mirror of
https://github.com/modelscope/modelscope.git
synced 2025-12-16 16:27:45 +01:00
33 lines
1002 B
Python
33 lines
1002 B
Python
|
|
# Copyright (c) Alibaba, Inc. and its affiliates.
|
||
|
|
|
||
|
|
import unittest
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from modelscope.metrics.sequence_classification_metric import \
|
||
|
|
SequenceClassificationMetric
|
||
|
|
from modelscope.utils.test_utils import test_level
|
||
|
|
|
||
|
|
|
||
|
|
class TestTextClsMetrics(unittest.TestCase):
|
||
|
|
|
||
|
|
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
||
|
|
def test_value(self):
|
||
|
|
metric = SequenceClassificationMetric()
|
||
|
|
outputs = {
|
||
|
|
'logits':
|
||
|
|
np.array([[2.0, 1.0, 0.5], [1.0, 1.5, 1.0], [2.0, 1.0, 3.0],
|
||
|
|
[2.4, 1.5, 4.0], [2.0, 1.0, 3.0], [2.4, 1.5, 1.7],
|
||
|
|
[2.0, 1.0, 0.5], [2.4, 1.5, 0.5]])
|
||
|
|
}
|
||
|
|
inputs = {'labels': np.array([0, 1, 2, 2, 0, 1, 2, 2])}
|
||
|
|
metric.add(outputs, inputs)
|
||
|
|
ret = metric.evaluate()
|
||
|
|
self.assertTrue(np.isclose(ret['f1'], 0.5))
|
||
|
|
self.assertTrue(np.isclose(ret['accuracy'], 0.5))
|
||
|
|
print(ret)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
unittest.main()
|