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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]),
# fractionReplaced=0.01,
fractionReplaced=hp.loguniform("fractionReplaced", np.log(0.0001), np.log(0.5)),
# fractionReplacedHof=0.005,
fractionReplacedHof=hp.loguniform(
"fractionReplacedHof", np.log(0.0001), np.log(0.5)
),
# npop=100,
npop=hp.qloguniform("npop", 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),
# weightAddNode=1,
weightAddNode=hp.loguniform("weightAddNode", np.log(0.0001), np.log(100)),
# weightInsertNode=3,
weightInsertNode=hp.loguniform("weightInsertNode", np.log(0.0001), np.log(100)),
# weightDeleteNode=3,
weightDeleteNode=hp.loguniform("weightDeleteNode", np.log(0.0001), np.log(100)),
# weightDoNothing=1,
weightDoNothing=hp.loguniform("weightDoNothing", np.log(0.0001), np.log(100)),
# weightMutateConstant=10,
weightMutateConstant=hp.loguniform(
"weightMutateConstant", np.log(0.0001), np.log(100)
),
# weightMutateOperator=1,
weightMutateOperator=hp.loguniform(
"weightMutateOperator", np.log(0.0001), np.log(100)
),
# weightRandomize=1,
weightRandomize=hp.loguniform("weightRandomize", np.log(0.0001), np.log(100)),
# weightSimplify=0.002,
weightSimplify=hp.choice("weightSimplify", [0.002]), # One of these is fixed.
# crossoverProbability=0.01,
crossoverProbability=hp.loguniform(
"crossoverProbability", np.log(0.00001), np.log(0.2)
),
# perturbationFactor=1.0,
perturbationFactor=hp.loguniform("perturbationFactor", np.log(0.0001), np.log(100)),
# maxsize=20,
maxsize=hp.choice("maxsize", [30]),
# warmupMaxsizeBy=0.0,
warmupMaxsizeBy=hp.uniform("warmupMaxsizeBy", 0.0, 0.5),
# useFrequency=True,
useFrequency=hp.choice("useFrequency", [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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