core.py 文件源码

def score(self, y_predicted, y_target, y_prob=None):
        """ Compute metrics on classifier predictions

        Parameters
        ----------
        y_predicted : np.array [n_samples]
            Predicted class labels
        y_target : np.array [n_samples]
            Target class labels
        y_prob : np.array [n_samples] or None, default=None
            predicted probabilties. If None, auc is not computed

        Returns
        -------
        scores : dict
            dictionary of scores for the following metrics:
            accuracy, matthews correlation coefficient, precision, recall, f1,
            support, confusion matrix, auc score
        """
        labels = set(y_target)
        labels.update(y_predicted)
        is_binary = len(labels) <= 2

        scores = {}
        scores['accuracy'] = metrics.accuracy_score(y_target, y_predicted)

        if is_binary:
            scores['mcc'] = metrics.matthews_corrcoef(y_target, y_predicted)
        else:
            scores['mcc'] = None

        (scores['precision'],
         scores['recall'],
         scores['f1'],
         scores['support']) = metrics.precision_recall_fscore_support(
             y_target, y_predicted
         )

        scores['confusion matrix'] = metrics.confusion_matrix(
            y_target, y_predicted, labels=list(labels)
        )

        if y_prob is not None:
            scores['auc score'] = metrics.roc_auc_score(
                y_target, y_prob + 1, average='weighted'
            )
        else:
            scores['auc score'] = None

        return scores


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