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# Copyright (c) 2020 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 division
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from __future__ import print_function
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import os
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import unittest
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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from paddle.hapi.model import to_list
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def accuracy(pred, label, topk=(1, )):
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maxk = max(topk)
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pred = np.argsort(pred)[:, ::-1][:, :maxk]
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correct = (pred == np.repeat(label, maxk, 1))
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batch_size = label.shape[0]
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res = []
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for k in topk:
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correct_k = correct[:, :k].sum()
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res.append(float(correct_k) / batch_size)
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return res
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def convert_to_one_hot(y, C):
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oh = np.random.choice(np.arange(C), C, replace=False).astype('float32') / C
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oh = np.tile(oh[np.newaxis, :], (y.shape[0], 1))
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for i in range(y.shape[0]):
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oh[i, int(y[i])] = 1.
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return oh
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class TestAccuracy(unittest.TestCase):
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def test_acc(self):
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paddle.disable_static()
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x = paddle.to_tensor(
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np.array([[0.1, 0.2, 0.3, 0.4], [0.1, 0.4, 0.3, 0.2],
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[0.1, 0.2, 0.4, 0.3], [0.1, 0.2, 0.3, 0.4]]))
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y = paddle.to_tensor(np.array([[0], [1], [2], [3]]))
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m = paddle.metric.Accuracy(name='my_acc')
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# check name
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self.assertEqual(m.name(), ['my_acc'])
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correct = m.compute(x, y)
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# check results
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self.assertEqual(m.update(correct), 0.75)
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self.assertEqual(m.accumulate(), 0.75)
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x = paddle.to_tensor(
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np.array([[0.1, 0.2, 0.3, 0.4], [0.1, 0.3, 0.4, 0.2],
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[0.1, 0.2, 0.4, 0.3], [0.1, 0.2, 0.3, 0.4]]))
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y = paddle.to_tensor(np.array([[0], [1], [2], [3]]))
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correct = m.compute(x, y)
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# check results
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self.assertEqual(m.update(correct), 0.5)
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self.assertEqual(m.accumulate(), 0.625)
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# check reset
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m.reset()
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self.assertEqual(m.total[0], 0.0)
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self.assertEqual(m.count[0], 0.0)
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paddle.enable_static()
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class TestAccuracyDynamic(unittest.TestCase):
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def setUp(self):
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self.topk = (1, )
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self.class_num = 5
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self.sample_num = 1000
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self.name = None
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def random_pred_label(self):
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label = np.random.randint(0, self.class_num,
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(self.sample_num, 1)).astype('int64')
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pred = np.random.randint(0, self.class_num,
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(self.sample_num, 1)).astype('int32')
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pred_one_hot = convert_to_one_hot(pred, self.class_num)
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pred_one_hot = pred_one_hot.astype('float32')
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return label, pred_one_hot
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def test_main(self):
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with fluid.dygraph.guard(fluid.CPUPlace()):
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acc = paddle.metric.Accuracy(topk=self.topk, name=self.name)
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for _ in range(10):
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label, pred = self.random_pred_label()
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label_var = paddle.to_tensor(label)
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pred_var = paddle.to_tensor(pred)
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state = to_list(acc.compute(pred_var, label_var))
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acc.update(* [s.numpy() for s in state])
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res_m = acc.accumulate()
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res_f = accuracy(pred, label, self.topk)
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assert np.all(np.isclose(np.array(res_m, dtype='float64'),
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np.array(res_f, dtype='float64'), rtol=1e-3)), \
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"Accuracy precision error: {} != {}".format(res_m, res_f)
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acc.reset()
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assert np.sum(acc.total) == 0
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assert np.sum(acc.count) == 0
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class TestAccuracyDynamicMultiTopk(TestAccuracyDynamic):
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def setUp(self):
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self.topk = (1, 5)
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self.class_num = 10
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self.sample_num = 1000
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self.name = "accuracy"
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class TestAccuracyStatic(TestAccuracyDynamic):
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def test_main(self):
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main_prog = fluid.Program()
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startup_prog = fluid.Program()
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main_prog.random_seed = 1024
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startup_prog.random_seed = 1024
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with fluid.program_guard(main_prog, startup_prog):
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pred = fluid.data(
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name='pred', shape=[None, self.class_num], dtype='float32')
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label = fluid.data(name='label', shape=[None, 1], dtype='int64')
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acc = paddle.metric.Accuracy(topk=self.topk, name=self.name)
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state = acc.compute(pred, label)
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exe = fluid.Executor(fluid.CPUPlace())
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compiled_main_prog = fluid.CompiledProgram(main_prog)
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for _ in range(10):
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label, pred = self.random_pred_label()
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state_ret = exe.run(compiled_main_prog,
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feed={'pred': pred,
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'label': label},
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fetch_list=[s.name for s in to_list(state)],
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return_numpy=True)
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acc.update(*state_ret)
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res_m = acc.accumulate()
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res_f = accuracy(pred, label, self.topk)
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assert np.all(np.isclose(np.array(res_m), np.array(res_f), rtol=1e-3)), \
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"Accuracy precision error: {} != {}".format(res_m, res_f)
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acc.reset()
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assert np.sum(acc.total) == 0
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assert np.sum(acc.count) == 0
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class TestAccuracyStaticMultiTopk(TestAccuracyStatic):
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def setUp(self):
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self.topk = (1, 5)
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self.class_num = 10
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self.sample_num = 100
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self.name = "accuracy"
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class TestPrecision(unittest.TestCase):
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def test_1d(self):
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paddle.disable_static()
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x = np.array([0.1, 0.5, 0.6, 0.7])
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y = np.array([1, 0, 1, 1])
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m = paddle.metric.Precision()
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 2. / 3.)
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x = paddle.to_tensor(np.array([0.1, 0.5, 0.6, 0.7, 0.2]))
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y = paddle.to_tensor(np.array([1, 0, 1, 1, 1]))
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 4. / 6.)
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paddle.enable_static()
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def test_2d(self):
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paddle.disable_static()
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x = np.array([0.1, 0.5, 0.6, 0.7]).reshape(-1, 1)
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y = np.array([1, 0, 1, 1]).reshape(-1, 1)
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m = paddle.metric.Precision()
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 2. / 3.)
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x = np.array([0.1, 0.5, 0.6, 0.7, 0.2]).reshape(-1, 1)
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y = np.array([1, 0, 1, 1, 1]).reshape(-1, 1)
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 4. / 6.)
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# check reset
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m.reset()
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self.assertEqual(m.tp, 0.0)
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self.assertEqual(m.fp, 0.0)
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self.assertEqual(m.accumulate(), 0.0)
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paddle.enable_static()
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class TestRecall(unittest.TestCase):
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def test_1d(self):
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paddle.disable_static()
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x = np.array([0.1, 0.5, 0.6, 0.7])
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y = np.array([1, 0, 1, 1])
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m = paddle.metric.Recall()
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 2. / 3.)
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x = paddle.to_tensor(np.array([0.1, 0.5, 0.6, 0.7]))
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y = paddle.to_tensor(np.array([1, 0, 0, 1]))
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 3. / 5.)
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# check reset
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m.reset()
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self.assertEqual(m.tp, 0.0)
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self.assertEqual(m.fn, 0.0)
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self.assertEqual(m.accumulate(), 0.0)
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paddle.enable_static()
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class TestAuc(unittest.TestCase):
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def test_auc_numpy(self):
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paddle.disable_static()
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x = np.array([[0.78, 0.22], [0.62, 0.38], [0.55, 0.45], [0.30, 0.70],
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[0.14, 0.86], [0.59, 0.41], [0.91, 0.08], [0.16, 0.84]])
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y = np.array([[0], [1], [1], [0], [1], [0], [0], [1]])
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m = paddle.metric.Auc()
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 0.8125)
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m.reset()
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self.assertEqual(m.accumulate(), 0.0)
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paddle.enable_static()
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def test_auc_tensor(self):
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paddle.disable_static()
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x = paddle.to_tensor(
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np.array([[0.78, 0.22], [0.62, 0.38], [0.55, 0.45], [0.30, 0.70],
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[0.14, 0.86], [0.59, 0.41], [0.91, 0.08], [0.16, 0.84]]))
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y = paddle.to_tensor(np.array([[0], [1], [1], [0], [1], [0], [0], [1]]))
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m = paddle.metric.Auc()
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m.update(x, y)
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r = m.accumulate()
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self.assertAlmostEqual(r, 0.8125)
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m.reset()
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self.assertEqual(m.accumulate(), 0.0)
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paddle.enable_static()
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if __name__ == '__main__':
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unittest.main()
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