c7303148fee708935d774139fab68fb38b43f58a,mlxtend/classifier/stacking_cv_classification.py,StackingCVClassifier,fit,#StackingCVClassifier#,92

Before Change

                          ((num+1), self.n_folds))

                if not self.use_probas:
                    prediction = model.fit(X[train_index], y[train_index])\
                        .predict(X[test_index])
                    prediction = prediction.reshape(prediction.shape[0], 1)
                else:
                    prediction = model.fit(X[train_index], y[train_index]).\
                        predict_proba(X[test_index])
                single_model_prediction = np.vstack([single_model_prediction.
                                                    astype(prediction.dtype),
                                                     prediction])

            all_model_predictions = np.hstack((all_model_predictions.
                                               astype(single_model_prediction.
                                                      dtype),
                                               single_model_prediction))

After Change

                prediction = model.fit(X[train_index], y[train_index])

                if not self.use_probas:
                    prediction = prediction.predict(X[test_index])
                    prediction = prediction.reshape(prediction.shape[0], 1)
                else:
                    prediction = prediction.predict_proba(X[test_index])
                single_model_prediction = np.vstack([single_model_prediction.
                                                    astype(prediction.dtype),
                                                     prediction])

            all_model_predictions = np.hstack((all_model_predictions.
                                               astype(single_model_prediction.
                                                      dtype),
                                               single_model_prediction))

Instances