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from gradient_free_optimizers.optimizers.core_optimizer import search_tracker |
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import pytest |
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import numpy as np |
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from gradient_free_optimizers import ( |
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HillClimbingOptimizer, |
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StochasticHillClimbingOptimizer, |
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RepulsingHillClimbingOptimizer, |
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SimulatedAnnealingOptimizer, |
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DownhillSimplexOptimizer, |
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RandomSearchOptimizer, |
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GridSearchOptimizer, |
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RandomRestartHillClimbingOptimizer, |
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PowellsMethod, |
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PatternSearch, |
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RandomAnnealingOptimizer, |
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ParallelTemperingOptimizer, |
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ParticleSwarmOptimizer, |
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EvolutionStrategyOptimizer, |
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BayesianOptimizer, |
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TreeStructuredParzenEstimators, |
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ForestOptimizer, |
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) |
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optimizers = ( |
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"Optimizer", |
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[ |
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(HillClimbingOptimizer), |
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(StochasticHillClimbingOptimizer), |
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(RepulsingHillClimbingOptimizer), |
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(SimulatedAnnealingOptimizer), |
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(DownhillSimplexOptimizer), |
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(RandomSearchOptimizer), |
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(GridSearchOptimizer), |
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(RandomRestartHillClimbingOptimizer), |
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(PowellsMethod), |
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(PatternSearch), |
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(RandomAnnealingOptimizer), |
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(ParallelTemperingOptimizer), |
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(ParticleSwarmOptimizer), |
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(EvolutionStrategyOptimizer), |
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(BayesianOptimizer), |
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(TreeStructuredParzenEstimators), |
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(ForestOptimizer), |
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], |
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) |
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def objective_function(para): |
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score = -para["x1"] * para["x1"] |
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return score |
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search_space = { |
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"x1": np.arange(0, 10, 1), |
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} |
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@pytest.mark.parametrize(*optimizers) |
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def test_opt_algos_0(Optimizer): |
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opt = Optimizer(search_space) |
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opt.search(objective_function, n_iter=15) |
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_ = opt.best_para |
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_ = opt.best_score |
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_ = opt.search_data |
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SimonBlanke /
Gradient-Free-Optimizers
