File size: 2,728 Bytes
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 | # 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
import numpy as np
import torch
from parameterized import parameterized
from monai.metrics import compute_roc_auc
TEST_CASE_1 = [
{
"y_pred": torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5]]),
"y": torch.tensor([[0], [1], [0], [1]]),
"to_onehot_y": True,
"softmax": True,
},
0.75,
]
TEST_CASE_2 = [{"y_pred": torch.tensor([[0.5], [0.5], [0.2], [8.3]]), "y": torch.tensor([[0], [1], [0], [1]])}, 0.875]
TEST_CASE_3 = [{"y_pred": torch.tensor([[0.5], [0.5], [0.2], [8.3]]), "y": torch.tensor([0, 1, 0, 1])}, 0.875]
TEST_CASE_4 = [{"y_pred": torch.tensor([0.5, 0.5, 0.2, 8.3]), "y": torch.tensor([0, 1, 0, 1])}, 0.875]
TEST_CASE_5 = [
{
"y_pred": torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5]]),
"y": torch.tensor([[0], [1], [0], [1]]),
"to_onehot_y": True,
"softmax": True,
"average": "none",
},
[0.75, 0.75],
]
TEST_CASE_6 = [
{
"y_pred": torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5], [0.1, 0.5]]),
"y": torch.tensor([[1, 0], [0, 1], [0, 0], [1, 1], [0, 1]]),
"softmax": True,
"average": "weighted",
},
0.56667,
]
TEST_CASE_7 = [
{
"y_pred": torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5], [0.1, 0.5]]),
"y": torch.tensor([[1, 0], [0, 1], [0, 0], [1, 1], [0, 1]]),
"softmax": True,
"average": "micro",
},
0.62,
]
TEST_CASE_8 = [
{
"y_pred": torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5]]),
"y": torch.tensor([[0], [1], [0], [1]]),
"to_onehot_y": True,
"other_act": lambda x: torch.log_softmax(x, dim=1),
},
0.75,
]
class TestComputeROCAUC(unittest.TestCase):
@parameterized.expand(
[TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7, TEST_CASE_8]
)
def test_value(self, input_data, expected_value):
result = compute_roc_auc(**input_data)
np.testing.assert_allclose(expected_value, result, rtol=1e-5)
if __name__ == "__main__":
unittest.main()
|