evoknn_jmlr.py

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

import knn, random
from deap import algorithms, base, creator, tools

def evalFitness(individual):
    return knn.classification_rate(features=individual), sum(individual)

creator.create("FitnessMulti", base.Fitness, weights=(1.0, -1.0))
creator.create("Individual", list, fitness=creator.FitnessMulti)

toolbox = base.Toolbox()
toolbox.register("bit", random.randint, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual,
                 toolbox.bit, n=13)
toolbox.register("population", tools.initRepeat, list, toolbox.individual, n=100)
toolbox.register("evaluate", evalFitness)
toolbox.register("mate", tools.cxUniform, indpb=0.1)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
toolbox.register("select", tools.selNSGA2)

population = toolbox.population()
fits = toolbox.map(toolbox.evaluate, population)
for fit, ind in zip(fits, population):
    ind.fitness.values = fit

for gen in range(50):
    offspring = algorithms.varOr(population, toolbox, lambda_=100, cxpb=0.5,mutpb=0.1)
    fits = toolbox.map(toolbox.evaluate, offspring)
    for fit, ind in zip(fits, offspring):
        ind.fitness.values = fit
    population = toolbox.select(offspring + population, k=100)