algorithms.py

#    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 <http://www.gnu.org/licenses/>.

"""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. Generally, 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 random

from deap import tools

from automoea import MOEATerminationDetection


def varAnd(population, toolbox, 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.

    :param population: A list of individuals to vary.
    :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
                    operators.
    :param cxpb: The probability of mating two individuals.
    :param mutpb: The probability of mutating an individual.
    :returns: A list of varied individuals that are independent of their
              parents.

    The variation 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 pairs of 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. Note that both operators are
    not applied systematicaly, the resulting individuals can be generated from
    crossover only, mutation only, crossover and mutation, and reproduction
    according to the given probabilities. Both probabilities should be in
    :math:`[0, 1]`.
    """
    offspring = [toolbox.clone(ind) for ind in population]

    # Apply crossover and mutation on the offspring
    for i in range(1, len(offspring), 2):
        if random.random() < cxpb:
            offspring[i - 1], offspring[i] = toolbox.mate(offspring[i - 1], offspring[i])
            del offspring[i - 1].fitness.values, offspring[i].fitness.values

    for i in range(len(offspring)):
        if random.random() < mutpb:
            offspring[i], = toolbox.mutate(offspring[i])
            del offspring[i].fitness.values

    return offspring


def eaSimple(population, toolbox, cxpb, mutpb, ngen, stats=None,
             halloffame=None, verbose=__debug__):
    """This algorithm reproduce the simplest evolutionary algorithm as
    presented in chapter 7 of [Back2000]_.

    :param population: A list of individuals.
    :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
                    operators.
    :param cxpb: The probability of mating two individuals.
    :param mutpb: The probability of mutating an individual.
    :param ngen: The number of generation.
    :param stats: A :class:`~deap.tools.Statistics` object that is updated
                  inplace, optional.
    :param halloffame: A :class:`~deap.tools.HallOfFame` object that will
                       contain the best individuals, optional.
    :param verbose: Whether or not to log the statistics.
    :returns: The final population
    :returns: A class:`~deap.tools.Logbook` with the statistics of the
              evolution

    The algorithm takes in a population and evolves it in place using the
    :meth:`varAnd` method. It returns the optimized population and a
    :class:`~deap.tools.Logbook` with the statistics of the evolution. The
    logbook will contain the generation number, the number of evalutions for
    each generation and the statistics if a :class:`~deap.tools.Statistics` is
    given as argument. The *cxpb* and *mutpb* arguments are passed to the
    :func:`varAnd` function. The pseudocode goes as follow ::

        evaluate(population)
        for g in range(ngen):
            population = select(population, len(population))
            offspring = varAnd(population, toolbox, cxpb, mutpb)
            evaluate(offspring)
            population = offspring

    As stated in the pseudocode above, the algorithm goes as follow. First, it
    evaluates the individuals with an invalid fitness. Second, it enters the
    generational loop where the selection procedure is applied to entirely
    replace the parental population. The 1:1 replacement ratio of this
    algorithm **requires** the selection procedure to be stochastic and to
    select multiple times the same individual, for example,
    :func:`~deap.tools.selTournament` and :func:`~deap.tools.selRoulette`.
    Third, it applies the :func:`varAnd` function to produce the next
    generation population. Fourth, it evaluates the new individuals and
    compute the statistics on this population. Finally, when *ngen*
    generations are done, the algorithm returns a tuple with the final
    population and a :class:`~deap.tools.Logbook` of the evolution.

    .. note::

        Using a non-stochastic selection method will result in no selection as
        the operator selects *n* individuals from a pool of *n*.

    This function expects the :meth:`toolbox.mate`, :meth:`toolbox.mutate`,
    :meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be
    registered in the toolbox.

    .. [Back2000] Back, Fogel and Michalewicz, "Evolutionary Computation 1 :
       Basic Algorithms and Operators", 2000.
    """
    logbook = tools.Logbook()
    logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])

    # 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)

    record = stats.compile(population) if stats else {}
    logbook.record(gen=0, nevals=len(invalid_ind), **record)
    if verbose:
        print logbook.stream

    # Begin the generational process
    for gen in range(1, ngen + 1):
        # Select the next generation individuals
        offspring = toolbox.select(population, len(population))

        # Vary the pool of individuals
        offspring = varAnd(offspring, toolbox, cxpb, mutpb)

        # Evaluate the individuals with an invalid fitness
        invalid_ind = [ind for ind in offspring 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(offspring)

        # Replace the current population by the offspring
        population[:] = offspring

        # Append the current generation statistics to the logbook
        record = stats.compile(population) if stats else {}
        logbook.record(gen=gen, nevals=len(invalid_ind), **record)
        if verbose:
            print logbook.stream

    return population, logbook


def varOr(population, toolbox, 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.

    :param population: A list of individuals to vary.
    :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
                    operators.
    :param lambda\_: The number of children to produce
    :param cxpb: The probability of mating two individuals.
    :param mutpb: The probability of mutating an individual.
    :returns: The final population
    :returns: A class:`~deap.tools.Logbook` with the statistics of the
              evolution

    The variation 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.")

    offspring = []
    for _ in xrange(lambda_):
        op_choice = random.random()
        if op_choice < cxpb:            # Apply crossover
            ind1, ind2 = map(toolbox.clone, random.sample(population, 2))
            ind1, ind2 = toolbox.mate(ind1, ind2)
            del ind1.fitness.values
            offspring.append(ind1)
        elif op_choice < cxpb + mutpb:  # Apply mutation
            ind = toolbox.clone(random.choice(population))
            ind, = toolbox.mutate(ind)
            del ind.fitness.values
            offspring.append(ind)
        else:                           # Apply reproduction
            offspring.append(random.choice(population))

    return offspring


def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
                   stats=None, halloffame=None, verbose=__debug__):
    """This is the :math:`(\mu + \lambda)` evolutionary algorithm.

    :param population: A list of individuals.
    :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
                    operators.
    :param mu: The number of individuals to select for the next generation.
    :param lambda\_: The number of children to produce at each generation.
    :param cxpb: The probability that an offspring is produced by crossover.
    :param mutpb: The probability that an offspring is produced by mutation.
    :param ngen: The number of generation.
    :param stats: A :class:`~deap.tools.Statistics` object that is updated
                  inplace, optional.
    :param halloffame: A :class:`~deap.tools.HallOfFame` object that will
                       contain the best individuals, optional.
    :param verbose: Whether or not to log the statistics.
    :returns: The final population
    :returns: A class:`~deap.tools.Logbook` with the statistics of the
              evolution.

    The algorithm takes in a population and evolves it in place using the
    :func:`varOr` function. It returns the optimized population and a
    :class:`~deap.tools.Logbook` with the statistics of the evolution. The
    logbook will contain the generation number, the number of evalutions for
    each generation and the statistics if a :class:`~deap.tools.Statistics` is
    given as argument. The *cxpb* and *mutpb* arguments are passed to the
    :func:`varOr` function. The pseudocode goes as follow ::

        evaluate(population)
        for g in range(ngen):
            offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
            evaluate(offspring)
            population = select(population + offspring, mu)

    First, the individuals having an invalid fitness are evaluated. Second,
    the evolutionary loop begins by producing *lambda_* offspring from the
    population, the offspring are generated by the :func:`varOr` function. The
    offspring are then evaluated and the next generation population is
    selected from both the offspring **and** the population. Finally, when
    *ngen* generations are done, the algorithm returns a tuple with the final
    population and a :class:`~deap.tools.Logbook` of the evolution.

    This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`,
    :meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be
    registered in the toolbox. This algorithm uses the :func:`varOr`
    variation.
    """
    logbook = tools.Logbook()
    logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])

    # 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)

    record = stats.compile(population) if stats is not None else {}
    logbook.record(gen=0, nevals=len(invalid_ind), **record)
    if verbose:
        print logbook.stream

    terminator = MOEATerminationDetection(30, 4, 10)
    
    # Begin the generational process
    #for gen in range(1, ngen + 1):
    gen = 0
    while not terminator.done():
        # Vary the population
        offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)

        # Evaluate the individuals with an invalid fitness
        invalid_ind = [ind for ind in offspring 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(offspring)

        # Select the next generation population
        P = [list(y) for y in [x.fitness.values for x in population[:]]]
        population[:] = toolbox.select(population + offspring, mu)
        Q = [list(y) for y in [x.fitness.values for x in population[:]]]

        
        # Update the statistics with the new population
        record = stats.compile(population) if stats is not None else {}
        logbook.record(gen=gen, nevals=len(invalid_ind), **record)
        if verbose:
            print logbook.stream
            
        terminator.update(P, Q, gen)
        terminator.status(gen)
        gen += 1
    return population, logbook

def eaMuCommaLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen,
                    stats=None, halloffame=None, verbose=__debug__):
    """This is the :math:`(\mu~,~\lambda)` evolutionary algorithm.

    :param population: A list of individuals.
    :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
                    operators.
    :param mu: The number of individuals to select for the next generation.
    :param lambda\_: The number of children to produce at each generation.
    :param cxpb: The probability that an offspring is produced by crossover.
    :param mutpb: The probability that an offspring is produced by mutation.
    :param ngen: The number of generation.
    :param stats: A :class:`~deap.tools.Statistics` object that is updated
                  inplace, optional.
    :param halloffame: A :class:`~deap.tools.HallOfFame` object that will
                       contain the best individuals, optional.
    :param verbose: Whether or not to log the statistics.
    :returns: The final population
    :returns: A class:`~deap.tools.Logbook` with the statistics of the
              evolution

    The algorithm takes in a population and evolves it in place using the
    :func:`varOr` function. It returns the optimized population and a
    :class:`~deap.tools.Logbook` with the statistics of the evolution. The
    logbook will contain the generation number, the number of evalutions for
    each generation and the statistics if a :class:`~deap.tools.Statistics` is
    given as argument. The *cxpb* and *mutpb* arguments are passed to the
    :func:`varOr` function. The pseudocode goes as follow ::

        evaluate(population)
        for g in range(ngen):
            offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
            evaluate(offspring)
            population = select(offspring, mu)

    First, the individuals having an invalid fitness are evaluated. Second,
    the evolutionary loop begins by producing *lambda_* offspring from the
    population, the offspring are generated by the :func:`varOr` function. The
    offspring are then evaluated and the next generation population is
    selected from **only** the offspring. Finally, when
    *ngen* generations are done, the algorithm returns a tuple with the final
    population and a :class:`~deap.tools.Logbook` of the evolution.

    .. note::

        Care must be taken when the lambda:mu ratio is 1 to 1 as a non-stochastic
        selection will result in no selection at all as
        the operator selects *lambda* individuals from a pool of *mu*.


    This function expects :meth:`toolbox.mate`, :meth:`toolbox.mutate`,
    :meth:`toolbox.select` and :meth:`toolbox.evaluate` aliases to be
    registered in the toolbox. This algorithm uses the :func:`varOr`
    variation.
    """
    assert lambda_ >= mu, "lambda must be greater or equal to mu."

    # 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)

    logbook = tools.Logbook()
    logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])

    record = stats.compile(population) if stats is not None else {}
    logbook.record(gen=0, nevals=len(invalid_ind), **record)
    if verbose:
        print logbook.stream

    # Begin the generational process
    for gen in range(1, ngen + 1):
        # Vary the population
        offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)

        # Evaluate the individuals with an invalid fitness
        invalid_ind = [ind for ind in offspring 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(offspring)

        # Select the next generation population
        population[:] = toolbox.select(offspring, mu)

        # Update the statistics with the new population
        record = stats.compile(population) if stats is not None else {}
        logbook.record(gen=gen, nevals=len(invalid_ind), **record)
        if verbose:
            print logbook.stream
    return population, logbook


def eaGenerateUpdate(toolbox, ngen, halloffame=None, stats=None,
                     verbose=__debug__):
    """This is algorithm implements the ask-tell model proposed in
    [Colette2010]_, where ask is called `generate` and tell is called `update`.

    :param toolbox: A :class:`~deap.base.Toolbox` that contains the evolution
                    operators.
    :param ngen: The number of generation.
    :param stats: A :class:`~deap.tools.Statistics` object that is updated
                  inplace, optional.
    :param halloffame: A :class:`~deap.tools.HallOfFame` object that will
                       contain the best individuals, optional.
    :param verbose: Whether or not to log the statistics.
    :returns: The final population
    :returns: A class:`~deap.tools.Logbook` with the statistics of the
              evolution

    The algorithm generates the individuals using the :func:`toolbox.generate`
    function and updates the generation method with the :func:`toolbox.update`
    function. It returns the optimized population and a
    :class:`~deap.tools.Logbook` with the statistics of the evolution. The
    logbook will contain the generation number, the number of evalutions for
    each generation and the statistics if a :class:`~deap.tools.Statistics` is
    given as argument. The pseudocode goes as follow ::

        for g in range(ngen):
            population = toolbox.generate()
            evaluate(population)
            toolbox.update(population)

    .. [Colette2010] Collette, Y., N. Hansen, G. Pujol, D. Salazar Aponte and
       R. Le Riche (2010). On Object-Oriented Programming of Optimizers -
       Examples in Scilab. In P. Breitkopf and R. F. Coelho, eds.:
       Multidisciplinary Design Optimization in Computational Mechanics,
       Wiley, pp. 527-565;

    """
    logbook = tools.Logbook()
    logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])

    for gen in xrange(ngen):
        # Generate a new population
        population = toolbox.generate()
        # Evaluate the individuals
        fitnesses = toolbox.map(toolbox.evaluate, population)
        for ind, fit in zip(population, fitnesses):
            ind.fitness.values = fit

        if halloffame is not None:
            halloffame.update(population)

        # Update the strategy with the evaluated individuals
        toolbox.update(population)

        record = stats.compile(population) if stats is not None else {}
        logbook.record(gen=gen, nevals=len(population), **record)
        if verbose:
            print logbook.stream

    return population, logbook