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simspeed.py
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simspeed.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2011 Radim Rehurek <radimrehurek@seznam.cz>
"""
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).
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)
if len(sys.argv) > 3:
NUMDOCS = int(sys.argv[3])
corpus_dense = list(itertools.islice(gensim.corpora.MmCorpus(sys.argv[1]), NUMDOCS))
corpus_sparse = list(itertools.islice(gensim.corpora.MmCorpus(sys.argv[2]), NUMDOCS))
else:
corpus_dense = gensim.corpora.MmCorpus(sys.argv[1])
corpus_sparse = gensim.corpora.MmCorpus(sys.argv[2])
# 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)
density = 100.0 * index_sparse.corpus.nnz / (index_sparse.corpus.shape[0] * index_sparse.corpus.shape[1])
logging.info("test 1: similarity of all vs. all (%i documents, %i features)" %
(len(corpus_dense), index_dense.numFeatures))
for chunks in [0, 1, 4, 8, 16, 64, 128, 256, 512, 1024]:
index_dense.chunks = chunks
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 chunks == 0:
logging.info("chunks=%i, time=%.4fs (%.2f docs/s)" % (chunks, taken, len(corpus_dense) / taken))
unchunked = sims
else:
queries = math.ceil(1.0 * len(corpus_dense) / chunks)
diff = numpy.mean(numpy.abs(unchunked - sims))
logging.info("chunks=%i, time=%.4fs (%.2f docs/s, %.2f queries/s), meandiff=%.3e" %
(chunks, taken, len(corpus_dense) / taken, queries / taken, diff))
del sims
index_dense.numBest = 10
logging.info("test 2: as above, but only ask for top-10 most similar for each document")
for chunks in [0, 1, 4, 8, 16, 64, 128, 256, 512, 1024]:
index_dense.chunks = chunks
start = time()
sims = [sim for sim in index_dense]
taken = time() - start
if chunks == 0:
queries = len(corpus_dense)
else:
queries = math.ceil(1.0 * len(corpus_dense) / chunks)
logging.info("chunks=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)" %
(chunks, taken, len(corpus_dense) / taken, queries / taken))
logging.info("test 3: sparse index all vs. all (%i documents, %i features, %.2f%% density)" %
(len(corpus_sparse), index_sparse.corpus.shape[1], density))
for chunks in [0, 5, 10, 100, 500, 1000, 5000]:
index_sparse.chunks = chunks
start = time()
sims = [sim for sim in index_sparse]
taken = time() - start
sims = numpy.asarray(sims)
if chunks == 0:
logging.info("chunks=%i, time=%.4fs (%.2f docs/s)" % (chunks, taken, len(corpus_sparse) / taken))
unchunked = sims
else:
queries = math.ceil(1.0 * len(corpus_sparse) / chunks)
diff = numpy.mean(numpy.abs(unchunked - sims))
logging.info("chunks=%i, time=%.4fs (%.2f docs/s, %.2f queries/s), meadiff=%.3e" %
(chunks, taken, len(corpus_sparse) / taken, queries / taken, diff))
del sims
# Difference between test #4 and test #1 is that the query in #4 is a gensim sparse
# corpus, while in #1, the index is used directly (numpy arrays). So #4 is slower,
# because it needs to convert sparse vecs to numpy arrays and normalize them to
# unit length=extra work.
query = corpus_dense[:1000]
logging.info("test 4: dense corpus of %i docs vs. index (%i documents, %i features)" %
(len(query), len(index_dense), index_dense.numFeatures))
for chunks in [1, 5, 10, 50, 100, 500, 1000]:
start = time()
if chunks > 1:
sims = []
for chunk in gensim.utils.chunkize_serial(query, chunks):
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) / chunks)
logging.info("chunks=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)" %
(chunks, taken, len(query) / taken, queries / taken))
# Same comment as for test #4.
query = corpus_sparse[:1000]
logging.info("test 5: sparse corpus of %i docs vs. index (%i documents, %i features, %.2f%% density)" %
(len(query), len(corpus_sparse), index_sparse.corpus.shape[1], density))
for chunks in [1, 5, 10, 100, 500, 1000]:
start = time()
if chunks > 1:
sims = []
for chunk in gensim.utils.chunkize_serial(query, chunks):
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) / chunks)
logging.info("chunks=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)" %
(chunks, taken, len(query) / taken, queries / taken))
logging.info("finished running %s" % program)