of the Searchlight algorithm""" __docformat__ = 'restructuredtext' if __debug__: from mvpa.base import debug from mvpa.datasets.mapped import MappedDataset from mvpa.measures.base import DatasetMeasure from mvpa.misc.state import StateVariable from mvpa.base.dochelpers import enhancedDocString class Searchlight(DatasetMeasure): """Runs a scalar `DatasetMeasure` on all possible spheres of a certain size within a dataset. The idea for a searchlight algorithm stems from a paper by :ref:`Kriegeskorte et al. (2006) `. """ spheresizes = StateVariable(enabled=False, doc="Number of features in each sphere.") def __init__(self, datameasure, radius=1.0, center_ids=None, **kwargs): """ :Parameters: datameasure: callable Any object that takes a :class:`~mvpa.datasets.base.Dataset` and returns some measure when called. radius: float All features within the radius around the center will be part of a sphere. Provided dataset should have a metric assigned (for NiftiDataset, voxel size is used to provide such a metric, hence radius should be specified in mm). center_ids: list(int) List of feature ids (not coordinates) the shall serve as sphere centers. By default all features will be used. **kwargs In additions this class supports all keyword arguments of its base-class :class:`~mvpa.measures.base.DatasetMeasure`. .. note:: If `Searchlight` is used as `SensitivityAnalyzer` one has to make sure that the specified scalar `DatasetMeasure` returns large (absolute) values for high sensitivities and small (absolute) values for low sensitivities. Especially when using error functions usually low values imply high performance and therefore high sensitivity. This would in turn result in sensitivity maps that have low (absolute) values indicating high sensitivites and this conflicts with the intended behavior of a `SensitivityAnalyzer`. """ DatasetMeasure.__init__(self, **(kwargs)) self.__datameasure = datameasure self.__radius = radius self.__center_ids = center_ids __doc__ = enhancedDocString('Searchlight', locals(), DatasetMeasure) def _call(self, dataset): """Perform the spheresearch. """ if not isinstance(dataset, MappedDataset) \ or dataset.mapper.metric is None: raise ValueError, "Searchlight only works with MappedDatasets " \ "that has metric assigned." if self.states.isEnabled('spheresizes'): self.spheresizes = [] if __debug__: if not self.__center_ids == None: nspheres = len(self.__center_ids) else: nspheres = dataset.nfeatures sphere_count = 0 # collect the results in a list -- you never know what you get results = [] # decide whether to run on all possible center coords or just a provided # subset if not self.__center_ids == None: generator = self.__center_ids else: generator = xrange(dataset.nfeatures) # put spheres around all features in the dataset and compute the # measure within them for f in generator: sphere = dataset.selectFeatures( dataset.mapper.getNeighbors(f, self.__radius), plain=True) # compute the datameasure and store in results measure = self.__datameasure(sphere) results.append(measure) # store the size of the sphere dataset if self.states.isEnabled('spheresizes'): self.spheresizes.append(sphere.nfeatures) if __debug__: sphere_count += 1 debug('SLC', "Doing %i spheres: %i (%i features) [%i%%]" \ % (nspheres, sphere_count, sphere.nfeatures, float(sphere_count)/nspheres*100,), cr=True) if __debug__: debug('SLC', '') # charge state self.raw_results = results # return raw results, base-class will take care of transformations return results #class OptimalSearchlight( object ): # def __init__( self, # searchlight, # test_radii, # verbose=False, # **kwargs ): # """ # """ # # results will end up here # self.__perfmeans = [] # self.__perfvars = [] # self.__chisquares = [] # self.__chanceprobs = [] # self.__spheresizes = [] # # # run searchligh for all radii in the list # for radius in test_radii: # if verbose: # print 'Using searchlight with radius:', radius # # compute the results # searchlight( radius, **(kwargs) ) # # self.__perfmeans.append( searchlight.perfmean ) # self.__perfvars.append( searchlight.perfvar ) # self.__chisquares.append( searchlight.chisquare ) # self.__chanceprobs.append( searchlight.chanceprob ) # self.__spheresizes.append( searchlight.spheresize ) # # # # now determine the best classification accuracy # best = N.array(self.__perfmeans).argmax( axis=0 ) # # # select the corresponding values of the best classification # # in all data tables # self.perfmean = best.choose(*(self.__perfmeans)) # self.perfvar = best.choose(*(self.__perfvars)) # self.chisquare = best.choose(*(self.__chisquares)) # self.chanceprob = best.choose(*(self.__chanceprobs)) # self.spheresize = best.choose(*(self.__spheresizes)) # # # store the best performing radius # self.bestradius = N.zeros( self.perfmean.shape, dtype='uint' ) # self.bestradius[searchlight.mask==True] = \ # best.choose( test_radii )[searchlight.mask==True] # # # #def makeSphericalROIMask( mask, radius, elementsize=None ): # """ # """ # # use default elementsize if none is supplied # if not elementsize: # elementsize = [ 1 for i in range( len(mask.shape) ) ] # else: # if len( elementsize ) != len( mask.shape ): # raise ValueError, 'elementsize does not match mask dimensions.' # # # rois will be drawn into this mask # roi_mask = N.zeros( mask.shape, dtype='int32' ) # # # while increase with every ROI # roi_id_counter = 1 # # # build spheres around every non-zero value in the mask # for center, spheremask in \ # algorithms.SpheresInMask( mask, # radius, # elementsize, # forcesphere = True ): # # # set all elements that match the current spheremask to the # # current ROI index value # roi_mask[spheremask] = roi_id_counter # # # increase ROI counter # roi_id_counter += 1 # # return roi_mask pymvpa-0.4.8/mvpa/measures/splitmeasure.py000066400000000000000000000133301174541445200207240ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """This is a `FeaturewiseDatasetMeasure` that uses another `FeaturewiseDatasetMeasure` and runs it multiple times on differents splits of a `Dataset`. """ __docformat__ = 'restructuredtext' import numpy as N from mvpa.measures.base import FeaturewiseDatasetMeasure from mvpa.datasets.splitters import NoneSplitter from mvpa.misc.state import StateVariable from mvpa.misc.transformers import FirstAxisMean if __debug__: from mvpa.base import debug class SplitFeaturewiseMeasure(FeaturewiseDatasetMeasure): """This is a `FeaturewiseDatasetMeasure` that uses another `FeaturewiseDatasetMeasure` and runs it multiple times on differents splits of a `Dataset`. When called with a `Dataset` it returns the mean sensitivity maps of all data splits. Additonally this class supports the `State` interface. Several postprocessing functions can be specififed to the constructor. The results of the functions specified in the `postproc` dictionary will be available via their respective keywords. """ maps = StateVariable(enabled=False, doc="To store maps per each split") def __init__(self, sensana, splitter=NoneSplitter, combiner=FirstAxisMean, **kwargs): """Cheap initialization. :Parameters: sensana : FeaturewiseDatasetMeasure that shall be run on the `Dataset` splits. splitter : Splitter used to split the `Dataset`. By convention the first dataset in the tuple returned by the splitter on each iteration is used to compute the sensitivity map. combiner This functor will be called on an array of sensitivity maps and the result will be returned by __call__(). The result of a combiner must be an 1d ndarray. """ # init base classes first FeaturewiseDatasetMeasure.__init__(self, **kwargs) self.__sensana = sensana """Sensitivity analyzer used to compute the sensitivity maps. """ self.__splitter = splitter """Splitter instance used to split the datasets.""" self.__combiner = combiner """Function to combine sensitivities to serve a result of __call__()""" def _call(self, dataset): """Compute sensitivity maps for all dataset splits and run the postprocessing functions afterward (if any). Returns a list of all computed sensitivity maps. Postprocessing results are available via the objects `State` interface. """ maps = [] # splitter for split in self.__splitter(dataset): # compute sensitivity using first dataset in split sensitivity = self.__sensana(split[0]) maps.append(sensitivity) self.maps = maps """Store the maps across splits""" # return all maps return self.__combiner(maps) class TScoredFeaturewiseMeasure(SplitFeaturewiseMeasure): """`SplitFeaturewiseMeasure` computing featurewise t-score of sensitivities across splits. """ def __init__(self, sensana, splitter, noise_level=0.0, **kwargs): """Cheap initialization. :Parameters: sensana : SensitivityAnalyzer that shall be run on the `Dataset` splits. splitter : Splitter used to split the `Dataset`. By convention the first dataset in the tuple returned by the splitter on each iteration is used to compute the sensitivity map. noise_level: float Theoretical output of the respective `SensitivityAnalyzer` for a pure noise pattern. For most algorithms this is probably zero, hence the default. """ # init base classes first # - get full sensitivity maps from SplittingSensitivityAnalyzer # - no postprocessing # - leave States handling to base class SplitFeaturewiseMeasure.__init__(self, sensana, splitter, combiner=N.array, **kwargs) self.__noise_level = noise_level """Output of the sensitivity analyzer when there is no signal.""" def _call(self, dataset, callables=[]): """Compute sensitivity maps for all dataset splits and return the featurewise t-score of them. """ # let base class compute the sensitivity maps maps = SplitFeaturewiseMeasure._call(self, dataset) # feature wise mean m = N.mean(maps, axis=0) #m = N.min(maps, axis=0) # featurewise variance v = N.var(maps, axis=0) # degrees of freedom (n-1 for one-sample t-test) df = maps.shape[0] - 1 # compute t-score t = (m - self.__noise_level) / N.sqrt(v * (1.0 / maps.shape[0])) if __debug__: debug('SA', 'T-score sensitivities computed for %d maps ' % maps.shape[0] + 'min=%f max=%f. mean(m)=%f mean(v)=%f Result min=%f max=%f mean(abs)=%f' % (N.min(maps), N.max(maps), N.mean(m), N.mean(v), N.min(t), N.max(t), N.mean(N.abs(t)))) return t pymvpa-0.4.8/mvpa/misc/000077500000000000000000000000001174541445200147445ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/misc/__init__.py000066400000000000000000000011041174541445200170510ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Import helper for PyMVPA misc modules""" __docformat__ = 'restructuredtext' if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.misc start') debug('INIT', 'mvpa.misc end') pymvpa-0.4.8/mvpa/misc/args.py000066400000000000000000000054721174541445200162620ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Helpers for arguments handling.""" __docformat__ = 'restructuredtext' def split_kwargs(kwargs, prefixes=[]): """Helper to separate kwargs into multiple groups :Parameters: prefixes : list of basestrings Each entry sets a prefix which puts entry with key starting with it into a separate group. Group '' corresponds to 'leftovers' :Output: dictionary with keys == `prefixes` """ if not ('' in prefixes): prefixes = prefixes + [''] result = [ [] for i in prefixes ] for k,v in kwargs.iteritems(): for i,p in enumerate(prefixes): if k.startswith(p): result[i].append((k.replace(p,'',1), v)) break resultd = dict((p,dict(x)) for p,x in zip(prefixes, result)) return resultd def group_kwargs(prefixes, assign=False, passthrough=False): """Decorator function to join parts of kwargs together :Parameters: prefixes : list of basestrings Prefixes to split based on. See `split_kwargs` assign : bool Flag to assign the obtained arguments to self.__kwargs passthrough : bool Flag to pass joined arguments as _kwargs argument. Usually it is sufficient to have either assign or passthrough. If none of those is True, decorator simply filters out mentioned groups from being passed to the method Example: if needed to join all args which start with 'slave' together under slave_kwargs parameter """ def decorated_method(method): def do_group_kwargs(self, *args_, **kwargs_): if '' in prefixes: raise ValueError, \ "Please don't put empty string ('') into prefixes" # group as needed splits = split_kwargs(kwargs_, prefixes) # adjust resultant kwargs__ kwargs__ = splits[''] for prefix in prefixes: skwargs = splits[prefix] k = '%skwargs' % prefix if k in kwargs__: # is unprobable but can happen raise ValueError, '%s is already given in the arguments' % k if passthrough: kwargs__[k] = skwargs if assign: setattr(self, '_%s' % k, skwargs) return method(self, *args_, **kwargs__) do_group_kwargs.func_name = method.func_name return do_group_kwargs return decorated_method pymvpa-0.4.8/mvpa/misc/attributes.py000066400000000000000000000200121174541445200174770ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Module with some special objects to be used as magic attributes with dedicated containers aka. `Collections`. """ __docformat__ = 'restructuredtext' from mvpa.misc.exceptions import UnknownStateError if __debug__: from mvpa.base import debug ################################################################## # Various attributes which will be collected into collections # class CollectableAttribute(object): """Base class for any custom behaving attribute intended to become part of a collection. Derived classes will have specific semantics: * StateVariable: conditional storage * AttributeWithUnique: easy access to a set of unique values within a container * Parameter: attribute with validity ranges. - ClassifierParameter: specialization to become a part of Classifier's params collection - KernelParameter: --//-- to become a part of Kernel Classifier's kernel_params collection Those CollectableAttributes are to be groupped into corresponding collections for each class by statecollector metaclass, ie it would be done on a class creation (ie not per each object) """ _instance_index = 0 def __init__(self, name=None, doc=None, index=None): if index is None: CollectableAttribute._instance_index += 1 index = CollectableAttribute._instance_index self._instance_index = index self.__doc__ = doc self.__name = name self._value = None self._isset = False self.reset() if __debug__: debug("COL", "Initialized new collectable #%d:%s" % (index,name) + `self`) # Instead of going for VProperty lets make use of virtual method def _getVirtual(self): return self._get() def _setVirtual(self, value): return self._set(value) def _get(self): return self._value def _set(self, val): if __debug__: # Since this call is quite often, don't convert # values to strings here, rely on passing them # withing msgargs debug("COL", "Setting %(self)s to %(val)s ", msgargs={'self':self, 'val':val}) self._value = val self._isset = True @property def isSet(self): return self._isset def reset(self): """Simply reset the flag""" if __debug__ and self._isset: debug("COL", "Reset %s to being non-modified" % self.name) self._isset = False # TODO XXX unify all bloody __str__ def __str__(self): res = "%s" % (self.name) if self.isSet: res += '*' # so we have the value already return res def _getName(self): return self.__name def _setName(self, name): """Set the name of parameter .. note:: Should not be called for an attribute which is already assigned to a collection """ if name is not None: if isinstance(name, basestring): if name[0] == '_': raise ValueError, \ "Collectable attribute name must not start " \ "with _. Got %s" % name else: raise ValueError, \ "Collectable attribute name must be a string. " \ "Got %s" % `name` self.__name = name # XXX should become vproperty? # YYY yoh: not sure... someone has to do performance testing # to see which is more effective. My wild guess is that # _[gs]etVirtual would be faster value = property(_getVirtual, _setVirtual) name = property(_getName) #, _setName) # XXX think that may be discard hasunique and just devise top # class DatasetAttribute class AttributeWithUnique(CollectableAttribute): """Container which also takes care about recomputing unique values XXX may be we could better link original attribute to additional attribute which actually stores the values (and do reverse there as well). Pros: * don't need to mess with getattr since it would become just another attribute Cons: * might be worse design in terms of comprehension * take care about _set, since we shouldn't allow change it externally For now lets do it within a single class and tune up getattr """ def __init__(self, name=None, hasunique=True, doc="Attribute with unique"): CollectableAttribute.__init__(self, name, doc) self._hasunique = hasunique self._resetUnique() if __debug__: debug("UATTR", "Initialized new AttributeWithUnique %s " % name + `self`) def reset(self): super(AttributeWithUnique, self).reset() self._resetUnique() def _resetUnique(self): self._uniqueValues = None def _set(self, *args, **kwargs): self._resetUnique() CollectableAttribute._set(self, *args, **kwargs) def _getUniqueValues(self): if self.value is None: return None if self._uniqueValues is None: # XXX we might better use Set, but yoh recalls that # N.unique was more efficient. May be we should check # on the the class and use Set only if we are not # dealing with ndarray (or lists/tuples) self._uniqueValues = N.unique(N.asanyarray(self.value)) return self._uniqueValues uniqueValues = property(fget=_getUniqueValues) hasunique = property(fget=lambda self:self._hasunique) # Hooks for comprehendable semantics and automatic collection generation class SampleAttribute(AttributeWithUnique): pass class FeatureAttribute(AttributeWithUnique): pass class DatasetAttribute(AttributeWithUnique): pass class StateVariable(CollectableAttribute): """Simple container intended to conditionally store the value """ def __init__(self, name=None, enabled=True, doc="State variable"): # Force enabled state regardless of the input # to facilitate testing if __debug__ and 'ENFORCE_STATES_ENABLED' in debug.active: enabled = True CollectableAttribute.__init__(self, name, doc) self._isenabled = enabled self._defaultenabled = enabled if __debug__: debug("STV", "Initialized new state variable %s " % name + `self`) def _get(self): if not self.isSet: raise UnknownStateError("Unknown yet value of %s" % (self.name)) return CollectableAttribute._get(self) def _set(self, val): if self.isEnabled: # XXX may be should have left simple assignment # self._value = val CollectableAttribute._set(self, val) elif __debug__: debug("COL", "Not setting disabled %(self)s to %(val)s ", msgargs={'self':self, 'val':val}) def reset(self): """Simply detach the value, and reset the flag""" CollectableAttribute.reset(self) self._value = None @property def isEnabled(self): return self._isenabled def enable(self, value=False): if self._isenabled == value: # Do nothing since it is already in proper state return if __debug__: debug("STV", "%s %s" % ({True: 'Enabling', False: 'Disabling'}[value], str(self))) self._isenabled = value def __str__(self): res = CollectableAttribute.__str__(self) if self.isEnabled: res += '+' # it is enabled but no value is assigned yet return res pymvpa-0.4.8/mvpa/misc/bv/000077500000000000000000000000001174541445200153535ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/misc/bv/__init__.py000066400000000000000000000011111174541445200174560ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Import helper for Brain Voyager""" if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.misc.bv') from mvpa.misc.bv.base import * if __debug__: debug('INIT', 'mvpa.misc.bv end') pymvpa-0.4.8/mvpa/misc/bv/base.py000066400000000000000000000032471174541445200166450ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Tiny snippets to interface with FSL easily.""" __docformat__ = 'restructuredtext' from mvpa.misc.io import ColumnData if __debug__: from mvpa.base import debug class BrainVoyagerRTC(ColumnData): """IO helper to read BrainVoyager RTC files. This is a textfile format that is used to specify stimulation protocols for data analysis in BrainVoyager. It looks like FileVersion: 2 Type: DesignMatrix NrOfPredictors: 4 NrOfDataPoints: 147 "fm_l_60dB" "fm_r_60dB" "fm_l_80dB" "fm_r_80dB" 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 Data is always read as `float` and header is actually ignored """ def __init__(self, source): """Read and write BrainVoyager RTC files. :Parameter: source: filename of an RTC file """ # init data from known format ColumnData.__init__(self, source, header=True, sep=None, headersep='"', dtype=float, skiplines=5) def toarray(self): """Returns the data as an array """ import numpy as N # return as array with time axis first return N.array([self[i] for i in self._header_order], dtype='float').T pymvpa-0.4.8/mvpa/misc/cmdline.py000066400000000000000000000254151174541445200167400ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Common functions and options definitions for command line __docformat__ = 'restructuredtext' Conventions: Every option (instance of optparse.Option) has prefix "opt". Lists of options has prefix opts (e.g. `opts.common`). Option name should be camelbacked version of .dest for the option. """ import mvpa # TODO? all options (opt*) might migrate to respective module? discuss from optparse import OptionParser, Option, OptionGroup, OptionConflictError # needed for verboseCallback from mvpa.base import verbose, externals # we need to make copies of the options if we place them into the # groups, since otherwise it is impossible to use them without using # the whole group. May be we should make some late creation of the # groups -- ie only if user requests a group, options are added to it. from mvpa.support import copy class Options(object): """Just a convinience placeholder for all available options """ pass class OptionGroups(object): """Group creation is delayed until instance is requested. This allows to overcome the problem of poluting handled cmdline options """ def __init__(self, parser): self._d = {} self._parser = parser def add(self, name, l, doc): self._d[name] = (doc, l) def _getGroup(self, name): try: doc, l = self._d[name] except KeyError: raise ValueError, "No group with name %s" % name opts = OptionGroup(self._parser, doc) #opts.add_options(copy.deepcopy(l)) # may be copy? try: opts.add_options(l) except OptionConflictError: print "Problem addition options to the group '%s'. Most probably" \ " the option was independently added already." % name raise return opts def __getattribute__(self, index): if index[0] == '_': return object.__getattribute__(self, index) if self._d.has_key(index): return self._getGroup(index) return object.__getattribute__(self, index) # TODO: try to make groups definition somewhat lazy, since now # whenever a group is created, those parameters are already known by # parser, although might not be listed in the list of used and not by # --help. But their specification on cmdline doesn't lead to # error/help msg. # # Conflict hanlder to resolve situation that we have the same option added # to some group and also available 'freely' # # set default version string, otherwise '--version' option is not enabled # can be overwritten later on by assigning to `parser.version` parser = OptionParser(version="%prog", add_help_option=False, conflict_handler="error") opt = Options() opts = OptionGroups(parser) # # Verbosity options # def _verboseCallback(option, optstr, value, parser): """Callback for -v|--verbose cmdline option """ if __debug__: debug("CMDLINE", "Setting verbose.level to %s" % str(value)) verbose.level = value optstr = optstr # pylint shut up setattr(parser.values, option.dest, value) opt.help = \ Option("-h", "--help", "--sos", action="help", help="Show this help message and exit") opt.verbose = \ Option("-v", "--verbose", "--verbosity", action="callback", callback=_verboseCallback, nargs=1, type="int", dest="verbose", default=0, help="Verbosity level of output") """Pre-cooked `optparse`'s option to specify verbose level""" commonopts_list = [opt.verbose, opt.help] if __debug__: from mvpa.base import debug def _debugCallback(option, optstr, value, parser): """Callback for -d|--debug cmdline option """ if value == "list": print "Registered debug IDs:" keys = debug.registered.keys() keys.sort() for k in keys: print "%-7s: %s" % (k, debug.registered[k]) print "Use ALL: to enable all of the debug IDs listed above." print "Use python regular expressions to select group. CLF.* will" \ " enable all debug entries starting with CLF (e.g. CLFBIN, CLFMC)" raise SystemExit, 0 optstr = optstr # pylint shut up debug.setActiveFromString(value) setattr(parser.values, option.dest, value) optDebug = Option("-d", "--debug", action="callback", callback=_debugCallback, nargs=1, type="string", dest="debug", default="", help="Debug entries to report. " + "Run with '-d list' to get a list of " + "registered entries") commonopts_list.append(optDebug) opts.add("common", commonopts_list, "Common generic options") # # Classifiers options # opt.clf = \ Option("--clf", type="choice", dest="clf", choices=['knn', 'svm', 'ridge', 'gpr', 'smlr'], default='svm', help="Type of classifier to be used. Default: svm") opt.radius = \ Option("-r", "--radius", action="store", type="float", dest="radius", default=5.0, help="Radius to be used (eg for the searchlight). Default: 5.0") opt.knearestdegree = \ Option("-k", "--k-nearest", action="store", type="int", dest="knearestdegree", default=3, help="Degree of k-nearest classifier. Default: 3") opts.add('KNN', [opt.radius, opt.knearestdegree], "Specification of kNN") opt.svm_C = \ Option("-C", "--svm-C", action="store", type="float", dest="svm_C", default=1.0, help="C parameter for soft-margin C-SVM classification. " \ "Default: 1.0") opt.svm_nu = \ Option("--nu", "--svm-nu", action="store", type="float", dest="svm_nu", default=0.1, help="nu parameter for soft-margin nu-SVM classification. " \ "Default: 0.1") opt.svm_gamma = \ Option("--gamma", "--svm-gamma", action="store", type="float", dest="svm_gamma", default=1.0, help="gamma parameter for Gaussian kernel of RBF SVM. " \ "Default: 1.0") opts.add('SVM', [opt.svm_nu, opt.svm_C, opt.svm_gamma], "SVM specification") opt.do_sweep = \ Option("--sweep", action="store_true", dest="do_sweep", default=False, help="Sweep through various classifiers") # Crossvalidation options opt.crossfolddegree = \ Option("-c", "--crossfold", action="store", type="int", dest="crossfolddegree", default=1, help="Degree of N-fold crossfold. Default: 1") opts.add('general', [opt.crossfolddegree], "Generalization estimates") # preprocess options opt.zscore = \ Option("--zscore", action="store_true", dest="zscore", default=0, help="Enable zscoring of dataset samples. Default: Off") opt.tr = \ Option("--tr", action="store", dest="tr", default=2.0, type='float', help="fMRI volume repetition time. Default: 2.0") opt.detrend = \ Option("--detrend", action="store_true", dest="detrend", default=0, help="Do linear detrending. Default: Off") opts.add('preproc', [opt.zscore, opt.tr, opt.detrend], "Preprocessing options") # Wavelets options if externals.exists('pywt'): import pywt def _waveletFamilyCallback(option, optstr, value, parser): """Callback for -w|--wavelet-family cmdline option """ wl_list = pywt.wavelist() wl_list_str = ", ".join( ['-1: None'] + ['%d:%s' % w for w in enumerate(wl_list)]) if value == "list": print "Available wavelet families: " + wl_list_str raise SystemExit, 0 wl_family = value try: # may be int? ;-) wl_family_index = int(value) if wl_family_index >= 0: try: wl_family = wl_list[wl_family_index] except IndexError: print "Index is out of range. " + \ "Following indexes with names are known: " + \ wl_list_str raise SystemExit, -1 else: wl_family = 'None' except ValueError: pass # Check the value wl_family = wl_family.lower() if wl_family == 'none': wl_family = None elif not wl_family in wl_list: print "Uknown family '%s'. Known are %s" % (wl_family, ', '.join(wl_list)) raise SystemExit, -1 # Store it in the parser setattr(parser.values, option.dest, wl_family) opt.wavelet_family = \ Option("-w", "--wavelet-family", callback=_waveletFamilyCallback, action="callback", type="string", dest="wavelet_family", default='-1', help="Wavelet family: string or index among the available. " + "Run with '-w list' to see available families") opt.wavelet_decomposition = \ Option("-W", "--wavelet-decomposition", action="store", type="choice", dest="wavelet_decomposition", default='dwt', choices=['dwt', 'dwp'], help="Wavelet decomposition: discrete wavelet transform "+ "(dwt) or packet (dwp)") opts.add('wavelet', [opt.wavelet_family, opt.wavelet_decomposition], "Wavelets mappers") # Box options opt.boxlength = \ Option("--boxlength", action="store", dest="boxlength", default=1, type='int', help="Length of the box in volumes (integer). Default: 1") opt.boxoffset = \ Option("--boxoffset", action="store", dest="boxoffset", default=0, type='int', help="Offset of the box from the event onset in volumes. Default: 0") opts.add('box', [opt.boxlength, opt.boxoffset], "Box options") # sample attributes opt.chunk = \ Option("--chunk", action="store", dest="chunk", default='0', help="Id of the data chunk. Default: 0") opt.chunkLimits = \ Option("--chunklimits", action="store", dest="chunklimits", default=None, help="Limit processing to a certain chunk of data given by start " \ "and end volume number (including lower, excluding upper " \ "limit). Numbering starts with zero.") opts.add('chunk', [opt.chunk, opt.chunkLimits], "Chunk options AKA Sample attributes XXX") pymvpa-0.4.8/mvpa/misc/data_generators.py000066400000000000000000000305001174541445200204560ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Miscelaneous data generators for unittests and demos""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.datasets import Dataset if __debug__: from mvpa.base import debug def multipleChunks(func, n_chunks, *args, **kwargs): """Replicate datasets multiple times raising different chunks Given some randomized (noisy) generator of a dataset with a single chunk call generator multiple times and place results into a distinct chunks """ for chunk in xrange(n_chunks): dataset_ = func(*args, **kwargs) dataset_.chunks[:] = chunk + 1 if chunk == 0: dataset = dataset_ else: dataset += dataset_ return dataset def dumbFeatureDataset(): """Create a very simple dataset with 2 features and 3 labels """ data = [[1, 0], [1, 1], [2, 0], [2, 1], [3, 0], [3, 1], [4, 0], [4, 1], [5, 0], [5, 1], [6, 0], [6, 1], [7, 0], [7, 1], [8, 0], [8, 1], [9, 0], [9, 1], [10, 0], [10, 1], [11, 0], [11, 1], [12, 0], [12, 1]] regs = ([1] * 8) + ([2] * 8) + ([3] * 8) return Dataset(samples=data, labels=regs) def dumbFeatureBinaryDataset(): """Very simple binary (2 labels) dataset """ data = [[1, 0], [1, 1], [2, 0], [2, 1], [3, 0], [3, 1], [4, 0], [4, 1], [5, 0], [5, 1], [6, 0], [6, 1], [7, 0], [7, 1], [8, 0], [8, 1], [9, 0], [9, 1], [10, 0], [10, 1], [11, 0], [11, 1], [12, 0], [12, 1]] regs = ([0] * 12) + ([1] * 12) return Dataset(samples=data, labels=regs) def normalFeatureDataset(perlabel=50, nlabels=2, nfeatures=4, nchunks=5, means=None, nonbogus_features=None, snr=3.0): """Generate a univariate dataset with normal noise and specified means. :Keywords: perlabel : int Number of samples per each label nlabels : int Number of labels in the dataset nfeatures : int Total number of features (including bogus features which carry no label-related signal) nchunks : int Number of chunks (perlabel should be multiple of nchunks) means : None or list of float or ndarray Specified means for each of features among nfeatures. nonbogus_features : None or list of int Indexes of non-bogus features (1 per label) snr : float Signal-to-noise ration assuming that signal has std 1.0 so we just divide random normal noise by snr Probably it is a generalization of pureMultivariateSignal where means=[ [0,1], [1,0] ] Specify either means or nonbogus_features so means get assigned accordingly """ data = N.random.standard_normal((perlabel*nlabels, nfeatures))/N.sqrt(snr) if (means is None) and (not nonbogus_features is None): if len(nonbogus_features) > nlabels: raise ValueError, "Can't assign simply a feature to a " + \ "class: more nonbogus_features than labels" means = N.zeros((len(nonbogus_features), nfeatures)) # pure multivariate -- single bit per feature for i in xrange(len(nonbogus_features)): means[i, nonbogus_features[i]] = 1.0 if not means is None: # add mean data += N.repeat(N.array(means, ndmin=2), perlabel, axis=0) # bring it 'under 1', since otherwise some classifiers have difficulties # during optimization data = 1.0/(N.max(N.abs(data))) * data labels = N.concatenate([N.repeat('L%d' % i, perlabel) for i in range(nlabels)]) chunks = N.concatenate([N.repeat(range(nchunks), perlabel/nchunks) for i in range(nlabels)]) ds = Dataset(samples=data, labels=labels, chunks=chunks, labels_map=True) ds.nonbogus_features = nonbogus_features return ds def pureMultivariateSignal(patterns, signal2noise = 1.5, chunks=None): """ Create a 2d dataset with a clear multivariate signal, but no univariate information. :: %%%%%%%%% % O % X % %%%%%%%%% % X % O % %%%%%%%%% """ # start with noise data = N.random.normal(size=(4*patterns, 2)) # add signal data[:2*patterns, 1] += signal2noise data[2*patterns:4*patterns, 1] -= signal2noise data[:patterns, 0] -= signal2noise data[2*patterns:3*patterns, 0] -= signal2noise data[patterns:2*patterns, 0] += signal2noise data[3*patterns:4*patterns, 0] += signal2noise # two conditions regs = N.array(([0] * patterns) + ([1] * 2 * patterns) + ([0] * patterns)) return Dataset(samples=data, labels=regs, chunks=chunks) def normalFeatureDataset__(dataset=None, labels=None, nchunks=None, perlabel=50, activation_probability_steps=1, randomseed=None, randomvoxels=False): """ NOT FINISHED """ raise NotImplementedError if dataset is None and labels is None: raise ValueError, \ "Provide at least labels or a background dataset" if dataset is None: nlabels = len(labels) else: nchunks = len(dataset.uniquechunks) N.random.seed(randomseed) # Create a sequence of indexes from which to select voxels to be used # for features if randomvoxels: indexes = N.random.permutation(dataset.nfeatures) else: indexes = N.arange(dataset.nfeatures) allind, maps = [], [] if __debug__: debug('DG', "Ugly creation of the copy of background") dtype = dataset.samples.dtype if not N.issubdtype(dtype, N.float): dtype = N.float totalsignal = N.zeros(dataset.samples.shape, dtype=dtype) for l in xrange(len(labels)): label = labels[l] if __debug__: debug('DG', "Simulating independent labels for %s" % label) # What sample ids belong to this label labelids = dataset.idsbylabels(label) # What features belong here and what is left over nfeatures = perlabel * activation_probability_steps ind, indexes = indexes[0:nfeatures], \ indexes[nfeatures+1:] allind += list(ind) # store what indexes we used # Create a dataset only for 'selected' features NB there is # sideeffect that selectFeatures will sort those ind provided ds = dataset.selectFeatures(ind) ds.samples[:] = 0.0 # zero them out # assign data prob = [1.0 - x*1.0/activation_probability_steps for x in xrange(activation_probability_steps)] # repeat so each feature gets itw own probabilities = N.repeat(prob, perlabel) if __debug__: debug('DG', 'For prob=%s probabilities=%s' % (prob, probabilities)) for chunk in ds.uniquechunks: chunkids = ds.idsbychunks(chunk) # samples in this chunk ids = list(set(chunkids).intersection(set(labelids))) chunkvalue = N.random.uniform() # random number to decide either # to 'activate' the voxel for id_ in ids: ds.samples[id_, :] = \ (chunkvalue <= probabilities).astype('float') maps.append(N.array(probabilities, copy=True)) signal = ds.map2Nifti(ds.samples) totalsignal[:, ind] += ds.samples # figure out average variance across all 'working' features wfeatures = dataset.samples[:, allind] meanstd = N.mean(N.std(wfeatures, 1)) if __debug__: debug('DG', "Mean deviation is %f" % meanstd) totalsignal *= meanstd * options.snr # add signal on top of background dataset.samples += totalsignal return dataset def getMVPattern(s2n): """Simple multivariate dataset""" return multipleChunks(pureMultivariateSignal, 6, 5, s2n, 1) def wr1996(size=200): """Generate '6d robot arm' dataset (Williams and Rasmussen 1996) Was originally created in order to test the correctness of the implementation of kernel ARD. For full details see: http://www.gaussianprocess.org/gpml/code/matlab/doc/regression.html#ard x_1 picked randomly in [-1.932, -0.453] x_2 picked randomly in [0.534, 3.142] r_1 = 2.0 r_2 = 1.3 f(x_1,x_2) = r_1 cos (x_1) + r_2 cos(x_1 + x_2) + N(0,0.0025) etc. Expected relevances: ell_1 1.804377 ell_2 1.963956 ell_3 8.884361 ell_4 34.417657 ell_5 1081.610451 ell_6 375.445823 sigma_f 2.379139 sigma_n 0.050835 """ intervals = N.array([[-1.932, -0.453], [0.534, 3.142]]) r = N.array([2.0, 1.3]) x = N.random.rand(size, 2) x *= N.array(intervals[:, 1]-intervals[:, 0]) x += N.array(intervals[:, 0]) if __debug__: for i in xrange(2): debug('DG', '%d columnt Min: %g Max: %g' % (i, x[:, i].min(), x[:, i].max())) y = r[0]*N.cos(x[:, 0] + r[1]*N.cos(x.sum(1))) + \ N.random.randn(size)*N.sqrt(0.0025) y -= y.mean() x34 = x + N.random.randn(size, 2)*0.02 x56 = N.random.randn(size, 2) x = N.hstack([x, x34, x56]) return Dataset(samples=x, labels=y) def sinModulated(n_instances, n_features, flat=False, noise=0.4): """ Generate a (quite) complex multidimensional non-linear dataset Used for regression testing. In the data label is a sin of a x^2 + uniform noise """ if flat: data = (N.arange(0.0, 1.0, 1.0/n_instances)*N.pi) data.resize(n_instances, n_features) else: data = N.random.rand(n_instances, n_features)*N.pi label = N.sin((data**2).sum(1)).round() label += N.random.rand(label.size)*noise return Dataset(samples=data, labels=label) def chirpLinear(n_instances, n_features=4, n_nonbogus_features=2, data_noise=0.4, noise=0.1): """ Generates simple dataset for linear regressions Generates chirp signal, populates n_nonbogus_features out of n_features with it with different noise level and then provides signal itself with additional noise as labels """ x = N.linspace(0, 1, n_instances) y = N.sin((10 * N.pi * x **2)) data = N.random.normal(size=(n_instances, n_features ))*data_noise for i in xrange(n_nonbogus_features): data[:, i] += y[:] labels = y + N.random.normal(size=(n_instances,))*noise return Dataset(samples=data, labels=labels) def linear_awgn(size=10, intercept=0.0, slope=0.4, noise_std=0.01, flat=False): """Generate a dataset from a linear function with AWGN (Added White Gaussian Noise). It can be multidimensional if 'slope' is a vector. If flat is True (in 1 dimesion) generate equally spaces samples instead of random ones. This is useful for the test phase. """ dimensions = 1 if isinstance(slope, N.ndarray): dimensions = slope.size if flat and dimensions == 1: x = N.linspace(0, 1, size)[:, N.newaxis] else: x = N.random.rand(size, dimensions) y = N.dot(x, slope)[:, N.newaxis] \ + (N.random.randn(*(x.shape[0], 1)) * noise_std) + intercept return Dataset(samples=x, labels=y) def noisy_2d_fx(size_per_fx, dfx, sfx, center, noise_std=1): """ """ x = [] y = [] labels = [] for fx in sfx: nx = N.random.normal(size=size_per_fx) ny = fx(nx) + N.random.normal(size=nx.shape, scale=noise_std) x.append(nx) y.append(ny) # whenever larger than first function value labels.append(N.array(ny < dfx(nx), dtype='int')) samples = N.array((N.hstack(x), N.hstack(y))).squeeze().T labels = N.hstack(labels).squeeze().T samples += N.array(center) return Dataset(samples=samples, labels=labels) def linear1d_gaussian_noise(size=100, slope=0.5, intercept=1.0, x_min=-2.0, x_max=3.0, sigma=0.2): """A straight line with some Gaussian noise. """ x = N.linspace(start=x_min, stop=x_max, num=size) noise = N.random.randn(size)*sigma y = x * slope + intercept + noise return Dataset(samples=x.reshape((-1, 1)), labels=y) pymvpa-0.4.8/mvpa/misc/errorfx.py000066400000000000000000000137001174541445200170060ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Error functions helpers. PyMVPA can use arbitrary function which takes 2 arguments: predictions and targets and spits out a scalar value. Functions below are for the convinience, and they confirm the agreement that 'smaller' is 'better'""" __docformat__ = 'restructuredtext' import numpy as N from numpy import trapz from mvpa.base import externals # Various helper functions def meanPowerFx(data): """Returns mean power Similar to var but without demeaning """ return N.mean(N.asanyarray(data)**2) def rootMeanPowerFx(data): """Returns root mean power to be comparable against RMSE """ return N.sqrt(meanPowerFx(data)) class _ErrorFx(object): """Common error function interface, computing the difference between some target and some predicted values. """ """XXX there is no reason to keep this class around imho -- it is just the skeleton for all the _ErrorFxs -- interface they must conform... and there is no reason to have all those ErrorFx as classes... may be they should be just functions?""" def __str__(self): """Print class name when asked for string """ return self.__class__.__name__ def __repr__(self): """Proper repr for _ErrorFx """ return self.__class__.__name__ + "()" def __call__(self, predicted, target): """Compute some error value from the given target and predicted values (both sequences). """ raise NotImplemented class RMSErrorFx(_ErrorFx): """Computes the root mean squared error of some target and some predicted values. """ def __call__(self, predicted, target): """Both 'predicted' and 'target' can be either scalars or sequences, but have to be of the same length. """ return N.sqrt(N.mean(N.subtract(predicted, target)**2)) class MeanMismatchErrorFx(_ErrorFx): """Computes the percentage of mismatches between some target and some predicted values. """ def __call__(self, predicted, target): """Both 'predicted' and 'target' can be either scalars or sequences, but have to be of the same length. """ return 1 - N.mean( predicted == target ) class AUCErrorFx(_ErrorFx): """Computes the area under the ROC for the given the target and predicted to make the prediction.""" def __call__(self, predicted, target): """Requires all arguments.""" # sort the target in descending order based on the predicted and # set to boolean self.t = t = N.asanyarray(target)[N.argsort(predicted)[::-1]] > 0 # calculate the true positives self.tp = tp = N.concatenate( ([0], N.cumsum(t)/t.sum(dtype=N.float), [1])) # calculate the false positives self.fp = fp = N.concatenate( ([0], N.cumsum(~t)/(~t).sum(dtype=N.float), [1])) return trapz(tp, fp) if externals.exists('scipy'): from scipy.stats import pearsonr class CorrErrorFx(_ErrorFx): """Computes the correlation between the target and the predicted values. Resultant value is the 1 - correlation coefficient, so minimization leads to the best value (at 0) """ def __call__(self, predicted, target): """Requires all arguments.""" return 1.0-pearsonr(predicted, target)[0] class CorrErrorPFx(_ErrorFx): """Computes p-value of correlation between the target and the predicted values. """ def __call__(self, predicted, target): """Requires all arguments.""" return pearsonr(predicted, target)[1] else: # slower(?) and bogus(p-value) implementations for non-scipy users # TODO: implement them more or less correcly with numpy # functionality class CorrErrorFx(_ErrorFx): """Computes the correlation between the target and the predicted values. Return 1-CC """ def __call__(self, predicted, target): """Requires all arguments.""" l = len(predicted) return 1.0 - N.corrcoef(N.reshape(predicted, l), N.reshape(target, l))[0,1] class CorrErrorPFx(_ErrorFx): """Computes p-value of correlation between the target and the predicted values. """ def __call__(self, predicted, target): """Requires all arguments.""" from mvpa.base import warning warning("p-value for correlation is implemented only when scipy is " "available. Bogus value -1.0 is returned otherwise") return -1.0 class RelativeRMSErrorFx(_ErrorFx): """Ratio between RMSE and root mean power of target output. So it can be considered as a scaled RMSE -- perfect reconstruction has values near 0, while no reconstruction has values around 1.0. Word of caution -- it is not commutative, ie exchange of predicted and target might lead to completely different answers """ def __call__(self, predicted, target): return RMSErrorFx()(predicted, target) / rootMeanPowerFx(target) class Variance1SVFx(_ErrorFx): """Ratio of variance described by the first singular value component. Of limited use -- left for the sake of not wasting it """ def __call__(self, predicted, target): data = N.vstack( (predicted, target) ).T # demean data_demeaned = data - N.mean(data, axis=0) u, s, vh = N.linalg.svd(data_demeaned, full_matrices=0) # assure sorting s.sort() s=s[::-1] cvar = s[0]**2 / N.sum(s**2) return cvar pymvpa-0.4.8/mvpa/misc/exceptions.py000066400000000000000000000031151174541445200174770ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Exception classes which might get thrown""" __docformat__ = 'restructuredtext' class UnknownStateError(Exception): """Thrown if the internal state of the class is not yet defined. Classifiers and Algorithms classes might have properties, which are not defined prior to training or invocation has happened. """ def __init__(self, msg=""): Exception.__init__(self) self.__msg = msg def __str__(self): return "Exception: " + self.__msg class DatasetError(Exception): """Thrown if there is an internal problem with a Dataset. ValueError exception is too generic to be used for any needed case, thus this one is created """ def __init__(self, msg=""): Exception.__init__(self) self.__msg = msg def __str__(self): return "Dataset handling exception: " + self.__msg class ConvergenceError(Exception): """Thrown if some algorithm does not converge to a solution. """ def __init__(self): Exception.__init__(self) class InvalidHyperparameterError(Exception): """Generic exception to be raised when setting improper values as hyperparameters.""" def __init__(self): Exception.__init__(self) pymvpa-0.4.8/mvpa/misc/fsl/000077500000000000000000000000001174541445200155305ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/misc/fsl/__init__.py000066400000000000000000000011441174541445200176410ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Import helper for FSL""" if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.misc.fsl') from mvpa.misc.fsl.base import * from mvpa.misc.fsl.flobs import * if __debug__: debug('INIT', 'mvpa.misc.fsl end') pymvpa-0.4.8/mvpa/misc/fsl/base.py000066400000000000000000000215531174541445200170220ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Tiny snippets to interface with FSL easily.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.misc.io import ColumnData from mvpa.misc.support import Event if __debug__: from mvpa.base import debug class FslEV3(ColumnData): """IO helper to read FSL's EV3 files. This is a three-column textfile format that is used to specify stimulation protocols for fMRI data analysis in FSL's FEAT module. Data is always read as `float`. """ def __init__(self, source): """Read and write FSL EV3 files. :Parameter: source: filename of an EV3 file """ # init data from known format ColumnData.__init__(self, source, header=['onsets', 'durations', 'intensities'], sep=None, dtype=float) def getNEVs(self): """Returns the number of EVs in the file. """ return self.nrows def getEV(self, evid): """Returns a tuple of (onset time, simulus duration, intensity) for a certain EV. """ return (self['onsets'][evid], self['durations'][evid], self['intensities'][evid]) def tofile(self, filename): """Write data to a FSL EV3 file. """ ColumnData.tofile(self, filename, header=False, header_order=['onsets', 'durations', 'intensities'], sep=' ') def toEvents(self, **kwargs): """Convert into a list of `Event` instances. :Parameters: kwargs Any keyword arugment provided would be replicated, through all the entries. Useful to specify label or even a chunk """ return \ [Event(onset=self['onsets'][i], duration=self['durations'][i], features=[self['intensities'][i]], **kwargs) for i in xrange(self.nevs)] onsets = property(fget=lambda self: self['onsets']) durations = property(fget=lambda self: self['durations']) intensities = property(fget=lambda self: self['intensities']) nevs = property(fget=getNEVs) class McFlirtParams(ColumnData): """Read and write McFlirt's motion estimation parameters from and to text files. """ header_def = ['rot1', 'rot2', 'rot3', 'x', 'y', 'z'] def __init__(self, source): """Initialize McFlirtParams :Parameter: source: str Filename of a parameter file. """ ColumnData.__init__(self, source, header=McFlirtParams.header_def, sep=None, dtype=float) def tofile(self, filename): """Write motion parameters to file. """ ColumnData.tofile(self, filename, header=False, header_order=McFlirtParams.header_def, sep=' ') def plot(self): """Produce a simple plot of the estimated translation and rotation parameters using. You still need to can pylab.show() or pylab.savefig() if you want to see/get anything. """ # import internally as it takes some time and might not be needed most # of the time import pylab as P # translations subplot P.subplot(211) P.plot(self.x) P.plot(self.y) P.plot(self.z) P.ylabel('Translations in mm') P.legend(('x', 'y', 'z'), loc=0) # rotations subplot P.subplot(212) P.plot(self.rot1) P.plot(self.rot2) P.plot(self.rot3) P.ylabel('Rotations in rad') P.legend(('rot1', 'rot2', 'rot3'), loc=0) def toarray(self): """Returns the data as an array with six columns (same order as in file). """ import numpy as N # return as array with time axis first return N.array([self[i] for i in McFlirtParams.header_def], dtype='float').T class FslGLMDesign(object): """Load FSL GLM design matrices from file. Be aware that such a desig matrix has its regressors in columns and the samples in its rows. """ def __init__(self, source): """ :Parameter: source: filename Compressed files will be read as well, if their filename ends with '.gz'. """ # XXX maybe load from array as well self._loadFile(source) def _loadFile(self, fname): """Helper function to load GLM definition from a file. """ # header info nwaves = 0 ntimepoints = 0 matrix_offset = 0 # open the file compressed or not if fname.endswith('.gz'): import gzip fh = gzip.open(fname, 'r') else: fh = open(fname, 'r') # read header for i, line in enumerate(fh): if line.startswith('/NumWaves'): nwaves = int(line.split()[1]) if line.startswith('/NumPoints'): ntimepoints = int(line.split()[1]) if line.startswith('/PPheights'): self.ppheights = [float(i) for i in line.split()[1:]] if line.startswith('/Matrix'): matrix_offset = i + 1 # done with the header, now revert to NumPy's loadtxt for convenience fh.close() self.mat = N.loadtxt(fname, skiprows=matrix_offset) # checks if not self.mat.shape == (ntimepoints, nwaves): raise IOError, "Design matrix file '%s' did not contain expected " \ "matrix size (expected %s, got %s)" \ % (fname, str((ntimepoints, nwaves)), self.mat.shape) def plot(self, style='lines', **kwargs): """Visualize the design matrix. :Parameters: style: 'lines', 'matrix' **kwargs: Additional arguments will be passed to the corresponding matplotlib plotting functions 'plot()' and 'pcolor()' for 'lines' and 'matrix' plots respectively. """ # import internally as it takes some time and might not be needed most # of the time import pylab as P if style == 'lines': # common y-axis yax = N.arange(0, self.mat.shape[0]) axcenters = [] col_offset = max(self.ppheights) # for all columns for i in xrange(self.mat.shape[1]): axcenter = i * col_offset P.plot(self.mat[:, i] + axcenter, yax, **kwargs) axcenters.append(axcenter) P.xticks(N.array(axcenters), range(self.mat.shape[1])) elif style == 'matrix': P.pcolor(self.mat, **kwargs) ticks = N.arange(1, self.mat.shape[1]+1) P.xticks(ticks - 0.5, ticks) else: raise ValueError, "Unknown plotting style '%s'" % style # labels and turn y-axis upside down P.ylabel('Samples (top to bottom)') P.xlabel('Regressors') P.ylim(self.mat.shape[0],0) def read_fsl_design(fsf_file): """Reads an FSL FEAT design.fsf file and return the content as a dictionary. :Parameters: fsf_file : filename, file-like """ # This function was originally contributed by Russell Poldrack if isinstance(fsf_file, basestring): infile = open(fsf_file, 'r') else: infile = fsf_file # target dict fsl = {} # loop over all lines for line in infile: line = line.strip() # if there is nothing on the line, do nothing if not line or line[0] == '#': continue # strip leading TCL 'set' key, value = line.split(None, 2)[1:] # fixup the 'y-' thing if value == 'y-': value = "y" # special case of variable keyword if line.count('_files('): # e.g. feat_files(1) -> feat_files key = key.split('(')[0] # decide which type we have for the value # int? if value.isdigit(): fsl[key] = int(value) else: # float? try: fsl[key] = float(value) except ValueError: # must be string then, but... # sometimes there are quotes, sometimes not, but if the value # should be a string we remove them, since the value is already # of this type fsl[key] = value.strip('"') return fsl pymvpa-0.4.8/mvpa/misc/fsl/flobs.py000066400000000000000000000105271174541445200172140ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Wrapper around FSLs halfcosbasis to generate HRF kernels""" __docformat__ = 'restructuredtext' import os import tempfile import shutil import numpy as N import math def makeFlobs(pre=0, rise=5, fall=5, undershoot=5, undershootamp=0.3, nsamples=1, resolution=0.05, nsecs=-1, nbasisfns=2): """Wrapper around the FSL tool halfcosbasis. This function uses halfcosbasis to generate samples of HRF kernels. Kernel parameters can be modified analogous to the Make_flobs GUI which is part of FSL. :: ^ /-\\ | / \\ 1 / \\ | / \\ | / \\ | / \\ -----/ \\ /----- | \\--/ | undershootamp | | | | | | | | | | pre rise fall undershoot Parameters 'pre', 'rise', 'fall', 'undershoot' and 'undershootamp' can be specified as 2-tuples (min-max range for sampling) and single value (setting exact values -- no sampling). If 'nsec' is negative, the length of the samples is determined automatically to include the whole kernel function (until it returns to baseline). 'nsec' has to be an integer value and is set to the next greater integer value if it is not. All parameters except for 'nsamples' and 'nbasisfns' are in seconds. """ # create tempdir and temporary parameter file pfile, pfilename = tempfile.mkstemp('pyflobs') wdir = tempfile.mkdtemp('pyflobs') # halfcosbasis can only handle >1 samples # so we simply compute two and later ignore the other if nsamples < 2: rnsamples = 2 else: rnsamples = nsamples # make range tuples if not supplied if not isinstance(pre, tuple): pre = (pre, pre) if not isinstance(rise, tuple): rise = (rise, rise) if not isinstance(fall, tuple): fall = (fall, fall) if not isinstance(undershoot, tuple): undershoot = (undershoot, undershoot) if not isinstance(undershootamp, tuple): undershootamp = (undershootamp, undershootamp) # calc minimum length of hrf if not specified # looks like it has to be an integer if nsecs < 0: nsecs = int( math.ceil( pre[1] \ + rise[1] \ + fall[1] \ + undershoot[1] \ + resolution ) ) else: nsecs = math.ceil(nsecs) # write parameter file pfile = os.fdopen( pfile, 'w' ) pfile.write(str(pre[0]) + ' ' + str(pre[1]) + '\n') pfile.write(str(rise[0]) + ' ' + str(rise[1]) + '\n') pfile.write(str(fall[0]) + ' ' + str(fall[1]) + '\n') pfile.write(str(undershoot[0]) + ' ' + str(undershoot[1]) + '\n') pfile.write('0 0\n0 0\n') pfile.write(str(undershootamp[0]) + ' ' + str(undershootamp[1]) + '\n') pfile.write('0 0\n') pfile.close() # call halfcosbasis to generate the hrf samples tochild, fromchild, childerror = os.popen3('halfcosbasis' + ' --hf=' + pfilename + ' --nbfs=' + str(nbasisfns) + ' --ns=' + str(nsecs) + ' --logdir=' + os.path.join(wdir, 'out') + ' --nhs=' + str(rnsamples) + ' --res=' + str(resolution) ) err = childerror.readlines() if len(err) > 0: print err raise RuntimeError, "Problem while running halfcosbasis." # read samples from file into an array hrfs = N.fromfile( os.path.join( wdir, 'out', 'hrfsamps.txt' ), sep = ' ' ) # reshape array to get one sample per row and 1d array only # for one sample hrf hrfs = \ hrfs.reshape( len(hrfs)/rnsamples, rnsamples).T[:nsamples].squeeze() # cleanup working dir (ignore errors) shutil.rmtree( wdir, True ) # remove paramter file os.remove( pfilename ) # and return an array return( hrfs ) pymvpa-0.4.8/mvpa/misc/fsl/melodic.py000066400000000000000000000047621174541445200175270ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Wrapper around the output of MELODIC (part of FSL)""" __docformat__ = 'restructuredtext' import os import numpy as N from mvpa.base import externals if externals.exists('nifti', raiseException=True): import nifti class MelodicResults( object ): """Easy access to MELODIC output. Only important information is available (important as judged by the author). """ def __init__( self, path ): """Reads all information from the given MELODIC output path. """ self.__outputpath = path self.__icapath = os.path.join( path, 'filtered_func_data.ica' ) self.__ic = \ nifti.NiftiImage( os.path.join( self.__icapath, 'melodic_IC' ) ) self.__funcdata = \ nifti.NiftiImage( os.path.join( self.__outputpath, 'filtered_func_data' ) ) self.__tmodes = N.fromfile( os.path.join( self.__icapath, 'melodic_Tmodes' ), sep = ' ' ).reshape( self.tr, self.nic ) self.__smodes = N.fromfile( os.path.join( self.__icapath, 'melodic_Smodes' ), sep = ' ' ) self.__icstats = N.fromfile( os.path.join( self.__icapath, 'melodic_ICstats' ), sep = ' ' ).reshape( self.nic, 4 ) # properties path = property( fget=lambda self: self.__respath ) ic = property( fget=lambda self: self.__ic ) nic = property( fget=lambda self: self.ic.extent[3] ) funcdata = property( fget=lambda self: self.__funcdata ) tr = property( fget=lambda self: self.funcdata.extent[3] ) tmodes = property( fget=lambda self: self.__tmodes ) smodes = property( fget=lambda self: self.__smodes ) icastats = property( fget=lambda self: self.__icstats ) relvar_per_ic = property( fget=lambda self: self.__icstats[:, 0] ) truevar_per_ic = property( fget=lambda self: self.__icstats[:, 1] ) pymvpa-0.4.8/mvpa/misc/fx.py000066400000000000000000000077341174541445200157460ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Misc. functions (in the mathematical sense)""" __docformat__ = 'restructuredtext' import numpy as N def singleGammaHRF(t, A=5.4, W=5.2, K=1.0): """Hemodynamic response function model. The version consists of a single gamma function (also see doubleGammaHRF()). :Parameters: t: float Time. A: float Time to peak. W: float Full-width at half-maximum. K: float Scaling factor. """ A = float(A) W = float(W) K = float(K) return K * (t / A) ** ((A ** 2) / (W ** 2) * 8.0 * N.log(2.0)) \ * N.e ** ((t - A) / -((W ** 2) / A / 8.0 / N.log(2.0))) def doubleGammaHRF(t, A1=5.4, W1=5.2, K1=1.0, A2=10.8, W2=7.35, K2=0.35): """Hemodynamic response function model. The version is using two gamma functions (also see singleGammaHRF()). :Parameters: t: float Time. A: float Time to peak. W: float Full-width at half-maximum. K: float Scaling factor. Parameters A, W and K exists individually for each of both gamma functions. """ A1 = float(A1) W1 = float(W1) K1 = float(K1) A2 = float(A2) W2 = float(W2) K2 = float(K2) return singleGammaHRF(t, A1, W1, K1) - singleGammaHRF(t, A2, W2, K2) def leastSqFit(fx, params, y, x=None, **kwargs): """Simple convenience wrapper around SciPy's optimize.leastsq. The advantage of using this wrapper instead of optimize.leastsq directly is, that it automatically constructs an appropriate error function and easily deals with 2d data arrays, i.e. each column with multiple values for the same function argument (`x`-value). :Parameters: fx: functor Function to be fitted to the data. It has to take a vector with function arguments (`x`-values) as the first argument, followed by an arbitrary number of (to be fitted) parameters. params: sequence Sequence of start values for all to be fitted parameters. During fitting all parameters in this sequences are passed to the function in the order in which they appear in this sequence. y: 1d or 2d array The data the function is fitted to. In the case of a 2d array, each column in the array is considered to be multiple observations or measurements of function values for the same `x`-value. x: Corresponding function arguments (`x`-values) for each datapoint, i.e. element in `y` or columns in `y', in the case of `y` being a 2d array. If `x` is not provided it will be generated by `N.arange(m)`, where `m` is either the length of `y` or the number of columns in `y`, if `y` is a 2d array. **kwargs: All additonal keyword arguments are passed to `fx`. :Returns: tuple: as returned by scipy.optimize.leastsq i.e. 2-tuple with list of final (fitted) parameters of `fx` and an integer value indicating success or failure of the fitting procedure (see leastsq docs for more information). """ # import here to not let the whole module depend on SciPy from scipy.optimize import leastsq y = N.asanyarray(y) if len(y.shape) > 1: nsamp, ylen = y.shape else: nsamp, ylen = (1, len(y)) # contruct matching x-values if necessary if x is None: x = N.arange(ylen) # transform x and y into 1d arrays if nsamp > 1: x = N.array([x] * nsamp).ravel() y = y.ravel() # define error function def efx(p): err = y - fx(x, *p, **kwargs) return err # do fit return leastsq(efx, params) pymvpa-0.4.8/mvpa/misc/io/000077500000000000000000000000001174541445200153535ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/misc/io/__init__.py000066400000000000000000000011061174541445200174620ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Import helper for IO helpers""" if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.misc.io') from mvpa.misc.io.base import * if __debug__: debug('INIT', 'mvpa.misc.io end') pymvpa-0.4.8/mvpa/misc/io/base.py000066400000000000000000000525201174541445200166430ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Some little helper for reading (and writing) common formats from and to disk.""" __docformat__ = 'restructuredtext' import numpy as N import mvpa.support.copy as copy from mvpa.base.dochelpers import enhancedDocString from re import sub as re_sub from mvpa.base import warning from mvpa.misc.support import Event if __debug__: from mvpa.base import debug class DataReader(object): """Base class for data readers. Every subclass has to put all information into to variable: `self._data`: ndarray The data array has to have the samples separating dimension along the first axis. `self._props`: dict All other meaningful information has to be stored in a dictionary. This class provides two methods (and associated properties) to retrieve this information. """ def __init__(self): """Cheap init. """ self._props = {} self._data = None def getPropsAsDict(self): """Return the dictionary with the data properties. """ return self._props def getData(self): """Return the data array. """ return self._data data = property(fget=getData, doc="Data array") props = property(fget=getPropsAsDict, doc="Property dict") class ColumnData(dict): """Read data that is stored in columns of text files. All read data is available via a dictionary-like interface. If column headers are available, the column names serve as dictionary keys. If no header exists an articfical key is generated: str(number_of_column). Splitting of text file lines is performed by the standard split() function (which gets passed the `sep` argument as separator string) and each element is converted into the desired datatype. Because data is read into a dictionary no two columns can have the same name in the header! Each column is stored as a list in the dictionary. """ def __init__(self, source, header=True, sep=None, headersep=None, dtype=float, skiplines=0): """Read data from file into a dictionary. :Parameters: source : basestring or dict If values is given as a string all data is read from the file and additonal keyword arguments can be sued to customize the read procedure. If a dictionary is passed a deepcopy is performed. header : bool or list of basestring Indicates whether the column names should be read from the first line (`header=True`). If `header=False` unique column names will be generated (see class docs). If `header` is a python list, it's content is used as column header names and its length has to match the number of columns in the file. sep : basestring or None Separator string. The actual meaning depends on the output format (see class docs). headersep : basestring or None Separator string used in the header. The actual meaning depends on the output format (see class docs). dtype : type or list(types) Desired datatype(s). Datatype per column get be specified by passing a list of types. skiplines : int Number of lines to skip at the beginning of the file. """ # init base class dict.__init__(self) # intialize with default self._header_order = None if isinstance(source, str): self._fromFile(source, header=header, sep=sep, headersep=headersep, dtype=dtype, skiplines=skiplines) elif isinstance(source, dict): for k, v in source.iteritems(): self[k] = v # check data integrity self._check() else: raise ValueError, 'Unkown source for ColumnData [%s]' \ % `type(source)` # generate missing properties for each item in the header classdict = self.__class__.__dict__ for k in self.keys(): if not classdict.has_key(k): getter = "lambda self: self._getAttrib('%s')" % (k) # Sanitarize the key, substitute ' []' with '_' k_ = re_sub('[[\] ]', '_', k) # replace multipe _s k_ = re_sub('__+', '_', k_) # remove quotes k_ = re_sub('["\']', '', k_) if __debug__: debug("IOH", "Registering property %s for ColumnData key %s" % (k_, k)) # make sure to import class directly into local namespace # otherwise following does not work for classes defined # elsewhere exec 'from %s import %s' % (self.__module__, self.__class__.__name__) exec "%s.%s = property(fget=%s)" % \ (self.__class__.__name__, k_, getter) # TODO!!! Check if it is safe actually here to rely on value of # k in lambda. May be it is treated as continuation and # some local space would override it???? #setattr(self.__class__, # k, # property(fget=lambda x: x._getAttrib("%s" % k))) # it seems to be error-prone due to continuation... __doc__ = enhancedDocString('ColumnData', locals()) def _getAttrib(self, key): """Return corresponding value if given key is known to current instance Is used for automatically added properties to the class. :Raises: ValueError: If `key` is not known to given instance :Returns: Value if `key` is known """ if self.has_key(key): return self[key] else: raise ValueError, "Instance %s has no data about %s" \ % (`self`, `key`) def __str__(self): s = self.__class__.__name__ if len(self.keys())>0: s += " %d rows, %d columns [" % \ (self.getNRows(), self.getNColumns()) s += reduce(lambda x, y: x+" %s" % y, self.keys()) s += "]" return s def _check(self): """Performs some checks for data integrity. """ length = None for k in self.keys(): if length == None: length = len(self[k]) else: if not len(self[k]) == length: raise ValueError, "Data integrity lost. Columns do not " \ "have equal length." def _fromFile(self, filename, header, sep, headersep, dtype, skiplines): """Loads column data from file -- clears object first. """ # make a clean table self.clear() file_ = open(filename, 'r') self._header_order = None [ file_.readline() for x in range(skiplines) ] """Simply skip some lines""" # make column names, either take header or generate if header == True: # read first line and split by 'sep' hdr = file_.readline().split(headersep) # remove bogus empty header titles hdr = filter(lambda x:len(x.strip()), hdr) self._header_order = hdr elif isinstance(header, list): hdr = header else: hdr = [ str(i) for i in xrange(len(file_.readline().split(sep))) ] # reset file to not miss the first line file_.seek(0) [ file_.readline() for x in range(skiplines) ] # string in lists: one per column tbl = [ [] for i in xrange(len(hdr)) ] # do per column dtypes if not isinstance(dtype, list): dtype = [dtype] * len(hdr) # parse line by line and feed into the lists for line in file_: # get rid of leading and trailing whitespace line = line.strip() # ignore empty lines and comment lines if not line or line.startswith('#'): continue l = line.split(sep) if not len(l) == len(hdr): raise RuntimeError, \ "Number of entries in line [%i] does not match number " \ "of columns in header [%i]." % (len(l), len(hdr)) for i, v in enumerate(l): if not dtype[i] is None: try: v = dtype[i](v) except ValueError: warning("Can't convert %s to desired datatype %s." % (`v`, `dtype`) + " Leaving original type") tbl[i].append(v) # check if not len(tbl) == len(hdr): raise RuntimeError, "Number of columns read from file does not " \ "match the number of header entries." # fill dict for i, v in enumerate(hdr): self[v] = tbl[i] def __iadd__(self, other): """Merge column data. """ # for all columns in the other object for k, v in other.iteritems(): if not self.has_key(k): raise ValueError, 'Unknown key [%s].' % `k` if not isinstance(v, list): raise ValueError, 'Can only merge list data, but got [%s].' \ % `type(v)` # now it seems to be ok # XXX check for datatype? self[k] += v # look for problems, like columns present in self, but not in other self._check() return self def selectSamples(self, selection): """Return new ColumnData with selected samples""" data = copy.deepcopy(self) for k, v in data.iteritems(): data[k] = [v[x] for x in selection] data._check() return data def getNColumns(self): """Returns the number of columns. """ return len(self.keys()) def tofile(self, filename, header=True, header_order=None, sep=' '): """Write column data to a text file. :Parameters: filename : basestring Target filename header : bool If `True` a column header is written, using the column keys. If `False` no header is written. header_order : None or list of basestring If it is a list of strings, they will be used instead of simply asking for the dictionary keys. However these strings must match the dictionary keys in number and identity. This argument type can be used to determine the order of the columns in the output file. The default value is `None`. In this case the columns will be in an arbitrary order. sep : basestring String that is written as a separator between to data columns. """ # XXX do the try: except: dance file_ = open(filename, 'w') # write header if header_order == None: if self._header_order is None: col_hdr = self.keys() else: # use stored order + newly added keys at the last columns col_hdr = self._header_order + \ list(set(self.keys()).difference( set(self._header_order))) else: if not len(header_order) == self.getNColumns(): raise ValueError, 'Header list does not match number of ' \ 'columns.' for k in header_order: if not self.has_key(k): raise ValueError, 'Unknown key [%s]' % `k` col_hdr = header_order if header == True: file_.write(sep.join(col_hdr) + '\n') # for all rows for r in xrange(self.getNRows()): # get attributes for all keys l = [str(self[k][r]) for k in col_hdr] # write to file with proper separator file_.write(sep.join(l) + '\n') file_.close() def getNRows(self): """Returns the number of rows. """ # no data no rows (after Bob Marley) if not len(self.keys()): return 0 # otherwise first key is as good as any other else: return len(self[self.keys()[0]]) ncolumns = property(fget=getNColumns) nrows = property(fget=getNRows) class SampleAttributes(ColumnData): """Read and write PyMVPA sample attribute definitions from and to text files. """ def __init__(self, source, literallabels=False, header=None): """Read PyMVPA sample attributes from disk. :Parameters: source: basestring Filename of an atrribute file literallabels: bool Either labels are given as literal strings header: None or bool or list of str If None, ['labels', 'chunks'] is assumed. Otherwise the same behavior as of `ColumnData` """ if literallabels: dtypes = [str, float] else: dtypes = float if header is None: header = ['labels', 'chunks'] ColumnData.__init__(self, source, header=header, sep=None, dtype=dtypes) def tofile(self, filename): """Write sample attributes to a text file. """ ColumnData.tofile(self, filename, header=False, header_order=['labels', 'chunks'], sep=' ') def getNSamples(self): """Returns the number of samples in the file. """ return self.getNRows() def toEvents(self, **kwargs): """Convert into a list of `Event` instances. Each change in the label or chunks value is taken as a new event onset. The length of an event is determined by the number of identical consecutive label-chunk combinations. Since the attributes list has no sense of absolute timing, both `onset` and `duration` are determined and stored in #samples units. :Parameters: kwargs Any keyword arugment provided would be replicated, through all the entries. """ events = [] prev_onset = 0 old_comb = None duration = 1 # over all samples for r in xrange(self.nrows): # the label-chunk combination comb = (self.labels[r], self.chunks[r]) # check if things changed if not comb == old_comb: # did we ever had an event if not old_comb is None: events.append( Event(onset=prev_onset, duration=duration, label=old_comb[0], chunk=old_comb[1], **kwargs)) # reset duration for next event duration = 1 # store the current samples as onset for the next event prev_onset = r # update the reference combination old_comb = comb else: # current event is lasting duration += 1 # push the last event in the pipeline if not old_comb is None: events.append( Event(onset=prev_onset, duration=duration, label=old_comb[0], chunk=old_comb[1], **kwargs)) return events nsamples = property(fget=getNSamples) class SensorLocations(ColumnData): """Base class for sensor location readers. Each subclass should provide x, y, z coordinates via the `pos_x`, `pos_y`, and `pos_z` attrbibutes. Axes should follow the following convention: x-axis: left -> right y-axis: anterior -> posterior z-axis: superior -> inferior """ def __init__(self, *args, **kwargs): """Pass arguments to ColumnData. """ ColumnData.__init__(self, *args, **kwargs) def locations(self): """Get the sensor locations as an array. :Returns: (nchannels x 3) array with coordinates in (x, y, z) """ return N.array((self.pos_x, self.pos_y, self.pos_z)).T class XAVRSensorLocations(SensorLocations): """Read sensor location definitions from a specific text file format. File layout is assumed to be 5 columns: 1. sensor name 2. some useless integer 3. position on x-axis 4. position on y-axis 5. position on z-axis """ def __init__(self, source): """Read sensor locations from file. :Parameter: source : filename of an attribute file """ SensorLocations.__init__( self, source, header=['names', 'some_number', 'pos_x', 'pos_y', 'pos_z'], sep=None, dtype=[str, int, float, float, float]) class TuebingenMEGSensorLocations(SensorLocations): """Read sensor location definitions from a specific text file format. File layout is assumed to be 7 columns: 1: sensor name 2: position on y-axis 3: position on x-axis 4: position on z-axis 5-7: same as 2-4, but for some outer surface thingie. Note that x and y seem to be swapped, ie. y as defined by SensorLocations conventions seems to be first axis and followed by x. Only inner surface coordinates are reported by `locations()`. """ def __init__(self, source): """Read sensor locations from file. :Parameter: source : filename of an attribute file """ SensorLocations.__init__( self, source, header=['names', 'pos_y', 'pos_x', 'pos_z', 'pos_y2', 'pos_x2', 'pos_z2'], sep=None, dtype=[str, float, float, float, float, float, float]) def design2labels(columndata, baseline_label=0, func=lambda x: x > 0.0): """Helper to convert design matrix into a list of labels Given a design, assign a single label to any given sample TODO: fix description/naming :Parameters: columndata : ColumnData Attributes where each known will be considered as a separate explanatory variable (EV) in the design. baseline_label What label to assign for samples where none of EVs was given a value func : functor Function which decides either a value should be considered :Output: list of labels which are taken from column names in ColumnData and baseline_label """ # doing it simple naive way but it should be of better control if # we decide to process columndata with non-numeric entries etc keys = columndata.keys() labels = [] for row in xrange(columndata.nrows): entries = [ columndata[key][row] for key in keys ] # which entries get selected selected = filter(lambda x: func(x[1]), zip(keys, entries)) nselected = len(selected) if nselected > 1: # if there is more than a single one -- we are in problem raise ValueError, "Row #%i with items %s has multiple entries " \ "meeting the criterion. Cannot decide on the label" % \ (row, entries) elif nselected == 1: label = selected[0][0] else: label = baseline_label labels.append(label) return labels __known_chunking_methods = { 'alllabels': 'Each chunk must contain instances of all labels' } def labels2chunks(labels, method="alllabels", ignore_labels=None): """Automagically decide on chunks based on labels :Parameters: labels labels to base chunking on method : basestring codename for method to use. Known are %s ignore_labels : list of basestring depends on the method. If method ``alllabels``, then don't seek for such labels in chunks. E.g. some 'reject' samples :rtype: list """ % __known_chunking_methods.keys() chunks = [] if ignore_labels is None: ignore_labels = [] alllabels = set(labels).difference(set(ignore_labels)) if method == 'alllabels': seenlabels = set() lastlabel = None chunk = 0 for label in labels: if label != lastlabel: if seenlabels == alllabels: chunk += 1 seenlabels = set() lastlabel = label if not label in ignore_labels: seenlabels.update([label]) chunks.append(chunk) chunks = N.array(chunks) # fix up a bit the trailer if seenlabels != alllabels: chunks[chunks == chunk] = chunk-1 chunks = list(chunks) else: errmsg = "Unknown method to derive chunks is requested. Known are:\n" for method, descr in __known_chunking_methods.iteritems(): errmsg += " %s : %s\n" % (method, descr) raise ValueError, errmsg return chunks pymvpa-0.4.8/mvpa/misc/io/eepbin.py000066400000000000000000000101741174541445200171720ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # # Derived from the EEP binary reader of the pybsig toolbox # (C) by Ingo Fruend # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Reader for binary EEP files.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.misc.io import DataReader class EEPBin(DataReader): """Read-access to binary EEP files. EEP files are used by *eeprobe* a software for analysing even-related potentials (ERP), which was developed at the Max-Planck Institute for Cognitive Neuroscience in Leipzig, Germany. http://www.ant-neuro.com/products/eeprobe EEP files consist of a plain text header and a binary data block in a single file. The header starts with a line of the form ';%d %d %d %g %g' % (Nchannels, Nsamples, Ntrials, t0, dt) where Nchannels, Nsamples, Ntrials are the numbers of channels, samples per trial and trials respectively. t0 is the time of the first sample of a trial relative to the stimulus onset and dt is the sampling interval. The binary data block consists of single precision floats arranged in the following way:: ,,... ,,... . ,,... ,,... """ def __init__(self, source): """Read EEP file and store header and data. :Parameter: source : str Filename. """ # init base class DataReader.__init__(self) # temp storage of number of samples nsamples = None # non-critical header components stored in temp dict hdr = {} infile = open(source, "r") # read file the end of header of EOF while True: # one line at a time line = infile.readline() # stop if EOH or EOF if not line or line.startswith(';EOH;'): break # no crap! line = line.strip() # all but first line as colon if not line.count(':'): # top header l = line.split() # extract critical information self._props['nchannels'] = int(l[0][1:]) self._props['ntimepoints'] = int(l[1]) self._props['t0'] = float(l[3]) self._props['dt'] = float(l[4]) nsamples = int(l[2]) else: # simply store non-critical extras l = line.split(':') key = l[0].lstrip(';') value = ':'.join(l[1:]) hdr[key] = value # post process channel name info -> list if hdr.has_key('channels'): self._props['channels'] = hdr['channels'].split() self._data = \ N.reshape(N.fromfile(infile, dtype='f'), \ (nsamples, self._props['nchannels'], self._props['ntimepoints'])) # cleanup infile.close() nchannels = property(fget=lambda self: self._props['nchannels'], doc="Number of channels") ntimepoints = property(fget=lambda self: self._props['ntimepoints'], doc="Number of data timepoints") nsamples = property(fget=lambda self: self._data.shape[0], doc="Number of trials/samples") t0 = property(fget=lambda self: self._props['t0'], doc="Relative start time of sampling interval") dt = property(fget=lambda self: self._props['dt'], doc="Time difference between two adjacent samples") channels = property(fget=lambda self: self._props['channels'], doc="List of channel names") pymvpa-0.4.8/mvpa/misc/io/hamster.py000066400000000000000000000140131174541445200173670ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Helper for simple storage facility via cPickle and optionally zlib""" __docformat__ = 'restructuredtext' import os from mvpa.base import externals _d_geti_ = dict.__getitem__ _d_seti_ = dict.__setitem__ _o_geta_ = dict.__getattribute__ _o_seta_ = dict.__setattr__ if externals.exists('cPickle', raiseException=True) and \ externals.exists('gzip', raiseException=True): import cPickle, gzip if __debug__: from mvpa.base import debug class Hamster(object): """Simple container class with basic IO capabilities. It is capable of storing itself in a file, or loading from a file using cPickle (optionally via zlib from compressed files). Any serializable object can be bound to a hamster to be stored. To undig burried hamster use Hamster(filename). Here is an example: >>> h = Hamster(bla='blai') >>> h.boo = N.arange(5) >>> h.dump(filename) ... >>> h = Hamster(filename) Since Hamster introduces methods `dump`, `asdict` and property 'registered', those names cannot be used to assign an attribute, nor provided in among constructor arguments. """ __ro_attr = set(object.__dict__.keys() + ['dump', 'registered', 'asdict']) """Attributes which come with being an object""" def __new__(cls, *args, **kwargs): if len(args) > 0: if len(kwargs) > 0: raise ValueError, \ "Do not mix positional and keyword arguments. " \ "Use a single positional argument -- filename, " \ "or any number of keyword arguments, without having " \ "filename specified" if len(args) == 1 and isinstance(args[0], basestring): filename = args[0] args = args[1:] if __debug__: debug('IOH', 'Undigging hamster from %s' % filename) # compressed or not -- that is the question if filename.endswith('.gz'): f = gzip.open(filename) else: f = open(filename) result = cPickle.load(f) if not isinstance(result, Hamster): warning("Loaded other than Hamster class from %s" % filename) return result else: raise ValueError, "Hamster accepts only a single positional " \ "argument and it must be a filename. Got %d " \ "arguments" % (len(args),) else: return object.__new__(cls) def __init__(self, *args, **kwargs): """Initialize Hamster. Providing a single parameter string would treat it as a filename from which to undig the data. Otherwise all keyword parameters are assigned into the attributes of the object. """ if len(args) > 0: if len(args) == 1 and isinstance(args[0], basestring): # it was a filename args = args[1:] else: raise RuntimeError, "Should not get here" # assign provided attributes for k,v in kwargs.iteritems(): setattr(self, k, v) object.__init__(self) def dump(self, filename, compresslevel='auto'): """Bury the hamster into the file :Parameter: filename: str Name of the target file. When writing to a compressed file the filename gets a '.gz' extension if not already specified. This is necessary as the constructor uses the extension to decide whether it loads from a compressed or uncompressed file. compresslevel: 'auto' or int Compression level setting passed to gzip. When set to 'auto', if filename ends with '.gz' `compresslevel` is set to 5, 0 otherwise. However, when `compresslevel` is set to 0 gzip is bypassed completely and everything is written to an uncompressed file. """ if compresslevel == 'auto': compresslevel = (0, 5)[int(filename.endswith('.gz'))] if compresslevel > 0 and not filename.endswith('.gz'): filename += '.gz' if __debug__: debug('IOH', 'Burying hamster into %s' % filename) if compresslevel == 0: f = open(filename, 'w') else: f = gzip.open(filename, 'w', compresslevel) cPickle.dump(self, f) f.close() def __repr__(self): reg_attr = ["%s=%s" % (k, repr(getattr(self, k))) for k in self.registered] return "%s(%s)" % (self.__class__.__name__, ", ".join(reg_attr)) # ??? actually seems to be ugly #def __str__(self): # registered = self.registered # return "%s with %d elements: %s" \ # % (self.__class__.__name__, # len(registered), # ", ".join(self.registered)) @property def registered(self): """List registered attributes. """ reg_attr = [k for k in self.__dict__.iterkeys() if not k in self.__ro_attr] reg_attr.sort() return reg_attr def __setattr__(self, k, v): """Just to prevent resetting read-only attributes, such as methods """ if k in self.__ro_attr: raise ValueError, "'%s' object attribute '%s' is read-only" \ % (self.__class__.__name__, k) object.__setattr__(self, k, v) def asdict(self): """Return registered data as dictionary """ return dict([(k, getattr(self, k)) for k in self.registered]) pymvpa-0.4.8/mvpa/misc/io/meg.py000066400000000000000000000104161174541445200164770ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """IO helper for MEG datasets.""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base import externals class TuebingenMEG(object): """Reader for MEG data from line-based textfile format. This class reads segmented MEG data from a textfile, which is created by converting the proprietary binary output files of a MEG device in Tuebingen (Germany) with an unkown tool. The file format is line-based, i.e. all timepoints for all samples/trials are written in a single line. Each line is prefixed with an identifier (using a colon as the delimiter between identifier and data). Two lines have a special purpose. The first 'Sample Number' is a list of timepoint ids, similar to `range(ntimepoints)` for each sample/trial (all concatenated into one line. The second 'Time' contains the timing information for each timepoint (relative to stimulus onset), again for all trials concatenated into a single line. All other lines contain various information (channels) recorded during the experiment. The meaning of some channels is unknown. Known ones are: M*: MEG channels EEG*: EEG channels ADC*: Analog to digital converter output Dataset properties are available from various class attributes. The `data` member provides all data from all channels (except for 'Sample Number' and 'Time') in a NumPy array (nsamples x nchannels x ntimepoints). The reader supports uncompressed as well as gzipped input files (or other file-like objects). """ def __init__(self, source): """Reader MEG data from texfiles or file-like objects. :Parameters: source: str | file-like Strings are assumed to be filenames (with `.gz` suffix compressed), while all other object types are treated as file-like objects. """ self.ntimepoints = None self.timepoints = None self.nsamples = None self.channelids = [] self.data = [] self.samplingrate = None # open textfiles if isinstance(source, str): if source.endswith('.gz'): externals.exists('gzip', raiseException=True) import gzip source = gzip.open(source, 'r') else: source = open(source, 'r') # read file for line in source: # split ID colon = line.find(':') # ignore lines without id if colon == -1: continue id = line[:colon] data = line[colon+1:].strip() if id == 'Sample Number': timepoints = N.fromstring(data, dtype=int, sep='\t') # one more as it starts with zero self.ntimepoints = int(timepoints.max()) + 1 self.nsamples = int(len(timepoints) / self.ntimepoints) elif id == 'Time': self.timepoints = N.fromstring(data, dtype=float, count=self.ntimepoints, sep='\t') self.samplingrate = self.ntimepoints \ / (self.timepoints[-1] - self.timepoints[0]) else: # load data self.data.append( N.fromstring(data, dtype=float, sep='\t').reshape( self.nsamples, self.ntimepoints)) # store id self.channelids.append(id) # reshape data from (channels x samples x timepoints) to # (samples x chanels x timepoints) self.data = N.swapaxes(N.array(self.data), 0, 1) def __str__(self): """Give a short summary. """ return '' \ % (self.nsamples, self.ntimepoints, len(self.channelids)) pymvpa-0.4.8/mvpa/misc/param.py000066400000000000000000000125451174541445200164250ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##g """Parameter representation""" __docformat__ = 'restructuredtext' import re import textwrap from mvpa.misc.state import CollectableAttribute if __debug__: from mvpa.base import debug _whitespace_re = re.compile('\n\s+|^\s+') __all__ = [ 'Parameter', 'KernelParameter' ] class Parameter(CollectableAttribute): """This class shall serve as a representation of a parameter. It might be useful if a little more information than the pure parameter value is required (or even only useful). Each parameter must have a value. However additional property can be passed to the constructor and will be stored in the object. BIG ASSUMPTION: stored values are not mutable, ie nobody should do cls.parameter1[:] = ... or we wouldn't know that it was changed Here is a list of possible property names: min - minimum value max - maximum value step - increment/decrement stepsize """ def __init__(self, default, name=None, doc=None, index=None, **kwargs): """Specify a parameter by its default value and optionally an arbitrary number of additional parameters. TODO: :Parameters: for Parameter """ self.__default = default CollectableAttribute.__init__(self, name=name, doc=doc, index=index) self.resetvalue() self._isset = False if __debug__: if kwargs.has_key('val'): raise ValueError, "'val' property name is illegal." # XXX probably is too generic... for k, v in kwargs.iteritems(): self.__setattr__(k, v) def __str__(self): res = CollectableAttribute.__str__(self) # it is enabled but no value is assigned yet res += '=%s' % (self.value,) return res def doc(self, indent=" ", width=70): """Docstring for the parameter to be used in lists of parameters :Returns: string or list of strings (if indent is None) """ paramsdoc = " %s" % self.name if hasattr(paramsdoc, 'allowedtype'): paramsdoc += " : %s" % self.allowedtype paramsdoc = [paramsdoc] try: doc = self.__doc__ if not doc.endswith('.'): doc += '.' try: doc += " (Default: %s)" % self.default except: pass # Explicitly deal with multiple spaces, for some reason # replace_whitespace is non-effective doc = _whitespace_re.sub(' ', doc) paramsdoc += [' ' + x for x in textwrap.wrap(doc, width=width-len(indent), replace_whitespace=True)] except Exception, e: pass if indent is None: return paramsdoc else: return ('\n' + indent).join(paramsdoc) # XXX should be named reset2default? correspondingly in # ParameterCollection as well def resetvalue(self): """Reset value to the default""" #CollectableAttribute.reset(self) if not self.isDefault: self._isset = True self.value = self.__default def _set(self, val): if self._value != val: if __debug__: debug("COL", "Parameter: setting %s to %s " % (str(self), val)) if hasattr(self, 'min') and val < self.min: raise ValueError, \ "Minimal value for parameter %s is %s. Got %s" % \ (self.name, self.min, val) if hasattr(self, 'max') and val > self.max: raise ValueError, \ "Maximal value for parameter %s is %s. Got %s" % \ (self.name, self.max, val) if hasattr(self, 'choices') and (not val in self.choices): raise ValueError, \ "Valid choices for parameter %s are %s. Got %s" % \ (self.name, self.choices, val) self._value = val self._isset = True elif __debug__: debug("COL", "Parameter: not setting %s since value is the same" \ % (str(self))) @property def isDefault(self): """Returns True if current value is bound to default one""" return self._value is self.default @property def equalDefault(self): """Returns True if current value is equal to default one""" return self._value == self.__default def setDefault(self, value): wasdefault = self.isDefault self.__default = value if wasdefault: self.resetvalue() self._isset = False # incorrect behavior #def reset(self): # """Override reset so we don't clean the flag""" # pass default = property(fget=lambda x:x.__default, fset=setDefault) value = property(fget=lambda x:x._value, fset=_set) class KernelParameter(Parameter): """Just that it is different beast""" pass pymvpa-0.4.8/mvpa/misc/plot/000077500000000000000000000000001174541445200157225ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/misc/plot/__init__.py000066400000000000000000000012451174541445200200350ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Import helper for miscalaneouse PyMVPA plotting functions (mvpa.misc.plot)""" __docformat__ = 'restructuredtext' if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.misc.plot start') from mvpa.misc.plot.base import * if __debug__: debug('INIT', 'mvpa.misc.plot end') pymvpa-0.4.8/mvpa/misc/plot/base.py000066400000000000000000000251341174541445200172130ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Misc. plotting helpers.""" __docformat__ = 'restructuredtext' import pylab as P import numpy as N from mvpa.datasets.splitters import NFoldSplitter from mvpa.clfs.distance import squared_euclidean_distance def plotErrLine(data, x=None, errtype='ste', curves=None, linestyle='--', fmt='o', perc_sigchg=False, baseline=None): """Make a line plot with errorbars on the data points. :Parameters: data: sequence of sequences First axis separates samples and second axis will appear as x-axis in the plot. x: sequence Value to be used as 'x-values' corresponding to the elements of the 2nd axis id `data`. If `None`, a sequence of ascending integers will be generated. errtype: 'ste' | 'std' Type of error value to be computed per datapoint. 'ste': standard error of the mean 'std': standard deviation curves: None | list of tuple(x, y) Each tuple represents an additional curve, with x and y coordinates of each point on the curve. linestyle: str matplotlib linestyle argument. Applied to either the additional curve or a the line connecting the datapoints. Set to 'None' to disable the line completely. fmt: str matplotlib plot style argument to be applied to the data points and errorbars. perc_sigchg: bool If `True` the plot will show percent signal changes relative to a baseline. baseline: float | None Baseline used for converting values into percent signal changes. If `None` and `perc_sigchg` is `True`, the absolute of the mean of the first feature (i.e. [:,0]) will be used as a baseline. :Example: Make dataset with 20 samples from a full sinus wave period, computed 100 times with individual noise pattern. >>> x = N.linspace(0, N.pi * 2, 20) >>> data = N.vstack([N.sin(x)] * 30) >>> data += N.random.normal(size=data.shape) Now, plot mean data points with error bars, plus a high-res version of the original sinus wave. >>> x = N.linspace(0, N.pi * 2, 200) >>> plotErrLine(data, curves=[(x, N.sin(x))]) >>> #P.show() """ data = N.asanyarray(data) if len(data.shape) < 2: data = N.atleast_2d(data) # compute mean signal course md = data.mean(axis=0) if baseline is None: baseline = N.abs(md[0]) if perc_sigchg: md /= baseline md -= 1.0 md *= 100.0 # not in-place to keep original data intact data = data / baseline data *= 100.0 # compute matching datapoint locations on x-axis if x is None: x = N.arange(len(md)) else: if not len(md) == len(x): raise ValueError, "The length of `x` (%i) has to match the 2nd " \ "axis of the data array (%i)" % (len(x), len(md)) # plot highres line if present if curves is not None: for c in curves: xc, yc = c # scales line array to same range as datapoints P.plot(xc, yc, linestyle=linestyle) # no line between data points linestyle = 'None' # compute error per datapoint if errtype == 'ste': err = data.std(axis=0) / N.sqrt(len(data)) elif errtype == 'std': err = data.std(axis=0) else: raise ValueError, "Unknown error type '%s'" % errtype # plot datapoints with error bars P.errorbar(x, md, err, fmt=fmt, linestyle=linestyle) def plotFeatureHist(dataset, xlim=None, noticks=True, perchunk=False, **kwargs): """Plot histograms of feature values for each labels. :Parameters: dataset: Dataset xlim: None | 2-tuple Common x-axis limits for all histograms. noticks: boolean If True, no axis ticks will be plotted. This is useful to save space in large plots. perchunk: boolean If True, one histogramm will be plotted per each label and each chunk, resulting is a histogram grid (labels x chunks). **kwargs: Any additional arguments are passed to matplotlib's hist(). """ lsplit = NFoldSplitter(1, attr='labels') csplit = NFoldSplitter(1, attr='chunks') nrows = len(dataset.uniquelabels) ncols = len(dataset.uniquechunks) def doplot(data): P.hist(data, **kwargs) if xlim is not None: P.xlim(xlim) if noticks: P.yticks([]) P.xticks([]) fig = 1 # for all labels for row, (ignore, ds) in enumerate(lsplit(dataset)): if perchunk: for col, (alsoignore, d) in enumerate(csplit(ds)): P.subplot(nrows, ncols, fig) doplot(d.samples.ravel()) if row == 0: P.title('C:' + str(d.uniquechunks[0])) if col == 0: P.ylabel('L:' + str(d.uniquelabels[0])) fig += 1 else: P.subplot(1, nrows, fig) doplot(ds.samples) P.title('L:' + str(ds.uniquelabels[0])) fig += 1 def plotSamplesDistance(dataset, sortbyattr=None): """Plot the euclidean distances between all samples of a dataset. :Parameters: dataset: Dataset Providing the samples. sortbyattr: None | str If None, the samples distances will be in the same order as their appearance in the dataset. Alternatively, the name of a samples attribute can be given, which wil then be used to sort/group the samples, e.g. to investigate the similarity samples by label or by chunks. """ if sortbyattr is not None: slicer = [] for attr in dataset.__getattribute__('unique' + sortbyattr): slicer += \ dataset.__getattribute__('idsby' + sortbyattr)(attr).tolist() samples = dataset.samples[slicer] else: samples = dataset.samples ed = N.sqrt(squared_euclidean_distance(samples)) P.imshow(ed) P.colorbar() def plotBars(data, labels=None, title=None, ylim=None, ylabel=None, width=0.2, offset=0.2, color='0.6', distance=1.0, yerr='ste', **kwargs): """Make bar plots with automatically computed error bars. Candlestick plot (multiple interleaved barplots) can be done, by calling this function multiple time with appropriatly modified `offset` argument. :Parameters: data: array (nbars x nobservations) | other sequence type Source data for the barplot. Error measure is computed along the second axis. labels: list | None If not None, a label from this list is placed on each bar. title: str An optional title of the barplot. ylim: 2-tuple Y-axis range. ylabel: str An optional label for the y-axis. width: float Width of a bar. The value should be in a reasonable relation to `distance`. offset: float Constant offset of all bar along the x-axis. Can be used to create candlestick plots. color: matplotlib color spec Color of the bars. distance: float Distance of two adjacent bars. yerr: 'ste' | 'std' | None Type of error for the errorbars. If `None` no errorbars are plotted. **kwargs: Any additional arguments are passed to matplotlib's `bar()` function. """ # determine location of bars xlocations = (N.arange(len(data)) * distance) + offset if yerr == 'ste': yerr = [N.std(d) / N.sqrt(len(d)) for d in data] elif yerr == 'std': yerr = [N.std(d) for d in data] else: # if something that we do not know just pass on pass # plot bars plot = P.bar(xlocations, [N.mean(d) for d in data], yerr=yerr, width=width, color=color, ecolor='black', **kwargs) if ylim: P.ylim(*(ylim)) if title: P.title(title) if labels: P.xticks(xlocations + width / 2, labels) if ylabel: P.ylabel(ylabel) # leave some space after last bar P.xlim(0, xlocations[-1] + width + offset) return plot def inverseCmap(cmap_name): """Create a new colormap from the named colormap, where it got reversed """ import matplotlib._cm as _cm import matplotlib as mpl try: cmap_data = eval('_cm._%s_data' % cmap_name) except: raise ValueError, "Cannot obtain data for the colormap %s" % cmap_name new_data = dict( [(k, [(v[i][0], v[-(i+1)][1], v[-(i+1)][2]) for i in xrange(len(v))]) for k,v in cmap_data.iteritems()] ) return mpl.colors.LinearSegmentedColormap('%s_rev' % cmap_name, new_data, _cm.LUTSIZE) def plotDatasetChunks(ds, clf_labels=None): """Quick plot to see chunk sctructure in dataset with 2 features if clf_labels is provided for the predicted labels, then incorrectly labeled samples will have 'x' in them """ if ds.nfeatures != 2: raise ValueError, "Can plot only in 2D, ie for datasets with 2 features" if P.matplotlib.get_backend() == 'TkAgg': P.ioff() if clf_labels is not None and len(clf_labels) != ds.nsamples: clf_labels = None colors = ('b', 'g', 'r', 'c', 'm', 'y', 'k', 'w') labels = ds.uniquelabels labels_map = dict(zip(labels, colors[:len(labels)])) for chunk in ds.uniquechunks: chunk_text = str(chunk) ids = ds.where(chunks=chunk) ds_chunk = ds[ids] for i in xrange(ds_chunk.nsamples): s = ds_chunk.samples[i] l = ds_chunk.labels[i] format = '' if clf_labels != None: if clf_labels[i] != ds_chunk.labels[i]: P.plot([s[0]], [s[1]], 'x' + labels_map[l]) P.text(s[0], s[1], chunk_text, color=labels_map[l], horizontalalignment='center', verticalalignment='center', ) dss = ds.samples P.axis((1.1 * N.min(dss[:, 0]), 1.1 * N.max(dss[:, 1]), 1.1 * N.max(dss[:, 0]), 1.1 * N.min(dss[:, 1]))) P.draw() P.ion() pymvpa-0.4.8/mvpa/misc/plot/erp.py000066400000000000000000000371571174541445200170770ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Basic ERP (here ERP = Event Related Plot ;-)) plotting Can be used for plotting not only ERP but any event-locked data """ import pylab as P import numpy as N import matplotlib as mpl from mvpa.base import warning from mvpa.mappers.boxcar import BoxcarMapper # # Few helper functions # import matplotlib.transforms as mlt def _offset(ax, x, y): """Provide offset in pixels :Parameters: x : int Offset in pixels for x y : int Offset in pixels for y Idea borrowed from http://www.scipy.org/Cookbook/Matplotlib/Transformations but then heavily extended to be compatible with many reincarnations of matplotlib """ d = dir(mlt) if 'offset_copy' in d: # tested with python-matplotlib 0.98.3-5 # ??? if pukes, might need to replace 2nd parameter from # ax to ax.get_figure() return mlt.offset_copy(ax.transData, ax, x=x, y=y, units='dots') elif 'BlendedAffine2D' in d: # some newer versions of matplotlib return ax.transData + \ mlt.Affine2D().translate(x,y) elif 'blend_xy_sep_transform' in d: trans = mlt.blend_xy_sep_transform(ax.transData, ax.transData) # Now we set the offset in pixels trans.set_offset((x, y), mlt.identity_transform()) return trans else: raise RuntimeError, \ "Lacking needed functions in matplotlib.transform " \ "for _offset. Please upgrade" def _make_centeredaxis(ax, loc, offset=5, ai=0, mult=1.0, format='%4g', label=None, **props): """Plot an axis which is centered at loc (e.g. 0) :Parameters: ax Axes from the figure loc Value to center at offset Relative offset (in pixels) for the labels ai : int Axis index: 0 for x, 1 for y mult Multiplier for the axis labels. ERPs for instance need to be inverted, thus labels too manually here since there is no easy way in matplotlib to invert an axis label : basestring or None If not -- put a label outside of the axis **props Given to underlying plotting functions Idea borrowed from http://www.mail-archive.com/matplotlib-users@lists.sourceforge.net \ /msg05669.html It sustained heavy refactoring/extension """ xmin, xmax = ax.get_xlim() ymin, ymax = ax.get_ylim() xlocs = [l for l in ax.xaxis.get_ticklocs() if l>=xmin and l<=xmax] ylocs = [l for l in ax.yaxis.get_ticklocs() if l>=ymin and l<=ymax] if ai == 0: hlocs = ylocs locs = xlocs vrange = [xmin, xmax] tdir = mpl.lines.TICKDOWN halignment = 'center' valignment = 'top' lhalignment = 'left' lvalignment = 'center' lx, ly = xmax, 0 ticklength = ax.xaxis.get_ticklines()[0]._markersize elif ai == 1: hlocs = xlocs locs = ylocs vrange = [ymin, ymax] tdir = mpl.lines.TICKLEFT halignment = 'right' valignment = 'center' lhalignment = 'center' lvalignment = 'bottom' lx, ly = 0, ymax ticklength = ax.yaxis.get_ticklines()[0]._markersize else: raise ValueError, "Illegal ai=%s" % ai args = [ (locs, [loc]*len(locs)), (vrange, [loc, loc]), [locs, (loc,)*len(locs)] ] offset_abs = offset + ticklength if ai == 1: # invert args = [ [x[1], x[0]] for x in args ] # shift the tick labels labels trans = _offset(ax, -offset_abs, 0) transl = _offset(ax, 0, offset) else: trans = _offset(ax, 0, -offset_abs) transl = _offset(ax, offset, 0) tickline, = ax.plot(linestyle='', marker=tdir, *args[0], **props) axline, = ax.plot(*args[1], **props) tickline.set_clip_on(False) axline.set_clip_on(False) for i, l in enumerate(locs): if l == 0: # no origin label continue coor = [args[2][0][i], args[2][1][i], format % (mult * l)] ax.text(horizontalalignment=halignment, verticalalignment=valignment, transform=trans, *coor) if label is not None: ax.text( #max(args[2][0]), max(args[2][1]), lx, ly, label, horizontalalignment=lhalignment, verticalalignment=lvalignment, fontsize=14, # fontweight='bold', transform=transl) def plotERP(data, SR=500, onsets=None, pre=0.2, pre_onset=None, post=None, pre_mean=None, color='r', errcolor=None, errtype=None, ax=P, ymult=1.0, *args, **kwargs): """Plot single ERP on existing canvas :Parameters: data: 1D or 2D ndarray The data array can either be 1D (samples over time) or 2D (trials x samples). In the first case a boxcar mapper is used to extract the respective trial timecourses given a list of trial onsets. In the latter case, each row of the data array is taken as the EEG signal timecourse of a particular trial. onsets: list(int) List of onsets (in samples not in seconds). SR: int Sampling rate (1/s) of the signal. pre: float Duration (in seconds) to be plotted prior to onset. pre_onset : float or None If data is already in epochs (2D) then pre_onset provides information on how many seconds pre-stimulus were used to generate them. If None, then pre_onset = pre post: float Duration (in seconds) to be plotted after the onset. pre_mean: float Duration (in seconds) at the beginning of the window which is used for deriving the mean of the signal. If None, pre_mean = pre errtype: None | 'ste' | 'std' | 'ci95' | list of previous three Type of error value to be computed per datapoint. 'ste': standard error of the mean 'std': standard deviation 'ci95': 95% confidence interval (1.96 * ste) None: no error margin is plotted (default) Optionally, multiple error types can be specified in a list. In that case all of them will be plotted. color: matplotlib color code Color to be used for plotting the mean signal timecourse. errcolor: matplotlib color code Color to be used for plotting the error margin. If None, use main color but with weak alpha level ax: Target where to draw. ymult: float Multiplier for the values. E.g. if negative-up ERP plot is needed: provide ymult=-1.0 *args, **kwargs Additional arguments to plot(). :Returns: array Mean ERP timeseries. """ if pre_mean is None: pre_mean = pre # set default pre_discard = 0 if onsets is not None: # if we need to extract ERPs if post is None: raise ValueError, \ "Duration post onsets must be provided if onsets are given" # trial timecourse duration duration = pre + post # We are working with a full timeline bcm = BoxcarMapper(onsets, boxlength = int(SR * duration), offset = -int(SR * pre)) erp_data = bcm(data) # override values since we are using Boxcar pre_onset = pre else: if pre_onset is None: pre_onset = pre if pre_onset < pre: warning("Pre-stimulus interval to plot %g is smaller than provided " "pre-stimulus captured interval %g, thus plot interval was " "adjusted" % (pre, pre_onset)) pre = pre_onset if post is None: # figure out post duration = float(data.shape[1]) / SR - pre_discard post = duration - pre else: duration = pre + post erp_data = data pre_discard = pre_onset - pre # Scale the data appropriately erp_data *= ymult # validity check -- we should have 2D matrix (trials x samples) if len(erp_data.shape) != 2: raise RuntimeError, \ "plotERP() supports either 1D data with onsets, or 2D data " \ "(trials x sample_points). Shape of the data at the point " \ "is %s" % erp_data.shape if not (pre_mean == 0 or pre_mean is None): # mean of pre-onset signal accross trials erp_baseline = N.mean( erp_data[:, int((pre_onset-pre_mean)*SR):int(pre_onset*SR)]) # center data on pre-onset mean # NOTE: make sure that we make a copy of the data to don't # alter the original. Better be safe than sorry erp_data = erp_data - erp_baseline # generate timepoints and error ranges to plot filled error area # top -> # bottom <- time_points = N.arange(erp_data.shape[1]) * 1.0 / SR - pre_onset # if pre != pre_onset if pre_discard > 0: npoints = int(pre_discard * SR) time_points = time_points[npoints:] erp_data = erp_data[:, npoints:] # select only time points of interest (if post is provided) if post is not None: npoints = int(duration * SR) time_points = time_points[:npoints] erp_data = erp_data[:, :npoints] # compute mean signal timecourse accross trials erp_mean = N.mean(erp_data, axis=0) # give sane default if errtype is None: errtype = [] if not isinstance(errtype, list): errtype = [errtype] for et in errtype: # compute error per datapoint if et in ['ste', 'ci95']: erp_stderr = erp_data.std(axis=0) / N.sqrt(len(erp_data)) if et == 'ci95': erp_stderr *= 1.96 elif et == 'std': erp_stderr = erp_data.std(axis=0) else: raise ValueError, "Unknown error type '%s'" % errtype time_points2w = N.hstack((time_points, time_points[::-1])) error_top = erp_mean + erp_stderr error_bottom = erp_mean - erp_stderr error2w = N.hstack((error_top, error_bottom[::-1])) if errcolor is None: errcolor = color # plot error margin pfill = ax.fill(time_points2w, error2w, edgecolor=errcolor, facecolor=errcolor, alpha=0.2, zorder=3) # plot mean signal timecourse ax.plot(time_points, erp_mean, lw=2, color=color, zorder=4, *args, **kwargs) # ax.xaxis.set_major_locator(P.MaxNLocator(4)) return erp_mean def plotERPs(erps, data=None, ax=None, pre=0.2, post=None, pre_onset=None, xlabel='time (s)', ylabel='$\mu V$', ylim=None, ymult=1.0, legend=None, xlformat='%4g', ylformat='%4g', loffset=10, alinewidth=2, **kwargs): """Plot multiple ERPs on a new figure. :Parameters: erps : list of tuples List of definitions of ERPs. Each tuple should consist of (label, color, onsets) or a dictionary which defines, label, color, onsets, data. Data provided in dictionary overrides 'common' data provided in the next argument ``data`` data Data for ERPs to be derived from 1D (samples) ax Where to draw (e.g. subplot instance). If None, new figure is created pre : float Duration (seconds) to be plotted prior to onset pre_onset : float or None If data is already in epochs (2D) then pre_onset provides information on how many seconds pre-stimulus were used to generate them. If None, then pre_onset = pre post : float or None Duration (seconds) to be plotted after the onset. If any data is provided with onsets, it can't be None. If None -- plots all time points after onsets ymult : float Multiplier for the values. E.g. if negative-up ERP plot is needed: provide ymult=-1.0 xlformat : basestring Format of the x ticks ylformat : basestring Format of the y ticks legend : basestring or None If not None, legend will be plotted with position argument provided in this argument loffset : int Offset in voxels for axes and tick labels. Different matplotlib frontends might have different opinions, thus offset value might need to be tuned specifically per frontend alinewidth : int Axis and ticks line width **kwargs Additional arguments provided to plotERP() :Examples: kwargs = {'SR' : eeg.SR, 'pre_mean' : 0.2} fig = plotERPs((('60db', 'b', eeg.erp_onsets['60db']), ('80db', 'r', eeg.erp_onsets['80db'])), data[:, eeg.sensor_mapping['Cz']], ax=fig.add_subplot(1,1,1,frame_on=False), pre=0.2, post=0.6, **kwargs) or fig = plotERPs((('60db', 'b', eeg.erp_onsets['60db']), {'color': 'r', 'onsets': eeg.erp_onsets['80db'], 'data' : data[:, eeg.sensor_mapping['Cz']]} ), data[:, eeg.sensor_mapping['Cz']], ax=fig.add_subplot(1,1,1,frame_on=False), pre=0.2, post=0.6, **kwargs) :Returns: current fig handler """ if ax is None: fig = P.figure(facecolor='white') fig.clf() ax = fig.add_subplot(111, frame_on=False) else: fig = P.gcf() # We don't want original axis being on ax.axison = True labels = [] for erp_def in erps: plot_data = data params = {'ymult' : ymult} # provide custom parameters per ERP if isinstance(erp_def, tuple) and len(erp_def) == 3: params.update( {'label': erp_def[0], 'color': erp_def[1], 'onsets': erp_def[2]}) elif isinstance(erp_def, dict): plot_data = erp_def.pop('data', None) params.update(erp_def) labels.append(params.get('label', '')) # absorb common parameters params.update(kwargs) if plot_data is None: raise ValueError, "Channel %s got no data provided" \ % params.get('label', 'UNKNOWN') plotERP(plot_data, pre=pre, pre_onset=pre_onset, post=post, ax=ax, **params) # plot_kwargs={'label':label}) if isinstance(erp_def, dict): erp_def['data'] = plot_data # return it back props = dict(color='black', linewidth=alinewidth, markeredgewidth=alinewidth, zorder=1, offset=loffset) def set_limits(): """Helper to set x and y limits""" ax.set_xlim( (-pre, post) ) if ylim != None: ax.set_ylim(*ylim) set_limits() _make_centeredaxis(ax, 0, ai=0, label=xlabel, **props) set_limits() _make_centeredaxis(ax, 0, ai=1, mult=N.sign(ymult), label=ylabel, **props) ax.yaxis.set_major_locator(P.NullLocator()) ax.xaxis.set_major_locator(P.NullLocator()) # legend obscures plotting a bit... seems to be plotting # everything twice. Thus disabled by default if legend is not None and N.any(N.array(labels) != ''): P.legend(labels, loc=legend) fig.canvas.draw() return fig pymvpa-0.4.8/mvpa/misc/plot/mri.py000066400000000000000000000421371174541445200170720ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Basic (f)MRI plotting with ability to interactively perform thresholding """ import pylab as P import numpy as N import matplotlib as mpl from mvpa.base import warning, externals if externals.exists('nifti', raiseException=True): from nifti import NiftiImage _interactive_backends = ['GTKAgg', 'TkAgg'] def plotMRI(background=None, background_mask=None, cmap_bg='gray', overlay=None, overlay_mask=None, cmap_overlay='autumn', vlim=(0.0, None), vlim_type=None, do_stretch_colors=False, add_info=True, add_hist=True, add_colorbar=True, fig=None, interactive=None, nrows=None, ncolumns=None ): """Very basic plotting of 3D data with interactive thresholding. Background/overlay could be nifti files names or NiftiImage objects, or 3D ndarrays. if no mask provided, only non-0 elements are plotted :Parameters: do_stretch_colors : bool Stratch color range to the data (not just to visible data) vlim 2 element tuple of low/upper bounds of values to plot vlim_type : None or 'symneg_z' If not None, then vlim would be treated accordingly: symneg_z z-score values of symmetric normal around 0, estimated by symmetrizing negative part of the distribution, which often could be assumed when total distribution is a mixture of by-chance performance normal around 0, and some other in the positive tail ncolumns : int or None Explicit starting number of columns into which position the slice renderings. If None, square arrangement would be used nrows : int or None Explicit starting number of rows into which position the slice renderings. If None, square arrangement would be used add_hist : bool or tuple (int, int) If True, add histogram and position automagically. If a tuple -- use as (row, column) add_info : bool or tuple (int, int) If True, add information and position automagically. If a tuple -- use as (row, column). Available colormaps are presented nicely on http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps TODO: * Make interface more attractive/usable * allow multiple overlays... or just unify for them all to be just a list of entries * handle cases properly when there is only one - background/overlay """ # if False: # for easy debugging impath = '/research/fusion/herrman/be37/fMRI' background = NiftiImage('%s/anat_slices_brain_inbold.nii.gz' % impath) background_mask = None overlay = NiftiImage('/research/fusion/herrman/code/CCe-1.nii.gz') overlay_mask = NiftiImage('%s/masks/example_func_brain_mask.nii.gz' % impath) do_stretch_colors = False add_info = True add_hist = True add_colorbar = True cmap_bg = 'gray' cmap_overlay = 'hot' # YlOrRd_r # P.cm.autumn fig = None # vlim describes value limits # clim color limits (same by default) vlim = [2.3, None] vlim_type = 'symneg_z' interactive = False # # process data arguments def handle_arg(arg): """Helper which would read in NiftiImage if necessary """ if isinstance(arg, basestring): arg = NiftiImage(arg) argshape = arg.data.shape # Assure that we have 3D (at least) if len(argshape)<3: arg.data = arg.data.reshape((1,)*(3-len(argshape)) + argshape) if isinstance(arg, N.ndarray): if len(arg.shape) != 3: raise ValueError, "For now just handling 3D volumes" return arg bg = handle_arg(background) if isinstance(bg, NiftiImage): # figure out aspect fov = (N.array(bg.header['pixdim']) * bg.header['dim'])[3:0:-1] # XXX might be vise-verse ;-) aspect = fov[2]/fov[1] bg = bg.data[...,::-1,::-1] # XXX custom for now else: aspect = 1.0 if bg is not None: bg_mask = handle_arg(background_mask) if isinstance(bg_mask, NiftiImage): bg_mask = bg_mask.data[...,::-1,::-1] # XXX if bg_mask is not None: bg_mask = bg_mask != 0 else: bg_mask = bg != 0 func = handle_arg(overlay) if func is not None: if isinstance(func, NiftiImage): func = func.data[..., ::-1, :] # XXX func_mask = handle_arg(overlay_mask) if isinstance(func_mask, NiftiImage): func_mask = func_mask.data[..., ::-1, :] # XXX if func_mask is not None: func_mask = func_mask != 0 else: func_mask = func != 0 # process vlim vlim = list(vlim) vlim_orig = vlim[:] add_dist2hist = [] if isinstance(vlim_type, basestring): if vlim_type == 'symneg_z': func_masked = func[func_mask] fnonpos = func_masked[func_masked<=0] fneg = func_masked[func_masked<0] # take together with sign-reverted negative values fsym = N.hstack((-fneg, fnonpos)) nfsym = len(fsym) # Estimate normal std under assumption of mean=0 std = N.sqrt(N.mean(abs(fsym)**2)) # convert vlim assuming it is z-scores for i,v in enumerate(vlim): if v is not None: vlim[i] = std * v # add a plot to histogram add_dist2hist = [(lambda x: nfsym/(N.sqrt(2*N.pi)*std)*N.exp(-(x**2)/(2*std**2)), {})] else: raise ValueError, 'Unknown specification of vlim=%s' % vlim + \ ' Known is: symneg' class Plotter(object): """ TODO """ #_store_attribs = ('vlim', 'fig', 'bg', 'bg_mask') def __init__(self, _locals): """TODO""" self._locals = _locals self.fig = _locals['fig'] def do_plot(self): """TODO""" # silly yarik didn't find proper way vlim = self._locals['vlim'] bg = self._locals['bg'] bg_mask = self._locals['bg_mask'] ncolumns = self._locals['ncolumns'] nrows = self._locals['nrows'] add_info = self._locals['add_info'] add_hist = self._locals['add_hist'] #print locals() if N.isscalar(vlim): vlim = (vlim, None) if vlim[0] is None: vlim = (N.min(func), vlim[1]) if vlim[1] is None: vlim = (vlim[0], N.max(func)) invert = vlim[1] < vlim[0] if invert: vlim = (vlim[1], vlim[0]) print "Not yet fully supported" # adjust lower bound if it is too low if vlim[0] < N.min(func[func_mask]): vlim = list(vlim) vlim[0] = N.min(func[func_mask]) vlim = tuple(vlim) bound_above = (max(vlim) < N.max(func)) bound_below = (min(vlim) > N.min(func)) # # reverse the map if needed cmap_ = cmap_overlay if not bound_below and bound_above: if cmap_.endswith('_r'): cmap_ = cmap_[:-2] else: cmap_ += '_r' func_cmap = eval("P.cm.%s" % cmap_) bg_cmap = eval("P.cm.%s" % cmap_bg) if do_stretch_colors: clim = (N.min(func), N.max(func))#vlim else: clim = vlim # # figure out 'extend' for colorbar and threshold string extend, thresh_str = { (True, True) : ('both', 'x in [%.3g, %.3g]' % tuple(vlim)), (True, False): ('min', 'x in [%.3g, +inf]' % vlim[0]), (False, True): ('max', 'x in (-inf, %.3g]' % vlim[1]), (False, False): ('neither', 'none') }[(bound_below, bound_above)] # # Figure out subplots dshape = func.shape nslices = func.shape[0] # Check if additional column/row information was provided and extend nrows/ncolumns for v in (add_hist, add_info): if v and not isinstance(v, bool): ncolumns = max(ncolumns, v[1]+1) nrows = max(nrows, v[0]+1) # more or less square alignment ;-) if ncolumns is None: ncolumns = int(N.sqrt(nslices)) ndcolumns = ncolumns nrows = max(nrows, int(N.ceil(nslices*1.0/ncolumns))) # Decide either we need more cells where to add hist and/or info nadd = bool(add_info) + bool(add_hist) while ncolumns*nrows - (nslices + nadd) < 0: ncolumns += 1 locs = ['' for i in xrange(ncolumns*nrows)] # Fill in predefined locations for v,vl in ((add_hist, 'hist'), (add_info, 'info')): if v and not isinstance(v, bool): locs[ncolumns*v[0] + v[1]] = vl # Fill in slices for islice in xrange(nslices): locs[locs.index('')] = islice # Fill the last available if necessary if add_hist and isinstance(add_hist, bool): locs[locs.index('')] = 'hist' if add_info and isinstance(add_info, bool): locs[locs.index('')] = 'info' print ncolumns, nrows print locs # should compare by backend? if P.matplotlib.get_backend() in _interactive_backends: P.ioff() if self.fig is None: self.fig = P.figure(facecolor='white', figsize=(4*ncolumns, 4*nrows)) else: self.fig.clf() fig = self.fig # fig.clf() # # how to threshold images thresholder = lambda x: N.logical_and(x>=vlim[0], x<=vlim[1]) ^ invert # # Draw all slices self.slices = [] for si in range(nslices)[::-1]: ax = fig.add_subplot(nrows, ncolumns, locs.index(si) + 1, frame_on=False) self.slices.append(ax) ax.axison = False slice_bg = bg[si] slice_bg_ = N.ma.masked_array(slice_bg, mask=N.logical_not(bg_mask[si]))#slice_bg<=0) slice_sl = func[si] in_thresh = thresholder(slice_sl) out_thresh = N.logical_not(in_thresh) slice_sl_ = N.ma.masked_array(slice_sl, mask=N.logical_or(out_thresh, N.logical_not(func_mask[si]))) kwargs = dict(aspect=aspect, origin='lower') # paste a blank white background first, since otherwise # recent matplotlib screws up those masked imshows im = ax.imshow(N.ones(slice_sl_.shape), cmap=bg_cmap, extent=(0, slice_bg.shape[0], 0, slice_bg.shape[1]), **kwargs) im.set_clim((0,1)) # ax.clim((0,1)) ax.imshow(slice_bg_, interpolation='bilinear', cmap=bg_cmap, **kwargs) im = ax.imshow(slice_sl_, interpolation='nearest', cmap=func_cmap, alpha=0.8, extent=(0, slice_bg.shape[0], 0, slice_bg.shape[1]), **kwargs) im.set_clim(*clim) if si == 0: im0 = im func_masked = func[func_mask] # # Add summary information func_thr = func[N.logical_and(func_mask, thresholder(func))] if add_info and len(func_thr): self.info = ax = fig.add_subplot(nrows, ncolumns, locs.index('info')+1, frame_on=False) # cb = P.colorbar(shrink=0.8) # #cb.set_clim(clim[0], clim[1]) ax.axison = False #if add_colorbar: # cb = P.colorbar(im, shrink=0.8, pad=0.0, drawedges=False, # extend=extend, cmap=func_cmap) stats = {'v':len(func_masked), 'vt': len(func_thr), 'm': N.mean(func_masked), 'mt': N.mean(func_thr), 'min': N.min(func_masked), 'mint': N.min(func_thr), 'max': N.max(func_masked), 'maxt': N.max(func_thr), 'mm': N.median(func_masked), 'mmt': N.median(func_thr), 'std': N.std(func_masked), 'stdt': N.std(func_thr), 'sthr': thresh_str} P.text(0, 0.5, """ Original: voxels = %(v)d range = [%(min).3g, %(max).3g] mean = %(m).3g median = %(mm).3g std = %(std).3g Thresholded: %(sthr)s: voxels = %(vt)d range = [%(mint).3g, %(maxt).3g] median = %(mt).3g mean = %(mmt).3g std = %(stdt).3g """ % stats, horizontalalignment='left', verticalalignment='center', transform = ax.transAxes, fontsize=14) if add_colorbar: kwargs_cb = {} #if add_hist: # kwargs_cb['cax'] = self.hist self.cb = cb = P.colorbar( im0, #self.hist, shrink=0.8, pad=0.0, drawedges=False, extend=extend, cmap=func_cmap, **kwargs_cb) cb.set_clim(*clim) # Add histogram if add_hist: self.hist = fig.add_subplot(nrows, ncolumns, locs.index('hist') + 1, frame_on=True) minv, maxv = N.min(func_masked), N.max(func_masked) if minv<0 and maxv>0: # then make it centered on 0 maxx = max(-minv, maxv) range_ = (-maxx, maxx) else: range_ = (minv, maxv) H = N.histogram(func_masked, range=range_, bins=31) H2 = P.hist(func_masked, bins=H[1], align='center', facecolor='#FFFFFF', hold=True) for a, kwparams in add_dist2hist: dbin = (H[1][1] - H[1][0]) P.plot(H2[1], [a(x) * dbin for x in H2[1]], **kwparams) if add_colorbar: cbrgba = cb.to_rgba(H2[1]) for face, facecolor, value in zip(H2[2], cbrgba, H2[1]): if not thresholder(value): color = '#FFFFFF' else: color = facecolor face.set_facecolor(color) fig.subplots_adjust(left=0.01, right=0.95, hspace=0.01) # , bottom=0.01 if ncolumns - int(bool(add_info) or bool(add_hist)) < 2: fig.subplots_adjust(wspace=0.4) else: fig.subplots_adjust(wspace=0.1) if P.matplotlib.get_backend() in _interactive_backends: P.draw() P.ion() def on_click(self, event): """Actions to perform on click """ if id(event.inaxes) != id(plotter.hist): return xdata, ydata, button = event.xdata, event.ydata, event.button print xdata, ydata, button vlim = self._locals['vlim'] if button == 1: vlim[0] = xdata elif button == 3: vlim[1] = xdata elif button == 2: vlim[0], vlim[1] = vlim[1], vlim[0] self.do_plot() plotter = Plotter(locals()) plotter.do_plot() if interactive is None: interactive = P.matplotlib.get_backend() in _interactive_backends # Global adjustments if interactive: # if P.matplotlib.is_interactive(): P.connect('button_press_event', plotter.on_click) P.show() plotter.fig.plotter = plotter return plotter.fig pymvpa-0.4.8/mvpa/misc/plot/topo.py000066400000000000000000000164621174541445200172660ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil; encoding: utf-8 -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # # The initial version of the code was contributed by Ingo Fründ and is # Coypright (c) 2008 by Ingo Fründ ingo.fruend@googlemail.com # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Plot parameter distributions on a head surface (topography plots).""" __docformat__ = 'restructuredtext' import numpy as N from mvpa.base import externals if externals.exists("pylab", raiseException=True): import pylab as P import numpy.ma as M if externals.exists("griddata", raiseException=True): from mvpa.support.griddata import griddata if externals.exists("scipy", raiseException=True): from scipy.optimize import leastsq # TODO : add optional plotting labels for the sensors def plotHeadTopography(topography, sensorlocations, plotsensors=False, resolution=51, masked=True, plothead=True, plothead_kwargs=None, **kwargs): """Plot distribution to a head surface, derived from some sensor locations. The sensor locations are first projected onto the best fitting sphere and finally projected onto a circle (by simply ignoring the z-axis). :Parameters: topography: array A vector of some values corresponding to each sensor. sensorlocations: (nsensors x 3) array 3D coordinates of each sensor. The order of the sensors has to match with the `topography` vector. plotsensors: bool If True, sensor will be plotted on their projected coordinates. No sensor are shown otherwise. plothead: bool If True, a head outline is plotted. plothead_kwargs: dict Additional keyword arguments passed to `plotHeadOutline()`. resolution: int Number of surface samples along both x and y-axis. masked: bool If True, all surface sample extending to head outline will be masked. **kwargs: All additional arguments will be passed to `pylab.imshow()`. :Returns: (map, head, sensors) The corresponding matplotlib objects are returned if plotted, ie. if plothead is set to `False`, `head` will be `None`. map The colormap that makes the actual plot, a matplotlib.image.AxesImage instance. head What is returned by `plotHeadOutline()`. sensors The dots marking the electrodes, a matplotlib.lines.Line2d instance. """ # give sane defaults if plothead_kwargs is None: plothead_kwargs = {} # error function to fit the sensor locations to a sphere def err(params): r, cx, cy, cz = params return (sensorlocations[:, 0] - cx) ** 2 \ + (sensorlocations[:, 1] - cy) ** 2 \ + (sensorlocations[:, 2] - cz) ** 2 \ - r ** 2 # initial guess of sphere parameters (radius and center) params = (1, 0, 0, 0) # do fit (r, cx, cy, cz), stuff = leastsq(err, params) # size of each square ssh = float(r) / resolution # half-size ss = ssh * 2.0 # full-size # Generate a grid and interpolate using the griddata module x = N.arange(cx - r, cx + r, ss) + ssh y = N.arange(cy - r, cy + r, ss) + ssh x, y = P.meshgrid(x, y) # project the sensor locations onto the sphere sphere_center = N.array((cx, cy, cz)) sproj = sensorlocations - sphere_center sproj = r * sproj / N.c_[N.sqrt(N.sum(sproj ** 2, axis=1))] sproj += sphere_center # fit topology onto xy projection of sphere topo = griddata(sproj[:, 0], sproj[:, 1], N.ravel(N.array(topography)), x, y) # mask values outside the head if masked: notinhead = N.greater_equal((x - cx) ** 2 + (y - cy) ** 2, (1.0 * r) ** 2) topo = M.masked_where(notinhead, topo) # show surface map = P.imshow(topo, origin="lower", extent=(-r, r, -r, r), **kwargs) P.axis('off') if plothead: # plot scaled head outline head = plotHeadOutline(scale=r, shift=(cx/2.0, cy/2.0), **plothead_kwargs) else: head = None if plotsensors: # plot projected sensor locations # reorder sensors so the ones below plotted first # TODO: please fix with more elegant solution zenum = [x[::-1] for x in enumerate(sproj[:, 2].tolist())] zenum.sort() indx = [ x[1] for x in zenum ] sensors = P.plot(sproj[indx, 0] - cx/2.0, sproj[indx, 1] - cy/2.0, 'wo') else: sensors = None return map, head, sensors def plotHeadOutline(scale=1, shift=(0, 0), color='k', linewidth='5', **kwargs): """Plots a simple outline of a head viewed from the top. The plot contains schematic representations of the nose and ears. The size of the head is basically a unit circle for nose and ears attached to it. :Parameters: scale: float Factor to scale the size of the head. shift: 2-tuple of floats Shift the center of the head circle by these values. color: matplotlib color spec The color the outline should be plotted in. linewidth: int Linewidth of the head outline. **kwargs: All additional arguments are passed to `pylab.plot()`. :Returns: Matplotlib lines2D object can be used to tweak the look of the head outline. """ rmax = 0.5 # factor used all the time fac = 2 * N.pi * 0.01 # Koordinates for the ears EarX1 = -1 * N.array( [.497, .510, .518, .5299, .5419, .54, .547, .532, .510, rmax * N.cos(fac * (54 + 42))]) EarY1 = N.array( [.0655, .0775, .0783, .0746, .0555, -.0055, -.0932, -.1313, -.1384, rmax * N.sin(fac * (54 + 42))]) EarX2 = N.array( [rmax * N.cos(fac * (54 + 42)), .510, .532, .547, .54, .5419, .5299, .518, .510, .497] ) EarY2 = N.array( [rmax * N.sin(fac * (54 + 42)), -.1384, -.1313, -.0932, -.0055, .0555, .0746, .0783, .0775, .0655] ) # Coordinates for the Head HeadX1 = N.fromfunction( lambda x: rmax * N.cos(fac * (x + 2)), (21,)) HeadY1 = N.fromfunction( lambda y: rmax * N.sin(fac * (y + 2)), (21,)) HeadX2 = N.fromfunction( lambda x: rmax * N.cos(fac * (x + 28)), (21,)) HeadY2 = N.fromfunction( lambda y: rmax * N.sin(fac * (y + 28)), (21,)) HeadX3 = N.fromfunction( lambda x: rmax * N.cos(fac * (x + 54)), (43,)) HeadY3 = N.fromfunction( lambda y: rmax * N.sin(fac * (y + 54)), (43,)) # Coordinates for the Nose NoseX = N.array([.18 * rmax, 0, -.18 * rmax]) NoseY = N.array([rmax - 0.004, rmax * 1.15, rmax - 0.004]) # Combine to one X = N.concatenate((EarX2, HeadX1, NoseX, HeadX2, EarX1, HeadX3)) Y = N.concatenate((EarY2, HeadY1, NoseY, HeadY2, EarY1, HeadY3)) X *= 2 * scale Y *= 2 * scale X += shift[0] Y += shift[1] return P.plot(X, Y, color=color, linewidth=linewidth) pymvpa-0.4.8/mvpa/misc/state.py000066400000000000000000001317671174541445200164550ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Classes to control and store state information. It was devised to provide conditional storage """ # XXX: MH: The use of `index` as variable name confuses me. IMHO `index` refers # to a position in a container (i.e. list access). However, in this # file it is mostly used in the context of a `key` for dictionary # access. Can we refactor that? __docformat__ = 'restructuredtext' import operator, copy from textwrap import TextWrapper import numpy as N from mvpa.misc.vproperty import VProperty from mvpa.misc.exceptions import UnknownStateError from mvpa.misc.attributes import CollectableAttribute, StateVariable from mvpa.base.dochelpers import enhancedDocString from mvpa.base import externals if __debug__: from mvpa.base import debug _in_ipython = externals.exists('running ipython env') # Separators around definitions, needed for ReST, but bogus for # interactive sessions _def_sep = ('`', '')[int(_in_ipython)] _object_getattribute = object.__getattribute__ _object_setattr = object.__setattr__ ################################################################### # Collections # # TODO: refactor into collections.py. state.py now has # little in common with the main part of this file # class Collection(object): """Container of some CollectableAttributes. :Groups: - `Public Access Functions`: `isKnown` - `Access Implementors`: `_getListing`, `_getNames` - `Mutators`: `__init__` - `R/O Properties`: `listing`, `names`, `items` XXX Seems to be not used and duplicating functionality: `_getListing` (thus `listing` property) """ def __init__(self, items=None, owner=None, name=None): """Initialize the Collection :Parameters: items : dict of CollectableAttribute's items to initialize with owner : object an object to which collection belongs name : basestring name of the collection (as seen in the owner, e.g. 'states') """ self.__owner = owner if items == None: items = {} self._items = items """Dictionary to contain registered states as keys and values signal either they are enabled """ self.__name = name def _setName(self, name): self.__name = name def __str__(self): num = len(self._items) if __debug__ and "ST" in debug.active: maxnumber = 1000 # I guess all else: maxnumber = 4 if self.__name is not None: res = self.__name else: res = "" res += "{" for i in xrange(min(num, maxnumber)): if i > 0: res += " " res += "%s" % str(self._items.values()[i]) if len(self._items) > maxnumber: res += "..." res += "}" if __debug__: if "ST" in debug.active: res += " owner:%s#%s" % (self.owner.__class__.__name__, id(self.owner)) return res def _cls_repr(self): """Collection specific part of __repr__ for a class containing it, ie a part of __repr__ for the owner object :Return: list of items to be appended within __repr__ after a .join() """ # XXX For now we do not expect any pure non-specialized # collection , thus just override in derived classes raise NotImplementedError, "Class %s should override _cls_repr" \ % self.__class__.__name__ def _is_initializable(self, index): """Checks if index could be assigned within collection via _initialize :Return: bool value for a given `index` It is to facilitate dynamic assignment of collections' items within derived classes' __init__ depending on the present collections in the class. """ # XXX Each collection has to provide what indexes it allows # to be set within constructor. Custom handling of some # arguments (like (dis|en)able_states) is to be performed # in _initialize # raise NotImplementedError, \ # "Class %s should override _is_initializable" \ # % self.__class__.__name__ # YYY lets just check if it is in the keys return index in self._items.keys() def _initialize(self, index, value): """Initialize `index` (no check performed) with `value` """ # by default we just set corresponding value self[index].value = value def __repr__(self): s = "%s(" % self.__class__.__name__ items_s = "" sep = "" for item in self._items: try: itemvalue = "%s" % `self._items[item].value` if len(itemvalue)>50: itemvalue = itemvalue[:10] + '...' + itemvalue[-10:] items_s += "%s'%s':%s" % (sep, item, itemvalue) sep = ', ' except: pass if items_s != "": s += "items={%s}" % items_s if self.owner is not None: s += "%sowner=%s" % (sep, `self.owner`) s += ")" return s # # XXX TODO: figure out if there is a way to define proper # __copy__'s for a hierarchy of classes. Probably we had # to define __getinitargs__, etc... read more... # #def __copy__(self): # TODO Remove or refactor? # def _copy_states_(self, fromstate, deep=False): # """Copy known here states from `fromstate` object into current object # # Crafted to overcome a problem mentioned above in the comment # and is to be called from __copy__ of derived classes # # Probably sooner than later will get proper __getstate__, # __setstate__ # """ # # Bad check... doesn't generalize well... # # if not issubclass(fromstate.__class__, self.__class__): # # raise ValueError, \ # # "Class %s is not subclass of %s, " % \ # # (fromstate.__class__, self.__class__) + \ # # "thus not eligible for _copy_states_" # # TODO: FOR NOW NO TEST! But this beast needs to be fixed... # operation = { True: copy.deepcopy, # False: copy.copy }[deep] # # if isinstance(fromstate, ClassWithCollections): # fromstate = fromstate.states # # self.enabled = fromstate.enabled # for name in self.names: # if fromstate.isKnown(name): # self._items[name] = operation(fromstate._items[name]) def isKnown(self, index): """Returns `True` if state `index` is known at all""" return self._items.has_key(index) def isSet(self, index=None): """If item (or any in the present or listed) was set :Parameters: index : None or basestring or list of basestring What items to check if they were set in the collection """ _items = self._items if not (index is None): if isinstance(index, basestring): self._checkIndex(index) # process just that single index return _items[index].isSet else: items = index # assume that we got some list else: items = self._items # go through all the items for index in items: self._checkIndex(index) if _items[index].isSet: return True return False def whichSet(self): """Return list of indexes which were set""" result = [] # go through all members and if any isSet -- return True for index,v in self._items.iteritems(): if v.isSet: result.append(index) return result def _checkIndex(self, index): """Verify that given `index` is a known/registered state. :Raise `KeyError`: if given `index` is not known """ # OPT: lets not reuse isKnown, to don't incure 1 more function # call if not self._items.has_key(index): raise KeyError, \ "%s of %s has no key '%s' registered" \ % (self.__class__.__name__, self.__owner.__class__.__name__, index) def add(self, item): """Add a new CollectableAttribute to the collection :Parameters: item : CollectableAttribute or of derived class. Must have 'name' assigned TODO: we should make it stricter to don't add smth of wrong type into Collection since it might lead to problems Also we might convert to __setitem__ """ # local binding name = item.name if not isinstance(item, CollectableAttribute): raise ValueError, \ "Collection can add only instances of " + \ "CollectableAttribute-derived classes. Got %s" % `item` if name is None: raise ValueError, \ "CollectableAttribute to be added %s must have 'name' set" % \ item self._items[name] = item if not self.owner is None: self._updateOwner(name) def remove(self, index): """Remove item from the collection """ self._checkIndex(index) self._updateOwner(index, register=False) discard = self._items.pop(index) def __getattribute__(self, index): """ """ #return all private and protected ones first since we will not have # collectable's with _ (we should not have!) if index[0] == '_': return _object_getattribute(self, index) _items = _object_getattribute(self, '_items') if index in _items: return _items[index].value return _object_getattribute(self, index) def __setattr__(self, index, value): if index[0] == '_': return _object_setattr(self, index, value) _items = _object_getattribute(self, '_items') if index in _items: _items[index].value = value else: _object_setattr(self, index, value) def __getitem__(self, index): _items = _object_getattribute(self, '_items') if index in _items: self._checkIndex(index) return _items[index] else: raise AttributeError("State collection %s has no %s attribute" % (self, index)) # Probably not needed -- enable if need arises # #def __setattr__(self, index, value): # if self._items.has_key(index): # self._updateOwner(index, register=False) # self._items[index] = value # self._updateOwner(index, register=True) # # _object_setattr(self, index, value) def get(self, index, default): """Access the value by a given index. Mimiquing regular dictionary behavior, if value cannot be obtained (i.e. if any exception is caught) return default value. """ try: return self[index].value except Exception, e: #if default is not None: return default #else: # raise e def _action(self, index, func, missingok=False, **kwargs): """Run specific func either on a single item or on all of them :Parameters: index : basestr Name of the state variable func Function (not bound) to call given an item, and **kwargs missingok : bool If True - do not complain about wrong index """ if isinstance(index, basestring): if index.upper() == 'ALL': for index_ in self._items: self._action(index_, func, missingok=missingok, **kwargs) else: try: self._checkIndex(index) func(self._items[index], **kwargs) except: if missingok: return raise elif operator.isSequenceType(index): for item in index: self._action(item, func, missingok=missingok, **kwargs) else: raise ValueError, \ "Don't know how to handle variable given by %s" % index def reset(self, index=None): """Reset the state variable defined by `index`""" if not index is None: indexes = [ index ] else: indexes = self.names if len(self.items): for index in indexes: # XXX Check if that works as desired self._action(index, self._items.values()[0].__class__.reset, missingok=False) def _getListing(self): """Return a list of registered states along with the documentation""" # lets assure consistent litsting order items = self._items.items() items.sort() return [ "%s%s%s: %s" % (_def_sep, str(x[1]), _def_sep, x[1].__doc__) for x in items ] def _getNames(self): """Return ids for all registered state variables""" return self._items.keys() def _getOwner(self): return self.__owner def _setOwner(self, owner): if not isinstance(owner, ClassWithCollections): raise ValueError, \ "Owner of the StateCollection must be ClassWithCollections object" if __debug__: try: strowner = str(owner) except: strowner = "UNDEF: <%s#%s>" % (owner.__class__, id(owner)) debug("ST", "Setting owner for %s to be %s" % (self, strowner)) if not self.__owner is None: # Remove attributes which were registered to that owner previousely self._updateOwner(register=False) self.__owner = owner if not self.__owner is None: self._updateOwner(register=True) def _updateOwner(self, index=None, register=True): """Define an entry within owner's __dict__ so ipython could easily complete it :Parameters: index : basestring or list of basestring Name of the attribute. If None -- all known get registered register : bool Register if True or unregister if False XXX Needs refactoring since we duplicate the logic of expansion of index value """ if not index is None: if not index in self._items: raise ValueError, \ "Attribute %s is not known to %s" % (index, self) indexes = [ index ] else: indexes = self.names ownerdict = self.owner.__dict__ selfdict = self.__dict__ owner_known = ownerdict['_known_attribs'] for index_ in indexes: if register: if index_ in ownerdict: raise RuntimeError, \ "Cannot register attribute %s within %s " % \ (index_, self.owner) + "since it has one already" ownerdict[index_] = self._items[index_] if index_ in selfdict: raise RuntimeError, \ "Cannot register attribute %s within %s " % \ (index_, self) + "since it has one already" selfdict[index_] = self._items[index_] owner_known[index_] = self.__name else: if index_ in ownerdict: # yoh doesn't think that we need to complain if False ownerdict.pop(index_) owner_known.pop(index_) if index_ in selfdict: selfdict.pop(index_) # Properties names = property(fget=_getNames) items = property(fget=lambda x:x._items) owner = property(fget=_getOwner, fset=_setOwner) name = property(fget=lambda x:x.__name, fset=_setName) # Virtual properties listing = VProperty(fget=_getListing) class ParameterCollection(Collection): """Container of Parameters for a stateful object. """ # def __init__(self, items=None, owner=None, name=None): # """Initialize the state variables of a derived class # # :Parameters: # items : dict # dictionary of states # """ # Collection.__init__(self, items, owner, name) # def _cls_repr(self): """Part of __repr__ for the owner object """ prefixes = [] for k in self.names: # list only params with not default values if self[k].isDefault: continue prefixes.append("%s=%s" % (k, self[k].value)) return prefixes def resetvalue(self, index, missingok=False): """Reset all parameters to default values""" from param import Parameter self._action(index, Parameter.resetvalue, missingok=False) class SampleAttributesCollection(Collection): """Container for data and attributes of samples (ie data/labels/chunks/...) """ # def __init__(self, items=None, owner=None, name=None): # """Initialize the state variables of a derived class # # :Parameters: # items : dict # dictionary of states # """ # Collection.__init__(self, items, owner, name) # def _cls_repr(self): """Part of __repr__ for the owner object """ return [] # TODO: return I guess samples/labels/chunks class StateCollection(Collection): """Container of StateVariables for a stateful object. :Groups: - `Public Access Functions`: `isKnown`, `isEnabled`, `isActive` - `Access Implementors`: `_getListing`, `_getNames`, `_getEnabled` - `Mutators`: `__init__`, `enable`, `disable`, `_setEnabled` - `R/O Properties`: `listing`, `names`, `items` - `R/W Properties`: `enabled` """ def __init__(self, items=None, owner=None): """Initialize the state variables of a derived class :Parameters: items : dict dictionary of states owner : ClassWithCollections object which owns the collection name : basestring literal description. Usually just attribute name for the collection, e.g. 'states' """ Collection.__init__(self, items=items, owner=owner) self.__storedTemporarily = [] """List to contain sets of enabled states which were enabled temporarily. """ # # XXX TODO: figure out if there is a way to define proper # __copy__'s for a hierarchy of classes. Probably we had # to define __getinitargs__, etc... read more... # #def __copy__(self): def _cls_repr(self): """Part of __repr__ for the owner object """ prefixes = [] for name, invert in ( ('enable', False), ('disable', True) ): states = self._getEnabled(nondefault=False, invert=invert) if len(states): prefixes.append("%s_states=%s" % (name, str(states))) return prefixes def _is_initializable(self, index): """Checks if index could be assigned within collection via setvalue """ return index in ['enable_states', 'disable_states'] def _initialize(self, index, value): if value is None: value = [] if index == 'enable_states': self.enable(value, missingok=True) elif index == 'disable_states': self.disable(value) else: raise ValueError, "StateCollection can accept only enable_states " \ "and disable_states arguments for the initialization. " \ "Got %s" % index def _copy_states_(self, fromstate, index=None, deep=False): """Copy known here states from `fromstate` object into current object :Parameters: fromstate : Collection or ClassWithCollections Source states to copy from index : None or list of basestring If not to copy all set state variables, index provides selection of what to copy deep : bool Optional control over the way to copy Crafted to overcome a problem mentioned above in the comment and is to be called from __copy__ of derived classes Probably sooner than later will get proper __getstate__, __setstate__ """ # Bad check... doesn't generalize well... # if not issubclass(fromstate.__class__, self.__class__): # raise ValueError, \ # "Class %s is not subclass of %s, " % \ # (fromstate.__class__, self.__class__) + \ # "thus not eligible for _copy_states_" # TODO: FOR NOW NO TEST! But this beast needs to be fixed... operation = { True: copy.deepcopy, False: copy.copy }[deep] if isinstance(fromstate, ClassWithCollections): fromstate = fromstate.states #self.enabled = fromstate.enabled _items, from_items = self._items, fromstate._items if index is None: # copy all set ones for name in fromstate.whichSet():#self.names: #if fromstate.isKnown(name): _items[name] = operation(from_items[name]) else: isKnown = fromstate.isKnown for name in index: if isKnown(name): _items[name] = operation(from_items[name]) def isEnabled(self, index): """Returns `True` if state `index` is enabled""" self._checkIndex(index) return self._items[index].isEnabled def isActive(self, index): """Returns `True` if state `index` is known and is enabled""" return self.isKnown(index) and self.isEnabled(index) def enable(self, index, value=True, missingok=False): """Enable state variable given in `index`""" self._action(index, StateVariable.enable, missingok=missingok, value=value) def disable(self, index): """Disable state variable defined by `index` id""" self._action(index, StateVariable.enable, missingok=False, value=False) # TODO XXX think about some more generic way to grab temporary # snapshot of CollectableAttributes to be restored later on... def _changeTemporarily(self, enable_states=None, disable_states=None, other=None): """Temporarily enable/disable needed states for computation Enable or disable states which are enabled in `other` and listed in `enable _states`. Use `resetEnabledTemporarily` to reset to previous state of enabled. `other` can be a ClassWithCollections object or StateCollection """ if enable_states == None: enable_states = [] if disable_states == None: disable_states = [] self.__storedTemporarily.append(self.enabled) other_ = other if isinstance(other, ClassWithCollections): other = other.states if not other is None: # lets take states which are enabled in other but not in # self add_enable_states = list(set(other.enabled).difference( set(enable_states)).intersection(self.names)) if len(add_enable_states)>0: if __debug__: debug("ST", "Adding states %s from %s to be enabled temporarily" % (add_enable_states, other_) + " since they are not enabled in %s" % (self)) enable_states += add_enable_states # Lets go one by one enabling only disabled once... but could be as # simple as self.enable(enable_states) self.disable(disable_states) def _resetEnabledTemporarily(self): """Reset to previousely stored set of enabled states""" if __debug__: debug("ST", "Resetting to previous set of enabled states") if len(self.enabled)>0: self.enabled = self.__storedTemporarily.pop() else: raise ValueError("Trying to restore not-stored list of enabled " \ "states") def _getEnabled(self, nondefault=True, invert=False): """Return list of enabled states :Parameters: nondefault : bool Either to return also states which are enabled simply by default invert : bool Would invert the meaning, ie would return disabled states """ if invert: fmatch = lambda y: not self.isEnabled(y) else: fmatch = lambda y: self.isEnabled(y) if nondefault: ffunc = fmatch else: ffunc = lambda y: fmatch(y) and \ self._items[y]._defaultenabled != self.isEnabled(y) return filter(ffunc, self.names) def _setEnabled(self, indexlist): """Given `indexlist` make only those in the list enabled It might be handy to store set of enabled states and then to restore it later on. It can be easily accomplished now:: >>> from mvpa.misc.state import ClassWithCollections, StateVariable >>> class Blah(ClassWithCollections): ... bleh = StateVariable(enabled=False, doc='Example') ... >>> blah = Blah() >>> states_enabled = blah.states.enabled >>> blah.states.enabled = ['bleh'] >>> blah.states.enabled = states_enabled """ for index in self._items.keys(): self.enable(index, index in indexlist) # Properties enabled = property(fget=_getEnabled, fset=_setEnabled) ################################################################## # Base classes (and metaclass) which use collections # # # Helper dictionaries for AttributesCollector # _known_collections = { # Quite a generic one but mostly in classifiers 'StateVariable': ("states", StateCollection), # For classifiers only 'Parameter': ("params", ParameterCollection), 'KernelParameter': ("kernel_params", ParameterCollection), # For datasets # XXX custom collections needed? 'SampleAttribute': ("sa", SampleAttributesCollection), 'FeatureAttribute': ("fa", SampleAttributesCollection), 'DatasetAttribute': ("dsa", SampleAttributesCollection), } _col2class = dict(_known_collections.values()) """Mapping from collection name into Collection class""" _COLLECTIONS_ORDER = ['sa', 'fa', 'dsa', 'params', 'kernel_params', 'states'] class AttributesCollector(type): """Intended to collect and compose StateCollection for any child class of this metaclass """ def __init__(cls, name, bases, dict): if __debug__: debug( "COLR", "AttributesCollector call for %s.%s, where bases=%s, dict=%s " \ % (cls, name, bases, dict)) super(AttributesCollector, cls).__init__(name, bases, dict) collections = {} for name, value in dict.iteritems(): if isinstance(value, CollectableAttribute): baseclassname = value.__class__.__name__ col = _known_collections[baseclassname][0] # XXX should we allow to throw exceptions here? if not collections.has_key(col): collections[col] = {} collections[col][name] = value # and assign name if not yet was set if value.name is None: value._setName(name) # !!! We do not keep copy of this attribute static in the class. # Due to below traversal of base classes, we should be # able to construct proper collections even in derived classes delattr(cls, name) # XXX can we first collect parent's states and then populate with ours? # TODO for base in bases: if hasattr(base, "__metaclass__") and \ base.__metaclass__ == AttributesCollector: # TODO take care about overriding one from super class # for state in base.states: # if state[0] = newcollections = base._collections_template if len(newcollections) == 0: continue if __debug__: debug("COLR", "Collect collections %s for %s from %s" % (newcollections, cls, base)) for col, collection in newcollections.iteritems(): newitems = collection.items if collections.has_key(col): collections[col].update(newitems) else: collections[col] = newitems if __debug__: debug("COLR", "Creating StateCollection template %s with collections %s" % (cls, collections.keys())) # if there is an explicit if hasattr(cls, "_ATTRIBUTE_COLLECTIONS"): for col in cls._ATTRIBUTE_COLLECTIONS: if not col in _col2class: raise ValueError, \ "Requested collection %s is unknown to collector" % \ col if not col in collections: collections[col] = None # TODO: check on conflict in names of Collections' items! since # otherwise even order is not definite since we use dict for # collections. # XXX should we switch to tuple? for col, colitems in collections.iteritems(): collections[col] = _col2class[col](colitems) setattr(cls, "_collections_template", collections) # # Expand documentation for the class based on the listed # parameters an if it is stateful # # TODO -- figure nice way on how to alter __init__ doc directly... textwrapper = TextWrapper(subsequent_indent=" ", initial_indent=" ", width=70) # Parameters paramsdoc = "" paramscols = [] for col in ('params', 'kernel_params'): if collections.has_key(col): paramscols.append(col) # lets at least sort the parameters for consistent output col_items = collections[col].items params = [(v._instance_index, k) for k,v in col_items.iteritems()] params.sort() paramsdoc += '\n'.join( [col_items[param].doc(indent=' ') for index,param in params]) + '\n' # Parameters collection could be taked hash of to decide if # any were changed? XXX may be not needed at all? setattr(cls, "_paramscols", paramscols) # States doc statesdoc = "" if collections.has_key('states'): paramsdoc += """ enable_states : None or list of basestring Names of the state variables which should be enabled additionally to default ones disable_states : None or list of basestring Names of the state variables which should be disabled """ statesdoc = " * " statesdoc += '\n * '.join(collections['states'].listing) statesdoc += "\n\n(States enabled by default are listed with `+`)" if __debug__: debug("COLR", "Assigning __statesdoc to be %s" % statesdoc) setattr(cls, "_statesdoc", statesdoc) if paramsdoc != "": if __debug__ and 'COLR' in debug.active: debug("COLR", "Assigning __paramsdoc to be %s" % paramsdoc) setattr(cls, "_paramsdoc", paramsdoc) if paramsdoc + statesdoc != "": cls.__doc__ = enhancedDocString(cls, *bases) class ClassWithCollections(object): """Base class for objects which contain any known collection Classes inherited from this class gain ability to access collections and their items as simple attributes. Access to collection items "internals" is done via attribute and interface of a corresponding `Collection`. """ _DEV__doc__ = """ TODO: rename 'descr'? -- it should simply be 'doc' -- no need to drag classes docstring imho. """ __metaclass__ = AttributesCollector def __new__(cls, *args, **kwargs): """Initialize ClassWithCollections object :Parameters: descr : basestring Description of the instance """ self = super(ClassWithCollections, cls).__new__(cls) s__dict__ = self.__dict__ # init variable # XXX: Added as pylint complained (rightfully) -- not sure if false # is the proper default self.__params_set = False # need to check to avoid override of enabled states in the case # of multiple inheritance, like both ClassWithCollectionsl and Harvestable if not s__dict__.has_key('_collections'): s__class__ = self.__class__ collections = copy.deepcopy(s__class__._collections_template) s__dict__['_collections'] = collections s__dict__['_known_attribs'] = {} """Dictionary to contain 'links' to the collections from each known attribute. Is used to gain some speed up in lookup within __getattribute__ and __setattr__ """ # Assign owner to all collections for col, collection in collections.iteritems(): if col in s__dict__: raise ValueError, \ "Object %s has already attribute %s" % \ (self, col) s__dict__[col] = collection collection.name = col collection.owner = self self.__params_set = False if __debug__: descr = kwargs.get('descr', None) debug("COL", "ClassWithCollections.__new__ was done " "for %s#%s with descr=%s" \ % (s__class__.__name__, id(self), descr)) return self def __init__(self, descr=None, **kwargs): if not self.__params_set: self.__descr = descr """Set humane description for the object""" # To avoid double initialization in case of multiple inheritance self.__params_set = True collections = self._collections # Assign attributes values if they are given among # **kwargs for arg, argument in kwargs.items(): set = False for collection in collections.itervalues(): if collection._is_initializable(arg): collection._initialize(arg, argument) set = True break if set: trash = kwargs.pop(arg) else: known_params = reduce( lambda x,y:x+y, [x.items.keys() for x in collections.itervalues()], []) raise TypeError, \ "Unexpected keyword argument %s=%s for %s." \ % (arg, argument, self) \ + " Valid parameters are %s" % known_params ## Initialize other base classes ## commented out since it seems to be of no use for now #if init_classes is not None: # # return back stateful arguments since they might be # # processed by underlying classes # kwargs.update(kwargs_stateful) # for cls in init_classes: # cls.__init__(self, **kwargs) #else: # if len(kwargs)>0: # known_params = reduce(lambda x, y: x + y, \ # [x.items.keys() for x in collections], # []) # raise TypeError, \ # "Unknown parameters %s for %s." % (kwargs.keys(), # self) \ # + " Valid parameters are %s" % known_params if __debug__: debug("COL", "ClassWithCollections.__init__ was done " "for %s#%s with descr=%s" \ % (self.__class__.__name__, id(self), descr)) #__doc__ = enhancedDocString('ClassWithCollections', locals()) def __getattribute__(self, index): # return all private ones first since smth like __dict__ might be # queried by copy before instance is __init__ed if index[0] == '_': return _object_getattribute(self, index) s_dict = _object_getattribute(self, '__dict__') # check if it is a known collection collections = s_dict['_collections'] if index in collections: return collections[index] # check if it is a part of any collection known_attribs = s_dict['_known_attribs'] if index in known_attribs: return collections[known_attribs[index]]._items[index].value # just a generic return return _object_getattribute(self, index) def __setattr__(self, index, value): if index[0] == '_': return _object_setattr(self, index, value) # Check if a part of a collection, and set appropriately s_dict = _object_getattribute(self, '__dict__') known_attribs = s_dict['_known_attribs'] if index in known_attribs: collections = s_dict['_collections'] collections[known_attribs[index]][index].value = value return value # Generic setattr return _object_setattr(self, index, value) # XXX not sure if we shouldn't implement anything else... def reset(self): for collection in self._collections.values(): collection.reset() def __str__(self): s = "%s:" % (self.__class__.__name__) if self.__descr is not None: s += "/%s " % self.__descr if hasattr(self, "_collections"): for col, collection in self._collections.iteritems(): s += " %d %s:%s" % (len(collection.items), col, str(collection)) return s def __repr__(self, prefixes=None, fullname=False): """String definition of the object of ClassWithCollections object :Parameters: fullname : bool Either to include full name of the module prefixes : list of strings What other prefixes to prepend to list of arguments """ if prefixes is None: prefixes = [] prefixes = prefixes[:] # copy list id_str = "" module_str = "" if __debug__: if 'MODULE_IN_REPR' in debug.active: fullname = True if 'ID_IN_REPR' in debug.active: id_str = '#%s' % id(self) if fullname: modulename = '%s' % self.__class__.__module__ if modulename != "__main__": module_str = "%s." % modulename # Collections' attributes collections = self._collections # we want them in this particular order for col in _COLLECTIONS_ORDER: collection = collections.get(col, None) if collection is None: continue prefixes += collection._cls_repr() # Description if present descr = self.__descr if descr is not None: prefixes.append("descr=%s" % repr(descr)) return "%s%s(%s)%s" % (module_str, self.__class__.__name__, ', '.join(prefixes), id_str) descr = property(lambda self: self.__descr, doc="Description of the object if any") class Harvestable(ClassWithCollections): """Classes inherited from this class intend to collect attributes within internal processing. Subclassing Harvestable we gain ability to collect any internal data from the processing which is especially important if an object performs something in loop and discards some intermidiate possibly interesting results (like in case of CrossValidatedTransferError and states of the trained classifier or TransferError). """ harvested = StateVariable(enabled=False, doc= """Store specified attributes of classifiers at each split""") _KNOWN_COPY_METHODS = [ None, 'copy', 'deepcopy' ] def __init__(self, harvest_attribs=None, copy_attribs='copy', **kwargs): """Initialize state of harvestable :Parameters: harvest_attribs : list of basestr or dicts What attributes of call to store and return within harvested state variable. If an item is a dictionary, following keys are used ['name', 'copy'] copy_attribs : None or basestr Default copying. If None -- no copying, 'copy' - shallow copying, 'deepcopy' -- deepcopying """ ClassWithCollections.__init__(self, **kwargs) self.__atribs = harvest_attribs self.__copy_attribs = copy_attribs self._setAttribs(harvest_attribs) def _setAttribs(self, attribs): """Set attributes to harvest Each attribute in self.__attribs must have following fields - name : functional (or arbitrary if 'obj' or 'attr' is set) description of the thing to harvest, e.g. 'transerror.clf.training_time' - obj : name of the object to harvest from (if empty, 'self' is assumed), e.g 'transerror' - attr : attribute of 'obj' to harvest, e.g. 'clf.training_time' - copy : None, 'copy' or 'deepcopy' - way to copy attribute """ if attribs: # force the state self.states.enable('harvested') self.__attribs = [] for i, attrib in enumerate(attribs): if isinstance(attrib, dict): if not 'name' in attrib: raise ValueError, \ "Harvestable: attribute must be a string or " + \ "a dictionary with 'name'" else: attrib = {'name': attrib} # assign default method to copy if not 'copy' in attrib: attrib['copy'] = self.__copy_attribs # check copy method if not attrib['copy'] in self._KNOWN_COPY_METHODS: raise ValueError, "Unknown method %s. Known are %s" % \ (attrib['copy'], self._KNOWN_COPY_METHODS) if not ('obj' in attrib or 'attr' in attrib): # Process the item to harvest # split into obj, attr. If obj is empty, then assume self split = attrib['name'].split('.', 1) if len(split)==1: obj, attr = split[0], None else: obj, attr = split attrib.update({'obj':obj, 'attr':attr}) if attrib['obj'] == '': attrib['obj'] = 'self' # TODO: may be enabling of the states?? self.__attribs.append(attrib) # place value back else: # just to make sure it is not None or 0 self.__attribs = [] def _harvest(self, vars): """The harvesting function: must obtain dictionary of variables from the caller. :Parameters: vars : dict Dictionary of available data. Most often locals() could be passed as `vars`. Mention that desired to be harvested private attributes better be bound locally to some variable :Returns: nothing """ if not self.states.isEnabled('harvested') or len(self.__attribs)==0: return if not self.states.isSet('harvested'): self.harvested = dict([(a['name'], []) for a in self.__attribs]) for attrib in self.__attribs: attrv = vars[attrib['obj']] # access particular attribute if needed if not attrib['attr'] is None: attrv = eval('attrv.%s' % attrib['attr']) # copy the value if needed attrv = {'copy':copy.copy, 'deepcopy':copy.deepcopy, None:lambda x:x}[attrib['copy']](attrv) self.harvested[attrib['name']].append(attrv) harvest_attribs = property(fget=lambda self:self.__attribs, fset=_setAttribs) pymvpa-0.4.8/mvpa/misc/stats.py000066400000000000000000000146301174541445200164600ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Little statistics helper""" __docformat__ = 'restructuredtext' from mvpa.base import externals if externals.exists('scipy', raiseException=True): import scipy.stats as stats import numpy as N import copy def chisquare(obs, exp=None): """Compute the chisquare value of a contingency table with arbitrary dimensions. If no expected frequencies are supplied, the total N is assumed to be equally distributed across all cells. Returns: chisquare-stats, associated p-value (upper tail) """ obs = N.array(obs) # get total number of observations nobs = N.sum(obs) # if no expected value are supplied assume equal distribution if exp == None: exp = N.ones(obs.shape) * nobs / N.prod(obs.shape) # make sure to have floating point data exp = exp.astype(float) # compute chisquare value chisq = N.sum((obs - exp )**2 / exp) # return chisq and probability (upper tail) return chisq, stats.chisqprob(chisq, N.prod(obs.shape) - 1) class DSMatrix(object): """DSMatrix allows for the creation of dissilimarity matrices using arbitrary distance metrics. """ # metric is a string def __init__(self, data_vectors, metric='spearman'): """Initialize DSMatrix :Parameters: data_vectors : ndarray m x n collection of vectors, where m is the number of exemplars and n is the number of features per exemplar metric : string Distance metric to use (e.g., 'euclidean', 'spearman', 'pearson', 'confusion') """ # init members self.full_matrix = [] self.u_triangle = None self.vector_form = None # this one we know straight away, so set it self.metric = metric # size of dataset (checking if we're dealing with a column vector only) num_exem = N.shape(data_vectors)[0] flag_1d = False # changed 4/26/09 to new way of figuring out if array is 1-D #if (isinstance(data_vectors, N.ndarray)): if (not(num_exem == N.size(data_vectors))): num_features = N.shape(data_vectors)[1] else: flag_1d = True num_features = 1 # generate output (dissimilarity) matrix dsmatrix = N.mat(N.zeros((num_exem, num_exem))) if (metric == 'euclidean'): #print 'Using Euclidean distance metric...' # down rows for i in range(num_exem): # across columns for j in range(num_exem): if (not(flag_1d)): dsmatrix[i, j] = N.linalg.norm( data_vectors[i, :] - data_vectors[j, :]) else: dsmatrix[i, j] = N.linalg.norm( data_vectors[i] - data_vectors[j]) elif (metric == 'spearman'): #print 'Using Spearman rank-correlation metric...' # down rows for i in range(num_exem): # across columns for j in range(num_exem): dsmatrix[i, j] = 1 - stats.spearmanr( data_vectors[i,:], data_vectors[j,:])[0] elif (metric == 'pearson'): #print 'Using Pearson correlation metric...' # down rows for i in range(num_exem): # across columns for j in range(num_exem): dsmatrix[i, j] = 1 - stats.pearsonr( data_vectors[i,:], data_vectors[j,:])[0] elif (metric == 'confusion'): #print 'Using confusion correlation metric...' # down rows for i in range(num_exem): # across columns for j in range(num_exem): if (not(flag_1d)): dsmatrix[i, j] = 1 - int( N.floor(N.sum(( data_vectors[i, :] == data_vectors[j, :] ).astype(N.int32)) / num_features)) else: dsmatrix[i, j] = 1 - int( data_vectors[i] == data_vectors[j]) self.full_matrix = dsmatrix def getTriangle(self): # if we need to create the u_triangle representation, do so if (self.u_triangle is None): self.u_triangle = N.triu(self.full_matrix) return self.u_triangle # create the dissimilarity matrix on the (upper) triangle of the two # two dissimilarity matrices; we can just reuse the same dissimilarity # matrix code, but since it will return a matrix, we need to pick out # either dsm[0,1] or dsm[1,0] # note: this is a bit of a kludge right now, but it's the only way to solve # certain problems: # 1. Set all 0-valued elements in the original matrix to -1 (an impossible # value for a dissimilarity matrix) # 2. Find the upper triangle of the matrix # 3. Create a vector from the upper triangle, but only with the # elements whose absolute value is greater than 0 -- this # will keep everything from the original matrix that wasn't # part of the zero'ed-out portion when we took the upper # triangle # 4. Set all the -1-valued elements in the vector to 0 (their # original value) # 5. Cast to numpy array def getVectorForm(self): if (self.vector_form is not None): return self.vector_form orig_dsmatrix = copy.deepcopy(self.getFullMatrix()) orig_dsmatrix[orig_dsmatrix == 0] = -1 orig_tri = N.triu(orig_dsmatrix) vector_form = orig_tri[abs(orig_tri) > 0] vector_form[vector_form == -1] = 0 vector_form = N.asarray(vector_form) self.vector_form = vector_form[0] return self.vector_form # XXX is there any reason to have these get* methods # instead of plain access to full_matrix and method? def getFullMatrix(self): return self.full_matrix def getMetric(self): return self.metric pymvpa-0.4.8/mvpa/misc/support.py000066400000000000000000000556171174541445200170500ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Support function -- little helpers in everyday life""" __docformat__ = 'restructuredtext' import numpy as N import re, os # for SmartVersion from distutils.version import Version from types import StringType, TupleType, ListType from mvpa.base import warning from mvpa.support.copy import copy, deepcopy from operator import isSequenceType if __debug__: from mvpa.base import debug def reuseAbsolutePath(file1, file2, force=False): """Use path to file1 as the path to file2 is no absolute path is given for file2 :Parameters: force : bool if True, force it even if the file2 starts with / """ if not file2.startswith(os.path.sep) or force: # lets reuse path to file1 return os.path.join(os.path.dirname(file1), file2.lstrip(os.path.sep)) else: return file2 def transformWithBoxcar(data, startpoints, boxlength, offset=0, fx=N.mean): """This function extracts boxcar windows from an array. Such a boxcar is defined by a starting point and the size of the window along the first axis of the array (`boxlength`). Afterwards a customizable function is applied to each boxcar individually (Default: averaging). :param data: An array with an arbitrary number of dimensions. :type data: array :param startpoints: Boxcar startpoints as index along the first array axis :type startpoints: sequence :param boxlength: Length of the boxcar window in #array elements :type boxlength: int :param offset: Optional offset between the configured starting point and the actual begining of the boxcar window. :type offset: int :rtype: array (len(startpoints) x data.shape[1:]) """ if boxlength < 1: raise ValueError, "Boxlength lower than 1 makes no sense." # check for illegal boxes for sp in startpoints: if ( sp + offset + boxlength - 1 > len(data)-1 ) \ or ( sp + offset < 0 ): raise ValueError, \ 'Illegal box: start: %i, offset: %i, length: %i' \ % (sp, offset, boxlength) # build a list of list where each sublist contains the indexes of to be # averaged data elements selector = [ range( i + offset, i + offset + boxlength ) \ for i in startpoints ] # average each box selected = [ fx( data[ N.array(box) ], axis=0 ) for box in selector ] return N.array( selected ) def _getUniqueLengthNCombinations_lt3(data, n): """Generates a list of lists containing all combinations of elements of data of length 'n' without repetitions. data: list n: integer This function is adapted from a Java version posted in some forum on the web as an answer to the question 'How can I generate all possible combinations of length n?'. Unfortunately I cannot remember which forum it was. NOTE: implementation is broken for n>2 """ if n > 2: raise ValueError, "_getUniqueLengthNCombinations_lt3 " \ "is broken for n>2, hence should not be used directly." # to be returned combos = [] # local function that will be called recursively to collect the # combination elements def take(data, occupied, depth, taken): for i, d in enumerate(data): # only do something if this element hasn't been touch yet if occupied[i] == False: # see whether will reached the desired length if depth < n-1: # flag the current element as touched occupied[i] = True # next level take(data, occupied, depth+1, taken + [d]) # if the current element would be set 'free', it would # results in ALL combinations of elements (obeying order # of elements) and not just in the unique sets of # combinations (without order) #occupied[i] = False else: # store the final combination combos.append(taken + [d]) # some kind of bitset that stores the status of each element # (contained in combination or not) occupied = [False] * len(data) # get the combinations take(data, occupied, 0, []) # return the result return combos def xuniqueCombinations(L, n): """Generator of unique combinations form a list L of objects in groups of size n. # XXX EO: I guess they are already sorted. # XXX EO: It seems to work well for n>20 :) :Parameters: L : list list of unique ids n : int grouping size Adopted from Li Daobing http://code.activestate.com/recipes/190465/ (MIT license, according to activestate.com's policy) """ if n==0: yield [] else: for i in xrange(len(L)-n+1): for cc in xuniqueCombinations(L[i+1:],n-1): yield [L[i]]+cc def _getUniqueLengthNCombinations_binary(L, n=None, sort=True): """Find all subsets of data :Parameters: L : list list of unique ids n : None or int If None, all possible subsets are returned. If n is specified (int), then only the ones of the length n are returned sort : bool Either to sort the resultant sequence Adopted from Alex Martelli: http://mail.python.org/pipermail/python-list/2001-January/067815.html """ N = len(L) if N > 20 and n == 1: warning("getUniqueLengthNCombinations_binary should not be used for " "large N") result = [] for X in range(2**N): x = [ L[i] for i in range(N) if X & (1L< 10000? No one would run it... return list(xuniqueCombinations(L, n)) def indentDoc(v): """Given a `value` returns a string where each line is indented Needed for a cleaner __repr__ output `v` - arbitrary """ return re.sub('\n', '\n ', str(v)) def idhash(val): """Craft unique id+hash for an object """ res = "%s" % id(val) if isinstance(val, list): val = tuple(val) try: res += ":%s" % hash(buffer(val)) except: try: res += ":%s" % hash(val) except: pass pass return res def isSorted(items): """Check if listed items are in sorted order. :Parameters: `items`: iterable container :return: `True` if were sorted. Otherwise `False` + Warning """ items_sorted = deepcopy(items) items_sorted.sort() equality = items_sorted == items # XXX yarik forgotten analog to isiterable if hasattr(equality, '__iter__'): equality = N.all(equality) return equality def isInVolume(coord, shape): """For given coord check if it is within a specified volume size. Returns True/False. Assumes that volume coordinates start at 0. No more generalization (arbitrary minimal coord) is done to save on performance """ for i in xrange(len(coord)): if coord[i] < 0 or coord[i] >= shape[i]: return False return True def version_to_tuple(v): """Convert literal string into a tuple, if possible of ints Tuple of integers constructed by splitting at '.' or interleaves of numerics and alpha numbers """ if isinstance(v, basestring): v = v.split('.') elif isinstance(v, tuple) or isinstance(v, list): # assure tuple pass else: raise ValueError, "Do not know how to treat version '%s'" % str(v) # Try to convert items into ints vres = [] regex = re.compile('(?P[0-9]*)' '(?P[~+-]*[A-Za-z]*)(?P.*)') for x in v: try: vres += [int(x)] except ValueError: # try to split into sequences of literals and numerics suffix = x while suffix != '': res = regex.search(suffix) if res: resd = res.groupdict() if resd['numeric'] != '': vres += [int(resd['numeric'])] if resd['alpha'] != '': vres += [resd['alpha']] suffix = resd['suffix'] else: # We can't detech anything meaningful -- let it go as is resd += [suffix] break v = tuple(vres) return v class SmartVersion(Version): """A bit evolved comparison of versions The reason for not using python's distutil.version is that it seems to have no clue about somewhat common conventions of using '-dev' or 'dev' or 'rc' suffixes for upcoming releases (so major version does contain upcoming release already). So here is an ad-hoc and not as nice implementation """ def parse(self, vstring): self.vstring = vstring self.version = version_to_tuple(vstring) def __str__(self): return self.vstring def __cmp__(self, other): if isinstance(other, (StringType, TupleType, ListType)): other = SmartVersion(other) elif isinstance(other, SmartVersion): pass elif isinstance(other, Version): other = SmartVersion(other.vstring) else: raise ValueError("Do not know how to treat version %s" % str(other)) # Do ad-hoc comparison of strings i = 0 s, o = self.version, other.version regex_prerelease = re.compile('~|-?dev|-?rc|-?svn|-?pre', re.I) for i in xrange(max(len(s), len(o))): if i < len(s): si = s[i] else: si = None if i < len(o): oi = o[i] else: oi = None if si == oi: continue for x,y,mult in ((si, oi, 1), (oi, si, -1)): if x is None: if isinstance(y, int): return -mult # we got '.1' suffix if isinstance(y, StringType): if (regex_prerelease.match(y)): return mult # so we got something to signal # pre-release, so first one won else: # otherwise the other one wins return -mult else: raise RuntimeError, "Should not have got here with %s" \ % y elif isinstance(x, int): if not isinstance(y, int): return mult return mult*cmp(x, y) # both are ints elif isinstance(x, StringType): if isinstance(y, StringType): return mult*cmp(x,y) return 0 def getBreakPoints(items, contiguous=True): """Return a list of break points. :Parameters: items : iterable list of items, such as chunks contiguous : bool if `True` (default) then raise Value Error if items are not contiguous, i.e. a label occur in multiple contiguous sets :raises: ValueError :return: list of indexes for every new set of items """ prev = None # pylint happiness event! known = [] """List of items which was already seen""" result = [] """Resultant list""" for index in xrange(len(items)): item = items[index] if item in known: if index > 0: if prev != item: # breakpoint if contiguous: raise ValueError, \ "Item %s was already seen before" % str(item) else: result.append(index) else: known.append(item) result.append(index) prev = item return result def RFEHistory2maps(history): """Convert history generated by RFE into the array of binary maps Example: history2maps(N.array( [ 3,2,1,0 ] )) results in array([[ 1., 1., 1., 1.], [ 1., 1., 1., 0.], [ 1., 1., 0., 0.], [ 1., 0., 0., 0.]]) """ # assure that it is an array history = N.array(history) nfeatures, steps = len(history), max(history) - min(history) + 1 history_maps = N.zeros((steps, nfeatures)) for step in xrange(steps): history_maps[step, history >= step] = 1 return history_maps class MapOverlap(object): """Compute some overlap stats from a sequence of binary maps. When called with a sequence of binary maps (e.g. lists or arrays) the fraction of mask elements that are non-zero in a customizable proportion of the maps is returned. By default this threshold is set to 1.0, i.e. such an element has to be non-zero in *all* maps. Three additional maps (same size as original) are computed: * overlap_map: binary map which is non-zero for each overlapping element. * spread_map: binary map which is non-zero for each element that is non-zero in any map, but does not exceed the overlap threshold. * ovstats_map: map of float with the raw elementwise fraction of overlap. All maps are available via class members. """ def __init__(self, overlap_threshold=1.0): """Nothing to be seen here. """ self.__overlap_threshold = overlap_threshold # pylint happiness block self.overlap_map = None self.spread_map = None self.ovstats_map = None def __call__(self, maps): """Returns fraction of overlapping elements. """ ovstats = N.mean(maps, axis=0) self.overlap_map = (ovstats >= self.__overlap_threshold ) self.spread_map = N.logical_and(ovstats > 0.0, ovstats < self.__overlap_threshold) self.ovstats_map = ovstats return N.mean(ovstats >= self.__overlap_threshold) class Event(dict): """Simple class to define properties of an event. The class is basically a dictionary. Any properties can be passed as keyword arguments to the constructor, e.g.: >>> ev = Event(onset=12, duration=2.45) Conventions for keys: `onset` The onset of the event in some unit. `duration` The duration of the event in the same unit as `onset`. `label` E.g. the condition this event is part of. `chunk` Group this event is part of (if any), e.g. experimental run. `features` Any amount of additional features of the event. This might include things like physiological measures, stimulus intensity. Must be a mutable sequence (e.g. list), if present. """ _MUSTHAVE = ['onset'] def __init__(self, **kwargs): # store everything dict.__init__(self, **kwargs) # basic checks for k in Event._MUSTHAVE: if not self.has_key(k): raise ValueError, "Event must have '%s' defined." % k def asDescreteTime(self, dt, storeoffset=False): """Convert `onset` and `duration` information into descrete timepoints. :Parameters: dt: float Temporal distance between two timepoints in the same unit as `onset` and `duration`. storeoffset: bool If True, the temporal offset between original `onset` and descretized `onset` is stored as an additional item in `features`. :Return: A copy of the original `Event` with `onset` and optionally `duration` replaced by their corresponding descrete timepoint. The new onset will correspond to the timepoint just before or exactly at the original onset. The new duration will be the number of timepoints covering the event from the computed onset timepoint till the timepoint exactly at the end, or just after the event. Note again, that the new values are expressed as #timepoint and not in their original unit! """ dt = float(dt) onset = self['onset'] out = deepcopy(self) # get the timepoint just prior the onset out['onset'] = int(N.floor(onset / dt)) if storeoffset: # compute offset offset = onset - (out['onset'] * dt) if out.has_key('features'): out['features'].append(offset) else: out['features'] = [offset] if out.has_key('duration'): # how many timepoint cover the event (from computed onset # to the one timepoint just after the end of the event out['duration'] = int(N.ceil((onset + out['duration']) / dt) \ - out['onset']) return out class HarvesterCall(object): def __init__(self, call, attribs=None, argfilter=None, expand_args=True, copy_attribs=True): """Initialize :Parameters: expand_args : bool Either to expand the output of looper into a list of arguments for call attribs : list of basestr What attributes of call to store and return later on? copy_attribs : bool Force copying values of attributes """ self.call = call """Call which gets called in the harvester.""" if attribs is None: attribs = [] if not isSequenceType(attribs): raise ValueError, "'attribs' have to specified as a sequence." if not (argfilter is None or isSequenceType(argfilter)): raise ValueError, "'argfilter' have to be a sequence or None." # now give it to me... self.argfilter = argfilter self.expand_args = expand_args self.copy_attribs = copy_attribs self.attribs = attribs class Harvester(object): """World domination helper: do whatever it is asked and accumulate results XXX Thinks about: - Might we need to deepcopy attributes values? - Might we need to specify what attribs to copy and which just to bind? """ def __init__(self, source, calls, simplify_results=True): """Initialize :Parameters: source Generator which produce food for the calls. calls : sequence of HarvesterCall instances Calls which are processed in the loop. All calls are processed in order of apperance in the sequence. simplify_results: bool Remove unecessary overhead in results if possible (nested lists and dictionaries). """ if not isSequenceType(calls): raise ValueError, "'calls' have to specified as a sequence." self.__source = source """Generator which feeds the harvester""" self.__calls = calls """Calls which gets called with each generated source""" self.__simplify_results = simplify_results def __call__(self, *args, **kwargs): """ """ # prepare complex result structure for all calls and their respective # attributes: calls x dict(attributes x loop iterations) results = [dict([('result', [])] + [(a, []) for a in c.attribs]) \ for c in self.__calls] # Lets do it! for (i, X) in enumerate(self.__source(*args, **kwargs)): for (c, call) in enumerate(self.__calls): # sanity check if i == 0 and call.expand_args and not isSequenceType(X): raise RuntimeError, \ "Cannot expand non-sequence result from %s" % \ `self.__source` # apply argument filter (and reorder) if requested if call.argfilter: filtered_args = [X[f] for f in call.argfilter] else: filtered_args = X if call.expand_args: result = call.call(*filtered_args) else: result = call.call(filtered_args) # # XXX pylint doesn't like `` for some reason # if __debug__: # debug("LOOP", "Iteration %i on call %s. Got result %s" % # (i, `self.__call`, `result`)) results[c]['result'].append(result) for attrib in call.attribs: attrv = call.call.__getattribute__(attrib) if call.copy_attribs: attrv = copy(attrv) results[c][attrib].append(attrv) # reduce results structure if self.__simplify_results: # get rid of dictionary if just the results are requested for (c, call) in enumerate(self.__calls): if not len(call.attribs): results[c] = results[c]['result'] if len(self.__calls) == 1: results = results[0] return results # XXX MH: this doesn't work in all cases, as you cannot have *args after a # kwarg. #def loop(looper, call, # unroll=True, attribs=None, copy_attribs=True, *args, **kwargs): # """XXX Loop twin brother # # Helper for those who just wants to do smth like # loop(blah, bleh, grgr) # instead of # Loop(blah, bleh)(grgr) # """ # print looper, call, unroll, attribs, copy_attribs # print args, kwargs # return Loop(looper=looper, call=call, unroll=unroll, # attribs=attribs, copy_attribs=copy_attribs)(*args, **kwargs) pymvpa-0.4.8/mvpa/misc/transformers.py000066400000000000000000000316501174541445200200500ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Simply functors that transform something.""" _DEV_DOC = """ Follow the convetion that functions start with lower case, and classes with uppercase letter. """ __docformat__ = 'restructuredtext' import numpy as N from mvpa.base import externals, warning from mvpa.misc.state import StateVariable, ClassWithCollections if __debug__: from mvpa.base import debug def Absolute(x): """ Returns the elementwise absolute of any argument. :Parameter: x: scalar | sequence """ return N.absolute(x) def OneMinus(x): """Returns elementwise '1 - x' of any argument.""" return 1 - x def Identity(x): """Return whatever it was called with.""" return x def FirstAxisMean(x): """Mean computed along the first axis.""" return N.mean(x, axis=0) def FirstAxisSumNotZero(x): """Sum computed over first axis of whether the values are not equal to zero.""" return (N.asarray(x)!=0).sum(axis=0) def SecondAxisMean(x): """Mean across 2nd axis Use cases: - to combine multiple sensitivities to get sense about mean sensitivity across splits """ return N.mean(x, axis=1) def SecondAxisSumOfAbs(x): """Sum of absolute values along the 2nd axis Use cases: - to combine multiple sensitivities to get sense about what features are most influential """ return N.abs(x).sum(axis=1) def SecondAxisMaxOfAbs(x): """Max of absolute values along the 2nd axis """ return N.abs(x).max(axis=1) def GrandMean(x): """Just what the name suggests.""" return N.mean(x) def L2Normed(x, norm=1.0, reverse=False): """Norm the values so that regular vector norm becomes `norm` More verbose: Norm that the sum of the squared elements of the returned vector becomes `norm`. """ xnorm = N.linalg.norm(x) return x * (norm/xnorm) def L1Normed(x, norm=1.0, reverse=False): """Norm the values so that L_1 norm (sum|x|) becomes `norm`""" xnorm = N.sum(N.abs(x)) return x * (norm/xnorm) def RankOrder(x, reverse=False): """Rank-order by value. Highest gets 0""" # XXX was Yarik on drugs? please simplify this beast nelements = len(x) indexes = N.arange(nelements) t_indexes = indexes if not reverse: t_indexes = indexes[::-1] tosort = zip(x, indexes) tosort.sort() ztosort = zip(tosort, t_indexes) rankorder = N.empty(nelements, dtype=int) rankorder[ [x[0][1] for x in ztosort] ] = \ [x[1] for x in ztosort] return rankorder def ReverseRankOrder(x): """Convinience functor""" return RankOrder(x, reverse=True) class OverAxis(object): """Helper to apply transformer over specific axis """ def __init__(self, transformer, axis=None): """Initialize transformer wrapper with an axis. :Parameters: transformer A callable to be used axis : None or int If None -- apply transformer across all the data. If some int -- over that axis """ self.transformer = transformer # sanity check if not (axis is None or isinstance(axis, int)): raise ValueError, "axis must be specified by integer" self.axis = axis def __call__(self, x, *args, **kwargs): transformer = self.transformer axis = self.axis if axis is None: return transformer(x, *args, **kwargs) x = N.asanyarray(x) shape = x.shape if axis >= len(shape): raise ValueError, "Axis given in constructor %d is higher " \ "than dimensionality of the data of shape %s" % (axis, shape) # WRONG! ;-) #for ind in xrange(shape[axis]): # results.append(transformer(x.take([ind], axis=axis), # *args, **kwargs)) # TODO: more elegant/speedy solution? shape_sweep = shape[:axis] + shape[axis+1:] shrinker = None """Either transformer reduces the dimensionality of the data""" #results = N.empty(shape_out, dtype=x.dtype) for index_sweep in N.ndindex(shape_sweep): if axis > 0: index = index_sweep[:axis] else: index = () index = index + (slice(None),) + index_sweep[axis:] x_sel = x[index] x_t = transformer(x_sel, *args, **kwargs) if shrinker is None: if N.isscalar(x_t) or x_t.shape == shape_sweep: results = N.empty(shape_sweep, dtype=x.dtype) shrinker = True elif x_t.shape == x_sel.shape: results = N.empty(x.shape, dtype=x.dtype) shrinker = False else: raise RuntimeError, 'Not handled by OverAxis kind of transformer' if shrinker: results[index_sweep] = x_t else: results[index] = x_t return results class DistPValue(ClassWithCollections): """Converts values into p-values under vague and non-scientific assumptions """ nulldist_number = StateVariable(enabled=True, doc="Number of features within the estimated null-distribution") positives_recovered = StateVariable(enabled=True, doc="Number of features considered to be positives and which were recovered") def __init__(self, sd=0, distribution='rdist', fpp=None, nbins=400, **kwargs): """L2-Norm the values, convert them to p-values of a given distribution. :Parameters: sd : int Samples dimension (if len(x.shape)>1) on which to operate distribution : string Which distribution to use. Known are: 'rdist' (later normal should be there as well) fpp : float At what p-value (both tails) if not None, to control for false positives. It would iteratively prune the tails (tentative real positives) until empirical p-value becomes less or equal to numerical. nbins : int Number of bins for the iterative pruning of positives WARNING: Highly experimental/slow/etc: no theoretical grounds have been presented in any paper, nor proven """ externals.exists('scipy', raiseException=True) ClassWithCollections.__init__(self, **kwargs) self.sd = sd if not (distribution in ['rdist']): raise ValueError, "Actually only rdist supported at the moment" \ " got %s" % distribution self.distribution = distribution self.fpp = fpp self.nbins = nbins def __call__(self, x): from mvpa.support.stats import scipy import scipy.stats as stats # some local bindings distribution = self.distribution sd = self.sd fpp = self.fpp nbins = self.nbins x = N.asanyarray(x) shape_orig = x.shape ndims = len(shape_orig) # (very) old numpy had different format of returned bins -- # there were not edges but center points. We care here about # center points, so we will transform edge points into center # points for newer versions of numpy numpy_center_points = externals.versions['numpy'] < (1, 1) # XXX May be just utilize OverAxis transformer? if ndims > 2: raise NotImplementedError, \ "TODO: add support for more than 2 dimensions" elif ndims == 1: x, sd = x[:, N.newaxis], 0 # lets transpose for convenience if sd == 0: x = x.T # Output p-values of x in null-distribution pvalues = N.zeros(x.shape) nulldist_number, positives_recovered = [], [] # finally go through all data nd = x.shape[1] if __debug__: if nd < x.shape[0]: warning("Number of features in DistPValue lower than number of" " items -- may be incorrect sd=%d was provided" % sd) for i, xx in enumerate(x): dist = stats.rdist(nd-1, 0, 1) xx /= N.linalg.norm(xx) if fpp is not None: if __debug__: debug('TRAN_', "starting adaptive adjustment i=%d" % i) # Adaptive adjustment for false negatives: Nxx, xxx, pN_emp_prev = len(xx), xx, 1.0 Nxxx = Nxx indexes = N.arange(Nxx) """What features belong to Null-distribution""" while True: hist, bins = N.histogram(xxx, bins=nbins, normed=False) pdf = hist.astype(float)/Nxxx if not numpy_center_points: # if we obtain edge points for bins -- take centers bins = 0.5 * (bins[0:-1] + bins[1:]) bins_halfstep = (bins[1] - bins[2])/2.0 # theoretical CDF # was really unstable -- now got better ;) dist_cdf = dist.cdf(bins) # otherwise just recompute manually # dist_pdf = dist.pdf(bins) # dist_pdf /= N.sum(dist_pdf) # XXX can't recall the function... silly # probably could use N.integrate cdf = N.zeros(nbins, dtype=float) #dist_cdf = cdf.copy() dist_prevv = cdf_prevv = 0.0 for j in range(nbins): cdf_prevv = cdf[j] = cdf_prevv + pdf[j] #dist_prevv = dist_cdf[j] = dist_prevv + dist_pdf[j] # what bins fall into theoretical 'positives' in both tails p = (0.5 - N.abs(dist_cdf - 0.5) < fpp/2.0) # amount in empirical tails -- if we match theoretical, we # should have total >= p pN_emp = N.sum(pdf[p]) # / (1.0 * nbins) if __debug__: debug('TRAN_', "empirical p=%.3f for theoretical p=%.3f" % (pN_emp, fpp)) if pN_emp <= fpp: # we are done break if pN_emp > pN_emp_prev: if __debug__: debug('TRAN_', "Diverging -- thus keeping last result " "with p=%.3f" % pN_emp_prev) # we better restore previous result indexes, xxx, dist = indexes_prev, xxx_prev, dist_prev break pN_emp_prev = pN_emp # very silly way for now -- just proceed by 1 bin keep = N.logical_and(xxx > bins[0], # + bins_halfstep, xxx < bins[-1]) # - bins_halfstep) if __debug__: debug('TRAN_', "Keeping %d out of %d elements" % (N.sum(keep), Nxxx)) # Preserve them if we need to "roll back" indexes_prev, xxx_prev, dist_prev = indexes, xxx, dist # we should remove those which we assume to be positives and # which should not belong to Null-dist xxx, indexes = xxx[keep], indexes[keep] # L2 renorm it xxx = xxx / N.linalg.norm(xxx) Nxxx = len(xxx) dist = stats.rdist(Nxxx-1, 0, 1) Nindexes = len(indexes) Nrecovered = Nxx - Nindexes nulldist_number += [Nindexes] positives_recovered += [Nrecovered] if __debug__: if distribution == 'rdist': assert(dist.args[0] == Nindexes-1) debug('TRAN', "Positives recovery finished with %d out of %d " "entries in Null-distribution, thus %d positives " "were recovered" % (Nindexes, Nxx, Nrecovered)) # And now we need to perform our duty -- assign p-values #dist = stats.rdist(Nindexes-1, 0, 1) pvalues[i, :] = dist.cdf(xx) # XXX we might add an option to transform it to z-scores? result = pvalues # charge state variables # XXX might want to populate them for non-adaptive handling as well self.nulldist_number = nulldist_number self.positives_recovered = positives_recovered # transpose if needed if sd == 0: result = result.T return result pymvpa-0.4.8/mvpa/misc/vproperty.py000066400000000000000000000020621174541445200173700ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """C++-like virtual properties""" __docformat__ = 'restructuredtext' class VProperty(object): """Provides "virtual" property: uses derived class's method """ def __init__(self, fget=None, fset=None, fdel=None, doc=''): for attr in ('fget', 'fset'): func = locals()[attr] if callable(func): setattr(self, attr, func.func_name) setattr(self, '__doc__', doc) def __get__(self, obj=None, type=None): if not obj: return 'property' if self.fget: return getattr(obj, self.fget)() def __set__(self, obj, arg): if self.fset: return getattr(obj, self.fset)(arg) pymvpa-0.4.8/mvpa/suite.py000066400000000000000000000110561174541445200155170ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """MultiVariate Pattern Analysis -- load helper If you don't like to specify exact location of any particular functionality within PyMVPA, please simply:: from mvpa.suite import * or import mvpa.suite """ __docformat__ = 'restructuredtext' from mvpa import * from mvpa.base import * from mvpa.base.config import * from mvpa.base.verbosity import * from mvpa.base.info import * if externals.exists('reportlab'): from mvpa.base.report import * else: from mvpa.base.report_dummy import Report from mvpa.algorithms.cvtranserror import * from mvpa.algorithms.hyperalignment import * from mvpa import clfs from mvpa.clfs.distance import * from mvpa.clfs.kernel import * from mvpa.clfs.base import * from mvpa.clfs.meta import * from mvpa.clfs.knn import * if externals.exists('lars'): from mvpa.clfs.lars import * if externals.exists('elasticnet'): from mvpa.clfs.enet import * if externals.exists('glmnet'): from mvpa.clfs.glmnet import * from mvpa.clfs.smlr import * from mvpa.clfs.blr import * from mvpa.clfs.gnb import * from mvpa.clfs.stats import * if externals.exists('libsvm') or externals.exists('shogun'): from mvpa.clfs.svm import * from mvpa.clfs.transerror import * from mvpa.clfs.warehouse import * from mvpa import datasets from mvpa.datasets import * # just to make testsuite happy from mvpa.datasets.base import * from mvpa.datasets.meta import * from mvpa.datasets.masked import * from mvpa.datasets.miscfx import * from mvpa.datasets.channel import * from mvpa.datasets.event import * from mvpa.datasets.eep import * if externals.exists('nifti'): from mvpa.datasets.nifti import * from mvpa.datasets import splitters from mvpa.datasets.splitters import * from mvpa import featsel from mvpa.featsel.base import * from mvpa.featsel.helpers import * from mvpa.featsel.ifs import * from mvpa.featsel.rfe import * from mvpa import mappers #from mvpa.mappers import * from mvpa.mappers.base import * from mvpa.mappers.metric import * from mvpa.mappers.mask import * from mvpa.mappers.svd import * from mvpa.mappers.procrustean import * from mvpa.mappers.boxcar import * from mvpa.mappers.samplegroup import * from mvpa.mappers.som import * from mvpa.mappers.array import * if externals.exists('scipy'): from mvpa.mappers.zscore import ZScoreMapper if externals.exists('mdp'): from mvpa.mappers.pca import * from mvpa.mappers.ica import * if externals.exists('mdp ge 2.4'): from mvpa.mappers.lle import * from mvpa import measures from mvpa.measures.anova import * from mvpa.measures.glm import * from mvpa.measures.irelief import * from mvpa.measures.base import * from mvpa.measures.noiseperturbation import * from mvpa.measures.searchlight import * from mvpa.measures.splitmeasure import * from mvpa.measures.corrstability import * from mvpa.support.copy import * from mvpa.misc.fx import * from mvpa.misc.errorfx import * from mvpa.misc.cmdline import * from mvpa.misc.data_generators import * from mvpa.misc.exceptions import * from mvpa.misc import * from mvpa.misc.io import * from mvpa.misc.io.eepbin import * from mvpa.misc.io.meg import * if externals.exists('cPickle') and externals.exists('gzip'): from mvpa.misc.io.hamster import * from mvpa.misc.fsl import * from mvpa.misc.bv import * from mvpa.misc.bv.base import * from mvpa.misc.param import * from mvpa.misc.state import * from mvpa.misc.support import * from mvpa.misc.transformers import * if externals.exists("nifti"): from mvpa.misc.fsl.melodic import * if externals.exists("pylab"): from mvpa.misc.plot import * from mvpa.misc.plot.erp import * if externals.exists(['griddata', 'scipy']): from mvpa.misc.plot.topo import * if externals.exists('nifti'): from mvpa.misc.plot.mri import plotMRI if externals.exists("scipy"): from mvpa.support.stats import scipy from mvpa.measures.corrcoef import * from mvpa.measures.ds import * from mvpa.clfs.ridge import * from mvpa.clfs.plr import * from mvpa.misc.stats import * from mvpa.clfs.gpr import * if externals.exists("pywt"): from mvpa.mappers.wavelet import * if externals.exists("pylab"): import pylab as P if externals.exists("lxml") and externals.exists("nifti"): from mvpa.atlases import * pymvpa-0.4.8/mvpa/support/000077500000000000000000000000001174541445200155255ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/support/__init__.py000066400000000000000000000014521174541445200176400ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Import helper for PyMVPA support modules mvpa.support is destined to contain temporary "fixes" to external modules (Python, scipy), which are known to be fixed in the recent releases but are not present in comodity distributions. Example could be copy module.""" __docformat__ = 'restructuredtext' if __debug__: from mvpa.base import debug debug('INIT', 'mvpa.support start') debug('INIT', 'mvpa.support end') pymvpa-0.4.8/mvpa/support/_copy.py000066400000000000000000000253641174541445200172220ustar00rootroot00000000000000"""Generic (shallow and deep) copying operations. Interface summary: import copy x = copy.copy(y) # make a shallow copy of y x = copy.deepcopy(y) # make a deep copy of y For module specific errors, copy.Error is raised. The difference between shallow and deep copying is only relevant for compound objects (objects that contain other objects, like lists or class instances). - A shallow copy constructs a new compound object and then (to the extent possible) inserts *the same objects* into it that the original contains. - A deep copy constructs a new compound object and then, recursively, inserts *copies* into it of the objects found in the original. Two problems often exist with deep copy operations that don't exist with shallow copy operations: a) recursive objects (compound objects that, directly or indirectly, contain a reference to themselves) may cause a recursive loop b) because deep copy copies *everything* it may copy too much, e.g. administrative data structures that should be shared even between copies Python's deep copy operation avoids these problems by: a) keeping a table of objects already copied during the current copying pass b) letting user-defined classes override the copying operation or the set of components copied This version does not copy types like module, class, function, method, nor stack trace, stack frame, nor file, socket, window, nor array, nor any similar types. Classes can use the same interfaces to control copying that they use to control pickling: they can define methods called __getinitargs__(), __getstate__() and __setstate__(). See the documentation for module "pickle" for information on these methods. """ import types from copy_reg import dispatch_table class Error(Exception): pass error = Error # backward compatibility try: from org.python.core import PyStringMap except ImportError: PyStringMap = None __all__ = ["Error", "copy", "deepcopy"] def copy(x): """Shallow copy operation on arbitrary Python objects. See the module's __doc__ string for more info. """ cls = type(x) copier = _copy_dispatch.get(cls) if copier: return copier(x) copier = getattr(cls, "__copy__", None) if copier: return copier(x) reductor = dispatch_table.get(cls) if reductor: rv = reductor(x) else: reductor = getattr(x, "__reduce_ex__", None) if reductor: rv = reductor(2) else: reductor = getattr(x, "__reduce__", None) if reductor: rv = reductor() else: raise Error("un(shallow)copyable object of type %s" % cls) return _reconstruct(x, rv, 0) _copy_dispatch = d = {} def _copy_immutable(x): return x for t in (type(None), int, long, float, bool, str, tuple, frozenset, type, xrange, types.ClassType, types.BuiltinFunctionType, type(Ellipsis), types.FunctionType): d[t] = _copy_immutable for name in ("ComplexType", "UnicodeType", "CodeType"): t = getattr(types, name, None) if t is not None: d[t] = _copy_immutable def _copy_with_constructor(x): return type(x)(x) for t in (list, dict, set): d[t] = _copy_with_constructor def _copy_with_copy_method(x): return x.copy() if PyStringMap is not None: d[PyStringMap] = _copy_with_copy_method def _copy_inst(x): if hasattr(x, '__copy__'): return x.__copy__() if hasattr(x, '__getinitargs__'): args = x.__getinitargs__() y = x.__class__(*args) else: y = _EmptyClass() y.__class__ = x.__class__ if hasattr(x, '__getstate__'): state = x.__getstate__() else: state = x.__dict__ if hasattr(y, '__setstate__'): y.__setstate__(state) else: y.__dict__.update(state) return y d[types.InstanceType] = _copy_inst del d def deepcopy(x, memo=None, _nil=[]): """Deep copy operation on arbitrary Python objects. See the module's __doc__ string for more info. """ if memo is None: memo = {} d = id(x) y = memo.get(d, _nil) if y is not _nil: return y cls = type(x) copier = _deepcopy_dispatch.get(cls) if copier: y = copier(x, memo) else: try: issc = issubclass(cls, type) except TypeError: # cls is not a class (old Boost; see SF #502085) issc = 0 if issc: y = _deepcopy_atomic(x, memo) else: copier = getattr(x, "__deepcopy__", None) if copier: y = copier(memo) else: reductor = dispatch_table.get(cls) if reductor: rv = reductor(x) else: reductor = getattr(x, "__reduce_ex__", None) if reductor: rv = reductor(2) else: reductor = getattr(x, "__reduce__", None) if reductor: rv = reductor() else: raise Error( "un(deep)copyable object of type %s" % cls) y = _reconstruct(x, rv, 1, memo) memo[d] = y _keep_alive(x, memo) # Make sure x lives at least as long as d return y _deepcopy_dispatch = d = {} def _deepcopy_atomic(x, memo): return x d[type(None)] = _deepcopy_atomic d[type(Ellipsis)] = _deepcopy_atomic d[int] = _deepcopy_atomic d[long] = _deepcopy_atomic d[float] = _deepcopy_atomic d[bool] = _deepcopy_atomic try: d[complex] = _deepcopy_atomic except NameError: pass d[str] = _deepcopy_atomic try: d[unicode] = _deepcopy_atomic except NameError: pass try: d[types.CodeType] = _deepcopy_atomic except AttributeError: pass d[type] = _deepcopy_atomic d[xrange] = _deepcopy_atomic d[types.ClassType] = _deepcopy_atomic d[types.BuiltinFunctionType] = _deepcopy_atomic d[types.FunctionType] = _deepcopy_atomic def _deepcopy_list(x, memo): y = [] memo[id(x)] = y for a in x: y.append(deepcopy(a, memo)) return y d[list] = _deepcopy_list def _deepcopy_tuple(x, memo): y = [] for a in x: y.append(deepcopy(a, memo)) d = id(x) try: return memo[d] except KeyError: pass for i in range(len(x)): if x[i] is not y[i]: y = tuple(y) break else: y = x memo[d] = y return y d[tuple] = _deepcopy_tuple def _deepcopy_dict(x, memo): y = {} memo[id(x)] = y for key, value in x.iteritems(): y[deepcopy(key, memo)] = deepcopy(value, memo) return y d[dict] = _deepcopy_dict if PyStringMap is not None: d[PyStringMap] = _deepcopy_dict def _keep_alive(x, memo): """Keeps a reference to the object x in the memo. Because we remember objects by their id, we have to assure that possibly temporary objects are kept alive by referencing them. We store a reference at the id of the memo, which should normally not be used unless someone tries to deepcopy the memo itself... """ try: memo[id(memo)].append(x) except KeyError: # aha, this is the first one :-) memo[id(memo)]=[x] def _deepcopy_inst(x, memo): if hasattr(x, '__deepcopy__'): return x.__deepcopy__(memo) if hasattr(x, '__getinitargs__'): args = x.__getinitargs__() args = deepcopy(args, memo) y = x.__class__(*args) else: y = _EmptyClass() y.__class__ = x.__class__ memo[id(x)] = y if hasattr(x, '__getstate__'): state = x.__getstate__() else: state = x.__dict__ state = deepcopy(state, memo) if hasattr(y, '__setstate__'): y.__setstate__(state) else: y.__dict__.update(state) return y d[types.InstanceType] = _deepcopy_inst def _reconstruct(x, info, deep, memo=None): if isinstance(info, str): return x assert isinstance(info, tuple) if memo is None: memo = {} n = len(info) assert n in (2, 3, 4, 5) callable, args = info[:2] if n > 2: state = info[2] else: state = {} if n > 3: listiter = info[3] else: listiter = None if n > 4: dictiter = info[4] else: dictiter = None if deep: args = deepcopy(args, memo) y = callable(*args) memo[id(x)] = y if listiter is not None: for item in listiter: if deep: item = deepcopy(item, memo) y.append(item) if dictiter is not None: for key, value in dictiter: if deep: key = deepcopy(key, memo) value = deepcopy(value, memo) y[key] = value if state: if deep: state = deepcopy(state, memo) if hasattr(y, '__setstate__'): y.__setstate__(state) else: if isinstance(state, tuple) and len(state) == 2: state, slotstate = state else: slotstate = None if state is not None: y.__dict__.update(state) if slotstate is not None: for key, value in slotstate.iteritems(): setattr(y, key, value) return y del d del types # Helper for instance creation without calling __init__ class _EmptyClass: pass def _test(): l = [None, 1, 2L, 3.14, 'xyzzy', (1, 2L), [3.14, 'abc'], {'abc': 'ABC'}, (), [], {}] l1 = copy(l) print l1==l l1 = map(copy, l) print l1==l l1 = deepcopy(l) print l1==l class C: def __init__(self, arg=None): self.a = 1 self.arg = arg if __name__ == '__main__': import sys file = sys.argv[0] else: file = __file__ self.fp = open(file) self.fp.close() def __getstate__(self): return {'a': self.a, 'arg': self.arg} def __setstate__(self, state): for key, value in state.iteritems(): setattr(self, key, value) def __deepcopy__(self, memo=None): new = self.__class__(deepcopy(self.arg, memo)) new.a = self.a return new c = C('argument sketch') l.append(c) l2 = copy(l) print l == l2 print l print l2 l2 = deepcopy(l) print l == l2 print l print l2 l.append({l[1]: l, 'xyz': l[2]}) l3 = copy(l) import repr print map(repr.repr, l) print map(repr.repr, l1) print map(repr.repr, l2) print map(repr.repr, l3) l3 = deepcopy(l) import repr print map(repr.repr, l) print map(repr.repr, l1) print map(repr.repr, l2) print map(repr.repr, l3) if __name__ == '__main__': _test() pymvpa-0.4.8/mvpa/support/copy.py000066400000000000000000000015431174541445200170540ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Support for python's copy module. """ __docformat__ = 'restructuredtext' import sys # We have to use deepcopy from python 2.5, since otherwise it fails to # copy sensitivity analyzers with assigned combiners which are just # functions not functors if sys.version_info[:2] >= (2, 6): # enforce absolute import _copy = __import__('copy', globals(), locals(), [], 0) copy = _copy.copy deepcopy = _copy.deepcopy else: from mvpa.support._copy import copy, deepcopy pymvpa-0.4.8/mvpa/support/griddata.py000066400000000000000000000031061174541445200176560ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Import griddata with preference to the version from matplotlib """ __docformat__ = 'restructuredtext' import sys from mvpa.base import externals if externals.exists('griddata', raiseException=True): if __debug__: from mvpa.base import debug try: if sys.version_info[:2] >= (2, 5): # enforce absolute import griddata = __import__('griddata', globals(), locals(), [], 0).griddata else: # little trick to be able to import 'griddata' package (which # has same name) oldname = __name__ # crazy name with close to zero possibility to cause whatever __name__ = 'iaugf9zrkjsbdv91' try: from griddata import griddata # restore old settings __name__ = oldname except ImportError: # restore old settings __name__ = oldname raise if __debug__: debug('EXT', 'Using python-griddata') except ImportError: from matplotlib.mlab import griddata if __debug__: debug('EXT', 'Using matplotlib.mlab.griddata') pymvpa-0.4.8/mvpa/support/stats.py000066400000000000000000000113001174541445200172300ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Fixer for rdist in scipy """ __docformat__ = 'restructuredtext' from mvpa.base import externals, warning, cfg if __debug__: from mvpa.base import debug if externals.exists('scipy', raiseException=True): import scipy import scipy.stats import scipy.stats as stats if not externals.exists('good scipy.stats.rdist'): if __debug__: debug("EXT", "Fixing up scipy.stats.rdist") # Lets fix it up, future imports of scipy.stats should carry fixed # version, isn't python is \emph{evil} ;-) import numpy as N from scipy.stats.distributions import rv_continuous from scipy import special import scipy.integrate # NB: Following function is copied from scipy SVN rev.5236 # and fixed with pow -> N.power (thanks Josef!) # FIXME: PPF does not work. class rdist_gen(rv_continuous): def _pdf(self, x, c): return N.power((1.0-x*x),c/2.0-1) / special.beta(0.5,c/2.0) def _cdf_skip(self, x, c): #error inspecial.hyp2f1 for some values see tickets 758, 759 return 0.5 + x/special.beta(0.5,c/2.0)* \ special.hyp2f1(0.5,1.0-c/2.0,1.5,x*x) def _munp(self, n, c): return (1-(n % 2))*special.beta((n+1.0)/2,c/2.0) # Lets try to avoid at least some of the numerical problems by removing points # around edges rdist = rdist_gen(a=-1.0, b=1.0, name="rdist", longname="An R-distributed", shapes="c", extradoc=""" R-distribution rdist.pdf(x,c) = (1-x**2)**(c/2-1) / B(1/2, c/2) for -1 <= x <= 1, c > 0. """ ) # Fix up number of arguments for veccdf's vectorize if rdist.veccdf.nin == 1: if __debug__: debug("EXT", "Fixing up veccdf.nin to make 2 for rdist") rdist.veccdf.nin = 2 scipy.stats.distributions.rdist_gen = scipy.stats.rdist_gen = rdist_gen scipy.stats.distributions.rdist = scipy.stats.rdist = rdist try: # Retest externals.exists('good scipy.stats.rdist', force=True, raiseException=True) except RuntimeError: warning("scipy.stats.rdist was not fixed with a monkey-patch. " "It remains broken") # Revert so if configuration stored, we know the true flow of things ;) cfg.set('externals', 'have good scipy.stats.rdist', 'no') if not externals.exists('good scipy.stats.rv_discrete.ppf'): # Local rebindings for ppf7 (7 is for the scipy version from # which code was borrowed) arr = N.asarray from scipy.stats.distributions import valarray, argsreduce from numpy import shape, place, any def ppf7(self,q,*args,**kwds): """ Percent point function (inverse of cdf) at q of the given RV :Parameters: q : array-like lower tail probability arg1, arg2, arg3,... : array-like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array-like, optional location parameter (default=0) :Returns: k : array-like quantile corresponding to the lower tail probability, q. """ loc = kwds.get('loc') args, loc = self._rv_discrete__fix_loc(args, loc) q,loc = map(arr,(q,loc)) args = tuple(map(arr,args)) cond0 = self._argcheck(*args) & (loc == loc) cond1 = (q > 0) & (q < 1) cond2 = (q==1) & cond0 cond = cond0 & cond1 output = valarray(shape(cond),value=self.badvalue,typecode='d') #output type 'd' to handle nin and inf place(output,(q==0)*(cond==cond), self.a-1) place(output,cond2,self.b) if any(cond): goodargs = argsreduce(cond, *((q,)+args+(loc,))) loc, goodargs = goodargs[-1], goodargs[:-1] place(output,cond,self._ppf(*goodargs) + loc) if output.ndim == 0: return output[()] return output scipy.stats.distributions.rv_discrete.ppf = ppf7 try: externals.exists('good scipy.stats.rv_discrete.ppf', force=True, raiseException=True) except RuntimeError: warning("rv_discrete.ppf was not fixed with a monkey-patch. " "It remains broken") cfg.set('externals', 'have good scipy.stats.rv_discrete.ppf', 'no') pymvpa-0.4.8/mvpa/tests/000077500000000000000000000000001174541445200151535ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/tests/__init__.py000066400000000000000000000140671174541445200172740ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit test interface for PyMVPA""" import unittest import numpy as np from mvpa import _random_seed, cfg from mvpa.base import externals, warning def collectTestSuites(): """Runs over all tests it knows and composes a dictionary with test suite instances as values and IDs as keys. IDs are the filenames of the unittest without '.py' extension and 'test_' prefix. During collection this function will run a full and verbose test for all known externals. """ # list all test modules (without .py extension) tests = [ # Basic data structures/manipulators 'test_externals', 'test_base', 'test_dochelpers', 'test_dataset', 'test_arraymapper', 'test_boxcarmapper', 'test_som', 'test_neighbor', 'test_maskeddataset', 'test_metadataset', 'test_splitter', 'test_state', 'test_params', 'test_eepdataset', # Misc supporting utilities 'test_config', 'test_stats', 'test_support', 'test_verbosity', 'test_report', 'test_datasetfx', 'test_cmdline', 'test_args', 'test_eepdataset', 'test_meg', # Classifiers (longer tests) 'test_kernel', 'test_clf', 'test_regr', 'test_knn', 'test_gnb', 'test_svm', 'test_plr', 'test_smlr', # Various algorithms 'test_svdmapper', 'test_procrust', 'test_hyperalignment', 'test_samplegroupmapper', 'test_transformers', 'test_transerror', 'test_clfcrossval', 'test_searchlight', 'test_rfe', 'test_ifs', 'test_datameasure', 'test_perturbsensana', 'test_splitsensana', # And the suite (all-in-1) 'test_suite', ] # provide people with a hint about the warnings that might show up in a # second warning('Testing for availability of external software packages. Test ' 'cases depending on missing packages will not be part of the test ' 'suite.') # So we could see all warnings about missing dependencies warning.maxcount = 1000 # fully test of externals externals.testAllDependencies() __optional_tests = [ ('scipy', 'ridge'), ('scipy', 'stats_sp'), ('scipy', 'datasetfx_sp'), (['lars','scipy'], 'lars'), ('nifti', 'niftidataset'), ('mdp', 'icamapper'), ('scipy', 'zscoremapper'), ('pywt', 'waveletmapper'), (['cPickle', 'gzip'], 'hamster'), ('nose', 'iohelpers'), # ('mdp', 'pcamapper'), ] if not cfg.getboolean('tests', 'lowmem', default='no'): __optional_tests += [(['nifti', 'lxml'], 'atlases')] # and now for the optional tests optional_tests = [] for external, testname in __optional_tests: if externals.exists(external): optional_tests.append('test_%s' % testname) # finally merge all of them tests += optional_tests # import all test modules for t in tests: # TODO: exclude tests which fail to import: e.g. on Windows # could get WindowsError due to missing msvcr90.dll exec 'import ' + t # instanciate all tests suites and return dict of them (with ID as key) return dict([(t[5:], eval(t + '.suite()')) for t in tests ]) def run(limit=None, verbosity=None): """Runs the full or a subset of the PyMVPA unittest suite. :Parameters: limit: None | list If None, the full test suite is run. Alternatively, a list with test IDs can be provides. IDs are the base filenames of the test implementation, e.g. the ID for the suite in 'mvpa/tests/test_niftidataset.py' is 'niftidataset'. verbosity: None | int Verbosity of unittests execution. If None, controlled by PyMVPA configuration tests/verbosity. Values higher than 2 enable all Python, NumPy and PyMVPA warnings """ if __debug__: from mvpa.base import debug # Lets add some targets which provide additional testing debug.active += ['CHECK_.*'] # collect all tests suites = collectTestSuites() if limit is None: # make global test suite (use them all) ts = unittest.TestSuite(suites.values()) else: ts = unittest.TestSuite([suites[s] for s in limit]) class TextTestRunnerPyMVPA(unittest.TextTestRunner): """Extend TextTestRunner to print out random seed which was used in the case of failure""" def run(self, test): """Run the bloody test and puke the seed value if failed""" result = super(TextTestRunnerPyMVPA, self).run(test) if not result.wasSuccessful(): print "MVPA_SEED=%s" % _random_seed if verbosity is None: verbosity = int(cfg.get('tests', 'verbosity', default=1)) if verbosity < 3: # no MVPA warnings during whole testsuite (but restore handlers later on) handler_backup = warning.handlers warning.handlers = [] # No python warnings (like ctypes version for slmr) import warnings warnings.simplefilter('ignore') # No numpy np_errsettings = np.geterr() np.seterr(**dict([(x, 'ignore') for x in np_errsettings])) # finally run it TextTestRunnerPyMVPA(verbosity=verbosity).run(ts) if verbosity < 3: # restore warning handlers warning.handlers = handler_backup np.seterr(**np_errsettings) pymvpa-0.4.8/mvpa/tests/badexternals/000077500000000000000000000000001174541445200176275ustar00rootroot00000000000000pymvpa-0.4.8/mvpa/tests/badexternals/cPickle_disabled.py000066400000000000000000000000221174541445200233740ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/ctypes.py000066400000000000000000000000221174541445200215020ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/griddata.py000066400000000000000000000000221174541445200217520ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/gzip.py000066400000000000000000000000221174541445200211440ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/hcluster.py000066400000000000000000000000221174541445200220240ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/libsvm.py000066400000000000000000000000221174541445200214670ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/mdp.py000066400000000000000000000000221174541445200207530ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/nifti.py000066400000000000000000000000221174541445200213040ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/pylab.py000066400000000000000000000000221174541445200213020ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/pywt.py000066400000000000000000000000221174541445200211760ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/rpy.py000066400000000000000000000000221174541445200210050ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/scikits.py000066400000000000000000000000221174541445200216440ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/scipy.py000066400000000000000000000000221174541445200213220ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/shogun.py000066400000000000000000000000221174541445200214760ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/badexternals/weave.py000066400000000000000000000000221174541445200213020ustar00rootroot00000000000000raise ImportError pymvpa-0.4.8/mvpa/tests/main.py000066400000000000000000000046471174541445200164640ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit test console interface for PyMVPA""" import unittest import sys import numpy as np from mvpa import _random_seed, cfg from mvpa.base import externals, warning from mvpa.tests import collectTestSuites def main(): if __debug__: from mvpa.base import debug # Lets add some targets which provide additional testing debug.active += ['CHECK_.*'] # NOTE: it had to be done here instead of test_clf.py for # instance, since for CHECK_RETRAIN it has to be set before object # gets created, ie while importing clfs.warehouse suites = collectTestSuites() # and make global test suite ts = unittest.TestSuite(suites.values()) class TextTestRunnerPyMVPA(unittest.TextTestRunner): """Extend TextTestRunner to print out random seed which was used in the case of failure""" def run(self, test): result = super(TextTestRunnerPyMVPA, self).run(test) if not result.wasSuccessful(): print "MVPA_SEED=%s" % _random_seed sys.exit(1) return result verbosity = int(cfg.get('tests', 'verbosity', default=1)) if verbosity < 3: # no MVPA warnings during whole testsuite (but restore handlers later on) handler_backup = warning.handlers warning.handlers = [] # No python warnings (like ctypes version for slmr) import warnings warnings.simplefilter('ignore') # No numpy np_errsettings = np.geterr() np.seterr(**dict([(x, 'ignore') for x in np_errsettings])) # finally run it TextTestRunnerPyMVPA(verbosity=verbosity).run(ts) if verbosity < 3: # restore warning handlers warning.handlers = handler_backup np.seterr(**np_errsettings) if __name__ == '__main__': main() pymvpa-0.4.8/mvpa/tests/runner.py000066400000000000000000000054061174541445200170430ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Helper module to enable profiling of the testcase If environment variable PROFILELEVEL is set it uses hotshot profiler for unittest.main() call. Value of PROFILELEVEL defines number of top busy functions to report. Environment variable PROFILELINES=1 makes hotshot store information per each line, so it could be easily inspected later on. Output: Profiler stores its Stats into a file named after original script (sys.argv[0]) with suffix".prof" appended Usage: Replace unittest.main() with import runner Visualization: kcachegrind provides nice interactive GUI to inspect profiler results. If PROFILELINES was set to 1, it provides information per each line. To convert .prof file into a file suitable for kcachegrind, use utility hotshot2calltree which comes in package kcachegrind-converters. Example: # profile and output 3 most expensive function calls PROFILELEVEL=3 PROFILELINES=1 PYTHONPATH=../ python test_searchlight.py # convert to kcachegrind format hotshot2calltree -o test_searchlight.py.kcache test_searchlight.py.prof # inspect kcachegrind test_searchlight.py.kcache """ import unittest import sys from os import environ from mvpa import _random_seed profilelevel = None if environ.has_key('PROFILELEVEL'): profilelevel = int(environ['PROFILELEVEL']) # Extend TestProgram to print out the seed which was used class TestProgramPyMVPA(unittest.TestProgram): def runTests(self): if self.verbosity: print "MVPA_SEED=%s:" % _random_seed, sys.stdout.flush() super(TestProgramPyMVPA, self).runTests() if profilelevel is None: TestProgramPyMVPA() else: profilelines = environ.has_key('PROFILELINES') import hotshot, hotshot.stats pname = "%s.prof" % sys.argv[0] prof = hotshot.Profile(pname, lineevents=profilelines) try: # actually return values are never setup # since unittest.main sys.exit's benchtime, stones = prof.runcall( unittest.main ) except SystemExit: pass print "Saving profile data into %s" % pname prof.close() if profilelevel > 0: # we wanted to see the summary right here # instead of just storing it into a file print "Loading profile data from %s" % pname stats = hotshot.stats.load(pname) stats.strip_dirs() stats.sort_stats('time', 'calls') stats.print_stats(profilelevel) pymvpa-0.4.8/mvpa/tests/test_args.py000066400000000000000000000054501174541445200175240ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA args helpers""" import unittest from mvpa.misc.args import * if __debug__: from mvpa.base import debug class ArgsHelpersTest(unittest.TestCase): def testBasic(self): """Test if we are not missing basic parts""" kwargs = {'a':1, 'slave_a':3, 'slave_z':4, 'slave_slave_z':5, 'c':3} res = split_kwargs(kwargs, ['slave_']) self.failUnless(res.has_key('slave_') and res.has_key('')) self.failUnless(res['slave_'] == {'a':3, 'z':4, 'slave_z':5}) self.failUnless(res[''] == {'a':1, 'c':3}) res = split_kwargs(kwargs) self.failUnless(res.keys() == ['']) self.failUnless(res[''] == kwargs) def testDecorator(self): """Test the group_kwargs decorator""" selftop = self class C1(object): @group_kwargs(prefixes=['slave_'], assign=True) def __init__(self, **kwargs): selftop.failUnless(hasattr(self, '_slave_kwargs')) self.method_passedempty() self.method_passed(1, custom_p1=144, bugax=1) self.method_filtered(1, custom_p1=123) @group_kwargs(prefixes=['custom_'], passthrough=True) def method_passedempty(self, **kwargs): # we must have it even though it is empty selftop.failUnless('custom_kwargs' in kwargs) @group_kwargs(prefixes=['custom_', 'buga'], passthrough=True) def method_passed(self, a, custom_kwargs, bugakwargs, **kwargs): # we must have it even though it is empty selftop.failUnless(custom_kwargs == {'p1':144}) selftop.failUnless(bugakwargs == {'x':1}) selftop.failUnless(not hasattr(self, '_custom_kwargs')) @group_kwargs(prefixes=['custom_']) def method_filtered(self, a, **kwargs): # we must have it even though it is empty selftop.failUnlessEqual(a, 1) selftop.failUnless(not 'custom_kwargs' in kwargs) def method(self): return 123 @group_kwargs(prefixes=['xxx']) def method_decorated(self): return 124 c1 = C1(slave_p1=1, p1=2) self.failUnless(c1.method() == 123) self.failUnless(c1.method_decorated() == 124) def suite(): return unittest.makeSuite(ArgsHelpersTest) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_arraymapper.py000066400000000000000000000176461174541445200211250ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA dense array mapper""" from mvpa.mappers.array import DenseArrayMapper from mvpa.mappers.metric import * import unittest import numpy as N class DenseArrayMapperTests(unittest.TestCase): def testForwardDenseArrayMapper(self): mask = N.ones((3,2)) map_ = DenseArrayMapper(mask) # test shape reports self.failUnless( map_.nfeatures == 6 ) # test 1sample mapping self.failUnless( ( map_.forward( N.arange(6).reshape(3,2) ) \ == [0,1,2,3,4,5]).all() ) # test 4sample mapping foursample = map_.forward( N.arange(24).reshape(4,3,2)) self.failUnless( ( foursample \ == [[0,1,2,3,4,5], [6,7,8,9,10,11], [12,13,14,15,16,17], [18,19,20,21,22,23]]).all() ) # check incomplete masks mask[1,1] = 0 map_ = DenseArrayMapper(mask) self.failUnless( map_.nfeatures == 5 ) self.failUnless( ( map_.forward( N.arange(6).reshape(3,2) ) \ == [0,1,2,4,5]).all() ) # check that it doesn't accept wrong dataspace self.failUnlessRaises( ValueError, map_.forward, N.arange(4).reshape(2,2) ) # check fail if neither mask nor shape self.failUnlessRaises(ValueError, DenseArrayMapper) # check that a full mask is automatically created when providing shape m = DenseArrayMapper(shape=(2, 3, 4)) mp = m.forward(N.arange(24).reshape(2, 3, 4)) self.failUnless((mp == N.arange(24)).all()) def testReverseDenseArrayMapper(self): mask = N.ones((3,2)) mask[1,1] = 0 map_ = DenseArrayMapper(mask) rmapped = map_.reverse(N.arange(1,6)) self.failUnless( rmapped.shape == (3,2) ) self.failUnless( rmapped[1,1] == 0 ) self.failUnless( rmapped[2,1] == 5 ) # check that it doesn't accept wrong dataspace self.failUnlessRaises( ValueError, map_, N.arange(6)) rmapped2 = map_.reverse(N.arange(1,11).reshape(2,5)) self.failUnless( rmapped2.shape == (2,3,2) ) self.failUnless( rmapped2[0,1,1] == 0 ) self.failUnless( rmapped2[1,1,1] == 0 ) self.failUnless( rmapped2[0,2,1] == 5 ) self.failUnless( rmapped2[1,2,1] == 10 ) def testDenseArrayMapperMetrics(self): """ Test DenseArrayMapperMetric """ mask = N.ones((3,2)) mask[1,1] = 0 # take space with non-square elements neighborFinder = DescreteMetric([0.5, 2]) map_ = DenseArrayMapper(mask, neighborFinder) # test getNeighbors # now it returns list of arrays #target = [N.array([0, 0]), N.array([0, 1]), # N.array([1, 0]), N.array([2, 0])] #result = map_.getNeighborIn([0, 0], 2) #self.failUnless(N.array(map(lambda x,y:(x==y).all(), result, target)).all()) # check by providing outId target = [0,1,2,3] result = map_.getNeighbors(0, 2.1) self.failUnless( result == target ) map__ = DenseArrayMapper(mask, elementsize=[0.5, 2]) self.failUnless( map__.getNeighbors(0, 2.1) == target, msg="DenseArrayMapper must accept elementsize parameter and set" + " DescreteMetric accordingly") self.failUnlessRaises(ValueError, DenseArrayMapper, mask, elementsize=[0.5]*3) """DenseArrayMapper must raise exception when not appropriatly sized elementsize was provided""" def testMapperAliases(self): mm=DenseArrayMapper(N.ones((3,4,2))) # We decided to don't have alias for reverse #self.failUnless((mm(N.arange(24)) == mm.reverse(N.arange(24))).all()) self.failUnless((mm(N.ones((3,4,2))) \ == mm.forward(N.ones((3,4,2)))).all()) def testGetInOutIdBehaviour(self): mask=N.zeros((3,4,2)) mask[0,0,1]=1 mask[2,1,0]=1 mask[0,3,1]=1 mm=DenseArrayMapper(mask) self.failUnless(mm.nfeatures==3) # 'In' self.failUnless((mm.getInIds() \ == N.array([[0, 0, 1],[0, 3, 1],[2, 1, 0]])).all()) self.failUnless((mm.getInId(1) == [0,3,1]).all()) # called with list gives nonzero() like output self.failUnless((mm.getInId(range(mm.nfeatures)) \ == mm.getInIds().T).all()) # 'Out' self.failUnlessRaises( ValueError, mm.getOutId, (0,0,0)) self.failUnless(mm.getOutId((0,0,1)) == 0 and mm.getOutId((0,3,1)) == 1 and mm.getOutId((2,1,0)) == 2) def testSelects(self): mask = N.ones((3,2)) mask[1,1] = 0 mask0 = mask.copy() data = N.arange(6).reshape(mask.shape) map_ = DenseArrayMapper(mask) # check if any exception is thrown if we get # out of the outIds self.failUnlessRaises(IndexError, map_.selectOut, [0,1,2,6]) # remove 1,2 map_.selectOut([0,3,4]) self.failUnless((map_.forward(data)==[0, 4, 5]).all()) # remove 1 more map_.selectOut([0,2]) self.failUnless((map_.forward(data)==[0, 5]).all()) # check if original mask wasn't perturbed self.failUnless((mask == mask0).all()) # do the same but using discardOut map_ = DenseArrayMapper(mask) map_.discardOut([1,2]) self.failUnless((map_.forward(data)==[0, 4, 5]).all()) map_.discardOut([1]) self.failUnless((map_.forward(data)==[0, 5]).all()) # check if original mask wasn't perturbed self.failUnless((mask == mask0).all()) def _testSelectReOrder(self): """ Test is desabled for now since if order is incorrect in __debug__ we just spit out a warning - no exception """ mask = N.ones((3,3)) mask[1,1] = 0 data = N.arange(9).reshape(mask.shape) map_ = DenseArrayMapper(mask) oldneighbors = map_.forward(data)[map_.getNeighbors(0, radius=2)] # just do place changes # by default - we don't sort/check order so it would screw things # up map_.selectOut([7, 1, 2, 3, 4, 5, 6, 0]) # we check if an item new outId==7 still has proper neighbors newneighbors = map_.forward(data)[map_.getNeighbors(7, radius=2)] self.failUnless( (oldneighbors != newneighbors ).any()) # disable since selectOut does not have 'sort' anymore # def testSelectOrder(self): # """ # Test if changing the order by doing selectOut preserves # neighborhood information -- but also apply sort in difference # to testSelectReOrder # """ # mask = N.ones((3,3)) # mask[1,1] = 0 # # data = N.arange(9).reshape(mask.shape) # map_ = DenseArrayMapper(mask) # oldneighbors = map_.forward(data)[map_.getNeighbors(0, radius=2)] # # map_ = DenseArrayMapper(mask) # map_.selectOut([7, 1, 2, 3, 4, 5, 6, 0], sort=True) # # we check if an item new outId==0 still has proper neighbors # newneighbors = map_.forward(data)[map_.getNeighbors(0, radius=2)] # self.failUnless( (oldneighbors == newneighbors ).all()) def suite(): return unittest.makeSuite(DenseArrayMapperTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_atlases.py000066400000000000000000000110221174541445200202140ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA atlases""" import unittest, re import numpy as N from mvpa.base import externals, warning if externals.exists('nifti', raiseException=True): from mvpa.atlases import * else: raise RuntimeError, "Don't run me if no nifti is present" import os from mvpa import pymvpa_dataroot class AtlasesTests(unittest.TestCase): """Basic tests for support of atlases such as the ones shipped with FSL """ def testTransformations(self): """TODO""" pass def testAtlases(self): """Basic testing of atlases""" tested = 0 for name in KNOWN_ATLASES.keys(): #filename = KNOWN_ATLASES[name] % {'name': name} try: atlas = Atlas(name=name) tested += 1 except IOError: # so we just don't have it continue #print isinstance(atlas.atlas, objectify.ObjectifiedElement) #print atlas.header.images.imagefile.get('offset') #print atlas.labelVoxel( (0, -7, 20) ) #print atlas[ 0, 0, 0 ] coord = (-63, -12, 22) # Atlas must have at least 1 level and that one must # have some labels self.failUnless(len(atlas.levels_dict[0].labels) > 0) for res in [ atlas[coord], atlas.labelPoint(coord) ]: self.failUnless(res.get('coord_queried', None) == coord, '%s: Comparison failed. Got %s and %s' % (name, res.get('coord_queried', None), coord)) self.failUnless('labels' in res) # all atlases so far are based on voxels self.failUnless('voxel_queried' in res) # test explicit level specification via slice, although bogus here # XXX levels in queries should be deprecated -- too much of # performance hit res0 = atlas[coord, range(atlas.Nlevels)] self.failUnless(res0 == res) #print atlas[ 0, -7, 20, [1,2,3] ] #print atlas[ (0, -7, 20), 1:2 ] #print atlas[ (0, -7, 20) ] #print atlas[ (0, -7, 20), : ] # print atlas.getLabels(0) if not tested: warning("No atlases were found -- thus no testing was done") def testFind(self): if not externals.exists('atlas_fsl'): return tshape = (182, 218, 182) # target shape of fsl atlas chosen by default atl = Atlas(name='HarvardOxford-Cortical') atl.levels_dict[0].find('Frontal Pole') self.failUnlessEqual( len(atl.find(re.compile('Fusiform'), unique=False)), 4) m = atl.getMap(1) self.failUnlessEqual(m.shape, tshape) self.failUnless(N.max(m)==100) self.failUnless(N.min(m)==0) ms = atl.getMaps('Fusiform') self.failUnlessEqual(len(ms), 4) self.failUnlessEqual(ms[0].shape, tshape) ms = atl.getMaps('ZaZaZa') self.failUnless(not len(ms)) self.failUnlessRaises(ValueError, atl.getMap, 'Fusiform') self.failUnless(len(atl.find('Fusiform', unique=False)) == 4) self.failUnlessEqual(atl.getMap('Fusiform', strategy='max').shape, tshape) # Test loading of custom atlas # for now just on the original file atl2 = Atlas(name='HarvardOxford-Cortical', image_file=atl._image_file) # we should get exactly the same maps from both in this dummy case self.failUnless((atl.getMap('Frontal Pole') == atl2.getMap('Frontal Pole')).all()) # Lets falsify and feed some crammy file as the atlas atl2 = Atlas(name='HarvardOxford-Cortical', image_file=os.path.join(pymvpa_dataroot, 'example4d.nii.gz')) # we should get not even comparable maps now ;) self.failUnless(atl.getMap('Frontal Pole').shape != atl2.getMap('Frontal Pole').shape) def suite(): return unittest.makeSuite(AtlasesTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_base.py000066400000000000000000000022031174541445200174730ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Test some base functionality which did not make it into a separate unittests""" import unittest from tempfile import mktemp from mvpa.base.info import wtf class TestBases(unittest.TestCase): def testWtf(self): """Very basic testing -- just to see if it doesn't crash""" try: wtf() except Exception, e: self.fail('Testing of systemInfo failed with "%s"' % str(e)) filename = mktemp('mvpa', 'test') wtf(filename) try: syslines = open(filename, 'r').readlines() except Exception, e: self.fail('Testing of dumping systemInfo into a file failed: %s' % str(e)) def suite(): return unittest.makeSuite(TestBases) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_boxcarmapper.py000066400000000000000000000075161174541445200212600ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA Boxcar mapper""" import unittest from mvpa.support.copy import deepcopy import numpy as N from mvpa.mappers.boxcar import BoxcarMapper from mvpa.datasets import Dataset class BoxcarMapperTests(unittest.TestCase): def testSimple(self): """Just the same tests as for transformWithBoxcar. Mention that BoxcarMapper doesn't apply function with each boxcar """ data = N.arange(10) sp = N.arange(10) # check if stupid thing don't work self.failUnlessRaises(ValueError, BoxcarMapper, sp, 0 ) # now do an identity transformation bcm = BoxcarMapper(sp, 1) trans = bcm(data) # ,0 is a feature below, so we get explicit 2D out of 1D self.failUnless( (trans[:,0] == data).all() ) # now check for illegal boxes self.failUnlessRaises(ValueError, BoxcarMapper(sp, 2), data) # now something that should work sp = N.arange(9) bcm = BoxcarMapper(sp,2) trans = bcm(data) self.failUnless( (trans == N.vstack((N.arange(9), N.arange(9)+1)).T ).all() ) # now test for proper data shape data = N.ones((10,3,4,2)) sp = [ 2, 4, 3, 5 ] trans = BoxcarMapper(sp, 4)(data) self.failUnless( trans.shape == (4,4,3,4,2) ) # test reverse data = N.arange(240).reshape(10, 3, 4, 2) sp = [ 2, 4, 3, 5 ] m = BoxcarMapper(sp, 2) mp = m.forward(data) self.failUnless(mp.shape == (4, 2, 3, 4, 2)) # try full reconstruct mr = m.reverse(mp) # shape has to match self.failUnless(mr.shape == data.shape) # first two samples where not in any of the boxcars and cannot be # reconstructed self.failUnless(N.sum(mr[:2]) == 0) # same for the last ones self.failUnless(N.sum(mr[7:]) == 0) # check proper reconstruction of non-conflicting sample self.failUnless((mr[2].ravel() == N.arange(48, 72)).all()) # check proper reconstruction of samples being part of multiple # mapped samples self.failUnless((mr[3].ravel() == N.arange(72, 96)).all()) # test reverse of a single sample singlesample = N.arange(48).reshape(2, 3, 4, 2) self.failUnless((singlesample == m.reverse(singlesample)).all()) # should not work for shape mismatch self.failUnlessRaises(ValueError, m.reverse, singlesample[0]) # check broadcasting of 'raw' samples into proper boxcars on forward() bc = m.forward(N.arange(24).reshape(3, 4, 2)) self.failUnless((bc == N.array(2 * [N.arange(24).reshape(3, 4, 2)])).all()) def testIds(self): data = N.arange(20).reshape( (10,2) ) bcm = BoxcarMapper([1, 4, 6], 3) trans = bcm(data) self.failUnlessEqual(bcm.isValidInId( [1] ), True) self.failUnlessEqual(bcm.isValidInId( [0,1] ), False) self.failUnlessEqual(bcm.isValidOutId( [1] ), True) self.failUnlessEqual(bcm.isValidOutId( [3] ), False) self.failUnlessEqual(bcm.isValidOutId( [0,1] ), True) self.failUnlessEqual(bcm.isValidOutId( [0,1,0] ), False) def suite(): return unittest.makeSuite(BoxcarMapperTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_clf.py000066400000000000000000001010451174541445200173310ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA basic Classifiers""" from mvpa.support.copy import deepcopy from mvpa.base import externals from mvpa.datasets import Dataset from mvpa.mappers.mask import MaskMapper from mvpa.datasets.splitters import NFoldSplitter, OddEvenSplitter from mvpa.misc.exceptions import UnknownStateError from mvpa.clfs.base import DegenerateInputError, FailedToTrainError from mvpa.clfs.meta import CombinedClassifier, \ BinaryClassifier, MulticlassClassifier, \ SplitClassifier, MappedClassifier, FeatureSelectionClassifier, \ TreeClassifier from mvpa.clfs.transerror import TransferError from mvpa.algorithms.cvtranserror import CrossValidatedTransferError from tests_warehouse import * from tests_warehouse_clfs import * # What exceptions to allow while testing degenerate cases. # If it pukes -- it is ok -- user will notice that something # is wrong _degenerate_allowed_exceptions = [DegenerateInputError, FailedToTrainError] if externals.exists('rpy'): import rpy _degenerate_allowed_exceptions += [rpy.RPyRException] class ClassifiersTests(unittest.TestCase): def setUp(self): self.clf_sign = SameSignClassifier() self.clf_less1 = Less1Classifier() # simple binary dataset self.data_bin_1 = Dataset( samples=[[0,0],[-10,-1],[1,0.1],[1,-1],[-1,1]], labels=[1, 1, 1, -1, -1], # labels chunks=[0, 1, 2, 2, 3]) # chunks def testDummy(self): clf = SameSignClassifier(enable_states=['training_confusion']) clf.train(self.data_bin_1) self.failUnlessRaises(UnknownStateError, clf.states.__getattribute__, "predictions") """Should have no predictions after training. Predictions state should be explicitely disabled""" if not _all_states_enabled: self.failUnlessRaises(UnknownStateError, clf.states.__getattribute__, "trained_dataset") self.failUnlessEqual(clf.training_confusion.percentCorrect, 100, msg="Dummy clf should train perfectly") self.failUnlessEqual(clf.predict(self.data_bin_1.samples), list(self.data_bin_1.labels)) self.failUnlessEqual(len(clf.predictions), self.data_bin_1.nsamples, msg="Trained classifier stores predictions by default") clf = SameSignClassifier(enable_states=['trained_dataset']) clf.train(self.data_bin_1) self.failUnless((clf.trained_dataset.samples == self.data_bin_1.samples).all()) self.failUnless((clf.trained_dataset.labels == self.data_bin_1.labels).all()) def testBoosted(self): # XXXXXXX # silly test if we get the same result with boosted as with a single one bclf = CombinedClassifier(clfs=[self.clf_sign.clone(), self.clf_sign.clone()]) self.failUnlessEqual(list(bclf.predict(self.data_bin_1.samples)), list(self.data_bin_1.labels), msg="Boosted classifier should work") self.failUnlessEqual(bclf.predict(self.data_bin_1.samples), self.clf_sign.predict(self.data_bin_1.samples), msg="Boosted classifier should have the same as regular") def testBoostedStatePropagation(self): bclf = CombinedClassifier(clfs=[self.clf_sign.clone(), self.clf_sign.clone()], enable_states=['feature_ids']) # check states enabling propagation self.failUnlessEqual(self.clf_sign.states.isEnabled('feature_ids'), _all_states_enabled) self.failUnlessEqual(bclf.clfs[0].states.isEnabled('feature_ids'), True) bclf2 = CombinedClassifier(clfs=[self.clf_sign.clone(), self.clf_sign.clone()], propagate_states=False, enable_states=['feature_ids']) self.failUnlessEqual(self.clf_sign.states.isEnabled('feature_ids'), _all_states_enabled) self.failUnlessEqual(bclf2.clfs[0].states.isEnabled('feature_ids'), _all_states_enabled) def testBinaryDecorator(self): ds = Dataset(samples=[ [0,0], [0,1], [1,100], [-1,0], [-1,-3], [ 0,-10] ], labels=[ 'sp', 'sp', 'sp', 'dn', 'sn', 'dp']) testdata = [ [0,0], [10,10], [-10, -1], [0.1, -0.1], [-0.2, 0.2] ] # labels [s]ame/[d]ifferent (sign), and [p]ositive/[n]egative first element clf = SameSignClassifier() # lets create classifier to descriminate only between same/different, # which is a primary task of SameSignClassifier bclf1 = BinaryClassifier(clf=clf, poslabels=['sp', 'sn'], neglabels=['dp', 'dn']) orig_labels = ds.labels[:] bclf1.train(ds) self.failUnless(bclf1.predict(testdata) == [['sp', 'sn'], ['sp', 'sn'], ['sp', 'sn'], ['dn', 'dp'], ['dn', 'dp']]) self.failUnless((ds.labels == orig_labels).all(), msg="BinaryClassifier should not alter labels") @sweepargs(clf=clfswh['binary']) def testClassifierGeneralization(self, clf): """Simple test if classifiers can generalize ok on simple data """ te = CrossValidatedTransferError(TransferError(clf), NFoldSplitter()) cve = te(datasets['uni2medium']) if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(cve < 0.25, msg="Got transfer error %g" % (cve)) @sweepargs(clf=clfswh[:] + regrswh[:]) def testSummary(self, clf): """Basic testing of the clf summary """ summary1 = clf.summary() self.failUnless('not yet trained' in summary1) clf.train(datasets['uni2small']) summary = clf.summary() # It should get bigger ;) self.failUnless(len(summary) > len(summary1)) self.failUnless(not 'not yet trained' in summary) @sweepargs(clf=clfswh[:] + regrswh[:]) def testDegenerateUsage(self, clf): """Test how clf handles degenerate cases """ # Whenever we have only 1 feature with only 0s in it ds1 = datasets['uni2small'][:, [0]] # XXX this very line breaks LARS in many other unittests -- # very interesting effect. but screw it -- for now it will be # this way ds1.samples[:] = 0.0 # all 0s #ds2 = datasets['uni2small'][[0], :] #ds2.samples[:] = 0.0 # all 0s clf.states._changeTemporarily( enable_states=['values', 'training_confusion']) # Good pukes are good ;-) # TODO XXX add # - ", ds2):" to test degenerate ds with 1 sample # - ds1 but without 0s -- just 1 feature... feature selections # might lead to 'surprises' due to magic in combiners etc for ds in (ds1, ): try: clf.train(ds) # should not crash or stall # could we still get those? summary = clf.summary() cm = clf.states.training_confusion # If succeeded to train/predict (due to # training_confusion) without error -- results better be # at "chance" continue if 'ACC' in cm.stats: self.failUnlessEqual(cm.stats['ACC'], 0.5) else: self.failUnless(N.isnan(cm.stats['CCe'])) except tuple(_degenerate_allowed_exceptions): pass clf.states._resetEnabledTemporarily() # TODO: sg - remove our limitations, meta -- also @sweepargs(clf=clfswh['!sg', '!plr', '!meta']) def test_single_class(self, clf): """Test if binary and multiclass can handle single class training/testing """ ds = datasets['uni2small']['labels', (0,)] try: err = TransferError(clf)( datasets['uni2small_test']['labels', (0,)], datasets['uni2small_train']['labels', (0,)]) except Exception, e: self.fail(str(e)) self.failUnless(err == 0.) # TODO: validate for regressions as well!!! def testSplitClassifier(self): ds = self.data_bin_1 clf = SplitClassifier(clf=SameSignClassifier(), splitter=NFoldSplitter(1), enable_states=['confusion', 'training_confusion', 'feature_ids']) clf.train(ds) # train the beast error = clf.confusion.error tr_error = clf.training_confusion.error clf2 = clf.clone() cv = CrossValidatedTransferError( TransferError(clf2), NFoldSplitter(), enable_states=['confusion', 'training_confusion']) cverror = cv(ds) tr_cverror = cv.training_confusion.error self.failUnlessEqual(error, cverror, msg="We should get the same error using split classifier as" " using CrossValidatedTransferError. Got %s and %s" % (error, cverror)) self.failUnlessEqual(tr_error, tr_cverror, msg="We should get the same training error using split classifier as" " using CrossValidatedTransferError. Got %s and %s" % (tr_error, tr_cverror)) self.failUnlessEqual(clf.confusion.percentCorrect, 100, msg="Dummy clf should train perfectly") self.failUnlessEqual(len(clf.confusion.sets), len(ds.uniquechunks), msg="Should have 1 confusion per each split") self.failUnlessEqual(len(clf.clfs), len(ds.uniquechunks), msg="Should have number of classifiers equal # of epochs") self.failUnlessEqual(clf.predict(ds.samples), list(ds.labels), msg="Should classify correctly") # feature_ids must be list of lists, and since it is not # feature-selecting classifier used - we expect all features # to be utilized # NOT ANYMORE -- for BoostedClassifier we have now union of all # used features across slave classifiers. That makes # semantics clear. If you need to get deeper -- use upcoming # harvesting facility ;-) # self.failUnlessEqual(len(clf.feature_ids), len(ds.uniquechunks)) # self.failUnless(N.array([len(ids)==ds.nfeatures # for ids in clf.feature_ids]).all()) # Just check if we get it at all ;-) summary = clf.summary() @sweepargs(clf_=clfswh['binary', '!meta']) def testSplitClassifierExtended(self, clf_): clf2 = clf_.clone() ds = datasets['uni2medium']#self.data_bin_1 clf = SplitClassifier(clf=clf_, #SameSignClassifier(), splitter=NFoldSplitter(1), enable_states=['confusion', 'feature_ids']) clf.train(ds) # train the beast error = clf.confusion.error cv = CrossValidatedTransferError( TransferError(clf2), NFoldSplitter(), enable_states=['confusion', 'training_confusion']) cverror = cv(ds) self.failUnless(abs(error-cverror)<0.01, msg="We should get the same error using split classifier as" " using CrossValidatedTransferError. Got %s and %s" % (error, cverror)) if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(error < 0.25, msg="clf should generalize more or less fine. " "Got error %s" % error) self.failUnlessEqual(len(clf.confusion.sets), len(ds.uniquechunks), msg="Should have 1 confusion per each split") self.failUnlessEqual(len(clf.clfs), len(ds.uniquechunks), msg="Should have number of classifiers equal # of epochs") #self.failUnlessEqual(clf.predict(ds.samples), list(ds.labels), # msg="Should classify correctly") def testHarvesting(self): """Basic testing of harvesting based on SplitClassifier """ ds = self.data_bin_1 clf = SplitClassifier(clf=SameSignClassifier(), splitter=NFoldSplitter(1), enable_states=['confusion', 'training_confusion', 'feature_ids'], harvest_attribs=['clf.feature_ids', 'clf.training_time'], descr="DESCR") clf.train(ds) # train the beast # Number of harvested items should be equal to number of chunks self.failUnlessEqual(len(clf.harvested['clf.feature_ids']), len(ds.uniquechunks)) # if we can blame multiple inheritance and ClassWithCollections.__init__ self.failUnlessEqual(clf.descr, "DESCR") def testMappedClassifier(self): samples = N.array([ [0,0,-1], [1,0,1], [-1,-1, 1], [-1,0,1], [1, -1, 1] ]) testdata3 = Dataset(samples=samples, labels=1) res110 = [1, 1, 1, -1, -1] res101 = [-1, 1, -1, -1, 1] res011 = [-1, 1, -1, 1, -1] clf110 = MappedClassifier(clf=self.clf_sign, mapper=MaskMapper(N.array([1,1,0]))) clf101 = MappedClassifier(clf=self.clf_sign, mapper=MaskMapper(N.array([1,0,1]))) clf011 = MappedClassifier(clf=self.clf_sign, mapper=MaskMapper(N.array([0,1,1]))) self.failUnlessEqual(clf110.predict(samples), res110) self.failUnlessEqual(clf101.predict(samples), res101) self.failUnlessEqual(clf011.predict(samples), res011) def testFeatureSelectionClassifier(self): from test_rfe import SillySensitivityAnalyzer from mvpa.featsel.base import \ SensitivityBasedFeatureSelection from mvpa.featsel.helpers import \ FixedNElementTailSelector # should give lowest weight to the feature with lowest index sens_ana = SillySensitivityAnalyzer() # should give lowest weight to the feature with highest index sens_ana_rev = SillySensitivityAnalyzer(mult=-1) # corresponding feature selections feat_sel = SensitivityBasedFeatureSelection(sens_ana, FixedNElementTailSelector(1, mode='discard')) feat_sel_rev = SensitivityBasedFeatureSelection(sens_ana_rev, FixedNElementTailSelector(1)) samples = N.array([ [0,0,-1], [1,0,1], [-1,-1, 1], [-1,0,1], [1, -1, 1] ]) testdata3 = Dataset(samples=samples, labels=1) # dummy train data so proper mapper gets created traindata = Dataset(samples=N.array([ [0, 0,-1], [1,0,1] ]), labels=[1,2]) # targets res110 = [1, 1, 1, -1, -1] res011 = [-1, 1, -1, 1, -1] # first classifier -- 0th feature should be discarded clf011 = FeatureSelectionClassifier(self.clf_sign, feat_sel, enable_states=['feature_ids']) self.clf_sign.states._changeTemporarily(enable_states=['values']) clf011.train(traindata) self.failUnlessEqual(clf011.predict(testdata3.samples), res011) # just silly test if we get values assigned in the 'ProxyClassifier' self.failUnless(len(clf011.values) == len(res110), msg="We need to pass values into ProxyClassifier") self.clf_sign.states._resetEnabledTemporarily() self.failUnlessEqual(len(clf011.feature_ids), 2) "Feature selection classifier had to be trained on 2 features" # first classifier -- last feature should be discarded clf011 = FeatureSelectionClassifier(self.clf_sign, feat_sel_rev) clf011.train(traindata) self.failUnlessEqual(clf011.predict(testdata3.samples), res110) def testFeatureSelectionClassifierWithRegression(self): from test_rfe import SillySensitivityAnalyzer from mvpa.featsel.base import \ SensitivityBasedFeatureSelection from mvpa.featsel.helpers import \ FixedNElementTailSelector if sample_clf_reg is None: # none regression was found, so nothing to test return # should give lowest weight to the feature with lowest index sens_ana = SillySensitivityAnalyzer() # corresponding feature selections feat_sel = SensitivityBasedFeatureSelection(sens_ana, FixedNElementTailSelector(1, mode='discard')) # now test with regression-based classifier. The problem is # that it is determining predictions twice from values and # then setting the values from the results, which the second # time is set to predictions. The final outcome is that the # values are actually predictions... dat = Dataset(samples=N.random.randn(4,10),labels=[-1,-1,1,1]) clf_reg = FeatureSelectionClassifier(sample_clf_reg, feat_sel) clf_reg.train(dat) res = clf_reg.predict(dat.samples) self.failIf((N.array(clf_reg.values)-clf_reg.predictions).sum()==0, msg="Values were set to the predictions in %s." % sample_clf_reg) def testTreeClassifier(self): """Basic tests for TreeClassifier """ ds = datasets['uni4medium'] # excluding PLR since that one can deal only with 0,1 labels ATM clfs = clfswh['binary', '!plr'] # pool of classifiers # Lets permute so each time we try some different combination # of the classifiers clfs = [clfs[i] for i in N.random.permutation(len(clfs))] # Test conflicting definition tclf = TreeClassifier(clfs[0], { 'L0+2' : (('L0', 'L2'), clfs[1]), 'L2+3' : ((2, 3), clfs[2])}) self.failUnlessRaises(ValueError, tclf.train, ds) """Should raise exception since label 2 is in both""" # Test insufficient definition tclf = TreeClassifier(clfs[0], { 'L0+5' : (('L0', 'L5'), clfs[1]), 'L2+3' : ((2, 3), clfs[2])}) self.failUnlessRaises(ValueError, tclf.train, ds) """Should raise exception since no group for L1""" # proper definition now tclf = TreeClassifier(clfs[0], { 'L0+1' : (('L0', 1), clfs[1]), 'L2+3' : ((2, 3), clfs[2])}) # Lets test train/test cycle using CVTE cv = CrossValidatedTransferError( TransferError(tclf), OddEvenSplitter(), enable_states=['confusion', 'training_confusion']) cverror = cv(ds) try: rtclf = repr(tclf) except: self.fail(msg="Could not obtain repr for TreeClassifier") # Test accessibility of .clfs self.failUnless(tclf.clfs['L0+1'] is clfs[1]) self.failUnless(tclf.clfs['L2+3'] is clfs[2]) cvtrc = cv.training_confusion cvtc = cv.confusion if cfg.getboolean('tests', 'labile', default='yes'): # just a dummy check to make sure everything is working self.failUnless(cvtrc != cvtc) self.failUnless(cverror < 0.3, msg="Got too high error = %s using %s" % (cverror, tclf)) # Test trailing nodes with no classifier tclf = TreeClassifier(clfs[0], { 'L0' : ((0,), None), 'L1+2+3' : ((1, 2, 3), clfswh['multiclass'][0])}) cv = CrossValidatedTransferError( TransferError(tclf), OddEvenSplitter(), enable_states=['confusion', 'training_confusion']) cverror = cv(ds) if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(cverror < 0.3, msg="Got too high error = %s using %s" % (cverror, tclf)) @sweepargs(clf=clfswh[:]) def testValues(self, clf): if isinstance(clf, MulticlassClassifier): # TODO: handle those values correctly return ds = datasets['uni2small'] clf.states._changeTemporarily(enable_states = ['values']) cv = CrossValidatedTransferError( TransferError(clf), OddEvenSplitter(), enable_states=['confusion', 'training_confusion']) cverror = cv(ds) #print clf.descr, clf.values[0] # basic test either we get 1 set of values per each sample self.failUnlessEqual(len(clf.values), ds.nsamples/2) clf.states._resetEnabledTemporarily() @sweepargs(clf=clfswh['linear', 'svm', 'libsvm', '!meta']) def testMulticlassClassifier(self, clf): oldC = None # XXX somewhat ugly way to force non-dataspecific C value. # Otherwise multiclass libsvm builtin and our MultiClass would differ # in results if clf.params.isKnown('C') and clf.C<0: oldC = clf.C clf.C = 1.0 # reset C to be 1 svm, svm2 = clf, clf.clone() svm2.states.enable(['training_confusion']) mclf = MulticlassClassifier(clf=svm, enable_states=['training_confusion']) svm2.train(datasets['uni2small_train']) mclf.train(datasets['uni2small_train']) s1 = str(mclf.training_confusion) s2 = str(svm2.training_confusion) self.failUnlessEqual(s1, s2, msg="Multiclass clf should provide same results as built-in " "libsvm's %s. Got %s and %s" % (svm2, s1, s2)) svm2.untrain() self.failUnless(svm2.trained == False, msg="Un-Trained SVM should be untrained") self.failUnless(N.array([x.trained for x in mclf.clfs]).all(), msg="Trained Boosted classifier should have all primary classifiers trained") self.failUnless(mclf.trained, msg="Trained Boosted classifier should be marked as trained") mclf.untrain() self.failUnless(not mclf.trained, msg="UnTrained Boosted classifier should not be trained") self.failUnless(not N.array([x.trained for x in mclf.clfs]).any(), msg="UnTrained Boosted classifier should have no primary classifiers trained") if oldC is not None: clf.C = oldC # XXX meta should also work but TODO @sweepargs(clf=clfswh['svm', '!meta']) def testSVMs(self, clf): knows_probabilities = 'probabilities' in clf.states.names and clf.params.probability enable_states = ['values'] if knows_probabilities: enable_states += ['probabilities'] clf.states._changeTemporarily(enable_states = enable_states) for traindata, testdata in [ (datasets['uni2small_train'], datasets['uni2small_test']) ]: clf.train(traindata) predicts = clf.predict(testdata.samples) # values should be different from predictions for SVMs we have self.failUnless( (predicts != clf.values).any() ) if knows_probabilities and clf.states.isSet('probabilities'): # XXX test more thoroughly what we are getting here ;-) self.failUnlessEqual( len(clf.probabilities), len(testdata.samples) ) clf.states._resetEnabledTemporarily() @sweepargs(clf=clfswh['retrainable']) def testRetrainables(self, clf): # we need a copy since will tune its internals later on clf = clf.clone() clf.states._changeTemporarily(enable_states = ['values'], # ensure that it does do predictions # while training disable_states=['training_confusion']) clf_re = clf.clone() # TODO: .retrainable must have a callback to call smth like # _setRetrainable clf_re._setRetrainable(True) # need to have high snr so we don't 'cope' with problematic # datasets since otherwise unittests would fail. dsargs = {'perlabel':50, 'nlabels':2, 'nfeatures':5, 'nchunks':1, 'nonbogus_features':[2,4], 'snr': 5.0} ## !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # NB datasets will be changed by the end of testing, so if # are to change to use generic datasets - make sure to copy # them here dstrain = deepcopy(datasets['uni2large_train']) dstest = deepcopy(datasets['uni2large_test']) clf.untrain() clf_re.untrain() trerr, trerr_re = TransferError(clf), \ TransferError(clf_re, disable_states=['training_confusion']) # Just check for correctness of retraining err_1 = trerr(dstest, dstrain) self.failUnless(err_1<0.3, msg="We should test here on easy dataset. Got error of %s" % err_1) values_1 = clf.values[:] # some times retraining gets into deeper optimization ;-) eps = 0.05 corrcoef_eps = 0.85 # just to get no failures... usually > 0.95 def batch_test(retrain=True, retest=True, closer=True): err = trerr(dstest, dstrain) err_re = trerr_re(dstest, dstrain) corr = N.corrcoef(clf.values, clf_re.values)[0,1] corr_old = N.corrcoef(values_1, clf_re.values)[0,1] if __debug__: debug('TEST', "Retraining stats: errors %g %g corr %g " "with old error %g corr %g" % (err, err_re, corr, err_1, corr_old)) self.failUnless(clf_re.states.retrained == retrain, ("Must fully train", "Must retrain instead of full training")[retrain]) self.failUnless(clf_re.states.repredicted == retest, ("Must fully test", "Must retest instead of full testing")[retest]) self.failUnless(corr > corrcoef_eps, msg="Result must be close to the one without retraining." " Got corrcoef=%s" % (corr)) if closer: self.failUnless(corr >= corr_old, msg="Result must be closer to current without retraining" " than to old one. Got corrcoef=%s" % (corr_old)) # Check sequential retraining/retesting for i in xrange(3): flag = bool(i!=0) # ok - on 1st call we should train/test, then retrain/retest # and we can't compare for closinest to old result since # we are working on the same data/classifier batch_test(retrain=flag, retest=flag, closer=False) # should retrain nicely if we change a parameter if 'C' in clf.params.names: clf.params.C *= 0.1 clf_re.params.C *= 0.1 batch_test() elif 'sigma_noise' in clf.params.names: clf.params.sigma_noise *= 100 clf_re.params.sigma_noise *= 100 batch_test() else: raise RuntimeError, \ 'Please implement testing while changing some of the ' \ 'params for clf %s' % clf # should retrain nicely if we change kernel parameter if hasattr(clf, 'kernel_params') and len(clf.kernel_params.names): clf.kernel_params.gamma = 0.1 clf_re.kernel_params.gamma = 0.1 # retest is false since kernel got recomputed thus # can't expect to use the same kernel batch_test(retest=not('gamma' in clf.kernel_params.names)) # should retrain nicely if we change labels oldlabels = dstrain.labels[:] dstrain.permuteLabels(status=True, assure_permute=True) self.failUnless((oldlabels != dstrain.labels).any(), msg="We should succeed at permutting -- now got the same labels") batch_test() # Change labels in testing oldlabels = dstest.labels[:] dstest.permuteLabels(status=True, assure_permute=True) self.failUnless((oldlabels != dstest.labels).any(), msg="We should succeed at permutting -- now got the same labels") batch_test() # should re-train if we change data # reuse trained SVM and its 'final' optimization point if not clf.__class__.__name__ in ['GPR']: # on GPR everything depends on the data ;-) oldsamples = dstrain.samples.copy() dstrain.samples[:] += dstrain.samples*0.05 self.failUnless((oldsamples != dstrain.samples).any()) batch_test(retest=False) clf.states._resetEnabledTemporarily() # test retrain() # TODO XXX -- check validity clf_re.retrain(dstrain); self.failUnless(clf_re.states.retrained) clf_re.retrain(dstrain, labels=True); self.failUnless(clf_re.states.retrained) clf_re.retrain(dstrain, traindataset=True); self.failUnless(clf_re.states.retrained) # test repredict() clf_re.repredict(dstest.samples); self.failUnless(clf_re.states.repredicted) self.failUnlessRaises(RuntimeError, clf_re.repredict, dstest.samples, labels=True, msg="for now retesting with anything changed makes no sense") clf_re._setRetrainable(False) def testGenericTests(self): """Test all classifiers for conformant behavior """ for clf_, traindata in \ [(clfswh['binary'], datasets['dumb2']), (clfswh['multiclass'], datasets['dumb'])]: traindata_copy = deepcopy(traindata) # full copy of dataset for clf in clf_: clf.train(traindata) self.failUnless( (traindata.samples == traindata_copy.samples).all(), "Training of a classifier shouldn't change original dataset") # TODO: enforce uniform return from predict?? #predicted = clf.predict(traindata.samples) #self.failUnless(isinstance(predicted, N.ndarray)) # Just simple test that all of them are syntaxed correctly self.failUnless(str(clf) != "") self.failUnless(repr(clf) != "") # TODO: unify str and repr for all classifiers # XXX TODO: should work on smlr, knn, ridgereg, lars as well! but now # they fail to train # GNB -- cannot train since 1 sample isn't sufficient to assess variance @sweepargs(clf=clfswh['!smlr', '!knn', '!gnb', '!lars', '!meta', '!ridge']) def testCorrectDimensionsOrder(self, clf): """To check if known/present Classifiers are working properly with samples being first dimension. Started to worry about possible problems while looking at sg where samples are 2nd dimension """ # specially crafted dataset -- if dimensions are flipped over # the same storage, problem becomes unseparable. Like in this case # incorrect order of dimensions lead to equal samples [0, 1, 0] traindatas = [ Dataset(samples=N.array([ [0, 0, 1.0], [1, 0, 0] ]), labels=[0, 1]), Dataset(samples=N.array([ [0, 0.0], [1, 1] ]), labels=[0, 1])] clf.states._changeTemporarily(enable_states = ['training_confusion']) for traindata in traindatas: clf.train(traindata) self.failUnlessEqual(clf.training_confusion.percentCorrect, 100.0, "Classifier %s must have 100%% correct learning on %s. Has %f" % (`clf`, traindata.samples, clf.training_confusion.percentCorrect)) # and we must be able to predict every original sample thus for i in xrange(traindata.nsamples): sample = traindata.samples[i,:] predicted = clf.predict([sample]) self.failUnlessEqual([predicted], traindata.labels[i], "We must be able to predict sample %s using " % sample + "classifier %s" % `clf`) clf.states._resetEnabledTemporarily() def suite(): return unittest.makeSuite(ClassifiersTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_clfcrossval.py000066400000000000000000000111221174541445200211020ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA classifier cross-validation""" from mvpa.datasets.splitters import NFoldSplitter from mvpa.datasets.meta import MetaDataset from mvpa.algorithms.cvtranserror import CrossValidatedTransferError from mvpa.clfs.transerror import TransferError from tests_warehouse import * from tests_warehouse import pureMultivariateSignal, getMVPattern from tests_warehouse_clfs import * class CrossValidationTests(unittest.TestCase): def testSimpleNMinusOneCV(self): data = getMVPattern(3) self.failUnless( data.nsamples == 120 ) self.failUnless( data.nfeatures == 2 ) self.failUnless( (data.labels == \ [0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0] * 6).all()) self.failUnless( (data.chunks == \ [k for k in range(1, 7) for i in range(20)]).all()) transerror = TransferError(sample_clf_nl) cv = CrossValidatedTransferError( transerror, NFoldSplitter(cvtype=1), enable_states=['confusion', 'training_confusion', 'samples_error']) results = cv(data) self.failUnless( results < 0.2 and results >= 0.0 ) # TODO: test accessibility of {training_,}confusion{,s} of # CrossValidatedTransferError self.failUnless(isinstance(cv.samples_error, dict)) self.failUnless(len(cv.samples_error) == data.nsamples) # one value for each origid self.failUnless(sorted(cv.samples_error.keys()) == sorted(data.origids)) for k, v in cv.samples_error.iteritems(): self.failUnless(len(v) == 1) def testNoiseClassification(self): # get a dataset with a very high SNR data = getMVPattern(10) # do crossval with default errorfx and 'mean' combiner transerror = TransferError(sample_clf_nl) cv = CrossValidatedTransferError(transerror, NFoldSplitter(cvtype=1)) # must return a scalar value result = cv(data) # must be perfect self.failUnless( result < 0.05 ) # do crossval with permuted regressors cv = CrossValidatedTransferError(transerror, NFoldSplitter(cvtype=1, permute=True, nrunspersplit=10) ) results = cv(data) # must be at chance level pmean = N.array(results).mean() self.failUnless( pmean < 0.58 and pmean > 0.42 ) def testHarvesting(self): # get a dataset with a very high SNR data = getMVPattern(10) # do crossval with default errorfx and 'mean' combiner transerror = TransferError(clfswh['linear'][0]) cv = CrossValidatedTransferError( transerror, NFoldSplitter(cvtype=1), harvest_attribs=['transerror.clf.training_time']) result = cv(data) self.failUnless(cv.harvested.has_key('transerror.clf.training_time')) self.failUnless(len(cv.harvested['transerror.clf.training_time'])>1) def testNMinusOneCVWithMetaDataset(self): # simple datasets with decreasing SNR data = MetaDataset([getMVPattern(3), getMVPattern(2), getMVPattern(1)]) self.failUnless( data.nsamples == 120 ) self.failUnless( data.nfeatures == 6 ) self.failUnless( (data.labels == \ [0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0] * 6).all()) self.failUnless( (data.chunks == \ [ k for k in range(1,7) for i in range(20) ] ).all() ) transerror = TransferError(sample_clf_nl) cv = CrossValidatedTransferError(transerror, NFoldSplitter(cvtype=1), enable_states=['confusion', 'training_confusion']) results = cv(data) self.failUnless(results < 0.2 and results >= 0.0, msg="We should generalize while working with " "metadataset. Got %s error" % results) # TODO: test accessibility of {training_,}confusion{,s} of # CrossValidatedTransferError def suite(): return unittest.makeSuite(CrossValidationTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_cmdline.py000066400000000000000000000017701174541445200202040ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA cmdline helpers""" import unittest from mvpa.misc.cmdline import * if __debug__: from mvpa.base import debug class CmdlineHelpersTest(unittest.TestCase): def testBasic(self): """Test if we are not missing basic parts""" globals_ = globals() for member in [#'_verboseCallback', 'parser', 'opt', 'opts']: self.failUnless(globals_.has_key(member), msg="We must have imported %s from mvpa.misc.cmdline!" % member) def suite(): return unittest.makeSuite(CmdlineHelpersTest) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_config.py000066400000000000000000000017711174541445200200370ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA dense array mapper""" import unittest from mvpa.base.config import ConfigManager class ConfigTests(unittest.TestCase): def testConfig(self): cfg = ConfigManager() # does nothing so far, but will be used to test the default # configuration from doc/examples/pymvpa.cfg # query for some non-existing option and check if default is returned query = cfg.get('dasgibtsdochnicht', 'neegarnicht', default='required') self.failUnless(query == 'required') def suite(): return unittest.makeSuite(ConfigTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_datameasure.py000066400000000000000000000356751174541445200210770ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA SplittingSensitivityAnalyzer""" from mvpa.base import externals from mvpa.featsel.base import FeatureSelectionPipeline, \ SensitivityBasedFeatureSelection, CombinedFeatureSelection from mvpa.clfs.transerror import TransferError from mvpa.algorithms.cvtranserror import CrossValidatedTransferError from mvpa.featsel.helpers import FixedNElementTailSelector, \ FractionTailSelector, RangeElementSelector from mvpa.featsel.rfe import RFE from mvpa.clfs.meta import SplitClassifier, MulticlassClassifier, \ FeatureSelectionClassifier from mvpa.clfs.smlr import SMLR, SMLRWeights from mvpa.misc.transformers import Absolute from mvpa.datasets.splitters import NFoldSplitter, NoneSplitter from mvpa.misc.transformers import Absolute, FirstAxisMean, \ SecondAxisSumOfAbs, DistPValue from mvpa.measures.base import SplitFeaturewiseDatasetMeasure from mvpa.measures.anova import OneWayAnova, CompoundOneWayAnova from mvpa.measures.irelief import IterativeRelief, IterativeReliefOnline, \ IterativeRelief_Devel, IterativeReliefOnline_Devel from tests_warehouse import * from tests_warehouse_clfs import * _MEASURES_2_SWEEP = [ OneWayAnova(), CompoundOneWayAnova(combiner=SecondAxisSumOfAbs), IterativeRelief(), IterativeReliefOnline(), IterativeRelief_Devel(), IterativeReliefOnline_Devel() ] if externals.exists('scipy'): from mvpa.measures.corrcoef import CorrCoef _MEASURES_2_SWEEP += [ CorrCoef(), # that one is good when small... handle later #CorrCoef(pvalue=True) ] class SensitivityAnalysersTests(unittest.TestCase): def setUp(self): self.dataset = datasets['uni2large'] @sweepargs(dsm=_MEASURES_2_SWEEP) def testBasic(self, dsm): data = datasets['dumbinv'] datass = data.samples.copy() # compute scores f = dsm(data) # check if nothing evil is done to dataset self.failUnless(N.all(data.samples == datass)) self.failUnless(f.shape == (4,)) self.failUnless(abs(f[1]) <= 1e-12, # some small value msg="Failed test with value %g instead of != 0.0" % f[1]) self.failUnless(f[0] > 0.1) # some reasonably large value # we should not have NaNs self.failUnless(not N.any(N.isnan(f))) # XXX meta should work too but doesn't # XXX also look below -- lars with stepwise segfaults if all states are enabled, # disabled for now -- do not have enough juice to debug lars code @sweepargs(clf=clfswh['has_sensitivity']) def testAnalyzerWithSplitClassifier(self, clf): """Test analyzers in split classifier """ # We need to skip some LARSes here _sclf = str(clf) if 'LARS(' in _sclf and "type='stepwise'" in _sclf: return # assumming many defaults it is as simple as mclf = SplitClassifier(clf=clf, enable_states=['training_confusion', 'confusion']) sana = mclf.getSensitivityAnalyzer(transformer=Absolute, enable_states=["sensitivities"]) # Test access to transformers and combiners self.failUnless(sana.transformer is Absolute) self.failUnless(sana.combiner is FirstAxisMean) # and lets look at all sensitivities # and we get sensitivity analyzer which works on splits map_ = sana(self.dataset) self.failUnlessEqual(len(map_), self.dataset.nfeatures) if cfg.getboolean('tests', 'labile', default='yes'): for conf_matrix in [sana.clf.training_confusion] \ + sana.clf.confusion.matrices: self.failUnless( conf_matrix.percentCorrect>75, msg="We must have trained on each one more or " \ "less correctly. Got %f%% correct on %d labels" % (conf_matrix.percentCorrect, len(self.dataset.uniquelabels))) errors = [x.percentCorrect for x in sana.clf.confusion.matrices] # XXX # That is too much to ask if the dataset is easy - thus # disabled for now #self.failUnless(N.min(errors) != N.max(errors), # msg="Splits should have slightly but different " \ # "generalization") # lets go through all sensitivities and see if we selected the right # features # XXX yoh: disabled checking of each map separately since in # BoostedClassifierSensitivityAnalyzer and # ProxyClassifierSensitivityAnalyzer # we don't have yet way to provide transformers thus internal call # to getSensitivityAnalyzer in _call of them is not parametrized if 'meta' in clf._clf_internals and len(map_.nonzero()[0])<2: # Some meta classifiers (5% of ANOVA) are too harsh ;-) return for map__ in [map_]: # + sana.combined_analyzer.sensitivities: selected = FixedNElementTailSelector( self.dataset.nfeatures - len(self.dataset.nonbogus_features))(map__) if cfg.getboolean('tests', 'labile', default='yes'): self.failUnlessEqual( list(selected), list(self.dataset.nonbogus_features), msg="At the end we should have selected the right features") @sweepargs(clf=clfswh['has_sensitivity']) def testMappedClassifierSensitivityAnalyzer(self, clf): """Test sensitivity of the mapped classifier """ # Assuming many defaults it is as simple as mclf = FeatureSelectionClassifier( clf, SensitivityBasedFeatureSelection( OneWayAnova(), FractionTailSelector(0.5, mode='select', tail='upper')), enable_states=['training_confusion']) sana = mclf.getSensitivityAnalyzer(transformer=Absolute, enable_states=["sensitivities"]) # and lets look at all sensitivities dataset = datasets['uni2medium'] # and we get sensitivity analyzer which works on splits map_ = sana(dataset) self.failUnlessEqual(len(map_), dataset.nfeatures) @sweepargs(svm=clfswh['linear', 'svm']) def testLinearSVMWeights(self, svm): # assumming many defaults it is as simple as sana = svm.getSensitivityAnalyzer(enable_states=["sensitivities"] ) # and lets look at all sensitivities map_ = sana(self.dataset) # for now we can do only linear SVM, so lets check if we raise # a concern svmnl = clfswh['non-linear', 'svm'][0] self.failUnlessRaises(NotImplementedError, svmnl.getSensitivityAnalyzer) @sweepargs(svm=clfswh['linear', 'svm']) def testLinearSVMWeights(self, svm): # assumming many defaults it is as simple as sana = svm.getSensitivityAnalyzer(enable_states=["sensitivities"] ) # and lets look at all sensitivities map_ = sana(self.dataset) # for now we can do only linear SVM, so lets check if we raise # a concern svmnl = clfswh['non-linear', 'svm'][0] self.failUnlessRaises(NotImplementedError, svmnl.getSensitivityAnalyzer) # XXX doesn't work easily with meta since it would need # to be explicitely passed to the slave classifier's # getSengetSensitivityAnalyzer @sweepargs(svm=clfswh['linear', 'svm', 'libsvm', '!sg', '!meta']) def testLinearSVMWeightsPerClass(self, svm): # assumming many defaults it is as simple as kwargs = dict(combiner=None, transformer=None, enable_states=["sensitivities"]) sana_split = svm.getSensitivityAnalyzer( split_weights=True, **kwargs) sana_full = svm.getSensitivityAnalyzer( force_training=False, **kwargs) # and lets look at all sensitivities ds2 = datasets['uni4large'].copy() ds2.zscore(baselinelabels = [2, 3]) ds2 = ds2['labels', [0,1]] map_split = sana_split(ds2) map_full = sana_full(ds2) self.failUnlessEqual(map_split.shape, (ds2.nfeatures, 2)) self.failUnlessEqual(map_full.shape, (ds2.nfeatures, )) # just to verify that we split properly and if we reconstruct # manually we obtain the same dmap = (-1*map_split[:, 1] + map_split[:, 0]) - map_full self.failUnless((N.abs(dmap) <= 1e-10).all()) #print "____" #print map_split #print SMLR().getSensitivityAnalyzer(combiner=None)(ds2) # for now we can do split weights for binary tasks only, so # lets check if we raise a concern self.failUnlessRaises(NotImplementedError, sana_split, datasets['uni3medium']) def testSplitFeaturewiseDatasetMeasure(self): ds = datasets['uni3small'] sana = SplitFeaturewiseDatasetMeasure( analyzer=SMLR( fit_all_weights=True).getSensitivityAnalyzer(combiner=None), splitter=NFoldSplitter(), combiner=None) sens = sana(ds) self.failUnless(sens.shape == ( len(ds.uniquechunks), ds.nfeatures, len(ds.uniquelabels))) # Lets try more complex example with 'boosting' ds = datasets['uni3medium'] sana = SplitFeaturewiseDatasetMeasure( analyzer=SMLR( fit_all_weights=True).getSensitivityAnalyzer(combiner=None), splitter=NoneSplitter(nperlabel=0.25, mode='first', nrunspersplit=2), combiner=None, enable_states=['splits', 'sensitivities']) sens = sana(ds) self.failUnless(sens.shape == (2, ds.nfeatures, 3)) splits = sana.splits self.failUnlessEqual(len(splits), 2) self.failUnless(N.all([s[0].nsamples == ds.nsamples/4 for s in splits])) # should have used different samples self.failUnless(N.any([splits[0][0].origids != splits[1][0].origids])) # and should have got different sensitivities self.failUnless(N.any(sens[0] != sens[1])) if not externals.exists('scipy'): return # Most evil example ds = datasets['uni2medium'] plain_sana = SVM().getSensitivityAnalyzer( combiner=None, transformer=DistPValue()) boosted_sana = SplitFeaturewiseDatasetMeasure( analyzer=SVM().getSensitivityAnalyzer( combiner=None, transformer=DistPValue(fpp=0.05)), splitter=NoneSplitter(nperlabel=0.8, mode='first', nrunspersplit=2), combiner=FirstAxisMean, enable_states=['splits', 'sensitivities']) # lets create feature selector fsel = RangeElementSelector(upper=0.1, lower=0.9, inclusive=True) sanas = dict(plain=plain_sana, boosted=boosted_sana) for k,sana in sanas.iteritems(): clf = FeatureSelectionClassifier(SVM(), SensitivityBasedFeatureSelection(sana, fsel), descr='SVM on p=0.2(both tails) using %s' % k) ce = CrossValidatedTransferError(TransferError(clf), NFoldSplitter()) error = ce(ds) sens = boosted_sana(ds) sens_plain = plain_sana(ds) # TODO: make a really unittest out of it -- not just runtime # bugs catcher # TODO -- unittests for sensitivity analyzers which use combiners # (linsvmweights for multi-class SVMs and smlrweights for SMLR) @sweepargs(basic_clf=clfswh['has_sensitivity']) def __testFSPipelineWithAnalyzerWithSplitClassifier(self, basic_clf): #basic_clf = LinearNuSVMC() multi_clf = MulticlassClassifier(clf=basic_clf) #svm_weigths = LinearSVMWeights(svm) # Proper RFE: aggregate sensitivities across multiple splits, # but also due to multi class those need to be aggregated # somehow. Transfer error here should be 'leave-1-out' error # of split classifier itself sclf = SplitClassifier(clf=basic_clf) rfe = RFE(sensitivity_analyzer= sclf.getSensitivityAnalyzer( enable_states=["sensitivities"]), transfer_error=trans_error, feature_selector=FeatureSelectionPipeline( [FractionTailSelector(0.5), FixedNElementTailSelector(1)]), train_clf=True) # and we get sensitivity analyzer which works on splits and uses # sensitivity selected_features = rfe(self.dataset) def testUnionFeatureSelection(self): # two methods: 5% highes F-scores, non-zero SMLR weights fss = [SensitivityBasedFeatureSelection( OneWayAnova(), FractionTailSelector(0.05, mode='select', tail='upper')), SensitivityBasedFeatureSelection( SMLRWeights(SMLR(lm=1, implementation="C")), RangeElementSelector(mode='select'))] fs = CombinedFeatureSelection(fss, combiner='union', enable_states=['selected_ids', 'selections_ids']) od, otd = fs(self.dataset) self.failUnless(fs.combiner == 'union') self.failUnless(len(fs.selections_ids)) self.failUnless(len(fs.selections_ids) <= self.dataset.nfeatures) # should store one set per methods self.failUnless(len(fs.selections_ids) == len(fss)) # no individual can be larger than union for s in fs.selections_ids: self.failUnless(len(s) <= len(fs.selected_ids)) # check output dataset self.failUnless(od.nfeatures == len(fs.selected_ids)) for i, id in enumerate(fs.selected_ids): self.failUnless((od.samples[:,i] == self.dataset.samples[:,id]).all()) # again for intersection fs = CombinedFeatureSelection(fss, combiner='intersection', enable_states=['selected_ids', 'selections_ids']) # simply run it for now -- can't think of additional tests od, otd = fs(self.dataset) def suite(): return unittest.makeSuite(SensitivityAnalysersTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_dataset.py000066400000000000000000000650741174541445200202250ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA dataset handling""" import unittest import random import numpy as N from mvpa.datasets import Dataset from mvpa.datasets.miscfx import zscore, aggregateFeatures from mvpa.mappers.mask import MaskMapper from mvpa.misc.exceptions import DatasetError from mvpa.support import copy from tests_warehouse import datasets class DatasetTests(unittest.TestCase): def testAddPatterns(self): """Test composition of new datasets by addition of existing ones """ data = Dataset(samples=range(5), labels=1, chunks=1) self.failUnlessEqual( data.uniquelabels, [1], msg="uniquelabels must be correctly recomputed") # simple sequence has to be a single pattern self.failUnlessEqual( data.nsamples, 1) # check correct pattern layout (1x5) self.failUnless( (data.samples == N.array([[0, 1, 2, 3, 4]])).all() ) # check for single labels and origin self.failUnless( (data.labels == N.array([1])).all() ) self.failUnless( (data.chunks == N.array([1])).all() ) # now try adding pattern with wrong shape self.failUnlessRaises( DatasetError, data.__iadd__, Dataset(samples=N.ones((2,3)), labels=1, chunks=1)) # now add two real patterns dss = datasets['uni2large'].samples data += Dataset(samples=dss[:2, :5], labels=2, chunks=2 ) self.failUnlessEqual( data.nfeatures, 5 ) self.failUnless((data.labels == N.array([1, 2, 2])).all() ) self.failUnless((data.chunks == N.array([1, 2, 2])).all() ) # test automatic origins data += Dataset(samples=dss[3:5, :5], labels=3) self.failUnless((data.chunks == N.array([1, 2, 2, 0, 1]) ).all()) # test unique class labels self.failUnless((data.uniquelabels == N.array([1, 2, 3]) ).all()) # test wrong label length self.failUnlessRaises(DatasetError, Dataset, samples=dss[:4, :5], labels=[ 1, 2, 3 ], chunks=2) # test wrong origin length self.failUnlessRaises(DatasetError, Dataset, samples=dss[:4, :5], labels=[ 1, 2, 3, 4 ], chunks=[ 2, 2, 2 ]) def testFeatureSelection(self): """Testing feature selection: sorted/not sorted, feature groups """ origdata = datasets['uni2large'].samples[:10, :20] data = Dataset(samples=origdata, labels=2, chunks=2 ) # define some feature groups data.defineFeatureGroups(N.repeat(range(4), 5)) unmasked = data.samples.copy() # default must be no mask self.failUnless( data.nfeatures == 20 ) features_to_select = [3, 0, 17] features_to_select_copy = copy.deepcopy(features_to_select) features_to_select_sorted = copy.deepcopy(features_to_select) features_to_select_sorted.sort() bsel = N.array([False]*20) bsel[ features_to_select ] = True # check selection with feature list for sel, issorted in \ [(data.selectFeatures( features_to_select, sort=False), False), (data.selectFeatures( features_to_select, sort=True), True), (data.select(slice(None), features_to_select), True), (data.select(slice(None), N.array(features_to_select)), True), (data.select(slice(None), bsel), True) ]: self.failUnless(sel.nfeatures == 3) # check size of the masked patterns self.failUnless(sel.samples.shape == (10, 3)) # check that the right features are selected fts = (features_to_select, features_to_select_sorted)[int(issorted)] self.failUnless((unmasked[:, fts] == sel.samples).all()) # check grouping information self.failUnless((sel._dsattr['featuregroups'] == [0, 0, 3]).all()) # check side effect on features_to_select parameter: self.failUnless(features_to_select==features_to_select_copy) # check selection by feature group id gsel = data.selectFeatures(groups=[2, 3]) self.failUnless(gsel.nfeatures == 10) self.failUnless(set(gsel._dsattr['featuregroups']) == set([2, 3])) def testSampleSelection(self): origdata = datasets['uni2large'].samples[:100, :10].T data = Dataset(samples=origdata, labels=2, chunks=2 ) self.failUnless( data.nsamples == 10 ) # set single pattern to enabled for sel in [ data.selectSamples(5), data.select(5), data.select(slice(5, 6)), ]: self.failUnless( sel.nsamples == 1 ) self.failUnless( data.nfeatures == 100 ) self.failUnless( sel.origids == [5] ) # check duplicate selections for sel in [ data.selectSamples([5, 5]), # Following ones would fail since select removes # repetitions (XXX) #data.select([5,5]), #data.select([5,5], 'all'), #data.select([5,5], slice(None)), ]: self.failUnless( sel.nsamples == 2 ) self.failUnless( (sel.samples[0] == data.samples[5]).all() ) self.failUnless( (sel.samples[0] == sel.samples[1]).all() ) self.failUnless( len(sel.labels) == 2 ) self.failUnless( len(sel.chunks) == 2 ) self.failUnless((sel.origids == [5, 5]).all()) self.failUnless( sel.samples.shape == (2, 100) ) # check selection by labels for sel in [ data.selectSamples(data.idsbylabels(2)), data.select(labels=2), data.select('labels', 2), data.select('labels', [2]), data['labels', [2]], data['labels': [2], 'labels':2], data['labels': [2]], ]: self.failUnless( sel.nsamples == data.nsamples ) self.failUnless( N.all(sel.samples == data.samples) ) # not present label for sel in [ data.selectSamples(data.idsbylabels(3)), data.select(labels=3), data.select('labels', 3), data.select('labels', [3]), ]: self.failUnless( sel.nsamples == 0 ) data = Dataset(samples=origdata, labels=[8, 9, 4, 3, 3, 3, 4, 2, 8, 9], chunks=2) for sel in [ data.selectSamples(data.idsbylabels([2, 3])), data.select('labels', [2, 3]), data.select('labels', [2, 3], labels=[1, 2, 3, 4]), data.select('labels', [2, 3], chunks=[1, 2, 3, 4]), data['labels':[2, 3], 'chunks':[1, 2, 3, 4]], data['chunks':[1, 2, 3, 4], 'labels':[2, 3]], ]: self.failUnless(N.all(sel.origids == [ 3., 4., 5., 7.])) # lets cause it to compute unique labels self.failUnless( (data.uniquelabels == [2, 3, 4, 8, 9]).all() ); # select some samples removing some labels completely sel = data.selectSamples(data.idsbylabels([3, 4, 8, 9])) self.failUnlessEqual(set(sel.uniquelabels), set([3, 4, 8, 9])) self.failUnless((sel.origids == [0, 1, 2, 3, 4, 5, 6, 8, 9]).all()) def testEvilSelects(self): """Test some obscure selections of samples via select() or __getitem__ """ origdata = datasets['uni2large'].samples[:100, :10].T data = Dataset(samples=origdata, # 0 1 2 3 4 5 6 7 8 9 labels=[8, 9, 4, 3, 3, 3, 3, 2, 8, 9], chunks=[1, 2, 3, 2, 3, 1, 5, 6, 3, 6]) # malformed getitem if __debug__: # check is enforced only in __debug__ self.failUnlessRaises(ValueError, data.__getitem__, 'labels', 'featu') # too many indicies self.failUnlessRaises(ValueError, data.__getitem__, 1, 1, 1) # various getitems which should carry the same result for sel in [ data.select('chunks', [2, 6], labels=[3, 2], features=slice(None)), data.select('all', 'all', labels=[2,3], chunks=[2, 6]), data['chunks', [2, 6], 'labels', [3, 2]], data[:, :, 'chunks', [2, 6], 'labels', [3, 2]], # get warnings but should work as the rest for now data[3:8, 'chunks', [2, 6, 2, 6], 'labels', [3, 2]], ]: self.failUnless(N.all(sel.origids == [3, 7])) self.failUnless(sel.nfeatures == 100) self.failUnless(N.all(sel.samples == origdata[ [3, 7] ])) target = origdata[ [3, 7] ] target = target[:, [1, 3] ] # various getitems which should carry the same result for sel in [ data.select('all', [1, 3], 'chunks', [2, 6], labels=[3, 2]), data[:, [1,3], 'chunks', [2, 6], 'labels', [3, 2]], data[:, [1,3], 'chunks', [2, 6], 'labels', [3, 2]], # get warnings but should work as the rest for now data[3:8, [1, 1, 3, 1], 'chunks', [2, 6, 2, 6], 'labels', [3, 2]], ]: self.failUnless(N.all(sel.origids == [3, 7])) self.failUnless(sel.nfeatures == 2) self.failUnless(N.all(sel.samples == target)) # Check if we get empty selection if requesting impossible self.failUnless(data.select(chunks=[23]).nsamples == 0) # Check .where() self.failUnless(N.all(data.where(chunks=[2, 6])==[1, 3, 7, 9])) self.failUnless(N.all(data.where(chunks=[2, 6], labels=[22, 3])==[3])) # both samples and features idx = data.where('all', [1, 3, 10], labels=[2, 3, 4]) self.failUnless(N.all(idx[1] == [1, 3, 10])) self.failUnless(N.all(idx[0] == range(2, 8))) # empty query self.failUnless(data.where() is None) # empty result self.failUnless(data.where(labels=[123]) == []) def testCombinedPatternAndFeatureMasking(self): data = Dataset(samples=N.arange( 20 ).reshape( (4, 5) ), labels=1, chunks=1) self.failUnless( data.nsamples == 4 ) self.failUnless( data.nfeatures == 5 ) fsel = data.selectFeatures([1, 2]) fpsel = fsel.selectSamples([0, 3]) self.failUnless( fpsel.nsamples == 2 ) self.failUnless( fpsel.nfeatures == 2 ) self.failUnless( (fpsel.samples == [[1, 2], [16, 17]]).all() ) def testPatternMerge(self): data1 = Dataset(samples=N.ones((5, 5)), labels=1, chunks=1 ) data2 = Dataset(samples=N.ones((3, 5)), labels=2, chunks=1 ) merged = data1 + data2 self.failUnless( merged.nfeatures == 5 ) l12 = [1]*5 + [2]*3 l1 = [1]*8 self.failUnless( (merged.labels == l12).all() ) self.failUnless( (merged.chunks == l1).all() ) data1 += data2 self.failUnless( data1.nfeatures == 5 ) self.failUnless( (data1.labels == l12).all() ) self.failUnless( (data1.chunks == l1).all() ) def testLabelRandomizationAndSampling(self): """ """ data = Dataset(samples=N.ones((5, 1)), labels=range(5), chunks=1 ) data += Dataset(samples=N.ones((5, 1))+1, labels=range(5), chunks=2 ) data += Dataset(samples=N.ones((5, 1))+2, labels=range(5), chunks=3 ) data += Dataset(samples=N.ones((5, 1))+3, labels=range(5), chunks=4 ) data += Dataset(samples=N.ones((5, 1))+4, labels=range(5), chunks=5 ) self.failUnless( data.samplesperlabel == {0:5, 1:5, 2:5, 3:5, 4:5} ) sample = data.getRandomSamples( 2 ) self.failUnless( sample.samplesperlabel.values() == [ 2, 2, 2, 2, 2 ] ) self.failUnless( (data.uniquechunks == range(1, 6)).all() ) # store the old labels origlabels = data.labels.copy() data.permuteLabels(True) self.failIf( (data.labels == origlabels).all() ) data.permuteLabels(False) self.failUnless( (data.labels == origlabels).all() ) # now try another object with the same data data2 = Dataset(samples=data.samples, labels=data.labels, chunks=data.chunks ) # labels are the same as the originals self.failUnless( (data2.labels == origlabels).all() ) # now permute in the new object data2.permuteLabels( True ) # must not affect the old one self.failUnless( (data.labels == origlabels).all() ) # but only the new one self.failIf( (data2.labels == origlabels).all() ) def testAttributes(self): """Test adding custom attributes to a dataset """ #class BlobbyDataset(Dataset): # pass # TODO: we can't assign attributes to those for now... ds = Dataset(samples=range(5), labels=1, chunks=1) self.failUnlessRaises(AttributeError, lambda x:x.blobs, ds) """Dataset.blobs should fail since .blobs wasn't yet registered""" #register new attribute but it would alter only new instances Dataset._registerAttribute("blobs", "_data", hasunique=True) ds = Dataset(samples=range(5), labels=1, chunks=1) self.failUnless(not ds.blobs != [ 0 ], msg="By default new attributes supposed to get 0 as the value") try: ds.blobs = [1, 2] self.fail(msg="Dataset.blobs=[1,2] should fail since " "there is 5 samples") except ValueError, e: pass try: ds.blobs = [1] except e: self.fail(msg="We must be able to assign the attribute") # Dataset still shouldn't have blobs... just BlobbyDataset #self.failUnlessRaises(AttributeError, lambda x:x.blobs, # Dataset(samples=range(5), labels=1, chunks=1)) def testRequiredAtrributes(self): """Verify that we have required attributes """ self.failUnlessRaises(DatasetError, Dataset) self.failUnlessRaises(DatasetError, Dataset, samples=[1]) self.failUnlessRaises(DatasetError, Dataset, labels=[1]) try: ds = Dataset(samples=[1], labels=[1]) except: self.fail(msg="samples and labels are 2 required parameters") assert(ds is not None) # silence pylint def testZScoring(self): """Test z-scoring transformation """ # dataset: mean=2, std=1 samples = N.array( (0,1,3,4,2,2,3,1,1,3,3,1,2,2,2,2) ).\ reshape((16, 1)) data = Dataset(samples=samples, labels=range(16), chunks=[0]*16) self.failUnlessEqual( data.samples.mean(), 2.0 ) self.failUnlessEqual( data.samples.std(), 1.0 ) zscore(data, perchunk=True) # check z-scoring check = N.array([-2,-1,1,2,0,0,1,-1,-1,1,1,-1,0,0,0,0], dtype='float64').reshape(16,1) self.failUnless( (data.samples == check).all() ) data = Dataset(samples=samples, labels=range(16), chunks=[0]*16) zscore(data, perchunk=False) self.failUnless( (data.samples == check).all() ) # check z-scoring taking set of labels as a baseline data = Dataset(samples=samples, labels=[0, 2, 2, 2, 1] + [2]*11, chunks=[0]*16) zscore(data, baselinelabels=[0, 1]) self.failUnless((samples == data.samples+1.0).all()) def test_zscore_reportedfailure(self): dataset = Dataset(samples=N.arange( 20 ).reshape( (4, 5) ), labels=1, chunks=1) zscore(dataset, mean=0, std=1, #N.ones(dataset.nfeatures), perchunk=True, pervoxel=True, targetdtype="float32") def testAggregation(self): data = Dataset(samples=N.arange( 20 ).reshape( (4, 5) ), labels=1, chunks=1) ag_data = aggregateFeatures(data, N.mean) self.failUnless(ag_data.nsamples == 4) self.failUnless(ag_data.nfeatures == 1) self.failUnless((ag_data.samples[:, 0] == [2, 7, 12, 17]).all()) def testApplyMapper(self): """Test creation of new dataset by applying a mapper""" mapper = MaskMapper(N.array([1, 0, 1])) dataset = Dataset(samples=N.arange(12).reshape( (4, 3) ), labels=1, chunks=1) seldataset = dataset.applyMapper(featuresmapper=mapper) self.failUnless( (dataset.selectFeatures([0, 2]).samples == seldataset.samples).all() ) # Lets do simple test on maskmapper reverse since it seems to # do evil things. Those checks are done only in __debug__ if __debug__: # should fail since in mask we have just 2 features now self.failUnlessRaises(ValueError, mapper.reverse, [10, 20, 30]) self.failUnlessRaises(ValueError, mapper.forward, [10, 20]) # XXX: the intended test is added as SampleGroupMapper test # self.failUnlessRaises(NotImplementedError, # dataset.applyMapper, None, [1]) # """We don't yet have implementation for samplesmapper -- # if we get one -- remove this check and place a test""" def testId(self): """Test Dataset.idhash() if it gets changed if any of the labels/chunks changes """ dataset = Dataset(samples=N.arange(12).reshape( (4, 3) ), labels=1, chunks=1) origid = dataset.idhash dataset.labels = [3, 1, 2, 3] # change all labels self.failUnless(origid != dataset.idhash, msg="Changing all labels should alter dataset's idhash") origid = dataset.idhash z = dataset.labels[1] self.failUnlessEqual(origid, dataset.idhash, msg="Accessing shouldn't change idhash") z = dataset.chunks self.failUnlessEqual(origid, dataset.idhash, msg="Accessing shouldn't change idhash") z[2] = 333 self.failUnless(origid != dataset.idhash, msg="Changing value in attribute should change idhash") origid = dataset.idhash dataset.samples[1, 1] = 1000 self.failUnless(origid != dataset.idhash, msg="Changing value in data should change idhash") origid = dataset.idhash dataset.permuteLabels(True) self.failUnless(origid != dataset.idhash, msg="Permutation also changes idhash") dataset.permuteLabels(False) self.failUnless(origid == dataset.idhash, msg="idhash should be restored after " "permuteLabels(False)") def testFeatureMaskConversion(self): dataset = Dataset(samples=N.arange(12).reshape((4, 3)), labels=1, chunks=1) mask = dataset.convertFeatureIds2FeatureMask(range(dataset.nfeatures)) self.failUnless(len(mask) == dataset.nfeatures) self.failUnless((mask == True).all()) self.failUnless( (dataset.convertFeatureMask2FeatureIds(mask) == range(3)).all()) mask[1] = False self.failUnless( (dataset.convertFeatureMask2FeatureIds(mask) == [0, 2]).all()) def testSummary(self): """Dummy test""" ds = datasets['uni2large'] ds = ds[N.random.permutation(range(ds.nsamples))[:20]] summary = ds.summary() self.failUnless(len(summary)>40) def testLabelsMapping(self): """Test mapping of the labels from strings to numericals """ od = {'apple':0, 'orange':1} samples = [[3], [2], [3]] labels_l = ['apple', 'orange', 'apple'] # test broadcasting of the label ds = Dataset(samples=samples, labels='orange') self.failUnless(N.all(ds.labels == ['orange']*3)) # Test basic mapping of litteral labels for ds in [Dataset(samples=samples, labels=labels_l, labels_map=od), # Figure out mapping Dataset(samples=samples, labels=labels_l, labels_map=True)]: self.failUnless(N.all(ds.labels == [0, 1, 0])) self.failUnless(ds.labels_map == od) ds_ = ds[1] self.failUnless(ds_.labels_map == od, msg='selectSamples should provide full mapping preserved') # We should complaint about insufficient mapping self.failUnlessRaises(ValueError, Dataset, samples=samples, labels=labels_l, labels_map = {'apple':0}) # Conformance to older behavior -- if labels are given in # strings, no mapping occur by default ds2 = Dataset(samples=samples, labels=labels_l) self.failUnlessEqual(ds2.labels_map, None) # We should label numerical labels if it was requested: od3 = {1:100, 2:101, 3:100} ds3 = Dataset(samples=samples, labels=[1, 2, 3], labels_map=od3) self.failUnlessEqual(ds3.labels_map, od3) self.failUnless(N.all(ds3.labels == [100, 101, 100])) ds3_ = ds3[1] self.failUnlessEqual(ds3.labels_map, od3) ds4 = Dataset(samples=samples, labels=labels_l) # Lets check setting the labels map ds = Dataset(samples=samples, labels=labels_l, labels_map=od) self.failUnlessRaises(ValueError, ds.setLabelsMap, {'orange': 1, 'nonorange': 3}) new_map = {'tasty':0, 'crappy':1} ds.labels_map = new_map.copy() self.failUnlessEqual(ds.labels_map, new_map) def testLabelsMappingAddDataset(self): """Adding datasets needs special care whenever labels mapping is used.""" samples = [[3], [2], [3]] l1 = ['a', 'b', 'a'] l2 = ['b', 'a', 'c'] ds1 = Dataset(samples=samples, labels=l1, labels_map={'a':1, 'b':2}) ds2 = Dataset(samples=samples, labels=l2, labels_map={'c':1, 'a':4, 'b':2}) # some dataset without mapping ds0 = Dataset(samples=samples, labels=l2) # original mappings lm1 = ds1.labels_map.copy() lm2 = ds2.labels_map.copy() ds3 = ds1 + ds2 self.failUnless(N.all(ds3.labels == N.hstack((ds1.labels, [2, 1, 5])))) self.failUnless(ds1.labels_map == lm1) self.failUnless(ds2.labels_map == lm2) # check iadd ds1 += ds2 self.failUnless(N.all(ds1.labels == ds3.labels)) # it should be deterministic self.failUnless(N.all(ds1.labels_map == ds3.labels_map)) # don't allow to add datasets where one of them doesn't have a labels_map # whenever the other one does self.failUnlessRaises(ValueError, ds1.__add__, ds0) self.failUnlessRaises(ValueError, ds1.__iadd__, ds0) def testCopy(self): # lets use some instance of somewhat evolved dataset ds = datasets['uni2small'] # Clone the beast ds_ = ds.copy() # verify that we have the same data self.failUnless(N.all(ds.samples == ds_.samples)) self.failUnless(N.all(ds.labels == ds_.labels)) self.failUnless(N.all(ds.chunks == ds_.chunks)) # modify and see if we don't change data in the original one ds_.samples[0, 0] = 1234 self.failUnless(N.any(ds.samples != ds_.samples)) self.failUnless(N.all(ds.labels == ds_.labels)) self.failUnless(N.all(ds.chunks == ds_.chunks)) ds_.labels = N.hstack(([123], ds_.labels[1:])) self.failUnless(N.any(ds.samples != ds_.samples)) self.failUnless(N.any(ds.labels != ds_.labels)) self.failUnless(N.all(ds.chunks == ds_.chunks)) ds_.chunks = N.hstack(([1234], ds_.chunks[1:])) self.failUnless(N.any(ds.samples != ds_.samples)) self.failUnless(N.any(ds.labels != ds_.labels)) self.failUnless(N.any(ds.chunks != ds_.chunks)) self.failUnless(N.any(ds.uniquelabels != ds_.uniquelabels)) self.failUnless(N.any(ds.uniquechunks != ds_.uniquechunks)) def testIdsonboundaries(self): """Test detection of transition points Shame on Yarik -- he didn't create unittests right away... damn me """ ds = Dataset(samples=N.array(range(10), ndmin=2).T, labels=[0,0,1,1,0,0,1,1,0,0], chunks=[0,0,0,0,0,1,1,1,1,1]) self.failUnless(ds.idsonboundaries() == [0,2,4,5,6,8], "We should have got ids whenever either chunk or " "label changes") self.failUnless(ds.idsonboundaries(attributes_to_track=['chunks']) == [0, 5]) # Preceding samples self.failUnless(ds.idsonboundaries(prior=1, post=-1, attributes_to_track=['chunks']) == [4, 9]) self.failUnless(ds.idsonboundaries(prior=2, post=-1, attributes_to_track=['chunks']) == [3, 4, 8, 9]) self.failUnless(ds.idsonboundaries(prior=2, post=-1, attributes_to_track=['chunks'], revert=True) == [0, 1, 2, 5, 6, 7]) self.failUnless(ds.idsonboundaries(prior=1, post=1, attributes_to_track=['chunks']) == [0, 1, 4, 5, 6, 9]) # all should be there self.failUnless(ds.idsonboundaries(prior=2) == range(10)) def suite(): return unittest.makeSuite(DatasetTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_datasetfx.py000066400000000000000000000104621174541445200205520ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA miscelaneouse functions operating on datasets""" import unittest import numpy as N from mvpa.base import externals from mvpa.datasets import Dataset from mvpa.datasets.miscfx import removeInvariantFeatures, coarsenChunks, \ SequenceStats from mvpa.misc.data_generators import normalFeatureDataset class MiscDatasetFxTests(unittest.TestCase): def testInvarFeaturesRemoval(self): r = N.random.normal(size=(3,1)) ds = Dataset(samples=N.hstack((N.zeros((3,2)), r)), labels=1) self.failUnless(ds.nfeatures == 3) dsc = removeInvariantFeatures(ds) self.failUnless(dsc.nfeatures == 1) self.failUnless((dsc.samples == r).all()) def testCoarsenChunks(self): """Just basic testing for now""" chunks = [1,1,2,2,3,3,4,4] ds = Dataset(samples=N.arange(len(chunks)).reshape( (len(chunks),1)), labels=[1]*8, chunks=chunks) coarsenChunks(ds, nchunks=2) chunks1 = coarsenChunks(chunks, nchunks=2) self.failUnless((chunks1 == ds.chunks).all()) self.failUnless((chunks1 == N.asarray([0,0,0,0,1,1,1,1])).all()) ds2 = Dataset(samples=N.arange(len(chunks)).reshape( (len(chunks),1)), labels=[1]*8) coarsenChunks(ds2, nchunks=2) self.failUnless((chunks1 == ds.chunks).all()) def testBinds(self): ds = normalFeatureDataset() ds_data = ds.samples.copy() ds_chunks = ds.chunks.copy() self.failUnless(N.all(ds.samples == ds_data)) # sanity check funcs = ['zscore', 'coarsenChunks'] if externals.exists('scipy'): funcs.append('detrend') for f in funcs: eval('ds.%s()' % f) self.failUnless(N.any(ds.samples != ds_data) or N.any(ds.chunks != ds_chunks), msg="We should have modified original dataset with %s" % f) ds.samples = ds_data.copy() ds.chunks = ds_chunks.copy() # and some which should just return results for f in ['aggregateFeatures', 'removeInvariantFeatures', 'getSamplesPerChunkLabel']: res = eval('ds.%s()' % f) self.failUnless(res is not None, msg='We should have got result from function %s' % f) self.failUnless(N.all(ds.samples == ds_data), msg="Function %s should have not modified original dataset" % f) def testSequenceStat(self): """Test sequence statistics """ order = 3 # Close to perfectly balanced one sp = N.array([-1, 1, 1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1], dtype=int) rp = SequenceStats(sp, order=order) self.failUnlessAlmostEqual(rp['sumabscorr'], 1.0) self.failUnlessAlmostEqual(N.max(rp['corrcoef'] * (len(sp)-1) + 1.0), 0.0) # Now some random but loong one still binary (boolean) sb = (N.random.random_sample((1000,)) >= 0.5) rb = SequenceStats(sb, order=order) # Now lets do multiclass with literal labels s5 = N.random.permutation(['f', 'bu', 'd', 0, 'zz']*200) r5 = SequenceStats(s5, order=order) # Degenerate one but still should be valid s1 = ['aaa']*100 r1 = SequenceStats(s1, order=order) # Generic conformance tests for r in (rp, rb, r5, r1): ulabels = r['ulabels'] nlabels = len(r['ulabels']) cbcounts = r['cbcounts'] self.failUnlessEqual(len(cbcounts), order) for cb in cbcounts: self.failUnlessEqual(N.asarray(cb).shape, (nlabels, nlabels)) # Check if str works fine sr = str(r) # TODO: check the content def suite(): return unittest.makeSuite(MiscDatasetFxTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_datasetfx_sp.py000066400000000000000000000126221174541445200212540ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA miscelaneouse functions operating on datasets and requiring SciPy""" import unittest import numpy as N from mvpa.base import externals from mvpa.datasets import Dataset from mvpa.datasets.miscfx import removeInvariantFeatures if externals.exists('scipy', raiseException=True): from scipy import linalg from mvpa.datasets.miscfx import detrend class MiscDatasetFxSpTests(unittest.TestCase): def testDetrend(self): thr = 1e-9; # threshold for comparison samples = N.array( [[1.0, 2, 3, 3, 2, 1], [-2.0, -4, -6, -6, -4, -2]], ndmin=2 ).T chunks = [0, 0, 0, 1, 1, 1] chunks_bad = [ 0, 0, 1, 1, 1, 0] target_all = N.array( [[-1.0, 0, 1, 1, 0, -1], [2, 0, -2, -2, 0, 2]], ndmin=2 ).T ds = Dataset(samples=samples, labels=chunks, chunks=chunks, copy_samples=True) detrend(ds, perchunk=False) self.failUnless(linalg.norm(ds.samples - target_all) < thr, msg="Detrend should have detrended all the samples at once") ds_bad = Dataset(samples=samples, labels=chunks, chunks=chunks_bad, copy_samples=True) self.failUnlessRaises(ValueError, detrend, ds_bad, perchunk=True) ds = Dataset(samples=samples, labels=chunks, chunks=chunks, copy_samples=True) detrend(ds, perchunk=True) self.failUnless(linalg.norm(ds.samples) < thr, msg="Detrend should have detrended each chunk separately") self.failUnless(ds.samples.shape == samples.shape, msg="Detrend must preserve the size of dataset") # small additional test for break points ds = Dataset(samples=N.array([[1.0, 2, 3, 1, 2, 3]], ndmin=2).T, labels=chunks, chunks=chunks, copy_samples=True) detrend(ds, perchunk=True) self.failUnless(linalg.norm(ds.samples) < thr, msg="Detrend should have removed all the signal") # tests of the regress version of detrend ds = Dataset(samples=samples, labels=chunks, chunks=chunks, copy_samples=True) detrend(ds, perchunk=False, model='regress', polyord=1) self.failUnless(linalg.norm(ds.samples - target_all) < thr, msg="Detrend should have detrended all the samples at once") ds = Dataset(samples=samples, labels=chunks, chunks=chunks, copy_samples=True) (res, reg) = detrend(ds, perchunk=True, model='regress', polyord=2) psamps = ds.samples.copy() self.failUnless(linalg.norm(ds.samples) < thr, msg="Detrend should have detrended each chunk separately") self.failUnless(ds.samples.shape == samples.shape, msg="Detrend must preserve the size of dataset") ods = Dataset(samples=samples, labels=chunks, chunks=chunks, copy_samples=True) opt_reg = reg.copy() (ores, oreg) = detrend(ods, perchunk=True, model='regress', opt_reg=opt_reg) dsamples = (ods.samples - psamps).sum() self.failUnless(abs(dsamples) <= 1e-10, msg="Detrend for polyord reg should be same as opt_reg " + \ "when popt_reg is the same as the polyord reg. But got %g" \ % dsamples) self.failUnless(linalg.norm(ds.samples) < thr, msg="Detrend should have detrended each chunk separately") # test of different polyord on each chunk target_mixed = N.array( [[-1.0, 0, 1, 0, 0, 0], [2.0, 0, -2, 0, 0, 0]], ndmin=2 ).T ds = Dataset(samples=samples, labels=chunks, chunks=chunks, copy_samples=True) (res, reg) = detrend(ds, perchunk=True, model='regress', polyord=[0,1]) self.failUnless(linalg.norm(ds.samples - target_mixed) < thr, msg="Detrend should have baseline corrected the first chunk, " + \ "but baseline and linear detrended the second.") # test applying detrend in sequence ds = Dataset(samples=samples, labels=chunks, chunks=chunks, copy_samples=True) (res, reg) = detrend(ds, perchunk=True, model='regress', polyord=1) opt_reg = reg[N.ix_(range(reg.shape[0]),[1,3])] final_samps = ds.samples.copy() ds = Dataset(samples=samples, labels=chunks, chunks=chunks, copy_samples=True) (res, reg) = detrend(ds, perchunk=True, model='regress', polyord=0) (res, reg) = detrend(ds, perchunk=True, model='regress', opt_reg=opt_reg) self.failUnless(linalg.norm(ds.samples - final_samps) < thr, msg="Detrend of polyord 1 should be same as detrend with " + \ "0 followed by opt_reg the same as a 1st order.") def suite(): return unittest.makeSuite(MiscDatasetFxSpTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_datasetng.py000066400000000000000000000013341174541445200205370ustar00rootroot00000000000000'''Tests for the dataset implementation''' import numpy as N from numpy.testing import assert_array_equal from nose.tools import ok_ from mvpa.datasets.base import _Dataset as Dataset def test_initSimple(): samples = N.arange(12).reshape((4,3)) labels = range(4) chunks = [1, 1, 2, 2] ds = Dataset.initSimple(samples, labels, chunks) assert_array_equal(ds.samples, samples) ok_(ds.sa.labels == labels) ok_(ds.sa.chunks == chunks) # XXX but why is this puking, or rather do we want to keep cluttering the # interface like this -- I'd prefer having it all inside the collection. ok_(ds.labels == labels) ok_(ds.chunks == chunks) ok_(sorted(ds.sa.names) == ['chunks', 'labels']) pymvpa-0.4.8/mvpa/tests/test_dochelpers.py000066400000000000000000000016031174541445200207140ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA dochelpers""" from mvpa.base.dochelpers import singleOrPlural import unittest import numpy as N class DochelpersTests(unittest.TestCase): def testBasic(self): self.failUnlessEqual(singleOrPlural('a', 'b', 1), 'a') self.failUnlessEqual(singleOrPlural('a', 'b', 0), 'b') self.failUnlessEqual(singleOrPlural('a', 'b', 123), 'b') # TODO: more unittests def suite(): return unittest.makeSuite(DochelpersTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_eepdataset.py000066400000000000000000000056251174541445200207130ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA EEP dataset""" import unittest import os.path import numpy as N from mvpa import pymvpa_dataroot from mvpa.base import externals from mvpa.datasets.eep import EEPDataset from mvpa.misc.io.eepbin import EEPBin class EEPDatasetTests(unittest.TestCase): def testLoad(self): eb = EEPBin(os.path.join(pymvpa_dataroot, 'eep.bin')) ds = [ EEPDataset(source, labels=[1, 2]) for source in (eb, os.path.join(pymvpa_dataroot, 'eep.bin')) ] for d in ds: self.failUnless(d.nsamples == 2) self.failUnless(d.nfeatures == 128) self.failUnless(d.channelids[23] == 'Pz') self.failUnless(N.round(d.t0 + 0.002, decimals=3) == 0) self.failUnless(N.round(d.dt - 0.002, decimals=3) == 0) self.failUnless(N.round(d.samplingrate) == 500) def testEEPBin(self): eb = EEPBin(os.path.join(pymvpa_dataroot, 'eep.bin')) self.failUnless(eb.nchannels == 32) self.failUnless(eb.nsamples == 2) self.failUnless(eb.ntimepoints == 4) self.failUnless(eb.t0 - eb.dt < 0.00000001) self.failUnless(len(eb.channels) == 32) self.failUnless(eb.data.shape == (2, 32, 4)) def testResampling(self): ds = EEPDataset(os.path.join(pymvpa_dataroot, 'eep.bin'), labels=[1, 2], labels_map={1:100, 2:101}) channelids = N.array(ds.channelids).copy() self.failUnless(N.round(ds.samplingrate) == 500.0) if not externals.exists('scipy'): return # should puke when called with nothing self.failUnlessRaises(ValueError, ds.resample) # now for real -- should divide nsamples into half rds = ds.resample(sr=250, inplace=False) # We should have not changed anything self.failUnless(N.round(ds.samplingrate) == 500.0) # by default do 'inplace' resampling ds.resample(sr=250) for d in [rds, ds]: self.failUnless(N.round(d.samplingrate) == 250) self.failUnless(d.nsamples == 2) self.failUnless(N.abs((d.dt - 1.0/250)/d.dt)<1e-5) self.failUnless(N.all(d.channelids == channelids)) # lets now see if we still have a mapper self.failUnless(d.O.shape == (2, len(channelids), 2)) # and labels_map self.failUnlessEqual(d.labels_map, {1:100, 2:101}) #self.failUnless(d.labels_map) def suite(): return unittest.makeSuite(EEPDatasetTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_enet.py000066400000000000000000000040461174541445200175230ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA least angle regression (ENET) classifier""" from mvpa import cfg from mvpa.clfs.enet import ENET from scipy.stats import pearsonr from tests_warehouse import * from mvpa.misc.data_generators import normalFeatureDataset class ENETTests(unittest.TestCase): def testENET(self): # not the perfect dataset with which to test, but # it will do for now. #data = datasets['dumb2'] # for some reason the R code fails with the dumb data data = datasets['chirp_linear'] clf = ENET() clf.train(data) # prediction has to be almost perfect # test with a correlation pre = clf.predict(data.samples) cor = pearsonr(pre, data.labels) if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(cor[0] > .8) def testENETState(self): #data = datasets['dumb2'] # for some reason the R code fails with the dumb data data = datasets['chirp_linear'] clf = ENET() clf.train(data) clf.states.enable('predictions') p = clf.predict(data.samples) self.failUnless((p == clf.predictions).all()) def testENETSensitivities(self): data = normalFeatureDataset(perlabel=10, nlabels=2, nfeatures=4) # use ENET on binary problem clf = ENET() clf.train(data) # now ask for the sensitivities WITHOUT having to pass the dataset # again sens = clf.getSensitivityAnalyzer(force_training=False)() self.failUnless(sens.shape == (data.nfeatures,)) def suite(): return unittest.makeSuite(ENETTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_externals.py000066400000000000000000000056361174541445200206030ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Test externals checking""" import unittest from mvpa import cfg from mvpa.base import externals from mvpa.support import copy class TestExternals(unittest.TestCase): def setUp(self): self.backup = [] # paranoid check self.cfgstr = str(cfg) # clean up externals cfg for proper testing if cfg.has_section('externals'): self.backup = copy.deepcopy(cfg.items('externals')) cfg.remove_section('externals') def tearDown(self): if len(self.backup): # wipe existing one completely if cfg.has_section('externals'): cfg.remove_section('externals') cfg.add_section('externals') for o,v in self.backup: cfg.set('externals', o,v) # paranoid check # since order can't be guaranteed, lets check # each item after sorting self.failUnlessEqual(sorted(self.cfgstr.split('\n')), sorted(str(cfg).split('\n'))) def testExternals(self): self.failUnlessRaises(ValueError, externals.exists, 'BoGuS') def testExternalsNoDoubleInvocation(self): # no external should be checking twice (unless specified # explicitely) class Checker(object): """Helper class to increment count of actual checks""" def __init__(self): self.checked = 0 def check(self): self.checked += 1 checker = Checker() externals._KNOWN['checker'] = 'checker.check()' externals.__dict__['checker'] = checker externals.exists('checker') self.failUnlessEqual(checker.checked, 1) externals.exists('checker') self.failUnlessEqual(checker.checked, 1) externals.exists('checker', force=True) self.failUnlessEqual(checker.checked, 2) externals.exists('checker') self.failUnlessEqual(checker.checked, 2) # restore original externals externals.__dict__.pop('checker') externals._KNOWN.pop('checker') def testExternalsCorrect2ndInvocation(self): # always fails externals._KNOWN['checker2'] = 'raise ImportError' self.failUnless(not externals.exists('checker2'), msg="Should be False on 1st invocation") self.failUnless(not externals.exists('checker2'), msg="Should be False on 2nd invocation as well") externals._KNOWN.pop('checker2') def suite(): return unittest.makeSuite(TestExternals) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_gnb.py000066400000000000000000000046321174541445200173370ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA GNB classifier""" from mvpa.clfs.gnb import GNB from tests_warehouse import * class GNBTests(unittest.TestCase): def testGNB(self): gnb = GNB() gnb_nc = GNB(common_variance=False) gnb_n = GNB(normalize=True) gnb_n_nc = GNB(normalize=True, common_variance=False) ds_tr = datasets['uni2medium_train'] ds_te = datasets['uni2medium_test'] # Generic silly coverage just to assure that it works in all # possible scenarios: bools = (True, False) # There should be better way... heh for cv in bools: # common_variance? for prior in ('uniform', 'laplacian_smoothing', 'ratio'): tp = None # predictions -- all above should # result in the same predictions for n in bools: # normalized? for ls in bools: # logspace? for es in ((), ('values')): gnb_ = GNB(common_variance=cv, prior=prior, normalize=n, logprob=ls, enable_states=es) gnb_.train(ds_tr) predictions = gnb_.predict(ds_te.samples) if tp is None: tp = predictions self.failUnless((predictions == tp), msg="%s failed to reproduce predictions" % gnb_) # if normalized -- check if values are such if n and 'values' in es: v = gnb_.values if ls: # in log space -- take exp ;) v = N.exp(v) d1 = N.sum(v, axis=1) - 1.0 self.failUnless(N.max(N.abs(d1)) < 1e-5) def suite(): return unittest.makeSuite(GNBTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_gpr.py000066400000000000000000000037741174541445200173670ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA GPR.""" import unittest from mvpa.base import externals from mvpa.misc import data_generators from mvpa.clfs.kernel import KernelLinear as GeneralizedLinearKernel from mvpa.clfs.gpr import GPR from tests_warehouse import * from numpy.testing import assert_array_equal, assert_array_almost_equal if __debug__: from mvpa.base import debug class GPRTests(unittest.TestCase): def test_basic(self): dataset = data_generators.linear1d_gaussian_noise() k = GeneralizedLinearKernel() clf = GPR(k) clf.train(dataset) y = clf.predict(dataset.samples) assert_array_equal(y.shape, dataset.labels.shape) def test_linear(self): pass def test_gpr_model_selection(self): """Smoke test for running model selection while getting GPRWeights """ if not externals.exists('openopt'): return dataset = datasets['uni2small'] #data_generators.linear1d_gaussian_noise() k = GeneralizedLinearKernel() clf = GPR(k, enable_states=['log_marginal_likelihood']) sa = clf.getSensitivityAnalyzer() # should be regular weights sa_ms = clf.getSensitivityAnalyzer(flavor='model_select') # with model selection def prints(): print clf.states.log_marginal_likelihood, clf.kernel.Sigma_p, clf.kernel.sigma_0 sa(dataset) lml = clf.states.log_marginal_likelihood sa_ms(dataset) lml_ms = clf.states.log_marginal_likelihood self.failUnless(lml_ms > lml) def suite(): return unittest.makeSuite(GPRTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_hamster.py000066400000000000000000000073541174541445200202400ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA Hamster""" import os import unittest from tempfile import mktemp import numpy as N from mvpa.misc.io.hamster import * class HamsterHelperTests(unittest.TestCase): def testSpecification(self): # more than 1 positional self.failUnlessRaises(ValueError, Hamster, "1", 2) # do not mix positional self.failUnlessRaises(ValueError, Hamster, "1", bu=123) # need to be a string self.failUnlessRaises(ValueError, Hamster, 1) # dump cannot be assigned self.failUnlessRaises(ValueError, Hamster, dump=123) # need to be an existing file self.failUnlessRaises(IOError, Hamster, "/dev/ZUMBARGAN123") hh=Hamster(budda=1, z=[123], fuga="123"); hh.h1=123; delattr(hh, 'budda') self.failUnless(`hh` == "Hamster(fuga='123', h1=123, z=[123])") def testSimpleStorage(self): ex1 = """eins zwei drei 0 1 2 3 4 5 """ ex2 = {'d1': N.random.normal(size=(4,4))} hamster = Hamster(ex1=ex1) hamster.d = ex2 hamster.boo = HamsterHelperTests total_dict = {'ex1' : ex1, 'd' : ex2, 'boo' : HamsterHelperTests} self.failUnless(hamster.asdict() == total_dict) self.failUnless(set(hamster.registered) == set(['ex1', 'd', 'boo'])) filename = mktemp('mvpa', 'test') filename_gz = filename + '.gz' filename_bogusgz = filename + '_bogus.gz' # dump hamster.dump(filename) hamster.dump(filename_gz) # allow to shoot yourself in the head hamster.dump(filename_bogusgz, compresslevel=0) self.failUnless(hamster.asdict() == total_dict) # We should have stored plain and gzipped versions gzplain = gzip.open(filename) self.failUnlessRaises(IOError, gzplain.readlines) gzipped = gzip.open(filename_gz) discard = gzipped.readlines() gzbogus = gzip.open(filename_bogusgz) self.failUnlessRaises(IOError, gzbogus.readlines) # load plain hamster2 = Hamster(filename) # check if we re-stored all the keys k = hamster.__dict__.keys(); k2 = hamster2.__dict__.keys(); self.failUnless(set(k) == set(k2)) # identity should be lost self.failUnless(hamster.ex1 is hamster.ex1) self.failUnless(not (hamster.ex1 is hamster2.ex1)) # lets compare self.failUnless(hamster.ex1 == hamster2.ex1) self.failUnless(hamster.d.keys() == hamster2.d.keys()) self.failUnless((hamster.d['d1'] == hamster2.d['d1']).all()) self.failUnless(hamster.boo == hamster2.boo) # not sure if that is a feature or a bug self.failUnless(hamster.boo is hamster2.boo) # cleanup os.remove(filename) os.remove(filename_gz) os.remove(filename_bogusgz) def testAssignment(self): ex1 = """eins zwei drei 0 1 2 3 4 5 """ ex2 = {'d1': N.random.normal(size=(4,4))} h = Hamster(ex1=ex1) h.ex2 = ex2 self.failUnless(hasattr(h, 'ex2')) h.ex2 = None self.failUnless(h.ex2 is None) h.ex2 = 123 self.failUnless(h.ex2 == 123) h.has_key = 123 def suite(): return unittest.makeSuite(HamsterHelperTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_hyperalignment.py000066400000000000000000000017601174541445200216160ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA ...""" # See other tests and test_procrust.py for some example on what to do ;) from mvpa.algorithms.hyperalignment import Hyperalignment # Somewhat slow but provides all needed ;) from tests_warehouse import * # if you need some classifiers #from tests_warehouse_clfs import * class HyperAlignmentTests(unittest.TestCase): def testBasicFunctioning(self): # TODO pass def testPossibleInputs(self): # get a dataset with a very high SNR pass def suite(): return unittest.makeSuite(HyperAlignmentTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_icamapper.py000066400000000000000000000046621174541445200205350ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA ICA mapper""" import unittest from mvpa.support.copy import deepcopy import numpy as N from mvpa.mappers.ica import ICAMapper from mvpa.datasets import Dataset class ICAMapperTests(unittest.TestCase): def setUp(self): # data: 40 sample feature line in 2d space (40x2; samples x features) samples = N.vstack([N.arange(40.) for i in range(2)]).T samples -= samples.mean() samples += N.random.normal(size=samples.shape, scale=0.1) self.ndlin = Dataset(samples=samples, labels=1, chunks=1) # data: 40 sample feature line in 50d space (40x50; samples x features) samples = N.vstack([N.arange(40.) for i in range(50)]).T samples -= samples.mean() samples += N.random.normal(size=samples.shape, scale=0.1) self.largefeat = Dataset(samples=samples, labels=1, chunks=1) self.pm = ICAMapper() def testSimpleICA(self): # train self.pm.train(self.ndlin) self.failUnlessEqual(self.pm.proj.shape, (2, 2)) # now project data into ICA space p = self.pm.forward(self.ndlin.samples) self.failUnlessEqual(p.shape, (40, 2)) # check that the mapped data can be fully recovered by 'reverse()' self.failUnless(N.abs(self.pm.reverse(p) - self.ndlin.samples).mean() \ < 0.0001) # def testAutoOptimzeICA(self): # # train # self.pm.train(self.largefeat) # # self.failUnlessEqual(self.pm.proj.shape, (50, 40)) # # # now project data into ICA space ## p = self.pm.forward(self.largefeat.samples) # # self.failUnless(p.shape[1] == 40) # print self.pm.proj # print self.pm.recon # print p # P.scatter(p[:20,0], p[:20,1],color='green') # P.scatter(p[20:,0], p[20:,1], color='red') # P.show() # self.failUnless(N.abs(self.pm.reverse(p) - self.largefeat.samples).mean() \ # < 0.0001) def suite(): return unittest.makeSuite(ICAMapperTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_ifs.py000066400000000000000000000055431174541445200173540ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA incremental feature search.""" from mvpa.datasets.masked import MaskedDataset from mvpa.featsel.ifs import IFS from mvpa.algorithms.cvtranserror import CrossValidatedTransferError from mvpa.clfs.transerror import TransferError from mvpa.datasets.splitters import NFoldSplitter from mvpa.featsel.helpers import FixedNElementTailSelector from tests_warehouse import * from tests_warehouse_clfs import * class IFSTests(unittest.TestCase): def getData(self): data = N.random.standard_normal(( 100, 2, 2, 2 )) labels = N.concatenate( ( N.repeat( 0, 50 ), N.repeat( 1, 50 ) ) ) chunks = N.repeat( range(5), 10 ) chunks = N.concatenate( (chunks, chunks) ) return MaskedDataset(samples=data, labels=labels, chunks=chunks) # XXX just testing based on a single classifier. Not sure if # should test for every known classifier since we are simply # testing IFS algorithm - not sensitivities @sweepargs(svm=clfswh['has_sensitivity', '!meta'][:1]) def testIFS(self, svm): # data measure and transfer error quantifier use the SAME clf! trans_error = TransferError(svm) data_measure = CrossValidatedTransferError(trans_error, NFoldSplitter(1)) ifs = IFS(data_measure, trans_error, feature_selector=\ # go for lower tail selection as data_measure will return # errors -> low is good FixedNElementTailSelector(1, tail='lower', mode='select'), ) wdata = self.getData() wdata_nfeatures = wdata.nfeatures tdata = self.getData() tdata_nfeatures = tdata.nfeatures sdata, stdata = ifs(wdata, tdata) # fail if orig datasets are changed self.failUnless(wdata.nfeatures == wdata_nfeatures) self.failUnless(tdata.nfeatures == tdata_nfeatures) # check that the features set with the least error is selected self.failUnless(len(ifs.errors)) e = N.array(ifs.errors) self.failUnless(sdata.nfeatures == e.argmin() + 1) # repeat with dataset where selection order is known signal = datasets['dumb2'] sdata, stdata = ifs(signal, signal) self.failUnless((sdata.samples[:,0] == signal.samples[:,0]).all()) def suite(): return unittest.makeSuite(IFSTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_iohelpers.py000066400000000000000000000225531174541445200205650ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA IO helpers""" import re import os import unittest from tempfile import mkstemp import numpy as N from nose.tools import ok_, assert_equal from mvpa import pymvpa_dataroot from mvpa.misc.io import * from mvpa.misc.fsl import * from mvpa.misc.bv import BrainVoyagerRTC class IOHelperTests(unittest.TestCase): def testColumnDataFromFile(self): ex1 = """eins zwei drei 0 1 2 3 4 5 """ file, fpath = mkstemp('mvpa', 'test') file = open(fpath, 'w') file.write(ex1) file.close() # intentionally rely on defaults d = ColumnData(fpath, header=True) # check header (sort because order in dict is unpredictable) self.failUnless(sorted(d.keys()) == ['drei','eins','zwei']) self.failUnless(d['eins'] == [0, 3]) self.failUnless(d['zwei'] == [1, 4]) self.failUnless(d['drei'] == [2, 5]) # make a copy d2 = ColumnData(d) # check if identical self.failUnless(sorted(d2.keys()) == ['drei','eins','zwei']) self.failUnless(d2['eins'] == [0, 3]) self.failUnless(d2['zwei'] == [1, 4]) self.failUnless(d2['drei'] == [2, 5]) # now merge back d += d2 # same columns? self.failUnless(sorted(d.keys()) == ['drei','eins','zwei']) # but more data self.failUnlessEqual(d['eins'], [0, 3, 0, 3]) self.failUnlessEqual(d['zwei'], [1, 4, 1, 4]) self.failUnlessEqual(d['drei'], [2, 5, 2, 5]) # test file write # TODO: check if correct header_order = ['drei', 'zwei', 'eins'] d.tofile(fpath, header_order=header_order) # test sample selection dsel = d.selectSamples([0, 2]) self.failUnlessEqual(dsel['eins'], [0, 0]) self.failUnlessEqual(dsel['zwei'], [1, 1]) self.failUnlessEqual(dsel['drei'], [2, 2]) # test if order is read from file when available d3 = ColumnData(fpath) self.failUnlessEqual(d3._header_order, header_order) # add another column -- should be appended as the last column # while storing d3['four'] = [0.1] * len(d3['eins']) d3.tofile(fpath) d4 = ColumnData(fpath) self.failUnlessEqual(d4._header_order, header_order + ['four']) # cleanup and ignore stupidity try: os.remove(fpath) except WindowsError: pass def testSamplesAttributes(self): sa = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes_literal.txt'), literallabels=True) ok_(sa.nrows == 1452, msg='There should be 1452 samples') # convert to event list, with some custom attr ev = sa.toEvents(funky='yeah') ok_(len(ev) == 17 * (max(sa.chunks) + 1), msg='Not all events got detected.') ok_(len([e for e in ev if e.has_key('funky')]) == len(ev), msg='All events need to have to custom arg "funky".') ok_(ev[0]['label'] == ev[-1]['label'] == 'rest', msg='First and last event are rest condition.') ok_(ev[-1]['onset'] + ev[-1]['duration'] == sa.nrows, msg='Something is wrong with the timiing of the events') def testFslEV(self): ex1 = """0.0 2.0 1 13.89 2 1 16 2.0 0.5 """ file, fpath = mkstemp('mvpa', 'test') file = open(fpath, 'w') file.write(ex1) file.close() # intentionally rely on defaults d = FslEV3(fpath) # check header (sort because order in dict is unpredictable) self.failUnless(sorted(d.keys()) == \ ['durations','intensities','onsets']) self.failUnless(d['onsets'] == [0.0, 13.89, 16.0]) self.failUnless(d['durations'] == [2.0, 2.0, 2.0]) self.failUnless(d['intensities'] == [1.0, 1.0, 0.5]) self.failUnless(d.getNEVs() == 3) self.failUnless(d.getEV(1) == (13.89, 2.0, 1.0)) # cleanup and ignore stupidity try: os.remove(fpath) except WindowsError: pass d = FslEV3(os.path.join(pymvpa_dataroot, 'fslev3.txt')) ev = d.toEvents() self.failUnless(len(ev) == 3) self.failUnless([e['duration'] for e in ev] == [9] * 3) self.failUnless([e['onset'] for e in ev] == [6, 21, 35]) self.failUnless([e['features'] for e in ev] == [[1],[1],[1]]) ev = d.toEvents(label='face', chunk=0, crap=True) ev[0]['label'] = 'house' self.failUnless(len(ev) == 3) self.failUnless([e['duration'] for e in ev] == [9] * 3) self.failUnless([e['onset'] for e in ev] == [6, 21, 35]) self.failUnless([e['features'] for e in ev] == [[1],[1],[1]]) self.failUnless([e['label'] for e in ev] == ['house', 'face', 'face']) self.failUnless([e['chunk'] for e in ev] == [0]*3) self.failUnless([e['crap'] for e in ev] == [True]*3) def testFslEV2(self): attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'smpl_attr.txt')) # check header (sort because order in dict is unpredictable) self.failUnless(sorted(attr.keys()) == \ ['chunks','labels']) self.failUnless(attr.nsamples == 3) def testBVRTC(self): """Simple testing of reading RTC files from BrainVoyager""" attr = BrainVoyagerRTC(os.path.join(pymvpa_dataroot, 'bv', 'smpl_model.rtc')) self.failUnlessEqual(attr.ncolumns, 4, "We must have 4 colums") self.failUnlessEqual(attr.nrows, 147, "We must have 147 rows") self.failUnlessEqual(attr._header_order, ['l_60 B', 'r_60 B', 'l_80 B', 'r_80 B'], "We must got column names correctly") self.failUnless(len(attr.r_60_B) == attr.nrows, "We must have got access to column by property") self.failUnless(attr.toarray() != None, "We must have got access to column by property") def testdesign2labels(self): """Simple testing of helper Design2Labels""" attr = BrainVoyagerRTC(os.path.join(pymvpa_dataroot, 'bv', 'smpl_model.rtc')) labels0 = design2labels(attr, baseline_label='silence') labels = design2labels(attr, baseline_label='silence', func=lambda x:x>0.5) Nsilence = lambda x:len(N.where(N.array(x) == 'silence')[0]) nsilence0 = Nsilence(labels0) nsilence = Nsilence(labels) self.failUnless(nsilence0 < nsilence, "We must have more silence if thr is higher") self.failUnlessEqual(len(labels), attr.nrows, "We must have the same number of labels as rows") self.failUnlessRaises(ValueError, design2labels, attr, baseline_label='silence', func=lambda x:x>-1.0) def testlabels2chunks(self): attr = BrainVoyagerRTC(os.path.join(pymvpa_dataroot, 'bv', 'smpl_model.rtc')) labels = design2labels(attr, baseline_label='silence') self.failUnlessRaises(ValueError, labels2chunks, labels, 'bugga') chunks = labels2chunks(labels) self.failUnlessEqual(len(labels), len(chunks)) # we must got them in sorted order chunks_sorted = N.sort(chunks) self.failUnless((chunks == chunks_sorted).all()) # for this specific one we must have just 4 chunks self.failUnless((N.unique(chunks) == range(4)).all()) def testSensorLocations(self): sl = XAVRSensorLocations(os.path.join(pymvpa_dataroot, 'xavr1010.dat')) for var in ['names', 'pos_x', 'pos_y', 'pos_z']: self.failUnless(len(eval('sl.' + var)) == 31) def testFslGLMDesign(self): glm = FslGLMDesign(os.path.join(pymvpa_dataroot, 'glm.mat')) self.failUnless(glm.mat.shape == (850, 6)) self.failUnless(len(glm.ppheights) == 6) def test_read_fsl_design(self): fname = os.path.join(pymvpa_dataroot, 'sample_design.fsf') # use our function design = read_fsl_design(fname) # and just load manually to see either we match fine set_lines = [x for x in open(fname).readlines() if x.startswith('set ')] assert_equal(len(set_lines), len(design)) # figure out which one is missing """TODO: would require the same special treatment for _files fields re_set = re.compile("set ([^)]*\)).*") for line in set_lines: key = re_set.search(line).groups()[0] if not key in design: raise AssertionError( "Key %s was not found in read FSL design" % key) key_list = [' '.join(l.split(None,2)[1:2]) for l in set_lines] for k in set(key_list): if len([key for key in key_list if key == k]) == 2: raise AssertionError( "Got the non-unique beast %s" % k) """ def suite(): return unittest.makeSuite(IOHelperTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_kernel.py000066400000000000000000000105151174541445200200460ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA kernels""" import unittest import numpy as N from mvpa.clfs.distance import squared_euclidean_distance, \ pnorm_w, pnorm_w_python # from mvpa.clfs.kernel import Kernel from tests_warehouse import datasets class KernelTests(unittest.TestCase): def testEuclidDist(self): # select some block of data from already generated data = datasets['uni4large'].samples[:5, :8] ed = squared_euclidean_distance(data) # XXX not sure if that is right: 'weight' seems to be given by # feature (i.e. column), but distance is between samples (i.e. rows) # current behavior is: true_size = (5, 5) self.failUnless(ed.shape == true_size) # slow version to compute distance matrix ed_manual = N.zeros(true_size, 'd') for i in range(true_size[0]): for j in range(true_size[1]): #ed_manual[i,j] = N.sqrt(((data[i,:] - data[j,:] )** 2).sum()) ed_manual[i,j] = ((data[i,:] - data[j,:] )** 2).sum() ed_manual[ed_manual < 0] = 0 self.failUnless(N.diag(ed_manual).sum() < 0.0000000001) self.failUnless(N.diag(ed).sum() < 0.0000000001) # let see whether Kernel does the same self.failUnless((ed - ed_manual).sum() < 0.0000001) def testPNorm_w(self): data0 = datasets['uni4large'].samples.T weight = N.abs(data0[11, :60]) self.failUnlessRaises(ValueError, pnorm_w_python, data0[:10,:2], p=1.2, heuristic='buga') self.failUnlessRaises(ValueError, pnorm_w_python, data0[:10,:2], weight=weight) self.failUnlessRaises(ValueError, pnorm_w_python, data0[:10,:2], data0[:10, :3], weight=weight) self.failUnlessRaises(ValueError, pnorm_w, data0[:10,:2], data0[:10, :3], weight=weight) self.failUnlessRaises(ValueError, pnorm_w, data0[:10,:2], weight=weight) # some sanity checks for did, (data1, data2, w) in enumerate( [ (data0[:2, :60], None, None), (data0[:2, :60], data0[3:4, 1:61], None), (data0[:2, :60], None, weight), (data0[:2, :60], data0[3:4, 1:61], weight), ]): # test different norms for p in [1, 2, 1.2]: kwargs = {'data1': data1, 'data2': data2, 'weight' : w, 'p' : p} d = pnorm_w(**kwargs) # default one # to assess how far we are kwargs0 = kwargs.copy() kwargs0['data2'] = N.zeros(data1.shape) d0 = pnorm_w(**kwargs0) d0norm = N.linalg.norm(d - d0, 'fro') # test different implementations for iid, d2 in enumerate( [pnorm_w_python(**kwargs), pnorm_w_python(use_sq_euclidean=True, **kwargs), pnorm_w_python(heuristic='auto', **kwargs), pnorm_w_python(use_sq_euclidean=False, **kwargs), pnorm_w_python(heuristic='auto', use_sq_euclidean=False, **kwargs), pnorm_w_python(heuristic='samples', use_sq_euclidean=False, **kwargs), pnorm_w_python(heuristic='features', use_sq_euclidean=False, **kwargs), ]): dnorm = N.linalg.norm(d2 - d, 'fro') self.failUnless(dnorm/d0norm < 1e-7, msg="Failed comparison of different implementations on " "data #%d, implementation #%d, p=%s. " "Norm of the difference is %g" % (did, iid, p, dnorm)) def suite(): return unittest.makeSuite(KernelTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_knn.py000066400000000000000000000034641174541445200173610ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA kNN classifier""" from mvpa.clfs.knn import kNN from tests_warehouse import * from tests_warehouse import pureMultivariateSignal from mvpa.clfs.distance import oneMinusCorrelation class KNNTests(unittest.TestCase): def testMultivariate(self): mv_perf = [] uv_perf = [] clf = kNN(k=10) for i in xrange(20): train = pureMultivariateSignal( 20, 3 ) test = pureMultivariateSignal( 20, 3 ) clf.train(train) p_mv = clf.predict( test.samples ) mv_perf.append( N.mean(p_mv==test.labels) ) clf.train(train.selectFeatures([0])) p_uv = clf.predict( test.selectFeatures([0]).samples ) uv_perf.append( N.mean(p_uv==test.labels) ) mean_mv_perf = N.mean(mv_perf) mean_uv_perf = N.mean(uv_perf) self.failUnless( mean_mv_perf > 0.9 ) self.failUnless( mean_uv_perf < mean_mv_perf ) def testKNNState(self): train = pureMultivariateSignal( 20, 3 ) test = pureMultivariateSignal( 20, 3 ) clf = kNN(k=10) clf.train(train) clf.states.enable('values') clf.states.enable('predictions') p = clf.predict(test.samples) self.failUnless(p == clf.predictions) self.failUnless(N.array(clf.values).shape == (80,2)) def suite(): return unittest.makeSuite(KNNTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_lars.py000066400000000000000000000040261174541445200175270ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA least angle regression (LARS) classifier""" from mvpa import cfg from mvpa.clfs.lars import LARS from scipy.stats import pearsonr from tests_warehouse import * from mvpa.misc.data_generators import normalFeatureDataset class LARSTests(unittest.TestCase): def testLARS(self): # not the perfect dataset with which to test, but # it will do for now. #data = datasets['dumb2'] # for some reason the R code fails with the dumb data data = datasets['chirp_linear'] clf = LARS(regression=True) clf.train(data) # prediction has to be almost perfect # test with a correlation pre = clf.predict(data.samples) cor = pearsonr(pre, data.labels) if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(cor[0] > .8) def testLARSState(self): #data = datasets['dumb2'] # for some reason the R code fails with the dumb data data = datasets['chirp_linear'] clf = LARS() clf.train(data) clf.states.enable('predictions') p = clf.predict(data.samples) self.failUnless((p == clf.predictions).all()) def testLARSSensitivities(self): data = datasets['chirp_linear'] # use LARS on binary problem clf = LARS() clf.train(data) # now ask for the sensitivities WITHOUT having to pass the dataset # again sens = clf.getSensitivityAnalyzer(force_training=False)() self.failUnless(sens.shape == (data.nfeatures,)) def suite(): return unittest.makeSuite(LARSTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_maskeddataset.py000066400000000000000000000310071174541445200213770ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA pattern handling""" from mvpa.datasets.masked import * from mvpa.misc.exceptions import DatasetError import unittest import numpy as N import random class MaskedDatasetTests(unittest.TestCase): def testCreateMaskedDataset(self): data = MaskedDataset(samples=[range(5)], labels=1, chunks=1) # simple sequence has to be a single pattern self.failUnlessEqual( data.nsamples, 1) # check correct pattern layout (1x5) self.failUnless( (data.samples == N.array([[0, 1, 2, 3, 4]])).all() ) # check for single label and origin self.failUnless( (data.labels == N.array([1])).all() ) self.failUnless( (data.chunks == N.array([1])).all() ) # now try adding pattern with wrong shape self.failUnlessRaises(DatasetError, data.__iadd__, MaskedDataset(samples=N.ones((2,3)), labels=1, chunks=1)) # now add two real patterns data += MaskedDataset(samples=N.random.standard_normal((2,5)), labels=2, chunks=2) self.failUnlessEqual( data.nsamples, 3 ) self.failUnless( (data.labels == N.array([1,2,2]) ).all() ) self.failUnless( (data.chunks == N.array([1,2,2]) ).all() ) # test unique class labels data += MaskedDataset(samples=N.random.standard_normal((2,5)), labels=3) self.failUnless( (data.uniquelabels == N.array([1,2,3]) ).all() ) # test wrong label length self.failUnlessRaises(DatasetError, MaskedDataset, samples=N.random.standard_normal((4,2,3,4)), labels=[1, 2, 3], chunks=2) # test wrong origin length self.failUnlessRaises( DatasetError, MaskedDataset, samples=N.random.standard_normal((4,2,3,4)), labels=[1, 2, 3, 4], chunks=[2, 2, 2]) def testShapeConversion(self): data = MaskedDataset(samples=N.arange(24).reshape((2,3,4)), labels=1, chunks=1) self.failUnlessEqual(data.nsamples, 2) self.failUnlessEqual(data.samples.shape, (2,12)) self.failUnless((data.samples == N.array([range(12),range(12,24)])).all()) def testPatternShape(self): data = MaskedDataset(samples=N.ones((10,2,3,4)), labels=1, chunks=1) self.failUnless(data.samples.shape == (10,24)) def testFeature2Coord(self): origdata = N.random.standard_normal((10,2,4,3,5)) data = MaskedDataset( samples=origdata, labels=2, chunks=2 ) def randomCoord(shape): return [ random.sample(range(size),1)[0] for size in shape ] # check 100 random coord2feature transformations for i in xrange(100): # choose random coord c = randomCoord((2,4,3,5)) # tranform to feature_id id = data.mapper.getOutId(c) # compare data from orig array (selected by coord) # and data from pattern array (selected by feature id) orig = origdata[:,c[0],c[1],c[2],c[3]] pat = data.samples[:, id] self.failUnless((orig == pat).all()) def testCoord2Feature(self): origdata = N.random.standard_normal((10,2,4,3,5)) data = MaskedDataset(samples=origdata, labels=2, chunks=2) def randomCoord(shape): return [ random.sample(range(size),1)[0] for size in shape ] for id in xrange(data.nfeatures): # transform to coordinate c = data.mapper.getInId(id) self.failUnlessEqual(len(c), 4) # compare data from orig array (selected by coord) # and data from pattern array (selected by feature id) orig = origdata[:,c[0],c[1],c[2],c[3]] pat = data.samples[:, id] self.failUnless((orig == pat).all()) def testFeatureSelection(self): origdata = N.random.standard_normal((10,2,4,3,5)) data = MaskedDataset(samples=origdata, labels=2, chunks=2) unmasked = data.samples.copy() # default must be no mask self.failUnless( data.nfeatures == 120 ) self.failUnless(data.mapper.getOutSize() == 120) # check that full mask uses all features sel = data.selectFeaturesByMask( N.ones((2,4,3,5)) ) self.failUnless( sel.nfeatures == data.samples.shape[1] ) self.failUnless( data.nfeatures == 120 ) # check partial array mask partial_mask = N.zeros((2,4,3,5), dtype='uint') partial_mask[0,0,2,2] = 1 partial_mask[1,2,2,0] = 1 sel = data.selectFeaturesByMask( partial_mask ) self.failUnless( sel.nfeatures == 2 ) self.failUnless( sel.mapper.getMask().shape == (2,4,3,5)) # check that feature selection does not change source data self.failUnless(data.nfeatures == 120) self.failUnlessEqual(data.mapper.getOutSize(), 120) # check selection with feature list sel = data.selectFeatures([0,37,119]) self.failUnless(sel.nfeatures == 3) # check size of the masked patterns self.failUnless( sel.samples.shape == (10,3) ) # check that the right features are selected self.failUnless( (unmasked[:,[0,37,119]]==sel.samples).all() ) def testPatternSelection(self): origdata = N.random.standard_normal((10,2,4,3,5)) data = MaskedDataset(samples=origdata, labels=2, chunks=2) self.failUnless( data.nsamples == 10 ) # set single pattern to enabled sel=data.selectSamples(5) self.failUnless( sel.nsamples == 1 ) self.failUnless( data.nsamples == 10 ) # check duplicate selections sel = data.selectSamples([5,5]) self.failUnless( sel.nsamples == 2 ) self.failUnless( (sel.samples[0] == sel.samples[1]).all() ) self.failUnless( len(sel.labels) == 2 ) self.failUnless( len(sel.chunks) == 2 ) self.failUnless( sel.samples.shape == (2,120) ) def testCombinedPatternAndFeatureMasking(self): data = MaskedDataset( samples=N.arange( 20 ).reshape( (4,5) ), labels=1, chunks=1 ) self.failUnless( data.nsamples == 4 ) self.failUnless( data.nfeatures == 5 ) fsel = data.selectFeatures([1,2]) fpsel = fsel.selectSamples([0,3]) self.failUnless( fpsel.nsamples == 2 ) self.failUnless( fpsel.nfeatures == 2 ) self.failUnless( (fpsel.samples == [[1,2],[16,17]]).all() ) def testOrigMaskExtraction(self): origdata = N.random.standard_normal((10,2,4,3)) data = MaskedDataset(samples=origdata, labels=2, chunks=2) # check with custom mask sel = data.selectFeatures([5]) self.failUnless( sel.samples.shape[1] == 1 ) origmask = sel.mapper.getMask() self.failUnless( origmask[0,1,2] == True ) self.failUnless( origmask.shape == (2,4,3) ) def testPatternMerge(self): data1 = MaskedDataset(samples=N.ones((5,5,1)), labels=1, chunks=1) data2 = MaskedDataset(samples=N.ones((3,5,1)), labels=2, chunks=1) merged = data1 + data2 self.failUnless(merged.nsamples == 8 ) self.failUnless((merged.labels == [ 1,1,1,1,1,2,2,2]).all()) self.failUnless((merged.chunks == [ 1,1,1,1,1,1,1,1]).all()) data1 += data2 self.failUnless(data1.nsamples == 8 ) self.failUnless((data1.labels == [ 1,1,1,1,1,2,2,2]).all()) self.failUnless((data1.chunks == [ 1,1,1,1,1,1,1,1]).all()) def testLabelRandomizationAndSampling(self): data = MaskedDataset(samples=N.ones((5,1)), labels=range(5), chunks=1) data += MaskedDataset(samples=N.ones((5,1))+1, labels=range(5), chunks=2) data += MaskedDataset(samples=N.ones((5,1))+2, labels=range(5), chunks=3) data += MaskedDataset(samples=N.ones((5,1))+3, labels=range(5), chunks=4) data += MaskedDataset(samples=N.ones((5,1))+4, labels=range(5), chunks=5) self.failUnless( data.samplesperlabel == {0:5, 1:5, 2:5, 3:5, 4:5} ) sample = data.getRandomSamples( 2 ) self.failUnless( sample.samplesperlabel.values() == [ 2,2,2,2,2 ] ) self.failUnless( (data.uniquechunks == range(1,6)).all() ) # store the old labels origlabels = data.labels.copy() data.permuteLabels(True) self.failIf( (data.labels == origlabels).all() ) data.permuteLabels(False) self.failUnless( (data.labels == origlabels).all() ) # now try another object with the same data data2 = MaskedDataset(samples=data.samples, labels=data.labels, chunks=data.chunks ) # labels are the same as the originals self.failUnless( (data2.labels == origlabels).all() ) # now permute in the new object data2.permuteLabels( True ) # must not affect the old one self.failUnless( (data.labels == origlabels).all() ) # but only the new one self.failIf( (data2.labels == origlabels).all() ) def testFeatureMasking(self): mask = N.zeros((5,3),dtype='bool') mask[2,1] = True; mask[4,0] = True data = MaskedDataset( samples=N.arange( 60 ).reshape( (4,5,3) ), labels=1, chunks=1, mask=mask) # check simple masking self.failUnless( data.nfeatures == 2 ) self.failUnless( data.mapper.getOutId( (2,1) ) == 0 and data.mapper.getOutId( (4,0) ) == 1 ) self.failUnlessRaises( ValueError, data.mapper.getOutId, (2,3) ) self.failUnless( data.mapper.getMask().shape == (5,3) ) self.failUnless( tuple(data.mapper.getInId( 1 )) == (4,0) ) # selection should be idempotent self.failUnless(data.selectFeaturesByMask( mask ).nfeatures == data.nfeatures ) # check that correct feature get selected self.failUnless( (data.selectFeatures([1]).samples[:,0] \ == N.array([12, 27, 42, 57]) ).all() ) self.failUnless(tuple( data.selectFeatures([1]).mapper.getInId(0) ) == (4,0) ) self.failUnless( data.selectFeatures([1]).mapper.getMask().sum() == 1 ) # check sugarings # Access to simple attributes and samples self.failUnless(N.all(data.I == data.origids)) self.failUnless(N.all(data.C == data.chunks)) self.failUnless(N.all(data.L == data.labels)) self.failUnless(N.all(data.S == data.samples)) self.failUnless(N.all(data.O == data.mapper.reverse(data.samples))) # Access to unique attributes self.failUnless(N.all(data.UC == data.uniquechunks)) self.failUnless(N.all(data.UL == data.uniquelabels)) # def testROIMasking(self): # mask=N.array([i/6 for i in range(60)], dtype='int').reshape(6,10) # data = MaskedDataset( # N.arange( 180 ).reshape( (3,6,10) ), 1, 1, mask=mask ) # # self.failIf( data.mapper.getMask().dtype == 'bool' ) # # check that the 0 masked features get cut # self.failUnless( data.nfeatures == 54 ) # self.failUnless( (data.samples[:,0] == [6,66,126]).all() ) # self.failUnless( data.mapper.getMask().shape == (6,10) ) # # featsel = data.selectFeatures([19]) # self.failUnless( (data.samples[:,19] == featsel.samples[:,0]).all() ) # # # check single ROI selection works # roisel = data.selectFeaturesByGroup([4]) # self.failUnless( (data.samples[:,19] == roisel.samples[:,1]).all() ) # # # check dual ROI selection works (plus keep feature order) # roisel = data.selectFeaturesByGroup([6,4]) # self.failUnless( (data.samples[:,19] == roisel.samples[:,1]).all() ) # self.failUnless( (data.samples[:,32] == roisel.samples[:,8]).all() ) # # # check if feature coords can be recovered # self.failUnless( (roisel.getCoordinate(8) == (3,8)).all() ) def suite(): return unittest.makeSuite(MaskedDatasetTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_meg.py000066400000000000000000000024621174541445200173400ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA MEG stuff""" import unittest import os.path from mvpa import pymvpa_dataroot from mvpa.base import externals from mvpa.misc.io.meg import TuebingenMEG class MEGTests(unittest.TestCase): def testTuebingenMEG(self): if not externals.exists('gzip'): return meg = TuebingenMEG(os.path.join(pymvpa_dataroot, 'tueb_meg.dat.gz')) # check basics self.failUnless(meg.channelids == ['BG1', 'MLC11', 'EEG02']) self.failUnless(meg.ntimepoints == 814) self.failUnless(meg.nsamples == 4) # check correct axis order (samples x channels x timepoints) self.failUnless(meg.data.shape == (4, 3, 814)) # check few values self.failUnless(meg.data[0, 1, 4] == -2.318207982e-14) self.failUnless(meg.data[3, 0, 808] == -4.30692876e-12) def suite(): return unittest.makeSuite(MEGTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_metadataset.py000066400000000000000000000233311174541445200210620ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA meta dataset handling""" import unittest import numpy as N import os.path from mvpa import pymvpa_dataroot from mvpa.support.copy import deepcopy from mvpa.base import externals from mvpa.datasets import Dataset from mvpa.datasets.meta import MetaDataset from mvpa.datasets.eep import EEPDataset from mvpa.mappers.base import CombinedMapper, ChainMapper from mvpa.mappers.array import DenseArrayMapper from mvpa.mappers.mask import MaskMapper from mvpa.mappers.boxcar import BoxcarMapper from mvpa.datasets.event import EventDataset from mvpa.misc.support import Event from mvpa.misc.exceptions import DatasetError class MetaDatasetTests(unittest.TestCase): def testSimple(self): # bunch of datasets datasets = [ Dataset(samples=N.arange(12).reshape((4,3)), labels=1), Dataset(samples=N.zeros((4,4)), labels=1), Dataset(samples=N.ones((4,2), dtype='float'), labels=1), ] mds = MetaDataset(datasets) # all together self.failUnless(mds.samples.shape == (4, 9)) # should do upcasting self.failUnless(mds.samples.dtype == 'float') # simple samples attrs self.failUnless((mds.labels == [1] * 4).all()) self.failUnless((mds.chunks == range(4)).all()) # do sample selection across all datasets mds1 = mds.selectSamples([0,3]) self.failUnless(mds1.samples.shape == (2, 9)) self.failUnless(\ (mds1.samples[0] == [0, 1, 2, 0, 0, 0, 0, 1, 1]).all()) self.failUnless(\ (mds1.samples[1] == [9, 10, 11, 0, 0, 0, 0, 1, 1]).all()) # more tricky feature selection on all datasets mds2 = mds.selectFeatures([1,4,8]) self.failUnless(\ (mds2.samples == [[ 1, 0, 1], [ 4, 0, 1], [ 7, 0, 1], [10, 0, 1]] ).all()) def testMapping(self): if not externals.exists('nifti'): return from mvpa.datasets.nifti import NiftiDataset eeds = EEPDataset(os.path.join(pymvpa_dataroot, 'eep.bin'), labels=[1,2]) nids = NiftiDataset(os.path.join(pymvpa_dataroot, 'example4d.nii.gz'), labels=[1,2]) plainds = Dataset(samples=N.arange(8).reshape((2,4)), labels=[1,2]) datasets = (eeds, plainds, nids) mds = MetaDataset(datasets) self.failUnless(mds.nfeatures == N.sum([d.nfeatures for d in datasets])) self.failUnless(mds.nsamples == 2) # try reverse mapping mr = mds.mapReverse(N.arange(mds.nfeatures)) self.failUnless(len(mr) == 3) self.failUnless(mr[1].shape == (plainds.nfeatures,)) def testCombinedMapper(self): # simple case: two array of different shape combined m = CombinedMapper([DenseArrayMapper(mask=N.ones((2,3,4))), MaskMapper(mask=N.array((1,1)))]) self.failUnless(m.getInSize() == 26) self.failUnless(m.getOutSize() == 26) d1 = N.ones((5,2,3,4)) d2_broken = N.ones((6,2)) + 1 d2 = N.ones((5,2)) + 1 # should not work for sample mismatch self.failUnlessRaises(ValueError, m.forward, (d1, d2_broken)) # check forward mapping (size and identity) mf = m.forward((d1, d2)) self.failUnless(mf.shape == (5, 26)) self.failUnless((mf[:,:24] == 1).all()) self.failUnless((mf[:,-2:] == 2).all()) # check reverse mapping self.failUnlessRaises(ValueError, m.reverse, N.arange(12)) mr = m.reverse(N.arange(26) + 1) self.failUnless(len(mr) == 2) self.failUnless((mr[0] == N.arange(24).reshape((2,3,4)) + 1).all()) self.failUnless((mr[1] == N.array((25,26))).all()) # check reverse mapping of multiple samples mr = m.reverse(N.array([N.arange(26) + 1 for i in range(4)])) self.failUnless(len(mr) == 2) self.failUnless( (mr[0] == N.array([N.arange(24).reshape((2,3,4)) + 1 for i in range(4)])).all()) self.failUnless( (mr[1] == N.array([N.array((25,26)) for i in range(4)])).all()) # check dummy train m.train(Dataset(samples=N.random.rand(10,26), labels=range(10))) self.failUnlessRaises(ValueError, m.train, Dataset(samples=N.random.rand(10,25), labels=range(10))) # check neighbor information # fail if invalid id self.failUnlessRaises(ValueError, m.getNeighbor, 26) # neighbors for last feature of first mapper, ie. # close in out space but infinite/undefined distance in in-space self.failUnless([n for n in m.getNeighbor(23, radius=2)] == [6, 7, 10, 11, 15, 18, 19, 21, 22, 23]) # check feature selection m.selectOut((23,25)) self.failUnless(m.getInSize() == 26) self.failUnless(m.getOutSize() == 2) # check reverse mapping of truncated mapper mr = m.reverse(N.array((99,88))) target1 = N.zeros((2,3,4)) target1[1,2,3] = 99 target2 = N.array((0, 88)) self.failUnless(len(mr) == 2) self.failUnless((mr[0] == target1).all()) self.failUnless((mr[1] == target2).all()) # check forward mapping self.failUnless((m.forward((d1, d2))[0] == (1, 2)).all()) # check copying mc = deepcopy(m) mc.selectOut([1]) self.failUnless(m.getOutSize() == 2) self.failUnless(mc.getOutSize() == 1) def testChainMapper(self): data = N.array([N.arange(24).reshape(3,4,2) + (i * 100) for i in range(10)]) startpoints = [ 2, 4, 3, 5 ] m = ChainMapper([BoxcarMapper(startpoints, 2), DenseArrayMapper(mask=N.ones((2, 3, 4, 2)))]) mp = m.forward(data) # 4 startpoint, with each two samples of shape (3,4,2) self.failUnless(mp.shape == (4, 48)) self.failUnless(m.reverse(N.arange(48)).shape == (2, 3, 4, 2)) # should behave a DenseArrayMapper alone self.failUnless((N.array([n for n in m.getNeighbor(24, radius=1.1)]) == N.array((0, 24, 25, 26, 32))).all()) def testEventDataset(self): # baisc checks self.failUnlessRaises(DatasetError, EventDataset) # simple data samples = N.arange(240).reshape(10, 2, 3, 4) # copy constructor does not work on non-2D data self.failUnlessRaises(DatasetError, EventDataset, samples=samples) # try case without extra features evs = [Event(onset=2, duration=2, label=1, chunk=2), Event(onset=5, duration=1, label=2, chunk=2), Event(onset=7, duration=2, label=3, chunk=4)] ds = EventDataset(samples=samples, events=evs) self.failUnless(ds.nfeatures == 48) self.failUnless(ds.nsamples == 3) self.failUnless((ds.labels == [1,2,3]).all()) self.failUnless((ds.chunks == [2,2,4]).all()) mr = ds.mapReverse(N.arange(48)) self.failUnless((mr == N.arange(48).reshape(2,2,3,4)).all()) # try case with extra features evs = [Event(onset=2, duration=2, label=1, features=[2,3]), Event(onset=5, duration=2, label=1, features=[4,5]), Event(onset=7, duration=2, label=1, features=[6,7]),] ds = EventDataset(samples=samples, events=evs) # we have 2 additional features self.failUnless(ds.nfeatures == 50) self.failUnless(ds.nsamples == 3) self.failUnless((ds.labels == [1,1,1]).all()) self.failUnless((ds.chunks == [0,1,2]).all()) # now for the long awaited -- map back into two distinct # feature spaces mr = ds.mapReverse(N.arange(50)) # we get two sets of feature spaces (samples and extra features) self.failUnless(len(mr) == 2) msamples, mxfeat = mr # the sample side should be identical to the case without extra features self.failUnless((msamples == N.arange(48).reshape(2,2,3,4)).all()) # the extra features should be flat self.failUnless((mxfeat == (48,49)).all()) # now take a look at orig = ds.O self.failUnless(len(mr) == 2) osamples, oextra = orig self.failUnless((oextra == [[2,3],[4,5],[6,7]]).all()) self.failUnless(osamples.shape == samples.shape) # check that all samples not covered by an event are zero filt = N.array([True,True,False,False,True,False,False,False,False,True]) self.failUnless(N.sum(osamples[filt]) == 0) self.failUnless((osamples[N.negative(filt)] > 0).all()) def testEventDatasetExtended(self): if not externals.exists('nifti'): return from mvpa.datasets.nifti import ERNiftiDataset try: ds = ERNiftiDataset( samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'), mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz'), events=[Event(onset=1,duration=5,label=1,chunk=1)], evconv=True, tr=2.0) except ValueError, e: self.fail("Failed to create a simple ERNiftiDataset from a volume" " with only 1 slice. Exception was:\n %s" % e) def suite(): return unittest.makeSuite(MetaDatasetTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_neighbor.py000066400000000000000000000117411174541445200203650ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA metrics""" from mvpa.mappers.metric import * from mvpa.clfs.distance import * import unittest import numpy as N class MetricTests(unittest.TestCase): """Basic tests for metrics: neighbors etc """ def testDistances(self): a = N.array([3,8]) b = N.array([6,4]) # test distances or yarik recalls unit testing ;) self.failUnless( cartesianDistance(a, b) == 5.0 ) self.failUnless( manhattenDistance(a, b) == 7 ) self.failUnless( absminDistance(a, b) == 4 ) def testDescreteMetric(self): """Descrete metrics tests -- elementsizes etc""" # who said that we will not use FSL's data # with negative dimensions? :-) elsize = [-2.5, 1.5] distance = 3 # use default function metric = DescreteMetric(elsize) # simple check target = N.array([ [1, 2], [2, 1], [2, 2], [2, 3], [3, 2] ]) self.failUnless( (metric.getNeighbors([2, 2], 2.6) == target).all()) # a bit longer one... not sure what for for point in metric.getNeighbor([2, 2], distance): self.failUnless( cartesianDistance(point, [2,2]) <= distance) # use manhattenDistance function metric = DescreteMetric(elsize, manhattenDistance) for point in metric.getNeighbor([2, 2], distance): self.failUnless( manhattenDistance(point, [2, 2]) <= distance) metric.elementsize = [10, 1.5] """We can reassign element size as a whole""" self.failUnless((metric.elementsize == [10, 1.5]).all()) try: metric.elementsize[1] = 1 self.fail( msg="We should not be able to reassign parts of elementsize") except RuntimeError: pass self.failUnless((metric.getNeighbors([2, 2], 2.6) == [t for t in target if t[0]==2]).all()) """Check if new elementsize is in effect for getNeighbors""" def testDescreteMetricCompatMask(self): """Test ability to provide compatmask """ # let's play fMRI: 3x3x3.3 mm and 2s TR, but in NIfTI we have it # reversed esize = [2, 3.3, 3, 3] # only the last three axis are spatial ones and compatible in terms # of a meaningful distance among them. metric = DescreteMetric(esize, compatmask=[0, 1, 1, 1]) # neighbors in compat space will be simply propagated along remaining # dimensions (somewhat like a backprojection from the subspace into the # original space) self.failUnless((metric.getNeighbors([0]*4, 1) == [[-1, 0, 0, 0], [0, 0, 0, 0], [1, 0, 0, 0]]).all()) # test different radius in compat and in remaining space # in this case usual spatial neighborhood, but no temporal self.failUnless((metric.getNeighbors([0]*4, [0, 4, 4, 4]) == [[0,-1,0,0],[0,0,-1,0],[0,0,0,-1],[0,0,0,0], [0,0,0,1],[0,0,1,0],[0,1,0,0]]).all()) # no spatial but temporal neighborhood self.failUnless((metric.getNeighbors([0]*4, [2, 0, 0, 0]) == [[-1,0,0,0],[0,0,0,0],[1,0,0,0]]).all()) # check axis scaling in non-compat space self.failUnless(len(metric.getNeighbors([0]*4, [7.9, 0, 0, 0])) == 9) # check if we can modify compatmask and still perform fine old_filter = metric.filter_coord.copy() cm = metric.compatmask cm[0] = 1 metric.compatmask = cm self.failUnless((metric.compatmask == [True]*4).all()) self.failUnless((metric.getNeighbors([0]*4, 3) == [[-1, 0, 0, 0], [ 0, 0, -1, 0], [ 0, 0, 0, -1], [ 0, 0, 0, 0], [ 0, 0, 0, 1], [ 0, 0, 1, 0], [ 1, 0, 0, 0]]).all()) self.failUnless(N.any(old_filter != metric.filter_coord)) def testGetNeighbors(self): """Test if generator getNeighbor and method getNeighbors return the right thing""" class BM(Metric): """ Class which overrides only getNeighbor """ def getNeighbor(self): for n in [4, 5, 6]: yield n class CM(Metric): """ Class which overrides only getNeighbor """ def getNeighbors(self): return [1, 2, 3] b = BM() self.failUnless(b.getNeighbors() == [4, 5, 6]) c = CM() self.failUnless([ x for x in c.getNeighbor()] == [1, 2, 3]) def suite(): """Create the suite -- pylint shut up""" return unittest.makeSuite(MetricTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_niftidataset.py000066400000000000000000000341241174541445200212470ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA nifti dataset""" import unittest import os import numpy as N from tempfile import mktemp from mvpa import pymvpa_dataroot from mvpa.datasets.nifti import * from mvpa.misc.exceptions import * from mvpa.misc.fsl import FslEV3 from mvpa.misc.support import Event class NiftiDatasetTests(unittest.TestCase): """Tests of various Nifti-based datasets """ def testNiftiDataset(self): """Basic testing of NiftiDataset """ data = NiftiDataset(samples=os.path.join(pymvpa_dataroot,'example4d'), labels=[1,2]) self.failUnless(data.nfeatures == 294912) self.failUnless(data.nsamples == 2) self.failUnless((data.mapper.metric.elementsize \ == data.niftihdr['pixdim'][3:0:-1]).all()) #check that mapper honours elementsize nb22 = N.array([i for i in data.mapper.getNeighborIn((1, 1, 1), 2.2)]) nb20 = N.array([i for i in data.mapper.getNeighborIn((1, 1, 1), 2.0)]) self.failUnless(nb22.shape[0] == 7) self.failUnless(nb20.shape[0] == 5) # Can't rely on released pynifties, so doing really vague testing # XXX self.failUnless(data.dt in [2.0, 2000.0]) self.failUnless(data.samplingrate in [5e-4, 5e-1]) merged = data + data self.failUnless(merged.nfeatures == 294912) self.failUnless(merged.nsamples == 4) # check that the header survives #self.failUnless(merged.niftihdr == data.niftihdr) for k in merged.niftihdr.keys(): self.failUnless(N.mean(merged.niftihdr[k] == data.niftihdr[k]) == 1) # throw away old dataset and see if new one survives del data self.failUnless(merged.samples[3, 120000] == merged.samples[1, 120000]) # check whether we can use a plain ndarray as mask mask = N.zeros((24, 96, 128), dtype='bool') mask[12, 20, 40] = True nddata = NiftiDataset(samples=os.path.join(pymvpa_dataroot,'example4d'), labels=[1,2], mask=mask) self.failUnless(nddata.nfeatures == 1) rmap = nddata.mapReverse([44]) self.failUnless(rmap.shape == (24, 96, 128)) self.failUnless(N.sum(rmap) == 44) self.failUnless(rmap[12, 20, 40] == 44) def testNiftiMapper(self): """Basic testing of map2Nifti """ data = NiftiDataset(samples=os.path.join(pymvpa_dataroot,'example4d'), labels=[1,2]) # test mapping of ndarray vol = data.map2Nifti(N.ones((294912,), dtype='int16')) self.failUnless(vol.data.shape == (24, 96, 128)) self.failUnless((vol.data == 1).all()) # test mapping of the dataset vol = data.map2Nifti(data) self.failUnless(vol.data.shape == (2, 24, 96, 128)) def testNiftiSelfMapper(self): """Test map2Nifti facility ran without arguments """ example_path = os.path.join(pymvpa_dataroot, 'example4d') example = NiftiImage(example_path) data = NiftiDataset(samples=example_path, labels=[1,2]) # Map read data to itself vol = data.map2Nifti() self.failUnless(vol.data.shape == example.data.shape) self.failUnless((vol.data == example.data).all()) data.samples[:] = 1 vol = data.map2Nifti() self.failUnless((vol.data == 1).all()) def testMultipleCalls(self): """Test if doing exactly the same operation twice yields the same result """ data = NiftiDataset(samples=os.path.join(pymvpa_dataroot,'example4d'), labels=1) data2 = NiftiDataset(samples=os.path.join(pymvpa_dataroot,'example4d'), labels=1) # Currently this test fails and I don't know why! # The problem occurs, because in the second call to # NiftiDataset.__init__() there is already a dsattr that has a 'mapper' # key, although dsattr is set to be an empty dict. Therefore the # constructor does not set the proper elementsize, because it thinks # there is already a mapper present. Actually this test is just looking # for a symptom of a buggy dsattr handling. # The tricky part is: I have no clue, what is going on... :( self.failUnless((data.mapper.metric.elementsize \ == data2.mapper.metric.elementsize).all()) def testERNiftiDataset(self): """Basic testing of ERNiftiDataset """ self.failUnlessRaises(DatasetError, ERNiftiDataset) # setup data sources tssrc = os.path.join(pymvpa_dataroot, 'bold') evsrc = os.path.join(pymvpa_dataroot, 'fslev3.txt') # masrc = os.path.join(pymvpa_dataroot, 'mask') evs = FslEV3(evsrc).toEvents() # more failure ;-) # no label! self.failUnlessRaises(ValueError, ERNiftiDataset, samples=tssrc, events=evs) # set some label for each ev for ev in evs: ev['label'] = 1 # for real! # using TR from nifti header ds = ERNiftiDataset(samples=tssrc, events=evs) # 40x20 volume, 9 volumes per sample + 1 intensity score = 7201 features self.failUnless(ds.nfeatures == 7201) self.failUnless(ds.nsamples == len(evs)) # check samples origsamples = getNiftiFromAnySource(tssrc).data for i, ev in enumerate(evs): self.failUnless((ds.samples[i][:-1] \ == origsamples[ev['onset']:ev['onset'] + ev['duration']].ravel() ).all()) # do again -- with conversion ds = ERNiftiDataset(samples=tssrc, events=evs, evconv=True, storeoffset=True) self.failUnless(ds.nsamples == len(evs)) # TR=2.5, 40x20 volume, 9 second per sample (4volumes), 1 intensity # score + 1 offset = 3202 features self.failUnless(ds.nfeatures == 3202) # map back into voxel space, should ignore addtional features nim = ds.map2Nifti() self.failUnless(nim.data.shape == origsamples.shape) # check shape of a single sample nim = ds.map2Nifti(ds.samples[0]) self.failUnless(nim.data.shape == (4, 1, 20, 40)) def testERNiftiDatasetMapping(self): """Some mapping testing -- more tests is better """ sample_size = (4, 3, 2) samples = N.arange(120).reshape((5,) + sample_size) dsmask = N.arange(24).reshape(sample_size)%2 ds = ERNiftiDataset(samples=NiftiImage(samples), events=[Event(onset=0, duration=2, label=1, chunk=1, features=[1000, 1001]), Event(onset=1, duration=2, label=2, chunk=1, features=[2000, 2001])], mask=dsmask) nfeatures = ds.mapper._mappers[1].getInSize() mask = N.zeros(sample_size) mask[0, 0, 0] = mask[1, 0, 1] = mask[0, 0, 1] = 1 # select only 3 # but since 0th is masked out in the dataset, we should end up # selecting only 2 from the dataset #sel_orig_features = [1, 7] # select using mask in volume and all features in the other part ds_sel = ds.selectFeatures( ds.mapper.forward([mask, [1]*nfeatures]).nonzero()[0]) # now tests self.failUnless((mask.reshape(24).nonzero()[0] == [0, 1, 7]).all()) self.failUnless(ds_sel.samples.shape == (2, 6), msg="We should have selected all samples, and 6 " "features (2 voxels at 2 timepoints + 2 features). " "Got %s" % (ds_sel.samples.shape,)) self.failUnless((ds_sel.samples[:, -2:] == [[1000, 1001], [2000, 2001]]).all(), msg="We should have selected additional features " "correctly. Got %s" % ds_sel.samples[:, -2:]) self.failUnless((ds_sel.samples[:, :-2] == [[ 1, 7, 25, 31], [ 25, 31, 49, 55]]).all(), msg="We should have selected original features " "correctly. Got %s" % ds_sel.samples[:, :-2]) def testNiftiDatasetFrom3D(self): """Test NiftiDataset based on 3D volume(s) """ tssrc = os.path.join(pymvpa_dataroot, 'bold') masrc = os.path.join(pymvpa_dataroot, 'mask') # Test loading of 3D volumes # it should puke if we are not enforcing 4D: self.failUnlessRaises(Exception, NiftiDataset, masrc, mask=masrc, labels=1, enforce4D=False) # by default we are enforcing it ds = NiftiDataset(masrc, mask=masrc, labels=1) plain_data = NiftiImage(masrc).data # Lets check if mapping back works as well self.failUnless(N.all(plain_data == \ ds.map2Nifti().data.reshape(plain_data.shape))) # test loading from a list of filenames # for now we should fail if trying to load a mix of 4D and 3D volumes self.failUnlessRaises(ValueError, NiftiDataset, (masrc, tssrc), mask=masrc, labels=1) # Lets prepare some custom NiftiImage dsfull = NiftiDataset(tssrc, mask=masrc, labels=1) ds_selected = dsfull['samples', [3]] nifti_selected = ds_selected.map2Nifti() # Load dataset from a mix of 3D volumes # (given by filenames and NiftiImages) labels = [123, 2, 123] ds2 = NiftiDataset((masrc, masrc, nifti_selected), mask=masrc, labels=labels) self.failUnless(ds2.nsamples == 3) self.failUnless((ds2.samples[0] == ds2.samples[1]).all()) self.failUnless((ds2.samples[2] == dsfull.samples[3]).all()) self.failUnless((ds2.labels == labels).all()) def testNiftiDatasetROIMaskNeighbors(self): """Test if we could request neighbors within spherical ROI whenever center is outside of the mask """ # check whether we can use a plain ndarray as mask mask_roi = N.zeros((24, 96, 128), dtype='bool') mask_roi[12, 20, 38:42] = True mask_roi[23, 20, 38:42] = True # far away ds_full = NiftiDataset(samples=os.path.join(pymvpa_dataroot,'example4d'), labels=[1,2]) ds_roi = NiftiDataset(samples=os.path.join(pymvpa_dataroot,'example4d'), labels=[1,2], mask=mask_roi) # Should just work since we are in the mask ids_roi = ds_roi.mapper.getNeighbors(ds_roi.mapper.getOutId((12, 20, 40)), radius=20) self.failUnless(len(ids_roi) == 4) # Trying to request feature outside of the mask self.failUnlessRaises(ValueError, ds_roi.mapper.getOutId, (12, 20, 37)) # Lets work around using full (non-masked) volume ids_out = [] for id_in in ds_full.mapper.getNeighborIn( (12, 20, 37), radius=20): try: ids_out.append(ds_roi.mapper.getOutId(id_in)) except ValueError: pass self.failUnless(ids_out == ids_roi) def testNiftiScalingData(self): """Test if loading scaled data works correctly Is relevant only for pynifti interface -- nibabel always does scaling """ # We first need to construct such one orig_filename = os.path.join(pymvpa_dataroot,'mask.nii.gz') filename = mktemp('mvpa', 'test_scl_nifti') + '.nii.gz' ni = NiftiImage(orig_filename) orig_value = ni.data[0, 3, 4] ni.data[0, 3, 4] = 5 # magic number # Modify the header hdr = ni.header hdr['scl_slope'] = 15 hdr['scl_inter'] = 100 ni.header = hdr ni.save(filename) # Load generated file with slope and intercept defined ds_scaled = NiftiDataset(filename, labels=1) # by default should scale ds_scaled_multi = NiftiDataset([orig_filename, filename], labels=1) ds_raw = NiftiDataset(filename, labels=1, scale_data=False) # by default should scale fid = ds_scaled.mapper.getOutId([0,3,4]) # Feature ID for that coordinate above self.failUnlessEqual(ds_scaled.samples[0, fid], 175) self.failUnlessEqual(ds_scaled_multi.samples[0, fid], orig_value) self.failUnlessEqual(ds_scaled_multi.samples[1, fid], 175) self.failUnlessEqual(ds_raw.samples[0, fid], 5) # In a ds obtained from a single volume -- scl_ are maintained self.failUnlessEqual(ds_scaled.niftihdr['scl_slope'], 15.0) self.failUnlessEqual(ds_scaled.niftihdr['scl_inter'], 100) # And reset in a group self.failUnlessEqual(ds_scaled_multi.niftihdr['scl_slope'], 1.0) self.failUnlessEqual(ds_scaled_multi.niftihdr['scl_inter'], 0) self.failUnlessEqual(ds_raw.niftihdr['scl_slope'], 15.0) self.failUnlessEqual(ds_raw.niftihdr['scl_inter'], 100) # Verify that map2nifti resets the scl_ fields: ni0 = ds_scaled.map2Nifti(ds_raw.samples) self.failUnlessEqual(ni0.header['scl_slope'], 1.0) self.failUnlessEqual(ni0.header['scl_inter'], 0.) # but original remains untouched self.failUnlessEqual(ds_scaled.niftihdr['scl_slope'], 15.0) self.failUnlessEqual(ds_scaled.niftihdr['scl_inter'], 100) # and data remains the same # (check just in case of evil "pynifti got brains") self.failUnlessEqual(ni0.data[0, 0, 3, 4], 5) os.remove(filename) def suite(): return unittest.makeSuite(NiftiDatasetTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_params.py000066400000000000000000000111201174541445200200420ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA Parameter class.""" import unittest, copy import numpy as N from mvpa.datasets import Dataset from mvpa.misc.state import ClassWithCollections, StateVariable from mvpa.misc.param import Parameter, KernelParameter from tests_warehouse_clfs import SameSignClassifier class ParametrizedClassifier(SameSignClassifier): p1 = Parameter(1.0) kp1 = KernelParameter(100.0) class ParametrizedClassifierExtended(ParametrizedClassifier): def __init__(self): ParametrizedClassifier.__init__(self) self.kernel_params.add(KernelParameter(200.0, doc="Very useful param", name="kp2")) class BlankClass(ClassWithCollections): pass class SimpleClass(ClassWithCollections): C = Parameter(1.0, min=0, doc="C parameter") class MixedClass(ClassWithCollections): C = Parameter(1.0, min=0, doc="C parameter") D = Parameter(3.0, min=0, doc="D parameter") state1 = StateVariable(doc="bogus") class ParamsTests(unittest.TestCase): def testBlank(self): blank = BlankClass() self.failUnlessRaises(AttributeError, blank.__getattribute__, 'states') self.failUnlessRaises(IndexError, blank.__getattribute__, '') def testSimple(self): simple = SimpleClass() self.failUnlessEqual(len(simple.params.items), 1) self.failUnlessRaises(AttributeError, simple.__getattribute__, 'dummy') self.failUnlessRaises(IndexError, simple.__getattribute__, '') self.failUnlessEqual(simple.C, 1.0) self.failUnlessEqual(simple.params.isSet("C"), False) self.failUnlessEqual(simple.params.isSet(), False) self.failUnlessEqual(simple.params["C"].isDefault, True) self.failUnlessEqual(simple.params["C"].equalDefault, True) simple.C = 1.0 # we are not actually setting the value if == default self.failUnlessEqual(simple.params["C"].isDefault, True) self.failUnlessEqual(simple.params["C"].equalDefault, True) simple.C = 10.0 self.failUnlessEqual(simple.params.isSet("C"), True) self.failUnlessEqual(simple.params.isSet(), True) self.failUnlessEqual(simple.params["C"].isDefault, False) self.failUnlessEqual(simple.params["C"].equalDefault, False) self.failUnlessEqual(simple.C, 10.0) simple.params["C"].resetvalue() self.failUnlessEqual(simple.params.isSet("C"), True) # TODO: Test if we 'train' a classifier f we get isSet to false self.failUnlessEqual(simple.C, 1.0) self.failUnlessRaises(AttributeError, simple.params.__getattribute__, 'B') def testMixed(self): mixed = MixedClass() self.failUnlessEqual(len(mixed.params.items), 2) self.failUnlessEqual(len(mixed.states.items), 1) self.failUnlessRaises(AttributeError, mixed.__getattribute__, 'kernel_params') self.failUnlessEqual(mixed.C, 1.0) self.failUnlessEqual(mixed.params.isSet("C"), False) self.failUnlessEqual(mixed.params.isSet(), False) mixed.C = 10.0 self.failUnlessEqual(mixed.params.isSet("C"), True) self.failUnlessEqual(mixed.params.isSet("D"), False) self.failUnlessEqual(mixed.params.isSet(), True) self.failUnlessEqual(mixed.D, 3.0) def testClassifier(self): clf = ParametrizedClassifier() self.failUnlessEqual(len(clf.params.items), 3) # + regression/retrainable self.failUnlessEqual(len(clf.kernel_params.items), 1) clfe = ParametrizedClassifierExtended() self.failUnlessEqual(len(clfe.params.items), 3) self.failUnlessEqual(len(clfe.kernel_params.items), 2) self.failUnlessEqual(len(clfe.kernel_params.listing), 2) # check assignment once again self.failUnlessEqual(clfe.kp2, 200.0) clfe.kp2 = 201.0 self.failUnlessEqual(clfe.kp2, 201.0) self.failUnlessEqual(clfe.kernel_params.isSet("kp2"), True) clfe.train(Dataset(samples=[[0,0]], labels=[1], chunks=[1])) self.failUnlessEqual(clfe.kernel_params.isSet("kp2"), False) self.failUnlessEqual(clfe.kernel_params.isSet(), False) self.failUnlessEqual(clfe.params.isSet(), False) def suite(): return unittest.makeSuite(ParamsTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_pcamapper.py000066400000000000000000000073661174541445200205500ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA PCA mapper""" import unittest from mvpa.support.copy import deepcopy import numpy as N from mvpa.mappers.pca import PCAMapper from mvpa.datasets import Dataset class PCAMapperTests(unittest.TestCase): def setUp(self): # data: 40 sample feature line in 20d space (40x20; samples x features) self.ndlin = Dataset(samples=N.concatenate( [N.arange(40) for i in range(20)]).reshape(20,-1).T, labels=1, chunks=1) # data: 10 sample feature line in 40d space # (10x40; samples x features) self.largefeat = Dataset(samples=N.concatenate( [N.arange(10) for i in range(40)]).reshape(40,-1).T, labels=1, chunks=1) self.pm = PCAMapper() def testSimplePCA(self): # train PCA self.pm.train(self.ndlin) self.failUnlessEqual(self.pm.mix.shape, (20, 20)) # now project data into PCA space p = self.pm.forward(self.ndlin.samples) # only first eigenvalue significant self.failUnless(self.pm.sv[:1] > 1.0) self.failUnless((self.pm.sv[1:] < 0.0001).all()) # only variance of first component significant var = p.var(axis=0) # test that only one component has variance self.failUnless(var[:1] > 1.0) self.failUnless((var[1:] < 0.0001).all()) # check that the mapped data can be fully recovered by 'reverse()' self.failUnless((N.round(self.pm.reverse(p)) == self.ndlin.samples).all()) def testAutoOptimizePCA(self): # train PCA self.pm.train(self.largefeat) # mixing matrix cannot be square # self.failUnlessEqual(self.pm.mix.shape, (10, 40)) # only first eigenvalue significant self.failUnless(self.pm.sv[:1] > 10) self.failUnless((self.pm.sv[1:] < 10).all()) # now project data into PCA space p = self.pm.forward(self.largefeat.samples) # only variance of first component significant var = p.var(axis=0) # test that only one component has variance self.failUnless(var[:1] > 1.0) self.failUnless((var[1:] < 0.0001).all()) # check that the mapped data can be fully recovered by 'reverse()' rp = self.pm.reverse(p) self.failUnlessEqual(rp.shape, self.largefeat.samples.shape) self.failUnless((N.round(rp) == self.largefeat.samples).all()) self.failUnlessEqual(self.pm.getInSize(), 40) # self.failUnlessEqual(self.pm.getOutSize(), 10) self.failUnlessEqual(self.pm.getOutSize(), 40) # copy mapper pm2 = deepcopy(self.pm) # now remove all but the first 2 components from the mapper pm2.selectOut([0,1]) # sanity check self.failUnlessEqual(pm2.getInSize(), 40) self.failUnlessEqual(pm2.getOutSize(), 2) # but orginal mapper must be left intact self.failUnlessEqual(self.pm.getInSize(), 40) # self.failUnlessEqual(self.pm.getOutSize(), 10) self.failUnlessEqual(self.pm.getOutSize(), 40) # data should still be fully recoverable by 'reverse()' rp2 = pm2.reverse(p[:,[0,1]]) self.failUnlessEqual(rp2.shape, self.largefeat.samples.shape) self.failUnless((N.round(rp2) == self.largefeat.samples).all()) def suite(): return unittest.makeSuite(PCAMapperTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_perturbsensana.py000066400000000000000000000040571174541445200216260ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA perturbation sensitivity analyzer.""" from mvpa.datasets.masked import MaskedDataset from mvpa.measures.noiseperturbation import NoisePerturbationSensitivity from mvpa.datasets.splitters import NFoldSplitter from mvpa.algorithms.cvtranserror import CrossValidatedTransferError from mvpa.clfs.transerror import TransferError from tests_warehouse import * from tests_warehouse_clfs import * class PerturbationSensitivityAnalyzerTests(unittest.TestCase): def setUp(self): data = N.random.standard_normal(( 100, 3, 4, 2 )) labels = N.concatenate( ( N.repeat( 0, 50 ), N.repeat( 1, 50 ) ) ) chunks = N.repeat( range(5), 10 ) chunks = N.concatenate( (chunks, chunks) ) mask = N.ones( (3, 4, 2) ) mask[0,0,0] = 0 mask[1,3,1] = 0 self.dataset = MaskedDataset(samples=data, labels=labels, chunks=chunks, mask=mask) def testPerturbationSensitivityAnalyzer(self): # compute N-1 cross-validation as datameasure cv = CrossValidatedTransferError( TransferError(sample_clf_lin), NFoldSplitter(cvtype=1)) # do perturbation analysis using gaussian noise pa = NoisePerturbationSensitivity(cv, noise=N.random.normal) # run analysis map = pa(self.dataset) # check for correct size of map self.failUnless(len(map) == 22) # dataset is noise -> mean sensitivity should be zero self.failUnless(-0.2 < map.mean() < 0.2) def suite(): return unittest.makeSuite(PerturbationSensitivityAnalyzerTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_plr.py000066400000000000000000000022701174541445200173620ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA logistic regression classifier""" from mvpa.clfs.plr import PLR from tests_warehouse import * class PLRTests(unittest.TestCase): def testPLR(self): data = datasets['dumb2'] clf = PLR() clf.train(data) # prediction has to be perfect self.failUnless((clf.predict(data.samples) == data.labels).all()) def testPLRState(self): data = datasets['dumb2'] clf = PLR() clf.train(data) clf.states.enable('values') clf.states.enable('predictions') p = clf.predict(data.samples) self.failUnless((p == clf.predictions).all()) self.failUnless(N.array(clf.values).shape == N.array(p).shape) def suite(): return unittest.makeSuite(PLRTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_pls.py000066400000000000000000000006631174541445200173670ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for Partial Least Squares measures.""" pymvpa-0.4.8/mvpa/tests/test_procrust.py000066400000000000000000000105351174541445200204510ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA Procrustean mapper""" import unittest import numpy as N from numpy.linalg import norm from mvpa.datasets import Dataset from tests_warehouse import datasets, sweepargs from mvpa.mappers.procrustean import ProcrusteanMapper class ProcrusteanMapperTests(unittest.TestCase): @sweepargs(oblique=(False,True)) def testSimple(self, oblique): d_orig = datasets['uni2large'].samples d_orig2 = datasets['uni4large'].samples for sdim, nf_s, nf_t, full_test \ in (('Same 2D', 2, 2, True), ('Same 10D', 10, 10, True), ('2D -> 3D', 2, 3, True), ('3D -> 2D', 3, 2, False)): # figure out some "random" rotation d = max(nf_s, nf_t) _u, _s, _vh = N.linalg.svd(d_orig[:, :d]) R = _vh[:nf_s, :nf_t] if nf_s == nf_t: # Test if it is indeed a rotation matrix ;) # Lets flip first axis if necessary if N.linalg.det(R) < 0: R[:, 0] *= -1.0 adR = N.abs(1.0 - N.linalg.det(R)) self.failUnless(adR < 1e-10, "Determinant of rotation matrix should " "be 1. Got it 1+%g" % adR) self.failUnless(norm(N.dot(R, R.T) - N.eye(R.shape[0])) < 1e-10) for s, scaling in ((0.3, True), (1.0, False)): pm = ProcrusteanMapper(scaling=scaling, oblique=oblique) pm2 = ProcrusteanMapper(scaling=scaling, oblique=oblique) t1, t2 = d_orig[23, 1], d_orig[22, 1] # Create source/target data d = d_orig[:, :nf_s] d_s = d + t1 d_t = N.dot(s * d, R) + t2 # train bloody mapper(s) pm.train(d_s, d_t) ds2 = Dataset(samples=d_s, labels=d_t) pm2.train(ds2) # verify that both created the same transformation npm2proj = norm(pm.proj - pm2.proj) self.failUnless(npm2proj <= 1e-10, msg="Got transformation different by norm %g." " Had to be less than 1e-10" % npm2proj) self.failUnless(norm(pm._offset_in - pm2._offset_in) <= 1e-10) self.failUnless(norm(pm._offset_out - pm2._offset_out) <= 1e-10) # do forward transformation on the same source data d_s_f = pm.forward(d_s) self.failUnlessEqual(d_s_f.shape, d_t.shape, msg="Mapped shape should be identical to the d_t") dsf = d_s_f - d_t ndsf = norm(dsf)/norm(d_t) if full_test: dsR = norm(s*R - pm.proj) if not oblique: self.failUnless(dsR <= 1e-12, msg="We should have got reconstructed rotation+scaling " "perfectly. Now got d scale*R=%g" % dsR) self.failUnless(N.abs(s - pm._scale) < 1e-12, msg="We should have got reconstructed scale " "perfectly. Now got %g for %g" % (pm._scale, s)) self.failUnless(ndsf <= 1e-12, msg="%s: Failed to get to the target space correctly." " normed error=%g" % (sdim, ndsf)) # Test if we get back d_s_f_r = pm.reverse(d_s_f) dsfr = d_s_f_r - d_s ndsfr = norm(dsfr)/norm(d_s) if full_test: self.failUnless(ndsfr <= 1e-12, msg="%s: Failed to reconstruct into source space correctly." " normed error=%g" % (sdim, ndsfr)) def suite(): return unittest.makeSuite(ProcrusteanMapperTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_regr.py000066400000000000000000000126351174541445200175320ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA Regressions""" from mvpa.base import externals from mvpa.support.copy import deepcopy from mvpa.datasets import Dataset from mvpa.mappers.mask import MaskMapper from mvpa.datasets.splitters import NFoldSplitter, OddEvenSplitter from mvpa.misc.errorfx import RMSErrorFx, RelativeRMSErrorFx, \ CorrErrorFx, CorrErrorPFx from mvpa.clfs.meta import SplitClassifier from mvpa.clfs.transerror import TransferError from mvpa.misc.exceptions import UnknownStateError from mvpa.algorithms.cvtranserror import CrossValidatedTransferError from tests_warehouse import * from tests_warehouse_clfs import * class RegressionsTests(unittest.TestCase): @sweepargs(ml=clfswh['regression']+regrswh[:]) def testNonRegressions(self, ml): """Test If binary regression-based classifiers have proper tag """ self.failUnless(('binary' in ml._clf_internals) != ml.regression, msg="Inconsistent markin with binary and regression features" " detected in %s having %s" % (ml, `ml._clf_internals`)) @sweepargs(regr=regrswh['regression']) def testRegressions(self, regr): """Simple tests on regressions """ ds = datasets['chirp_linear'] cve = CrossValidatedTransferError( TransferError(regr, CorrErrorFx()), splitter=NFoldSplitter(), enable_states=['training_confusion', 'confusion']) corr = cve(ds) self.failUnless(corr == cve.confusion.stats['CCe']) splitregr = SplitClassifier(regr, splitter=OddEvenSplitter(), enable_states=['training_confusion', 'confusion']) splitregr.train(ds) split_corr = splitregr.confusion.stats['CCe'] split_corr_tr = splitregr.training_confusion.stats['CCe'] for confusion, error in ((cve.confusion, corr), (splitregr.confusion, split_corr), (splitregr.training_confusion, split_corr_tr), ): #TODO: test confusion statistics # Part of it for now -- CCe for conf in confusion.summaries: stats = conf.stats self.failUnless(stats['CCe'] < 0.5) self.failUnlessEqual(stats['CCe'], stats['Summary CCe']) s0 = confusion.asstring(short=True) s1 = confusion.asstring(short=False) for s in [s0, s1]: self.failUnless(len(s) > 10, msg="We should get some string representation " "of regression summary. Got %s" % s) self.failUnless(error < 0.2, msg="Regressions should perform well on a simple " "dataset. Got correlation error of %s " % error) # Test access to summary statistics # YOH: lets start making testing more reliable. # p-value for such accident to have is verrrry tiny, # so if regression works -- it better has at least 0.5 ;) # otherwise fix it! ;) #if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(confusion.stats['CCe'] < 0.5) split_predictions = splitregr.predict(ds.samples) # just to check if it works fine # To test basic plotting #import pylab as P #cve.confusion.plot() #P.show() @sweepargs(clf=clfswh['regression']) def testRegressionsClassifiers(self, clf): """Simple tests on regressions being used as classifiers """ # check if we get values set correctly clf.states._changeTemporarily(enable_states=['values']) self.failUnlessRaises(UnknownStateError, clf.states['values']._get) cv = CrossValidatedTransferError( TransferError(clf), NFoldSplitter(), enable_states=['confusion', 'training_confusion']) ds = datasets['uni2small'] cverror = cv(ds) self.failUnless(len(clf.values) == ds['chunks', 1].nsamples) clf.states._resetEnabledTemporarily() @sweepargs(regr=regrswh['regression', 'has_sensitivity']) def testSensitivities(self, regr): """Inspired by a snippet leading to segfault from Daniel Kimberg lead to segfaults due to inappropriate access of SVs thinking that it is a classification problem (libsvm keeps SVs at None for those, although reports nr_class to be 2. """ myds = Dataset(samples=N.random.normal(size=(10,5)), labels=N.random.normal(size=10)) sa = regr.getSensitivityAnalyzer() try: res = sa(myds) except Exception, e: self.fail('Failed to obtain a sensitivity due to %r' % (e,)) self.failUnless(res.shape == (myds.nfeatures,)) # TODO: extend the test -- checking for validity of sensitivities etc def suite(): return unittest.makeSuite(RegressionsTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_report.py000066400000000000000000000074041174541445200201040ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA simple report facility""" import unittest, os from tempfile import mktemp from mvpa.base import verbose, externals from mvpa.base.report_dummy import Report as DummyReport _test_classes = [ DummyReport ] from tests_warehouse import sweepargs if externals.exists('reportlab', raiseException=False): from mvpa.base.report import Report _test_classes += [ Report ] if __debug__: from mvpa.base import debug class ReportTest(unittest.TestCase): """Just basic testing of reports -- pretty much that nothing fails """ @sweepargs(rc=_test_classes) def testBasic(self, rc): """Test all available reports, real or dummy for just working """ dirname = mktemp('mvpa', 'test_report') report = rc('UnitTest report', title="Sample report for testing", path=dirname) isdummy = isinstance(report, DummyReport) ohandlers = verbose.handlers verbose.handlers = [report] verbose.level = 3 verbose(1, "Starting") verbose(2, "Level 2") if not isdummy: self.failUnless(len(report._story) == 2, msg="We should have got some lines from verbose") if __debug__: odhandlers = debug.handlers debug.handlers = [report] oactive = debug.active debug.active = ['TEST'] + debug.active debug('TEST', "Testing report as handler for debug") if not isdummy: self.failUnless(len(report._story) == 4, msg="We should have got some lines from debug") debug.active = oactive debug.handlers = odhandlers os.makedirs(dirname) if externals.exists('pylab plottable'): if not isdummy: clen = len(report._story) import pylab as P P.ioff() P.close('all') P.figure() P.plot([1, 2], [3, 2]) P.figure() P.plot([2, 10], [3, 2]) P.title("Figure 2 must be it") report.figures() if not isdummy: self.failUnless( len(report._story) == clen+2, msg="We should have got some lines from figures") report.text("Dugi bugi") # make sure we don't puke on xml like text with crap report.text("$lkj&*()^$%#%") report.text("locals:\n%s globals:\n%s" % (`locals()`, `globals()`)) # bloody XML - just to check that there is no puke report.xml("Dugi bugi") report.save() if externals.exists('pylab'): import pylab as P P.close('all') P.ion() # cleanup if os.path.exists(dirname): # poor man recursive remove for f in os.listdir(dirname): try: os.remove(os.path.join(dirname, f)) except: # could be a directory... but no deeper ones expected for f2 in os.listdir(os.path.join(dirname, f)): os.remove(os.path.join(dirname, f, f2)) os.rmdir(os.path.join(dirname, f)) os.rmdir(dirname) verbose.handlers = ohandlers def suite(): return unittest.makeSuite(ReportTest) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_rfe.py000066400000000000000000000410541174541445200173440ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA recursive feature elimination""" from mvpa.datasets.splitters import NFoldSplitter from mvpa.algorithms.cvtranserror import CrossValidatedTransferError from mvpa.datasets.masked import MaskedDataset from mvpa.measures.base import FeaturewiseDatasetMeasure from mvpa.featsel.rfe import RFE from mvpa.featsel.base import \ SensitivityBasedFeatureSelection, \ FeatureSelectionPipeline from mvpa.featsel.helpers import \ NBackHistoryStopCrit, FractionTailSelector, FixedErrorThresholdStopCrit, \ MultiStopCrit, NStepsStopCrit, \ FixedNElementTailSelector, BestDetector, RangeElementSelector from mvpa.clfs.meta import FeatureSelectionClassifier, SplitClassifier from mvpa.clfs.transerror import TransferError, ConfusionBasedError from mvpa.clfs.stats import MCNullDist from mvpa.misc.transformers import Absolute, FirstAxisMean from mvpa.misc.state import UnknownStateError from tests_warehouse import * from tests_warehouse_clfs import * class SillySensitivityAnalyzer(FeaturewiseDatasetMeasure): """Simple one which just returns xrange[-N/2, N/2], where N is the number of features """ def __init__(self, mult=1, **kwargs): FeaturewiseDatasetMeasure.__init__(self, **kwargs) self.__mult = mult def __call__(self, dataset): """Train linear SVM on `dataset` and extract weights from classifier. """ return( self.__mult *( N.arange(dataset.nfeatures) - int(dataset.nfeatures/2) )) class RFETests(unittest.TestCase): def getData(self): return datasets['uni2medium_train'] def getDataT(self): return datasets['uni2medium_test'] def testBestDetector(self): bd = BestDetector() # for empty history -- no best self.failUnless(bd([]) == False) # we got the best if we have just 1 self.failUnless(bd([1]) == True) # we got the best if we have the last minimal self.failUnless(bd([1, 0.9, 0.8]) == True) # test for alternative func bd = BestDetector(func=max) self.failUnless(bd([0.8, 0.9, 1.0]) == True) self.failUnless(bd([0.8, 0.9, 1.0]+[0.9]*9) == False) self.failUnless(bd([0.8, 0.9, 1.0]+[0.9]*10) == False) # test to detect earliest and latest minimum bd = BestDetector(lastminimum=True) self.failUnless(bd([3, 2, 1, 1, 1, 2, 1]) == True) bd = BestDetector() self.failUnless(bd([3, 2, 1, 1, 1, 2, 1]) == False) def testNBackHistoryStopCrit(self): """Test stopping criterion""" stopcrit = NBackHistoryStopCrit() # for empty history -- no best but just go self.failUnless(stopcrit([]) == False) # should not stop if we got 10 more after minimal self.failUnless(stopcrit( [1, 0.9, 0.8]+[0.9]*(stopcrit.steps-1)) == False) # should stop if we got 10 more after minimal self.failUnless(stopcrit( [1, 0.9, 0.8]+[0.9]*stopcrit.steps) == True) # test for alternative func stopcrit = NBackHistoryStopCrit(BestDetector(func=max)) self.failUnless(stopcrit([0.8, 0.9, 1.0]+[0.9]*9) == False) self.failUnless(stopcrit([0.8, 0.9, 1.0]+[0.9]*10) == True) # test to detect earliest and latest minimum stopcrit = NBackHistoryStopCrit(BestDetector(lastminimum=True)) self.failUnless(stopcrit([3, 2, 1, 1, 1, 2, 1]) == False) stopcrit = NBackHistoryStopCrit(steps=4) self.failUnless(stopcrit([3, 2, 1, 1, 1, 2, 1]) == True) def testFixedErrorThresholdStopCrit(self): """Test stopping criterion""" stopcrit = FixedErrorThresholdStopCrit(0.5) self.failUnless(stopcrit([]) == False) self.failUnless(stopcrit([0.8, 0.9, 0.5]) == False) self.failUnless(stopcrit([0.8, 0.9, 0.4]) == True) # only last error has to be below to stop self.failUnless(stopcrit([0.8, 0.4, 0.6]) == False) def testNStepsStopCrit(self): """Test stopping criterion""" stopcrit = NStepsStopCrit(2) self.failUnless(stopcrit([]) == False) self.failUnless(stopcrit([0.8, 0.9]) == True) self.failUnless(stopcrit([0.8]) == False) def testMultiStopCrit(self): """Test multiple stop criteria""" stopcrit = MultiStopCrit([FixedErrorThresholdStopCrit(0.5), NBackHistoryStopCrit(steps=4)]) # default 'or' mode # nback triggers self.failUnless(stopcrit([1, 0.9, 0.8]+[0.9]*4) == True) # threshold triggers self.failUnless(stopcrit([1, 0.9, 0.2]) == True) # alternative 'and' mode stopcrit = MultiStopCrit([FixedErrorThresholdStopCrit(0.5), NBackHistoryStopCrit(steps=4)], mode = 'and') # nback triggers not self.failUnless(stopcrit([1, 0.9, 0.8]+[0.9]*4) == False) # threshold triggers not self.failUnless(stopcrit([1, 0.9, 0.2]) == False) # only both satisfy self.failUnless(stopcrit([1, 0.9, 0.4]+[0.4]*4) == True) def testFeatureSelector(self): """Test feature selector""" # remove 10% weekest selector = FractionTailSelector(0.1) data = N.array([3.5, 10, 7, 5, -0.4, 0, 0, 2, 10, 9]) # == rank [4, 5, 6, 7, 0, 3, 2, 9, 1, 8] target10 = N.array([0, 1, 2, 3, 5, 6, 7, 8, 9]) target30 = N.array([0, 1, 2, 3, 7, 8, 9]) self.failUnlessRaises(UnknownStateError, selector.__getattribute__, 'ndiscarded') self.failUnless((selector(data) == target10).all()) selector.felements = 0.30 # discard 30% self.failUnless(selector.felements == 0.3) self.failUnless((selector(data) == target30).all()) self.failUnless(selector.ndiscarded == 3) # se 3 were discarded selector = FixedNElementTailSelector(1) # 0 1 2 3 4 5 6 7 8 9 data = N.array([3.5, 10, 7, 5, -0.4, 0, 0, 2, 10, 9]) self.failUnless((selector(data) == target10).all()) selector.nelements = 3 self.failUnless(selector.nelements == 3) self.failUnless((selector(data) == target30).all()) self.failUnless(selector.ndiscarded == 3) # test range selector # simple range 'above' self.failUnless((RangeElementSelector(lower=0)(data) == \ N.array([0,1,2,3,7,8,9])).all()) self.failUnless((RangeElementSelector(lower=0, inclusive=True)(data) == \ N.array([0,1,2,3,5,6,7,8,9])).all()) self.failUnless((RangeElementSelector(lower=0, mode='discard', inclusive=True)(data) == \ N.array([4])).all()) # simple range 'below' self.failUnless((RangeElementSelector(upper=2)(data) == \ N.array([4,5,6])).all()) self.failUnless((RangeElementSelector(upper=2, inclusive=True)(data) == \ N.array([4,5,6,7])).all()) self.failUnless((RangeElementSelector(upper=2, mode='discard', inclusive=True)(data) == \ N.array([0,1,2,3,8,9])).all()) # ranges self.failUnless((RangeElementSelector(lower=2, upper=9)(data) == \ N.array([0,2,3])).all()) self.failUnless((RangeElementSelector(lower=2, upper=9, inclusive=True)(data) == \ N.array([0,2,3,7,9])).all()) self.failUnless((RangeElementSelector(upper=2, lower=9, mode='discard', inclusive=True)(data) == RangeElementSelector(lower=2, upper=9, inclusive=False)(data)).all()) # non-0 elements -- should be equivalent to N.nonzero()[0] self.failUnless((RangeElementSelector()(data) == \ N.nonzero(data)[0]).all()) @sweepargs(clf=clfswh['has_sensitivity', '!meta']) def testSensitivityBasedFeatureSelection(self, clf): # sensitivity analyser and transfer error quantifier use the SAME clf! sens_ana = clf.getSensitivityAnalyzer() # of features to remove Nremove = 2 # because the clf is already trained when computing the sensitivity # map, prevent retraining for transfer error calculation # Use absolute of the svm weights as sensitivity fe = SensitivityBasedFeatureSelection(sens_ana, feature_selector=FixedNElementTailSelector(2), enable_states=["sensitivity", "selected_ids"]) wdata = self.getData() wdata_nfeatures = wdata.nfeatures tdata = self.getDataT() tdata_nfeatures = tdata.nfeatures sdata, stdata = fe(wdata, tdata) # fail if orig datasets are changed self.failUnless(wdata.nfeatures == wdata_nfeatures) self.failUnless(tdata.nfeatures == tdata_nfeatures) # silly check if nfeatures got a single one removed self.failUnlessEqual(wdata.nfeatures, sdata.nfeatures+Nremove, msg="We had to remove just a single feature") self.failUnlessEqual(tdata.nfeatures, stdata.nfeatures+Nremove, msg="We had to remove just a single feature in testing as well") self.failUnlessEqual(len(fe.sensitivity), wdata_nfeatures, msg="Sensitivity have to have # of features equal to original") self.failUnlessEqual(len(fe.selected_ids), sdata.nfeatures, msg="# of selected features must be equal the one in the result dataset") def testFeatureSelectionPipeline(self): sens_ana = SillySensitivityAnalyzer() wdata = self.getData() wdata_nfeatures = wdata.nfeatures tdata = self.getDataT() tdata_nfeatures = tdata.nfeatures # test silly one first ;-) self.failUnlessEqual(sens_ana(wdata)[0], -int(wdata_nfeatures/2)) # OLD: first remove 25% == 6, and then 4, total removing 10 # NOW: test should be independent of the numerical number of features feature_selections = [SensitivityBasedFeatureSelection( sens_ana, FractionTailSelector(0.25)), SensitivityBasedFeatureSelection( sens_ana, FixedNElementTailSelector(4)) ] # create a FeatureSelection pipeline feat_sel_pipeline = FeatureSelectionPipeline( feature_selections=feature_selections, enable_states=['nfeatures', 'selected_ids']) sdata, stdata = feat_sel_pipeline(wdata, tdata) self.failUnlessEqual(len(feat_sel_pipeline.feature_selections), len(feature_selections), msg="Test the property feature_selections") desired_nfeatures = int(N.ceil(wdata_nfeatures*0.75)) self.failUnlessEqual(feat_sel_pipeline.nfeatures, [wdata_nfeatures, desired_nfeatures], msg="Test if nfeatures get assigned properly." " Got %s!=%s" % (feat_sel_pipeline.nfeatures, [wdata_nfeatures, desired_nfeatures])) self.failUnlessEqual(list(feat_sel_pipeline.selected_ids), range(int(wdata_nfeatures*0.25)+4, wdata_nfeatures)) # TODO: should later on work for any clfs_with_sens @sweepargs(clf=clfswh['has_sensitivity', '!meta'][:1]) def testRFE(self, clf): # sensitivity analyser and transfer error quantifier use the SAME clf! sens_ana = clf.getSensitivityAnalyzer() trans_error = TransferError(clf) # because the clf is already trained when computing the sensitivity # map, prevent retraining for transfer error calculation # Use absolute of the svm weights as sensitivity rfe = RFE(sens_ana, trans_error, feature_selector=FixedNElementTailSelector(1), train_clf=False) wdata = self.getData() wdata_nfeatures = wdata.nfeatures tdata = self.getDataT() tdata_nfeatures = tdata.nfeatures sdata, stdata = rfe(wdata, tdata) # fail if orig datasets are changed self.failUnless(wdata.nfeatures == wdata_nfeatures) self.failUnless(tdata.nfeatures == tdata_nfeatures) # check that the features set with the least error is selected if len(rfe.errors): e = N.array(rfe.errors) self.failUnless(sdata.nfeatures == wdata_nfeatures - e.argmin()) else: self.failUnless(sdata.nfeatures == wdata_nfeatures) # silly check if nfeatures is in decreasing order nfeatures = N.array(rfe.nfeatures).copy() nfeatures.sort() self.failUnless( (nfeatures[::-1] == rfe.nfeatures).all() ) # check if history has elements for every step self.failUnless(set(rfe.history) == set(range(len(N.array(rfe.errors))))) # Last (the largest number) can be present multiple times even # if we remove 1 feature at a time -- just need to stop well # in advance when we have more than 1 feature left ;) self.failUnless(rfe.nfeatures[-1] == len(N.where(rfe.history ==max(rfe.history))[0])) # XXX add a test where sensitivity analyser and transfer error do not # use the same classifier def testJamesProblem(self): percent = 80 dataset = datasets['uni2small'] rfesvm_split = LinearCSVMC() fs = \ RFE(sensitivity_analyzer=rfesvm_split.getSensitivityAnalyzer(), transfer_error=TransferError(rfesvm_split), feature_selector=FractionTailSelector( percent / 100.0, mode='select', tail='upper'), update_sensitivity=True) clf = FeatureSelectionClassifier( clf = LinearCSVMC(), # on features selected via RFE feature_selection = fs) # update sensitivity at each step (since we're not using the # same CLF as sensitivity analyzer) clf.states.enable('feature_ids') cv = CrossValidatedTransferError( TransferError(clf), NFoldSplitter(cvtype=1), enable_states=['confusion'], expose_testdataset=True) #cv = SplitClassifier(clf) try: error = cv(dataset) except Exception, e: self.fail('CrossValidation cannot handle classifier with RFE ' 'feature selection. Got exception: %s' % e) self.failUnless(error < 0.2) def __testMatthiasQuestion(self): rfe_clf = LinearCSVMC(C=1) rfesvm_split = SplitClassifier(rfe_clf) clf = \ FeatureSelectionClassifier( clf = LinearCSVMC(C=1), feature_selection = RFE( sensitivity_analyzer = rfesvm_split.getSensitivityAnalyzer( combiner=FirstAxisMean, transformer=N.abs), transfer_error=ConfusionBasedError( rfesvm_split, confusion_state="confusion"), stopping_criterion=FixedErrorThresholdStopCrit(0.20), feature_selector=FractionTailSelector( 0.2, mode='discard', tail='lower'), update_sensitivity=True)) splitter = NFoldSplitter(cvtype=1) no_permutations = 1000 cv = CrossValidatedTransferError( TransferError(clf), splitter, null_dist=MCNullDist(permutations=no_permutations, tail='left'), enable_states=['confusion']) error = cv(datasets['uni2small']) self.failUnless(error < 0.4) self.failUnless(cv.states.null_prob < 0.05) def suite(): return unittest.makeSuite(RFETests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_ridge.py000066400000000000000000000031461174541445200176620ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA ridge regression classifier""" from mvpa.clfs.ridge import RidgeReg from scipy.stats import pearsonr from tests_warehouse import * class RidgeRegTests(unittest.TestCase): def testRidgeReg(self): # not the perfect dataset with which to test, but # it will do for now. data = datasets['dumb'] clf = RidgeReg() clf.train(data) # prediction has to be almost perfect # test with a correlation pre = clf.predict(data.samples) cor = pearsonr(pre,data.labels) self.failUnless(cor[0] > .8) # do again for fortran implementation # DISABLE for now, at it is known to be broken # clf = RidgeReg(implementation='gradient') # clf.train(data) # cor = pearsonr(clf.predict(data.samples), data.labels) # print cor # self.failUnless(cor[0] > .8) def testRidgeRegState(self): data = datasets['dumb'] clf = RidgeReg() clf.train(data) clf.states.enable('predictions') p = clf.predict(data.samples) self.failUnless((p == clf.predictions).all()) def suite(): return unittest.makeSuite(RidgeRegTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_samplegroupmapper.py000066400000000000000000000037571174541445200223430ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA SampleGroup mapper""" import unittest from mvpa.support.copy import deepcopy import numpy as N from mvpa.mappers.samplegroup import SampleGroupMapper from mvpa.datasets import Dataset class SampleGroupMapperTests(unittest.TestCase): def testSimple(self): data = N.arange(24).reshape(8,3) labels = [0, 1] * 4 chunks = N.repeat(N.array((0,1)),4) # correct results csamples = [[3, 4, 5], [6, 7, 8], [15, 16, 17], [18, 19, 20]] clabels = [0, 1, 0, 1] cchunks = [0, 0, 1, 1] ds = Dataset(samples=data, labels=labels, chunks=chunks) # default behavior m = SampleGroupMapper() # error if not trained self.failUnlessRaises(RuntimeError, m, data) # train mapper first m.train(ds) self.failUnless((m.forward(ds.samples) == csamples).all()) self.failUnless((m.forward(ds.labels) == clabels).all()) self.failUnless((m.forward(ds.chunks) == cchunks).all()) # directly apply to dataset # using untrained mapper! mapped = ds.applyMapper(samplesmapper=SampleGroupMapper()) self.failUnless(mapped.nsamples == 4) self.failUnless(mapped.nfeatures == 3) self.failUnless((mapped.samples == csamples).all()) self.failUnless((mapped.labels == clabels).all()) self.failUnless((mapped.chunks == cchunks).all()) # make sure origids get regenerated self.failUnless((mapped.origids == range(4)).all()) def suite(): return unittest.makeSuite(SampleGroupMapperTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_searchlight.py000066400000000000000000000066451174541445200210740ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA searchlight algorithm""" from mvpa.base import externals from mvpa.datasets.masked import MaskedDataset from mvpa.measures.searchlight import Searchlight from mvpa.datasets.splitters import NFoldSplitter from mvpa.algorithms.cvtranserror import CrossValidatedTransferError from mvpa.clfs.transerror import TransferError from tests_warehouse import * from tests_warehouse_clfs import * class SearchlightTests(unittest.TestCase): def setUp(self): self.dataset = datasets['3dlarge'] def testSearchlight(self): # compute N-1 cross-validation for each sphere transerror = TransferError(sample_clf_lin) cv = CrossValidatedTransferError( transerror, NFoldSplitter(cvtype=1)) # contruct radius 1 searchlight sl = Searchlight(cv, radius=1.0, transformer=N.array, enable_states=['spheresizes', 'raw_results']) # run searchlight results = sl(self.dataset) # check for correct number of spheres self.failUnless(len(results) == 106) # verify if we can map correctly back results_ospace = self.dataset.mapper.reverse(results) # check for chance-level performance across all spheres self.failUnless(0.4 < results.mean() < 0.6) # check resonable sphere sizes self.failUnless(len(sl.spheresizes) == 106) self.failUnless(max(sl.spheresizes) == 7) self.failUnless(min(sl.spheresizes) == 4) # check base-class state self.failUnlessEqual(len(sl.raw_results), 106) def testPartialSearchlightWithFullReport(self): # compute N-1 cross-validation for each sphere transerror = TransferError(sample_clf_lin) cv = CrossValidatedTransferError( transerror, NFoldSplitter(cvtype=1), combiner=N.array) # contruct radius 1 searchlight sl = Searchlight(cv, radius=1.0, transformer=N.array, center_ids=[3,50]) # run searchlight results = sl(self.dataset) # only two spheres but error for all CV-folds self.failUnlessEqual(results.shape, (2, len(self.dataset.uniquechunks))) def testChiSquareSearchlight(self): # only do partial to save time if not externals.exists('scipy'): return from mvpa.misc.stats import chisquare transerror = TransferError(sample_clf_lin) cv = CrossValidatedTransferError( transerror, NFoldSplitter(cvtype=1), enable_states=['confusion']) def getconfusion(data): cv(data) return chisquare(cv.confusion.matrix)[0] # contruct radius 1 searchlight sl = Searchlight(getconfusion, radius=1.0, center_ids=[3,50]) # run searchlight results = sl(self.dataset) self.failUnless(len(results) == 2) def suite(): return unittest.makeSuite(SearchlightTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_smlr.py000066400000000000000000000041431174541445200175430ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA sparse multinomial logistic regression classifier""" from mvpa.clfs.smlr import SMLR from tests_warehouse import * from mvpa.misc.data_generators import normalFeatureDataset class SMLRTests(unittest.TestCase): def testSMLR(self): data = datasets['dumb'] clf = SMLR() clf.train(data) # prediction has to be perfect # # XXX yoh: whos said that?? ;-) # # There is always a tradeoff between learning and # generalization errors so... but in this case the problem is # more interesting: absent bias disallows to learn data you # have here -- there is no solution which would pass through # (0,0) predictions = clf.predict(data.samples) self.failUnless((predictions == data.labels).all()) def testSMLRState(self): data = datasets['dumb'] clf = SMLR() clf.train(data) clf.states.enable('values') clf.states.enable('predictions') p = N.asarray(clf.predict(data.samples)) self.failUnless((p == clf.predictions).all()) self.failUnless(N.array(clf.values).shape[0] == N.array(p).shape[0]) def testSMLRSensitivities(self): data = normalFeatureDataset(perlabel=10, nlabels=2, nfeatures=4) # use SMLR on binary problem, but not fitting all weights clf = SMLR(fit_all_weights=False) clf.train(data) # now ask for the sensitivities WITHOUT having to pass the dataset # again sens = clf.getSensitivityAnalyzer(force_training=False)() self.failUnless(sens.shape == (data.nfeatures,)) def suite(): return unittest.makeSuite(SMLRTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_som.py000066400000000000000000000035011174541445200173610ustar00rootroot00000000000000#emacs: -*- mode: python-mode; py-indent-offset: 4; indent-tabs-mode: nil -*- #ex: set sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA SOM mapper""" import unittest import numpy as N from mvpa import cfg from mvpa.mappers.som import SimpleSOMMapper from mvpa.datasets import Dataset class SOMMapperTests(unittest.TestCase): def testSimpleSOM(self): colors = [[0., 0., 0.], [0., 0., 1.], [0., 1., 0.], [1., 0., 0.], [0., 1., 1.], [1., 0., 1.], [1., 1., 0.], [1., 1., 1.]] ds = Dataset(samples=colors, labels=1) # only small SOM for speed reasons som = SimpleSOMMapper((10, 5), 200, learning_rate=0.05) # no acces when nothing is there self.failUnlessRaises(RuntimeError, som._accessKohonen) self.failUnlessRaises(RuntimeError, som.getInSize) self.failUnlessRaises(RuntimeError, som.getOutSize) som.train(ds) self.failUnless(som.getInSize() == 3) self.failUnless(som.getOutSize() == (10,5)) fmapped = som(colors) self.failUnless(fmapped.shape == (8, 2)) for fm in fmapped: self.failUnless(som.isValidOutId(fm)) # reverse mapping rmapped = som.reverse(fmapped) if cfg.getboolean('tests', 'labile', default='yes'): # should approximately restore the input, but could fail # with bas initialisation self.failUnless((N.round(rmapped) == ds.samples).all()) def suite(): return unittest.makeSuite(SOMMapperTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_splitsensana.py000066400000000000000000000054731174541445200213010ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA SplittingSensitivityAnalyzer""" from mvpa.datasets.splitters import NFoldSplitter from mvpa.measures.splitmeasure import SplitFeaturewiseMeasure, \ TScoredFeaturewiseMeasure from mvpa.misc.data_generators import normalFeatureDataset from mvpa.misc.transformers import Absolute from tests_warehouse import * from tests_warehouse_clfs import * class SplitSensitivityAnalyserTests(unittest.TestCase): # XXX meta should work TODO @sweepargs(svm=clfswh['linear', 'svm', '!meta']) def testAnalyzer(self, svm): dataset = datasets['uni2small'] svm_weigths = svm.getSensitivityAnalyzer() sana = SplitFeaturewiseMeasure( svm_weigths, NFoldSplitter(cvtype=1), enable_states=['maps']) maps = sana(dataset) nchunks = len(dataset.uniquechunks) nfeatures = dataset.nfeatures self.failUnless(len(maps) == nfeatures, msg='Lengths of the map %d is different from number of features %d' % (len(maps), nfeatures)) self.failUnless(sana.states.isKnown('maps')) allmaps = N.array(sana.maps) self.failUnless(allmaps[:,0].mean() == maps[0]) self.failUnless(allmaps.shape == (nchunks, nfeatures)) @sweepargs(svm=clfswh['linear', 'svm', '!meta']) def testTScoredAnalyzer(self, svm): self.dataset = normalFeatureDataset(perlabel=100, nlabels=2, nchunks=20, nonbogus_features=[0,1], nfeatures=4, snr=10) svm_weigths = svm.getSensitivityAnalyzer() sana = TScoredFeaturewiseMeasure( svm_weigths, NFoldSplitter(cvtype=1), enable_states=['maps']) t = sana(self.dataset) # correct size? self.failUnlessEqual(t.shape, (4,)) # check reasonable sensitivities t = Absolute(t) self.failUnless(N.mean(t[:2]) > N.mean(t[2:])) # check whether SplitSensitivityAnalyzer 'maps' state is accessible self.failUnless(sana.states.isKnown('maps')) self.failUnless(N.array(sana.maps).shape == (20,4)) def suite(): return unittest.makeSuite(SplitSensitivityAnalyserTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_splitter.py000066400000000000000000000351341174541445200204400ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA pattern handling""" from mvpa.datasets.masked import MaskedDataset from mvpa.datasets.splitters import NFoldSplitter, OddEvenSplitter, \ NoneSplitter, HalfSplitter, \ CustomSplitter, NGroupSplitter import unittest import numpy as N class SplitterTests(unittest.TestCase): def setUp(self): self.data = \ MaskedDataset(samples=N.random.normal(size=(100,10)), labels=[ i%4 for i in range(100) ], chunks=[ i/10 for i in range(100)]) def testSimplestCVPatGen(self): # create the generator nfs = NFoldSplitter(cvtype=1) # now get the xval pattern sets One-Fold CV) xvpat = [ (train, test) for (train,test) in nfs(self.data) ] self.failUnless( len(xvpat) == 10 ) for i,p in enumerate(xvpat): self.failUnless( len(p) == 2 ) self.failUnless( p[0].nsamples == 90 ) self.failUnless( p[1].nsamples == 10 ) self.failUnless( p[1].chunks[0] == i ) def testOddEvenSplit(self): oes = OddEvenSplitter() splits = [ (train, test) for (train, test) in oes(self.data) ] self.failUnless(len(splits) == 2) for i,p in enumerate(splits): self.failUnless( len(p) == 2 ) self.failUnless( p[0].nsamples == 50 ) self.failUnless( p[1].nsamples == 50 ) self.failUnless((splits[0][1].uniquechunks == [1, 3, 5, 7, 9]).all()) self.failUnless((splits[0][0].uniquechunks == [0, 2, 4, 6, 8]).all()) self.failUnless((splits[1][0].uniquechunks == [1, 3, 5, 7, 9]).all()) self.failUnless((splits[1][1].uniquechunks == [0, 2, 4, 6, 8]).all()) # check if it works on pure odd and even chunk ids moresplits = [ (train, test) for (train, test) in oes(splits[0][0])] for split in moresplits: self.failUnless(split[0] != None) self.failUnless(split[1] != None) def testHalfSplit(self): hs = HalfSplitter() splits = [ (train, test) for (train, test) in hs(self.data) ] self.failUnless(len(splits) == 2) for i,p in enumerate(splits): self.failUnless( len(p) == 2 ) self.failUnless( p[0].nsamples == 50 ) self.failUnless( p[1].nsamples == 50 ) self.failUnless((splits[0][1].uniquechunks == [0, 1, 2, 3, 4]).all()) self.failUnless((splits[0][0].uniquechunks == [5, 6, 7, 8, 9]).all()) self.failUnless((splits[1][1].uniquechunks == [5, 6, 7, 8, 9]).all()) self.failUnless((splits[1][0].uniquechunks == [0, 1, 2, 3, 4]).all()) # check if it works on pure odd and even chunk ids moresplits = [ (train, test) for (train, test) in hs(splits[0][0])] for split in moresplits: self.failUnless(split[0] != None) self.failUnless(split[1] != None) def testNGroupSplit(self): """Test NGroupSplitter alongside with the reversal of the order of spit out datasets """ # Test 2 groups like HalfSplitter first hs = NGroupSplitter(2) hs_reversed = NGroupSplitter(2, reverse=True) for isreversed, splitter in enumerate((hs, hs_reversed)): splits = list(splitter(self.data)) self.failUnless(len(splits) == 2) for i, p in enumerate(splits): self.failUnless( len(p) == 2 ) self.failUnless( p[0].nsamples == 50 ) self.failUnless( p[1].nsamples == 50 ) self.failUnless((splits[0][1-isreversed].uniquechunks == [0, 1, 2, 3, 4]).all()) self.failUnless((splits[0][isreversed].uniquechunks == [5, 6, 7, 8, 9]).all()) self.failUnless((splits[1][1-isreversed].uniquechunks == [5, 6, 7, 8, 9]).all()) self.failUnless((splits[1][isreversed].uniquechunks == [0, 1, 2, 3, 4]).all()) # check if it works on pure odd and even chunk ids moresplits = list(hs(splits[0][0])) for split in moresplits: self.failUnless(split[0] != None) self.failUnless(split[1] != None) # now test more groups s5 = NGroupSplitter(5) s5_reversed = NGroupSplitter(5, reverse=True) # get the splits for isreversed, s5splitter in enumerate((s5, s5_reversed)): splits = list(s5splitter(self.data)) # must have 10 splits self.failUnless(len(splits) == 5) # check split content self.failUnless((splits[0][1-isreversed].uniquechunks == [0, 1]).all()) self.failUnless((splits[0][isreversed].uniquechunks == [2, 3, 4, 5, 6, 7, 8, 9]).all()) self.failUnless((splits[1][1-isreversed].uniquechunks == [2, 3]).all()) self.failUnless((splits[1][isreversed].uniquechunks == [0, 1, 4, 5, 6, 7, 8, 9]).all()) # ... self.failUnless((splits[4][1-isreversed].uniquechunks == [8, 9]).all()) self.failUnless((splits[4][isreversed].uniquechunks == [0, 1, 2, 3, 4, 5, 6, 7]).all()) # Test for too many groups def splitcall(spl, dat): return [ (train, test) for (train, test) in spl(dat) ] s20 = NGroupSplitter(20) self.assertRaises(ValueError,splitcall,s20,self.data) def testCustomSplit(self): #simulate half splitter hs = CustomSplitter([(None,[0,1,2,3,4]),(None,[5,6,7,8,9])]) splits = list(hs(self.data)) self.failUnless(len(splits) == 2) for i,p in enumerate(splits): self.failUnless( len(p) == 2 ) self.failUnless( p[0].nsamples == 50 ) self.failUnless( p[1].nsamples == 50 ) self.failUnless((splits[0][1].uniquechunks == [0, 1, 2, 3, 4]).all()) self.failUnless((splits[0][0].uniquechunks == [5, 6, 7, 8, 9]).all()) self.failUnless((splits[1][1].uniquechunks == [5, 6, 7, 8, 9]).all()) self.failUnless((splits[1][0].uniquechunks == [0, 1, 2, 3, 4]).all()) # check fully customized split with working and validation set specified cs = CustomSplitter([([0,3,4],[5,9])]) splits = list(cs(self.data)) self.failUnless(len(splits) == 1) for i,p in enumerate(splits): self.failUnless( len(p) == 2 ) self.failUnless( p[0].nsamples == 30 ) self.failUnless( p[1].nsamples == 20 ) self.failUnless((splits[0][1].uniquechunks == [5, 9]).all()) self.failUnless((splits[0][0].uniquechunks == [0, 3, 4]).all()) # full test with additional sampling and 3 datasets per split cs = CustomSplitter([([0,3,4],[5,9],[2])], nperlabel=[3,4,1], nrunspersplit=3) splits = list(cs(self.data)) self.failUnless(len(splits) == 3) for i,p in enumerate(splits): self.failUnless( len(p) == 3 ) self.failUnless( p[0].nsamples == 12 ) self.failUnless( p[1].nsamples == 16 ) self.failUnless( p[2].nsamples == 4 ) # lets test selection of samples by ratio and combined with # other ways cs = CustomSplitter([([0,3,4],[5,9],[2])], nperlabel=[[0.3, 0.6, 1.0, 0.5], 0.5, 'all'], nrunspersplit=3) csall = CustomSplitter([([0,3,4],[5,9],[2])], nrunspersplit=3) # lets craft simpler dataset #ds = Dataset(samples=N.arange(12), labels=[1]*6+[2]*6, chunks=1) splits = list(cs(self.data)) splitsall = list(csall(self.data)) self.failUnless(len(splits) == 3) ul = self.data.uniquelabels self.failUnless(((N.array(splitsall[0][0].samplesperlabel.values()) *[0.3, 0.6, 1.0, 0.5]).round().astype(int) == N.array(splits[0][0].samplesperlabel.values())).all()) self.failUnless(((N.array(splitsall[0][1].samplesperlabel.values())*0.5 ).round().astype(int) == N.array(splits[0][1].samplesperlabel.values())).all()) self.failUnless((N.array(splitsall[0][2].samplesperlabel.values()) == N.array(splits[0][2].samplesperlabel.values())).all()) def testNoneSplitter(self): nos = NoneSplitter() splits = [ (train, test) for (train, test) in nos(self.data) ] self.failUnless(len(splits) == 1) self.failUnless(splits[0][0] == None) self.failUnless(splits[0][1].nsamples == 100) nos = NoneSplitter(mode='first') splits = [ (train, test) for (train, test) in nos(self.data) ] self.failUnless(len(splits) == 1) self.failUnless(splits[0][1] == None) self.failUnless(splits[0][0].nsamples == 100) # test sampling tools # specified value nos = NoneSplitter(nrunspersplit=3, nperlabel=10) splits = [ (train, test) for (train, test) in nos(self.data) ] self.failUnless(len(splits) == 3) for split in splits: self.failUnless(split[0] == None) self.failUnless(split[1].nsamples == 40) self.failUnless(split[1].samplesperlabel.values() == [10,10,10,10]) # auto-determined nos = NoneSplitter(nrunspersplit=3, nperlabel='equal') splits = [ (train, test) for (train, test) in nos(self.data) ] self.failUnless(len(splits) == 3) for split in splits: self.failUnless(split[0] == None) self.failUnless(split[1].nsamples == 100) self.failUnless(split[1].samplesperlabel.values() == [25,25,25,25]) def testLabelSplitter(self): oes = OddEvenSplitter(attr='labels') splits = [ (first, second) for (first, second) in oes(self.data) ] self.failUnless((splits[0][0].uniquelabels == [0,2]).all()) self.failUnless((splits[0][1].uniquelabels == [1,3]).all()) self.failUnless((splits[1][0].uniquelabels == [1,3]).all()) self.failUnless((splits[1][1].uniquelabels == [0,2]).all()) def testCountedSplitting(self): # count > #chunks, should result in 10 splits nchunks = len(self.data.uniquechunks) for strategy in NFoldSplitter._STRATEGIES: for count, target in [ (nchunks*2, nchunks), (nchunks, nchunks), (nchunks-1, nchunks-1), (3, 3), (0, 0), (1, 1) ]: nfs = NFoldSplitter(cvtype=1, count=count, strategy=strategy) splits = [ (train, test) for (train,test) in nfs(self.data) ] self.failUnless(len(splits) == target) chosenchunks = [int(s[1].uniquechunks) for s in splits] # Check if "lastsplit" dsattr was assigned appropriately nsplits = len(splits) if nsplits > 0: # dummy-proof testing of last split for ds_ in splits[-1]: self.failUnless(ds_._dsattr['lastsplit']) # test all now for isplit,split in enumerate(splits): for ds_ in split: ds_._dsattr['lastsplit'] == isplit==nsplits-1 # Check results of different strategies if strategy == 'first': self.failUnlessEqual(chosenchunks, range(target)) elif strategy == 'equidistant': if target == 3: self.failUnlessEqual(chosenchunks, [0, 3, 7]) elif strategy == 'random': # none is selected twice self.failUnless(len(set(chosenchunks)) == len(chosenchunks)) self.failUnless(target == len(chosenchunks)) else: raise RuntimeError, "Add unittest for strategy %s" \ % strategy def testDiscardedBoundaries(self): splitters = [NFoldSplitter(), NFoldSplitter(discard_boundary=(0,1)), # discard testing NFoldSplitter(discard_boundary=(1,0)), # discard training NFoldSplitter(discard_boundary=(2,0)), # discard 2 from training NFoldSplitter(discard_boundary=1), # discard from both OddEvenSplitter(discard_boundary=(1,0)), OddEvenSplitter(discard_boundary=(0,1)), HalfSplitter(discard_boundary=(1,0)), ] split_sets = [list(s(self.data)) for s in splitters] counts = [[(len(s[0].chunks), len(s[1].chunks)) for s in split_set] for split_set in split_sets] nodiscard_tr = [c[0] for c in counts[0]] nodiscard_te = [c[1] for c in counts[0]] # Discarding in testing: self.failUnless(nodiscard_tr == [c[0] for c in counts[1]]) self.failUnless(nodiscard_te[1:-1] == [c[1] + 2 for c in counts[1][1:-1]]) # at the beginning/end chunks, just a single element self.failUnless(nodiscard_te[0] == counts[1][0][1] + 1) self.failUnless(nodiscard_te[-1] == counts[1][-1][1] + 1) # Discarding in training for d in [1,2]: self.failUnless(nodiscard_te == [c[1] for c in counts[1+d]]) self.failUnless(nodiscard_tr[0] == counts[1+d][0][0] + d) self.failUnless(nodiscard_tr[-1] == counts[1+d][-1][0] + d) self.failUnless(nodiscard_tr[1:-1] == [c[0] + d*2 for c in counts[1+d][1:-1]]) # Discarding in both -- should be eq min from counts[1] and [2] counts_min = [(min(c1[0], c2[0]), min(c1[1], c2[1])) for c1,c2 in zip(counts[1], counts[2])] self.failUnless(counts_min == counts[4]) # TODO: test all those odd/even etc splitters... YOH: did # visually... looks ok;) #for count in counts[5:]: # print count def suite(): return unittest.makeSuite(SplitterTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_state.py000066400000000000000000000253541174541445200177150ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA State parent class""" import unittest, copy import numpy as N from mvpa.base import externals from mvpa.misc.state import StateVariable, ClassWithCollections, \ ParameterCollection, _def_sep from mvpa.misc.param import * from mvpa.misc.exceptions import UnknownStateError if __debug__: from mvpa.base import debug class TestClassEmpty(ClassWithCollections): pass class TestClassBlank(ClassWithCollections): # We can force to have 'states' present even though we don't have # any StateVariable defined here -- it might be added later on at run time _ATTRIBUTE_COLLECTIONS = ['states'] pass class TestClassBlankNoExplicitStates(ClassWithCollections): pass class TestClassProper(ClassWithCollections): state1 = StateVariable(enabled=False, doc="state1 doc") state2 = StateVariable(enabled=True, doc="state2 doc") class TestClassProperChild(TestClassProper): state4 = StateVariable(enabled=False, doc="state4 doc") class TestClassParametrized(TestClassProper, ClassWithCollections): p1 = Parameter(0) state0 = StateVariable(enabled=False) def __init__(self, **kwargs): # XXX make such example when we actually need to invoke # constructor # TestClassProper.__init__(self, **kwargs) ClassWithCollections.__init__(self, **kwargs) class StateTests(unittest.TestCase): def testBlankState(self): empty = TestClassEmpty() blank = TestClassBlank() blank2 = TestClassBlank() self.failUnlessRaises(AttributeError, empty.__getattribute__, 'states') self.failUnlessEqual(blank.states.items, {}) self.failUnless(blank.states.enabled == []) self.failUnlessRaises(AttributeError, blank.__getattribute__, 'dummy') self.failUnlessRaises(AttributeError, blank.__getattribute__, '_') # we shouldn't use _registerState now since metaclass statecollector wouldn't # update the states... may be will be implemented in the future if necessity comes return # add some state variable blank._registerState('state1', False) self.failUnless(blank.states == ['state1']) self.failUnless(blank.states.isEnabled('state1') == False) self.failUnless(blank.states.enabled == []) self.failUnlessRaises(UnknownStateError, blank.__getattribute__, 'state1') # assign value now blank.state1 = 123 # should have no effect since the state variable wasn't enabled self.failUnlessRaises(UnknownStateError, blank.__getattribute__, 'state1') # lets enable and assign blank.states.enable('state1') blank.state1 = 123 self.failUnless(blank.state1 == 123) # we should not share states across instances at the moment, so an arbitrary # object could carry some custom states self.failUnless(blank2.states == []) self.failUnlessRaises(AttributeError, blank2.__getattribute__, 'state1') def testProperState(self): proper = TestClassProper() proper2 = TestClassProper(enable_states=['state1'], disable_states=['state2']) # disable_states should override anything in enable_states proper3 = TestClassProper(enable_states=['all'], disable_states='all') self.failUnlessEqual(len(proper3.states.enabled), 0, msg="disable_states should override anything in enable_states") proper.state2 = 1000 value = proper.state2 self.failUnlessEqual(proper.state2, 1000, msg="Simple assignment/retrieval") proper.states.disable('state2') proper.state2 = 10000 self.failUnlessEqual(proper.state2, 1000, msg="Simple assignment after being disabled") proper4 = copy.deepcopy(proper) proper.states.reset('state2') self.failUnlessRaises(UnknownStateError, proper.__getattribute__, 'state2') """Must be blank after being reset""" self.failUnlessEqual(proper4.state2, 1000, msg="Simple assignment after being reset in original instance") proper.states.enable(['state2']) self.failUnlessEqual(set(proper.states.names), set(['state1', 'state2'])) if __debug__ and 'ENFORCE_STATES_ENABLED' in debug.active: # skip testing since all states are on now return self.failUnless(proper.states.enabled == ['state2']) self.failUnless(set(proper2.states.enabled) == set(['state1'])) self.failUnlessRaises(AttributeError, proper.__getattribute__, 'state12') # if documentary on the state is appropriate self.failUnlessEqual(proper2.states.listing, ['%sstate1+%s: state1 doc' % (_def_sep, _def_sep), '%sstate2%s: state2 doc' % (_def_sep, _def_sep)]) # if __str__ lists correct number of states str_ = str(proper2) self.failUnless(str_.find('2 states:') != -1) # check if disable works self.failUnless(set(proper2.states.enabled), set(['state1'])) proper2.states.disable("all") self.failUnlessEqual(set(proper2.states.enabled), set()) proper2.states.enable("all") self.failUnlessEqual(len(proper2.states.enabled), 2) proper2.state1, proper2.state2 = 1,2 self.failUnlessEqual(proper2.state1, 1) self.failUnlessEqual(proper2.state2, 2) # now reset them proper2.states.reset('all') self.failUnlessRaises(UnknownStateError, proper2.__getattribute__, 'state1') self.failUnlessRaises(UnknownStateError, proper2.__getattribute__, 'state2') def testGetSaveEnabled(self): """Check if we can store/restore set of enabled states""" if __debug__ and 'ENFORCE_STATES_ENABLED' in debug.active: # skip testing since all states are on now return proper = TestClassProper() enabled_states = proper.states.enabled proper.states.enable('state1') self.failUnless(enabled_states != proper.states.enabled, msg="New enabled states should differ from previous") self.failUnless(set(proper.states.enabled) == set(['state1', 'state2']), msg="Making sure that we enabled all states of interest") proper.states.enabled = enabled_states self.failUnless(enabled_states == proper.states.enabled, msg="List of enabled states should return to original one") # TODO: make test for _copy_states_ or whatever comes as an alternative def testStoredTemporarily(self): proper = TestClassProper() properch = TestClassProperChild(enable_states=["state1"]) if __debug__ and 'ENFORCE_STATES_ENABLED' in debug.active: # skip testing since all states are on now return self.failUnlessEqual(proper.states.enabled, ["state2"]) proper.states._changeTemporarily( enable_states=["state1"], other=properch) self.failUnlessEqual(set(proper.states.enabled), set(["state1", "state2"])) proper.states._resetEnabledTemporarily() self.failUnlessEqual(proper.states.enabled, ["state2"]) # allow to enable disable without other instance proper.states._changeTemporarily( enable_states=["state1", "state2"]) self.failUnlessEqual(set(proper.states.enabled), set(["state1", "state2"])) proper.states._resetEnabledTemporarily() self.failUnlessEqual(proper.states.enabled, ["state2"]) def testProperStateChild(self): """ Simple test if child gets state variables from the parent as well """ proper = TestClassProperChild() self.failUnlessEqual(set(proper.states.names), set(['state1', 'state2', 'state4'])) def testStateVariables(self): """To test new states""" class S1(ClassWithCollections): v1 = StateVariable(enabled=True, doc="values1 is ...") v1XXX = StateVariable(enabled=False, doc="values1 is ...") class S2(ClassWithCollections): v2 = StateVariable(enabled=True, doc="values12 is ...") class S1_(S1): pass class S1__(S1_): v1__ = StateVariable(enabled=False) class S12(S1__, S2): v12 = StateVariable() s1, s2, s1_, s1__, s12 = S1(), S2(), S1_(), S1__(), S12() self.failUnlessEqual(s1.states.isEnabled("v1"), True) s1.v1 = 12 s12.v1 = 120 s2.v2 = 100 self.failUnlessEqual(len(s2.states.listing), 1) self.failUnlessEqual(s1.v1, 12) try: tempvalue = s1__.v1__ self.fail("Should have puked since values were not enabled yet") except: pass def testParametrized(self): self.failUnlessRaises(TypeError, TestClassParametrized, p2=34, enable_states=['state1'], msg="Should raise an exception if argument doesn't correspond to" "any parameter") a = TestClassParametrized(p1=123, enable_states=['state1']) self.failUnlessEqual(a.p1, 123, msg="We must have assigned value to instance") self.failUnless('state1' in a.states.enabled, msg="state1 must have been enabled") if (__debug__ and 'ID_IN_REPR' in debug.active): # next tests would fail due to ID in the tails return # validate that string representation of the object is valid and consistent a_str = `a` try: import test_state exec "a2=%s" % a_str except Exception, e: self.fail(msg="Failed to generate an instance out of " "representation %s. Got exception: %s" % (a_str, e)) a2_str = `a2` self.failUnless(a2_str == a_str, msg="Generated object must have the same repr. Got %s and %s" % (a_str, a2_str)) # Test at least that repr of collection is of correct syntax aparams_str = `a.params` try: import test_state exec "aparams2=%s" % aparams_str except Exception, e: self.fail(msg="Failed to generate an instance out of " "representation %s of params. Got exception: %s" % (aparams_str, e)) def suite(): return unittest.makeSuite(StateTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_stats.py000066400000000000000000000113621174541445200177250ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA stats helpers""" from mvpa.base import externals from mvpa.clfs.stats import MCNullDist, FixedNullDist, NullDist from mvpa.datasets import Dataset from mvpa.measures.glm import GLM from mvpa.measures.anova import OneWayAnova, CompoundOneWayAnova from mvpa.misc.fx import doubleGammaHRF, singleGammaHRF from tests_warehouse import * from mvpa import cfg from numpy.testing import assert_array_almost_equal # Prepare few distributions to test #kwargs = {'permutations':10, 'tail':'any'} nulldist_sweep = [ MCNullDist(permutations=30, tail='any'), MCNullDist(permutations=30, tail='right')] if externals.exists('scipy'): from mvpa.support.stats import scipy from scipy.stats import f_oneway from mvpa.clfs.stats import rv_semifrozen nulldist_sweep += [ MCNullDist(scipy.stats.norm, permutations=30, tail='any'), MCNullDist(scipy.stats.norm, permutations=30, tail='right'), MCNullDist(rv_semifrozen(scipy.stats.norm, loc=0), permutations=30, tail='right'), MCNullDist(scipy.stats.expon, permutations=30, tail='right'), FixedNullDist(scipy.stats.norm(0, 10.0), tail='any'), FixedNullDist(scipy.stats.norm(0, 10.0), tail='right'), scipy.stats.norm(0, 0.1) ] class StatsTests(unittest.TestCase): """Unittests for various statistics""" @sweepargs(null=nulldist_sweep[1:]) def testNullDistProb(self, null): """Testing null dist probability""" if not isinstance(null, NullDist): return ds = datasets['uni2small'] null.fit(OneWayAnova(), ds) # check reasonable output. # p-values for non-bogus features should significantly different, # while bogus (0) not prob = null.p([20, 0, 0, 0, 0, N.nan]) # XXX this is labile! it also needs checking since the F-scores # of the MCNullDists using normal distribution are apparently not # distributed that way, hence the test often (if not always) fails. if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(N.abs(prob[0]) < 0.05, msg="Expected small p, got %g" % prob[0]) if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless((N.abs(prob[1:]) > 0.05).all(), msg="Bogus features should have insignificant p." " Got %s" % (N.abs(prob[1:]),)) # has to have matching shape if not isinstance(null, FixedNullDist): # Fixed dist is univariate ATM so it doesn't care # about dimensionality and gives 1 output value self.failUnlessRaises(ValueError, null.p, [5, 3, 4]) def testAnova(self): """Do some extended testing of OneWayAnova in particular -- compound estimation """ m = OneWayAnova() # default must be not compound ? mc = CompoundOneWayAnova(combiner=None) ds = datasets['uni2medium'] # For 2 labels it must be identical for both and equal to # simple OneWayAnova a, ac = m(ds), mc(ds) self.failUnless(a.shape == (ds.nfeatures,)) self.failUnless(ac.shape == (ds.nfeatures, len(ds.uniquelabels))) self.failUnless((ac[:, 0] == ac[:, 1]).all()) self.failUnless((a == ac[:, 1]).all()) ds = datasets['uni4large'] ac = mc(ds) if cfg.getboolean('tests', 'labile', default='yes'): # All non-bogus features must be high for a corresponding feature self.failUnless((ac[(N.array(ds.nonbogus_features), N.arange(4))] >= 1).all()) # All features should have slightly but different CompoundAnova # values. I really doubt that there will be a case when this # test would fail just to being 'labile' self.failUnless(N.max(N.std(ac, axis=1))>0, msg='In compound anova, we should get different' ' results for different labels. Got %s' % ac) def suite(): """Create the suite""" return unittest.makeSuite(StatsTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_stats_sp.py000066400000000000000000000277441174541445200204420ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA stats helpers -- those requiring scipy""" from test_stats import * externals.exists('scipy', raiseException=True) from scipy import signal from mvpa.misc.stats import chisquare class StatsTestsScipy(unittest.TestCase): """Unittests for various statistics which use scipy""" def testChiSquare(self): """Test chi-square distribution""" # test equal distribution tbl = N.array([[5, 5], [5, 5]]) chi, p = chisquare(tbl) self.failUnless( chi == 0.0 ) self.failUnless( p == 1.0 ) # test non-equal distribution tbl = N.array([[4, 0], [0, 4]]) chi, p = chisquare(tbl) self.failUnless(chi == 8.0) self.failUnless(p < 0.05) def testNullDistProbAny(self): """Test 'any' tail statistics estimation""" if not externals.exists('scipy'): return # test 'any' mode from mvpa.measures.corrcoef import CorrCoef ds = datasets['uni2medium'] null = MCNullDist(permutations=20, tail='any') null.fit(CorrCoef(), ds) # 100 and -100 should both have zero probability on their respective # tails pm100 = null.p([-100] + [0]*(ds.nfeatures-1)) p100 = null.p([100] + [0]*(ds.nfeatures-1)) assert_array_almost_equal(pm100, p100) # With 20 samples isn't that easy to get reliable sampling for # non-parametric, so we can allow somewhat low significance # ;-) self.failUnless(pm100[0] <= 0.1) self.failUnless(p100[0] <= 0.1) self.failUnless(N.all(pm100[1:] >= 0.1)) self.failUnless(N.all(pm100[1:] >= 0.1)) # same test with just scalar measure/feature null.fit(CorrCoef(), ds.selectFeatures([0])) self.failUnlessAlmostEqual(null.p(-100), null.p(100)) self.failUnless(null.p(100) <= 0.1) @sweepargs(nd=nulldist_sweep) def testDatasetMeasureProb(self, nd): """Test estimation of measures statistics""" if not externals.exists('scipy'): # due to null_t requirement return ds = datasets['uni2medium'] m = OneWayAnova(null_dist=nd, enable_states=['null_t']) score = m(ds) score_nonbogus = N.mean(score[ds.nonbogus_features]) score_bogus = N.mean(score[ds.bogus_features]) # plausability check self.failUnless(score_bogus < score_nonbogus) null_prob_nonbogus = m.null_prob[ds.nonbogus_features] null_prob_bogus = m.null_prob[ds.bogus_features] self.failUnless((null_prob_nonbogus < 0.05).all(), msg="Nonbogus features should have a very unlikely value. Got %s" % null_prob_nonbogus) # the others should be a lot larger self.failUnless(N.mean(N.abs(null_prob_bogus)) > N.mean(N.abs(null_prob_nonbogus))) if isinstance(nd, MCNullDist): # MCs are not stable with just 10 samples... so lets skip them return if cfg.getboolean('tests', 'labile', default='yes'): # Failed on c94ec26eb593687f25d8c27e5cfdc5917e352a69 # with MVPA_SEED=833393575 self.failUnless((N.abs(m.null_t[ds.nonbogus_features]) >= 5).all(), msg="Nonbogus features should have high t-score. Got %s" % (m.null_t[ds.nonbogus_features])) bogus_min = min(N.abs(m.null_t[ds.bogus_features])) self.failUnless(bogus_min < 4, msg="Some bogus features should have low t-score of %g." "Got (t,p,sens):%s" % (bogus_min, zip(m.null_t[ds.bogus_features], m.null_prob[ds.bogus_features], score[ds.bogus_features]))) def testNegativeT(self): """Basic testing of the sign in p and t scores """ from mvpa.measures.base import FeaturewiseDatasetMeasure class BogusMeasure(FeaturewiseDatasetMeasure): """Just put high positive into first 2 features, and high negative into 2nd two """ def _call(self, dataset): """just a little helper... pylint shut up!""" res = N.random.normal(size=(dataset.nfeatures,)) res[0] = res[1] = 100 res[2] = res[3] = -100 return res nd = FixedNullDist(scipy.stats.norm(0, 0.1), tail='any') m = BogusMeasure(null_dist=nd, enable_states=['null_t']) ds = datasets['uni2small'] score = m(ds) t, p = m.null_t, m.null_prob self.failUnless((p>=0).all()) self.failUnless((t[:2] > 0).all()) self.failUnless((t[2:4] < 0).all()) def testMatchDistribution(self): """Some really basic testing for matchDistribution """ from mvpa.clfs.stats import matchDistribution, rv_semifrozen ds = datasets['uni2medium'] # large to get stable stats data = ds.samples[:, ds.bogus_features[0]] # choose bogus feature, which # should have close to normal distribution # Lets test ad-hoc rv_semifrozen floc = rv_semifrozen(scipy.stats.norm, loc=0).fit(data) self.failUnless(floc[0] == 0) fscale = rv_semifrozen(scipy.stats.norm, scale=1.0).fit(data) self.failUnless(fscale[1] == 1) flocscale = rv_semifrozen(scipy.stats.norm, loc=0, scale=1.0).fit(data) self.failUnless(flocscale[1] == 1 and flocscale[0] == 0) full = scipy.stats.norm.fit(data) for res in [floc, fscale, flocscale, full]: self.failUnless(len(res) == 2) data_mean = N.mean(data) for loc in [None, data_mean]: for test in ['p-roc', 'kstest']: # some really basic testing matched = matchDistribution( data=data, distributions = ['scipy', ('norm', {'name': 'norm_fixed', 'loc': 0.2, 'scale': 0.3})], test=test, loc=loc, p=0.05) # at least norm should be in there names = [m[2] for m in matched] if test == 'p-roc': if cfg.getboolean('tests', 'labile', default='yes'): # we can guarantee that only for norm_fixed self.failUnless('norm' in names) self.failUnless('norm_fixed' in names) inorm = names.index('norm_fixed') # and it should be at least in the first # 30 best matching ;-) self.failUnless(inorm <= 30) def testRDistStability(self): """Test either rdist distribution performs nicely """ try: # actually I haven't managed to cause this error scipy.stats.rdist(1.32, 0, 1).pdf(-1.0+N.finfo(float).eps) except Exception, e: self.fail('Failed to compute rdist.pdf due to numeric' ' loss of precision. Exception was %s' % e) try: # this one should fail with recent scipy with error # ZeroDivisionError: 0.0 cannot be raised to a negative power # XXX: There is 1 more bug in etch's scipy.stats or numpy # (vectorize), so I have to put 2 elements in the # queried x's, otherwise it # would puke. But for now that fix is not here # # value = scipy.stats.rdist(1.32, 0, 1).cdf( # [-1.0+N.finfo(float).eps, 0]) # # to cause it now just run this unittest only with # nosetests -s test_stats:StatsTests.testRDistStability # NB: very cool way to store the trace of the execution #import pydb #pydb.debugger(dbg_cmds=['bt', 'l', 's']*300 + ['c']) scipy.stats.rdist(1.32, 0, 1).cdf(-1.0+N.finfo(float).eps) except IndexError, e: self.fail('Failed due to bug which leads to InvalidIndex if only' ' scalar is provided to cdf') except Exception, e: self.fail('Failed to compute rdist.cdf due to numeric' ' loss of precision. Exception was %s' % e) v = scipy.stats.rdist(10000, 0, 1).cdf([-0.1]) self.failUnless(v>=0, v<=1) def testAnovaCompliance(self): ds = datasets['uni2large'] fwm = OneWayAnova() f = fwm(ds) f_sp = f_oneway(ds['labels', [1]].samples, ds['labels', [0]].samples) # SciPy needs to compute the same F-scores assert_array_almost_equal(f, f_sp[0]) def testGLM(self): """Test GLM """ # play fmri # full-blown HRF with initial dip and undershoot ;-) hrf_x = N.linspace(0, 25, 250) hrf = doubleGammaHRF(hrf_x) - singleGammaHRF(hrf_x, 0.8, 1, 0.05) # come up with an experimental design samples = 1800 fast_er_onsets = N.array([10, 200, 250, 500, 600, 900, 920, 1400]) fast_er = N.zeros(samples) fast_er[fast_er_onsets] = 1 # high resolution model of the convolved regressor model_hr = N.convolve(fast_er, hrf)[:samples] # downsample the regressor to fMRI resolution tr = 2.0 model_lr = signal.resample(model_hr, int(samples / tr / 10), window='ham') # generate artifical fMRI data: two voxels one is noise, one has # something baseline = 800.0 wsignal = baseline + 2 * model_lr + \ N.random.randn(int(samples / tr / 10)) * 0.2 nsignal = baseline + N.random.randn(int(samples / tr / 10)) * 0.5 # build design matrix: bold-regressor and constant X = N.array([model_lr, N.repeat(1, len(model_lr))]).T # two 'voxel' dataset data = Dataset(samples=N.array((wsignal, nsignal, nsignal)).T, labels=1) # check GLM betas glm = GLM(X, combiner=None) betas = glm(data) # betas for each feature and each regressor self.failUnless(betas.shape == (data.nfeatures, X.shape[1])) self.failUnless(N.absolute(betas[:, 1] - baseline < 10).all(), msg="baseline betas should be huge and around 800") self.failUnless(betas[0][0] > betas[1, 0], msg="feature (with signal) beta should be larger than for noise") if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(N.absolute(betas[1, 0]) < 0.5) self.failUnless(N.absolute(betas[0, 0]) > 1.0) # check GLM zscores glm = GLM(X, voi='zstat', combiner=None) zstats = glm(data) self.failUnless(zstats.shape == betas.shape) self.failUnless((zstats[:, 1] > 1000).all(), msg='constant zstats should be huge') if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(N.absolute(betas[0, 0]) > betas[1][0], msg='with signal should have higher zstats') def testBinomdistPPF(self): """Test if binomial distribution works ok after possibly a monkey patch """ bdist = scipy.stats.binom(100, 0.5) self.failUnless(bdist.ppf(1.0) == 100) self.failUnless(bdist.ppf(0.9) <= 60) self.failUnless(bdist.ppf(0.5) == 50) self.failUnless(bdist.ppf(0) == -1) def suite(): """Create the suite""" return unittest.makeSuite(StatsTestsScipy) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_suite.py000066400000000000000000000015351174541445200177210ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit test for PyMVPA mvpa.suite() of being loading ok""" import unittest class SuiteTest(unittest.TestCase): def testBasic(self): """Test if we are loading fine""" try: exec "from mvpa.suite import *" except Exception, e: self.fail(msg="Cannot import everything from mvpa.suite." "Getting %s" % e) def suite(): return unittest.makeSuite(SuiteTest) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_support.py000066400000000000000000000223351174541445200203050ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA serial feature inclusion algorithm""" from mvpa.misc.support import * from mvpa.datasets.splitters import NFoldSplitter from mvpa.clfs.transerror import TransferError from tests_warehouse import * from tests_warehouse import getMVPattern from tests_warehouse_clfs import * from mvpa.clfs.distance import oneMinusCorrelation from mvpa.support.copy import deepcopy class SupportFxTests(unittest.TestCase): def testTransformWithBoxcar(self): data = N.arange(10) sp = N.arange(10) # check if stupid thing don't work self.failUnlessRaises(ValueError, transformWithBoxcar, data, sp, 0 ) # now do an identity transformation trans = transformWithBoxcar(data, sp, 1) self.failUnless( (trans == data).all() ) # now check for illegal boxes self.failUnlessRaises(ValueError, transformWithBoxcar, data, sp, 2) # now something that should work sp = N.arange(9) trans = transformWithBoxcar( data, sp, 2) self.failUnless( ( trans == \ [0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5] ).all() ) # now test for proper data shape data = N.ones((10,3,4,2)) sp = [ 2, 4, 3, 5 ] trans = transformWithBoxcar( data, sp, 4) self.failUnless( trans.shape == (4,3,4,2) ) def testEvent(self): self.failUnlessRaises(ValueError, Event) ev = Event(onset=2.5) # all there? self.failUnless(ev.items() == [('onset', 2.5)]) # conversion self.failUnless(ev.asDescreteTime(dt=2).items() == [('onset', 1)]) evc = ev.asDescreteTime(dt=2, storeoffset=True) self.failUnless(evc.has_key('features')) self.failUnless(evc['features'] == [0.5]) # same with duration included evc = Event(onset=2.5, duration=3.55).asDescreteTime(dt=2) self.failUnless(evc['duration'] == 3) def testMofNCombinations(self): self.failUnlessEqual( getUniqueLengthNCombinations( range(3), 1 ), [[0],[1],[2]] ) self.failUnlessEqual( getUniqueLengthNCombinations( range(4), 2 ), [[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]] ) self.failUnlessEqual( getUniqueLengthNCombinations( range(4), 3 ), [[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]] ) def testBreakPoints(self): items_cont = [0, 0, 0, 1, 1, 1, 3, 3, 2] items_noncont = [0, 0, 1, 1, 0, 3, 2] self.failUnlessRaises(ValueError, getBreakPoints, items_noncont) self.failUnlessEqual(getBreakPoints(items_noncont, contiguous=False), [0, 2, 4, 5, 6]) self.failUnlessEqual(getBreakPoints(items_cont), [0, 3, 6, 8]) self.failUnlessEqual(getBreakPoints(items_cont, contiguous=False), [0, 3, 6, 8]) def testMapOverlap(self): mo = MapOverlap() maps = [[1,0,1,0], [1,0,0,1], [1,0,1,0]] overlap = mo(maps) self.failUnlessEqual(overlap, 1./len(maps[0])) self.failUnless((mo.overlap_map == [1,0,0,0]).all()) self.failUnless((mo.spread_map == [0,0,1,1]).all()) self.failUnless((mo.ovstats_map == [1,0,2./3,1./3]).all()) mo = MapOverlap(overlap_threshold=0.5) overlap = mo(maps) self.failUnlessEqual(overlap, 2./len(maps[0])) self.failUnless((mo.overlap_map == [1,0,1,0]).all()) self.failUnless((mo.spread_map == [0,0,0,1]).all()) self.failUnless((mo.ovstats_map == [1,0,2./3,1./3]).all()) def testHarvester(self): # do very simple list comprehension self.failUnlessEqual( [(-1)*i for i in range(5)], Harvester(xrange, [HarvesterCall(lambda x: (-1)*x, expand_args=False)]) (5)) # do clf cross-validation on a dataset with a very high SNR cv = Harvester(NFoldSplitter(cvtype=1), [HarvesterCall(TransferError(sample_clf_nl), argfilter=[1,0])]) data = getMVPattern(10) err = N.array(cv(data)) # has to be perfect self.failUnless((err < 0.1).all()) self.failUnlessEqual(err.shape, (len(data.uniquechunks),)) # now same stuff but two classifiers at once cv = Harvester(NFoldSplitter(cvtype=1), [HarvesterCall(TransferError(sample_clf_nl), argfilter=[1,0]), HarvesterCall(TransferError(sample_clf_nl), argfilter=[1,0])]) err = N.array(cv(data)) self.failUnlessEqual(err.shape, (2,len(data.uniquechunks))) # only one again, but this time remember confusion matrix cv = Harvester(NFoldSplitter(cvtype=1), [HarvesterCall(TransferError(sample_clf_nl, enable_states=['confusion']), argfilter=[1,0], attribs=['confusion'])]) res = cv(data) self.failUnless(isinstance(res, dict)) self.failUnless(res.has_key('confusion') and res.has_key('result')) self.failUnless(len(res['result']) == len(data.uniquechunks)) @sweepargs(pair=[(N.random.normal(size=(10,20)), N.random.normal(size=(10,20))), ([1,2,3,0], [1,3,2,0]), ((1,2,3,1), (1,3,2,1))]) def testIdHash(self, pair): a, b = pair a1 = deepcopy(a) a_1 = idhash(a) self.failUnless(a_1 == idhash(a), msg="Must be of the same idhash") self.failUnless(a_1 != idhash(b), msg="Must be of different idhash") if isinstance(a, N.ndarray): self.failUnless(a_1 != idhash(a.T), msg=".T must be of different idhash") if not isinstance(a, tuple): self.failUnless(a_1 != idhash(a1), msg="Must be of different idhash") a[2] += 1; a_2 = idhash(a) self.failUnless(a_1 != a_2, msg="Idhash must change") else: a_2 = a_1 a = a[2:]; a_3 = idhash(a) self.failUnless(a_2 != a_3, msg="Idhash must change after slicing") def testCorrelation(self): # data: 20 samples, 80 features X = N.random.rand(20,80) C = 1 - oneMinusCorrelation(X, X) # get nsample x nssample correlation matrix self.failUnless(C.shape == (20, 20)) # diagonal is 1 self.failUnless((N.abs(N.diag(C) - 1).mean() < 0.00001).all()) # now two different Y = N.random.rand(5,80) C2 = 1 - oneMinusCorrelation(X, Y) # get nsample x nssample correlation matrix self.failUnless(C2.shape == (20, 5)) # external validity check -- we are dealing with correlations self.failUnless(C2[10,2] - N.corrcoef(X[10], Y[2])[0,1] < 0.000001) def test_version_to_tuple(self): """Test conversion of versions from strings """ self.failUnless(version_to_tuple('0.0.01') == (0, 0, 1)) self.failUnless(version_to_tuple('0.7.1rc3') == (0, 7, 1, 'rc', 3)) def testSmartVersion(self): """Test our ad-hoc SmartVersion """ SV = SmartVersion for v1, v2 in ( ('0.0.1', '0.0.2'), ('0.0.1', '0.1'), ('0.0.1', '0.1.0'), ('0.0.1', '0.0.1a'), # this might be a bit unconventional? ('0.0.1', '0.0.1+svn234'), ('0.0.1+svn234', '0.0.1+svn235'), ('0.0.1dev1', '0.0.1'), ('0.0.1dev1', '0.0.1rc3'), ('0.7.1rc3', '0.7.1'), ('0.0.1-dev1', '0.0.1'), ('0.0.1-svn1', '0.0.1'), ('0.0.1~p', '0.0.1'), ('0.0.1~prior.1.2', '0.0.1'), ): self.failUnless(SV(v1) < SV(v2), msg="Failed to compare %s to %s" % (v1, v2)) self.failUnless(SV(v2) > SV(v1), msg="Failed to reverse compare %s to %s" % (v2, v1)) # comparison to strings self.failUnless(SV(v1) < v2, msg="Failed to compare %s to string %s" % (v1, v2)) self.failUnless(v1 < SV(v2), msg="Failed to compare string %s to %s" % (v1, v2)) # to tuples self.failUnless(SV(v1) < version_to_tuple(v2), msg="Failed to compare %s to tuple of %s" % (v1, v2)) self.failUnless(version_to_tuple(v1) < SV(v2), msg="Failed to compare tuple of %s to %s" % (v1, v2)) def suite(): return unittest.makeSuite(SupportFxTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_svdmapper.py000066400000000000000000000076351174541445200206000ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA SVD mapper""" import unittest from mvpa.support.copy import deepcopy import numpy as N from mvpa.datasets import Dataset from mvpa.mappers.svd import SVDMapper class SVDMapperTests(unittest.TestCase): def setUp(self): # data: 40 sample feature line in 20d space (40x20; samples x features) self.ndlin = Dataset(samples=N.concatenate( [N.arange(40) for i in range(20)]).reshape(20,-1).T, labels=1, chunks=1) # data: 10 sample feature line in 40d space # (10x40; samples x features) self.largefeat = Dataset(samples=N.concatenate( [N.arange(10) for i in range(40)]).reshape(40,-1).T, labels=1, chunks=1) def testSimpleSVD(self): pm = SVDMapper() # train SVD pm.train(self.ndlin) self.failUnlessEqual(pm.proj.shape, (20, 20)) # now project data into PCA space p = pm.forward(self.ndlin.samples) # only first eigenvalue significant self.failUnless(pm.sv[:1] > 1.0) self.failUnless((pm.sv[1:] < 0.0001).all()) # only variance of first component significant var = p.var(axis=0) # test that only one component has variance self.failUnless(var[:1] > 1.0) self.failUnless((var[1:] < 0.0001).all()) # check that the mapped data can be fully recovered by 'reverse()' pr = pm.reverse(p) self.failUnlessEqual(pr.shape, (40,20)) self.failUnless(N.abs(pm.reverse(p) - self.ndlin.samples).sum() < 0.0001) def testMoreSVD(self): pm = SVDMapper() # train SVD pm.train(self.largefeat) # mixing matrix cannot be square self.failUnlessEqual(pm.proj.shape, (40, 10)) # only first singular value significant self.failUnless(pm.sv[:1] > 10) self.failUnless((pm.sv[1:] < 10).all()) # now project data into SVD space p = pm.forward(self.largefeat.samples) # only variance of first component significant var = p.var(axis=0) # test that only one component has variance self.failUnless(var[:1] > 1.0) self.failUnless((var[1:] < 0.0001).all()) # check that the mapped data can be fully recovered by 'reverse()' rp = pm.reverse(p) self.failUnlessEqual(rp.shape, self.largefeat.samples.shape) self.failUnless((N.round(rp) == self.largefeat.samples).all()) self.failUnlessEqual(pm.getInSize(), 40) self.failUnlessEqual(pm.getOutSize(), 10) # copy mapper pm2 = deepcopy(pm) # now remove all but the first 2 components from the mapper pm2.selectOut([0,1]) # sanity check self.failUnlessEqual(pm2.getInSize(), 40) self.failUnlessEqual(pm2.getOutSize(), 2) # but orginal mapper must be left intact self.failUnlessEqual(pm.getInSize(), 40) self.failUnlessEqual(pm.getOutSize(), 10) # data should still be fully recoverable by 'reverse()' rp2 = pm2.reverse(p[:,[0,1]]) self.failUnlessEqual(rp2.shape, self.largefeat.samples.shape) self.failUnless(N.abs(rp2 - self.largefeat.samples).sum() < 0.0001) # now make new random data and do forward->reverse check data = N.random.normal(size=(98,40)) data_f = pm.forward(data) self.failUnlessEqual(data_f.shape, (98,10)) data_r = pm.reverse(data_f) self.failUnlessEqual(data_r.shape, (98,40)) def suite(): return unittest.makeSuite(SVDMapperTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_svm.py000066400000000000000000000156011174541445200173740ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for SVM classifier""" from mvpa.datasets.splitters import NFoldSplitter from mvpa.clfs.meta import ProxyClassifier from mvpa.clfs.transerror import TransferError from mvpa.algorithms.cvtranserror import CrossValidatedTransferError from tests_warehouse import pureMultivariateSignal from tests_warehouse import * from tests_warehouse_clfs import * class SVMTests(unittest.TestCase): # @sweepargs(nl_clf=clfswh['non-linear', 'svm'] ) # @sweepargs(nl_clf=clfswh['non-linear', 'svm'] ) def testMultivariate(self): mv_perf = [] mv_lin_perf = [] uv_perf = [] l_clf = clfswh['linear', 'svm'][0] nl_clf = clfswh['non-linear', 'svm'][0] #orig_keys = nl_clf.param._params.keys() #nl_param_orig = nl_clf.param._params.copy() # l_clf = LinearNuSVMC() # XXX ??? not sure what below meant and it is obsolete if # using SG... commenting out for now # for some reason order is not preserved thus dictionaries are not # the same any longer -- lets compare values #self.failUnlessEqual([nl_clf.param._params[k] for k in orig_keys], # [nl_param_orig[k] for k in orig_keys], # msg="New instance mustn't override values in previously created") ## and keys separately #self.failUnlessEqual(set(nl_clf.param._params.keys()), # set(orig_keys), # msg="New instance doesn't change set of parameters in original") # We must be able to deepcopy not yet trained SVMs now import mvpa.support.copy as copy try: nl_clf.untrain() nl_clf_copy = copy.deepcopy(nl_clf) except: self.fail(msg="Failed to deepcopy not-yet trained SVM %s" % nl_clf) for i in xrange(20): train = pureMultivariateSignal( 20, 3 ) test = pureMultivariateSignal( 20, 3 ) # use non-linear CLF on 2d data nl_clf.train(train) p_mv = nl_clf.predict(test.samples) mv_perf.append(N.mean(p_mv==test.labels)) # use linear CLF on 2d data l_clf.train(train) p_lin_mv = l_clf.predict(test.samples) mv_lin_perf.append(N.mean(p_lin_mv==test.labels)) # use non-linear CLF on 1d data nl_clf.train(train.selectFeatures([0])) p_uv = nl_clf.predict(test.selectFeatures([0]).samples) uv_perf.append(N.mean(p_uv==test.labels)) mean_mv_perf = N.mean(mv_perf) mean_mv_lin_perf = N.mean(mv_lin_perf) mean_uv_perf = N.mean(uv_perf) # non-linear CLF has to be close to perfect self.failUnless( mean_mv_perf > 0.9 ) # linear CLF cannot learn this problem! self.failUnless( mean_mv_perf > mean_mv_lin_perf ) # univariate has insufficient information self.failUnless( mean_uv_perf < mean_mv_perf ) # XXX for now works only with linear... think it through -- should # work non-linear, shouldn't it? # now all non-linear have C>0 thus skipped anyways # TODO: For some reason libsvm's weight assignment has no effect # as well -- need to be fixed :-/ @sweepargs(clf=clfswh['svm', 'sg', '!regression', '!gnpp', '!meta']) def testCperClass(self, clf): try: if clf.C > 0: # skip those with fixed C return except: # classifier has no C return if clf.C < -5: # too soft margin helps to fight disbalance, thus skip # it in testing return #print clf ds = datasets['uni2small'].copy() ds__ = datasets['uni2small'].copy() # # ballanced set # Lets add a bit of noise to drive classifier nuts. same # should be done for disballanced set ds__.samples = ds__.samples + 0.5 * N.random.normal(size=(ds__.samples.shape)) # # disballanced set # lets overpopulate label 0 times = 10 ds_ = ds.selectSamples(range(ds.nsamples) + range(ds.nsamples/2) * times) ds_.samples = ds_.samples + 0.7 * N.random.normal(size=(ds_.samples.shape)) spl = ds_.samplesperlabel #print ds_.labels, ds_.chunks cve = CrossValidatedTransferError(TransferError(clf), NFoldSplitter(), enable_states='confusion') e = cve(ds__) if cfg.getboolean('tests', 'labile', default='yes'): # without disballance we should already have some hits self.failUnless(cve.confusion.stats["P'"][1] > 0) e = cve(ds_) if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(cve.confusion.stats["P'"][1] < 5, msg="With disballance we should have almost no " "hits. Got %f" % cve.confusion.stats["P'"][1]) #print "D:", cve.confusion.stats["P'"][1], cve.confusion.stats['MCC'][1] # Set '1 C per label' # recreate cvte since previous might have operated on copies cve = CrossValidatedTransferError(TransferError(clf), NFoldSplitter(), enable_states='confusion') oldC = clf.C ratio = N.sqrt(float(spl[0])/spl[1]) clf.C = (-1/ratio, -1*ratio) try: e_ = cve(ds_) # reassign C clf.C = oldC except: clf.C = oldC raise #print "B:", cve.confusion.stats["P'"][1], cve.confusion.stats['MCC'][1] if cfg.getboolean('tests', 'labile', default='yes'): # Finally test if we get any 'hit' for minor category. In the # classifier, which has way to 'ballance' should be non-0 self.failUnless(cve.confusion.stats["P'"][1] > 0) def testSillyness(self): """Test if we raise exceptions on incorrect specifications """ if externals.exists('libsvm') or externals.exists('shogun'): self.failUnlessRaises(TypeError, SVM, C=1.0, nu=2.3) if externals.exists('libsvm'): self.failUnlessRaises(TypeError, libsvm.SVM, C=1.0, nu=2.3) self.failUnlessRaises(TypeError, LinearNuSVMC, C=2.3) self.failUnlessRaises(TypeError, LinearCSVMC, nu=2.3) if externals.exists('shogun'): self.failUnlessRaises(TypeError, sg.SVM, C=10, kernel_type='RBF', coef0=3) def suite(): return unittest.makeSuite(SVMTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_transerror.py000066400000000000000000000761711174541445200210010ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA classifier cross-validation""" import unittest from mvpa.support.copy import copy from mvpa.base import externals from mvpa.datasets import Dataset from mvpa.datasets.splitters import OddEvenSplitter from mvpa.clfs.meta import MulticlassClassifier from mvpa.clfs.transerror import \ TransferError, ConfusionMatrix, ConfusionBasedError from mvpa.algorithms.cvtranserror import CrossValidatedTransferError from mvpa.clfs.stats import MCNullDist from mvpa.misc.exceptions import UnknownStateError from tests_warehouse import datasets, sweepargs from tests_warehouse_clfs import * class ErrorsTests(unittest.TestCase): def testConfusionMatrix(self): data = N.array([1,2,1,2,2,2,3,2,1], ndmin=2).T reg = [1,1,1,2,2,2,3,3,3] regl = [1,2,1,2,2,2,3,2,1] correct_cm = [[2,0,1],[1,3,1],[0,0,1]] # Check if we are ok with any input type - either list, or N.array, or tuple for t in [reg, tuple(reg), list(reg), N.array(reg)]: for p in [regl, tuple(regl), list(regl), N.array(regl)]: cm = ConfusionMatrix(targets=t, predictions=p) # check table content self.failUnless((cm.matrix == correct_cm).all()) # Do a bit more thorough checking cm = ConfusionMatrix() self.failUnlessRaises(ZeroDivisionError, lambda x:x.percentCorrect, cm) """No samples -- raise exception""" cm.add(reg, regl) self.failUnlessEqual(len(cm.sets), 1, msg="Should have a single set so far") self.failUnlessEqual(cm.matrix.shape, (3,3), msg="should be square matrix (len(reglabels) x len(reglabels)") self.failUnlessRaises(ValueError, cm.add, reg, N.array([1])) """ConfusionMatrix must complaint if number of samples different""" # check table content self.failUnless((cm.matrix == correct_cm).all()) # lets add with new labels (not yet known) cm.add(reg, N.array([1,4,1,2,2,2,4,2,1])) self.failUnlessEqual(cm.labels, [1,2,3,4], msg="We should have gotten 4th label") matrices = cm.matrices # separate CM per each given set self.failUnlessEqual(len(matrices), 2, msg="Have gotten two splits") self.failUnless((matrices[0].matrix + matrices[1].matrix == cm.matrix).all(), msg="Total votes should match the sum across split CMs") # check pretty print # just a silly test to make sure that printing works self.failUnless(len(cm.asstring( header=True, summary=True, description=True))>100) self.failUnless(len(str(cm))>100) # and that it knows some parameters for printing self.failUnless(len(cm.asstring(summary=True, header=False))>100) # lets check iadd -- just itself to itself cm += cm self.failUnlessEqual(len(cm.matrices), 4, msg="Must be 4 sets now") # lets check add -- just itself to itself cm2 = cm + cm self.failUnlessEqual(len(cm2.matrices), 8, msg="Must be 8 sets now") self.failUnlessEqual(cm2.percentCorrect, cm.percentCorrect, msg="Percent of corrrect should remain the same ;-)") self.failUnlessEqual(cm2.error, 1.0-cm.percentCorrect/100.0, msg="Test if we get proper error value") def testDegenerateConfusion(self): # We must not just puke -- some testing splits might # have just a single target label for orig in ([1], [1, 1], [0], [0, 0]): cm = ConfusionMatrix(targets=orig, predictions=orig, values=orig) scm = str(cm) self.failUnless(cm.stats['ACC%'] == 100) def testConfusionMatrixACC(self): reg = [0,0,1,1] regl = [1,0,1,0] cm = ConfusionMatrix(targets=reg, predictions=regl) self.failUnless('ACC% 50' in str(cm)) def testConfusionMatrixWithMappings(self): data = N.array([1,2,1,2,2,2,3,2,1], ndmin=2).T reg = [1,1,1,2,2,2,3,3,3] regl = [1,2,1,2,2,2,3,2,1] correct_cm = [[2,0,1], [1,3,1], [0,0,1]] lm = {'apple':1, 'orange':2, 'shitty apple':1, 'candy':3} cm = ConfusionMatrix(targets=reg, predictions=regl, labels_map=lm) # check table content self.failUnless((cm.matrix == correct_cm).all()) # assure that all labels are somewhere listed ;-) s = str(cm) for l in lm.keys(): self.failUnless(l in s) @sweepargs(l_clf=clfswh['linear', 'svm']) def testConfusionBasedError(self, l_clf): train = datasets['uni2medium_train'] # to check if we fail to classify for 3 labels test3 = datasets['uni3medium_train'] err = ConfusionBasedError(clf=l_clf) terr = TransferError(clf=l_clf) self.failUnlessRaises(UnknownStateError, err, None) """Shouldn't be able to access the state yet""" l_clf.train(train) e, te = err(None), terr(train) self.failUnless(abs(e-te) < 1e-10, msg="ConfusionBasedError (%.2g) should be equal to TransferError " "(%.2g) on traindataset" % (e, te)) # this will print nasty WARNING but it is ok -- it is just checking code # NB warnings are not printed while doing whole testing self.failIf(terr(test3) is None) # try copying the beast terr_copy = copy(terr) @sweepargs(l_clf=clfswh['linear', 'svm']) def testNullDistProb(self, l_clf): train = datasets['uni2medium'] num_perm = 10 # define class to estimate NULL distribution of errors # use left tail of the distribution since we use MeanMatchFx as error # function and lower is better terr = TransferError( clf=l_clf, null_dist=MCNullDist(permutations=num_perm, tail='left')) # check reasonable error range err = terr(train, train) self.failUnless(err < 0.4) # Lets do the same for CVTE cvte = CrossValidatedTransferError( TransferError(clf=l_clf), OddEvenSplitter(), null_dist=MCNullDist(permutations=num_perm, tail='left', enable_states=['dist_samples'])) cv_err = cvte(train) # check that the result is highly significant since we know that the # data has signal null_prob = terr.states.null_prob if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(null_prob <= 0.1, msg="Failed to check that the result is highly significant " "(got %f) since we know that the data has signal" % null_prob) self.failUnless(cvte.states.null_prob <= 0.1, msg="Failed to check that the result is highly significant " "(got p(cvte)=%f) since we know that the data has signal" % cvte.states.null_prob) # and we should be able to access the actual samples of the distribution self.failUnlessEqual(len(cvte.null_dist.states.dist_samples), num_perm) @sweepargs(l_clf=clfswh['linear', 'svm']) def testPerSampleError(self, l_clf): train = datasets['uni2medium'] terr = TransferError(clf=l_clf, enable_states=['samples_error']) err = terr(train, train) se = terr.samples_error # one error per sample self.failUnless(len(se) == train.nsamples) # for this simple test it can only be correct or misclassified # (boolean) self.failUnless( N.sum(N.array(se.values(), dtype='float') \ - N.array(se.values(), dtype='b')) == 0) @sweepargs(clf=clfswh['multiclass']) def testAUC(self, clf): """Test AUC computation """ if isinstance(clf, MulticlassClassifier): # TODO: handle those values correctly return clf.states._changeTemporarily(enable_states = ['values']) # uni2 dataset with reordered labels ds2 = datasets['uni2small'].copy() ds2.labels = 1 - ds2.labels # revert labels # same with uni3 ds3 = datasets['uni3small'].copy() ul = ds3.uniquelabels nl = ds3.labels.copy() for l in xrange(3): nl[ds3.labels == ul[l]] = ul[(l+1)%3] ds3.labels = nl for ds in [datasets['uni2small'], ds2, datasets['uni3small'], ds3]: cv = CrossValidatedTransferError( TransferError(clf), OddEvenSplitter(), enable_states=['confusion', 'training_confusion']) cverror = cv(ds) stats = cv.confusion.stats Nlabels = len(ds.uniquelabels) # so we at least do slightly above chance self.failUnless(stats['ACC'] > 1.2 / Nlabels) auc = stats['AUC'] if (Nlabels == 2) or (Nlabels > 2 and auc[0] is not N.nan): mauc = N.min(stats['AUC']) if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(mauc > 0.55, msg='All AUCs must be above chance. Got minimal ' 'AUC=%.2g among %s' % (mauc, stats['AUC'])) clf.states._resetEnabledTemporarily() def testConfusionPlot(self): """Based on existing cell dataset results. Let in for possible future testing, but is not a part of the unittests suite """ #from matplotlib import rc as rcmpl #rcmpl('font',**{'family':'sans-serif','sans-serif':['DejaVu Sans']}) ##rcmpl('text', usetex=True) ##rcmpl('font', family='sans', style='normal', variant='normal', ## weight='bold', stretch='normal', size='large') #import numpy as N #from mvpa.clfs.transerror import \ # TransferError, ConfusionMatrix, ConfusionBasedError array = N.array uint8 = N.uint8 sets = [ (array([47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44], dtype=uint8), array([40, 39, 47, 43, 45, 41, 44, 41, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 38, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 46, 45, 38, 44, 39, 46, 38, 39, 39, 38, 43, 45, 41, 44, 40, 46, 42, 38, 40, 47, 43, 45, 41, 44, 40, 46, 42, 38, 39, 40, 43, 45, 41, 44, 39, 46, 42, 47, 38, 38, 43, 45, 41, 44, 38, 46, 42, 47, 38, 39, 43, 45, 41, 44, 40, 46, 42, 47, 38, 38, 43, 45, 41, 44, 40, 46, 42, 47, 38, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 47, 43, 45, 41, 44, 40, 46, 42, 47, 38, 38, 43, 45, 41, 44, 40, 46, 42, 39, 39, 38, 43, 45, 41, 44, 47, 46, 42, 47, 38, 39, 43, 45, 40, 44, 40, 46, 42, 47, 39, 40, 43, 45, 41, 44, 38, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 41, 47, 39, 38, 46, 45, 41, 44, 40, 46, 42, 40, 38, 38, 43, 45, 41, 44, 40, 45, 42, 47, 39, 39, 43, 45, 41, 44, 38, 46, 42, 47, 38, 42, 43, 45, 41, 44, 39, 46, 42, 39, 39, 39, 47, 45, 41, 44], dtype=uint8)), (array([40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43], dtype=uint8), array([40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 47, 46, 42, 47, 39, 40, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 39, 46, 42, 47, 47, 47, 43, 45, 41, 44, 40, 46, 42, 43, 39, 38, 43, 45, 41, 44, 38, 38, 42, 38, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 40, 38, 43, 45, 41, 44, 40, 40, 42, 47, 40, 40, 43, 45, 41, 44, 38, 38, 42, 47, 38, 38, 47, 45, 41, 44, 40, 46, 42, 47, 39, 40, 43, 45, 41, 44, 40, 46, 42, 47, 47, 39, 43, 45, 41, 44, 40, 46, 42, 39, 39, 42, 43, 45, 41, 44, 40, 46, 42, 47, 39, 39, 43, 45, 41, 44, 47, 46, 42, 40, 39, 39, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 40, 44, 40, 46, 42, 47, 39, 39, 43, 45, 41, 44, 38, 46, 42, 47, 39, 39, 43, 45, 41, 44, 40, 46, 46, 47, 38, 39, 43, 45, 41, 44, 40, 46, 42, 47, 38, 39, 43, 45, 41, 44, 40, 46, 42, 39, 39, 38, 47, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43], dtype=uint8)), (array([45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47], dtype=uint8), array([45, 41, 44, 40, 46, 42, 47, 39, 46, 43, 45, 41, 44, 40, 46, 42, 47, 39, 39, 43, 45, 41, 44, 38, 46, 42, 47, 38, 39, 43, 45, 41, 44, 40, 46, 42, 47, 38, 39, 43, 45, 41, 44, 40, 46, 42, 47, 39, 43, 43, 45, 40, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 47, 40, 43, 45, 41, 44, 40, 47, 42, 38, 47, 38, 43, 45, 41, 44, 40, 40, 42, 47, 39, 39, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 38, 46, 42, 47, 39, 39, 43, 45, 41, 44, 40, 46, 42, 47, 40, 38, 43, 45, 41, 44, 40, 46, 38, 38, 39, 38, 43, 45, 41, 44, 39, 46, 42, 47, 40, 39, 43, 45, 38, 44, 38, 46, 42, 47, 47, 40, 43, 45, 41, 44, 40, 40, 42, 47, 40, 38, 43, 39, 41, 44, 41, 46, 42, 39, 39, 38, 38, 45, 41, 44, 38, 46, 40, 46, 46, 46, 43, 45, 38, 44, 40, 46, 42, 39, 39, 45, 43, 45, 41, 44, 38, 46, 42, 38, 39, 39, 43, 45, 41, 38, 40, 46, 42, 47, 38, 39, 43, 45, 41, 44, 40, 46, 42, 40], dtype=uint8)), (array([39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40], dtype=uint8), array([39, 38, 43, 45, 41, 44, 40, 46, 38, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 41, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 38, 43, 47, 38, 38, 43, 45, 41, 44, 39, 46, 42, 39, 39, 38, 43, 45, 41, 44, 43, 46, 42, 47, 39, 39, 43, 45, 41, 44, 40, 46, 42, 47, 39, 40, 43, 45, 41, 44, 40, 46, 42, 39, 38, 38, 43, 45, 40, 44, 47, 46, 38, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 38, 39, 38, 43, 45, 41, 44, 40, 46, 42, 38, 39, 38, 43, 45, 47, 44, 45, 46, 42, 38, 39, 41, 43, 45, 41, 44, 38, 38, 42, 39, 40, 40, 43, 45, 41, 39, 40, 46, 42, 47, 39, 40, 43, 45, 41, 44, 40, 47, 42, 47, 38, 38, 43, 45, 41, 44, 47, 46, 42, 47, 40, 47, 43, 45, 41, 44, 40, 46, 42, 47, 38, 39, 43, 45, 41, 44, 40, 46, 42, 39, 38, 43, 45, 46, 44, 38, 46, 42, 47, 38, 44, 43, 45, 42, 44, 41, 46, 42, 47, 47, 38, 43, 45, 41, 44, 38, 46, 42, 39, 39, 38, 43, 45, 41, 44, 40], dtype=uint8)), (array([46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45], dtype=uint8), array([46, 42, 39, 38, 38, 43, 45, 41, 44, 40, 46, 42, 47, 47, 42, 43, 45, 42, 44, 40, 46, 42, 38, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 47, 40, 43, 45, 41, 44, 41, 46, 42, 38, 39, 38, 43, 45, 41, 44, 38, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 46, 38, 38, 43, 45, 41, 44, 39, 46, 42, 47, 39, 40, 43, 45, 41, 44, 40, 46, 42, 47, 39, 39, 43, 45, 41, 44, 40, 47, 42, 47, 38, 39, 43, 45, 41, 44, 39, 46, 42, 47, 39, 46, 43, 45, 41, 44, 39, 46, 42, 39, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 38, 38, 43, 45, 41, 44, 40, 46, 42, 39, 39, 38, 43, 45, 41, 44, 40, 38, 42, 46, 39, 38, 43, 45, 41, 44, 38, 46, 42, 46, 46, 38, 43, 45, 41, 44, 40, 46, 42, 47, 47, 38, 38, 45, 41, 44, 38, 38, 42, 43, 39, 40, 43, 45, 41, 44, 38, 46, 42, 47, 38, 39, 47, 45, 46, 44, 40, 46, 42, 47, 40, 38, 43, 45, 41, 44, 40, 46, 42, 47, 40, 38, 43, 45, 41, 44, 38, 46, 42, 38, 39, 38, 47, 45], dtype=uint8)), (array([41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39], dtype=uint8), array([41, 44, 38, 46, 42, 47, 39, 47, 40, 45, 41, 44, 40, 46, 42, 38, 40, 38, 43, 45, 41, 44, 40, 46, 42, 38, 38, 38, 43, 45, 41, 44, 46, 38, 42, 40, 38, 39, 43, 45, 41, 44, 41, 46, 42, 47, 47, 38, 43, 45, 41, 44, 40, 46, 42, 38, 39, 39, 43, 45, 41, 44, 38, 46, 42, 47, 43, 39, 43, 45, 41, 44, 40, 46, 42, 38, 39, 38, 43, 45, 41, 44, 40, 46, 42, 40, 39, 38, 43, 45, 41, 44, 38, 46, 42, 39, 39, 39, 43, 45, 41, 44, 40, 46, 42, 39, 38, 47, 43, 45, 38, 44, 40, 38, 42, 47, 38, 38, 43, 45, 41, 44, 40, 38, 46, 47, 38, 38, 43, 45, 41, 44, 41, 46, 42, 40, 38, 38, 40, 45, 41, 44, 40, 40, 42, 43, 38, 40, 43, 39, 41, 44, 40, 40, 42, 47, 38, 46, 43, 45, 41, 44, 47, 41, 42, 43, 40, 47, 43, 45, 41, 44, 41, 38, 42, 40, 39, 40, 43, 45, 41, 44, 39, 43, 42, 47, 39, 40, 43, 45, 41, 44, 42, 46, 42, 47, 40, 46, 43, 45, 41, 44, 38, 46, 42, 47, 47, 38, 43, 45, 41, 44, 40, 38, 39, 47, 38], dtype=uint8)), (array([38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46], dtype=uint8), array([39, 43, 45, 41, 44, 40, 46, 42, 47, 38, 38, 43, 45, 41, 44, 41, 46, 42, 47, 47, 39, 43, 45, 41, 44, 40, 46, 42, 47, 38, 39, 43, 45, 41, 44, 40, 46, 42, 47, 39, 40, 43, 45, 41, 44, 40, 46, 42, 47, 45, 38, 43, 45, 41, 44, 38, 46, 42, 47, 38, 39, 43, 45, 41, 44, 40, 46, 42, 39, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 40, 39, 43, 45, 41, 44, 40, 39, 42, 40, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 38, 46, 42, 39, 39, 47, 43, 45, 41, 44, 40, 46, 42, 47, 39, 39, 43, 45, 41, 44, 40, 46, 42, 46, 47, 39, 47, 45, 41, 44, 40, 46, 42, 47, 39, 39, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 38, 46, 42, 47, 39, 38, 43, 45, 42, 44, 39, 47, 42, 39, 39, 47, 43, 47, 40, 44, 40, 46, 42, 39, 39, 38, 39, 45, 41, 44, 40, 46, 42, 47, 38, 38, 43, 45, 41, 44, 46, 38, 42, 47, 39, 43, 43, 45, 41, 44, 40, 46], dtype=uint8)), (array([42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45, 41, 44, 40, 46, 42, 47, 39, 38, 43, 45], dtype=uint8), array([42, 38, 38, 40, 43, 45, 41, 44, 39, 46, 42, 47, 39, 38, 43, 45, 41, 44, 39, 38, 42, 47, 41, 40, 43, 45, 41, 44, 40, 41, 42, 47, 38, 46, 43, 45, 41, 44, 41, 41, 42, 40, 39, 39, 43, 45, 41, 44, 46, 45, 42, 39, 39, 40, 43, 45, 41, 44, 40, 46, 42, 40, 44, 38, 43, 41, 41, 44, 39, 46, 42, 39, 39, 39, 43, 45, 41, 44, 40, 43, 42, 47, 39, 39, 43, 45, 41, 44, 40, 47, 42, 38, 46, 39, 47, 45, 41, 44, 39, 46, 42, 47, 41, 38, 43, 45, 41, 44, 42, 46, 42, 46, 39, 38, 43, 45, 41, 44, 41, 46, 42, 46, 39, 38, 43, 45, 41, 44, 40, 46, 42, 38, 38, 38, 43, 45, 41, 44, 38, 46, 42, 39, 40, 43, 43, 45, 41, 44, 39, 38, 40, 40, 38, 38, 43, 45, 41, 44, 41, 40, 42, 39, 39, 39, 43, 45, 41, 44, 40, 46, 42, 47, 40, 40, 43, 45, 41, 44, 40, 46, 42, 41, 39, 39, 43, 45, 41, 44, 40, 38, 42, 40, 39, 46, 43, 45, 41, 44, 47, 46, 42, 47, 39, 38, 43, 45, 41, 44, 41, 46, 42, 43, 39, 39, 43, 45], dtype=uint8))] labels_map = {'12kHz': 40, '20kHz': 41, '30kHz': 42, '3kHz': 38, '7kHz': 39, 'song1': 43, 'song2': 44, 'song3': 45, 'song4': 46, 'song5': 47} try: cm = ConfusionMatrix(sets=sets, labels_map=labels_map) except: self.fail() self.failUnless('3kHz / 38' in cm.asstring()) if externals.exists("pylab plottable"): import pylab as P P.figure() labels_order = ("3kHz", "7kHz", "12kHz", "20kHz","30kHz", None, "song1","song2","song3","song4","song5") #print cm #fig, im, cb = cm.plot(origin='lower', labels=labels_order) fig, im, cb = cm.plot(labels=labels_order[1:2] + labels_order[:1] + labels_order[2:], numbers=True) self.failUnless(cm._plotted_confusionmatrix[0,0] == cm.matrix[1,1]) self.failUnless(cm._plotted_confusionmatrix[0,1] == cm.matrix[1,0]) self.failUnless(cm._plotted_confusionmatrix[1,1] == cm.matrix[0,0]) self.failUnless(cm._plotted_confusionmatrix[1,0] == cm.matrix[0,1]) P.close(fig) fig, im, cb = cm.plot(labels=labels_order, numbers=True) P.close(fig) # P.show() def testConfusionPlot2(self): """Based on a sample confusion which plots incorrectly """ array = N.array uint8 = N.uint8 sets = [(array([1, 2]), array([1, 1]), array([[ 0.54343765, 0.45656235], [ 0.92395853, 0.07604147]])), (array([1, 2]), array([1, 1]), array([[ 0.98030832, 0.01969168], [ 0.78998763, 0.21001237]])), (array([1, 2]), array([1, 1]), array([[ 0.86125263, 0.13874737], [ 0.83674113, 0.16325887]])), (array([1, 2]), array([1, 1]), array([[ 0.57870383, 0.42129617], [ 0.59702509, 0.40297491]])), (array([1, 2]), array([1, 1]), array([[ 0.89530255, 0.10469745], [ 0.69373919, 0.30626081]])), (array([1, 2]), array([1, 1]), array([[ 0.75015218, 0.24984782], [ 0.9339767 , 0.0660233 ]])), (array([1, 2]), array([1, 2]), array([[ 0.97826616, 0.02173384], [ 0.38620638, 0.61379362]])), (array([2]), array([2]), array([[ 0.46893776, 0.53106224]]))] try: cm = ConfusionMatrix(sets=sets) except: self.fail() if externals.exists("pylab plottable"): import pylab as P #P.figure() #print cm fig, im, cb = cm.plot(origin='lower', numbers=True) #P.plot() self.failUnless((cm._plotted_confusionmatrix == cm.matrix).all()) P.close(fig) #fig, im, cb = cm.plot(labels=labels_order, numbers=True) #P.close(fig) #P.show() def suite(): return unittest.makeSuite(ErrorsTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_transformers.py000066400000000000000000000112171174541445200213130ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA transformers.""" import unittest import numpy as N from mvpa.base import externals from mvpa.misc.transformers import Absolute, OneMinus, RankOrder, \ ReverseRankOrder, L1Normed, L2Normed, OverAxis, \ DistPValue, FirstAxisSumNotZero from tests_warehouse import sweepargs, datasets from mvpa.base import cfg class TransformerTests(unittest.TestCase): def setUp(self): self.d1 = N.array([ 1, 0, -1, -2, -3]) self.d2 = N.array([ 2.3, 0, -1, 2, -30, 1]) def testAbsolute(self): # generate 100 values (gaussian noise mean -1000 -> all negative) out = Absolute(N.random.normal(-1000, size=100)) self.failUnless(out.min() >= 0) self.failUnless(len(out) == 100) def testAbsolute2(self): target = self.d1 out = OneMinus(N.arange(5)) self.failUnless((out == target).all()) def testFirstAxisSumNotZero(self): src = [[ 1, -22.9, 6.8, 0], [ -.8, 7, 0, 0.0], [88, 0, 0.0, 0], [0, 0, 0, 0.0]] target = N.array([ 3, 2, 1, 0]) out = FirstAxisSumNotZero(src) self.failUnless((out == target).all()) def testRankOrder(self): nelements = len(self.d2) out = RankOrder(self.d2) outr = ReverseRankOrder(self.d2) uout = N.unique(out) uoutr = N.unique(outr) self.failUnless((uout == N.arange(nelements)).all(), msg="We should get all indexes. Got just %s" % uout) self.failUnless((uoutr == N.arange(nelements)).all(), msg="We should get all indexes. Got just %s" % uoutr) self.failUnless((out+outr+1 == nelements).all()) self.failUnless((out == [ 0, 3, 4, 1, 5, 2]).all()) def testL2Norm(self): out = L2Normed(self.d2) self.failUnless(N.abs(N.sum(out*out)-1.0) < 1e-10) def testL1Norm(self): out = L1Normed(self.d2) self.failUnless(N.abs(N.sum(N.abs(out))-1.0) < 1e-10) def testOverAxis(self): data = datasets['uni4large'].samples[:120,0].reshape((2,3,4,5)) # Simple transformer/combiner which collapses across given # dimension, e.g. sum for axis in [None, 0, 1, 2]: oversum = OverAxis(N.sum, axis=axis)(data) sum_ = N.sum(data, axis=axis) self.failUnless(N.all(sum_ == oversum)) # Transformer which doesn't modify dimensionality of the data data = data.reshape((6, -1)) overnorm = OverAxis(L2Normed, axis=1)(data) self.failUnless(N.linalg.norm(overnorm)!=1.0) for d in overnorm: self.failUnless(N.abs(N.linalg.norm(d) - 1.0)<0.00001) overnorm = OverAxis(L2Normed, axis=0)(data) self.failUnless(N.linalg.norm(overnorm)!=1.0) for d in overnorm.T: self.failUnless(N.abs(N.linalg.norm(d) - 1.0)<0.00001) def testDistPValue(self): """Basic testing of DistPValue""" if not externals.exists('scipy'): return ndb = 200 ndu = 20 nperd = 2 pthr = 0.05 Nbins = 400 # Lets generate already normed data (on sphere) and add some nonbogus features datau = (N.random.normal(size=(nperd, ndb))) dist = N.sqrt((datau * datau).sum(axis=1)) datas = (datau.T / dist.T).T tn = datax = datas[0, :] dataxmax = N.max(N.abs(datax)) # now lets add true positive features tp = [-dataxmax * 1.1] * (ndu/2) + [dataxmax * 1.1] * (ndu/2) x = N.hstack((datax, tp)) # lets add just pure normal to it x = N.vstack((x, N.random.normal(size=x.shape))).T for distPValue in (DistPValue(), DistPValue(fpp=0.05)): result = distPValue(x) self.failUnless((result>=0).all) self.failUnless((result<=1).all) if cfg.getboolean('tests', 'labile', default='yes'): self.failUnless(distPValue.positives_recovered[0] > 10) self.failUnless((N.array(distPValue.positives_recovered) + N.array(distPValue.nulldist_number) == ndb + ndu).all()) self.failUnless(distPValue.positives_recovered[1] == 0) def suite(): return unittest.makeSuite(TransformerTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_verbosity.py000066400000000000000000000133531174541445200206170ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA verbose and debug output""" import unittest, re from StringIO import StringIO from mvpa.base.verbosity import OnceLogger from mvpa.base import verbose, error if __debug__: from mvpa.base import debug debug.register('1', 'id 1') # needed for testing debug.register('2', 'id 2') ## XXX There must be smth analogous in python... don't know it yet # And it is StringIO #class StringStream(object): # def __init__(self): # self.__str = "" # # def __repr__(self): # return self.__str # # def write(self, s): # self.__str += s # # def clean(self): # self.__str = "" # class VerboseOutputTest(unittest.TestCase): def setUp(self): self.msg = "Test level 2" # output stream self.sout = StringIO() self.once = OnceLogger(handlers=[self.sout]) # set verbose to 4th level self.__oldverbosehandlers = verbose.handlers verbose.handlers = [] # so debug doesn't spoil it verbose.level = 4 if __debug__: self.__olddebughandlers = debug.handlers self.__olddebugactive = debug.active debug.active = ['1', '2', 'SLC'] debug.handlers = [self.sout] debug.offsetbydepth = False verbose.handlers = [self.sout] def tearDown(self): if __debug__: debug.active = self.__olddebugactive debug.handlers = self.__olddebughandlers debug.offsetbydepth = True verbose.handlers = self.__oldverbosehandlers self.sout.close() def testVerboseAbove(self): """Test if it doesn't output at higher levels""" verbose(5, self.msg) self.failUnlessEqual(self.sout.getvalue(), "") def testVerboseBelow(self): """Test if outputs at lower levels and indents by default with spaces """ verbose(2, self.msg) self.failUnlessEqual(self.sout.getvalue(), " %s\n" % self.msg) def testVerboseIndent(self): """Test indent symbol """ verbose.indent = "." verbose(2, self.msg) self.failUnlessEqual(self.sout.getvalue(), "..%s\n" % self.msg) verbose.indent = " " # restore def testVerboseNegative(self): """Test if chokes on negative level""" self.failUnlessRaises( ValueError, verbose._setLevel, -10 ) def testNoLF(self): """Test if it works fine with no newline (LF) symbol""" verbose(2, self.msg, lf=False) verbose(2, " continue ", lf=False) verbose(2, "end") verbose(0, "new %s" % self.msg) self.failUnlessEqual(self.sout.getvalue(), " %s continue end\nnew %s\n" % \ (self.msg, self.msg)) def testCR(self): """Test if works fine with carriage return (cr) symbol""" verbose(2, self.msg, cr=True) verbose(2, "rewrite", cr=True) verbose(1, "rewrite 2", cr=True) verbose(1, " add", cr=False, lf=False) verbose(1, " finish") target = '\r %s\r \rrewrite' % self.msg + \ '\r \rrewrite 2 add finish\n' self.failUnlessEqual(self.sout.getvalue(), target) def testOnceLogger(self): """Test once logger""" self.once("X", self.msg) self.once("X", self.msg) self.failUnlessEqual(self.sout.getvalue(), self.msg+"\n") self.once("Y", "XXX", 2) self.once("Y", "XXX", 2) self.once("Y", "XXX", 2) self.failUnlessEqual(self.sout.getvalue(), self.msg+"\nXXX\nXXX\n") def testError(self): """Test error message""" error(self.msg, critical=False) # should not exit self.failUnless(self.sout.getvalue().startswith("ERROR")) if __debug__: def testDebug(self): verbose.handlers = [] # so debug doesn't spoil it debug.active = ['1', '2', 'SLC'] # do not offset for this test debug('SLC', self.msg, lf=False) self.failUnlessRaises(ValueError, debug, 3, 'bugga') #Should complain about unknown debug id svalue = self.sout.getvalue() regexp = "\[SLC\] DBG(?:{.*})?: %s" % self.msg rematch = re.match(regexp, svalue) self.failUnless(rematch, msg="Cannot match %s with regexp %s" % (svalue, regexp)) def testDebugRgexp(self): verbose.handlers = [] # so debug doesn't spoil it debug.active = ['.*'] # we should have enabled all of them self.failUnlessEqual(set(debug.active), set(debug.registered.keys())) debug.active = ['S.*', 'CLF'] self.failUnlessEqual(set(debug.active), set(filter(lambda x:x.startswith('S'), debug.registered.keys())+['CLF'])) debug.active = ['SG', 'CLF'] self.failUnlessEqual(set(debug.active), set(['SG', 'CLF']), msg="debug should do full line matching") debug.offsetbydepth = True # TODO: More tests needed for debug output testing def suite(): return unittest.makeSuite(VerboseOutputTest) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_waveletmapper.py000066400000000000000000000127611174541445200214470ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA Wavelet mappers""" from mvpa.base import externals externals.exists('pywt', raiseException=True) import unittest from mvpa.support.copy import deepcopy import numpy as N from mvpa.mappers.boxcar import BoxcarMapper from mvpa.mappers.wavelet import * from mvpa.datasets import Dataset from tests_warehouse import datasets class WaveletMappersTests(unittest.TestCase): def testSimpleWDM(self): """ """ ds = datasets['uni2medium'] d2d = ds.samples ws = 15 # size of timeline for wavelet sp = N.arange(ds.nsamples-ws*2) + ws # create 3D instance (samples x timepoints x channels) bcm = BoxcarMapper(sp, ws) d3d = bcm(d2d) # use wavelet mapper wdm = WaveletTransformationMapper() d3d_wd = wdm(d3d) d3d_swap = d3d.swapaxes(1,2) self.failUnlessRaises(ValueError, WaveletTransformationMapper, wavelet='bogus') self.failUnlessRaises(ValueError, WaveletTransformationMapper, mode='bogus') # use wavelet mapper for wdm, wdm_swap in ((WaveletTransformationMapper(), WaveletTransformationMapper(dim=2)), (WaveletPacketMapper(), WaveletPacketMapper(dim=2))): for dd, dd_swap in ((d3d, d3d_swap), (d2d, None)): dd_wd = wdm(dd) if dd_swap is not None: dd_wd_swap = wdm_swap(dd_swap) self.failUnless((dd_wd == dd_wd_swap.swapaxes(1,2)).all(), msg="We should have got same result with swapped " "dimensions and explicit mentioining of it. " "Got %s and %s" % (dd_wd, dd_wd_swap)) # some sanity checks self.failUnless(dd_wd.shape[0] == dd.shape[0]) if not isinstance(wdm, WaveletPacketMapper): # we can do reverse only for DWT dd_rev = wdm.reverse(dd_wd) # inverse transform might be not exactly as the # input... but should be very close ;-) self.failUnlessEqual(dd_rev.shape, dd.shape, msg="Shape should be the same after iDWT") diff = N.linalg.norm(dd - dd_rev) ornorm = N.linalg.norm(dd) self.failUnless(diff/ornorm < 1e-10) def testSimpleWP1Level(self): """ """ ds = datasets['uni2large'] d2d = ds.samples ws = 50 # size of timeline for wavelet sp = (N.arange(ds.nsamples - ws*2) + ws)[:4] # create 3D instance (samples x timepoints x channels) bcm = BoxcarMapper(sp, ws) d3d = bcm(d2d) # use wavelet mapper wdm = WaveletPacketMapper(level=2, wavelet='sym2') d3d_wd = wdm(d3d) # Check dimensionality d3d_wds, d3ds = d3d_wd.shape, d3d.shape self.failUnless(len(d3d_wds) == len(d3ds)+1) self.failUnless(d3d_wds[1] * d3d_wds[2] >= d3ds[1]) self.failUnless(d3d_wds[0] == d3ds[0]) self.failUnless(d3d_wds[-1] == d3ds[-1]) #print d2d.shape, d3d.shape, d3d_wd.shape if externals.exists('pywt wp reconstruct'): # Test reverse -- should be identical # we can do reverse only for DWT d3d_rev = wdm.reverse(d3d_wd) # inverse transform might be not exactly as the # input... but should be very close ;-) self.failUnlessEqual(d3d_rev.shape, d3d.shape, msg="Shape should be the same after iDWT") diff = N.linalg.norm(d3d - d3d_rev) ornorm = N.linalg.norm(d3d) if externals.exists('pywt wp reconstruct fixed'): self.failUnless(diff/ornorm < 1e-10) else: self.failUnlessRaises(NotImplementedError, wdm.reverse, d3d_wd) def _testCompareToOld(self): """Good just to compare if I didn't screw up anything... treat it as a regression test """ import mvpa.mappers.wavelet_ as wavelet_ ds = datasets['uni2medium'] d2d = ds.samples ws = 16 # size of timeline for wavelet sp = N.arange(ds.nsamples-ws*2) + ws # create 3D instance (samples x timepoints x channels) bcm = BoxcarMapper(sp, ws) d3d = bcm(d2d) # use wavelet mapper for wdm, wdm_ in ((WaveletTransformationMapper(), wavelet_.WaveletTransformationMapper()), (WaveletPacketMapper(), wavelet_.WaveletPacketMapper()),): d3d_wd = wdm(d3d) d3d_wd_ = wdm_(d3d) self.failUnless((d3d_wd == d3d_wd_).all(), msg="We should have got same result with old and new code. Got %s and %s" % (d3d_wd, d3d_wd_)) def suite(): return unittest.makeSuite(WaveletMappersTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/test_zscoremapper.py000066400000000000000000000037461174541445200213100ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Unit tests for PyMVPA ZScore mapper""" import unittest from mvpa.base import externals externals.exists('scipy', raiseException=True) from mvpa.support.copy import deepcopy import numpy as N from mvpa.datasets import Dataset from mvpa.mappers.zscore import ZScoreMapper from mvpa.datasets.miscfx import zscore from tests_warehouse import datasets class ZScoreMapperTests(unittest.TestCase): """Test simple ZScoreMapper """ def setUp(self): """Setup sample datasets """ # data: 40 sample feature line in 20d space (40x20; samples x features) self.dss = [ Dataset(samples=N.concatenate( [N.arange(40) for i in range(20)]).reshape(20,-1).T, labels=1, chunks=1), ] + datasets.values() def testCompareToZscore(self): """Test by comparing to results of elderly z-score function """ for ds in self.dss: ds1 = deepcopy(ds) ds2 = deepcopy(ds) zsm = ZScoreMapper() zsm.train(ds1) ds1z = zsm.forward(ds1.samples) zscore(ds2, perchunk=False) self.failUnless(N.linalg.norm(ds1z - ds2.samples) < 1e-12) self.failUnless((ds1.samples == ds.samples).all(), msg="It seems we modified original dataset!") ds0 = zsm.reverse(ds1z) self.failUnless(N.linalg.norm(ds0 - ds.samples) < 1e-12, msg="Can't reconstruct from z-scores") def suite(): return unittest.makeSuite(ZScoreMapperTests) if __name__ == '__main__': import runner pymvpa-0.4.8/mvpa/tests/tests_warehouse.py000066400000000000000000000203131174541445200207500ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Miscelaneous functions/datasets to be used in the unit tests""" __docformat__ = 'restructuredtext' from os import environ import unittest, traceback, sys import numpy as N from mvpa import cfg from mvpa.datasets import Dataset from mvpa.datasets.splitters import OddEvenSplitter from mvpa.datasets.masked import MaskedDataset from mvpa.clfs.base import Classifier from mvpa.misc.state import ClassWithCollections from mvpa.misc.data_generators import * __all__ = [ 'datasets', 'sweepargs', 'N', 'unittest', '_all_states_enabled' ] if __debug__: from mvpa.base import debug __all__.append('debug') _all_states_enabled = 'ENFORCE_STATES_ENABLED' in debug.active else: _all_states_enabled = False def sweepargs(**kwargs): """Decorator function to sweep over a given set of classifiers :Parameters: clfs : list of `Classifier` List of classifiers to run method on Often some unittest method can be ran on multiple classifiers. So this decorator aims to do that """ def unittest_method(method): def do_sweep(*args_, **kwargs_): def untrain_clf(argvalue): """Little helper""" if isinstance(argvalue, Classifier): # clear classifier after its use -- just to be sure ;-) argvalue.retrainable = False argvalue.untrain() failed_tests = {} for argname in kwargs.keys(): for argvalue in kwargs[argname]: if isinstance(argvalue, Classifier): # clear classifier before its use argvalue.untrain() if isinstance(argvalue, ClassWithCollections): argvalue.states.reset() # update kwargs_ kwargs_[argname] = argvalue # do actual call try: if __debug__: debug('TEST', 'Running %s on args=%s and kwargs=%s' % (method.__name__, `args_`, `kwargs_`)) method(*args_, **kwargs_) except AssertionError, e: estr = str(e) etype, value, tb = sys.exc_info() # literal representation of exception tb, so # we could group them later on eidstr = ' '.join( [l for l in traceback.format_exception(etype, value, tb) if not ('do_sweep' in l or 'unittest.py' in l or 'AssertionError' in l or 'Traceback (most' in l)]) # Store exception information for later on groupping if not eidstr in failed_tests: failed_tests[eidstr] = [] failed_tests[eidstr].append( # skip top-most tb in sweep_args (argname, `argvalue`, tb.tb_next, estr)) if __debug__: msg = "%s on %s=%s" % (estr, argname, `argvalue`) debug('TEST', 'Failed unittest: %s\n%s' % (eidstr, msg)) untrain_clf(argvalue) # TODO: handle different levels of unittests properly if cfg.getboolean('tests', 'quick', False): # on TESTQUICK just run test for 1st entry in the list, # the rest are omitted # TODO: proper partitioning of unittests break if len(failed_tests): # Lets now create a single AssertionError exception which would nicely # incorporate all failed exceptions multiple = len(failed_tests) != 1 # is it unique? # if so, we don't need to reinclude traceback since it # would be spitted out anyways below estr = "" cestr = "lead to failures of unittest %s" % method.__name__ if multiple: estr += "\n Different scenarios %s (specific tracebacks are below):" % cestr else: estr += "\n Single scenario %s:" % cestr for ek, els in failed_tests.iteritems(): estr += '\n' if multiple: estr += ek estr += " on\n %s" % (" ".join( ["%s=%s%s\n" % (ea, eav, # Why didn't I just do regular for loop? ;) ":\n ".join([x for x in [' ', es] if x != ''])) for ea, eav, etb, es in els])) etb = els[0][2] # take first one... they all should be identical raise AssertionError(estr), None, etb do_sweep.func_name = method.func_name return do_sweep if len(kwargs) > 1: raise NotImplementedError return unittest_method # Define datasets to be used all over. Split-half later on is used to # split into training/testing # snr_scale = cfg.getAsDType('tests', 'snr scale', float, default=1.0) specs = {'large' : { 'perlabel': 99, 'nchunks': 11, 'nfeatures': 20, 'snr': 8 * snr_scale}, 'medium' :{ 'perlabel': 24, 'nchunks': 6, 'nfeatures': 14, 'snr': 8 * snr_scale}, 'small' : { 'perlabel': 12, 'nchunks': 4, 'nfeatures': 6, 'snr' : 14 * snr_scale} } nonbogus_pool = [0, 1, 3, 5] datasets = {} for kind, spec in specs.iteritems(): # set of univariate datasets for nlabels in [ 2, 3, 4 ]: basename = 'uni%d%s' % (nlabels, kind) nonbogus_features=nonbogus_pool[:nlabels] bogus_features = filter(lambda x:not x in nonbogus_features, range(spec['nfeatures'])) dataset = normalFeatureDataset( nlabels=nlabels, nonbogus_features=nonbogus_features, **spec) dataset.nonbogus_features = nonbogus_features dataset.bogus_features = bogus_features oes = OddEvenSplitter() splits = [(train, test) for (train, test) in oes(dataset)] for i, replication in enumerate( ['test', 'train'] ): dataset_ = splits[0][i] dataset_.nonbogus_features = nonbogus_features dataset_.bogus_features = bogus_features datasets["%s_%s" % (basename, replication)] = dataset_ # full dataset datasets[basename] = dataset # sample 3D total = 2*spec['perlabel'] nchunks = spec['nchunks'] data = N.random.standard_normal(( total, 3, 6, 6 )) labels = N.concatenate( ( N.repeat( 0, spec['perlabel'] ), N.repeat( 1, spec['perlabel'] ) ) ) chunks = N.asarray(range(nchunks)*(total/nchunks)) mask = N.ones( (3, 6, 6) ) mask[0,0,0] = 0 mask[1,3,2] = 0 datasets['3d%s' % kind] = MaskedDataset(samples=data, labels=labels, chunks=chunks, mask=mask) # some additional datasets datasets['dumb2'] = dumbFeatureBinaryDataset() datasets['dumb'] = dumbFeatureDataset() # dataset with few invariant features _dsinv = dumbFeatureDataset() _dsinv.samples = N.hstack((_dsinv.samples, N.zeros((_dsinv.nsamples, 1)), N.ones((_dsinv.nsamples, 1)))) datasets['dumbinv'] = _dsinv # Datasets for regressions testing datasets['sin_modulated'] = multipleChunks(sinModulated, 4, 30, 1) datasets['sin_modulated_test'] = sinModulated(30, 1, flat=True) # simple signal for linear regressors datasets['chirp_linear'] = multipleChunks(chirpLinear, 6, 50, 10, 2, 0.3, 0.1) datasets['chirp_linear_test'] = chirpLinear(20, 5, 2, 0.4, 0.1) datasets['wr1996'] = multipleChunks(wr1996, 4, 50) datasets['wr1996_test'] = wr1996(50) pymvpa-0.4.8/mvpa/tests/tests_warehouse_clfs.py000066400000000000000000000051401174541445200217600ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Provides `clfs` dictionary with instances of all available classifiers.""" __docformat__ = 'restructuredtext' # Some global imports useful through out unittests from mvpa.base import cfg # # first deal with classifiers which do not have external deps # from mvpa.clfs.smlr import SMLR from mvpa.clfs.knn import * from mvpa.clfs.warehouse import clfswh, regrswh from mvpa.base import externals # if have ANY svm implementation if externals.exists('libsvm') or externals.exists('shogun'): from mvpa.clfs.svm import * # # Few silly classifiers # class SameSignClassifier(Classifier): """Dummy classifier which reports +1 class if both features have the same sign, -1 otherwise""" _clf_internals = ['notrain2predict'] def __init__(self, **kwargs): Classifier.__init__(self, **kwargs) def _train(self, data): # we don't need that ;-) pass def _predict(self, data): datalen = len(data) values = [] for d in data: values.append(2*int( (d[0]>=0) == (d[1]>=0) )-1) self.predictions = values self.values = values # just for the sake of having values return values class Less1Classifier(SameSignClassifier): """Dummy classifier which reports +1 class if abs value of max less than 1""" def _predict(self, data): datalen = len(data) values = [] for d in data: values.append(2*int(max(d)<=1)-1) self.predictions = values return values # Sample universal classifiers (linear and non-linear) which should be # used whenever it doesn't matter what classifier it is for testing # some higher level creations -- chosen so it is the fastest universal # one. Also it should not punch state.py in the face how it is # happening with kNN... sample_clf_lin = SMLR(lm=0.1)#sg.svm.LinearCSVMC(svm_impl='libsvm') #if externals.exists('shogun'): # sample_clf_nl = sg.SVM(kernel_type='RBF', svm_impl='libsvm') #else: #classical one which was used for a while #and surprisingly it is not bad at all for the unittests sample_clf_nl = kNN(k=5) # and also a regression-based classifier r = clfswh['linear', 'regression', 'has_sensitivity'] if len(r) > 0: sample_clf_reg = r[0] else: sample_clf_reg = None pymvpa-0.4.8/setup.cfg.win000066400000000000000000000002441174541445200154630ustar00rootroot00000000000000[build_ext] # configure paths for libsvm include-dirs = 3rd/libsvm library-dirs = 3rd/libsvm [build] # configure python to use a free compiler compiler = mingw32 pymvpa-0.4.8/setup.py000077500000000000000000000130731174541445200145670ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Python distutils setup for PyMVPA""" from numpy.distutils.core import setup, Extension import os import sys from glob import glob # some config settings bind_libsvm = 'local' # choices: 'local', 'system', None libsvmc_extra_sources = [] libsvmc_include_dirs = [] libsvmc_libraries = [] extra_link_args = [] libsvmc_library_dirs = [] # platform-specific settings if sys.platform == "darwin": extra_link_args.append("-bundle") if sys.platform.startswith('linux'): # need to look for numpy (header location changes with v1.3) libsvmc_include_dirs += ['/usr/include/numpy'] # when libsvm is forced -- before it was used only in cases # when libsvm was available at system level, hence we switch # from local to system at this point # TODO: Deprecate --with-libsvm for 0.5 for arg in ('--with-libsvm', '--with-system-libsvm'): if not sys.argv.count(arg): continue # clean argv if necessary (or distutils will complain) sys.argv.remove(arg) # assure since default is 'auto' wouldn't fail if it is N/A bind_libsvm = 'system' # when no libsvm bindings are requested explicitly if sys.argv.count('--no-libsvm'): # clean argv if necessary (or distutils will complain) sys.argv.remove('--no-libsvm') bind_libsvm = None # if requested: if bind_libsvm == 'local': # we will provide libsvm sources later on # if libsvm.a is available locally -- use it #if os.path.exists(os.path.join('build', 'libsvm', 'libsvm.a')): libsvm_3rd_path = os.path.join('3rd', 'libsvm') libsvmc_include_dirs += [libsvm_3rd_path] libsvmc_extra_sources = [os.path.join(libsvm_3rd_path, 'svm.cpp')] elif bind_libsvm == 'system': # look for libsvm in some places, when local one is not used libsvmc_libraries += ['svm'] if not sys.platform.startswith('win'): libsvmc_include_dirs += [ '/usr/include/libsvm-3.0/libsvm', '/usr/include/libsvm-2.0/libsvm', '/usr/include/libsvm', '/usr/local/include/libsvm', '/usr/local/include/libsvm-2.0/libsvm', '/usr/local/include'] else: # no clue on windows pass elif bind_libsvm is None: pass else: raise ValueError("Shouldn't happen that we get bind_libsvm=%r" % (bind_libsvm,)) # define the extension modules libsvmc_ext = Extension( 'mvpa.clfs.libsvmc._svmc', sources = libsvmc_extra_sources + ['mvpa/clfs/libsvmc/svmc.i'], include_dirs = libsvmc_include_dirs, library_dirs = libsvmc_library_dirs, libraries = libsvmc_libraries, language = 'c++', extra_link_args = extra_link_args, swig_opts = ['-I' + d for d in libsvmc_include_dirs]) smlrc_ext = Extension( 'mvpa.clfs.libsmlrc.smlrc', sources = [ 'mvpa/clfs/libsmlrc/smlr.c' ], #library_dirs = library_dirs, libraries = ['m'], # extra_compile_args = ['-O0'], extra_link_args = extra_link_args, language = 'c') ext_modules = [smlrc_ext] if bind_libsvm: ext_modules.append(libsvmc_ext) # Notes on the setup # Version scheme is: major.minor.patch # define the setup setup(name = 'pymvpa', version = '0.4.8', author = 'Michael Hanke, Yaroslav Halchenko, Per B. Sederberg', author_email = 'pkg-exppsy-pymvpa@lists.alioth.debian.org', license = 'MIT License', url = 'http://v04.pymvpa.org', description = 'Multivariate pattern analysis', long_description = \ "PyMVPA is a Python module intended to ease pattern classification " \ "analyses of large datasets. It provides high-level abstraction of " \ "typical processing steps and a number of implementations of some " \ "popular algorithms. While it is not limited to neuroimaging data " \ "it is eminently suited for such datasets.\n" \ "PyMVPA is truly free software (in every respect) and " \ "additionally requires nothing but free-software to run.", # please maintain alphanumeric order packages = [ 'mvpa', 'mvpa.algorithms', 'mvpa.atlases', 'mvpa.base', 'mvpa.clfs', 'mvpa.clfs.libsmlrc', 'mvpa.clfs.libsvmc', 'mvpa.clfs.sg', 'mvpa.datasets', 'mvpa.featsel', 'mvpa.mappers', 'mvpa.measures', 'mvpa.misc', 'mvpa.misc.bv', 'mvpa.misc.fsl', 'mvpa.misc.io', 'mvpa.misc.plot', 'mvpa.support', 'mvpa.tests', 'mvpa.tests.badexternals', ], data_files = [('mvpa/data', [f for f in glob(os.path.join('mvpa', 'data', '*')) if os.path.isfile(f)]), ('mvpa/data/bv', [f for f in glob(os.path.join('mvpa', 'data', 'bv', '*')) if os.path.isfile(f)])], scripts = glob(os.path.join('bin', '*')), ext_modules = ext_modules ) pymvpa-0.4.8/tools/000077500000000000000000000000001174541445200142065ustar00rootroot00000000000000pymvpa-0.4.8/tools/Makefile000066400000000000000000000002231174541445200156430ustar00rootroot00000000000000TARGETS=pdfbook TDIR=../build/tools all: $(TARGETS) pdfbook: mkdir -p $(TDIR) gcc pdfbook.c -o $(TDIR)/pdfbook clean:: rm -f $(TDIR)/pdfbook pymvpa-0.4.8/tools/apigen.py000066400000000000000000000362331174541445200160320ustar00rootroot00000000000000"""Attempt to generate templates for module reference with Sphinx XXX - we exclude extension modules To include extension modules, first identify them as valid in the ``_uri2path`` method, then handle them in the ``_parse_module`` script. We get functions and classes by parsing the text of .py files. Alternatively we could import the modules for discovery, and we'd have to do that for extension modules. This would involve changing the ``_parse_module`` method to work via import and introspection, and might involve changing ``discover_modules`` (which determines which files are modules, and therefore which module URIs will be passed to ``_parse_module``). NOTE: this is a modified version of a script originally shipped with the PyMVPA project, which we've adapted for NIPY use. PyMVPA and NIPY are both BSD-licensed projects. """ # Stdlib imports import os import re # Functions and classes class ApiDocWriter(object): ''' Class for automatic detection and parsing of API docs to Sphinx-parsable reST format''' # only separating first two levels rst_section_levels = ['*', '=', '-', '~', '^'] def __init__(self, package_name, rst_extension='.rst', package_skip_patterns=None, module_skip_patterns=None, ): ''' Initialize package for parsing Parameters ---------- package_name : string Name of the top-level package. *package_name* must be the name of an importable package rst_extension : string, optional Extension for reST files, default '.rst' package_skip_patterns : None or sequence of {strings, regexps} Sequence of strings giving URIs of packages to be excluded Operates on the package path, starting at (including) the first dot in the package path, after *package_name* - so, if *package_name* is ``sphinx``, then ``sphinx.util`` will result in ``.util`` being passed for earching by these regexps. If is None, gives default. Default is: ['\.tests$'] module_skip_patterns : None or sequence Sequence of strings giving URIs of modules to be excluded Operates on the module name including preceding URI path, back to the first dot after *package_name*. For example ``sphinx.util.console`` results in the string to search of ``.util.console`` If is None, gives default. Default is: ['\.setup$', '\._'] ''' if package_skip_patterns is None: package_skip_patterns = ['\\.tests$'] if module_skip_patterns is None: module_skip_patterns = ['\\.setup$', '\\._'] self.package_name = package_name self.rst_extension = rst_extension self.package_skip_patterns = package_skip_patterns self.module_skip_patterns = module_skip_patterns def get_package_name(self): return self._package_name def set_package_name(self, package_name): ''' Set package_name >>> docwriter = ApiDocWriter('sphinx') >>> import sphinx >>> docwriter.root_path == sphinx.__path__[0] True >>> docwriter.package_name = 'docutils' >>> import docutils >>> docwriter.root_path == docutils.__path__[0] True ''' # It's also possible to imagine caching the module parsing here self._package_name = package_name self.root_module = __import__(package_name) self.root_path = self.root_module.__path__[0] self.written_modules = None package_name = property(get_package_name, set_package_name, None, 'get/set package_name') def _get_object_name(self, line): ''' Get second token in line >>> docwriter = ApiDocWriter('sphinx') >>> docwriter._get_object_name(" def func(): ") 'func' >>> docwriter._get_object_name(" class Klass(object): ") 'Klass' >>> docwriter._get_object_name(" class Klass: ") 'Klass' ''' name = line.split()[1].split('(')[0].strip() # in case we have classes which are not derived from object # ie. old style classes return name.rstrip(':') def _uri2path(self, uri): ''' Convert uri to absolute filepath Parameters ---------- uri : string URI of python module to return path for Returns ------- path : None or string Returns None if there is no valid path for this URI Otherwise returns absolute file system path for URI Examples -------- >>> docwriter = ApiDocWriter('sphinx') >>> import sphinx >>> modpath = sphinx.__path__[0] >>> res = docwriter._uri2path('sphinx.builder') >>> res == os.path.join(modpath, 'builder.py') True >>> res = docwriter._uri2path('sphinx') >>> res == os.path.join(modpath, '__init__.py') True >>> docwriter._uri2path('sphinx.does_not_exist') ''' if uri == self.package_name: return os.path.join(self.root_path, '__init__.py') path = uri.replace('.', os.path.sep) path = path.replace(self.package_name + os.path.sep, '') path = os.path.join(self.root_path, path) # XXX maybe check for extensions as well? if os.path.exists(path + '.py'): # file path += '.py' elif os.path.exists(os.path.join(path, '__init__.py')): path = os.path.join(path, '__init__.py') else: return None return path def _path2uri(self, dirpath): ''' Convert directory path to uri ''' relpath = dirpath.replace(self.root_path, self.package_name) if relpath.startswith(os.path.sep): relpath = relpath[1:] return relpath.replace(os.path.sep, '.') def _parse_module(self, uri): ''' Parse module defined in *uri* ''' filename = self._uri2path(uri) if filename is None: # nothing that we could handle here. return ([],[]) f = open(filename, 'rt') functions, classes = self._parse_lines(f) f.close() return functions, classes def _parse_lines(self, linesource): ''' Parse lines of text for functions and classes ''' functions = [] classes = [] for line in linesource: if line.startswith('def ') and line.count('('): # exclude private stuff name = self._get_object_name(line) if not name.startswith('_'): functions.append(name) elif line.startswith('class '): # exclude private stuff name = self._get_object_name(line) if not name.startswith('_'): classes.append(name) else: pass functions.sort() classes.sort() return functions, classes def generate_api_doc(self, uri): '''Make autodoc documentation template string for a module Parameters ---------- uri : string python location of module - e.g 'sphinx.builder' Returns ------- S : string Contents of API doc ''' # get the names of all classes and functions functions, classes = self._parse_module(uri) if not len(functions) and not len(classes): print 'WARNING: Empty -',uri # dbg return '' # Make a shorter version of the uri that omits the package name for # titles uri_short = re.sub(r'^%s\.' % self.package_name,'',uri) ad = '.. AUTO-GENERATED FILE -- DO NOT EDIT!\n\n' chap_title = uri_short ad += (chap_title+'\n'+ self.rst_section_levels[1] * len(chap_title) + '\n\n') # Set the chapter title to read 'module' for all modules except for the # main packages if '.' in uri: title = 'Module: :mod:`' + uri_short + '`' else: title = ':mod:`' + uri_short + '`' ad += title + '\n' + self.rst_section_levels[2] * len(title) if len(classes): ad += '\nInheritance diagram for ``%s``:\n\n' % uri ad += '.. inheritance-diagram:: %s \n' % uri ad += ' :parts: 3\n' ad += '\n.. automodule:: ' + uri + '\n' ad += '\n.. currentmodule:: ' + uri + '\n' multi_class = len(classes) > 1 multi_fx = len(functions) > 1 if multi_class: ad += '\n' + 'Classes' + '\n' + \ self.rst_section_levels[2] * 7 + '\n' elif len(classes) and multi_fx: ad += '\n' + 'Class' + '\n' + \ self.rst_section_levels[2] * 5 + '\n' for c in classes: ad += '\n:class:`' + c + '`\n' \ + self.rst_section_levels[multi_class + 2 ] * \ (len(c)+9) + '\n\n' ad += '\n.. autoclass:: ' + c + '\n' # must NOT exclude from index to keep cross-refs working ad += ' :members:\n' \ ' :undoc-members:\n' \ ' :show-inheritance:\n' if multi_fx: ad += '\n' + 'Functions' + '\n' + \ self.rst_section_levels[2] * 9 + '\n\n' elif len(functions) and multi_class: ad += '\n' + 'Function' + '\n' + \ self.rst_section_levels[2] * 8 + '\n\n' for f in functions: # must NOT exclude from index to keep cross-refs working ad += '\n.. autofunction:: ' + uri + '.' + f + '\n\n' return ad def _survives_exclude(self, matchstr, match_type): ''' Returns True if *matchstr* does not match patterns ``self.package_name`` removed from front of string if present Examples -------- >>> dw = ApiDocWriter('sphinx') >>> dw._survives_exclude('sphinx.okpkg', 'package') True >>> dw.package_skip_patterns.append('^\\.badpkg$') >>> dw._survives_exclude('sphinx.badpkg', 'package') False >>> dw._survives_exclude('sphinx.badpkg', 'module') True >>> dw._survives_exclude('sphinx.badmod', 'module') True >>> dw.module_skip_patterns.append('^\\.badmod$') >>> dw._survives_exclude('sphinx.badmod', 'module') False ''' if match_type == 'module': patterns = self.module_skip_patterns elif match_type == 'package': patterns = self.package_skip_patterns else: raise ValueError('Cannot interpret match type "%s"' % match_type) # Match to URI without package name L = len(self.package_name) if matchstr[:L] == self.package_name: matchstr = matchstr[L:] for pat in patterns: try: pat.search except AttributeError: pat = re.compile(pat) if pat.search(matchstr): return False return True def discover_modules(self): ''' Return module sequence discovered from ``self.package_name`` Parameters ---------- None Returns ------- mods : sequence Sequence of module names within ``self.package_name`` Examples -------- >>> dw = ApiDocWriter('sphinx') >>> mods = dw.discover_modules() >>> 'sphinx.util' in mods True >>> dw.package_skip_patterns.append('\.util$') >>> 'sphinx.util' in dw.discover_modules() False >>> ''' modules = [self.package_name] # raw directory parsing for dirpath, dirnames, filenames in os.walk(self.root_path): # Check directory names for packages root_uri = self._path2uri(os.path.join(self.root_path, dirpath)) for dirname in dirnames[:]: # copy list - we modify inplace package_uri = '.'.join((root_uri, dirname)) if (self._uri2path(package_uri) and self._survives_exclude(package_uri, 'package')): modules.append(package_uri) else: dirnames.remove(dirname) # Check filenames for modules for filename in filenames: module_name = filename[:-3] module_uri = '.'.join((root_uri, module_name)) if (self._uri2path(module_uri) and self._survives_exclude(module_uri, 'module')): modules.append(module_uri) return sorted(modules) def write_modules_api(self, modules,outdir): # write the list written_modules = [] for m in modules: api_str = self.generate_api_doc(m) if not api_str: continue # write out to file outfile = os.path.join(outdir, m + self.rst_extension) fileobj = open(outfile, 'wt') fileobj.write(api_str) fileobj.close() written_modules.append(m) self.written_modules = written_modules def write_api_docs(self, outdir): """Generate API reST files. Parameters ---------- outdir : string Directory name in which to store files We create automatic filenames for each module Returns ------- None Notes ----- Sets self.written_modules to list of written modules """ if not os.path.exists(outdir): os.mkdir(outdir) # compose list of modules modules = self.discover_modules() self.write_modules_api(modules,outdir) def write_index(self, outdir, froot='gen', relative_to=None): """Make a reST API index file from written files Parameters ---------- path : string Filename to write index to outdir : string Directory to which to write generated index file froot : string, optional root (filename without extension) of filename to write to Defaults to 'gen'. We add ``self.rst_extension``. relative_to : string path to which written filenames are relative. This component of the written file path will be removed from outdir, in the generated index. Default is None, meaning, leave path as it is. """ if self.written_modules is None: raise ValueError('No modules written') # Get full filename path path = os.path.join(outdir, froot+self.rst_extension) # Path written into index is relative to rootpath if relative_to is not None: relpath = outdir.replace(relative_to + os.path.sep, '') else: relpath = outdir idx = open(path,'wt') w = idx.write w('.. AUTO-GENERATED FILE -- DO NOT EDIT!\n\n') w('.. toctree::\n\n') for f in self.written_modules: w(' %s\n' % os.path.join(relpath,f)) idx.close() pymvpa-0.4.8/tools/bib2rst_ref.py000077500000000000000000000255311174541445200167740ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## import _bibtex import re def compareBibByDate(a, b): """Sorting helper.""" x = a[1][1] y = b[1][1] if x.has_key('year'): if y.has_key('year'): if x['year'].isdigit(): if y['year'].isdigit(): # x and y have dates xyear = int( x['year'] ) yyear = int( y['year'] ) comp = cmp(xyear, yyear) if comp == 0: return compareBibByAuthor(a,b) else: return (-1)*comp else: # x has date, y not -> y is first return 1 else: if y['year'][0].isdigit(): return -1 else: return compareBibByAuthor(a,b) else: # only x has date return 1 else: if y.has_key('year'): return -1 else: # neither nor y have dates return compareBibByAuthor(a, b) def compareBibByAuthor(a,b): """Sorting helper.""" x = a[1][1] y = b[1][1] if x.has_key('author'): if y.has_key('author'): return cmp(joinAuthorList(x['author']), joinAuthorList(y['author'])) else: # only x has author return 1 else: if y.has_key('author'): return -1 else: # neither nor y have authors return 0 def formatSurname(s, keep_full = False): """Recieves a string with surname(s) and returns a string with nicely concatenated surnames or initals (with dots). """ # clean spaces s = s.strip() # go home if empty if not len(s): return '' if not keep_full: # only keep initial s = s[0] if len(s) == 1: # add final dot s += '.' return s def formatAuthor(s, full_surname = False): """ Takes a string as argument an tries to determine the lastname and surname(s) of a single author. Returns a string with 'lastname, surname(s)'. The function takes care of 'von's and other funny prefixes. """ s = s.strip() # nothing? take ball, go home if not len(s): return s if s.count(','): # assume we have 'lastname, surname(s)' slist = s.split(',') # take lastname verbatim lastname = slist[0].strip() # remerge possible surnames with spaces if any surnames = u' '.join(slist[1:]) # get nicely formated surnames concat with spaces surname = u' '.join( [ formatSurname(i, full_surname) for i in surnames.split() ] ) else: # assume last entity is lastname the rest is surnames # check for lastname prefixes slist = s.split() if len(slist) < 2: # only lastname -> finished return slist[0] # check for order if len(slist[-1]) == 1 or slist[-1].endswith('.'): # seems like we have lastname->surname order if slist[0] in ('von', 'van'): lastname = slist[0] + ' ' + slist[1] surnames = u' '.join(slist[2:]) else: lastname = slist[0] surnames = u' '.join(slist[1:]) else: # the lastname is last lastname = slist[-1] if slist[-2] in ('von', 'van'): lastname = slist[-2] + u' ' + lastname surnames = u' '.join(slist[:-2]) else: surnames = u' '.join(slist[:-1]) surname = u' '.join( [ formatSurname(i, full_surname) for i in surnames.split() ] ) return lastname + u', ' + surname def joinAuthorList(alist): """ Nicely concatenate a list of author with ', ' and a final ' & '. Each author is passed to formatAuthor() internally. """ if not len(alist) > 1: return formatAuthor(alist[0]) ret = u', '.join( [ formatAuthor(a) for a in alist[:-1] ] ) ret += u' & ' + formatAuthor( alist[-1] ) return ret def formatProperty(string, indent, max_length = 80): """ Helper function to place linebreaks and indentation for pretty printing. """ length = len(string) lines = [] pos = 0 while pos < length: if not pos == 0: justify = ''.ljust(indent) line_length = max_length - indent else: justify = '' line_length = max_length if length - pos > line_length: lastspace = string.rfind(' ', pos + 1, pos + line_length) else: lastspace = length if lastspace == -1 or lastspace < indent + 1: lastspace = string.find(' ', pos + line_length) # if no space in the whole string if lastspace == -1: lastspace = length lines.append(justify + string[pos:lastspace]) pos = lastspace + 1 return '\n'.join(lines) class BibTeX(dict): """Read bibtex file as dictionary. Each entry is accessible by its bibtex ID. An entry is a two-tuple `(item_type, dict)`, where `item_type` is eg. article, book, ... and `dict` is a dictionary with all bibtex properties for the respective item. In this dictionary all properties are store as plain strings, except for the list of authors (which is a list of strings) and the pages which is a two-tuple with first and last page. """ def __init__(self, filename = None): if not filename == None: self.open(filename) # spaces to be used for indentation self.indent = 17 # maximum line length self.line_length = 80 def open(self, filename): """Read and parse bibtex file using python-bibtex.""" # figure out what the second argument means file = _bibtex.open_file(filename, 1) while 1: entry = _bibtex.next(file) if entry == None: break eprops = {} for k,v in entry[4].iteritems(): # figure out what the last argument really does # leaving in -1 seems to be save value = _bibtex.expand(file, v, 0)[2] try: value = unicode(value, 'utf-8') except UnicodeDecodeError, e: print "ERROR: Failed to decode string '%s'" % value raise if k.lower() == 'author': value = value.split(' and ') if k.lower() == 'pages': value = tuple(value.replace('-', ' ').split()) eprops[k] = value # bibtex key is dict key self[entry[0]] = (entry[1],eprops) def __str__(self): """Pretty print in bibtex format.""" bibstring = '' for k, v in self.iteritems(): bibstring += '@' + v[0] + ' { ' + k for ek, ev in v[1].iteritems(): if ek.lower() == 'author': ev = ' and '.join(ev) if ek.lower() == 'pages': ev = '--'.join(ev) keyname = ' ' + ek bibstring += ',\n' bibstring += formatProperty( keyname.ljust(15) + '= {' + ev + '}', self.indent, self.line_length ) bibstring += "\n}\n\n" return bibstring.encode(self.enc) def bib2rst_references(bib): """Compose the reference page.""" # do it in unicode rst = u'' intro = open('doc/misc/references.in').readlines() rst += intro[0] rst += " #\n # THIS IS A GENERATED FILE -- DO NOT EDIT!\n #\n" rst += ''.join(intro[1:]) rst += '\n\n' biblist = bib.items() biblist.sort(compareBibByAuthor) for id, (cat, prop) in biblist: # put reference target for citations rst += '.. _' + id + ':\n\n' # compose the citation as the list item label cit = u'' # initial details equal for all item types if prop.has_key('author'): cit += u'**' + joinAuthorList(prop['author']) + u'**' if prop.has_key('year'): cit += ' (' + prop['year'] + ').' if prop.has_key('title'): cit += ' ' + smoothRsT(prop['title']) if not prop['title'].endswith('.'): cit += '.' # appendix for journal articles if cat.lower() == 'article': # needs to have journal, volume, pages cit += ' *' + prop['journal'] + '*' if prop.has_key('volume'): cit += ', *' + prop['volume'] + '*' if prop.has_key('pages'): cit += ', ' + '-'.join(prop['pages']) elif cat.lower() == 'book': # needs to have publisher, address cit += ' ' + prop['publisher'] cit += ': ' + prop['address'] elif cat.lower() == 'manual': cit += ' ' + prop['address'] else: print "WARNING: Cannot handle bibtex item type:", cat cit += '.' # beautify citation with linebreaks and proper indentation # damn, no. list label has to be a single line... :( #rst += formatProperty(cit, 0) rst += cit # place optional paper summary if prop.has_key('pymvpa-summary'): rst += '\n *' + formatProperty(prop['pymvpa-summary'], 2) + '*\n' # make keywords visible if prop.has_key('pymvpa-keywords'): rst += '\n Keywords: ' \ + ', '.join([':keyword:`' + kw.strip() + '`' for kw in prop['pymvpa-keywords'].split(',')]) \ + '\n' # place DOI link if prop.has_key('doi'): rst += '\n DOI: ' if not prop['doi'].startswith('http://dx.doi.org/'): rst += 'http://dx.doi.org/' rst += prop['doi'] rst += '\n' # use URL if no DOI available elif prop.has_key('url'): rst += '\n URL: ' + prop['url'] + '\n' rst += '\n\n' # end list with blank line rst += '\n\n' return rst.encode('utf-8') def smoothRsT(s): """Replace problematic stuff with less problematic stuff.""" s = re.sub("``", '"', s) # assuming that empty strings to not occur in a bib file s = re.sub("''", '"', s) return s # do it bib = BibTeX('doc/misc/references.bib') refpage = open('doc/references.rst', 'w') refpage.write(bib2rst_references(bib)) refpage.close() pymvpa-0.4.8/tools/bisect_mtrand.sh000077500000000000000000000010051174541445200173570ustar00rootroot00000000000000#!/bin/bash # This is the script to be used with git bisect to figure out first # commit when testBasic starts to fail # to use it just do # git bisect start yoh/master maint/0.4 -- # git bisect run tools/bisect_mtrand.sh # where yoh/master is known place where that test fails # and maint/0.4 where it doesn't # # it would stop at the first bad commit make clean make || exit 125 # skip commits where build is broken PYTHONPATH=$PWD nosetests mvpa/tests/test_datameasure.py:SensitivityAnalysersTests.testBasic pymvpa-0.4.8/tools/build_modref_templates.py000077500000000000000000000010371174541445200212750ustar00rootroot00000000000000#!/usr/bin/env python """Script to auto-generate our API docs.""" import os from apigen import ApiDocWriter if __name__ == '__main__': package = 'mvpa' outdir = os.path.join('build', 'doc', 'modref') docwriter = ApiDocWriter(package, rst_extension='.rst') #docwriter.package_skip_patterns += ['\\.fixes$', # '\\.externals$'] docwriter.write_api_docs(outdir) #docwriter.write_index(outdir, 'gen', relative_to='api') print '%d files written' % len(docwriter.written_modules) pymvpa-0.4.8/tools/daily_tests.sh000077500000000000000000000067471174541445200171070ustar00rootroot00000000000000#!/bin/bash # emacs: -*- mode: shell-script; indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=sh sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # Helper to run all the tests daily at night # set -e # what branches to test BRANCHES='master yoh/master mh/master' # maint/0.4' # where to send reports EMAILS='yoh@onerussian.com,michael.hanke@gmail.com' precmd= #precmd="echo C: " # XXX repo="git://git.debian.org/git/pkg-exppsy/pymvpa.git" ds=`date +"20%y%m%d_%H%M%S"` topdir=$HOME/proj/pymvpa logdir="$topdir/logs/daily/pymvpa_tests-$ds" tmpfile="$logdir/tmp.log" logfile="$logdir/all.log" # Remove trap "rm -fr $logdir/pymvpa $logdir/tmp.log;" EXIT mkdir -p "$logdir" indent() { sed -e 's/^/ /g' } do_checkout() { $precmd git clean -df | indent $precmd git reset --hard #if [ ! $branch = 'master' ]; then $precmd git checkout -b $branch origin/$branch || : #fi $precmd git checkout $branch } do_build() { $precmd make clean $precmd make } do_clean() { # verify that cleaning works as desired $precmd make clean $precmd git clean -n | grep -q . \ && { git clean -n; return 1; } \ || return 0 } # Not yet can do fine scale unittest separation since maint/0.4, nor # master have it that way... leaving it for future. #MAKE_TESTS="unittest unittest-optimization unittest-debug unittest-badexternals MAKE_TESTS="unittests testmanual testsuite testapiref testsphinx testexamples testcfg" for c in $MAKE_TESTS; do eval "do_$c() { $precmd make $c; }" done # What actions/tests to run per each branch ACTIONS="checkout build $MAKE_TESTS clean" # Counters failed=0 succeeded=0 # skip the tests we can't fully trust to sleep well export MVPA_TESTS_LABILE=no # Lets use backend allowing to draw without DISPLAY export MVPA_MATPLOTLIB_BACKEND=agg # need to be a function to share global failed/succeded sweep() { cd $logdir echo "I:" $(date) # checkout the repository echo "I: Cloning repository" $precmd git clone -q $repo 2>&1 | indent $precmd cd pymvpa # no need to check here since checkout would fail below otherwise # # Sweep through the branches and actionsto test # branches_with_problems= for branch in $BRANCHES; do branch_has_problems= echo echo "I: ---------------{ Branch $branch }--------------" for action in $ACTIONS; do echo -n "I: $action " cmd="do_$action" if $cmd >| $tmpfile 2>&1 ; then echo " ok" succeeded=$(($succeeded+1)) else branch_has_problems+=" $action" failed=$(($failed+1)) echo " ! FAILED ! Output was:" cat $tmpfile | indent fi done if [ "x$branch_has_problems" != x ]; then branches_with_problems+="\n $branch: $branch_has_problems" fi done echo "I: Succeeded $succeeded actions, failed $failed actions." if [ "x$branches_with_problems" != x ]; then echo -e "I: Branches which experienced problems: $branches_with_problems" fi echo echo "I:" $(date) echo "I: Exiting. Logfile $logfile" } sweep >| $logfile 2>&1 # Email only if any test has failed #[ ! $failed = 0 ] && \ # Email always since it is better to see that indeed everything is smooth # and to confirm that it is tested daily cat $logfile | mail -s "PyMVPA: daily testing: +$succeeded/-$failed" $EMAILS pymvpa-0.4.8/tools/epydoc000077500000000000000000000022541174541445200154220ustar00rootroot00000000000000#!/usr/bin/python # # Call the command line interface for Epydoc. # # Make sure that we don't get confused between an epydoc.py script and # the real epydoc package. import sys, os.path from docutils.nodes import NodeVisitor NodeVisitor.optional = ('note') if os.path.exists(os.path.join(sys.path[0], 'epydoc.py')): del sys.path[0] from epydoc.cli import cli # suppress multitude of warnings about profile data missing # import warnings # defaul is supposed to print it only once # warnings.simplefilter('default', Warning) # epydoc uses its own # lets override it completely import epydoc.log import os epydoc_warnings = os.environ.get('MVPA_EPYDOC_WARNINGS', 'all').lower() _logged_warnings = set() if epydoc_warnings == 'all': pass elif epydoc_warnings == 'once': warning_orig = epydoc.log.warning def once_warning(*messages): smsg = str(messages) if not (smsg in _logged_warnings): warning_orig(*messages) _logged_warnings.add(smsg) epydoc.log.warning = once_warning elif epydoc_warnings == 'none': epydoc.log.warning = lambda *x:None else: print "ERROR: Unknown control for epydoc warnings %s. " \ "Known are all, once, none" % epydoc_warnings cli() pymvpa-0.4.8/tools/ex2rst000077500000000000000000000177301174541445200153730ustar00rootroot00000000000000#!/usr/bin/env python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Helper to automagically generate ReST versions of examples""" __docformat__ = 'restructuredtext' import os import sys import re import glob from optparse import OptionParser def exfile2rst(filename): """Open a Python script and convert it into an ReST string. """ # output string s = '' # open source file xfile = open(filename) # parser status vars inheader = True indocs = False doc2code = False code2doc = False # an empty line found in the example enables the check for a potentially # indented docstring starting on the next line (as an attempt to exclude # function or class docstrings) last_line_empty = False # indentation of indented docstring, which is removed from the RsT output # since we typically do not want an indentation there. indent_level = 0 for line in xfile: # skip header if inheader and \ not (line.startswith('"""') or line.startswith("'''")): continue # determine end of header if inheader and (line.startswith('"""') or line.startswith("'''")): inheader = False # strip comments and remove trailing whitespace if not indocs and last_line_empty: # first remove leading whitespace and store indent level cleanline = line[:line.find('#')].lstrip() indent_level = len(line) - len(cleanline) - 1 cleanline = cleanline.rstrip() else: cleanline = line[:line.find('#')].rstrip() if not indocs and line == '\n': last_line_empty = True else: last_line_empty = False # if we have something that should go into the text if indocs \ or (cleanline.startswith('"""') or cleanline.startswith("'''")): proc_line = None # handle doc start if not indocs: # guarenteed to start with """ if len(cleanline) > 3 \ and (cleanline.endswith('"""') \ or cleanline.endswith("'''")): # single line doc code2doc = True doc2code = True proc_line = cleanline[3:-3] else: # must be start of multiline block indocs = True code2doc = True # rescue what is left on the line proc_line = cleanline[3:] # strip """ else: # we are already in the docs # handle doc end if cleanline.endswith('"""') or cleanline.endswith("'''"): indocs = False doc2code = True # rescue what is left on the line proc_line = cleanline[:-3] # reset the indentation indent_level = 0 else: # has to be documentation # if the indentation is whitespace remove it, other wise # keep it (accounts for some variation in docstring # styles real_indent = \ indent_level - len(line[:indent_level].lstrip()) proc_line = line[real_indent:] if code2doc: code2doc = False s += '\n' if proc_line: s += proc_line.rstrip() + '\n' else: if doc2code: doc2code = False s += '\n::\n' # has to be code s += ' %s' % line xfile.close() return s def exfile2rstfile(filename, opts): """ """ # doc filename dfilename = os.path.basename(filename[:-3]) + '.rst' # open dest file dfile = open(os.path.join(opts.outdir, os.path.basename(dfilename)), 'w') # place header dfile.write('.. AUTO-GENERATED FILE -- DO NOT EDIT!\n\n') # place cross-ref target dfile.write('.. _example_' + dfilename[:-4] + ':\n\n') # write converted ReST dfile.write(exfile2rst(filename)) if opts.sourceref: # write post example see also box dfile.write("\n.. seealso::\n The full source code of this example is " "included in the %s source distribution (`%s`).\n" % (opts.project, filename)) dfile.close() def main(): parser = OptionParser( \ usage="%prog [options] [...]", \ version="%prog 0.1", description="""\ %prog converts Python scripts into restructered text (ReST) format suitable for integration into the Sphinx documentation framework. Its key feature is that it extracts stand-alone (unassigned) single, or multiline triple-quote docstrings and moves them out of the code listing so that they are rendered as regular ReST, while at the same time maintaining their position relative to the listing. The detection of such docstrings is exclusively done by parsing the raw code so it is never actually imported into a running Python session. Docstrings have to be written using triple quotes (both forms " and ' are possible). It is recommend that such docstrings are preceded and followed by an empty line. Intended docstring can make use of the full linewidth from the second docstring line on. If the indentation of multiline docstring is maintained for all lines, the respective indentation is removed in the ReST output. The parser algorithm automatically excludes file headers and starts with the first (module-level) docstring instead. """ ) # define options parser.add_option('--verbose', action='store_true', dest='verbose', default=False, help='print status messages') parser.add_option('-x', '--exclude', action='append', dest='excluded', help="""\ Use this option to exclude single files from the to be parsed files. This is especially useful to exclude files when parsing complete directories. This option can be specified multiple times. """) parser.add_option('-o', '--outdir', action='store', dest='outdir', type='string', default=None, help="""\ Target directory to write the ReST output to. This is a required option. """) parser.add_option('--no-sourceref', action='store_false', default=True, dest='sourceref', help="""\ If specified, the source reference section will be suppressed. """) parser.add_option('--project', type='string', action='store', default='', dest='project', help="""\ Name of the project that contains the examples. This name is used in the 'seealso' source references. Default: '' """) # parse options (opts, args) = parser.parse_args() # read sys.argv[1:] by default # check for required options if opts.outdir is None: print('Required option -o, --outdir not specified.') sys.exit(1) # build up list of things to parse toparse = [] for t in args: # expand dirs if os.path.isdir(t): # add all python files in that dir toparse += glob.glob(os.path.join(t, '*.py')) else: toparse.append(t) # filter parse list if not opts.excluded is None: toparse = [t for t in toparse if not t in opts.excluded] toparse_list = toparse toparse = set(toparse) if len(toparse) != len(toparse_list): print('Ignoring duplicate parse targets.') if not os.path.exists(opts.outdir): os.mkdir(outdir) # finally process all examples for t in toparse: exfile2rstfile(t, opts) if __name__ == '__main__': main() pymvpa-0.4.8/tools/fillin_states000077500000000000000000000055301174541445200167770ustar00rootroot00000000000000#!/usr/bin/python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """""" __docformat__ = 'restructuredtext' if not __name__ == "__main__": raise ValueError, "Go away -- nothing to look here for as a module" import sys, os, re from optparse import OptionParser from os import environ, path from textwrap import wrap from mvpa.base import verbose from mvpa.misc.cmdline import parser, opts parser.usage = """Usage: %s [options] infile outfile...""" % sys.argv[0] parser.option_groups = [opts.common] (options, files) = parser.parse_args() if len(files)!=2: print "Need input and output files" sys.exit(1) if files[0] == files[1]: print "For now provide two different names" sys.exit(1) infile = open(files[0], 'r') inlines = infile.readlines() infile.close() outlines = [] reg = re.compile('^.. IncludeStates: +(?P\S*) +(?P\S*)') i = 0 while i < len(inlines): line = inlines[i] i += 1 outlines.append(line) reg_res = reg.search(line) if not reg_res: continue else: verbose(2, "Line: %s" % line) d = reg_res.groupdict() # import asked module exec "from mvpa.%s import %s as Victim" % (d['path'], d['cls']) states = Victim._collections_template['states'].items keys = states.keys() keys.sort() isthere = inlines[i+1].startswith("Supported st") suffix = ['', 's'][len(keys)>1] outlines.append(""" Supported state%s: ================== ============================================== ========= State Name Description Default ------------------ ---------------------------------------------- --------- """%suffix) for k in keys: verbose(3, " " + k) v = states[k] doc = v.__doc__ if not doc.endswith('.'): doc += '.' doc = wrap(doc, 46) k, e = k[:], ['Disabled', 'Enabled'][v.isEnabled] for d in doc: new_line = "%-19s %-46s %s" % \ (k, d, e) k, e = "", "" outlines.append(new_line.rstrip()+"\n") outlines.append("""================== ============================================== =========\n""") if isthere: count = 0 # aborb "older" lines until we hit good one while count < 2: count += inlines[i].startswith('======') i += 1 outfile = open(files[1], 'w') [outfile.write(x) for x in outlines] outfile.close() pymvpa-0.4.8/tools/mpkg_wrapper.py000066400000000000000000000015531174541445200172620ustar00rootroot00000000000000# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """Simple wrapper to use setuptools extension bdist_mpkg with PyMVPA distutils setup.py. This script is a minimal version of a wrapper script shipped with the bdist_mpkg packge. """ __docformat__ = 'restructuredtext' import sys import setuptools import bdist_mpkg def main(): del sys.argv[0] sys.argv.insert(1, 'bdist_mpkg') g = dict(globals()) g['__file__'] = sys.argv[0] g['__name__'] = '__main__' execfile(sys.argv[0], g, g) if __name__ == '__main__': main() pymvpa-0.4.8/tools/pdfbook.c000066400000000000000000000223721174541445200160040ustar00rootroot00000000000000/* * pdfbook.c Rearrange pages in a PDF file into signatures. * * Authors: Tigran Aivazian * Jaap Eldering * Roman Buchert * Pierre Francois * * Based on the algorithm from psutils/psbook.c, which was * written by Angus J. C. Duggan 1991-1995. * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License version 2 as * published by the Free Software Foundation. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software Foundation, * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301, USA * */ #include #include #include #include #include #include #define TMP_INFILE_BASE "input" #define TMP_OUTFILE_BASE "output" #define DEF_PAPERSIZE "a4" char *program; static void usage(void) { fprintf(stderr, "Usage: %s [OPTION]... infile outfile\n" " or: %s [-q] [-p ] -o infile outfile\n" "Rearrange pages for printing as booklet.\n\n" "Options:\n" " -q suppress verbose output\n" " -d debug mode: do not cleanup temporary files\n" " -2 place 2 pages on 1 page of output\n" " -s group pages together in groups of size \n" " must be positive and divisible by 4\n" " -r reduce the last book to minimum required number of pages\n" " -p set the paper size to: a3, a4, a5, b3, b4, b5, letter, legal\n" " or executive (default is determined from infile, %s if unknown)\n" " -o pass directly to LaTeX pdfpages `includepdf' command\n" " see the pdfpages package documentation for possible options\n", program, program, DEF_PAPERSIZE); fflush(stderr); exit(1); } char *alloc_and_copy (char *str) { char *new_str; if ( (new_str = strdup(str)) == NULL ) { fprintf(stderr, "%s: error allocating memory\n", program); exit(1); } return (new_str); } int check_papersize (char *p) { if ((strlen(p) == 2) && ((*p == 'a') || (*p == 'b')) && ((p[1] >= '3') && (p[1] <= '5'))) { return (1); /* a3, a4, a5, b3, b4 or b5 */ } if ((strcmp(p, "letter") == 0) || (strcmp(p, "legal") == 0) || (strcmp(p, "executive") == 0)) { return (1); /* letter, legal or executive */ } return (0); /* invalid paper size */ } char *make_tempdir() { static char dirtemplate[L_tmpnam+10]; char *dirname; if ( (dirname = tempnam(NULL, "pdfbk")) == NULL ) { fprintf(stderr, "%s: error generating temporary directory\n", program); exit(1); } strcpy(dirtemplate, dirname); strcat(dirtemplate, "XXXXXX"); if ( (dirname = mkdtemp(dirtemplate)) == NULL ) { fprintf(stderr, "%s: error generating temporary directory\n", program); exit(1); } return dirname; } char *allocstr(char *format, ...) { va_list ap; char *str; char tmp[2]; int len, n; va_start(ap,format); len = vsnprintf(tmp,1,format,ap); va_end(ap); if ( (str = (char *) malloc(len+1))==NULL ) return NULL; va_start(ap,format); n = vsnprintf(str,len+1,format,ap); va_end(ap); if ( n==-1 || n>len ) { fprintf(stderr, "%s: error allocating memory\n", program); exit(1); } return str; } int main(int argc, char *argv[]) { char *infile = NULL; char *outfile = NULL; char *tmpdir; char *tmptexfile; char *tmpinfile; char *tmpoutfile; FILE *fp, *fout; int *actualpg; static char cmdline[1024]; int quiet = 0; int debug = 0; int nup = 0; int pdfcustom = 0; int signature = 0; int reducelastbook = 0; int npages = 0; int maxpage; int completebooks = 0; int restpages = 0; int restsignature = 0; char *pdfcustom_str = NULL; char *papersize_str = NULL; char origpapersize[32]; int i, c; program = argv[0]; tmpdir = make_tempdir(); while ( (c = getopt(argc, argv, "2qds:o:p:r")) != -1 ) { switch (c) { case 's': signature = atoi(optarg); if (signature < 1 || signature % 4) usage(); break; case 'r': reducelastbook = 1; break; case '2': nup = 1; break; case 'o': pdfcustom = 1; pdfcustom_str = alloc_and_copy(optarg); break; case 'p': papersize_str = alloc_and_copy(optarg); if (check_papersize(papersize_str) == 0) { fprintf(stderr, "%s: bad paper size `%s'\n", program, papersize_str); usage (); } break; case 'q': quiet = 1; break; case 'd': debug = 1; break; default: usage(); } } if ( optind /dev/null 2>&1 < /dev/null", tmpdir, tmptexfile); if (system(cmdline)) { fprintf(stderr, "%s: Failed to generate output, see \"%s/%s.log\" for details\n", program, tmpdir, TMP_OUTFILE_BASE); exit(1); } sprintf(cmdline, "cp %s %s", tmpoutfile, outfile); if (system(cmdline)) { fprintf(stderr, "%s: Failed to write \"%s\" file\n", program, outfile); exit(1); } if (!debug) { sprintf(cmdline, "rm -rf %s", tmpdir); system(cmdline); } return 0; } pymvpa-0.4.8/tools/profile000077500000000000000000000121711174541445200155760ustar00rootroot00000000000000#!/usr/bin/python # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## """""" __docformat__ = 'restructuredtext' import sys, os from optparse import OptionParser from os import environ, path from mvpa.base.verbosity import LevelLogger from mvpa.misc.cmdline import opt if __name__ == "__main__": usage = """Usage: %s [options] ... """ % sys.argv[0] # default options convert2kcache = True displaykcachegrinder = True printstats = False pfilename = None pstatsfilename = None profilelines = True profilelevel = 10 # how many most hungry to list in stats run = True # either to run profiling at all verbose = LevelLogger(handlers=[sys.stderr]) verbose.level = 1 # do we need to know lots by default? :-) verbose.indent = '> ' # to discriminate easily with program output verbose(2, "Initial cmdline params: %s" % `sys.argv`) removed = sys.argv.pop(0) verbose(3, "Removed %s" % removed) verbose(3, "Remained ", sys.argv) if not len(sys.argv): verbose(0, "No python module to profile specified.") print usage sys.exit(1) while sys.argv[0].startswith('-'): if sys.argv[0] in ["-v", "--verbose"]: verbose.level = int(sys.argv[1]) sys.argv.pop(0) elif sys.argv[0] in ["-l", "--level"]: profilelevel = int(sys.argv[1]) sys.argv.pop(0) elif sys.argv[0] in ["-o", "--output-file"]: pfilename = sys.argv[1] sys.argv.pop(0) elif sys.argv[0] in ["-O", "--output-statsfile"]: pstatsfilename = sys.argv[1] sys.argv.pop(0) elif sys.argv[0] in ["-s", "--stats"]: printstats = True verbose(2, "Enabling printing stats") convert2kcache = False displaykcachegrinder = False verbose(2, "Disabling conversion to kcache") elif sys.argv[0] in ["-n", "--no-run"]: run = False verbose(2, "Disabling running main. Just do conversions and stats") elif sys.argv[0] in ["-P", "--no-profilelines"]: profilelines = False verbose(2, "Disabling profiling lines") elif sys.argv[0] in ["-K", "--no-kcache"]: convert2kcache = False displaykcachegrinder = False verbose(2, "Disabling conversion to kcache") else: verbose(0, "UNKNOWN parameter %s. Exiting" % sys.argv[0]) print usage sys.exit(1) sys.argv.pop(0) cmdname = sys.argv[0] dirname = path.dirname(cmdname) (root, ext) = path.splitext(path.basename(cmdname)) verbose(2, "Adding '%s' to sys.path " % dirname) sys.path.append(dirname) verbose(3, "sys.path is %s " % `sys.path`) # now do profiling import hotshot if pfilename is None: pfilename = cmdname + ".prof" if run: verbose(1, "Importing %s " % root) try: exec "import %s as runnable" % root except SystemExit: pass if not runnable.__dict__.has_key('main'): verbose(0, "OOPS: file/module %s has no function main defined" \ % cmdname) sys.exit(1) verbose(2, "Creating profiler instance") prof = hotshot.Profile(pfilename, lineevents=profilelines) try: # actually return values are never setup # since unittest.main sys.exit's verbose(1, "Calling main() with a profiler") results = prof.runcall( runnable.main ) verbose(4, "Results of profiler call are %s" % `results`) except SystemExit: pass verbose(1, "Saving profile data into %s" % pfilename) prof.close() verbose(2, "Closed profiler") if printstats or pstatsfilename: import hotshot.stats verbose(1, "Loading profile file to print statistics") stats = hotshot.stats.load(pfilename) if printstats: stats.strip_dirs() stats.sort_stats('time', 'calls') stats.print_stats(profilelevel) if pstatsfilename: stats.dump_stats(pstatsfilename) kfilename = pfilename + ".kcache" if convert2kcache: cmd = "hotshot2calltree -o %s %s" % (kfilename, pfilename) verbose(1, "Converting to kcache") verbose(3, "Calling '%s'" % cmd) if os.system(cmd): verbose(0, "!!! Make sure to install kcachegrind-converters ;-)") sys.exit(1) if displaykcachegrinder: verbose(1, "Running kcachegrind") if os.system('kcachegrind %s' % kfilename): verbose(0, "!!! Make sure to install kcachegrind ;-)") sys.exit(1) else: print "Go away -- nothing to look here for as a module" pymvpa-0.4.8/tools/refactor.sh000077500000000000000000000025271174541445200163600ustar00rootroot00000000000000#!/bin/bash # emacs: -*- mode: shell-script; c-basic-offset: 4; tab-width: 4; indent-tabs-mode: t -*- # vi: set ft=sh sts=4 ts=4 sw=4 noet: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the PyMVPA package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## git grep -l mvpa | grep -v refactor | \ xargs sed -i \ -e 's,optHelp,opt.help,g' \ -e "s,optVerbose,opt.verbose,g" \ -e "s,optClf,opt.clf,g" \ -e "s,optRadius,opt.radius,g" \ -e "s,optKNearestDegree,opt.knearestdegree,g" \ -e "s,optSVMC,opt.svm_C,g" \ -e "s,optSVMNu,opt.svm_nu,g" \ -e "s,optSVMGamma,opt.svm_gamma,g" \ -e "s,optCrossfoldDegree,opt.crossfolddegree,g" \ -e "s,optZScore,opt.zscore,g" \ -e "s,optTr,opt.tr,g" \ -e "s,optDetrend,opt.detrend,g" \ -e "s,optBoxLength,opt.boxlength,g" \ -e "s,optBoxOffset,opt.boxoffset,g" \ -e "s,optChunk,opt.chunk,g" \ -e "s,optChunkLimits,opt.chunklimits,g" \ -e "s,optsCommon,opts.common,g" \ -e "s,optsKNN,opts.KNN,g" \ -e "s,optsSVM,opts.SVM,g" \ -e "s,optsGener,opts.general,g" \ -e "s,optsPreproc,opts.preproc,g" \ -e "s,optsBox,opts.box,g" \ -e "s,optsChunk,opts.chunk,g" ## Uncomment and move up any additional refactorings which needed # -e 's,training_confusions,confusion,g' pymvpa-0.4.8/tools/sitemap.sh000077500000000000000000000011621174541445200162070ustar00rootroot00000000000000#!/bin/bash # # Generate a XML sitemap to make the search engines love the website. # baseurl="http://v04.pymvpa.org" siteroot="build/website" cat << EOT $baseurl/ $(stat -c '%y' $siteroot/index.html | cut -d ' ' -f 1,1) 1.0 EOT for file in $(find $siteroot/ -maxdepth 1 -name '*.html' -o -name '*.pdf'); do cat << EOT $baseurl/$(basename $file) $(stat -c '%y' $file | cut -d ' ' -f 1,1) EOT done echo "" pymvpa-0.4.8/tools/state_refactor.sh000077500000000000000000000034221174541445200175530ustar00rootroot00000000000000#!/bin/bash known_states="\(all_label_counts\|confusion\|confusions\|emp_error\|errors\|history\|ndiscarded\|nfeatures\|null_errors\|predictions\|raw_predictions\|raw_values\|results\|selected_ids\|sensitivities\|sensitivity\|splits\|state[123]\|trained_confusion\|trained_confusions\|transerrors\|values\)" sed -i \ -e 's/\(\W\)State\.)/\1Stateful\./g' \ -e 's/State\.__init/Stateful\.__init/g' \ -e 's/State\.__str/Stateful\.__str/g' \ -e 's/\.enableState/\.states\.enable/g' \ -e 's/\.enableStates/\.states\.enable/g' \ -e 's/\.enabledStates/\.states\.enabled/g' \ -e 's/\.disableState/\.states\.disable/g' \ -e 's/\.disableStates/\.states\.disable/g' \ -e 's/\.listStates/\.states\.listing/g' \ -e 's/\.hasState/\.states\.isKnown/g' \ -e 's/\.isStateEnabled/\.states\.isEnabled/g' \ -e 's/\._enableStatesTemporarily/\.states\._enableTemporarily/g' \ -e 's/\.isStateActive/\.states\.isActive/g' \ -e "s/\(\w\)[[]\([\"']\)$known_states\2[]]/\1\.\3/g" \ -e "s/self\._registerState(\([\"']\)$known_states\1\,* */\2 = StateVariable(/g" \ $@ exit 0 sed -i \ -e 's/(State)/(Stateful)/g' \ -e 's/import State/import StateVariable, Stateful/g' \ -e "s/self\._registerState(\([\"']\)$known_states\1\,* */\2 = StateVariable(/g" \ $@ exit 0 obtained list of all state variables ever known by grep '_registerState(' *py `find ../mvpa -iname \*.py` 2>/dev/null| \ grep -v 'def _reg' | sed -e "s/.*(\([\"']\)\([^ ,]*\)\1.*/\2/g" | \ grep -v mvpa | sort | uniq | tr '\n' '\|'; echo (State) Stateful enableState enable enabledStates enabled listStates listing states items _enableStatesTemporarily _enableTemporarily _getRegisteredStates _getNames hasState isKnown isStateEnabled isEnabled isStateActive isActive listStates _getListing GONE: enableStates disableStates __enabledisableall pymvpa-0.4.8/tools/valgrind-python000077500000000000000000000002541174541445200172620ustar00rootroot00000000000000#!/bin/bash path=$(dirname $0) valgrind --tool=memcheck --leak-check=full --show-reachable=yes --leak-resolution=med --suppressions=$path/valgrind-python.supp python $* pymvpa-0.4.8/tools/valgrind-python.supp000066400000000000000000000171121174541445200202460ustar00rootroot00000000000000# # This is a valgrind suppression file that should be used when using valgrind. # # Here's an example of running valgrind: # # cd python/dist/src # valgrind --tool=memcheck --suppressions=Misc/valgrind-python.supp \ # ./python -E -tt ./Lib/test/regrtest.py -u bsddb,network # # You must edit Objects/obmalloc.c and uncomment Py_USING_MEMORY_DEBUGGER # to use the preferred suppressions with Py_ADDRESS_IN_RANGE. # # If you do not want to recompile Python, you can uncomment # suppressions for PyObject_Free and PyObject_Realloc. # # See Misc/README.valgrind for more information. # all tool names: Addrcheck,Memcheck,cachegrind,helgrind,massif { ADDRESS_IN_RANGE/Invalid read of size 4 Memcheck:Addr4 fun:Py_ADDRESS_IN_RANGE } { ADDRESS_IN_RANGE/Invalid read of size 4 Memcheck:Value4 fun:Py_ADDRESS_IN_RANGE } { ADDRESS_IN_RANGE/Invalid read of size 8 (x86_64 aka amd64) Memcheck:Value8 fun:Py_ADDRESS_IN_RANGE } { ADDRESS_IN_RANGE/Conditional jump or move depends on uninitialised value Memcheck:Cond fun:Py_ADDRESS_IN_RANGE } # # Leaks (including possible leaks) # Hmmm, I wonder if this masks some real leaks. I think it does. # Will need to fix that. # { Handle PyMalloc confusing valgrind (possibly leaked) Memcheck:Leak fun:realloc fun:_PyObject_GC_Resize fun:COMMENT_THIS_LINE_TO_DISABLE_LEAK_WARNING } { Handle PyMalloc confusing valgrind (possibly leaked) Memcheck:Leak fun:malloc fun:_PyObject_GC_New fun:COMMENT_THIS_LINE_TO_DISABLE_LEAK_WARNING } { Handle PyMalloc confusing valgrind (possibly leaked) Memcheck:Leak fun:malloc fun:_PyObject_GC_NewVar fun:COMMENT_THIS_LINE_TO_DISABLE_LEAK_WARNING } # # Non-python specific leaks # { Handle pthread issue (possibly leaked) Memcheck:Leak fun:calloc fun:allocate_dtv fun:_dl_allocate_tls_storage fun:_dl_allocate_tls } { Handle pthread issue (possibly leaked) Memcheck:Leak fun:memalign fun:_dl_allocate_tls_storage fun:_dl_allocate_tls } { ADDRESS_IN_RANGE/Invalid read of size 4 Memcheck:Addr4 fun:PyObject_Free } { ADDRESS_IN_RANGE/Invalid read of size 4 Memcheck:Value4 fun:PyObject_Free } { ADDRESS_IN_RANGE/Conditional jump or move depends on uninitialised value Memcheck:Cond fun:PyObject_Free } { ADDRESS_IN_RANGE/Invalid read of size 4 Memcheck:Addr4 fun:PyObject_Realloc } { ADDRESS_IN_RANGE/Invalid read of size 4 Memcheck:Value4 fun:PyObject_Realloc } { ADDRESS_IN_RANGE/Conditional jump or move depends on uninitialised value Memcheck:Cond fun:PyObject_Realloc } ### ### All the suppressions below are for errors that occur within libraries ### that Python uses. The problems to not appear to be related to Python's ### use of the libraries. ### { Generic ubuntu ld problems Memcheck:Addr8 obj:/lib/ld-2.4.so obj:/lib/ld-2.4.so obj:/lib/ld-2.4.so obj:/lib/ld-2.4.so } { Generic gentoo ld problems Memcheck:Cond obj:/lib/ld-2.3.4.so obj:/lib/ld-2.3.4.so obj:/lib/ld-2.3.4.so obj:/lib/ld-2.3.4.so } { DBM problems, see test_dbm Memcheck:Param write(buf) fun:write obj:/usr/lib/libdb1.so.2 obj:/usr/lib/libdb1.so.2 obj:/usr/lib/libdb1.so.2 obj:/usr/lib/libdb1.so.2 fun:dbm_close } { DBM problems, see test_dbm Memcheck:Value8 fun:memmove obj:/usr/lib/libdb1.so.2 obj:/usr/lib/libdb1.so.2 obj:/usr/lib/libdb1.so.2 obj:/usr/lib/libdb1.so.2 fun:dbm_store fun:dbm_ass_sub } { DBM problems, see test_dbm Memcheck:Cond obj:/usr/lib/libdb1.so.2 obj:/usr/lib/libdb1.so.2 obj:/usr/lib/libdb1.so.2 fun:dbm_store fun:dbm_ass_sub } { DBM problems, see test_dbm Memcheck:Cond fun:memmove obj:/usr/lib/libdb1.so.2 obj:/usr/lib/libdb1.so.2 obj:/usr/lib/libdb1.so.2 obj:/usr/lib/libdb1.so.2 fun:dbm_store fun:dbm_ass_sub } { GDBM problems, see test_gdbm Memcheck:Param write(buf) fun:write fun:gdbm_open } { ZLIB problems, see test_gzip Memcheck:Cond obj:/lib/libz.so.1.2.3 obj:/lib/libz.so.1.2.3 fun:deflate } { Avoid problems w/readline doing a putenv and leaking on exit Memcheck:Leak fun:malloc fun:xmalloc fun:sh_set_lines_and_columns fun:_rl_get_screen_size fun:_rl_init_terminal_io obj:/lib/libreadline.so.4.3 fun:rl_initialize } ### ### These occur from somewhere within the SSL, when running ### test_socket_sll. They are too general to leave on by default. ### ###{ ### somewhere in SSL stuff ### Memcheck:Cond ### fun:memset ###} ###{ ### somewhere in SSL stuff ### Memcheck:Value4 ### fun:memset ###} ### ###{ ### somewhere in SSL stuff ### Memcheck:Cond ### fun:MD5_Update ###} ### ###{ ### somewhere in SSL stuff ### Memcheck:Value4 ### fun:MD5_Update ###} # # All of these problems come from using test_socket_ssl # { from test_socket_ssl Memcheck:Cond fun:BN_bin2bn } { from test_socket_ssl Memcheck:Cond fun:BN_num_bits_word } { from test_socket_ssl Memcheck:Value4 fun:BN_num_bits_word } { from test_socket_ssl Memcheck:Cond fun:BN_mod_exp_mont_word } { from test_socket_ssl Memcheck:Cond fun:BN_mod_exp_mont } { from test_socket_ssl Memcheck:Param write(buf) fun:write obj:/usr/lib/libcrypto.so.0.9.7 } { from test_socket_ssl Memcheck:Cond fun:RSA_verify } { from test_socket_ssl Memcheck:Value4 fun:RSA_verify } { from test_socket_ssl Memcheck:Value4 fun:DES_set_key_unchecked } { from test_socket_ssl Memcheck:Value4 fun:DES_encrypt2 } { from test_socket_ssl Memcheck:Cond obj:/usr/lib/libssl.so.0.9.7 } { from test_socket_ssl Memcheck:Value4 obj:/usr/lib/libssl.so.0.9.7 } { from test_socket_ssl Memcheck:Cond fun:BUF_MEM_grow_clean } { from test_socket_ssl Memcheck:Cond fun:memcpy fun:ssl3_read_bytes } { from test_socket_ssl Memcheck:Cond fun:SHA1_Update } { from test_socket_ssl Memcheck:Value4 fun:SHA1_Update } # custom suppressions for yoh { Memcheck:Cond obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so } { Memcheck:Addr4 obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/i686/cmov/libdl-2.7.so obj:/lib/ld-2.7.so obj:/lib/i686/cmov/libdl-2.7.so fun:dlopen fun:_PyImport_GetDynLoadFunc fun:_PyImport_LoadDynamicModule } { Memcheck:Cond obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/i686/cmov/libdl-2.7.so obj:/lib/ld-2.7.so obj:/lib/i686/cmov/libdl-2.7.so fun:dlopen fun:_PyImport_GetDynLoadFunc fun:_PyImport_LoadDynamicModule obj:/usr/bin/python2.4 obj:/usr/bin/python2.4 } { Memcheck:Cond obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/i686/cmov/libdl-2.7.so obj:/lib/ld-2.7.so obj:/lib/i686/cmov/libdl-2.7.so fun:dlopen fun:_PyImport_GetDynLoadFunc fun:_PyImport_LoadDynamicModule obj:/usr/bin/python2.4 obj:/usr/bin/python2.4 obj:/usr/bin/python2.4 } { Memcheck:Addr4 obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/ld-2.7.so obj:/lib/i686/cmov/libdl-2.7.so obj:/lib/ld-2.7.so obj:/lib/i686/cmov/libdl-2.7.so }