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# disables sklearn warnings |
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def warn(*args, **kwargs): |
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pass |
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import warnings |
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warnings.warn = warn |
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from sklearn.datasets import load_breast_cancer |
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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.ensemble import RandomForestClassifier |
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from sklearn.ensemble import ExtraTreesClassifier |
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from xgboost import XGBClassifier |
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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 GradientBoostingClassifier_(para, X, y): |
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gbc = GradientBoostingClassifier( |
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n_estimators=para["n_estimators"], max_depth=para["max_depth"], min_samples_split=para["min_samples_split"], min_samples_leaf=para["min_samples_leaf"] |
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) |
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scores = cross_val_score(gbc, X, y, cv=3) |
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return scores.mean() |
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def RandomForestClassifier_(para, X, y): |
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rfc = RandomForestClassifier( |
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n_estimators=para["n_estimators"], max_depth=para["max_depth"], min_samples_split=para["min_samples_split"], min_samples_leaf=para["min_samples_leaf"] |
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) |
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scores = cross_val_score(rfc, X, y, cv=3) |
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return scores.mean() |
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def ExtraTreesClassifier_(para, X, y): |
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etc = ExtraTreesClassifier( |
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n_estimators=para["n_estimators"], max_depth=para["max_depth"], min_samples_split=para["min_samples_split"], min_samples_leaf=para["min_samples_leaf"] |
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) |
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scores = cross_val_score(etc, X, y, cv=3) |
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return scores.mean() |
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def XGBoost_(para, X, y): |
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etc = XGBClassifier( |
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n_estimators=para["n_estimators"], max_depth=para["max_depth"] |
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) |
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scores = cross_val_score(etc, X, y, cv=3) |
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return scores.mean() |
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search_config = { |
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GradientBoostingClassifier_: { |
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"n_estimators": range(50, 300, 5), |
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"max_depth": range(2, 10), |
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"min_samples_split": range(2, 20), |
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"min_samples_leaf": range(2, 20), |
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}, |
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RandomForestClassifier_: { |
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"n_estimators": range(5, 100, 1), |
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"max_depth": range(2, 20), |
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"min_samples_split": range(2, 20), |
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"min_samples_leaf": range(2, 20), |
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}, |
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ExtraTreesClassifier_: { |
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"n_estimators": range(50, 300, 5), |
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"max_depth": range(2, 20), |
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"min_samples_split": range(2, 20), |
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"min_samples_leaf": range(2, 20), |
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}, |
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XGBoost_: { |
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"n_estimators": range(50, 300, 5), |
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"max_depth": range(2, 20), |
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}, |
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} |
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opt = Hyperactive(search_config, n_jobs=4, n_iter=100) |
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opt.search(X, y) |
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SimonBlanke /
Hyperactive
