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82 lines
3.0 KiB
82 lines
3.0 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import numpy as np
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from op_test import OpTest
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class TestAucOp(OpTest):
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def setUp(self):
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self.op_type = "auc"
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pred = np.random.random((128, 2)).astype("float32")
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indices = np.random.randint(0, 2, (128, 2))
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labels = np.random.randint(0, 2, (128, 1))
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num_thresholds = 200
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self.inputs = {'Out': pred, 'Indices': indices, 'Label': labels}
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self.attrs = {'curve': 'ROC', 'num_thresholds': num_thresholds}
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# NOTE: sklearn use a different way to generate thresholds
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# which will cause the result differs slightly:
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# from sklearn.metrics import roc_curve, auc
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# fpr, tpr, thresholds = roc_curve(labels, pred)
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# auc_value = auc(fpr, tpr)
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# we caculate AUC again using numpy for testing
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kepsilon = 1e-7 # to account for floating point imprecisions
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thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
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for i in range(num_thresholds - 2)]
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thresholds = [0.0 - kepsilon] + thresholds + [1.0 + kepsilon]
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# caculate TP, FN, TN, FP count
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tp_list = np.ndarray((num_thresholds, ))
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fn_list = np.ndarray((num_thresholds, ))
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tn_list = np.ndarray((num_thresholds, ))
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fp_list = np.ndarray((num_thresholds, ))
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for idx_thresh, thresh in enumerate(thresholds):
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tp, fn, tn, fp = 0, 0, 0, 0
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for i, lbl in enumerate(labels):
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if lbl:
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if pred[i, 0] >= thresh:
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tp += 1
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else:
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fn += 1
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else:
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if pred[i, 0] >= thresh:
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fp += 1
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else:
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tn += 1
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tp_list[idx_thresh] = tp
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fn_list[idx_thresh] = fn
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tn_list[idx_thresh] = tn
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fp_list[idx_thresh] = fp
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epsilon = 1e-6
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tpr = (tp_list.astype("float32") + epsilon) / (
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tp_list + fn_list + epsilon)
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fpr = fp_list.astype("float32") / (fp_list + tn_list + epsilon)
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rec = (tp_list.astype("float32") + epsilon) / (
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tp_list + fp_list + epsilon)
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x = fpr[:num_thresholds - 1] - fpr[1:]
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y = (tpr[:num_thresholds - 1] + tpr[1:]) / 2.0
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auc_value = np.sum(x * y)
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self.outputs = {'AUC': auc_value}
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def test_check_output(self):
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self.check_output()
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if __name__ == "__main__":
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unittest.main()
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