Source code for deeppavlov.metrics.roc_auc_score

# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from typing import List, Union

import numpy as np
import sklearn.metrics

from deeppavlov.core.common.metrics_registry import register_metric


[docs]@register_metric('roc_auc') def roc_auc_score(y_true: Union[List[List[float]], List[List[int]], np.ndarray], y_pred: Union[List[List[float]], List[List[int]], np.ndarray]) -> float: """ Compute Area Under the Curve (AUC) from prediction scores. Args: y_true: true binary labels y_pred: target scores, can either be probability estimates of the positive class Returns: Area Under the Curve (AUC) from prediction scores """ try: return sklearn.metrics.roc_auc_score(np.squeeze(np.array(y_true)), np.squeeze(np.array(y_pred)), average="macro") except ValueError: return 0.