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from sklearn.model_selection import cross_val_score |
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from sklearn.ensemble import GradientBoostingClassifier |
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from sklearn.datasets import load_iris |
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from hyperactive import Hyperactive |
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5
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6
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iris_data = load_iris() |
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7
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X = iris_data.data |
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y = iris_data.target |
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9
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10
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def model(para, X_train, y_train): |
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11
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model = GradientBoostingClassifier( |
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12
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n_estimators=para["n_estimators"], |
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max_depth=para["max_depth"], |
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min_samples_split=para["min_samples_split"], |
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15
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) |
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16
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scores = cross_val_score(model, X_train, y_train, cv=3) |
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return scores.mean(), model |
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20
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search_config = { |
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model: { |
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"n_estimators": range(10, 200, 10), |
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"max_depth": range(2, 12), |
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"min_samples_split": range(2, 12), |
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} |
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} |
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# Without scatter initialization |
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opt = SimulatedAnnealingOptimizer( |
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30
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search_config, optimizer="HillClimbing", n_iter=20, random_state=0, scatter_init=False |
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) |
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opt.fit(X, y) |
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35
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# With scatter initialization |
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opt = Hyperactive( |
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search_config, optimizer="HillClimbing", n_iter=20, random_state=0, scatter_init=True |
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) |
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opt.fit(X, y) |
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SimonBlanke /
Hyperactive
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