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130 lines
4.1 KiB
130 lines
4.1 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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from __future__ import print_function
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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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from paddle.fluid import metrics
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import paddle.fluid as fluid
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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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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((1 + slide_steps) * (num_thresholds + 1) + 1,
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).astype("int64")
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stat_neg = np.zeros((1 + slide_steps) * (num_thresholds + 1) + 1,
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).astype("int64")
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self.inputs = {
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'Predict': pred,
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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 = metrics.Auc(name="auc",
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curve='ROC',
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num_thresholds=num_thresholds)
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python_auc.update(pred, labels)
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pos = python_auc._stat_pos * 2
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pos.append(1)
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neg = python_auc._stat_neg * 2
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neg.append(1)
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self.outputs = {
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'AUC': np.array(python_auc.eval()),
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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()
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class TestGlobalAucOp(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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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': pred,
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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 = metrics.Auc(name="auc",
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curve='ROC',
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num_thresholds=num_thresholds)
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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.eval()),
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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()
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class TestAucOpError(unittest.TestCase):
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def test_errors(self):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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def test_type1():
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data1 = fluid.data(name="input1", shape=[-1, 2], dtype="int")
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label1 = fluid.data(name="label1", shape=[-1], dtype="int")
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result1 = fluid.layers.auc(input=data1, label=label1)
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self.assertRaises(TypeError, test_type1)
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def test_type2():
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data2 = fluid.data(
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name="input2", shape=[-1, 2], dtype="float32")
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label2 = fluid.data(name="label2", shape=[-1], dtype="float32")
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result2 = fluid.layers.auc(input=data2, label=label2)
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self.assertRaises(TypeError, test_type2)
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if __name__ == '__main__':
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unittest.main()
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