da729dab14194dba84e75571f08f927efbc19865,yellowbrick/classifier/confusion_matrix.py,ConfusionMatrix,score,#ConfusionMatrix#Any#Any#,159

Before Change

        y_pred = self.predict(X)

        // Encode the target with the supplied label encoder
        if self.label_encoder:
            try:
                y = self.label_encoder.inverse_transform(y)
                y_pred = self.label_encoder.inverse_transform(y_pred)
            except AttributeError:
                // if a mapping is passed to class apply it here.
                y = np.array([self.label_encoder[x] for x in y])
                y_pred = np.array([self.label_encoder[x] for x in y_pred])

        // Compute the confusion matrix and class counts
        self.confusion_matrix_ = confusion_matrix_metric(
            y, y_pred, labels=self.classes_, sample_weight=self.sample_weight
        )
        self.class_counts_ = self.class_counts(y)

After Change

        y = self._decode_labels(y)
        y_pred = self._decode_labels(y_pred)
        labels = self._labels()
        if labels is None:
            labels = self.classes_

        // Compute the confusion matrix and class counts
        self.confusion_matrix_ = confusion_matrix_metric(
            y, y_pred, labels=labels, sample_weight=self.sample_weight
        )
        self.class_counts_ = dict(zip(*np.unique(y, return_counts=True)))

Instances