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| import numpy as np | |
| from hyperopt import hp, fmin, tpe, Trials | |
| binary_operators = ["*", "/", "+", "-"] | |
| unary_operators = ["sin", "cos", "exp", "log"] | |
| space = dict( | |
| # model_selection="best", | |
| model_selection=hp.choice("model_selection", ["accuracy"]), | |
| # binary_operators=None, | |
| binary_operators=hp.choice("binary_operators", [binary_operators]), | |
| # unary_operators=None, | |
| unary_operators=hp.choice("unary_operators", [unary_operators]), | |
| # populations=100, | |
| populations=hp.qloguniform("populations", np.log(10), np.log(1000), 1), | |
| # niterations=4, | |
| niterations=hp.choice( | |
| "niterations", [10000] | |
| ), # We will quit automatically based on a clock. | |
| # ncyclesperiteration=100, | |
| ncyclesperiteration=hp.qloguniform( | |
| "ncyclesperiteration", np.log(10), np.log(5000), 1 | |
| ), | |
| # alpha=0.1, | |
| alpha=hp.loguniform("alpha", np.log(0.0001), np.log(1000)), | |
| # annealing=False, | |
| annealing=hp.choice("annealing", [False, True]), | |
| # fraction_replaced=0.01, | |
| fraction_replaced=hp.loguniform("fraction_replaced", np.log(0.0001), np.log(0.5)), | |
| # fraction_replaced_hof=0.005, | |
| fraction_replaced_hof=hp.loguniform( | |
| "fraction_replaced_hof", np.log(0.0001), np.log(0.5) | |
| ), | |
| # population_size=100, | |
| population_size=hp.qloguniform("population_size", np.log(20), np.log(1000), 1), | |
| # parsimony=1e-4, | |
| parsimony=hp.loguniform("parsimony", np.log(0.0001), np.log(0.5)), | |
| # topn=10, | |
| topn=hp.qloguniform("topn", np.log(2), np.log(50), 1), | |
| # weight_add_node=1, | |
| weight_add_node=hp.loguniform("weight_add_node", np.log(0.0001), np.log(100)), | |
| # weight_insert_node=3, | |
| weight_insert_node=hp.loguniform("weight_insert_node", np.log(0.0001), np.log(100)), | |
| # weight_delete_node=3, | |
| weight_delete_node=hp.loguniform("weight_delete_node", np.log(0.0001), np.log(100)), | |
| # weight_do_nothing=1, | |
| weight_do_nothing=hp.loguniform("weight_do_nothing", np.log(0.0001), np.log(100)), | |
| # weight_mutate_constant=10, | |
| weight_mutate_constant=hp.loguniform( | |
| "weight_mutate_constant", np.log(0.0001), np.log(100) | |
| ), | |
| # weight_mutate_operator=1, | |
| weight_mutate_operator=hp.loguniform( | |
| "weight_mutate_operator", np.log(0.0001), np.log(100) | |
| ), | |
| # weight_randomize=1, | |
| weight_randomize=hp.loguniform("weight_randomize", np.log(0.0001), np.log(100)), | |
| # weight_simplify=0.002, | |
| weight_simplify=hp.choice("weight_simplify", [0.002]), # One of these is fixed. | |
| # crossover_probability=0.01, | |
| crossover_probability=hp.loguniform( | |
| "crossover_probability", np.log(0.00001), np.log(0.2) | |
| ), | |
| # perturbation_factor=1.0, | |
| perturbation_factor=hp.loguniform( | |
| "perturbation_factor", np.log(0.0001), np.log(100) | |
| ), | |
| # maxsize=20, | |
| maxsize=hp.choice("maxsize", [30]), | |
| # warmup_maxsize_by=0.0, | |
| warmup_maxsize_by=hp.uniform("warmup_maxsize_by", 0.0, 0.5), | |
| # use_frequency=True, | |
| use_frequency=hp.choice("use_frequency", [True, False]), | |
| # optimizer_nrestarts=3, | |
| optimizer_nrestarts=hp.quniform("optimizer_nrestarts", 1, 10, 1), | |
| # optimize_probability=1.0, | |
| optimize_probability=hp.uniform("optimize_probability", 0.0, 1.0), | |
| # optimizer_iterations=10, | |
| optimizer_iterations=hp.quniform("optimizer_iterations", 1, 10, 1), | |
| # tournament_selection_p=1.0, | |
| tournament_selection_p=hp.uniform("tournament_selection_p", 0.0, 1.0), | |
| ) | |