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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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from sklearn.datasets import load_iris |
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from sklearn.model_selection import cross_val_score |
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from sklearn.tree import DecisionTreeClassifier |
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from hyperactive import Hyperactive |
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data = load_iris() |
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X = data.data |
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y = data.target |
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random_state = 1 |
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n_jobs = 2 |
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View Code Duplication |
def model(para, X_train, y_train): |
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model = DecisionTreeClassifier( |
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criterion=para["criterion"], |
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max_depth=para["max_depth"], |
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min_samples_split=para["min_samples_split"], |
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min_samples_leaf=para["min_samples_leaf"], |
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) |
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scores = cross_val_score(model, X_train, y_train, cv=2) |
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return scores.mean(), model |
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search_config = { |
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model: { |
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"criterion": ["gini", "entropy"], |
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"max_depth": range(1, 11), |
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"min_samples_split": range(2, 11), |
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"min_samples_leaf": range(1, 11), |
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} |
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} |
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warm_start = {model: {"max_depth": [1]}} |
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def test_TabuOptimizer(): |
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opt0 = Hyperactive( |
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search_config, |
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optimizer="TabuSearch", |
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n_iter=1, |
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random_state=random_state, |
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verbosity=0, |
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n_jobs=1, |
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warm_start=warm_start, |
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) |
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opt0.fit(X, y) |
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opt1 = Hyperactive( |
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search_config, |
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optimizer="TabuSearch", |
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n_iter=30, |
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random_state=random_state, |
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verbosity=0, |
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n_jobs=n_jobs, |
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warm_start=warm_start, |
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) |
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opt1.fit(X, y) |
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assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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SimonBlanke /
Hyperactive
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