parser.add_argument('-v', '--validate', dest='validate', default=None, nargs=1, required=False, help="Validate a given model") parser.add_argument('-k', '--classify', dest='classify', default=None, nargs=1, required=False, help="Save classification into given output file") return parser.parse_args() if __name__ == '__main__': app_args = get_args() load_dataset(app_args.num_samples) pyvotune.set_debug(app_args.debug_mode) rng = random.Random() gene_pool = get_gene_pool(rng) if app_args.classify: if not app_args.validate: log.error("Need path to model -v") sys.exit(0) classify_models(app_args.validate[0], app_args.classify[0]) sys.exit(1) elif app_args.validate: validate_models(app_args.validate[0]) sys.exit(1)
log = pyvotune.log.logger() def reproduce(offspring_cs, variator, rng, args): if isinstance(variator, collections.Iterable): for op in variator: offspring_cs = op(random=rng, candidates=offspring_cs, args=args) return offspring_cs else: return [variator(random=rng, candidates=offspring_cs, args=args)] if __name__ == '__main__': pyvotune.set_debug(True) # Dummy data n_features = 28 * 28 rng = random.Random() ################################# # Initialize PyvoTune Generator # ################################# gen = pyvotune.Generate( initial_state={ 'sparse': False }, gene_pool=pyvotune.sklearn.get_classifiers(n_features, rng) + pyvotune.sklearn.get_decomposers(n_features, rng) +
def setUp(self): pyvotune.set_debug(False)
'--classify', dest='classify', default=None, nargs=1, required=False, help="Save classification into given output file") return parser.parse_args() if __name__ == '__main__': app_args = get_args() load_dataset(app_args.num_samples) pyvotune.set_debug(app_args.debug_mode) rng = random.Random() gene_pool = get_gene_pool(rng) if app_args.classify: if not app_args.validate: log.error("Need path to model -v") sys.exit(0) classify_models(app_args.validate[0], app_args.classify[0]) sys.exit(1) elif app_args.validate: validate_models(app_args.validate[0]) sys.exit(1)
return pipeline def test_individual(pipeline, test_X, test_y, display=False): observed_y = pipeline.predict(test_X) f1 = sklearn.metrics.f1_score(test_y, observed_y) if display: print sklearn.metrics.classification_report(test_y, observed_y) return round(f1 * 100., 2) if __name__ == '__main__': pyvotune.set_debug(False) ############################ # Load the initial dataset # ############################ data = sklearn.datasets.load_digits() X = data['data'] y = data['target'] print X.shape # Split the dataset into training, testing and then validation parts train_X, temp_X, train_y, temp_y = train_test_split(X, y, test_size=0.25) test_X, validate_X, test_y, validate_y = train_test_split( temp_X, temp_y, test_size=0.5)
comma_me(err_pct) + "%" print "" print "Actual Solution:" "E = m *", SPEED_OF_LIGHT, "*", SPEED_OF_LIGHT print "Best Solution:", best_eq print "Actual C:", SPEED_OF_LIGHT**2 print "Our C:", best_eq(1) print "Diff:", abs(best_eq(1) - SPEED_OF_LIGHT**2) print "Diff Pct:", round( abs(best_eq(1) - SPEED_OF_LIGHT**2) / (SPEED_OF_LIGHT**2.) * 100, 2) print "Fitness", best.fitness print "MSE", sum_errs / samps if __name__ == '__main__': pyvotune.set_debug(False) gen = pyvotune.Generate(gene_pool=[mass, someconst, oper], max_length=10, noop_frequency=0.2) ea = inspyred.ec.GA(random.Random()) ea.terminator = [ inspyred.ec.terminators.time_termination, inspyred.ec.terminators.average_fitness_termination ] ea.observer = inspyred.ec.observers.stats_observer ea.variator = [ pyvotune.variators.param_reset_mutation,