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import numpy as np
from hyperopt import Trials, fmin, hp, tpe
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),
)
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