forked from huawei/mindspore2022
107 lines
3.4 KiB
Python
107 lines
3.4 KiB
Python
# Copyright 2020 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 accuracy"""
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import math
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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 Accuracy
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def test_classification_accuracy():
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"""test_classification_accuracy"""
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x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
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y = Tensor(np.array([1, 0, 1]))
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y2 = Tensor(np.array([[0, 1], [1, 0], [0, 1]]))
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metric = Accuracy('classification')
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metric.clear()
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metric.update(x, y)
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accuracy = metric.eval()
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accuracy2 = metric(x, y2)
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assert math.isclose(accuracy, 2 / 3)
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assert math.isclose(accuracy2, 2 / 3)
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def test_classification_accuracy_indexes_awareness():
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"""A indexes aware version of test_classification_accuracy"""
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x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
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y = Tensor(np.array([1, 0, 1]))
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y2 = Tensor(np.array([0, 0, 1]))
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metric = Accuracy('classification').set_indexes([0, 2])
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metric.clear()
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metric.update(x, y, y2)
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accuracy = metric.eval()
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assert math.isclose(accuracy, 1 / 3)
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@pytest.mark.parametrize('indexes', [0, [0., 2.], [0., 1], ['1', '0']])
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def test_set_indexes(indexes):
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pat_str = "For 'set_indexes', the argument 'indexes' should be a list and all its elements should " \
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"be int, please check whether it is correct."
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with pytest.raises(ValueError, match=pat_str):
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_ = Accuracy('classification').set_indexes(indexes)
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def test_multilabel_accuracy():
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x = Tensor(np.array([[0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]]))
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y = Tensor(np.array([[0, 1, 1, 1], [0, 1, 1, 1], [0, 0, 0, 1]]))
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metric = Accuracy('multilabel')
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metric.clear()
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metric.update(x, y)
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accuracy = metric.eval()
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assert accuracy == 1 / 3
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def test_shape_accuracy():
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x = Tensor(np.array([[0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]]))
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y = Tensor(np.array([[0, 1, 1, 1], [0, 1, 1, 1]]))
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metric = Accuracy('multilabel')
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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_shape_accuracy2():
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x = Tensor(np.array([[0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]]))
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y = Tensor(np.array([0, 1, 1, 1]))
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metric = Accuracy('multilabel')
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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_shape_accuracy3():
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x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
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y = Tensor(np.array([[1, 0, 1], [1, 1, 1]]))
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metric = Accuracy('classification')
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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_shape_accuracy4():
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x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
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y = Tensor(np.array(1))
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metric = Accuracy('classification')
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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_type_accuracy():
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with pytest.raises(TypeError):
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Accuracy('test')
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