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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_breast_cancer |
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from hyperactive import Hyperactive |
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data = load_breast_cancer() |
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X, y = data.data, data.target |
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def model(para, X_train, y_train): |
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model = GradientBoostingClassifier( |
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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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) |
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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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# this defines the model and hyperparameter search space |
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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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opt = Hyperactive(search_config, n_iter=100, n_jobs=2) |
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# search best hyperparameter for given data |
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opt.fit(X, y) |
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SimonBlanke /
Hyperactive
