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import pytest |
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import time |
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import numpy as np |
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4
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from sklearn.datasets import load_breast_cancer |
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5
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from sklearn.model_selection import cross_val_score |
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from sklearn.tree import DecisionTreeClassifier |
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from ._parametrize import optimizers |
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10
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11
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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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15
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search_space = { |
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"x1": np.arange(0, 100000, 0.1), |
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} |
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View Code Duplication |
@pytest.mark.parametrize(*optimizers) |
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def test_max_score_0(Optimizer): |
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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, 100, 0.1), |
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} |
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max_score = -9999 |
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opt = Optimizer( |
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search_space, |
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initialize={"warm_start": [{"x1": 99}]}, |
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) |
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opt.search( |
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objective_function, |
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n_iter=100, |
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max_score=max_score, |
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) |
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print("\n Results head \n", opt.search_data.head()) |
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print("\n Results tail \n", opt.search_data.tail()) |
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print("\nN iter:", len(opt.search_data)) |
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assert -100 > opt.best_score > max_score |
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50
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51
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View Code Duplication |
@pytest.mark.parametrize(*optimizers) |
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def test_max_score_1(Optimizer): |
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def objective_function(para): |
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score = -para["x1"] * para["x1"] |
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time.sleep(0.01) |
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return score |
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search_space = { |
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"x1": np.arange(0, 100, 0.1), |
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} |
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max_score = -9999 |
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64
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c_time = time.time() |
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opt = Optimizer(search_space, initialize={"warm_start": [{"x1": 99}]}) |
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opt.search( |
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objective_function, |
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n_iter=100000, |
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max_score=max_score, |
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) |
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diff_time = time.time() - c_time |
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print("\n Results head \n", opt.search_data.head()) |
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print("\n Results tail \n", opt.search_data.tail()) |
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print("\nN iter:", len(opt.search_data)) |
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assert diff_time < 1 |
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SimonBlanke /
Gradient-Free-Optimizers
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