# 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 numpy as np
from typing import List, Tuple
from sklearn.metrics import f1_score
from deeppavlov.core.common.metrics_registry import register_metric
from deeppavlov.models.classifiers.utils import labels2onehot
[docs]@register_metric('classification_f1')
def classification_fmeasure(y_true: List[list], y_predicted: List[Tuple[list, dict]], average="macro") -> float:
"""
Calculate F1-measure macro
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} ) \
where probability is float or keras.tensor
average: determines the type of averaging performed on the data
Returns:
F1-measure
"""
classes = np.array(list(y_predicted[0][1].keys()))
y_true_one_hot = labels2onehot(y_true, classes)
y_pred_labels = [y_predicted[i][0] for i in range(len(y_predicted))]
y_pred_one_hot = labels2onehot(y_pred_labels, classes)
return f1_score(y_true_one_hot, y_pred_one_hot, average=average)
[docs]@register_metric('classification_f1_weighted')
def classification_fmeasure_weighted(y_true: List[list], y_predicted: List[Tuple[list, dict]],
average="weighted") -> float:
"""
Calculate F1-measure weighted
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} ) \
where probability is float or keras.tensor
average: determines the type of averaging performed on the data
Returns:
F1-measure
"""
classes = np.array(list(y_predicted[0][1].keys()))
y_true_one_hot = labels2onehot(y_true, classes)
y_pred_labels = [y_predicted[i][0] for i in range(len(y_predicted))]
y_pred_one_hot = labels2onehot(y_pred_labels, classes)
return f1_score(y_true_one_hot, y_pred_one_hot, average=average)