Total Complexity | 0 |
Total Lines | 38 |
Duplicated Lines | 0 % |
Changes | 0 |
1 | import numpy as np |
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2 | from sklearn.datasets import load_diabetes |
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3 | from sklearn.tree import DecisionTreeRegressor |
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4 | |||
5 | |||
6 | from hyperactive.search_config import SearchConfig |
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7 | from hyperactive.optimization.gradient_free_optimizers import ( |
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8 | HillClimbingOptimizer, |
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9 | RandomRestartHillClimbingOptimizer, |
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10 | RandomSearchOptimizer, |
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11 | ) |
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12 | from hyperactive.optimization.talos import TalosOptimizer |
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13 | |||
14 | from experiments.sklearn import SklearnExperiment |
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15 | from experiments.test_function import AckleyFunction |
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16 | |||
17 | |||
18 | data = load_diabetes() |
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19 | X, y = data.data, data.target |
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20 | |||
21 | |||
22 | search_config1 = SearchConfig( |
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23 | max_depth=list(np.arange(2, 15, 1)), |
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24 | min_samples_split=list(np.arange(2, 25, 2)), |
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25 | ) |
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26 | |||
27 | |||
28 | TalosOptimizer() |
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29 | |||
30 | experiment1 = SklearnExperiment() |
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31 | experiment1.setup(DecisionTreeRegressor, X, y, cv=4) |
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32 | |||
33 | |||
34 | optimizer = HillClimbingOptimizer() |
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35 | optimizer.add_search(experiment1, search_config1, n_iter=100) |
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36 | hyper = optimizer |
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37 | hyper.run(max_time=5) |
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38 |