FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
https://doi.org/10.1109/TVCG.2022.3141040
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15 lines
603 B
15 lines
603 B
# first line: 457
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@memory.cache
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def executeModel():
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create_global_function()
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global estimator
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params = {"C": (0.0001, 10000), "gamma": (0.0001, 10000)}
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svc_bayesopt = BayesianOptimization(estimator, params)
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svc_bayesopt.maximize(init_points=10, n_iter=25, acq='ucb')
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bestParams = svc_bayesopt.max['params']
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estimator = SVC(C=bestParams.get('C'), gamma=bestParams.get('gamma'), probability=True)
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estimator.fit(XData, yData)
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yPredict = estimator.predict(XData)
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yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')
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