kiez.hubness_reduction.CSLS

class kiez.hubness_reduction.CSLS(nn_algo: NNAlgorithm, verbose: int = 0, **kwargs)

Hubness reduction with Cross-domain similarity local scaling.

Uses the formula presented in [1].

References

[1]

Lample, G., Conneau, A., Ranzato, M., Denoyer, L., & Jégou, H. (2018) Word translation without parallel data In: 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings. https://openreview.net/forum?id=H196sainb

__init__(nn_algo: NNAlgorithm, verbose: int = 0, **kwargs)

Methods

__init__(nn_algo[, verbose])
fit(source[, target])
kneighbors([k])
transform(neigh_dist, neigh_ind, query)	Transform distance between test and training data with CSLS.

transform(neigh_dist, neigh_ind, query) → Tuple[T, T]

Transform distance between test and training data with CSLS.

Parameters:

• neigh_dist (np.ndarray, shape (n_query, n_neighbors)) – Distance matrix of test objects (rows) against their individual k nearest neighbors among the training data (columns).

• neigh_ind (np.ndarray, shape (n_query, n_neighbors)) – Neighbor indices corresponding to the values in neigh_dist

• query – Ignored

Returns:

CSLS distances, and corresponding neighbor indices

Return type:

hub_reduced_dist, neigh_ind

Notes

The returned distances are NOT sorted! If you use this class directly, you will need to sort the returned matrices according to hub_reduced_dist.