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import pytest |
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import time |
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import numpy as np |
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import pandas as pd |
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from ._parametrize import optimizers |
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def objective_function(para): |
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score = -(para["x1"] * para["x1"] + para["x2"] * para["x2"]) |
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return score |
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search_space = { |
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"x1": np.arange(-20, 200, 1), |
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"x2": np.arange(-20, 200, 1), |
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} |
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err = 0.001 |
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n_iter = 5 |
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View Code Duplication |
@pytest.mark.parametrize(*optimizers) |
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def test_random_state_0(Optimizer): |
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opt0 = Optimizer(search_space, initialize={"random": 1}) |
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opt0.search( |
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objective_function, |
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n_iter=n_iter, |
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random_state=1, |
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) |
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opt1 = Optimizer(search_space, initialize={"random": 1}) |
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opt1.search( |
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objective_function, |
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n_iter=n_iter, |
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random_state=1, |
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) |
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assert abs(opt0.best_score - opt1.best_score) < err |
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View Code Duplication |
@pytest.mark.parametrize(*optimizers) |
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def test_random_state_1(Optimizer): |
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opt0 = Optimizer(search_space, initialize={"random": 1}) |
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opt0.search( |
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objective_function, |
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n_iter=n_iter, |
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random_state=10, |
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) |
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opt1 = Optimizer(search_space, initialize={"random": 1}) |
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opt1.search( |
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objective_function, |
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n_iter=n_iter, |
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random_state=10, |
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) |
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assert abs(opt0.best_score - opt1.best_score) < err |
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View Code Duplication |
@pytest.mark.parametrize(*optimizers) |
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def test_random_state_2(Optimizer): |
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opt0 = Optimizer(search_space, initialize={"random": 1}) |
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opt0.search( |
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objective_function, |
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n_iter=n_iter, |
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random_state=1, |
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) |
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opt1 = Optimizer(search_space, initialize={"random": 1}) |
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opt1.search( |
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objective_function, |
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n_iter=n_iter, |
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random_state=10, |
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) |
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assert abs(opt0.best_score - opt1.best_score) > err |
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82
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@pytest.mark.parametrize(*optimizers) |
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def test_no_random_state_0(Optimizer): |
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opt0 = Optimizer(search_space, initialize={"random": 1}) |
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opt0.search(objective_function, n_iter=n_iter) |
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opt1 = Optimizer(search_space, initialize={"random": 1}) |
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opt1.search(objective_function, n_iter=n_iter) |
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90
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assert abs(opt0.best_score - opt1.best_score) > err |
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SimonBlanke /
Gradient-Free-Optimizers
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