def test_v1_3_0_deprecated_metrics():
from pytorch_lightning.metrics.functional.classification import to_onehot
with pytest.deprecated_call(match='will be removed in v1.3'):
to_onehot(torch.tensor([1, 2, 3]))
from pytorch_lightning.metrics.functional.classification import to_categorical
with pytest.deprecated_call(match='will be removed in v1.3'):
to_categorical(torch.tensor([[0.2, 0.5], [0.9, 0.1]]))
from pytorch_lightning.metrics.functional.classification import get_num_classes
with pytest.deprecated_call(match='will be removed in v1.3'):
get_num_classes(pred=torch.tensor([0, 1]), target=torch.tensor([1, 1]))
x_binary = torch.tensor([0, 1, 2, 3])
y_binary = torch.tensor([0, 1, 2, 3])
from pytorch_lightning.metrics.functional.classification import roc
with pytest.deprecated_call(match='will be removed in v1.3'):
roc(pred=x_binary, target=y_binary)
from pytorch_lightning.metrics.functional.classification import _roc
with pytest.deprecated_call(match='will be removed in v1.3'):
_roc(pred=x_binary, target=y_binary)
x_multy = torch.tensor([
[0.85, 0.05, 0.05, 0.05],
[0.05, 0.85, 0.05, 0.05],
[0.05, 0.05, 0.85, 0.05],
[0.05, 0.05, 0.05, 0.85],
])
y_multy = torch.tensor([0, 1, 3, 2])
from pytorch_lightning.metrics.functional.classification import multiclass_roc
with pytest.deprecated_call(match='will be removed in v1.3'):
multiclass_roc(pred=x_multy, target=y_multy)
from pytorch_lightning.metrics.functional.classification import average_precision
with pytest.deprecated_call(match='will be removed in v1.3'):
average_precision(pred=x_binary, target=y_binary)
from pytorch_lightning.metrics.functional.classification import precision_recall_curve
with pytest.deprecated_call(match='will be removed in v1.3'):
precision_recall_curve(pred=x_binary, target=y_binary)
from pytorch_lightning.metrics.functional.classification import multiclass_precision_recall_curve
with pytest.deprecated_call(match='will be removed in v1.3'):
multiclass_precision_recall_curve(pred=x_multy, target=y_multy)
from pytorch_lightning.metrics.functional.reduction import reduce
with pytest.deprecated_call(match='will be removed in v1.3'):
reduce(torch.tensor([0, 1, 1, 0]), 'sum')
from pytorch_lightning.metrics.functional.reduction import class_reduce
with pytest.deprecated_call(match='will be removed in v1.3'):
class_reduce(
torch.randint(1, 10, (50, )).float(),
torch.randint(10, 20, (50, )).float(),
torch.randint(1, 100, (50, )).float())
def test_average_precision_constant_values():
# Check the average_precision_score of a constant predictor is
# the TPR
# Generate a dataset with 25% of positives
target = torch.zeros(100, dtype=torch.float)
target[::4] = 1
# And a constant score
pred = torch.ones(100)
# The precision is then the fraction of positive whatever the recall
# is, as there is only one threshold:
assert average_precision(pred, target).item() == .25
def forward(self,
pred: torch.Tensor,
target: torch.Tensor,
sample_weight: Optional[Sequence] = None) -> torch.Tensor:
"""
Actual metric computation
Args:
pred: predicted labels
target: groundtruth labels
sample_weight: the weights per sample
Return:
torch.Tensor: classification score
"""
return average_precision(pred=pred,
target=target,
sample_weight=sample_weight,
pos_label=self.pos_label)
def test_average_precision(scores, target, expected_score):
assert average_precision(scores, target) == expected_score
