pairwise_distances(X) should always have 0 diagonal

In the case of euclidean distances, we explicitly set the diagonal of unary pairwise distances to 0: https://github.com/scikit-learn/scikit-learn/blob/e170d479acff31aadd9d2ab404952b23f7994aab/sklearn/metrics/pairwise.py#L257 as well as in pairwise_distances_chunked: https://github.com/scikit-learn/scikit-learn/blob/e170d479acff31aadd9d2ab404952b23f7994aab/sklearn/metrics/pairwise.py#L1279. We also do so for cosine distances: https://github.com/scikit-learn/scikit-learn/blob/e170d479acff31aadd9d2ab404952b23f7994aab/sklearn/metrics/pairwise.py#L550.

We should zero the diagonal of the output for all metrics through pairwise_distances and pairwise_distances_chunked (where Y is None or Y is X) to reduce the effect of imprecision during distance calculation.

That is, for all metric we eventually want:

assert not np.any(pairwise_distances(X, metric=metric)[np.diag_indices(X.shape[0])])

and a similar assertion for pairwise_distances_chunked

I say eventually because I propose that:

• if current output satisfies np.allclose(pairwise_distances(X, metric=metric)[np.diag_indices(X.shape[0])], 0), we just set it to 0
• if current output does not satisfy that condition, we only raise a FutureWarning saying that “The specified metric is not a valid dissimilarity metric; it does not return metric(x, x) == 0 for some values. In version 0.23, the diagonal of pairwise_distances(X) will be set to 0.” We do this because at the moment we do not strictly require that the metric be a valid dissimilarity measure.
• we do the same for pairwise_distances_chunked

[jeremiedbb]

I don’t think so because you can use euclidean_distances instead of pairwise_distances and you still want 0s on the diagonal in that case.

Btw, np.fill_diagonal(array, 0) does the job and is more readable 😃

[jnothman]

I’m proposing that we stop supporting them, but it would be worth tracing the history of that test to work out if it was well motivated