def test_print(self):
result = Result(self.cardinality, "Test")
result.number_of_classes = 0
result.update(torch.Tensor([[0.4, 0.3], [0.3, 0.4], [0.4, 0.3],
[0.3, 0.4]]),
labels=[0, 1, 1, 0])
result.computing_result()
result = Result(self.cardinality)
result.number_of_classes = 0
result.update(torch.Tensor([[0.4, 0.3], [0.3, 0.4], [0.4, 0.3],
[0.3, 0.4]]),
labels=[0, 1, 1, 0])
result.computing_result()
def test_results_no_class(self):
result = Result(self.cardinality)
result.update(torch.Tensor([[0.4, 0.3], [0.3, 0.4], [0.4, 0.3],
[0.3, 0.4]]),
labels=[0, 1, 1, 0])
result.number_of_classes = 0
result_dump = pickle.dumps({
"Result": {
"1": {
"Test": result,
"Train": result
},
"2": {
"Test": result,
"Train": result
}
}
})
mockOpen = mock_open(read_data=result_dump)
with patch('builtins.open', mockOpen):
results = Results(path_model="model/", name_model="Test")
results.load_files()
results.compute_results(condition="Test")
self.assertEqual(results.global_TP, 0)
self.assertEqual(results.global_FP, 0)
self.assertEqual(results.global_FN, 0)
self.assertEqual(results.macro_precision, 0)
self.assertEqual(results.macro_recall, 0)
self.assertTrue(math.isclose(results.micro_recall, 0))
self.assertTrue(math.isclose(results.micro_precision, 0))
def test_no_class(self):
result = Result(self.cardinality)
result.number_of_classes = 0
result.update(torch.Tensor([[0.4, 0.3], [0.3, 0.4], [0.4, 0.3],
[0.3, 0.4]]),
labels=[0, 1, 1, 0])
result.computing_result()
self.assertEqual(result.micro_precision, 0)
self.assertEqual(result.micro_recall, 0)