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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12 lines
690 B
12 lines
690 B
# first line: 600
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@memory.cache
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def estimator(n_estimators, eta, max_depth, subsample, colsample_bytree):
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# initialize model
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print('loopingQSAR')
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n_estimators = int(n_estimators)
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max_depth = int(max_depth)
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model = XGBClassifier(n_estimators=n_estimators, eta=eta, max_depth=max_depth, subsample=subsample, colsample_bytree=colsample_bytree, n_jobs=-1, random_state=RANDOM_SEED, silent=True, verbosity = 0, use_label_encoder=False)
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# set in cross-validation
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result = cross_validate(model, XData, yData, cv=crossValidation, scoring='accuracy')
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# result is mean of test_score
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return np.mean(result['test_score'])
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