def get_accuracy(targets, outputs, k=1, ignore_index=None):
""" Get the accuracy top-k accuracy between two tensors.
Args:
targets (1 - 2D :class:`torch.Tensor`): Target or true vector against which to measure
saccuracy
outputs (1 - 3D :class:`torch.Tensor`): Prediction or output vector
ignore_index (int, optional): Specifies a target index that is ignored
Returns:
:class:`tuple` consisting of accuracy (:class:`float`), number correct (:class:`int`) and
total (:class:`int`)
Example:
>>> import torch
>>> from torchnlp.metrics import get_accuracy
>>> targets = torch.LongTensor([1, 2, 3, 4, 5])
>>> outputs = torch.LongTensor([1, 2, 2, 3, 5])
>>> accuracy, n_correct, n_total = get_accuracy(targets, outputs, ignore_index=3)
>>> accuracy
0.8
>>> n_correct
4
>>> n_total
5
"""
n_correct = 0.0
for target, output in zip(targets, outputs):
if not torch.is_tensor(target):
target = torch.LongTensor([target])
if not torch.is_tensor(output):
output = torch.LongTensor([[output]])
predictions = output.topk(k=min(k, len(output)), dim=0)[0]
for prediction in predictions:
if not torch.is_tensor(prediction):
prediction = torch.LongTensor([prediction])
if torch_equals_ignore_index(target,
prediction,
ignore_index=ignore_index):
n_correct += 1
break
return n_correct / len(targets), n_correct, len(targets)
def test_torch_equals_ignore_index():
source = torch.LongTensor([1, 2, 3])
target = torch.LongTensor([1, 2, 4])
assert torch_equals_ignore_index(source, target, ignore_index=3)
assert not torch_equals_ignore_index(source, target)
