skhubness.reduction.MutualProximity¶

class
skhubness.reduction.
MutualProximity
(method: str = 'normal', verbose: int = 0, **kwargs)[source]¶ Hubness reduction with Mutual Proximity [1].
 Parameters
 method: ‘normal’ or ‘empiric’, default = ‘normal’
Model distance distribution with ‘method’.
‘normal’ or ‘gaussi’ model distance distributions with independent Gaussians (fast)
‘empiric’ or ‘exact’ model distances with the empiric distributions (slow)
 verbose: int, default = 0
If verbose > 0, show progress bar.
References
 1
Schnitzer, D., Flexer, A., Schedl, M., & Widmer, G. (2012). Local and global scaling reduce hubs in space. The Journal of Machine Learning Research, 13(1), 2871–2902.

__init__
(method: str = 'normal', verbose: int = 0, **kwargs)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([method, verbose])Initialize self.
fit
(neigh_dist, neigh_ind[, X, assume_sorted])Fit the model using neigh_dist and neigh_ind as training data.
fit_transform
(neigh_dist, neigh_ind, X[, …])Equivalent to call .fit().transform()
transform
(neigh_dist, neigh_ind[, X, …])Transform distance between test and training data with Mutual Proximity.

fit
(neigh_dist, neigh_ind, X=None, assume_sorted=None, *args, **kwargs) → skhubness.reduction.mutual_proximity.MutualProximity[source]¶ Fit the model using neigh_dist and neigh_ind as training data.
 Parameters
 neigh_dist: np.ndarray, shape (n_samples, n_neighbors)
Distance matrix of training objects (rows) against their individual k nearest neighbors (columns).
 neigh_ind: np.ndarray, shape (n_samples, n_neighbors)
Neighbor indices corresponding to the values in neigh_dist.
 X: ignored
 assume_sorted: ignored

fit_transform
(neigh_dist, neigh_ind, X, assume_sorted=True, return_distance=True, *args, **kwargs)[source]¶ Equivalent to call .fit().transform()

transform
(neigh_dist, neigh_ind, X=None, assume_sorted=None, *args, **kwargs)[source]¶ Transform distance between test and training data with Mutual Proximity.
 Parameters
 neigh_dist: np.ndarray
Distance matrix of test objects (rows) against their individual k nearest neighbors among the training data (columns).
 neigh_ind: np.ndarray
Neighbor indices corresponding to the values in neigh_dist
 X: ignored
 assume_sorted: ignored
 Returns
 hub_reduced_dist, neigh_ind
Mutual Proximity distances, and corresponding neighbor indices
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. Classes from
skhubness.neighbors
do this automatically.