93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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# """test_roc"""
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import numpy as np
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import pytest
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from mindspore import Tensor
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from mindspore.nn.metrics import ROC
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def test_roc():
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"""test_roc_binary"""
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x = Tensor(np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]]))
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y = Tensor(np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]]))
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metric = ROC(pos_label=1)
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metric.clear()
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metric.update(x, y)
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fpr, tpr, thresholds = metric.eval()
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assert np.equal(fpr, np.array([0, 0.4, 0.4, 0.6, 1])).all()
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assert np.equal(tpr, np.array([0, 0, 0.25, 0.75, 1])).all()
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assert np.equal(thresholds, np.array([4, 3, 2, 1, 0])).all()
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def test_roc2():
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"""test_roc_multiclass"""
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x = Tensor(np.array([[0.28, 0.55, 0.15, 0.05], [0.10, 0.20, 0.05, 0.05], [0.20, 0.05, 0.15, 0.05],
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[0.05, 0.05, 0.05, 0.75]]))
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y = Tensor(np.array([0, 1, 2, 3]))
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metric = ROC(class_num=4)
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metric.clear()
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metric.update(x, y)
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fpr, tpr, thresholds = metric.eval()
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list1 = [np.array([0., 0., 0.33333333, 0.66666667, 1.]), np.array([0., 0.33333333, 0.33333333, 1.]),
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np.array([0., 0.33333333, 1.]), np.array([0., 0., 1.])]
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list2 = [np.array([0., 1., 1., 1., 1.]), np.array([0., 0., 1., 1.]),
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np.array([0., 1., 1.]), np.array([0., 1., 1.])]
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list3 = [np.array([1.28, 0.28, 0.2, 0.1, 0.05]), np.array([1.55, 0.55, 0.2, 0.05]),
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np.array([1.15, 0.15, 0.05]), np.array([1.75, 0.75, 0.05])]
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assert fpr[0].shape == list1[0].shape
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assert np.equal(tpr[1], list2[1]).all()
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assert np.equal(thresholds[2], list3[2]).all()
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def test_roc_update1():
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x = Tensor(np.array([[0.2, 0.5, 0.7], [0.3, 0.1, 0.2], [0.9, 0.6, 0.5]]))
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metric = ROC()
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metric.clear()
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with pytest.raises(ValueError):
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metric.update(x)
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def test_roc_update2():
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x = Tensor(np.array([[0.2, 0.5, 0.7], [0.3, 0.1, 0.2], [0.9, 0.6, 0.5]]))
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y = Tensor(np.array([1, 0]))
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metric = ROC()
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metric.clear()
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with pytest.raises(ValueError):
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metric.update(x, y)
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def test_roc_init1():
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with pytest.raises(TypeError):
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ROC(pos_label=1.2)
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def test_roc_init2():
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with pytest.raises(TypeError):
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ROC(class_num="class_num")
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def test_roc_runtime():
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metric = ROC()
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metric.clear()
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with pytest.raises(RuntimeError):
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metric.eval()
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