1 | import argparse
|
---|
2 | import os
|
---|
3 | import sys
|
---|
4 | import numpy as np
|
---|
5 | from deap import creator, base, tools, algorithms
|
---|
6 | from FramsticksLib import FramsticksLib
|
---|
7 |
|
---|
8 | # Note: this may be less efficient than running the evolution directly in Framsticks, so if performance is key, compare both options.
|
---|
9 |
|
---|
10 |
|
---|
11 | # The list of criteria includes 'vertpos', 'velocity', 'distance', 'vertvel', 'lifespan', 'numjoints', 'numparts', 'numneurons', 'numconnections'.
|
---|
12 | OPTIMIZATION_CRITERIA = ['vertpos'] # Single or multiple criteria. Names from the standard-eval.expdef dictionary, e.g. ['vertpos', 'velocity'].
|
---|
13 |
|
---|
14 |
|
---|
15 | def frams_evaluate(frams_cli, individual):
|
---|
16 | genotype = individual[0] # individual[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 an invalid genotype (such as X[@][@] with "Don't simulate genotypes with warnings" option) or for some other reason
|
---|
25 | fitness = [-1] * len(OPTIMIZATION_CRITERIA) # fitness of -1 is intended to discourage further propagation of this genotype via selection ("this one is very poor")
|
---|
26 | print("Error '%s': could not evaluate genotype '%s', returning fitness %s" % (str(e), genotype, fitness))
|
---|
27 | return fitness
|
---|
28 |
|
---|
29 |
|
---|
30 | def frams_crossover(frams_cli, individual1, individual2):
|
---|
31 | geno1 = individual1[0] # individual[0] because we can't (?) have a simple str as a deap genotype/individual, only list of str.
|
---|
32 | geno2 = individual2[0] # individual[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 |
|
---|
38 | def frams_mutate(frams_cli, individual):
|
---|
39 | individual[0] = frams_cli.mutate([individual[0]])[0] # individual[0] because we can't (?) have a simple str as a deap genotype/individual, only list of str.
|
---|
40 | return individual,
|
---|
41 |
|
---|
42 |
|
---|
43 | def frams_getsimplest(frams_cli, genetic_format):
|
---|
44 | return frams_cli.getSimplest(genetic_format)
|
---|
45 |
|
---|
46 |
|
---|
47 | def 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 |
|
---|
71 | def 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('-simsettings', required=False, help='The name of the .sim file with settings for evaluation, mutation, crossover, and similarity estimation. If not given, "eval-allcriteria.sim" is assumed by default. Must be compatible with the "standard-eval" expdef.')
|
---|
76 | 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.')
|
---|
77 | return parser.parse_args()
|
---|
78 |
|
---|
79 |
|
---|
80 | def ensureDir(string):
|
---|
81 | if os.path.isdir(string):
|
---|
82 | return string
|
---|
83 | else:
|
---|
84 | raise NotADirectoryError(string)
|
---|
85 |
|
---|
86 |
|
---|
87 | if __name__ == "__main__":
|
---|
88 | # A demo run: optimize OPTIMIZATION_CRITERIA
|
---|
89 |
|
---|
90 | # random.seed(123) # see FramsticksLib.DETERMINISTIC below, set to True if you want full determinism
|
---|
91 | FramsticksLib.DETERMINISTIC = False # must be set before FramsticksLib() constructor call
|
---|
92 | parsed_args = parseArguments()
|
---|
93 | framsLib = FramsticksLib(parsed_args.path, parsed_args.exe, parsed_args.simsettings)
|
---|
94 |
|
---|
95 | toolbox = prepareToolbox(framsLib, '1' if parsed_args.genformat is None else parsed_args.genformat)
|
---|
96 |
|
---|
97 | POPSIZE = 10
|
---|
98 | GENERATIONS = 100
|
---|
99 |
|
---|
100 | pop = toolbox.population(n=POPSIZE)
|
---|
101 | hof = tools.HallOfFame(5)
|
---|
102 | stats = tools.Statistics(lambda ind: ind.fitness.values)
|
---|
103 | stats.register("avg", np.mean)
|
---|
104 | stats.register("stddev", np.std)
|
---|
105 | stats.register("min", np.min)
|
---|
106 | stats.register("max", np.max)
|
---|
107 |
|
---|
108 | print('Evolution with population size %d for %d generations, optimization criteria: %s' % (POPSIZE, GENERATIONS, OPTIMIZATION_CRITERIA))
|
---|
109 | pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.2, mutpb=0.9, ngen=GENERATIONS, stats=stats, halloffame=hof, verbose=True)
|
---|
110 | print('Best individuals:')
|
---|
111 | for best in hof:
|
---|
112 | print(best.fitness, '\t-->\t', best[0])
|
---|