source: framspy/FramsticksEvolution.py @ 961

Last change on this file since 961 was 961, checked in by Maciej Komosinski, 8 months ago

Cosmetic

File size: 5.2 KB
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1import argparse
2import os
3import sys
4import numpy as np
5from deap import creator, base, tools, algorithms
6from FramsticksCLI import FramsticksCLI
7
8# Note: this is much less efficient than running the evolution directly in Framsticks, so use only when required or when poor performance is acceptable!
9
10
11# The list of criteria includes 'vertpos', 'velocity', 'distance', 'vertvel', 'lifespan', 'numjoints', 'numparts', 'numneurons', 'numconnections'.
12OPTIMIZATION_CRITERIA = ['vertpos']  # Single or multiple criteria. Names from the standard-eval.expdef dictionary, e.g. ['vertpos', 'velocity'].
13
14
15def frams_evaluate(frams_cli, individual):
16        genotype = individual[0]  # [0] because we can't (?) have a simple str as a deap genotype/individual, only list of str.
17        data = frams_cli.evaluate(genotype)
18        # print("Evaluated '%s'" % genotype, 'evaluation is:', data)
19        try:
20                first_genotype_data = data[0]
21                evaluation_data = first_genotype_data["evaluations"]
22                default_evaluation_data = evaluation_data[""]
23                fitness = [default_evaluation_data[crit] for crit in OPTIMIZATION_CRITERIA]
24        except (KeyError, TypeError) as e:  # the evaluation may have failed for invalid genotypes (or some other reason)
25                fitness = [-1] * len(OPTIMIZATION_CRITERIA)
26                print("Error '%s': could not evaluate genotype '%s', returning fitness %s" % (str(e), genotype, fitness))
27        return fitness
28
29
30def frams_crossover(frams_cli, individual1, individual2):
31        geno1 = individual1[0]  # [0] because we can't (?) have a simple str as a deap genotype/individual, only list of str.
32        geno2 = individual2[0]  # [0] because we can't (?) have a simple str as a deap genotype/individual, only list of str.
33        individual1[0] = frams_cli.crossOver(geno1, geno2)
34        individual2[0] = frams_cli.crossOver(geno1, geno2)
35        return individual1, individual2
36
37
38def frams_mutate(frams_cli, individual):
39        individual[0] = frams_cli.mutate(individual[0])
40        return individual,
41
42
43def frams_getsimplest(frams_cli, genetic_format):
44        return frams_cli.getSimplest(genetic_format)
45
46
47def prepareToolbox(frams_cli, genetic_format):
48        creator.create("FitnessMax", base.Fitness, weights=[1.0] * len(OPTIMIZATION_CRITERIA))
49        creator.create("Individual", list, fitness=creator.FitnessMax)  # would be nice to have "str" instead of unnecessary "list of str"
50
51        toolbox = base.Toolbox()
52        toolbox.register("attr_simplest_genotype", frams_getsimplest, frams_cli, genetic_format)  # "Attribute generator"
53        # (failed) struggle to have an individual which is a simple str, not a list of str
54        # toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_frams)
55        # https://stackoverflow.com/questions/51451815/python-deap-library-using-random-words-as-individuals
56        # https://github.com/DEAP/deap/issues/339
57        # https://gitlab.com/santiagoandre/deap-customize-population-example/-/blob/master/AGbasic.py
58        # https://groups.google.com/forum/#!topic/deap-users/22g1kyrpKy8
59        toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_simplest_genotype, 1)
60        toolbox.register("population", tools.initRepeat, list, toolbox.individual)
61        toolbox.register("evaluate", frams_evaluate, frams_cli)
62        toolbox.register("mate", frams_crossover, frams_cli)
63        toolbox.register("mutate", frams_mutate, frams_cli)
64        if len(OPTIMIZATION_CRITERIA) <= 1:
65                toolbox.register("select", tools.selTournament, tournsize=5)
66        else:
67                toolbox.register("select", tools.selNSGA2)
68        return toolbox
69
70
71def parseArguments():
72        parser = argparse.ArgumentParser(description='Run this program with "python -u %s" if you want to disable buffering of its output.' % sys.argv[0])
73        parser.add_argument('-path', type=ensureDir, required=True, help='Path to Framsticks CLI without trailing slash.')
74        parser.add_argument('-exe', required=False, help='Executable name. If not given, "frams.exe" or "frams.linux" is assumed.')
75        parser.add_argument('-genformat', required=False, help='Genetic format for the demo run, for example 4, 9, or B. If not given, f1 is assumed.')
76        return parser.parse_args()
77
78
79def ensureDir(string):
80        if os.path.isdir(string):
81                return string
82        else:
83                raise NotADirectoryError(string)
84
85
86if __name__ == "__main__":
87        # A demo run: optimize OPTIMIZATION_CRITERIA
88
89        # random.seed(123)  # see FramsticksCLI.DETERMINISTIC below, set to True if you want full determinism
90        FramsticksCLI.DETERMINISTIC = False  # must be set before FramsticksCLI() constructor call
91        parsed_args = parseArguments()
92        framsCLI = FramsticksCLI(parsed_args.path, parsed_args.exe)
93
94        toolbox = prepareToolbox(framsCLI, '1' if parsed_args.genformat is None else parsed_args.genformat)
95
96        POPSIZE = 10
97        GENERATIONS = 100
98
99        pop = toolbox.population(n=POPSIZE)
100        hof = tools.HallOfFame(5)
101        stats = tools.Statistics(lambda ind: ind.fitness.values)
102        stats.register("avg", np.mean)
103        stats.register("stddev", np.std)
104        stats.register("min", np.min)
105        stats.register("max", np.max)
106
107        print('Evolution with population size %d for %d generations, optimization criteria: %s' % (POPSIZE, GENERATIONS, OPTIMIZATION_CRITERIA))
108        pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.2, mutpb=0.9, ngen=GENERATIONS, stats=stats, halloffame=hof, verbose=True)
109        print('Best individuals:')
110        for best in hof:
111                print(best.fitness, '\t-->\t', best[0])
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