-
-
Notifications
You must be signed in to change notification settings - Fork 4.4k
/
matutils.py
1568 lines (1276 loc) · 50.3 KB
/
matutils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2011 Radim Rehurek <radimrehurek@seznam.cz>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
"""This module contains math helper functions."""
from __future__ import with_statement
from itertools import chain
import logging
import math
from gensim import utils
import numpy as np
import scipy.sparse
from scipy.stats import entropy
import scipy.linalg
from scipy.linalg.lapack import get_lapack_funcs
from scipy.linalg.special_matrices import triu
from scipy.special import psi # gamma function utils
from six import iteritems, itervalues, string_types
from six.moves import xrange, zip as izip
logger = logging.getLogger(__name__)
def blas(name, ndarray):
"""Helper for getting BLAS function, used :func:`scipy.linalg.get_blas_funcs`.
Parameters
----------
name : str
Name(s) of BLAS functions without type prefix.
ndarray : numpy.ndarray
Arrays can be given to determine optimal prefix of BLAS routines.
Returns
-------
fortran object
Fortran function for needed operation.
"""
return scipy.linalg.get_blas_funcs((name,), (ndarray,))[0]
def argsort(x, topn=None, reverse=False):
"""Get indices of the `topn` smallest elements in array `x`.
Parameters
----------
x : array_like
Array to sort.
topn : int, optional
Number of indices of the smallest(greatest) elements to be returned if given,
otherwise - indices of all elements will be returned in ascending(descending) order.
reverse : bool, optional
If True - return the `topn` greatest elements, in descending order.
Returns
-------
numpy.ndarray
Array of `topn` indices that.sort the array in the required order.
"""
x = np.asarray(x) # unify code path for when `x` is not a np array (list, tuple...)
if topn is None:
topn = x.size
if topn <= 0:
return []
if reverse:
x = -x
if topn >= x.size or not hasattr(np, 'argpartition'):
return np.argsort(x)[:topn]
# np >= 1.8 has a fast partial argsort, use that!
most_extreme = np.argpartition(x, topn)[:topn]
return most_extreme.take(np.argsort(x.take(most_extreme))) # resort topn into order
def corpus2csc(corpus, num_terms=None, dtype=np.float64, num_docs=None, num_nnz=None, printprogress=0):
"""Convert a streamed corpus in BoW format into a sparse matrix `scipy.sparse.csc_matrix`,
with documents as columns.
Notes
-----
If the number of terms, documents and non-zero elements is known, you can pass
them here as parameters and a more memory efficient code path will be taken.
Parameters
----------
corpus : iterable of iterable of (int, number)
Input corpus in BoW format
num_terms : int, optional
If provided, the `num_terms` attributes in the corpus will be ignored.
dtype : data-type, optional
Data type of output matrix.
num_docs : int, optional
If provided, the `num_docs` attributes in the corpus will be ignored.
num_nnz : int, optional
If provided, the `num_nnz` attributes in the corpus will be ignored.
printprogress : int, optional
Print progress for every `printprogress` number of documents,
If 0 - nothing will be printed.
Returns
-------
scipy.sparse.csc_matrix
Sparse matrix inferred based on `corpus`.
See Also
--------
:class:`~gensim.matutils.Sparse2Corpus`
"""
try:
# if the input corpus has the `num_nnz`, `num_docs` and `num_terms` attributes
# (as is the case with MmCorpus for example), we can use a more efficient code path
if num_terms is None:
num_terms = corpus.num_terms
if num_docs is None:
num_docs = corpus.num_docs
if num_nnz is None:
num_nnz = corpus.num_nnz
except AttributeError:
pass # not a MmCorpus...
if printprogress:
logger.info("creating sparse matrix from corpus")
if num_terms is not None and num_docs is not None and num_nnz is not None:
# faster and much more memory-friendly version of creating the sparse csc
posnow, indptr = 0, [0]
indices = np.empty((num_nnz,), dtype=np.int32) # HACK assume feature ids fit in 32bit integer
data = np.empty((num_nnz,), dtype=dtype)
for docno, doc in enumerate(corpus):
if printprogress and docno % printprogress == 0:
logger.info("PROGRESS: at document #%i/%i", docno, num_docs)
posnext = posnow + len(doc)
indices[posnow: posnext] = [feature_id for feature_id, _ in doc]
data[posnow: posnext] = [feature_weight for _, feature_weight in doc]
indptr.append(posnext)
posnow = posnext
assert posnow == num_nnz, "mismatch between supplied and computed number of non-zeros"
result = scipy.sparse.csc_matrix((data, indices, indptr), shape=(num_terms, num_docs), dtype=dtype)
else:
# slower version; determine the sparse matrix parameters during iteration
num_nnz, data, indices, indptr = 0, [], [], [0]
for docno, doc in enumerate(corpus):
if printprogress and docno % printprogress == 0:
logger.info("PROGRESS: at document #%i", docno)
indices.extend([feature_id for feature_id, _ in doc])
data.extend([feature_weight for _, feature_weight in doc])
num_nnz += len(doc)
indptr.append(num_nnz)
if num_terms is None:
num_terms = max(indices) + 1 if indices else 0
num_docs = len(indptr) - 1
# now num_docs, num_terms and num_nnz contain the correct values
data = np.asarray(data, dtype=dtype)
indices = np.asarray(indices)
result = scipy.sparse.csc_matrix((data, indices, indptr), shape=(num_terms, num_docs), dtype=dtype)
return result
def pad(mat, padrow, padcol):
"""Add additional rows/columns to `mat`. The new rows/columns will be initialized with zeros.
Parameters
----------
mat : numpy.ndarray
Input 2D matrix
padrow : int
Number of additional rows
padcol : int
Number of additional columns
Returns
-------
numpy.matrixlib.defmatrix.matrix
Matrix with needed padding.
"""
if padrow < 0:
padrow = 0
if padcol < 0:
padcol = 0
rows, cols = mat.shape
return np.bmat([
[mat, np.matrix(np.zeros((rows, padcol)))],
[np.matrix(np.zeros((padrow, cols + padcol)))],
])
def zeros_aligned(shape, dtype, order='C', align=128):
"""Get array aligned at `align` byte boundary.
Parameters
----------
shape : int or (int, int)
Shape of array.
dtype : data-type
Data type of array.
order : {'C', 'F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory.
align : int, optional
Boundary for alignment in bytes.
Returns
-------
numpy.ndarray
Aligned array.
"""
nbytes = np.prod(shape, dtype=np.int64) * np.dtype(dtype).itemsize
buffer = np.zeros(nbytes + align, dtype=np.uint8) # problematic on win64 ("maximum allowed dimension exceeded")
start_index = -buffer.ctypes.data % align
return buffer[start_index: start_index + nbytes].view(dtype).reshape(shape, order=order)
def ismatrix(m):
"""Check does `m` numpy.ndarray or `scipy.sparse` matrix.
Parameters
----------
m : object
Candidate for matrix
Returns
-------
bool
True if `m` is matrix, False otherwise.
"""
return isinstance(m, np.ndarray) and m.ndim == 2 or scipy.sparse.issparse(m)
def any2sparse(vec, eps=1e-9):
"""Convert a numpy.ndarray or `scipy.sparse` vector into gensim BoW format.
Parameters
----------
vec : {`numpy.ndarray`, `scipy.sparse`}
Input vector
eps : float, optional
Value used for threshold, all coordinates less than `eps` will not be presented in result.
Returns
-------
list of (int, float)
Vector in BoW format.
"""
if isinstance(vec, np.ndarray):
return dense2vec(vec, eps)
if scipy.sparse.issparse(vec):
return scipy2sparse(vec, eps)
return [(int(fid), float(fw)) for fid, fw in vec if np.abs(fw) > eps]
def scipy2scipy_clipped(matrix, topn, eps=1e-9):
"""Get a `scipy.sparse` vector / matrix consisting of 'topn' elements of the greatest magnitude (absolute value).
Parameters
----------
matrix : `scipy.sparse`
Input vector / matrix.
topn : int
Number of greatest (by module) elements, that will be in result.
eps : float
PARAMETER IGNORED.
Returns
-------
`scipy.sparse.csr.csr_matrix`
Clipped matrix.
"""
if not scipy.sparse.issparse(matrix):
raise ValueError("'%s' is not a scipy sparse vector." % matrix)
if topn <= 0:
return scipy.sparse.csr_matrix([])
# Return clipped sparse vector if input is a sparse vector.
if matrix.shape[0] == 1:
# use np.argpartition/argsort and only form tuples that are actually returned.
biggest = argsort(abs(matrix.data), topn, reverse=True)
indices, data = matrix.indices.take(biggest), matrix.data.take(biggest)
return scipy.sparse.csr_matrix((data, indices, [0, len(indices)]))
# Return clipped sparse matrix if input is a matrix, processing row by row.
else:
matrix_indices = []
matrix_data = []
matrix_indptr = [0]
# calling abs() on entire matrix once is faster than calling abs() iteratively for each row
matrix_abs = abs(matrix)
for i in range(matrix.shape[0]):
v = matrix.getrow(i)
v_abs = matrix_abs.getrow(i)
# Sort and clip each row vector first.
biggest = argsort(v_abs.data, topn, reverse=True)
indices, data = v.indices.take(biggest), v.data.take(biggest)
# Store the topn indices and values of each row vector.
matrix_data.append(data)
matrix_indices.append(indices)
matrix_indptr.append(matrix_indptr[-1] + min(len(indices), topn))
matrix_indices = np.concatenate(matrix_indices).ravel()
matrix_data = np.concatenate(matrix_data).ravel()
# Instantiate and return a sparse csr_matrix which preserves the order of indices/data.
return scipy.sparse.csr.csr_matrix(
(matrix_data, matrix_indices, matrix_indptr),
shape=(matrix.shape[0], np.max(matrix_indices) + 1)
)
def scipy2sparse(vec, eps=1e-9):
"""Convert a scipy.sparse vector BoW format.
Parameters
----------
vec : `scipy.sparse`
Sparse vector
eps : float, optional
Value used for threshold, all coordinates less than `eps` will not be presented in result.
Returns
-------
list of (int, float)
Vector in BoW format.
"""
vec = vec.tocsr()
assert vec.shape[0] == 1
return [(int(pos), float(val)) for pos, val in zip(vec.indices, vec.data) if np.abs(val) > eps]
class Scipy2Corpus(object):
"""Convert a sequence of dense/sparse vectors into a streamed gensim corpus object.
See Also
--------
:func:`~gensim.matutils.corpus2csc`
"""
def __init__(self, vecs):
"""
Parameters
----------
vecs : iterable of {`numpy.ndarray`, `scipy.sparse`}
Input vectors.
"""
self.vecs = vecs
def __iter__(self):
for vec in self.vecs:
if isinstance(vec, np.ndarray):
yield full2sparse(vec)
else:
yield scipy2sparse(vec)
def __len__(self):
return len(self.vecs)
def sparse2full(doc, length):
"""Convert a document in BoW format into dense numpy array.
Parameters
----------
doc : list of (int, number)
Document in BoW format
length : int
Length of result vector
Returns
-------
numpy.ndarray
Dense variant of `doc` vector.
See Also
--------
:func:`~gensim.matutils.full2sparse`
"""
result = np.zeros(length, dtype=np.float32) # fill with zeroes (default value)
# convert indices to int as numpy 1.12 no longer indexes by floats
doc = ((int(id_), float(val_)) for (id_, val_) in doc)
doc = dict(doc)
# overwrite some of the zeroes with explicit values
result[list(doc)] = list(itervalues(doc))
return result
def full2sparse(vec, eps=1e-9):
"""Convert a dense array into the BoW format.
Parameters
----------
vec : numpy.ndarray
Input dense vector
eps : float
Threshold value, if coordinate in `vec` < eps, this will not be presented in result.
Returns
-------
list of (int, float)
BoW format of `vec`.
See Also
--------
:func:`~gensim.matutils.sparse2full`
"""
vec = np.asarray(vec, dtype=float)
nnz = np.nonzero(abs(vec) > eps)[0]
return list(zip(nnz, vec.take(nnz)))
dense2vec = full2sparse
def full2sparse_clipped(vec, topn, eps=1e-9):
"""Like :func:`~gensim.matutils.full2sparse`, but only return the `topn` elements of the greatest magnitude (abs).
Parameters
----------
vec : numpy.ndarray
Input dense vector
topn : int
Number of greatest (abs) elements that will be presented in result.
eps : float
Threshold value, if coordinate in `vec` < eps, this will not be presented in result.
Returns
-------
list of (int, float)
Clipped vector in BoW format.
See Also
--------
:func:`~gensim.matutils.full2sparse`
"""
# use np.argpartition/argsort and only form tuples that are actually returned.
# this is about 40x faster than explicitly forming all 2-tuples to run sort() or heapq.nlargest() on.
if topn <= 0:
return []
vec = np.asarray(vec, dtype=float)
nnz = np.nonzero(abs(vec) > eps)[0]
biggest = nnz.take(argsort(abs(vec).take(nnz), topn, reverse=True))
return list(zip(biggest, vec.take(biggest)))
def corpus2dense(corpus, num_terms, num_docs=None, dtype=np.float32):
"""Convert corpus into a dense numpy array (documents will be columns).
Parameters
----------
corpus : iterable of iterable of (int, number)
Input corpus in BoW format.
num_terms : int
Number of terms in dictionary (will be used as size of output vector.
num_docs : int, optional
Number of documents in corpus.
dtype : data-type, optional
Data type of output matrix
Returns
-------
numpy.ndarray
Dense array that present `corpus`.
See Also
--------
:class:`~gensim.matutils.Dense2Corpus`
"""
if num_docs is not None:
# we know the number of documents => don't bother column_stacking
docno, result = -1, np.empty((num_terms, num_docs), dtype=dtype)
for docno, doc in enumerate(corpus):
result[:, docno] = sparse2full(doc, num_terms)
assert docno + 1 == num_docs
else:
result = np.column_stack(sparse2full(doc, num_terms) for doc in corpus)
return result.astype(dtype)
class Dense2Corpus(object):
"""Treat dense numpy array as a streamed gensim corpus in BoW format.
Notes
-----
No data copy is made (changes to the underlying matrix imply changes in the corpus).
See Also
--------
:func:`~gensim.matutils.corpus2dense`
:class:`~gensim.matutils.Sparse2Corpus`
"""
def __init__(self, dense, documents_columns=True):
"""
Parameters
----------
dense : numpy.ndarray
Corpus in dense format.
documents_columns : bool, optional
If True - documents will be column, rows otherwise.
"""
if documents_columns:
self.dense = dense.T
else:
self.dense = dense
def __iter__(self):
"""Iterate over corpus
Yields
------
list of (int, float)
Document in BoW format.
"""
for doc in self.dense:
yield full2sparse(doc.flat)
def __len__(self):
return len(self.dense)
class Sparse2Corpus(object):
"""Convert a matrix in scipy.sparse format into a streaming gensim corpus.
See Also
--------
:func:`~gensim.matutils.corpus2csc`
:class:`~gensim.matutils.Dense2Corpus`
"""
def __init__(self, sparse, documents_columns=True):
"""
Parameters
----------
sparse : `scipy.sparse`
Corpus scipy sparse format
documents_columns : bool, optional
If True - documents will be column, rows otherwise.
"""
if documents_columns:
self.sparse = sparse.tocsc()
else:
self.sparse = sparse.tocsr().T # make sure shape[1]=number of docs (needed in len())
def __iter__(self):
"""
Yields
------
list of (int, float)
Document in BoW format.
"""
for indprev, indnow in izip(self.sparse.indptr, self.sparse.indptr[1:]):
yield list(zip(self.sparse.indices[indprev:indnow], self.sparse.data[indprev:indnow]))
def __len__(self):
return self.sparse.shape[1]
def __getitem__(self, document_index):
"""Get a single document in the corpus by its index.
Parameters
----------
document_index : int
Index of document
Returns
-------
list of (int, number)
Document in BoW format.
"""
indprev = self.sparse.indptr[document_index]
indnow = self.sparse.indptr[document_index + 1]
return list(zip(self.sparse.indices[indprev:indnow], self.sparse.data[indprev:indnow]))
def veclen(vec):
"""Calculate length of vector
Parameters
----------
vec : list of (int, number)
Input vector in BoW format.
Returns
-------
float
Length of `vec`.
"""
if len(vec) == 0:
return 0.0
length = 1.0 * math.sqrt(sum(val**2 for _, val in vec))
assert length > 0.0, "sparse documents must not contain any explicit zero entries"
return length
def ret_normalized_vec(vec, length):
"""Normalize vector.
Parameters
----------
vec : list of (int, number)
Input vector in BoW format.
length : float
Length of vector
Returns
-------
list of (int, number)
Normalized vector in BoW format.
"""
if length != 1.0:
return [(termid, val / length) for termid, val in vec]
else:
return list(vec)
def ret_log_normalize_vec(vec, axis=1):
log_max = 100.0
if len(vec.shape) == 1:
max_val = np.max(vec)
log_shift = log_max - np.log(len(vec) + 1.0) - max_val
tot = np.sum(np.exp(vec + log_shift))
log_norm = np.log(tot) - log_shift
vec -= log_norm
else:
if axis == 1: # independently normalize each sample
max_val = np.max(vec, 1)
log_shift = log_max - np.log(vec.shape[1] + 1.0) - max_val
tot = np.sum(np.exp(vec + log_shift[:, np.newaxis]), 1)
log_norm = np.log(tot) - log_shift
vec = vec - log_norm[:, np.newaxis]
elif axis == 0: # normalize each feature
k = ret_log_normalize_vec(vec.T)
return k[0].T, k[1]
else:
raise ValueError("'%s' is not a supported axis" % axis)
return vec, log_norm
blas_nrm2 = blas('nrm2', np.array([], dtype=float))
blas_scal = blas('scal', np.array([], dtype=float))
def unitvec(vec, norm='l2', return_norm=False):
"""Scale a vector to unit length.
Parameters
----------
vec : {numpy.ndarray, scipy.sparse, list of (int, float)}
Input vector in any format
norm : {'l1', 'l2'}, optional
Normalization that will be used.
return_norm : bool, optional
If True - returns the length of vector `vec`.
Returns
-------
numpy.ndarray, scipy.sparse, list of (int, float)}
Normalized vector in same format as `vec`.
float
Length of `vec` before normalization.
Notes
-----
Zero-vector will be unchanged.
"""
if norm not in ('l1', 'l2'):
raise ValueError("'%s' is not a supported norm. Currently supported norms are 'l1' and 'l2'." % norm)
if scipy.sparse.issparse(vec):
vec = vec.tocsr()
if norm == 'l1':
veclen = np.sum(np.abs(vec.data))
if norm == 'l2':
veclen = np.sqrt(np.sum(vec.data ** 2))
if veclen > 0.0:
if return_norm:
return vec / veclen, veclen
else:
return vec / veclen
else:
if return_norm:
return vec, 1.
else:
return vec
if isinstance(vec, np.ndarray):
vec = np.asarray(vec, dtype=float)
if norm == 'l1':
veclen = np.sum(np.abs(vec))
if norm == 'l2':
veclen = blas_nrm2(vec)
if veclen > 0.0:
if return_norm:
return blas_scal(1.0 / veclen, vec), veclen
else:
return blas_scal(1.0 / veclen, vec)
else:
if return_norm:
return vec, 1
else:
return vec
try:
first = next(iter(vec)) # is there at least one element?
except StopIteration:
return vec
if isinstance(first, (tuple, list)) and len(first) == 2: # gensim sparse format
if norm == 'l1':
length = float(sum(abs(val) for _, val in vec))
if norm == 'l2':
length = 1.0 * math.sqrt(sum(val ** 2 for _, val in vec))
assert length > 0.0, "sparse documents must not contain any explicit zero entries"
if return_norm:
return ret_normalized_vec(vec, length), length
else:
return ret_normalized_vec(vec, length)
else:
raise ValueError("unknown input type")
def cossim(vec1, vec2):
"""Get cosine similarity between two sparse vectors.
The similarity is a number between <-1.0, 1.0>, higher is more similar.
Parameters
----------
vec1 : list of (int, float)
Vector in BoW format
vec2 : list of (int, float)
Vector in BoW format
Returns
-------
float
Cosine similarity between `vec1` and `vec2`.
"""
vec1, vec2 = dict(vec1), dict(vec2)
if not vec1 or not vec2:
return 0.0
vec1len = 1.0 * math.sqrt(sum(val * val for val in itervalues(vec1)))
vec2len = 1.0 * math.sqrt(sum(val * val for val in itervalues(vec2)))
assert vec1len > 0.0 and vec2len > 0.0, "sparse documents must not contain any explicit zero entries"
if len(vec2) < len(vec1):
vec1, vec2 = vec2, vec1 # swap references so that we iterate over the shorter vector
result = sum(value * vec2.get(index, 0.0) for index, value in iteritems(vec1))
result /= vec1len * vec2len # rescale by vector lengths
return result
def softcossim(vec1, vec2, similarity_matrix):
"""Get Soft Cosine Measure between two vectors given a term similarity matrix.
Return Soft Cosine Measure between two sparse vectors given a sparse term similarity matrix
in the :class:`scipy.sparse.csc_matrix` format. The similarity is a number between <-1.0, 1.0>,
higher is more similar.
Parameters
----------
vec1 : list of (int, float)
A query vector in the BoW format.
vec2 : list of (int, float)
A document vector in the BoW format.
similarity_matrix : {:class:`scipy.sparse.csc_matrix`, :class:`scipy.sparse.csr_matrix`}
A term similarity matrix, typically produced by
:meth:`~gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.similarity_matrix`.
Returns
-------
`similarity_matrix.dtype`
The Soft Cosine Measure between `vec1` and `vec2`.
Raises
------
ValueError
When the term similarity matrix is in an unknown format.
See Also
--------
:meth:`gensim.models.keyedvectors.WordEmbeddingsKeyedVectors.similarity_matrix`
A term similarity matrix produced from term embeddings.
:class:`gensim.similarities.docsim.SoftCosineSimilarity`
A class for performing corpus-based similarity queries with Soft Cosine Measure.
References
----------
Soft Cosine Measure was perhaps first defined by [sidorovetal14]_.
.. [sidorovetal14] Grigori Sidorov et al., "Soft Similarity and Soft Cosine Measure: Similarity
of Features in Vector Space Model", 2014, http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2043/1921.
"""
if not isinstance(similarity_matrix, scipy.sparse.csc_matrix):
if isinstance(similarity_matrix, scipy.sparse.csr_matrix):
similarity_matrix = similarity_matrix.T
else:
raise ValueError('unknown similarity matrix format')
if not vec1 or not vec2:
return 0.0
vec1 = dict(vec1)
vec2 = dict(vec2)
word_indices = sorted(set(chain(vec1, vec2)))
dtype = similarity_matrix.dtype
vec1 = np.array([vec1[i] if i in vec1 else 0 for i in word_indices], dtype=dtype)
vec2 = np.array([vec2[i] if i in vec2 else 0 for i in word_indices], dtype=dtype)
dense_matrix = similarity_matrix[[[i] for i in word_indices], word_indices].todense()
vec1len = vec1.T.dot(dense_matrix).dot(vec1)[0, 0]
vec2len = vec2.T.dot(dense_matrix).dot(vec2)[0, 0]
assert \
vec1len > 0.0 and vec2len > 0.0, \
u"sparse documents must not contain any explicit zero entries and the similarity matrix S " \
u"must satisfy x^T * S * x > 0 for any nonzero bag-of-words vector x."
result = vec1.T.dot(dense_matrix).dot(vec2)[0, 0]
result /= math.sqrt(vec1len) * math.sqrt(vec2len) # rescale by vector lengths
return np.clip(result, -1.0, 1.0)
def isbow(vec):
"""Checks if vector passed is in BoW format.
Parameters
----------
vec : object
Input vector in any format
Returns
-------
bool
True if vector in BoW format, False otherwise.
"""
if scipy.sparse.issparse(vec):
vec = vec.todense().tolist()
try:
id_, val_ = vec[0] # checking first value to see if it is in bag of words format by unpacking
int(id_), float(val_)
except IndexError:
return True # this is to handle the empty input case
except (ValueError, TypeError):
return False
return True
def convert_vec(vec1, vec2, num_features=None):
"""Convert vectors to dense format
Parameters
----------
vec1 : {scipy.sparse, list of (int, float)}
Input vector.
vec2 : {scipy.sparse, list of (int, float)}
Input vector.
num_features : int, optional
Number of features in vector.
Returns
-------
(numpy.ndarray, numpy.ndarray)
(`vec1`, `vec2`) in dense format.
"""
if scipy.sparse.issparse(vec1):
vec1 = vec1.toarray()
if scipy.sparse.issparse(vec2):
vec2 = vec2.toarray() # converted both the vectors to dense in case they were in sparse matrix
if isbow(vec1) and isbow(vec2): # if they are in bag of words format we make it dense
if num_features is not None: # if not None, make as large as the documents drawing from
dense1 = sparse2full(vec1, num_features)
dense2 = sparse2full(vec2, num_features)
return dense1, dense2
else:
max_len = max(len(vec1), len(vec2))
dense1 = sparse2full(vec1, max_len)
dense2 = sparse2full(vec2, max_len)
return dense1, dense2
else:
# this conversion is made because if it is not in bow format, it might be a list within a list after conversion
# the scipy implementation of Kullback fails in such a case so we pick up only the nested list.
if len(vec1) == 1:
vec1 = vec1[0]
if len(vec2) == 1:
vec2 = vec2[0]
return vec1, vec2
def kullback_leibler(vec1, vec2, num_features=None):
"""Calculate Kullback-Leibler distance between two probability distributions using `scipy.stats.entropy`.
Parameters
----------
vec1 : {scipy.sparse, numpy.ndarray, list of (int, float)}
Distribution vector.
vec2 : {scipy.sparse, numpy.ndarray, list of (int, float)}
Distribution vector.
num_features : int, optional
Number of features in vector.
Returns
-------
float
Kullback-Leibler distance between `vec1` and `vec2`.
Value in range [0, +∞) where values closer to 0 mean less distance (and a higher similarity).
"""
vec1, vec2 = convert_vec(vec1, vec2, num_features=num_features)
return entropy(vec1, vec2)
def jensen_shannon(vec1, vec2, num_features=None):
"""Calculate Jensen-Shannon distance between two probability distributions using `scipy.stats.entropy`.
Parameters
----------
vec1 : {scipy.sparse, numpy.ndarray, list of (int, float)}
Distribution vector.
vec2 : {scipy.sparse, numpy.ndarray, list of (int, float)}
Distribution vector.
num_features : int, optional
Number of features in vector.
Returns
-------
float
Jensen-Shannon distance between `vec1` and `vec2`.
Notes
-----
This is symmetric and finite "version" of :func:`gensim.matutils.kullback_leibler`.
"""
vec1, vec2 = convert_vec(vec1, vec2, num_features=num_features)
avg_vec = 0.5 * (vec1 + vec2)
return 0.5 * (entropy(vec1, avg_vec) + entropy(vec2, avg_vec))
def hellinger(vec1, vec2):
"""Calculate Hellinger distance between two probability distributions.
Parameters
----------
vec1 : {scipy.sparse, numpy.ndarray, list of (int, float)}
Distribution vector.
vec2 : {scipy.sparse, numpy.ndarray, list of (int, float)}
Distribution vector.
Returns
-------
float
Hellinger distance between `vec1` and `vec2`.
Value in range [0, 1], where 0 is min distance (max similarity) and 1 is max distance (min similarity).
"""
if scipy.sparse.issparse(vec1):
vec1 = vec1.toarray()
if scipy.sparse.issparse(vec2):
vec2 = vec2.toarray()
if isbow(vec1) and isbow(vec2):
# if it is a BoW format, instead of converting to dense we use dictionaries to calculate appropriate distance
vec1, vec2 = dict(vec1), dict(vec2)
indices = set(list(vec1.keys()) + list(vec2.keys()))
sim = np.sqrt(
0.5 * sum((np.sqrt(vec1.get(index, 0.0)) - np.sqrt(vec2.get(index, 0.0)))**2 for index in indices)
)
return sim
else:
sim = np.sqrt(0.5 * ((np.sqrt(vec1) - np.sqrt(vec2))**2).sum())
return sim