kiez.hubness_reduction.LocalScaling

class kiez.hubness_reduction.LocalScaling(method: str = 'standard', **kwargs)[source]

Hubness reduction with Local Scaling.

Uses the formula presented in [1].

Parameters:

  • k (int, default = 5) – Number of neighbors to consider for the rescaling

  • method ('standard' or 'nicdm', default = 'standard') – Perform local scaling with the specified variant: - ‘standard’ or ‘ls’ rescale distances using the distance to the k-th neighbor - ‘nicdm’ rescales distances using a statistic over distances to k neighbors

  • verbose (int, default = 0) – If verbose > 0, show progress bar.

References

__init__(method: str = 'standard', **kwargs)[source]

Methods

__init__([method])

fit(source[, target])

kneighbors([k])

transform(neigh_dist, neigh_ind[, query])

Transform distance between test and training data with Mutual Proximity.
transform(neigh_dist, neigh_ind, query=None) → Tuple[T, T][source]

Transform distance between test and training data with Mutual Proximity.

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:

Local scaling distances, and corresponding neighbor indices

Return type:

hub_reduced_dist, neigh_ind

Raises:

ValueError – If wrong self.method was supplied

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.