Bug in binary precision

Basic tests of binary precision seem to fail:

precision = Precision(average=True)

y_pred = torch.rand(10, 1)
y = torch.randint(0, 2, size=(10,)).type(torch.LongTensor)

precision.update((y_pred, y))

np_y = y.numpy().ravel()
np_y_pred = (y_pred.numpy().ravel() > 0.5).astype('int')

precision_score(np_y, np_y_pred), precision.compute()

@jasonkriss could you please confirm this ?

EDIT: Another failing test: https://github.com/pytorch/ignite/pull/333#issuecomment-442643530

Probably, the error is at binary to categorical mapping and counting class-0 similarly to class-1. But in binary case we should ignore class-0.

There is also input “binary or categorical” checking is missing if user tries to mix both in several updates.

[alykhantejani]

Once this is merged we should probably cut a release. I think for bugfixes we should release often, just to avoid anyone running for too long with a buggy version

[alykhantejani]

So we could follow the pytorch path here. 0.2 can contain the backwards incompatible changes and this can be 0.1.2, we can switch the warning message to say 0.2 and all the current tickets assigned to 0.1.2 we can just move to 0.2 and that can be the next release (pending no further bug fixes).

wdyt?