# 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
#
# 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
# limitations under the License.
import sklearn.metrics
import numpy as np
from typing import List, Tuple
from deeppavlov.core.common.metrics_registry import register_metric
from deeppavlov.models.classifiers.utils import labels2onehot
def roc_auc_score_np(y_true: [list, np.ndarray], y_pred: [list, 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.array(y_true), np.array(y_pred), average="macro")
except ValueError:
return 0.
[docs]@register_metric('classification_roc_auc')
def classification_roc_auc_score(y_true: List[list], y_predicted: List[Tuple[list, dict]]) -> float:
"""
Compute Area Under the Curve (AUC) from prediction scores.
Args:
y_true: true binary labels
y_predicted: predictions. \
Each prediction is a tuple of two elements \
(predicted_labels, dictionary like {"label_i": probability_i} )
Returns:
Area Under the Curve (AUC) from prediction scores
"""
classes = np.array(list(y_predicted[0][1].keys()))
y_true_one_hot = labels2onehot(y_true, classes)
y_pred_probas = [list(y_predicted[i][1].values()) for i in range(len(y_predicted))]
auc_score = roc_auc_score_np(y_true_one_hot, y_pred_probas)
return auc_score