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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import time |
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import pytest |
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import random |
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import numpy as np |
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from gradient_free_optimizers import BayesianOptimizer |
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from sklearn.gaussian_process import GaussianProcessRegressor |
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from sklearn.gaussian_process.kernels import Matern, WhiteKernel, RBF |
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from ._base_para_test import _base_para_test_func |
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from gradient_free_optimizers import RandomSearchOptimizer |
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def objective_function_nan(para): |
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rand = random.randint(0, 1) |
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if rand == 0: |
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return 1 |
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else: |
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return np.nan |
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def objective_function_m_inf(para): |
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rand = random.randint(0, 1) |
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if rand == 0: |
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return 1 |
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else: |
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return -np.inf |
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def objective_function_inf(para): |
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rand = random.randint(0, 1) |
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if rand == 0: |
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return 1 |
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else: |
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return np.inf |
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search_space_ = {"x1": np.arange(0, 20, 1)} |
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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 = {"x1": np.arange(-10, 11, 1)} |
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search_space2 = {"x1": np.arange(-10, 51, 1)} |
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search_space3 = {"x1": np.arange(-50, 11, 1)} |
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opt1 = RandomSearchOptimizer(search_space) |
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opt2 = RandomSearchOptimizer(search_space2) |
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opt3 = RandomSearchOptimizer(search_space3) |
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opt4 = RandomSearchOptimizer(search_space_) |
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opt5 = RandomSearchOptimizer(search_space_) |
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opt6 = RandomSearchOptimizer(search_space_) |
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opt1.search(objective_function, n_iter=30) |
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opt2.search(objective_function, n_iter=30) |
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opt3.search(objective_function, n_iter=30) |
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opt4.search(objective_function_nan, n_iter=30) |
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opt5.search(objective_function_m_inf, n_iter=30) |
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opt6.search(objective_function_inf, n_iter=30) |
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search_data1 = opt1.search_data |
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search_data2 = opt2.search_data |
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search_data3 = opt3.search_data |
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search_data4 = opt4.search_data |
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search_data5 = opt5.search_data |
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search_data6 = opt6.search_data |
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View Code Duplication |
class GPR: |
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def __init__(self): |
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nu_param = 0.5 |
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matern = Matern( |
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# length_scale=length_scale_param, |
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# length_scale_bounds=length_scale_bounds_param, |
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nu=nu_param, |
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) |
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self.gpr = GaussianProcessRegressor( |
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kernel=matern + RBF() + WhiteKernel(), n_restarts_optimizer=1 |
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) |
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def fit(self, X, y): |
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self.gpr.fit(X, y) |
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def predict(self, X, return_std=False): |
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return self.gpr.predict(X, return_std=return_std) |
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bayesian_optimizer_para = [ |
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({"gpr": GPR()}), |
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({"xi": 0.001}), |
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({"xi": 0.5}), |
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({"xi": 0.9}), |
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({"warm_start_smbo": None}), |
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({"warm_start_smbo": search_data1}), |
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({"warm_start_smbo": search_data2}), |
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({"warm_start_smbo": search_data3}), |
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({"warm_start_smbo": search_data4}), |
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({"warm_start_smbo": search_data5}), |
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({"warm_start_smbo": search_data6}), |
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({"max_sample_size": 10000000}), |
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({"max_sample_size": 10000}), |
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({"max_sample_size": 1000000000}), |
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({"sampling": False}), |
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({"sampling": {"random": 1}}), |
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({"sampling": {"random": 100000000}}), |
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({"rand_rest_p": 0}), |
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({"rand_rest_p": 0.5}), |
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({"rand_rest_p": 1}), |
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({"rand_rest_p": 10}), |
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({"replacement": True}), |
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({"replacement": False}), |
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] |
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pytest_wrapper = ("opt_para", bayesian_optimizer_para) |
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@pytest.mark.parametrize(*pytest_wrapper) |
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def test_bayesian_para(opt_para): |
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_base_para_test_func(opt_para, BayesianOptimizer) |
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def test_warm_start_0(): |
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opt = BayesianOptimizer(search_space, warm_start_smbo=search_data1) |
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assert len(opt.X_sample) == 30 |
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SimonBlanke /
Gradient-Free-Optimizers
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