def main(argv):
global TrainSet
global TestSet
letters = "i:t"
keywords = ["input=", "test="]
trainfile = ""
testfile = ""
# run the algorithm by: python MainGA --input=train.txt --test=test.txt
try:
opts, arg = getopt.getopt(sys.argv[1:], letters, keywords)
except getopt.GetoptError:
print "GetoptError: -i <trainfile>"
sys.exit(2)
for opt, arg in opts:
if opt in ("-i", "--input"):
trainfile = arg
if opt in ("-t", "--test"):
testfile = arg
if trainfile:
trainF = open(trainfile, "r")
TrainSet = DataSet(False, trainF, iB, sB)
trainF.close()
TrainSet.set_dataset_filename(str(trainfile))
TrainSet.set_generations(GENERATIONS)
TrainSet.set_pop_size(POPULATION)
TrainSet.set_mutation_rate(MUTATION_RATE)
genome = G1DList.G1DList(SIZE_OF_CHROMOSOMES)
# * 2 to ascending and descending sorts, otherwise you'll have just ascending order
genome.setParams(rangemin=1, rangemax=(TrainSet.FeaturesNum) * 2 * sB)
genome.evaluator.set(eval_func) # change here if you want a different fitness function
ga = GSimpleGA.GSimpleGA(genome)
ga.setGenerations(GENERATIONS) # changes the # of generations(default 100)
ga.setPopulationSize(POPULATION) # changes the # of individuals(default 80)
# ga.setMutationRate(MUTATION_RATE) # --> use it when you want to change the Mutation Rate (default 0.02)
# ga.setCrossoverRate(CROSS_OVER_RATE) # --> use it when you want to change the Crossover Rate (default 0.8)
# ga.setMultiProcessing(True) # --> please read this: http://pyevolve.sourceforge.net/wordpress/?p=843
ga.evolve(freq_stats=10)
chromosome = ga.bestIndividual().getInternalList() # the chromosome selected by GA
# print chromosome ---> you can print the chromosome to see what was selected
TrainSet.set_best_individual(chromosome)
TrainSet.sort_dataset_by_chromosome(chromosome)
TrainSet.write_scores(isTrain=True)
else:
sys.exit("GA_Algorithm: A train file is required.")
if testfile:
testF = open(testfile, "r")
TestSet = DataSet(True, testF, iB, sB)
testF.close()
TestSet.set_dataset_filename(str(testfile))
TestSet.set_generations(GENERATIONS)
TestSet.set_pop_size(POPULATION)
TestSet.set_mutation_rate(MUTATION_RATE)
TestSet.isTest = True
TestSet.sort_dataset_by_chromosome(
TrainSet.get_best_individual()
) # order the testset with chromosome found by GA with trainset
TestSet.write_scores()
