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import GPy |
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from sklearn.gaussian_process import GaussianProcessRegressor |
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from sklearn.gaussian_process.kernels import Matern |
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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, y): |
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gbc = 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(gbc, X, y, cv=3) |
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return scores.mean() |
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search_config = { |
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model: { |
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"n_estimators": range(10, 100, 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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class GPR0: |
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def __init__(self): |
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self.kernel = GPy.kern.RBF(input_dim=1, variance=1., lengthscale=1.) |
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def fit(self, X, y): |
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self.m = GPy.models.GPRegression(X, y, self.kernel) |
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self.m.optimize(messages=True) |
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def predict(self, X): |
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return self.m.predict(X) |
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class GPR1: |
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def __init__(self): |
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self.gpr = GaussianProcessRegressor( |
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kernel=Matern(nu=2.5), normalize_y=True, n_restarts_optimizer=10 |
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) |
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def fit(self, X, y): |
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self.gpr.fit(X, y) |
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def predict(self, X): |
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return self.gpr.predict(X, return_std=True) |
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opt = Hyperactive(X, y) |
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opt.search(search_config, n_iter=30, optimizer="Bayesian") |
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bayes_opt = {"Bayesian": {"gpr": GPR0()}} |
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opt = Hyperactive(X, y) |
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opt.search(search_config, n_iter=30, optimizer=bayes_opt) |
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bayes_opt = {"Bayesian": {"gpr": GPR1()}} |
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opt = Hyperactive(X, y) |
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opt.search(search_config, n_iter=30, optimizer=bayes_opt) |
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SimonBlanke /
Hyperactive
