def test_correct_score_calculation_multi_label_micro(self):
evaluator = evaluators.AUROCEvaluator(model_output_key=None,
batch_target_key='target',
average='micro')
output = torch.tensor(
[[0.6, 0.2], [0.7, 0.2], [0.6, 0.6], [0.3, 0.55]],
dtype=torch.float32)
batch = {
'target':
torch.tensor([[1, 1], [0, 1], [1, 0], [0, 1]], dtype=torch.float32)
}
evaluator.step(output, batch)
output = torch.tensor([[0.6, 0.4]], dtype=torch.float32)
batch = {'target': torch.tensor([[1, 1]], dtype=torch.float32)}
evaluator.step(output, batch)
res = evaluator.calculate()
correct = metrics.roc_auc_score(y_score=np.array(
[0.6, 0.7, 0.6, 0.3, 0.6, 0.2, 0.2, 0.6, 0.55, 0.4]),
y_true=np.array(
[1, 0, 1, 0, 1, 1, 1, 0, 1, 1]))
self.assertAlmostEqual(res.score, correct)
def test_correct_score_calculation_binary(self):
evaluator = evaluators.AUROCEvaluator(model_output_key=None,
batch_target_key='target',
average='macro')
output = torch.tensor([0.9, 0.2, 0.8, 0.3], dtype=torch.float32)
batch = {'target': torch.tensor([1, 1, 0, 0], dtype=torch.float32)}
evaluator.step(output, batch)
output = torch.tensor([0.2, 0.98, 0.76], dtype=torch.float32)
batch = {'target': torch.tensor([1, 1, 0], dtype=torch.float32)}
evaluator.step(output, batch)
res = evaluator.calculate()
self.assertAlmostEqual(res.score, 0.5)