deap-0.7.1/0000755000076500000240000000000011650301263012657 5ustar felixstaff00000000000000deap-0.7.1/CHANGELOG.txt0000644000076500000240000000132011641072614014710 0ustar felixstaff000000000000002011-06-01 Reorganized modules in a more permanent API. 2011-05-23 Modified structure of the project so that deap include both dtm and eap. 2011-04-02 Added support for numpy.ndarray in the creator. 2011-03-20 Renamed the nsga2 and spea2 selections to selNSGA2 and selSPEA2. 2011-03-19 Moved all the operators from the toolbox to a new operators module. Also, this new module contains the other operators that are the History, Hall of Fame, Stats and Milestone. 2011-03-19 Changed version number in eap/__init__.py file. 2011-03-17 Tagged revision 0.7-a1. 2011-03-17 Added the Hillis coevolution example. 2011-03-17 Added the CHANGELOG file to log every change in DEAP from now.deap-0.7.1/deap/0000755000076500000240000000000011650301263013570 5ustar felixstaff00000000000000deap-0.7.1/deap/__init__.py0000644000076500000240000000613411650277637015726 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """ DEAP (Distributed Evolutionary Algorithms in Python) is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black box type of frameworks. It also incorporates easy parallelism where users need not concern themselves with gory implementation details like synchronization and load balancing, only functional decomposition. The five founding hypotheses of DEAP are: (1) The user knows best. Users should be able to understand the internal mechanisms of the framework so that they can extend them easily to better suit their specific needs. (2) User needs in terms of algorithms and operators are so vast that it would be unrealistic to think of implementing them all in a single framework. However, it should be possible to build basic tools and generic mechanisms that enable easy user implementation of most any EA variant. (3) Speedy prototyping of ideas is often more precious than speedy execution of programs. Moreover, code compactness and clarity is also very precious. (4) Even though interpreted, Python is fast enough to execute EAs. Whenever execution time becomes critical, compute intensive components can always be recoded in C. Many efficient numerical libraries are already available through Python APIs. (5) Easy parallelism can alleviate slow execution. And these hypotheses lead to the following objectives: **Rapid prototyping** Provide an environment allowing users to quickly implement their own algorithms without compromise. **Parallelization made easy** Allow for straightforward parallelization; users should not be forced to specify more than the granularity level of their functional decomposition. **Adaptive load balancing** The workload should automatically and dynamically be distributed among available compute units; user intervention should be optional and limited to hints of relative loads of tasks. **Preach by examples** Although the aim of the framework is not to provide ready made solutions, it should nevertheless come with a substantial set of real-world examples to guide the apprenticeship of users. """ __author__ = "Francois-Michel De Rainville and Felix-Antoine Fortin" __version__ = "0.7" __revision__ = "0.7.1" deap-0.7.1/deap/algorithms.py0000644000076500000240000004715111641072614016330 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """The :mod:`algorithms` module is intended to contain some specific algorithms in order to execute very common evolutionary algorithms. The method used here are more for convenience than reference as the implementation of every evolutionary algorithm may vary infinitely. Most of the algorithms in this module use operators registered in the toolbox. Generaly, the keyword used are :meth:`mate` for crossover, :meth:`mutate` for mutation, :meth:`~deap.select` for selection and :meth:`evaluate` for evaluation. You are encouraged to write your own algorithms in order to make them do what you really want them to do. """ import logging import random _logger = logging.getLogger("deap.algorithms") def varSimple(toolbox, population, cxpb, mutpb): """Part of the :func:`~deap.algorithmes.eaSimple` algorithm applying only the variation part (crossover followed by mutation). The modified individuals have their fitness invalidated. The individuals are not cloned so there can be twice a reference to the same individual. This function expects :meth:`toolbox.mate` and :meth:`toolbox.mutate` aliases to be registered in the toolbox. """ # Apply crossover and mutation on the offspring for ind1, ind2 in zip(population[::2], population[1::2]): if random.random() < cxpb: toolbox.mate(ind1, ind2) del ind1.fitness.values, ind2.fitness.values for ind in population: if random.random() < mutpb: toolbox.mutate(ind) del ind.fitness.values return population def varAnd(toolbox, population, cxpb, mutpb): """Part of an evolutionary algorithm applying only the variation part (crossover **and** mutation). The modified individuals have their fitness invalidated. The individuals are cloned so returned population is independent of the input population. The variator goes as follow. First, the parental population :math:`P_\mathrm{p}` is duplicated using the :meth:`toolbox.clone` method and the result is put into the offspring population :math:`P_\mathrm{o}`. A first loop over :math:`P_\mathrm{o}` is executed to mate consecutive individuals. According to the crossover probability *cxpb*, the individuals :math:`\mathbf{x}_i` and :math:`\mathbf{x}_{i+1}` are mated using the :meth:`toolbox.mate` method. The resulting children :math:`\mathbf{y}_i` and :math:`\mathbf{y}_{i+1}` replace their respective parents in :math:`P_\mathrm{o}`. A second loop over the resulting :math:`P_\mathrm{o}` is executed to mutate every individual with a probability *mutpb*. When an individual is mutated it replaces its not mutated version in :math:`P_\mathrm{o}`. The resulting :math:`P_\mathrm{o}` is returned. This variation is named *And* beceause of its propention to apply both crossover and mutation on the individuals. Both probabilities should be in :math:`[0, 1]`. """ offspring = [toolbox.clone(ind) for ind in population] # Apply crossover and mutation on the offspring for ind1, ind2 in zip(offspring[::2], offspring[1::2]): if random.random() < cxpb: toolbox.mate(ind1, ind2) del ind1.fitness.values, ind2.fitness.values for ind in offspring: if random.random() < mutpb: toolbox.mutate(ind) del ind.fitness.values return offspring def eaSimple(toolbox, population, cxpb, mutpb, ngen, stats=None, halloffame=None): """This algorithm reproduce the simplest evolutionary algorithm as presented in chapter 7 of Back, Fogel and Michalewicz, "Evolutionary Computation 1 : Basic Algorithms and Operators", 2000. It uses :math:`\lambda = \kappa = \mu` and goes as follow. It first initializes the population (:math:`P(0)`) by evaluating every individual presenting an invalid fitness. Then, it enters the evolution loop that begins by the selection of the :math:`P(g+1)` population. Then the crossover operator is applied on a proportion of :math:`P(g+1)` according to the *cxpb* probability, the resulting and the untouched individuals are placed in :math:`P'(g+1)`. Thereafter, a proportion of :math:`P'(g+1)`, determined by *mutpb*, is mutated and placed in :math:`P''(g+1)`, the untouched individuals are transferred :math:`P''(g+1)`. Finally, those new individuals are evaluated and the evolution loop continues until *ngen* generations are completed. Briefly, the operators are applied in the following order :: evaluate(population) for i in range(ngen): offspring = select(population) offspring = mate(offspring) offspring = mutate(offspring) evaluate(offspring) population = offspring This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`, :meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be registered in the toolbox. """ _logger.info("Start of evolution") # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in population if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit if halloffame is not None: halloffame.update(population) if stats is not None: stats.update(population) # Begin the generational process for gen in range(ngen): _logger.info("Evolving generation %i", gen) # Select and clone the next generation individuals offsprings = toolbox.select(population, len(population)) offsprings = map(toolbox.clone, offsprings) # Variate the pool of individuals offsprings = varSimple(toolbox, offsprings, cxpb, mutpb) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offsprings if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # Update the hall of fame with the generated individuals if halloffame is not None: halloffame.update(offsprings) _logger.debug("Evaluated %i individuals", len(invalid_ind)) # Replace the current population by the offsprings population[:] = offsprings # Update the statistics with the new population if stats is not None: stats.update(population) # Log statistics on the current generation if stats is not None: print stats _logger.info("End of (successful) evolution") return population def varOr(toolbox, population, lambda_, cxpb, mutpb): """Part of an evolutionary algorithm applying only the variation part (crossover, mutation **or** reproduction). The modified individuals have their fitness invalidated. The individuals are cloned so returned population is independent of the input population. The variator goes as follow. On each of the *lambda_* iteration, it selects one of the three operations; crossover, mutation or reproduction. In the case of a crossover, two individuals are selected at random from the parental population :math:`P_\mathrm{p}`, those individuals are cloned using the :meth:`toolbox.clone` method and then mated using the :meth:`toolbox.mate` method. Only the first child is appended to the offspring population :math:`P_\mathrm{o}`, the second child is discarded. In the case of a mutation, one individual is selected at random from :math:`P_\mathrm{p}`, it is cloned and then mutated using using the :meth:`toolbox.mutate` method. The resulting mutant is appended to :math:`P_\mathrm{o}`. In the case of a reproduction, one individual is selected at random from :math:`P_\mathrm{p}`, cloned and appended to :math:`P_\mathrm{o}`. This variation is named *Or* beceause an offspring will never result from both operations crossover and mutation. The sum of both probabilities shall be in :math:`[0, 1]`, the reproduction probability is 1 - *cxpb* - *mutpb*. """ assert (cxpb + mutpb) <= 1.0, ("The sum of the crossover and mutation " "probabilities must be smaller or equal to 1.0.") offsprings = [] for _ in xrange(lambda_): op_choice = random.random() if op_choice < cxpb: # Apply crossover ind1, ind2 = [toolbox.clone(ind) for ind in random.sample(population, 2)] toolbox.mate(ind1, ind2) del ind1.fitness.values offsprings.append(ind1) elif op_choice < cxpb + mutpb: # Apply mutation ind = toolbox.clone(random.choice(population)) toolbox.mutate(ind) del ind.fitness.values offsprings.append(ind) else: # Apply reproduction offsprings.append(random.choice(population)) return offsprings def varLambda(toolbox, population, lambda_, cxpb, mutpb): """Part of the :func:`~deap.algorithms.eaMuPlusLambda` and :func:`~deap.algorithms.eaMuCommaLambda` algorithms that produce the lambda new individuals. The modified individuals have their fitness invalidated. The individuals are not cloned so there can be twice a reference to the same individual. This function expects :meth:`toolbox.mate` and :meth:`toolbox.mutate` aliases to be registered in the toolbox. """ assert (cxpb + mutpb) <= 1.0, ("The sum of the crossover and mutation " "probabilities must be smaller or equal to 1.0.") offsprings = [] nb_offsprings = 0 while nb_offsprings < lambda_: op_choice = random.random() if op_choice < cxpb: # Apply crossover ind1, ind2 = random.sample(population, 2) ind1 = toolbox.clone(ind1) ind2 = toolbox.clone(ind2) toolbox.mate(ind1, ind2) del ind1.fitness.values, ind2.fitness.values offsprings.append(ind1) offsprings.append(ind2) nb_offsprings += 2 elif op_choice < cxpb + mutpb: # Apply mutation ind = random.choice(population) # select ind = toolbox.clone(ind) # clone toolbox.mutate(ind) del ind.fitness.values offsprings.append(ind) nb_offsprings += 1 else: # Apply reproduction offsprings.append(random.choice(population)) nb_offsprings += 1 # Remove the exedant of offsprings if nb_offsprings > lambda_: del offsprings[lambda_:] return offsprings def eaMuPlusLambda(toolbox, population, mu, lambda_, cxpb, mutpb, ngen, stats=None, halloffame=None): """This is the :math:`(\mu + \lambda)` evolutionary algorithm. First, the individuals having an invalid fitness are evaluated. Then, the evolutionary loop begins by producing *lambda* offspring from the population, the offspring are generated by a crossover, a mutation or a reproduction proportionally to the probabilities *cxpb*, *mutpb* and 1 - (cxpb + mutpb). The offspring are then evaluated and the next generation population is selected from both the offspring **and** the population. Briefly, the operators are applied as following :: evaluate(population) for i in range(ngen): offspring = generate_offspring(population) evaluate(offspring) population = select(population + offspring) This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`, :meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be registered in the toolbox. .. note:: Both produced individuals from a crossover are put in the offspring pool. """ _logger.info("Start of evolution") # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in population if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit _logger.debug("Evaluated %i individuals", len(invalid_ind)) if halloffame is not None: halloffame.update(population) if stats is not None: stats.update(population) # Begin the generational process for gen in range(ngen): _logger.info("Evolving generation %i", gen) # Variate the population offsprings = varLambda(toolbox, population, lambda_, cxpb, mutpb) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offsprings if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit _logger.debug("Evaluated %i individuals", len(invalid_ind)) # Update the hall of fame with the generated individuals if halloffame is not None: halloffame.update(offsprings) # Select the next generation population population[:] = toolbox.select(population + offsprings, mu) # Update the statistics with the new population if stats is not None: stats.update(population) # Log statistics on the current generation if stats is not None: _logger.debug(stats) _logger.info("End of (successful) evolution") return population def eaMuCommaLambda(toolbox, population, mu, lambda_, cxpb, mutpb, ngen, stats=None, halloffame=None): """This is the :math:`(\mu~,~\lambda)` evolutionary algorithm. First, the individuals having an invalid fitness are evaluated. Then, the evolutionary loop begins by producing *lambda* offspring from the population, the offspring are generated by a crossover, a mutation or a reproduction proportionally to the probabilities *cxpb*, *mutpb* and 1 - (cxpb + mutpb). The offspring are then evaluated and the next generation population is selected **only** from the offspring. Briefly, the operators are applied as following :: evaluate(population) for i in range(ngen): offspring = generate_offspring(population) evaluate(offspring) population = select(offspring) This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`, :meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be registered in the toolbox. .. note:: Both produced individuals from the crossover are put in the offspring pool. """ assert lambda_ >= mu, "lambda must be greater or equal to mu." _logger.info("Start of evolution") # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in population if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit _logger.debug("Evaluated %i individuals", len(invalid_ind)) if halloffame is not None: halloffame.update(population) if stats is not None: stats.update(population) # Begin the generational process for gen in range(ngen): _logger.info("Evolving generation %i", gen) # Variate the population offsprings = varLambda(toolbox, population, lambda_, cxpb, mutpb) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offsprings if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit _logger.debug("Evaluated %i individuals", len(invalid_ind)) # Update the hall of fame with the generated individuals if halloffame is not None: halloffame.update(offsprings) # Select the next generation population population[:] = toolbox.select(offsprings, mu) # Update the statistics with the new population if stats is not None: stats.update(population) # Log statistics on the current generation if stats is not None: _logger.debug(stats) _logger.info("End of (successful) evolution") return population def varSteadyState(toolbox, population): """Part of the :func:`~deap.algorithms.eaSteadyState` algorithm that produce the new individual by crossover of two randomly selected parents and mutation on one randomly selected child. The modified individual has its fitness invalidated. The individuals are not cloned so there can be twice a reference to the same individual. This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate` and :meth:`toolbox.select` aliases to be registered in the toolbox. """ # Select two individuals for crossover p1, p2 = random.sample(population, 2) p1 = toolbox.clone(p1) p2 = toolbox.clone(p2) toolbox.mate(p1, p2) # Randomly choose amongst the offsprings the returned child and mutate it child = random.choice((p1, p2)) toolbox.mutate(child) return child, def eaSteadyState(toolbox, population, ngen, stats=None, halloffame=None): """The steady-state evolutionary algorithm. Every generation, a single new individual is produced and put in the population producing a population of size :math:`lambda+1`, then :math:`lambda` individuals are kept according to the selection operator present in the toolbox. This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`, :meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be registered in the toolbox. """ _logger.info("Start of evolution") # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in population if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit if halloffame is not None: halloffame.update(population) # Begin the generational process for gen in range(ngen): _logger.info("Evolving generation %i", gen) # Variate the population child, = varSteadyState(toolbox, population) # Evaluate the produced child child.fitness.values = toolbox.evaluate(child) # Update the hall of fame if halloffame is not None: halloffame.update((child,)) # Select the next generation population population[:] = toolbox.select(population + [child], len(population)) # Update the statistics with the new population if stats is not None: stats.update(population) # Log statistics on the current generation if stats is not None: _logger.debug(stats) _logger.info("End of (successful) evolution") return population deap-0.7.1/deap/base.py0000644000076500000240000004206511641072614015070 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """The :mod:`~deap.base` module provides basic structures to build evolutionary algorithms. It contains only two simple containers that are a basic N-ary :class:`~deap.base.Tree`, usefull for implementing genetic programing, and a virtual :class:`~deap.base.Fitness` class used as base class, for the fitness member of any individual. """ import copy import operator import functools from collections import deque from itertools import izip, repeat, count class Toolbox(object): """A toolbox for evolution that contains the evolutionary operators. At first the toolbox contains two simple methods. The first method :meth:`~deap.toolbox.clone` duplicates any element it is passed as argument, this method defaults to the :func:`copy.deepcopy` function. The second method :meth:`~deap.toolbox.map` applies the function given as first argument to every items of the iterables given as next arguments, this method defaults to the :func:`map` function. You may populate the toolbox with any other function by using the :meth:`~deap.base.Toolbox.register` method. """ def __init__(self): self.register("clone", copy.deepcopy) self.register("map", map) def register(self, alias, method, *args, **kargs): """Register a *method* in the toolbox under the name *alias*. You may provide default arguments that will be passed automatically when calling the registered method. Fixed arguments can then be overriden at function call time. The following code block is a example of how the toolbox is used. :: >>> def func(a, b, c=3): ... print a, b, c ... >>> tools = Toolbox() >>> tools.register("myFunc", func, 2, c=4) >>> tools.myFunc(3) 2 3 4 """ pfunc = functools.partial(method, *args, **kargs) pfunc.__name__ = alias setattr(self, alias, pfunc) def unregister(self, alias): """Unregister *alias* from the toolbox.""" delattr(self, alias) def decorate(self, alias, *decorators): """Decorate *alias* with the specified *decorators*, *alias* has to be a registered function in the current toolbox. Decorate uses the signature preserving decoration function :func:`~deap.tools.decorate`. """ from tools import decorate partial_func = getattr(self, alias) method = partial_func.func args = partial_func.args kargs = partial_func.keywords for decorator in decorators: method = decorate(decorator)(method) setattr(self, alias, functools.partial(method, *args, **kargs)) class Fitness(object): """The fitness is a measure of quality of a solution. If *values* are provided as a tuple, the fitness is initalized using those values, otherwise it is empty (or invalid). Fitnesses may be compared using the ``>``, ``<``, ``>=``, ``<=``, ``==``, ``!=``. The comparison of those operators is made lexicographically. Maximization and minimization are taken care off by a multiplication between the :attr:`weights` and the fitness :attr:`values`. The comparison can be made between fitnesses of different size, if the fitnesses are equal until the extra elements, the longer fitness will be superior to the shorter. .. note:: When comparing fitness values that are **minimized**, ``a > b`` will return :data:`True` if *a* is **smaller** than *b*. """ weights = None """The weights are used in the fitness comparison. They are shared among all fitnesses of the same type. When subclassing ``Fitness``, ``weights`` must be defined as a tuple where each element is associated to an objective. A negative weight element corresponds to the minimization of the associated objective and positive weight to the maximization. .. note:: If weights is not defined during subclassing, the following error will occur at instantiation of a subclass fitness object: ``TypeError: Can't instantiate abstract with abstract attribute weights.`` """ wvalues = () """Contains the weighted values of the fitness, the multiplication with the weights is made when the values are set via the property :attr:`values`. Multiplication is made on setting of the values for efficiency. Generally it is unnecessary to manipulate *wvalues* as it is an internal attribute of the fitness used in the comparison operators. """ def __init__(self, values=()): if self.weights is None: raise TypeError("Can't instantiate abstract %r with abstract " "attribute weights." % (self.__class__)) if len(values) > 0: self.values = values def getValues(self): return tuple(map(operator.div, self.wvalues, self.weights)) def setValues(self, values): self.wvalues = tuple(map(operator.mul, values, self.weights)) def delValues(self): self.wvalues = () values = property(getValues, setValues, delValues, ("Fitness values. Use directly ``individual.fitness.values = values`` " "in order to set the fitness and ``del individual.fitness.values`` " "in order to clear (invalidate) the fitness. The (unweighted) fitness " "can be directly accessed via ``individual.fitness.values``.")) @property def valid(self): """Asses if a fitness is valid or not.""" return len(self.wvalues) != 0 def isDominated(self, other): """In addition to the comparison operators that are used to sort lexically the fitnesses, this method returns :data:`True` if this fitness is dominated by the *other* fitness and :data:`False` otherwise. The weights are used to compare minimizing and maximizing fitnesses. If there is more fitness values than weights, the last weight get repeated until the end of the comparison. """ not_equal = False for self_wvalue, other_wvalue in izip(self.wvalues, other.wvalues): if self_wvalue > other_wvalue: return False elif self_wvalue < other_wvalue: not_equal = True return not_equal def __gt__(self, other): return not self.__le__(other) def __ge__(self, other): return not self.__lt__(other) def __le__(self, other): if not other: # Protection against yamling return False return self.wvalues <= other.wvalues def __lt__(self, other): if not other: # Protection against yamling return False return self.wvalues < other.wvalues def __eq__(self, other): if not other: # Protection against yamling return False return self.wvalues == other.wvalues def __ne__(self, other): return not self.__eq__(other) def __deepcopy__(self, memo): """Replace the basic deepcopy function with a faster one. It assumes that the elements in the :attr:`values` tuple are immutable and the fitness does not contain any other object than :attr:`values` and :attr:`weights`. """ if len(self.wvalues) > 0: return self.__class__(self.values) else: return self.__class__() def __str__(self): """Return the values of the Fitness object.""" return str(self.values) def __repr__(self): """Return the Python code to build a copy of the object.""" module = self.__module__ name = self.__class__.__name__ return "%s.%s(%r)" % (module, name, self.values) class Tree(list): """Basic N-ary tree class. A tree is initialized from the list `content`. The first element of the list is the root of the tree, then the following elements are the nodes. Each node can be either a list or a single element. In the case of a list, it is considered as a subtree, otherwise a leaf. """ class NodeProxy(object): __slots__ = ['obj'] def __new__(cls, obj, *args, **kargs): if isinstance(obj, cls): return obj inst = object.__new__(cls) inst.obj = obj return inst def getstate(self): """Return the state of the NodeProxy: the proxied object.""" return self.obj @property def size(self): """Return the size of a leaf: 1.""" return 1 @property def height(self): """Return the height of a leaf: 0.""" return 0 @property def root(self): """Return the root of a leaf: itself.""" return self def __eq__(self, other): return self.obj == other.obj def __getattr__(self, attr): return getattr(self.obj, attr) def __call__(self, *args, **kargs): return self.obj(*args, **kargs) def __repr__(self): return self.obj.__repr__() def __str__(self): return self.obj.__str__() @classmethod def convertNode(cls, node): """Convert node into the proper object either a Tree or a Node.""" if isinstance(node, cls): if len(node) == 1: return cls.NodeProxy(node[0]) return node elif isinstance(node, list): if len(node) > 1: return cls(node) else: return cls.NodeProxy(node[0]) else: return cls.NodeProxy(node) def __init__(self, content=None): """Initialize a tree with a list `content`. The first element of the list is the root of the tree, then the following elements are the nodes. A node could be a list, then representing a subtree. """ for elem in content: self.append(self.convertNode(elem)) def getstate(self): """Return the state of the Tree as a list of arbitrary elements. It is mainly used for pickling a Tree object. """ return [elem.getstate() for elem in self] def __reduce__(self): """Return the class init, the object's state and the object's dict in a tuple. The function is used to pickle Tree. """ return (self.__class__, (self.getstate(),), self.__dict__) def __deepcopy__(self, memo): """Deepcopy a Tree by first converting it back to a list of list.""" new = self.__class__(copy.deepcopy(self.getstate())) new.__dict__.update(copy.deepcopy(self.__dict__, memo)) return new def __setitem__(self, key, value): """Set the item at `key` with the corresponding `value`.""" list.__setitem__(self, key, self.convertNode(value)) def __setslice__(self, i, j, value): """Set the slice at `i` to `j` with the corresponding `value`.""" list.__setslice__(self, i, j, self.convertNode(value)) def __str__(self): """Return the tree in its original form, a list, as a string.""" return list.__str__(self) def __repr__(self): """Return the Python code to build a copy of the object.""" module = self.__module__ name = self.__class__.__name__ return "%s.%s(%s)" % (module, name, list.__repr__(self)) @property def root(self): """Return the root element of the tree. The root node of a tree is the node with no parents. There is at most one root node in a rooted tree. """ return self[0] @property def size(self): """Return the number of nodes in the tree. The size of a node is the number of descendants it has including itself. """ return sum(elem.size for elem in self) @property def height(self): """Return the height of the tree. The height of a tree is the length of the path from the root to the deepest node in the tree. A (rooted) tree with only one node (the root) has a height of zero. """ try: return max(elem.height for elem in self[1:])+1 except ValueError: return 0 @property def iter(self): """Return a generator function that iterates on the element of the tree in linear time using depth first algorithm. >>> t = Tree([1,2,3[4,5,[6,7]],8]) >>> [i for i in t.iter]: [1, 2, 3, 4, 5, 6, 7, 8] """ for elem in self: if isinstance(elem, Tree): for elem2 in elem.iter: yield elem2 else: yield elem @property def iter_leaf(self): """Return a generator function that iterates on the leaf of the tree in linear time using depth first algorithm. >>> t = Tree([1,2,3,[4,5,[6,7]],8]) >>> [i for i in t.iter_leaf] [2, 3, 5, 7, 8] """ for elem in self[1:]: if isinstance(elem, Tree): for elem2 in elem.iter_leaf: yield elem2 else: yield elem @property def iter_leaf_idx(self): """Return a generator function that iterates on the leaf indices of the tree in linear time using depth first algorithm. >>> t = Tree([1,2,3,[4,[5,6,7],[8,9]],[10,11]]); >>> [i for i in t.iter_leaf_idx] [1, 2, 5, 6, 8, 10] """ def leaf_idx(tree, total): total[0] += 1 for elem in tree[1:]: if isinstance(elem, Tree): for elem2 in leaf_idx(elem, total): yield total[0] else: yield total[0] total[0] += 1 return leaf_idx(self, [0]) def searchSubtreeDF(self, index): """Search the subtree with the corresponding index based on a depth-first search. """ if index == 0: return self total = 0 for child in self: if total == index: return child nbr_child = child.size if nbr_child + total > index: return child.searchSubtreeDF(index-total) total += nbr_child def setSubtreeDF(self, index, subtree): """Replace the tree with the corresponding index by subtree based on a depth-first search. """ if index == 0: try: self[:] = subtree except TypeError: del self[1:] self[0] = subtree return total = 0 for i, child in enumerate(self): if total == index: self[i] = subtree return nbr_child = child.size if nbr_child + total > index: child.setSubtreeDF(index-total, subtree) return total += nbr_child def searchSubtreeBF(self, index): """Search the subtree with the corresponding index based on a breadth-first search. """ if index == 0: return self queue = deque(self[1:]) for i in xrange(index): subtree = queue.popleft() if isinstance(subtree, Tree): queue.extend(subtree[1:]) return subtree def setSubtreeBF(self, index, subtree): """Replace the subtree with the corresponding index by subtree based on a breadth-first search. """ if index == 0: try: self[:] = subtree except TypeError: del self[1:] self[0] = subtree return queue = deque(izip(repeat(self, len(self[1:])), count(1))) for i in xrange(index): elem = queue.popleft() parent = elem[0] child = elem[1] if isinstance(parent[child], Tree): tree = parent[child] queue.extend(izip(repeat(tree, len(tree[1:])), count(1))) parent[child] = subtree deap-0.7.1/deap/benchmarks/0000755000076500000240000000000011650301263015705 5ustar felixstaff00000000000000deap-0.7.1/deap/benchmarks/__init__.py0000644000076500000240000002117111641072614020025 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . """ Regroup typical EC benchmarks functions to import easily and benchmark examples. """ import random from math import sin, cos, pi, exp, e, sqrt from operator import mul from functools import reduce # Unimodal def rand(individual): """Random test objective function. :math:`f_{\\text{Rand}}(\mathbf{x}) = \\text{\\texttt{random}}(0,1)` """ return random.random(), def plane(individual): """Plane test objective function. :math:`f_{\\text{Plane}}(\mathbf{x}) = x_0` """ return individual[0], def sphere(individual): """Sphere test objective function. :math:`f_{\\text{Sphere}}(\mathbf{x}) = \sum_{i=1}^Nx_i^2` """ return sum(gene * gene for gene in individual), def cigar(individual): """Cigar test objective function. :math:`f_{\\text{Cigar}}(\mathbf{x}) = x_0^2 + 10^6\\sum_{i=1}^N\,x_i^2` """ return individual[0]**2 + 1e6 * sum(gene * gene for gene in individual), def rosenbrock(individual): """Rosenbrock test objective function. :math:`f_{\\text{Rosenbrock}}(\\mathbf{x}) = \\sum_{i=1}^{N-1} (1-x_i)^2 + 100 (x_{i+1} - x_i^2 )^2` .. plot:: _scripts/rosenbrock.py :width: 67 % """ return sum(100 * (x * x - y)**2 + (1. - x)**2 \ for x, y in zip(individual[:-1], individual[1:])), def h1(individual): """ Simple two-dimensional function containing several local maxima, H1 has a unique maximum value of 2.0 at the point (8.6998, 6.7665). From : The Merits of a Parallel Genetic Algorithm in Solving Hard Optimization Problems, A. J. Knoek van Soest and L. J. R. Richard Casius, J. Biomech. Eng. 125, 141 (2003) :math:`f_{\\text{H1}}(x_1, x_2) = \\frac{\sin(x_1 - \\frac{x_2}{8})^2 + \ \\sin(x_2 + \\frac{x_1}{8})^2}{\\sqrt{(x_1 - 8.6998)^2 + \ (x_2 - 6.7665)^2} + 1}` .. plot:: _scripts/h1.py :width: 67 % """ num = (sin(individual[0] - individual[1] / 8))**2 + (sin(individual[1] + individual[0] / 8))**2 denum = ((individual[0] - 8.6998)**2 + (individual[1] - 6.7665)**2)**0.5 + 1 return num / denum, # # Multimodal def ackley(individual): """Ackley test objective function. :math:`f_{\\text{Ackley}}(\\mathbf{x}) = 20 - 20\cdot\exp\left(-0.2\sqrt{\\frac{1}{N} \ \sum_{i=1}^N x_i^2} \\right)\ + e - \ \exp\left(\\frac{1}{N}\sum_{i=1}^N \\cos(2\pi x_i) \ \\right)` .. plot:: _scripts/ackley.py :width: 67 % """ N = len(individual) return 20 - 20 * exp(-0.2*sqrt(1.0/N * sum(x**2 for x in individual))) \ + e - exp(1.0/N * sum(cos(2*pi*x) for x in individual)), def bohachevsky(individual): """Bohachevsky test objective function :math:`f_{\\text{Bohachevsky}}(\mathbf{x}) = \sum_{i=1}^{N-1}(x_i^2 + 2x_{i+1}^2 - \ 0.3\cos(3\pi x_i) - 0.4\cos(4\pi x_{i+1}) + 0.7)` .. plot:: _scripts/bohachevsky.py :width: 67 % """ return sum(x**2 + 2*x1**2 - 0.3*cos(3*pi*x) - 0.4*cos(4*pi*x1) + 0.7 for x, x1 in zip(individual[:-1], individual[1:])), def griewank(individual): """Griewank test objective function :math:`f_{\\text{Griewank}}(\\mathbf{x}) = \\frac{1}{4000}\\sum_{i=1}^N\,x_i^2 - \ \prod_{i=1}^N\\cos\\left(\\frac{x_i}{\sqrt{i}}\\right) + 1` .. plot:: _scripts/griewank.py :width: 67 % """ return 1.0/4000.0 * sum(x**2 for x in individual) - \ reduce(mul, (cos(x/sqrt(i+1.0)) for i, x in enumerate(individual)), 1) + 1, def rastrigin(individual): """Rastrigin test objective function. :math:`f_{\\text{Rastrigin}}(\\mathbf{x}) = 10N \sum_{i=1}^N x_i^2 - 10 \\cos(2\\pi x_i)` .. plot:: _scripts/rastrigin.py :width: 67 % """ return 10 * len(individual) + sum(gene * gene - 10 * \ cos(2 * pi * gene) for gene in individual), def rastrigin_scaled(individual): """Scaled Rastrigin test objective function :math:`f_{\\text{RastScaled}}(\mathbf{x}) = 10N + \sum_{i=1}^N \ \left(10^{\left(\\frac{i-1}{N-1}\\right)} x_i \\right)^2 x_i)^2 - \ 10\cos\\left(2\\pi 10^{\left(\\frac{i-1}{N-1}\\right)} x_i \\right)` """ N = len(individual) return 10*N + sum((10**(i/(N-1))*x)**2 - 10*cos(2*pi*10**(i/(N-1))*x) for i, x in enumerate(individual)), def rastrigin_skew(individual): """Skewed Rastrigin test objective function :math:`f_{\\text{RastSkew}}(\mathbf{x}) = 10N \sum_{i=1}^N \left(y_i^2 - 10 \\cos(2\\pi x_i)\\right)` :math:`\\text{with } y_i = \ \\begin{cases} \ 10\\cdot x_i & \\text{ if } x_i > 0,\\\ \ x_i & \\text{ otherwise } \ \\end{cases}` """ N = len(individual) return 10*N + sum((10*x if x > 0 else x)**2 - 10*cos(2*pi*(10*x if x > 0 else x)) for x in individual), def schaffer(individual): """Schaffer test objective function. :math:`f_{\\text{Schaffer}}(\mathbf{x}) = \sum_{i=1}^{N-1} (x_i^2+x_{i+1}^2)^{0.25} \cdot \ \\left[ \sin^2(50\cdot(x_i^2+x_{i+1}^2)^{0.10}) + 1.0 \ \\right]` """ return sum((x**2+x1**2)**0.25 * ((sin(50*(x**2+x1**2)**0.1))**2+1.0) for x, x1 in zip(individual[:-1], individual[1:])), def schwefel(individual): """Schwefel test objective function. :math:`f_{\\text{Schwefel}}(\mathbf{x}) = 418.9828872724339\cdot N - \ \sum_{i=1}^N\,x_i\sin\\left(\sqrt{|x_i|}\\right)` .. plot:: _scripts/schwefel.py :width: 67 % """ N = len(individual) return 418.9828872724339*N-sum(x*sin(sqrt(abs(x))) for x in individual), def himmelblau(individual): """The Himmelblau's function is multimodal with 4 defined minimums in :math:`[-6, 6]^2`. :math:`f_{\\text{Himmelblau}}(x_1, x_2) = (x_1^2 + x_2 - 11)^2 + (x_1 + x_2^2 -7)^2` .. plot:: _scripts/himmelblau.py :width: 67 % """ return (individual[0] * individual[0] + individual[1] - 11)**2 + \ (individual[0] + individual[1] * individual[1] - 7)**2, def shekel(individual, a, c): """The Shekel multimodal function can have any number of maxima. The number of maxima is given by the length of any of the arguments *a* or *c*, *a* is a matrix of size :math:`M\\times N`, where *M* is the number of maxima and *N* the number of dimensions and *c* is a :math:`M\\times 1` vector. The matrix :math:`\\mathcal{A}` can be seen as the position of the maxima and the vector :math:`\\mathbf{c}`, the width of the maxima. :math:`f_\\text{Shekel}(\mathbf{x}) = \\sum_{i = 1}^{M} \\frac{1}{c_{i} + \\sum_{j = 1}^{N} (x_{j} - a_{ij})^2 }` The following figure uses :math:`\\mathcal{A} = \\begin{bmatrix} 0.5 & 0.5 \\\\ 0.25 & 0.25 \\\\ 0.25 & 0.75 \\\\ 0.75 & 0.25 \\\\ 0.75 & 0.75 \\end{bmatrix}` and :math:`\\mathbf{c} = \\begin{bmatrix} 0.002 \\\\ 0.005 \\\\ 0.005 \\\\ 0.005 \\\\ 0.005 \\end{bmatrix}`, thus defining 5 maximums in :math:`\\mathbb{R}^2`. .. plot:: _scripts/shekel.py :width: 67 % """ return sum((1. / (c[i] + sum((x - a[i][j])**2 for j, x in enumerate(individual)))) for i in range(len(c))), # Multiobjectives def kursawe(individual): """Kursawe multiobjective function. :math:`f_{\\text{Kursawe}1}(\\mathbf{x}) = \\sum_{i=1}^{N-1} -10 e^{-0.2 \\sqrt{x_i^2 + x_{i+1}^2} }` :math:`f_{\\text{Kursawe}2}(\\mathbf{x}) = \\sum_{i=1}^{N} |x_i|^{0.8} + 5 \\sin(x_i^3)` .. plot:: _scripts/kursawe.py :width: 100 % """ f1 = sum(-10 * exp(-0.2 * sqrt(x * x + y * y)) for x, y in zip(individual[:-1], individual[1:])) f2 = sum(abs(x)**0.8 + 5 * sin(x * x * x) for x in individual) return f1, f2deap-0.7.1/deap/cma.py0000644000076500000240000003170511645601567014726 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . # Special thanks to Nikolaus Hansen for providing major part of # this code. The CMA-ES algorithm is provided in many other languages # and advanced versions at http://www.lri.fr/~hansen/cmaesintro.html. """A module that provides support for the Covariance Matrix Adaptation Evolution Strategy. """ import logging from math import sqrt, log, exp import numpy import random # Only used to seed numpy.random import sys # Used to get maxint import copy numpy.random.seed(random.randint(0, sys.maxint)) _logger = logging.getLogger("deap.cma") def esCMA(toolbox, population, ngen, halloffame=None, statistics=None): """The CMA-ES algorithm as described in Hansen, N. (2006). *The CMA Evolution Strategy: A Comparing Rewiew.* The provided *population* should be a list of one or more individuals. """ _logger.info("Start of evolution") for g in xrange(ngen): _logger.info("Evolving generation %i", g) # Evaluate the individuals for ind in population: ind.fitness.values = toolbox.evaluate(ind) if halloffame is not None: halloffame.update(population) # Update the Strategy with the evaluated individuals toolbox.update(population) if statistics is not None: statistics.update(population) _logger.debug(statistics) _logger.info("End of (successful) evolution") class CMAStrategy(object): """ Additional configuration may be passed through the *params* argument as a dictionary, +----------------+---------------------------+----------------------------+ | Parameter | Default | Details | +================+===========================+============================+ | ``lambda_`` | ``floor(4 + 3 * log(N))`` | Number of children to | | | | produce at each generation,| | | | ``N`` is the individual's | | | | size. | +----------------+---------------------------+----------------------------+ | ``mu`` | ``floor(lambda_ / 2)`` | The number of parents to | | | | keep from the | | | | lambda children. | +----------------+---------------------------+----------------------------+ | ``weights`` | ``"superlinear"`` | Decrease speed, can be | | | | ``"superlinear"``, | | | | ``"linear"`` or | | | | ``"equal"``. | +----------------+---------------------------+----------------------------+ | ``cs`` | ``(mueff + 2) / | Cumulation constant for | | | (N + mueff + 3)`` | step-size. | +----------------+---------------------------+----------------------------+ | ``damps`` | ``1 + 2 * max(0, sqrt(( | Damping for step-size. | | | mueff - 1) / (N + 1)) - 1)| | | | + cs`` | | +----------------+---------------------------+----------------------------+ | ``ccum`` | ``4 / (N + 4)`` | Cumulation constant for | | | | covariance matrix. | +----------------+---------------------------+----------------------------+ | ``ccov1`` | ``2 / ((N + 1.3)^2 + | Learning rate for rank-one | | | mueff)`` | update. | +----------------+---------------------------+----------------------------+ | ``ccovmu`` | ``2 * (mueff - 2 + 1 / | Learning rate for rank-mu | | | mueff) / ((N + 2)^2 + | update. | | | mueff)`` | | +----------------+---------------------------+----------------------------+ """ def __init__(self, centroid, sigma, **kargs): self.params = kargs # Create a centroid as a numpy array self.centroid = numpy.array(centroid) self.dim = len(self.centroid) self.sigma = sigma self.pc = numpy.zeros(self.dim) self.ps = numpy.zeros(self.dim) self.chiN = sqrt(self.dim) * (1 - 1. / (4. * self.dim) + \ 1. / (21. * self.dim**2)) self.B = numpy.identity(self.dim) self.C = numpy.identity(self.dim) self.diagD = numpy.ones(self.dim) self.BD = self.B * self.diagD self.cond = 1 self.lambda_ = self.params.get("lambda_", int(4 + 3 * log(self.dim))) self.update_count = 0 self.computeParams(self.params) def generate(self, ind_init): """Generate a population from the current strategy using the centroid individual as parent. """ arz = numpy.random.standard_normal((self.lambda_, self.dim)) arz = self.centroid + self.sigma * numpy.dot(arz, self.BD.T) return [ind_init(arzi) for arzi in arz] def update(self, population): """Update the current covariance matrix strategy. """ population.sort(key=lambda ind: ind.fitness, reverse=True) old_centroid = self.centroid self.centroid = numpy.dot(self.weights, population[0:self.mu]) c_diff = self.centroid - old_centroid # Cumulation : update evolution path self.ps = (1 - self.cs) * self.ps \ + sqrt(self.cs * (2 - self.cs) * self.mueff) / self.sigma \ * numpy.dot(self.B, (1. / self.diagD) \ * numpy.dot(self.B.T, c_diff)) hsig = float((numpy.linalg.norm(self.ps) / sqrt(1. - (1. - self.cs)**(2. * (self.update_count + 1.))) / self.chiN < (1.4 + 2. / (self.dim + 1.)))) self.update_count += 1 self.pc = (1 - self.cc) * self.pc + hsig \ * sqrt(self.cc * (2 - self.cc) * self.mueff) / self.sigma \ * c_diff # Update covariance matrix artmp = population[0:self.mu] - old_centroid self.C = (1 - self.ccov1 - self.ccovmu + (1 - hsig) \ * self.ccov1 * self.cc * (2 - self.cc)) * self.C \ + self.ccov1 * numpy.outer(self.pc, self.pc) \ + self.ccovmu * numpy.dot((self.weights * artmp.T), artmp) \ / self.sigma**2 self.sigma *= numpy.exp((numpy.linalg.norm(self.ps) / self.chiN - 1.) \ * self.cs / self.damps) self.diagD, self.B = numpy.linalg.eigh(self.C) indx = numpy.argsort(self.diagD) self.cond = self.diagD[indx[-1]]/self.diagD[indx[0]] self.diagD = self.diagD[indx]**0.5 self.B = self.B[:, indx] self.BD = self.B * self.diagD arz = numpy.random.standard_normal((self.lambda_, self.dim)) arz = self.centroid + self.sigma * numpy.dot(arz, self.BD.T) for ind, arzi in zip(population, arz): del ind[:] ind.extend(arzi) def computeParams(self, params): """Those parameters depends on lambda and need to computed again if it changes during evolution. """ self.mu = params.get("mu", self.lambda_ / 2) rweights = params.get("weights", "superlinear") if rweights == "superlinear": self.weights = log(self.mu + 0.5) - \ numpy.log(numpy.arange(1, self.mu + 1)) elif rweights == "linear": self.weights = self.mu + 0.5 - numpy.arange(1, self.mu + 1) elif rweights == "equal": self.weights = numpy.ones(self.mu) else: pass # Print some warning ? self.weights /= sum(self.weights) self.mueff = 1. / sum(self.weights**2) self.cc = params.get("ccum", 4. / (self.dim + 4.)) self.cs = params.get("cs", (self.mueff + 2.) / (self.dim + self.mueff + 3.)) self.ccov1 = params.get("ccov1", 2. / ((self.dim + 1.3)**2 + \ self.mueff)) self.ccovmu = params.get("ccovmu", 2. * (self.mueff - 2. + \ 1. / self.mueff) / \ ((self.dim + 2.)**2 + self.mueff)) self.ccovmu = min(1 - self.ccov1, self.ccovmu) self.damps = 1. + 2. * max(0, sqrt((self.mueff - 1.) / \ (self.dim + 1.)) - 1.) + self.cs self.damps = params.get("damps", self.damps) class CMA1pLStrategy(object): def __init__(self, parent, sigma, **kargs): self.parent = parent self.sigma = sigma self.dim = len(self.parent) self.C = numpy.identity(self.dim) self.A = numpy.identity(self.dim) self.pc = numpy.zeros(self.dim) self.computeParams(kargs) self.psucc = self.ptarg def computeParams(self, params): # Selection : self.lambda_ = params.get("lambda_", 1) # Step size control : self.d = params.get("d", 1.0 + self.dim/(2.0*self.lambda_)) self.ptarg = params.get("ptarg", 1.0/(5+sqrt(self.lambda_)/2.0)) self.cp = params.get("cp", self.ptarg*self.lambda_/(2+self.ptarg*self.lambda_)) # Covariance matrix adaptation self.cc = params.get("cc", 2.0/(self.dim+2.0)) self.ccov = params.get("ccov", 2.0/(self.dim**2 + 6.0)) self.pthresh = params.get("pthresh", 0.44) def generate(self, ind_init): # self.y = numpy.dot(self.A, numpy.random.standard_normal(self.dim)) arz = numpy.random.standard_normal((self.lambda_, self.dim)) arz = self.parent + self.sigma * numpy.dot(arz, self.A.T) return [ind_init(arzi) for arzi in arz] def update(self, population): population.sort(key=lambda ind: ind.fitness, reverse=True) lambda_succ = sum(self.parent.fitness <= ind.fitness for ind in population) p_succ = float(lambda_succ) / self.lambda_ self.psucc = (1-self.cp)*self.psucc + self.cp*p_succ if self.parent.fitness <= population[0].fitness: x_step = (population[0] - numpy.array(self.parent)) / self.sigma self.parent = copy.deepcopy(population[0]) if self.psucc < self.pthresh: self.pc = (1 - self.cc)*self.pc + sqrt(self.cc * (2 - self.cc)) * x_step self.C = (1-self.ccov)*self.C + self.ccov * numpy.dot(self.pc, self.pc.T) else: self.pc = (1 - self.cc)*self.pc self.C = (1-self.ccov)*self.C + self.ccov * (numpy.dot(self.pc, self.pc.T) + self.cc*(2-self.cc)*self.C) self.sigma = self.sigma * exp(1.0/self.d * (self.psucc - self.ptarg)/(1.0-self.ptarg)) # We use Cholesky since for now we have no use of eigen decomposition # Basically, Cholesky returns a matrix A as C = A*A.T # Eigen decomposition returns two matrix B and D^2 as C = B*D^2*B.T = B*D*D*B.T # So A == B*D # To compute the new individual we need to multiply each vector z by A # as y = centroid + sigma * A*z # So the Cholesky is more straightforward as we don't need to compute # the squareroot of D^2, and multiply B and D in order to get A, we directly get A. # This can't be done (without cost) with the standard CMA-ES as the eigen decomposition is used # to compute covariance matrix inverse in the step-size evolutionary path computation. self.A = numpy.linalg.cholesky(self.C) arz = numpy.random.standard_normal((self.lambda_, self.dim)) arz = self.parent + self.sigma * numpy.dot(arz, self.A.T) for ind, arzi in zip(population, arz): del ind[:] ind.extend(arzi) deap-0.7.1/deap/creator.py0000644000076500000240000001475711641072614015624 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """The :mod:`~deap.creator` module is the heart and soul of DEAP, it allows to create, at runtime, classes that will fulfill the needs of your evolutionary algorithms. This module follows the meta-factory paradigm by allowing to create new classes via both composition and inheritance. Attributes both datas and functions are added to existing types in order to create new types empowered with user specific evolutionary computation capabilities. In effect, new classes can be built from any imaginable type, from :class:`list` to :class:`set`, :class:`dict`, :class:`~deap.base.Tree` and more, providing the possibility to implement genetic algorithms, genetic programming, evolution strategies, particle swarm optimizers, and many more. """ import array import copy class_replacers = {} """Some classes in Python's standard library as well as third party library may be in part incompatible with the logic used in DEAP. In order to palliate to this problem, the method :func:`create` uses the dictionary `class_replacers` to identify if the base type provided is problematic, and if so the new class inherits from the replacement class instead of the original base class. `class_replacers` keys are classes to be replaced and the values are the replacing classes. """ try: import numpy (numpy.ndarray, numpy.array) except ImportError: # Numpy is not present, skip the definition of the replacement class. pass except AttributeError: # Numpy is present, but there is either no ndarray or array in numpy, # also skip the definition of the replacement class. pass else: class _numpy_array(numpy.ndarray): def __getslice__(self, i, j): """Overrides the getslice from numpy.ndarray that returns a shallow copy of the slice. """ return numpy.ndarray.__getslice__(self, i, j).copy() def __deepcopy__(self, memo): """Overrides the deepcopy from numpy.ndarray that does not copy the object's attributes. """ copy_ = numpy.ndarray.__deepcopy__(self, memo) copy_.__dict__.update(copy.deepcopy(self.__dict__, memo)) return copy_ @staticmethod def __new__(cls, iterable): """Creates a new instance of a numpy.ndarray from a function call""" return numpy.array(list(iterable)).view(cls) def __array_finalize__(self, obj): # __init__ will reinitialize every member of the subclass. # this might not be desirable for example in the case of an ES. self.__init__() # Instead, e could use the following that will simply deepcopy # every member that is present in the original class # This is significantly slower. #if self.__class__ == obj.__class__: # self.__dict__.update(copy.deepcopy(obj.__dict__)) class_replacers[numpy.ndarray] = _numpy_array class _array(array.array): @staticmethod def __new__(cls, seq=()): return super(_array, cls).__new__(cls, cls.typecode, seq) def __deepcopy__(self, memo): """Overrides the deepcopy from array.array that does not copy the object's attributes and class type. """ cls = self.__class__ copy_ = cls.__new__(cls, self) memo[id(self)] = copy_ copy_.__dict__.update(copy.deepcopy(self.__dict__, memo)) return copy_ def __reduce__(self): return (self.__class__, (list(self),), self.__dict__) class_replacers[array.array] = _array def create(name, base, **kargs): """Creates a new class named *name* inheriting from *base* in the :mod:`~deap.creator` module. The new class can have attributes defined by the subsequent keyword arguments passed to the function create. If the argument is a class (without the parenthesis), the __init__ function is called in the initialization of an instance of the new object and the returned instance is added as an attribute of the class' instance. Otherwise, if the argument is not a class, (for example an :class:`int`), it is added as a "static" attribute of the class. The following is used to create a class :class:`Foo` inheriting from the standard :class:`list` and having an attribute :attr:`bar` being an empty dictionary and a static attribute :attr:`spam` initialized to 1. :: create("Foo", list, bar=dict, spam=1) This above line is exactly the same as defining in the :mod:`creator` module something like the following. :: def Foo(list): spam = 1 def __init__(self): self.bar = dict() """ dict_inst = {} dict_cls = {} for obj_name, obj in kargs.iteritems(): if hasattr(obj, "__call__"): dict_inst[obj_name] = obj else: dict_cls[obj_name] = obj # Check if the base class has to be replaced if base in class_replacers: base = class_replacers[base] # A DeprecationWarning is raised when the object inherits from the # class "object" which leave the option of passing arguments, but # raise a warning stating that it will eventually stop permitting # this option. Usually this happens when the base class does not # override the __init__ method from object. def initType(self, *args, **kargs): """Replace the __init__ function of the new type, in order to add attributes that were defined with **kargs to the instance. """ for obj_name, obj in dict_inst.iteritems(): setattr(self, obj_name, obj()) if base.__init__ is not object.__init__: base.__init__(self, *args, **kargs) else: base.__init__(self) objtype = type(name, (base,), dict_cls) objtype.__init__ = initType globals()[name] = objtype deap-0.7.1/deap/dtm/0000755000076500000240000000000011650301263014354 5ustar felixstaff00000000000000deap-0.7.1/deap/dtm/__init__.py0000644000076500000240000000242611641072614016476 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . # DTM : Distributed Task Manager # Alpha version __author__ = "Marc-Andre Gardner" __version__ = "0.2" __revision__ = "0.2.1" from deap.dtm.manager import Control _dtmObj = Control() setOptions = _dtmObj.setOptions start = _dtmObj.start map = _dtmObj.map map_async = _dtmObj.map_async apply = _dtmObj.apply apply_async = _dtmObj.apply_async imap = _dtmObj.imap imap_unordered = _dtmObj.imap_unordered filter = _dtmObj.filter repeat = _dtmObj.repeat waitForAll = _dtmObj.waitForAll testAllAsync = _dtmObj.testAllAsync getWorkerId = _dtmObj.getWorkerId # Control shall not be imported that way del Controldeap-0.7.1/deap/dtm/abstractCommManager.py0000644000076500000240000000534011641072614020647 0ustar felixstaff00000000000000import threading from abc import ABCMeta, abstractmethod, abstractproperty class AbstractCommThread(threading.Thread): __metaclass__ = ABCMeta def __init__(self, recvQ, sendQ, mainThreadEvent, exitEvent, commReadyEvent, randomGenerator, cmdlineArgs): threading.Thread.__init__(self) self.recvQ = recvQ self.sendQ = sendQ self.exitStatus = exitEvent self.msgSendTag = 2 self.wakeUpMainThread = mainThreadEvent self.random = randomGenerator self.commReadyEvent = commReadyEvent self.cmdArgs = cmdlineArgs self.traceMode = False self.traceTo = None @abstractproperty def poolSize(self): """ Return the number of effective workers (for instance, with MPI, this is the number of slots asked with the -n or -np option) """ pass @abstractproperty def workerId(self): """ Return an ID for this worker such as each worker gets a different ID. This must be an immutable type (int, string, tuple, etc.) """ pass @abstractproperty def isRootWorker(self): """ Return True if this worker is the "root worker", that is the worker which will start the main task. The position of this worker in the hosts is not important, but one and only one worker should be designated as the root worker (the others should receive False). """ pass @abstractproperty def isLaunchProcess(self): """ If this function returns True, the main thread will wait for the termination of this thread, and then exit without executing any task. This may be useful for the backends which have to launch themselves the remote DTM processes. """ pass @abstractmethod def setTraceModeOn(self, xmlLogger): """ Used for logging purposes. The xmlLogger arg is an xml.etree object which can be use by the backend to log some informations. The log format is not currently specified, and the backend may choose to ignore this call (and not log anything). """ pass @abstractmethod def iterOverIDs(self): """ Return an iterable over all the worker IDs. For instance, if your workers IDs are integers between 0 and 63, it should then return range(0,64). """ pass @abstractmethod def run(self): """ The main method, which will be call to start the communication backend. This is the place where you should import your special modules, and insert the communication loop. """ pass deap-0.7.1/deap/dtm/commManagerMpi4py.py0000644000076500000240000001521411641072614020267 0ustar felixstaff00000000000000try: import Queue except ImportError: import queue as Queue import time import threading try: import cPickle except ImportError: import pickle as cPickle import array import copy import logging try: from lxml import etree except ImportError: try: import xml.etree.cElementTree as etree except ImportError: # Python 2.5 import xml.etree.ElementTree as etree from deap.dtm.dtmTypes import * from deap.dtm.abstractCommManager import AbstractCommThread _logger = logging.getLogger("dtm.communication") DTM_MPI_MIN_LATENCY = 0.005 DTM_MPI_MAX_LATENCY = 0.01 DTM_CONCURRENT_RECV_LIMIT = 1000 DTM_CONCURRENT_SEND_LIMIT = 1000 class CommThread(AbstractCommThread): def __init__(self, recvQ, sendQ, mainThreadEvent, exitEvent, commReadyEvent, randomGenerator, cmdlineArgs): AbstractCommThread.__init__(self, recvQ, sendQ, mainThreadEvent, exitEvent, commReadyEvent, randomGenerator, cmdlineArgs) @property def poolSize(self): return self.pSize @property def workerId(self): return self.currentId @property def isRootWorker(self): return self.currentId == 0 @property def isLaunchProcess(self): return False def setTraceModeOn(self, xmlLogger): self.traceMode = True self.traceTo = xmlLogger def iterOverIDs(self): return range(self.pSize) def run(self): from mpi4py import MPI def mpiSend(msg, dest): # Pickle and send over MPI arrayBuf = array.array('b') arrayBuf.fromstring(cPickle.dumps(msg, cPickle.HIGHEST_PROTOCOL)) b = MPI.COMM_WORLD.Isend([arrayBuf, MPI.CHAR], dest=dest, tag=self.msgSendTag) if self.traceMode: etree.SubElement(self.traceTo, "msg", {"direc" : "out", "type" : str(msg.msgType), "otherWorker" : str(dest), "msgtag" : str(self.msgSendTag), "time" : repr(time.time())}) self.msgSendTag += 1 return b, arrayBuf assert MPI.Is_initialized(), "Error in MPI Init!" self.pSize = MPI.COMM_WORLD.Get_size() self.currentId = MPI.COMM_WORLD.Get_rank() self.commReadyEvent.set() # Notify the main thread that we are ready if self.currentId == 0 and MPI.Query_thread() > 0: # Warn only once _logger.warning("MPI was initialized with a thread level of %i, which is higher than MPI_THREAD_SINGLE." " The current MPI implementations do not always handle well the MPI_THREAD_MULTIPLE or MPI_THREAD_SERIALIZED modes." " As DTM was designed to work with the base, safe mode (MPI_THREAD_SINGLE), it is strongly suggested to change" " the 'thread_level' variable or your mpi4py settings in 'site-packages/mpi4py/rc.py', unless you have strong" " motivations to keep that setting. This may bring both stability and performance improvements.", MPI.Query_thread()) lRecvWaiting = [] lSendWaiting = [] countSend = 0 countRecv = 0 lMessageStatus = MPI.Status() working = True countRecvNotTransmit = 0 countRecvTimeInit = time.time() while working: recvSomething = False sendSomething = False if self.exitStatus.is_set(): # Exiting # Warning : the communication thread MUST clear the sendQ # BEFORE leaving (the exiting orders must be send) working = False while len(lRecvWaiting) < DTM_CONCURRENT_RECV_LIMIT and MPI.COMM_WORLD.Iprobe(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=lMessageStatus): # We received something lBuf = array.array('b', (0,)) lBuf = lBuf * lMessageStatus.Get_elements(MPI.CHAR) lRecvWaiting.append((lBuf, MPI.COMM_WORLD.Irecv([lBuf, MPI.CHAR], source=lMessageStatus.Get_source(), tag=lMessageStatus.Get_tag()), lMessageStatus.Get_tag())) lMessageStatus = MPI.Status() recvSomething = True for i, reqTuple in enumerate(lRecvWaiting): if reqTuple[1].Test(): countRecv += 1 dataS = cPickle.loads(reqTuple[0].tostring()) if self.traceMode: etree.SubElement(self.traceTo, "msg", {"direc" : "in", "type" : str(dataS.msgType), "otherWorker" : str(dataS.senderWid), "msgtag" : str(reqTuple[2]), "time" : repr(time.time())}) self.recvQ.put(dataS) lRecvWaiting[i] = None recvSomething = True # Wake up the main thread if there's a sufficient number # of pending receives countRecvNotTransmit += 1 if countRecvNotTransmit > 50 or (time.time() - countRecvTimeInit > 0.1 and countRecvNotTransmit > 0): countRecvNotTransmit = 0 countRecvTimeInit = time.time() self.wakeUpMainThread.set() lRecvWaiting = filter(lambda d: not d is None, lRecvWaiting) if not isinstance(lRecvWaiting, list): lRecvWaiting = list(lRecvWaiting) while len(lSendWaiting) < DTM_CONCURRENT_SEND_LIMIT: # Send all pending sends, under the limit of # DTM_CONCURRENT_SEND_LIMIT try: sendMsg = self.sendQ.get_nowait() countSend += 1 sendMsg.sendTime = time.time() commA, buf1 = mpiSend(sendMsg, sendMsg.receiverWid) lSendWaiting.append((commA, buf1)) sendSomething = True except Queue.Empty: break lSendWaiting = filter(lambda d: not d[0].Test(), lSendWaiting) if not isinstance(lSendWaiting, list): # Python 3 lSendWaiting = list(lSendWaiting) if not recvSomething: time.sleep(self.random.uniform(DTM_MPI_MIN_LATENCY, DTM_MPI_MAX_LATENCY)) while len(lSendWaiting) > 0: # Send the lasts messages before shutdown lSendWaiting = filter(lambda d: not d[0].Test(), lSendWaiting) if not isinstance(lSendWaiting, list): # Python 3 lSendWaiting = list(lSendWaiting) time.sleep(self.random.uniform(DTM_MPI_MIN_LATENCY, DTM_MPI_MAX_LATENCY)) del lSendWaiting del lRecvWaiting deap-0.7.1/deap/dtm/commManagerTCP.py0000644000076500000240000003345311641072614017540 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . from multiprocessing.connection import Client,Listener import Queue import time import threading import sys import os import socket import subprocess from deap.dtm.abstractCommManager import AbstractCommThread DTM_TCP_MIN_LATENCY = 0.005 DTM_TCP_MAX_LATENCY = 0.01 DTM_CONCURRENT_RECV_LIMIT = 1000 DTM_CONCURRENT_SEND_LIMIT = 1000 BASE_COMM_PORT = 10011 class CommThread(AbstractCommThread): def __init__(self, recvQ, sendQ, mainThreadEvent, exitEvent, commReadyEvent, randomGenerator, cmdlineArgs): AbstractCommThread.__init__(self, recvQ, sendQ, mainThreadEvent, exitEvent, commReadyEvent, randomGenerator, cmdlineArgs) self.props = dict(dtmTCPGlobalLaunch = True, dtmTCPLocalLaunch = False, dtmTCPLineNo = -1, dtmTCPhosts = '', dtmTCPstartPort = BASE_COMM_PORT, dtmTCPnPortsByHost = -1, dtmTCPnbrWorkers = -1, dtmTCPmaxWorkersByHost = -1, dtmTCPisRootWorker = False) self.userArgs = "" indexArg = 1 while indexArg < len(cmdlineArgs): keyDataF = cmdlineArgs[indexArg][2:].split('=') if keyDataF[0] in self.props.keys(): self.props[keyDataF[0]] = keyDataF[1] else: self.userArgs += cmdlineArgs[indexArg] + " " indexArg += 1 if self.props['dtmTCPhosts'] == '': cmdlineArgs = cmdlineArgs[1].split(' ') self.userArgs = "" indexArg = 1 while indexArg < len(cmdlineArgs): keyDataF = cmdlineArgs[indexArg][2:].split('=') if keyDataF[0] in self.props.keys(): self.props[keyDataF[0]] = keyDataF[1] else: self.userArgs += cmdlineArgs[indexArg] + " " indexArg += 1 @property def poolSize(self): return len(self.tabConn)+1 # +1 because tabConn does not include ourselves @property def workerId(self): return (self.workerHost, self.workerHostId) @property def isRootWorker(self): return bool(int(self.props['dtmTCPisRootWorker'])) @property def isLaunchProcess(self): return bool(int(self.props['dtmTCPGlobalLaunch'])) or bool(int(self.props['dtmTCPLocalLaunch'])) def iterOverIDs(self): return self.tabConn.keys() + [self.workerId] def setTraceModeOn(self, xmlLog): # Profiling is not currently enabled with TCP return def run(self): # Looking if we are in "launch mode" or "execute mode" if int(self.props['dtmTCPGlobalLaunch']): # Global launch. Create a SSH connection with each node, and # execute a local launch on them. Then wait for termination sshWait = [] lineTab = [] maxWorkersByHost = 1 totalWorkers = 0 with open(self.props['dtmTCPhosts']) as hostFile: for line in hostFile: if line[0] == '#' or line[0] == '\n' or line[0] == '\r': continue lineTab.append(line.replace("\n", "").replace("\r", "").split(" ")) totalWorkers += int(lineTab[-1][1].split("=")[1]) if int(lineTab[-1][1].split("=")[1]) > maxWorkersByHost: maxWorkersByHost = int(lineTab[-1][1].split("=")[1]) for lineCount,hostH in enumerate(lineTab): if hostH[0] == 'localhost' or hostH[0] == '127.0.0.1' or hostH[0] == socket.gethostname(): # Local launch if len(lineTab) > 1: sys.stderr.write("Warning : 'localhost' used as a global hostname!\nDTM will probably hang.\nReplace localhost by the fully qualified name or run only on this host.") sys.stderr.flush() sshWait.append(subprocess.Popen(["python"+str(sys.version_info[0])+"."+str(sys.version_info[1]), self.cmdArgs[0], " --dtmTCPGlobalLaunch=0 --dtmTCPLocalLaunch=1 --dtmTCPLineNo="+str(lineCount) +\ " --dtmTCPhosts=" + str(self.props['dtmTCPhosts']) + " --dtmTCPnPortsByHost=" + str(maxWorkersByHost*(totalWorkers-1)) +\ " --dtmTCPstartPort="+ str(self.props['dtmTCPstartPort']) + " --dtmTCPnbrWorkers="+str(hostH[1].split("=")[1])+\ " --dtmTCPmaxWorkersByHost="+ str(maxWorkersByHost)+" "+ self.userArgs], stdout=sys.stdout, stderr=sys.stderr)) else: # Remote launch sshWait.append(subprocess.Popen(["/usr/bin/ssh", "-x", hostH[0], "cd "+str(os.getcwd()) +";", "python"+\ str(sys.version_info[0])+"."+str(sys.version_info[1])+" " + self.cmdArgs[0] +\ " --dtmTCPGlobalLaunch=0 --dtmTCPLocalLaunch=1 --dtmTCPLineNo="+str(lineCount) +\ " --dtmTCPhosts=" + str(self.props['dtmTCPhosts']) + " --dtmTCPnPortsByHost=" + str(maxWorkersByHost*(totalWorkers-1)) +\ " --dtmTCPstartPort="+ str(self.props['dtmTCPstartPort']) + " --dtmTCPnbrWorkers="+str(hostH[1].split("=")[1])+\ " --dtmTCPmaxWorkersByHost="+ str(maxWorkersByHost)+" "+ self.userArgs], stdout=sys.stdout, stderr=sys.stderr)) for connJob in sshWait: connJob.wait() self.commReadyEvent.set() return elif int(self.props['dtmTCPLocalLaunch']): # Local launch. Makes use of subprocess.popen to launch DTM workers # After that, wait until termination dtmLocalWorkersList = [] for i in range(int(self.props['dtmTCPnbrWorkers'])): if i == 0 and int(self.props['dtmTCPLineNo']) == 0: dtmLocalWorkersList.append(subprocess.Popen(["python"+str(sys.version_info[0])+"."+str(sys.version_info[1]), self.cmdArgs[0], " --dtmTCPGlobalLaunch=0 --dtmTCPLocalLaunch=0 --dtmTCPLineNo="+str(self.props['dtmTCPLineNo']) +\ " --dtmTCPhosts=" + str(self.props['dtmTCPhosts']) + " --dtmTCPnPortsByHost=" + str(self.props['dtmTCPnPortsByHost']) +\ " --dtmTCPstartPort="+ str(self.props['dtmTCPstartPort']) + " --dtmTCPnbrWorkers="+str(i)+\ " --dtmTCPmaxWorkersByHost="+ str(self.props['dtmTCPmaxWorkersByHost']) +" --dtmTCPisRootWorker=1", self.userArgs])) else: dtmLocalWorkersList.append(subprocess.Popen(["python"+str(sys.version_info[0])+"."+str(sys.version_info[1]), self.cmdArgs[0], " --dtmTCPGlobalLaunch=0 --dtmTCPLocalLaunch=0 --dtmTCPLineNo="+str(self.props['dtmTCPLineNo']) +\ " --dtmTCPhosts=" + str(self.props['dtmTCPhosts']) + " --dtmTCPnPortsByHost=" + str(self.props['dtmTCPnPortsByHost']) +\ " --dtmTCPstartPort="+ str(self.props['dtmTCPstartPort']) + " --dtmTCPnbrWorkers="+str(i)+\ " --dtmTCPmaxWorkersByHost="+ str(self.props['dtmTCPmaxWorkersByHost']), self.userArgs])) for dtmW in dtmLocalWorkersList: dtmW.wait() self.commReadyEvent.set() return # Here, we are ready to start DTM # We have to open the TCP ports and create the communication sockets # This is somewhat tricky, because two workers on the same node # must not have the same port number, and we do not want to bind # n^2 different ports (which will be the trivial way). # # Each worker as an ID which is a tuple (host, unique number) self.tabConn = {} listListeners = {} lineTab = [] self.workerHostId = int(self.props['dtmTCPnbrWorkers']) maxWorkersByHost = int(self.props['dtmTCPmaxWorkersByHost']) fullyhost = "boumbadaboum" with open(self.props['dtmTCPhosts']) as hostFile: for hostline in hostFile: if hostline[0] == '#' or hostline[0] == '\n' or hostline[0] == '\r': continue lineTab.append(hostline.replace("\n", "").replace("\r", "").split(" ")) if len(lineTab)-1 == int(self.props['dtmTCPLineNo']): fullyhost = lineTab[-1][0] # If our line number is GREATER than the other OR our uniq ID is # GREATER than the ID of another worker on our node, # we create the Client which will attempt to connect to the # listener opened by the other. for lineC,hostInfos in enumerate(lineTab): remoteIp = socket.gethostbyname(hostInfos[0]) if lineC > int(self.props['dtmTCPLineNo']): for i in range(0, int(hostInfos[1].split("=")[1])): try: listListeners[(hostInfos[0], i)] = Listener((fullyhost, int(self.props['dtmTCPstartPort']) +\ lineC*maxWorkersByHost + i + self.workerHostId*maxWorkersByHost*len(lineTab))) except Exception as ex: sys.stderr.write(str(("ERROR IN : ", "Listener", self.props['dtmTCPLineNo'], self.workerHostId, lineC, i, maxWorkersByHost, len(lineTab), " < ", remoteIp, lineC*maxWorkersByHost + i + self.workerHostId*maxWorkersByHost*len(lineTab))) + "\n\n") raise ex elif lineC == int(self.props['dtmTCPLineNo']): self.workerHost = hostInfos[0] for i in range(0, int(hostInfos[1].split("=")[1])): if i == self.workerHostId: continue # Pas de connexion avec nous-memes if i > self.workerHostId: try: listListeners[(hostInfos[0], i)] = Listener(('127.0.0.1', int(self.props['dtmTCPstartPort']) + lineC*maxWorkersByHost + i + self.workerHostId*maxWorkersByHost*len(lineTab))) except Exception as ex: sys.stderr.write(str(("ERROR IN : ", "Listener", self.props['dtmTCPLineNo'], self.workerHostId, lineC, i, maxWorkersByHost, len(lineTab), " => ", remoteIp, int(self.props['dtmTCPLineNo'])*maxWorkersByHost + i + self.workerHostId*maxWorkersByHost*len(lineTab))) +"\n\n\n") raise ex else: self.tabConn[(hostInfos[0], i)] = Client(('127.0.0.1', int(self.props['dtmTCPstartPort']) + lineC*maxWorkersByHost + self.workerHostId + i*maxWorkersByHost*len(lineTab))) else: # lineC < int(self.props['dtmTCPLineNo']) for i in range(0, int(hostInfos[1].split("=")[1])): self.tabConn[(hostInfos[0], i)] = Client((remoteIp, int(self.props['dtmTCPstartPort']) +\ int(self.props['dtmTCPLineNo'])*maxWorkersByHost + self.workerHostId + i*maxWorkersByHost*len(lineTab))) for le in listListeners.keys(): conn = listListeners[le].accept() self.tabConn[le] = conn self.commReadyEvent.set() working = True countRecvNotTransmit = 0 countRecvTimeInit = time.time() while working: recvSomething = False sendSomething = False if self.exitStatus.is_set(): # Exit # Warning : the communication thread MUST clear the sendQ # BEFORE leaving (the exiting orders must be send) working = False # Check for incoming messages for conn in self.tabConn.values(): if conn.poll(): # We received something try: self.recvQ.put(conn.recv()) countRecvNotTransmit += 1 except EOFError: working = False recvSomething = True if countRecvNotTransmit > 50 or (time.time() - countRecvTimeInit > 0.1 and countRecvNotTransmit > 0): countRecvNotTransmit = 0 countRecvTimeInit = time.time() self.wakeUpMainThread.set() while True: # Send all our messages try: sendMsg = self.sendQ.get_nowait() self.tabConn[sendMsg.receiverWid].send(sendMsg) sendSomething = True except Queue.Empty: break if not recvSomething: time.sleep(self.random.uniform(DTM_TCP_MIN_LATENCY, DTM_TCP_MAX_LATENCY)) deap-0.7.1/deap/dtm/dtmTypes.py0000644000076500000240000001535611641072614016556 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . DTM_TASK_STATE_IDLE = 0 DTM_TASK_STATE_RUNNING = 1 DTM_TASK_STATE_WAITING = 2 DTM_TASK_STATE_FINISH = 3 DTM_MSG_EXIT = 0 DTM_MSG_TASK = 1 DTM_MSG_REQUEST_TASK = 2 DTM_MSG_RESULT = 3 DTM_MSG_ACK_RECEIVED_TASK = 4 DTM_WAIT_NONE = 0 DTM_WAIT_SOME = 1 DTM_WAIT_ALL = 2 DTM_WAIT_ANY = 3 class TaskContainer(object): """ Contains all the information about a task (running or not) """ __slots__ = ('tid', # Unique ID of the task 'creatorWid', # ID of the worker who creates this task 'creatorTid', # ID of the task who creates this task (parent) 'taskIndex', # Position into the parents task childs list 'taskRoute', # Worker path followed by the job before begin 'creationTime', # Time at the job creation 'target', # Target function (or callable object) of the task 'args', # Arguments (list) 'kwargs', # Key-worded arguments (dictionnary) 'threadObject', # Python representation of the thread 'taskState') # State of the task (DTM_TASK_*) def __init__(self, **kwargs): self.__setstate__(kwargs) def __getstate__(self): d = {} for a in self.__slots__: d[a] = self.__getattribute__(a) return d def __setstate__(self, state): for t in state: self.__setattr__(t, state[t]) def __lt__(self, other): return self.creationTime < other.creationTime class ResultContainer(object): """ Used to store the result of a task so it can be sent """ __slots__ = ('tid', # ID of the task which produced these results 'parentTid', # Parent ID (waiting for these results) 'taskIndex', # Position into the parents task childs list 'execTime', # Total execution time (NOT waiting time) 'success', # False if an exception occured 'result') # The result; if an exception occured, contains it def __init__(self, **kwargs): self.__setstate__(kwargs) def __getstate__(self): d = {} for a in self.__slots__: d[a] = self.__getattribute__(a) return d def __setstate__(self, state): for t in state: self.__setattr__(t, state[t]) class ExceptedResultContainer(object): """ Keep the information of a result waited on the task creator side """ __slots__ = ('tids', # List of IDs of the tasks which produce these results 'waitingOn', # Is the parent task waiting on this result? 'finished', # Boolean : is the task finished (i.e. result received)? 'success', # Boolean : False if unfinished or if an exception occured 'callbackFunc', # Callback function, FOR USE IN DTM, NO ARGUMENTS PASSED, or None 'result') # Result, or the exception occured def __init__(self, **kwargs): self.__setstate__(kwargs) def __getstate__(self): d = {} for a in self.__slots__: d[a] = self.__getattribute__(a) return d def __setstate__(self, state): for t in state: self.__setattr__(t, state[t]) class WaitInfoContainer(object): """ Keep a track on the pending child tasks of a parent task. """ __slots__ = ('threadObject', # Python representation of the thread 'eventObject', # threading.Event flag (to wake up the thread) 'waitBeginningTime', # Time when the thread started waiting (0 if not) 'tasksWaitingCount', # How many tasks are we waiting on 'waitingMode', # DTM_WAIT_* : waiting mode (None, One, Any, All) 'rWaitingDict') # List of ExceptedResultContainer, key : the first task ID def __init__(self, **kwargs): self.__setstate__(kwargs) def __getstate__(self): d = {} for a in self.__slots__: d[a] = self.__getattribute__(a) return d def __setstate__(self, state): for t in state: self.__setattr__(t, state[t]) class StatsContainer(object): """ Contains stats about a target """ __slots__ = ('rAvg', # RELATIVE average execution time 'rStdDev', # RELATIVE standard deviation of the exec time 'rSquareSum', # Square sum of the RELATIVE exec times 'execCount') # Number of executions def __init__(self, **kwargs): self.__setstate__(kwargs) def __getstate__(self): d = {} for a in self.__slots__: d[a] = self.__getattribute__(a) return d def __setstate__(self, state): for t in state: self.__setattr__(t, state[t]) class MessageContainer(object): """ Generic message container If msgType == DTM_MSG_EXIT: msg = (Exit code, exit message) If msgType == DTM_MSG_TASK: msg = [TaskContainer, TaskContainer, TaskContainer, ...] If msgType == DTM_MSG_REQUEST_TASK: msg = None If msgType == DTM_MSG_RESULT: msg = [ResultContainer, ResultContainer, ResultContainer, ...] If msgType == DTM_MSG_ACK_RECEIVED_TASK: msg = AckId """ __slots__ = ('msgType', # Message type (DTM_MSG_*) 'senderWid', # Worker id of the sender 'receiverWid', # Worker id of the receiver 'loadsDict', # Load dictionnary of the sender 'targetsStats', # Stats on the tasks of the sender 'prepTime', # Time when it was ready to send 'sendTime', # Time when sent 'ackNbr', # ACK number (optionnal for some operations) 'msg') # Message (varies with msgType) def __init__(self, **kwargs): self.__setstate__(kwargs) def __getstate__(self): d = {} for a in self.__slots__: d[a] = self.__getattribute__(a) return d def __setstate__(self, state): for t in state: self.__setattr__(t, state[t]) deap-0.7.1/deap/dtm/loadBalancerPDB.py0000644000076500000240000002513111641072614017632 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import threading try: import Queue except ImportError: import queue as Queue import time import copy import logging try: from lxml import etree except ImportError: try: import xml.etree.cElementTree as etree except ImportError: # Python 2.5 import xml.etree.ElementTree as etree _logger = logging.getLogger("dtm.loadBalancing") DTM_ASK_FOR_TASK_DELAY = 2.5 DTM_SEND_TASK_DELAY = 0.001 DTM_RESTART_QUEUE_BLOCKING_FROM = 1. class LoadInfoContainer(object): """ Contains load information of a worker """ __slots__ = ('loadCurrentExec', # Load of the current exec task 'loadExecQ', # Load of the exec queue 'loadWaitingRestartQ', # Load of the waiting for restart queue 'loadWaitingQ', # Load of the waiting queue 'seqNbr', # Sequence number, only ++ by the worker which is its info 'infoUpToDate', # Boolean : is the dictionnary of the OTHER worker up to date with our info? 'ackWaiting') # List of ACKs (messages transmitted) def __init__(self,args): self.__setstate__(args) def __getstate__(self): d = {} for a in self.__slots__: d[a] = self.__getattribute__(a) return d def __setstate__(self,state): for t in state: self.__setattr__(t,state[t]) def sumRealLoad(self): return self.loadCurrentExec+self.loadExecQ+self.loadWaitingRestartQ class LoadBalancer(object): """ """ def __init__(self, workersIterator, workerId, execQueue, randomizer): self.wid = workerId self.ws = {} self.execQ = execQueue # Les autres queues ne sont pas necessaires self.wIter = workersIterator self.dLock = threading.Lock() self.tmpRecvJob = False self.localRandom = randomizer self.xmlLogger = None for w in workersIterator: self.ws[w] = LoadInfoContainer({'loadCurrentExec' : 0., 'loadExecQ' : 0., 'loadWaitingRestartQ' : 0., 'loadWaitingQ' : 0., 'seqNbr' : 0., 'infoUpToDate' : False, 'ackWaiting' : []}) self.totalExecLoad, self.totalEQueueLoad, self.totalWaitingRQueueLoad, self.totalWaitingQueueLoad = 0., 0., 0., 0. self.sendDelayMultiplier = DTM_SEND_TASK_DELAY*float(len(self.ws))**0.5 self.lastTaskSend = time.time() - self.sendDelayMultiplier self.lastQuerySend = time.time() def setTraceModeOn(self, xmlLogger): self.xmlLogger = xmlLogger def getNodesDict(self): return self.ws def updateSelfStatus(self, statusTuple): self.ws[self.wid].loadCurrentExec = statusTuple[0] self.ws[self.wid].loadExecQ = statusTuple[1] self.ws[self.wid].loadWaitingRestartQ = statusTuple[2] self.ws[self.wid].loadWaitingQ = statusTuple[3] self.ws[self.wid].seqNbr += 1 def mergeNodeStatus(self, otherDict): for wId in otherDict: if len(self.ws[wId].ackWaiting) == 0 and otherDict[wId].seqNbr > self.ws[wId].seqNbr and wId != self.wid: remInfoUpToDate = self.ws[wId].infoUpToDate self.ws[wId] = otherDict[wId] # Les deux dernieres infos sont "personnelles" self.ws[wId].infoUpToDate = remInfoUpToDate self.ws[wId].ackWaiting = [] def notifyTaskReceivedFrom(self, fromId): self.ws[fromId].infoUpToDate = True self.tmpRecvJob = True def acked(self, fromWorker, ackN): try: self.ws[fromWorker].ackWaiting.remove(ackN) except ValueError: print("ERROR : Tentative to delete an already received or non-existant ACK!", self.ws[fromWorker].ackWaiting, ackN) def takeDecision(self): MAX_PROB = 1. MIN_PROB = 0.00 sendTasksList = [] sendNotifList = [] if not self.xmlLogger is None: decisionLog = etree.SubElement(self.xmlLogger, "decision", {"time" : repr(time.time()), "selfLoad" : repr(self.ws[self.wid].loadCurrentExec)+","+repr(self.ws[self.wid].loadExecQ)+","+repr(self.ws[self.wid].loadWaitingRestartQ)+","+repr(self.ws[self.wid].loadWaitingQ)}) for workerId in self.ws: etree.SubElement(decisionLog, "workerKnownState", {"load" : repr(self.ws[workerId].loadCurrentExec)+","+repr(self.ws[workerId].loadExecQ)+","+repr(self.ws[workerId].loadWaitingRestartQ)+","+repr(self.ws[workerId].loadWaitingQ)}) listLoads = self.ws.values() self.totalExecLoad, self.totalEQueueLoad, self.totalWaitingRQueueLoad, self.totalWaitingQueueLoad = 0., 0., 0., 0. totalSum2 = 0. for r in listLoads: self.totalExecLoad += r.loadCurrentExec self.totalEQueueLoad += r.loadExecQ self.totalWaitingRQueueLoad += r.loadWaitingRestartQ self.totalWaitingQueueLoad += r.loadWaitingQ totalSum2 += (r.loadCurrentExec+r.loadExecQ+r.loadWaitingRestartQ)**2 avgLoad = (self.totalExecLoad + self.totalEQueueLoad + self.totalWaitingRQueueLoad) / float(len(self.ws)) stdDevLoad = (totalSum2/float(len(self.ws)) - avgLoad**2)**0.5 selfLoad = self.ws[self.wid].sumRealLoad() diffLoad = selfLoad - avgLoad listPossibleSendTo = list(filter(lambda d: d[1].infoUpToDate and d[1].sumRealLoad() > avgLoad, self.ws.items())) if selfLoad == 0 and len(listPossibleSendTo) > 0 and avgLoad != 0 and self.ws[self.wid].loadWaitingRestartQ < DTM_RESTART_QUEUE_BLOCKING_FROM: # Algorithme d'envoi de demandes de taches self.lastQuerySend = time.time() txtList = "" for worker in listPossibleSendTo: sendNotifList.append(worker[0]) txtList +=str(worker[0])+"," self.ws[worker[0]].infoUpToDate = False if not self.xmlLogger is None: etree.SubElement(decisionLog, "action", {"time" : repr(time.time()), "type" : "sendrequest", "destination":txtList}) if self.ws[self.wid].loadExecQ > 0 and diffLoad > -stdDevLoad and avgLoad != 0 and stdDevLoad != 0: # Algorithme d'envoi de taches def scoreFunc(loadi): if loadi < (avgLoad-2*stdDevLoad) or loadi == 0: return MAX_PROB # Si le load du worker est vraiment tres bas, forte probabilite de lui envoyer des taches elif loadi > (avgLoad + stdDevLoad): return MIN_PROB # Si le load du worker est tres haut, tres faible probabilite de lui envoyer des taches else: a = (MIN_PROB-MAX_PROB)/(3*stdDevLoad) b = MIN_PROB - a*(avgLoad + stdDevLoad) return a*loadi + b # Lineaire entre Avg-2*stdDev et Avg+stdDev scores = [(None,0)] * (len(self.ws)-1) # Gagne-t-on vraiment du temps en prechargeant la liste? i = 0 for worker in self.ws: if worker == self.wid: continue scores[i] = (worker, scoreFunc(self.ws[worker].sumRealLoad())) i += 1 if not self.xmlLogger is None: etree.SubElement(decisionLog, "action", {"time" : repr(time.time()), "type" : "checkavail", "destination":str(scores)}) while diffLoad > 0.00000001 and len(scores) > 0 and self.ws[self.wid].loadExecQ > 0.: selectedIndex = self.localRandom.randint(0,len(scores)-1) if self.localRandom.random() > scores[selectedIndex][1]: del scores[selectedIndex] continue widToSend = scores[selectedIndex][0] loadForeign = self.ws[widToSend] diffLoadForeign = loadForeign.sumRealLoad() - avgLoad sendT = 0. if diffLoadForeign < 0: # On veut lui envoyer assez de taches pour que son load = loadAvg sendT = diffLoadForeign*-1 if diffLoadForeign*-1 < self.ws[self.wid].loadExecQ else self.ws[self.wid].loadExecQ elif diffLoadForeign < stdDevLoad: # On veut lui envoyer assez de taches pour son load = loadAvg + stdDev sendT = stdDevLoad - diffLoadForeign if stdDevLoad - diffLoadForeign < self.ws[self.wid].loadExecQ else self.ws[self.wid].loadExecQ else: # On envoie une seule tache sendT = 0. tasksIDs, retiredTime = self.execQ.getTasksIDsWithExecTime(sendT) tasksObj = [] for tID in tasksIDs: t = self.execQ.getSpecificTask(tID) if not t is None: tasksObj.append(t) if len(tasksObj) > 0: diffLoad -= retiredTime self.ws[self.wid].loadExecQ -= retiredTime self.ws[widToSend].loadExecQ += retiredTime ackNbr = len(self.ws[widToSend].ackWaiting) self.ws[widToSend].ackWaiting.append(ackNbr) sendTasksList.append((widToSend, tasksObj, ackNbr)) del scores[selectedIndex] # On s'assure de ne pas reprendre le meme worker if not self.xmlLogger is None: etree.SubElement(decisionLog, "action", {"time" : repr(time.time()), "type" : "sendtasks", "destination":str([b[0] for b in sendTasksList]), "tasksinfo" : str([len(b[1]) for b in sendTasksList])}) self.lastTaskSend = time.time() return sendNotifList, sendTasksListdeap-0.7.1/deap/dtm/manager.py0000644000076500000240000017544211641072614016362 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import threading import time import math import random try: import Queue except ImportError: import queue as Queue import copy import pickle import logging import sys import os PRETTY_PRINT_SUPPORT = False try: from lxml import etree PRETTY_PRINT_SUPPORT = True except ImportError: try: import xml.etree.cElementTree as etree except ImportError: # Python 2.5 import xml.etree.ElementTree as etree from deap.dtm.dtmTypes import * logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) _logger = logging.getLogger("dtm.control") # Constantes DTM_CONTROL_THREAD_LATENCY = 0.05 MSG_COMM_TYPE = 0 MSG_SENDER_INFO = 1 MSG_NODES_INFOS = 2 DTM_LOGDIR_DEFAULT_NAME = "DtmLog" try: from math import erf except ImportError: def erf(x): # See http://stackoverflow.com/questions/457408/is-there-an-easily-available-implementation-of-erf-for-python # save the sign of x sign = 1 if x < 0: sign = -1 x = abs(x) # constants a1 = 0.254829592 a2 = -0.284496736 a3 = 1.421413741 a4 = -1.453152027 a5 = 1.061405429 p = 0.3275911 # A&S formula 7.1.26 t = 1.0 / (1.0 + p * x) y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * math.exp(-x * x) return sign * y # erf(-x) = -erf(x) class TaskIdGenerator(object): """ A thread-safe ID generator. A DTM task ID is a tuple looking like (workerId, uniqId) With MPI, workerId is an integer (rank) and the uniqIds start from 0, but workerId can be virtually any immutable object (as it is used as a dictionnary key) """ def __init__(self, rank, initId=0): self.r = rank self.wtid = 0 self.generatorLock = threading.Lock() @property def tid(self): self.generatorLock.acquire() newId = self.wtid self.wtid += 1 self.generatorLock.release() return (self.r, newId) class ExecInfo(object): """ Contains the information about the current task """ def __init__(self, tStats, tStatsLock): self.eLock = threading.Lock() self.tStats = tStats self.tStatsLock = tStatsLock self.eCurrent = None self.mainThread = threading.currentThread() self.piSqrt = math.sqrt(math.pi) self.sqrt2 = 2 ** 0.5 def _execTimeRemaining(self, mean, stdDev, alreadyDone): if alreadyDone == 0.: # If not started yet, return mean return mean # # Else compute the mass center of the remaining part of the gaussian # For instance, if we have a gaussian (mu, sigma) = (3, 4) # and that the task is executing for 3 seconds, then we may estimate # that it will probably finished at : # int(x*gaussian) / int(gaussian) over [3, +inf[ # (where int is integral) # # We get 6.192 seconds, that is an expected remaining time of # 6.192 - 3 = 3.192 seconds # Evaluate primitive at point 'alreadyDone' commonPart = erf(self.sqrt2 * (alreadyDone - mean) / (stdDev * 2)) areaPart1 = 0.5 * commonPart massCenterPart1 = (self.sqrt2 / (4 * self.piSqrt)) * (-2 * stdDev * math.exp(-0.5 * ((alreadyDone - mean) ** 2) / (stdDev ** 2)) + mean * (self.piSqrt) * (self.sqrt2) * commonPart) # Evaluate primitive at the infinite # erf(+inf) = 1, so areaPart2 = 0.5 # exp(-inf) = 0, and erf(+inf) = 1, so massCenterPart2 = mean / 2. if areaPart1 == areaPart2: # So far from the mean that erf(alreadyDone - moyenne) = 1 previsionPoint = alreadyDone + 0.5 # Hack : lets assume that there's 0.5 sec left... else: previsionPoint = (massCenterPart2 - massCenterPart1) / (areaPart2 - areaPart1) return previsionPoint - alreadyDone def acquire(self, forTask, blocking=True): if self.eLock.acquire(blocking): self.eCurrent = forTask return True else: return False def isLocked(self): if self.eLock.acquire(False): self.eLock.release() return False return True def release(self): self.eCurrent = None self.eLock.release() def getLoad(self): try: self.tStatsLock.acquire() tInfo = self.tStats.get(self.eCurrent.target, StatsContainer(rAvg=1., rStdDev=1., rSquareSum=0., execCount=0)) val = self._execTimeRemaining(tInfo.rAvg, tInfo.rStdDev, self.eCurrent.threadObject.timeExec) self.tStatsLock.release() return val except AttributeError: self.tStatsLock.release() return 0. class TaskQueue(object): """ """ def __init__(self, tasksStatsStruct, tasksStatsLock): self.tStats = tasksStatsStruct self.tStatsLock = tasksStatsLock self.previousLoad = 0. self.piSqrt = math.sqrt(math.pi) self.sqrt2 = 2 ** 0.5 self._tQueue = Queue.PriorityQueue() self._tDict = {} self._actionLock = threading.Lock() self.changed = True def _getTimeInfoAbout(self, task): if task.threadObject is None: timeDone = 0. else: timeDone = task.threadObject.timeExec self.tStatsLock.acquire() tInfo = self.tStats.get(task.target, StatsContainer(rAvg=1., rStdDev=1., rSquareSum=0., execCount=0)) self.tStatsLock.release() return self._execTimeRemaining(tInfo.rAvg, tInfo.rStdDev, timeDone) def _execTimeRemaining(self, mean, stdDev, alreadyDone): if alreadyDone == 0. or stdDev == 0: return mean # # Else compute the mass center of the remaining part of the gaussian # For instance, if we have a gaussian (mu, sigma) = (3, 4) # and that the task is executing for 3 seconds, then we may estimate # that it will probably finished at : # int(x*gaussian) / int(gaussian) over [3, +inf[ # (where int is integral) # # We get 6.192 seconds, that is an expected remaining time of # 6.192 - 3 = 3.192 seconds # Evaluate primitive at point 'alreadyDone' commonPart = erf(self.sqrt2 * (alreadyDone - mean) / (stdDev * 2)) areaPart1 = 0.5 * commonPart massCenterPart1 = (self.sqrt2 / (4 * self.piSqrt)) * (-2 * stdDev * math.exp(-0.5 * ((alreadyDone - mean) ** 2) / (stdDev ** 2)) + mean * (self.piSqrt) * (self.sqrt2) * commonPart) # Evaluate primitive at the infinite # erf(+inf) = 1, so areaPart2 = 0.5 # exp(-inf) = 0, and erf(+inf) = 1, so massCenterPart2 = mean / 2. if areaPart1 == areaPart2: # So far from the mean that erf(alreadyDone - moyenne) = 1 previsionPoint = alreadyDone + 0.5 # Hack : lets assume that there's 0.5 sec left... else: previsionPoint = (massCenterPart2 - massCenterPart1) / (areaPart2 - areaPart1) return previsionPoint - alreadyDone def put(self, taskObject): self.putList((taskObject,)) def putList(self, tasksList): self._actionLock.acquire() for taskObject in tasksList: self._tDict[taskObject.tid] = taskObject self._tQueue.put(taskObject) self.changed = True self._actionLock.release() def getTask(self): self._actionLock.acquire() while True: try: taskObject = self._tQueue.get_nowait() except Queue.Empty: self._actionLock.release() raise Queue.Empty if taskObject.tid in self._tDict: del self._tDict[taskObject.tid] self.changed = True self._actionLock.release() return taskObject def isTaskIn(self, taskId): return taskId in self._tDict def _getApproximateLoad(self): return self.previousLoad def getLen(self): return len(self._tDict) def getLoad(self): tmpLoad = 0. self._actionLock.acquire() if not self.changed: self._actionLock.release() return self.previousLoad for tid, tObj in self._tDict.items(): tmpLoad += self._getTimeInfoAbout(tObj) self.previousLoad = tmpLoad self.changed = False self._actionLock.release() return self.previousLoad def getSpecificTask(self, taskId): self._actionLock.acquire() if taskId in self._tDict: task = self._tDict[taskId] del self._tDict[taskId] self.changed = True else: task = None self._actionLock.release() return task def getTaskByExecTime(self, execTimeWanted, maxDiff= -1.): # maxDiff is the max difference wanted # If there is no task with the execTimeWanted +- maxDiff, # return None # maxDiff = -1 means that there is no max difference mostClose = None self._actionLock.acquire() for tid, tObj in self._tDict.items(): timeDiff = math.abs(execTimeWanted - self._getTimeInfoAbout(tObj)) if mostClose is None or mostClose[1] > timeDiff: mostClose = (tid, timeDiff) self._actionLock.release() if mostClose is None or (maxDiff >= 0 and mostClose[1] > maxDiff): return None else: return self.getSpecificTask(mostClose[0]) def getTasksIDsWithExecTime(self, execTimeWanted, maxDiff= -1.): # Return task list containing tasks which the durations approximately # sum to execTimeWanted returnList = [] totalTime = 0. self._actionLock.acquire() for tid, tObj in self._tDict.items(): timeInfo = self._getTimeInfoAbout(tObj) if totalTime + timeInfo <= execTimeWanted: returnList.append(tid) totalTime += timeInfo # # Hack to avoid the starting bug (few jobs but very long) # if len(returnList) == 0 and maxDiff == -1 and len(self._tDict) > 1: it = list(filter(lambda x: self._getTimeInfoAbout(x[1]) != 0, self._tDict.items())) if len(it) != 0: returnList.append(it[0][0]) totalTime += self._getTimeInfoAbout(it[0][1]) self._actionLock.release() return returnList, totalTime class Control(object): """ Control is the main DTM class. The dtm object you receive when you use ``from deap import dtm`` is a proxy over an instance of this class. Most of its methods are used by your program, in the execution tasks; however, two of thems (start() and setOptions()) MUST be called in the MainThread (i.e. the thread started by the Python interpreter). As this class is instancied directly in the module, initializer takes no arguments. """ def __init__(self): self.sTime = time.time() # Key : Target, Value : StatsContainer self.tasksStats = {} self.tasksStatsLock = threading.Lock() # Global execution lock self.dtmExecLock = ExecInfo(self.tasksStats, self.tasksStatsLock) self.waitingThreadsQueue = TaskQueue(self.tasksStats, self.tasksStatsLock) # Key : task ID, Value : WaitInfoContainer self.waitingThreads = {} self.waitingThreadsLock = threading.Lock() self.launchTaskLock = threading.Lock() self.waitingForRestartQueue = TaskQueue(self.tasksStats, self.tasksStatsLock) self.execQueue = TaskQueue(self.tasksStats, self.tasksStatsLock) self.recvQueue = Queue.Queue() # Contains MessageContainer self.sendQueue = Queue.Queue() # Contains MessageContainer self.exitStatus = threading.Event() self.commExitNotification = threading.Event() self.commReadyEvent = threading.Event() self.exitState = (None, None) self.exitSetHere = False # Will stop the main thread until an event occurs self.runningFlag = threading.Event() self.runningFlag.set() self.commManagerType = "deap.dtm.commManagerMpi4py" self.loadBalancerType = "deap.dtm.loadBalancerPDB" self.printExecSummary = True self.isStarted = False self.traceMode = False self.traceRoot = None # Root element of the XML log self.traceTasks = None # XML elements self.traceComm = None # defined if traceMode == True self.traceLoadB = None self.traceLock = threading.Lock() self.refTime = 1. self.loadBalancer = None self.lastRetValue = None self.dtmRandom = random.Random() def _doCleanUp(self): """ Clean up function, called at this end of the execution. Should NOT be called by the user. """ #if self.printExecSummary: #_logger.info("[%s] did %i tasks", str(self.workerId), self._DEBUG_COUNT_EXEC_TASKS) if self.exitSetHere: for n in self.commThread.iterOverIDs(): if n == self.workerId: continue self.sendQueue.put(MessageContainer(msgType = DTM_MSG_EXIT, senderWid = self.workerId, receiverWid = n, loadsDict = None, targetsStats = None, prepTime = time.time(), sendTime = 0, ackNbr = -1, msg = (0, "Exit with success"))) self.commExitNotification.set() self.commThread.join() del self.execQueue del self.sendQueue del self.recvQueue countThreads = sum([1 for th in threading.enumerate() if not th.daemon]) if countThreads > 1: _logger.warning("[%s] There's more than 1 active thread (%i) at the exit.", str(self.workerId), threading.activeCount()) if self.commThread.isRootWorker: if self.printExecSummary: msgT = " Tasks execution times summary :\n" for target, times in self.tasksStats.items(): msgT += "\t" + str(target) + " : Avg=" + str(times.rAvg * self.refTime) + ", StdDev=" + str(times.rStdDev * self.refTime) + "\n\n" _logger.info(msgT) _logger.info("DTM execution ended (no more tasks)") _logger.info("Total execution time : %s", str(time.time() - self.sTime)) if self.traceMode: _logger.info("Writing log to the log files...") self.traceLock.acquire() fstat = etree.SubElement(self.traceRoot, "finalStats") for target, times in self.tasksStats.items(): etree.SubElement(fstat, "taskinfo", {"target" : str(target), "avg" : repr(times.rAvg * self.refTime), "stddev" : repr(times.rStdDev * self.refTime), "execcount" : str(times.execCount)}) flog = open(DTM_LOGDIR_DEFAULT_NAME + "/log" + str(self.workerId) + ".xml", 'w') if PRETTY_PRINT_SUPPORT: flog.write(etree.tostring(self.traceRoot, pretty_print=True)) else: flog.write(etree.tostring(self.traceRoot)) flog.close() self.traceLock.release() if self.exitSetHere: if self.lastRetValue[0]: return self.lastRetValue[1] else: raise self.lastRetValue[1] def _addTaskStat(self, taskKey, timeExec): # The execution time is based on the calibration ref time comparableLoad = timeExec / self.refTime # We do not keep up all the execution times, but # update the mean and stddev realtime self.tasksStatsLock.acquire() if not taskKey in self.tasksStats: self.tasksStats[taskKey] = StatsContainer(rAvg = timeExec, rStdDev = 0., rSquareSum = timeExec * timeExec, execCount = 1) else: oldAvg = self.tasksStats[taskKey].rAvg oldStdDev = self.tasksStats[taskKey].rStdDev oldSum2 = self.tasksStats[taskKey].rSquareSum oldExecCount = self.tasksStats[taskKey].execCount self.tasksStats[taskKey].rAvg = (timeExec + oldAvg * oldExecCount) / (oldExecCount + 1) self.tasksStats[taskKey].rSquareSum = oldSum2 + timeExec * timeExec self.tasksStats[taskKey].rStdDev = abs(self.tasksStats[taskKey].rSquareSum / (oldExecCount + 1) - self.tasksStats[taskKey].rAvg ** 2) ** 0.5 self.tasksStats[taskKey].execCount = oldExecCount + 1 self.tasksStatsLock.release() def _calibrateExecTime(self, runsN=3): """ Small calibration test, run at startup Should not be called by user """ timesList = [] for r in range(runsN): timeS = time.clock() a = 0. for i in range(10000): a = math.sqrt(self.dtmRandom.random() / (self.dtmRandom.uniform(0, i) + 1)) strT = "" for i in range(5000): strT += str(self.dtmRandom.randint(0, 9999)) for i in range(500): pickStr = pickle.dumps(strT) strT = pickle.loads(pickStr) timesList.append(time.clock() - timeS) return sorted(timesList)[int(runsN / 2)] def _getLoadTuple(self): return (self.dtmExecLock.getLoad(), self.execQueue.getLoad(), self.waitingForRestartQueue.getLoad(), self.waitingThreadsQueue.getLoad()) def _returnResult(self, idToReturn, resultInfo): """ Called by the execution threads when they have to return a result Should NOT be called explicitly by the user """ if idToReturn == self.workerId: self._dispatchResults((resultInfo,)) else: self.sendQueue.put(MessageContainer(msgType = DTM_MSG_RESULT, senderWid = self.workerId, receiverWid = idToReturn, loadsDict = self.loadBalancer.getNodesDict(), targetsStats = self.tasksStats, prepTime = time.time(), sendTime = 0, ackNbr = -1, msg = (resultInfo,))) def _updateStats(self, msg): """ Called by the control thread to update its dictionnary Should NOT be called explicitly by the user """ self.loadBalancer.mergeNodeStatus(msg.loadsDict) self.tasksStatsLock.acquire() for key, val in msg.targetsStats.items(): if not key in self.tasksStats or val.execCount > self.tasksStats[key].execCount: self.tasksStats[key] = val self.tasksStatsLock.release() def _dispatchResults(self, resultsList): """ Called by the control thread when a message is received; Dispatch it to the task waiting for it. Should NOT be called explicitly by the user """ for result in resultsList: self.waitingThreadsLock.acquire() # We look for the task waiting for each result foundKey = None for taskKey in self.waitingThreads[result.parentTid].rWaitingDict: try: self.waitingThreads[result.parentTid].rWaitingDict[taskKey].tids.remove(result.tid) except ValueError: # The ID is not in this waiting list, continue with other worker continue foundKey = taskKey if isinstance(self.waitingThreads[result.parentTid].rWaitingDict[taskKey].result, list): if not result.success: # Exception occured self.waitingThreads[result.parentTid].rWaitingDict[taskKey].result = result.result else: self.waitingThreads[result.parentTid].rWaitingDict[taskKey].result[result.taskIndex] = result.result break assert not foundKey is None, "Parent task not found for result dispatch!" # Debug if len(self.waitingThreads[result.parentTid].rWaitingDict[foundKey].tids) == 0: # All tasks done self.waitingThreads[result.parentTid].rWaitingDict[foundKey].finished = True if isinstance(self.waitingThreads[result.parentTid].rWaitingDict[foundKey].result, list): self.waitingThreads[result.parentTid].rWaitingDict[foundKey].success = True canRestart = False if self.waitingThreads[result.parentTid].rWaitingDict[foundKey].waitingOn == True or self.waitingThreads[result.parentTid].waitingMode == DTM_WAIT_ALL: if (self.waitingThreads[result.parentTid].waitingMode == DTM_WAIT_ALL and len(self.waitingThreads[result.parentTid].rWaitingDict) == 1)\ or self.waitingThreads[result.parentTid].waitingMode == DTM_WAIT_ANY: canRestart = True elif self.waitingThreads[result.parentTid].waitingMode == DTM_WAIT_SOME: canRestart = True for rKey in self.waitingThreads[result.parentTid].rWaitingDict: if self.waitingThreads[result.parentTid].rWaitingDict[rKey].waitingOn and rKey != foundKey: canRestart = False if not self.waitingThreads[result.parentTid].rWaitingDict[taskKey].callbackFunc is None: self.waitingThreads[result.parentTid].rWaitingDict[taskKey].callbackFunc() if canRestart: wTask = self.waitingThreadsQueue.getSpecificTask(result.parentTid) assert not wTask is None self.waitingForRestartQueue.put(wTask) self.waitingThreadsLock.release() def _startNewTask(self): """ Start a new task (if there's one available) Return True if so Should NOT be called explicitly by the user """ taskLauched = False self.launchTaskLock.acquire() if not self.dtmExecLock.isLocked(): try: wTask = self.waitingForRestartQueue.getTask() self.dtmExecLock.acquire(wTask) wTask.threadObject.waitingFlag.set() taskLauched = True except Queue.Empty: pass if not taskLauched: try: newTask = self.execQueue.getTask() if self.traceMode: self.traceLock.acquire() newTaskElem = etree.SubElement(self.traceTasks, "task", {"id" : str(newTask.tid), "creatorTid" : str(newTask.creatorTid), "creatorWid" : str(newTask.creatorWid), "taskIndex" : str(newTask.taskIndex), "creationTime" : repr(newTask.creationTime)}) try: newTaskTarget = etree.SubElement(newTaskElem, "target", {"name" : str(newTask.target.__name__)}) except AttributeError: newTaskTarget = etree.SubElement(newTaskElem, "target", {"name" : str(newTask.target)}) for i, a in enumerate(newTask.args): newTaskTarget.set("arg" + str(i), str(a)) for k in newTask.kwargs: newTaskTarget.set("kwarg_" + str(k), str(newTask.kwargs[k])) newTaskPath = etree.SubElement(newTaskElem, "path", {"data" : str(newTask.taskRoute)}) self.traceLock.release() newThread = DtmThread(newTask, self, newTaskElem) else: newThread = DtmThread(newTask, self) self.dtmExecLock.acquire(newTask) newThread.start() taskLauched = True except Queue.Empty: pass self.launchTaskLock.release() return taskLauched def _main(self): """ Main loop of the control thread Should NOT be called explicitly by the user """ timeBegin = time.time() while True: self.runningFlag.wait() # WARNING, may deadlock on very specific conditions self.runningFlag.clear() # (if the _last_ task do a set() between those 2 lines, nothing will wake up the main thread) while True: try: recvMsg = self.recvQueue.get_nowait() if recvMsg.msgType == DTM_MSG_EXIT: self.exitStatus.set() self.exitState = (recvMsg.msg[1], recvMsg.msg[0]) break elif recvMsg.msgType == DTM_MSG_TASK: self.execQueue.putList(recvMsg.msg) self.loadBalancer.updateSelfStatus(self._getLoadTuple()) self.sendQueue.put(MessageContainer(msgType = DTM_MSG_ACK_RECEIVED_TASK, senderWid = self.workerId, receiverWid = recvMsg.senderWid, loadsDict = self.loadBalancer.getNodesDict(), targetsStats = self.tasksStats, prepTime = time.time(), sendTime = 0, ackNbr = -1, msg = recvMsg.ackNbr)) self._updateStats(recvMsg) elif recvMsg.msgType == DTM_MSG_RESULT: self._dispatchResults(recvMsg.msg) self._updateStats(recvMsg) elif recvMsg.msgType == DTM_MSG_REQUEST_TASK: self._updateStats(recvMsg) elif recvMsg.msgType == DTM_MSG_ACK_RECEIVED_TASK: self.loadBalancer.acked(recvMsg.senderWid, recvMsg.msg) self._updateStats(recvMsg) else: _logger.warning("[%s] Unknown message type %s received will be ignored.", str(self.workerId), str(recvMsg.msgType)) except Queue.Empty: break if self.exitStatus.is_set(): break currentNodeStatus = self._getLoadTuple() self.loadBalancer.updateSelfStatus(currentNodeStatus) sendUpdateList, sendTasksList = self.loadBalancer.takeDecision() self.tasksStatsLock.acquire() for sendInfo in sendTasksList: self.sendQueue.put(MessageContainer(msgType = DTM_MSG_TASK, senderWid = self.workerId, receiverWid = sendInfo[0], loadsDict = self.loadBalancer.getNodesDict(), targetsStats = self.tasksStats, prepTime = time.time(), sendTime = 0, ackNbr = sendInfo[2], msg = sendInfo[1])) for updateTo in sendUpdateList: self.sendQueue.put(DtmMessageContainer(msgType = DTM_MSG_REQUEST_TASK, senderWid = self.workerId, receiverWid = updateTo, loadsDict = self.loadBalancer.getNodesDict(), targetsStats = self.tasksStats, prepTime = time.time(), sendTime = 0, ackNbr = -1, msg = None)) self.tasksStatsLock.release() self._startNewTask() return self._doCleanUp() def setOptions(self, *args, **kwargs): """ Set a DTM global option. .. warning:: This function must be called BEFORE ``start()``. It is also the user responsability to ensure that the same option is set on every worker. Currently, the supported options are : * **communicationManager** : can be *deap.dtm.mpi4py* (default) or *deap.dtm.commManagerTCP*. * **loadBalancer** : currently only the default *PDB* is available. * **printSummary** : if set, DTM will print a task execution summary at the end (mean execution time of each tasks, how many tasks did each worker do, ...) * **setTraceMode** : if set, will enable a special DTM tracemode. In this mode, DTM logs all its activity in XML files (one by worker). Mainly for DTM debug purpose, but can also be used for profiling. This function can be called more than once. Any unknown parameter will have no effect. """ if self.isStarted: if self.commThread.isRootWorker: _logger.warning("dtm.setOptions() was called after dtm.start(); options will not be considered") return for opt in kwargs: if opt == "communicationManager": self.commManagerType = kwargs[opt] elif opt == "loadBalancer": self.loadBalancerType = kwargs[opt] elif opt == "printSummary": self.printExecSummary = kwargs[opt] elif opt == "setTraceMode": self.traceMode = kwargs[opt] elif self.commThread.isRootWorker: _logger.warning("Unknown option '%s'", opt) def start(self, initialTarget, *args, **kwargs): """ Start the execution with the target `initialTarget`. Calling this function create and launch the first task on the root worker (defined by the communication manager, for instance, with MPI, the root worker is the worker with rank 0.). .. warning:: This function must be called only ONCE, and after the target has been parsed by the Python interpreter. """ self.isStarted = True try: tmpImport = __import__(self.commManagerType, globals(), locals(), ['CommThread'], 0) if not hasattr(tmpImport, 'CommThread'): raise ImportError CommThread = tmpImport.CommThread except ImportError: _logger.warning("Warning : %s is not a suitable communication manager. Default to commManagerMpi4py.", self.commManagerType) tmpImport = __import__('deap.dtm.commManagerMpi4py', globals(), locals(), ['CommThread'], 0) CommThread = tmpImport.CommThread try: tmpImport = __import__(self.loadBalancerType, globals(), locals(), ['LoadBalancer'], 0) if not hasattr(tmpImport, 'LoadBalancer'): raise ImportError LoadBalancer = tmpImport.LoadBalancer except ImportError: _logger.warning("Warning : %s is not a suitable load balancer. Default to loadBalancerPDB.", self.loadBalancerType) tmpImport = __import__('deap.dtm.loadBalancerPDB', globals(), locals(), ['LoadBalancer'], 0) LoadBalancer = tmpImport.LoadBalancer self.commThread = CommThread(self.recvQueue, self.sendQueue, self.runningFlag, self.commExitNotification, self.commReadyEvent, self.dtmRandom, sys.argv) self.commThread.start() self.refTime = self._calibrateExecTime() self.commReadyEvent.wait() if self.commThread.isLaunchProcess: sys.exit() self.poolSize = self.commThread.poolSize self.workerId = self.commThread.workerId self.idGenerator = TaskIdGenerator(self.workerId) self.loadBalancer = LoadBalancer(self.commThread.iterOverIDs(), self.workerId, self.execQueue, self.dtmRandom) if self.traceMode: self.traceLock.acquire() self.traceRoot = etree.Element("dtm", {"version" : str(0.7), "workerId" : str(self.workerId), "timeBegin" : repr(self.sTime)}) self.traceTasks = etree.SubElement(self.traceRoot, "tasksLog") self.traceComm = etree.SubElement(self.traceRoot, "commLog") self.traceLoadB = etree.SubElement(self.traceRoot, "loadBalancerLog") self.traceLock.release() self.commThread.setTraceModeOn(self.traceComm) self.loadBalancer.setTraceModeOn(self.traceLoadB) if self.commThread.isRootWorker: if self.traceMode: # Create the log folder try: os.mkdir(DTM_LOGDIR_DEFAULT_NAME) except OSError: _logger.warning("Log folder '" + DTM_LOGDIR_DEFAULT_NAME + "' already exists!") _logger.info("DTM started with %i workers", self.poolSize) _logger.info("DTM load balancer is %s, and communication manager is %s", self.loadBalancerType, self.commManagerType) initTask = TaskContainer(tid = self.idGenerator.tid, creatorWid = self.workerId, creatorTid = None, taskIndex = 0, taskRoute = [self.workerId], creationTime = time.time(), target = initialTarget, args = args, kwargs = kwargs, threadObject = None, taskState = DTM_TASK_STATE_IDLE) self.execQueue.put(initTask) return self._main() # The following methods are NOT called by the control thread, but by the EXECUTION THREADS # All the non-local objects used MUST be thread-safe def map(self, function, *iterables, **kwargs): """ A parallel equivalent of the :func:`map` built-in function. It blocks till the result is ready. This method chops the iterables into a number of chunks determined by DTM in order to get the most efficient use of the workers. It takes any number of iterables (though it will shrink all of them to the len of the smallest one), and any others kwargs that will be transmitted as is to the *function* target. """ cThread = threading.currentThread() currentId = cThread.tid zipIterable = list(zip(*iterables)) listResults = [None] * len(zipIterable) listTasks = [] listTids = [] for index, elem in enumerate(zipIterable): task = TaskContainer(tid = self.idGenerator.tid, creatorWid = self.workerId, creatorTid = currentId, taskIndex = index, taskRoute = [self.workerId], creationTime = time.time(), target = function, args = elem, kwargs = kwargs, threadObject = None, taskState = DTM_TASK_STATE_IDLE) listTasks.append(task) listTids.append(task.tid) if self.traceMode: self.traceLock.acquire() newTaskElem = etree.SubElement(cThread.xmlTrace, "event", {"type" : "map", "time" : repr(time.time()), "target" : str(function.__name__), "childTasks" : str(listTids)}) self.traceLock.release() self.waitingThreadsLock.acquire() if currentId not in self.waitingThreads.keys(): self.waitingThreads[currentId] = WaitInfoContainer(threadObject = cThread, eventObject = cThread.waitingFlag, waitBeginningTime = 0, tasksWaitingCount = 0, waitingMode = DTM_WAIT_NONE, rWaitingDict = {}) resultKey = listTids[0] self.waitingThreads[currentId].rWaitingDict[resultKey] = ExceptedResultContainer(tids = listTids, waitingOn = True, finished = False, success = False, callbackFunc = None, result = listResults) self.waitingThreads[currentId].tasksWaitingCount += len(listTasks) self.waitingThreads[currentId].waitingMode = DTM_WAIT_SOME self.waitingThreadsQueue.put(cThread.taskInfo) self.waitingThreads[currentId].waitBeginningTime = time.time() self.waitingThreadsLock.release() self.execQueue.putList(listTasks) cThread.waitForResult() self.waitingThreadsLock.acquire() ret = self.waitingThreads[currentId].rWaitingDict[resultKey].result if self.waitingThreads[currentId].rWaitingDict[resultKey].success == False: # Exception occured del self.waitingThreads[currentId].rWaitingDict[resultKey] self.waitingThreadsLock.release() raise ret else: del self.waitingThreads[currentId].rWaitingDict[resultKey] self.waitingThreadsLock.release() return ret def map_async(self, function, iterable, callback=None): """ A non-blocking variant of the :func:`~deap.dtm.taskmanager.Control.map` method which returns a :class:`~deap.dtm.taskmanager.AsyncResult` object. .. note:: As on version 0.2, callback is not implemented. """ cThread = threading.currentThread() currentId = cThread.tid listResults = [None] * len(iterable) listTasks = [] listTids = [] for index, elem in enumerate(iterable): task = TaskContainer(tid = self.idGenerator.tid, creatorWid = self.workerId, creatorTid = currentId, taskIndex = index, taskRoute = [self.workerId], creationTime = time.time(), target = function, args = (elem,), kwargs = {}, threadObject = None, taskState = DTM_TASK_STATE_IDLE) listTasks.append(task) listTids.append(task.tid) resultKey = listTids[0] if self.traceMode: newTaskElem = etree.SubElement(cThread.xmlTrace, "event", {"type" : "map_async", "time" : repr(time.time()), "target" : str(function.__name__), "childTasks" : str(listTids)}) self.waitingThreadsLock.acquire() if currentId not in self.waitingThreads.keys(): self.waitingThreads[currentId] = WaitInfoContainer(threadObject = cThread, eventObject = cThread.waitingFlag, waitBeginningTime = 0, tasksWaitingCount = 0, waitingMode = DTM_WAIT_NONE, rWaitingDict = {}) self.waitingThreads[currentId].rWaitingDict[resultKey] = ExceptedResultContainer(tids = listTids, waitingOn = False, finished = False, success = False, callbackFunc = None, result = listResults) self.waitingThreads[currentId].waitingMode = DTM_WAIT_NONE asyncRequest = AsyncResult(self, self.waitingThreads[currentId], resultKey) self.waitingThreads[currentId].rWaitingDict[resultKey].callbackFunc = asyncRequest._dtmCallback self.waitingThreadsLock.release() self.execQueue.putList(listTasks) self.runningFlag.set() return asyncRequest def apply(self, function, *args, **kwargs): """ Equivalent of the :func:`apply` built-in function. It blocks till the result is ready. Given this blocks, :func:`~deap.dtm.taskmanager.Control.apply_async()` is better suited for performing work in parallel. Additionally, the passed in function is only executed in one of the workers of the pool. """ cThread = threading.currentThread() currentId = cThread.tid task = TaskContainer(tid = self.idGenerator.tid, creatorWid = self.workerId, creatorTid = currentId, taskIndex = 0, taskRoute = [self.workerId], creationTime = time.time(), target = function, args = args, kwargs = kwargs, threadObject = None, taskState = DTM_TASK_STATE_IDLE) if self.traceMode: newTaskElem = etree.SubElement(cThread.xmlTrace, "event", {"type" : "apply", "time" : repr(time.time()), "target" : str(function.__name__), "childTasks" : str([task.tid])}) self.waitingThreadsLock.acquire() if currentId not in self.waitingThreads.keys(): self.waitingThreads[currentId] = WaitInfoContainer(threadObject = cThread, eventObject = cThread.waitingFlag, waitBeginningTime = 0, tasksWaitingCount = 0, waitingMode = DTM_WAIT_NONE, rWaitingDict = {}) resultKey = task.tid self.waitingThreads[currentId].rWaitingDict[resultKey] = ExceptedResultContainer(tids = [task.tid], waitingOn = True, finished = False, success = False, callbackFunc = None, result = [None]) self.waitingThreads[currentId].tasksWaitingCount += 1 self.waitingThreads[currentId].waitingMode = DTM_WAIT_SOME self.waitingThreadsQueue.put(cThread.taskInfo) self.waitingThreads[currentId].waitBeginningTime = time.time() self.waitingThreadsLock.release() self.execQueue.put(task) cThread.waitForResult() self.waitingThreadsLock.acquire() ret = self.waitingThreads[currentId].rWaitingDict[resultKey].result if self.waitingThreads[currentId].rWaitingDict[resultKey].success == False: # Exception occured del self.waitingThreads[currentId].rWaitingDict[resultKey] self.waitingThreadsLock.release() raise ret else: del self.waitingThreads[currentId].rWaitingDict[resultKey] self.waitingThreadsLock.release() return ret[0] def apply_async(self, function, *args, **kwargs): """ A non-blocking variant of the :func:`~deap.dtm.taskmanager.Control.apply` method which returns a :class:`~deap.dtm.taskmanager.AsyncResult` object. """ cThread = threading.currentThread() currentId = cThread.tid task = TaskContainer(tid = self.idGenerator.tid, creatorWid = self.workerId, creatorTid = currentId, taskIndex = 0, taskRoute = [self.workerId], creationTime = time.time(), target = function, args = args, kwargs = kwargs, threadObject = None, taskState = DTM_TASK_STATE_IDLE) if self.traceMode: newTaskElem = etree.SubElement(cThread.xmlTrace, "event", {"type" : "apply_async", "time" : repr(time.time()), "target" : str(function.__name__), "childTasks" : str([task.tid])}) self.waitingThreadsLock.acquire() if currentId not in self.waitingThreads.keys(): self.waitingThreads[currentId] = WaitInfoContainer(threadObject = cThread, eventObject = cThread.waitingFlag, waitBeginningTime = 0, tasksWaitingCount = 0, waitingMode = DTM_WAIT_NONE, rWaitingDict = {}) resultKey = task.tid self.waitingThreads[currentId].rWaitingDict[resultKey] = ExceptedResultContainer(tids = [task.tid], waitingOn = False, finished = False, success = False, callbackFunc = None, result = [None]) self.waitingThreads[currentId].waitingMode = DTM_WAIT_NONE asyncRequest = AsyncResult(self, self.waitingThreads[currentId], resultKey) self.waitingThreads[currentId].rWaitingDict[resultKey].callbackFunc = asyncRequest._dtmCallback self.waitingThreadsLock.release() self.execQueue.put(task) self.runningFlag.set() return asyncRequest def imap(self, function, iterable, chunksize=1): """ An equivalent of :func:`itertools.imap`. The chunksize argument can be used to tell DTM how many elements should be computed at the same time. For very long iterables using a large value for chunksize can make make the job complete much faster than using the default value of 1. """ currentIndex = 0 while currentIndex < len(iterable): maxIndex = currentIndex + chunksize if currentIndex + chunksize < len(iterable) else len(iterable) asyncResults = [None] * (maxIndex - currentIndex) for i in range(currentIndex, maxIndex): asyncResults[i % chunksize] = self.apply_async(function, iterable[i]) for result in asyncResults: ret = result.get() yield ret currentIndex = maxIndex def imap_unordered(self, function, iterable, chunksize=1): """ Not implemented yet. """ raise NotImplementedError def filter(self, function, iterable): """ Same behavior as the built-in :func:`filter`. The filtering is done localy, but the computation is distributed. """ results = self.map(function, iterable) return [item for result, item in zip(results, iterable) if result] def repeat(self, function, n, *args, **kwargs): """ Repeat the function *function* *n* times, with given args and keyworded args. Return a list containing the results. """ cThread = threading.currentThread() currentId = cThread.tid listResults = [None] * n listTasks = [] listTids = [] for index in range(n): task = TaskContainer(tid = self.idGenerator.tid, creatorWid = self.workerId, creatorTid = currentId, taskIndex = index, taskRoute = [self.workerId], creationTime = time.time(), target = function, args = args, kwargs = kwargs, threadObject = None, taskState = DTM_TASK_STATE_IDLE) listTasks.append(task) listTids.append(task.tid) self.waitingThreadsLock.acquire() if currentId not in self.waitingThreads.keys(): self.waitingThreads[currentId] = WaitInfoContainer(threadObject = cThread, eventObject = cThread.waitingFlag, waitBeginningTime = 0, tasksWaitingCount = 0, waitingMode = DTM_WAIT_NONE, rWaitingDict = {}) resultKey = listTids[0] self.waitingThreads[currentId].rWaitingDict[resultKey] = ExceptedResultContainer(tids = listTids, waitingOn = True, finished = False, success = False, callbackFunc = None, result = listResults) self.waitingThreads[currentId].tasksWaitingCount += len(listTasks) self.waitingThreads[currentId].waitingMode = DTM_WAIT_SOME self.waitingThreadsQueue.put(cThread.taskInfo) self.waitingThreads[currentId].waitBeginningTime = time.time() self.waitingThreadsLock.release() self.execQueue.putList(listTasks) cThread.waitForResult() self.waitingThreadsLock.acquire() ret = self.waitingThreads[currentId].rWaitingDict[resultKey].result if self.waitingThreads[currentId].rWaitingDict[resultKey].success == False: # Exception occured del self.waitingThreads[currentId].rWaitingDict[resultKey] self.waitingThreadsLock.release() raise ret else: del self.waitingThreads[currentId].rWaitingDict[resultKey] self.waitingThreadsLock.release() return ret def waitForAll(self): """ Wait for all pending asynchronous results. When this function returns, DTM guarantees that all ready() call on asynchronous tasks will return true. """ threadId = threading.currentThread().tid self.waitingThreadsLock.acquire() if threadId in self.waitingThreads and len(self.waitingThreads[threadId].rWaitingDict) > 0: self.waitingThreads[threadId].waitingMode = DTM_WAIT_ALL self.waitingThreadsQueue.put(threading.currentThread().taskInfo) self.waitingThreadsLock.release() threading.currentThread().waitForResult() self.waitingThreadsLock.acquire() self.waitingThreads[threadId].waitingMode = DTM_WAIT_NONE self.waitingThreadsLock.release() else: self.waitingThreadsLock.release() return return None def testAllAsync(self): """ Check whether all pending asynchronous tasks are done. It does not lock if it is not the case, but returns false. """ threadId = threading.currentThread().tid self.waitingThreadsLock.acquire() if threadId in self.waitingThreads: ret = len(self.waitingThreads[threadId].rWaitingDict) self.waitingThreadsLock.release() return False else: self.waitingThreadsLock.release() return True def getWorkerId(self): """ Return a unique ID for the current worker. Depending of the communication manager type, it can be virtually any Python immutable type. .. note:: With MPI, the value returned is the MPI slot number. """ return self.workerId class DtmThread(threading.Thread): """ DTM execution threads. Those are one of the main parts of DTM. They should not be created or called directly by the user. """ def __init__(self, structInfo, controlThread, xmlTrace=None): threading.Thread.__init__(self) self.taskInfo = structInfo # TaskContainer self.taskInfo.threadObject = self # Remind that we are the exec thread self.tid = structInfo.tid self.t = structInfo.target self.control = controlThread self.waitingFlag = threading.Event() self.waitingFlag.clear() self.timeExec = 0 self.timeBegin = 0 if structInfo.creatorTid is None: self.isRootTask = True else: self.isRootTask = False self.xmlTrace = xmlTrace def run(self): # The lock is already acquired for us self.taskInfo.taskState = DTM_TASK_STATE_RUNNING success = True self.timeBegin = time.time() if not self.xmlTrace is None: # Debug output in xml object self.control.traceLock.acquire() etree.SubElement(self.xmlTrace, "event", {"type" : "begin", "worker" : str(self.control.workerId), "time" : repr(self.timeBegin)}) self.control.traceLock.release() try: returnedR = self.t(*self.taskInfo.args, **self.taskInfo.kwargs) except Exception as expc: returnedR = expc strWarn = "An exception of type " + str(type(expc)) + " occured on worker " + str(self.control.workerId) + " while processing task " + str(self.tid) _logger.warning(strWarn) _logger.warning("This will be transfered to the parent task.") _logger.warning("Exception details : " + str(expc)) success = False self.timeExec += time.time() - self.timeBegin self.control.dtmExecLock.release() if not self.xmlTrace is None: # Debug output in xml object self.control.traceLock.acquire() etree.SubElement(self.xmlTrace, "event", {"type" : "end", "worker" : str(self.control.workerId), "time" : repr(time.time()), "execTime" : repr(self.timeExec), "success" : str(success)}) self.control.traceLock.release() if success: try: self.control._addTaskStat(self.t.__name__, self.timeExec) except AttributeError: self.control._addTaskStat(str(self.t), self.timeExec) if self.isRootTask: # Is this task the root task (launch by dtm.start)? If so, we quit self.control.lastRetValue = (success, returnedR) self.control.exitSetHere = True self.control.exitStatus.set() else: # Else, tell the communication thread to return the result resultStruct = ResultContainer(tid = self.tid, parentTid = self.taskInfo.creatorTid, taskIndex = self.taskInfo.taskIndex, execTime = self.timeExec, success = success, result = returnedR) self.control._returnResult(self.taskInfo.creatorWid, resultStruct) # Tell the control thread that something happened self.control._startNewTask() if self.isRootTask: self.control.runningFlag.set() self.control.waitingThreadsLock.acquire() if self.tid in self.control.waitingThreads.keys(): del self.control.waitingThreads[self.tid] self.control.waitingThreadsLock.release() self.taskInfo.taskState = DTM_TASK_STATE_FINISH def waitForResult(self): # Clear the execution lock, and sleep beginTimeWait = time.time() self.timeExec += beginTimeWait - self.timeBegin self.control.dtmExecLock.release() self.taskInfo.taskState = DTM_TASK_STATE_WAITING if not self.xmlTrace is None: # Debug output in xml object self.control.traceLock.acquire() etree.SubElement(self.xmlTrace, "event", {"type" : "sleep", "worker" : str(self.control.workerId), "time" : repr(beginTimeWait)}) self.control.traceLock.release() self.control._startNewTask() self.control.runningFlag.set() self.waitingFlag.wait() self.waitingFlag.clear() # At this point, we already have acquired the execution lock self.taskInfo.taskState = DTM_TASK_STATE_RUNNING self.timeBegin = time.time() if not self.xmlTrace is None: # Debug output in xml object self.control.traceLock.acquire() etree.SubElement(self.xmlTrace, "event", {"type" : "wakeUp", "worker" : str(self.control.workerId), "time" : repr(self.timeBegin)}) self.control.traceLock.release() class AsyncResult(object): """ The class of the result returned by :func:`~deap.dtm.taskmanager.Control.map_async()` and :func:`~deap.dtm.taskmanager.Control.apply_async()`. """ def __init__(self, control, waitingInfo, taskKey): self.control = control self.resultReturned = False self.resultSuccess = False self.resultVal = None self.taskKey = taskKey self.dictWaitingInfo = waitingInfo def _dtmCallback(self): # Used by DTM to inform the object that the job is done self.resultSuccess = self.dictWaitingInfo.rWaitingDict[self.taskKey].success self.resultVal = self.dictWaitingInfo.rWaitingDict[self.taskKey].result self.resultReturned = True del self.dictWaitingInfo.rWaitingDict[self.taskKey] def get(self): """ Return the result when it arrives. .. note:: This is a blocking call : caller will wait in this function until the result is ready. To check for the avaibility of the result, use :func:`~deap.dtm.taskmanager.AsyncResult.ready()`. """ if not self.resultReturned: self.wait() if self.resultSuccess: return self.resultVal else: raise self.resultVal def wait(self): """ Wait until the result is available """ self.control.waitingThreadsLock.acquire() if self.ready(): # This test MUST be protected by the mutex on waitingThreads self.control.waitingThreadsLock.release() return self.control.waitingThreads[threading.currentThread().tid].waitingMode = DTM_WAIT_SOME self.control.waitingThreads[threading.currentThread().tid].rWaitingDict[self.taskKey].waitingOn = True #self.dictWaitingInfo.waitingMode = DTM_WAIT_SOME #self.dictWaitingInfo.rWaitingDict[self.taskKey].waitingOn = True self.control.waitingThreadsQueue.put(threading.currentThread().taskInfo) self.control.waitingThreadsLock.release() threading.currentThread().waitForResult() def ready(self): """ Return whether the asynchronous task has completed. """ return self.resultReturned def successful(self): """ Return whether the task completed without error. Will raise AssertionError if the result is not ready. """ if not self.resultReturned: raise AssertionError("Call AsyncResult.successful() before the results were ready!") return self.resultSuccess deap-0.7.1/deap/gp.py0000644000076500000240000006403211641072614014562 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """The :mod:`gp` module provides the methods and classes to perform Genetic Programming with DEAP. It essentially contains the classes to build a Genetic Program Tree, and the functions to evaluate it. This module support both strongly and loosely typed GP. """ import copy import random import base from itertools import repeat from collections import defaultdict # Define the name of type for any types. __type__ = None ## GP Tree utility functions def evaluate(expr, pset=None): """Evaluate the expression *expr* into a string if *pset* is None or into Python code if *pset* is not None. """ def _stringify(expr): try: func = expr[0] return str(func(*[_stringify(value) for value in expr[1:]])) except TypeError: return str(expr) if not pset is None: try: return eval(_stringify(expr), pset.func_dict) except MemoryError: raise MemoryError,("DEAP : Error in tree evaluation :" " Python cannot evaluate a tree with a height bigger than 90. " "To avoid this problem, you should use bloat control on your " "operators. See the DEAP documentation for more information. " "DEAP will now abort.") else: return _stringify(expr) def evaluateADF(seq): """Evaluate a list of ADF and return a dict mapping the ADF name with its lambda function. """ adfdict = {} for i, expr in enumerate(reversed(seq[1:])): func = lambdify(expr.pset, expr) adfdict.update({expr.pset.__name__ : func}) for expr2 in reversed(seq[1:i+1]): expr2.pset.func_dict.update(adfdict) return adfdict def lambdify(pset, expr): """Return a lambda function of the expression *expr*. .. note:: This function is a stripped version of the lambdify function of sympy0.6.6. """ expr = evaluate(expr) args = ",".join(a for a in pset.arguments) lstr = "lambda %s: %s" % (args, expr) try: return eval(lstr, dict(pset.func_dict)) except MemoryError: raise MemoryError,("DEAP : Error in tree evaluation :" " Python cannot evaluate a tree with a height bigger than 90. " "To avoid this problem, you should use bloat control on your " "operators. See the DEAP documentation for more information. " "DEAP will now abort.") def lambdifyList(expr): """Return a lambda function created from a list of trees. The first element of the list is the main tree, and the following elements are automatically defined functions (ADF) that can be called by the first tree. """ adfdict = evaluateADF(expr) expr[0].pset.func_dict.update(adfdict) return lambdify(expr[0].pset, expr[0]) ## Loosely + Strongly Typed GP class Primitive(object): """Class that encapsulates a primitive and when called with arguments it returns the Python code to call the primitive with the arguments. >>> import operator >>> pr = Primitive(operator.mul, (int, int), int) >>> pr("1", "2") 'mul(1, 2)' """ def __init__(self, primitive, args, ret = __type__): self.name = primitive.__name__ self.arity = len(args) self.args = args self.ret = ret args = ", ".join(repeat("%s", self.arity)) self.seq = "%s(%s)" % (self.name, args) def __call__(self, *args): return self.seq % args def __repr__(self): return self.name class Operator(Primitive): """Class that encapsulates an operator and when called with arguments it returns the Python code to call the operator with the arguments. It acts as the Primitive class, but instead of returning a function and its arguments, it returns an operator and its operands. >>> import operator >>> op = Operator(operator.mul, (int, int), int) >>> op("1", "2") '(1 * 2)' >>> op2 = Operator(operator.neg, (int,), int) >>> op2(1) '-(1)' """ symbols = {"add" : "+", "sub" : "-", "mul" : "*", "div" : "/", "neg" : "-", "and_" : "and", "or_" : "or", "not_" : "not", "lt" : "<", "eq" : "==", "gt" : ">", "geq" : ">=", "leq" : "<="} def __init__(self, operator, args, ret = __type__): Primitive.__init__(self, operator, args, ret) if len(args) == 1: self.seq = "%s(%s)" % (self.symbols[self.name], "%s") elif len(args) == 2: self.seq = "(%s %s %s)" % ("%s", self.symbols[self.name], "%s") else: raise ValueError("Operator arity can be either 1 or 2.") class Terminal(object): """Class that encapsulates terminal primitive in expression. Terminals can be symbols, values, or 0-arity functions. """ def __init__(self, terminal, ret = __type__): self.ret = ret try: self.value = terminal.__name__ except AttributeError: self.value = terminal def __call__(self): return self def __repr__(self): return str(self.value) class Ephemeral(Terminal): """Class that encapsulates a terminal which value is set at run-time. The value of the `Ephemeral` can be regenerated with the method `regen`. """ def __init__(self, func, ret = __type__): self.func = func Terminal.__init__(self, self.func(), ret) def regen(self): """Regenerate the ephemeral value.""" self.value = self.func() class EphemeralGenerator(object): """Class that generates `Ephemeral` to be added to an expression.""" def __init__(self, ephemeral, ret = __type__): self.ret = ret self.name = ephemeral.__name__ self.func = ephemeral def __call__(self): return Ephemeral(self.func, self.ret) def __repr__(self): return self.name class PrimitiveSetTyped(object): """Class that contains the primitives that can be used to solve a Strongly Typed GP problem. The set also defined the researched function return type, and input arguments type and number. """ def __init__(self, name, in_types, ret_type, prefix = "ARG"): self.terminals = defaultdict(list) self.primitives = defaultdict(list) self.arguments = [] self.func_dict = dict() self.terms_count = 0 self.prims_count = 0 self.adfs_count = 0 self.__name__ = name self.ret = ret_type self.ins = in_types for i, type_ in enumerate(in_types): self.arguments.append(prefix + ("%s" % i)) PrimitiveSetTyped.addTerminal(self, self.arguments[-1], type_) def renameArguments(self, new_args): """Rename function arguments with new arguments name *new_args*. """ for i, argument in enumerate(self.arguments): if new_args.has_key(argument): self.arguments[i] = new_args[argument] for terminals in self.terminals.values(): for terminal in terminals: if ( isinstance(terminal, Terminal) and new_args.has_key(terminal.value) ): terminal.value = new_args[terminal.value] def addPrimitive(self, primitive, in_types, ret_type): """Add a primitive to the set. *primitive* is a callable object or a function. *in_types* is a list of argument's types the primitive takes. *ret_type* is the type returned by the primitive. """ try: prim = Operator(primitive, in_types, ret_type) except (KeyError, ValueError): prim = Primitive(primitive, in_types, ret_type) self.primitives[ret_type].append(prim) self.func_dict[primitive.__name__] = primitive self.prims_count += 1 def addTerminal(self, terminal, ret_type): """Add a terminal to the set. *terminal* is an object, or a function with no arguments. *ret_type* is the type of the terminal. """ if callable(terminal): self.func_dict[terminal.__name__] = terminal prim = Terminal(terminal, ret_type) self.terminals[ret_type].append(prim) self.terms_count += 1 def addEphemeralConstant(self, ephemeral, ret_type): """Add an ephemeral constant to the set. An ephemeral constant is a no argument function that returns a random value. The value of the constant is constant for a Tree, but may differ from one Tree to another. *ephemeral* function with no arguments that returns a random value. *ret_type* is the type of the object returned by the function. """ prim = EphemeralGenerator(ephemeral, ret_type) self.terminals[ret_type].append(prim) self.terms_count += 1 def addADF(self, adfset): """Add an Automatically Defined Function (ADF) to the set. *adfset* is a PrimitiveSetTyped containing the primitives with which the ADF can be built. """ prim = Primitive(adfset, adfset.ins, adfset.ret) self.primitives[adfset.ret].append(prim) self.prims_count += 1 @property def terminalRatio(self): """Return the ratio of the number of terminals on the number of all kind of primitives. """ return self.terms_count / float(self.terms_count + self.prims_count) class PrimitiveSet(PrimitiveSetTyped): """Class same as :class:`~deap.gp.PrimitiveSetTyped`, except there is no definition of type. """ def __init__(self, name, arity, prefix="ARG"): args = [__type__]*arity PrimitiveSetTyped.__init__(self, name, args, __type__, prefix) def addPrimitive(self, primitive, arity): """Add primitive *primitive* with arity *arity* to the set.""" assert arity > 0, "arity should be >= 1" args = [__type__] * arity PrimitiveSetTyped.addPrimitive(self, primitive, args, __type__) def addTerminal(self, terminal): """Add a terminal to the set.""" PrimitiveSetTyped.addTerminal(self, terminal, __type__) def addEphemeralConstant(self, ephemeral): """Add an ephemeral constant to the set.""" PrimitiveSetTyped.addEphemeralConstant(self, ephemeral, __type__) class PrimitiveTree(base.Tree): """Tree class faster than base Tree, optimized for Primitives.""" pset = None def _getstate(self): state = [] for elem in self: try: state.append(elem._getstate()) except AttributeError: state.append(elem) return state def __deepcopy__(self, memo): """Deepcopy a Tree by first converting it back to a list of list. This deepcopy is faster than the default implementation. From quick testing, up to 1.6 times faster, and at least 2 times less function calls. """ new = self.__class__(self._getstate()) new.__dict__.update(copy.deepcopy(self.__dict__, memo)) return new # Expression generation functions def genFull(pset, min_, max_, type_=__type__): """Generate an expression where each leaf has a the same depth between *min* and *max*. """ def condition(max_depth): """Expression generation stops when the depth is zero.""" return max_depth == 0 return _generate(pset, min_, max_, condition, type_) def genGrow(pset, min_, max_, type_=__type__): """Generate an expression where each leaf might have a different depth between *min* and *max*. """ def condition(max_depth): """Expression generation stops when the depth is zero or when it is randomly determined that a a node should be a terminal. """ return max_depth == 0 or random.random() < pset.terminalRatio return _generate(pset, min_, max_, condition, type_) def genRamped(pset, min_, max_, type_=__type__): """Generate an expression with a PrimitiveSet *pset*. Half the time, the expression is generated with :func:`~deap.gp.genGrow`, the other half, the expression is generated with :func:`~deap.gp.genFull`. """ method = random.choice((genGrow, genFull)) return method(pset, min_, max_, type_) def _generate(pset, min_, max_, condition, type_=__type__): def genExpr(max_depth, type_): if condition(max_depth): term = random.choice(pset.terminals[type_]) expr = term() else: prim = random.choice(pset.primitives[type_]) expr = [prim] args = (genExpr(max_depth-1, arg) for arg in prim.args) expr.extend(args) return expr max_depth = random.randint(min_, max_) expr = genExpr(max_depth, type_) if not isinstance(expr, list): expr = [expr] return expr ###################################### # GP Crossovers # ###################################### def cxUniformOnePoint(ind1, ind2): """Randomly select in each individual and exchange each subtree with the point as root between each individual. """ try: index1 = random.randint(1, ind1.size-1) index2 = random.randint(1, ind2.size-1) except ValueError: return ind1, ind2 sub1 = ind1.searchSubtreeDF(index1) sub2 = ind2.searchSubtreeDF(index2) ind1.setSubtreeDF(index1, sub2) ind2.setSubtreeDF(index2, sub1) return ind1, ind2 ## Strongly Typed GP crossovers def cxTypedOnePoint(ind1, ind2): """Randomly select in each individual and exchange each subtree with the point as root between each individual. Since the node are strongly typed, the operator then make sure the type of second node correspond to the type of the first node. If it doesn't, it randomly selects another point in the second individual and try again. It tries up to *5* times before giving up on the crossover. .. note:: This crossover is subject to change for a more effective method of selecting the crossover points. """ # choose the crossover point in each individual try: index1 = random.randint(1, ind1.size-1) index2 = random.randint(1, ind2.size-1) except ValueError: return ind1, ind2 subtree1 = ind1.searchSubtreeDF(index1) subtree2 = ind2.searchSubtreeDF(index2) type1 = subtree1.root.ret type2 = subtree2.root.ret # try to mate the trees # if no crossover point is found after 5 it gives up trying # mating individuals. tries = 0 MAX_TRIES = 5 while not (type1 == type2) and tries < MAX_TRIES: index2 = random.randint(1, ind2.size-1) subtree2 = ind2.searchSubtreeDF(index2) type2 = subtree2.root.ret tries += 1 if type1 == type2: sub1 = ind1.searchSubtreeDF(index1) sub2 = ind2.searchSubtreeDF(index2) ind1.setSubtreeDF(index1, sub2) ind2.setSubtreeDF(index2, sub1) return ind1, ind2 def cxOnePointLeafBiased(ind1, ind2, cxtermpb): """Randomly select crossover point in each individual and exchange each subtree with the point as root between each individual. This operator takes another parameter *cxtermpb*, which set the probability to choose between a terminal or non-terminal crossover point. For instance, as defined by Koza, non-terminal primitives are selected for 90% of the crossover points, and terminals for 10%, so *cxtermpb* should be set to 0.1. """ size1, size2 = ind1.size, ind2.size if size1 == 1 or size2 == 1: return ind1, ind2 # Those were not implemented with set because random.choice() # works only on sequencable iterables (it is not clear whether # it would be faster to perform the conversion set->list or # directly use lists) termsList1 = [termIndex for termIndex in ind1.iter_leaf_idx] termsList2 = [termIndex for termIndex in ind2.iter_leaf_idx] primList1 = [i for i in xrange(1,size1) if i not in termsList1] primList2 = [i for i in xrange(1,size2) if i not in termsList2] if random.random() < cxtermpb or len(primList1) == 0: # Choose a terminal from the first parent index1 = random.choice(termsList1) subtree1 = ind1.searchSubtreeDF(index1) else: # Choose a primitive (non-terminal) from the first parent index1 = random.choice(primList1) subtree1 = ind1.searchSubtreeDF(index1) if random.random() < cxtermpb or len(primList2) == 0: # Choose a terminal from the second parent index2 = random.choice(termsList2) subtree2 = ind2.searchSubtreeDF(index2) else: # Choose a primitive (non-terminal) from the second parent index2 = random.choice(primList2) subtree2 = ind2.searchSubtreeDF(index2) ind1.setSubtreeDF(index1, subtree2) ind2.setSubtreeDF(index2, subtree1) return ind1, ind2 ## Strongly Typed GP crossovers def cxTypedOnePointLeafBiased(ind1, ind2, cxtermpb): """Randomly select crossover point in each individual and exchange each subtree with the point as root between each individual. Since the node are strongly typed, the operator then make sure the type of second node correspond to the type of the first node. If it doesn't, it randomly selects another point in the second individual and try again. It tries up to *5* times before giving up on the crossover. This operator takes another parameter *cxtermpb*, which set the probability to choose between a terminal or non-terminal crossover point. For instance, as defined by Koza, non-terminal primitives are selected for 90% of the crossover points, and terminals for 10%, so *cxtermpb* should be set to 0.1. .. note:: This crossover is subject to change for a more effective method of selecting the crossover points. """ size1, size2 = ind1.size, ind2.size if size1 == 1 or size2 == 1: return ind1, ind2 # Those were not implemented with set because random.choice() # works only on sequencable iterables (it is not clear whether # it would be faster to perform the conversion set->list or # directly use lists) termsList1 = [termIndex for termIndex in ind1.iter_leaf_idx] termsList2 = [termIndex for termIndex in ind2.iter_leaf_idx] primList1 = [i for i in xrange(1,size1) if i not in termsList1] primList2 = [i for i in xrange(1,size2) if i not in termsList2] if random.random() < cxtermpb or len(primList1) == 0: # Choose a terminal from the first parent index1 = random.choice(termsList1) subtree1 = ind1.searchSubtreeDF(index1) else: # Choose a primitive (non-terminal) from the first parent index1 = random.choice(primList1) subtree1 = ind1.searchSubtreeDF(index1) if random.random() < cxtermpb or len(primList2) == 0: # Choose a terminal from the second parent index2 = random.choice(termsList2) subtree2 = ind2.searchSubtreeDF(index2) else: # Choose a primitive (non-terminal) from the second parent index2 = random.choice(primList2) subtree2 = ind2.searchSubtreeDF(index2) type1 = subtree1.root.ret type2 = subtree2.root.ret # try to mate the trees # if no crossover point is found after MAX_CX_TRY # the children are returned without modifications. tries = 0 MAX_CX_TRY = 5 while not (type1 is type2) and tries != MAX_CX_TRY: if random.random() < cxtermpb or len(primList2) == 0: index2 = random.choice(termsList2) subtree2 = ind2.searchSubtreeDF(index2) else: index2 = random.choice(primList2) subtree2 = ind2.searchSubtreeDF(index2) type2 = subtree2.root.ret tries += 1 if type1 is type2: ind1.setSubtreeDF(index1, subtree2) ind2.setSubtreeDF(index2, subtree1) return ind1, ind2 ###################################### # GP Mutations # ###################################### def mutUniform(individual, expr): """Randomly select a point in the Tree, then replace the subtree with the point as a root by a randomly generated expression. The expression is generated using the method `expr`. """ index = random.randint(0, individual.size-1) individual.setSubtreeDF(index, expr(pset=individual.pset)) return individual, ## Strongly Typed GP mutations def mutTypedUniform(individual, expr): """The mutation of strongly typed GP expression is pretty easy. First, it finds a subtree. Second, it has to identify the return type of the root of this subtree. Third, it generates a new subtree with a root's type corresponding to the original subtree root's type. Finally, the old subtree is replaced by the new subtree. """ index = random.randint(0, individual.size-1) subtree = individual.searchSubtreeDF(index) individual.setSubtreeDF(index, expr(pset=individual.pset, type_=subtree.root.ret)) return individual, def mutTypedNodeReplacement(individual): """This operator mutates the individual *individual* that are subjected to it. The operator randomly chooses a primitive in the individual and replaces it with a randomly selected primitive in *pset* that takes the same number of arguments. This operator works on strongly typed trees as on normal GP trees. """ if individual.size < 2: return individual, index = random.randint(1, individual.size-1) node = individual.searchSubtreeDF(index) if node.size == 1: subtree = random.choice(individual.pset.terminals[node.root.ret])() individual.setSubtreeDF(index, subtree) else: # We're going to replace one of the *node* children index = random.randint(1, len(node) - 1) if node[index].size > 1: prim_set = individual.pset.primitives[node[index].root.ret] repl_node = random.choice(prim_set) while repl_node.args != node[index].root.args: repl_node = random.choice(prim_set) node[index][0] = repl_node else: term_set = individual.pset.terminals[node[index].root.ret] repl_node = random.choice(term_set)() node[index] = repl_node return individual, def mutTypedEphemeral(individual, mode): """This operator works on the constants of the tree *ind*. In *mode* ``"one"``, it will change the value of **one** of the individual ephemeral constants by calling its generator function. In *mode* ``"all"``, it will change the value of **all** the ephemeral constants. This operator works on strongly typed trees as on normal GP trees. """ if mode not in ["one", "all"]: raise ValueError("Mode must be one of \"one\" or \"all\"") ephemerals = [] for i in xrange(1, individual.size): subtree = individual.searchSubtreeDF(i) if hasattr(subtree.root.obj, 'regen'): ephemerals.append(i) if len(ephemerals) > 0: if mode == "one": ephemerals = [random.choice(ephemerals)] elif mode == "all": pass for i in ephemerals: individual.searchSubtreeDF(i).regen() return individual, def mutShrink(individual): """This operator shrinks the individual *individual* that are subjected to it. The operator randomly chooses a branch in the individual and replaces it with one of the branch's arguments (also randomly chosen). This operator is not usable with STGP. """ if individual.size < 3 or individual.height <= 2: return individual, # We don't want to "shrink" the root index = random.randint(1, individual.size-2) # Shrinking a terminal is useless while individual.searchSubtreeDF(index).size == 1: index = random.randint(1, individual.size-2) deleted_node = individual.searchSubtreeDF(index) repl_subtree_index = random.randint(1, len(deleted_node)-1) individual.setSubtreeDF(index, deleted_node[repl_subtree_index]) return individual, def mutTypedInsert(individual): """This operator mutate the GP tree of the *individual* passed and the primitive set *expr*, by inserting a new branch at a random position in a tree, using the original subtree at this position as one argument, and if necessary randomly selecting terminal primitives to complete the arguments of the inserted node. Note that the original subtree will become one of the children of the new primitive inserted, but not perforce the first (its position is randomly selected if the new primitive has more than one child) This operator works on strongly typed trees as on normal GP trees. """ pset = individual.pset index = random.randint(0, individual.size-1) node = individual.searchSubtreeDF(index) if node.size > 1: # We do not need to deepcopy the leafs node = copy.deepcopy(node) new_primitive = random.choice(pset.primitives[node.root.ret]) inserted_list = [new_primitive] for i in xrange(0, new_primitive.arity): # Why don't we use expr to create the other subtrees? # Bloat control? new_child = random.choice(pset.terminals[new_primitive.args[i]]) inserted_list.append(new_child()) inserted_list[random.randint(1, new_primitive.arity)] = node individual.setSubtreeDF(index, inserted_list) return individual, if __name__ == "__main__": import doctest doctest.testmod() deap-0.7.1/deap/tests/0000755000076500000240000000000011650301263014732 5ustar felixstaff00000000000000deap-0.7.1/deap/tests/__init__.py0000644000076500000240000000126511641072614017054 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . deap-0.7.1/deap/tests/test_pickle.py0000644000076500000240000000773411641072614017632 0ustar felixstaff00000000000000 import sys import unittest import array import pickle import operator from test import test_support sys.path.append("..") import creator import base import gp import tools def func(): return "True" class Pickling(unittest.TestCase): def setUp(self): creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("IndList", list, fitness=creator.FitnessMax) creator.create("IndArray", array.array, typecode='f', fitness=creator.FitnessMax) creator.create("IndTree", base.Tree, fitness=creator.FitnessMax) self.toolbox = base.Toolbox() self.toolbox.register("func", func) self.toolbox.register("lambda_func", lambda: "True") def test_pickle_fitness(self): fitness = creator.FitnessMax() fitness.values = (1.0,) fitness_s = pickle.dumps(fitness) fitness_l = pickle.loads(fitness_s) self.failUnlessEqual(fitness, fitness_l, "Unpickled fitness != pickled fitness") def test_pickle_ind_list(self): ind = creator.IndList([1.0, 2.0, 3.0]) ind.fitness.values = (4.0,) ind_s = pickle.dumps(ind) ind_l = pickle.loads(ind_s) self.failUnlessEqual(ind, ind_l, "Unpickled individual list != pickled individual list") self.failUnlessEqual(ind.fitness, ind_l.fitness, "Unpickled individual fitness != pickled individual fitness") def test_pickle_ind_array(self): ind = creator.IndArray([1.0, 2.0, 3.0]) ind.fitness.values = (4.0,) ind_s = pickle.dumps(ind) ind_l = pickle.loads(ind_s) self.failUnlessEqual(ind, ind_l, "Unpickled individual array != pickled individual array") self.failUnlessEqual(ind.fitness, ind_l.fitness, "Unpickled individual fitness != pickled individual fitness") def test_pickle_tree(self): ind = creator.IndTree(["+", 1, 2]) ind.fitness.values = (1.0,) ind_s = pickle.dumps(ind) ind_l = pickle.loads(ind_s) msg = "Unpickled individual %s != pickled individual %s" % (str(ind), str(ind_l)) self.failUnlessEqual(ind, ind_l, msg) msg = "Unpickled fitness %s != pickled fitness %s" % (str(ind.fitness), str(ind_l.fitness)) self.failUnlessEqual(ind.fitness, ind_l.fitness, msg) def test_pickle_population(self): ind1 = creator.IndList([1.0,2.0,3.0]) ind1.fitness.values = (1.0,) ind2 = creator.IndList([4.0,5.0,6.0]) ind2.fitness.values = (2.0,) ind3 = creator.IndList([7.0,8.0,9.0]) ind3.fitness.values = (3.0,) pop = [ind1, ind2, ind3] pop_s = pickle.dumps(pop) pop_l = pickle.loads(pop_s) self.failUnlessEqual(pop[0], pop_l[0], "Unpickled individual list != pickled individual list") self.failUnlessEqual(pop[0].fitness, pop_l[0].fitness, "Unpickled individual fitness != pickled individual fitness") self.failUnlessEqual(pop[1], pop_l[1], "Unpickled individual list != pickled individual list") self.failUnlessEqual(pop[1].fitness, pop_l[1].fitness, "Unpickled individual fitness != pickled individual fitness") self.failUnlessEqual(pop[2], pop_l[2], "Unpickled individual list != pickled individual list") self.failUnlessEqual(pop[2].fitness, pop_l[2].fitness, "Unpickled individual fitness != pickled individual fitness") if not sys.version_info < (2, 7): def test_pickle_partial(self): func_s = pickle.dumps(self.toolbox.func) func_l = pickle.loads(func_s) self.failUnlessEqual(self.toolbox.func(), func_l()) @unittest.expectedFailure def test_pickle_lambda(self): func_s = pickle.dumps(self.toolbox.lambda_func) func_l = pickle.loads(func_s) self.failUnlessEqual(self.toolbox.lambda_func(), func_l()) if __name__ == "__main__": suite = unittest.TestLoader().loadTestsFromTestCase(Pickling) unittest.TextTestRunner(verbosity=2).run(suite) deap-0.7.1/deap/tools.py0000644000076500000240000015461511641072614015323 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """The :mod:`~deap.tools` module contains the operators for evolutionary algorithms. They are used to modify, select and move the individuals in their environment. The set of operators it contains are readily usable in the :class:`~deap.base.Toolbox`. In addition to the basic operators this module also contains utility tools to enhance the basic algorithms with :class:`Statistics`, :class:`HallOfFame`, :class:`Checkpoint`, and :class:`History`. """ from __future__ import division import bisect import copy import inspect import math import random from itertools import chain from operator import attrgetter, eq from collections import defaultdict from functools import partial try: import yaml CHECKPOINT_USE_YAML = True try: from yaml import CDumper as Dumper # CLoader and CDumper are much from yaml import CLoader as Loader # faster than default ones, but except ImportError: # requires LibYAML to be compiled from yaml import Dumper from yaml import Loader except ImportError: CHECKPOINT_USE_YAML = False # If yaml ain't present, use try: # pickling to dump import cPickle as pickle # cPickle is much faster than except ImportError: # pickle but only present under import pickle # CPython def initRepeat(container, func, n): """Call the function *container* with a generator function corresponding to the calling *n* times the function *func*. This helper function can can be used in conjunction with a Toolbox to register a generator of filled containers, as individuals or population. >>> initRepeat(list, random.random, 2) # doctest: +ELLIPSIS, ... # doctest: +NORMALIZE_WHITESPACE [0.4761..., 0.6302...] """ return container(func() for _ in xrange(n)) def initIterate(container, generator): """Call the function *container* with an iterable as its only argument. The iterable must be returned by the method or the object *generator*. This helper function can can be used in conjunction with a Toolbox to register a generator of filled containers, as individuals or population. >>> from random import sample >>> from functools import partial >>> gen_idx = partial(sample, range(10), 10) >>> initIterate(list, gen_idx) [4, 5, 3, 6, 0, 9, 2, 7, 1, 8] """ return container(generator()) def initCycle(container, seq_func, n=1): """Call the function *container* with a generator function corresponding to the calling *n* times the functions present in *seq_func*. This helper function can can be used in conjunction with a Toolbox to register a generator of filled containers, as individuals or population. >>> func_seq = [lambda:1 , lambda:'a', lambda:3] >>> initCycle(list, func_seq, 2) [1, 'a', 3, 1, 'a', 3] """ return container(func() for _ in xrange(n) for func in seq_func) class History(object): """The :class:`History` class helps to build a genealogy of all the individuals produced in the evolution. It contains two attributes, the :attr:`genealogy_tree` that is a dictionary of lists indexed by individual, the list contain the indices of the parents. The second attribute :attr:`genealogy_history` contains every individual indexed by their individual number as in the genealogy tree. The produced genealogy tree is compatible with `NetworkX `_, here is how to plot the genealogy tree :: hist = History() # Do some evolution and fill the history import matplotlib.pyplot as plt import networkx as nx g = nx.DiGraph(hist.genealogy_tree) nx.draw_springs(g) plt.show() .. note:: The genealogy tree might get very big if your population and/or the number of generation is large. """ def __init__(self): self.genealogy_index = 0 self.genealogy_history = dict() self.genealogy_tree = dict() def populate(self, individuals): """Populate the history with the initial *individuals*. An attribute :attr:`history_index` is added to every individual, this index will help to track the parents and the children through evolution. This index will be modified by the :meth:`update` method when a child is produced. Modifying the internal :attr:`genealogy_index` of the history or the :attr:`history_index` of an individual may lead to unpredictable results and corruption of the history. """ for ind in individuals: self.genealogy_index += 1 ind.history_index = self.genealogy_index self.genealogy_history[self.genealogy_index] = copy.deepcopy(ind) self.genealogy_tree[self.genealogy_index] = list() def update(self, *individuals): """Update the history with the new *individuals*. The index present in their :attr:`history_index` attribute will be used to locate their parents and modified to a unique one to keep track of those new individuals. """ parent_indices = [ind.history_index for ind in individuals] for ind in individuals: self.genealogy_index += 1 ind.history_index = self.genealogy_index self.genealogy_history[self.genealogy_index] = copy.deepcopy(ind) self.genealogy_tree[self.genealogy_index] = parent_indices @property def decorator(self): """Property that returns an appropriate decorator to enhance the operators of the toolbox. The returned decorator assumes that the individuals are returned by the operator. First the decorator calls the underlying operation and then calls the update function with what has been returned by the operator as argument. Finally, it returns the individuals with their history parameters modified according to the update function. """ def decFunc(func): def wrapFunc(*args, **kargs): individuals = func(*args, **kargs) self.update(*individuals) return individuals return wrapFunc return decFunc class Checkpoint(object): """A checkpoint is a file containing the state of any object that has been hooked. While initializing a checkpoint, add the objects that you want to be dumped by appending keyword arguments to the initializer or using the :meth:`add`. By default the checkpoint tries to use the YAML format which is human readable, if PyYAML is not installed, it uses pickling which is not readable. You can force the use of pickling by setting the argument *yaml* to :data:`False`. In order to use efficiently this module, you must understand properly the assignment principles in Python. This module use the *pointers* you passed to dump the object, for example the following won't work as desired :: >>> my_object = [1, 2, 3] >>> cp = Checkpoint(obj=my_object) >>> my_object = [3, 5, 6] >>> cp.dump("example") >>> cp.load("example.ems") >>> cp["obj"] [1, 2, 3] In order to dump the new value of ``my_object`` it is needed to change its internal values directly and not touch the *label*, as in the following :: >>> my_object = [1, 2, 3] >>> cp = Checkpoint(obj=my_object) >>> my_object[:] = [3, 5, 6] >>> cp.dump("example") >>> cp.load("example.ems") >>> cp["obj"] [3, 5, 6] """ def __init__(self, yaml=True, **kargs): # self.zipped = zipped self._dict = kargs if CHECKPOINT_USE_YAML and yaml: self.use_yaml = True else: self.use_yaml = False def add(self, **kargs): """Add objects to the list of objects to be dumped. The object is added under the name specified by the argument's name. Keyword arguments are mandatory in this function. """ self._dict.update(*kargs) def remove(self, *args): """Remove objects with the specified name from the list of objects to be dumped. """ for element in args: del self._dict[element] def __getitem__(self, value): return self._dict[value] def dump(self, prefix): """Dump the current registered objects in a file named *prefix.ecp*, the randomizer state is always added to the file and available under the ``"randomizer_state"`` tag. """ # if not self.zipped: cp_file = open(prefix + ".ecp", "w") # else: # file = gzip.open(prefix + ".ems.gz", "w") cp = self._dict.copy() cp.update({"randomizer_state" : random.getstate()}) if self.use_yaml: cp_file.write(yaml.dump(ms, Dumper=Dumper)) else: pickle.dump(cp, cp_file) cp_file.close() def load(self, filename): """Load a checkpoint file retrieving the dumped objects, it is not safe to load a checkpoint file in a checkpoint object that contains references as all conflicting names will be updated with the new values. """ if self.use_yaml: self._dict.update(yaml.load(open(filename, "r"), Loader=Loader)) else: self._dict.update(pickle.load(open(filename, "r"))) def mean(seq): """Returns the arithmetic mean of the sequence *seq* = :math:`\{x_1,\ldots,x_n\}` as :math:`A = \\frac{1}{n} \sum_{i=1}^n x_i`. """ return sum(seq) / len(seq) def median(seq): """Returns the median of *seq* - the numeric value separating the higher half of a sample from the lower half. If there is an even number of elements in *seq*, it returns the mean of the two middle values. """ sseq = sorted(seq) length = len(seq) if length % 2 == 1: return sseq[int((length - 1) / 2)] else: return (sseq[int((length - 1) / 2)] + sseq[int(length / 2)]) / 2 def var(seq): """Returns the variance :math:`\sigma^2` of *seq* = :math:`\{x_1,\ldots,x_n\}` as :math:`\sigma^2 = \\frac{1}{N} \sum_{i=1}^N (x_i - \\mu )^2`, where :math:`\\mu` is the arithmetic mean of *seq*. """ return abs(sum(x*x for x in seq) / len(seq) - mean(seq)**2) def std(seq): """Returns the square root of the variance :math:`\sigma^2` of *seq*. """ return var(seq)**0.5 class Statistics(object): """A statistics object that holds the required data for as long as it exists. When created the statistics object receives a *key* argument that is used to get the required data, if not provided the *key* argument defaults to the identity function. A statistics object can be represented as a 4 dimensional matrix. Along the first axis (wich length is given by the *n* argument) are independent statistics objects that are used on different collections given this index in the :meth:`update` method. The second axis is the function it-self, each element along the second axis (indexed by their name) will represent a different function. The third axis is the accumulator of statistics. each time the update function is called the new statistics are added using the registered functions at the end of this axis. The fourth axis is used when the entered data is an iterable (for example a multiobjective fitness). Data can be retrieved by different means in a statistics object. One can use directly the registered function name with an *index* argument that represent the first axis of the matrix. This method returns the last entered data. :: >>> s = Statistics(n=2) >>> s.register("Mean", mean) >>> s.update([1, 2, 3, 4], index=0) >>> s.update([5, 6, 7, 8], index=1) >>> s.Mean(0) [2.5] >>> s.Mean(1) [6.5] An other way to obtain the statistics is to use directly the ``[]``. In that case all dimensions must be specified. This is how stats that have been registered earlier in the process can be retrieved. :: >>> s.update([10, 20, 30, 40], index=0) >>> s.update([50, 60, 70, 80], index=1) >>> s[0]["Mean"][0] [2.5] >>> s[1]["Mean"][0] [6.5] >>> s[0]["Mean"][1] [25] >>> s[1]["Mean"][1] [65] Finally, the fourth dimension is used when stats are needed on lists of lists. The stats are computed on the matching indices of each list. :: >>> s = Statistics() >>> s.register("Mean", mean) >>> s.update([[1, 2, 3], [4, 5, 6]]) >>> s.Mean() [2.5, 3.5, 4.5] >>> s[0]["Mean"][-1][0] 2.5 """ class Data(defaultdict): def __init__(self): defaultdict.__init__(self, list) def __str__(self): return "\n".join("%s %s" % (key, ", ".join(map(str, stat[-1]))) for key, stat in self.iteritems()) def __init__(self, key=lambda x: x, n=1): self.key = key self.functions = {} self.data = tuple(self.Data() for _ in xrange(n)) def __getitem__(self, index): return self.data[index] def _getFuncValue(self, name, index=0): return self.data[index][name][-1] def register(self, name, function): """Register a function *function* that will be apply on the sequence each time :func:`~deap.tools.Statistics.update` is called. The function result will be accessible by using the string given by the argument *name* as a function of the statistics object. >>> s = Statistics() >>> s.register("Mean", mean) >>> s.update([1,2,3,4,5,6,7]) >>> s.Mean() [4.0] """ self.functions[name] = function setattr(self, name, partial(self._getFuncValue, name)) def update(self, seq, index=0): """Apply to the input sequence *seq* each registered function and store each result in a list specific to the function and the data index *index*. >>> s = Statistics() >>> s.register("Mean", mean) >>> s.register("Max", max) >>> s.update([4,5,6,7,8]) >>> s.Max() [8] >>> s.Mean() [6.0] >>> s.update([1,2,3]) >>> s.Max() [3] >>> s[0]["Max"] [[8], [3]] >>> s[0]["Mean"] [[6.0], [2.0]] """ # Transpose the values data = self.data[index] try: values = zip(*(self.key(elem) for elem in seq)) except TypeError: values = zip(*[(self.key(elem),) for elem in seq]) for key, func in self.functions.iteritems(): data[key].append(map(func, values)) def __str__(self): return "\n".join("%s %s" % (key, ", ".join(map(str, stat[-1]))) for key, stat in self.data[-1].iteritems()) class HallOfFame(object): """The hall of fame contains the best individual that ever lived in the population during the evolution. It is sorted at all time so that the first element of the hall of fame is the individual that has the best first fitness value ever seen, according to the weights provided to the fitness at creation time. The class :class:`HallOfFame` provides an interface similar to a list (without being one completely). It is possible to retrieve its length, to iterate on it forward and backward and to get an item or a slice from it. """ def __init__(self, maxsize): self.maxsize = maxsize self.keys = list() self.items = list() def update(self, population): """Update the hall of fame with the *population* by replacing the worst individuals in it by the best individuals present in *population* (if they are better). The size of the hall of fame is kept constant. """ if len(self) < self.maxsize: # Items are sorted with the best fitness first self.items = sorted(chain(self, population), key=attrgetter("fitness"), reverse=True)[:self.maxsize] self.items = [copy.deepcopy(item) for item in self.items] # The keys are the fitnesses in reverse order to allow the use # of the bisection algorithm self.keys = map(attrgetter("fitness"), reversed(self.items)) else: for ind in population: if ind.fitness > self[-1].fitness: # Delete the worst individual from the front self.remove(-1) # Insert the new individual self.insert(ind) def insert(self, item): """Insert a new individual in the hall of fame using the :func:`~bisect.bisect_right` function. The inserted individual is inserted on the right side of an equal individual. Inserting a new individual in the hall of fame also preserve the hall of fame's order. This method **does not** check for the size of the hall of fame, in a way that inserting a new individual in a full hall of fame will not remove the worst individual to maintain a constant size. """ item = copy.deepcopy(item) i = bisect.bisect_right(self.keys, item.fitness) self.items.insert(len(self) - i, item) self.keys.insert(i, item.fitness) def remove(self, index): """Remove the specified *index* from the hall of fame.""" del self.keys[len(self) - (index % len(self) + 1)] del self.items[index] def clear(self): """Clear the hall of fame.""" del self.items[:] del self.keys[:] def __len__(self): return len(self.items) def __getitem__(self, i): return self.items[i] def __iter__(self): return iter(self.items) def __reversed__(self): return reversed(self.items) def __str__(self): return str(self.items) + "\n" + str(self.keys) class ParetoFront(HallOfFame): """The Pareto front hall of fame contains all the non-dominated individuals that ever lived in the population. That means that the Pareto front hall of fame can contain an infinity of different individuals. The size of the front may become very large if it is used for example on a continuous function with a continuous domain. In order to limit the number of individuals, it is possible to specify a similarity function that will return :data:`True` if the genotype of two individuals are similar. In that case only one of the two individuals will be added to the hall of fame. By default the similarity function is :func:`operator.__eq__`. Since, the Pareto front hall of fame inherits from the :class:`HallOfFame`, it is sorted lexicographically at every moment. """ def __init__(self, similar=eq): self.similar = similar HallOfFame.__init__(self, None) def update(self, population): """Update the Pareto front hall of fame with the *population* by adding the individuals from the population that are not dominated by the hall of fame. If any individual in the hall of fame is dominated it is removed. """ for ind in population: is_dominated = False has_twin = False to_remove = [] for i, hofer in enumerate(self): # hofer = hall of famer if ind.fitness.isDominated(hofer.fitness): is_dominated = True break elif hofer.fitness.isDominated(ind.fitness): to_remove.append(i) elif ind.fitness == hofer.fitness and self.similar(ind, hofer): has_twin = True break for i in reversed(to_remove): # Remove the dominated hofer self.remove(i) if not is_dominated and not has_twin: self.insert(ind) ###################################### # GA Crossovers # ###################################### def cxTwoPoints(ind1, ind2): """Execute a two points crossover on the input individuals. The two individuals are modified in place. This operation apply on an individual composed of a list of attributes and act as follow :: >>> ind1 = [A(1), ..., A(i), ..., A(j), ..., A(m)] #doctest: +SKIP >>> ind2 = [B(1), ..., B(i), ..., B(j), ..., B(k)] >>> # Crossover with mating points 1 < i < j <= min(m, k) + 1 >>> cxTwoPoints(ind1, ind2) >>> print ind1, len(ind1) [A(1), ..., B(i), ..., B(j-1), A(j), ..., A(m)], m >>> print ind2, len(ind2) [B(1), ..., A(i), ..., A(j-1), B(j), ..., B(k)], k This function use the :func:`~random.randint` function from the python base :mod:`random` module. """ size = min(len(ind1), len(ind2)) cxpoint1 = random.randint(1, size) cxpoint2 = random.randint(1, size - 1) if cxpoint2 >= cxpoint1: cxpoint2 += 1 else: # Swap the two cx points cxpoint1, cxpoint2 = cxpoint2, cxpoint1 ind1[cxpoint1:cxpoint2], ind2[cxpoint1:cxpoint2] \ = ind2[cxpoint1:cxpoint2], ind1[cxpoint1:cxpoint2] return ind1, ind2 def cxOnePoint(ind1, ind2): """Execute a one point crossover on the input individuals. The two individuals are modified in place. This operation apply on an individual composed of a list of attributes and act as follow :: >>> ind1 = [A(1), ..., A(n), ..., A(m)] #doctest: +SKIP >>> ind2 = [B(1), ..., B(n), ..., B(k)] >>> # Crossover with mating point i, 1 < i <= min(m, k) >>> cxOnePoint(ind1, ind2) >>> print ind1, len(ind1) [A(1), ..., B(i), ..., B(k)], k >>> print ind2, len(ind2) [B(1), ..., A(i), ..., A(m)], m This function use the :func:`~random.randint` function from the python base :mod:`random` module. """ size = min(len(ind1), len(ind2)) cxpoint = random.randint(1, size - 1) ind1[cxpoint:], ind2[cxpoint:] = ind2[cxpoint:], ind1[cxpoint:] return ind1, ind2 def cxUniform(ind1, ind2, indpb): """Execute a uniform crossover that modify in place the two individuals. The genes are swapped according to the *indpb* probability. This function use the :func:`~random.random` function from the python base :mod:`random` module. """ size = min(len(ind1), len(ind2)) for i in xrange(size): if random.random() < indpb: ind1[i], ind2[i] = ind2[i], ind1[i] return ind1, ind2 def cxPartialyMatched(ind1, ind2): """Execute a partially matched crossover (PMX) on the input individuals. The two individuals are modified in place. This crossover expect iterable individuals of indices, the result for any other type of individuals is unpredictable. Moreover, this crossover consists of generating two children by matching pairs of values in a certain range of the two parents and swapping the values of those indexes. For more details see Goldberg and Lingel, "Alleles, loci, and the traveling salesman problem", 1985. For example, the following parents will produce the two following children when mated with crossover points ``a = 2`` and ``b = 4``. :: >>> ind1 = [0, 1, 2, 3, 4] >>> ind2 = [1, 2, 3, 4, 0] >>> cxPartialyMatched(ind1, ind2) >>> print ind1 [0, 2, 3, 1, 4] >>> print ind2 [2, 3, 1, 4, 0] This function use the :func:`~random.randint` function from the python base :mod:`random` module. """ size = min(len(ind1), len(ind2)) p1, p2 = [0]*size, [0]*size # Initialize the position of each indices in the individuals for i in xrange(size): p1[ind1[i]] = i p2[ind2[i]] = i # Choose crossover points cxpoint1 = random.randint(0, size) cxpoint2 = random.randint(0, size - 1) if cxpoint2 >= cxpoint1: cxpoint2 += 1 else: # Swap the two cx points cxpoint1, cxpoint2 = cxpoint2, cxpoint1 # Apply crossover between cx points for i in xrange(cxpoint1, cxpoint2): # Keep track of the selected values temp1 = ind1[i] temp2 = ind2[i] # Swap the matched value ind1[i], ind1[p1[temp2]] = temp2, temp1 ind2[i], ind2[p2[temp1]] = temp1, temp2 # Position bookkeeping p1[temp1], p1[temp2] = p1[temp2], p1[temp1] p2[temp1], p2[temp2] = p2[temp2], p2[temp1] return ind1, ind2 def cxUniformPartialyMatched(ind1, ind2, indpb): """Execute a uniform partially matched crossover (UPMX) on the input individuals. The two individuals are modified in place. This crossover expect iterable individuals of indices, the result for any other type of individuals is unpredictable. Moreover, this crossover consists of generating two children by matching pairs of values chosen at random with a probability of *indpb* in the two parents and swapping the values of those indexes. For more details see Cicirello and Smith, "Modeling GA performance for control parameter optimization", 2000. For example, the following parents will produce the two following children when mated with the chosen points ``[0, 1, 0, 0, 1]``. :: >>> ind1 = [0, 1, 2, 3, 4] #doctest: +SKIP >>> ind2 = [1, 2, 3, 4, 0] >>> cxUniformPartialyMatched(ind1, ind2) >>> print ind1 [4, 2, 1, 3, 0] >>> print ind2 [2, 1, 3, 0, 4] This function use the :func:`~random.random` and :func:`~random.randint` functions from the python base :mod:`random` module. """ size = min(len(ind1), len(ind2)) p1, p2 = [0]*size, [0]*size # Initialize the position of each indices in the individuals for i in xrange(size): p1[ind1[i]] = i p2[ind2[i]] = i for i in xrange(size): if random.random < indpb: # Keep track of the selected values temp1 = ind1[i] temp2 = ind2[i] # Swap the matched value ind1[i], ind1[p1[temp2]] = temp2, temp1 ind2[i], ind2[p2[temp1]] = temp1, temp2 # Position bookkeeping p1[temp1], p1[temp2] = p1[temp2], p1[temp1] p2[temp1], p2[temp2] = p2[temp2], p2[temp1] return ind1, ind2 def cxBlend(ind1, ind2, alpha): """Executes a blend crossover that modify in-place the input individuals. The blend crossover expect individuals formed of a list of floating point numbers. This function use the :func:`~random.random` function from the python base :mod:`random` module. """ size = min(len(ind1), len(ind2)) for i in xrange(size): gamma = (1. + 2. * alpha) * random.random() - alpha x1 = ind1[i] x2 = ind2[i] ind1[i] = (1. - gamma) * x1 + gamma * x2 ind2[i] = gamma * x1 + (1. - gamma) * x2 return ind1, ind2 def cxSimulatedBinary(ind1, ind2, nu): """Executes a simulated binary crossover that modify in-place the input individuals. The simulated binary crossover expect individuals formed of a list of floating point numbers. This function use the :func:`~random.random` function from the python base :mod:`random` module. """ size = min(len(ind1), len(ind2)) for i in xrange(size): rand = random.random() if rand <= 0.5: beta = 2. * rand else: beta = 1. / (2. * (1. - rand)) beta **= 1. / (nu + 1.) x1 = ind1[i] x2 = ind2[i] ind1[i] = 0.5 * (((1 + beta) * x1) + ((1 - beta) * x2)) ind2[i] = 0.5 * (((1 - beta) * x1) + ((1 + beta) * x2)) return ind1, ind2 ###################################### # Messy Crossovers # ###################################### def cxMessyOnePoint(ind1, ind2): """Execute a one point crossover that will in most cases change the individuals size. This operation apply on an individual composed of a list of attributes and act as follow :: >>> ind1 = [A(1), ..., A(i), ..., A(m)] #doctest: +SKIP >>> ind2 = [B(1), ..., B(j), ..., B(n)] >>> # Crossover with mating points i, j, 1 <= i <= m, 1 <= j <= n >>> cxMessyOnePoint(ind1, ind2) >>> print ind1, len(ind1) [A(1), ..., A(i - 1), B(j), ..., B(n)], n + j - i >>> print ind2, len(ind2) [B(1), ..., B(j - 1), A(i), ..., A(m)], m + i - j This function use the :func:`~random.randint` function from the python base :mod:`random` module. """ cxpoint1 = random.randint(0, len(ind1)) cxpoint2 = random.randint(0, len(ind2)) ind1[cxpoint1:], ind2[cxpoint2:] = ind2[cxpoint2:], ind1[cxpoint1:] return ind1, ind2 ###################################### # ES Crossovers # ###################################### def cxESBlend(ind1, ind2, alpha): """Execute a blend crossover on both, the individual and the strategy. The individuals must have a :attr:`strategy` attribute. Adjustement of the minimal strategy shall be done after the call to this function using a decorator, for example :: def checkStrategy(minstrategy): def decMinStrategy(func): def wrapMinStrategy(*args, **kargs): children = func(*args, **kargs) for child in children: if child.strategy < minstrategy: child.strategy = minstrategy return children return wrapMinStrategy return decMinStrategy toolbox.register("mate", tools.cxEsBlend, alpha=ALPHA) toolbox.decorate("mate", checkStrategy(minstrategy=0.01)) """ size = min(len(ind1), len(ind2)) for indx in xrange(size): # Blend the values gamma = (1. + 2. * alpha) * random.random() - alpha x1 = ind1[indx] x2 = ind2[indx] ind1[indx] = (1. - gamma) * x1 + gamma * x2 ind2[indx] = gamma * x1 + (1. - gamma) * x2 # Blend the strategies gamma = (1. + 2. * alpha) * random.random() - alpha s1 = ind1.strategy[indx] s2 = ind2.strategy[indx] ind1.strategy[indx] = (1. - gamma) * s1 + gamma * s2 ind2.strategy[indx] = gamma * s1 + (1. - gamma) * s2 return ind1, ind2 def cxESTwoPoints(ind1, ind2): """Execute a classical two points crossover on both the individual and its strategy. The crossover points for the individual and the strategy are the same. """ size = min(len(ind1), len(ind2)) pt1 = random.randint(1, size) pt2 = random.randint(1, size - 1) if pt2 >= pt1: pt2 += 1 else: # Swap the two cx points pt1, pt2 = pt2, pt1 ind1[pt1:pt2], ind2[pt1:pt2] = ind2[pt1:pt2], ind1[pt1:pt2] ind1.strategy[pt1:pt2], ind2.strategy[pt1:pt2] = \ ind2.strategy[pt1:pt2], ind1.strategy[pt1:pt2] return ind1, ind2 ###################################### # GA Mutations # ###################################### def mutGaussian(individual, mu, sigma, indpb): """This function applies a gaussian mutation of mean *mu* and standard deviation *sigma* on the input individual and returns the mutant. The *individual* is left intact and the mutant is an independant copy. This mutation expects an iterable individual composed of real valued attributes. The *mutIndxPb* argument is the probability of each attribute to be mutated. .. note:: The mutation is not responsible for constraints checking, because there is too many possibilities for resetting the values. Which way is closer to the representation used is up to you. One easy way to add constraint checking to an operator is to use the function decoration in the toolbox. See the multi-objective example (moga_kursawefct.py) for an explicit example. This function uses the :func:`~random.random` and :func:`~random.gauss` functions from the python base :mod:`random` module. """ for i in xrange(len(individual)): if random.random() < indpb: individual[i] += random.gauss(mu, sigma) return individual, def mutShuffleIndexes(individual, indpb): """Shuffle the attributes of the input individual and return the mutant. The *individual* is left intact and the mutant is an independent copy. The *individual* is expected to be iterable. The *shuffleIndxPb* argument is the probability of each attribute to be moved. This function uses the :func:`~random.random` and :func:`~random.randint` functions from the python base :mod:`random` module. """ size = len(individual) for i in xrange(size): if random.random() < indpb: swap_indx = random.randint(0, size - 2) if swap_indx >= i: swap_indx += 1 individual[i], individual[swap_indx] = \ individual[swap_indx], individual[i] return individual, def mutFlipBit(individual, indpb): """Flip the value of the attributes of the input individual and return the mutant. The *individual* is left intact and the mutant is an independent copy. The *individual* is expected to be iterable and the values of the attributes shall stay valid after the ``not`` operator is called on them. The *flipIndxPb* argument is the probability of each attribute to be flipped. This function uses the :func:`~random.random` function from the python base :mod:`random` module. """ for indx in xrange(len(individual)): if random.random() < indpb: individual[indx] = not individual[indx] return individual, ###################################### # ES Mutations # ###################################### def mutESLogNormal(individual, c, indpb): """Mutate an evolution strategy according to its :attr:`strategy` attribute as described in *Beyer and Schwefel, 2002, Evolution strategies - A Comprehensive Introduction*. The individual is first mutated by a normal distribution of mean 0 and standard deviation of :math:`\\boldsymbol{\sigma}_{t-1}` then the strategy is mutated according to an extended log normal rule, :math:`\\boldsymbol{\sigma}_t = \\exp(\\tau_0 \mathcal{N}_0(0, 1)) \\left[ \\sigma_{t-1, 1}\\exp(\\tau \mathcal{N}_1(0, 1)), \ldots, \\sigma_{t-1, n} \\exp(\\tau \mathcal{N}_n(0, 1))\\right]`, with :math:`\\tau_0 = \\frac{c}{\\sqrt{2n}}` and :math:`\\tau = \\frac{c}{\\sqrt{2\\sqrt{n}}}`. A recommended choice is :math:`c=1` when using a :math:`(10, 100)` evolution strategy (Beyer and Schwefel, 2002). The strategy shall be the same size as the individual. Each index (strategy and attribute) is mutated with probability *indpb*. In order to limit the strategy, use a decorator as shown in the :func:`cxESBlend` function. """ size = len(individual) t = c / math.sqrt(2. * math.sqrt(size)) t0 = c / math.sqrt(2. * size) n = random.gauss(0, 1) t0_n = t0 * n for indx in xrange(size): if random.random() < indpb: individual[indx] += individual.strategy[indx] * random.gauss(0, 1) individual.strategy[indx] *= math.exp(t0_n + t * random.gauss(0, 1)) return individual, ###################################### # Selections # ###################################### def selRandom(individuals, k): """Select *k* individuals at random from the input *individuals* with replacement. The list returned contains references to the input *individuals*. This function uses the :func:`~random.choice` function from the python base :mod:`random` module. """ return [random.choice(individuals) for i in xrange(k)] def selBest(individuals, k): """Select the *k* best individuals among the input *individuals*. The list returned contains references to the input *individuals*. """ return sorted(individuals, key=attrgetter("fitness"), reverse=True)[:k] def selWorst(individuals, k): """Select the *k* worst individuals among the input *individuals*. The list returned contains references to the input *individuals*. """ return sorted(individuals, key=attrgetter("fitness"))[:k] def selTournament(individuals, k, tournsize): """Select *k* individuals from the input *individuals* using *k* tournaments of *tournSize* individuals. The list returned contains references to the input *individuals*. This function uses the :func:`~random.choice` function from the python base :mod:`random` module. """ chosen = [] for i in xrange(k): chosen.append(random.choice(individuals)) for j in xrange(tournsize - 1): aspirant = random.choice(individuals) if aspirant.fitness > chosen[i].fitness: chosen[i] = aspirant return chosen def selRoulette(individuals, k): """Select *k* individuals from the input *individuals* using *k* spins of a roulette. The selection is made by looking only at the first objective of each individual. The list returned contains references to the input *individuals*. This function uses the :func:`~random.random` function from the python base :mod:`random` module. .. warning:: The roulette selection by definition cannot be used for minimization or when the fitness can be smaller or equal to 0. """ s_inds = sorted(individuals, key=attrgetter("fitness"), reverse=True) sum_fits = sum(ind.fitness.values[0] for ind in individuals) chosen = [] for i in xrange(k): u = random.random() * sum_fits sum_ = 0 for ind in s_inds: sum_ += ind.fitness.values[0] if sum_ > u: chosen.append(ind) break return chosen ###################################### # Non-Dominated Sorting (NSGA-II) # ###################################### def selNSGA2(individuals, k): """Apply NSGA-II selection operator on the *individuals*. Usually, the size of *individuals* will be larger than *k* because any individual present in *individuals* will appear in the returned list at most once. Having the size of *individuals* equals to *n* will have no effect other than sorting the population according to a non-domination scheme. The list returned contains references to the input *individuals*. For more details on the NSGA-II operator see Deb, Pratab, Agarwal, and Meyarivan, "A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II", 2002. """ pareto_fronts = sortFastND(individuals, k) chosen = list(chain(*pareto_fronts[:-1])) k = k - len(chosen) if k > 0: chosen.extend(sortCrowdingDist(pareto_fronts[-1], k)) return chosen def sortFastND(individuals, k, first_front_only=False): """Sort the first *k* *individuals* according the the fast non-dominated sorting algorithm. """ N = len(individuals) pareto_fronts = [] if k == 0: return pareto_fronts pareto_fronts.append([]) pareto_sorted = 0 dominating_inds = [0] * N dominated_inds = [list() for i in xrange(N)] # Rank first Pareto front for i in xrange(N): for j in xrange(i+1, N): if individuals[j].fitness.isDominated(individuals[i].fitness): dominating_inds[j] += 1 dominated_inds[i].append(j) elif individuals[i].fitness.isDominated(individuals[j].fitness): dominating_inds[i] += 1 dominated_inds[j].append(i) if dominating_inds[i] == 0: pareto_fronts[-1].append(i) pareto_sorted += 1 if not first_front_only: # Rank the next front until all individuals are sorted or the given # number of individual are sorted N = min(N, k) while pareto_sorted < N: pareto_fronts.append([]) for indice_p in pareto_fronts[-2]: for indice_d in dominated_inds[indice_p]: dominating_inds[indice_d] -= 1 if dominating_inds[indice_d] == 0: pareto_fronts[-1].append(indice_d) pareto_sorted += 1 return [[individuals[index] for index in front] for front in pareto_fronts] def sortCrowdingDist(individuals, k): """Sort the individuals according to the crowding distance.""" if len(individuals) == 0: return [] distances = [0.0] * len(individuals) crowding = [(ind, i) for i, ind in enumerate(individuals)] number_objectives = len(individuals[0].fitness.values) inf = float("inf") # It is four times faster to compare with a local # variable than create the float("inf") each time for i in xrange(number_objectives): crowding.sort(key=lambda element: element[0].fitness.values[i]) distances[crowding[0][1]] = float("inf") distances[crowding[-1][1]] = float("inf") for j in xrange(1, len(crowding) - 1): if distances[crowding[j][1]] < inf: distances[crowding[j][1]] += \ crowding[j + 1][0].fitness.values[i] - \ crowding[j - 1][0].fitness.values[i] sorted_dist = sorted([(dist, i) for i, dist in enumerate(distances)], key=lambda value: value[0], reverse=True) return (individuals[index] for dist, index in sorted_dist[:k]) ###################################### # Strength Pareto (SPEA-II) # ###################################### def selSPEA2(individuals, k): """Apply SPEA-II selection operator on the *individuals*. Usually, the size of *individuals* will be larger than *n* because any individual present in *individuals* will appear in the returned list at most once. Having the size of *individuals* equals to *n* will have no effect other than sorting the population according to a strength Pareto scheme. The list returned contains references to the input *individuals*. For more details on the SPEA-II operator see Zitzler, Laumanns and Thiele, "SPEA 2: Improving the strength Pareto evolutionary algorithm", 2001. """ N = len(individuals) L = len(individuals[0].fitness.values) K = math.sqrt(N) strength_fits = [0] * N fits = [0] * N dominating_inds = [list() for i in xrange(N)] for i in xrange(N): for j in xrange(i + 1, N): if individuals[i].fitness.isDominated(individuals[j].fitness): strength_fits[j] += 1 dominating_inds[i].append(j) elif individuals[j].fitness.isDominated(individuals[i].fitness): strength_fits[i] += 1 dominating_inds[j].append(i) for i in xrange(N): for j in dominating_inds[i]: fits[i] += strength_fits[j] # Choose all non-dominated individuals chosen_indices = [i for i in xrange(N) if fits[i] < 1] if len(chosen_indices) < k: # The archive is too small for i in xrange(N): distances = [0.0] * N for j in xrange(i + 1, N): dist = 0.0 for l in xrange(L): val = individuals[i].fitness.values[l] - \ individuals[j].fitness.values[l] dist += val * val distances[j] = dist kth_dist = _randomizedSelect(distances, 0, N - 1, K) density = 1.0 / (kth_dist + 2.0) fits[i] += density next_indices = [(fits[i], i) for i in xrange(N) \ if not i in chosen_indices] next_indices.sort() #print next_indices chosen_indices += [i for _, i in next_indices[:k - len(chosen_indices)]] elif len(chosen_indices) > k: # The archive is too large N = len(chosen_indices) distances = [[0.0] * N for i in xrange(N)] sorted_indices = [[0] * N for i in xrange(N)] for i in xrange(N): for j in xrange(i + 1, N): dist = 0.0 for l in xrange(L): val = individuals[chosen_indices[i]].fitness.values[l] - \ individuals[chosen_indices[j]].fitness.values[l] dist += val * val distances[i][j] = dist distances[j][i] = dist distances[i][i] = -1 # Insert sort is faster than quick sort for short arrays for i in xrange(N): for j in xrange(1, N): l = j while l > 0 and distances[i][j] < distances[i][sorted_indices[i][l - 1]]: sorted_indices[i][l] = sorted_indices[i][l - 1] l -= 1 sorted_indices[i][l] = j size = N to_remove = [] while size > k: # Search for minimal distance min_pos = 0 for i in xrange(1, N): for j in xrange(1, size): dist_i_sorted_j = distances[i][sorted_indices[i][j]] dist_min_sorted_j = distances[min_pos][sorted_indices[min_pos][j]] if dist_i_sorted_j < dist_min_sorted_j: min_pos = i break elif dist_i_sorted_j > dist_min_sorted_j: break # Remove minimal distance from sorted_indices for i in xrange(N): distances[i][min_pos] = float("inf") distances[min_pos][i] = float("inf") for j in xrange(1, size - 1): if sorted_indices[i][j] == min_pos: sorted_indices[i][j] = sorted_indices[i][j + 1] sorted_indices[i][j + 1] = min_pos # Remove corresponding individual from chosen_indices to_remove.append(min_pos) size -= 1 for index in reversed(sorted(to_remove)): del chosen_indices[index] return [individuals[i] for i in chosen_indices] def _randomizedSelect(array, begin, end, i): """Allows to select the ith smallest element from array without sorting it. Runtime is expected to be O(n). """ if begin == end: return array[begin] q = _randomizedPartition(array, begin, end) k = q - begin + 1 if i < k: return _randomizedSelect(array, begin, q, i) else: return _randomizedSelect(array, q + 1, end, i - k) def _randomizedPartition(array, begin, end): i = random.randint(begin, end) array[begin], array[i] = array[i], array[begin] return _partition(array, begin, end) def _partition(array, begin, end): x = array[begin] i = begin - 1 j = end + 1 while True: j -= 1 while array[j] > x: j -= 1 i += 1 while array[i] < x: i += 1 if i < j: array[i], array[j] = array[j], array[i] else: return j ###################################### # Replacement Strategies (ES) # ###################################### ###################################### # Migrations # ###################################### def migRing(populations, k, selection, replacement=None, migarray=None): """Perform a ring migration between the *populations*. The migration first select *k* emigrants from each population using the specified *selection* operator and then replace *k* individuals from the associated population in the *migarray* by the emigrants. If no *replacement* operator is specified, the immigrants will replace the emigrants of the population, otherwise, the immigrants will replace the individuals selected by the *replacement* operator. The migration array, if provided, shall contain each population's index once and only once. If no migration array is provided, it defaults to a serial ring migration (1 -- 2 -- ... -- n -- 1). Selection and replacement function are called using the signature ``selection(populations[i], k)`` and ``replacement(populations[i], k)``. It is important to note that the replacement strategy must select *k* **different** individuals. For example, using a traditional tournament for replacement strategy will thus give undesirable effects, two individuals will most likely try to enter the same slot. """ if migarray is None: migarray = range(1, len(populations)) + [0] immigrants = [[] for i in xrange(len(migarray))] emigrants = [[] for i in xrange(len(migarray))] for from_deme in xrange(len(migarray)): emigrants[from_deme].extend(selection(populations[from_deme], k)) if replacement is None: # If no replacement strategy is selected, replace those who migrate immigrants[from_deme] = emigrants[from_deme] else: # Else select those who will be replaced immigrants[from_deme].extend(replacement(populations[from_deme], k)) mig_buf = emigrants[0] for from_deme, to_deme in enumerate(migarray[1:]): from_deme += 1 # Enumerate starts at 0 for i, immigrant in enumerate(immigrants[to_deme]): indx = populations[to_deme].index(immigrant) populations[to_deme][indx] = emigrants[from_deme][i] to_deme = migarray[0] for i, immigrant in enumerate(immigrants[to_deme]): indx = populations[to_deme].index(immigrant) populations[to_deme][indx] = mig_buf[i] ###################################### # Decoration tool # ###################################### # This function is a simpler version of the decorator module (version 3.2.0) # from Michele Simionato available at http://pypi.python.org/pypi/decorator. # Copyright (c) 2005, Michele Simionato # All rights reserved. # Modified by Francois-Michel De Rainville, 2010 def decorate(decorator): """Decorate a function preserving its signature. There is two way of using this function, first as a decorator passing the decorator to use as argument, for example :: @decorate(a_decorator) def myFunc(arg1, arg2, arg3="default"): do_some_work() return "some_result" Or as a decorator :: @decorate def myDecorator(func): def wrapFunc(*args, **kargs): decoration_work() return func(*args, **kargs) return wrapFunc @myDecorator def myFunc(arg1, arg2, arg3="default"): do_some_work() return "some_result" Using the :mod:`inspect` module, we can retrieve the signature of the decorated function, what is not possible when not using this method. :: print inspect.getargspec(myFunc) It shall return something like :: (["arg1", "arg2", "arg3"], None, None, ("default",)) This function is a simpler version of the decorator module (version 3.2.0) from Michele Simionato available at http://pypi.python.org/pypi/decorator. """ def wrapDecorate(func): # From __init__ assert func.__name__ if inspect.isfunction(func): argspec = inspect.getargspec(func) defaults = argspec[-1] signature = inspect.formatargspec(formatvalue=lambda val: "", *argspec)[1:-1] elif inspect.isclass(func): argspec = inspect.getargspec(func.__init__) defaults = argspec[-1] signature = inspect.formatargspec(formatvalue=lambda val: "", *argspec)[1:-1] if not signature: raise TypeError("You are decorating a non function: %s" % func) # From create src = ("def %(name)s(%(signature)s):\n" " return _call_(%(signature)s)\n") % dict(name=func.__name__, signature=signature) # From make evaldict = dict(_call_=decorator(func)) reserved_names = set([func.__name__] + \ [arg.strip(' *') for arg in signature.split(',')]) for name in evaldict.iterkeys(): if name in reserved_names: raise NameError("%s is overridden in\n%s" % (name, src)) try: # This line does all the dirty work of reassigning the signature code = compile(src, "", "single") exec code in evaldict except: raise RuntimeError("Error in generated code:\n%s" % src) new_func = evaldict[func.__name__] # From update new_func.__source__ = src new_func.__name__ = func.__name__ new_func.__doc__ = func.__doc__ new_func.__dict__ = func.__dict__.copy() new_func.func_defaults = defaults new_func.__module__ = func.__module__ return new_func return wrapDecorate if __name__ == "__main__": import doctest import random random.seed(64) doctest.run_docstring_examples(initRepeat, globals()) random.seed(64) doctest.run_docstring_examples(initIterate, globals()) doctest.run_docstring_examples(initCycle, globals()) doctest.run_docstring_examples(Statistics.register, globals()) doctest.run_docstring_examples(Statistics.update, globals()) deap-0.7.1/doc/0000755000076500000240000000000011650301263013424 5ustar felixstaff00000000000000deap-0.7.1/doc/_images/0000755000076500000240000000000011650301263015030 5ustar felixstaff00000000000000deap-0.7.1/doc/_images/deap.png0000644000076500000240000010423511641072614016461 0ustar felixstaff00000000000000‰PNG  IHDRtºÒCß pHYs.#.#x¥?vtEXtSoftwareAdobe ImageReadyqÉe<ˆ*IDATxÚìÝpTç½çy¥ÛjIýC-au ñË‘'æ.’ål e0±ãÊbíÝ[[¡¶â1{ËIªÆNmìª-ã© þÃví”ÉVìT͵kÆïìâ*¸{kfÀ&ÞÄ®!¹A.$¦›€¢Õ²Ô’àt·¤÷‘š‹1ÑÒéþžçœó~¥(8­ó£û<Ÿó}¾Ï×¾øâ‹*î3páÍä[•ÞÊ7¿u€] 8ž‡]Ì \¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜B¸L!\¦.S€)„ ÀÂ` á0…p˜r»Ec¹D¡)~årFýßYýu}çµï}¾huM„] .A¸à…Âd^p-5¸%äŒcåÙ@ò­iÛêP_}5ÑjßdÜPÌ ¡NŽ8á‚s„ñ\¢p9“5º¯LþßÓV½˜bxñe„q]áux½ÁZ{uÍd¥‰Øá‚dî‰ñÔX.Qüu¥µÅË.&ÆÈÁk¿Sí‹S†êšh­¿­ÖßÎÁý.ØÒTˆp:gt¿qÌÏ5‘O©_×ÏÔð‡:jýíPçT}@G„ ¶‘5ºs—º³S‚]jÌËÇÔ¯¡þ]ê{7uê;ýSY§h‚pAkc¹„1|`2V(WÛE;»RÈ#‹Ó(@„ Ú™Oencø£©^ŒYvÈ´®ª}‘@}g¨aµ¿¾Óë ²s@á‚.ŠE ÆÈ'õP1‘O îU¿ª¦z4ÔÏ[jXCƒC¸`±±\BŠáj„ÌÞ0¯Ø£!ÕóZ­¿-Ô°:4o5“& Ò¬A¦Pù=|ZýH¾Uí‹44­khz”Z¨ÂQã©tÿN2Ñ}žO $ßR¿jým Më˜1eG¸ ¡PÈÆúwÒOÁBjç§z^S¿B „æ­ihZÇ>€² \¨¬¬Ñ}ñ_z BÅe&R=¿lhz´±y… `áBEK.¼Éôm])d‡úw©_þPGCÓ£2Àœ.”ÙÄxj ùæ¥áÔØ•½a Å&.¼Ùд.ÙäõÙ'0+„ e“5º‡R;‘ƒì ;ºÖ÷±¡iÝü–-Ì•€Ò.”ÁÈà^š5:éhª_þPÇü[¡NvœÇÍÇϧÕ7É´‘LgŠ¿ïM¹üuf<Þ;4ó¿ [C×ÿNW,Zü¦¥ñêŠ6ÞøgXèTo:3u¥Ÿú—KþÚÂWþÌh~æ'ÖÕÕ\û¿!¿/ÖÚXü~ÙÂÆPoòÏüË7t5ºÕ×+—3c¹Dñw&ò©üxßµ?p¥)e8£n’¯ÿ¿¾šhµïê39ýä³×¬õ·³ÃÝék_|ñ{ÁÌ@”Æ æìv êÔH¾Ué­|ó[8‘¬E|Ÿ¼o82õµ”1C…tµG¯ 6îŸÊ ®%Ê«˜¨¯§¦¾³ƒ#‰>K^L1…,¦êë²ÖÆàÔWP ã©|¾o<—(L……BFýŽUC•Z›Ç,¦ÕêkM¤ÖßÎdÂÜH]¨C©#ƒ{‰Ü@½!Î_°Åyá‚É÷êM'Ó5H¦¾¡Œþ¯9X79ƈ-lli ¿á9'0[“×{:óI¼¯ø…â¬kºbÑb¡i#0c¹ÄÄxJ}ü&Ÿ²Kµ?ÔQ¬nP¿Š‰‡’pÁ½Ô,Ý¿“~D „ „ *>TÉxoúÔù´-¢„Òã5ÌX¶°Q=˜RÜìToZ]þ§z‡¦¾Ú#J(1nP—|¬µñþX”¨˜VÖ蟌NO Ι‘íuÔúÛ}5¨“¬pÁ-˜j_¤åî­ÎèÅ@¸`;ÅڄɯJn›5L3¦<Ø„k£ùâ…_|pLšpÛ¬A]ûËN¥L£€[MŒ§²F÷X.‘›üê–þnŬ!04ÐdpXÎËs@»G» ŸÄûÔW«æKk¥«}òyf1hà©&Ü(L]þÉÛvWu¼bÎØ5õ‹ ŽWŒ²—&ÓFÕ¾H ¾ÓêT7Þ „ ¶—5ºû{^e%L1,Xú¼}ßæ´U,OØìÂÌAƒf¬íXÌHN²ÿø9…ƒ†µ+3s ŽQ¬P(f  3 ¡†ÕþúNC.Øï"¿pöÅœqŒ]44­k^ô´ßà´R|Jù»cŸ©¯.™òPÞ‘†f¬íXB9ì(™6ö?7)?ÇÞ˜•h8¨.üâ;{v4–KŒ îuÕ”‡r©õ·©›pz4.Ø@¡Q㮡þ]ì ”Âã ܵ`K¸yááÂŃŠ²ˆµ†7¬Šñ<¶pª7½ûP‚"…² d„½ÃŒ‘—†?¢C¼yÕ¾HhÞê@¨S}eo.hgdpoªç—\ê˜Ã[›½z=.X›)ì?~n÷¡8ƒ R¸3SØì3j”*d2b e™‚ûx¼úykB «I´0–K¤z^eÌ5<YôS[4b \°à®b4¯F;ö S e™H@¦€Ê¥ áæ̘ \°ó PÞw4[Ì’ \T¬S`ºÚ£Vµ3Ì€ô b4¯®}Ê”,¬ó­]±ø¯WÅXΊýFß#S°JqÆDcó&–™ \üà>êy•¾¬(¯Z[ËÒ­:'¦„ ŠÏ*Õ¸Â%ëÒ3Ì®!RÔM4Ü°*¶aU;uL¨¨B!sqð½‘Á½ôhÔ‡?ÔÑÐôhhÞjÖ˜ \¨ìÅŸ<ó¢1ró2¿å‰pd“žod„ •Ãô[ 36?¼\4(d@y%ÓF1Rdúƒ¶Ö®X\œ-Å®@yeî‹“¥ {ÙzbºáB%ïþ‡\8û"uJ¨4m=.Th\±cßIJldýÊöÍ/_ÖÚÈ®€IGâ};ö TÁ.Š… êò'a„I…BF +ÔmuÐvQ,dhhZÇ® \(Ï[ nÞYô4áã è©Ø‘A4ؘ­b¥Òë{ŽRª`S$Œ˜³‰ñTº']lÊã 46oҶĘpÁ&7,Ì^­¿ÍsÓUw¥a.Ùlw£V]Êe÷¡8ã g`®f¥8bǾT*9@W{T]þÌ•@‰˜á$ Mëæ·l¡é#áÂì ™þžWy¸Æê˜ñ¶ã:ýÕº}Ÿ/:·«kb<•Ï÷Mæ—3c¹ÄäïäSùñ>bˆëÍoybþ‚-„ `Œæw|p‚™Õάóm~h9ÅÒ˜9Vx}ÏÑ=‡ì ‡‰†ƒ?ZEL˜9VPwP,]ï<¡†‘MNd&\БëžOluíT(¨ÃW­öEjýíž;‚–\6…Ɉ!11žšï›ü&Ÿrmâ Ç‚¥Ï[ž.˜Œx\éxëW¶«aåA¬@Ä(#ƒ{i¬à†»ôù ¶1.Ìd¨gªç5÷Ô©¡­Ößî¯ïœs‚ŒbÜ ¾fnõÕ=ÓU<ÞÀ‚¥Ï‡æ­&\ VláH¼ï¿Š+¸EL V b`W.|E¡9ŸxÎ •KêP€¿¾óÚ4;º>hpÃQ³¶Ë#᱈P ª\1<»q%U Ä ì "¸=\PÔÏ>}ÊÁÃC ê;ý¡N§.Öš5ºs—º'¿:7h¨õ·-l{Ù’êÂbÌÖ“ÝÇcLb¸%\HÝö÷¼J×01.LrêT5õ‡:CóV»ê/2Å”Á>à¼ðت)„ ¥Ø}(þÊ®ÃÄ ¸†Ji—0Fó¯ìåæpÁÍo{ûÃýÇÏq] rºÚ£Û_C™´nNõ¦ÕåïbW rž|ì>J44Ô¿óó oR°€ŠŽõ,}ÞÍí.Ø.Y(.[Žlb„µ&ÆSÉ7í1Th ׆ ûŸûùÛ2Å(aÐ   ·ÿäJô¹ý£`b„W—'\0Ë^ C+èù“îß92øž]ò)u-¹çµòæ . 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nS¶@P–8àC›qÊèîÁ–2"""'¸vM⢵‚ÝR×¥!-`PFDDäÇïømW·–3 }_ÒeDDDN0sæLÀ£>jéX;ƒ?üp‹œˆ}ʈˆˆˆ4€ã”iƒ2"""" `PFDDD¤ ʈˆˆˆ4€A‘0(#"""ÒeDDDDÀ ŒˆˆˆH”iƒ2"""" `PFDDD¤ ʈˆˆˆ4€A‘0(#"""ÒeDDDDÀ ŒˆˆˆH”iƒ2"""" `PFDDD¤ ʈˆˆˆ4€A‘0(#"""ÒeDDDDÀ ŒˆˆˆH”iƒ2"""" `PFDDD¤ÿ©“­䆖þIEND®B`‚deap-0.7.1/doc/_scripts/0000755000076500000240000000000011650301263015252 5ustar felixstaff00000000000000deap-0.7.1/doc/_scripts/ackley.py0000644000076500000240000000135611641072614017106 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import sys try: import numpy as np except: exit() sys.path.append("../..") from deap import benchmarks def ackley_arg0(sol): return benchmarks.ackley(sol)[0] fig = plt.figure() # ax = Axes3D(fig, azim = -29, elev = 50) ax = Axes3D(fig) X = np.arange(-30, 30, 0.5) Y = np.arange(-30, 30, 0.5) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = ackley_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, norm = LogNorm(), cmap = cm.jet, linewidth=0.2, ) plt.xlabel("x") plt.ylabel("y") plt.show()deap-0.7.1/doc/_scripts/bohachevsky.py0000644000076500000240000000137511641072614020145 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import sys try: import numpy as np except: exit() sys.path.append("../..") from deap import benchmarks def bohachevsky_arg0(sol): return benchmarks.bohachevsky(sol)[0] fig = plt.figure() ax = Axes3D(fig, azim = -29, elev = 50) # ax = Axes3D(fig) X = np.arange(-15, 15, 0.5) Y = np.arange(-15, 15, 0.5) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = bohachevsky_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, norm = LogNorm(), cmap = cm.jet, linewidth=0.2, ) plt.xlabel("x") plt.ylabel("y") plt.show()deap-0.7.1/doc/_scripts/griewank.py0000644000076500000240000000134111641072614017437 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import sys try: import numpy as np except: exit() sys.path.append("../..") from deap import benchmarks def griewank_arg0(sol): return benchmarks.griewank(sol)[0] fig = plt.figure() ax = Axes3D(fig, azim = -29, elev = 40) # ax = Axes3D(fig) X = np.arange(-50, 50, 0.5) Y = np.arange(-50, 50, 0.5) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = griewank_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, cmap = cm.jet, linewidth=0.2, ) plt.xlabel("x") plt.ylabel("y") plt.show()deap-0.7.1/doc/_scripts/h1.py0000644000076500000240000000134211641072614016141 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import sys try: import numpy as np except: exit() sys.path.append("../..") from deap import benchmarks def h1_arg0(sol): return benchmarks.h1(sol)[0] fig = plt.figure() # ax = Axes3D(fig, azim = -29, elev = 50) ax = Axes3D(fig) X = np.arange(-25, 25, 0.5) Y = np.arange(-25, 25, 0.5) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = h1_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, norm = LogNorm(), cmap = cm.jet, linewidth=0.2, ) plt.xlabel("x") plt.ylabel("y") plt.show()deap-0.7.1/doc/_scripts/himmelblau.py0000644000076500000240000000117011641072614017747 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import sys try: import numpy as np except: exit() sys.path.append("../..") from deap import benchmarks def untuple(sol): return benchmarks.himmelblau(sol)[0] fig = plt.figure() ax = Axes3D(fig, azim = -29, elev = 49) X = np.arange(-6, 6, 0.1) Y = np.arange(-6, 6, 0.1) X, Y = np.meshgrid(X, Y) Z = np.array(map(untuple, zip(X,Y))) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, norm = LogNorm(), cmap = cm.jet, linewidth=0.2, ) plt.xlabel("x") plt.ylabel("y") plt.show()deap-0.7.1/doc/_scripts/kursawe.py0000644000076500000240000000167511641072614017323 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import sys try: import numpy as np except: exit() sys.path.append("../..") from deap import benchmarks X = np.arange(-5, 5, 0.1) Y = np.arange(-5, 5, 0.1) X, Y = np.meshgrid(X, Y) Z1 = np.zeros(X.shape) Z2 = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z1[i,j], Z2[i,j] = benchmarks.kursawe((X[i,j],Y[i,j])) fig = plt.figure(figsize=(12,5)) ax = fig.add_subplot(1, 2, 1, projection='3d') ax.plot_surface(X, Y, Z1, rstride = 1, cstride = 1, cmap = cm.jet, linewidth=0.2, ) plt.xlabel("x") plt.ylabel("y") ax = fig.add_subplot(1, 2, 2, projection='3d') ax.plot_surface(X, Y, Z2, rstride = 1, cstride = 1, cmap = cm.jet, linewidth=0.2, ) plt.xlabel("x") plt.ylabel("y") plt.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0, hspace=0) plt.show()deap-0.7.1/doc/_scripts/rastrigin.py0000644000076500000240000000131511641072614017633 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import sys try: import numpy as np except: exit() sys.path.append("../..") from deap import benchmarks def rastrigin_arg0(sol): return benchmarks.rastrigin(sol)[0] fig = plt.figure() ax = Axes3D(fig, azim = -29, elev = 50) X = np.arange(-5, 5, 0.1) Y = np.arange(-5, 5, 0.1) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = rastrigin_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, cmap = cm.jet, linewidth=0.2, ) plt.xlabel("x") plt.ylabel("y") plt.show()deap-0.7.1/doc/_scripts/rosenbrock.py0000644000076500000240000000136611641072614020006 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import sys try: import numpy as np except: exit() sys.path.append("../..") from deap import benchmarks def rosenbrock_arg0(sol): return benchmarks.rosenbrock(sol)[0] fig = plt.figure() # ax = Axes3D(fig, azim = -29, elev = 50) ax = Axes3D(fig) X = np.arange(-2, 2, 0.1) Y = np.arange(-1, 3, 0.1) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = rosenbrock_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, norm = LogNorm(), cmap = cm.jet, linewidth=0.2, ) plt.xlabel("x") plt.ylabel("y") plt.show()deap-0.7.1/doc/_scripts/schwefel.py0000644000076500000240000000134311641072614017432 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import sys try: import numpy as np except: exit() sys.path.append("../..") from deap import benchmarks def schwefel_arg0(sol): return benchmarks.schwefel(sol)[0] fig = plt.figure() # ax = Axes3D(fig, azim = -29, elev = 50) ax = Axes3D(fig) X = np.arange(-500, 500, 10) Y = np.arange(-500, 500, 10) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = schwefel_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, cmap = cm.jet, linewidth=0.2, ) plt.xlabel("x") plt.ylabel("y") plt.show()deap-0.7.1/doc/_scripts/shekel.py0000644000076500000240000000167711641072614017117 0ustar felixstaff00000000000000from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import sys try: import numpy as np except: exit() sys.path.append("../..") from deap import benchmarks #NUMMAX = 5 #A = 10 * np.random.rand(NUMMAX, 2) #C = np.random.rand(NUMMAX) A = [[0.5, 0.5], [0.25, 0.25], [0.25, 0.75], [0.75, 0.25], [0.75, 0.75]] C = [0.002, 0.005, 0.005, 0.005, 0.005] print A print C def shekel_arg0(sol): return benchmarks.shekel(sol, A, C)[0] fig = plt.figure() # ax = Axes3D(fig, azim = -29, elev = 50) ax = Axes3D(fig) X = np.arange(0, 1, 0.01) Y = np.arange(0, 1, 0.01) X, Y = np.meshgrid(X, Y) Z = np.zeros(X.shape) for i in xrange(X.shape[0]): for j in xrange(X.shape[1]): Z[i,j] = shekel_arg0((X[i,j],Y[i,j])) ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, norm = LogNorm(), cmap = cm.jet, linewidth=0.2, ) plt.xlabel("x") plt.ylabel("y") plt.show()deap-0.7.1/doc/_templates/0000755000076500000240000000000011650301263015561 5ustar felixstaff00000000000000deap-0.7.1/doc/_templates/indexcontent.html0000644000076500000240000001121211641072614021153 0ustar felixstaff00000000000000{% extends "defindex.html" %} {% block tables %}

DEAP (Distributed Evolutionary Algorithms in Python) is a novel evolutionary computation framework for rapid prototyping and testing of ideas. Its design departs from most other existing frameworks in that it seeks to make algorithms explicit and data structures transparent, as opposed to the more common black box type of frameworks. It also incorporates easy parallelism where users need not concern themselves with gory implementation details like synchronization and load balancing, only functional decomposition.

The five founding hypotheses of DEAP are:

  1. The user knows best. Users should be able to understand the internal mechanisms of the framework so that they can extend them easily to better suit their specific needs.
  2. User needs in terms of algorithms and operators are so vast that it would be unrealistic to think of implementing them all in a single framework. However, it should be possible to build basic tools and generic mechanisms that enable easy user implementation of most any EA variant.
  3. Speedy prototyping of ideas is often more precious than speedy execution of programs. Moreover, code compactness and clarity is also very precious.
  4. Even though interpreted, Python is fast enough to execute EAs. Whenever execution time becomes critical, compute intensive components can always be recoded in C. Many efficient numerical libraries are already available through Python APIs.
  5. Easy parallelism can alleviate slow execution.

And these hypotheses lead to the following objectives:

Rapid prototyping
Provide an environment allowing users to quickly implement their own algorithms without compromise.
Parallelization made easy
Allow for straightforward parallelization; users should not be forced to specify more than the granularity level of their functional decomposition.
Adaptive load balancing
The workload should automatically and dynamically be distributed among available compute units; user intervention should be optional and limited to hints of relative loads of tasks.
Preach by examples
Although the aim of the framework is not to provide ready made solutions, it should nevertheless come with a substantial set of real-world examples to guide the apprenticeship of users.

Parts of the documentation:

Tutorial
start here

Examples
complete examples using common problems

Library Reference
the complete api of the framework

What's New?
list of major changes to the framework

Indices and tables:

Global Module Index
quick access to all modules

General Index
all functions, classes, terms

Search page
search this documentation

Complete Table of Contents
lists all sections and subsections

Meta information:

Reporting bugs

{% endblock %}deap-0.7.1/doc/_templates/indexsidebar.html0000644000076500000240000000121011643625342021113 0ustar felixstaff00000000000000

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    • {% endblock %} deap-0.7.1/doc/api/0000755000076500000240000000000011650301263014175 5ustar felixstaff00000000000000deap-0.7.1/doc/api/algo.rst0000644000076500000240000000175111641072614015662 0ustar felixstaff00000000000000Algorithms ========== .. automodule:: deap.algorithms .. autofunction:: deap.algorithms.eaSimple(toolbox, population, cxpb, mutpb, ngen[, stats, halloffame]) .. autofunction:: deap.algorithms.varSimple .. autofunction:: deap.algorithms.varAnd .. autofunction:: deap.algorithms.eaMuPlusLambda(toolbox, population, mu, lambda_, cxpb, mutpb, ngen[, stats, halloffame]) .. autofunction:: deap.algorithms.eaMuCommaLambda(toolbox, population, mu, lambda_, cxpb, mutpb, ngen[, stats, halloffame]) .. autofunction:: deap.algorithms.varLambda .. autofunction:: deap.algorithms.varOr .. autofunction:: deap.algorithms.eaSteadyState(toolbox, population, ngen[, stats, halloffame]) .. autofunction:: deap.algorithms.varSteadyState Covariance Matrix Adaptation Evolution Strategy =============================================== .. autofunction:: deap.cma.esCMA(toolbox, population, sigma, ngen[, halloffame, **kargs]) .. autoclass:: deap.cma.CMAStrategy(population, sigma[, params]) :members:deap-0.7.1/doc/api/benchmarks.rst0000644000076500000240000000012011641072614017042 0ustar felixstaff00000000000000========== Benckmarks ========== .. automodule:: deap.benchmarks :members: deap-0.7.1/doc/api/core.rst0000644000076500000240000000307611641072614015672 0ustar felixstaff00000000000000.. _core: Core Architecture ================= The core architecture of DEAP is composed of two simple structures, the :mod:`~deap.creator` and the :class:`~deap.base.toolbox`. The former provides structuring capabilities, while the latter adds genericity potential to every algorithm. Both structures are described in detail in the following sections. Creator ------- .. automodule:: deap.creator .. autofunction:: deap.creator.create(name, base[, attribute[, ...]]) .. autodata:: deap.creator.class_replacers Toolbox ------- The :class:`Toolbox` is a container for the tools that are selected by the user. The toolbox is manually populated with the desired tools that best apply with the chosen representation and algorithm from the user's point of view. This way it is possible to build algorithms that are totally decoupled from the operator set, as one only need to update the toolbox in order to make the algorithm run with a different operator set as the algorithms are built to use aliases instead of direct function names. .. autoclass:: deap.base.Toolbox .. automethod:: deap.base.Toolbox.register(alias, function[, argument[, ...]]) .. automethod:: deap.base.Toolbox.unregister(alias) .. automethod:: deap.base.Toolbox.decorate(alias, decorator[, decorator[, ...]]) Additional Base Types --------------------- Even if there is a very large variety of implemented types in Python, we must provide additional ones for sake of completeness. Fitness +++++++ .. autoclass:: deap.base.Fitness([values]) :members: Tree ++++ .. autoclass:: deap.base.Tree([content]) :members:deap-0.7.1/doc/api/dtm.rst0000644000076500000240000003643511641072614015533 0ustar felixstaff00000000000000.. _dtm-bases: Distributed Task Manager Overview ================================= DTM is a distributed task manager which works over many communication layers. Currently, all modern MPI implementations are supported through `mpi4py `_, and an experimental TCP backend is available. .. warning:: As on version 0.2, DTM is still in *alpha stage*, meaning that some specific bugs may appear; the communication and operation layers are quite stable, though. Feel free to report bugs and performance issues if you isolate a problematic situation. DTM Main Features ----------------- DTM has some very interesting features : - Offers a similar interface to the Python's multiprocessing module - Automatically balances the load between workers (and therefore supports heterogeneous networks and different task duration) - Supports an arbitrary number of workers without changing a byte in the program - Abstracts the user from the communication management (the same program can be run over MPI, TCP or multiprocessing just by changing the communication manager) - Provides easy-to-use parallelization paradigms - Offers a trace mode, which can be used to tune the performance of the running program (still experimental) Introductory example -------------------- First, lets take a look to a very simple distribution example. The sequential program we want to parallelize reads as follow : :: def op(x): return x + 1./x # Or any operation if __name__ == "__main__": nbrs = range(1, 1000) results = map(op, nbrs) This program simply applies an arbitrary operation to each item of a list. Although it is a very trivial program (and therefore would not benefit from a parallelization), lets assume we want to distribute the workload over a cluster. We just import the task manager, and use the DTM parallel version of :func:`map` : :: from deap import dtm def op(x): return x + 1./x # Or any operation def main(): nbrs = range(1, 1000) results = dtm.map(op, nbrs) dtm.start(main) And we are done! This program can now run over MPI, with an arbitrary number of processors, without changing anything. We just run it like a normal MPI program (here with OpenMPI) :: $ mpirun -n * python myProgram.py The operation done in the op() function can be virtually any operation, including other DTM calls (which in turn may also spawn sub-tasks, and so on). .. note:: The encapsulation of the main execution code into a function is required by DTM, in order to be able to control which worker will start the execution. Functions documentation ----------------------- .. module:: deap.dtm .. autoclass:: deap.dtm.manager.Control :members: .. autoclass:: deap.dtm.manager.AsyncResult :members: DTM launching ------------- DTM can support many communication environment. The only real restriction on the choice of a communication backend is that is must provide an access from each worker to every other (that is, the worker 0 must be able to directly communicate with all other workers, and so for the worker 1, 2, etc.). Currently, two main backends are available : one using MPI through the `mpi4py `_ layer, and another using TCP (with SSH for the launch process). If you already have a functionnal MPI installation, you should choose the mpi4py backend, as it can provide better performances in some cases and easier configuration. On the other side, MPI implementations may cause some strange errors when you use low-level system operations (such as fork). In any case, both backends should be ready for production use, and as the backend switch is as easy as changing only one line in your script, this choice should not be an issue. Launch DTM with the mpi4py backend ++++++++++++++++++++++++++++++++++ When this backend is used, DTM delegates the start-up process to MPI. In this way, any MPI option can be used. For instance : :: mpirun -n 32 -hostfile myHosts -npernode 4 -bind-to-core python yourScript.py --yourScriptParams ... Will launch your script on 32 workers, distributed over the hosts listed in 'myHosts', without exceeding 4 workers by host, and will bind DTM workers to a CPU core. The bind-to-core option can provide a good performance improvement in some environments, and does not affect the behavior of DTM at all. Launch DTM with the pure-TCP backend ++++++++++++++++++++++++++++++++++++ The TCP backend includes a launcher which works with SSH in order to start remote DTM workers. Therefore, your execution environment must provide a SSH access to every host, and a shared file system such as NFS (or, at least, ensure that the script you want to execute and the DTM libraries are located in a common path for every worker). You also have to provide a host file which follows the same synthax as the MPI host files, for instance : :: firstComputer.myNetwork.com slots=4 otherComputer.myNetwork.com slots=6 221.32.118.3 slots=4 .. warning:: The hostnames / addresses you write in this file must be accessible and translable on every machine used. For instance, putting 'localhost' in this file among other remote hosts **will fail** because each host will try to bind ports for 'localhost' (which will fail, as this is not a network-accessible address). Then you can launch DTM with this file : :: python myScript.py --dtmTCPhosts=myHostFile .. note:: Do not forget to explicitly set the communication manager to **deap.dtm.commManagerTCP** : :: dtm.setOptions(communicationManager="deap.dtm.commManagerTCP") This backend can also be useful if you want to launch local jobs. If your hostfile contains only 'localhost' or '127.0.0.1', DTM will start all the workers locally, without using SSH. Troubleshooting and Pitfalls ---------------------------- Here are the most common errors or problems reported with DTM. Some of them are caused by a bad use of the task manager, others are limitations from the libraries and programs used by DTM. Isolation per worker ++++++++++++++++++++ In DTM, the atomic independent working units are called workers. They are separate processes, and do not share any information other than those from the communications (explicitly called). Therefore, two variables cannot interfere if they are used in different workers, and your program should not rely on this. Thus, one has to be extremely careful about which data is global, and which is local to a task. For instance, consider the following program : :: from deap import dtm foo = [0] def bar(n): foo[0] += n return foo[0] def main(): listNbr = range(30) results = dtm.map(bar, listNbr) print("Results : " + str(results)) dtm.start(main) Although it is syntactically correct, it may not produce the result you are waiting for. On a serial evaluation (using the built-in :func:`map` function), it simply produces a list containing the sums of numbers from 0 to 30 (it is a quite odd approach, but it works) : :: Results : [0, 1, 3, 6, 10, 15, 21, 28, 36, 45, 55, 66, 78, 91, 105, 120, 136, 153, 171, 190, 210, 231, 253, 276, 300, 325, 351, 378, 406, 435] But with DTM, as foo is not shared between workers, the program generate a completely unpredictable output, for instance : :: Results : [0, 1, 2, 5, 5, 10, 11, 17, 25, 20, 30, 36, 48, 43, 57, 72, 88, 65, 83, 107, 127, 104, 126, 150, 150, 175, 176, 203, 203, 232] The reverse problem should also be taken into account. If an object keeps its state, it is generally not a good idea to make it global (accessible from all tasks). For instance, if you create a log like this : :: from deap import dtm logfile = open('myLogFile', 'w+') def task1(args): # [...] logfile.write("Log task 1") # [...] def task2(args): # [...] logfile.write("Log task 2") # [...] def main(): listNbr = range(100) statusTask1 = dtm.map_async(task1, listNBr) statusTask2 = dtm.map_async(task2, listNBr) # [...] dtm.start() You may experience some unusual outputs, as task1 and task2 writings will probably overlap (because they use the same resource if, by chance, they are executed on the same worker, which will probably happens). In doubt, use local variables. Exceptions ++++++++++ When an Python exception occurs during a task execution, DTM catchs it (and try to run another task on this worker). This exception is then raised in the *parent task*. If there is no such task (the task where the exception occurs is the root task), then it is thrown and DTM stops its execution. The moment when the exception will be raised in the parent tasks depends on the child task type : if it is a synchronous call (like :func:`~deap.dtm.manager.Control.apply` or :func:`~deap.dtm.manager.Control.map`), it is raised when the parent awake (i.e. as if it has been raised by the DTM function itself). If it is an asynchronous call (like :func:`~deap.dtm.manager.Control.apply_async` or :func:`~deap.dtm.manager.Control.map_async`), the exception is raised when the parent task performs a :func:`~deap.dtm.manager.AsyncResult.get` on the :class:`~deap.dtm.manager.AsyncResult` object. Also, the :func:`~deap.dtm.manager.AsyncResult.successful` will return *False* if an exception occured, without raising it. .. note:: When DTM catches an exception, it outputs a warning on the standard error output stating the exception type and arguments. This warning does not mean that the exception has been raised in the parent task (actually, in some situations, it may take a lot of time if every workers are busy); it is logged only for information purpose. MPI and threads +++++++++++++++ Recent MPI implementations supports four levels of threading : single, funneled, serialized and multiple. However, many environments (like Infiniband backend) do not support other level than single. In that case, if you use the `mpi4py `_ backend, make sure that mpi4py does not initialize MPI environment in another mode than single (as DTM has been designed so that only the communication thread makes MPI calls, this mode works well even if there is more than one active thread in a DTM worker). This setting can be changed in the file "site-packages/mpi4py/rc.py", with the variable `thread_level`. Cooperative multitasking ++++++++++++++++++++++++ DTM works on a cooperative philosophy. There is no preemption system to interrupt an executing thread (and eventually starts one with a higher priority). When a task begins its execution, the worker will execute it until the task returns or makes a DTM synchronous call, like map or apply. If the task enters an infinite loop or reaches a dead-lock state, then the worker will also be in dead-lock -- it will be able to transfer its other tasks to other workers though. DTM is not a fair scheduler, and thus cannot guarantee any execution delay or avoid any case of starvation; it just tries to reach the best execution time knowing some information about the tasks. Pickling ++++++++ When dealing with non trivial programs, you may experience error messages like this : :: PicklingError: Can't pickle : attribute lookup __main__.**** failed This is because DTM makes use of the Python :mod:`pickle` module in order to serialize data and function calls (so they can be transferred from one worker to another). Although the pickle module is very powerful (it handles recursive and shared objects, multiple references, etc.), it has some limitations. Most of the time, a pickling error can be easily solved by adding `__setstate__() `_ and `__getstate__() `_ methods to the problematic class (see the `Python documentation `_ for more details about the pickling protocol). This may also be used to accelerate pickling : by defining your own pickling methods, you can speedup the pickling operation (the same way you can speedup the deepcopy operation by defining your own `__deepcopy__()` method. If your program use thousands of objects from the same class, it may be worthwhile. Take also note of the following Python interpreter limitations : * As on version 2.6, partial functions cannot be pickled. Python 2.7 works fine. * Lambda functions cannot be pickled in every Python version (up to 3.2). User should use normal functions, or tools from functools, or ensure that its parallelization never need to explicitly transfer a lambda function (not its result, but the lambda object itself) from a worker to another. * Functions are usually never pickled : they are just referenced, and should be importable in the unpickling environment, even if they are standard functions (defined with the keyword **def**). For instance, consider this (faulty) code : :: from deap import dtm def main(): def bar(n): return n**2 listNbr = range(30) results = dtm.map(bar, listNbr) dtm.start(main) On the execution, this will produce an error like : :: TypeError: can't pickle function objects Because the pickler will not be able to find a global reference to the function *bar()*. The same restriction applies on classes and modules. Asynchronous tasks and active waiting +++++++++++++++++++++++++++++++++++++ DTM supports both synchronous and asynchronous tasks (that do not stop the parent task). For the asynchronous tasks, DTM returns an object with an API similar to the Python :class:`multiprocessing.pool.AsyncResult`. This object offers some convenient functions to wait on a result, or test if the task is done. However, some issues may appear in DTM with a program like that : :: from deap import dtm def myTask(param): # [...] Long treatment return param+2 def main(): listTasks = range(100) asyncReturn = dtm.map_async(myTask, listTasks) while not asyncReturn.ready(): continue # Other instructions... dtm.start(main) This active waiting (by looping while the result is not available), although syntactically valid, might produce unexpected "half-deadlocks". Keep in mind that DTM is not a preemptive system : if you load a worker only for waiting on another, the load balancer may not work properly. For instance, it may consider that as the parent task is still working, the asynchronous child tasks can still wait, or that one of the child tasks should remain on the same worker than its parent to balance the load between workers. As the load balancer is not completely deterministic, the child tasks should eventually complete, but in an unpredictable time. It is way better to do something like this : :: def main(): listTasks = range(100) asyncReturn = dtm.map_async(myTask, listTasks) asyncReturn.wait() # or dtm.waitForAll() # [...] By calling one of these functions, you effectively notify DTM that you are now waiting for those asynchronous tasks, and willing to let the worker do another job. The call will then return to your parent function when all the results will be available. deap-0.7.1/doc/api/gp.rst0000644000076500000240000000103611641072614015342 0ustar felixstaff00000000000000Genetic Programming =================== .. automodule:: deap.gp .. autoclass:: deap.gp.PrimitiveSet :members: .. autoclass:: deap.gp.PrimitiveTree :members: .. autoclass:: deap.gp.Primitive :members: .. autoclass:: deap.gp.Operator :members: .. autoclass:: deap.gp.Terminal :members: .. autoclass:: deap.gp.Ephemeral :members: .. autofunction:: deap.gp.evaluate .. autofunction:: deap.gp.evaluateADF .. autofunction:: deap.gp.lambdify .. autofunction:: deap.gp.lambdifyList .. autoclass:: deap.gp.PrimitiveSetTyped :members:deap-0.7.1/doc/api/index.rst0000644000076500000240000000275511641072614016054 0ustar felixstaff00000000000000API --- The API of DEAP is built around a clean and lean core composed of two simple structures: a :mod:`~deap.creator` and :class:`~deap.base.Toolbox`. The former allows creation of classes, at run-time, via inheritance and composition and the latter contains the tools that are required by the evolutionary algorithm. The core functionalities of DEAP are levered by several peripheral modules. The :mod:`~deap.tools` module provides a bunch of operator that are commonly used in evolutionary computation such as initialization, mutation, crossover and selection functions. It also contains all sort of tools to gather information about the evolution such as statistics on almost every thing, genealogy of the produced individuals, hall-of-fame of the best individuals seen, and checkpoints allowing to restart an evolution from its last state. The :mod:`~deap.algorithms` module contains five of the most frequently used algorithms in EC: generational, steady-state, :math:`(\mu\ ,\ \lambda)`, :math:`(\mu+\lambda)`, and CMA-ES. These are readily usable for the most common population layouts. Specific genetic programming tools are provided in the :mod:`~deap.gp` module. A complete and efficient (and stand alone) parallelization module, base on MPI, providing some very useful parallel method is provided in the :mod:`~deap.dtm` module. Finally, common benchmark functions are readily implemented in the :mod:`~deap.benchmarks`. .. toctree:: :maxdepth: 2 :numbered: core tools algo gp dtm benchmarksdeap-0.7.1/doc/api/tools.rst0000644000076500000240000001455511641072614016106 0ustar felixstaff00000000000000Evolutionary Tools ================== .. automodule:: deap.tools Operators --------- The operator set does the minimum job for transforming or selecting individuals. This means, for example, that providing two individuals to the crossover will transform those individuals in-place. The responsibility of making offspring(s) independent of their parent(s) and invalidating the fitness is left to the user and is generally fulfilled in the algorithms by calling :func:`toolbox.clone` on an individuals to duplicate it and ``del`` on the :attr:`values` attribute of the individual's fitness to invalidate to invalidate this last one. Here is a list of the implemented operators in DEAP. ============================ =========================================== ========================================= ======================= ================ Initialization Crossover Mutation Selection Migration ============================ =========================================== ========================================= ======================= ================ :func:`initRepeat` :func:`cxOnePoint` :func:`mutGaussian` :func:`selTournament` :func:`migRing` :func:`initIterate` :func:`cxTwoPoints` :func:`mutShuffleIndexes` :func:`selRoulette` .. :func:`initCycle` :func:`cxUniform` :func:`mutFlipBit` :func:`selNSGA2` .. :func:`~deap.gp.genFull` :func:`cxPartialyMatched` :func:`mutESLogNormal` :func:`selSPEA2` .. :func:`~deap.gp.genGrow` :func:`cxUniformPartialyMatched` :func:`~deap.gp.mutUniform` :func:`selRandom` .. :func:`~deap.gp.genRamped` :func:`cxBlend` :func:`~deap.gp.mutTypedUniform` :func:`selBest` .. .. :func:`cxESBlend` :func:`~deap.gp.mutTypedNodeReplacement` :func:`selWorst` .. .. :func:`cxESTwoPoints` :func:`~deap.gp.mutTypedEphemeral` .. .. .. :func:`cxSimulatedBinary` :func:`~deap.gp.mutShrink` .. .. .. :func:`cxMessyOnePoint` :func:`~deap.gp.mutTypedInsert` .. .. .. :func:`~deap.gp.cxUniformOnePoint` .. .. .. .. :func:`~deap.gp.cxTypedOnePoint` .. .. .. .. :func:`~deap.gp.cxOnePointLeafBiased` .. .. .. .. :func:`~deap.gp.cxTypedOnePointLeafBiased` .. .. .. ============================ =========================================== ========================================= ======================= ================ Initialization ++++++++++++++ .. autofunction:: deap.tools.initRepeat .. autofunction:: deap.tools.initIterate .. autofunction:: deap.tools.initCycle .. autofunction:: deap.gp.genFull .. autofunction:: deap.gp.genGrow .. autofunction:: deap.gp.genRamped Crossover +++++++++ .. autofunction:: deap.tools.cxTwoPoints .. autofunction:: deap.tools.cxOnePoint .. autofunction:: deap.tools.cxUniform .. autofunction:: deap.tools.cxPartialyMatched .. autofunction:: deap.tools.cxUniformPartialyMatched .. autofunction:: deap.tools.cxBlend .. autofunction:: deap.tools.cxESBlend .. autofunction:: deap.tools.cxESTwoPoints .. autofunction:: deap.tools.cxSimulatedBinary .. autofunction:: deap.tools.cxMessyOnePoint .. autofunction:: deap.gp.cxUniformOnePoint .. autofunction:: deap.gp.cxTypedOnePoint .. autofunction:: deap.gp.cxOnePointLeafBiased(ind1, ind2, cxtermpb) .. autofunction:: deap.gp.cxTypedOnePointLeafBiased(ind1, ind2, cxtermpb) Mutation ++++++++ .. autofunction:: deap.tools.mutGaussian .. autofunction:: deap.tools.mutShuffleIndexes .. autofunction:: deap.tools.mutFlipBit .. autofunction:: deap.tools.mutESLogNormal .. autofunction:: deap.gp.mutUniform .. autofunction:: deap.gp.mutTypedUniform .. autofunction:: deap.gp.mutTypedNodeReplacement .. autofunction:: deap.gp.mutTypedEphemeral .. autofunction:: deap.gp.mutShrink .. autofunction:: deap.gp.mutTypedInsert Selection +++++++++ .. autofunction:: deap.tools.selTournament .. autofunction:: deap.tools.selRoulette .. autofunction:: deap.tools.selNSGA2 .. autofunction:: deap.tools.selSPEA2 .. autofunction:: deap.tools.selRandom .. autofunction:: deap.tools.selBest .. autofunction:: deap.tools.selWorst Migration +++++++++ .. autofunction:: deap.tools.migRing(populations, n, selection[, replacement, migarray]) Statistics ---------- .. autoclass:: deap.tools.Statistics([key][, n]) :members: .. autofunction:: deap.tools.mean .. autofunction:: deap.tools.median .. autofunction:: deap.tools.var .. autofunction:: deap.tools.std Hall-Of-Fame ------------ .. autoclass:: deap.tools.HallOfFame .. automethod:: deap.tools.HallOfFame.update .. automethod:: deap.tools.HallOfFame.insert .. automethod:: deap.tools.HallOfFame.remove .. automethod:: deap.tools.HallOfFame.clear .. autoclass:: deap.tools.ParetoFront([similar]) .. automethod:: deap.tools.ParetoFront.update Checkpoint ---------- .. autoclass:: deap.tools.Checkpoint([yaml,object[, ...]]) .. automethod:: deap.tools.Checkpoint.dump(prefix) .. automethod:: deap.tools.Checkpoint.load(filename) .. automethod:: deap.tools.Checkpoint.add(object[, ...]) .. automethod:: deap.tools.Checkpoint.remove(object[, ...]) History ------- .. autoclass:: deap.tools.History .. automethod:: deap.tools.History.populate .. automethod:: deap.tools.History.update(individual[, ...]) .. autoattribute:: deap.tools.History.decorator Other ----- .. autofunction:: deap.tools.decoratedeap-0.7.1/doc/bugs.rst0000644000076500000240000000020711641072614015122 0ustar felixstaff00000000000000Reporting a Bug =============== You can report a bug on the issue list on google code. ``_deap-0.7.1/doc/conf.py0000644000076500000240000001570111641072614014734 0ustar felixstaff00000000000000# -*- coding: utf-8 -*- # # DEAP documentation build configuration file, created by # sphinx-quickstart on Sat Jan 30 13:21:43 2010. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, time # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.append("..") #sys.path.append(os.path.abspath('_ext/')) import deap # -- General configuration ----------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.todo', 'sphinx.ext.pngmath', 'sphinx.ext.intersphinx'] try: import matplotlib except: pass else: extensions += ['matplotlib.sphinxext.only_directives', 'matplotlib.sphinxext.plot_directive'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8' # The master toctree document. master_doc = 'contents' # General information about the project. project = u'DEAP' copyright = u'2009-%s, François-Michel De Rainville, Félix-Antoine Fortin and Marc-André Gardner' % time.strftime('%Y') # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = deap.__version__ # The full version, including alpha/beta/rc tags. release = deap.__revision__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of documents that shouldn't be included in the build. #unused_docs = [] # List of directories, relative to source directory, that shouldn't be searched # for source files. exclude_trees = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'default' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, the todo will be printed in the documentation todo_include_todos = True # Search in python documentation intersphinx_mapping = {'http://docs.python.org/': None} # Reload the cached values every 5 days intersphinx_cache_limit = 5 # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. html_theme = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = {'collapsiblesidebar': True} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". #html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. html_use_smartypants = True # Custom sidebar templates, maps document names to template names. html_sidebars = { 'index': 'indexsidebar.html', } # Additional templates that should be rendered to pages, maps page names to # template names. html_additional_pages = { 'index': 'indexcontent.html', } # If false, no module index is generated. #html_use_modindex = True # If false, no index is generated. html_use_index = True # If true, the index is split into individual pages for each letter. html_split_index = True # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = '' # Output file base name for HTML help builder. htmlhelp_basename = 'DEAP-doc' # -- Options for LaTeX output -------------------------------------------------- # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('contents', 'DEAP.tex', u'DEAP Documentation', u'François-Michel De Rainville, Félix-Antoine Fortin', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # Additional stuff for the LaTeX preamble. #latex_preamble = '' # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_use_modindex = True deap-0.7.1/doc/contents.rst0000644000076500000240000000022611641072614016020 0ustar felixstaff00000000000000=================== DEAP's Master Index =================== .. toctree:: :maxdepth: 2 api/index tutorials/index examples/index whatsnew bugs deap-0.7.1/doc/examples/0000755000076500000240000000000011650301263015242 5ustar felixstaff00000000000000deap-0.7.1/doc/examples/artificial_ant.rst0000644000076500000240000000733011641072614020755 0ustar felixstaff00000000000000.. _artificial-ant: ========================= GP Artificial Ant Problem ========================= The Artificial Ant problem is a more sophisticated yet classical GP problem, in which the evolved individuals have to control an artificial ant so that it can eat all the food located in a given environment. This example shows how DEAP can easily deal with more complex problems, including an intricate system of functions and resources (including a small simulator). For more information about this problem, see :ref:`refPapersAnt`. Primitives set used =================== We use the standard primitives set for the Artificial Ant problem : :: pset = gp.PrimitiveSet("MAIN", 0) pset.addPrimitive(ant.if_food_ahead, 2) pset.addPrimitive(prog2, 2) pset.addPrimitive(prog3, 3) pset.addTerminal(ant.move_forward) pset.addTerminal(ant.turn_left) pset.addTerminal(ant.turn_right) - if_food_ahead is a primitive which executes its first argument if there is food in front of the ant; else, it executes its second argument. - prog2 and prog3 are the equivalent of the lisp PROGN2 and PROGN3 functions. They execute their children in order, from the first to the last. For instance, prog2 will first execute its first argument, then its second. - move_forward makes the artificial ant move one front. This is a terminal. - turn_right and turn_left makes the artificial ant turning clockwise and counter-clockwise, without changing its position. Those are also terminals. .. note:: There is no external input as in symbolic regression or parity. Although those functions are obviously not already built-in in Python, it is very easy to define them : :: def progn(*args): for arg in args: arg() def prog2(out1, out2): return partial(progn,out1,out2) def prog3(out1, out2, out3): return partial(progn,out1,out2,out3) def if_then_else(condition, out1, out2): out1() if condition() else out2() class AntSimulator(object): # [...] def if_food_ahead(self, out1, out2): return partial(if_then_else, self.sense_food, out1, out2) Partial functions are a powerful feature of Python which allow to create functions on the fly. For more detailed information, please refer to the Python documentation of :func:`functools.partial`. Evaluation function =================== The evaluation function use an instance of a simulator class to evaluate the individual. Each individual is given 600 moves on the simulator map (obtained from an external file). The fitness of each individual corresponds to the number of pieces of food picked up. In this example, we are using a classical trail, the *Santa Fe trail*, in which there is 89 pieces of food. Therefore, a perfect individual would achieve a fitness of 89. .. literalinclude:: ../../examples/gp_ant.py :pyobject: evalArtificialAnt Where `ant` is the instance of the simulator used. The :func:`~deap.gp.evaluate` function is a convenience one provided by DEAP and returning an executable Python program from a GP individual and its primitives function set. Complete example ================ Except for the simulator code (about 75 lines), the code does not fundamentally differ from the :ref:`Symbolic Regression example `. Note that as the problem is harder, improving the selection pressure by increasing the size of the tournament to 7 allows to achieve better performance. .. literalinclude:: ../../examples/gp_ant.py :lines: 54- .. note:: The import of the Python standard library modules are not shown. .. _refPapersAnt: Reference ========= *John R. Koza, "Genetic Programming I: On the Programming of Computers by Means of Natural Selection", MIT Press, 1992, pages 147-161.* deap-0.7.1/doc/examples/dtm_onemax.rst0000644000076500000240000000607011641072614020137 0ustar felixstaff00000000000000.. _dtm_onemax: ========================================== DTM + EAP = DEAP : a Distributed Evolution ========================================== As part of the DEAP framework, EAP offers an easy DTM integration. As the EAP algorithms use a map function stored in the toolbox to spawn the individuals evaluations (by default, this is simply the traditional Python :func:`map`), the parallelization can be made very easily, by replacing the map operator in the toolbox : :: from deap import dtm tools.register("map", dtm.map) Thereafter, ensure that your main code is in enclosed in a Python function (for instance, main), and just add the last line : :: dtm.start(main) For instance, take a look at the short version of the onemax. This is how it may be parallelized : :: from deap import dtm creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("map", dtm.map) def main(): random.seed(64) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaSimple(toolbox, pop, cxpb=0.5, mutpb=0.2, ngen=40, stats=stats, halloffame=hof) logging.info("Best individual is %s, %s", hof[0], hof[0].fitness.values) return pop, stats, hof dtm.start(main) # Launch the first task As one can see, parallelization requires almost no changes at all (an import, the selection of the distributed map and the starting instruction), even with a non-trivial program. This program can now be run on a multi-cores computer, on a small cluster or on a supercomputer, without any changes, as long as those environments provide a MPI implementation. .. note:: In this specific case, the distributed version would be actually *slower* than the serial one, because of the extreme simplicity of the evaluation function (which takes *less than 0.1 ms* to execute), as the small overhead generated by the serialization, load-balancing, treatment and transfer of the tasks and the results is not balanced by a gain in the evaluation time. In more complex, real-life problems (for instance sorting networks), the benefit of a distributed version is fairly noticeable. deap-0.7.1/doc/examples/dtmPi.rst0000644000076500000240000000444111641072614017061 0ustar felixstaff00000000000000.. _dtmPi: ========================= A Pi Calculation with DTM ========================= A simple yet interesting use of DTM is the calculation of :math:`\pi` with a Monte Carlo approach. This approach is quite straightforward : if you randomly throw *n* darts on a unit square, approximately :math:`\frac{n * \pi}{4}` will be inside a quadrant delimited by (0,1) and (1,0). Therefore, if a huge quantity of darts are thrown, one could estimate :math:`\pi` simply by computing the ratio between the number of darts inside and outside the quadrant. A comprehensive explanation of the algorithm can be found `here `_ .. note:: This example is intended to show a simple parallelization of an actual algorithm. It should not be taken as a good :math:`\pi` calculation algorithm (it is not). A possible serial Python code reads as follow : :: from random import random from math import hypot def test(tries): # Each run of this function makes some tries # and return the number of darts inside the quadrant (r < 1) return sum(hypot(random(), random()) < 1 for i in xrange(tries)) def calcPi(n, t): expr = (test(t) for i in range(n)) pi2 = 4. * sum(expr) / (n*t) print("pi = " + str(pi2)) return pi2 piVal = calcPi(1000, 10000) With DTM, you can now take advantage of the parallelization, and distribute the calls to the function *test()*. There are many ways to do so, but a mere one is to use :func:`~deap.dtm.manager.Control.repeat`, which repeats a function an arbitrary number of times, and returns a results list. In this case, the program may look like this : :: from math import hypot from random import random from deap import dtm def test(tries): # Each run of this function makes some tries # and return the number of darts inside the quadrant (r < 1) return sum(hypot(random(), random()) < 1 for i in xrange(tries)) def calcPi(n, t): expr = dtm.repeat(test, n, t) pi2 = 4. * sum(expr) / (n*t) print("pi = " + str(pi2)) return pi2 piVal = dtm.start(calcPi, 1000, 10000) And so, without any major changes (and not at all in the *test()* function), this computation can be distributed.deap-0.7.1/doc/examples/index.rst0000644000076500000240000000075411641072614017116 0ustar felixstaff00000000000000Examples -------- This section contains some documented examples of common toy problems often encountered in the evolutionary computation community. Note that there are several other examples in the ``deap/examples`` sub-directory of the framework. These can be used has ground work for implementing your own flavour of evolutionary algorithms. .. toctree:: :maxdepth: 2 :numbered: onemax short_onemax knapsack symbreg parity multiplexer artificial_ant spambase dtmPi dtm_onemaxdeap-0.7.1/doc/examples/knapsack.rst0000644000076500000240000001100211641072614017566 0ustar felixstaff00000000000000============================== Individual Inheriting from Set ============================== Again for this example we will use a very simple problem, the 0-1 Knapsack. The purpose of this example is to show the simplicity of DEAP and the ease to inherit from anyting else than a simple list or array. Many evolutionary algorithm textbooks mention that the best way to have an efficient algorithm to have a representation close the problem. Here, what can be closer to a bag than a set? Lets make our individuals inherit from the :class:`set` class. :: creator.create("FitnessMulti", base.Fitness, weights=(-1.0, 1.0)) creator.create("Individual", set, fitness=creator.FitnessMulti) That's it. You now have individuals that are in fact sets, they have the usual attribute :attr:`fitness`. The fitness is a minimization of the first objective (the weight of the bag) and a maximization of the second objective (the value of the bag). We will now create a dictionary of 100 random items to map the values and weights. :: # The items' name is an integer, and value is a (weight, value) 2-tuple items = dict([(i, (random.randint(1, 10), random.uniform(0, 100))) for i in xrange(100)]) We now need to initialize a population and the individuals therein. For this we will need a :class:`~deap.base.Toolbox` to register our generators since sets can also be created with an input iterable. :: toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_item", random.choice, items.keys()) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, n=30) toolbox.register("population", tools.initRepeat, list) Voilà! The *last* thing to do is to define our evaluation function. :: def evalKnapsack(individual): weight = 0.0 value = 0.0 for item in individual: weight += items[item][0] value += items[item][1] if len(individual) > MAX_ITEM or weight > MAX_WEIGHT: return 10000, 0 # Ensure overweighted bags are dominated return weight, value Everything is ready for evolution. Ho no wait, since DEAP's developers are lazy, there is no crossover and mutation operators that can be applied directly on sets. Lets define some. For example, a crossover, producing two child from two parents, could be that the first child is the intersection of the two sets and the second child their absolute difference. :: def cxSet(ind1, ind2): """Apply a crossover operation on input sets. The first child is the intersection of the two sets, the second child is the difference of the two sets. """ temp = set(ind1) # Used in order to keep type ind1 &= ind2 # Intersection (inplace) ind2 ^= temp # Symmetric Difference (inplace) A mutation operator could randomly add or remove an element from the set input individual. :: def mutSet(individual): """Mutation that pops or add an element.""" if random.random() < 0.5: if len(individual) > 0: # Can't pop from an empty set mutant.pop() else: mutant.add(random.choice(items.keys())) .. note:: The outcome of this mutation is dependent of the python you use. The :meth:`set.pop` function is not consistent between versions of python. See the sources of the actual example for a version that will be stable but more complicated. From here, everything else is just as usual, register the operators in the toolbox, and use or write an algorithm. Here we will use the :func:`~deap.algorithms.eaMuPlusLambda` algorithm and the SPEA-II selection scheme. :: toolbox.register("evaluate", evalKnapsack) toolbox.register("mate", cxSet) toolbox.register("mutate", mutSet) toolbox.register("select", tools.selSPEA2) pop = toolbox.population(n=MU) hof = tools.ParetoFront() stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaMuPlusLambda(toolbox, pop, MU, LAMBDA, CXPB, MUTPB, MAXGEN, stats, hof) Finally, a :class:`~deap.tools.ParetoFront` may be used to retrieve the best non dominated individuals of the evolution and a :class:`~deap.tools.Statistics` object is created for compiling four different statistics over the generations. The complete `Knapsack Genetic Algorithm `_ code is available. deap-0.7.1/doc/examples/multiplexer.rst0000644000076500000240000000600011641072614020347 0ustar felixstaff00000000000000.. _mux: ========================== GP Multiplexer 3-8 problem ========================== The multiplexer problem is another extensively used GP problem. Basically, it trains a program to reproduce the behavior of an electronic `multiplexer `_ (mux). Usually, a 3-8 multiplexer is used (3 address entries, from A0 to A2, and 8 data entries, from D0 to D7), but virtually any size of multiplexer can be used. This problem was first defined by Koza (see :ref:`refPapersMux`). Primitives set used =================== The primitive set is almost the same as the set used in :ref:`Parity `. Three Boolean operators (and, or and not), imported from :mod:`operator`, and a specific if-then-else primitive, which return either its second or third argument depending on the value of the first one. :: pset = gp.PrimitiveSet("MAIN", MUX_TOTAL_LINES, "IN") pset.addPrimitive(operator.and_, 2) pset.addPrimitive(operator.or_, 2) pset.addPrimitive(operator.not_, 1) pset.addPrimitive(if_then_else, 3) pset.addTerminal(1) pset.addTerminal(0) As usual, we also add two terminals, a Boolean true and a Boolean false. Evaluation function =================== To speed up the evaluation, the computation of the input/output pairs is done at start up, instead of at each evaluation call. This pre-computation also allows to easily tune the multiplexer size, by changing the value of *MUX_SELECT_LINES*. :: MUX_SELECT_LINES = 3 MUX_IN_LINES = 2 ** MUX_SELECT_LINES MUX_TOTAL_LINES = MUX_SELECT_LINES + MUX_IN_LINES # input : [A0 A1 A2 D0 D1 D2 D3 D4 D5 D6 D7] for a 8-3 mux inputs = [[0] * MUX_TOTAL_LINES for i in range(2 ** MUX_TOTAL_LINES)] outputs = [None] * (2 ** MUX_TOTAL_LINES) for i in range(2 ** MUX_TOTAL_LINES): value = i divisor = 2 ** MUX_TOTAL_LINES # Fill the input bits for j in range(MUX_TOTAL_LINES): divisor /= 2 if value >= divisor: inputs[i][j] = 1 value -= divisor # Determine the corresponding output indexOutput = MUX_SELECT_LINES for j, k in enumerate(inputs[i][:MUX_SELECT_LINES]): if k: indexOutput += 2 ** j outputs[i] = inputs[i][indexOutput] After that, the evaluation function is trivial, as we have both inputs and output values. The fitness is then the number of well predicted outputs over the 2048 cases (for a 3-8 multiplexer). :: def evalMultiplexer(individual): func = toolbox.lambdify(expr=individual) good = sum((func(*(inputs[i])) == outputs[i] for i in range(2 ** MUX_TOTAL_LINES))) return good, Complete Example ================ .. literalinclude:: ../../examples/gp_multiplexer.py :lines: 20- .. note:: The import of the Python standard library modules are not shown. .. _refPapersMux: Reference ========= *John R. Koza, "Genetic Programming I: On the Programming of Computers by Means of Natural Selection", MIT Press, 1992, pages 170-187.* deap-0.7.1/doc/examples/onemax.rst0000644000076500000240000002571311641072614017300 0ustar felixstaff00000000000000.. _ga-onemax: ========================= One Max Genetic Algorithm ========================= This is the first complete program built with DEAP. It will help new users to overview some of the possibilities in DEAP. The problem is very simple, we search for a :data:`1` filled list individual. This problem is widely used in the evolutionary computation community since it is very simple and it illustrates well the potential of evolutionary algorithms. Setting Things Up ================= Here we use the one max problem to show how simple can be an evolutionary algorithm with DEAP. The first thing to do is to elaborate the structures of the algorithm. It is pretty obvious in this case that an individual that can contain a series of `booleans` is the most interesting kind of structure available. DEAP does not contain any explicit individual structure since it is simply a container of attributes associated with a fitness. Instead, it provides a convenient method for creating types called the creator. ------- Creator ------- The creator is a class factory that can build at run-time new classes that inherit from a base classe. It is very useful since an individual can be any type of container from list to n-ary tree. The creator allows build complex new structures convenient for evolutionary computation. Let see an example of how to use the creator to setup an individual that contains an array of booleans and a maximizing fitness. We will first need to import the :mod:`deap.base` and :mod:`deap.creator` modules. The creator defines at first a single function :func:`~deap.creator.create` that is used to create types. The :func:`~deap.creator.create` function takes at least 2 arguments plus additional optional arguments. The first argument *name* is the actual name of the type that we want to create. The second argument *base* is the base classe that the new type created should inherit from. Finally the optional arguments are members to add to the new type, for example a :attr:`fitness` for an individual or :attr:`speed` for a particle. :: from deap import base from deap import creator creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) The first line creates a maximizing fitness by replacing, in the base type :class:`~deap.base.Fitness`, the pure virtual :attr:`~deap.base.Fitness.weights` attribute by ``(1.0,)`` that means to maximize a single objective fitness. The second line creates an :class:`Individual` class that inherits the properties of :class:`list` and has a :attr:`fitness` attribute of the type :class:`FitnessMax` that was just created. In this last step, two things are of major importance. The first is the comma following the ``1.0`` in the weights declaration, even when implementing a single objective fitness, the weights (and values) must be iterable. We won't repeat it enough, in DEAP single objective is a special case of multiobjective. The second important thing is how the just created :class:`FitnessMax` can be used directly as if it was part of the :mod:`~deap.creator`. This is not magic. ------- Toolbox ------- A :class:`~deap.base.Toolbox` can be found in the base module. It is intended to store functions with their arguments. The toolbox contains two methods, :meth:`~deap.base.Toolbox.register` and :meth:`~deap.base.Toolbox.unregister` that are used to do the tricks. :: toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) In this code block we registered a generation function and two initialization functions. The generator :meth:`toolbox.attr_bool` when called, will draw a random integer between 0 and 1. The two initializers for their part will produce respectively initialized individuals and populations. Again, looking a little closer shows that their is no magic. The registration of tools in the toolbox only associates an *alias* to an already existing function and freezes part of its arguments. This allows to call the alias as if the majority of the (or every) arguments have already been given. For example, the :meth:`attr_bool` generator is made from the :func:`~random.randint` that takes two arguments *a* and *b*, with ``a <= n <= b``, where *n* is the returned integer. Here, we fix ``a = 0`` and ``b = 1``. It is the same thing for the initializers. This time, the :func:`~deap.tools.initRepeat` is frozen with predefined arguments. In the case of the :meth:`individual` method, :func:`~deap.tools.initRepeat` takes 3 arguments, a class that is a container -- here the :class:`Individual` is derived from a :class:`list` --, a function to fill the container and the number of times the function shall be repeated. When called, the :meth:`individual` method will thus return an individual initialized with what would be returned by 100 calls to the :meth:`attr_bool` method. Finally, the :meth:`population` method uses the same paradigm, but we don't fix the number of individuals that it should contain. The Evaluation Function ======================= The evaluation function is pretty simple in this case, we need to count the number of ones in the individual. This is done by the following lines of code. :: def evalOneMax(individual): return sum(individual), The returned value must be an iterable of length equal to the number of objectives (weights). The Genetic Operators ===================== There is two way of using operators, the first one, is to simply call the function from the :mod:`~deap.tools` module and the second one is to register them with their argument in a toolbox as for the initialization methods. The most convenient way is to register them in the toolbox, because it allows to easily switch between operators if desired. The toolbox method is also used in the algorithms, see the `one max short version `_ for an example. Registering the operators and their default arguments in the toolbox is done as follow. :: toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) The evaluation is given the alias evaluate. Having a single argument being the individual to evaluate we don't need to fix any, the individual will be given later in the algorithm. The mutation, for its part, needs an argument to be fixed (the independent probability of each attribute to be mutated *indpb*). In the algorithms the :meth:`mutate` function is called with the signature ``mutant, = toolbox.mutate(mutant)``. This is the most convenient way because each mutation takes a different number of arguments, having those arguments fixed in the toolbox leave open most of the possibilities to change the mutation (or crossover, or selection, or evaluation) operator later in your researches. Evolving the Population ======================= ----------------------- Creating the Population ----------------------- Before evolving it, we need to instantiate a population. This step is done effortless using the method we registered in the toolbox. :: pop = toolbox.population(n=300) ``pop`` will be a :class:`list` composed of 300 individuals, *n* is the parameter left open earlier in the toolbox. The next thing to do is to evaluate this brand new population. :: # Evaluate the entire population fitnesses = map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit We first :func:`map` the evaluation function to every individual, then assign their respective fitness. Note that the order in ``fitnesses`` and ``population`` are the same. ----------------------- The Appeal of Evolution ----------------------- The evolution of the population is the last thing to accomplish. Let say that we want to evolve for a fixed number of generation :data:`MAXGEN`, the evolution will then begin with a simple for statement. :: for g in range(MAXGEN): evolve... Is that simple enough? Lets continue with more complicated things, selecting, mating and mutating the population. The crossover and mutation operators provided within DEAP usually take respectively 2 and 1 individual(s) on input and return 2 and 1 modified individual(s), they also modify inplace these individuals. In a simple GA, the first step is to select the next generation. :: offspring = [toolbox.clone(ind) for ind in toolbox.select(pop, len(pop))] This step creates an offspring list that is an exact copy of the selected individuals. The :meth:`toolbox.clone` method ensure that we don't own a reference to the individuals but an completely independent instance. Next, a simple GA would replace the parents by the produced children directly in the population. This is what is done by the following lines of code, where a crossover is applied with probability :data:`CXPB` and a mutation with probability :data:`MUTPB`. The ``del`` statement simply invalidate the fitness of the modified individuals. :: # Apply crossover and mutation on the offspring for child1, child2 in zip(offspring[::2], offspring[1::2]): if random.random() < CXPB: toolbox.mate(child1, child2) del child1.fitness.values del child2.fitness.values for mutant in offspring: if random.random() < MUTPB: toolbox.mutate(mutant) del mutant.fitness.values The population now needs to be re-evaluated, we then apply the evaluation as seen earlier, but this time only on the individuals with an invalid fitness. :: # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit And finally, last but not least, we replace the old population by the offspring. :: pop = offspring This is the end of the evolution part, it will continue until the predefined number of generation are accomplished. Although, some statistics may be gathered on the population, the following lines print the min, max, mean and standard deviation of the population. :: # Gather all the fitnesses in one list and print the stats fits = [ind.fitness.values[0] for ind in pop] length = len(pop) mean = sum(fits) / length sum2 = sum(x*x for x in fits) std = abs(sum2 / length - mean**2)**0.5 print " Min %s" % min(fits) print " Max %s" % max(fits) print " Avg %s" % mean print " Std %s" % std A :class:`~deap.tools.Statistics` object has been defined to facilitate how statistics are gathered. It is not presented here so that we can focus on the core and not the gravitating helper objects of DEAP. The complete `One Max Genetic Algorithm `_ code is available. It may be a little different but it does the overall same thing.deap-0.7.1/doc/examples/parity.rst0000644000076500000240000000471011641072614017313 0ustar felixstaff00000000000000.. _parity: ====================== GP Even-Parity problem ====================== Parity is one of the classical GP problems. The goal is to find a program that produces the value of the Boolean even parity given n independent Boolean inputs. Usually, 6 Boolean inputs are used (Parity-6), and the goal is to match the good parity bit value for each of the :math:`2^6 = 64` possible entries. The problem can be made harder by increasing the number of inputs (in the DEAP implementation, this number can easily be tuned, as it is fixed by a constant named PARITY_FANIN_M). For more information about this problem, see :ref:`refPapersParity`. Primitives set used =================== Parity uses standard Boolean operators as primitives, available in the Python operator module : :: pset = gp.PrimitiveSet("MAIN", PARITY_FANIN_M, "IN") pset.addPrimitive(operator.and_, 2) pset.addPrimitive(operator.or_, 2) pset.addPrimitive(operator.xor, 2) pset.addPrimitive(operator.not_, 1) pset.addTerminal(1) pset.addTerminal(0) In addition to the *n* inputs, we add two constant terminals, one at 0, one at 1. .. note:: As Python is a dynamic typed language, you can mix Boolean operators and integers without any issue. Evaluation function =================== In this implementation, the fitness of a Parity individual is simply the number of successful cases. Thus, the fitness is maximized, and the maximum value is 64 in the case of a 6 inputs problems. :: def evalParity(individual): func = toolbox.lambdify(expr=individual) good = sum(func(*inputs[i]) == outputs[i] for i in xrange(PARITY_SIZE_M)) return good, `inputs` and `outputs` are two pre-generated lists, to speedup the evaluation, mapping a given input vector to the good output bit. The Python :func:`sum` function works on booleans (false is interpreted as 0 and true as 1), so the evaluation function boils down to sum the number of successful tests : the higher this sum, the better the individual. Complete example ================ The other parts of the program are greatly the same as the :ref:`Symbolic Regression algorithm ` : .. literalinclude:: ../../examples/gp_parity.py :lines: 20- .. note:: The import of the Python standard library modules are not shown. .. _refPapersParity: Reference ========= *John R. Koza, "Genetic Programming II: Automatic Discovery of Reusable Programs", MIT Press, 1994, pages 157-199.* deap-0.7.1/doc/examples/short_onemax.rst0000644000076500000240000000331611641072614020512 0ustar felixstaff00000000000000.. _short-ga-onemax: =============================== Short One Max Genetic Algorithm =============================== The short one max genetic algorithm example is very similar to one max example. The only difference is that it makes use of the :mod:`~deap.algorithms` module that implements some basic evolutionary algorithms. The initialization are the same so we will skip this phase. The algorithms impemented use specific functions from the toolbox, in this case :func:`evaluate`, :func:`mate`, :func:`mutate` and :func:`~deap.Toolbox.select` must be registered. :: toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) The toolbox is then passed to the algorithm and the algorithm uses the registered function. :: pop = toolbox.population() hof = tools.HallOfFame(1) algorithms.eaSimple(toolbox, pop, cxpb=0.5, mutpb=0.2, ngen=40, halloffame=hof) The short GA One max example makes use of a :class:`~deap.tools.HallOfFame` in order to keep track of the best individual to appear in the evolution (it keeps it even in the case it extinguishes). All algorithms from the :mod:`~deap.algorithms` module do take a *halloffame* argument that gets updated after every evaluation section.======= The short GA One max example makes use of a :class:`~eap.halloffame.HallOfFame` in order to keep track of the best individual to appear in the evolution (it keeps them even in the case they extinguish). All algorithms from the :mod:`eap.algorithms` module do take a *halloffame* argument that gets updated after every evaluation section of the basic algorithms. deap-0.7.1/doc/examples/spambase.rst0000644000076500000240000001324211641072614017576 0ustar felixstaff00000000000000.. _spambase: ======================= STGP example : Spambase ======================= This problem is a classification example using STGP (Strongly Typed Genetic Programming). The evolved programs work on floating-point values AND Booleans values. The programs must return a Boolean value which must be true if e-mail is spam, and false otherwise. It uses a base of emails (saved in *spambase.csv*, see :ref:`refPapersSpam`), from which it randomly chooses 400 emails to evaluate each individual. .. warning:: Don't expect too much from this program as it is quite basic and not oriented toward performance. It is given to illustrate the use of strongly-typed GP with DEAP. From a machine learning perspective, it is mainly wrong. Primitives set used =================== Strongly-typed GP is a more generic GP where each primitive, in addition to have an arity and a corresponding function, has also a specific return type and specific parameter(s) type. In this way, each primitive is someway describe as a pure C function, where each parameter has to be one of the good type, and where the return value type is specified before run time. .. note:: Actually, when the user does not specify return or parameters type, a default type is selected by DEAP. On standard GP, because all the primitives use this default type, this behaves as there was no type requirement. We define a typed primitive set almost the same way than a normal one, but we have to specify the types used. :: pset = gp.PrimitiveSetTyped("MAIN", itertools.repeat("float", 57), "bool", "IN") # boolean operators pset.addPrimitive(operator.and_, ["bool", "bool"], "bool") pset.addPrimitive(operator.or_, ["bool", "bool"], "bool") pset.addPrimitive(operator.not_, ["bool"], "bool") # floating point operators def safeDiv(left, right): try: return left / right except ZeroDivisionError: return 0 pset.addPrimitive(operator.add, ["float","float"], "float") pset.addPrimitive(operator.sub, ["float","float"], "float") pset.addPrimitive(operator.mul, ["float","float"], "float") pset.addPrimitive(safeDiv, ["float","float"], "float") # logic operators def if_then_else(input, output1, output2): if input: return output1 else: return output2 pset.addPrimitive(operator.lt, ["float", "float"], "bool") pset.addPrimitive(operator.eq, ["float", "float"], "bool") pset.addPrimitive(if_then_else, ["bool", "float", "float"], "float") # terminals pset.addEphemeralConstant(lambda: random.random() * 100, "float") pset.addTerminal(0, "bool") pset.addTerminal(1, "bool") On the first line, we see the declaration of a typed primitive set with :class:`~deap.gp.PrimitiveSetTyped`. The first argument remains the set name, but the next ones are the type of the entries (in this case, we have 57 float entries and one Boolean output; we could have written `float` 57 times, but it is fairly quicker and more understandable to use the :func:`itertools.repeat` function). The last argument remains the entries prefix. After that, we define the primitives themselves. The definition of a typed primitive has two additional parameters : a list containing the parameters type, in order, and the return type. .. note:: The types specified do not have to be real Python or C types. In the above example, we may rename "float" in "type1" and "bool" in "type2" without any issue. For the same reason, DEAP nor Python actually check if a given parameter has the good type. The terminals set is then filled, with at least one terminal of each type, and that is for the primitive set declaration. Evaluation function =================== The evaluation function is very simple : it picks 400 mails at random in the spam database, and then checks if the prediction made by the individual matches the expected Boolean output. The count of well predicted emails is returned as the fitness of the individual (which is so, at most, 400). :: def evalSpambase(individual): # Transform the tree expression in a callable function func = toolbox.lambdify(expr=individual) # Randomly sample 400 mails in the spam database (defined before) spam_samp = random.sample(spam, 400) # Evaluate the sum of correctly identified mail as spam result = sum(bool(func(*mail[:57])) is bool(mail[57]) for mail in spam_samp) return result, Toolbox ======= The toolbox used is very similar to the one presented in the symbolic regression example, but notice that we now use specific STGP operators for crossovers and mutations : :: toolbox.register("evaluate", evalSpambase) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("mate", gp.cxTypedOnePoint) toolbox.register("expr_mut", gp.genFull, min_=0, max_=2) toolbox.register("mutate", gp.mutTypedUniform, expr=toolbox.expr_mut)) Complete Example ================ This is the complete code for the spambase example. Although it does not really differ from the other problems, it is interesting to note how Python can decrease the programming time. Indeed, the spam database is in csv form : with many frameworks, you would have to manually read it, or use a non-standard library, but with Python, you can use the built-in module :mod:`csv` and, within 2 lines, it is done! The data is now in the matrix *spam* and can easily be used through all the program : .. literalinclude:: ../../examples/gp_spambase.py :lines: 20- .. note:: Some of the import of the Python standard library modules are not shown. .. _refPapersSpam: Reference ========= Data are from the Machine learning repository, http://www.ics.uci.edu/~mlearn/MLRepository.htmldeap-0.7.1/doc/examples/symbreg.rst0000644000076500000240000002172511641072614017460 0ustar felixstaff00000000000000.. _symbreg: ======================================================== A Genetic Programming Introduction : Symbolic Regression ======================================================== Symbolic regression is one of the best known problems in GP (see :ref:`refPapersSymbreg`). It is commonly used as a tuning problem for new algorithms, but is also widely used with real-life distributions, where other regression methods may not work. It is conceptually a simple problem, and therefore makes a good introductory example for the GP framework in DEAP. All symbolic regression problems use an arbitrary data distribution, and try to fit the most accurately the data with a symbolic formula. Usually, a measure like the RMSE (Root Mean Square Error) is used to measure an individual's fitness. In this example, we use a classical distribution, the quartic polynomial :math:`(x^4 + x^3 + x^2 + x)`, a one-dimension distribution. *20* equidistant points are generated in the range [-1, 1], and are used to evaluate the fitness. Creating the primitives set =========================== One of the most crucial aspect of a GP program is the choice of the primitives set. They should make good building blocks for the individuals so the evolution can succeed. In this problem, we use a classical set of primitives, which are basic arithmetic functions : :: def safeDiv(left, right): try: return left / right except ZeroDivisionError: return 0 pset = gp.PrimitiveSet("MAIN", 1) pset.addPrimitive(operator.add, 2) pset.addPrimitive(operator.sub, 2) pset.addPrimitive(operator.mul, 2) pset.addPrimitive(safeDiv, 2) pset.addPrimitive(operator.neg, 1) pset.addPrimitive(math.cos, 1) pset.addPrimitive(math.sin, 1) pset.addEphemeralConstant(lambda: random.randint(-1,1)) The redefinition of the division is made to protect it against a zero division error (which would crash the program). The other functions are simply a mapping from the Python :mod:`operator` module. The number following the function is the *arity* of the primitive, that is the number of entries it takes (this will be used by DTM to build the individuals from the primitives). On the last line, we declare an :class:`~deap.gp.Ephemeral` constant. This is a special terminal type, which does not have a fixed value. When the program appends an ephemeral constant terminal to a tree, the function it contains is executed, and its result is inserted as a constant terminal. In this case, those constant terminals can take the values -1, 0 or 1. The second argument of :class:`~deap.gp.PrimitiveSet` is the number of inputs. Here, as we have only a one dimension regression problem, there is only one input, but it could have as many as you want. By default, those inputs are named "ARGx", where "x" is a number, but you can easily rename them : :: pset.renameArguments({"ARG0" : "x"}) Creator ======= As any evolutionary program, symbolic regression needs (at least) two object types : an individual containing the genotype and a fitness. We can easily create them with the creator : :: creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin, pset=pset) The first line creates the fitness object (this is a minimization problem, so the weight is negative). The `weights` argument must be an iterable of weights, even if there is only one fitness measure. The second line create the individual object itself. Very straightforward, we can see that it will be based upon a tree, to which we add two attributes : a fitness and the primitive set. If, for any reason, the user would want to add any other attribute (for instance, a file in which the individual will be saved), it would be as easy as adding this attribute of any type to this line. After this declaration, any individual produced will contain those wanted attributes. Toolbox ======= Now, we want to register some parameters specific to the evolution process. In DEAP, this is done through the toolbox : :: toolbox = base.Toolbox() toolbox.register("expr", gp.genRamped, pset=pset, min_=1, max_=2) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("lambdify", gp.lambdify, pset=pset) def evalSymbReg(individual): # Transform the tree expression in a callable function func = toolbox.lambdify(expr=individual) # Evaluate the sum of squared difference between the expression # and the real function : x**4 + x**3 + x**2 + x values = (x/10. for x in xrange(-10,10)) diff_func = lambda x: (func(x)-(x**4 + x**3 + x**2 + x))**2 diff = sum(map(diff_func, values)) return diff, toolbox.register("evaluate", evalSymbReg) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("mate", gp.cxUniformOnePoint) toolbox.register("expr_mut", gp.genFull, min_=0, max_=2) toolbox.register('mutate', gp.mutUniform, expr=toolbox.expr_mut) First, a toolbox instance is created (in some problem types like coevolution, you may consider creating more than one toolbox). Then, we can register any parameters. The first lines register how to create an individual (by calling gp.genRamped with the previously defined primitive set), and how to create the population (by repeating the individual initialization). We may now introduce the evaluation function, which will receive an individual as input, and return the corresponding fitness. This function uses the `lambdify` function previously defined to transform the individual into its executable form -- that is, a program. After that, the evaluation is only simple maths, where the difference between the values produced by the evaluated individual and the real values are squared and summed to compute the RMSE, which is returned as the fitness of the individual. .. warning:: Even if the fitness only contains one measure, keep in mind that DEAP stores it as an iterable. Knowing that, you can understand why the evaluation function must return a tuple value (even if it is a 1-tuple) : :: return diff, Returning only the value would produce strange behaviors and errors, as the selection and stats functions relies on the fact that the fitness is always an iterable. Afterwards, we register the evaluation function. We also choose the selection method (a tournament of size 3), the mate method (one point crossover with uniform probability over all the nodes), the mutation method (an uniform probability mutation which may append a new full sub-tree to a node). At this point, any structure with an access to the toolbox instance will also have access to all of those registered parameters. Of course, the user could register other parameters basing on his needs. Statistics ========== Although optional, statistics are often useful in evolutionary programming. DEAP offers a simple class which can handle most of the "boring work". In this case, we want to keep four measures over the fitness distribution : the mean, the standard deviation, the minimum and the maximum. :: stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) Launching the evolution ======================= At this point, DEAP has all the information needed to begin the evolutionary process, but nothing has been initialized. We can start the evolution by creating the population and then call a pre-made algorithm, as eaSimple : :: def main(): logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) pop = toolbox.population(n=300) # Population length hof = tools.HallOfFame(1) algorithms.eaSimple(toolbox, pop, 0.5, 0.1, 40, stats, halloffame=hof) logging.info("Best individual is %s, %s", gp.evaluate(hof[0]), hof[0].fitness) return pop, stats, hof if __name__ == "__main__": main() The hall of fame is a specific structure which contains the *n* best individuals (here, the best one only). The first line set up the Python logger used by DEAP to provide information, warnings or errors about the evolution. Its level can be adjusted according to the desired verbosity level (see :mod:`logging`). Complete Example ================ .. literalinclude:: ../../examples/gp_symbreg.py :lines: 21- .. note:: The import of the Python standard library modules are not shown. .. _refPapersSymbreg: Reference ========= *John R. Koza, "Genetic Programming: On the Programming of Computers by Means of Natural Selection", MIT Press, 1992, pages 162-169.*deap-0.7.1/doc/Makefile0000644000076500000240000000606011641072614015073 0ustar felixstaff00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = _build # Internal variables. 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Distributed Task Manager ------------------------ Distributing tasks on multiple computers is taken care of by the distributed task manager module :mod:`~deap.dtm`. Its API similar to the multiprocessing module allows it to be very easy to use. In the :ref:`last section ` a complete algorithm was exposed with the :func:`toolbox.map` left to the default :func:`map`. In order to parallelize the evaluation the operation to do is to replace this map with the one provided by the dtm module and tell to dtm which function is the main program here it is the :func:`main` function. :: from deap import dtm toolbox.register("map", dtm.map) def main(): # My evolutionary algorithm pass if __name__ == "__main__": dtm.start(main) That's it. The map operation contained in the toolbox will now be parallel. The next time you run the algorithm, it will run on the number of cores specified to the ``mpirun`` command used to run the python script. The usual bash command to use dtm will be : .. code-block:: bash $ mpirun [options] python my_script.py Multiprocessing Module ---------------------- Using the :mod:`multiprocessing` module is exactly similar to using the distributed task manager. The only operation to do is to replace in the toolbox the appropriate function by the parallel one. :: import multiprocessing pool = multiprocessing.pool() toolbox.register("map", pool.map) # Continue on with the evolutionary algorithm .. note:: While Python 2.6 is required for the multiprocessing module, the pickling of partial function is possible only since Python 2.7 (or 3.1), earlier version of Python may throw some strange errors when using partial function in the multiprocessing :func:`multiprocessing.Pool.map`. This may be avoided by creating local function outside of the toolbox (in Python version 2.6). .. note:: The pickling of lambda function is not yet available in Python. .. Parallel Evaluation .. ------------------- .. The multiprocessing example shows how to use the :mod:`multiprocessing` module .. in order to enhance the computing power during the evaluations. First the .. toolbox contains a method named :func:`~deap.map`, this method has the same .. function as the built-in :func:`map` function. In order to use the .. multiprocessing module into the built-in :mod:`~deap.algorithms`, the only .. thing to do is to replace the map operation by a parallel one. Then the .. difference between the `Multiprocessing One Max Example .. `_ and the `Regular One .. Max Example `_ is the .. addition of these two lines .. :: .. .. # Process Pool of 4 workers .. pool = multiprocessing.Pool(processes=4) .. tools.register("map", pool.map) .. .. Parallel Variation .. ------------------ .. .. The paralellization of the variation operators is not directly supported in .. the algorithms, although it is still possible. What one needs is to create its .. own algorithm (from one in the algorithm module for example) and change the .. desired lines in order to use the :meth:`~deap.toolbox.map` method from the .. toolbox. This may be achieved for example, for the crossover operation from .. the :func:`~deap.algorithms.eaSimple` algorithm by replacing the crossover part .. of the algorithms by .. :: .. .. parents1 = list() .. parents2 = list() .. to_replace = list() .. for i in range(1, len(offsprings), 2): .. if random.random() < cxpb: .. parents1.append(offsprings[i - 1]) .. parents2.append(offsprings[i]) .. to_replace.append(i - 1) .. to_replace.append(i) .. .. children = tools.map(tools.mate, (parents1, parents2)) .. .. for i, child in zip(to_replace, children): .. del child.fitness.values .. offsprings[i] = child .. .. Since the multiprocessing map does take a single iterable we must .. bundle/unbundle the parents, respectively by creating a tuple in the .. :func:`tools.map` function of the preceding code example and the following .. decorator on the crossover function. .. :: .. .. def unbundle(func): .. def wrapUnbundle(bundled): .. return func(*bundled) .. return wrapUnbundle .. .. tools.decorate("mate", unbundle) deap-0.7.1/doc/tutorials/index.rst0000644000076500000240000000136711641072614017327 0ustar felixstaff00000000000000Tutorial -------- Although this tutorial doesn't make reference directly to the complete API of the framework, we think it is the place to start to understand the principles of DEAP. The core of the architecture is based on the :mod:`~deap.creator` and the :class:`~deap.base.Toolbox`. Their usage to create types at run-time (!) is shown in the first part of this tutorial. Then, a next step is taken in the direction of building generic algorithms by showing how to use the different tools present in the framework. Finally, we conclude on how those algorithms can be made parallel with minimal changes to the overall code (generally, adding a single line of code will do the job). .. toctree:: :maxdepth: 2 :numbered: types next_step distributiondeap-0.7.1/doc/tutorials/next_step.rst0000644000076500000240000003351211641072614020226 0ustar felixstaff00000000000000Next Step Toward Evolution ========================== Before starting with complex algorithms, we will see some basis of DEAP. First, we will start by creating simple individuals (as seen in the :ref:`creating-types` tutorial) and make them interact with each other using different operators. Afterwards, we will learn how to use the algorithms and other tools. A First Individual ------------------ First import the required modules and register the different fonctions required to create individuals that are a list of floats with a minimizing two objectives fitness. :: from deap import base from deap import creator from deap import tools import random IND_SIZE = 5 creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("attr_float", random.random) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=IND_SIZE) The first individual can now be built by adding the appropriate line to the script. :: ind1 = creator.individual() Printing the individual ``ind1`` and checking if its fitness is valid will give something like this :: print ind1 # [0.86..., 0.27..., 0.70..., 0.03..., 0.87...] print in1.fitness.valid # False The individual is printed as its base class representation (here a list) and the fitness is invalid because it contains no values. Evaluation ---------- The evaluation is the most personal part of an evolutionary algorithm, it is the only part of the library that you must write your-self. A typical evaluation function takes one individual as argument and return its fitness as a :class:`tuple`. As shown in the in the :ref:`core` section, a fitness is a list of floating point values and has a property :attr:`valid` to know if this individual shall be re-evaluated. The fitness is set by setting the :attr:`~deap.base.Fitness.values` to the associated :class:`tuple`. For example, the following evaluates the previously created individual ``ind1`` and assign its fitness to the corresponding values. :: def eval(individual): # Do some hard computing on the individual a = sum(individual) b = len(individual) return a, 1. / b ind1.fitness.values = eval(ind1) print ind1.fitness.valid # True print ind1.fitness # (2.73, 0.2) Dealing with single objective fitness is not different, the evaluation function **must** return a tuple because single-objective is treated as a special case of multi-objective. Mutation -------- The next kind of operator that we will present is the mutation operator. There is a variety of mutation operators in the :mod:`deap.tools` module. Each mutation has its own characteristics and may be applied to different type of individual. Be careful to read the documentation of the selected operator in order to avoid undesirable behaviour. The general rule for mutation operators is that they **only** mutate, this means that an independent copy must be made prior to mutating the individual if the original individual has to be kept or is a *reference* to an other individual (see the selection operator). In order to apply a mutation (here a gaussian mutation) on the individual ``ind1``, simply apply the desired function. :: mutant = toolbox.clone(ind1) ind2 = tools.mutGaussian(mutant, sigma=0.2, indpb=0.2) del mutant.fitness.values The fitness' values are deleted because they not related to the individual anymore. As stated above, the mutation does mutate and only mutate an individual it is not responsible of invalidating the fitness nor anything else. The following shows that ``ind2`` and ``mutant`` are in fact the same individual. :: print ind2 is mutant # True print mutant is ind1 # False Crossover --------- The second kind of operator that we will present is the crossover operator. There is a variety of crossover operators in the :mod:`deap.tools` module. Each crossover has its own characteristics and may be applied to different type of individuals. Be careful to read the documentation of the selected operator in order to avoid undesirable behaviour. The general rule for crossover operators is that they **only** mate individuals, this means that an independent copies must be made prior to mating the individuals if the original individuals have to be kept or is are *references* to other individuals (see the selection operator). Lets apply a crossover operation to produce the two children that are cloned beforehand. :: child1, child2 = [toolbox.clone(ind) for ind in (ind1, ind2)] tools.cxBlend(child1, child2, 0.5) del child1.fitness.values del child2.fitness.values .. note:: Just as a remark on the language, the form ``toolbox.clone([ind1, ind2])`` cannot be used because if ``ind1`` and ``ind2`` are referring to the same location in memory (the same individual) there will be a single independent copy of the individual and the second one will be a reference to this same independent copy. This is caused by the mechanism that prevents recursive loops. The first time the individual is seen, it is put in the "memo" dictionary, the next time it is seen the deep copy stops for that object and puts a reference to that previously created deep copy. Care should be taken when deep copying containers. Selection --------- Selection is made among a population by the selection operators that are available in the :mod:`deap.operators` module. The selection operator usually takes as first argument an iterable container of individuals and the number of individuals to select. It returns a list containing the references to the selected individuals. The selection is made as follow. :: selected = tools.selBest([child1, child2], 2) print child1 in selected # True .. warning:: It is **very** important here to note that the selection operators does not duplicate any individual during the selection process. If an individual is selected twice and one of either object is modified, the other will also be modified. Only a reference to the individual is copied. Just like every other operator it selects and only selects. Usually duplication of the entire population will be made after selection. :: selected = toolbox.select(population, LAMBDA) offsprings = [toolbox.clone(ind) for ind in selected] Using the Toolbox ----------------- The toolbox is intended to contain all the evolutionary tools, from the object initializers to the evaluation operator. It allows easy configuration of each algorithms. The toolbox has basically two methods, :meth:`~deap.toolbox.Toolbox.register` and :meth:`~deap.toolbox.Toolbox.unregister`, that are used to add or remove tools from the toolbox. A shown :ref:`earlier ` for initialization. This part of the tutorial will focus on registration of the evolutionary tools in the toolbox rather than the initialization tools. The usual names for the evolutionary tools are :func:`~deap.mate`, :func:`~deap.mutate`, :func:`~deap.evaluate` and :func:`~deap.select`. Here is how they are registered in the toolbox. :: from deap import base from deap import tools toolbox = base.Toolbox() def evaluateInd(individual): # Do some computation return result, toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", evaluateInd) Using the toolbox for registering tools helps keeping the rest of the algorithms independent from the operator set. Using this scheme makes it very easy to locate and change any tool in the toolbox if needed. .. _using-tools: Using the Tools +++++++++++++++ When building evolutionary algorithms the toolbox is used to contain the operators, which are called using their generic name. For example, here is a very small sample of what looks like a simple generational evolutionary algorithm. :: for g in range(NGEN): # Select the next generation individuals offsprings = toolbox.select(pop, len(pop)) # Clone the selected individuals offsprings = map(toolbox.clone, offsprings) # Apply crossover on the offsprings for child1, child2 in zip(offsprings[::2], offsprings[1::2]): if random.random() < CXPB: toolbox.mate(child1, child2) del child1.fitness.values del child2.fitness.values # Apply mutation on the offsprings for mutant in offsprings: if random.random() < MUTPB: toolbox.mutate(mutant) del mutant.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offsprings if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # The population is entirely replaced by the offsprings pop[:] = offsprings This is a complete algorithm. It is generic enough to accept any kind of individual and any operator, as long as the operators are suitable for the chosen individual type. As shown in the last example, the usage of the toolbox allows to write algorithms that are as close as possible to the pseudo code. Now it is up to you to write and experiment your own. Tool Decoration +++++++++++++++ Tool decoration is a very powerful feature that helps to control very precise thing during an evolution without changing anything in the algorithm or operators. A decorator is a wrapper that is called instead of a function. It is asked to make some initialization and termination work before and after the actual function is called. For example, in the case of a constrained domain, one can apply a decorator to the mutation and crossover in order to keep any individual from being out-of-bound. The following defines a decorator that checks if any attribute in the list is out-of-bound and clips it if it is the case. The decorator is defined using three functions in order to receive the *min* and *max* arguments. Whenever the mutation or crossover is called, bounds will be check on the resulting individuals. :: def checkBounds(min, max): def decCheckBounds(func): def wrapCheckBounds(*args, **kargs): offsprings = func(*args, **kargs) for child in offsprings: for i in xrange(len(child)): if child[i] > max: child[i] = max elif child[i] < min: child[i] = min return offsprings return wrapCheckBounds return decCheckBounds toolbox.register("mate", tools.cxBlend, alpha=0.2) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=2) toolbox.decorate("mate", checkbound(MIN, MAX)) toolbox.decorate("mutate", checkbound(MIN, MAX)) This will work on crossover and mutation because both return a tuple of individuals. The mutation is often considered to return a single individual but again like for the evaluation, the single individual case is a special case of the multiple individual case. Note that their are various ways of defining decorator that are not presented here. `Here `_ is a very good tutorial on decorators by Bruce Eckel and `here `_ is a list of proposed decorators for various purposes. Variations ---------- Variations allows to build simple algorithms using predefined small parts. In order to use a variation, the toolbox must be setuped to contain the required operators. For example in the lastly presented complete algorithm, the crossover and mutation are regrouped in the :func:`~deap.algorithms.varSimple` function, this function requires the toolbox to contain a :func:`~deap.mate` and a :func:`~deap.mutate` functions. The variations can be used to simplify the writing of an algorithm as follow. :: from deap import algorithms for g in range(NGEN): # Select and clone the next generation individuals offsprings = map(toolbox.clone, toolbox.select(pop, len(pop))) # Apply crossover and mutation on the offsprings offsprings = algorithms.varSimple(offsprings, CXPB, MUTPB) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offsprings if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # The population is entirely replaced by the offsprings pop[:] = offsprings This last example shows that using the variations makes it straight forward to build algorithms that are very close to the pseudo-code. Algorithms ---------- There is several algorithms implemented in a couple modules and examples, but principally in the :mod:`~deap.algorithms` module. They are very simple and reflect the basic types of evolutionary algorithms present in the literature. The algorithms use a :class:`~deap.base.Toolbox` as defined in the last sections. In order to setup a toolbox for an algorithm, you must register the desired operators under a specified names, refer to the documentation of the selected algorithm for more details. Once the toolbox is ready, it is time to launch the algorithm. The simple evolutionary algorithm takes 5 arguments, a *toolbox*, a *population*, a propability of mating each individual at each generation (*cxpb*), a propability of mutating each individual at each generation (*mutpb*) and a max number of generations (*ngen*). :: from deap import algorithms algorithms.eaSimple(tools, pop, cxpb=0.5, mutpb=0.2, ngen=50) The best way to understand what the simple evolutionary algorithm does, it to take a look at the documentation or the source code Now that you built your own evolutionary algorithm in python, you are welcome to gives us feedback and appreciation. We would also really like to hear about your project and success stories with DEAP. deap-0.7.1/doc/tutorials/types.rst0000644000076500000240000003072311641072614017362 0ustar felixstaff00000000000000.. _creating-types: Creating Types ============== This tutorial shows how types are created using the creator and initialized using the toolbox. Fitness ------- The provided :class:`~deap.base.Fitness` class is an abstract class that needs a :attr:`~deap.base.Fitness.weights` attribute in order to be functional. A minimizing fitness is built using negatives weights. For example, the following line creates, in the :mod:`~deap.creator`, a ready to use single objective minimizing fitness named :class:`FitnessMin`. :: creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) The :attr:`~deap.base.Fitness.weights` argument must be a tuple so that multi objective and single objective fitnesses can be treated the same way. A :class:`FitnessMulti` would be created the same way but using ``weights=(1.0, -1.0)`` rendering a fitness that maximize the first objective and minimize the second one. The weights can also be used to variate the importance of each objective one against another. This means that the weights can be any real number and only the sign is used to determine if a maximization of minimization is done. An example of where the weights can be useful is in the crowding distance sort made in the NSGA-II selection algorithm. Individual ---------- Simply by thinking of the different flavours of evolutionary algorithms (GA, GP, ES, PSO, DE, ...), we notice that an extremely large variety of individuals are possible. Here is a guide on how to create some of those individuals using the :mod:`~deap.creator` and initializing them using a :class:`~deap.base.Toolbox`. List of Floats ++++++++++++++ The first individual created will be a simple list containing floats. In order to produce this kind of individual, we need to create an :class:`Individual` class, using the creator, that will inherit from the standard :class:`list` and have a :attr:`fitness` attribute. Then we will initialize this list using the :func:`~deap.tools.initRepeat` helper function that will repeat ``n`` times the float generator that has been registered under the :func:`attr_float` alias of the toolbox. Note that the :func:`attr_float` is a direct reference to the :func:`~random.random` function. :: from deap import base from deap import creator from deap import tools import random creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_float", random.random) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=IND_SIZE) Calling :func:`toolbox.individual` will readily return a complete individual composed of ``IND_SIZE`` floating point numbers with a maximizing single objective fitness attribute. Permutation +++++++++++ An individual for the permutation representation is almost similar to the general list individual. In fact they both inherit from the basic :class:`list` type. The only difference is that instead of filling the list with a series of floats, we need to generate a random permutation and provide that permutation to the individual. First, the individual class is created the exact same way as the previous one. Then, an :func:`indices` function is added to the toolbox referring to the :func:`~random.sample` function, sample is used instead of :func:`~random.shuffle` because this last one does not return the shuffled list. The indices function returns a complete permutation of the numbers between ``0`` and ``IND_SIZE - 1``. Finally, the individual is initialized with the :func:`~deap.tools.initIterate` function which gives to the individual an iterable of what is produced by the call to the indices function. :: from deap import base from deap import creator from deap import tools import random creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("indices", random.sample, range(IND_SIZE), IND_SIZE) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.indices) Calling :func:`toolbox.individual` will readily return a complete individual that is a permutation of the integers ``0`` to ``IND_SIZE`` with a minimizing single objective fitness attribute. Arithmetic Expression +++++++++++++++++++++ The next individual that is commonly used is a prefix tree of mathematical expressions. This time a :class:`~deap.gp.PrimitiveSet` must be defined containing all possible mathematical operators that our individual can use. Here the set is called ``MAIN`` and has a single variable defined by the arity. Operators :func:`~operator.add`, :func:`~operator.sub`, and :func:`~operator.mul` are added to the primitive set with each an arity of 2. Next, the :class:`Individual` class is created as before but having an additional static attribute :attr:`pset` set to remember the global primitive set. This time the content of the individuals will be generated by the :func:`~deap.gp.genRamped` function that generate trees in a list format based on a ramped procedure. Once again, the individual is initialised using the :func:`~deap.tools.initIterate` function to give the complete generated iterable to the individual class. :: from deap import base from deap import creator from deap import gp from deap import tools import operator pset = gp.PrimitiveSet("MAIN", arity=1) pset.addPrimitive(operator.add, 2) pset.addPrimitive(operator.sub, 2) pset.addPrimitive(operator.mul, 2) creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin, pset=pset) toolbox = base.Toolbox() toolbox.register("expr", gp.genRamped, pset=pset, min_=1, max_=2) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) Calling :func:`toolbox.individual` will readily return a complete individual that is an arithmetic expression in the form of a prefix tree with a minimizing single objective fitness attribute. Evolution Strategy ++++++++++++++++++ Evolution strategies individuals are slightly different as they contain generally two list, one for the actual individual and one for its mutation parameters. This time instead of using the list base class we will inherit from an :class:`array.array` for both the individual and the strategy. Since there is no helper function to generate two different vectors in a single object we must define this function our-self. The :func:`initES` function receives two classes and instantiate them generating itself the random numbers in the intervals provided for individuals of a given size. :: from deap import base from deap import creator from deap import tools import array import random creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode="d", fitness=creator.FitnessMin, strategy=None) creator.create("Strategy", array.array, typecode="d") def initES(icls, scls, size, imin, imax, smin, smax): ind = icls(random.uniform(imin, imax) for _ in range(size)) ind.strategy = scls(random.uniform(smin, smax) for _ in range(size)) return ind toolbox = base.Toolbox() toolbox.register("individual", initES, creator.Individual, creator.Strategy, IND_SIZE, MIN_VALUE, MAX_VALUE, MIN_STRATEGY, MAX_STRATEGY) Calling :func:`toolbox.individual` will readily return a complete evolution strategy with a strategy vector and a minimizing single objective fitness attribute. Particle ++++++++ A particle is another special type of individual as it usually has a speed and generally remember its best position. This type of individual is created (once again) the same way inheriting from a list. This time :attr:`speed`, :attr:`best` and speed limits attributes are added to the object. Again, an initialization function :func:`initParticle` is also registered to produce the individual receiving the particle class, size, domain, and speed limits as arguments. :: from deap import base from deap import creator from deap import tools import random creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Particle", list, fitness=creator.FitnessMax, speed=None, smin=None, smax=None, best=None) def initParticle(pcls, size, pmin, pmax, smin, smax): part = pcls(random.uniform(pmin, pmax) for _ in xrange(size)) part.speed = [random.uniform(smin, smax) for _ in xrange(size)] part.smin = smin part.smax = smax return part toolbox = base.Toolbox() toolbox.register("particle", initParticle, creator.Particle, size=2, pmin=-6, pmax=6, smin=-3, smax=3) Calling :func:`toolbox.individual` will readily return a complete particle with a speed vector and a maximizing two objectives fitness attribute. A Funky One +++++++++++ Supposing your problem have very specific needs. It is also possible to build custom individuals very easily. The next individual created is a list of alternating integers and floating point numbers, using the :func:`~deap.tools.initCycle` function. :: from deap import base from deap import creator from deap import tools import random creator.create("FitnessMax", base.Fitness, weights=(1.0, 1.0)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_int", random.randint, INT_MIN, INT_MAX) toolbox.register("attr_flt", random.uniform, FLT_MIN, FLT_MAX) toolbox.register("individual", tools.initCycle, creator.Individual, (toolbox.attr_int, toolbox.attr_flt), n=N_CYCLES) Calling :func:`toolbox.individual` will readily return a complete individual of the form ``[int float int float ... int float]`` with a maximizing two objectives fitness attribute. Population ---------- Population are much like individuals, instead of being initialized with attributes it is filled with individuals, strategies or particles. Bag +++ A bag population is the most commonly used type, it has no particular ordering although it is generally implemented using a list. Since the bag has no particular attribute it does not need any special class. The population is initialized using directly the toolbox and the :func:`~deap.tools.initRepeat` function. :: toolbox.register("population", tools.initRepeat, list, toolbox.individual) Calling :func:`toolbox.population` will readily return a complete population in a list providing a number of times the repeat helper must be repeated as an argument of the population function. Grid ++++ A grid population is a special case of structured population where neighbouring individuals have a direct effect on each other. The individuals are distributed in grid where each cell contains a single individual. (Sadly?) It is no different than the list implementation of the bag population other that it is composed of lists of individuals. :: toolbox.register("row", tools.initRepeat, list, toolbox.individual, n=N_COL) toolbox.register("population", tools.initRepeat, list, toolbox.row, n=N_ROW) Calling :func:`toolbox.population` will readily return a complete population where the individuals are accessible using two indices for example ``pop[r][c]``. For the moment there is no algorithm specialized for structured population, we are waiting your submissions. Swarm +++++ A swarm is used in particle swarm optimization, it is different in the sens that it contains a network of communication. The simplest network is the complete connection where each particle knows the best position of that ever been visited by any other particle. This is generally implemented by copying that global best position to a :attr:`gbest` attribute and the global best fitness to a :attr:`gbestfit` attribute. :: creator.create("Swarm", list, gbest=None, gbestfit=creator.FitnessMax) toolbox.register("swarm", tools.initRepeat, creator.Swarm, toolbox.particle) Calling :func:`toolbox.population` will readily return a complete swarm. After each evaluation the :attr:`gbest` and :attr:`gbestfit` are set to reflect the best found position and fitness. Demes +++++ A deme is a sub-population that is contained in a population. It is similar has an island in the island model. Demes being only sub-population are in fact no different than population other than by their names. Here we create a population containing 3 demes each having a different number of individuals using the *n* argument of the :func:`~deap.tools.initRepeat` function. :: toolbox.register("deme", tools.initRepeat, list, toolbox.individual) DEME_SIZES = 10, 50, 100 population = [toolbox.deme(n=i) for i in DEME_SIZES]deap-0.7.1/doc/whatsnew.rst0000644000076500000240000000426511641072614016032 0ustar felixstaff00000000000000=========== What's New? =========== Here is a (incomplete) log of the changes made to DEAP over time. Release 0.7 =========== - Modified structure so that DTM is a module of DEAP. - Restructured modules in a more permanent and coherent way. - The toolbox is now in the module base. - The operators have been moved to the tools module. - Checkpoint, Statistics, History and Hall-of-Fame are now also in the tools module. - Moved the GP specific operators to the gp module. - Renamed some operator for coherence. - Reintroduced a convenient, coherent and simple Statistics module. - Changed the Milestone module name for the more common Checkpoint name. - Eliminated the confusing *content_init* and *size_init* keywords in the toolbox. - Refactored the whole documentation in a more structured manner. - Added a benchmark module containing some of the most classic benchmark functions. - Added a lots of examples again : - Differential evolution (*x2*); - Evolution strategy : One fifth rule; - *k*-nearest neighbours feature selection; - One Max Multipopulation; - Particle Swarm Optimization; - Hillis' coevolution of sorting networks; - CMA-ES :math:`1+\lambda`. Release 0.6 =========== - Operator modify in-place the individuals (simplify a lot the algorithms). - Toolbox now contains two basic methods, map and clone that are useful in the algorithms. - The two methods can be replaced (as usual) to modify the behaviour of the algorithms. - Added new module History (compatible with NetworkX). - Genetic programming is now possible with Automatically Defined Functions (ADFs). - Algorithms now refers to literature algorithms. - Added new examples : - Coevolution; - Variable length genotype; - Multiobjective; - Inheriting from a Set; - Using ADFs; - Multiprocessing. - Basic operators can now be enhanced with decorators to do all sort of funny stuff. Release 0.5 =========== - Added a new module Milestone. - Enhanced Fitness efficiency when comparing fitnesses. - Replaced old base types with python built-in types. - Added an example of deriving from sets. - Added SPEA-II algorithm. - Fitnesses are no more extended when assigning value, the values are simply assigned.deap-0.7.1/examples/0000755000076500000240000000000011650301263014475 5ustar felixstaff00000000000000deap-0.7.1/examples/cmaes1pl_minfct.py0000644000076500000240000000460411641072614020125 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import sys import random import logging logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) # random.seed(64) # Random must be seeded before importing cma because it is # used to seed numpy.random # This will be fixed in future release. from deap import base from deap import benchmarks from deap import cma from deap import creator from deap import tools N=5 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin) # The centroid is set to a vector of 5.0 see http://www.lri.fr/~hansen/cmaes_inmatlab.html # for more details about the rastrigin and other tests for CMA-ES toolbox = base.Toolbox() toolbox.register("attr", random.uniform, -1, 5) toolbox.register("evaluate", benchmarks.sphere) def main(): # The CMA-ES algorithm takes a population of one individual as argument parent = tools.initRepeat(creator.Individual, toolbox.attr, N) parent.fitness.values = toolbox.evaluate(parent) strategy = cma.CMA1pLStrategy(parent, sigma=5.0, lambda_=8) toolbox.register("update", strategy.update) pop = strategy.generate(creator.Individual) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) # The CMA-ES algorithm converge with good probability with those settings cma.esCMA(toolbox, pop, ngen=600, halloffame=hof, statistics=stats) logging.info("Best individual is %s, %s", hof[0], hof[0].fitness.values) if __name__ == "__main__": main() deap-0.7.1/examples/cmaes_minfct.py0000644000076500000240000000442311641072614017507 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import sys import random import logging logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) random.seed(128) # Random must be seeded before importing cma because it is # used to seed numpy.random # This will be fixed in future release. from deap import base from deap import benchmarks from deap import cma from deap import creator from deap import tools # Problem size N=30 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("evaluate", benchmarks.rastrigin) def main(): # The CMA-ES algorithm takes a population of one individual as argument # The centroid is set to a vector of 5.0 see http://www.lri.fr/~hansen/cmaes_inmatlab.html # for more details about the rastrigin and other tests for CMA-ES strategy = cma.CMAStrategy(centroid=[5.0]*N, sigma=5.0, lambda_=20*N) pop = strategy.generate(creator.Individual) hof = tools.HallOfFame(1) toolbox.register("update", strategy.update) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) # The CMA-ES algorithm converge with good probability with those settings cma.esCMA(toolbox, pop, ngen=250, halloffame=hof, statistics=stats) # print "Best individual is %s, %s" % (hof[0], hof[0].fitness.values) return hof[0].fitness.values[0] if __name__ == "__main__": main() deap-0.7.1/examples/coev_hillis.py0000644000076500000240000001267611641072614017370 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random from deap import algorithms from deap import base from deap import creator from deap import tools import sortingnetwork as sn INPUTS = 12 def evalNetwork(host, parasite, dimension): network = sn.SortingNetwork(dimension, host) return network.assess(parasite), def genWire(dimension): return (random.randrange(dimension), random.randrange(dimension)) def genNetwork(dimension, min_size, max_size): size = random.randint(min_size, max_size) return [genWire(dimension) for i in xrange(size)] def getParasite(dimension): return [random.choice((0, 1)) for i in range(dimension)] def mutNetwork(individual, dimension, mutpb, addpb, delpb, indpb): if random.random() < mutpb: for index, elem in enumerate(individual): if random.random() < indpb: individual[index] = genWire(dimension) if random.random() < addpb: index = random.randint(0, len(individual)) individual.insert(index, genWire(dimension)) if random.random() < delpb: index = random.randrange(len(individual)) del individual[index] return individual def mutParasite(individual, indmut, indpb): for i in individual: if random.random() < indpb: indmut(i) return individual creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Host", list, fitness=creator.FitnessMin) creator.create("Parasite", list, fitness=creator.FitnessMax) htoolbox = base.Toolbox() ptoolbox = base.Toolbox() htoolbox.register("network", genNetwork, dimension=INPUTS, min_size=9, max_size=12) htoolbox.register("individual", tools.initIterate, creator.Host, htoolbox.network) htoolbox.register("population", tools.initRepeat, list, htoolbox.individual) ptoolbox.register("parasite", getParasite, dimension=INPUTS) ptoolbox.register("individual", tools.initRepeat, creator.Parasite, ptoolbox.parasite, 20) ptoolbox.register("population", tools.initRepeat, list, ptoolbox.individual) htoolbox.register("evaluate", evalNetwork, dimension=INPUTS) htoolbox.register("mate", tools.cxTwoPoints) htoolbox.register("mutate", mutNetwork, dimension=INPUTS, mutpb=0.2, addpb=0.01, delpb=0.01, indpb=0.05) htoolbox.register("select", tools.selTournament, tournsize=3) ptoolbox.register("mate", tools.cxTwoPoints) ptoolbox.register("indMutate", tools.mutFlipBit, indpb=0.05) ptoolbox.register("mutate", mutParasite, indmut=ptoolbox.indMutate, indpb=0.05) ptoolbox.register("select", tools.selTournament, tournsize=3) def cloneHost(individual): """Specialized copy function that will work only on a list of tuples with no other member than a fitness. """ clone = individual.__class__(individual) clone.fitness.values = individual.fitness.values return clone def cloneParasite(individual): """Specialized copy function that will work only on a list of lists with no other member than a fitness. """ clone = individual.__class__(list(seq) for seq in individual) clone.fitness.values = individual.fitness.values return clone htoolbox.register("clone", cloneHost) ptoolbox.register("clone", cloneParasite) def main(): random.seed(64) hosts = htoolbox.population(n=300) parasites = ptoolbox.population(n=300) hof = tools.HallOfFame(1) hstats = tools.Statistics(lambda ind: ind.fitness.values) hstats.register("Avg", tools.mean) hstats.register("Std", tools.std) hstats.register("Min", min) hstats.register("Max", max) MAXGEN = 50 H_CXPB, H_MUTPB = 0.5, 0.3 P_CXPB, P_MUTPB = 0.5, 0.3 fits = htoolbox.map(htoolbox.evaluate, hosts, parasites) for host, parasite, fit in zip(hosts, parasites, fits): host.fitness.values = parasite.fitness.values = fit hof.update(hosts) hstats.update(hosts) for g in range(MAXGEN): print "-- Generation %i --" % g hosts = htoolbox.select(hosts, len(hosts)) hosts = [htoolbox.clone(ind) for ind in hosts] parasites = ptoolbox.select(parasites, len(parasites)) parasites = [ptoolbox.clone(ind) for ind in parasites] hosts = algorithms.varSimple(htoolbox, hosts, H_CXPB, H_MUTPB) parasites = algorithms.varSimple(ptoolbox, parasites, P_CXPB, P_MUTPB) fits = htoolbox.map(htoolbox.evaluate, hosts, parasites) for host, parasite, fit in zip(hosts, parasites, fits): host.fitness.values = parasite.fitness.values = fit hof.update(hosts) hstats.update(hosts) print hstats best_network = sn.SortingNetwork(INPUTS, hof[0]) print best_network print best_network.draw() print "%i errors" % best_network.assess() return hosts, hstats, hof if __name__ == "__main__": main()deap-0.7.1/examples/coev_symbreg.py0000644000076500000240000001151711641072614017545 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random from deap import base from deap import creator from deap import gp from deap import tools # gp_symbreg already defines some useful structures import gp_symbreg creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("IndGA", list, fitness=creator.FitnessMax) def refFunc(x): return x**4 + x**3 + x**2 + x def evalSymbReg(expr, data): # Transform the tree expression in a callable function func = tools_gp.lambdify(expr=expr) # Evaluate the sum of squared difference between the expression # and the real function : x**4 + x**3 + x**2 + x values = data diff_func = lambda x: (func(x)-refFunc(x))**2 diff = sum(map(diff_func, values)) return diff, toolbox_ga = base.Toolbox() toolbox_ga.register("float", random.uniform, -1, 1) toolbox_ga.register("individual", tools.initRepeat, creator.IndGA, toolbox_ga.float, 10) toolbox_ga.register("population", tools.initRepeat, list, toolbox_ga.individual) toolbox_ga.register("select", tools.selTournament, tournsize=3) toolbox_ga.register("mate", tools.cxTwoPoints) toolbox_ga.register("mutate", tools.mutGaussian, mu=0, sigma=0.01, indpb=0.05) tools_gp = gp_symbreg.toolbox def main(): pop_ga = toolbox_ga.population(n=200) pop_gp = tools_gp.population(n=200) stats_ga = tools.Statistics(lambda ind: ind.fitness.values) stats_ga.register("Avg", tools.mean) stats_ga.register("Std", tools.std) stats_ga.register("Min", min) stats_ga.register("Max", max) stats_gp = tools.Statistics(lambda ind: ind.fitness.values) stats_gp.register("Avg", tools.mean) stats_gp.register("Std", tools.std) stats_gp.register("Min", min) stats_gp.register("Max", max) best_ga = tools.selRandom(pop_ga, 1)[0] best_gp = tools.selRandom(pop_gp, 1)[0] for ind in pop_gp: ind.fitness.values = evalSymbReg(ind, best_ga) for ind in pop_ga: ind.fitness.values = evalSymbReg(best_gp, ind) CXPB, MUTPB, NGEN = 0.5, 0.2, 50 # Begin the evolution for g in range(NGEN): print "-- Generation %i --" % g # Select and clone the offsprings off_ga = toolbox_ga.select(pop_ga, len(pop_ga)) off_gp = tools_gp.select(pop_gp, len(pop_gp)) off_ga = [toolbox_ga.clone(ind) for ind in off_ga] off_gp = [tools_gp.clone(ind) for ind in off_gp] # Apply crossover and mutation for ind1, ind2 in zip(off_ga[::2], off_ga[1::2]): if random.random() < CXPB: toolbox_ga.mate(ind1, ind2) del ind1.fitness.values del ind2.fitness.values for ind1, ind2 in zip(off_gp[::2], off_gp[1::2]): if random.random() < CXPB: tools_gp.mate(ind1, ind2) del ind1.fitness.values del ind2.fitness.values for ind in off_ga: if random.random() < MUTPB: toolbox_ga.mutate(ind) del ind.fitness.values for ind in off_gp: if random.random() < MUTPB: tools_gp.mutate(ind) del ind.fitness.values # Evaluate the individuals with an invalid fitness for ind in off_ga: if not ind.fitness.valid: ind.fitness.values = evalSymbReg(best_gp, ind) for ind in off_gp: if not ind.fitness.valid: ind.fitness.values = evalSymbReg(ind, best_ga) # Replace the old population by the offsprings pop_ga = off_ga pop_gp = off_gp stats_ga.update(pop_ga) stats_gp.update(pop_gp) best_ga = tools.selBest(pop_ga, 1)[0] best_gp = tools.selBest(pop_gp, 1)[0] print " -- GA statistics --" print stats_ga print " -- GP statistics --" print stats_gp print "Best individual GA is %s, %s" % (best_ga, best_ga.fitness.values) print "Best individual GP is %s, %s" % (gp.evaluate(best_gp), best_gp.fitness.values) return pop_ga, pop_gp, stats_ga, stats_gp, best_ga, best_gp if __name__ == "__main__": main() deap-0.7.1/examples/de_basic.py0000644000076500000240000000503511641072614016610 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import random import array from deap import base from deap import benchmarks from deap import creator from deap import tools # Problem dimension NDIM = 10 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register("attr_float", random.uniform, -3, 3) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, NDIM) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("select", tools.selRandom, k=3) toolbox.register("evaluate", benchmarks.sphere) def main(): # Differential evolution parameters CR = 0.25 F = 1 MU = 300 NGEN = 200 pop = toolbox.population(n=MU); hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) # Evaluate the individuals fitnesses = toolbox.map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit for g in range(NGEN): for k, agent in enumerate(pop): a,b,c = toolbox.select(pop) y = toolbox.clone(agent) index = random.randrange(NDIM) for i, value in enumerate(agent): if i == index or random.random() < CR: y[i] = a[i] + F*(b[i]-c[i]) y.fitness.values = toolbox.evaluate(y) if y.fitness > agent.fitness: pop[k] = y hof.update(pop) stats.update(pop) print "-- Generation %i --" % g print stats print "Best individual is ", hof[0], hof[0].fitness.values[0] if __name__ == "__main__": main() deap-0.7.1/examples/de_sphere.py0000644000076500000240000000667311641072614017026 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import random import array from deap import base from deap import benchmarks from deap import creator from deap import tools # Problem dimension NDIM = 10 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin) def mutDE(y, a, b, c, f): size = len(y) for i in range(len(y)): y[i] = a[i] + f*(b[i]-c[i]) return y def cxBinomial(x, y, cr): size = len(x) index = random.randrange(size) for i in range(size): if i == index or random.random() < cr: x[i] = y[i] return x def cxExponential(x, y, cr): size = len(x) index = random.randrange(size) # Loop on the indices index -> end, then on 0 -> index for i in range(index, size) + range(0, index): x[i] = y[i] if random.random() < cr: break return x toolbox = base.Toolbox() toolbox.register("attr_float", random.uniform, -3, 3) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, NDIM) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("mutate", mutDE, f=0.8) toolbox.register("mate", cxExponential, cr=0.8) toolbox.register("select", tools.selRandom, k=3) toolbox.register("evaluate", benchmarks.griewank) def main(): # Differential evolution parameters MU = NDIM * 10 NGEN = 200 pop = toolbox.population(n=MU); hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) # Evaluate the individuals fitnesses = toolbox.map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit for g in range(NGEN): children = [] for agent in pop: # We must clone everything to ensure independance a, b, c = [toolbox.clone(ind) for ind in toolbox.select(pop)] x = toolbox.clone(agent) y = toolbox.clone(agent) y = toolbox.mutate(y, a, b, c) z = toolbox.mate(x, y) del z.fitness.values children.append(z) fitnesses = toolbox.map(toolbox.evaluate, children) for ind, fit, i in zip(children, fitnesses, range(len(fitnesses))): ind.fitness.values = fit if ind.fitness > pop[i].fitness: pop[i] = ind hof.update(pop) stats.update(pop) print "-- Generation %i --" % g print stats print "Best individual is ", hof[0] print "with fitness", hof[0].fitness.values[0] if __name__ == "__main__": main() deap-0.7.1/examples/dtm_piCalc.py0000644000076500000240000000210411641072614017110 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """ Calculation of Pi using a Monte Carlo method. """ from math import hypot from random import random from deap import dtm def test(tries): return sum(hypot(random(), random()) < 1 for i in xrange(tries)) def calcPi(n, t): expr = dtm.repeat(test, n, t) pi_value = 4. * sum(expr) / float(n*t) print("pi = " + str(pi_value)) return pi_value dtm.start(calcPi, 2000, 5000)deap-0.7.1/examples/dtm_primes.py0000644000076500000240000000246711641072614017230 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """ Distributed computation of the prime numbers below 45,000 using DTM. """ import time import math from deap import dtm def primaryTest(nbr): if nbr <= 2: return True for i in range(2, int(math.sqrt(nbr))+1): if nbr % i == 0: return False return True def main(upTo): beginTime = time.time() listNbr = range(3,upTo,2) print("Computation begin...") listPrimes = dtm.filter(primaryTest, listNbr) print("Found " + str(len(listPrimes)) + " prime numbers between 3 and " + str(upTo)) print("Computation time : " + str(time.time()-beginTime)) dtm.start(main, 45003) deap-0.7.1/examples/dtm_recurse.py0000644000076500000240000000253611641072614017376 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """ A very simple example of recursive nested tasks. Each task maps 2 others tasks, each of these 2 tasks maps 2 others, etc., up to RECURSIVITY_LEVEL. Setting RECURSIVITY_LEVEL to a high value (typically more than 16) may produce some strange OS errors (as the kernel will probably complain about the number of active threads... """ from deap import dtm RECURSIVITY_LEVEL = 12 def recursiveFunc(level): if level == 1: return 1 else: args = [level-1] * 2 s = sum(dtm.map(recursiveFunc, args)) if level == RECURSIVITY_LEVEL: print("2^"+str(level)+" = " + str(s)) return s dtm.start(recursiveFunc, RECURSIVITY_LEVEL) deap-0.7.1/examples/dtm_Sssched.py0000644000076500000240000000431511641072614017317 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """ DTM version of the sssched utility by Devert and Gagne (http://marmakoide.org/content/code/sssched.html) Execute concurrently a list of independant tasks specified in a file passed with the -c command line option. """ import subprocess import time import sys import optparse import logging from deap import dtm _logger = logging.getLogger("dtm.sssched") def executor(taskCmd): _logger.info("Lauching %s on worker %s", taskCmd, dtm.getWorkerId()) return subprocess.call(taskCmd, stdout=sys.stdout, stderr=sys.stderr) def main(fTasks): tasksList = [] with open(fTasks) as f: for line in f: line = line.strip() if len(line) > 0: tasksList.append(line) retVals = dtm.map(executor, tasksList) if sum(retVals) > 0: _logger.warning("Some tasks did not run well (return code > 0) :") for i in xrange(len(retVals)): if retVals[i] > 0: _logger.warning("%s returns %s", tasksList[i], retVals[i]) _logger.info("Done") if __name__ == '__main__': cmdLine = optparse.OptionParser(description = "Simple script scheduler (sssched) for remote job management") cmdLine.add_option("-c", "--commands", dest = "commandsFileName", action = "store", help = "File with list of commands to perform (default:\"commands.lst\")", type = "string") (options, args) = cmdLine.parse_args() dtm.start(main, options.commandsFileName)deap-0.7.1/examples/es_fctmin.py0000644000076500000240000000603311641072614017025 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import sys import random import logging logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) from deap import algorithms from deap import base from deap import benchmarks from deap import creator from deap import tools IND_SIZE = 30 MIN_VALUE = 4 MAX_VALUE = 5 MIN_STRATEGY = 0.5 MAX_STRATEGY = 3 creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode="d", fitness=creator.FitnessMin, strategy=None) creator.create("Strategy", array.array, typecode="d") # Individual generator def generateES(icls, scls, size, imin, imax, smin, smax): ind = icls(random.uniform(imin, imax) for _ in range(size)) ind.strategy = scls(random.uniform(smin, smax) for _ in range(size)) return ind def checkStrategy(minstrategy): def decMinStrategy(func): def wrapMinStrategy(*args, **kargs): children = func(*args, **kargs) for child in children: if child.strategy < minstrategy: child.strategy = minstrategy return children return wrapMinStrategy return decMinStrategy # Structure initializers toolbox = base.Toolbox() toolbox.register("individual", generateES, creator.Individual, creator.Strategy, IND_SIZE, MIN_VALUE, MAX_VALUE, MIN_STRATEGY, MAX_STRATEGY) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("mate", tools.cxESBlend, alpha=0.1) toolbox.register("mutate", tools.mutESLogNormal, c=1.0, indpb=0.03) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", benchmarks.sphere) toolbox.decorate("mate", checkStrategy(0.5)) toolbox.decorate("mutate", checkStrategy(0.5)) def main(): random.seed() MU, LAMBDA = 10, 100 pop = toolbox.population(n=MU) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaMuCommaLambda(toolbox, pop, mu=MU, lambda_=LAMBDA, cxpb=0.6, mutpb=0.3, ngen=500, stats=stats, halloffame=hof) logging.info("Best individual is %s, %s", hof[0], hof[0].fitness.values) return pop, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/es_onefifth.py0000644000076500000240000000524211641072614017350 0ustar felixstaff00000000000000 # This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import random from deap import base from deap import creator from deap import benchmarks IND_SIZE = 10 tools = base.Toolbox() creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin) def update(ind, mu, std): for i, mu_i in enumerate(mu): ind[i] = random.gauss(mu_i,std) tools.register("update", update) tools.register("evaluate", benchmarks.sphere) def main(): """Implements the One-Fifth rule algorithm as expressed in : Kern, S., S.D. Muller, N. Hansen, D. Buche, J. Ocenasek and P. Koumoutsakos (2004). Learning Probability Distributions in Continuous Evolutionary Algorithms - A Comparative Review. Natural Computing, 3(1), pp. 77-112. However instead of parent and offspring the algorithm is expressed in terms of best and worst. Best is equivalent to the parent, and worst to the offspring. Instead of producing a new individual each time, we have defined a function which updates the worst individual using the best one as the mean of the gaussian and the sigma computed as the standard deviation. """ random.seed(64) interval = (-3,7) mu = (random.uniform(interval[0], interval[1]) for _ in xrange(IND_SIZE)) sigma = (interval[1] - interval[0])/2.0 alpha = 2.0**(1.0/IND_SIZE) best = creator.Individual(mu) best.fitness.values = tools.evaluate(best) worst = creator.Individual((0.0,)*IND_SIZE) NGEN = 1500 for g in xrange(NGEN): tools.update(worst, best, sigma) worst.fitness.values = tools.evaluate(worst) if best.fitness <= worst.fitness: sigma = sigma * alpha best, worst = worst, best else: sigma = sigma * alpha**(-0.25) print "Generation", g, "- Fitness:", best.fitness.values[0] print "Best individual is ", best, best.fitness.values[0] return best if __name__ == "__main__": main() deap-0.7.1/examples/ga_evosn.py0000644000076500000240000001144011641072614016655 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import sys import random import logging logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) from deap import algorithms from deap import base from deap import creator from deap import tools import sortingnetwork as sn INPUTS = 6 def evalEvoSN(individual, dimension): network = sn.SortingNetwork(dimension, individual) return network.assess(), network.length, network.depth def genWire(dimension): return (random.randrange(dimension), random.randrange(dimension)) def genNetwork(dimension, min_size, max_size): size = random.randint(min_size, max_size) return [genWire(dimension) for i in xrange(size)] def mutWire(individual, dimension, indpb): for index, elem in enumerate(individual): if random.random() < indpb: individual[index] = genWire(dimension) def mutAddWire(individual, dimension): index = random.randint(0, len(individual)) individual.insert(index, genWire(dimension)) def mutDelWire(individual): index = random.randrange(len(individual)) del individual[index] creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0, -1.0)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() # Gene initializer toolbox.register("network", genNetwork, dimension=INPUTS, min_size=9, max_size=12) # Structure initializers toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.network) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("evaluate", evalEvoSN, dimension=INPUTS) toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", mutWire, dimension=INPUTS, indpb=0.05) toolbox.register("addwire", mutAddWire, dimension=INPUTS) toolbox.register("delwire", mutDelWire) toolbox.register("select", tools.selNSGA2) def main(): random.seed(64) population = toolbox.population(n=300) hof = tools.ParetoFront() stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) CXPB, MUTPB, ADDPB, DELPB, NGEN = 0.5, 0.2, 0.01, 0.01, 40 # Evaluate every individuals fitnesses = toolbox.map(toolbox.evaluate, population) for ind, fit in zip(population, fitnesses): ind.fitness.values = fit hof.update(population) stats.update(population) # Begin the evolution for g in xrange(NGEN): print "-- Generation %i --" % g offsprings = [toolbox.clone(ind) for ind in population] # Apply crossover and mutation for ind1, ind2 in zip(offsprings[::2], offsprings[1::2]): if random.random() < CXPB: toolbox.mate(ind1, ind2) del ind1.fitness.values del ind2.fitness.values # Note here that we have a different sheme of mutation than in the # original algorithm, we use 3 different mutations subsequently. for ind in offsprings: if random.random() < MUTPB: toolbox.mutate(ind) del ind.fitness.values if random.random() < ADDPB: toolbox.addwire(ind) del ind.fitness.values if random.random() < DELPB: toolbox.delwire(ind) del ind.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offsprings if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit print " Evaluated %i individuals" % len(invalid_ind) population = toolbox.select(population+offsprings, len(offsprings)) hof.update(population) stats.update(population) print stats best_network = sn.SortingNetwork(INPUTS, hof[0]) print best_network print best_network.draw() print "%i errors, length %i, depth %i" % hof[0].fitness.values return population, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/ga_knapsack.py0000644000076500000240000000725211641072614017324 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import sys import random import logging logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) from deap import algorithms from deap import base from deap import creator from deap import tools IND_SIZE = 30 MAX_ITEM = 50 MAX_WEIGHT = 50 NBR_ITEMS = 20 # Create the item dictionary, items' name is an integer, and value is # a (weight, value) 2-uple. The items will be created during the runtime # to enable reproducibility. It must be available in the global space # because of the evaluation items = {} creator.create("Fitness", base.Fitness, weights=(-1.0, 1.0)) creator.create("Individual", set, fitness=creator.Fitness) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_item", random.randrange, NBR_ITEMS) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_item, IND_SIZE) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalKnapsack(individual): weight = 0.0 value = 0.0 for item in individual: weight += items[item][0] value += items[item][1] if len(individual) > MAX_ITEM or weight > MAX_WEIGHT: return 10000, 0 # Ensure overweighted bags are dominated return weight, value def cxSet(ind1, ind2): """Apply a crossover operation on input sets. The first child is the intersection of the two sets, the second child is the difference of the two sets. """ temp = set(ind1) # Used in order to keep type ind1 &= ind2 # Intersection (inplace) ind2 ^= temp # Symmetric Difference (inplace) def mutSet(individual): """Mutation that pops or add an element.""" if random.random() < 0.5: if len(individual) > 0: # We cannot pop from an empty set individual.remove(random.choice(sorted(tuple(individual)))) else: individual.add(random.randrange(NBR_ITEMS)) toolbox.register("evaluate", evalKnapsack) toolbox.register("mate", cxSet) toolbox.register("mutate", mutSet) toolbox.register("select", tools.selNSGA2) def main(): random.seed(64) NGEN = 50 MU = 50 LAMBDA = 100 # Create random items and store them in the items' dictionary. for i in xrange(NBR_ITEMS): items[i] = (random.randint(1, 10), random.uniform(0, 100)) pop = toolbox.population(n=MU) hof = tools.ParetoFront() stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaMuPlusLambda(toolbox, pop, MU, LAMBDA, 0.7, 0.2, NGEN, stats, halloffame=hof) logging.info("Best individual for measure 1 is %s, %s", hof[0], hof[0].fitness.values) logging.info("Best individual for measure 2 is %s, %s", hof[-1], hof[-1].fitness.values) return pop, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/ga_knn.py0000644000076500000240000000553611641072614016322 0ustar felixstaff00000000000000#!/usr/bin/env python2.7 # This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import csv import logging import random import sys import knn from deap import algorithms from deap import base from deap import creator from deap import tools logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) # kNN parameters import knn FILE="heart_scale.csv" N_TRAIN=175 K=1 # Read data from file data = csv.reader(open(FILE, "rb")) trainset = list() trainlabels = list() rows = [row for row in data] random.shuffle(rows) for row in rows: trainlabels.append(float(row[0])) trainset.append([float(e) for e in row[1:]]) classifier = knn.KNN(K) classifier.train(trainset[:N_TRAIN], trainlabels[:N_TRAIN]) def evalClassifier(individual): labels = classifier.predict(trainset[N_TRAIN:], individual) return sum(1 for x, y in zip(labels, trainlabels) if x == y) / float(len(trainlabels)), \ sum(individual) / float(classifier.ndim) creator.create("FitnessMulti", base.Fitness, weights=(1.0, -1.0)) creator.create("Individual", list, fitness=creator.FitnessMulti) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, classifier.ndim) toolbox.register("population", tools.initRepeat, list, toolbox.individual) # Operator registering toolbox.register("evaluate", evalClassifier) toolbox.register("mate", tools.cxUniform, indpb=0.1) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selNSGA2) def main(): # random.seed(64) MU, LAMBDA = 100, 200 pop = toolbox.population(n=MU) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaMuPlusLambda(toolbox, pop, mu=MU, lambda_=LAMBDA, cxpb=0.5, mutpb=0.2, ngen=40, stats=stats, halloffame=hof) logging.info("Best individual is %s, %s", hof[0], hof[0].fitness.values) return pop, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/ga_kursawefct.py0000644000076500000240000000563611641072614017713 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import logging import sys import random logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) from deap import algorithms from deap import base from deap import benchmarks from deap import creator from deap import tools creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0)) creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMin) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_float", random.uniform, -5, 5) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, 3) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def checkBounds(min, max): def decCheckBounds(func): def wrapCheckBounds(*args, **kargs): offsprings = func(*args, **kargs) for child in offsprings: for i in xrange(len(child)): if child[i] > max: child[i] = max elif child[i] < min: child[i] = min return offsprings return wrapCheckBounds return decCheckBounds toolbox.register("evaluate", benchmarks.kursawe) toolbox.register("mate", tools.cxBlend, alpha=1.5) toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=3, indpb=0.3) toolbox.register("select", tools.selNSGA2) toolbox.decorate("mate", checkBounds(-5, 5)) toolbox.decorate("mutate", checkBounds(-5, 5)) def main(): random.seed(64) MU, LAMBDA = 50, 100 pop = toolbox.population(n=MU) hof = tools.ParetoFront() stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaMuPlusLambda(toolbox, pop, mu=MU, lambda_=LAMBDA, cxpb=0.5, mutpb=0.2, ngen=50, stats=stats, halloffame=hof) logging.info("Best individual for measure 1 is %s, %s", hof[0], hof[0].fitness.values) logging.info("Best individual for measure 2 is %s, %s", hof[-1], hof[-1].fitness.values) return pop, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/ga_onemax.py0000644000076500000240000000716411641072614017022 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), # Operator registering toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) def main(): random.seed(64) pop = toolbox.population(n=300) CXPB, MUTPB, NGEN = 0.5, 0.2, 40 print "Start of evolution" # Evaluate the entire population fitnesses = map(toolbox.evaluate, pop) for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit print " Evaluated %i individuals" % len(pop) # Begin the evolution for g in range(NGEN): print "-- Generation %i --" % g # Select the next generation individuals offsprings = toolbox.select(pop, len(pop)) # Clone the selected individuals offsprings = map(toolbox.clone, offsprings) # Apply crossover and mutation on the offsprings for child1, child2 in zip(offsprings[::2], offsprings[1::2]): if random.random() < CXPB: toolbox.mate(child1, child2) del child1.fitness.values del child2.fitness.values for mutant in offsprings: if random.random() < MUTPB: toolbox.mutate(mutant) del mutant.fitness.values # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offsprings if not ind.fitness.valid] fitnesses = map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit print " Evaluated %i individuals" % len(invalid_ind) # The population is entirely replaced by the offsprings pop[:] = offsprings # Gather all the fitnesses in one list and print the stats fits = [ind.fitness.values[0] for ind in pop] length = len(pop) mean = sum(fits) / length sum2 = sum(x*x for x in fits) std = abs(sum2 / length - mean**2)**0.5 print " Min %s" % min(fits) print " Max %s" % max(fits) print " Avg %s" % mean print " Std %s" % std print "-- End of (successful) evolution --" best_ind = tools.selBest(pop, 1)[0] print "Best individual is %s, %s" % (best_ind, best_ind.fitness.values) if __name__ == "__main__": main() deap-0.7.1/examples/ga_onemax_multidemic.py0000644000076500000240000000607711641072614021240 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import random from deap import algorithms from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("migrate", tools.migRing, k=5, selection=tools.selBest, replacement=tools.selRandom) def main(): random.seed(64) NBR_DEMES = 3 MU = 300 NGEN = 40 CXPB = 0.5 MUTPB = 0.2 MIG_RATE = 5 demes = [toolbox.population(n=MU) for _ in xrange(NBR_DEMES)] hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values, 4) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) for idx, deme in enumerate(demes): for ind in deme: ind.fitness.values = toolbox.evaluate(ind) stats.update(deme, idx) hof.update(deme) stats.update(demes[0]+demes[1]+demes[2], 3) gen = 1 while gen <= NGEN and stats.Max(3)[0] < 100.0: print "-- Generation %i --" % gen for idx, deme in enumerate(demes): print "-- Deme %i --" % (idx+1) deme[:] = [toolbox.clone(ind) for ind in toolbox.select(deme, len(deme))] algorithms.varSimple(toolbox, deme, cxpb=CXPB, mutpb=MUTPB) for ind in deme: ind.fitness.values = toolbox.evaluate(ind) stats.update(deme, idx) hof.update(deme) print stats[idx] if gen % MIG_RATE == 0: toolbox.migrate(demes) stats.update(demes[0]+demes[1]+demes[2], 3) print "-- Population --" print stats[3] gen += 1 return demes, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/ga_onemax_numpy.py0000644000076500000240000000375611641072614020255 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import numpy import sys import random import logging logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) from deap import algorithms from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", numpy.ndarray, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return numpy.sum(individual), toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) def main(): random.seed(64) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaSimple(toolbox, pop, cxpb=0.5, mutpb=0.2, ngen=40, stats=stats, halloffame=hof) return pop, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/ga_onemax_short.py0000644000076500000240000000415711641072614020240 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import sys import random import logging logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) from deap import algorithms from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) def main(): random.seed(64) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaSimple(toolbox, pop, cxpb=0.5, mutpb=0.2, ngen=40, stats=stats, halloffame=hof) logging.info("Best individual is %s, %s", hof[0], hof[0].fitness.values) return pop, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/ga_tsp.py0000644000076500000240000000501011641072614016325 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import sys import logging import random import json logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) from deap import algorithms from deap import base from deap import creator from deap import tools # gr*.json contains the distance map in list of list style in JSON format # Optimal solutions are : gr17 = 2085, gr24 = 1272, gr120 = 6942 tsp = json.load(open("gr17.json", "r")) distance_map = tsp["DistanceMatrix"] IND_SIZE = tsp["TourSize"] creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", array.array, typecode='i', fitness=creator.FitnessMin) toolbox = base.Toolbox() # Attribute generator toolbox.register("indices", random.sample, xrange(IND_SIZE), IND_SIZE) # Structure initializers toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.indices) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalTSP(individual): distance = distance_map[individual[-1]][individual[0]] for gene1, gene2 in zip(individual[0:-1], individual[1:]): distance += distance_map[gene1][gene2] return distance, toolbox.register("mate", tools.cxPartialyMatched) toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", evalTSP) def main(): random.seed(169) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaSimple(toolbox, pop, 0.7, 0.2, 40, stats, hof) logging.info("Best individual is %s, %s", hof[0], hof[0].fitness.values) return pop, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/gp_adf_symbreg.py0000644000076500000240000001376411641072614020037 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import random import operator import math from deap import base from deap import creator from deap import gp from deap import tools # Define new functions def safeDiv(left, right): try: return left / right except ZeroDivisionError: return 0 adfset2 = gp.PrimitiveSet("ADF2", 2) adfset2.addPrimitive(operator.add, 2) adfset2.addPrimitive(operator.sub, 2) adfset2.addPrimitive(operator.mul, 2) adfset2.addPrimitive(safeDiv, 2) adfset2.addPrimitive(operator.neg, 1) adfset2.addPrimitive(math.cos, 1) adfset2.addPrimitive(math.sin, 1) adfset1 = gp.PrimitiveSet("ADF1", 2) adfset1.addPrimitive(operator.add, 2) adfset1.addPrimitive(operator.sub, 2) adfset1.addPrimitive(operator.mul, 2) adfset1.addPrimitive(safeDiv, 2) adfset1.addPrimitive(operator.neg, 1) adfset1.addPrimitive(math.cos, 1) adfset1.addPrimitive(math.sin, 1) adfset1.addADF(adfset2) adfset0 = gp.PrimitiveSet("ADF0", 2) adfset0.addPrimitive(operator.add, 2) adfset0.addPrimitive(operator.sub, 2) adfset0.addPrimitive(operator.mul, 2) adfset0.addPrimitive(safeDiv, 2) adfset0.addPrimitive(operator.neg, 1) adfset0.addPrimitive(math.cos, 1) adfset0.addPrimitive(math.sin, 1) adfset0.addADF(adfset1) adfset0.addADF(adfset2) pset = gp.PrimitiveSet("MAIN", 1) pset.addPrimitive(operator.add, 2) pset.addPrimitive(operator.sub, 2) pset.addPrimitive(operator.mul, 2) pset.addPrimitive(safeDiv, 2) pset.addPrimitive(operator.neg, 1) pset.addPrimitive(math.cos, 1) pset.addPrimitive(math.sin, 1) pset.addEphemeralConstant(lambda: random.randint(-1, 1)) pset.addADF(adfset0) pset.addADF(adfset1) pset.addADF(adfset2) pset.renameArguments({"ARG0" : "x"}) creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("ADF0", gp.PrimitiveTree, pset=adfset0) creator.create("ADF1", gp.PrimitiveTree, pset=adfset1) creator.create("ADF2", gp.PrimitiveTree, pset=adfset2) creator.create("MAIN", gp.PrimitiveTree, pset=pset) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() toolbox.register('adf_expr0', gp.genFull, pset=adfset0, min_=1, max_=2) toolbox.register('adf_expr1', gp.genFull, pset=adfset1, min_=1, max_=2) toolbox.register('adf_expr2', gp.genFull, pset=adfset2, min_=1, max_=2) toolbox.register('main_expr', gp.genRamped, pset=pset, min_=1, max_=2) toolbox.register('ADF0', tools.initIterate, creator.ADF0, toolbox.adf_expr0) toolbox.register('ADF1', tools.initIterate, creator.ADF1, toolbox.adf_expr1) toolbox.register('ADF2', tools.initIterate, creator.ADF2, toolbox.adf_expr2) toolbox.register('MAIN', tools.initIterate, creator.MAIN, toolbox.main_expr) func_cycle = [toolbox.MAIN, toolbox.ADF0, toolbox.ADF1, toolbox.ADF2] toolbox.register('individual', tools.initCycle, creator.Individual, func_cycle) toolbox.register('population', tools.initRepeat, list, toolbox.individual) def evalSymbReg(individual): # Transform the tree expression in a callable function func = toolbox.lambdify(expr=individual) # Evaluate the sum of squared difference between the expression # and the real function : x**4 + x**3 + x**2 + x values = (x/10. for x in xrange(-10, 10)) diff_func = lambda x: (func(x)-(x**4 + x**3 + x**2 + x))**2 diff = sum(map(diff_func, values)) return diff, toolbox.register('lambdify', gp.lambdifyList) toolbox.register('evaluate', evalSymbReg) toolbox.register('select', tools.selTournament, tournsize=3) toolbox.register('mate', gp.cxUniformOnePoint) toolbox.register('expr', gp.genFull, min_=1, max_=2) toolbox.register('mutate', gp.mutUniform, expr=toolbox.expr) def main(): random.seed(1024) ind = toolbox.individual() pop = toolbox.population(n=100) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) CXPB, MUTPB, NGEN = 0.5, 0.2, 40 # Evaluate the entire population for ind in pop: ind.fitness.values = toolbox.evaluate(ind) hof.update(pop) stats.update(pop) for g in range(NGEN): print "-- Generation %i --" % g # Select the offsprings offsprings = toolbox.select(pop, len(pop)) # Clone the offsprings offsprings = [toolbox.clone(ind) for ind in offsprings] # Apply crossover and mutation for ind1, ind2 in zip(offsprings[::2], offsprings[1::2]): for tree1, tree2 in zip(ind1, ind2): if random.random() < CXPB: toolbox.mate(tree1, tree2) del ind1.fitness.values del ind2.fitness.values for ind in offsprings: for tree in ind: if random.random() < MUTPB: toolbox.mutate(tree) del ind.fitness.values # Evaluate the individuals with an invalid fitness invalids = [ind for ind in offsprings if not ind.fitness.valid] for ind in invalids: ind.fitness.values = toolbox.evaluate(ind) # Replacement of the population by the offspring pop = offsprings hof.update(pop) stats.update(pop) print stats print 'Best individual : ', gp.evaluate(hof[0][0]), hof[0].fitness return pop, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/gp_ant.py0000644000076500000240000001563011641072614016331 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . """ This example is from "John R. Koza. Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, Cambridge, MA, USA, 1992.". The problem is called The Artificial Ant Problem. The goal of this example is to show how to use DEAP and its GP framework with with complex system of functions and object. Given an AntSimulator ant, this solution should get the 89 pieces of food within 543 moves. ant.routine = ant.if_food_ahead(ant.move_forward, prog3(ant.turn_left, prog2(ant.if_food_ahead(ant.move_forward, ant.turn_right), prog2(ant.turn_right, prog2(ant.turn_left, ant.turn_right))), prog2(ant.if_food_ahead(ant.move_forward, ant.turn_left), ant.move_forward))) Best solution found with DEAP: prog3(prog3(move_forward, turn_right, if_food_ahead(if_food_ahead(prog3(move_forward, move_forward, move_forward), prog2(turn_left, turn_right)), turn_left)), if_food_ahead(turn_left, turn_left), if_food_ahead(move_forward, turn_right)) fitness = (89,) """ import sys import logging import copy import random from functools import partial from deap import algorithms from deap import base from deap import creator from deap import tools from deap import gp logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) def progn(*args): for arg in args: arg() def prog2(out1, out2): return partial(progn,out1,out2) def prog3(out1, out2, out3): return partial(progn,out1,out2,out3) def if_then_else(condition, out1, out2): out1() if condition() else out2() class AntSimulator(object): direction = ["north","east","south","west"] dir_row = [1, 0, -1, 0] dir_col = [0, 1, 0, -1] def __init__(self, max_moves): self.max_moves = max_moves self.moves = 0 self.eaten = 0 self.routine = None def _reset(self): self.row = self.row_start self.col = self.col_start self.dir = 1 self.moves = 0 self.eaten = 0 self.matrix_exc = copy.deepcopy(self.matrix) @property def position(self): return (self.row, self.col, self.direction[self.dir]) def turn_left(self): if self.moves < self.max_moves: self.moves += 1 self.dir = (self.dir - 1) % 4 def turn_right(self): if self.moves < self.max_moves: self.moves += 1 self.dir = (self.dir + 1) % 4 def move_forward(self): if self.moves < self.max_moves: self.moves += 1 self.row = (self.row + self.dir_row[self.dir]) % self.matrix_row self.col = (self.col + self.dir_col[self.dir]) % self.matrix_col if self.matrix_exc[self.row][self.col] == "food": self.eaten += 1 self.matrix_exc[self.row][self.col] = "passed" def sense_food(self): ahead_row = (self.row + self.dir_row[self.dir]) % self.matrix_row ahead_col = (self.col + self.dir_col[self.dir]) % self.matrix_col return self.matrix_exc[ahead_row][ahead_col] == "food" def if_food_ahead(self, out1, out2): return partial(if_then_else, self.sense_food, out1, out2) def run(self,routine): self._reset() while self.moves < self.max_moves: routine() def parse_matrix(self, matrix): self.matrix = list() for i, line in enumerate(matrix): self.matrix.append(list()) for j, col in enumerate(line): if col == "#": self.matrix[-1].append("food") elif col == ".": self.matrix[-1].append("empty") elif col == "S": self.matrix[-1].append("empty") self.row_start = self.row = i self.col_start = self.col = j self.dir = 1 self.matrix_row = len(self.matrix) self.matrix_col = len(self.matrix[0]) self.matrix_exc = copy.deepcopy(self.matrix) ant = AntSimulator(600) pset = gp.PrimitiveSet("MAIN", 0) pset.addPrimitive(ant.if_food_ahead, 2) pset.addPrimitive(prog2, 2) pset.addPrimitive(prog3, 3) pset.addTerminal(ant.move_forward) pset.addTerminal(ant.turn_left) pset.addTerminal(ant.turn_right) creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax, pset=pset) toolbox = base.Toolbox() # Attribute generator toolbox.register("expr_init", gp.genFull, pset=pset, min_=1, max_=2) # Structure initializers toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr_init) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalArtificialAnt(individual): # Transform the tree expression to functionnal Python code routine = gp.evaluate(individual, pset) # Run the generated routine ant.run(routine) return ant.eaten, toolbox.register("evaluate", evalArtificialAnt) toolbox.register("select", tools.selTournament, tournsize=7) toolbox.register("mate", gp.cxUniformOnePoint) toolbox.register("expr_mut", gp.genFull, min_=0, max_=2) toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut) def main(): random.seed(69) trail_file = open("santafe_trail.txt") ant.parse_matrix(trail_file) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaSimple(toolbox, pop, 0.5, 0.2, 40, stats, halloffame=hof) logging.info("Best individual is %s, %s", gp.evaluate(hof[0]), hof[0].fitness) return pop, hof, stats if __name__ == "__main__": main() deap-0.7.1/examples/gp_multiplexer.py0000644000076500000240000000665711641072614020132 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import sys import random import operator import logging from deap import algorithms from deap import base from deap import creator from deap import tools from deap import gp logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) def if_then_else(condition, out1, out2): return out1 if condition else out2 # Initialize Multiplexer problem input and output vectors MUX_SELECT_LINES = 3 MUX_IN_LINES = 2 ** MUX_SELECT_LINES MUX_TOTAL_LINES = MUX_SELECT_LINES + MUX_IN_LINES # input : [A0 A1 A2 D0 D1 D2 D3 D4 D5 D6 D7] for a 8-3 mux inputs = [[0] * MUX_TOTAL_LINES for i in range(2 ** MUX_TOTAL_LINES)] outputs = [None] * (2 ** MUX_TOTAL_LINES) for i in range(2 ** MUX_TOTAL_LINES): value = i divisor = 2 ** MUX_TOTAL_LINES # Fill the input bits for j in range(MUX_TOTAL_LINES): divisor /= 2 if value >= divisor: inputs[i][j] = 1 value -= divisor # Determine the corresponding output indexOutput = MUX_SELECT_LINES for j, k in enumerate(inputs[i][:MUX_SELECT_LINES]): if k: indexOutput += 2 ** j outputs[i] = inputs[i][indexOutput] pset = gp.PrimitiveSet("MAIN", MUX_TOTAL_LINES, "IN") pset.addPrimitive(operator.and_, 2) pset.addPrimitive(operator.or_, 2) pset.addPrimitive(operator.not_, 1) pset.addPrimitive(if_then_else, 3) pset.addTerminal(1) pset.addTerminal(0) creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax, pset=pset) toolbox = base.Toolbox() toolbox.register("expr", gp.genFull, pset=pset, min_=2, max_=4) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("lambdify", gp.lambdify, pset=pset) def evalMultiplexer(individual): func = toolbox.lambdify(expr=individual) good = sum((func(*(inputs[i])) == outputs[i] for i in range(2 ** MUX_TOTAL_LINES))) return good, toolbox.register("evaluate", evalMultiplexer) toolbox.register("select", tools.selTournament, tournsize=7) toolbox.register("mate", gp.cxUniformOnePoint) toolbox.register("expr_mut", gp.genGrow, min_=0, max_=2) toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut) def main(): random.seed() pop = toolbox.population(n=500) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaSimple(toolbox, pop, 0.8, 0.1, 40, stats, halloffame=hof) logging.info("Best individual is %s, %s", gp.evaluate(hof[0]), hof[0].fitness) return pop, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/gp_parity.py0000644000076500000240000000610611641072614017055 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import sys import random import operator import logging from deap import algorithms from deap import base from deap import creator from deap import tools from deap import gp logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) # Initialize Parity problem input and output matrices PARITY_FANIN_M = 6 PARITY_SIZE_M = 2**PARITY_FANIN_M inputs = [None] * PARITY_SIZE_M outputs = [None] * PARITY_SIZE_M for i in xrange(PARITY_SIZE_M): inputs[i] = [None] * PARITY_FANIN_M value = i dividor = PARITY_SIZE_M parity = 1 for j in xrange(PARITY_FANIN_M): dividor /= 2 if value >= dividor: inputs[i][j] = 1 parity = int(not parity) value -= dividor else: inputs[i][j] = 0 outputs[i] = parity pset = gp.PrimitiveSet("MAIN", PARITY_FANIN_M, "IN") pset.addPrimitive(operator.and_, 2) pset.addPrimitive(operator.or_, 2) pset.addPrimitive(operator.xor, 2) pset.addPrimitive(operator.not_, 1) pset.addTerminal(1) pset.addTerminal(0) creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax, pset=pset) toolbox = base.Toolbox() toolbox.register("expr", gp.genFull, pset=pset, min_=3, max_=5) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("lambdify", gp.lambdify, pset=pset) def evalParity(individual): func = toolbox.lambdify(expr=individual) good = sum(func(*inputs[i]) == outputs[i] for i in xrange(PARITY_SIZE_M)) return good, toolbox.register("evaluate", evalParity) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("mate", gp.cxUniformOnePoint) toolbox.register("expr_mut", gp.genGrow, min_=0, max_=2) toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut) def main(): random.seed(21) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaSimple(toolbox, pop, 0.5, 0.2, 40, stats, halloffame=hof) logging.info("Best individual is %s, %s", gp.evaluate(hof[0]), hof[0].fitness) return pop, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/gp_spambase.py0000644000076500000240000001011611641072614017334 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import sys import random import operator import logging import csv import itertools from deap import algorithms from deap import base from deap import creator from deap import tools from deap import gp logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) # Read the spam list features and put it in a list of lists. # The dataset is from http://archive.ics.uci.edu/ml/datasets/Spambase # This example is a copy of the OpenBEAGLE example : # http://beagle.gel.ulaval.ca/refmanual/beagle/html/d2/dbe/group__Spambase.html spamReader = csv.reader(open("spambase.csv")) spam = list(list(float(elem) for elem in row) for row in spamReader) # defined a new primitive set for strongly typed GP pset = gp.PrimitiveSetTyped("MAIN", itertools.repeat("float", 57), "bool", "IN") # boolean operators pset.addPrimitive(operator.and_, ["bool", "bool"], "bool") pset.addPrimitive(operator.or_, ["bool", "bool"], "bool") pset.addPrimitive(operator.not_, ["bool"], "bool") # floating point operators # Define a safe division function def safeDiv(left, right): try: return left / right except ZeroDivisionError: return 0 pset.addPrimitive(operator.add, ["float","float"], "float") pset.addPrimitive(operator.sub, ["float","float"], "float") pset.addPrimitive(operator.mul, ["float","float"], "float") pset.addPrimitive(safeDiv, ["float","float"], "float") # logic operators # Define a new if-then-else function def if_then_else(input, output1, output2): if input: return output1 else: return output2 pset.addPrimitive(operator.lt, ["float", "float"], "bool") pset.addPrimitive(operator.eq, ["float", "float"], "bool") pset.addPrimitive(if_then_else, ["bool", "float", "float"], "float") # terminals pset.addEphemeralConstant(lambda: random.random() * 100, "float") pset.addTerminal(0, "bool") pset.addTerminal(1, "bool") creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax, pset=pset) toolbox = base.Toolbox() toolbox.register("expr", gp.genRamped, pset=pset, type_=pset.ret, min_=1, max_=2) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("lambdify", gp.lambdify, pset=pset) def evalSpambase(individual): # Transform the tree expression in a callable function func = toolbox.lambdify(expr=individual) # Randomly sample 400 mails in the spam database spam_samp = random.sample(spam, 400) # Evaluate the sum of correctly identified mail as spam result = sum(bool(func(*mail[:57])) is bool(mail[57]) for mail in spam_samp) return result, toolbox.register("evaluate", evalSpambase) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("mate", gp.cxTypedOnePoint) toolbox.register("expr_mut", gp.genFull, min_=0, max_=2) toolbox.register("mutate", gp.mutTypedUniform, expr=toolbox.expr_mut) def main(): pop = toolbox.population(n=100) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaSimple(toolbox, pop, 0.5, 0.2, 40, stats, halloffame=hof) logging.info("Best individual is %s, %s", gp.evaluate(hof[0]), hof[0].fitness) return pop, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/gp_symbreg.py0000644000076500000240000000605511641072614017220 0ustar felixstaff00000000000000# This file is part of EAP. # # EAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # EAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with EAP. If not, see . import sys import operator import math import logging import random from deap import algorithms from deap import base from deap import creator from deap import tools from deap import gp # Define new functions def safeDiv(left, right): try: return left / right except ZeroDivisionError: return 0 pset = gp.PrimitiveSet("MAIN", 1) pset.addPrimitive(operator.add, 2) pset.addPrimitive(operator.sub, 2) pset.addPrimitive(operator.mul, 2) pset.addPrimitive(safeDiv, 2) pset.addPrimitive(operator.neg, 1) pset.addPrimitive(math.cos, 1) pset.addPrimitive(math.sin, 1) pset.addEphemeralConstant(lambda: random.randint(-1,1)) pset.renameArguments({"ARG0" : "x"}) creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin, pset=pset) toolbox = base.Toolbox() toolbox.register("expr", gp.genRamped, pset=pset, min_=1, max_=2) toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr) toolbox.register("population", tools.initRepeat, list, toolbox.individual) toolbox.register("lambdify", gp.lambdify, pset=pset) def evalSymbReg(individual): # Transform the tree expression in a callable function func = toolbox.lambdify(expr=individual) # Evaluate the sum of squared difference between the expression # and the real function : x**4 + x**3 + x**2 + x values = (x/10. for x in xrange(-10,10)) diff_func = lambda x: (func(x)-(x**4 + x**3 + x**2 + x))**2 diff = sum(map(diff_func, values)) return diff, toolbox.register("evaluate", evalSymbReg) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("mate", gp.cxUniformOnePoint) toolbox.register("expr_mut", gp.genFull, min_=0, max_=2) toolbox.register('mutate', gp.mutUniform, expr=toolbox.expr_mut) def main(): random.seed(318) logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) algorithms.eaSimple(toolbox, pop, 0.5, 0.1, 40, stats, halloffame=hof) logging.info("Best individual is %s, %s", gp.evaluate(hof[0]), hof[0].fitness) return pop, stats, hof if __name__ == "__main__": main() deap-0.7.1/examples/gr120.json0000644000076500000240000021543211641072614016237 0ustar felixstaff00000000000000{ "TourSize" : 120, "OptTour" : [1, 76, 59, 15, 30, 29, 120, 32, 92, 28, 45, 78, 86, 94, 81, 22, 66, 31, 117, 85, 18, 19, 25, 108, 43, 79, 52, 33, 100, 58, 91, 68, 65, 69, 113, 107, 20, 46, 50, 44, 75, 14, 87, 74, 105, 40, 72, 38, 7, 56, 41, 42, 98, 17, 118, 49, 13, 51, 11, 23, 9, 103, 119, 3, 82, 2, 115, 21, 93, 53, 64, 109, 88, 97, 12, 95, 77, 39, 63, 5, 27, 80, 101, 102, 48, 110, 112, 106, 114, 73, 57, 83, 67, 37, 62, 99, 10, 35, 104, 36, 84, 6, 89, 55, 47, 71, 26, 4, 34, 116, 70, 8, 54, 90, 96, 111, 24, 60, 16, 61], "OptDistance" : 6942, 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-1.0,-0.375,1.0,-0.333333,-0.509434,-0.374429,-1.0,-1.0,0.557252,-1.0,-1.0,-1.0,-1.0,1.0 -1.0,0.125,-1.0,-0.333333,-0.132075,-0.232877,-1.0,1.0,0.251908,-1.0,-0.580645,0,-1.0,-1.0 -1.0,0.166667,1.0,1.0,-0.132075,-0.69863,-1.0,-1.0,0.175573,-1.0,-0.870968,0,-1.0,0.5 1.0,0.583333,1.0,1.0,0.245283,-0.269406,-1.0,1.0,-0.435115,1.0,-0.516129,0,1.0,-1.0deap-0.7.1/examples/knn.py0000644000076500000240000000530511641072614015645 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import numpy import operator class KNN(object): def __init__(self, k): self.k = k self.data = None self.labels = None self.ndim = 0 def train(self, data, labels): self.data = numpy.array(data) self.labels = numpy.array(labels) self.classes = numpy.unique(self.labels) self.ndim = len(self.data[0]) def predict(self, data, features=None): data = numpy.array(data) if features is None: features = numpy.ones(self.data.shape[1]) else: features = numpy.array(features) if data.ndim == 1: dist = self.data - data elif data.ndim == 2: dist = numpy.zeros((data.shape[0],) + self.data.shape) for i, d in enumerate(data): dist[i, :, :] = self.data - d else: raise ValueError("Cannot process data with dimensionality > 2") dist = features * dist dist = dist * dist dist = numpy.sum(dist, -1) dist = numpy.sqrt(dist) nns = numpy.argsort(dist) if data.ndim == 1: classes = {cls : 0 for cls in self.classes} for n in nns[:self.k]: classes[self.labels[n]] += 1 labels = sorted(classes.iteritems(), key=operator.itemgetter(1))[-1][0] elif data.ndim == 2: labels = list() for i, d in enumerate(data): classes = {cls : 0 for cls in self.classes} for n in nns[i, :self.k]: classes[self.labels[n]] += 1 labels.append(sorted(classes.iteritems(), key=operator.itemgetter(1))[-1][0]) return labels if __name__ == "__main__": trainset = [[1, 0], [1, 1], [1, 2]] trainlabels = [1, 2, 3] knn = KNN(1) knn.train(trainset, trainlabels) print "Single Data ===========" print knn.predict([1, 0], [1, 1]) print "Multiple Data ===========" print knn.predict([[1, 3], [1, 0]], [1, 1]) deap-0.7.1/examples/mpga_onemax.py0000644000076500000240000000401211641072614017344 0ustar felixstaff00000000000000#!/usr/bin/env python2.7 # This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import array import logging import multiprocessing import random import sys logging.basicConfig(level=logging.INFO, stream=sys.stdout) import random from deap import algorithms from deap import base from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", array.array, typecode='b', fitness=creator.FitnessMax) toolbox = base.Toolbox() # Attribute generator toolbox.register("attr_bool", random.randint, 0, 1) # Structure initializers toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual): return sum(individual), toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoints) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) # Process Pool of 4 workers pool = multiprocessing.Pool(processes=4) toolbox.register("map", pool.map) if __name__ == "__main__": random.seed(64) pop = toolbox.population(n=300) hof = tools.HallOfFame(1) algorithms.eaSimple(toolbox, pop, cxpb=0.5, mutpb=0.2, ngen=40, halloffame=hof) logging.info("Best individual is %s, %s", hof[0], hof[0].fitness.values) deap-0.7.1/examples/pso_basic.py0000644000076500000240000000622311641072614017021 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . import operator import random from deap import base from deap import benchmarks from deap import creator from deap import tools creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Particle", list, fitness=creator.FitnessMax, speed=list, smin=None, smax=None, best=None) def generate(size, pmin, pmax, smin, smax): part = creator.Particle(random.uniform(pmin, pmax) for _ in xrange(size)) part.speed = [random.uniform(smin, smax) for _ in xrange(size)] part.smin = smin part.smax = smax return part def updateParticle(part, best, phi1, phi2): u1 = (random.uniform(0, phi1) for _ in range(len(part))) u2 = (random.uniform(0, phi2) for _ in range(len(part))) v_u1 = map(operator.mul, u1, map(operator.sub, part.best, part)) v_u2 = map(operator.mul, u2, map(operator.sub, best, part)) part.speed = map(operator.add, part.speed, map(operator.add, v_u1, v_u2)) for i, speed in enumerate(part.speed): if speed < part.smin: part.speed[i] = part.smin elif speed > part.smax: part.speed[i] = part.smax part[:] = map(operator.add, part, part.speed) toolbox = base.Toolbox() toolbox.register("particle", generate, size=2, pmin=-6, pmax=6, smin=-3, smax=3) toolbox.register("population", tools.initRepeat, list, toolbox.particle) toolbox.register("update", updateParticle, phi1=2.0, phi2=2.0) toolbox.register("evaluate", benchmarks.h1) def main(): pop = toolbox.population(n=5) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register("Avg", tools.mean) stats.register("Std", tools.std) stats.register("Min", min) stats.register("Max", max) GEN = 1000 best = None for g in xrange(GEN): print "-- Generation %i --" % g for part in pop: part.fitness.values = toolbox.evaluate(part) if not part.best or part.best.fitness < part.fitness: part.best = creator.Particle(part) part.best.fitness.values = part.fitness.values if not best or best.fitness < part.fitness: best = creator.Particle(part) best.fitness.values = part.fitness.values for part in pop: toolbox.update(part, best) # Gather all the fitnesses in one list and print the stats stats.update(pop) print stats print " Best so far: %s - %s" % (best, best.fitness) return pop, stats, best if __name__ == "__main__": main() deap-0.7.1/examples/sortingnetwork.py0000644000076500000240000001116711641072614020161 0ustar felixstaff00000000000000# This file is part of DEAP. # # DEAP is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as # published by the Free Software Foundation, either version 3 of # the License, or (at your option) any later version. # # DEAP 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 Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with DEAP. If not, see . try: from itertools import product except ImportError: def product(*args, **kwds): # product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy # product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111 pools = map(tuple, args) * kwds.get('repeat', 1) result = [[]] for pool in pools: result = [x+[y] for x in result for y in pool] for prod in result: yield tuple(prod) class SortingNetwork(list): """Sorting network class. From Wikipedia : A sorting network is an abstract mathematical model of a network of wires and comparator modules that is used to sort a sequence of numbers. Each comparator connects two wires and sort the values by outputting the smaller value to one wire, and a larger value to the other. """ def __init__(self, dimension, connectors = []): self.dimension = dimension for wire1, wire2 in connectors: self.addConnector(wire1, wire2) def addConnector(self, wire1, wire2): """Add a connector between wire1 and wire2 in the network.""" if wire1 == wire2: return if wire1 > wire2: wire1, wire2 = wire2, wire1 try: last_level = self[-1] except IndexError: # Empty network, create new level and connector self.append([(wire1, wire2)]) return for wires in last_level: if wires[1] >= wire1 and wires[0] <= wire2: self.append([(wire1, wire2)]) return last_level.append((wire1, wire2)) def sort(self, values): """Sort the values in-place based on the connectors in the network.""" for level in self: for wire1, wire2 in level: if values[wire1] > values[wire2]: values[wire1], values[wire2] = values[wire2], values[wire1] def assess(self, cases=None): """Try to sort the **cases** using the network, return the number of misses. If **cases** is None, test all possible cases according to the network dimensionality. """ if cases is None: cases = product(range(2), repeat=self.dimension) misses = 0 ordered = [[0]*(self.dimension-i) + [1]*i for i in range(self.dimension+1)] for sequence in cases: sequence = list(sequence) self.sort(sequence) misses += (sequence != ordered[sum(sequence)]) return misses def draw(self): """Return an ASCII representation of the network.""" str_wires = [["-"]*7 * self.depth] str_wires[0][0] = "0" str_wires[0][1] = " o" str_spaces = [] for i in xrange(1, self.dimension): str_wires.append(["-"]*7 * self.depth) str_spaces.append([" "]*7 * self.depth) str_wires[i][0] = str(i) str_wires[i][1] = " o" for index, level in enumerate(self): for wire1, wire2 in level: str_wires[wire1][(index+1)*6] = "x" str_wires[wire2][(index+1)*6] = "x" for i in xrange(wire1, wire2): str_spaces[i][(index+1)*6+1] = "|" for i in xrange(wire1+1, wire2): str_wires[i][(index+1)*6] = "|" network_draw = "".join(str_wires[0]) for line, space in zip(str_wires[1:], str_spaces): network_draw += "\n" network_draw += "".join(space) network_draw += "\n" network_draw += "".join(line) return network_draw @property def depth(self): """Return the number of parallel steps that it takes to sort any input. 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Windows ======= Not tried yet ... tell us if it works. Options ======= You might want to install this software somewhere else by addind the prefix options to the installation. $ python setup.py install --prefix=somewhere/else Other basic options are provided by the building tools of Python, see http://docs.python.org/install/ for more information.deap-0.7.1/LICENSE.txt0000644000076500000240000001672511641072614014522 0ustar felixstaff00000000000000 GNU LESSER GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. This version of the GNU Lesser General Public License incorporates the terms and conditions of version 3 of the GNU General Public License, supplemented by the additional permissions listed below. 0. Additional Definitions. 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Each version is given a distinguishing version number. If the Library as you received it specifies that a certain numbered version of the GNU Lesser General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that published version or of any later version published by the Free Software Foundation. If the Library as you received it does not specify a version number of the GNU Lesser General Public License, you may choose any version of the GNU Lesser General Public License ever published by the Free Software Foundation. If the Library as you received it specifies that a proxy can decide whether future versions of the GNU Lesser General Public License shall apply, that proxy's public statement of acceptance of any version is permanent authorization for you to choose that version for the Library. deap-0.7.1/PKG-INFO0000644000076500000240000000312311650301263013753 0ustar felixstaff00000000000000Metadata-Version: 1.0 Name: deap Version: 0.7.1 Summary: Distributed Evolutionary Algorithms in Python Home-page: http://deap.googlecode.com Author: deap Development Team Author-email: deap-users@googlegroups.com License: LGPL Download-URL: http://code.google.com/p/deap/downloads/list Description: DEAP stands for Distributed Evolutionary Algorithm in Python, it is dedicated to people who wish to learn how to use evolutionary algorithms and to those who wish to rediscover evolutionary algorithms. DEAP is the proof that evolutionary algorithms do **not** need to be neither complex or complicated. EAP is part of the DEAP project, that also includes some facilities for the automatic distribution and parallelization of tasks over a cluster of computers. The D part of DEAP, called DTM, is under intense development and currently available as an alpha version. DTM currently provides two and a half ways to distribute work loads around a clusters or LAN of workstations, based MPI and TCP communication managers. Keywords: evolutionary algorithms,genetic algorithms,genetic programming,cma-es,ga,gp,es,pso Platform: any Classifier: Development Status :: 4 - Beta Classifier: Intended Audience :: Developers Classifier: Intended Audience :: Education Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL) Classifier: Programming Language :: Python Classifier: Topic :: Scientific/Engineering Classifier: Topic :: Software Development deap-0.7.1/README.txt0000644000076500000240000000132411641072614014362 0ustar felixstaff00000000000000DEAP stands for Distributed Evolutionary Algorithm in Python, it is dedicated to people who wish to learn how to use evolutionary algorithms and to those who wish to rediscover evolutionary algorithms. DEAP is the proof that evolutionary algorithms do **not** need to be neither complex or complicated. EAP is part of the DEAP project, that also includes some facilities for the automatic distribution and parallelization of tasks over a cluster of computers. The D part of DEAP, called DTM, is under intense development and currently available as an alpha version. DTM currently provides two and a half ways to distribute work loads around a clusters or LAN of workstations, based MPI and TCP communication managers. deap-0.7.1/setup.py0000644000076500000240000000211211650301232014361 0ustar felixstaff00000000000000#!/usr/bin/env python from distutils.core import setup import deap setup(name='deap', version=deap.__revision__, description='Distributed Evolutionary Algorithms in Python', long_description=open('README.txt').read(), author='deap Development Team', author_email='deap-users@googlegroups.com', url='http://deap.googlecode.com', download_url='http://code.google.com/p/deap/downloads/list', packages=['deap', 'deap.benchmarks', 'deap.dtm', 'deap.tests'], platforms=['any'], keywords=['evolutionary algorithms','genetic algorithms','genetic programming','cma-es','ga','gp','es','pso'], license='LGPL', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Intended Audience :: Education', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)', 'Programming Language :: Python', 'Topic :: Scientific/Engineering', 'Topic :: Software Development', ], )