import argparse import sys import numpy as np from FramsticksLib import FramsticksLib, DissimMethod from ..frams_base.experiment_frams_niching import ExperimentFramsNiching from ..frams_base.experiment_frams_islands import ExperimentFramsIslands from ..numerical_example.numerical_example import ExperimentNumerical from ..numerical_example.numerical_islands_example import ExperimentNumericalIslands from ..structures.individual import Individual from ..utils import ensureDir GENERATIONS = 10 SETTINGS_TO_TEST_NUMERIC = { 'hof_size': [0, 10], 'popsize': [8], 'archive': [8], 'pmut': [0.7], 'pxov': [0.2], 'tournament': [5], 'initialgenotype':[np.array([100, 100, 100, 100]), np.array([-100,-100])] } SETTINGS_TO_TEST_NUMERIC_ISLAND = { 'hof_size': [0, 10], 'popsize': [8], 'archive': [8], 'pmut': [0.7], 'pxov': [0.2], 'tournament': [5], 'migration_interval': [1,5], 'number_of_populations':[1,5], 'initialgenotype':[np.array([100, 100, 100, 100]), np.array([-100,-100])] } SETTINGS_TO_TEST_FRAMS_NICHING = { 'opt': ['velocity', 'vertpos'], 'max_numparts': [None], 'max_numjoints': [20], 'max_numneurons': [20], 'max_numconnections': [None], 'max_numgenochars': [20], 'hof_size': [0, 10], 'normalize': ['none', 'max', 'sum'], 'dissim': [DissimMethod.GENE_LEVENSHTEIN],# DissimMethod.PHENE_STRUCT_OPTIM, DissimMethod.PHENE_DESCRIPTORS, DissimMethod.PHENE_DENSITY_COUNT, DissimMethod.PHENE_DENSITY_FREQ], 'crowding_dissim': [DissimMethod.FITNESS, DissimMethod.GENE_LEVENSHTEIN, DissimMethod.PHENE_STRUCT_OPTIM],#, DissimMethod.PHENE_STRUCT_OPTIM, DissimMethod.PHENE_DESCRIPTORS, DissimMethod.PHENE_DENSITY_COUNT, DissimMethod.PHENE_DENSITY_FREQ], 'fit': ['niching', 'novelty', 'nsga2', 'nslc', 'raw'], 'genformat': ['1'], 'popsize': [8], 'archive': [8], 'initialgenotype': [None], 'pmut': [0.7], 'pxov': [0.2], 'tournament': [5] } SETTINGS_TO_TEST_FRAMS_ISLANDS = { 'opt': ['velocity', 'vertpos'], 'max_numparts': [None], 'max_numjoints': [20], 'max_numneurons': [20], 'max_numconnections': [None], 'max_numgenochars': [20], 'hof_size': [0, 10], 'migration_interval': [1,5], 'number_of_populations':[1,5], 'genformat': ['1'], 'popsize': [8], 'initialgenotype': [None], 'pmut': [0.7], 'pxov': [0.2], 'tournament': [5] } def test_run_experiment_numerical(params): # multiple criteria not supported here. If needed, use FramsticksEvolution.py experiment = ExperimentNumerical( hof_size=params['hof_size'], popsize=params['popsize'], save_only_best=True,) experiment.evolve(hof_savefile=None, generations=GENERATIONS, initialgenotype=params['initialgenotype'], pmut=params['pmut'], pxov=params['pxov'], tournament_size=params['tournament']) def test_run_experiment_numerical_islands(params): # multiple criteria not supported here. If needed, use FramsticksEvolution.py experiment = ExperimentNumericalIslands(hof_size=params['hof_size'], popsize=params['popsize'], save_only_best=True, migration_interval=params['migration_interval'], number_of_populations=params['number_of_populations']) experiment.evolve(hof_savefile=None, generations=GENERATIONS, initialgenotype=params['initialgenotype'], pmut=params['pmut'], pxov=params['pxov'], tournament_size=params['tournament']) def test_run_experiment_frams_niching(params): # multiple criteria not supported here. If needed, use FramsticksEvolution.py opt_criteria = params['opt'].split(",") framsLib = FramsticksLib( parsed_args.path, parsed_args.lib, parsed_args.sim) constrains = {"max_numparts": params['max_numparts'], "max_numjoints": params['max_numjoints'], "max_numneurons": params['max_numneurons'], "max_numconnections": params['max_numconnections'], "max_numgenochars": params['max_numgenochars'], } old_fitness_set_negative_to_zero = Individual.fitness_set_negative_to_zero # save a copy of the current value to restore later Individual.fitness_set_negative_to_zero = True # niching must have it set to True, see "-fitness_set_negative_to_zero" argument in experiment_abc.py experiment = ExperimentFramsNiching(frams_lib=framsLib, optimization_criteria=opt_criteria, hof_size=params['hof_size'], constraints=constrains, normalize=params['normalize'], dissim=params['dissim'], crowding_dissim=params['crowding_dissim'], fit=params['fit'], genformat=params['genformat'], popsize=params['popsize'], archive_size=params['archive'], save_only_best=True, knn_niching=5, knn_nslc=5) experiment.evolve(hof_savefile=None, generations=GENERATIONS, initialgenotype=params['initialgenotype'], pmut=params['pmut'], pxov=params['pxov'], tournament_size=params['tournament']) Individual.fitness_set_negative_to_zero = old_fitness_set_negative_to_zero # restore original value def test_run_experiment_frams_island(params): # multiple criteria not supported here. If needed, use FramsticksEvolution.py opt_criteria = params['opt'].split(",") framsLib = FramsticksLib( parsed_args.path, parsed_args.lib, parsed_args.sim) constrains = {"max_numparts": params['max_numparts'], "max_numjoints": params['max_numjoints'], "max_numneurons": params['max_numneurons'], "max_numconnections": params['max_numconnections'], "max_numgenochars": params['max_numgenochars'], } experiment = ExperimentFramsIslands(frams_lib=framsLib, optimization_criteria=opt_criteria, hof_size=params['hof_size'], constraints=constrains, genformat=params['genformat'], popsize=params['popsize'], migration_interval=params['migration_interval'], number_of_populations=params['number_of_populations'], save_only_best=True) experiment.evolve(hof_savefile=None, generations=GENERATIONS, initialgenotype=params['initialgenotype'], pmut=params['pmut'], pxov=params['pxov'], tournament_size=params['tournament']) def parseArguments(): parser = argparse.ArgumentParser( description='Run this program with "python -u %s" if you want to disable buffering of its output.' % sys.argv[0]) parser.add_argument('-path', type=ensureDir, required=True, help='Path to Framsticks CLI without trailing slash.') parser.add_argument('-lib', required=False, help='Library name. If not given, "frams-objects.dll" or "frams-objects.so" is assumed depending on the platform.') parser.add_argument('-sim', required=False, default="eval-allcriteria.sim", 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. If you want to provide more files, separate them with a semicolon ';'.") return parser.parse_args() def get_params_sets(settings): params_sets = [] for k in settings.keys(): temp_param_set = [] for value in settings[k]: if params_sets: for exsiting_set in params_sets: copy_of_set = exsiting_set.copy() copy_of_set[k] = value temp_param_set.append(copy_of_set) else: temp_param_set.append({k: value}) params_sets = temp_param_set return params_sets def cover_to_test(params, run_exp): run_exp(params) return 1 def run_tests(): results = [] print("TESTING NUMERICAL") params_sets = get_params_sets(SETTINGS_TO_TEST_NUMERIC) print(f"Starting executing {len(params_sets)} experiments") results.extend([cover_to_test(params, test_run_experiment_numerical) for params in params_sets]) print("TESTING NUMERICAL ISLANDS") params_sets = get_params_sets(SETTINGS_TO_TEST_NUMERIC_ISLAND) print(f"Starting executing {len(params_sets)} experiments") results.extend([cover_to_test(params,test_run_experiment_numerical_islands) for params in params_sets]) print("TESTING FRAMS NICHING") params_sets = get_params_sets(SETTINGS_TO_TEST_FRAMS_NICHING) print(f"Starting executing {len(params_sets)} experiments") results.extend([cover_to_test(params, test_run_experiment_frams_niching) for params in params_sets]) print("TESTING FRAMS ISLANDS") params_sets = get_params_sets(SETTINGS_TO_TEST_FRAMS_ISLANDS) print(f"Starting executing {len(params_sets)} experiments") results.extend([cover_to_test(params,test_run_experiment_frams_island) for params in params_sets]) print(f"Passed tests: {sum(results)} / {len(results)}") if __name__ == "__main__": parsed_args = parseArguments() run_tests()