wehub-resource-sync
117 lines
3.4 KiB
Python
117 lines
3.4 KiB
Python
# 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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import paddle
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class TestAucSinglePredOp(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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pred0 = pred[:, 0].reshape(128, 1)
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labels = np.random.randint(0, 2, (128, 1)).astype("int64")
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num_thresholds = 200
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slide_steps = 1
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stat_pos = np.zeros(
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(1 + slide_steps) * (num_thresholds + 1) + 1,
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).astype("int64")
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stat_neg = np.zeros(
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(1 + slide_steps) * (num_thresholds + 1) + 1,
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).astype("int64")
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self.inputs = {
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'Predict': pred0,
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'Label': labels,
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"StatPos": stat_pos,
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"StatNeg": stat_neg,
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}
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self.attrs = {
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'curve': 'ROC',
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'num_thresholds': num_thresholds,
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"slide_steps": slide_steps,
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}
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python_auc = paddle.metric.Auc(
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name="auc", curve='ROC', num_thresholds=num_thresholds
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)
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for i in range(128):
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pred[i][1] = pred[i][0]
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python_auc.update(pred, labels)
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pos = python_auc._stat_pos.tolist() * 2
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pos.append(1)
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neg = python_auc._stat_neg.tolist() * 2
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neg.append(1)
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self.outputs = {
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'AUC': np.array(python_auc.accumulate()),
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'StatPosOut': np.array(pos),
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'StatNegOut': np.array(neg),
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}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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class TestAucGlobalSinglePredOp(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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pred0 = pred[:, 0].reshape(128, 1)
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labels = np.random.randint(0, 2, (128, 1)).astype("int64")
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num_thresholds = 200
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slide_steps = 0
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stat_pos = np.zeros((1, (num_thresholds + 1))).astype("int64")
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stat_neg = np.zeros((1, (num_thresholds + 1))).astype("int64")
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self.inputs = {
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'Predict': pred0,
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'Label': labels,
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"StatPos": stat_pos,
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"StatNeg": stat_neg,
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}
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self.attrs = {
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'curve': 'ROC',
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'num_thresholds': num_thresholds,
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"slide_steps": slide_steps,
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}
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python_auc = paddle.metric.Auc(
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name="auc", curve='ROC', num_thresholds=num_thresholds
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)
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for i in range(128):
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pred[i][1] = pred[i][0]
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python_auc.update(pred, labels)
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pos = python_auc._stat_pos
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neg = python_auc._stat_neg
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self.outputs = {
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'AUC': np.array(python_auc.accumulate()),
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'StatPosOut': np.array([pos]),
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'StatNegOut': np.array([neg]),
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}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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if __name__ == "__main__":
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unittest.main()
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