e8d6fd1a77df735a6362003014515d8249617011,deslib/des/des_knn.py,DESKNN,predict_proba_with_ds,#DESKNN#Any#Any#Any#Any#Any#Any#,343

Before Change

                          Probability estimates for all test examples.
        

        if query.shape[0] != probabilities.shape[0]:
            raise ValueError(
                "The arrays query and predictions must have the same number"
                " of samples. query.shape is {}"
                "and predictions.shape is {}".format(query.shape,
                                                     predictions.shape))

        accuracy, diversity = self.estimate_competence(query,
                                                       neighbors,
                                                       distances=distances,
                                                       predictions=predictions)
        if self.DFP:
            accuracy = accuracy * DFP_mask

        // This method always performs selection. There is no weighted version.
        selected_classifiers = self.select(accuracy, diversity)
        ensemble_proba = probabilities[
                         np.arange(probabilities.shape[0])[:, None],
                         selected_classifiers, :]

        predicted_proba = np.mean(ensemble_proba, axis=1)

        return predicted_proba

    def _check_parameters(self):

After Change

        // This method always performs selection. There is no weighted version.
        selected_classifiers = self.select(accuracy, diversity)

        if self.voting == "hard":
            votes = predictions[np.arange(predictions.shape[0])[:, None],
                                selected_classifiers]
            votes = sum_votes_per_class(votes, self.n_classes_)
            predicted_proba = votes / votes.sum(axis=1)[:, None]
        else:
            ensemble_proba = probabilities[
                             np.arange(probabilities.shape[0])[:, None],
                             selected_classifiers, :]

            predicted_proba = np.mean(ensemble_proba, axis=1)

        return predicted_proba

    def _check_parameters(self):
        Check if the parameters passed as argument are correct.

Instances