SimonBlanke /
Hyperactive
| @@ 41-65 (lines=25) @@ | ||
| 38 | opt.search(X, y) |
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| 39 | ||
| 40 | ||
| 41 | def test_sklearn(): |
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| 42 | from sklearn.tree import DecisionTreeClassifier |
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| 43 | ||
| 44 | def model(para, X_train, y_train): |
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| 45 | model = DecisionTreeClassifier( |
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| 46 | criterion=para["criterion"], |
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| 47 | max_depth=para["max_depth"], |
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| 48 | min_samples_split=para["min_samples_split"], |
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| 49 | min_samples_leaf=para["min_samples_leaf"], |
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| 50 | ) |
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| 51 | scores = cross_val_score(model, X_train, y_train, cv=3) |
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| 52 | ||
| 53 | return scores.mean() |
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| 54 | ||
| 55 | search_config = { |
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| 56 | model: { |
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| 57 | "criterion": ["gini", "entropy"], |
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| 58 | "max_depth": range(1, 21), |
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| 59 | "min_samples_split": range(2, 21), |
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| 60 | "min_samples_leaf": range(1, 21), |
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| 61 | } |
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| 62 | } |
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| 63 | ||
| 64 | opt = Hyperactive(search_config) |
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| 65 | opt.search(X, y) |
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| 66 | # opt.predict(X) |
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| 67 | # opt.score(X, y) |
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| 68 | ||
| @@ 14-38 (lines=25) @@ | ||
| 11 | X, y = data.data, data.target |
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| 12 | ||
| 13 | ||
| 14 | def test_meta_learn(): |
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| 15 | from sklearn.tree import DecisionTreeClassifier |
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| 16 | ||
| 17 | def model(para, X_train, y_train): |
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| 18 | model = DecisionTreeClassifier( |
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| 19 | criterion=para["criterion"], |
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| 20 | max_depth=para["max_depth"], |
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| 21 | min_samples_split=para["min_samples_split"], |
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| 22 | min_samples_leaf=para["min_samples_leaf"], |
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| 23 | ) |
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| 24 | scores = cross_val_score(model, X_train, y_train, cv=3) |
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| 25 | ||
| 26 | return scores.mean() |
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| 27 | ||
| 28 | search_config = { |
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| 29 | model: { |
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| 30 | "criterion": ["gini", "entropy"], |
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| 31 | "max_depth": range(1, 21), |
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| 32 | "min_samples_split": range(2, 21), |
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| 33 | "min_samples_leaf": range(1, 21), |
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| 34 | } |
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| 35 | } |
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| 36 | ||
| 37 | opt = Hyperactive(search_config, meta_learn=True) |
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| 38 | opt.search(X, y) |
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| 39 | ||
| 40 | ||
| 41 | def test_sklearn(): |
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