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e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | # Copyright 2020 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from typing import Tuple
import numpy as np
import torch
from parameterized import parameterized
from monai.metrics import compute_confusion_metric
metric_name = "true positive rate"
# test the metric under 2 class classification task
TEST_CASES_2_CLS_CLF = [
[
{
"y_pred": torch.tensor([[0.4], [0.4], [0.4], [0.6], [0.9], [0.9]]),
"y": torch.tensor([[1], [0], [0], [1], [1], [1]]),
"bin_mode": "threshold",
"metric_name": metric_name,
},
0.75,
],
[
{
"y_pred": torch.tensor([[0], [0], [0], [1], [1], [1]]),
"y": torch.tensor([[1], [0], [0], [1], [1], [1]]),
"bin_mode": None,
"metric_name": metric_name,
},
0.75,
],
]
# test the metric under multi-class classification task
average_list = ["micro", "macro", "weighted"]
multi_class_result_list = [0.4, 0.33333334, 0.4]
TEST_CASES_M_CLS_CLF = []
y_pred = torch.tensor([[0.4, 0.8, 1], [0.4, 0.8, 0], [0.4, 0.2, 0.7], [0.6, 0.1, 0.2], [0.2, 0.1, 0.8]])
y = torch.tensor([[1], [0], [2], [1], [2]])
for i in range(len(average_list)):
average = average_list[i]
test_case = [
{
"y_pred": y_pred,
"y": y,
"to_onehot_y": True,
"metric_name": metric_name,
"bin_mode": "mutually_exclusive",
"average": average,
},
multi_class_result_list[i],
]
TEST_CASES_M_CLS_CLF.append(test_case)
# test the metric under multi-label classification task
multi_label_result_list = [0.75, 0.80555556, 0.75]
TEST_CASES_M_LABEL_CLF = []
y_pred = torch.tensor([[0.4, 0.8, 1], [0.4, 0.8, 0], [0.4, 0.2, 0.7], [0.6, 0.1, 0.2], [0.2, 0.1, 0.8]])
y = torch.tensor([[0, 1, 1], [0, 1, 0], [0, 1, 1], [1, 0, 1], [0, 0, 1]])
for i in range(len(average_list)):
average = average_list[i]
test_case = [
{
"y_pred": y_pred,
"y": y,
"metric_name": metric_name,
"bin_mode": "threshold",
"bin_threshold": [0.3, 0.6, 0.5],
"average": average,
},
multi_label_result_list[i],
]
TEST_CASES_M_LABEL_CLF.append(test_case)
# test the metric under 2D segmentation task
def produce_seg_input(shape: Tuple):
y = torch.cat([torch.ones(shape), torch.zeros(shape)])
y_pred = torch.rand_like(y) * 0.5
return y, y_pred
seg_result_list_2d = [0, 0, 0, 1, 1, 1]
basic_shape: Tuple
basic_shape = (5, 5, 3, 3)
y, y_org = produce_seg_input(basic_shape)
TEST_CASES_2D_SEG = []
ct = 0
for y_pred in [y_org, 1 - y_org]:
for i in range(len(average_list)):
average = average_list[i]
test_case = [
{"y_pred": y_pred, "y": y, "metric_name": metric_name, "bin_mode": "threshold", "average": average},
seg_result_list_2d[ct],
]
ct += 1
TEST_CASES_2D_SEG.append(test_case)
# test the metric under 3D segmentation task
seg_result_list_3d = [1, 1, 1, 0, 0, 0]
basic_shape = (2, 5, 3, 3, 3)
y, y_org = produce_seg_input(basic_shape)
TEST_CASES_3D_SEG = []
ct = 0
for y_pred in [y_org, -y_org]:
for i in range(len(average_list)):
average = average_list[i]
test_case = [
{
"y_pred": y_pred,
"y": y,
"activation": "sigmoid",
"metric_name": metric_name,
"bin_mode": "threshold",
"average": average,
},
seg_result_list_3d[ct],
]
ct += 1
TEST_CASES_3D_SEG.append(test_case)
class TestComputeTprClf(unittest.TestCase):
@parameterized.expand(TEST_CASES_2_CLS_CLF + TEST_CASES_M_CLS_CLF + TEST_CASES_M_LABEL_CLF)
def test_value(self, input_data, expected_value):
result = compute_confusion_metric(**input_data)
np.testing.assert_allclose(expected_value, result, rtol=1e-7)
class TestComputeTprSeg(unittest.TestCase):
@parameterized.expand(TEST_CASES_2D_SEG + TEST_CASES_3D_SEG)
def test_value(self, input_data, expected_value):
result = compute_confusion_metric(**input_data)
self.assertEqual(expected_value, result)
if __name__ == "__main__":
unittest.main()
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