forked from piskvorky/gensim
-
Notifications
You must be signed in to change notification settings - Fork 6
/
simspeed.py
executable file
·163 lines (145 loc) · 7.32 KB
/
simspeed.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
#!/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
"""
USAGE: %(program)s CORPUS_DENSE.mm CORPUS_SPARSE.mm [NUMDOCS]
Run speed test of similarity queries. Only use the first NUMDOCS documents of \
each corpus for testing (or use all if no NUMDOCS is given).
The two sample corpora can be downloaded from http://nlp.fi.muni.cz/projekty/gensim/wikismall.tgz
Example: ./simspeed.py wikismall.dense.mm wikismall.sparse.mm 5000
"""
import logging
import sys
import itertools
import os
import math
from time import time
import numpy
import scipy.sparse
import gensim
if __name__ == '__main__':
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
logging.info("running %s" % " ".join(sys.argv))
# check and process cmdline input
program = os.path.basename(sys.argv[0])
if len(sys.argv) < 3:
print(globals()['__doc__'] % locals())
sys.exit(1)
corpus_dense = gensim.corpora.MmCorpus(sys.argv[1])
corpus_sparse = gensim.corpora.MmCorpus(sys.argv[2])
NUMTERMS = corpus_sparse.num_terms
if len(sys.argv) > 3:
NUMDOCS = int(sys.argv[3])
corpus_dense = list(itertools.islice(corpus_dense, NUMDOCS))
corpus_sparse = list(itertools.islice(corpus_sparse, NUMDOCS))
# create the query index to be tested (one for dense input, one for sparse)
index_dense = gensim.similarities.MatrixSimilarity(corpus_dense)
index_sparse = gensim.similarities.SparseMatrixSimilarity(corpus_sparse, num_terms=NUMTERMS)
density = 100.0 * index_sparse.index.nnz / (index_sparse.index.shape[0] * index_sparse.index.shape[1])
# Difference between test #1 and test #3 is that the query in #1 is a gensim iterable
# corpus, while in #3, the index is used directly (numpy arrays). So #1 is slower,
# because it needs to convert sparse vecs to numpy arrays and normalize them to
# unit length=extra work, which #3 avoids.
query = list(itertools.islice(corpus_dense, 1000))
logging.info("test 1 (dense): dense corpus of %i docs vs. index (%i documents, %i dense features)" %
(len(query), len(index_dense), index_dense.num_features))
for chunksize in [1, 4, 8, 16, 64, 128, 256, 512, 1024]:
start = time()
if chunksize > 1:
sims = []
for chunk in gensim.utils.chunkize_serial(query, chunksize):
sim = index_dense[chunk]
sims.extend(sim)
else:
sims = [index_dense[vec] for vec in query]
assert len(sims) == len(query) # make sure we have one result for each query document
taken = time() - start
queries = math.ceil(1.0 * len(query) / chunksize)
logging.info("chunksize=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)" %
(chunksize, taken, len(query) / taken, queries / taken))
# Same comment as for test #1 but vs. test #4.
query = list(itertools.islice(corpus_sparse, 1000))
logging.info("test 2 (sparse): sparse corpus of %i docs vs. sparse index (%i documents, %i features, %.2f%% density)" %
(len(query), len(corpus_sparse), index_sparse.index.shape[1], density))
for chunksize in [1, 5, 10, 100, 500, 1000]:
start = time()
if chunksize > 1:
sims = []
for chunk in gensim.utils.chunkize_serial(query, chunksize):
sim = index_sparse[chunk]
sims.extend(sim)
else:
sims = [index_sparse[vec] for vec in query]
assert len(sims) == len(query) # make sure we have one result for each query document
taken = time() - start
queries = math.ceil(1.0 * len(query) / chunksize)
logging.info("chunksize=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)" %
(chunksize, taken, len(query) / taken, queries / taken))
logging.info("test 3 (dense): similarity of all vs. all (%i documents, %i dense features)" %
(len(corpus_dense), index_dense.num_features))
for chunksize in [0, 1, 4, 8, 16, 64, 128, 256, 512, 1024]:
index_dense.chunksize = chunksize
start = time()
# `sims` stores the entire N x N sim matrix in memory!
# this is not necessary, but i added it to test the accuracy of the result
# (=report mean diff below)
sims = [sim for sim in index_dense]
taken = time() - start
sims = numpy.asarray(sims)
if chunksize == 0:
logging.info("chunksize=%i, time=%.4fs (%.2f docs/s)" % (chunksize, taken, len(corpus_dense) / taken))
unchunksizeed = sims
else:
queries = math.ceil(1.0 * len(corpus_dense) / chunksize)
diff = numpy.mean(numpy.abs(unchunksizeed - sims))
logging.info("chunksize=%i, time=%.4fs (%.2f docs/s, %.2f queries/s), meandiff=%.3e" %
(chunksize, taken, len(corpus_dense) / taken, queries / taken, diff))
del sims
index_dense.num_best = 10
logging.info("test 4 (dense): as above, but only ask for the top-10 most similar for each document")
for chunksize in [0, 1, 4, 8, 16, 64, 128, 256, 512, 1024]:
index_dense.chunksize = chunksize
start = time()
sims = [sim for sim in index_dense]
taken = time() - start
if chunksize == 0:
queries = len(corpus_dense)
else:
queries = math.ceil(1.0 * len(corpus_dense) / chunksize)
logging.info("chunksize=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)" %
(chunksize, taken, len(corpus_dense) / taken, queries / taken))
index_dense.num_best = None
logging.info("test 5 (sparse): similarity of all vs. all (%i documents, %i features, %.2f%% density)" %
(len(corpus_sparse), index_sparse.index.shape[1], density))
for chunksize in [0, 5, 10, 100, 500, 1000, 5000]:
index_sparse.chunksize = chunksize
start = time()
sims = [sim for sim in index_sparse]
taken = time() - start
sims = numpy.asarray(sims)
if chunksize == 0:
logging.info("chunksize=%i, time=%.4fs (%.2f docs/s)" % (chunksize, taken, len(corpus_sparse) / taken))
unchunksizeed = sims
else:
queries = math.ceil(1.0 * len(corpus_sparse) / chunksize)
diff = numpy.mean(numpy.abs(unchunksizeed - sims))
logging.info("chunksize=%i, time=%.4fs (%.2f docs/s, %.2f queries/s), meandiff=%.3e" %
(chunksize, taken, len(corpus_sparse) / taken, queries / taken, diff))
del sims
index_sparse.num_best = 10
logging.info("test 6 (sparse): as above, but only ask for the top-10 most similar for each document")
for chunksize in [0, 5, 10, 100, 500, 1000, 5000]:
index_sparse.chunksize = chunksize
start = time()
sims = [sim for sim in index_sparse]
taken = time() - start
if chunksize == 0:
queries = len(corpus_sparse)
else:
queries = math.ceil(1.0 * len(corpus_sparse) / chunksize)
logging.info("chunksize=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)" %
(chunksize, taken, len(corpus_sparse) / taken, queries / taken))
index_sparse.num_best = None
logging.info("finished running %s" % program)