159 lines
6.3 KiB
Python
159 lines
6.3 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#
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# Copyright (C) 2011 Radim Rehurek <radimrehurek@seznam.cz>
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# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
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"""
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USAGE: %(program)s CORPUS_DENSE.mm CORPUS_SPARSE.mm [NUMDOCS]
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Run speed test of similarity queries. Only use the first NUMDOCS documents of \
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each corpus for testing (or use all if no NUMDOCS is given).
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The two sample corpora can be downloaded from http://nlp.fi.muni.cz/projekty/gensim/wikismall.tgz
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Example: ./simspeed2.py wikismall.dense.mm wikismall.sparse.mm
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"""
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import logging
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import sys
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import itertools
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import os
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import math
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from time import time
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import gensim
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if __name__ == '__main__':
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logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
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logging.info("running %s", " ".join(sys.argv))
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# check and process cmdline input
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program = os.path.basename(sys.argv[0])
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if len(sys.argv) < 3:
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print(globals()['__doc__'] % locals())
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sys.exit(1)
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corpus_dense = gensim.corpora.MmCorpus(sys.argv[1])
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corpus_sparse = gensim.corpora.MmCorpus(sys.argv[2])
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dense_features, sparse_features = corpus_dense.num_terms, corpus_sparse.num_terms
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if len(sys.argv) > 3:
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NUMDOCS = int(sys.argv[3])
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corpus_dense = list(itertools.islice(corpus_dense, NUMDOCS))
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corpus_sparse = list(itertools.islice(corpus_sparse, NUMDOCS))
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# create the query index to be tested (one for dense input, one for sparse)
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index_dense = gensim.similarities.Similarity('/tmp/tstdense', corpus_dense, dense_features)
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index_sparse = gensim.similarities.Similarity('/tmp/tstsparse', corpus_sparse, sparse_features)
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density = 100.0 * sum(shard.num_nnz for shard in index_sparse.shards) / (len(index_sparse) * sparse_features)
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logging.info(
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"test 1 (dense): similarity of all vs. all (%i documents, %i dense features)",
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len(corpus_dense), index_dense.num_features
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)
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for chunksize in [1, 8, 32, 64, 128, 256, 512, 1024, index_dense.shardsize]:
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index_dense.chunksize = chunksize
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start = time()
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for sim in index_dense:
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pass
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taken = time() - start
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queries = math.ceil(1.0 * len(corpus_dense) / chunksize)
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logging.info(
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"chunksize=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)",
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chunksize, taken, len(corpus_dense) / taken, queries / taken
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)
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index_dense.num_best = 10
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logging.info("test 2 (dense): as above, but only ask for the top-10 most similar for each document")
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for chunksize in [1, 8, 32, 64, 128, 256, 512, 1024, index_dense.shardsize]:
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index_dense.chunksize = chunksize
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start = time()
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sims = [sim for sim in index_dense]
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taken = time() - start
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queries = math.ceil(1.0 * len(corpus_dense) / chunksize)
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logging.info(
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"chunksize=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)",
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chunksize, taken, len(corpus_dense) / taken, queries / taken
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)
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index_dense.num_best = None
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logging.info(
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"test 3 (sparse): similarity of all vs. all (%i documents, %i features, %.2f%% density)",
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len(corpus_sparse), index_sparse.num_features, density
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)
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for chunksize in [1, 5, 10, 100, 256, 500, 1000, index_sparse.shardsize]:
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index_sparse.chunksize = chunksize
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start = time()
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for sim in index_sparse:
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pass
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taken = time() - start
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queries = math.ceil(1.0 * len(corpus_sparse) / chunksize)
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logging.info(
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"chunksize=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)",
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chunksize, taken, len(corpus_sparse) / taken, queries / taken
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)
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index_sparse.num_best = 10
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logging.info("test 4 (sparse): as above, but only ask for the top-10 most similar for each document")
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for chunksize in [1, 5, 10, 100, 256, 500, 1000, index_sparse.shardsize]:
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index_sparse.chunksize = chunksize
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start = time()
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for sim in index_sparse:
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pass
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taken = time() - start
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queries = math.ceil(1.0 * len(corpus_sparse) / chunksize)
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logging.info(
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"chunksize=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)",
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chunksize, taken, len(corpus_sparse) / taken, queries / taken
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)
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index_sparse.num_best = None
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# Difference between test #5 and test #1 is that the query in #5 is a gensim iterable
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# corpus, while in #1, the index is used directly (numpy arrays). So #5 is slower,
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# because it needs to convert sparse vecs to numpy arrays and normalize them to
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# unit length=extra work, which #1 avoids.
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query = list(itertools.islice(corpus_dense, 1000))
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logging.info(
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"test 5 (dense): dense corpus of %i docs vs. index (%i documents, %i dense features)",
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len(query), len(index_dense), index_dense.num_features
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)
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for chunksize in [1, 8, 32, 64, 128, 256, 512, 1024]:
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start = time()
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if chunksize > 1:
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sims = []
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for chunk in gensim.utils.chunkize_serial(query, chunksize):
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_ = index_dense[chunk]
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else:
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for vec in query:
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_ = index_dense[vec]
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taken = time() - start
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queries = math.ceil(1.0 * len(query) / chunksize)
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logging.info(
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"chunksize=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)",
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chunksize, taken, len(query) / taken, queries / taken
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)
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# Same comment as for test #5.
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query = list(itertools.islice(corpus_dense, 1000))
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logging.info(
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"test 6 (sparse): sparse corpus of %i docs vs. sparse index (%i documents, %i features, %.2f%% density)",
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len(query), len(corpus_sparse), index_sparse.num_features, density
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)
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for chunksize in [1, 5, 10, 100, 500, 1000]:
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start = time()
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if chunksize > 1:
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sims = []
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for chunk in gensim.utils.chunkize_serial(query, chunksize):
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_ = index_sparse[chunk]
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else:
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for vec in query:
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_ = index_sparse[vec]
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taken = time() - start
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queries = math.ceil(1.0 * len(query) / chunksize)
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logging.info(
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"chunksize=%i, time=%.4fs (%.2f docs/s, %.2f queries/s)",
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chunksize, taken, len(query) / taken, queries / taken
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)
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logging.info("finished running %s", program)
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