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import pytest |
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import numpy as np |
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3
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4
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from ._parametrize import optimizers |
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6
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7
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@pytest.mark.parametrize(*optimizers) |
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8
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def test_results_0(Optimizer): |
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9
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search_space = {"x1": np.arange(-10, 1, 1)} |
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10
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11
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def objective_function(para): |
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12
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score = -para["x1"] * para["x1"] |
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return score |
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14
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15
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initialize = {"random": 2} |
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17
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opt = Optimizer(search_space, initialize=initialize) |
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opt.search( |
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objective_function, |
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n_iter=30, |
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memory=False, |
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verbosity={"print_results": False, "progress_bar": False}, |
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) |
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results_set = set(opt.search_data["x1"]) |
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search_space_set = set(search_space["x1"]) |
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assert results_set.issubset(search_space_set) |
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30
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31
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@pytest.mark.parametrize(*optimizers) |
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def test_results_1(Optimizer): |
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search_space = {"x1": np.arange(-10, 1, 1), "x2": np.arange(-10, 1, 1)} |
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34
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35
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def objective_function(para): |
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36
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score = -(para["x1"] * para["x1"] + para["x2"] * para["x2"]) |
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return score |
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39
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initialize = {"random": 2} |
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40
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41
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opt = Optimizer(search_space, initialize=initialize) |
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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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46
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verbosity={"print_results": False, "progress_bar": False}, |
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47
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) |
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48
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49
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results_set_x1 = set(opt.search_data["x1"]) |
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50
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search_space_set_x1 = set(search_space["x1"]) |
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52
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assert results_set_x1.issubset(search_space_set_x1) |
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53
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54
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results_set_x2 = set(opt.search_data["x2"]) |
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search_space_set_x2 = set(search_space["x2"]) |
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56
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57
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assert results_set_x2.issubset(search_space_set_x2) |
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58
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SimonBlanke /
Gradient-Free-Optimizers
