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113 lines
3.2 KiB
113 lines
3.2 KiB
2 years ago
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from sklearn.metrics import hamming_loss
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from sklearn.metrics import accuracy_score as accuracy_metric
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from sklearn.metrics import multilabel_confusion_matrix
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from sklearn.metrics import precision_recall_fscore_support
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from sklearn.metrics import average_precision_score
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from sklearn.preprocessing import binarize
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import numpy as np
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__all__ = [
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"multi_hot_encode", "hamming_distance", "accuracy_score",
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"precision_recall_fscore", "mean_average_precision"
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]
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def multi_hot_encode(logits, threshold=0.5):
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"""
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Encode logits to multi-hot by elementwise for multilabel
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"""
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return binarize(logits, threshold=threshold)
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def hamming_distance(output, target):
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"""
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Soft metric based label for multilabel classification
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Returns:
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The smaller the return value is, the better model is.
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"""
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return hamming_loss(target, output)
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def accuracy_score(output, target, base="sample"):
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"""
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Hard metric for multilabel classification
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Args:
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output:
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target:
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base: ["sample", "label"], default="sample"
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if "sample", return metric score based sample,
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if "label", return metric score based label.
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Returns:
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accuracy:
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"""
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assert base in ["sample", "label"], 'must be one of ["sample", "label"]'
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if base == "sample":
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accuracy = accuracy_metric(target, output)
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elif base == "label":
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mcm = multilabel_confusion_matrix(target, output)
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tns = mcm[:, 0, 0]
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fns = mcm[:, 1, 0]
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tps = mcm[:, 1, 1]
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fps = mcm[:, 0, 1]
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accuracy = (sum(tps) + sum(tns)) / (
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sum(tps) + sum(tns) + sum(fns) + sum(fps))
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return accuracy
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def precision_recall_fscore(output, target):
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"""
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Metric based label for multilabel classification
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Returns:
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precisions:
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recalls:
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fscores:
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"""
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precisions, recalls, fscores, _ = precision_recall_fscore_support(target,
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output)
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return precisions, recalls, fscores
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def mean_average_precision(logits, target):
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"""
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Calculate average precision
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Args:
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logits: probability from network before sigmoid or softmax
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target: ground truth, 0 or 1
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"""
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if not (isinstance(logits, np.ndarray) and isinstance(target, np.ndarray)):
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raise TypeError("logits and target should be np.ndarray.")
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aps = []
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for i in range(target.shape[1]):
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ap = average_precision_score(target[:, i], logits[:, i])
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aps.append(ap)
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return np.mean(aps)
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