pax_global_header00006660000000000000000000000064130675131160014515gustar00rootroot0000000000000052 comment=d9ef6840a30c784fef1c7ed2c8f8c5302d5204e3 AppRecommender-0.7.5/000077500000000000000000000000001306751311600144275ustar00rootroot00000000000000AppRecommender-0.7.5/.gitignore000066400000000000000000000005031306751311600164150ustar00rootroot00000000000000*.pyc TODO doc/doxygen-1.7.3 doc/html/ popcon-data.tgz popcon-data/ psql-out udd-import.log udd.sql.gz *.log .vagrant/ *.swp apprecommender.egg-info/ apprecommender/tests/test_data/.sample_axi/ apprecommender/tests/test_data/.sample_axi/ apprecommender/tests/.apt_run/ apprecommender/web/submissions/ .travis.yml.container AppRecommender-0.7.5/.gitlab-ci.yml000066400000000000000000000004101306751311600170560ustar00rootroot00000000000000before_script: - ./install_dependencies.sh - sudo pip2 install flake8 - sudo /usr/bin/python2 -m apprecommender.main.cli --init - sudo /usr/bin/python2 -m apprecommender.main.cli --train test: script: - flake8 . - /usr/bin/python2 setup.py test AppRecommender-0.7.5/99app-recommender000066400000000000000000000002541306751311600176130ustar00rootroot00000000000000DPkg::Post-Invoke {"[ ! -x /usr/bin/apprec-apt ] || apprec-apt --post-invoke";}; DPkg::Pre-Install-Pkgs {"[ ! -x /usr/bin/apprec-apt ] || apprec-apt --pre-install-pkgs";}; AppRecommender-0.7.5/GPLv3.txt000066400000000000000000001045141306751311600160700ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 17. Interpretation of Sections 15 and 16. If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . AppRecommender-0.7.5/README000066400000000000000000000031751306751311600153150ustar00rootroot00000000000000AppRecommender - Application recommender for GNU/Linux systems --------------------------------------------------------------- Install dependencies --------------------- # apt-get install python python-xapian python-apt python-cluster python-simplejson python-numpy apt-xapian-index python-xdg debtags python-pip python-sklearn python-matplotlib python-fuzzywuzzy -y sudo update-apt-xapian-index pip install setuptools Run AppRecommender in Terminal ------------------------------ $ cd ./bin $ ./apprec.py -s cb Run "$ ./apprec.py -h" to view the recommender strategies Prepare AppRecommender data --------------------------- Run the following commands: $ ./install_dependencies.sh $ cd ./bin $ ./apprec.py --init Run Machine Learning Training ---------------------------- $ cd ./bin $ ./apprec.py --train License ------- Copyright 2011 Tassia Cames Araujo Copyright 2015-2016 Lucas Albuquerque Medeiros de Moura Copyright 2015-2016 Luciano Prestes Cavalcanti This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . AppRecommender-0.7.5/README.md000066400000000000000000000024171306751311600157120ustar00rootroot00000000000000AppRecommender [![Build Status](https://gitlab.com/AppRecommender/AppRecommender/badges/master/build.svg)](https://gitlab.com/AppRecommender/AppRecommender/builds) =============================================================== Application recommender for GNU/Linux systems --------------------------------------------------------------- Install dependencies --------------------- $ apt-get install python python-xapian python-apt python-cluster python-webpy python-simplejson python-numpy apt-xapian-index python-xdg debtags python-pip python-sklearn python-matplotlib python-stemmer -y $ sudo update-apt-xapian-index $ pip install setuptools Run AppRecommender web UI -------------------------- $ cd ./src/web $ ./server.py Open a browser and access http://localhost:8080 More info at https://github.com/tassia/AppRecommender/wiki Run AppRecommender in Terminal ------------------------------ $ cd ./bin $ ./apprec.py -s cb Run "$ ./apprec.py -h" to view the recommender strategies Prepare AppRecommender data --------------------------- Run the following commands: $ ./install_dependencies.sh $ cd ./bin $ ./apprec.py --init Run Machine Learning Training ---------------------------- $ cd ./bin $ ./apprec.py --train AppRecommender-0.7.5/Vagrantfile000066400000000000000000000005711306751311600166170ustar00rootroot00000000000000# -*- mode: ruby -*- # vi: set ft=ruby : Vagrant.configure(2) do |config| config.vm.box = "debian/jessie64" config.vm.box_check_update = false config.vm.network :forwarded_port, host: 8080, guest: 8080 config.vm.provision :shell, path: "vagrant/bootstrap.sh", privileged: false config.vm.provider "virtualbox" do |vm| vm.memory = 1024 vm.cpus = 2 end end AppRecommender-0.7.5/app_recommender.cfg000066400000000000000000000023541306751311600202540ustar00rootroot00000000000000# Config file for AppRecommender [general] # logging level debug = 0 verbose = 0 [data_sources] # base_dir = /var/lib/apprecommender/ base_dir = ~/.app-recommender/ user_data_dir = user_data/ output = apprec.log # filters for valid packages filters_dir = filters pkgs_filter = desktopapps # package information indexes axi = /var/lib/apt-xapian-index/index axi_programs = axi_programs axi_desktopapps = axi_desktopapps # old, reindex, cluster, recluster #index_mode = old # popcon indexes # check if there are popcon indexes available popcon = 0 popcon_programs = popcon_programs popcon_desktopapps = popcon_desktopapps popcon_index = popcon_desktopapps popcon_dir = popcon-entries # number of popcon submission for indexing max_popcon = 100000000 # popcon clustering clusters_dir = clusters_dir_full k_medoids = 100 # Connection to DDE dde_url = http://46.4.235.200:8000/q/udd/packages/all/%s?t=json self.dde_server = 46.4.235.200 self.dde_port = 8000 [recommender] # search weighting scheme ('trad' or 'bm25') weight = bm25 # bm25 parameters bm25_k1 = 1.2 bm25_k2 = 0 bm25_k3 = 7 bm25_b = 0.75 bm25_nl = 0.5 # recommendation strategy strategy = cb # user content profile size profile_size = 50 # neighborhood size k_neighbors = 50 popcon_profiling = full AppRecommender-0.7.5/apprecommender/000077500000000000000000000000001306751311600174305ustar00rootroot00000000000000AppRecommender-0.7.5/apprecommender/Makefile000066400000000000000000000000771306751311600210740ustar00rootroot00000000000000doc: ./generate_doc.sh clean : @find . -name \*.pyc -delete AppRecommender-0.7.5/apprecommender/__init__.py000066400000000000000000000001661306751311600215440ustar00rootroot00000000000000# \mainpage Application recommender for GNU/Linux systems # Main repository: http://github.com/tassia/AppRecommender AppRecommender-0.7.5/apprecommender/apt_cache.py000066400000000000000000000013441306751311600217130ustar00rootroot00000000000000#!/usr/bin/env python import apt import xapian class AptCache: DEFAULT_AXI_PATH = "/var/lib/apt-xapian-index/index" def __init__(self): self.axi = xapian.Database(AptCache.DEFAULT_AXI_PATH) self.cache = apt.Cache() def __getitem__(self, pkg_name): return self.get(pkg_name) def __contains__(self, pkg_name): return self.xapian_has_pkg(pkg_name) and pkg_name in self.cache def get(self, pkg_name): if self.xapian_has_pkg(pkg_name): return self.cache[pkg_name] raise KeyError("The cache has no package named '{}'".format(pkg_name)) def xapian_has_pkg(self, pkg_name): term = 'XP' + pkg_name return self.axi.get_termfreq(term) > 0L AppRecommender-0.7.5/apprecommender/config.py000066400000000000000000000226051306751311600212540ustar00rootroot00000000000000#!/usr/bin/env python """ config - python module for configuration options. """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import sys import os import logging import logging.handlers from ConfigParser import ConfigParser, MissingSectionHeaderError from apprecommender.singleton import Singleton class Config(Singleton): """ Class to handle configuration options. """ def __init__(self): """ Set default configuration options. """ try: self.config_parser = ConfigParser() self.config_parser.read( ['/etc/apprecommender/recommender.conf', os.path.expanduser('~/.app_recommender.rc'), os.path.expanduser('app_recommender.cfg')]) self.home_dir = os.path.expanduser('~/.apprecommender') except (MissingSectionHeaderError), err: logging.error("Error in config file syntax: %s", str(err)) os.abort() if not hasattr(self, 'initialized'): # data_source options self.base_dir = os.environ.get( 'APPREC_DATA', '/var/lib/apprecommender') self.base_dir = os.path.expanduser(self.base_dir) self.output = os.path.join(self.home_dir, 'apprec.log') self.user_data_dir = os.path.join(self.base_dir, "user_data/") # general options self.debug = 0 self.verbose = 0 # filters for valid packages self.filters_dir = os.path.join(self.base_dir, "filters") self.pkgs_filter = os.path.join(self.filters_dir, "desktopapps") # package information packages self.axi = "/var/lib/apt-xapian-index/index" self.axi_programs = os.path.join(self.base_dir, "axi_programs") self.axi_desktopapps = os.path.join(self.base_dir, "axi_desktopapps") # popcon indexes self.index_mode = "old" # check if there are popcon indexes available self.popcon = 0 self.popcon_programs = os.path.join(self.base_dir, "popcon_programs") self.popcon_desktopapps = os.path.join(self.base_dir, "popcon_desktopapps") self.popcon_index = self.popcon_desktopapps self.popcon_dir = os.path.join(self.base_dir, "popcon-entries") self.max_popcon = 1000 # popcon clustering self.clusters_dir = os.path.join(self.base_dir, "clusters-dir") self.k_medoids = 100 # self.dde_url = "http://dde.debian.net/dde/" \ # "q/udd/packs/all/%s?t=json" self.dde_url = "http://46.4.235.200:8000/" \ "q/udd/packages/prio-debian-sid/%s?t=json" self.dde_server = "46.4.235.200" self.dde_port = 8000 # recomender options self.strategy = "cb" self.weight = "bm25" self.bm25_k1 = 1.2 self.bm25_k2 = 0 self.bm25_k3 = 7 self.bm25_b = 0.75 self.bm25_nl = 0.5 # user content profile size self.profile_size = 10 self.num_recommendations = 8 self.because = False # neighborhood size self.k_neighbors = 50 # popcon profiling method: full, voted self.popcon_profiling = "full" self.load_config_file() self.initialized = 1 logging.info("Basic config") def read_option(self, section, option): """ Read option from configuration file if it is defined there or return default value. """ var = "self.%s" % option if self.config_parser.has_option(section, option): return self.config_parser.get(section, option) else: return eval(var) def load_config_file(self): """ Load options from configuration file and command line arguments. """ self.debug = int(self.read_option('general', 'debug')) self.debug = int(self.read_option('general', 'verbose')) self.base_dir = os.environ.get( 'APPREC_DATA', '/var/lib/apprecommender') self.base_dir = os.path.expanduser(self.base_dir) self.output = os.path.join( self.home_dir, self.read_option('data_sources', 'output')) self.user_data_dir = os.path.join( self.base_dir, self.read_option('data_sources', 'user_data_dir')) self.filters_dir = os.path.join( self.base_dir, self.read_option('data_sources', 'filters_dir')) self.pkgs_filter = os.path.join( self.filters_dir, self.read_option('data_sources', 'pkgs_filter')) self.axi = self.read_option('data_sources', 'axi') self.axi_programs = os.path.join( self.base_dir, self.read_option('data_sources', 'axi_programs')) self.axi_desktopapps = os.path.join( self.base_dir, self.read_option('data_sources', 'axi_desktopapps')) # self.index_mode = self.read_option('data_sources', 'index_mode') self.popcon = int(self.read_option('data_sources', 'popcon')) self.popcon_programs = os.path.join( self.base_dir, self.read_option('data_sources', 'popcon_programs')) self.popcon_desktopapps = os.path.join( self.base_dir, self.read_option('data_sources', 'popcon_desktopapps')) self.popcon_index = os.path.join( self.base_dir, self.read_option('data_sources', 'popcon_index')) self.popcon_dir = os.path.join( self.base_dir, self.read_option('data_sources', 'popcon_dir')) self.max_popcon = int(self.read_option('data_sources', 'max_popcon')) self.clusters_dir = os.path.join( self.base_dir, self.read_option('data_sources', 'clusters_dir')) self.k_medoids = int(self.read_option('data_sources', 'k_medoids')) self.dde_url = self.read_option('data_sources', 'dde_url') self.dde_server = self.read_option('data_sources', 'dde_server') self.dde_port = self.read_option('data_sources', 'dde_port') self.weight = self.read_option('recommender', 'weight') self.bm25_k1 = float(self.read_option('recommender', 'bm25_k1')) self.bm25_k2 = float(self.read_option('recommender', 'bm25_k2')) self.bm25_k3 = float(self.read_option('recommender', 'bm25_k3')) self.bm25_b = float(self.read_option('recommender', 'bm25_b')) self.bm25_nl = float(self.read_option('recommender', 'bm25_nl')) self.strategy = self.read_option('recommender', 'strategy') self.profile_size = int( self.read_option('recommender', 'profile_size')) self.k_neighbors = int( self.read_option('recommender', 'k_neighbors')) self.popcon_profiling = self.read_option( 'recommender', 'popcon_profiling') def set_logger(self): """ Configure application logger and log level. """ self.logger = logging.getLogger('') # root logger is used by default self.logger.setLevel(logging.WARNING) if self.debug == 1: log_level = logging.DEBUG elif self.verbose == 1: log_level = logging.INFO else: log_level = logging.WARNING console_handler = logging.StreamHandler(sys.stdout) console_handler.setFormatter(logging.Formatter( '%(levelname)s: %(message)s')) console_handler.setLevel(log_level) self.logger.addHandler(console_handler) if not os.path.exists(self.home_dir): os.makedirs(self.home_dir) file_handler = logging.handlers.RotatingFileHandler(self.output, maxBytes=50000000, backupCount=5) log_format = '%(asctime)s %(levelname)-8s %(message)s' file_handler.setFormatter(logging.Formatter( log_format, datefmt='%Y-%m-%d %H:%M:%S')) file_handler.setLevel(log_level) self.logger.addHandler(file_handler) logging.info("Set up logger") AppRecommender-0.7.5/apprecommender/data.py000066400000000000000000000246711306751311600207250ustar00rootroot00000000000000#!/usr/bin/env python """ data - python module for data sources classes and methods. """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import xapian import logging import random import operator import math import commands from apprecommender.utils import print_progress_bar from apprecommender.data_classification import time_weight from apprecommender.error import Error def axi_get_pkgs(axi): pkgs_names = [] for docid in range(1, axi.get_lastdocid() + 1): try: doc = axi.get_document(docid) except: pass docterms_XP = [t.term for t in doc.termlist() if t.term.startswith("XP")] pkgs_names.append(docterms_XP[0].lstrip('XP')) return pkgs_names def axi_search_pkgs(axi, pkgs_list): terms = ["XP" + item for item in pkgs_list] query = xapian.Query(xapian.Query.OP_OR, terms) enquire = xapian.Enquire(axi) enquire.set_query(query) mset = enquire.get_mset(0, axi.get_doccount()) return mset def axi_search_pkg_tags(axi, pkg): enquire = xapian.Enquire(axi) enquire.set_query(xapian.Query("XP" + pkg)) matches = enquire.get_mset(0, 1) if not matches: logging.debug("Package %s not found in items repository" % pkg) return False for m in matches: tags = [term.term for term in axi.get_document(m.docid).termlist() if term.term.startswith("XT")] if not tags: return "notags" else: return tags def print_index(index): output = "\n---\n" + xapian.Database.__repr__(index) + "\n---\n" for term in index.allterms(): output += term.term + "\n" output += str([index.get_document(posting.docid).get_data() for posting in index.postlist(term.term)]) output += "\n---" return output def get_user_installed_pkgs(): dpkg_output = commands.getoutput('/usr/bin/dpkg --get-selections') packages = [pkg.split('\t')[0] for pkg in dpkg_output.splitlines() if 'deinstall' not in pkg.split('\t')[-1]] packages = set(packages) return packages def get_all_terms(index, docs, content_filter, normalized_weights): # Store all terms in one single document terms_packages = {} terms_doc = xapian.Document() for d in docs: package = d.document.get_data() for term in index.get_document(d.docid).termlist(): if content_filter(term.term): if normalized_weights: terms_doc.add_term(term.term, int(math.ceil( normalized_weights[d.docid]))) else: terms_doc.add_term(term.term) if term.term.startswith('XP'): continue elif term.term in terms_packages: terms_packages[term.term].append(package) else: terms_packages[term.term] = [package] return (terms_doc, terms_packages) def get_tfidf_terms_weights(terms_doc, index, terms_package, time_context=0): # Compute sublinear tfidf for each term weights = {} for term in terms_doc.termlist(): try: # Even if it shouldn't raise error... # math.log: ValueError: math domain error tf = 1 + math.log(term.wdf) idf = math.log(index.get_doccount() / float(index.get_termfreq(term.term))) tfidf = tf * idf weights[term.term] = tfidf if time_context: weight = time_weight(term.term, terms_package[term.term]) weights[term.term] *= weight except: pass # print_best_weight_terms(terms_package) return weights def tfidf_weighting(index, docs, content_filter, normalized_weights=0, time_context=0): """ Return a dictionary of terms and weights of all terms of a set of documents, based on the frequency of terms in the selected set (docids). """ terms_doc, terms_packages = get_all_terms(index, docs, content_filter, normalized_weights) weights = get_tfidf_terms_weights(terms_doc, index, terms_packages, time_context) sorted_weights = list(reversed(sorted(weights.items(), key=operator.itemgetter(1)))) return sorted_weights def tfidf_plus(index, docs, content_filter, time_context=0): """ Return a dictionary of terms and weights of all terms of a set of documents, based on the frequency of terms in the selected set (docids). """ normalized_weigths = {} population = [d.weight for d in docs] mean = sum(population) / len(population) variance = sum([(p - mean) * (p - mean) for p in population]) / len(population) standard_deviation = math.sqrt(variance) for d in docs: if standard_deviation > 1: # values between [0-1] would cause the opposite effect normalized_weigths[d.docid] = d.weight / standard_deviation else: normalized_weigths[d.docid] = d.weight return tfidf_weighting(index, docs, content_filter, normalized_weigths, time_context) def split_pkg_data(user_pkg, partition_size): round_partition = {} for i in range(partition_size): if len(user_pkg) > 0: random_key = random.choice(user_pkg.keys()) else: logging.critical("Empty user_pkg.") raise Error round_partition[random_key] = user_pkg.pop(random_key) return round_partition class FilteredXapianIndex(xapian.WritableDatabase): """ Filtered Xapian Index """ def __init__(self, terms, index_path, path): xapian.WritableDatabase.__init__(self, path, xapian.DB_CREATE_OR_OVERWRITE) index = xapian.Database(index_path) for docid in range(1, index.get_lastdocid() + 1): try: doc = index.get_document(docid) docterms = [term.term for term in doc.termlist()] tagged = False for t in terms: if t in docterms: tagged = True if tagged: self.add_document(doc) logging.info("Added doc %d." % docid) else: logging.info("Discarded doc %d." % docid) except: logging.info("Doc %d not found in axi." % docid) logging.info("Filter: %s" % terms) logging.info("Index size: %d" % index.get_doccount()) logging.info("Filtered Index size: %d (lastdocid: %d)." % (self.get_doccount(), self.get_lastdocid())) def __str__(self): return print_index(self) class SampleAptXapianIndex(xapian.WritableDatabase): """ Sample data source for packages information, generated from a list of packages. """ def __init__(self, pkgs_list, axi, path): xapian.WritableDatabase.__init__(self, path, xapian.DB_CREATE_OR_OVERWRITE) self.sample = axi_search_pkgs(axi, pkgs_list) len_sample = len(self.sample) for index, package in enumerate(self.sample): self.doc_id = self.add_document(axi.get_document(package.docid)) print_progress_bar(index + 1, len_sample) def __str__(self): return print_index(self) class PopconSubmission(): def __init__(self, path, user_id=0, binary=1): self.packages = dict() self.path = path self.binary = binary self.load() if user_id: self.user_id = user_id def __str__(self): output = "\nPopularity-contest submission ID " + self.user_id for pkg, weight in self.packages.items(): output += "\n " + pkg + ": " + str(weight) return output def get_filtered(self, filter_list): filtered = {} for pkg in self.packages.keys(): if pkg in filter_list: filtered[pkg] = self.packages[pkg] return filtered def load(self, binary=1): """ Parse a popcon submission, generating the names of the valid packages in the vote. """ with open(self.path) as submission: for line in submission: if line.startswith("POPULARITY"): self.user_id = line.split()[2].lstrip("ID:") self.arch = line.split()[3].lstrip("ARCH:") elif not line.startswith("END-POPULARITY"): data = line.rstrip('\n').split() if len(data) > 2: pkg = data[2] if len(data) > 3: exec_file = data[3] # Binary weight if self.binary: self.packages[pkg] = 1 # Weights inherited from Enrico's anapop # No executable files to track elif exec_file == '': self.packages[pkg] = 1 # Recently used packages elif len(data) == 4: self.packages[pkg] = 10 # Unused packages elif data[4] == '': self.packages[pkg] = 3 # Recently installed packages elif data[4] == '': self.packages[pkg] = 8 AppRecommender-0.7.5/apprecommender/data_classification.py000066400000000000000000000070511306751311600237710ustar00rootroot00000000000000#!/usr/bin/env python import commands import calendar import logging import math import operator import time pkgs_times = {} pkgs_time_weight = {} best_weight_terms = {} user_tfidf_weights = {} def pkg_name_with_error(pkg): return len(pkg.split()) > 1 def get_time_from_package(pkg, pkg_bin=True): if pkg_name_with_error(pkg): pkgs_times[pkg] = [None, None] if pkg in pkgs_times: modify, access = pkgs_times[pkg] else: modify = get_time('Y', pkg, pkg_bin) access = get_time('X', pkg, pkg_bin) pkgs_times[pkg] = [modify, access] return pkgs_times[pkg] def get_alternative_pkg(pkg): dpkg_command = "dpkg -L {0}| grep /usr/bin/" dpkg_command += " || dpkg -L {0}| grep /usr/sbin/" pkg_bin = commands.getoutput(dpkg_command.format(pkg)) possible_pkgs = {} for pkg_path in pkg_bin.splitlines(): possible_pkgs[pkg_path] = get_time('X', pkg_path) if bool(possible_pkgs): return sorted(possible_pkgs.items(), key=operator.itemgetter(1))[0][0] return None def get_time(option, pkg, pkg_bin=True): stat_base = "stat -c '%{option}'" stat_base += " `which {package}`" if pkg_bin else " {package}" stat_error = 'stat:' stat_time = stat_base.format(option=option, package=pkg) pkg_time = commands.getoutput(stat_time) if stat_error not in pkg_time: return pkg_time return None def linear_percent_function(modify, access, time_now): modify, access = int(modify), int(access) time_access = access - modify time_actual = time_now - modify percent = float(time_access) / float(time_actual) return percent def get_pkg_time_weight(pkg): modify, access = get_time_from_package(pkg) if not modify and not access: modify, access = get_time_from_package(get_alternative_pkg(pkg)) if not modify and not access: return 0 time_now = calendar.timegm(time.gmtime()) return linear_percent_function(modify, access, time_now) def calculate_time_curve(pkg_time_weight): if not pkg_time_weight: return 0 const_a = 10 lambda_value = 1 return const_a * (1 / math.exp((1 - pkg_time_weight) * lambda_value)) def time_weight(term, term_list): weight = [] weight_len = 5 weight_delta = 0.2 for pkg in term_list: if pkg in pkgs_time_weight: weight.append(pkgs_time_weight[pkg]) else: pkg_time_weight = get_pkg_time_weight(pkg) pkgs_time_weight[pkg] = pkg_time_weight weight.append(calculate_time_curve(pkg_time_weight)) weight = list(reversed(sorted(weight))) if len(weight) < weight_len: for i in range(len(weight), weight_len): weight.append(weight[-1] - weight_delta) time_weight = float(sum(weight[0:weight_len])) / float(weight_len) best_weight_terms[term] = time_weight return time_weight def print_best_weight_terms(terms_package): index = 0 total = 0 logging.info("BEST TERMS") for term in sorted(best_weight_terms, key=best_weight_terms.get, reverse=True): if index < 10: logging.info("\n") logging.info(term, best_weight_terms[term]) logging.info('-') for pkg in terms_package[term]: logging.info("[{0}: {1} {2}]\n".format(pkg, get_pkg_time_weight(pkg), get_alternative_pkg(pkg))) total += 1 if total > 5: break total = 0 index += 1 AppRecommender-0.7.5/apprecommender/decider.py000066400000000000000000000200471306751311600214040ustar00rootroot00000000000000#!/usr/bin/env python import commands import re import xapian from apprecommender.apt_cache import AptCache from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS class PkgInitDecider(): """ Class used to decide if a package can be considered to recommended to an user or not. """ INVALID_PREFIXES = {'ruby', 'python', 'python3', 'golang', 'gir', 'texlive'} INVALID_SUFFIXES = {'examples', 'dbg', 'data', 'dev', 'utils', 'common', 'fonts', 'png', 'core', 'default'} def __init__(self): self.cache = AptCache() self.user_role_programs = self.get_user_role_programs() def is_in_apt_cache(self, pkg): return pkg in self.cache def get_package_dependencies(self, pkg): return [dep[0].name for dep in pkg.dependencies] def get_user_installed_packages(self): manual_installed = commands.getoutput('apt-mark showmanual') return manual_installed.splitlines() def get_user_role_programs(self): user_pkgs = self.get_user_installed_packages() user_programs = set() for pkg in user_pkgs: if pkg in self.cache: pkg_candidate = self.cache[pkg].candidate pkg_tags = pkg_candidate.record.get('Tag', None) if not pkg_tags: continue if 'role::program' in pkg_tags: user_programs.add(pkg) return user_programs def is_section_doc(self, pkg_section): return pkg_section == 'doc' def is_valid_dependency(self, pkg_tags, pkg_section): tags_dep = 'role::program' in pkg_tags or 'devel::editor' in pkg_tags section_dep = pkg_section == 'interpreters' return tags_dep or section_dep def is_program_dependencies_installed(self, pkg): pkg_dependencies = self.get_package_dependencies(pkg) dep_programs = set() for dep in pkg_dependencies: if dep in self.cache: pkg = self.cache[dep].candidate if pkg is not None: pkg_tags = pkg.record.get('Tag', None) pkg_section = pkg.section if pkg_tags is None: continue is_valid_dependency = self.is_valid_dependency( pkg_tags, pkg_section) if is_valid_dependency: dep_programs.add(dep) return len(dep_programs - self.user_role_programs) == 0 def is_pkg_a_prefix_or_suffix(self, pkg): splited_pkg = pkg.split('-') if len(splited_pkg) == 1: return False pkg_prefix = splited_pkg[0] for prefix in PkgInitDecider.INVALID_PREFIXES: if pkg_prefix.startswith(prefix): return True for suffix in PkgInitDecider.INVALID_SUFFIXES: if (splited_pkg[-1].endswith(suffix) or splited_pkg[-2].endswith(suffix)): return True return False def __call__(self, pkg): if not self.is_in_apt_cache(pkg): return False pkg_candidate = self.cache[pkg].candidate if not pkg_candidate: return False if not self.is_program_dependencies_installed(pkg_candidate): return False if self.is_pkg_a_prefix_or_suffix(pkg): return False if self.is_section_doc(pkg_candidate.section): return False return True class PkgMatchDecider(xapian.MatchDecider): """ Extend xapian.MatchDecider to not consider installed packages. """ def __init__(self, pkgs_list): """ Set initial parameters. """ xapian.MatchDecider.__init__(self) self.pkgs_list = pkgs_list def __call__(self, xapian_document): """ True if the package is not already installed and is not a lib or a xapian_document. """ pkg = xapian_document.get_data() is_new = pkg not in self.pkgs_list is_new = is_new and ':' not in pkg if "kde" in pkg: return is_new and "kde" in self.pkgs_list if "gnome" in pkg: return is_new and "gnome" in self.pkgs_list if re.match(r'^lib.*', pkg) or re.match(r'.*doc$', pkg): return False return is_new class PkgExpandDecider(xapian.ExpandDecider): """ Extend xapian.ExpandDecider to consider packages only. """ def __init__(self, pkgs_list): """ Set initial parameters. """ xapian.ExpandDecider.__init__(self) self.pkgs_list = pkgs_list def __call__(self, term): """ True if the term is a package. """ pkg = term.lstrip("XP") is_new_pkg = pkg not in self.pkgs_list and term.startswith("XP") if "kde" in pkg: return is_new_pkg and "kde" in self.pkgs_list if "gnome" in pkg: return is_new_pkg and "gnome" in self.pkgs_list return is_new_pkg class PkgReverseDependeciesDecider(xapian.MatchDecider): """ Extend xapian.MatchDecider to consider only packages on valid list """ def __init__(self, reverse_dependencies, user_installed_pkgs): """ Set initial parameters. """ xapian.MatchDecider.__init__(self) self.reverse_dependencies = reverse_dependencies self.pkg_init_decider = PkgInitDecider() self.pkg_match_decider = PkgMatchDecider(user_installed_pkgs) self.cache = AptCache() def __call__(self, xapian_document): """ True if the package is on pkg_list """ pkg = xapian_document.get_data() if pkg not in self.reverse_dependencies: return False if pkg not in self.cache: return False if self.cache[pkg].section == 'doc': return False decider = self.pkg_init_decider pkg_candidate = self.cache[pkg].candidate if not decider.is_program_dependencies_installed(pkg_candidate): return False return self.pkg_match_decider(xapian_document) class TagExpandDecider(xapian.ExpandDecider): """ Extend xapian.ExpandDecider to consider tags only. """ def __call__(self, term): """ True if the term is a package tag. """ return term.startswith("XT") class FilterTag(xapian.ExpandDecider): """ Extend xapian.ExpandDecider to consider only tag terms. """ def __init__(self, valid_tags): """ Set initial parameters. """ xapian.ExpandDecider.__init__(self) self.valid_tags = valid_tags def __call__(self, term): """ Return true if the term is a tag, else false. """ if self.valid_tags: is_valid = term.lstrip("XT") in self.valid_tags else: is_valid = 1 return term.startswith("XT") and is_valid class FilterDescription(xapian.ExpandDecider): """ Extend xapian.ExpandDecider to consider only package description terms. """ def __init__(self): xapian.ExpandDecider.__init__(self) self.stop_words = ENGLISH_STOP_WORDS def __call__(self, term): """ Return true if the term or its stemmed version is part of a package description. """ if term not in self.stop_words: return term.islower() and re.search('[a-z]', term) return False class FilterTag_or_Description(xapian.ExpandDecider): """ Extend xapian.ExpandDecider to consider only package description terms. """ def __init__(self, valid_tags): """ Set initial parameters. """ xapian.ExpandDecider.__init__(self) self.valid_tags = valid_tags def __call__(self, term): """ Return true if the term or its stemmed version is part of a package description. """ is_tag = FilterTag(self.valid_tags)(term) is_description = FilterDescription()(term) return is_tag or is_description AppRecommender-0.7.5/apprecommender/error.py000066400000000000000000000016661306751311600211440ustar00rootroot00000000000000#!/usr/bin/env python """ error.py - python module for error definition. """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ class Error(Exception): """ Base class for exceptions. """ pass AppRecommender-0.7.5/apprecommender/evaluation.py000066400000000000000000000404621306751311600221570ustar00rootroot00000000000000#!/usr/bin/env python """ evaluation - python module for classes and methods related to recommenders evaluation. """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import math import logging from abc import ABCMeta, abstractmethod from collections import defaultdict from apprecommender.error import Error from apprecommender.singleton import Singleton from apprecommender.recommender import RecommendationResult from apprecommender.user import User from apprecommender.data import split_pkg_data class Metric(Singleton): """ Base class for metrics. Strategy design pattern. """ def get_errors(self, evaluation): """ Compute prediction errors. """ keys = evaluation.predicted_item_scores.keys() keys.extend(evaluation.real_item_scores.keys()) errors = [] for k in keys: if k not in evaluation.real_item_scores: evaluation.real_item_scores[k] = 0.0 if k not in evaluation.predicted_item_scores: evaluation.predicted_item_scores[k] = 0.0 errors.append(float(evaluation.predicted_item_scores[k] - evaluation.real_item_scores[k])) return errors class SimpleAccuracy(Metric): """ Classification accuracy metric which consider classes sizes. """ def __init__(self): """ Set metric description. """ self.desc = " S_Accuracy " def run(self, evaluation): """ Compute metric. """ simple_accurary = float((evaluation.repository_size - evaluation.false_positive_len) - evaluation.false_negative_len) return simple_accurary / evaluation.repository_size class Accuracy(Metric): """ Classification accuracy metric which consider classes sizes. """ def __init__(self): """ Set metric description. """ self.desc = " Accuracy " def run(self, evaluation): """ Compute metric. """ error_1 = (float(evaluation.false_positive_len) / (evaluation.repository_size - evaluation.real_relevant_len)) error_2 = (float(evaluation.false_negative_len) / evaluation.real_relevant_len) accuracy = 1 - (float(error_1 + error_2) / 2) return accuracy class Precision(Metric): """ Classification accuracy metric defined as the percentage of relevant itens among the predicted ones. """ def __init__(self): """ Set metric description. """ self.desc = " Precision " def run(self, evaluation): """ Compute metric. """ precision = float(evaluation.true_positive_len) if not precision: return 0.0 return precision / float(evaluation.predicted_relevant_len) class Recall(Metric): """ Classification ccuracy metric defined as the percentage of relevant itens which were predicted as so. """ def __init__(self): """ Set metric description. """ self.desc = " Recall " def run(self, evaluation): """ Compute metric. """ recall = float(evaluation.true_positive_len) if not recall: return 0.0 return recall / evaluation.real_relevant_len class FPR(Metric): """ False positive rate (used for ploting ROC curve). """ def __init__(self): """ Set metric description. """ self.desc = " FPR " def run(self, evaluation): """ Compute metric. """ if not evaluation.false_positive_len: return 0.0 return (float(evaluation.false_positive_len) / evaluation.real_negative_len) class MCC(Metric): """ Matthews correlation coefficient. """ def __init__(self): """ Set metric description. """ self.desc = " MCC " def run(self, evaluation): """ Compute metric. """ VP = evaluation.true_positive_len FP = evaluation.false_positive_len FN = evaluation.false_negative_len VN = evaluation.true_negative_len if ((VP + FP) == 0 or (VP + FN) == 0 or (VN + FP) == 0 or (VN + FN) == 0): return 0 MCC = (((VP * VN) - (FP * FN)) / math.sqrt((VP + FP) * (VP + FN) * (VN + FP) * (VN + FN))) return MCC class F_score(Metric): """ Classification accuracy metric which correlates precision and recall into an unique measure. """ def __init__(self, k): """ Set metric description. """ self.desc = " F(%.1f) " % k self.k = k def run(self, evaluation): """ Compute metric. """ p = Precision().run(evaluation) r = Recall().run(evaluation) if ((self.k * self.k * p) + r) > 0: return float(((1 + (self.k * self.k)) * ((p * r) / ((self.k * self.k * p) + r)))) else: return 0 class MAE(Metric): """ Prediction accuracy metric defined as the mean absolute error. """ def __init__(self): """ Set metric description. """ self.desc = " MAE " def run(self, evaluation): """ Compute metric. """ errors = self.get_errors(evaluation) return sum(errors) / len(errors) class MSE(Metric): """ Prediction accuracy metric defined as the mean square error. """ def __init__(self): """ Set metric description. """ self.desc = " MSE " def run(self, evaluation): """ Compute metric. """ errors = self.get_errors(evaluation) square_errors = [pow(x, 2) for x in errors] return sum(square_errors) / len(square_errors) class RMSE(MSE): """ Prediction accuracy metric defined as the root mean square error. """ def __init__(self): """ Set metric description. """ self.desc = " RMSE " def run(self, evaluation): """ Compute metric. """ return math.sqrt(MSE.run(evaluation)) class Coverage(Metric): """ Evaluation metric defined as the percentage of itens covered by the recommender (have been recommended at least once). """ def __init__(self): """ Set initial parameters. """ self.desc = " Coverage " def run(self, evaluations_set): """ Compute metric. """ covered = set() for evaluation in evaluations_set: covered.update(set(evaluation.predicted_relevant)) return float(len(covered)) / evaluation.repository_size class Evaluation: """ Class designed to perform prediction evaluation, given data and metric. """ def __init__(self, predicted, real, repository_size): """ Set initial parameters. """ self.repository_size = repository_size self.predicted_item_scores = predicted.item_score self.predicted_relevant = predicted.get_prediction() self.predicted_relevant_len = len(self.predicted_relevant) self.real_item_scores = real.item_score self.real_relevant = real.get_prediction() self.real_relevant_len = len(self.real_relevant) self.true_positive = [v[0] for v in self.predicted_relevant if v[0] in [w[0] for w in self.real_relevant]] self.false_positive = [v[0] for v in self.predicted_relevant if not v[0] in [w[0] for w in self.real_relevant]] self.false_negative = [v[0] for v in self.real_relevant if not v[0] in [w[0] for w in self.predicted_relevant]] self.true_positive_len = len(self.true_positive) self.false_positive_len = len(self.false_positive) self.false_negative_len = len(self.false_negative) self.real_negative_len = self.repository_size - self.real_relevant_len self.true_negative_len = (self.real_negative_len - len(self.false_positive)) logging.debug("TP: %d" % len(self.true_positive)) logging.debug("FP: %d" % len(self.false_positive)) logging.debug("FN: %d" % len(self.false_negative)) logging.debug("TN: %d" % self.true_negative_len) logging.debug("Repo_size: %d" % self.repository_size) logging.debug("Relevant: %d" % len(self.real_relevant)) logging.debug("Irrelevant: %d" % self.real_negative_len) def run(self, metric): """ Perform the evaluation with the given metric. """ return metric.run(self) class CrossValidation: __metaclass__ = ABCMeta """ Class designed to perform cross-validation process. :param partition_proportion: A value that should dictates the number of data that will be on the traning data, the rest of data will be used as the validation set. :param rounds: The number of rounds that cross validation will be used on. :param rec: The recommendation strategy used. :param metrics_list: Array of the metrics that will be used to evaluate the algorithm. :param result_proportion: The percentage of recommendations that should be used based on the total number of packages available. """ def __init__(self, partition_proportion, rounds, rec, metrics_list, result_proportion): """ Set initial parameters. """ if partition_proportion < 1 and partition_proportion > 0: self.partition_proportion = partition_proportion else: logging.critical("Partition proportion must be a value" "in the interval [0,1].") raise Error self.rounds = rounds self.recommender = rec self.metrics_list = metrics_list self.cross_results = defaultdict(list) self.result_proportion = result_proportion @abstractmethod def get_evaluation(self, predicted_result, real_result): raise NotImplementedError("Method not implemented.") @abstractmethod def get_model(self): raise NotImplementedError("Method not implemented.") def get_result_size(self): result_size = (self.recommender.items_repository.get_doccount() * self.result_proportion) result_size = int(result_size) logging.debug("size %d" % result_size) return result_size @abstractmethod def get_real_results(self, round_partition): raise NotImplementedError("Method not implemented.") def get_partition_size(self, cross_item_score): return int(len(cross_item_score) * self.partition_proportion) @abstractmethod def get_predicted_results(self, round_user, round_partition, result_size): raise NotImplementedError("Method not implemented.") @abstractmethod def get_user_score(self, user): raise NotImplementedError("Method not implemented.") def reset_cross_item_score(self, cross_item_score, round_partition): while len(round_partition) > 0: item, score = round_partition.popitem() cross_item_score[item] = score return cross_item_score def run_metrics(self, predicted_result, real_result): logging.debug("Predicted result: %s", predicted_result) evaluation = self.get_evaluation(predicted_result, real_result) for metric in self.metrics_list: result = evaluation.run(metric) self.cross_results[metric.desc].append(result) def run(self, user): """ Perform cross-validation. """ ''' A dictionary containing all the usefull user packages. Its key is a package name, and its value is the input vector associated with that package. ''' cross_item_score = self.get_user_score(user) # The amount of data that will be used to train the algorithm partition_size = self.get_partition_size(cross_item_score) # main iteration for r in range(self.rounds): round_partition = split_pkg_data(cross_item_score, partition_size) logging.debug("Round partition: %s", str(round_partition)) logging.debug("Cross item-score: %s", str(cross_item_score)) # The algorithm model created with the selected training data. round_model = self.get_model(round_partition) result_size = self.get_result_size() if not result_size: logging.critical("Recommendation size is zero.") raise Error predicted_result = self.get_predicted_results( round_model, cross_item_score, result_size) if not predicted_result.size: logging.critical("No recommendation produced" " Abort cross-validation.") raise Error # partition is considered the expected result real_result = self.get_real_results(cross_item_score) self.run_metrics(predicted_result, real_result) # moving back items from round_partition to cross_item_score cross_item_score = self.reset_cross_item_score(cross_item_score, round_partition) class CrossValidationRecommender(CrossValidation): def get_evaluation(self, predicted_result, real_result): num_docs = self.recommender.items_repository.get_doccount() return Evaluation(predicted_result, real_result, num_docs) def get_model(self, cross_item_score): return User(cross_item_score) def get_pkg_score(self, user): cross_item_score = {} for pkg in user.pkg_profile: cross_item_score[pkg] = user.item_score[pkg] return cross_item_score def get_real_results(self, round_partition): return RecommendationResult(round_partition) def get_predicted_results(self, round_user, round_partition, result_size): return self.recommender.get_recommendation(round_user, result_size) def get_user_score(user): cross_item_score = {} for pkg in user.pkg_profile: cross_item_score[pkg] = user.item_score[pkg] return cross_item_score def __str__(self): """ String representation of the object. """ str = "\n" metrics_desc = "" for metric in self.metrics_list: metrics_desc += "%s|" % (metric.desc) str += "| Round |%s\n" % metrics_desc for r in range(self.rounds): metrics_result = "" for metric in self.metrics_list: metrics_result += (" %2.1f%% |" % (self.cross_results[metric.desc][r] * 100)) str += "| %d |%s\n" % (r, metrics_result) metrics_mean = "" for metric in self.metrics_list: mean = float(sum(self.cross_results[metric.desc]) / len(self.cross_results[metric.desc])) metrics_mean += " %2.1f%% |" % (mean * 100) str += "| Mean |%s\n" % (metrics_mean) return str AppRecommender-0.7.5/apprecommender/experiments/000077500000000000000000000000001306751311600217735ustar00rootroot00000000000000AppRecommender-0.7.5/apprecommender/experiments/README000066400000000000000000000003631306751311600226550ustar00rootroot00000000000000AppRecommender experiments and tests --------------------------------------- Install dependencies: # apt-get install \ python-unittest2 python-gnuplot gnuplot # cd ./src; git clone https://github.com/rueckstiess/expsuite (deprecated tests) AppRecommender-0.7.5/apprecommender/experiments/deprecated/000077500000000000000000000000001306751311600240735ustar00rootroot00000000000000AppRecommender-0.7.5/apprecommender/experiments/deprecated/README000066400000000000000000000001111306751311600247440ustar00rootroot00000000000000Experiments handled by expsuite: https://github.com/rueckstiess/expsuite AppRecommender-0.7.5/apprecommender/experiments/deprecated/clustering-suite.py000077500000000000000000000040211306751311600277530ustar00rootroot00000000000000#!/usr/bin/env python """ recommender suite - recommender experiments suite """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import sys import os import logging sys.path.insert(0, '../') from apprecommender.config import Config from apprecommender.data import PopconXapianIndex if __name__ == '__main__': cfg = Config() cfg.index_mode = "recluster" logging.info("Starting clustering experiments") logging.info("Medoids: %d\t Max popcon:%d" % (cfg.k_medoids, cfg.max_popcon)) cfg.popcon_dir = os.path.expanduser( "~/org/popcon.debian.org/popcon-mail/popcon-entries/") cfg.popcon_index = cfg.popcon_index + ("_%dmedoids%dmax" % (cfg.k_medoids, cfg.max_popcon)) cfg.clusters_dir = cfg.clusters_dir + ("_%dmedoids%dmax" % (cfg.k_medoids, cfg.max_popcon)) pxi = PopconXapianIndex(cfg) logging.info("Overall dispersion: %f\n" % pxi.cluster_dispersion) # Write clustering log output = open(("results/clustering/%dmedoids%dmax" % (cfg.k_medoids, cfg.max_popcon)), 'w') output.write("# k_medoids\tmax_popcon\tdispersion\n") output.write("%d %f\n" % (cfg.k_medoids, cfg.max_popcon, pxi.cluster_dispersion)) output.close() AppRecommender-0.7.5/apprecommender/experiments/deprecated/experiments.cfg000066400000000000000000000012221306751311600271140ustar00rootroot00000000000000[DEFAULT] repetitions = 1 iterations = 10 path = 'results' experiment = 'grid' weight = ['bm25', 'trad'] ;profile_size = range(10,100,10) ;sample = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] sample = [0.6, 0.7, 0.8, 0.9] [content] strategy = ['cb','cbt','cbd'] [clustering] experiment = 'single' ;iterations = 4 ;medoids = range(2,6) iterations = 6 medoids = [100,500,1000,5000,10000,50000] ;disabled for this experiment weight = 0 profile_size = 0 sample = 0 [colaborative] users_repository=["data/popcon","data/popcon-100","data/popcon-500","data/popcon-1000","data/popcon-5000","data/popcon-10000","data/popcon-50000"] neighbors = range(10,1010,50) AppRecommender-0.7.5/apprecommender/experiments/deprecated/k-suite.py000077500000000000000000000173731306751311600260440ustar00rootroot00000000000000#!/usr/bin/env python """ k-suite - experiment different neighborhood sizes """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import os import sys import random import Gnuplot sys.path.insert(0, '../') from apprecommender.config import Config from apprecommender.recommender import Recommender, RecommendationResult from apprecommender.user import User, PopconSystem from apprecommender.evaluation import (Evaluation, Precision, Recall, FPR, F_score, MCC) def plot_roc(k, roc_points, log_file): g = Gnuplot.Gnuplot() g('set style data points') g.xlabel('False Positive Rate') g.ylabel('True Positive Rate') g('set xrange [0:1.0]') g('set yrange [0:1.0]') g.title("Setup: %s-k%d" % (log_file.split("/")[-1], k)) g.plot(Gnuplot.Data([[0, 0], [1, 1]], with_="lines lt 7"), Gnuplot.Data(roc_points)) g.hardcopy(log_file + ("-k%.3d.png" % k), terminal="png") g.hardcopy(log_file + ("-k%.3d.ps" % k), terminal="postscript", enhanced=1, color=1) def plot_summary(precision, f05, mcc, log_file): g = Gnuplot.Gnuplot() g('set style data lines') g.xlabel('Neighborhood (k)') g.title("Setup: %s-size20" % (log_file.split("/")[-1])) g.plot( Gnuplot.Data([[k, sum(precision[k]) / len(precision[k])] for k in precision.keys()], title="P"), Gnuplot.Data([[k, sum(f05[k]) / len(f05[k])] for k in f05.keys()], title="F05"), Gnuplot.Data([[k, sum(mcc[k]) / len(mcc[k])] for k in mcc.keys()], title="MCC")) g.hardcopy(log_file + (".png"), terminal="png") g.hardcopy(log_file + (".ps"), terminal="postscript", enhanced=1, color=1) class ExperimentResults: def __init__(self, repo_size): self.repository_size = repo_size self.precision = [] self.recall = [] self.fpr = [] self.f05 = [] self.mcc = [] def add_result(self, ranking, sample): predicted = RecommendationResult(dict.fromkeys(ranking, 1)) real = RecommendationResult(sample) evaluation = Evaluation(predicted, real, self.repository_size) self.precision.append(evaluation.run(Precision())) self.recall.append(evaluation.run(Recall())) self.fpr.append(evaluation.run(FPR())) self.f05.append(evaluation.run(F_score(0.5))) self.mcc.append(evaluation.run(MCC())) def get_roc_point(self): tpr = self.recall fpr = self.fpr if not tpr or not fpr: return [0, 0] return [sum(fpr) / len(fpr), sum(tpr) / len(tpr)] def get_precision_summary(self): if not self.precision: return 0 return sum(self.precision) / len(self.precision) def get_f05_summary(self): if not self.f05: return 0 return sum(self.f05) / len(self.f05) def get_mcc_summary(self): if not self.mcc: return 0 return sum(self.mcc) / len(self.mcc) if __name__ == '__main__': if len(sys.argv) < 3: print "Usage: k-suite strategy_str sample_file" exit(1) threshold = 20 iterations = 30 neighbors = [3, 5, 10, 50, 100, 150, 200, 300, 400, 500] cfg = Config() cfg.strategy = sys.argv[1] sample_file = sys.argv[2] population_sample = [] with open(sample_file, 'r') as f: for line in f.readlines(): user_id = line.strip('\n') population_sample.append( os.path.join(cfg.popcon_dir, user_id[:2], user_id)) # setup dictionaries and files roc_summary = {} recommended = {} precision_summary = {} f05_summary = {} mcc_summary = {} sample_dir = ("results/k-suite/%s" % sample_file.split('/')[-1]) if not os.path.exists(sample_dir): os.makedirs(sample_dir) log_file = os.path.join(sample_dir, cfg.strategy) with open(log_file, 'w') as f: f.write("# %s\n\n" % sample_file.split('/')[-1]) f.write("# strategy %s recommendation_size %d iterations %d\n\n" % (cfg.strategy, threshold, iterations)) f.write("# k coverage \tprecision \tf05 \tmcc\n\n") for k in neighbors: roc_summary[k] = [] recommended[k] = set() precision_summary[k] = [] f05_summary[k] = [] mcc_summary[k] = [] with open(log_file + "-k%.3d" % k, 'w') as f: f.write("# %s\n\n" % sample_file.split('/')[-1]) f.write("# strategy-k %s-k%.3d\n\n" % (cfg.strategy, k)) f.write("# roc_point \tprecision \tf05 \tmcc\n\n") # main loop per user for submission_file in population_sample: user = PopconSystem(submission_file) user.filter_pkg_profile(cfg.pkgs_filter) user.maximal_pkg_profile() for k in neighbors: cfg.k_neighbors = k rec = Recommender(cfg) repo_size = rec.items_repository.get_doccount() results = ExperimentResults(repo_size) # n iterations for same recommender and user for n in range(iterations): # Fill sample profile profile_len = len(user.pkg_profile) item_score = {} for pkg in user.pkg_profile: item_score[pkg] = user.item_score[pkg] sample = {} sample_size = int(profile_len * 0.9) for i in range(sample_size): key = random.choice(item_score.keys()) sample[key] = item_score.pop(key) iteration_user = User(item_score) recommendation = rec.get_recommendation( iteration_user, threshold) if hasattr(recommendation, "ranking"): results.add_result(recommendation.ranking, sample) recommended[k] = recommended[ k].union(recommendation.ranking) # save summary roc_point = results.get_roc_point() roc_summary[k].append(roc_point) precision = results.get_precision_summary() precision_summary[k].append(precision) f05 = results.get_f05_summary() f05_summary[k].append(f05) mcc = results.get_mcc_summary() mcc_summary[k].append(mcc) with open(log_file + "-k%.3d" % k, 'a') as f: f.write("[%.2f,%.2f] \t%.4f \t%.4f \t%.4f\n" % (roc_point[0], roc_point[1], precision, f05, mcc)) # back to main flow with open(log_file, 'a') as f: plot_summary(precision_summary, f05_summary, mcc_summary, log_file) for k in neighbors: coverage = len(recommended[size]) / float(repo_size) # noqa ?? f.write("%3d \t%.2f \t%.4f \t%.4f \t%.4f\n" % (k, coverage, float(sum(precision_summary[ k])) / len(precision_summary[k]), float(sum(f05_summary[k])) / len(f05_summary[k]), float(sum(mcc_summary[k])) / len(mcc_summary[k]))) plot_roc(k, roc_summary[k], log_file) AppRecommender-0.7.5/apprecommender/experiments/deprecated/runner.py000077500000000000000000000166551306751311600257760ustar00rootroot00000000000000#!/usr/bin/env python """ recommender suite - recommender experiments suite """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import expsuite import sys import logging import random import Gnuplot sys.path.insert(0, '../') from apprecommender.config import Config from apprecommender.data import PopconXapianIndex from apprecommender.recommender import Recommender, RecommendationResult from apprecommender.evaluation import (Evaluation, Accuracy, Precision, Recall, F1) from apprecommender.user import LocalSystem, User class ClusteringSuite(expsuite.PyExperimentSuite): def reset(self, params, rep): self.cfg = Config() self.cfg.popcon_index = "../tests/test_data/.sample_pxi" self.cfg.popcon_dir = "../tests/test_data/popcon_dir" self.cfg.clusters_dir = "../tests/test_data/clusters_dir" if params['name'] == "clustering": logging.info("Starting 'clustering' experiments suite...") self.cfg.index_mode = "recluster" def iterate(self, params, rep, n): if params['name'] == "clustering": logging.info("Running iteration %d" % params['medoids'][n]) self.cfg.k_medoids = params['medoids'][n] pxi = PopconXapianIndex(self.cfg) result = {'k_medoids': params['medoids'][n], 'dispersion': pxi.cluster_dispersion} else: result = {} return result class ContentBasedSuite(expsuite.PyExperimentSuite): def reset(self, params, rep): if params['name'].startswith("content"): cfg = Config() # if the index was not built yet # app_axi = AppAptXapianIndex(cfg.axi,"results/arnaldo/AppAxi") cfg.axi = "data/AppAxi" cfg.index_mode = "old" cfg.weight = params['weight'] self.rec = Recommender(cfg) self.rec.set_strategy(params['strategy']) self.repo_size = self.rec.items_repository.get_doccount() self.user = LocalSystem() self.user.app_pkg_profile(self.rec.items_repository) self.sample_size = int( len(self.user.pkg_profile) * params['sample']) # iteration should be set to 10 in config file # self.profile_size = range(10,101,10) def iterate(self, params, rep, n): if params['name'].startswith("content"): item_score = dict.fromkeys(self.user.pkg_profile, 1) # Prepare partition sample = {} for i in range(self.sample_size): key = random.choice(item_score.keys()) sample[key] = item_score.pop(key) # Get full recommendation user = User(item_score) recommendation = self.rec.get_recommendation(user, self.repo_size) # Write recall log recall_file = "results/content/recall/%s-%s-%.2f-%d" % \ (params['strategy'], params[ 'weight'], params['sample'], n) output = open(recall_file, 'w') output.write("# weight=%s\n" % params['weight']) output.write("# strategy=%s\n" % params['strategy']) output.write("# sample=%f\n" % params['sample']) output.write("\n%d %d %d\n" % (self.repo_size, len(item_score), self.sample_size)) notfound = [] ranks = [] for pkg in sample.keys(): if pkg in recommendation.ranking: ranks.append(recommendation.ranking.index(pkg)) else: notfound.append(pkg) for r in sorted(ranks): output.write(str(r) + "\n") if notfound: output.write("Out of recommendation:\n") for pkg in notfound: output.write(pkg + "\n") output.close() # Plot metrics summary accuracy = [] precision = [] recall = [] f1 = [] g = Gnuplot.Gnuplot() g('set style data lines') g.xlabel('Recommendation size') for size in range(1, len(recommendation.ranking) + 1, 100): predicted = RecommendationResult( dict.fromkeys(recommendation.ranking[:size], 1)) real = RecommendationResult(sample) evaluation = Evaluation(predicted, real, self.repo_size) accuracy.append([size, evaluation.run(Accuracy())]) precision.append([size, evaluation.run(Precision())]) recall.append([size, evaluation.run(Recall())]) f1.append([size, evaluation.run(F1())]) g.plot(Gnuplot.Data(accuracy, title="Accuracy"), Gnuplot.Data(precision, title="Precision"), Gnuplot.Data(recall, title="Recall"), Gnuplot.Data(f1, title="F1")) g.hardcopy(recall_file + "-plot.ps", enhanced=1, color=1) # Iteration log result = {'iteration': n, 'weight': params['weight'], 'strategy': params['strategy'], 'accuracy': accuracy[20], 'precision': precision[20], 'recall:': recall[20], 'f1': f1[20]} return result # class CollaborativeSuite(expsuite.PyExperimentSuite): # def reset(self, params, rep): # if params['name'].startswith("collaborative"): # # def iterate(self, params, rep, n): # if params['name'].startswith("collaborative"): # for root, dirs, files in os.walk(self.source_dir): # for popcon_file in files: # submission = PopconSubmission( # os.path.join(root,popcon_file)) # user = User(submission.packages) # user.maximal_pkg_profile() # rec.get_recommendation(user) # precision = 0 # result = {'weight': params['weight'], # 'strategy': params['strategy'], # 'profile_size': self.profile_size[n], # 'accuracy': accuracy, # 'precision': precision, # 'recall:': recall, # 'f1': } # else: # result = {} # return result if __name__ == '__main__': if "clustering" in sys.argv or len(sys.argv) < 3: ClusteringSuite().start() if "content" in sys.argv or len(sys.argv) < 3: ContentBasedSuite().start() # if "collaborative" in sys.argv or len(sys.argv)<3: # CollaborativeSuite().start() AppRecommender-0.7.5/apprecommender/experiments/deprecated/strategies-suite.py000077500000000000000000000267161306751311600277650ustar00rootroot00000000000000#!/usr/bin/env python """ recommender suite - recommender experiments suite """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import sys import random import Gnuplot sys.path.insert(0, '../') from apprecommender.config import Config from apprecommender.recommender import Recommender, RecommendationResult from apprecommender.user import User, PopconSystem from apprecommender.evaluation import (Evaluation, Accuracy, Precision, Recall, F_score) # iterations = 3 # sample_proportions = [0.9] # weighting = [('bm25',1.2)] # collaborative = ['knn'] # content_based = [] # hybrid = ['knnco'] # profile_size = [50,100] # popcon_size = ["1000"] # neighbors = [50] iterations = 10 sample_proportions = [0.5, 0.6, 0.7, 0.8, 0.9] weighting = [('bm25', 1.2), ('bm25', 1.6), ('bm25', 2.0), ('trad', 0)] content_based = ['cb', 'cbt', 'cbd', 'cbh', 'cb_eset', 'cbt_eset', 'cbd_eset', 'cbh_eset'] collaborative = ['knn_eset', 'knn', 'knn_plus'] hybrid = ['knnco', 'knnco_eset'] profile_size = range(20, 100, 20) # popcon_size = [1000,10000,50000,'full'] neighbors = range(10, 510, 50) def write_recall_log(label, n, sample, recommendation, profile_size, repo_size, log_file): # Write recall log output = open(("%s-%d" % (log_file, n)), 'w') output.write("# %s-n\n" % label["description"]) output.write("# %s-%d\n" % (label["values"], n)) output.write("\n%d %d %d\n" % (repo_size, profile_size, len(sample))) if hasattr(recommendation, "ranking"): notfound = [] ranks = [] for pkg in sample.keys(): if pkg in recommendation.ranking: ranks.append(recommendation.ranking.index(pkg)) else: notfound.append(pkg) for r in sorted(ranks): output.write(str(r) + "\n") if notfound: output.write("Out of recommendation:\n") for pkg in notfound: output.write(pkg + "\n") output.close() def plot_summary(precision, recall, f1, f05, accuracy, log_file): # Plot metrics summary g = Gnuplot.Gnuplot() g('set style data lines') g.xlabel('Recommendation size') g.title("Setup: %s" % log_file.split("/")[-1]) g.plot(Gnuplot.Data(accuracy, title="Accuracy"), Gnuplot.Data(precision, title="Precision"), Gnuplot.Data(recall, title="Recall"), Gnuplot.Data(f1, title="F_1"), Gnuplot.Data(f05, title="F_0.5")) g.hardcopy(log_file + ".png", terminal="png") g.hardcopy(log_file + ".ps", terminal="postscript", enhanced=1, color=1) g('set logscale x') g('replot') g.hardcopy(log_file + "-logscale.png", terminal="png") g.hardcopy(log_file + "-logscale.ps", terminal="postscript", enhanced=1, color=1) def get_label(cfg, sample_proportion): label = {} if cfg.strategy in content_based: label["description"] = "strategy-filter-profile-k1_bm25-sample" label["values"] = ("%s-profile%d-%s-kbm%.1f-sample%.1f" % (cfg.strategy, cfg.profile_size, cfg.pkgs_filter.split("/")[-1], cfg.bm25_k1, sample_proportion)) elif cfg.strategy in collaborative: label["description"] = "strategy-knn-filter-k1_bm25-sample" label["values"] = ("%s-k%d-%s-kbm%.1f-sample%.1f" % (cfg.strategy, cfg.k_neighbors, cfg.pkgs_filter.split("/")[-1], cfg.bm25_k1, sample_proportion)) elif cfg.strategy in hybrid: label["description"] = "strategy-knn-filter-profile-k1_bm25-sample" label["values"] = ("%s-k%d-profile%d-%s-kbm%.1f-sample%.1f" % (cfg.strategy, cfg.k_neighbors, cfg.profile_size, cfg.pkgs_filter.split("/")[-1], cfg.bm25_k1, sample_proportion)) else: print "Unknown strategy" return label class ExperimentResults: def __init__(self, repo_size): self.repository_size = repo_size self.accuracy = {} self.precision = {} self.recall = {} self.f1 = {} self.f05 = {} points = [1] + range(10, 200, 10) + range( 200, self.repository_size, 100) for size in points: self.accuracy[size] = [] self.precision[size] = [] self.recall[size] = [] self.f1[size] = [] self.f05[size] = [] def add_result(self, ranking, sample): for size in self.accuracy.keys(): predicted = RecommendationResult(dict.fromkeys(ranking[:size], 1)) real = RecommendationResult(sample) evaluation = Evaluation(predicted, real, self.repository_size) self.accuracy[size].append(evaluation.run(Accuracy())) self.precision[size].append(evaluation.run(Precision())) self.recall[size].append(evaluation.run(Recall())) self.f1[size].append(evaluation.run(F_score(1))) self.f05[size].append(evaluation.run(F_score(0.5))) def get_precision_summary(self): summary = [[size, sum(values) / len(values)] for size, values in self.precision.items()] return sorted(summary) def get_recall_summary(self): summary = [[size, sum(values) / len(values)] for size, values in self.recall.items()] return sorted(summary) def get_f1_summary(self): summary = [[size, sum(values) / len(values)] for size, values in self.f1.items()] return sorted(summary) def get_f05_summary(self): summary = [[size, sum(values) / len(values)] for size, values in self.f05.items()] return sorted(summary) def get_accuracy_summary(self): summary = [[size, sum(values) / len(values)] for size, values in self.accuracy.items()] return sorted(summary) def best_precision(self): size = max(self.precision, key=lambda x: max(self.precision[x])) return (size, max(self.precision[size])) def best_f1(self): size = max(self.f1, key=lambda x: max(self.f1[x])) return (size, max(self.f1[size])) def best_f05(self): size = max(self.f05, key=lambda x: max(self.f05[x])) return (size, max(self.f05[size])) def run_strategy(cfg, user): for weight in weighting: cfg.weight = weight[0] cfg.bm25_k1 = weight[1] rec = Recommender(cfg) repo_size = rec.items_repository.get_doccount() for proportion in sample_proportions: results = ExperimentResults(repo_size) label = get_label(cfg, proportion) log_file = "results/strategies/" + label["values"] for n in range(iterations): # Fill sample profile profile_size = len(user.pkg_profile) item_score = {} for pkg in user.pkg_profile: item_score[pkg] = user.item_score[pkg] sample = {} sample_size = int(profile_size * proportion) for i in range(sample_size): key = random.choice(item_score.keys()) sample[key] = item_score.pop(key) iteration_user = User(item_score) recommendation = rec.get_recommendation( iteration_user, repo_size) write_recall_log( label, n, sample, recommendation, profile_size, repo_size, log_file) if hasattr(recommendation, "ranking"): results.add_result(recommendation.ranking, sample) with open(log_file, 'w') as f: precision_10 = sum(results.precision[10]) / len( results.precision[10]) f1_10 = sum(results.f1[10]) / len(results.f1[10]) f05_10 = sum(results.f05[10]) / len(results.f05[10]) f.write("# %s\n# %s\n\ncoverage %d\n\n" % (label["description"], label["values"], recommendation.size)) f.write("# best results (recommendation size; metric)\n") f.write( "precision (%d; %.2f)\nf1 (%d; %.2f)\nf05 (%d; %.2f)\n\n" % (results.best_precision()[0], results.best_precision()[1], results.best_f1()[0], results.best_f1()[1], results.best_f05()[0], results.best_f05()[1])) f.write("# recommendation size 10\nprecision (10; %.2f)\nf1 (10; %.2f)\nf05 (10; %.2f)" % # noqa (precision_10, f1_10, f05_10)) precision = results.get_precision_summary() recall = results.get_recall_summary() f1 = results.get_f1_summary() f05 = results.get_f05_summary() accuracy = results.get_accuracy_summary() plot_summary(precision, recall, f1, f05, accuracy, log_file) def run_content(user, cfg): for strategy in content_based: cfg.strategy = strategy for size in profile_size: cfg.profile_size = size run_strategy(cfg, user) def run_collaborative(user, cfg): for strategy in collaborative: cfg.strategy = strategy for k in neighbors: cfg.k_neighbors = k # for size in popcon_size: # if size: # cfg.popcon_desktopapps = popcon_desktopapps+"_"+size # cfg.popcon_programs = popcon_programs+"_"+size run_strategy(cfg, user) def run_hybrid(user, cfg): for strategy in hybrid: cfg.strategy = strategy for k in neighbors: cfg.k_neighbors = k # for size in popcon_size: # if size: # cfg.popcon_desktopapps = popcon_desktopapps+"_"+size # cfg.popcon_programs = popcon_programs+"_"+size for size in profile_size: cfg.profile_size = size run_strategy(cfg, user) if __name__ == '__main__': # user = LocalSystem() # user = # RandomPopcon(cfg.popcon_dir,os.path.join(cfg.filters_dir,"desktopapps")) cfg = Config() user = PopconSystem( "/root/.app-recommender/popcon-entries/8b/8b44fcdbcf676e711a153d5db09979d7") # noqa # user = # PopconSystem("/root/.app-recommender/popcon-entries/4a/4a67a295ec14826db2aa1d90be2f1623") user.filter_pkg_profile(cfg.pkgs_filter) user.maximal_pkg_profile() if "content" in sys.argv or len(sys.argv) < 2: run_content(user, cfg) if "collaborative" in sys.argv or len(sys.argv) < 2: run_collaborative(user, cfg) if "hybrid" in sys.argv or len(sys.argv) < 2: run_hybrid(user, cfg) AppRecommender-0.7.5/apprecommender/experiments/extract-sample-db.py000077500000000000000000000036711306751311600256730ustar00rootroot00000000000000#! /usr/bin/env python """ sample-popcon - extract a sample from popcon population """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import xapian import sys if __name__ == '__main__': try: sample_file = sys.argv[1] popcon = xapian.WritableDatabase(sys.argv[2], xapian.DB_OPEN) except: print "Usage: extract-sample-db sample_file popcon_index" exit(1) enquire = xapian.Enquire(popcon) print sample_file.split("/") new_popcon = xapian.WritableDatabase( sys.argv[2] + "-" + sample_file.split("/")[-1], xapian.DB_CREATE_OR_OVERWRITE) print ("Popcon repository size: %d" % popcon.get_doccount()) for submission in open(sample_file): print "ID" + submission.strip() query = xapian.Query("ID" + submission.strip()) enquire.set_query(query) mset = enquire.get_mset(0, 20) for m in mset: print "Adding doc %s" % m.docid new_popcon.add_document(popcon.get_document(m.docid)) print "Removing doc %s" % m.docid popcon.delete_document(m.docid) print ("Popcon repository size: %d" % popcon.get_doccount()) print ("Popcon repository size: %d" % new_popcon.get_doccount()) AppRecommender-0.7.5/apprecommender/experiments/hybrid.py000077500000000000000000000223521306751311600236350ustar00rootroot00000000000000#!/usr/bin/env python """ hybrid-suite """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import Gnuplot import numpy import os import random import sys from apprecommender.config import Config from apprecommender.recommender import Recommender, RecommendationResult from apprecommender.evaluation import Evaluation, Precision, F_score from apprecommender.user import User, PopconSystem # hybrid_strategies = ['knnco','knnco_eset'] if __name__ == '__main__': if len(sys.argv) < 2: print "Usage: hybrid strategy sample_file" exit(1) iterations = 20 profile_size = [10, 40, 70, 100, 170, 240] neighbor_size = [3, 10, 50, 70, 100, 150, 200] # iterations = 1 # profile_size = [10,20,30] # neighbor_size = [10,20,30] cfg = Config() population_sample = [] strategy = sys.argv[1] sample_file = sys.argv[2] sample_str = sample_file.split('/')[-1] with open(sample_file, 'r') as f: for line in f.readlines(): user_id = line.strip('\n') population_sample.append( os.path.join(cfg.popcon_dir, user_id[:2], user_id)) sample_dir = ("results/hybrid/%s/%s" % (sample_str, strategy)) if not os.path.exists(sample_dir): os.makedirs(sample_dir) cfg.strategy = strategy p_10_summary = {} f05_100_summary = {} c_10 = {} c_100 = {} log_file = os.path.join(sample_dir, sample_str + "-" + cfg.strategy) graph_10 = {} graph_100 = {} graph_10_jpg = {} graph_100_jpg = {} comment_10 = {} comment_100 = {} for k in neighbor_size: graph_10[k] = log_file + ("-neighborhood%.3d-010.png" % k) graph_100[k] = log_file + ("-neighborhood%.3d-100.png" % k) graph_10_jpg[k] = graph_10[k].strip(".png") + ".jpg" graph_100_jpg[k] = graph_100[k].strip(".png") + ".jpg" comment_10[k] = graph_10_jpg[k] + ".comment" comment_100[k] = graph_100_jpg[k] + ".comment" with open(comment_10[k], 'w') as f: f.write("# %s\n" % sample_str) f.write("# strategy %s\n# threshold 10\n# iterations %d\n\n" % (cfg.strategy, iterations)) f.write("# neighborhood\tprofile\tmean_p_10\tdev_p_10\tc_10\n\n") with open(comment_100[k], 'w') as f: f.write("# %s\n" % sample_str) f.write("# strategy %s\n# threshold 100\n# iterations %d\n\n" % (cfg.strategy, iterations)) f.write( '# neighborhood\tprofile\tmean_f05_100\tdev_f05_100\tc_100\n\n') # noqa c_10[k] = {} c_100[k] = {} p_10_summary[k] = {} f05_100_summary[k] = {} for size in profile_size: c_10[k][size] = set() c_100[k][size] = set() p_10_summary[k][size] = [] f05_100_summary[k][size] = [] with open(log_file + "-neighborhood%.3d-profile%.3d" % (k, size), 'w') as f: f.write("# %s\n" % sample_str) f.write("# strategy %s-neighborhood%.3d-profile%.3d\n\n" % (cfg.strategy, k, size)) f.write("# p_10\t\tf05_100\n\n") # main loop per user for submission_file in population_sample: user = PopconSystem(submission_file) user.filter_pkg_profile(cfg.pkgs_filter) user.maximal_pkg_profile() for k in neighbor_size: cfg.k_neighbors = k for size in profile_size: cfg.profile_size = size rec = Recommender(cfg) repo_size = rec.items_repository.get_doccount() p_10 = [] f05_100 = [] for n in range(iterations): # Fill sample profile profile_len = len(user.pkg_profile) item_score = {} for pkg in user.pkg_profile: item_score[pkg] = user.item_score[pkg] sample = {} sample_size = int(profile_len * 0.9) for i in range(sample_size): key = random.choice(item_score.keys()) sample[key] = item_score.pop(key) iteration_user = User(item_score) recommendation = rec.get_recommendation( iteration_user, repo_size) if hasattr(recommendation, "ranking"): ranking = recommendation.ranking real = RecommendationResult(sample) predicted_10 = RecommendationResult( dict.fromkeys(ranking[:10], 1)) evaluation = Evaluation(predicted_10, real, repo_size) p_10.append(evaluation.run(Precision())) predicted_100 = RecommendationResult( dict.fromkeys(ranking[:100], 1)) evaluation = Evaluation(predicted_100, real, repo_size) f05_100.append(evaluation.run(F_score(0.5))) c_10[k][size] = c_10[k][size].union( recommendation.ranking[:10]) c_100[k][size] = c_100[k][size].union( recommendation.ranking[:100]) # save summary if p_10: p_10_summary[k][size].append(numpy.mean(p_10)) if f05_100: f05_100_summary[k][size].append(numpy.mean(f05_100)) path = log_file + "-neighborhood%.3d-profile%.3d" % (k, size) with open(path, 'a') as f: f.write("%.4f\t\t%.4f\n" % (numpy.mean(p_10), numpy.mean(f05_100))) # back to main flow coverage_10 = {} coverage_100 = {} for k in neighbor_size: coverage_10[k] = {} coverage_100[k] = {} with open(comment_10[k], 'a') as f: for size in profile_size: coverage_10[k][size] = len(c_10[k][size]) / float(repo_size) f.write("%3d\t\t%3d\t\t%.4f\t%.4f\t%.4f\n" % (k, size, numpy.mean(p_10_summary[k][size]), numpy.std(p_10_summary[k][size]), coverage_10[k][size])) with open(comment_100[k], 'a') as f: for size in profile_size: coverage_100[k][size] = len(c_100[k][size]) / float(repo_size) f.write("%3d\t\t%3d\t\t%.4f\t%.4f\t%.4f\n" % (k, size, numpy.mean(f05_100_summary[k][size]), numpy.std(f05_100_summary[k][size]), coverage_100[k][size])) for k in neighbor_size: # plot results summary g = Gnuplot.Gnuplot() g('set style data lines') g('set yrange [0:1.0]') g.xlabel('Profile size') g.title("Setup: %s-neighborhood%3d (threshold 10)" % (cfg.strategy, k)) g.plot(Gnuplot.Data(sorted([[i, numpy.mean(p_10_summary[k][i]), numpy.std(p_10_summary[k][i])] for i in p_10_summary[k].keys( )]), title="Precision"), Gnuplot.Data(sorted([[i, numpy.mean(p_10_summary[k][i]), numpy.std(p_10_summary[k][i])] for i in p_10_summary[k].keys( )]), title="Deviation", with_="yerrorbar lt 2 pt 6"), Gnuplot.Data( sorted( [[i, coverage_10[k][i]] for i in coverage_10[k].keys()]), title="Coverage")) g.hardcopy(graph_10[k], terminal="png") g = Gnuplot.Gnuplot() g('set style data lines') g('set yrange [0:1.0]') g.xlabel('Profile size') g.title("Setup: %s-neighborhood%3d (threshold 100)" % (cfg.strategy, k)) g.plot(Gnuplot.Data(sorted([[i, numpy.mean(f05_100_summary[k][i]), numpy.std(f05_100_summary[k][i])] for i in f05_100_summary[k].keys( )]), title="F05"), Gnuplot.Data(sorted([[i, numpy.mean(f05_100_summary[k][i]), numpy.std(f05_100_summary[k][i])] for i in f05_100_summary[k].keys( )]), title="Deviation", with_="yerrorbar lt 2 pt 6"), Gnuplot.Data( sorted( [[i, coverage_100[k][i]] for i in coverage_100[k].keys()]), title="Coverage")) g.hardcopy(graph_100[k], terminal="png") AppRecommender-0.7.5/apprecommender/experiments/misc-popcon.py000077500000000000000000000054671306751311600246130ustar00rootroot00000000000000#! /usr/bin/env python """ misc_popcon - misc experiments with popcon data """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import Gnuplot import xapian def profile_population(): popcon = xapian.Database( "/home/tassia/.app-recommender/popcon_desktopapps") popcon_size = popcon.get_doccount() print "User repository size: %d" % popcon_size profiles_size = [] for n in range(1, popcon_size): user = popcon.get_document(n) profile = [t.term for t in user.termlist()] profiles_size.append(len(profile)) profile_population = [(n, profiles_size.count(n)) for n in range(max(profiles_size) + 1) if profiles_size.count(n) > 0] ranges_population = [] ranges_percentage = [] ranges = range(0, 601, 50) for maximum in ranges[1:]: minimum = ranges[ranges.index(maximum) - 1] valid = [x[1] for x in profile_population if x[0] > minimum and x[0] <= maximum] ranges_population.append((maximum, sum(valid))) ranges_percentage.append((maximum, sum(valid) / float(popcon_size))) g = Gnuplot.Gnuplot() g('set style data points') # give gnuplot an arbitrary command g.xlabel('Desktop profile size') g.ylabel('Population size') g.plot(profile_population) g.hardcopy('profile_population.png', terminal="png") g.hardcopy('profile_population.ps', terminal="postscript", enhanced=1, color=1) g.reset() g.xlabel('Range Desktop profile size') g.ylabel('Population size') g.plot(ranges_population) g.hardcopy('ranges_profile_population.png', terminal="png") g.hardcopy('ranges_profile_population.ps', terminal="postscript", enhanced=1, color=1) g.reset() g.xlabel('Range Desktop profile size') g.ylabel('Population percentage') g.plot(ranges_percentage) g.hardcopy('ranges_profile_percentage.png', terminal="png") g.hardcopy('ranges_profile_percentage.ps', terminal="postscript", enhanced=1, color=1) if __name__ == '__main__': profile_population() AppRecommender-0.7.5/apprecommender/experiments/popcon-population.py000077500000000000000000000056321306751311600260440ustar00rootroot00000000000000#! /usr/bin/env python """ misc_popcon - misc experiments with popcon data """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import Gnuplot import xapian import os def get_population_profile(popcon): profiles_size = [] for n in range(1, popcon.get_doccount()): user = popcon.get_document(n) pkgs_profile = [ t.term for t in user.termlist() if t.term.startswith("XP")] if len(pkgs_profile) < 10: print "-- profile<10:", user.get_data() profiles_size.append(len(pkgs_profile)) max_profile = max(profiles_size) population_profile = [(n, profiles_size.count(n)) for n in range(max_profile + 1) if profiles_size.count(n) > 0] return population_profile, max_profile def get_profile_ranges(population_profile, max_profile, popcon_size): ranges = range(0, 251, 50) ranges.append(max_profile) ranges_population = [] ranges_percentage = [] for maximum in ranges[1:]: minimum = ranges[ranges.index(maximum) - 1] valid = [x[1] for x in population_profile if x[0] > minimum and x[0] <= maximum] ranges_population.append((maximum, sum(valid))) ranges_percentage.append((maximum, sum(valid) / float(popcon_size))) return ranges_population, ranges_percentage def plot(data, xlabel, ylabel, output): g = Gnuplot.Gnuplot() g('set style data points') g.xlabel(xlabel) g.ylabel(ylabel) g.plot(data) g.hardcopy(output + ".png", terminal="png") g.hardcopy(output + ".ps", terminal="postscript", enhanced=1, color=1) if __name__ == '__main__': popcon = xapian.Database( os.path.expanduser("~/.app-recommender/popcon_desktopapps")) print ("Popcon repository size: %d" % popcon.get_doccount()) profile_population, max_profile = get_population_profile(popcon) ranges_population, ranges_percentage = get_profile_ranges( profile_population, max_profile, popcon.get_doccount()) print "Population per profile range (up to index)" print ranges_population plot(profile_population, "Desktop profile size", "Population size", "results/misc-popcon/profile_population") AppRecommender-0.7.5/apprecommender/experiments/pure.py000077500000000000000000000224211306751311600233240ustar00rootroot00000000000000#!/usr/bin/env python """ profile-suite - experiment different profile sizes """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import os import sys import random import Gnuplot import numpy from apprecommender.config import Config from apprecommender.evaluation import Evaluation, Precision, F_score from apprecommender.recommender import Recommender, RecommendationResult from apprecommender.user import User, PopconSystem if __name__ == '__main__': if len(sys.argv) < 2: print "Usage: pure strategy_category sample_file" exit(1) iterations = 20 profile_size = [10, 20, 40, 60, 80, 100, 140, 170, 200, 240] neighbor_size = [3, 5, 10, 20, 30, 50, 70, 100, 150, 200] content_strategies = [ 'cb', 'cbt', 'cbd', 'cbh', 'cb_eset', 'cbt_eset', 'cbd_eset', 'cbh_eset'] collaborative_strategies = ['knn_eset', 'knn', 'knn_plus'] # iterations = 1 # profile_size = [10,20,30] # neighbor_size = [3,5,10,20,30,50] # content_strategies = ['cb'] # collaborative_strategies = ['knn'] strategy_category = sys.argv[1] if strategy_category == "content": strategies = content_strategies sizes = profile_size option_str = "profile" elif strategy_category == "collaborative": strategies = collaborative_strategies sizes = neighbor_size option_str = "neighborhood" else: print "Usage: profile-suite strategy_category sample_file" exit(1) cfg = Config() population_sample = [] sample_file = sys.argv[2] sample_str = sample_file.split('/')[-1] with open(sample_file, 'r') as f: for line in f.readlines(): user_id = line.strip('\n') population_sample.append( os.path.join(cfg.popcon_dir, user_id[:2], user_id)) sample_dir = ("results/%s/%s" % (strategy_category, sample_str)) if not os.path.exists(sample_dir): os.makedirs(sample_dir) for strategy in strategies: cfg.strategy = strategy p_10_summary = {} f05_100_summary = {} c_10 = {} c_100 = {} log_file = os.path.join(sample_dir, sample_str + "-" + cfg.strategy) graph_10 = log_file + "-10.png" graph_100 = log_file + "-100.png" graph_10_jpg = graph_10.strip(".png") + ".jpg" graph_100_jpg = graph_100.strip(".png") + ".jpg" comment_10 = graph_10_jpg + ".comment" comment_100 = graph_100_jpg + ".comment" with open(comment_10, 'w') as f: f.write("# sample %s\n" % sample_str) f.write("# strategy %s\n# threshold 10\n# iterations %d\n\n" % (cfg.strategy, iterations)) f.write("# %s\tmean_p_10\tdev_p_10\tc_10\n\n" % option_str) with open(comment_100, 'w') as f: f.write("# sample %s\n" % sample_str) f.write("# strategy %s\n# threshold 100\n# iterations %d\n\n" % (cfg.strategy, iterations)) f.write("# %s\t\tmean_f05_100\t\tdev_f05_100\t\tc_100\n\n" % option_str) for size in sizes: c_10[size] = set() c_100[size] = set() p_10_summary[size] = [] f05_100_summary[size] = [] with open(log_file + "-%s%.3d" % (option_str, size), 'w') as f: f.write("# sample %s\n" % sample_str) f.write("# strategy %s-%s%.3d\n\n" % (cfg.strategy, option_str, size)) f.write("# p_10\tf05_100\n\n") # main loop per user for submission_file in population_sample: user = PopconSystem(submission_file) user.filter_pkg_profile(cfg.pkgs_filter) user.maximal_pkg_profile() for size in sizes: cfg.profile_size = size cfg.k_neighbors = size rec = Recommender(cfg) repo_size = rec.items_repository.get_doccount() p_10 = [] f05_100 = [] for n in range(iterations): # Fill sample profile profile_len = len(user.pkg_profile) item_score = {} for pkg in user.pkg_profile: item_score[pkg] = user.item_score[pkg] sample = {} sample_size = int(profile_len * 0.9) for i in range(sample_size): key = random.choice(item_score.keys()) sample[key] = item_score.pop(key) iteration_user = User(item_score) recommendation = rec.get_recommendation( iteration_user, repo_size) if hasattr(recommendation, "ranking"): ranking = recommendation.ranking real = RecommendationResult(sample) predicted_10 = RecommendationResult( dict.fromkeys(ranking[:10], 1)) evaluation = Evaluation(predicted_10, real, repo_size) p_10.append(evaluation.run(Precision())) predicted_100 = RecommendationResult( dict.fromkeys(ranking[:100], 1)) evaluation = Evaluation(predicted_100, real, repo_size) f05_100.append(evaluation.run(F_score(0.5))) c_10[size] = c_10[size].union( recommendation.ranking[:10]) c_100[size] = c_100[size].union( recommendation.ranking[:100]) # save summary if p_10: p_10_summary[size].append(numpy.mean(p_10)) if f05_100: f05_100_summary[size].append(numpy.mean(f05_100)) with open(log_file + "-%s%.3d" % (option_str, size), 'a') as f: f.write("%.4f \t%.4f\n" % (numpy.mean(p_10), numpy.mean(f05_100))) # back to main flow coverage_10 = {} coverage_100 = {} with open(comment_10, 'a') as f: for size in sizes: coverage_10[size] = len(c_10[size]) / float(repo_size) f.write("%3d\t\t%.4f\t\t%.4f\t\t%.4f\n" % (size, numpy.mean(p_10_summary[size]), numpy.std(p_10_summary[size]), coverage_10[size])) with open(comment_100, 'a') as f: for size in sizes: coverage_100[size] = len(c_100[size]) / float(repo_size) f.write("%3d\t\t%.4f\t\t%.4f\t\t%.4f\n" % (size, numpy.mean(f05_100_summary[size]), numpy.std(f05_100_summary[size]), coverage_100[size])) # plot results summary g = Gnuplot.Gnuplot() g('set style data lines') g('set yrange [0:1.0]') g.xlabel('%s size' % option_str.capitalize()) g.title("Setup: %s (threshold 10)" % cfg.strategy) g.plot(Gnuplot.Data(sorted([[k, numpy.mean(p_10_summary[k]), numpy.std(p_10_summary[k])] for k in p_10_summary.keys( )]), title="Precision"), Gnuplot.Data(sorted([[k, numpy.mean(p_10_summary[k]), numpy.std(p_10_summary[k])] for k in p_10_summary.keys( )]), title="Deviation", with_="yerrorbar lt 2 pt 6"), Gnuplot.Data( sorted( [[k, coverage_10[k]] for k in coverage_10.keys()]), title="Coverage")) g.hardcopy(graph_10, terminal="png") g = Gnuplot.Gnuplot() g('set style data lines') g('set yrange [0:1.0]') g.xlabel('%s size' % option_str.capitalize()) g.title("Setup: %s (threshold 100)" % cfg.strategy) g.plot(Gnuplot.Data(sorted([[k, numpy.mean(f05_100_summary[k]), numpy.std(f05_100_summary[k])] for k in f05_100_summary.keys( )]), title="F05"), Gnuplot.Data(sorted([[k, numpy.mean(f05_100_summary[k]), numpy.std(f05_100_summary[k])] for k in f05_100_summary.keys( )]), title="Deviation", with_="yerrorbar lt 2 pt 6"), Gnuplot.Data( sorted( [[k, coverage_100[k]] for k in coverage_100.keys()]), title="Coverage")) g.hardcopy(graph_100, terminal="png") AppRecommender-0.7.5/apprecommender/experiments/roc-sample.py000077500000000000000000000227151306751311600244210ustar00rootroot00000000000000#!/usr/bin/env python """ recommender suite - recommender experiments suite """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import os import sys import random import Gnuplot import numpy from apprecommender.config import Config from apprecommender.recommender import Recommender, RecommendationResult from apprecommender.user import User, PopconSystem from apprecommender.evaluation import (Evaluation, Precision, F_score, FPR, Recall) def plot_roc(results, log_file, mean=0): g = Gnuplot.Gnuplot() g('set style data lines') g.xlabel('False Positive Rate') g.ylabel('True Positive Rate') g('set xrange [0:1.0]') g('set yrange [0:1.0]') g.title("Setup: %s" % log_file.split("/")[-1]) g('set label "C %.4f" at 0.68,0.2' % results.coverage()) g('set label "AUC %.4f" at 0.68,0.15' % results.get_auc()) g('set label "P(10) %.2f +- %.2f" at 0.68,0.10' % (numpy.mean(results.precision[10]), numpy.std(results.precision[10]))) g('set label "F05(100) %.2f +- %.2f" at 0.68,0.05' % (numpy.mean(results.f05[100]), numpy.std(results.f05[100]))) if mean == 1: g.plot(Gnuplot.Data(results.get_roc_points(), title="mean ROC"), Gnuplot.Data([[0, 0], [1, 1]], with_="lines lt 7")) g.hardcopy(log_file + "-roc-mean.png", terminal="png") g.hardcopy(log_file + "-roc-mean.ps", terminal="postscript", enhanced=1, color=1) else: g.plot( Gnuplot.Data( results.get_roc_points(), title="ROC", with_="xyerrorbars"), Gnuplot.Data([[0, 0], [1, 1]], with_="lines lt 7")) g.hardcopy(log_file + "-roc.png", terminal="png") g.hardcopy(log_file + "-roc.ps", terminal="postscript", enhanced=1, color=1) def get_label(cfg): label = {} if cfg.strategy in content_based: label["description"] = "strategy-profile" label["values"] = ("%s-profile%.3d" % (cfg.strategy, cfg.profile_size)) elif cfg.strategy in collaborative: label["description"] = "strategy-knn" label["values"] = ("%s-k%.3d" % (cfg.strategy, cfg.k_neighbors)) elif cfg.strategy in hybrid: label["description"] = "strategy-knn-profile" label["values"] = ("%s-k%.3d-profile%.3d" % (cfg.strategy, cfg.k_neighbors, cfg.profile_size)) return label class ExperimentResults: def __init__(self, repo_size): self.repository_size = repo_size self.precision = {} self.recall = {} self.fpr = {} self.f05 = {} self.recommended = {} self.thresholds = [1] + range(10, self.repository_size, 10) for size in self.thresholds: self.precision[size] = [] self.recall[size] = [] self.fpr[size] = [] self.f05[size] = [] self.recommended[size] = set() def add_result(self, ranking, sample): for size in self.thresholds: recommendation = ranking[:size] self.recommended[size] = self.recommended[ size].union(recommendation) predicted = RecommendationResult(dict.fromkeys(recommendation, 1)) real = RecommendationResult(sample) evaluation = Evaluation(predicted, real, self.repository_size) self.precision[size].append(evaluation.run(Precision())) self.recall[size].append(evaluation.run(Recall())) self.f05[size].append(evaluation.run(F_score(0.5))) self.fpr[size].append(evaluation.run(FPR())) def precision_summary(self): return [[size, numpy.mean(self.precision[size])] for size in self.thresholds] def recall_summary(self): return [[size, numpy.mean(self.recall[size])] for size in self.thresholds] def f05_summary(self): return [[size, numpy.mean(self.f05[size])] for size in self.thresholds] def coverage_summary(self): return [[size, self.coverage(size)] for size in self.thresholds] def coverage(self, size=0): if not size: size = self.thresholds[-1] return len(self.recommended[size]) / float(self.repository_size) def precision(self, size): return numpy.mean(self.precision[size]) def get_auc(self): roc_points = self.get_roc_points() x_roc = [p[0] for p in roc_points] y_roc = [p[1] for p in roc_points] x_roc.insert(0, 0) y_roc.insert(0, 0) x_roc.append(1) y_roc.append(1) return numpy.trapz(y=y_roc, x=x_roc) # Average ROC by threshold (= size of recommendation) def get_roc_points(self): points = [] for size in self.recall.keys(): tpr = self.recall[size] fpr = self.fpr[size] points.append( [numpy.mean(fpr), numpy.mean(tpr), numpy.std(fpr), numpy.std(tpr)]) return sorted(points) def run_strategy(cfg, sample_file): rec = Recommender(cfg) repo_size = rec.items_repository.get_doccount() results = ExperimentResults(repo_size) label = get_label(cfg) population_sample = [] sample_str = sample_file.split('/')[-1] with open(sample_file, 'r') as f: for line in f.readlines(): user_id = line.strip('\n') population_sample.append( os.path.join(cfg.popcon_dir, user_id[:2], user_id)) sample_dir = ("results/roc-sample/%s" % sample_str) if not os.path.exists(sample_dir): os.makedirs(sample_dir) log_file = os.path.join(sample_dir, label["values"]) # n iterations per population user for submission_file in population_sample: user = PopconSystem(submission_file) user.filter_pkg_profile(cfg.pkgs_filter) user.maximal_pkg_profile() for n in range(iterations): # Fill sample profile profile_len = len(user.pkg_profile) item_score = {} for pkg in user.pkg_profile: item_score[pkg] = user.item_score[pkg] sample = {} sample_size = int(profile_len * 0.9) for i in range(sample_size): key = random.choice(item_score.keys()) sample[key] = item_score.pop(key) iteration_user = User(item_score) recommendation = rec.get_recommendation(iteration_user, repo_size) if hasattr(recommendation, "ranking"): results.add_result(recommendation.ranking, sample) plot_roc(results, log_file) plot_roc(results, log_file, 1) with open(log_file + "-roc.jpg.comment", 'w') as f: f.write("# %s\n# %s\n\n" % (label["description"], label["values"])) f.write("# roc AUC\n%.4f\n\n" % results.get_auc()) f.write( "# threshold\tmean_fpr\tdev_fpr\t\tmean_tpr\tdev_tpr\t\tcoverage\n") # noqa for size in results.thresholds: f.write("%4d\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f\n" % (size, numpy.mean(results.fpr[size]), numpy.std(results.fpr[size]), numpy.mean(results.recall[size]), numpy.std(results.recall[size]), numpy.mean(results.coverage(size)))) def run_content(cfg, sample_file): for size in profile_size: cfg.profile_size = size run_strategy(cfg, sample_file) def run_collaborative(cfg, sample_file): for k in neighbors: cfg.k_neighbors = k run_strategy(cfg, sample_file) def run_hybrid(cfg, sample_file): for k in neighbors: cfg.k_neighbors = k for size in profile_size: cfg.profile_size = size run_strategy(cfg, sample_file) if __name__ == '__main__': if len(sys.argv) < 2: print "Usage: sample-roc strategy_str [popcon_sample_path]" exit(1) # iterations = 3 # content_based = ['cb'] # collaborative = ['knn_eset'] # hybrid = ['knnco'] # profile_size = [50,100] # neighbors = [50] iterations = 20 content_based = ['cb', 'cbt', 'cbd', 'cbh', 'cb_eset', 'cbt_eset', 'cbd_eset', 'cbh_eset'] collaborative = ['knn_eset', 'knn', 'knn_plus'] hybrid = ['knnco', 'knnco_eset'] profile_size = [10, 20, 50, 100, 200] neighbors = [200] # neighbors = [3,10,50,100,200] # profile_size = [10,20,40,60,80,100,140,170,200,240] # neighbors = [3,5,10,20,30,50,70,100,150,200] cfg = Config() cfg.strategy = sys.argv[1] sample_file = sys.argv[2] if cfg.strategy in content_based: run_content(cfg, sample_file) if cfg.strategy in collaborative: run_collaborative(cfg, sample_file) if cfg.strategy in hybrid: run_hybrid(cfg, sample_file) AppRecommender-0.7.5/apprecommender/experiments/roc-single.py000077500000000000000000000251211306751311600244130ustar00rootroot00000000000000#!/usr/bin/env python """ recommender suite - recommender experiments suite """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import os import sys import random import Gnuplot import numpy import shutil from apprecommender.config import Config from apprecommender.recommender import Recommender, RecommendationResult from apprecommender.evaluation import (Evaluation, Precision, Recall, F_score, FPR) from apprecommender.user import User, PopconSystem def write_recall_log(label, n, sample, recommendation, profile_size, repo_size, log_file): # Write recall log output = open(("%s-%.2d" % (log_file, n)), 'w') output.write("# %s-n\n" % label["description"]) output.write("# %s-%.2d\n" % (label["values"], n)) output.write("\n# repository profile sample\n%d %d %d\n" % (repo_size, profile_size, len(sample))) if hasattr(recommendation, "ranking"): notfound = [] ranks = [] for pkg in sample.keys(): if pkg in recommendation.ranking: ranks.append(recommendation.ranking.index(pkg)) else: notfound.append(pkg) for r in sorted(ranks): output.write(str(r) + "\n") if notfound: output.write("# out of recommendation:\n") for pkg in notfound: output.write(pkg + "\n") output.close() def plot_summary(results, log_file): # Plot metrics summary g = Gnuplot.Gnuplot() g('set style data lines') g('set yrange [0:1.0]') g.xlabel('Threshold (recommendation size)') g.title("Setup: %s" % log_file.split("/")[-1]) g.plot(Gnuplot.Data(results.precision_summary(), title="Precision"), Gnuplot.Data(results.recall_summary(), title="Recall"), Gnuplot.Data(results.f05_summary(), title="F05"), Gnuplot.Data(results.coverage_summary(), title="Coverage")) g.hardcopy(log_file + ".png", terminal="png") g.hardcopy(log_file + ".ps", terminal="postscript", enhanced=1, color=1) g('set logscale x') g('replot') g.hardcopy(log_file + "-logscale.png", terminal="png") g.hardcopy(log_file + "-logscale.ps", terminal="postscript", enhanced=1, color=1) def plot_roc(results, log_file): g = Gnuplot.Gnuplot() g('set style data lines') g.xlabel('False Positive Rate') g.ylabel('True Positive Rate') g('set xrange [0:1.0]') g('set yrange [0:1.0]') g.title("Setup: %s" % log_file.split("/")[-1]) g('set label "C %.2f" at 0.8,0.25' % results.coverage()) g('set label "AUC %.2f" at 0.8,0.2' % results.get_auc()) g('set label "P(10) %.2f" at 0.8,0.15' % numpy.mean(results.precision[10])) g('set label "P(20) %.2f" at 0.8,0.10' % numpy.mean(results.precision[20])) g('set label "F05(100) %.2f" at 0.8,0.05' % numpy.mean(results.f05[100])) g.plot(Gnuplot.Data(results.get_roc_points(), title="ROC"), Gnuplot.Data([[0, 0], [1, 1]], with_="lines lt 7")) g.hardcopy(log_file + "-roc.png", terminal="png") g.hardcopy(log_file + "-roc.ps", terminal="postscript", enhanced=1, color=1) def get_label(cfg): label = {} if cfg.strategy in content_based: label["description"] = "strategy-profile" label["values"] = ("%s-profile%.3d" % (cfg.strategy, cfg.profile_size)) elif cfg.strategy in collaborative: label["description"] = "strategy-knn" label["values"] = ("%s-k%.3d" % (cfg.strategy, cfg.k_neighbors)) elif cfg.strategy in hybrid: label["description"] = "strategy-knn-profile" label["values"] = ("%s-k%.3d-profile%.3d" % (cfg.strategy, cfg.k_neighbors, cfg.profile_size)) return label class ExperimentResults: def __init__(self, repo_size): self.repository_size = repo_size self.precision = {} self.recall = {} self.fpr = {} self.f05 = {} self.recommended = {} self.thresholds = [1] + range(10, self.repository_size, 10) for size in self.thresholds: self.precision[size] = [] self.recall[size] = [] self.fpr[size] = [] self.f05[size] = [] self.recommended[size] = set() def add_result(self, ranking, sample): for size in self.thresholds: recommendation = ranking[:size] self.recommended[size] = self.recommended[ size].union(recommendation) predicted = RecommendationResult(dict.fromkeys(recommendation, 1)) real = RecommendationResult(sample) evaluation = Evaluation(predicted, real, self.repository_size) print evaluation.run(Precision()) self.precision[size].append(evaluation.run(Precision())) self.recall[size].append(evaluation.run(Recall())) self.f05[size].append(evaluation.run(F_score(0.5))) self.fpr[size].append(evaluation.run(FPR())) def precision_summary(self): return [[size, numpy.mean(self.precision[size])] for size in self.thresholds] def recall_summary(self): return [[size, numpy.mean(self.recall[size])] for size in self.thresholds] def f05_summary(self): return [[size, numpy.mean(self.f05[size])] for size in self.thresholds] def coverage_summary(self): return [[size, self.coverage(size)] for size in self.thresholds] def coverage(self, size=0): if not size: size = self.thresholds[-1] return len(self.recommended[size]) / float(self.repository_size) def precision(self, size): return numpy.mean(self.precision[size]) def get_auc(self): roc_points = self.get_roc_points() x_roc = [p[0] for p in roc_points] y_roc = [p[1] for p in roc_points] x_roc.insert(0, 0) y_roc.insert(0, 0) x_roc.append(1) y_roc.append(1) return numpy.trapz(y=y_roc, x=x_roc) # Average ROC by threshold (= size of recommendation) def get_roc_points(self): points = [] for size in self.recall.keys(): tpr = self.recall[size] fpr = self.fpr[size] points.append([sum(fpr) / len(fpr), sum(tpr) / len(tpr)]) return sorted(points) def run_strategy(cfg, user): rec = Recommender(cfg) repo_size = rec.items_repository.get_doccount() results = ExperimentResults(repo_size) label = get_label(cfg) user_dir = ("results/roc-suite/%s/%s" % (user.user_id[:8], cfg.strategy)) if not os.path.exists(user_dir): os.makedirs(user_dir) log_file = os.path.join(user_dir, label["values"]) for n in range(iterations): # Fill sample profile profile_len = len(user.pkg_profile) item_score = {} for pkg in user.pkg_profile: item_score[pkg] = user.item_score[pkg] sample = {} sample_size = int(profile_len * 0.9) for i in range(sample_size): key = random.choice(item_score.keys()) sample[key] = item_score.pop(key) iteration_user = User(item_score) recommendation = rec.get_recommendation(iteration_user, repo_size) write_recall_log( label, n, sample, recommendation, profile_len, repo_size, log_file) if hasattr(recommendation, "ranking"): results.add_result(recommendation.ranking, sample) with open(log_file + "-roc.jpg.comment", 'w') as f: f.write("# %s\n# %s\n\n" % (label["description"], label["values"])) f.write("# roc AUC\n%.4f\n\n" % results.get_auc()) f.write("# threshold\tprecision\trecall\t\tf05\t\tcoverage\n") for size in results.thresholds: f.write("%4d\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f\n" % (size, numpy.mean(results.precision[size]), numpy.mean(results.recall[size]), numpy.mean(results.f05[size]), numpy.mean(results.coverage(size)))) shutil.copy(log_file + "-roc.jpg.comment", log_file + ".jpg.comment") shutil.copy(log_file + "-roc.jpg.comment", log_file + "-logscale.jpg.comment") plot_roc(results, log_file) plot_summary(results, log_file) def run_content(user, cfg): for size in profile_size: cfg.profile_size = size run_strategy(cfg, user) def run_collaborative(user, cfg): for k in neighbors: cfg.k_neighbors = k run_strategy(cfg, user) def run_hybrid(user, cfg): for k in neighbors: cfg.k_neighbors = k for size in profile_size: cfg.profile_size = size run_strategy(cfg, user) if __name__ == '__main__': if len(sys.argv) < 2: print "Usage: roc-suite strategy_str [popcon_submission_path]" exit(1) # iterations = 3 # content_based = ['cb'] # collaborative = ['knn_eset'] # hybrid = ['knnco'] # profile_size = [50,100] # neighbors = [50] iterations = 20 content_based = ['cb', 'cbt', 'cbd', 'cbh', 'cb_eset', 'cbt_eset', 'cbd_eset', 'cbh_eset'] collaborative = ['knn_eset', 'knn', 'knn_plus'] hybrid = ['knnco', 'knnco_eset'] profile_size = [10, 20, 40, 60, 80, 100, 140, 170, 200, 240] neighbors = [3, 5, 10, 20, 30, 50, 70, 100, 150, 200] cfg = Config() cfg.strategy = sys.argv[1] # user = # PopconSystem("/root/.app-recommender/popcon-entries/4a/4a67a295ec14826db2aa1d90be2f1623") user = PopconSystem( "/root/.app-recommender/popcon-entries/8b/8b44fcdbcf676e711a153d5db09979d7") # noqa # user = PopconSystem(sys.argv[1]) user.filter_pkg_profile(cfg.pkgs_filter) user.maximal_pkg_profile() if cfg.strategy in content_based: run_content(user, cfg) if cfg.strategy in collaborative: run_collaborative(user, cfg) if cfg.strategy in hybrid: run_hybrid(user, cfg) AppRecommender-0.7.5/apprecommender/experiments/sample-popcon-arch.py000077500000000000000000000027121306751311600260420ustar00rootroot00000000000000#! /usr/bin/env python """ sample-popcon-arch - extract a sample of a specific arch """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import sys from apprecommender.user import RandomPopcon if __name__ == '__main__': try: size = int(sys.argv[1]) arch = sys.argv[2] popcon_dir = sys.argv[3] pkgs_filter = sys.argv[4] except: print "Usage: sample-popcon-arch size arch popcon_dir pkgs_filter" exit(1) sample_file = ("results/misc-popcon/sample-%s-%d" % (arch, size)) with open(sample_file, 'w') as f: for n in range(1, size + 1): user = RandomPopcon(popcon_dir, arch, pkgs_filter) f.write(user.user_id + '\n') print "sample", n AppRecommender-0.7.5/apprecommender/experiments/sample-popcon.py000077500000000000000000000040561306751311600251320ustar00rootroot00000000000000#! /usr/bin/env python """ sample-popcon - extract a sample from popcon population """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import xapian import os import sys def extract_sample(size, popcon, min_profile, max_profile, output): sample = [] for n in range(1, popcon.get_doccount() + 1): user = popcon.get_document(n) pkgs_profile = [ t.term for t in user.termlist() if t.term.startswith("XP")] print len(pkgs_profile) if(len(pkgs_profile) > min_profile and len(pkgs_profile) <= max_profile): sample.append(user.get_data()) print n, len(sample) if len(sample) == size: break with open(("%s-%d-%d" % (output, min_profile, max_profile)), 'w') as f: for s in sample: f.write(s + '\n') if __name__ == '__main__': popcon = xapian.Database( os.path.expanduser("~/.app-recommender/popcon_desktopapps")) print ("Popcon repository size: %d" % popcon.get_doccount()) try: min_profile = int(sys.argv[1]) max_profile = int(sys.argv[2]) size = int(sys.argv[3]) except: print "Usage: sample-popcon min_profile max_profile sample_size" exit(1) sample_file = "results/misc-popcon/sample" extract_sample(size, popcon, min_profile, max_profile, sample_file) AppRecommender-0.7.5/apprecommender/generate_doc.sh000077500000000000000000000022461306751311600224120ustar00rootroot00000000000000#!/bin/env bash # # generate_doc.sh - shell script to generate documentation using doxygen. # # Copyright (C) 2010 Tassia Camoes # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # 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, see . # Get project version from git repository TAG=$(git describe --tags --abbrev=0) echo "Generating documentation for git tag $TAG" sed -i "s/^PROJECT_NUMBER.*$/PROJECT_NUMBER\t\t= $TAG/" ../doc/doxy_config rm -Rf ../doc/html ../doc/doxygen-1.7.3 ../doc/doxy_config scp -r html/ tassia@eclipse.ime.usp.br: echo "---> Remember to place doc in the right location on server side." mv html/ ../doc/ AppRecommender-0.7.5/apprecommender/initialize.py000066400000000000000000000113431306751311600221450ustar00rootroot00000000000000#!/usr/bin/env python import commands import data import datetime import os import shutil import xapian from apprecommender.config import Config from apprecommender.decider import PkgInitDecider class Initialize: AXI_SAMPLES = ['sample', 'filter'] DEFAULT_AXI_PATH = "/var/lib/apt-xapian-index/index" EXCLUDED_TAGS = ['culture::', 'devel::lang', 'hardware::', 'implemented-in::', 'interface::', 'iso15924::', 'made-of::', 'network::', 'protocol::', 'role::', 'scope::', 'secteam::', 'special::', 'uitoolkit::', 'x11::', 'TODO'] def __init__(self): self.config = Config() self.pkg_init_decider = PkgInitDecider() def get_tags(self): command = "cat /var/lib/debtags/vocabulary" \ "| grep 'Tag:'" \ "| egrep -v '%s'" \ "| awk '{print $2}'" % '|'.join(Initialize.EXCLUDED_TAGS) tags = commands.getoutput(command).splitlines() return tags def get_axipkgs(self, axi_tag='XP', axi_path=DEFAULT_AXI_PATH): axi = xapian.Database(axi_path) all_terms = set() for n in range(1, axi.get_lastdocid()): doc = 0 try: doc = axi.get_document(n) except: pass if doc: xp_terms = None for t in doc.termlist(): if t.term.startswith(axi_tag): xp_terms = t.term break if xp_terms: pkg_name = xp_terms.lstrip(axi_tag) if pkg_name.startswith('M'): pkg_name = pkg_name.lstrip('M') if self.pkg_init_decider(pkg_name): all_terms.add(pkg_name) return all_terms def indexer_axi(self, axi_sample, filters_path, terms=[]): axi_path = Initialize.DEFAULT_AXI_PATH axi = xapian.Database(axi_path) base_dir = self.config.base_dir begin_time = datetime.datetime.now() # axi sample based on the pkgs sample provided by command line if axi_sample is 'sample': with open(filters_path) as valid: pkgs_list = [line.strip() for line in valid] filter_str = 'axi_' + filters_path.split('/')[-1] print"\nIndexing packages on xapian" index = data.SampleAptXapianIndex(pkgs_list, axi, os.path.join(base_dir, filter_str)) print "Axi size: %d" % axi.get_doccount() print "Packages list length: %d" % len(pkgs_list) print "Sample index size: %d" % index.get_doccount() # axi filtered by terms provided by command line if axi_sample is "filter": terms_str = "_".join([t.split("::")[-1] for t in terms]) index = data.FilteredXapianIndex(terms, axi, os.path.join(base_dir, "axi_" + terms_str)) print "Axi size: %d" % axi.get_doccount() print "Terms filter: %s" % terms print "Filtered index size: %d" % index.get_doccount() end_time = datetime.datetime.now() print "Indexing completed at %s" % end_time delta = end_time - begin_time print "Time elapsed: %d seconds." % delta.seconds def prepare_data(self): try: if os.path.exists(self.config.base_dir): if os.path.exists(self.config.user_data_dir): shutil.rmtree(self.config.user_data_dir) if os.path.exists(self.config.axi_desktopapps): shutil.rmtree(self.config.axi_desktopapps) if os.path.exists(self.config.filters_dir): shutil.rmtree(self.config.filters_dir) os.makedirs(self.config.filters_dir) except OSError: raise tags = self.get_tags() tags_path = "{}/debtags".format(self.config.filters_dir) self.save_list(tags, tags_path) pkgs = self.get_axipkgs() pkgs_path = "{}/desktopapps".format(self.config.filters_dir) self.save_list(pkgs, pkgs_path) self.indexer_axi('sample', pkgs_path) def get_role_program_pkgs(self): command = "cat /var/lib/debtags/package-tags | " \ "grep 'role::program' | " \ "awk -F: '{ print $1}'" pkgs = commands.getoutput(command).splitlines() return pkgs def save_list(self, elements, path): with open(path, 'w') as text: text.write('\n'.join(elements) + '\n') AppRecommender-0.7.5/apprecommender/main/000077500000000000000000000000001306751311600203545ustar00rootroot00000000000000AppRecommender-0.7.5/apprecommender/main/__init__.py000066400000000000000000000000001306751311600224530ustar00rootroot00000000000000AppRecommender-0.7.5/apprecommender/main/app_recommender.py000066400000000000000000000023061306751311600240670ustar00rootroot00000000000000#!/usr/bin/env python import logging import datetime from apprecommender.recommender import Recommender from apprecommender.user import LocalSystem from apprecommender.config import Config class AppRecommender: def __init__(self): self.recommender = Recommender() self.config = Config() def make_recommendation(self, reference_pkgs=None, print_recommendation=True): begin_time = datetime.datetime.now() logging.info("Computation started at %s" % begin_time) if not reference_pkgs: reference_pkgs = [] user = LocalSystem(reference_pkgs) recommendation_size = Config().num_recommendations user_recommendation = (self.recommender.get_recommendation( user, recommendation_size)) logging.info("Recommending applications for user %s" % user.user_id) if print_recommendation: print(user_recommendation) end_time = datetime.datetime.now() logging.info("Computation completed at %s" % end_time) delta = end_time - begin_time logging.info("Time elapsed: %d seconds." % delta.seconds) return user_recommendation AppRecommender-0.7.5/apprecommender/main/apt_run.py000066400000000000000000000065221306751311600224030ustar00rootroot00000000000000#!/usr/bin/env python import argparse import os import shutil import re from apprecommender.config import Config from apprecommender.main.app_recommender import AppRecommender from apprecommender.user import LocalSystem class AptRun: def __init__(self): apt_folder = os.path.join(Config().base_dir, "apt_run/") self.set_folder(apt_folder) def set_folder(self, folder_path): folder_path = os.path.expanduser(folder_path) folder_path = os.path.abspath(folder_path) if folder_path[-1] != '/': folder_path += '/' self.apt_folder = folder_path self.installed_pkgs_file = self.apt_folder + "installed_pkgs.txt" def enable(self): if not self.is_enable(): os.makedirs(self.apt_folder) return True return False def disable(self): if self.is_enable(): shutil.rmtree(self.apt_folder) return True return False def is_enable(self): return os.path.exists(self.apt_folder) def get_user_pkgs(self): user = LocalSystem() user_pkgs = user.pkg_profile return user_pkgs def pre_install_pkgs(self): if self.is_enable(): user_pkgs = self.get_user_pkgs() with open(self.installed_pkgs_file, 'w') as text: text.write("\n".join(user_pkgs)) def post_invoke(self): if not self.is_enable(): return [] with open(self.installed_pkgs_file, 'r') as text: pre_installed_pkgs = set([line.strip() for line in text]) pkgs = self.get_user_pkgs() pos_installed_pkgs = set([pkg.strip() for pkg in pkgs]) installed_pkgs = list(pos_installed_pkgs - pre_installed_pkgs) return installed_pkgs def get_recommendation_pkgs(self, installed_pkgs): app_recommender = AppRecommender() app_recommender.recommender.set_strategy('cbpkg') rec = app_recommender.make_recommendation( reference_pkgs=installed_pkgs, print_recommendation=False) pkgs_regex = re.compile(r'^\d:\s([^\s]+)', re.MULTILINE) pkgs = pkgs_regex.findall(rec.__str__()) return pkgs def make_recommendations(self, installed_pkgs): if len(installed_pkgs) > 0: pkgs = self.get_recommendation_pkgs(installed_pkgs) if len(pkgs) > 0: print '\nApprecommeder: The following packages are' \ ' interesting' for pkg in pkgs: print ' - {}'.format(pkg) def get_args(): aptrun_description = 'Integration of AppRecommender with apt' parser = argparse.ArgumentParser(description=aptrun_description) parser.add_argument( '--pre-install-pkgs', help='Indentify installed packages before install the new packages', action='store_true') parser.add_argument( '--post-invoke', help='Indentify the installed packages and makes recommendation', action='store_true') args = vars(parser.parse_args()) return args def main(): args = get_args() apt_run = AptRun() if args['pre_install_pkgs']: apt_run.pre_install_pkgs() elif args['post_invoke']: installed_pkgs = apt_run.post_invoke() apt_run.make_recommendations(installed_pkgs) if __name__ == '__main__': main() AppRecommender-0.7.5/apprecommender/main/cli.py000066400000000000000000000100641306751311600214760ustar00rootroot00000000000000#!/usr/bin/env python import xapian from apprecommender.main.app_recommender import AppRecommender from apprecommender.config import Config from apprecommender.initialize import Initialize from apprecommender.strategy import (MachineLearning, MachineLearningBVA, MachineLearningBOW, MachineLearningTrainError) from apprecommender.main import collect_user_data from apprecommender.main import show_classifications from apprecommender.main.apt_run import AptRun from apprecommender.main.options import get_parser SUCCESS = 0 ERROR_INIT = 1 ERROR_TRAIN = 2 PERMISSION_DENIED = 3 ERROR_INIT_TRAIN = 4 def parse_options(args, config): if args['strategy']: config.strategy = args['strategy'] if args['debug']: config.debug = 1 if args['verbose']: config.verbose = 1 if args['profile_size']: config.profile_size = args['profile_size'] if args['because']: config.because = True if args['num_recommendations']: config.num_recommendations = args['num_recommendations'] def run_apprecommender(reference_pkgs): try: app_recommender = AppRecommender() app_recommender.make_recommendation(reference_pkgs) return SUCCESS except xapian.DatabaseOpeningError: return ERROR_INIT except IOError: if "ml" in Config().strategy: return ERROR_TRAIN except OSError: return PERMISSION_DENIED def run_initialize(): print "Initializing AppRecommender" initialize = Initialize() try: initialize.prepare_data() except OSError: return PERMISSION_DENIED return SUCCESS def run_train(): print "Training machine learning" try: MachineLearning.train(MachineLearningBVA) MachineLearning.train(MachineLearningBOW) except IOError: return PERMISSION_DENIED except MachineLearningTrainError: return ERROR_INIT_TRAIN return SUCCESS def run(args): if args['update']: init_result = run_initialize() if init_result != SUCCESS: return init_result train_result = run_train() if train_result != SUCCESS: return train_result return SUCCESS elif args['init']: return run_initialize() elif args['train']: return run_train() elif args['contribute']: collect_user_data.main() elif args['show_classifications']: show_classifications.main() elif args['enable_apt']: try: apt_run = AptRun() if apt_run.enable(): print 'AppRecommender now makes recommendations when you ' \ ' install new packages with apt' else: print 'This is already enabled' return SUCCESS except OSError: return PERMISSION_DENIED elif args['disable_apt']: try: apt_run = AptRun() if apt_run.disable(): print 'AppRecommender now dont makes recommendations when' \ 'you install new packages with apt' else: print 'This is already disabled' return SUCCESS except OSError: return PERMISSION_DENIED else: config = Config() parse_options(args, config) reference_pkgs = args['packages'] return run_apprecommender(reference_pkgs) def main(): parser = get_parser() args = vars(parser.parse_args()) result = run(args) if result is ERROR_INIT: print "\n" print "Please, Initialize AppRecommender" print "Run: apprec.py --init" elif result is ERROR_TRAIN: print "\n" print "Please, run Machine Learning Training" print "Run: apprec.py --train" elif result is PERMISSION_DENIED: print "Please, run this command as sudo" elif result is ERROR_INIT_TRAIN: print 'Error: You need install more packages to use machine' \ ' learning recommendations' if __name__ == '__main__': main() AppRecommender-0.7.5/apprecommender/main/collect_user_data.py000066400000000000000000000303141306751311600244030ustar00rootroot00000000000000#!/usr/bin/env python import binascii import commands import datetime as dt import logging import os import re import subprocess import tarfile import time import xapian from apprecommender.apt_cache import AptCache from apprecommender.config import Config from apprecommender.data import get_user_installed_pkgs from apprecommender.data_classification import get_alternative_pkg from apprecommender.main.app_recommender import AppRecommender from apprecommender.main.ml_cross_validation import ml_cross_validation from apprecommender.ml.data import MachineLearningData from apprecommender.ml.pkg_time import PkgTime from apprecommender.utils import print_progress_bar LOG_PATH = os.path.expanduser('~/app_recommender_log') SUFIX = dt.datetime.now().strftime('%Y%m%d%H%M') LOG_PATH += SUFIX ALL_INSTALLED_PKGS = LOG_PATH + '/all_pkgs.txt' MANUAL_INSTALLED_PKGS_PATH = LOG_PATH + '/manual_installed_pkgs.txt' PKGS_TIME_PATH = LOG_PATH + '/pkgs_time.txt' HISTORY = LOG_PATH + '/user_history.txt' PKGS_BINARY = LOG_PATH + '/pkgs_binary.txt' RECOMMENDATION_PATH = LOG_PATH + '/{0}_recommendation.txt' USER_PREFERENCES = LOG_PATH + '/user_preferences.txt' POPCON_SUBMISSION = LOG_PATH + '/popcon-submission' PC_INFORMATIONS = LOG_PATH + '/pc_informations.txt' RECOMMENDATIONS_TIME = LOG_PATH + '/recommendations_time.txt' PKGS_DEPENDENCIES = [] RE_MATCH = re.compile(r'^\d:\s([^\s]+)', re.MULTILINE) def create_log_folder(): print "Creating log folder" if not os.path.exists(LOG_PATH): os.mkdir(LOG_PATH, 0755) def create_file(file_path): if not os.path.exists(file_path): with open(file_path, 'a'): os.utime(file_path, None) return True return False def delete_file(file_path): if os.path.exists(file_path): os.remove(file_path) def save_list(list_to_save, file_path): delete_file(file_path) create_file(file_path) with open(file_path, 'w') as text: for element in list_to_save: text.write(str(element) + '\n') def rename_file(original_name, new_name): os.rename(original_name, new_name) def get_submission_id(submission_header): fields = submission_header.split(' ') return fields[2][3:] def create_popularity_contest_file(): if os.path.exists('popularity-contest.conf'): return host_id = binascii.hexlify(os.urandom(16)) with open('popularity-contest.conf', 'w') as text: text.write('MY_HOSTID="{0}"\n'.format(host_id)) text.write('PARTICIPATE="no"\n') text.write('USEHTTP="no"\n') text.write('DAY="4"\n') def collect_popcon_submission(): print "Collecting popularity-contest submission" create_popularity_contest_file() popcon = subprocess.Popen('./popularity-contest', shell=False, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) popcon_output = popcon.stdout.read() popcon_parse = popcon_output.splitlines() submission_id = get_submission_id(popcon_parse[0]) submission = [line for line in popcon_parse] save_list(submission, POPCON_SUBMISSION) file_name = LOG_PATH + "/" + submission_id + ".txt" rename_file(POPCON_SUBMISSION, file_name) def collect_manual_installed_pkgs(): print "Collecting manual installed pkgs" if create_file(MANUAL_INSTALLED_PKGS_PATH): packages = commands.getoutput('apt-mark showmanual') packages = [pkg for pkg in packages.splitlines()] save_list(packages, MANUAL_INSTALLED_PKGS_PATH) def collect_all_user_pkgs(): print "Collecting all user packages" if create_file(ALL_INSTALLED_PKGS): packages = get_user_installed_pkgs() save_list(packages, ALL_INSTALLED_PKGS) def collect_pkgs_time(): print "Collecting packages time" pkg_time = PkgTime() if create_file(PKGS_TIME_PATH): manual_pkgs = [] with open(MANUAL_INSTALLED_PKGS_PATH, 'r') as text: manual_pkgs = [line.strip() for line in text] pkgs_time = pkg_time.get_packages_time(manual_pkgs) pkg_time.save_package_time(pkgs_time, PKGS_TIME_PATH) def get_pkg_binary(pkg): stat_command = "which {0}".format(pkg) pkg_bin = commands.getoutput(stat_command.format(pkg)) if pkg_bin: return pkg return get_alternative_pkg(pkg) def get_pkgs_of_recommendation(strategy): app_recommender = AppRecommender() app_recommender.recommender.set_strategy(strategy) rec = app_recommender.make_recommendation(print_recommendation=False) pkgs = RE_MATCH.findall(rec.__str__()) return pkgs def collect_user_preferences(): Config().num_recommendations = 6 strategies = ['cb', 'cb_eset', 'cbtm', 'mlbva', 'mlbva_eset', 'mlbow', 'mlbow_eset'] recommendations = {} recommendations_time = [] print "Preparing recommendations..." len_strategies = len(strategies) for index, strategy in enumerate(strategies): first_time = int(round(time.time() * 1000)) recommendations[strategy] = get_pkgs_of_recommendation(strategy) last_time = int(round(time.time() * 1000)) recommendations_time.append("{0}: {1}".format(strategy, last_time - first_time)) print_progress_bar(index + 1, len_strategies) all_recommendations = set(sum(recommendations.values(), [])) all_recommendations = sorted(list(all_recommendations)) index = 0 user_preferences = {} all_rec_len = len(all_recommendations) message = "\n\nPackage [{0}/{1}] - {2} \n" message += "Description: {3}\n\n" message += "Rank a package recommendation with 1-4\n" message += "1 - Bad\n" message += "2 - Redundant\n" message += "3 - Useful\n" message += "4 - Useful Surprise\n\n" message += "exit - Cancel collect data\n\n" message += "Rank: " message_error = "\nPlease use digits 1-4 to rank a package: " apt_cache = AptCache() for i in range(len(all_recommendations)): pkg = all_recommendations[i] pkg_description = apt_cache[pkg].versions[0].description rank = -1 raw_message = message.format((index + 1), all_rec_len, pkg, pkg_description) clear_prints() print "\n\nCollecting user preferences" if i > 0: print "\n" for j in range(i): prev_pkg = all_recommendations[j] print( "[{0}/{1}] {2}, rank: {3}".format(j + 1, all_rec_len, prev_pkg, user_preferences[prev_pkg])) while rank < 1 or rank > 4: try: rank = raw_input(raw_message) if rank == 'exit': break rank = int(rank) except: rank = -2 raw_message = message_error if rank == 'exit': exit(2) user_preferences[pkg] = rank index += 1 preferences_list = ["{0}:{1}".format(package, user_preferences[package]) for package in all_recommendations] for rec_key, rec_value in recommendations.iteritems(): save_list(rec_value, RECOMMENDATION_PATH.format(rec_key)) save_list(preferences_list, USER_PREFERENCES) save_list(recommendations_time, RECOMMENDATIONS_TIME) def get_uninstalled_dependencies(): user_pkgs = [] unistalled_pkgs = [] user_pkgs = get_user_installed_pkgs() for pkg in PKGS_DEPENDENCIES: if pkg not in user_pkgs: unistalled_pkgs.append(pkg) return unistalled_pkgs def check_dependencies(): unistalled_pkgs = get_uninstalled_dependencies() unistalled_dependencies = '' if len(unistalled_pkgs) > 0: unistalled_dependencies = ''.join(str(pkg) + ' ' for pkg in unistalled_pkgs) return unistalled_dependencies def collect_pc_informations(): print "Collecting PC informations" informations = [] linux_kernel_version = commands.getoutput('uname -s -r') distribution_version = commands.getoutput('lsb_release -a') distribution_version = distribution_version.splitlines() processor = commands.getoutput("cat /proc/cpuinfo | grep 'model name'") processor = processor.splitlines()[0].split(':')[1].strip() processor = "Processor: {0}".format(processor) informations.append(linux_kernel_version) informations.extend(distribution_version) informations.append(processor) save_list(informations, PC_INFORMATIONS) def collect_user_data(): collect_pc_informations() collect_all_user_pkgs() collect_manual_installed_pkgs() collect_pkgs_time() collect_popcon_submission() def initial_prints(): experiment_agree = """ Participate in this package survey? This survey has the objective to collect user feedback to allow the comparison between some new strategies for AppRecommender. This survey will them present a series of recommended packages that can be evaluates as follows: Bad: A package that you would not install on your system. Redundant: You already have a package that provides the same functionality as the one being recommended. Useful: A package that you may install on you system. Useful Surprise: A package that you would install on your system, but the recommendatio also surprised you in a positive way. This survey will also collect the result of some cross validation metrics generated for the AppRecommender strategies that use machine learning. Therefore, this survey will collect the following data: * A list of your evaluations for the recommended packages. * The result of the cross validation metrics for AppRecommender machine learning strategies. * The time in seconds that it took for each strategies to genarate its recommendations Once this process is over, a tar.gz file will be generated on your HOME dir. After that just send this file over to: lucas.moura128@gmail.com lucianopcbr@gmail.com terceiro@debian.org """ print experiment_agree def user_accept_collect_data(): accept_message = "\nDo you want to participate on this survey ? [y, N]: " accept_input = raw_input(accept_message) return accept_input.lower() == 'y' def clear_prints(): print '\n' * 80 os.system('clear') def train_machine_learning(): try: print "Training machine learning" os.system("cp {} {}".format( MachineLearningData.PKGS_CLASSIFICATIONS, LOG_PATH)) except xapian.DatabaseOpeningError: print "\n\nPlease check if you prepared the AppRecommender data" print "Try to run the following commands:" print " $ cd .." print " $ sudo apprec --init\n" exit(1) except IOError: print "\n\nPlease run the train command before executing this script:" print " $ sudo apprec --train\n" def run_cross_validation(): print "Collecting cross validations" strategies = ['mlbva', 'mlbva_eset', 'mlbow', 'mlbow_eset'] len_strategies = len(strategies) for index, strategy in enumerate(strategies): ml_cross_validation(LOG_PATH, strategy) print_progress_bar(index + 1, len_strategies) def make_tarfile(output_filename, source_dir): with tarfile.open(output_filename, "w:gz") as tar: tar.add(source_dir, arcname=os.path.basename(source_dir)) def main(): logging.getLogger().disabled = True initial_prints() if not user_accept_collect_data(): exit(1) create_log_folder() train_machine_learning() run_cross_validation() collect_user_preferences() make_tarfile(LOG_PATH + '.tar.gz', LOG_PATH) subprocess.call(['rm', '-rf', LOG_PATH]) print "\n\nFinished: All files and recommendations were collected" print "Collect data folder: {0}.tar.gz\n".format(LOG_PATH) print "Please, send this file to either one of these emails:\n" print "lucas.moura128@gmail.com, lucianopcbr@gmail.com, terceiro@debian.org" # noqa if __name__ == '__main__': main() AppRecommender-0.7.5/apprecommender/main/ml_cross_validation.py000066400000000000000000000072341306751311600247670ustar00rootroot00000000000000#!/usr/bin/python import datetime as dt import logging import os import pickle import sys import getopt from apprecommender.ml.cross_validation import (CrossValidationBVA, CrossValidationBOW) from apprecommender.evaluation import (SimpleAccuracy, Precision, Recall, FPR, F_score) from apprecommender.ml.data import MachineLearningData from apprecommender.ml.bag_of_words import BagOfWords from apprecommender.config import Config BASE_DIR = Config().base_dir CROSS_VALIDATION_FOLDER = BASE_DIR + '/cross_validation_data/' def get_strategy(ml_strategy_str, pkg_data, partition_size, rounds, metrics_list, labels): if ml_strategy_str == 'bow': return CrossValidationBOW( pkg_data, partition_size, rounds, metrics_list, labels) else: return CrossValidationBVA( pkg_data, partition_size, rounds, metrics_list, labels) def get_pkg_data(ml_strategy_str, ml_data, labels): if ml_strategy_str == 'bow': path = BagOfWords.BAG_OF_WORDS_PKGS_CLASSIFICATION else: path = MachineLearningData.PKGS_CLASSIFICATIONS with open(path, 'ra') as pkgs_classification: return pickle.load(pkgs_classification) def ml_cross_validation(folder_path, ml_strategy_str): logger = logging.getLogger('') logger.setLevel(logging.CRITICAL) if folder_path[-1] != '/': folder_path += '/' if not os.path.exists(folder_path): os.mkdir(folder_path) partition_size = 0.8 rounds = 5 metrics_list = [SimpleAccuracy(), Precision(), Recall(), FPR(), F_score(1)] labels = ['RU', 'U', 'NU'] ml_data = MachineLearningData() pkg_data = get_pkg_data(ml_strategy_str, ml_data, labels) ml_cross_validation = get_strategy( ml_strategy_str, pkg_data, partition_size, rounds, metrics_list, labels) cross_validaton_file = 'cross_validation_result_{}_{}_{}_{}.txt'.format( ml_strategy_str, rounds, partition_size, dt.datetime.now().strftime('%Y%m%d%H%M')) ml_cross_validation.run(None) cross_validation_file_path = folder_path + cross_validaton_file with open(cross_validation_file_path, 'w') as result: result.write(ml_cross_validation.__str__()) return ml_cross_validation def print_help(): print "\n" print "Usage: get_axipkgs" print " -h, --help \t\t Show this help" print " -s, --strategy \t Set machine_learning of one strategy" \ ", exemple: bva, bow" print "\n" print " [ strategy options ] " print " bva - run cross_validation from mlbva, Binary Vector" \ " Approach" print " bow - run cross_validation from mlbow, Bag of Words" if __name__ == '__main__': short_options = "hdvo:s:" long_options = ["help", "strategy"] valid_strategies = ['bva', 'bow'] ml_strategy_str = None try: opts, args = getopt.getopt(sys.argv[1:], short_options, long_options) except getopt.GetoptError as error: logging.error("Bad syntax: %s" % str(error)) print_help() sys.exit() for option, param in opts: if option in ("-h", "--help"): print_help() sys.exit() elif option in ("-s", "--strategy"): ml_strategy_str = param else: print_help() assert False, "unhandled option" if ml_strategy_str in valid_strategies: print ml_cross_validation(CROSS_VALIDATION_FOLDER, ml_strategy_str) print("Cross validation results saved on: %s" % (CROSS_VALIDATION_FOLDER)) else: print_help() AppRecommender-0.7.5/apprecommender/main/options.py000066400000000000000000000043071306751311600224250ustar00rootroot00000000000000import argparse def get_parser(): apprec_description = 'Package recommender system for Debian (and derived) \ distros' parser = argparse.ArgumentParser(description=apprec_description) parser.add_argument( '-s', '--strategy', help='select strategy to run apprecommender (default: cb)', type=str) parser.add_argument( '-d', '--debug', help='run apprecommender on debug mode', type=int) parser.add_argument( '-v', '--verbose', help='run apprecommender on verbose mode', type=int) parser.add_argument( '-z', '--profile-size', help='set the profile size of an user on apprecommender', type=int) parser.add_argument( '-i', '--init', help='initialize apprecommender database', action='store_true') parser.add_argument( '-t', '--train', help='train machine learning algorithms', action='store_true') parser.add_argument( '-b', '--because', help="display which user's packages generated a recommendation", action='store_true') parser.add_argument( '-n', '--num-recommendations', help='set the number of packages that will be recommended', type=int) parser.add_argument( '-c', '--contribute', help='classify recommendations and help apprecommender to improve', action='store_true') parser.add_argument( '-p', '--packages', help="Add reference package for strategy 'cbpkg'", type=str, nargs='+', default=[]) parser.add_argument( '--show-classifications', help='Show the user classifications for machine learning algorithms', action='store_true') parser.add_argument( '-e', '--enable-apt', help='Enable recommendations when install a package with apt', action='store_true') parser.add_argument( '-r', '--disable-apt', help='Disable recommendations when install a package with apt', action='store_true') parser.add_argument( '--update', help='Run both init and train commands', action='store_true') return parser AppRecommender-0.7.5/apprecommender/main/show_classifications.py000066400000000000000000000022321306751311600251430ustar00rootroot00000000000000#!/usr/bin/python import os import pickle from apprecommender.ml.pkg_time import PkgTime from apprecommender.config import Config def main(): path = Config().user_data_dir + 'pkgs_classifications.txt' if not os.path.exists(path): print 'Could not find file pkgs_classification' print 'Have you run apprec --train ?' exit(-1) pkg_time = PkgTime() pkgs_times = pkg_time.get_package_data() with open(path, 'ra') as data: pkg_classification = pickle.load(data) classifications = {'RU': [], 'U': [], 'NU': []} for pkg, values in pkg_classification.iteritems(): classifications[values[-1]].append(pkg) for classification, pkgs in classifications.iteritems(): print '\n' print 'Classification: {}'.format(classification) print '\n' for pkg in sorted(pkgs): pkg_text = '{} \t' if len(pkg) < 15: pkg_text += '\t' if len(pkg) < 7: pkg_text += '\t' pkg_text += ' {}' print pkg_text.format(pkg, pkgs_times[pkg][1].strip()) print '\nNum pkgs: {}'.format(len(pkg_classification)) AppRecommender-0.7.5/apprecommender/ml/000077500000000000000000000000001306751311600200405ustar00rootroot00000000000000AppRecommender-0.7.5/apprecommender/ml/__init__.py000066400000000000000000000000001306751311600221370ustar00rootroot00000000000000AppRecommender-0.7.5/apprecommender/ml/bag_of_words.py000066400000000000000000000121771306751311600230550ustar00rootroot00000000000000import os import pickle from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import GaussianNB from apprecommender.apt_cache import AptCache from apprecommender.config import Config from apprecommender.ml.data import MachineLearningData class BagOfWords(): USER_DATA_DIR = Config().user_data_dir BAG_OF_WORDS_DIR = USER_DATA_DIR + 'bag_of_words/' BAG_OF_WORDS_MODEL = BAG_OF_WORDS_DIR + 'bag_of_words_model.pickle' BAG_OF_WORDS_TERMS = BAG_OF_WORDS_DIR + 'bag_of_words_terms.pickle' BAG_OF_WORDS_DEBTAGS = BAG_OF_WORDS_DIR + 'bag_of_words_debtags.pickle' BAG_OF_WORDS_PKGS_CLASSIFICATION = BAG_OF_WORDS_DIR + \ 'bow_pkgs_classification.pickle' MODEL_ALREADY_CREATED = 1 CREATED_MODEL = 0 @staticmethod def save(bag_of_words, file_path): with open(file_path, 'wb') as text: pickle.dump(bag_of_words, text) @staticmethod def load(file_path): with open(file_path, 'rb') as text: bag_of_words = pickle.load(text) return bag_of_words def __init__(self): self.vectorizer = TfidfVectorizer( max_df=0.8, max_features=5000, min_df=5, stop_words='english', use_idf=True) def check_dir(self): return os.path.exists(BagOfWords.BAG_OF_WORDS_DIR) def combine_pkg_info(self, description, debtags, section): description.extend(debtags) description.append(section) return description def classify_pkg(self, attribute_vector, transform=True): if transform: pkg_feature = self.vectorizer.transform([attribute_vector]) pkg_feature = pkg_feature.toarray() else: pkg_feature = attribute_vector label = self.classifier.predict(pkg_feature) return label[0] def create_pkg_data(self, pkg, axi, cache, ml_data): description = self.get_pkg_description(pkg, cache, ml_data) debtags = self.get_pkg_debtags(pkg, axi, ml_data) section = self.get_pkg_section(pkg, cache, ml_data) return ' '.join(self.combine_pkg_info(description, debtags, section)) def get_pkgs_classification(self, pkgs_list): pkgs_classification = [] with open(MachineLearningData.PKGS_CLASSIFICATIONS) as pkgs: pkgs_data = pickle.load(pkgs) for pkg_name in pkgs_list: pkgs_classification.append(pkgs_data[pkg_name][-1]) return pkgs_classification def get_pkg_description(self, pkg, cache, ml_data): return ml_data.get_pkg_terms(cache, pkg) def get_pkg_debtags(self, pkg, axi, ml_data): return map(lambda x: x.replace('::', '_'), ml_data.get_pkg_debtags(axi, pkg)) def get_pkg_section(self, pkg, cache, ml_data): return ml_data.get_pkg_section(cache, pkg) def get_used_terms_and_debtags(self, features_lists): terms, debtags = [], [] for feature in features_lists: if '_' in feature: debtags.append(feature.replace('_', '::')) else: terms.append(feature) return terms, debtags def prepare_data(self, pkg_list, axi, cache, ml_data): pkgs_description = [] pkgs_classification = [] for pkg in pkg_list: pkg_data = self.create_pkg_data(pkg, axi, cache, ml_data) pkgs_description.append(pkg_data) pkgs_classification = self.get_pkgs_classification(pkg_list) return (pkgs_description, pkgs_classification) def save_features(self, features, path): if not self.check_dir(): os.mkdir(BagOfWords.BAG_OF_WORDS_DIR) with open(path, 'wa') as feature_file: pickle.dump(features, feature_file) def save_pkgs_features(self, path, pkgs_list, features_array, pkg_classification): pkgs_classification = {} for index, pkg in enumerate(pkgs_list): value = features_array[index, :].tolist() value.append(pkg_classification[index]) pkgs_classification[pkg] = value with open(path, 'wa') as bow_pkgs_classification: pickle.dump(pkgs_classification, bow_pkgs_classification) def train_model(self, pkgs_list, axi, save_files=True): cache = AptCache() ml_data = MachineLearningData() pkgs_description, pkg_classification = self.prepare_data( pkgs_list, axi, cache, ml_data) pkg_features = self.vectorizer.fit_transform(pkgs_description) features_array = pkg_features.toarray() terms, debtags = self.get_used_terms_and_debtags( self.vectorizer.get_feature_names()) self.classifier = GaussianNB() self.classifier.fit(features_array, pkg_classification) path = BagOfWords.BAG_OF_WORDS_PKGS_CLASSIFICATION if save_files: self.save_features(terms, BagOfWords.BAG_OF_WORDS_TERMS) self.save_features(debtags, BagOfWords.BAG_OF_WORDS_DEBTAGS) self.save_pkgs_features( path, pkgs_list, features_array, pkg_classification) return BagOfWords.CREATED_MODEL AppRecommender-0.7.5/apprecommender/ml/bayes_matrix.py000066400000000000000000000152471306751311600231120ustar00rootroot00000000000000import pickle import numpy as np class BayesMatrix: ''' This class contains the implementation of the naive bayes algorithm using only matrix operations in order to perform its tasks. ''' @staticmethod def save(bayes_matrix, file_path): with open(file_path, 'wb') as text: pickle.dump(bayes_matrix, text) @staticmethod def load(file_path): with open(file_path, 'rb') as text: bayes_matrix = pickle.load(text) return bayes_matrix ''' data: A matrix on the format p x a, where p is the number of packages that will be used to train the algorithm and a is the number of features that a package has. classifications: A matrix that holds the temporal classification labels for the packages found on the attributes' matrix. This matrix is a column one, where the number of lines represents the number of packages used to train the algorithm. order_of_classifications: A array with the possible classifications on crescent order labels: A column matrix that holds the possible labels that can be used to classify a given package. adjacency: A matrix in the format l x p, where l is the number of possible classifications labels. This is a binary matrix that holds which packages were classified with some given label. histogram: A column vector l x 1, where l is the possible classification labels. This matrix holds the values of how many packages p were classified with a given label l. label_probability: A column matrix that holds the individual probability for each label l for the given training data. feature_per_label: A l x a matrix that hold for each label, the number of times a feature a was present on the label. prob: A l x a matrix that holds the probability of a given feature a to be present in a given label l. diag_histogram: A diagonal matrix for the histogram one. attribute_vector: A vector 1 x a with the values of features to get a classification for this vector based on the training ''' def __init__(self): self.data = None self.classifications = None self.labels = None self.adjacecy = None self.histogram = None self.label_probability = None self.feature_per_label = None self.prob = None self.diag_histogram = None def training(self, data_matrix, classifications, order_of_classifications): self.data = data_matrix.astype(float) order_of_classifications = order_of_classifications[:] self.used_order_of_classifications = ( self.get_used_order_of_classifications(classifications, order_of_classifications)) self.labels = (self.convert_possible_labels_to_number( self.used_order_of_classifications).astype(float)) num_packages = self.data.shape[0] num_labels = self.labels.shape[0] self.classifications = (self.convert_classifications_to_number( classifications, self.used_order_of_classifications).astype(float)) self.adjacency = self.get_adjacent_matrix(num_labels, num_packages).astype(float) self.histogram = self.adjacency.dot(np.ones((num_packages, 1))) self.label_probability = self.histogram / num_packages self.feature_per_label = self.adjacency * self.data self.diag_histogram = np.diag(np.array(self.histogram)[:, 0]) self.prob = np.linalg.inv( self.diag_histogram) * self.feature_per_label def get_classification(self, attribute_vector): attribute_vector = attribute_vector.astype(float) prob_vector = self.prob.copy() label_probability_log = np.log(self.label_probability + 1) indexes_one = np.matrix(np.where(attribute_vector == 1.0)[1]) indexes_one = indexes_one.tolist()[0] indexes_zero = np.matrix(np.where(attribute_vector == 0.0)[1]) indexes_zero = indexes_zero.tolist()[0] prob_vector[:, indexes_one] = 1 + prob_vector[:, indexes_one] prob_vector[:, indexes_zero] = 2 - prob_vector[:, indexes_zero] prob_vector = np.log(prob_vector) prob_vector = prob_vector * np.ones((prob_vector.shape[1], 1)) prob_vector = label_probability_log + prob_vector line, col = np.unravel_index(prob_vector.argmax(), prob_vector.shape) best_prob_index = line return self.used_order_of_classifications[best_prob_index] def convert_possible_labels_to_number(self, order_of_classifications): numbers = "" for i in range(len(order_of_classifications)): numbers += "{0};".format(i) return np.matrix(numbers[0:-1]) def convert_classifications_to_number(self, classifications, order_of_classifications): numbers = "" for i in range(len(classifications)): number = order_of_classifications.index(classifications[i]) numbers += "{0};".format(number) return np.matrix(numbers[0:-1]) def get_adjacent_matrix(self, num_labels, num_packages): adjacent_matrix = np.zeros((num_labels, num_packages)) for i in range(len(self.classifications)): index = int(self.classifications[i].item()) adjacent_matrix[index][i] = 1 return adjacent_matrix def get_used_order_of_classifications(self, classifications, order_of_classifications): used_classifications = set() num_possible_classifications = len(order_of_classifications) for name in classifications: if len(used_classifications) == num_possible_classifications: return order_of_classifications used_classifications.add(name[0].item()) for name in order_of_classifications[:]: if name not in used_classifications: index = order_of_classifications.index(name) del order_of_classifications[index] return order_of_classifications AppRecommender-0.7.5/apprecommender/ml/cross_validation.py000066400000000000000000000302231306751311600237550ustar00rootroot00000000000000import numpy as np import xapian from abc import ABCMeta from collections import defaultdict from apprecommender.evaluation import CrossValidation, Metric from apprecommender.strategy import XAPIAN_DATABASE_PATH from apprecommender.ml.bag_of_words import BagOfWords from apprecommender.ml.bayes_matrix import BayesMatrix from apprecommender.ml.utils import create_column_matrix, create_binary_matrix NOT_NECESSARY = 1 class OverallAccuracy(Metric): ''' Simple comparison between generated predictions and real target values. ''' def __init__(self): self.desc = 'Overall_Accuracy' def run(self, evaluation): predicted = evaluation.predicted_results target = evaluation.real_results overall_accuracy = np.sum(predicted == target) overall_accuracy /= float(predicted.shape[0]) return overall_accuracy * 100 class ConfusionMatrix(): def __init__(self, predicted_results, real_results): self.predicted_results = predicted_results self.real_results = real_results self.repository_size = len(predicted_results) self.true_positive_len = 0 self.true_negative_len = 0 self.false_positive_len = 0 self.false_negative_len = 0 self.predicted_relevant_len = 0 self.real_relevant_len = 0 self.real_negative_len = 0 def run(self): matrix_values = np.zeros(shape=(2, 2)) num_classification = len(self.predicted_results) for i in range(num_classification): row = self.predicted_results[i][0] column = self.real_results[i][0] matrix_values[row][column] += 1 self.true_positive_len = matrix_values[1][1] self.true_negative_len = matrix_values[0][0] self.false_positive_len = matrix_values[1][0] self.false_negative_len = matrix_values[0][1] self.predicted_relevant_len = (self.true_positive_len + self.false_positive_len) self.real_relevant_len = (self.true_positive_len + self.false_negative_len) self.real_negative_len = self.repository_size - self.real_relevant_len def __str__(self): result = 'TP: {0}\nFP: {1}'.format(self.true_positive_len, self.false_positive_len) result += '\nFN: {0}\nTN: {1}\n'.format(self.false_negative_len, self.true_negative_len) return result class Evaluation(): ''' :param predicted_results: The classifications generated by the machine learning algorithm. This variable will be a column matrix, where the number of lines is equal to the number of data used to validate the algorithm. :param real_results: The real classifications of the data used to test the machine learning algorithm. This variable will be a column matrix, where the number of lines is equal to the number of data used to validate the algorithm. :possible_classification: An list containg the labels that a input vector can be classified upon. ''' def __init__(self, predicted_results, real_results, possible_classifications): self.predicted_results = predicted_results self.real_results = real_results self.possible_classifications = possible_classifications self.repository_size = predicted_results.shape[0] self.classes_outputs = {} self.create_classification_outputs() def create_classification_outputs(self): for classification in self.possible_classifications: self.classes_outputs[classification] = ( self.create_confusion_matrix(classification)) def create_confusion_matrix(self, classification): default_value = 0 # Create binary classifications using the one received as parameter binary_predictions = create_binary_matrix(self.predicted_results, classification, default_value) binary_real = create_binary_matrix(self.real_results, classification, default_value) confusion_matrix = ConfusionMatrix(binary_predictions, binary_real) confusion_matrix.run() return confusion_matrix def run(self, metric): results = {} for label, confusion_matrix in self.classes_outputs.iteritems(): results[label] = metric.run(confusion_matrix) return results class CrossValidationMachineLearning(CrossValidation): __metaclass__ = ABCMeta def __init__(self, pkg_data, partition_proportion, rounds, metrics_list, labels): self.pkg_data = pkg_data self.labels = labels self.label_groups = {} self.round_label_groups = [] self.round_num_data = [] self.evaluation = None self.overall_accuracy = [] super(CrossValidationMachineLearning, self).__init__(partition_proportion, rounds, None, metrics_list, 0) def __str__(self): result_str = '' metrics_mean = {} num_data = len(self.pkg_data) result_str += 'Model used {0}\n'.format(self.label) result_str += 'Num data used: {0}\n'.format(num_data) for label in self.labels: result_str += 'Num of data marked as {0}: {1}\n'.format( label, len(self.label_groups[label])) result_str += '\n\n' for r in range(self.rounds): result_str += 'Round {0}:\n\n'.format(r) result_str += 'Overall Accuracy: {0}\n'.format( self.overall_accuracy[r]) result_str += 'Training data used: {0}\n'.format( self.round_num_data[r]) for label in self.labels: result_str += 'Data marked as {0}: {1}\n'.format( label, len(self.round_label_groups[r][label])) result_str += '\n' result_str += '\n\n' for metric in self.metrics_list: result_str += '{0}:\n'.format(metric.desc) metrics_mean[metric.desc] = 0 for r in range(self.rounds): result_str += '\tRound {0}:\n'.format(r) mean = 0 for label in self.labels: result = self.cross_results[metric.desc][r][label] mean += result result_str += '\t\tClass {0}: {1}\n'.format(label, result) mean /= len(self.labels) result_str += '\t\tMean: {0}\n\n'.format(mean) metrics_mean[metric.desc] += mean metrics_mean[metric.desc] /= self.rounds result_str += '\n\n' result_str += 'Average results:\n' result_str += '---------------\n' for metric in self.metrics_list: result_str += '{0}: {1}\n'.format(metric.desc, metrics_mean[metric.desc]) return result_str def create_labels_groups(self, data): label_groups = {} label_groups.fromkeys(self.labels) label_groups = defaultdict(lambda: [], label_groups) for input_vector in data.values(): label = input_vector[-1] label_groups[label].append(input_vector) return label_groups def get_model(self, cross_item_score): ''' This function should get the data that will be used as training data, train the algorithm with this data and return the generated model ''' self.round_num_data.append(len(cross_item_score)) self.round_label_groups.append( self.create_labels_groups(cross_item_score)) def get_user_score(self, user): user_score = self.pkg_data self.label_groups = self.create_labels_groups(user_score) return user_score def get_real_results(self, round_partition): ''' This method should return the real labels for the validation set used on the algorithm. ''' classifications = [] for input_vector in round_partition.values(): classifications.append(input_vector[-1]) # make classifications a column array return create_column_matrix(classifications) def get_result_size(self): return NOT_NECESSARY def get_evaluation(self, predicted_result, real_result): self.evaluation = Evaluation(predicted_result, real_result, self.labels) return self.evaluation def run_metrics(self, predicted_result, real_result): super(CrossValidationMachineLearning, self).run_metrics( predicted_result, real_result) self.overall_accuracy.append(OverallAccuracy().run(self.evaluation)) class CrossValidationBVA(CrossValidationMachineLearning): def __init__(self, pkg_data, partition_proportion, rounds, metrics_list, labels): super(CrossValidationBVA, self).__init__( pkg_data, partition_proportion, rounds, metrics_list, labels) self.label = "Binary vector model" def get_model(self, cross_item_score): super(CrossValidationBVA, self).get_model(cross_item_score) bayes_matrix = BayesMatrix() all_matrix = (np.matrix(cross_item_score.values())) data_matrix = all_matrix[0:, 0:-1] classifications = all_matrix[0:, -1] bayes_matrix.training(data_matrix, classifications, self.labels) return bayes_matrix ''' :param round_user: The model created by the machine learning algorithm. :param round_partition: The data that will be used to evaluate the machine learning algorithm. :param result_size: Not necessary for this context ''' def get_predicted_results(self, round_user, round_partition, result_size=0): ''' This method should generate the predictions for the packages received. It basically needs to used the generated model and use it to generate the prediction. ''' predicted_results = [] for pkg, input_vector in round_partition.iteritems(): input_vector = np.matrix(input_vector[:-1]) predicted_results.append( round_user.get_classification(input_vector)) return create_column_matrix(predicted_results) class CrossValidationBOW(CrossValidationMachineLearning): def __init__(self, pkg_data, partition_proportion, rounds, metrics_list, labels): super(CrossValidationBOW, self).__init__( pkg_data, partition_proportion, rounds, metrics_list, labels) self.axi = xapian.Database(XAPIAN_DATABASE_PATH) self.label = "Bag of words model" def get_model(self, cross_item_score): super(CrossValidationBOW, self).get_model(cross_item_score) pkgs_list = cross_item_score.keys() bag_of_words = BagOfWords() bag_of_words.train_model(pkgs_list, self.axi, save_files=False) return bag_of_words ''' :param round_user: The model created by the machine learning algorithm. :param round_partition: The data that will be used to evaluate the machine learning algorithm. :param result_size: Not necessary for this context ''' def get_predicted_results(self, round_user, round_partition, result_size=0): ''' This method should generate the predictions for the packages received. It basically needs to used the generated model and use it to generate the prediction. ''' predicted_results = [] for pkg in round_partition.keys(): predicted_results.append( round_user.classify_pkg(pkg)[0]) return create_column_matrix(predicted_results) AppRecommender-0.7.5/apprecommender/ml/data.py000066400000000000000000000161521306751311600213300ustar00rootroot00000000000000from os import path from os import makedirs import Stemmer import pickle import xapian from apprecommender.apt_cache import AptCache from apprecommender.config import Config from apprecommender.decider import FilterTag, FilterDescription from apprecommender.ml.pkg_time import PkgTime class MachineLearningData(): XAPIAN_DATABASE_PATH = Config().axi_desktopapps USER_DATA_DIR = Config().user_data_dir BASE_DIR = Config().base_dir PKG_DATA_PATH = USER_DATA_DIR + 'pkg_data.txt' PKGS_CLASSIFICATIONS = USER_DATA_DIR + 'pkgs_classifications.txt' MACHINE_LEARNING_TERMS = USER_DATA_DIR + 'machine_learning_terms.txt' MACHINE_LEARNING_DEBTAGS = USER_DATA_DIR + 'machine_learning_debtags.txt' MACHINE_LEARNING_TRAINING = USER_DATA_DIR + 'machine_learning_training.txt' def __init__(self): self.axi = xapian.Database(MachineLearningData.XAPIAN_DATABASE_PATH) self.stemmer = Stemmer.Stemmer('english') valid_tags = [] with open(path.join(Config().filters_dir, "debtags")) as tags: valid_tags = [line.strip() for line in tags if not line.startswith("#")] self.filter_tag = FilterTag(valid_tags) self.filter_description = FilterDescription() def create_data(self, labels): if not path.exists(MachineLearningData.USER_DATA_DIR): makedirs(MachineLearningData.USER_DATA_DIR) pkgs = self.get_pkgs_classification(labels) cache = AptCache() terms_name = self.get_terms_for_all_pkgs(cache, pkgs.keys()) debtags_name = self.get_debtags_for_all_pkgs(self.axi, pkgs.keys()) debtags_name = self.filter_debtags(debtags_name) debtags_name = sorted(debtags_name) terms_name = self.filter_terms(terms_name) terms_name = sorted(terms_name) pkgs_classifications = ( self.get_pkgs_table_classification(self.axi, pkgs, cache, debtags_name, terms_name)) try: self.save_pkg_data( terms_name, MachineLearningData.MACHINE_LEARNING_TERMS) self.save_pkg_data( debtags_name, MachineLearningData.MACHINE_LEARNING_DEBTAGS) self.save_pkg_data( pkgs_classifications, MachineLearningData.PKGS_CLASSIFICATIONS) except IOError: raise return pkgs_classifications def get_pkgs_classification(self, labels): pkgs_percent = {} pkgs_classification = {} pkg_time = PkgTime() pkg_data = pkg_time.get_package_data() for name, time_values in pkg_data.iteritems(): _, access = time_values pkgs_percent[name] = access pkgs = pkgs_percent.keys() pkgs = sorted(pkgs, key=lambda pkg: pkgs_percent[pkg]) pkgs = list(reversed(pkgs)) if len(pkgs) > len(labels): size = len(pkgs) / len(labels) for index, label in enumerate(labels): index_begin = size * index index_end = index_begin + size classifications = dict.fromkeys(pkgs[index_begin:index_end], label) pkgs_classification.update(classifications) index_begin = size * len(labels) if index_begin < len(labels): classifications = dict.fromkeys(pkgs[index_begin], label[-1]) pkgs_classification.update(classifications) else: for index, pkg in enumerate(pkgs): pkgs_classification[pkg] = labels[index] return pkgs_classification def get_pkg_data(self, axi, pkg_name, data_type): pkg_name = 'XP' + pkg_name query = xapian.Query(xapian.Query.OP_OR, [pkg_name]) enquire = xapian.Enquire(axi) enquire.set_query(query) mset = enquire.get_mset(0, 10) pkg_info = [] for pkg in mset: for term in axi.get_document(pkg.docid).termlist(): pkg_term = term.term if pkg_term.startswith(data_type): pkg_info.append(pkg_term[len(data_type):]) elif data_type == 'term': if pkg_term[0].islower(): pkg_info.append(pkg_term) return pkg_info def get_pkg_debtags(self, axi, pkg_name): return self.get_pkg_data(axi, pkg_name, 'XT') def get_pkg_terms(self, cache, pkg_name): description = self.get_pkg_description(cache, pkg_name) tokens = description.lower().split() stems = [self.stemmer.stemWord(token) for token in tokens if self.filter_description(token)] return stems def get_pkg_section(self, cache, pkg_name): return cache[pkg_name].section def get_pkg_description(self, cache, pkg_name): if pkg_name not in cache: return [] else: description = cache[pkg_name].candidate.description return description.strip() def get_debtags_name(self, file_path): with open(file_path, 'r') as text: debtags_name = [debtag.strip() for debtag in text] return debtags_name def create_row_table_list(self, labels_name, pkg_elements): row_list = [] for debtag in labels_name: row_list.append(1 if debtag in pkg_elements else 0) return row_list def get_terms_for_all_pkgs(self, cache, pkgs): pkg_terms = set() for pkg in pkgs: pkg_terms = pkg_terms | set(self.get_pkg_terms(cache, pkg)) return pkg_terms def get_debtags_for_all_pkgs(self, axi, pkgs): pkg_debtags = set() for pkg in pkgs: pkg_debtags = pkg_debtags | set(self.get_pkg_debtags(axi, pkg)) return pkg_debtags def filter_terms(self, terms): filtered_terms = [] for term in terms: if self.filter_description(term): filtered_terms.append(term) return filtered_terms def filter_debtags(self, debtags): filtered_debtags = [] for tag in debtags: if self.filter_tag('XT' + tag): filtered_debtags.append(tag) return filtered_debtags def get_pkgs_table_classification(self, axi, pkgs, cache, debtags_name, terms_name): pkgs_classification = {} for key, value in pkgs.iteritems(): pkgs_classification[key] = [] debtags = self.get_pkg_debtags(axi, key) debtags = self.create_row_table_list(debtags_name, debtags) pkgs_classification[key].extend(debtags) terms = self.get_pkg_terms(cache, key) terms = self.create_row_table_list(list(terms_name), terms) pkgs_classification[key].extend(terms) pkgs_classification[key].append(value) return pkgs_classification def save_pkg_data(self, pkg_data, file_path): try: ml_data = open(file_path, 'wb') with ml_data: pickle.dump(pkg_data, ml_data) except IOError: raise AppRecommender-0.7.5/apprecommender/ml/pkg_time.py000066400000000000000000000064571306751311600222250ustar00rootroot00000000000000#!/usr/bin/env python import commands import os import re from apprecommender.config import Config from apprecommender.data_classification import get_time_from_package from apprecommender.user import LocalSystem from apprecommender.utils import print_progress_bar USER_DATA_DIR = Config().user_data_dir class PkgTime: def create_pkg_data(self): user = LocalSystem() user_pkgs = user.pkg_profile pkgs_time = self.get_packages_time(user_pkgs) self.save_package_time(pkgs_time) return pkgs_time def get_best_time(self, pkg): valid_path_regex = re.compile( r'/usr/bin/|/usr/game/|/usr/lib/.+/') invalid_path_regex = re.compile( r'(.+-){2,}|/usr/lib/mime/packages/|/init.d/|/media/') invalid_files_regex = re.compile( r'\.desktop|\.conf|\.egg-info|\.txt') pkg_files = commands.getoutput('dpkg -L {}'.format(pkg)) bestatime, bestmtime = 0, 0 for pkg_file in pkg_files.splitlines(): if invalid_path_regex.search(pkg_file): continue if invalid_files_regex.search(pkg_file): continue if os.path.isdir(pkg_file): continue if valid_path_regex.search(pkg_file): modify, access = get_time_from_package(pkg_file, pkg_bin=False) modify = int(modify) if modify else 0 access = int(access) if access else 0 if access > bestatime: bestatime = access bestmtime = modify return (bestmtime, bestatime) def get_package_data(self, file_path=USER_DATA_DIR + 'pkg_data.txt'): if os.path.isfile(file_path): pkgs_time = {} with open(file_path, 'r') as pkg_data: for pkg_line in pkg_data: name, modify, access = pkg_line.split(' ') pkgs_time[name] = [modify, access] return pkgs_time else: return self.create_pkg_data() def get_packages_time(self, pkgs, verbose=False): pkgs_time = {} len_pkgs = len(pkgs) for index, pkg in enumerate(pkgs): modify, access = self.get_best_time(pkg) if modify and access: if verbose: print 'ADD: {}'.format(pkg) pkgs_time[pkg] = [] pkgs_time[pkg].append(modify) pkgs_time[pkg].append(access) else: if verbose: print 'NOT: {} {} {}'.format(pkg, modify, access) print_progress_bar(index + 1, len_pkgs) return pkgs_time def print_package_time(self, pkgs_time): for key, value in pkgs_time.iteritems(): print "{} : Modify {}, Access {}".format(key, value[0], value[1]) def save_package_time(self, pkgs_time, file_path=USER_DATA_DIR + 'pkg_data.txt'): with open(file_path, 'w') as pkg_data: pkg_str = "{pkg} {modify} {access}\n" for pkg, times in pkgs_time.iteritems(): pkg_line = pkg_str.format(pkg=pkg, modify=times[0], access=times[1]) pkg_data.write(pkg_line) AppRecommender-0.7.5/apprecommender/ml/utils.py000066400000000000000000000004551306751311600215560ustar00rootroot00000000000000from numpy import array, where INVALID_PARAMETERS = -1 def create_column_matrix(matrix): matrix = array(matrix) matrix.shape = (len(matrix), 1) return matrix def create_binary_matrix(original_vector, value, default_value): return where(original_vector == value, 1, default_value) AppRecommender-0.7.5/apprecommender/recommender.py000066400000000000000000000144341306751311600223100ustar00rootroot00000000000000#!/usr/bin/env python """ recommender - python module for classes related to recommenders. """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import heapq import inspect import logging import operator import os import xapian from collections import namedtuple from fuzzywuzzy import fuzz from operator import attrgetter import apprecommender.strategy from apprecommender.apt_cache import AptCache from apprecommender.config import Config class RecommendationResult: """ Class designed to describe a recommendation result: items and scores. """ def __init__(self, item_score, ranking=0, limit=0, user_profile=None): """ Set initial parameters. """ self.item_score = item_score self.size = len(item_score) self.limit = limit self.cache = AptCache() self.pkg_descriptions = {} if ranking: self.ranking = ranking if user_profile: self.fill_pkg_descriptions(user_profile) def fill_pkg_descriptions(self, user_profile): for pkg in user_profile: description = self.cache[pkg].candidate.description self.pkg_descriptions[pkg] = description.lower() def __str__(self): """ String representation of the object. """ result = self.get_prediction(self.limit) rec_str = '\n' index = 1 for pkg, _ in result: summary = self.cache[pkg].candidate.summary description = self.cache[pkg].candidate.description rec_str += '{}: {} \t {}\n'.format( index, pkg.ljust(20), summary) if self.pkg_descriptions: because_pkgs = self.get_because(description.lower()) rec_str += ' because you installed: \t {}\n\n'.format( ', '.join(because_pkgs)) index += 1 return rec_str def get_because(self, rec_description): because = [] PkgRatio = namedtuple('PkgRatio', ['pkg', 'ratio']) for pkg, description in self.pkg_descriptions.iteritems(): ratio = fuzz.ratio(rec_description, description) because.append(PkgRatio(pkg, ratio)) pkgs = heapq.nlargest(4, because, key=attrgetter('ratio')) return [pkg for pkg, _ in pkgs] def get_prediction(self, limit=0): """ Return prediction based on recommendation size (number of items). """ sorted_result = sorted(self.item_score.items(), key=operator.itemgetter(1)) if not limit or limit > self.size: limit = self.size return list(reversed(sorted_result[-limit:])) class Recommender: """ Class designed to play the role of recommender. """ def __init__(self): """ Set initial parameters. """ self.cfg = Config() # Load xapian indexes self.axi_desktopapps = xapian.Database(self.cfg.axi_desktopapps) if self.cfg.popcon: self.popcon_desktopapps = xapian.Database( self.cfg.popcon_desktopapps) # Load valid programs, desktopapps and tags # format: one package or tag name per line self.valid_desktopapps = [] self.valid_tags = [] logging.info("Loading recommender filters") with open(os.path.join(self.cfg.filters_dir, "desktopapps")) as pkgs: self.valid_desktopapps = [line.strip() for line in pkgs if not line.startswith("#")] with open(os.path.join(self.cfg.filters_dir, "debtags")) as tags: self.valid_tags = [line.strip() for line in tags if not line.startswith("#")] # Set xapian index weighting scheme if self.cfg.weight == "bm25": self.weight = xapian.BM25Weight(self.cfg.bm25_k1, self.cfg.bm25_k2, self.cfg.bm25_k3, self.cfg.bm25_b, self.cfg.bm25_nl) else: self.weight = xapian.TradWeight() self.set_strategy(self.cfg.strategy) def get_all_strategies(self): cls_members = inspect.getmembers(apprecommender.strategy, inspect.isclass) valid_strategies = {} for name, obj in cls_members: class_strategies = getattr(obj, 'get_valid_strategies', None) if class_strategies: valid_strategies = dict(class_strategies(), **valid_strategies) return valid_strategies def set_strategy(self, strategy_str, n=0): """ Set the recommendation strategy. """ valid_strategies = self.get_all_strategies() profile_size = n if n else self.cfg.profile_size self.items_repository = self.axi_desktopapps self.valid_pkgs = self.valid_desktopapps logging.info("Setting recommender strategy to \'%s\'" % strategy_str) if strategy_str in valid_strategies: strategy_type = valid_strategies[strategy_str] class_name = strategy_type.class_name content_type = strategy_type.content_type self.strategy = eval(class_name)( content_type, profile_size) else: logging.info("Strategy not defined.") self.strategy = None def get_recommendation(self, user, result_size=100): """ Produces recommendation using previously loaded strategy. """ if self.strategy is None: return "" return self.strategy.run(self, user, result_size) AppRecommender-0.7.5/apprecommender/singleton.py000066400000000000000000000023601306751311600220050ustar00rootroot00000000000000#!/usr/bin/env python """ singleton - python class that implements singleton design pattern. """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ class Singleton(object): """ Base class for inheritance of only-one-instance classes. Singleton design pattern. """ def __new__(cls, *args, **kwargs): """ Creates a new instance of the class only if none already exists. """ if '_inst' not in vars(cls): cls._inst = object.__new__(cls) return cls._inst AppRecommender-0.7.5/apprecommender/strategy.py000066400000000000000000000403531306751311600216510ustar00rootroot00000000000000#!/usr/bin/env python """ strategy - python module for classes and methods related to recommendation strategies. """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import collections import logging import operator import pickle import re import recommender import subprocess import xapian import numpy as np from abc import ABCMeta, abstractmethod from apprecommender.apt_cache import AptCache from apprecommender.config import Config from apprecommender.decider import (PkgMatchDecider, PkgReverseDependeciesDecider) from apprecommender.ml.bag_of_words import BagOfWords from apprecommender.ml.bayes_matrix import BayesMatrix from apprecommender.ml.data import MachineLearningData from apprecommender.utils import get_class_and_module_name XAPIAN_DATABASE_PATH = Config().axi_desktopapps USER_DATA_DIR = Config().user_data_dir PKGS_CLASSIFICATIONS_INDICES = (USER_DATA_DIR + 'pkgs_classifications_indices.txt') StrategyType = collections.namedtuple('StrategyType', 'class_name content_type') class RecommendationStrategy(object): """ Base class for recommendation strategies. """ def run(self, rec, user, recommendation_size): raise NotImplementedError class ContentBased(RecommendationStrategy): """ Content-based recommendation strategy based on Apt-xapian-index. """ @classmethod def get_valid_strategies(cls): class_name = get_class_and_module_name(cls) valid_strategies = {'cb': StrategyType(class_name, 'mix'), 'cbt': StrategyType(class_name, 'tag'), 'cbd': StrategyType(class_name, 'desc'), 'cbh': StrategyType(class_name, 'half'), 'cbtm': StrategyType(class_name, 'time'), 'cb_eset': StrategyType(class_name, 'mix_eset'), 'cbt_eset': StrategyType(class_name, 'tag_eset'), 'cbd_eset': StrategyType(class_name, 'desc_eset'), 'cbh_eset': StrategyType(class_name, 'half_eset')} return valid_strategies def __init__(self, content, profile_size): self.description = "Content-based" self.content = content self.profile_size = profile_size def get_sugestion_from_profile(self, rec, user, profile, recommendation_size, because=True, pkg_decider=None): query = xapian.Query(xapian.Query.OP_OR, profile) enquire = xapian.Enquire(rec.items_repository) enquire.set_weighting_scheme(rec.weight) enquire.set_query(query) user_profile = None if pkg_decider is None: pkg_decider = PkgMatchDecider(user.installed_pkgs) # Retrieve matching packages try: mset = enquire.get_mset(0, recommendation_size, None, pkg_decider) except xapian.DatabaseError as error: logging.critical("Content-based strategy: " + error.get_msg()) # Compose result dictionary item_score = {} ranking = [] for m in mset: item_score[m.document.get_data()] = m.weight ranking.append(m.document.get_data()) if because and Config().because: user_profile = user.pkg_profile result = recommender.RecommendationResult( item_score, ranking, user_profile=user_profile) return result def run(self, rec, user, rec_size): """ Perform recommendation strategy. """ logging.debug("Composing user profile...") profile = user.content_profile(rec.items_repository, self.content, self.profile_size, rec.valid_tags) logging.debug(profile) result = self.get_sugestion_from_profile(rec, user, profile, rec_size) return result class PackageReference(ContentBased): @classmethod def get_valid_strategies(cls): class_name = get_class_and_module_name(cls) valid_strategies = {'cbpkg': StrategyType(class_name, 'mix')} return valid_strategies def __init__(self, content, profile_size): ContentBased.__init__(self, content, profile_size) self.content = content self.description = 'Package-reference' self.profile_size = profile_size self.cache = AptCache() self.pkgs_regex = re.compile(r'^\s+(?:\|)?(.+)$', re.MULTILINE) def get_reverse_dependencies_pkgs(self, reference_pkgs): reverse_dependencies_pkgs = [] for pkg in reference_pkgs: command = 'apt-cache rdepends {}'.format(pkg) rdepends = subprocess.check_output(command, stderr=subprocess.STDOUT, shell=True) rdepends_pkgs = self.pkgs_regex.findall(rdepends) reverse_dependencies_pkgs += rdepends_pkgs return reverse_dependencies_pkgs def content_profile_for_reference_pkgs(self, reference_pkgs): content_profile = reference_pkgs[:] for pkg in reference_pkgs: content_profile += pkg.split('-') content_profile = list(set(content_profile)) return content_profile def run(self, rec, user, rec_size): reference_pkgs = user.reference_pkgs reverse_dependencies_pkgs = self.get_reverse_dependencies_pkgs( reference_pkgs) pkg_decider = PkgReverseDependeciesDecider(reverse_dependencies_pkgs, user.installed_pkgs) profile = user.content_profile(rec.items_repository, self.content, self.profile_size, rec.valid_tags) profile += self.content_profile_for_reference_pkgs(reference_pkgs) rec.items_repository = xapian.Database(Config().axi) result = self.get_sugestion_from_profile( rec, user, profile, rec_size, pkg_decider=pkg_decider) return result class MachineLearningTrainError(Exception): def __init__(self, value=''): self.value = value def __str__(self): return repr(self.value) class MachineLearning(ContentBased): __metaclass__ = ABCMeta PKGS_CLASSIFICATIONS = None def __init__(self, content, profile_size, suggestion_size=200): ContentBased.__init__(self, content, profile_size) self.content = content self.description = 'Machine-learning' self.profile_size = profile_size self.suggestion_size = suggestion_size self.cache = AptCache() self.ml_data = MachineLearningData() self.axi = xapian.Database(XAPIAN_DATABASE_PATH) def display_recommended_terms(self, terms_name, debtags_name, item_score, rec_size): sorted_result = sorted(item_score.items(), key=operator.itemgetter(1)) sorted_result = list(reversed(sorted_result)) sorted_result = [pkg[0] for pkg in sorted_result][0:rec_size] sorted_result = list(reversed(sorted_result)) for pkg in sorted_result: pkg_terms = self.ml_data.get_pkg_terms(self.cache, pkg) pkg_debtags = self.ml_data.get_pkg_debtags(self.axi, pkg) terms_match = [] for term in pkg_terms: if term in terms_name: terms_match.append(term) debtags_match = [] for debtag in pkg_debtags: if debtag in debtags_name: debtags_match.append(debtag) print "\n\n=" print "{0}".format(pkg) print "debtags:" print debtags_match print "-" print "terms:" print terms_match print "=" def get_item_score(self, pkgs_score, pkgs_classifications): item_score = {} order = ['RU', 'U', 'NU'] order_values = [0, 1000, 2000] for pkg, classification in pkgs_classifications.iteritems(): item_score[pkg] = order_values[order.index(classification)] item_score[pkg] += pkgs_score[pkg] return item_score def get_pkgs_and_scores(self, rec, user): profile = user.content_profile(rec.items_repository, self.content, self.suggestion_size, rec.valid_tags) content_based = self.get_sugestion_from_profile( rec, user, profile, self.suggestion_size, because=False) pkgs, pkgs_score = [], {} for pkg_line in str(content_based).splitlines()[1:]: pkg = re.search(r'\d+:\s([\w-]+)', pkg_line) if not pkg.groups(): continue pkg = pkg.groups()[0] pkg_score = int(pkg_line.split(':')[0].strip()) pkgs.append(pkg) pkgs_score[pkg] = self.suggestion_size - pkg_score return pkgs, pkgs_score def get_pkgs_classifications(self, pkgs, terms_name, debtags_name): ml_strategy = self.get_ml_strategy() pkgs_classifications = {} kwargs = {} kwargs['terms_name'] = terms_name kwargs['debtags_name'] = debtags_name kwargs['ml_strategy'] = ml_strategy for pkg in pkgs: if pkg not in self.cache: continue attribute_vector = self.prepare_pkg_data( pkg, **kwargs) classification = self.get_pkg_classification( ml_strategy, attribute_vector) pkgs_classifications[pkg] = classification return pkgs_classifications def load_terms_and_debtags(self): terms_name = [] debtags_name = [] terms_path = self.get_terms_path() debtags_path = self.get_debtags_path() with open(terms_path, 'rb') as terms: terms_name = pickle.load(terms) with open(debtags_path, 'rb') as debtags: debtags_name = pickle.load(debtags) return terms_name, debtags_name @staticmethod def train(cls): ml_data = MachineLearningData() labels = ['RU', 'U', 'NU'] try: MachineLearning.PKGS_CLASSIFICATIONS = ml_data.create_data( labels) if len(MachineLearning.PKGS_CLASSIFICATIONS) >= 10: cls.run_train(MachineLearning.PKGS_CLASSIFICATIONS) else: raise MachineLearningTrainError() except IOError: raise @abstractmethod def get_debtags_path(self): raise NotImplementedError("Method not implemented.") @abstractmethod def get_ml_strategy(self): raise NotImplementedError("Method not implemented.") @abstractmethod def get_pkg_classification(self, ml_strategy, attribute_vector): raise NotImplementedError("Method not implemented.") @abstractmethod def get_terms_path(self): raise NotImplementedError("Method not implemented.") @abstractmethod def prepare_pkg_data(self, pkg, **kwargs): raise NotImplementedError("Method not implemented.") @abstractmethod def run_train(cls, pkgs_classifications): raise NotImplementedError("Method not implemented.") def run(self, rec, user, rec_size): user_profile = None terms_name, debtags_name = self.load_terms_and_debtags() pkgs, pkgs_score = self.get_pkgs_and_scores(rec, user) pkgs_classifications = self.get_pkgs_classifications(pkgs, terms_name, debtags_name) item_score = self.get_item_score(pkgs_score, pkgs_classifications) if Config().because: user_profile = user.pkg_profile return recommender.RecommendationResult( item_score, limit=rec_size, user_profile=user_profile) class MachineLearningBVA(MachineLearning): @classmethod def get_valid_strategies(cls): class_name = get_class_and_module_name(cls) valid_strategies = {'mlbva': StrategyType(class_name, 'mlbva_mix'), 'mlbva_eset': StrategyType(class_name, 'mlbva_mix_eset')} return valid_strategies @classmethod def run_train(cls, pkgs_classifications): all_matrix = (np.matrix(pkgs_classifications.values())) data_matrix = all_matrix[0:, 0:-1] classifications = all_matrix[0:, -1] order_of_classifications = ['NU', 'U', 'RU'] bayes_matrix = BayesMatrix() bayes_matrix.training(data_matrix, classifications, order_of_classifications) BayesMatrix.save(bayes_matrix, MachineLearningData.MACHINE_LEARNING_TRAINING) def __init__(self, content, profile_size, suggestion_size=200): super(MachineLearningBVA, self).__init__( content, profile_size, suggestion_size) self.description = "Machine-learning-binary-vector-approach" def get_debtags_path(self): return MachineLearningData.MACHINE_LEARNING_DEBTAGS def get_ml_strategy(self): return BayesMatrix.load( MachineLearningData.MACHINE_LEARNING_TRAINING) def get_pkg_classification(self, ml_strategy, attribute_vector): return ml_strategy.get_classification(attribute_vector) def get_terms_path(self): return MachineLearningData.MACHINE_LEARNING_TERMS def prepare_pkg_data(self, pkg, **kwargs): terms_name = kwargs['terms_name'] debtags_name = kwargs['debtags_name'] pkg_terms = self.ml_data.get_pkg_terms(self.cache, pkg) pkg_debtags = self.ml_data.get_pkg_debtags(self.axi, pkg) debtags_attributes = self.ml_data.create_row_table_list( debtags_name, pkg_debtags) terms_attributes = self.ml_data.create_row_table_list( terms_name, pkg_terms) attribute_vector = terms_attributes + debtags_attributes attribute_vector = np.matrix(attribute_vector) return attribute_vector class MachineLearningBOW(MachineLearning): @classmethod def get_valid_strategies(cls): class_name = get_class_and_module_name(cls) valid_strategies = {'mlbow': StrategyType(class_name, 'mlbow_mix'), 'mlbow_eset': StrategyType(class_name, 'mlbow_mix_eset')} return valid_strategies def __init__(self, content, profile_size, suggestion_size=200): super(MachineLearningBOW, self).__init__( content, profile_size, suggestion_size) self.description = "Machine-learning-bag-of-words" def get_debtags_path(self): return BagOfWords.BAG_OF_WORDS_DEBTAGS def get_ml_strategy(self): return BagOfWords.load( BagOfWords.BAG_OF_WORDS_MODEL) def get_pkg_classification(self, ml_strategy, attribute_vector): return ml_strategy.classify_pkg(attribute_vector) def get_terms_path(self): return BagOfWords.BAG_OF_WORDS_TERMS def prepare_pkg_data(self, pkg, **kwargs): ml_strategy = kwargs['ml_strategy'] attribute_vector = ml_strategy.create_pkg_data( pkg, self.axi, self.cache, self.ml_data) return attribute_vector @classmethod def run_train(cls, pkgs_classifications): bag_of_words = BagOfWords() pkgs_list = pkgs_classifications.keys() axi = xapian.Database(XAPIAN_DATABASE_PATH) bag_of_words.train_model(pkgs_list, axi) BagOfWords.save(bag_of_words, BagOfWords.BAG_OF_WORDS_MODEL) AppRecommender-0.7.5/apprecommender/tests/000077500000000000000000000000001306751311600205725ustar00rootroot00000000000000AppRecommender-0.7.5/apprecommender/tests/test_apt_run.py000066400000000000000000000042301306751311600236520ustar00rootroot00000000000000#!/usr/bin/env python import os import shutil import unittest from mock import patch from apprecommender.main.apt_run import AptRun from apprecommender.user import LocalSystem class AptRunTests(unittest.TestCase): TEST_FOLDER = 'apprecommender/tests/.apt_run' def setUp(self): if os.path.exists(AptRunTests.TEST_FOLDER): shutil.rmtree(AptRunTests.TEST_FOLDER) def test_enable_apt_run(self): apt_run = AptRun() apt_run.set_folder(AptRunTests.TEST_FOLDER) apt_run.enable() result = apt_run.is_enable() self.assertTrue(result) def test_dont_enable_apt_run_if_already_enabled(self): apt_run = AptRun() apt_run.set_folder(AptRunTests.TEST_FOLDER) apt_run.enable() result = apt_run.enable() self.assertFalse(result) def test_disable_apt_run(self): apt_run = AptRun() apt_run.set_folder(AptRunTests.TEST_FOLDER) os.makedirs(AptRunTests.TEST_FOLDER) apt_run.disable() result = apt_run.is_enable() self.assertFalse(result) def test_dont_disable_apt_run_if_already_disabled(self): apt_run = AptRun() apt_run.set_folder(AptRunTests.TEST_FOLDER) result = apt_run.disable() self.assertFalse(result) def test_pre_install_pkgs(self): apt_run = AptRun() apt_run.set_folder(AptRunTests.TEST_FOLDER) apt_run.enable() apt_run.pre_install_pkgs() user = LocalSystem() assert_pkgs = user.pkg_profile pkgs = [] with open(apt_run.installed_pkgs_file, 'r') as text: pkgs = [line.strip() for line in text] self.assertListEqual(assert_pkgs, pkgs) @patch('apprecommender.main.apt_run.AptRun.get_user_pkgs') def test_post_invoke(self, mock_user_pkgs): mock_user_pkgs.return_value = ['vim', 'gedit'] apt_run = AptRun() apt_run.set_folder(AptRunTests.TEST_FOLDER) apt_run.enable() with open(apt_run.installed_pkgs_file, 'w') as text: text.write('gedit') installed_pkgs = apt_run.post_invoke() self.assertEqual(['vim'], installed_pkgs) AppRecommender-0.7.5/apprecommender/tests/test_data.py000066400000000000000000000054151306751311600231210ustar00rootroot00000000000000#!/usr/bin/env python """ dataTests - Data classes test case """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import unittest import xapian from apprecommender.data import PopconSubmission, axi_search_pkg_tags from apprecommender.config import Config class AxiSearchTests(unittest.TestCase): @classmethod def setUpClass(self): cfg = Config() self.axi = xapian.Database(cfg.axi) def test_search_pkg_tags(self): tags = axi_search_pkg_tags(self.axi, 'vim') self.assertEqual(set(tags), set(['XTscope::application', 'XTrole::program', 'XTimplemented-in::c', 'XTworks-with::unicode', 'XTworks-with::text', 'XTuse::editing', 'XTinterface::commandline', 'XTdevel::editor', 'XTuitoolkit::ncurses', 'XTinterface::text-mode'])) class PopconSubmissionTests(unittest.TestCase): @classmethod def setUpClass(self): self.submission_path = "apprecommender/tests/test_data/test_popcon" self.submission = PopconSubmission(self.submission_path) def test_user_id(self): with open(self.submission_path) as popcon_file: user_id = popcon_file.readline().split()[2].lstrip("ID:") self.assertEqual(self.submission.user_id, user_id) def test_load(self): with open(self.submission_path) as popcon_file: size = len(popcon_file.readlines()) self.assertEqual(len(self.submission.packages), size - 2) def test_str(self): output = "\nPopularity-contest submission ID " output += "8b44fcdbcf676e711a153d5db099test\n " output += "dash: 1\n perl-base: 1\n libusbmuxd1: 1\n " output += "libc6-i686: 1\n libc6: 1" self.assertEqual(self.submission.__str__(), output) AppRecommender-0.7.5/apprecommender/tests/test_data/000077500000000000000000000000001306751311600225425ustar00rootroot00000000000000AppRecommender-0.7.5/apprecommender/tests/test_data/popcon_dir/000077500000000000000000000000001306751311600246765ustar00rootroot00000000000000AppRecommender-0.7.5/apprecommender/tests/test_data/popcon_dir/test_popcon_0000066400000000000000000000005441306751311600274000ustar00rootroot00000000000000POPULARITY-CONTEST-0 TIME:1309407492 ID:8b44fcdbcf676e711a153d5db0test_0 ARCH:i386 POPCONVER:1.52 1309407475 1303670994 gimp /usr/bin/perl 1309407451 1303670982 inkscape /lib/i686/cmov/libc-2.11.2.so 1309407450 1303670973 imagination /lib/ld-2.11.2.so 1309407434 1295654294 dash /bin/dash 0 0 libusbmuxd1 END-POPULARITY-CONTEST-0 TIME:1309407492 AppRecommender-0.7.5/apprecommender/tests/test_data/popcon_dir/test_popcon_1000066400000000000000000000003631306751311600274000ustar00rootroot00000000000000POPULARITY-CONTEST-0 TIME:1309407492 ID:8b44fcdbcf676e711a153d5db0test_1 ARCH:i386 POPCONVER:1.52 1309407475 1303670994 gimp /usr/bin/perl 1309407434 1295654294 dash /bin/dash 0 0 libusbmuxd1 END-POPULARITY-CONTEST-0 TIME:1309407492 AppRecommender-0.7.5/apprecommender/tests/test_data/popcon_dir/test_popcon_2000066400000000000000000000005411306751311600273770ustar00rootroot00000000000000POPULARITY-CONTEST-0 TIME:1309407492 ID:8b44fcdbcf676e711a153d5db0test_2 ARCH:i386 POPCONVER:1.52 1309407475 1303670994 iceweasel /usr/bin/perl 1309407451 1303670982 python /lib/i686/cmov/libc-2.11.2.so 1309407450 1303670973 libc6 /lib/ld-2.11.2.so 1309407434 1295654294 dash /bin/dash 0 0 libusbmuxd1 END-POPULARITY-CONTEST-0 TIME:1309407492 AppRecommender-0.7.5/apprecommender/tests/test_data/popcon_dir/test_popcon_3000066400000000000000000000004701306751311600274010ustar00rootroot00000000000000POPULARITY-CONTEST-0 TIME:1309407492 ID:8b44fcdbcf676e711a153d5db0test_3 ARCH:i386 POPCONVER:1.52 1309407475 1303670994 eog /usr/bin/perl 1309407451 1303670982 nautilus /lib/i686/cmov/libc-2.11.2.so 1309407450 1303670973 libc6 /lib/ld-2.11.2.so 0 0 libusbmuxd1 END-POPULARITY-CONTEST-0 TIME:1309407492 AppRecommender-0.7.5/apprecommender/tests/test_data/popcon_dir/test_popcon_4000066400000000000000000000004731306751311600274050ustar00rootroot00000000000000POPULARITY-CONTEST-0 TIME:1309407492 ID:8b44fcdbcf676e711a153d5db0test_4 ARCH:i386 POPCONVER:1.52 1309407475 1303670994 konqueror /usr/bin/perl 1309407451 1303670982 kedit /lib/i686/cmov/libc-2.11.2.so 1309407450 1303670973 libc6 /lib/ld-2.11.2.so 0 0 libusbmuxd1 END-POPULARITY-CONTEST-0 TIME:1309407492 AppRecommender-0.7.5/apprecommender/tests/test_data/popcon_dir/test_popcon_5000066400000000000000000000003701306751311600274020ustar00rootroot00000000000000POPULARITY-CONTEST-0 TIME:1309407492 ID:8b44fcdbcf676e711a153d5db0test_5 ARCH:i386 POPCONVER:1.52 1309407475 1303670994 konqueror /usr/bin/perl 1309407434 1295654294 dash /bin/dash 0 0 libusbmuxd1 END-POPULARITY-CONTEST-0 TIME:1309407492 AppRecommender-0.7.5/apprecommender/tests/test_data/popcon_dir/test_popcon_6000066400000000000000000000005411306751311600274030ustar00rootroot00000000000000POPULARITY-CONTEST-0 TIME:1309407492 ID:8b44fcdbcf676e711a153d5db0test_6 ARCH:i386 POPCONVER:1.52 1309407475 1303670994 perl-base /usr/bin/perl 1309407451 1303670982 eog /lib/i686/cmov/libc-2.11.2.so 1309407450 1303670973 nautilus /lib/ld-2.11.2.so 1309407434 1295654294 dash /bin/dash 0 0 libusbmuxd1 END-POPULARITY-CONTEST-0 TIME:1309407492 AppRecommender-0.7.5/apprecommender/tests/test_data/popcon_dir/test_popcon_7000066400000000000000000000005401306751311600274030ustar00rootroot00000000000000POPULARITY-CONTEST-0 TIME:1309407492 ID:8b44fcdbcf676e711a153d5db0test_7 ARCH:i386 POPCONVER:1.52 1309407475 1303670994 apticron /usr/bin/perl 1309407451 1303670982 aptitude /lib/i686/cmov/libc-2.11.2.so 1309407450 1303670973 apt /lib/ld-2.11.2.so 1309407434 1295654294 dash /bin/dash 0 0 libusbmuxd1 END-POPULARITY-CONTEST-0 TIME:1309407492 AppRecommender-0.7.5/apprecommender/tests/test_data/popcon_dir/test_popcon_8000066400000000000000000000005401306751311600274040ustar00rootroot00000000000000POPULARITY-CONTEST-0 TIME:1309407492 ID:8b44fcdbcf676e711a153d5db0test_8 ARCH:i386 POPCONVER:1.52 1309407475 1303670994 apticron /usr/bin/perl 1309407451 1303670982 eog /lib/i686/cmov/libc-2.11.2.so 1309407450 1303670973 nautilus /lib/ld-2.11.2.so 1309407434 1295654294 dash /bin/dash 0 0 libusbmuxd1 END-POPULARITY-CONTEST-0 TIME:1309407492 AppRecommender-0.7.5/apprecommender/tests/test_data/popcon_dir/test_popcon_9000066400000000000000000000005451306751311600274120ustar00rootroot00000000000000POPULARITY-CONTEST-0 TIME:1309407492 ID:8b44fcdbcf676e711a153d5db0test_9 ARCH:i386 POPCONVER:1.52 1309407475 1303670994 perl-base /usr/bin/perl 1309407451 1303670982 libc6-i686 /lib/i686/cmov/libc-2.11.2.so 1309407450 1303670973 libc6 /lib/ld-2.11.2.so 1309407434 1295654294 dash /bin/dash 0 0 libusbmuxd1 END-POPULARITY-CONTEST-0 TIME:1309407492 AppRecommender-0.7.5/apprecommender/tests/test_data/tags_filter000066400000000000000000000124471306751311600250000ustar00rootroot00000000000000accessibility::input accessibility::ocr accessibility::screen-magnify accessibility::screen-reader accessibility::speech accessibility::speech-recognition admin::accounting admin::automation admin::backup admin::benchmarking admin::boot admin::cluster admin::configuring admin::file-distribution admin::filesystem admin::forensics admin::hardware admin::install admin::issuetracker admin::kernel admin::logging admin::login admin::monitoring admin::package-management admin::power-management admin::recovery admin::user-management admin::virtualization biology::emboss biology::format:aln biology::format:fasta biology::format:nexus biology::nucleic-acids biology::peptidic devel::bugtracker devel::buildtools devel::code-generator devel::compiler devel::debian devel::debugger devel::doc devel::docsystem devel::ecma-cli devel::editor devel::examples devel::ide devel::interpreter devel::i18n devel::library devel::machinecode devel::modelling devel::packaging devel::prettyprint devel::profiler devel::rcs devel::rpc devel::runtime devel::testing-qa devel::ui-builder devel::web field::arts field::astronomy field::aviation field::biology field::biology:bioinformatics field::biology:molecular field::biology:structural field::chemistry field::computer-science field::electronics field::finance field::genealogy field::geography field::geology field::linguistics field::mathematics field::medicine field::medicine:imaging field::meteorology field::physics field::religion field::statistics game::adventure game::arcade game::board game::board:chess game::card game::demos game::fps game::mud game::platform game::puzzle game::rpg game::rpg:rogue game::simulation game::sport game::sport:racing game::strategy game::tetris game::toys game::typing junior::arcade junior::games-gl junior::meta mail::filters mail::imap mail::list mail::notification mail::pop mail::smtp mail::delivery-agent mail::transport-agent mail::user-agent office::finance office::groupware office::presentation office::project-management office::spreadsheet works-with::3dmodel works-with::archive works-with::audio works-with::biological-sequence works-with::bugs works-with::calendar works-with::db works-with::dictionary works-with::dtp works-with::fax works-with::file works-with::font works-with::graphs works-with::im works-with::logfile works-with::mail works-with::music-notation works-with::network-traffic works-with::people works-with::pim works-with::image works-with::image:raster works-with::image:vector works-with::semantic-data works-with::software:package works-with::software:running works-with::software:source works-with::spreadsheet works-with::text works-with::unicode works-with::vcs works-with::video works-with-format::bib works-with-format::chm works-with-format::diff works-with-format::djvu works-with-format::docbook works-with-format::dvi works-with-format::elf works-with-format::epub works-with-format::gif works-with-format::xml:gpx works-with-format::html works-with-format::ical works-with-format::info works-with-format::iso9660 works-with-format::jpg works-with-format::json works-with-format::ldif works-with-format::man works-with-format::mp3 works-with-format::mpc works-with-format::odf works-with-format::oggtheora works-with-format::oggvorbis works-with-format::pdf works-with-format::plaintext works-with-format::png works-with-format::po works-with-format::postscript works-with-format::rdf:json-ld works-with-format::rdf:n3 works-with-format::rdf:ntriples works-with-format::rdf:turtle works-with-format::rdf:xml works-with-format::sgml works-with-format::svg works-with-format::swf works-with-format::tar works-with-format::tex works-with-format::tiff works-with-format::vrml works-with-format::wav works-with-format::xml works-with-format::xml:rss works-with-format::xml:xslt works-with-format::zip system::cloud system::embedded system::laptop system::mobile system::server system::virtual security::antivirus security::authentication security::cryptography security::firewall security::forensics security::ids security::integrity security::log-analyzer security::privacy sound::compression sound::midi sound::mixer sound::player sound::recorder sound::sequencer sound::speech suite::apache suite::bsd suite::debian suite::eclipse suite::emacs suite::gforge suite::gimp suite::gkrellm suite::gnome suite::gnu suite::gnustep suite::gpe suite::kde suite::mozilla suite::netscape suite::openoffice suite::openstack suite::opie suite::postgresql suite::roxen suite::samba suite::sugar suite::webmin suite::xfce suite::xmms2 suite::zope use::analysing use::browsing use::calculating use::chatting use::checking use::comparing use::compressing use::configuring use::converting use::dialing use::downloading use::driver use::editing use::entertaining use::filtering use::gameplaying use::learning use::login use::measuring use::monitor use::organizing use::playing use::printing use::proxying use::routing use::searching use::scanning use::simulating use::storing use::synchronizing use::timekeeping use::transmission use::typesetting use::viewing use::text-formatting web::application web::appserver web::blog web::browser web::cms web::cgi web::commerce web::forum web::microblog web::portal web::scripting web::search-engine web::server web::wiki science::calculation science::modelling science::data-acquisition science::plotting science::bibliography science::publishing science::visualisation AppRecommender-0.7.5/apprecommender/tests/test_data/test_popcon000066400000000000000000000005451306751311600250260ustar00rootroot00000000000000POPULARITY-CONTEST-0 TIME:1309407492 ID:8b44fcdbcf676e711a153d5db099test ARCH:i386 POPCONVER:1.52 1309407475 1303670994 perl-base /usr/bin/perl 1309407451 1303670982 libc6-i686 /lib/i686/cmov/libc-2.11.2.so 1309407450 1303670973 libc6 /lib/ld-2.11.2.so 1309407434 1295654294 dash /bin/dash 0 0 libusbmuxd1 END-POPULARITY-CONTEST-0 TIME:1309407492 AppRecommender-0.7.5/apprecommender/tests/test_data_classification.py000066400000000000000000000005561306751311600261750ustar00rootroot00000000000000#!/usr/bin/env python import unittest from apprecommender.data_classification import linear_percent_function class DataClassificationTests(unittest.TestCase): def test_linear_percent_function(self): modify, access, time_now = 100, 175, 200 percent = linear_percent_function(modify, access, time_now) self.assertEqual(0.75, percent) AppRecommender-0.7.5/apprecommender/tests/test_decider.py000066400000000000000000000070361306751311600236100ustar00rootroot00000000000000import unittest from apprecommender.decider import PkgInitDecider class PkgInitDeciderTests(unittest.TestCase): def setUp(self): self.pkg_init_decider = PkgInitDecider() def test_python_pkg_regex(self): pkg = 'python-test' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'python3-test' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) def test_ruby_pkg_regex(self): pkg = 'ruby-test' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) def test_texlive_pkg_regex(self): pkg = 'texlive-test' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) def test_pkg_gir_regex(self): pkg = 'gir1.2-test' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) def test_pkg_golang_regex(self): pkg = 'golang-test' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) def test_pkg_data_regex(self): pkg = 'test-data' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) def test_pkg_dev_regex(self): pkg = 'test-dev' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) def test_pkg_utils_regex(self): pkg = 'test-utils' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-utils-1.9' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) def test_pkg_common_regex(self): pkg = 'test-common' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) def test_pkg_fonts_regex(self): pkg = 'test-fonts' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) def test_pkg_png_regex(self): pkg = 'test-png' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) def test_pkg_core_regex(self): pkg = 'test-core' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) def test_pkg_default_regex(self): pkg = 'test-default' self.assertTrue(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) pkg = 'test-pkg' self.assertFalse(self.pkg_init_decider.is_pkg_a_prefix_or_suffix(pkg)) AppRecommender-0.7.5/apprecommender/tests/test_evaluation.py000066400000000000000000000067711306751311600243650ustar00rootroot00000000000000#!/usr/bin/env python """ evaluationTests - Evaluation class test case """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import unittest from apprecommender.evaluation import (Accuracy, Precision, Recall, Coverage, Evaluation, CrossValidationRecommender) from apprecommender.recommender import RecommendationResult class MetricsTests(unittest.TestCase): @classmethod def setUpClass(self): repository = ['apple', 'grape', 'pineaple', 'melon', 'watermelon', 'orange'] real = RecommendationResult(dict.fromkeys(['apple', 'grape', 'pineaple', 'melon'], 1)) predicted = RecommendationResult(dict.fromkeys(['apple', 'grape', 'orange'], 1)) self.evaluation = Evaluation(predicted, real, len(repository)) def test_class_accuracy(self): accuracy = Accuracy().run(self.evaluation) self.assertEqual(accuracy, 0.5) def test_precision(self): precision = Precision().run(self.evaluation) self.assertEqual("%.2f" % precision, "0.67") def test_recall(self): recall = Recall().run(self.evaluation) self.assertEqual(recall, 0.5) def test_coverage(self): evaluations_set = set() evaluations_set.add(self.evaluation) coverage = Coverage().run(evaluations_set) self.assertEqual(coverage, 0.5) def test_evaluation(self): self.assertEqual(self.evaluation.true_positive, ['apple', 'grape']) self.assertEqual(self.evaluation.false_positive, ['orange']) self.assertEqual(self.evaluation.false_negative, ['pineaple', 'melon']) class CrossValidationTests(unittest.TestCase): def create_cross_validation(self, partition_proportion, rounds, rec, metrics_list, result_proportion): return CrossValidationRecommender(partition_proportion, rounds, rec, metrics_list, result_proportion) def test_get_partition_size(self): cross_validation = self.create_cross_validation(0.7, 1, None, [], 1) cross_item_score = {'item1': [1, 0, 1], 'item2': [1, 0, 1], 'item3': [1, 0, 1], 'item4': [1, 0, 1], 'item5': [1, 0, 1], 'item6': [1, 0, 1], 'item7': [1, 0, 1], 'item8': [1, 0, 1], 'item9': [1, 0, 1], 'item10': [1, 0, 1]} expected_result = 7 self.assertEqual(expected_result, cross_validation.get_partition_size(cross_item_score)) AppRecommender-0.7.5/apprecommender/tests/test_initialize.py000066400000000000000000000010571306751311600243470ustar00rootroot00000000000000#!/usr/bin/env python import unittest from apprecommender.initialize import Initialize class InitializeTests(unittest.TestCase): def test_get_tags(self): initialize = Initialize() tags = initialize.get_tags() self.assertTrue(len(tags) > 0) combined_tags = ''.join(tags) for tag in Initialize.EXCLUDED_TAGS: self.assertTrue(tag not in combined_tags) def test_get_axipkgs(self): initialize = Initialize() pkgs = initialize.get_axipkgs() self.assertTrue(len(pkgs) > 0) AppRecommender-0.7.5/apprecommender/tests/test_ml/000077500000000000000000000000001306751311600222415ustar00rootroot00000000000000AppRecommender-0.7.5/apprecommender/tests/test_ml/test_bayes_matrix.py000066400000000000000000000104271306751311600263450ustar00rootroot00000000000000#!/usr/bin/env python import unittest import numpy as np from apprecommender.ml.bayes_matrix import BayesMatrix class PkgClassificationTests(unittest.TestCase): def setUp(self): self.bayes_matrix = BayesMatrix() def test_training(self): data_matrix = np.matrix("1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0;\ 0 1 1 1 0") classifications = np.matrix([[1], [2], [0], [1], [2]]) order_of_classifications = [0, 1, 2] prob = np.matrix("1 0 0 1 1; 1 0 1 0.5 0.5; 0 1 1 0.5 0.5").astype( float) self.bayes_matrix = BayesMatrix() self.bayes_matrix.training(data_matrix, classifications, order_of_classifications) self.assertTrue(np.allclose(prob, self.bayes_matrix.prob)) def test_get_classification(self): self.bayes_matrix = BayesMatrix() self.bayes_matrix.data = np.matrix( "1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0;\ 0 1 1 1 0").astype(float) self.bayes_matrix.labels = np.matrix("0; 1; 2").astype(float) self.bayes_matrix.label_probability = (np.matrix( "0.2; 0.4; 0.4").astype(float)) self.bayes_matrix.order_of_classifications = [0, 1, 2] self.bayes_matrix.used_order_of_classifications = [0, 1, 2] self.bayes_matrix.prob = np.matrix( "1 0 0 1 1; 1 0 1 0.5 0.5; 0 1 1 0.5 0.5").astype(float) attribute_vector = np.matrix("1 0 1 1 0") self.assertEqual( 1, self.bayes_matrix.get_classification(attribute_vector)) def test_convert_classifications_to_numbers(self): classifications = np.matrix([['M'], ['B'], ['G']]) order_of_classifications = ['B', 'M', 'G'] expected = np.array([[1], [0], [2]]) actual = self.bayes_matrix.convert_classifications_to_number( classifications, order_of_classifications) for index, label in enumerate(expected): self.assertEqual(label[0], actual[index, 0]) def test_get_used_order_of_classifications(self): classifications = np.matrix([['B'], ['G']]) order_of_classifications = ['B', 'M', 'G'] expected = ['B', 'G'] actual = self.bayes_matrix.get_used_order_of_classifications( classifications, order_of_classifications) self.assertEquals(expected, actual) classifications = np.matrix([['M'], ['M'], ['B'], ['G']]) order_of_classifications = ['B', 'M', 'G'] expected = ['B', 'M', 'G'] actual = self.bayes_matrix.get_used_order_of_classifications( classifications, order_of_classifications) self.assertEquals(expected, actual) classifications = np.matrix([['M'], ['G']]) order_of_classifications = ['B', 'M', 'G'] expected = ['M', 'G'] actual = self.bayes_matrix.get_used_order_of_classifications( classifications, order_of_classifications) classifications = np.matrix([['B']]) order_of_classifications = ['B', 'M', 'G'] expected = ['B'] actual = self.bayes_matrix.get_used_order_of_classifications( classifications, order_of_classifications) classifications = np.matrix([['B'], ['D'], ['F']]) order_of_classifications = ['A', 'B', 'C', 'D', 'E', 'F', 'G'] expected = ['B', 'D', 'F'] actual = self.bayes_matrix.get_used_order_of_classifications( classifications, order_of_classifications) self.assertEquals(expected, actual) classifications = np.matrix([['B'], ['D'], ['F']]) order_of_classifications = ['A', 'B', 'C', 'D', 'E', 'F'] expected = ['B', 'D', 'F'] actual = self.bayes_matrix.get_used_order_of_classifications( classifications, order_of_classifications) self.assertEquals(expected, actual) def test_dont_change_labels_when_run_training(self): data_matrix = np.matrix("1 0 1 0 1; 0 1 1 0 1; 1 0 0 1 1; 1 0 1 1 0") classifications = np.matrix([[1], [2], [1], [2]]) labels = [0, 1, 2] self.bayes_matrix = BayesMatrix() self.bayes_matrix.training(data_matrix, classifications, labels) expected_labels = [0, 1, 2] self.assertEquals(expected_labels, labels) AppRecommender-0.7.5/apprecommender/tests/test_ml/test_evaluation.py000066400000000000000000000266571306751311600260410ustar00rootroot00000000000000import unittest from collections import OrderedDict from numpy import array, matrix from apprecommender.ml.cross_validation import (ConfusionMatrix, CrossValidationBVA, Evaluation) from apprecommender.evaluation import (SimpleAccuracy, Precision, Recall, FPR, F_score, MCC, Accuracy) class ConfusionMatrixTest(unittest.TestCase): def test_confusion_matrix_values(self): predicted_results = array([[1], [0], [1], [0]]) real_results = array([[1], [0], [0], [1]]) confusion_matrix = ConfusionMatrix(predicted_results, real_results) confusion_matrix.run() tp, tn, fp, fn = 1, 1, 1, 1 self.assertEqual(tp, confusion_matrix.true_positive_len) self.assertEqual(tn, confusion_matrix.true_negative_len) self.assertEqual(fp, confusion_matrix.false_positive_len) self.assertEqual(fn, confusion_matrix.false_negative_len) predicted_results = array([[1], [1], [1], [0]]) real_results = array([[1], [1], [0], [1]]) confusion_matrix = ConfusionMatrix(predicted_results, real_results) confusion_matrix.run() tp, tn, fp, fn = 2, 0, 1, 1 self.assertEqual(tp, confusion_matrix.true_positive_len) self.assertEqual(tn, confusion_matrix.true_negative_len) self.assertEqual(fp, confusion_matrix.false_positive_len) self.assertEqual(fn, confusion_matrix.false_negative_len) class EvaluationTest(unittest.TestCase): def create_evaluation(self, predicted_values, real_values, labels): return Evaluation(predicted_values, real_values, labels) def test_evaluation_simple_accuracy(self): labels = [1, 2, 3] predicted_values = array([[1], [2], [3], [2], [1]]) real_values = array([[1], [2], [2], [1], [3]]) metric = SimpleAccuracy() evaluation = self.create_evaluation(predicted_values, real_values, labels) results = evaluation.run(metric) expected_label_1 = 0.6 expected_label_2 = 0.6 expected_label_3 = 0.6 self.assertEquals(expected_label_1, results[1]) self.assertEquals(expected_label_2, results[2]) self.assertEquals(expected_label_3, results[3]) class CrossValidationTests(unittest.TestCase): def create_cross_validation_ml(self, pkg_data, partition_proportion, rounds, metrics_list, labels): return CrossValidationBVA( pkg_data, partition_proportion, rounds, metrics_list, labels) def compare_column_matrix(self, expected_matrix, actual_matrix): self.assertEquals(expected_matrix.shape, actual_matrix.shape) num_itens = actual_matrix.shape[0] for i in range(num_itens): self.assertEqual(expected_matrix[i, 0], actual_matrix[i, 0]) def test_get_real_results(self): cross_validation_ml = self.create_cross_validation_ml( None, 0.1, 1, [], []) test_data = {'test1': [1, 0, 1, 0, 'T'], 'test2': [1, 1, 1, 0, 'F']} expected_result = array([['T'], ['F']]) actual_result = cross_validation_ml.get_real_results(test_data) self.compare_column_matrix(expected_result, actual_result) def test_cross_validation_process(self): data_matrix = (('test1', [1, 0, 1, 0, 1, 1, 1, 1, 'G']), ('test2', [0, 1, 0, 1, 1, 1, 1, 0, 'M']), ('test3', [1, 0, 1, 0, 1, 0, 0, 1, 'B']), ('test4', [0, 0, 0, 0, 1, 1, 1, 1, 'M']), ('test5', [1, 1, 1, 1, 0, 0, 0, 1, 'G']), ('test6', [1, 1, 0, 0, 1, 1, 0, 0, 'M']), ('test7', [0, 0, 1, 0, 1, 0, 1, 1, 'B']), ('test8', [1, 1, 1, 0, 0, 0, 1, 0, 'G']), ('test9', [0, 1, 1, 0, 0, 1, 1, 1, 'B']), ('test10', [1, 1, 1, 0, 1, 0, 0, 1, 'G'])) data_matrix = OrderedDict(data_matrix) partition_proportion = 0.7 rounds = 1 metrics_list = [SimpleAccuracy()] labels = ['B', 'M', 'G'] cross_validation_ml = CrossValidationBVA( data_matrix, partition_proportion, rounds, metrics_list, labels) self.assertEquals(len(data_matrix), len(cross_validation_ml.get_user_score(None))) expected_num_B = 3 # noqa expected_num_G = 4 # noqa expected_num_M = 3 # noqa for label in labels: self.assertEqual(eval('expected_num_{0}'.format(label)), len(cross_validation_ml.label_groups[label])) expected_num_data = 10 self.assertEqual(expected_num_data, len(cross_validation_ml.pkg_data)) bayes_model = cross_validation_ml.get_model(data_matrix) data = array([[1, 0, 1, 0, 1, 1, 1, 1], [0, 1, 0, 1, 1, 1, 1, 0], [1, 0, 1, 0, 1, 0, 0, 1], [0, 0, 0, 0, 1, 1, 1, 1], [1, 1, 1, 1, 0, 0, 0, 1], [1, 1, 0, 0, 1, 1, 0, 0], [0, 0, 1, 0, 1, 0, 1, 1], [1, 1, 1, 0, 0, 0, 1, 0], [0, 1, 1, 0, 0, 1, 1, 1], [1, 1, 1, 0, 1, 0, 0, 1]]) labels_number = matrix([[0], [1], [2]]) classifications_numbers = matrix([[2], [1], [0], [1], [2], [1], [0], [2], [0], [2]]) histogram = array([[3], [3], [4]]) actual_labels_number = bayes_model.labels actual_classifications_number = bayes_model.classifications actual_histogram = bayes_model.histogram self.assertEquals(data.shape, bayes_model.data.shape) self.compare_column_matrix(labels_number, actual_labels_number) self.compare_column_matrix(classifications_numbers, actual_classifications_number) self.compare_column_matrix(histogram, actual_histogram) predictions = cross_validation_ml.get_predicted_results(bayes_model, data_matrix) expected_shape = (10, 1) self.assertEquals(expected_shape, predictions.shape) for i in range(len(predictions)): self.assertIn(predictions[i, 0], labels) real_results = cross_validation_ml.get_real_results(data_matrix) cross_validation_ml.run_metrics(predictions, real_results) class MetricsTest(unittest.TestCase): def test_simple_accuracy(self): predicted_results = array([[1], [1], [0], [0]]) actual_results = array([[1], [1], [0], [1]]) labels = [0, 1] metric = SimpleAccuracy() evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0.75 expected_0 = 0.75 self.assertEquals(expected_1, results[1]) self.assertEquals(expected_0, results[0]) def test_precision(self): predicted_results = array([[1], [1], [0], [0]]) actual_results = array([[1], [1], [0], [1]]) labels = [0, 1] metric = Precision() evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 1 expected_0 = 0.5 self.assertEquals(expected_1, results[1]) self.assertEquals(expected_0, results[0]) predicted_results = array([[0], [0], [0], [0]]) evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0 expected_0 = 0.25 self.assertEquals(expected_1, results[1]) self.assertEquals(expected_0, results[0]) def test_recall(self): predicted_results = array([[1], [1], [0], [0], [1]]) actual_results = array([[1], [1], [0], [1], [1]]) labels = [0, 1] metric = Recall() evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0.75 expected_0 = 1 self.assertEquals(expected_1, results[1]) self.assertEquals(expected_0, results[0]) predicted_results = array([[0], [0], [1], [0], [0]]) actual_results = array([[1], [1], [0], [1], [1]]) evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0 expected_0 = 0 self.assertEquals(expected_1, results[1]) self.assertEquals(expected_0, results[0]) def test_fpr(self): predicted_results = array([[1], [1], [0], [1], [0]]) actual_results = array([[0], [0], [0], [1], [0]]) labels = [0, 1] metric = FPR() evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0.5 expected_0 = 0 self.assertEquals(expected_1, results[1]) self.assertEquals(expected_0, results[0]) def test_f1_score(self): predicted_results = array([[1], [1], [0], [1], [0], [1]]) actual_results = array([[1], [1], [1], [0], [1], [1]]) labels = [0, 1] metric = F_score(1) evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0.6666666666666666 expected_0 = 0 self.assertAlmostEqual(expected_1, results[1]) self.assertEqual(expected_0, results[0]) def test_f0_5_score(self): predicted_results = array([[1], [1], [0], [1], [0], [1]]) actual_results = array([[1], [1], [1], [0], [1], [1]]) labels = [0, 1] metric = F_score(0.5) evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0.71428571 expected_0 = 0 self.assertAlmostEqual(expected_1, results[1]) self.assertEqual(expected_0, results[0]) def test_f2_score(self): predicted_results = array([[1], [1], [0], [1], [0], [1]]) actual_results = array([[1], [1], [1], [0], [1], [1]]) labels = [0, 1] metric = F_score(2) evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1 = 0.625 expected_0 = 0 self.assertAlmostEqual(expected_1, results[1]) self.assertEqual(expected_0, results[0]) def test_mcc_score(self): predicted_results = array([[1], [1], [0], [1], [0], [1]]) actual_results = array([[1], [1], [1], [0], [0], [1]]) labels = [0, 1] metric = MCC() evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1, expected_0 = 0.25, 0.25 self.assertAlmostEqual(expected_1, results[1]) self.assertEqual(expected_0, results[0]) def test_accuracy(self): predicted_results = array([[1], [1], [0], [1], [0], [1]]) actual_results = array([[1], [1], [1], [0], [0], [1]]) labels = [0, 1] metric = Accuracy() evaluation = Evaluation(predicted_results, actual_results, labels) results = evaluation.run(metric) expected_1, expected_0 = 0.625, 0.625 self.assertAlmostEqual(expected_1, results[1]) self.assertEqual(expected_0, results[0]) AppRecommender-0.7.5/apprecommender/tests/test_ml/test_pkg_classification.py000066400000000000000000000051231306751311600275070ustar00rootroot00000000000000#!/usr/bin/env python import unittest import xapian from mock import patch from apprecommender.apt_cache import AptCache from apprecommender.ml.data import MachineLearningData class PkgClassificationTests(unittest.TestCase): def setUp(self): self.ml_data = MachineLearningData() self.cache = AptCache() def test_get_pkg_debtags(self): vim_debtags = ['devel::editor', 'implemented-in::c', 'interface::commandline', 'interface::text-mode', 'role::program', 'scope::application', 'uitoolkit::ncurses', 'use::editing', 'works-with::text', 'works-with::unicode'] axi_path = "/var/lib/apt-xapian-index/index" axi = xapian.Database(axi_path) vim_debtags_result = self.ml_data.get_pkg_debtags(axi, 'vim') for debtag in vim_debtags: self.assertTrue(debtag in vim_debtags_result) @patch('apprecommender.ml.data.MachineLearningData.get_pkg_description') def test_get_pkg_terms(self, mock_description): mock_description.return_value = 'Vim is an text editor written in C' vim_terms = [u'vim', u'text', u'editor'] vim_terms_result = self.ml_data.get_pkg_terms(self.cache, 'vim') for term in vim_terms: self.assertTrue(term in vim_terms_result) def test_create_row_table_list(self): labels_name = ['devel::editor', 'implemented-in::c', 'complet', 'contain', 'syntax', 'unix', 'version'] pkg_elements = ['implemented-in::c', 'complet'] row_list_to_assert = [0, 1, 1, 0, 0, 0, 0] row_list = self.ml_data.create_row_table_list(labels_name, pkg_elements) self.assertEqual(row_list_to_assert, row_list) @patch('apprecommender.ml.data.MachineLearningData.get_pkg_description') def test_get_pkg_classification(self, mock_description): mock_description.return_value = 'vim is an text editor written in c' axi_path = "/var/lib/apt-xapian-index/index" axi = xapian.Database(axi_path) pkgs = {'vim': 'EX'} debtags_name = ['devel::editor', 'implemented-in::c', 'devel::interpreter', 'devel::lang:python'] terms_name = ['vim', 'editor', 'python'] assert_pkgs_classification = {'vim': [1, 1, 0, 0, 1, 1, 0, 'EX']} pkgs_classification = self.ml_data.get_pkgs_table_classification( axi, pkgs, self.cache, debtags_name, terms_name) self.assertEqual(assert_pkgs_classification, pkgs_classification) AppRecommender-0.7.5/apprecommender/tests/test_ml/test_pkg_time.py000066400000000000000000000070611306751311600254550ustar00rootroot00000000000000#!/usr/bin/env python import unittest from mock import patch from apprecommender.ml.pkg_time import PkgTime class PkgTimeTests(unittest.TestCase): def setUp(self): self.pkg_time = PkgTime() @patch('commands.getoutput') @patch('apprecommender.ml.pkg_time.get_time_from_package') def test_invalid_paths_get_best_time(self, mock_time, mock_command): mock_time.return_value = [10, 10] mock_command.return_value = '/usr/lib/a-b-c/\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 0) self.assertEqual(modify, 0) mock_command.return_value = '/usr/lib/a-b-c-d/\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 0) self.assertEqual(modify, 0) mock_command.return_value = '/usr/lib/mime/packages/test\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 0) self.assertEqual(modify, 0) mock_command.return_value = '/etc/init.d/test\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 0) self.assertEqual(modify, 0) mock_command.return_value = '/media/files/test\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 0) self.assertEqual(modify, 0) @patch('commands.getoutput') @patch('apprecommender.ml.pkg_time.get_time_from_package') def test_valid_paths_get_best_time(self, mock_time, mock_command): mock_time.return_value = [10, 10] mock_command.return_value = '/usr/lib/a-b/\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 10) self.assertEqual(modify, 10) mock_time.return_value = [20, 20] mock_command.return_value = '/usr/bin/test\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 20) self.assertEqual(modify, 20) mock_time.return_value = [30, 30] mock_command.return_value = '/usr/game/test\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 30) self.assertEqual(modify, 30) mock_time.return_value = [40, 40] mock_command.return_value = '/usr/lib/test/test\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 40) self.assertEqual(modify, 40) @patch('commands.getoutput') @patch('apprecommender.ml.pkg_time.get_time_from_package') def test_invalid_files_get_best_time(self, mock_time, mock_command): mock_time.return_value = [10, 10] mock_command.return_value = '/usr/bin/test.desktop\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 0) self.assertEqual(modify, 0) mock_time.return_value = [10, 10] mock_command.return_value = '/usr/games/test.conf\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 0) self.assertEqual(modify, 0) mock_time.return_value = [10, 10] mock_command.return_value = '/usr/lib/python/test.egg-info\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 0) self.assertEqual(modify, 0) mock_time.return_value = [10, 10] mock_command.return_value = '/usr/lib/test/test.txt\n' access, modify = self.pkg_time.get_best_time('test') self.assertEqual(access, 0) self.assertEqual(modify, 0) AppRecommender-0.7.5/apprecommender/tests/test_ml/test_utils.py000066400000000000000000000012621306751311600250130ustar00rootroot00000000000000import unittest from numpy import array from apprecommender.ml.utils import create_binary_matrix class MLUtilsTest(unittest.TestCase): def test_create_binary_matrix(self): test_matrix = array([[2], [3], [2], [4]]) classification = 2 default_value = 0 expected_result = array([[1], [0], [1], [0]]) actual_result = create_binary_matrix(test_matrix, classification, default_value) self.assertEquals(expected_result.shape, actual_result.shape) num_data = len(actual_result) for i in range(num_data): self.assertEqual(expected_result[i][0], actual_result[i][0]) AppRecommender-0.7.5/apprecommender/tests/test_recommender.py000066400000000000000000000077241306751311600245150ustar00rootroot00000000000000#!/usr/bin/env python """ recommenderTests - Recommender class test case """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import unittest from apprecommender.recommender import RecommendationResult, Recommender from apprecommender.user import User from apprecommender.config import Config from apprecommender.strategy import (ContentBased, MachineLearningBVA, MachineLearningBOW, PackageReference) class RecommendationResultTests(unittest.TestCase): @classmethod def setUpClass(self): self.result = RecommendationResult({"gimp": 1.5, "inkscape": 3.0, "eog": 1}) def test_str(self): rec = '\n1: inkscape \t vector-based drawing program\n' rec += '2: gimp \t GNU Image Manipulation Program\n' # noqa rec += '3: eog \t Eye of GNOME graphics viewer program\n' # noqa self.assertEqual(self.result.__str__(), rec) def test_get_prediction(self): prediction = [("inkscape", 3.0), ("gimp", 1.5), ("eog", 1)] self.assertEqual(self.result.get_prediction(), prediction) class RecommenderTests(unittest.TestCase): @classmethod def setUpClass(self): cfg = Config() cfg.popcon_index = "test_data/.sample_pxi" cfg.popcon_dir = "test_data/popcon_dir" cfg.clusters_dir = "test_data/clusters_dir" cfg.popcon = 0 self.rec = Recommender() def test_set_strategy(self): self.rec.set_strategy("cb") self.assertIsInstance(self.rec.strategy, ContentBased) self.assertEqual(self.rec.strategy.content, "mix") self.rec.set_strategy("cbt") self.assertIsInstance(self.rec.strategy, ContentBased) self.assertEqual(self.rec.strategy.content, "tag") self.rec.set_strategy("cbd") self.assertIsInstance(self.rec.strategy, ContentBased) self.assertEqual(self.rec.strategy.content, "desc") self.rec.set_strategy("cbtm") self.assertIsInstance(self.rec.strategy, ContentBased) self.assertEqual(self.rec.strategy.content, "time") self.rec.set_strategy("mlbva") self.assertIsInstance(self.rec.strategy, MachineLearningBVA) self.assertEqual(self.rec.strategy.content, "mlbva_mix") self.rec.set_strategy("mlbow") self.assertIsInstance(self.rec.strategy, MachineLearningBOW) self.assertEqual(self.rec.strategy.content, "mlbow_mix") self.rec.set_strategy("mlbva_eset") self.assertIsInstance(self.rec.strategy, MachineLearningBVA) self.assertEqual(self.rec.strategy.content, "mlbva_mix_eset") self.rec.set_strategy("mlbow_eset") self.assertIsInstance(self.rec.strategy, MachineLearningBOW) self.assertEqual(self.rec.strategy.content, "mlbow_mix_eset") self.rec.set_strategy("cbpkg") self.assertIsInstance(self.rec.strategy, PackageReference) self.assertEqual(self.rec.strategy.content, "mix") def test_get_recommendation(self): user = User({"inkscape": 1, "gimp": 1, "eog": 1, "vim": 1}) result = self.rec.get_recommendation(user) self.assertIsInstance(result, RecommendationResult) self.assertGreater(len(result.item_score), 0) AppRecommender-0.7.5/apprecommender/tests/test_run.py000066400000000000000000000025661306751311600230200ustar00rootroot00000000000000#!/usr/bin/env python import unittest import logging import apprecommender.main.cli as apprec from apprecommender.main.options import get_parser from apprecommender.config import Config from apprecommender.ml.data import MachineLearningData class RunTests(unittest.TestCase): def setUp(self): logging.getLogger().disabled = True self.axi_desktopapps = Config().axi_desktopapps parser = get_parser() self.args = vars(parser.parse_args('')) def tearDown(self): Config().axi_desktopapps = self.axi_desktopapps def test_success_run_apprec(self): logging.getLogger().disabled = False result = apprec.run(self.args) self.assertEqual(apprec.SUCCESS, result) def test_error_init_on_run_apprec(self): Config().axi_desktopapps = "asd" result = apprec.run(self.args) self.assertEqual(apprec.ERROR_INIT, result) def test_error_train_on_run_apprec(self): config = Config() strategy = config.strategy config.strategy = 'mlbva' training_path = MachineLearningData.MACHINE_LEARNING_TRAINING MachineLearningData.MACHINE_LEARNING_TRAINING = "error.txt" result = apprec.run(self.args) config.strategy = strategy MachineLearningData.MACHINE_LEARNING_TRAINING = training_path self.assertEqual(apprec.ERROR_TRAIN, result) AppRecommender-0.7.5/apprecommender/tests/test_singleton.py000066400000000000000000000021331306751311600242040ustar00rootroot00000000000000#!/usr/bin/env python """ singletonTests - Singleton class test case """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import unittest from apprecommender.singleton import Singleton class SingletonTests(unittest.TestCase): def test_creation(self): object_1 = Singleton() object_2 = Singleton() self.assertEqual(id(object_1), id(object_2)) AppRecommender-0.7.5/apprecommender/tests/test_strategy.py000066400000000000000000000040131306751311600240430ustar00rootroot00000000000000#!/usr/bin/env python """ strategyTests - Recommendation strategies classes test case """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import unittest import xapian from apprecommender.decider import (PkgMatchDecider, PkgExpandDecider, TagExpandDecider) class PkgMatchDeciderTests(unittest.TestCase): def setUp(self): pkgs_list = ["gimp", "eog", "inkscape"] self.decider = PkgMatchDecider(pkgs_list) self.doc = xapian.Document() def test_match(self): self.doc.set_data("emacs") self.assertTrue(self.decider(self.doc)) def test_no_match(self): self.doc.set_data("gimp") self.assertFalse(self.decider(self.doc)) class PkgExpandDeciderTests(unittest.TestCase): def setUp(self): pkgs_list = ["gimp", "eog", "inkscape"] self.decider = PkgExpandDecider(pkgs_list) def test_match(self): self.assertTrue(self.decider("XPemacs")) def test_no_match(self): self.assertFalse(self.decider("XTgimp")) class TagExpandDeciderTests(unittest.TestCase): def setUp(self): self.decider = TagExpandDecider() def test_match(self): self.assertTrue(self.decider("XTgimp")) def test_no_match(self): self.assertFalse(self.decider("gimp")) AppRecommender-0.7.5/apprecommender/tests/test_user.py000066400000000000000000000146031306751311600231650ustar00rootroot00000000000000#!/usr/bin/env python """ userTests - User class test case """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import unittest import xapian from mock import patch from apprecommender.user import User, LocalSystem, FilterTag, FilterDescription from apprecommender.config import Config from apprecommender.data import SampleAptXapianIndex class FilterTagTests(unittest.TestCase): @classmethod def setUpClass(self): tags_filter = "apprecommender/tests/test_data/tags_filter" self.debtag = "use::searching" with open(tags_filter) as valid_tags: self.valid_tags = [line.strip() for line in valid_tags] def test_call_true(self): self.assertTrue(FilterTag(self.valid_tags)("XT" + self.debtag)) def test_call_false(self): self.assertFalse(FilterTag(self.valid_tags)(self.debtag)) class FilterDescriptionTests(unittest.TestCase): def test_call_true(self): self.assertTrue(FilterDescription()("program")) def test_call_false(self): self.assertFalse(FilterDescription()("XTprogram")) class UserTests(unittest.TestCase): @classmethod def setUpClass(self): cfg = Config() self.axi = xapian.Database(cfg.axi) packages = ["gimp", "aaphoto", "eog", "emacs", "dia", "ferret", "festival", "file", "inkscape", "xpdf"] path = "apprecommender/tests/test_data/.sample_axi" self.sample_axi = SampleAptXapianIndex(packages, self.axi, path) self.user = User({"gimp": 1, "aaphoto": 1, "eog": 1, "emacs": 1}) def test_hash(self): new_user = User(dict()) self.assertIsNotNone(new_user.id) self.assertNotEqual(self.user.id, new_user.id) def test_profile_default(self): new_user = User(dict()) desktop = set(["x11", "accessibility", "game", "junior", "office", "interface::x11"]) self.assertEqual(new_user.demographic_profile, desktop) def test_profile_desktop(self): self.user.set_demographic_profile(set(["desktop"])) desktop = set(["x11", "accessibility", "game", "junior", "office", "interface::x11"]) self.assertEqual(self.user.demographic_profile, desktop) def test_profile_admin(self): self.user.set_demographic_profile(set(["admin"])) admin = set(["admin", "hardware", "mail", "protocol", "network", "security", "web", "interface::web"]) self.assertEqual(self.user.demographic_profile, admin) def test_profile_devel(self): self.user.set_demographic_profile(set(["devel"])) devel = set(["devel", "role::devel-lib", "role::shared-lib"]) self.assertEqual(self.user.demographic_profile, devel) def test_profile_art(self): self.user.set_demographic_profile(set(["art"])) art = set(["field::arts", "sound"]) self.assertEqual(self.user.demographic_profile, art) def test_profile_science(self): self.user.set_demographic_profile(set(["science"])) science = set(["science", "biology", "field::astronomy", "field::aviation", "field::biology", "field::chemistry", "field::eletronics", "field::finance", "field::geography", "field::geology", "field::linguistics", "field::mathematics", "field::medicine", "field::meteorology", "field::physics", "field::statistics"]) self.assertEqual(self.user.demographic_profile, science) def test_multi_profile(self): self.user.set_demographic_profile(set(["devel", "art"])) devel_art = set(["devel", "role::devel-lib", "role::shared-lib", "field::arts", "sound"]) self.assertEqual(self.user.demographic_profile, devel_art) self.user.set_demographic_profile(set(["art", "admin", "desktop"])) desktop_art_admin = set(["x11", "accessibility", "game", "junior", "office", "interface::x11", "field::arts", "sound", "admin", "hardware", "mail", "protocol", "network", "security", "web", "interface::web"]) self.assertEqual(self.user.demographic_profile, desktop_art_admin) def test_items(self): self.assertEqual(set(self.user.items()), set(["gimp", "aaphoto", "eog", "emacs"])) def test_maximal_pkg_profile(self): old_pkg_profile = self.user.items() aaphoto_deps = ["libc6", "libgomp1", "libjasper1", "libjpeg62", "libpng12-0"] libc6_deps = ["libc-bin", "libgcc1"] for pkg in aaphoto_deps + libc6_deps: self.user.item_score[pkg] = 1 self.assertEqual(old_pkg_profile, self.user.maximal_pkg_profile()) class LocalSystemTest(unittest.TestCase): def setUp(self): self.user = LocalSystem() @patch('commands.getoutput') @patch('glob.glob', return_value=['test']) def test_get_apt_installed_pkgs(self, mock_glob, mock_command): mock_command.return_value = 'Commandline: apt install test1 test2\n' apt_pkgs = self.user.get_apt_installed_pkgs() self.assertEqual(2, len(apt_pkgs)) self.assertIn('test1', apt_pkgs) self.assertIn('test2', apt_pkgs) mock_command.return_value = 'Commandline: apt remove test1 test2\n' apt_pkgs = self.user.get_apt_installed_pkgs() self.assertEqual(0, len(apt_pkgs)) mock_command.return_value = 'Commandline: apt upgrade\n' apt_pkgs = self.user.get_apt_installed_pkgs() self.assertEqual(0, len(apt_pkgs)) AppRecommender-0.7.5/apprecommender/user.py000066400000000000000000000412361306751311600207660ustar00rootroot00000000000000#!/usr/bin/env python """ user - python module for classes and methods related to recommenders' users. """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import commands import datetime import glob import logging import os import pickle import random import re import xapian import apprecommender.data as data from apprecommender.apt_cache import AptCache from apprecommender.config import Config from apprecommender.decider import (FilterTag, FilterDescription, FilterTag_or_Description) from apprecommender.error import Error from apprecommender.singleton import Singleton class DemographicProfile(Singleton): def __init__(self): self.admin = set(["admin", "hardware", "mail", "protocol", "network", "security", "web", "interface::web"]) self.devel = set(["devel", "role::devel-lib", "role::shared-lib"]) self.desktop = set(["x11", "accessibility", "game", "junior", "office", "interface::x11"]) self.art = set(["field::arts", "sound"]) self.science = set(["science", "biology", "field::astronomy", "field::aviation", "field::biology", "field::chemistry", "field::eletronics", "field::finance", "field::geography", "field::geology", "field::linguistics", "field::mathematics", "field::medicine", "field::meteorology", "field::physics", "field::statistics"]) def __call__(self, profiles_set): demographic_profile = set() for profile in profiles_set: profile_str = "self." + profile demographic_profile = (demographic_profile | eval(profile_str, {}, {"self": self})) return demographic_profile class User: """ Define a user of a recommender. """ def __init__(self, item_score, user_id=0, arch=0, demo_profiles_set=0, reference_pkgs=None): """ Set initial user attributes. pkg_profile gets the whole set of items, a random user_id is set if none was provided and the demographic profile defaults to 'desktop'. """ self.item_score = item_score self.pkg_profile = self.items() self.installed_pkgs = data.get_user_installed_pkgs() self.arch = arch self.reference_pkgs = reference_pkgs if reference_pkgs else [] if user_id: self.user_id = user_id else: random.seed() self.id = random.getrandbits(128) if not demo_profiles_set: profiles_set = set(["desktop"]) self.set_demographic_profile(profiles_set) def items(self): """ Return the set of user items. """ return self.item_score.keys() def set_demographic_profile(self, profiles_set): """ Set demographic profle based on labels in 'profiles_set'. """ self.demographic_profile = DemographicProfile()(profiles_set) def content_profile(self, items_repository, content, size, valid_tags=0, time_context=0): """ Get user profile for a specific type of content: packages tags, description or both (mixed and half-half profiles) """ if content == "tag": profile = self.tfidf_profile(items_repository, size, FilterTag(valid_tags), time_context) elif content == "desc": profile = self.tfidf_profile(items_repository, size, FilterDescription(), time_context) elif content == 'mlbow_mix' or content == 'mlbva_mix': self.pkg_profile = self.get_most_usefull_pkgs() profile = self.tfidf_profile(items_repository, size, FilterTag_or_Description(valid_tags), time_context) elif content == "mix": profile = self.tfidf_profile(items_repository, size, FilterTag_or_Description(valid_tags), time_context) elif content == "half": tag_profile = self.tfidf_profile(items_repository, size, FilterTag(valid_tags), time_context) desc_profile = self.tfidf_profile(items_repository, size, FilterDescription(), time_context) profile = tag_profile[:size / 2] + desc_profile[:size / 2] elif content == "time": tag_profile = self.tfidf_profile(items_repository, size, FilterTag(valid_tags), time_context=1) desc_profile = self.tfidf_profile(items_repository, size, FilterDescription(), time_context=1) profile = tag_profile[:size / 2] + desc_profile[:size / 2] elif content == "tag_eset": profile = self.eset_profile(items_repository, size, FilterTag(valid_tags)) elif content == "desc_eset": profile = self.eset_profile(items_repository, size, FilterDescription()) elif content == 'mlbow_mix_eset' or content == 'mlbva_mix_eset': self.pkg_profile = self.get_most_usefull_pkgs() profile = self.eset_profile(items_repository, size, FilterTag_or_Description(valid_tags)) elif content == "mix_eset": profile = self.eset_profile(items_repository, size, FilterTag_or_Description(valid_tags)) elif content == "half_eset": tag_profile = self.eset_profile(items_repository, size, FilterTag(valid_tags)) desc_profile = self.eset_profile(items_repository, size, FilterDescription()) profile = tag_profile[:size / 2] + desc_profile[:size / 2] else: logging.debug("Unknown content type %s." % content) raise Error logging.debug("User %s profile: %s" % (content, profile)) return profile def tfidf_profile(self, items_repository, size, content_filter, time_context=0): """ Return the most relevant tags for the user list of packages based on the sublinear tfidf weight of packages' tags. """ docs = data.axi_search_pkgs(items_repository, self.pkg_profile) # weights = data.tfidf_plus(items_repository,docs,content_filter) weights = data.tfidf_weighting(items_repository, docs, content_filter, time_context=time_context) # Eliminate duplicated stemmed term profile = self._eliminate_duplicated([w[0] for w in weights], size) return profile def eset_profile(self, items_repository, size, content_filter): """ Return most relevant tags for a list of packages. """ # Store package documents in a relevant set enquire = xapian.Enquire(items_repository) docs = data.axi_search_pkgs(items_repository, self.pkg_profile) rset_packages = xapian.RSet() for d in docs: rset_packages.add_document(d.docid) # Get expanded query terms (statistically good differentiators) eset_tags = enquire.get_eset(size * 2, rset_packages, xapian.Enquire.INCLUDE_QUERY_TERMS, 1, content_filter) # Eliminate duplicated stemmed term profile = self._eliminate_duplicated([res.term for res in eset_tags], size) return profile def _eliminate_duplicated(self, sorted_list, size): profile = sorted_list[:size] next_index = size duplicate = 1 while duplicate: duplicate = 0 for term in profile[:]: if term.startswith("Z"): for p in profile[:]: if p.startswith(term.lstrip("Z")): duplicate = 1 profile.remove(p) if len(sorted_list) > next_index: profile.append(sorted_list[next_index]) next_index += 1 return profile def filter_pkg_profile(self, filter_list_or_file): """ Return list of packages from profile listed in the filter_file. """ if type(filter_list_or_file).__name__ == "list": valid_pkgs = filter_list_or_file elif type(filter_list_or_file).__name__ == "str": try: with open(filter_list_or_file) as valid: valid_pkgs = [line.strip() for line in valid] except IOError: logging.critical("Could not open profile filter file: %s" % filter_list_or_file) raise Error else: logging.debug("No filter provided for user profiling.") return self.pkg_profile old_profile_size = len(self.pkg_profile) for pkg in self.pkg_profile[:]: # iterate list copy if pkg not in valid_pkgs: self.pkg_profile.remove(pkg) logging.debug("Discarded package %s during profile filtering" % pkg) profile_size = len(self.pkg_profile) logging.debug("Filtered package profile: reduced packages profile size \ from %d to %d." % (old_profile_size, profile_size)) return self.pkg_profile def maximal_pkg_profile(self): """ Return list of packages that are not dependence of any other package in the list. """ cache = AptCache() old_profile_size = len(self.pkg_profile) for p in self.pkg_profile[:]: # iterate list copy if p in cache: pkg = cache[p] if pkg.candidate: for dep in pkg.candidate.dependencies: for or_dep in dep.or_dependencies: if or_dep.name in self.pkg_profile: self.pkg_profile.remove(or_dep.name) profile_size = len(self.pkg_profile) logging.debug("Maximal package profile: reduced packages profile size \ from %d to %d." % (old_profile_size, profile_size)) return self.pkg_profile def get_most_usefull_pkgs(self): classification_path = Config().user_data_dir classification_path += 'pkgs_classifications.txt' if not os.path.exists(classification_path): return -1 with open(classification_path, 'ra') as data: pkg_classification = pickle.load(data) classifications = {'RU': [], 'U': [], 'NU': []} for pkg, values in pkg_classification.iteritems(): classifications[values[-1]].append(pkg) return classifications['RU'] class RandomPopcon(User): def __init__(self, submissions_dir, arch=0, pkgs_filter=0): """ Set initial parameters. """ len_profile = 0 match_arch = False while len_profile < 100 or not match_arch: path = random.choice([os.path.join(root, submission) for root, dirs, files in os.walk(submissions_dir) for submission in files]) user = PopconSystem(path) print arch print user.arch if arch and user.arch == arch: match_arch = True print "match" if pkgs_filter: user.filter_pkg_profile(pkgs_filter) len_profile = len(user.pkg_profile) print "p", len_profile submission = data.PopconSubmission(path) User.__init__(self, submission.packages, submission.user_id, submission.arch) class PopconSystem(User): def __init__(self, path, user_id=0): """ Set initial parameters. """ submission = data.PopconSubmission(path) if not user_id: user_id = submission.user_id User.__init__(self, submission.packages, user_id, submission.arch) class LocalSystem(User): """ Extend the class User to consider the packages installed on the local system as the set of selected itens. """ def __init__(self, reference_pkgs=None): """ Set initial parameters. """ user_pkgs = self.get_manual_installed_pkgs() item_score = {pkg: 1 for pkg in user_pkgs} self.user_id = "local-" + str(datetime.datetime.now()) User.__init__(self, item_score, reference_pkgs=reference_pkgs) def get_system_pkgs(self): system_pkgs = [] all_pkgs = commands.getoutput("dpkg-query -Wf \ '${Package;-40}${Priority}\n'") priority_terms = set(['important', 'required', 'standard']) for line in all_pkgs.splitlines(): line_split = line.split(' ') pkg_name = line_split[0] pkg_priority = line_split[-1].strip() if (pkg_priority in priority_terms): system_pkgs.append(pkg_name) return system_pkgs def get_apt_installed_pkgs(self): apt_pkgs = set() apt_log = glob.glob('/var/log/apt/history.log*') installed_pkgs_regex = re.compile( r'^Commandline:.+apt.+install\s(.+)', re.MULTILINE) no_remove_pkgs_regex = re.compile(r'--no-remove') automatic_remove_regex = re.compile(r'APT::Get::AutomaticRemove=true') apt_log = reversed(sorted(apt_log)) for log in apt_log: command = 'zcat' if log.endswith('gz') else 'cat' history_files = commands.getoutput( '{} {} | grep "Commandline:"'.format(command, log)) ''' The log_files will hold packages with this format: Commandline: apt install google-chrome-stable Therefore it is necessary to perform another filter on it to only get the package name. ''' for apt_command in history_files.splitlines(): installed_pkgs = installed_pkgs_regex.search(apt_command) no_remove_pkgs = no_remove_pkgs_regex.search(apt_command) automatic_remove_pkgs = automatic_remove_regex.search( apt_command) if (installed_pkgs and not no_remove_pkgs and not automatic_remove_pkgs): pkgs = set(installed_pkgs.group(1).split()) apt_pkgs |= pkgs return apt_pkgs def __get_manual_marked_pkgs(self): list_manual = commands.getoutput('apt-mark showmanual') list_manual = list_manual.splitlines() return set([pkg for pkg in list_manual]) def __remove_lib_packages(self, pkgs): return set([pkg for pkg in pkgs if not re.match(r'^lib', pkg)]) def __remove_apt_packages(self, pkgs): return set([pkg for pkg in pkgs if not re.match(r'^apt', pkg)]) def get_manual_installed_pkgs(self): apt_pkgs = self.get_apt_installed_pkgs() manual_pkgs = self.__get_manual_marked_pkgs() system_pkgs = self.get_system_pkgs() pkgs = apt_pkgs.intersection(manual_pkgs) pkgs = self.__remove_lib_packages(pkgs) pkgs = self.__remove_apt_packages(pkgs) pkgs -= set(system_pkgs) return pkgs AppRecommender-0.7.5/apprecommender/utils.py000066400000000000000000000016231306751311600211440ustar00rootroot00000000000000#!/usr/bin/env python import sys def print_progress_bar(number, n_numbers, message='Progress', bar_length=40): percent = float(number) / float(n_numbers) hashes = '#' * int(round(percent * bar_length)) spaces = ' ' * (bar_length - len(hashes)) percent = int(round(percent * 100)) percent_message = ("\r{}: [{}] [{} / {}] {}%".format(message, hashes + spaces, number, n_numbers, percent)) sys.stdout.write(percent_message) sys.stdout.flush() if number == n_numbers: print '\n' def get_class_and_module_name(cls): class_name = cls.__name__ module_name = cls.__module__ return module_name + '.' + class_name AppRecommender-0.7.5/bin/000077500000000000000000000000001306751311600151775ustar00rootroot00000000000000AppRecommender-0.7.5/bin/apprec.py000077500000000000000000000054511306751311600170330ustar00rootroot00000000000000#!/usr/bin/env python """ AppRecommender - A GNU/Linux application recommender """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import sys import xapian sys.path.insert(0, '../') from apprecommender.app_recommender import AppRecommender from apprecommender.initialize import Initialize from apprecommender.load_options import LoadOptions from apprecommender.config import Config from apprecommender.strategy import (MachineLearning, MachineLearningBVA, MachineLearningBOW) from apprecommender.data_collect import collect_user_data SUCCESS = 0 ERROR_INIT = 1 ERROR_TRAIN = 2 def check_for_flag(options, short_flag, long_flag): for option, _ in options: if option in (short_flag, long_flag): return True return False def run_apprecommender(options): try: app_recommender = AppRecommender() app_recommender.make_recommendation() return SUCCESS except xapian.DatabaseOpeningError: return ERROR_INIT except IOError: if "ml" in Config().strategy: return ERROR_TRAIN def run(): load_options = LoadOptions() load_options.load() options = load_options.options if check_for_flag(options, '-i', '--init'): print "Initializing AppRecommender" initialize = Initialize() initialize.prepare_data() return SUCCESS elif check_for_flag(options, '-t', '--train'): print "Training machine learning" MachineLearning.train(MachineLearningBVA) MachineLearning.train(MachineLearningBOW) return SUCCESS elif check_for_flag(options, '-c', '--contribute'): collect_user_data.main() else: return run_apprecommender(load_options.options) def main(): result = run() if result is ERROR_INIT: print "\n" print "Please, Initialize AppRecommender" print "Run: apprec.py --init" elif result is ERROR_TRAIN: print "\n" print "Please, run Machine Learning Training" print "Run: apprec.py --train" if __name__ == '__main__': main() AppRecommender-0.7.5/bin/apt_config/000077500000000000000000000000001306751311600173105ustar00rootroot00000000000000AppRecommender-0.7.5/bin/apt_config/__init__.py000066400000000000000000000000001306751311600214070ustar00rootroot00000000000000AppRecommender-0.7.5/bin/apt_config/add_apt_conf.py000077500000000000000000000020341306751311600222650ustar00rootroot00000000000000#!/usr/bin/python import os import sys sys.path.insert(0, "{0}/../../".format(os.path.dirname(__file__))) APT_FOLDER = "/home/%s/.app-recommender/apt_conf" % os.getenv("SUDO_USER") APT_CONF_FILE = '/etc/apt/apt.conf.d/99app-recommender' FILES_FOLDER = os.path.dirname(os.path.abspath(__file__)) POST_INVOKE_PATH = FILES_FOLDER + "/post_invoke.py" PRE_INSTALL_PKGS_PATH = FILES_FOLDER + "/pre_install_pkgs.py" INSTALLED_PKGS_FILE = APT_FOLDER + "/installed_pkgs.txt" def main(): post_invoke = 'DPkg::Post-Invoke {"python %s";};' % POST_INVOKE_PATH pre_install_pkgs = ('DPkg::Pre-Install-Pkgs {"python %s";};' % PRE_INSTALL_PKGS_PATH) print("Creating folder: {}".format(APT_FOLDER)) if not os.path.exists(APT_FOLDER): os.makedirs(APT_FOLDER) print("Creating file: {}".format(APT_CONF_FILE)) with open(APT_CONF_FILE, 'w') as text: text.write(post_invoke) text.write('\n') text.write(pre_install_pkgs) text.write('\n') if __name__ == "__main__": main() AppRecommender-0.7.5/bin/apt_config/clear_apt_conf.py000077500000000000000000000003171306751311600226250ustar00rootroot00000000000000#!/usr/bin/python import os import shutil from add_apt_conf import APT_FOLDER, APT_CONF_FILE def main(): os.remove(APT_CONF_FILE) shutil.rmtree(APT_FOLDER) if __name__ == "__main__": main() AppRecommender-0.7.5/bin/apt_config/post_invoke.py000066400000000000000000000011131306751311600222160ustar00rootroot00000000000000#!/usr/bin/python import commands from add_apt_conf import INSTALLED_PKGS_FILE def main(): with open(INSTALLED_PKGS_FILE, 'r') as text: pre_installed_pkgs = set([line.strip() for line in text]) pkgs = commands.getoutput("apt-mark showmanual") pos_installed_pkgs = set([line.strip() for line in pkgs.splitlines()]) installed_pkgs = list(pos_installed_pkgs - pre_installed_pkgs) if len(installed_pkgs) > 0: print("\n\n") print("installed packages: {}".format(installed_pkgs)) print("\n\n") if __name__ == "__main__": main() AppRecommender-0.7.5/bin/apt_config/pre_install_pkgs.py000066400000000000000000000003261306751311600232230ustar00rootroot00000000000000#!/usr/bin/python import commands from add_apt_conf import INSTALLED_PKGS_FILE def main(): commands.getoutput("apt-mark showmanual > {}".format(INSTALLED_PKGS_FILE)) if __name__ == "__main__": main() AppRecommender-0.7.5/bin/cross_validation.py000077500000000000000000000050221306751311600211160ustar00rootroot00000000000000#!/usr/bin/env python """ CrossValidation - python module for classes and methods related to recommenders evaluation. """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import os import logging import datetime import sys sys.path.insert(0, '../') from apprecommender.config import Config from apprecommender.evaluation import (CrossValidation, Precision, Recall, F_score, FPR, Accuracy) from apprecommender.recommender import Recommender from apprecommender.user import PopconSystem if __name__ == '__main__': cfg = Config() rec = Recommender() # user = LocalSystem() # user = RandomPopcon(cfg.popcon_dir) # user = RandomPopcon(cfg.popcon_dir,os.path.join(cfg.filters_dir, # "desktopapps")) popcon_entries = "~/.app-recommender/popcon-entries/" \ "00/0001166d0737c6dffb083071e5ee69f5" user = PopconSystem(os.path.expanduser(popcon_entries)) user.filter_pkg_profile(os.path.join(cfg.filters_dir, "desktopapps")) user.maximal_pkg_profile() begin_time = datetime.datetime.now() metrics = [] metrics.append(Precision()) metrics.append(Recall()) metrics.append(F_score(0.5)) metrics.append(Accuracy()) metrics.append(FPR()) validation = CrossValidation(0.9, 20, rec, metrics, 0.005) validation.run(user) print validation end_time = datetime.datetime.now() delta = end_time - begin_time logging.info("Cross-validation for user %s" % user.user_id) logging.info("Recommender strategy: %s" % rec.strategy.description) logging.debug("Cross-validation started at %s" % begin_time) logging.debug("Cross-validation completed at %s" % end_time) logging.info("Time elapsed: %d seconds." % delta.seconds) AppRecommender-0.7.5/bin/demo_rec.py000077500000000000000000000106651306751311600173410ustar00rootroot00000000000000#!/usr/bin/env python """ DemoRecommender - demonstration of a GNU/Linux application recommender. """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import os import commands import re import sys sys.path.insert(0, '../') import xapian from debian import debtags # ??? from apprecommender.strategy import PkgMatchDecider DB_PATH = "/var/lib/debtags/package-tags" INDEX_PATH = os.path.expanduser("~/.app-recommender/debtags_index") def load_debtags_db(path): """ Load debtags database. """ debtags_db = debtags.DB() tag_filter = re.compile(r"^special::.+$|^.+::TODO$") try: debtags_db.read(open(path, "r"), lambda x: not tag_filter.match(x)) except IOError: print >> sys.stderr, ("IOError: could not open debtags file \'%s\'" % path) exit(1) return debtags_db def get_system_pkgs(): """ Return set of system packages. """ dpkg_output = commands.getoutput('/usr/bin/dpkg --get-selections') return dpkg_output.replace('install', '\t').split() def get_most_relevant_tags(debtags_db, pkgs_list): """ Return most relevant tags considering a list of packages. """ relevant_db = debtags_db.choose_packages(pkgs_list) relevance_index = debtags.relevance_index_function(debtags_db, relevant_db) sorted_relevant_tags = sorted(relevant_db.iter_tags(), lambda a, b: cmp(relevance_index(a), relevance_index(b))) return normalize_tags(' '.join(sorted_relevant_tags[-50:])) def normalize_tags(string): """ Normalize tag string so that it can be indexed and retrieved. """ return string.replace(':', '_').replace('-', '\'') def create_debtags_index(debtags_db, index_path): """ Create a xapian index for debtags info based on file 'debtags_db' and place it at 'index_path'. """ if not os.path.exists(index_path): os.makedirs(index_path) print "Creating new debtags xapian index at \'%s\'" % index_path debtags_index = xapian.WritableDatabase(index_path, xapian.DB_CREATE_OR_OVERWRITE) for pkg, tags in debtags_db.iter_packages_tags(): doc = xapian.Document() doc.set_data(pkg) for tag in tags: doc.add_term(normalize_tags(tag)) print "indexing ", debtags_index.add_document(doc) return debtags_index def load_debtags_index(debtags_db, reindex): """ Load an existing or new debtags index, based on boolean reindex. """ if not reindex: try: print ("Opening existing debtags xapian index at \'%s\'" % INDEX_PATH) debtags_index = xapian.Database(INDEX_PATH) except xapian.DatabaseError: print "Could not open debtags xapian index" reindex = 1 if reindex: debtags_index = create_debtags_index(debtags_db, INDEX_PATH) return debtags_index if __name__ == '__main__': reindex = 0 if len(sys.argv) == 2: DB_PATH = sys.argv[1] reindex = 1 print "reindex true" elif len(sys.argv) > 2: print >> sys.stderr, ("Usage: %s [PATH_TO_DEBTAGS_DATABASE]" % sys.argv[0]) sys.exit(1) debtags_db = load_debtags_db(DB_PATH) installed_pkgs = get_system_pkgs() best_tags = get_most_relevant_tags(debtags_db, installed_pkgs) debtags_index = load_debtags_index(debtags_db, reindex) qp = xapian.QueryParser() query = qp.parse_query(best_tags) enquire = xapian.Enquire(debtags_index) enquire.set_query(query) mset = enquire.get_mset(0, 20, None, PkgMatchDecider(installed_pkgs)) for m in mset: print "%2d: %s" % (m.rank, m.document.get_data()) AppRecommender-0.7.5/bin/example/000077500000000000000000000000001306751311600166325ustar00rootroot00000000000000AppRecommender-0.7.5/bin/example/dpkg_debian6000066400000000000000000000367561306751311600211130ustar00rootroot00000000000000acpi 1.5-2 acpi-support-base 0.137-5 acpid 1:2.0.7-1 adduser 3.112+nmu2 alsa-base 1.0.23+dfsg-2 alsa-utils 1.0.23-3 anacron 2.3-14 apt 0.8.10.3 apt-listchanges 2.85.7 apt-utils 0.8.10.3 aptitude 0.6.3-3.2 at 3.1.12-1 base-files 6.0 base-passwd 3.5.22 bash 4.1-3 bash-completion 1:1.2-3 bc 1.06.95-2 bind9-host 1:9.7.2.dfsg.P3-1.1 bsd-mailx 8.1.2-0.20100314cvs-1 bsdmainutils 8.0.13 bsdutils 1:2.17.2-9 busybox 1:1.17.1-8 bzip2 1.0.5-6 capplets-data 1:2.30.1-2 console-setup 1.68 console-terminus 4.30-2 consolekit 0.4.1-4 coreutils 8.5-1 cpio 2.11-4 cpp 4:4.4.5-1 cpp-4.4 4.4.5-8 cron 3.0pl1-116 cups 1.4.4-7 cups-bsd 1.4.4-7 cups-client 1.4.4-7 cups-common 1.4.4-7 cups-ppdc 1.4.4-7 dash 0.5.5.1-7.4 dbus 1.2.24-4 dbus-x11 1.2.24-4 dc 1.06.95-2 debconf 1.5.36.1 debconf-i18n 1.5.36.1 debian-archive-keyring 2010.08.28 debian-faq 4.0.4+nmu1 debianutils 3.4 defoma 0.11.11 desktop-base 6.0.5 desktop-file-utils 0.15-2 diffutils 1:3.0-1 discover 2.1.2-5 discover-data 2.2010.10.18 dmidecode 2.9-1.2 dmsetup 2:1.02.48-5 dmz-cursor-theme 0.4.3 dnsutils 1:9.7.2.dfsg.P3-1.1 doc-debian 4.0.2 doc-linux-text 2008.08-1 docbook-xml 4.5-7 dosfstools 3.0.9-1 dpkg 1.15.8.10 e2fslibs 1.41.12-2 e2fsprogs 1.41.12-2 eject 2.1.5+deb1+cvs20081104-7.1 eog 2.30.2-1 epiphany-browser 2.30.6-1 epiphany-browser-data 2.30.6-1 esound-common 0.2.41-8 evince 2.30.3-2 evince-common 2.30.3-2 evolution 2.30.3-5 evolution-common 2.30.3-5 evolution-data-server 2.30.3-2 evolution-data-server-common 2.30.3-2 exim4 4.72-6 exim4-base 4.72-6 exim4-config 4.72-6 exim4-daemon-light 4.72-6 file 5.04-5 file-roller 2.30.2-2 findutils 4.4.2-1+b1 fontconfig 2.8.0-2.1 fontconfig-config 2.8.0-2.1 ftp 0.17-23 gcc-4.4-base 4.4.5-8 gconf2 2.28.1-6 gconf2-common 2.28.1-6 gdm3 2.30.5-6 gedit 2.30.4-1 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ttf-ubuntu-font-family 0 0 humanity-icon-theme 0 0 libisccfg60 0 0 evolution-data-server-common 0 0 ttf-indic-fonts-core 0 0 libbonoboui2-common 0 0 libss2 0 0 libwps-0.1-1 0 0 language-pack-gnome-en 0 0 libcouchdb-glib-1.0-2 0 0 ttf-kacst-one 0 0 libsepol1 0 0 libxapian15 0 0 libtheora0 0 0 python-ibus 0 0 xfonts-100dpi 0 0 manpages 0 0 libblkid1 0 0 libmission-control-plugins0 0 0 libgtop2-common 0 0 ttf-liberation 0 0 espeak-data 0 0 ubuntu-minimal 0 0 libarchive1 0 0 libopenobex1 0 0 libgsl0ldbl 0 0 gconf2-common 0 0 manpages-dev 0 0 libsmbclient 0 0 libmusicbrainz4c2a 0 0 libelf1 0 0 libmeanwhile1 0 0 libatm1 0 0 e2fslibs 0 0 xbitmaps 0 0 ttf-punjabi-fonts 0 0 xserver-xorg-video-all 0 0 libcurl3-gnutls 0 0 xkb-data 0 0 gcc-4.4-base 0 0 hunspell-en-ca 0 0 libcolamd2.7.1 0 0 x-ttcidfont-conf 0 0 python-gnupginterface 0 0 libmagickwand3 0 0 libxaw7 0 0 libhunspell-1.2-0 0 0 ubuntu-extras-keyring 0 0 example-content 0 0 ttf-wqy-microhei 0 0 libgexiv2-0 0 0 gnome-session-canberra 0 0 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python-protobuf 0 0 hwdata 0 0 python-ubuntuone-client 0 0 gnome-utils 0 0 libavahi-gobject0 0 0 libboost-iostreams1.42.0 0 0 libglib2.0-data 0 0 usb-creator-common 0 0 libtag1c2a 0 0 libgs8 0 0 libnice0 0 0 xfonts-mathml 0 0 adium-theme-ubuntu 0 0 brasero-common 0 0 fglrx-modaliases 0 0 libcdparanoia0 0 0 gnome-themes-selected 0 0 libprotobuf6 0 0 libwnck-common 0 0 bogofilter 0 0 libmpfr4 0 0 libisofs6 0 0 libgraphite3 0 0 mobile-broadband-provider-info 0 0 gnome-themes-ubuntu 0 0 ttf-opensymbol 0 0 libdns66 0 0 libgnomekbd-common 0 0 libespeak1 0 0 totem-common 0 0 python-ubuntuone-storageprotocol 0 0 libebackend1.2-0 0 0 libfs6 0 0 libgtk2.0-common 0 0 libdotconf1.0 0 0 python-pyinotify 0 0 libpoppler7 0 0 libx11-data 0 0 liblircclient0 0 0 branding-ubuntu 0 0 libxxf86dga1 0 0 ubuntu-keyring 0 0 ubuntu-sounds 0 0 gnome-accessibility-themes 0 0 bogofilter-common 0 0 libiec61883-0 0 0 evolution-common 0 0 ttf-unfonts-core 0 0 libcdio-paranoia0 0 0 libpcap0.8 0 0 compiz 0 0 libbluetooth3 0 0 ttf-freefont 0 0 libdatrie1 0 0 libcdio-cdda0 0 0 nvidia-current-modaliases 0 0 libraptor1 0 0 iso-codes 0 0 libcwidget3 0 0 xfonts-encodings 0 0 libxmuu1 0 0 command-not-found-data 0 0 libidl0 0 0 libclutter-1.0-0 0 0 libxslt1.1 0 0 libgcr0 0 0 libspectre1 0 0 checkbox 0 0 ubufox 0 0 libgoocanvas3 0 0 dmz-cursor-theme 0 0 python-xdg 0 0 light-themes 0 0 hunspell-en-us 0 0 libdrm-nouveau1 0 0 libgnomeui-common 0 0 app-install-data 0 0 libatspi1.0-0 0 0 libgnome-bluetooth8 0 0 xcursor-themes 0 0 libpci3 0 0 libgadu3 0 0 screensaver-default-images 0 0 xfonts-75dpi 0 0 ubuntu-docs 0 0 libclutter-gtk-0.10-0 0 0 librdf0 0 0 libgdict-1.0-6 0 0 libidn11 0 0 libbsd0 0 0 python-louis 0 0 libx86-1 0 0 libopencc1 0 0 libnewt0.52 0 0 libacl1 0 0 libkpathsea5 0 0 libedata-book1.2-2 0 0 ttf-lao 0 0 hicolor-icon-theme 0 0 libgomp1 0 0 libgdata-google1.2-1 0 0 libgc1c2 0 0 libdesktopcouch-glib-1.0-2 0 0 libraw1394-11 0 0 libtelepathy-farsight0 0 0 libgweather-common 0 0 gsfonts 0 0 libgd2-xpm 0 0 libntfs-3g79 0 0 python-avahi 0 0 libdjvulibre-text 0 0 libisccc60 0 0 python-pexpect 0 0 ttf-thai-tlwg 0 0 liborc-0.4-0 0 0 libsdl1.2debian-pulseaudio 0 0 capplets-data 0 0 libtelepathy-logger1 0 0 libwavpack1 0 0 libspeechd2 0 0 libgpm2 0 0 libavc1394-0 0 0 libwebkit-1.0-common 0 0 notify-osd-icons 0 0 libwpg-0.1-1 0 0 libreadline6 0 0 libwbclient0 END-POPULARITY-CONTEST-0 TIME:1312866728AppRecommender-0.7.5/bin/get_pkgs_inst.py000077500000000000000000000014121306751311600204120ustar00rootroot00000000000000#!/usr/bin/env python # # get_pkgs_inst.py - get tuple (package,installation) from popcon results file # # results_file: org/popcon.debian.org/popcon-mail/results import sys from operator import itemgetter if __name__ == '__main__': if len(sys.argv) < 2: print "Usage: get_pkgs_inst popcon_results_path" exit(1) results_path = sys.argv[1] pkgs_inst = {} with open(results_path) as results: for line in results: if line.startswith("Package"): fields = line.split() inst = int(fields[2]) + int(fields[3]) + int(fields[4]) pkgs_inst[fields[1]] = inst sorted_by_inst = sorted(pkgs_inst.items(), key=itemgetter(1)) for pkg, inst in sorted_by_inst: print pkg, inst AppRecommender-0.7.5/bin/indexer_popcon.py000077500000000000000000000037501306751311600205750ustar00rootroot00000000000000#!/usr/bin/env python """ popindex.py - generate a popcon index to be used by the recommender as the users repository, based on filters provided by config """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import os import sys sys.path.insert(0, '../') import logging import datetime from apprecommender.config import Config from apprecommender.data import FilteredPopconXapianIndex if __name__ == '__main__': base_dir = os.path.expanduser("~/.app-recommender/") axi_path = os.path.join(base_dir, "axi_XD") path = os.path.join(base_dir, "popcon_XD") popcon_dir = os.path.join(base_dir, "popcon-entries") tags_filter = os.path.join(base_dir, "filters/debtags") # set up config for logging cfg = Config() begin_time = datetime.datetime.now() logging.info("Popcon indexing started at %s" % begin_time) # use config file or command line options index = FilteredPopconXapianIndex(path, popcon_dir, axi_path, tags_filter) end_time = datetime.datetime.now() logging.info("Popcon indexing completed at %s" % end_time) logging.info("Number of documents (submissions): %d" % index.get_doccount()) delta = end_time - begin_time logging.info("Time elapsed: %d seconds." % delta.seconds) AppRecommender-0.7.5/bin/pkgindex.py000077500000000000000000000040431306751311600173660ustar00rootroot00000000000000#!/usr/bin/env python """ pkgindex.py - generate a pkgs index to be used by the recommender as the items repository, based on the pkgs filter provided by config """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import os import sys sys.path.insert(0, '../') import logging import datetime import xapian from apprecommender.config import Config import apprecommender.data as data if __name__ == '__main__': cfg = Config() begin_time = datetime.datetime.now() logging.info("Sample package indexing started at %s" % begin_time) with open(cfg.pkgs_filter) as valid: pkgs_list = [line.strip() for line in valid] logging.info("Packages list length: %d" % len(pkgs_list)) # use config file or command line options pkgs_filter = cfg.pkgs_filter.lstrip(cfg.filters_dir) pkgindex = data.SampleAptXapianIndex(pkgs_list, xapian.Database(cfg.axi), os.path.join(cfg.base_dir, "axi_" + pkgs_filter)) end_time = datetime.datetime.now() logging.info("Sample package indexing completed at %s" % end_time) logging.info("Number of documents (packages): %d" % pkgindex.get_doccount()) delta = end_time - begin_time logging.info("Time elapsed: %d seconds." % delta.seconds) AppRecommender-0.7.5/bin/popindex.py000077500000000000000000000034751306751311600174130ustar00rootroot00000000000000#!/usr/bin/env python """ popindex.py - generate a popcon index to be used by the recommender as the users repository, based on filters provided by config """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import sys sys.path.insert(0, '../') import logging import datetime from apprecommender.config import Config from apprecommender.data import PopconXapianIndex if __name__ == '__main__': cfg = Config() begin_time = datetime.datetime.now() logging.info("Popcon indexing started at %s" % begin_time) # use config file or command line options popindex = PopconXapianIndex(cfg) end_time = datetime.datetime.now() logging.info("Popcon indexing completed at %s" % end_time) logging.info("Number of documents (submissions): %d" % popindex.get_doccount()) delta = end_time - begin_time logging.info("Time elapsed: %d seconds." % delta.seconds) if cfg.index_mode == "cluster" or cfg.index_mode == "recluster": logging.info("Medoids: %d\tDispersion:%f" % (cfg.k_medoids, popindex.cluster_dispersion)) AppRecommender-0.7.5/bin/rank_terms.py000077500000000000000000000017241306751311600177250ustar00rootroot00000000000000#!/usr/bin/env python # # rank_terms.py - rank index terms by frequency import sys import xapian from operator import itemgetter if __name__ == '__main__': if "-h" in sys.argv or not len(sys.argv) == 4: print "\nUsage: rank_terms.py INDEX TERMS_FILE PREFIX\n" else: try: index = xapian.Database(sys.argv[1]) except: print "Could no open xapian index at %s" % sys.argv[1] try: with open(sys.argv[2]) as terms_file: terms_list = [line.strip() for line in terms_file] print terms_list frequencies = {} for term in terms_list: frequencies[term] = index.get_termfreq(sys.argv[3] + term) sorted_freqs = sorted(frequencies.items(), key=itemgetter(1)) except: print "Could not extract terms list from %s" % sys.argv[2] for term, freq in sorted_freqs: print term, str(freq) AppRecommender-0.7.5/bin/user_profiling.py000077500000000000000000000034231306751311600206050ustar00rootroot00000000000000#!/usr/bin/env python """ user_profiling - example script for testing user profiling """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ import sys import xapian sys.path.insert(0, '../') import apprecommender.user as user repo = xapian.Database("~/.app-recommender/axi_programs") user = user.LocalSystem() with open("~/.app-recommender/filters/debtags") as tags: valid_tags = [line.strip() for line in tags if not line.startswith("#")] size = 20 print "\nTF-IDF profiles" print "\nby tag: ", user.content_profile(repo, "tag", size, valid_tags) print "\nby desc: ", user.content_profile(repo, "desc", size) print "\nmix:", user.content_profile(repo, "mix", size, valid_tags) print "\nhalf", user.content_profile(repo, "half", size, valid_tags) print "\nEset profile" print "\nby tag: ", user.content_profile(repo, "tag_eset", size, valid_tags) print "\nby desc:", user.content_profile(repo, "desc_eset", size) print "\nmix: ", user.content_profile(repo, "mix_eset", size, valid_tags) print "\nhalf", user.content_profile(repo, "half_eset", size, valid_tags) AppRecommender-0.7.5/collect_data.sh000077500000000000000000000024441306751311600174100ustar00rootroot00000000000000#!/bin/bash echo "Para executar o AppRecommender as seguintes dependencias serao instaladas:" echo "" echo "python python-xapian python-apt python-cluster python-webpy" echo "python-simplejson python-numpy apt-xapian-index python-xdg debtags" echo "python-pip python-sklearn python-matplotlib python-stemmer" echo "" echo "Apos a instalação das dependencias os pacotes serao indexados ao xapian, que é o banco de dados utilizado pelo AppRecommender" echo "" cd bin/data_collect/ ./install_dependencies.sh cd - cd bin/ echo "" echo "Agora os dados do AppRecommender serao inicializados" ./apprec.py --init cd data_collect/ echo "" echo "Iniciando a coleta de dados" ./collect_user_data.py echo "" echo "Desinstalando as dependencias do AppRecommender" ./remove_dependencies.sh echo "" echo "" echo "" echo "Compacte o arquivo de log que está na home" echo "o nome do arquivo comeca com 'app_recommender_log'" echo "$ cd ~" echo "$ tar -zcvf [nome_da_pasta].tar.gz [nome_da_pasta]" echo "" echo "Envie o arquivo compactado para um dos seguintes emails:" echo "lucianopcbr@gmail.com" echo "lucas.moura128@gmail.com" echo "" echo "Como titulo do e-mail utilize 'coleta de dados'" echo "" echo "" echo "Obrigado por colaborar com nosso trabalho" echo "" echo "Att," echo "Lucas Moura e Luciano Prestes" echo "" AppRecommender-0.7.5/doc/000077500000000000000000000000001306751311600151745ustar00rootroot00000000000000AppRecommender-0.7.5/doc/doxy_config000066400000000000000000002117521306751311600174370ustar00rootroot00000000000000# Doxyfile 1.7.3 # This file describes the settings to be used by the documentation system # doxygen (www.doxygen.org) for a project. # # All text after a hash (#) is considered a comment and will be ignored. # The format is: # TAG = value [value, ...] # For lists items can also be appended using: # TAG += value [value, ...] # Values that contain spaces should be placed between quotes (" "). #--------------------------------------------------------------------------- # Project related configuration options #--------------------------------------------------------------------------- # This tag specifies the encoding used for all characters in the config file # that follow. The default is UTF-8 which is also the encoding used for all # text before the first occurrence of this tag. Doxygen uses libiconv (or the # iconv built into libc) for the transcoding. See # http://www.gnu.org/software/libiconv for the list of possible encodings. DOXYFILE_ENCODING = UTF-8 # The PROJECT_NAME tag is a single word (or a sequence of words surrounded # by quotes) that should identify the project. PROJECT_NAME = AppRecommender # The PROJECT_NUMBER tag can be used to enter a project or revision number. # This could be handy for archiving the generated documentation or # if some version control system is used. PROJECT_NUMBER = v0.3 # Using the PROJECT_BRIEF tag one can provide an optional one line description for a project that appears at the top of each page and should give viewer a quick idea about the purpose of the project. Keep the description short. PROJECT_BRIEF = # With the PROJECT_LOGO tag one can specify an logo or icon that is # included in the documentation. The maximum height of the logo should not # exceed 55 pixels and the maximum width should not exceed 200 pixels. # Doxygen will copy the logo to the output directory. PROJECT_LOGO = # The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) # base path where the generated documentation will be put. # If a relative path is entered, it will be relative to the location # where doxygen was started. If left blank the current directory will be used. OUTPUT_DIRECTORY = . # If the CREATE_SUBDIRS tag is set to YES, then doxygen will create # 4096 sub-directories (in 2 levels) under the output directory of each output # format and will distribute the generated files over these directories. # Enabling this option can be useful when feeding doxygen a huge amount of # source files, where putting all generated files in the same directory would # otherwise cause performance problems for the file system. CREATE_SUBDIRS = NO # The OUTPUT_LANGUAGE tag is used to specify the language in which all # documentation generated by doxygen is written. Doxygen will use this # information to generate all constant output in the proper language. # The default language is English, other supported languages are: # Afrikaans, Arabic, Brazilian, Catalan, Chinese, Chinese-Traditional, # Croatian, Czech, Danish, Dutch, Esperanto, Farsi, Finnish, French, German, # Greek, Hungarian, Italian, Japanese, Japanese-en (Japanese with English # messages), Korean, Korean-en, Lithuanian, Norwegian, Macedonian, Persian, # Polish, Portuguese, Romanian, Russian, Serbian, Serbian-Cyrillic, Slovak, # Slovene, Spanish, Swedish, Ukrainian, and Vietnamese. OUTPUT_LANGUAGE = English # If the BRIEF_MEMBER_DESC tag is set to YES (the default) Doxygen will # include brief member descriptions after the members that are listed in # the file and class documentation (similar to JavaDoc). # Set to NO to disable this. BRIEF_MEMBER_DESC = YES # If the REPEAT_BRIEF tag is set to YES (the default) Doxygen will prepend # the brief description of a member or function before the detailed description. # Note: if both HIDE_UNDOC_MEMBERS and BRIEF_MEMBER_DESC are set to NO, the # brief descriptions will be completely suppressed. REPEAT_BRIEF = YES # This tag implements a quasi-intelligent brief description abbreviator # that is used to form the text in various listings. Each string # in this list, if found as the leading text of the brief description, will be # stripped from the text and the result after processing the whole list, is # used as the annotated text. Otherwise, the brief description is used as-is. # If left blank, the following values are used ("$name" is automatically # replaced with the name of the entity): "The $name class" "The $name widget" # "The $name file" "is" "provides" "specifies" "contains" # "represents" "a" "an" "the" ABBREVIATE_BRIEF = # If the ALWAYS_DETAILED_SEC and REPEAT_BRIEF tags are both set to YES then # Doxygen will generate a detailed section even if there is only a brief # description. ALWAYS_DETAILED_SEC = NO # If the INLINE_INHERITED_MEMB tag is set to YES, doxygen will show all # inherited members of a class in the documentation of that class as if those # members were ordinary class members. Constructors, destructors and assignment # operators of the base classes will not be shown. INLINE_INHERITED_MEMB = NO # If the FULL_PATH_NAMES tag is set to YES then Doxygen will prepend the full # path before files name in the file list and in the header files. If set # to NO the shortest path that makes the file name unique will be used. FULL_PATH_NAMES = NO # If the FULL_PATH_NAMES tag is set to YES then the STRIP_FROM_PATH tag # can be used to strip a user-defined part of the path. Stripping is # only done if one of the specified strings matches the left-hand part of # the path. The tag can be used to show relative paths in the file list. # If left blank the directory from which doxygen is run is used as the # path to strip. STRIP_FROM_PATH = # The STRIP_FROM_INC_PATH tag can be used to strip a user-defined part of # the path mentioned in the documentation of a class, which tells # the reader which header file to include in order to use a class. # If left blank only the name of the header file containing the class # definition is used. Otherwise one should specify the include paths that # are normally passed to the compiler using the -I flag. STRIP_FROM_INC_PATH = # If the SHORT_NAMES tag is set to YES, doxygen will generate much shorter # (but less readable) file names. This can be useful if your file system # doesn't support long names like on DOS, Mac, or CD-ROM. SHORT_NAMES = NO # If the JAVADOC_AUTOBRIEF tag is set to YES then Doxygen # will interpret the first line (until the first dot) of a JavaDoc-style # comment as the brief description. If set to NO, the JavaDoc # comments will behave just like regular Qt-style comments # (thus requiring an explicit @brief command for a brief description.) JAVADOC_AUTOBRIEF = NO # If the QT_AUTOBRIEF tag is set to YES then Doxygen will # interpret the first line (until the first dot) of a Qt-style # comment as the brief description. If set to NO, the comments # will behave just like regular Qt-style comments (thus requiring # an explicit \brief command for a brief description.) QT_AUTOBRIEF = NO # The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make Doxygen # treat a multi-line C++ special comment block (i.e. a block of //! or /// # comments) as a brief description. This used to be the default behaviour. # The new default is to treat a multi-line C++ comment block as a detailed # description. Set this tag to YES if you prefer the old behaviour instead. MULTILINE_CPP_IS_BRIEF = NO # If the INHERIT_DOCS tag is set to YES (the default) then an undocumented # member inherits the documentation from any documented member that it # re-implements. INHERIT_DOCS = YES # If the SEPARATE_MEMBER_PAGES tag is set to YES, then doxygen will produce # a new page for each member. If set to NO, the documentation of a member will # be part of the file/class/namespace that contains it. SEPARATE_MEMBER_PAGES = NO # The TAB_SIZE tag can be used to set the number of spaces in a tab. # Doxygen uses this value to replace tabs by spaces in code fragments. TAB_SIZE = 4 # This tag can be used to specify a number of aliases that acts # as commands in the documentation. An alias has the form "name=value". # For example adding "sideeffect=\par Side Effects:\n" will allow you to # put the command \sideeffect (or @sideeffect) in the documentation, which # will result in a user-defined paragraph with heading "Side Effects:". # You can put \n's in the value part of an alias to insert newlines. ALIASES = # Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C # sources only. Doxygen will then generate output that is more tailored for C. # For instance, some of the names that are used will be different. The list # of all members will be omitted, etc. OPTIMIZE_OUTPUT_FOR_C = NO # Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java # sources only. Doxygen will then generate output that is more tailored for # Java. For instance, namespaces will be presented as packages, qualified # scopes will look different, etc. OPTIMIZE_OUTPUT_JAVA = NO # Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran # sources only. Doxygen will then generate output that is more tailored for # Fortran. OPTIMIZE_FOR_FORTRAN = NO # Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL # sources. Doxygen will then generate output that is tailored for # VHDL. OPTIMIZE_OUTPUT_VHDL = NO # Doxygen selects the parser to use depending on the extension of the files it # parses. With this tag you can assign which parser to use for a given extension. # Doxygen has a built-in mapping, but you can override or extend it using this # tag. The format is ext=language, where ext is a file extension, and language # is one of the parsers supported by doxygen: IDL, Java, Javascript, CSharp, C, # C++, D, PHP, Objective-C, Python, Fortran, VHDL, C, C++. For instance to make # doxygen treat .inc files as Fortran files (default is PHP), and .f files as C # (default is Fortran), use: inc=Fortran f=C. Note that for custom extensions # you also need to set FILE_PATTERNS otherwise the files are not read by doxygen. EXTENSION_MAPPING = # If you use STL classes (i.e. std::string, std::vector, etc.) but do not want # to include (a tag file for) the STL sources as input, then you should # set this tag to YES in order to let doxygen match functions declarations and # definitions whose arguments contain STL classes (e.g. func(std::string); v.s. # func(std::string) {}). This also makes the inheritance and collaboration # diagrams that involve STL classes more complete and accurate. BUILTIN_STL_SUPPORT = NO # If you use Microsoft's C++/CLI language, you should set this option to YES to # enable parsing support. CPP_CLI_SUPPORT = NO # Set the SIP_SUPPORT tag to YES if your project consists of sip sources only. # Doxygen will parse them like normal C++ but will assume all classes use public # instead of private inheritance when no explicit protection keyword is present. SIP_SUPPORT = NO # For Microsoft's IDL there are propget and propput attributes to indicate getter # and setter methods for a property. Setting this option to YES (the default) # will make doxygen replace the get and set methods by a property in the # documentation. This will only work if the methods are indeed getting or # setting a simple type. If this is not the case, or you want to show the # methods anyway, you should set this option to NO. IDL_PROPERTY_SUPPORT = YES # If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC # tag is set to YES, then doxygen will reuse the documentation of the first # member in the group (if any) for the other members of the group. By default # all members of a group must be documented explicitly. DISTRIBUTE_GROUP_DOC = NO # Set the SUBGROUPING tag to YES (the default) to allow class member groups of # the same type (for instance a group of public functions) to be put as a # subgroup of that type (e.g. under the Public Functions section). Set it to # NO to prevent subgrouping. Alternatively, this can be done per class using # the \nosubgrouping command. SUBGROUPING = YES # When TYPEDEF_HIDES_STRUCT is enabled, a typedef of a struct, union, or enum # is documented as struct, union, or enum with the name of the typedef. So # typedef struct TypeS {} TypeT, will appear in the documentation as a struct # with name TypeT. When disabled the typedef will appear as a member of a file, # namespace, or class. And the struct will be named TypeS. This can typically # be useful for C code in case the coding convention dictates that all compound # types are typedef'ed and only the typedef is referenced, never the tag name. TYPEDEF_HIDES_STRUCT = NO # The SYMBOL_CACHE_SIZE determines the size of the internal cache use to # determine which symbols to keep in memory and which to flush to disk. # When the cache is full, less often used symbols will be written to disk. # For small to medium size projects (<1000 input files) the default value is # probably good enough. For larger projects a too small cache size can cause # doxygen to be busy swapping symbols to and from disk most of the time # causing a significant performance penalty. # If the system has enough physical memory increasing the cache will improve the # performance by keeping more symbols in memory. Note that the value works on # a logarithmic scale so increasing the size by one will roughly double the # memory usage. The cache size is given by this formula: # 2^(16+SYMBOL_CACHE_SIZE). The valid range is 0..9, the default is 0, # corresponding to a cache size of 2^16 = 65536 symbols SYMBOL_CACHE_SIZE = 0 #--------------------------------------------------------------------------- # Build related configuration options #--------------------------------------------------------------------------- # If the EXTRACT_ALL tag is set to YES doxygen will assume all entities in # documentation are documented, even if no documentation was available. # Private class members and static file members will be hidden unless # the EXTRACT_PRIVATE and EXTRACT_STATIC tags are set to YES EXTRACT_ALL = NO # If the EXTRACT_PRIVATE tag is set to YES all private members of a class # will be included in the documentation. EXTRACT_PRIVATE = NO # If the EXTRACT_STATIC tag is set to YES all static members of a file # will be included in the documentation. EXTRACT_STATIC = NO # If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs) # defined locally in source files will be included in the documentation. # If set to NO only classes defined in header files are included. EXTRACT_LOCAL_CLASSES = YES # This flag is only useful for Objective-C code. When set to YES local # methods, which are defined in the implementation section but not in # the interface are included in the documentation. # If set to NO (the default) only methods in the interface are included. EXTRACT_LOCAL_METHODS = NO # If this flag is set to YES, the members of anonymous namespaces will be # extracted and appear in the documentation as a namespace called # 'anonymous_namespace{file}', where file will be replaced with the base # name of the file that contains the anonymous namespace. By default # anonymous namespaces are hidden. EXTRACT_ANON_NSPACES = NO # If the HIDE_UNDOC_MEMBERS tag is set to YES, Doxygen will hide all # undocumented members of documented classes, files or namespaces. # If set to NO (the default) these members will be included in the # various overviews, but no documentation section is generated. # This option has no effect if EXTRACT_ALL is enabled. HIDE_UNDOC_MEMBERS = NO # If the HIDE_UNDOC_CLASSES tag is set to YES, Doxygen will hide all # undocumented classes that are normally visible in the class hierarchy. # If set to NO (the default) these classes will be included in the various # overviews. This option has no effect if EXTRACT_ALL is enabled. HIDE_UNDOC_CLASSES = NO # If the HIDE_FRIEND_COMPOUNDS tag is set to YES, Doxygen will hide all # friend (class|struct|union) declarations. # If set to NO (the default) these declarations will be included in the # documentation. HIDE_FRIEND_COMPOUNDS = NO # If the HIDE_IN_BODY_DOCS tag is set to YES, Doxygen will hide any # documentation blocks found inside the body of a function. # If set to NO (the default) these blocks will be appended to the # function's detailed documentation block. HIDE_IN_BODY_DOCS = NO # The INTERNAL_DOCS tag determines if documentation # that is typed after a \internal command is included. If the tag is set # to NO (the default) then the documentation will be excluded. # Set it to YES to include the internal documentation. INTERNAL_DOCS = NO # If the CASE_SENSE_NAMES tag is set to NO then Doxygen will only generate # file names in lower-case letters. If set to YES upper-case letters are also # allowed. This is useful if you have classes or files whose names only differ # in case and if your file system supports case sensitive file names. Windows # and Mac users are advised to set this option to NO. CASE_SENSE_NAMES = YES # If the HIDE_SCOPE_NAMES tag is set to NO (the default) then Doxygen # will show members with their full class and namespace scopes in the # documentation. If set to YES the scope will be hidden. HIDE_SCOPE_NAMES = NO # If the SHOW_INCLUDE_FILES tag is set to YES (the default) then Doxygen # will put a list of the files that are included by a file in the documentation # of that file. SHOW_INCLUDE_FILES = YES # If the FORCE_LOCAL_INCLUDES tag is set to YES then Doxygen # will list include files with double quotes in the documentation # rather than with sharp brackets. FORCE_LOCAL_INCLUDES = NO # If the INLINE_INFO tag is set to YES (the default) then a tag [inline] # is inserted in the documentation for inline members. INLINE_INFO = YES # If the SORT_MEMBER_DOCS tag is set to YES (the default) then doxygen # will sort the (detailed) documentation of file and class members # alphabetically by member name. If set to NO the members will appear in # declaration order. SORT_MEMBER_DOCS = YES # If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the # brief documentation of file, namespace and class members alphabetically # by member name. If set to NO (the default) the members will appear in # declaration order. SORT_BRIEF_DOCS = NO # If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen # will sort the (brief and detailed) documentation of class members so that # constructors and destructors are listed first. If set to NO (the default) # the constructors will appear in the respective orders defined by # SORT_MEMBER_DOCS and SORT_BRIEF_DOCS. # This tag will be ignored for brief docs if SORT_BRIEF_DOCS is set to NO # and ignored for detailed docs if SORT_MEMBER_DOCS is set to NO. SORT_MEMBERS_CTORS_1ST = NO # If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the # hierarchy of group names into alphabetical order. If set to NO (the default) # the group names will appear in their defined order. SORT_GROUP_NAMES = NO # If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be # sorted by fully-qualified names, including namespaces. If set to # NO (the default), the class list will be sorted only by class name, # not including the namespace part. # Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES. # Note: This option applies only to the class list, not to the # alphabetical list. SORT_BY_SCOPE_NAME = NO # If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper type resolution of all parameters of a function it will reject a # match between the prototype and the implementation of a member function even if there is only one candidate or it is obvious which candidate to choose by doing a simple string match. By disabling STRICT_PROTO_MATCHING doxygen # will still accept a match between prototype and implementation in such cases. STRICT_PROTO_MATCHING = NO # The GENERATE_TODOLIST tag can be used to enable (YES) or # disable (NO) the todo list. This list is created by putting \todo # commands in the documentation. GENERATE_TODOLIST = YES # The GENERATE_TESTLIST tag can be used to enable (YES) or # disable (NO) the test list. This list is created by putting \test # commands in the documentation. GENERATE_TESTLIST = YES # The GENERATE_BUGLIST tag can be used to enable (YES) or # disable (NO) the bug list. This list is created by putting \bug # commands in the documentation. GENERATE_BUGLIST = YES # The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or # disable (NO) the deprecated list. This list is created by putting # \deprecated commands in the documentation. GENERATE_DEPRECATEDLIST= YES # The ENABLED_SECTIONS tag can be used to enable conditional # documentation sections, marked by \if sectionname ... \endif. ENABLED_SECTIONS = # The MAX_INITIALIZER_LINES tag determines the maximum number of lines # the initial value of a variable or macro consists of for it to appear in # the documentation. If the initializer consists of more lines than specified # here it will be hidden. Use a value of 0 to hide initializers completely. # The appearance of the initializer of individual variables and macros in the # documentation can be controlled using \showinitializer or \hideinitializer # command in the documentation regardless of this setting. MAX_INITIALIZER_LINES = 30 # Set the SHOW_USED_FILES tag to NO to disable the list of files generated # at the bottom of the documentation of classes and structs. If set to YES the # list will mention the files that were used to generate the documentation. SHOW_USED_FILES = YES # If the sources in your project are distributed over multiple directories # then setting the SHOW_DIRECTORIES tag to YES will show the directory hierarchy # in the documentation. The default is NO. SHOW_DIRECTORIES = NO # Set the SHOW_FILES tag to NO to disable the generation of the Files page. # This will remove the Files entry from the Quick Index and from the # Folder Tree View (if specified). The default is YES. SHOW_FILES = YES # Set the SHOW_NAMESPACES tag to NO to disable the generation of the # Namespaces page. # This will remove the Namespaces entry from the Quick Index # and from the Folder Tree View (if specified). The default is YES. SHOW_NAMESPACES = YES # The FILE_VERSION_FILTER tag can be used to specify a program or script that # doxygen should invoke to get the current version for each file (typically from # the version control system). Doxygen will invoke the program by executing (via # popen()) the command , where is the value of # the FILE_VERSION_FILTER tag, and is the name of an input file # provided by doxygen. Whatever the program writes to standard output # is used as the file version. See the manual for examples. FILE_VERSION_FILTER = # The LAYOUT_FILE tag can be used to specify a layout file which will be parsed # by doxygen. The layout file controls the global structure of the generated # output files in an output format independent way. The create the layout file # that represents doxygen's defaults, run doxygen with the -l option. # You can optionally specify a file name after the option, if omitted # DoxygenLayout.xml will be used as the name of the layout file. LAYOUT_FILE = #--------------------------------------------------------------------------- # configuration options related to warning and progress messages #--------------------------------------------------------------------------- # The QUIET tag can be used to turn on/off the messages that are generated # by doxygen. Possible values are YES and NO. If left blank NO is used. QUIET = NO # The WARNINGS tag can be used to turn on/off the warning messages that are # generated by doxygen. Possible values are YES and NO. If left blank # NO is used. WARNINGS = YES # If WARN_IF_UNDOCUMENTED is set to YES, then doxygen will generate warnings # for undocumented members. If EXTRACT_ALL is set to YES then this flag will # automatically be disabled. WARN_IF_UNDOCUMENTED = YES # If WARN_IF_DOC_ERROR is set to YES, doxygen will generate warnings for # potential errors in the documentation, such as not documenting some # parameters in a documented function, or documenting parameters that # don't exist or using markup commands wrongly. WARN_IF_DOC_ERROR = YES # The WARN_NO_PARAMDOC option can be enabled to get warnings for # functions that are documented, but have no documentation for their parameters # or return value. If set to NO (the default) doxygen will only warn about # wrong or incomplete parameter documentation, but not about the absence of # documentation. WARN_NO_PARAMDOC = NO # The WARN_FORMAT tag determines the format of the warning messages that # doxygen can produce. The string should contain the $file, $line, and $text # tags, which will be replaced by the file and line number from which the # warning originated and the warning text. Optionally the format may contain # $version, which will be replaced by the version of the file (if it could # be obtained via FILE_VERSION_FILTER) WARN_FORMAT = "$file:$line: $text" # The WARN_LOGFILE tag can be used to specify a file to which warning # and error messages should be written. If left blank the output is written # to stderr. WARN_LOGFILE = #--------------------------------------------------------------------------- # configuration options related to the input files #--------------------------------------------------------------------------- # The INPUT tag can be used to specify the files and/or directories that contain # documented source files. You may enter file names like "myfile.cpp" or # directories like "/usr/src/myproject". Separate the files or directories # with spaces. INPUT = ../src/ # This tag can be used to specify the character encoding of the source files # that doxygen parses. Internally doxygen uses the UTF-8 encoding, which is # also the default input encoding. Doxygen uses libiconv (or the iconv built # into libc) for the transcoding. See http://www.gnu.org/software/libiconv for # the list of possible encodings. INPUT_ENCODING = UTF-8 # If the value of the INPUT tag contains directories, you can use the # FILE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp # and *.h) to filter out the source-files in the directories. If left # blank the following patterns are tested: # *.c *.cc *.cxx *.cpp *.c++ *.d *.java *.ii *.ixx *.ipp *.i++ *.inl *.h *.hh # *.hxx *.hpp *.h++ *.idl *.odl *.cs *.php *.php3 *.inc *.m *.mm *.dox *.py # *.f90 *.f *.for *.vhd *.vhdl FILE_PATTERNS = # The RECURSIVE tag can be used to turn specify whether or not subdirectories # should be searched for input files as well. Possible values are YES and NO. # If left blank NO is used. RECURSIVE = YES # The EXCLUDE tag can be used to specify files and/or directories that should # excluded from the INPUT source files. This way you can easily exclude a # subdirectory from a directory tree whose root is specified with the INPUT tag. EXCLUDE = # The EXCLUDE_SYMLINKS tag can be used select whether or not files or # directories that are symbolic links (a Unix file system feature) are excluded # from the input. EXCLUDE_SYMLINKS = NO # If the value of the INPUT tag contains directories, you can use the # EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude # certain files from those directories. Note that the wildcards are matched # against the file with absolute path, so to exclude all test directories # for example use the pattern */test/* EXCLUDE_PATTERNS = # The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names # (namespaces, classes, functions, etc.) that should be excluded from the # output. The symbol name can be a fully qualified name, a word, or if the # wildcard * is used, a substring. Examples: ANamespace, AClass, # AClass::ANamespace, ANamespace::*Test EXCLUDE_SYMBOLS = # The EXAMPLE_PATH tag can be used to specify one or more files or # directories that contain example code fragments that are included (see # the \include command). EXAMPLE_PATH = # If the value of the EXAMPLE_PATH tag contains directories, you can use the # EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp # and *.h) to filter out the source-files in the directories. If left # blank all files are included. EXAMPLE_PATTERNS = # If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be # searched for input files to be used with the \include or \dontinclude # commands irrespective of the value of the RECURSIVE tag. # Possible values are YES and NO. If left blank NO is used. EXAMPLE_RECURSIVE = NO # The IMAGE_PATH tag can be used to specify one or more files or # directories that contain image that are included in the documentation (see # the \image command). IMAGE_PATH = # The INPUT_FILTER tag can be used to specify a program that doxygen should # invoke to filter for each input file. Doxygen will invoke the filter program # by executing (via popen()) the command , where # is the value of the INPUT_FILTER tag, and is the name of an # input file. Doxygen will then use the output that the filter program writes # to standard output. # If FILTER_PATTERNS is specified, this tag will be # ignored. INPUT_FILTER = # The FILTER_PATTERNS tag can be used to specify filters on a per file pattern # basis. # Doxygen will compare the file name with each pattern and apply the # filter if there is a match. # The filters are a list of the form: # pattern=filter (like *.cpp=my_cpp_filter). See INPUT_FILTER for further # info on how filters are used. If FILTER_PATTERNS is empty or if # non of the patterns match the file name, INPUT_FILTER is applied. FILTER_PATTERNS = # If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using # INPUT_FILTER) will be used to filter the input files when producing source # files to browse (i.e. when SOURCE_BROWSER is set to YES). FILTER_SOURCE_FILES = NO # The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file # pattern. A pattern will override the setting for FILTER_PATTERN (if any) # and it is also possible to disable source filtering for a specific pattern # using *.ext= (so without naming a filter). This option only has effect when # FILTER_SOURCE_FILES is enabled. FILTER_SOURCE_PATTERNS = #--------------------------------------------------------------------------- # configuration options related to source browsing #--------------------------------------------------------------------------- # If the SOURCE_BROWSER tag is set to YES then a list of source files will # be generated. Documented entities will be cross-referenced with these sources. # Note: To get rid of all source code in the generated output, make sure also # VERBATIM_HEADERS is set to NO. SOURCE_BROWSER = YES # Setting the INLINE_SOURCES tag to YES will include the body # of functions and classes directly in the documentation. INLINE_SOURCES = NO # Setting the STRIP_CODE_COMMENTS tag to YES (the default) will instruct # doxygen to hide any special comment blocks from generated source code # fragments. Normal C and C++ comments will always remain visible. STRIP_CODE_COMMENTS = YES # If the REFERENCED_BY_RELATION tag is set to YES # then for each documented function all documented # functions referencing it will be listed. REFERENCED_BY_RELATION = YES # If the REFERENCES_RELATION tag is set to YES # then for each documented function all documented entities # called/used by that function will be listed. REFERENCES_RELATION = YES # If the REFERENCES_LINK_SOURCE tag is set to YES (the default) # and SOURCE_BROWSER tag is set to YES, then the hyperlinks from # functions in REFERENCES_RELATION and REFERENCED_BY_RELATION lists will # link to the source code. # Otherwise they will link to the documentation. REFERENCES_LINK_SOURCE = YES # If the USE_HTAGS tag is set to YES then the references to source code # will point to the HTML generated by the htags(1) tool instead of doxygen # built-in source browser. The htags tool is part of GNU's global source # tagging system (see http://www.gnu.org/software/global/global.html). You # will need version 4.8.6 or higher. USE_HTAGS = NO # If the VERBATIM_HEADERS tag is set to YES (the default) then Doxygen # will generate a verbatim copy of the header file for each class for # which an include is specified. Set to NO to disable this. VERBATIM_HEADERS = YES #--------------------------------------------------------------------------- # configuration options related to the alphabetical class index #--------------------------------------------------------------------------- # If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index # of all compounds will be generated. Enable this if the project # contains a lot of classes, structs, unions or interfaces. ALPHABETICAL_INDEX = YES # If the alphabetical index is enabled (see ALPHABETICAL_INDEX) then # the COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns # in which this list will be split (can be a number in the range [1..20]) COLS_IN_ALPHA_INDEX = 5 # In case all classes in a project start with a common prefix, all # classes will be put under the same header in the alphabetical index. # The IGNORE_PREFIX tag can be used to specify one or more prefixes that # should be ignored while generating the index headers. IGNORE_PREFIX = #--------------------------------------------------------------------------- # configuration options related to the HTML output #--------------------------------------------------------------------------- # If the GENERATE_HTML tag is set to YES (the default) Doxygen will # generate HTML output. GENERATE_HTML = YES # The HTML_OUTPUT tag is used to specify where the HTML docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `html' will be used as the default path. HTML_OUTPUT = html # The HTML_FILE_EXTENSION tag can be used to specify the file extension for # each generated HTML page (for example: .htm,.php,.asp). If it is left blank # doxygen will generate files with .html extension. HTML_FILE_EXTENSION = .html # The HTML_HEADER tag can be used to specify a personal HTML header for # each generated HTML page. If it is left blank doxygen will generate a # standard header. HTML_HEADER = # The HTML_FOOTER tag can be used to specify a personal HTML footer for # each generated HTML page. If it is left blank doxygen will generate a # standard footer. HTML_FOOTER = # The HTML_STYLESHEET tag can be used to specify a user-defined cascading # style sheet that is used by each HTML page. It can be used to # fine-tune the look of the HTML output. If the tag is left blank doxygen # will generate a default style sheet. Note that doxygen will try to copy # the style sheet file to the HTML output directory, so don't put your own # stylesheet in the HTML output directory as well, or it will be erased! HTML_STYLESHEET = # The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. # Doxygen will adjust the colors in the stylesheet and background images # according to this color. Hue is specified as an angle on a colorwheel, # see http://en.wikipedia.org/wiki/Hue for more information. # For instance the value 0 represents red, 60 is yellow, 120 is green, # 180 is cyan, 240 is blue, 300 purple, and 360 is red again. # The allowed range is 0 to 359. HTML_COLORSTYLE_HUE = 220 # The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of # the colors in the HTML output. For a value of 0 the output will use # grayscales only. A value of 255 will produce the most vivid colors. HTML_COLORSTYLE_SAT = 100 # The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to # the luminance component of the colors in the HTML output. Values below # 100 gradually make the output lighter, whereas values above 100 make # the output darker. The value divided by 100 is the actual gamma applied, # so 80 represents a gamma of 0.8, The value 220 represents a gamma of 2.2, # and 100 does not change the gamma. HTML_COLORSTYLE_GAMMA = 80 # If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML # page will contain the date and time when the page was generated. Setting # this to NO can help when comparing the output of multiple runs. HTML_TIMESTAMP = YES # If the HTML_ALIGN_MEMBERS tag is set to YES, the members of classes, # files or namespaces will be aligned in HTML using tables. If set to # NO a bullet list will be used. HTML_ALIGN_MEMBERS = YES # If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML # documentation will contain sections that can be hidden and shown after the # page has loaded. For this to work a browser that supports # JavaScript and DHTML is required (for instance Mozilla 1.0+, Firefox # Netscape 6.0+, Internet explorer 5.0+, Konqueror, or Safari). HTML_DYNAMIC_SECTIONS = YES # If the GENERATE_DOCSET tag is set to YES, additional index files # will be generated that can be used as input for Apple's Xcode 3 # integrated development environment, introduced with OSX 10.5 (Leopard). # To create a documentation set, doxygen will generate a Makefile in the # HTML output directory. Running make will produce the docset in that # directory and running "make install" will install the docset in # ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find # it at startup. # See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html # for more information. GENERATE_DOCSET = NO # When GENERATE_DOCSET tag is set to YES, this tag determines the name of the # feed. A documentation feed provides an umbrella under which multiple # documentation sets from a single provider (such as a company or product suite) # can be grouped. DOCSET_FEEDNAME = "Doxygen generated docs" # When GENERATE_DOCSET tag is set to YES, this tag specifies a string that # should uniquely identify the documentation set bundle. This should be a # reverse domain-name style string, e.g. com.mycompany.MyDocSet. Doxygen # will append .docset to the name. DOCSET_BUNDLE_ID = org.doxygen.Project # When GENERATE_PUBLISHER_ID tag specifies a string that should uniquely identify # the documentation publisher. This should be a reverse domain-name style # string, e.g. com.mycompany.MyDocSet.documentation. DOCSET_PUBLISHER_ID = org.doxygen.Publisher # The GENERATE_PUBLISHER_NAME tag identifies the documentation publisher. DOCSET_PUBLISHER_NAME = Publisher # If the GENERATE_HTMLHELP tag is set to YES, additional index files # will be generated that can be used as input for tools like the # Microsoft HTML help workshop to generate a compiled HTML help file (.chm) # of the generated HTML documentation. GENERATE_HTMLHELP = NO # If the GENERATE_HTMLHELP tag is set to YES, the CHM_FILE tag can # be used to specify the file name of the resulting .chm file. You # can add a path in front of the file if the result should not be # written to the html output directory. CHM_FILE = # If the GENERATE_HTMLHELP tag is set to YES, the HHC_LOCATION tag can # be used to specify the location (absolute path including file name) of # the HTML help compiler (hhc.exe). If non-empty doxygen will try to run # the HTML help compiler on the generated index.hhp. HHC_LOCATION = # If the GENERATE_HTMLHELP tag is set to YES, the GENERATE_CHI flag # controls if a separate .chi index file is generated (YES) or that # it should be included in the master .chm file (NO). GENERATE_CHI = NO # If the GENERATE_HTMLHELP tag is set to YES, the CHM_INDEX_ENCODING # is used to encode HtmlHelp index (hhk), content (hhc) and project file # content. CHM_INDEX_ENCODING = # If the GENERATE_HTMLHELP tag is set to YES, the BINARY_TOC flag # controls whether a binary table of contents is generated (YES) or a # normal table of contents (NO) in the .chm file. BINARY_TOC = NO # The TOC_EXPAND flag can be set to YES to add extra items for group members # to the contents of the HTML help documentation and to the tree view. TOC_EXPAND = NO # If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and # QHP_VIRTUAL_FOLDER are set, an additional index file will be generated # that can be used as input for Qt's qhelpgenerator to generate a # Qt Compressed Help (.qch) of the generated HTML documentation. GENERATE_QHP = NO # If the QHG_LOCATION tag is specified, the QCH_FILE tag can # be used to specify the file name of the resulting .qch file. # The path specified is relative to the HTML output folder. QCH_FILE = # The QHP_NAMESPACE tag specifies the namespace to use when generating # Qt Help Project output. For more information please see # http://doc.trolltech.com/qthelpproject.html#namespace QHP_NAMESPACE = org.doxygen.Project # The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating # Qt Help Project output. For more information please see # http://doc.trolltech.com/qthelpproject.html#virtual-folders QHP_VIRTUAL_FOLDER = doc # If QHP_CUST_FILTER_NAME is set, it specifies the name of a custom filter to # add. For more information please see # http://doc.trolltech.com/qthelpproject.html#custom-filters QHP_CUST_FILTER_NAME = # The QHP_CUST_FILT_ATTRS tag specifies the list of the attributes of the # custom filter to add. For more information please see # # Qt Help Project / Custom Filters. QHP_CUST_FILTER_ATTRS = # The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this # project's # filter section matches. # # Qt Help Project / Filter Attributes. QHP_SECT_FILTER_ATTRS = # If the GENERATE_QHP tag is set to YES, the QHG_LOCATION tag can # be used to specify the location of Qt's qhelpgenerator. # If non-empty doxygen will try to run qhelpgenerator on the generated # .qhp file. QHG_LOCATION = # If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files # will be generated, which together with the HTML files, form an Eclipse help # plugin. To install this plugin and make it available under the help contents # menu in Eclipse, the contents of the directory containing the HTML and XML # files needs to be copied into the plugins directory of eclipse. The name of # the directory within the plugins directory should be the same as # the ECLIPSE_DOC_ID value. After copying Eclipse needs to be restarted before # the help appears. GENERATE_ECLIPSEHELP = NO # A unique identifier for the eclipse help plugin. When installing the plugin # the directory name containing the HTML and XML files should also have # this name. ECLIPSE_DOC_ID = org.doxygen.Project # The DISABLE_INDEX tag can be used to turn on/off the condensed index at # top of each HTML page. The value NO (the default) enables the index and # the value YES disables it. DISABLE_INDEX = NO # This tag can be used to set the number of enum values (range [0,1..20]) # that doxygen will group on one line in the generated HTML documentation. # Note that a value of 0 will completely suppress the enum values from appearing in the overview section. ENUM_VALUES_PER_LINE = 4 # The GENERATE_TREEVIEW tag is used to specify whether a tree-like index # structure should be generated to display hierarchical information. # If the tag value is set to YES, a side panel will be generated # containing a tree-like index structure (just like the one that # is generated for HTML Help). For this to work a browser that supports # JavaScript, DHTML, CSS and frames is required (i.e. any modern browser). # Windows users are probably better off using the HTML help feature. GENERATE_TREEVIEW = NO # By enabling USE_INLINE_TREES, doxygen will generate the Groups, Directories, # and Class Hierarchy pages using a tree view instead of an ordered list. USE_INLINE_TREES = YES # If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be # used to set the initial width (in pixels) of the frame in which the tree # is shown. TREEVIEW_WIDTH = 250 # When the EXT_LINKS_IN_WINDOW option is set to YES doxygen will open # links to external symbols imported via tag files in a separate window. EXT_LINKS_IN_WINDOW = NO # Use this tag to change the font size of Latex formulas included # as images in the HTML documentation. The default is 10. Note that # when you change the font size after a successful doxygen run you need # to manually remove any form_*.png images from the HTML output directory # to force them to be regenerated. FORMULA_FONTSIZE = 10 # Use the FORMULA_TRANPARENT tag to determine whether or not the images # generated for formulas are transparent PNGs. Transparent PNGs are # not supported properly for IE 6.0, but are supported on all modern browsers. # Note that when changing this option you need to delete any form_*.png files # in the HTML output before the changes have effect. FORMULA_TRANSPARENT = YES # Enable the USE_MATHJAX option to render LaTeX formulas using MathJax # (see http://www.mathjax.org) which uses client side Javascript for the # rendering instead of using prerendered bitmaps. Use this if you do not # have LaTeX installed or if you want to formulas look prettier in the HTML # output. When enabled you also need to install MathJax separately and # configure the path to it using the MATHJAX_RELPATH option. USE_MATHJAX = NO # When MathJax is enabled you need to specify the location relative to the # HTML output directory using the MATHJAX_RELPATH option. The destination # directory should contain the MathJax.js script. For instance, if the mathjax # directory is located at the same level as the HTML output directory, then # MATHJAX_RELPATH should be ../mathjax. The default value points to the mathjax.org site, so you can quickly see the result without installing # MathJax, but it is strongly recommended to install a local copy of MathJax # before deployment. MATHJAX_RELPATH = http://www.mathjax.org/mathjax # When the SEARCHENGINE tag is enabled doxygen will generate a search box # for the HTML output. The underlying search engine uses javascript # and DHTML and should work on any modern browser. Note that when using # HTML help (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets # (GENERATE_DOCSET) there is already a search function so this one should # typically be disabled. For large projects the javascript based search engine # can be slow, then enabling SERVER_BASED_SEARCH may provide a better solution. SEARCHENGINE = YES # When the SERVER_BASED_SEARCH tag is enabled the search engine will be # implemented using a PHP enabled web server instead of at the web client # using Javascript. Doxygen will generate the search PHP script and index # file to put on the web server. The advantage of the server # based approach is that it scales better to large projects and allows # full text search. The disadvantages are that it is more difficult to setup # and does not have live searching capabilities. SERVER_BASED_SEARCH = NO #--------------------------------------------------------------------------- # configuration options related to the LaTeX output #--------------------------------------------------------------------------- # If the GENERATE_LATEX tag is set to YES (the default) Doxygen will # generate Latex output. GENERATE_LATEX = NO # The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `latex' will be used as the default path. LATEX_OUTPUT = latex # The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be # invoked. If left blank `latex' will be used as the default command name. # Note that when enabling USE_PDFLATEX this option is only used for # generating bitmaps for formulas in the HTML output, but not in the # Makefile that is written to the output directory. LATEX_CMD_NAME = latex # The MAKEINDEX_CMD_NAME tag can be used to specify the command name to # generate index for LaTeX. If left blank `makeindex' will be used as the # default command name. MAKEINDEX_CMD_NAME = makeindex # If the COMPACT_LATEX tag is set to YES Doxygen generates more compact # LaTeX documents. This may be useful for small projects and may help to # save some trees in general. COMPACT_LATEX = NO # The PAPER_TYPE tag can be used to set the paper type that is used # by the printer. Possible values are: a4, letter, legal and # executive. If left blank a4wide will be used. PAPER_TYPE = a4wide # The EXTRA_PACKAGES tag can be to specify one or more names of LaTeX # packages that should be included in the LaTeX output. EXTRA_PACKAGES = # The LATEX_HEADER tag can be used to specify a personal LaTeX header for # the generated latex document. The header should contain everything until # the first chapter. If it is left blank doxygen will generate a # standard header. Notice: only use this tag if you know what you are doing! LATEX_HEADER = # If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated # is prepared for conversion to pdf (using ps2pdf). The pdf file will # contain links (just like the HTML output) instead of page references # This makes the output suitable for online browsing using a pdf viewer. PDF_HYPERLINKS = YES # If the USE_PDFLATEX tag is set to YES, pdflatex will be used instead of # plain latex in the generated Makefile. Set this option to YES to get a # higher quality PDF documentation. USE_PDFLATEX = YES # If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \\batchmode. # command to the generated LaTeX files. This will instruct LaTeX to keep # running if errors occur, instead of asking the user for help. # This option is also used when generating formulas in HTML. LATEX_BATCHMODE = NO # If LATEX_HIDE_INDICES is set to YES then doxygen will not # include the index chapters (such as File Index, Compound Index, etc.) # in the output. LATEX_HIDE_INDICES = NO # If LATEX_SOURCE_CODE is set to YES then doxygen will include # source code with syntax highlighting in the LaTeX output. # Note that which sources are shown also depends on other settings # such as SOURCE_BROWSER. LATEX_SOURCE_CODE = NO #--------------------------------------------------------------------------- # configuration options related to the RTF output #--------------------------------------------------------------------------- # If the GENERATE_RTF tag is set to YES Doxygen will generate RTF output # The RTF output is optimized for Word 97 and may not look very pretty with # other RTF readers or editors. GENERATE_RTF = NO # The RTF_OUTPUT tag is used to specify where the RTF docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `rtf' will be used as the default path. RTF_OUTPUT = rtf # If the COMPACT_RTF tag is set to YES Doxygen generates more compact # RTF documents. This may be useful for small projects and may help to # save some trees in general. COMPACT_RTF = NO # If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated # will contain hyperlink fields. The RTF file will # contain links (just like the HTML output) instead of page references. # This makes the output suitable for online browsing using WORD or other # programs which support those fields. # Note: wordpad (write) and others do not support links. RTF_HYPERLINKS = NO # Load stylesheet definitions from file. Syntax is similar to doxygen's # config file, i.e. a series of assignments. You only have to provide # replacements, missing definitions are set to their default value. RTF_STYLESHEET_FILE = # Set optional variables used in the generation of an rtf document. # Syntax is similar to doxygen's config file. RTF_EXTENSIONS_FILE = #--------------------------------------------------------------------------- # configuration options related to the man page output #--------------------------------------------------------------------------- # If the GENERATE_MAN tag is set to YES (the default) Doxygen will # generate man pages GENERATE_MAN = NO # The MAN_OUTPUT tag is used to specify where the man pages will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `man' will be used as the default path. MAN_OUTPUT = man # The MAN_EXTENSION tag determines the extension that is added to # the generated man pages (default is the subroutine's section .3) MAN_EXTENSION = .3 # If the MAN_LINKS tag is set to YES and Doxygen generates man output, # then it will generate one additional man file for each entity # documented in the real man page(s). These additional files # only source the real man page, but without them the man command # would be unable to find the correct page. The default is NO. MAN_LINKS = NO #--------------------------------------------------------------------------- # configuration options related to the XML output #--------------------------------------------------------------------------- # If the GENERATE_XML tag is set to YES Doxygen will # generate an XML file that captures the structure of # the code including all documentation. GENERATE_XML = NO # The XML_OUTPUT tag is used to specify where the XML pages will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `xml' will be used as the default path. XML_OUTPUT = xml # The XML_SCHEMA tag can be used to specify an XML schema, # which can be used by a validating XML parser to check the # syntax of the XML files. XML_SCHEMA = # The XML_DTD tag can be used to specify an XML DTD, # which can be used by a validating XML parser to check the # syntax of the XML files. XML_DTD = # If the XML_PROGRAMLISTING tag is set to YES Doxygen will # dump the program listings (including syntax highlighting # and cross-referencing information) to the XML output. Note that # enabling this will significantly increase the size of the XML output. XML_PROGRAMLISTING = YES #--------------------------------------------------------------------------- # configuration options for the AutoGen Definitions output #--------------------------------------------------------------------------- # If the GENERATE_AUTOGEN_DEF tag is set to YES Doxygen will # generate an AutoGen Definitions (see autogen.sf.net) file # that captures the structure of the code including all # documentation. Note that this feature is still experimental # and incomplete at the moment. GENERATE_AUTOGEN_DEF = NO #--------------------------------------------------------------------------- # configuration options related to the Perl module output #--------------------------------------------------------------------------- # If the GENERATE_PERLMOD tag is set to YES Doxygen will # generate a Perl module file that captures the structure of # the code including all documentation. Note that this # feature is still experimental and incomplete at the # moment. GENERATE_PERLMOD = NO # If the PERLMOD_LATEX tag is set to YES Doxygen will generate # the necessary Makefile rules, Perl scripts and LaTeX code to be able # to generate PDF and DVI output from the Perl module output. PERLMOD_LATEX = NO # If the PERLMOD_PRETTY tag is set to YES the Perl module output will be # nicely formatted so it can be parsed by a human reader. # This is useful # if you want to understand what is going on. # On the other hand, if this # tag is set to NO the size of the Perl module output will be much smaller # and Perl will parse it just the same. PERLMOD_PRETTY = YES # The names of the make variables in the generated doxyrules.make file # are prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. # This is useful so different doxyrules.make files included by the same # Makefile don't overwrite each other's variables. PERLMOD_MAKEVAR_PREFIX = #--------------------------------------------------------------------------- # Configuration options related to the preprocessor #--------------------------------------------------------------------------- # If the ENABLE_PREPROCESSING tag is set to YES (the default) Doxygen will # evaluate all C-preprocessor directives found in the sources and include # files. ENABLE_PREPROCESSING = YES # If the MACRO_EXPANSION tag is set to YES Doxygen will expand all macro # names in the source code. If set to NO (the default) only conditional # compilation will be performed. Macro expansion can be done in a controlled # way by setting EXPAND_ONLY_PREDEF to YES. MACRO_EXPANSION = NO # If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES # then the macro expansion is limited to the macros specified with the # PREDEFINED and EXPAND_AS_DEFINED tags. EXPAND_ONLY_PREDEF = NO # If the SEARCH_INCLUDES tag is set to YES (the default) the includes files # in the INCLUDE_PATH (see below) will be search if a #include is found. SEARCH_INCLUDES = YES # The INCLUDE_PATH tag can be used to specify one or more directories that # contain include files that are not input files but should be processed by # the preprocessor. INCLUDE_PATH = # You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard # patterns (like *.h and *.hpp) to filter out the header-files in the # directories. If left blank, the patterns specified with FILE_PATTERNS will # be used. INCLUDE_FILE_PATTERNS = # The PREDEFINED tag can be used to specify one or more macro names that # are defined before the preprocessor is started (similar to the -D option of # gcc). The argument of the tag is a list of macros of the form: name # or name=definition (no spaces). If the definition and the = are # omitted =1 is assumed. To prevent a macro definition from being # undefined via #undef or recursively expanded use the := operator # instead of the = operator. PREDEFINED = # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then # this tag can be used to specify a list of macro names that should be expanded. # The macro definition that is found in the sources will be used. # Use the PREDEFINED tag if you want to use a different macro definition that overrules the definition found in the source code. EXPAND_AS_DEFINED = # If the SKIP_FUNCTION_MACROS tag is set to YES (the default) then # doxygen's preprocessor will remove all references to function-like macros # that are alone on a line, have an all uppercase name, and do not end with a # semicolon, because these will confuse the parser if not removed. SKIP_FUNCTION_MACROS = YES #--------------------------------------------------------------------------- # Configuration::additions related to external references #--------------------------------------------------------------------------- # The TAGFILES option can be used to specify one or more tagfiles. # Optionally an initial location of the external documentation # can be added for each tagfile. The format of a tag file without # this location is as follows: # # TAGFILES = file1 file2 ... # Adding location for the tag files is done as follows: # # TAGFILES = file1=loc1 "file2 = loc2" ... # where "loc1" and "loc2" can be relative or absolute paths or # URLs. If a location is present for each tag, the installdox tool # does not have to be run to correct the links. # Note that each tag file must have a unique name # (where the name does NOT include the path) # If a tag file is not located in the directory in which doxygen # is run, you must also specify the path to the tagfile here. TAGFILES = # When a file name is specified after GENERATE_TAGFILE, doxygen will create # a tag file that is based on the input files it reads. GENERATE_TAGFILE = # If the ALLEXTERNALS tag is set to YES all external classes will be listed # in the class index. If set to NO only the inherited external classes # will be listed. ALLEXTERNALS = NO # If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed # in the modules index. If set to NO, only the current project's groups will # be listed. EXTERNAL_GROUPS = YES # The PERL_PATH should be the absolute path and name of the perl script # interpreter (i.e. the result of `which perl'). PERL_PATH = /usr/bin/perl #--------------------------------------------------------------------------- # Configuration options related to the dot tool #--------------------------------------------------------------------------- # If the CLASS_DIAGRAMS tag is set to YES (the default) Doxygen will # generate a inheritance diagram (in HTML, RTF and LaTeX) for classes with base # or super classes. Setting the tag to NO turns the diagrams off. Note that # this option also works with HAVE_DOT disabled, but it is recommended to # install and use dot, since it yields more powerful graphs. CLASS_DIAGRAMS = YES # You can define message sequence charts within doxygen comments using the \msc # command. Doxygen will then run the mscgen tool (see # http://www.mcternan.me.uk/mscgen/) to produce the chart and insert it in the # documentation. The MSCGEN_PATH tag allows you to specify the directory where # the mscgen tool resides. If left empty the tool is assumed to be found in the # default search path. MSCGEN_PATH = # If set to YES, the inheritance and collaboration graphs will hide # inheritance and usage relations if the target is undocumented # or is not a class. HIDE_UNDOC_RELATIONS = YES # If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is # available from the path. This tool is part of Graphviz, a graph visualization # toolkit from AT&T and Lucent Bell Labs. The other options in this section # have no effect if this option is set to NO (the default) HAVE_DOT = YES # The DOT_NUM_THREADS specifies the number of dot invocations doxygen is # allowed to run in parallel. When set to 0 (the default) doxygen will # base this on the number of processors available in the system. You can set it # explicitly to a value larger than 0 to get control over the balance # between CPU load and processing speed. DOT_NUM_THREADS = 0 # By default doxygen will write a font called Helvetica to the output # directory and reference it in all dot files that doxygen generates. # When you want a differently looking font you can specify the font name # using DOT_FONTNAME. You need to make sure dot is able to find the font, # which can be done by putting it in a standard location or by setting the # DOTFONTPATH environment variable or by setting DOT_FONTPATH to the directory # containing the font. DOT_FONTNAME = FreeSans.ttf # The DOT_FONTSIZE tag can be used to set the size of the font of dot graphs. # The default size is 10pt. DOT_FONTSIZE = 10 # By default doxygen will tell dot to use the output directory to look for the # FreeSans.ttf font (which doxygen will put there itself). If you specify a # different font using DOT_FONTNAME you can set the path where dot # can find it using this tag. DOT_FONTPATH = # If the CLASS_GRAPH and HAVE_DOT tags are set to YES then doxygen # will generate a graph for each documented class showing the direct and # indirect inheritance relations. Setting this tag to YES will force the # the CLASS_DIAGRAMS tag to NO. CLASS_GRAPH = YES # If the COLLABORATION_GRAPH and HAVE_DOT tags are set to YES then doxygen # will generate a graph for each documented class showing the direct and # indirect implementation dependencies (inheritance, containment, and # class references variables) of the class with other documented classes. COLLABORATION_GRAPH = YES # If the GROUP_GRAPHS and HAVE_DOT tags are set to YES then doxygen # will generate a graph for groups, showing the direct groups dependencies GROUP_GRAPHS = YES # If the UML_LOOK tag is set to YES doxygen will generate inheritance and # collaboration diagrams in a style similar to the OMG's Unified Modeling # Language. UML_LOOK = YES # If set to YES, the inheritance and collaboration graphs will show the # relations between templates and their instances. TEMPLATE_RELATIONS = NO # If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDE_GRAPH, and HAVE_DOT # tags are set to YES then doxygen will generate a graph for each documented # file showing the direct and indirect include dependencies of the file with # other documented files. INCLUDE_GRAPH = YES # If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDED_BY_GRAPH, and # HAVE_DOT tags are set to YES then doxygen will generate a graph for each # documented header file showing the documented files that directly or # indirectly include this file. INCLUDED_BY_GRAPH = YES # If the CALL_GRAPH and HAVE_DOT options are set to YES then # doxygen will generate a call dependency graph for every global function # or class method. Note that enabling this option will significantly increase # the time of a run. So in most cases it will be better to enable call graphs # for selected functions only using the \callgraph command. CALL_GRAPH = NO # If the CALLER_GRAPH and HAVE_DOT tags are set to YES then # doxygen will generate a caller dependency graph for every global function # or class method. Note that enabling this option will significantly increase # the time of a run. So in most cases it will be better to enable caller # graphs for selected functions only using the \callergraph command. CALLER_GRAPH = NO # If the GRAPHICAL_HIERARCHY and HAVE_DOT tags are set to YES then doxygen # will generate a graphical hierarchy of all classes instead of a textual one. GRAPHICAL_HIERARCHY = YES # If the DIRECTORY_GRAPH, SHOW_DIRECTORIES and HAVE_DOT tags are set to YES # then doxygen will show the dependencies a directory has on other directories # in a graphical way. The dependency relations are determined by the #include # relations between the files in the directories. DIRECTORY_GRAPH = YES # The DOT_IMAGE_FORMAT tag can be used to set the image format of the images # generated by dot. Possible values are png, svg, gif or svg. # If left blank png will be used. DOT_IMAGE_FORMAT = png # The tag DOT_PATH can be used to specify the path where the dot tool can be # found. If left blank, it is assumed the dot tool can be found in the path. DOT_PATH = # The DOTFILE_DIRS tag can be used to specify one or more directories that # contain dot files that are included in the documentation (see the # \dotfile command). DOTFILE_DIRS = # The MSCFILE_DIRS tag can be used to specify one or more directories that # contain msc files that are included in the documentation (see the # \mscfile command). MSCFILE_DIRS = # The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of # nodes that will be shown in the graph. If the number of nodes in a graph # becomes larger than this value, doxygen will truncate the graph, which is # visualized by representing a node as a red box. Note that doxygen if the # number of direct children of the root node in a graph is already larger than # DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note # that the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH. DOT_GRAPH_MAX_NODES = 50 # The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the # graphs generated by dot. A depth value of 3 means that only nodes reachable # from the root by following a path via at most 3 edges will be shown. Nodes # that lay further from the root node will be omitted. Note that setting this # option to 1 or 2 may greatly reduce the computation time needed for large # code bases. Also note that the size of a graph can be further restricted by # DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction. MAX_DOT_GRAPH_DEPTH = 0 # Set the DOT_TRANSPARENT tag to YES to generate images with a transparent # background. This is disabled by default, because dot on Windows does not # seem to support this out of the box. Warning: Depending on the platform used, # enabling this option may lead to badly anti-aliased labels on the edges of # a graph (i.e. they become hard to read). DOT_TRANSPARENT = NO # Set the DOT_MULTI_TARGETS tag to YES allow dot to generate multiple output # files in one run (i.e. multiple -o and -T options on the command line). This # makes dot run faster, but since only newer versions of dot (>1.8.10) # support this, this feature is disabled by default. DOT_MULTI_TARGETS = YES # If the GENERATE_LEGEND tag is set to YES (the default) Doxygen will # generate a legend page explaining the meaning of the various boxes and # arrows in the dot generated graphs. GENERATE_LEGEND = YES # If the DOT_CLEANUP tag is set to YES (the default) Doxygen will # remove the intermediate dot files that are used to generate # the various graphs. DOT_CLEANUP = YES AppRecommender-0.7.5/doc/header-python000077500000000000000000000015131306751311600176710ustar00rootroot00000000000000#!/usr/bin/env python """ Program - Description """ __author__ = "Tassia Camoes Araujo " __copyright__ = "Copyright (C) 2011 Tassia Camoes Araujo" __license__ = """ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. 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, see . """ AppRecommender-0.7.5/fig/000077500000000000000000000000001306751311600151745ustar00rootroot00000000000000AppRecommender-0.7.5/fig/app_rec.dia000066400000000000000000000202611306751311600172650ustar00rootroot00000000000000]r8}px^m2ꉞkvLGƾ)h%sKT).&hK>(,<H$d&_W_4ՇkJh5Kj<-J>\?l6},pdoI_\w]]}{y Mo7*|>\߇ϋ4ٮ׻VvdW_}*;:O˹"Os}NiI&uIyߴٷT ~/窻Fc.UGr7152=>pqqlNMƛ*},pCݤۨ?N6 kր^|7 Y>~m3w{Ԣ,_aڝٿY|>^mw/|vgk|=HFR(;i8þݩQ/:0J/b>ED N~^jF*m/߾fWW?.,~A?\9rԙ:yy׻e0?#Q#M8eT"\- Cqx/i@-,IWUx? 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I@HB$ $$Iyt| 3ggCFD8{s~EpBN\Hץ7j/]AWJ\Fkx'L7iZƛ0ۦn [Br  7@nrMᙌՠq!F ՠBl!|! n'̏I\^0u P.@]u2TQ2ٌˉ2^G2 hHD$bX[6ͭqCL4~I+# Q #1wPa$V*l_]`dM|]Z[EhaR(xm?wl&;&ƀephZ£ o? GX|]qpɍ8QupI!(LCC#G?B#GB$4\aUx9e@o|u ZG%/ݿUl 4@hB  4@hBGtgP͈7(S) J#Ж G45"Q4Ԝ_u3lwط+YpkZjjj7YmV[\ &蝿(Aͮi ݔB͘' Ю4X,ee.77yqgv9{HNO2u@|d'0:Mr?nx͓EB /#0MRj߅Qw]uceI7=ҋ~$ Pp5w)6(?vc˨ճZw(ִk<ƊoߴJv+.eؖ mĢ7&HIO޵Q눝rDٶpvpppu =AYtr(C1J\WNpIslQqrwSR!wѬ}G׀wKxr_&.G46 YOzB'd=! YOzB' f< l fP5ģDF܆mvqc~HlpЪP'Ѐxtt6IMM7m|M1o 7-"Aoz 7@ozn+p En@]]!1"78ҝx ON(ܕ`ܔysz xv7@oz 7@o@ƐJNy0!b0"&D+ )2mAcF赦Fe/-~p6*}r %jAڛEv1K[T3x AXi ڳ,ٰR~6_j|WF+m]Ơ_566v.?BJv:OQuUQunyy^P'oCWgȠR |_o|(ִKiuOTW%jb+qU.?oEݼ^W0Kܢw>Qބ&ь9au0s}18#<[hg̈_h_о}A /h_о}]ug d5Uz+F5+d(9J/|077WRݖ{Y޸~e0e-FںGo*/o`ybs&}MlLǺ,x2p[5n1uzn̓ٲ͔Z7k2f]/1Ͷ$Ni jv#Zoȟ+`ЄweF2l$ؕztǥmxx oÆÆ=~|cP0Ip ^ߗ,p , R}]&fN 9zLi(A65"JQD %(AD "JQdS#ȦF65 #QV0ҩGJ/{FO07 [7W^F:9ZKRc|]FE4!|A /_ |A /_ |Ahp 1įѫSu10)3}ǠYlT9_LJW(]/9wP?cВZ:0 8.̼v,SF!˒Y\~>zGakO>]?3[Cux 02ltN&y.7:ś'(/~e;il].܏qmޅ{[wV!B9*p_zK4ʋ S?q{NVj.Wm:}2W^q[Kye4:&_ILQ)J~_c-Nkt<;wwhQCY(aA n˜/=Ns VdT WЮCoFMcܔyVM5jx*M{HCr!Ձ>!,eu!C=$8+|("y3jY.pEjǧHZmY^?jb^0Qޑŏ`Es(9skE]5@bQ"rjV(Wzgl5b[[F(wdJ ~4I_A{V)Fj%^ɛ[.˨9wUZZD/fJQV>UIT/yD4ks.qj:q 4T3̞&)ڨk^TmPսLZzetkyMs^=feC]}0ސښ]:zF֜:]'t 6װ\} }cM.ie֬l*z5W;rVc4/CZxj3:Z[`Oj.we9^YKV'Ț ύXJ桱D[)>+a^5: PS&k, 5 )3;M888:g ޴>%& &eL-$\=ל[&ܤX9C:g69ǜy'jW |Vt%ol#nTa@`@`@`@4ZF_M?#ؒ %z׆3J宍p)#O3ȝ& 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