1# Copyright 2020 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15"""test recall""" 16import math 17import numpy as np 18import pytest 19 20from mindspore import Tensor 21from mindspore.nn.metrics import Recall 22 23 24def test_classification_recall(): 25 """test_classification_recall""" 26 x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) 27 y = Tensor(np.array([1, 0, 1])) 28 y2 = Tensor(np.array([[0, 1], [1, 0], [0, 1]])) 29 metric = Recall('classification') 30 metric.clear() 31 metric.update(x, y) 32 recall = metric.eval() 33 recall2 = metric(x, y2) 34 35 assert np.equal(recall, np.array([1, 0.5])).all() 36 assert np.equal(recall2, np.array([1, 0.5])).all() 37 38 39def test_multilabel_recall(): 40 x = Tensor(np.array([[0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]])) 41 y = Tensor(np.array([[0, 1, 1, 1], [0, 1, 1, 1], [0, 0, 0, 1]])) 42 metric = Recall('multilabel') 43 metric.clear() 44 metric.update(x, y) 45 recall = metric.eval() 46 47 assert np.equal(recall, np.array([2 / 3, 2 / 3, 1])).all() 48 49 50def test_average_recall(): 51 x = Tensor(np.array([[0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]])) 52 y = Tensor(np.array([[0, 1, 1, 1], [0, 1, 1, 1], [0, 0, 0, 1]])) 53 metric = Recall('multilabel') 54 metric.clear() 55 metric.update(x, y) 56 recall = metric.eval(True) 57 58 assert math.isclose(recall, (2 / 3 + 2 / 3 + 1) / 3) 59 60 61def test_num_recall(): 62 x = Tensor(np.array([[0.2, 0.5, 0.7], [0.3, 0.1, 0.2], [0.9, 0.6, 0.5]])) 63 y = Tensor(np.array([1, 0])) 64 metric = Recall('classification') 65 metric.clear() 66 67 with pytest.raises(ValueError): 68 metric.update(x, y) 69