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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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n_iter_0 = 100 |
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random_state = 0 |
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n_jobs = 1 |
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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=3) |
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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, 21), |
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"min_samples_split": range(2, 21), |
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"min_samples_leaf": range(1, 21), |
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} |
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} |
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def test_HillClimbingOptimizer(): |
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opt = Hyperactive(search_config, optimizer="HillClimbing") |
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opt.fit(X, y) |
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def test_StochasticHillClimbingOptimizer(): |
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opt = Hyperactive(search_config, optimizer="StochasticHillClimbing") |
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opt.fit(X, y) |
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def test_TabuOptimizer(): |
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opt = Hyperactive(search_config, optimizer="TabuSearch") |
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opt.fit(X, y) |
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def test_RandomSearchOptimizer(): |
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opt = Hyperactive(search_config, optimizer="RandomSearch") |
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opt.fit(X, y) |
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def test_RandomRestartHillClimbingOptimizer(): |
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opt = Hyperactive(search_config, optimizer="RandomRestartHillClimbing") |
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opt.fit(X, y) |
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def test_RandomAnnealingOptimizer(): |
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opt = Hyperactive(search_config, optimizer="RandomAnnealing") |
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opt.fit(X, y) |
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def test_SimulatedAnnealingOptimizer(): |
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opt = Hyperactive(search_config, optimizer="SimulatedAnnealing") |
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opt.fit(X, y) |
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def test_StochasticTunnelingOptimizer(): |
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opt = Hyperactive(search_config, optimizer="StochasticTunneling") |
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opt.fit(X, y) |
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def test_ParallelTemperingOptimizer(): |
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opt = Hyperactive(search_config, optimizer="ParallelTempering") |
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opt.fit(X, y) |
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def test_ParticleSwarmOptimizer(): |
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opt = Hyperactive(search_config, optimizer="ParticleSwarm") |
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opt.fit(X, y) |
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def test_EvolutionStrategyOptimizer(): |
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opt = Hyperactive(search_config, optimizer="EvolutionStrategy") |
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opt.fit(X, y) |
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def test_BayesianOptimizer(): |
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opt = Hyperactive(search_config, optimizer="Bayesian") |
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opt.fit(X, y) |
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SimonBlanke /
Hyperactive
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