pai4sk.metrics.pairwise.word_movers_distance(W, X, Y=None, use_gpu=True)

Computes word movers distance between samples in X and Y.

The details of the approximation algorithm can be found here: “”.

  • W (ndarray, shape: (n_features, n_dimensions)) – Embedding vectors
  • X (array-like, sparse-matrix, shape (n_samples_x, n_features)) – Input dataset. When MPI is used, it is preferred to have X of larger size than Y when X and Y are of unequal sizes as the MPI distribution of data is performed for X only.
  • Y (array-like, sparse-matrix, shape (n_samples_y, n_features), optional) – Input dataset. If None, the output will be the pairwise similarities between all samples in X.

D – Word Movers Distance

Return type:

array-like, shape (n_samples_x, n_samples_y)