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import pytest |
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from tqdm import tqdm |
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import numpy as np |
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from ._parametrize import pytest_parameter |
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View Code Duplication |
@pytest.mark.parametrize(*pytest_parameter) |
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def test_exploration_0(Optimizer): |
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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(-50, 1, 1), |
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"x2": np.arange(0, 10, 1), |
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} |
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init1 = { |
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"x1": -50, |
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"x2": 1, |
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} |
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init2 = { |
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"x1": -49, |
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"x2": 2, |
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} |
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opt = Optimizer(search_space) |
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opt.search( |
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objective_function, |
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n_iter=50, |
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memory=False, |
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verbosity={"print_results": False, "progress_bar": False,}, |
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initialize={"warm_start": [init1, init2]}, |
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) |
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uniques_2nd_dim = list(opt.results["x2"].values) |
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print("\n uniques_2nd_dim \n", uniques_2nd_dim, "\n") |
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print("\n Results head \n", opt.results.head()) |
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print("\n Results tail \n", opt.results.tail()) |
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print("\nN iter:", len(opt.results)) |
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assert 0 in uniques_2nd_dim |
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View Code Duplication |
@pytest.mark.parametrize(*pytest_parameter) |
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def test_exploration_1(Optimizer): |
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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(-50, 1, 1), |
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"x2": np.arange(-10, 1, 1), |
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} |
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init1 = { |
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"x1": -50, |
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"x2": -1, |
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} |
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init2 = { |
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"x1": -49, |
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"x2": -2, |
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} |
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opt = Optimizer(search_space) |
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opt.search( |
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objective_function, |
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n_iter=50, |
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memory=False, |
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verbosity={"print_results": False, "progress_bar": False,}, |
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initialize={"warm_start": [init1]}, |
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) |
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uniques_2nd_dim = list(opt.results["x2"].values) |
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print("\n uniques_2nd_dim \n", uniques_2nd_dim, "\n") |
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print("\n Results head \n", opt.results.head()) |
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print("\n Results tail \n", opt.results.tail()) |
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print("\nN iter:", len(opt.results)) |
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assert 0 in uniques_2nd_dim |
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