metrics.pairwise.word_movers_distance¶
-
pai4sk.metrics.pairwise.
word_movers_distance
(W, X, Y=None, use_gpu=True, optimization_level=1, num_cpu_threads=None)¶ Computes word movers distance between samples in X and Y.
The details of the approximation algorithm can be found here: “http://export.arxiv.org/abs/1812.02091”.
- Parameters
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 inX
.optimization_level (integer, mimimum is 0 and maximum is 4. Using a higher value increases runtime.) –
num_cpu_threads (integer, maximum number of CPU threads used by each process.) –
- Returns
D – Word Movers Distance
- Return type
array-like, shape (n_samples_x, n_samples_y)