fix

tests/metrics/functional/test_classification.py

@@ -1,34 +1,9 @@
 import pytest
 import torch
 
-from pytorch_lightning import seed_everything
 from pytorch_lightning.metrics.functional.classification import dice_score
 
 
-@pytest.mark.parametrize(['sample_weight', 'pos_label', "exp_shape"], [
-    pytest.param(1, 1., 42),
-    pytest.param(None, 1., 42),
-])
-def test_binary_clf_curve(sample_weight, pos_label, exp_shape):
-    # TODO: move back the pred and target to test func arguments
-    #  if you fix the array inside the function, you'd also have fix the shape,
-    #  because when the array changes, you also have to fix the shape
-    seed_everything(0)
-    pred = torch.randint(low=51, high=99, size=(100, ), dtype=torch.float) / 100
-    target = torch.tensor([0, 1] * 50, dtype=torch.int)
-    if sample_weight is not None:
-        sample_weight = torch.ones_like(pred) * sample_weight
-
-    fps, tps, thresh = _binary_clf_curve(preds=pred, target=target, sample_weights=sample_weight, pos_label=pos_label)
-
-    assert isinstance(tps, torch.Tensor)
-    assert isinstance(fps, torch.Tensor)
-    assert isinstance(thresh, torch.Tensor)
-    assert tps.shape == (exp_shape, )
-    assert fps.shape == (exp_shape, )
-    assert thresh.shape == (exp_shape, )
-
-
 @pytest.mark.parametrize(['pred', 'target', 'expected'], [
     pytest.param([[0, 0], [1, 1]], [[0, 0], [1, 1]], 1.),
     pytest.param([[1, 1], [0, 0]], [[0, 0], [1, 1]], 0.),