# check and process input arguments
if len(sys.argv) < 3:
print(globals()['__doc__'] % locals())
sys.exit(1)
language = sys.argv[1]
method = sys.argv[2].strip().lower()
logging.info("loading corpus mappings")
config = dmlcorpus.DmlConfig('%s_%s' % (gensim_build.PREFIX, language),
resultDir=gensim_build.RESULT_DIR, acceptLangs=[language])
logging.info("loading word id mapping from %s" % config.resultFile('wordids.txt'))
id2word = dmlcorpus.DmlCorpus.loadDictionary(config.resultFile('wordids.txt'))
logging.info("loaded %i word ids" % len(id2word))
corpus = dmlcorpus.DmlCorpus.load(config.resultFile('.pkl'))
input = MmCorpus(config.resultFile('_%s.mm' % method))
assert len(input) == len(corpus), "corpus size mismatch (%i vs %i): run ./gensim_genmodel.py again" % (len(input), len(corpus))
# initialize structure for similarity queries
if method == 'lsi' or method == 'rp': # for these methods, use dense vectors
index = MatrixSimilarity(input, numBest=MAX_SIMILAR + 1, numFeatures=input.numTerms)
else:
index = SparseMatrixSimilarity(input, numBest=MAX_SIMILAR + 1)
index.normalize = False # do not normalize query vectors during similarity queries (the index is already built normalized, so it would be a no-op)
generateSimilar(corpus, index, method) # for each document, print MAX_SIMILAR nearest documents to a xml file, in dml-cz specific format
logging.info("finished running %s" % program)
method = sys.argv[2].strip().lower()
logging.info("loading corpus mappings")
config = dmlcorpus.DmlConfig('%s_%s' % (gensim_build.PREFIX, language),
resultDir=gensim_build.RESULT_DIR,
acceptLangs=[language])
logging.info("loading word id mapping from %s",
config.resultFile('wordids.txt'))
id2word = dmlcorpus.DmlCorpus.loadDictionary(
config.resultFile('wordids.txt'))
logging.info("loaded %i word ids", len(id2word))
corpus = dmlcorpus.DmlCorpus.load(config.resultFile('.pkl'))
input = MmCorpus(config.resultFile('_%s.mm' % method))
assert len(input) == len(corpus), \
"corpus size mismatch (%i vs %i): run ./gensim_genmodel.py again" % (len(input), len(corpus))
# initialize structure for similarity queries
if method == 'lsi' or method == 'rp': # for these methods, use dense vectors
index = MatrixSimilarity(input,
num_best=MAX_SIMILAR + 1,
num_features=input.numTerms)
else:
index = SparseMatrixSimilarity(input, num_best=MAX_SIMILAR + 1)
index.normalize = False
generateSimilar(corpus, index, method)
logging.info("finished running %s", program)
resultDir=gensim_build.RESULT_DIR,
acceptLangs=[language])
logging.info("loading word id mapping from %s" %
config.resultFile('wordids.txt'))
id2word = dmlcorpus.DmlCorpus.loadDictionary(
config.resultFile('wordids.txt'))
logging.info("loaded %i word ids" % len(id2word))
corpus = dmlcorpus.DmlCorpus.load(config.resultFile('.pkl'))
input = MmCorpus(config.resultFile('_%s.mm' % method))
assert len(input) == len(
corpus
), "corpus size mismatch (%i vs %i): run ./gensim_genmodel.py again" % (
len(input), len(corpus))
# initialize structure for similarity queries
if method == 'lsi' or method == 'rp': # for these methods, use dense vectors
index = MatrixSimilarity(input,
numBest=MAX_SIMILAR + 1,
numFeatures=input.numTerms)
else:
index = SparseMatrixSimilarity(input, numBest=MAX_SIMILAR + 1)
index.normalize = False # do not normalize query vectors during similarity queries (the index is already built normalized, so it would be a no-op)
generateSimilar(
corpus, index, method
) # for each document, print MAX_SIMILAR nearest documents to a xml file, in dml-cz specific format
logging.info("finished running %s" % program)
# check and process input arguments
if len(sys.argv) < 3:
print(globals()['__doc__'] % locals())
sys.exit(1)
language = sys.argv[1]
method = sys.argv[2].strip().lower()
logging.info("loading corpus mappings")
config = dmlcorpus.DmlConfig('%s_%s' % (gensim_build.PREFIX, language),
resultDir=gensim_build.RESULT_DIR, acceptLangs=[language])
logging.info("loading word id mapping from %s", config.resultFile('wordids.txt'))
id2word = dmlcorpus.DmlCorpus.loadDictionary(config.resultFile('wordids.txt'))
logging.info("loaded %i word ids", len(id2word))
corpus = dmlcorpus.DmlCorpus.load(config.resultFile('.pkl'))
input = MmCorpus(config.resultFile('_%s.mm' % method))
assert len(input) == len(corpus), \
"corpus size mismatch (%i vs %i): run ./gensim_genmodel.py again" % (len(input), len(corpus))
# initialize structure for similarity queries
if method == 'lsi' or method == 'rp': # for these methods, use dense vectors
index = MatrixSimilarity(input, num_best=MAX_SIMILAR + 1, num_features=input.numTerms)
else:
index = SparseMatrixSimilarity(input, num_best=MAX_SIMILAR + 1)
index.normalize = False
generateSimilar(corpus, index, method)
logging.info("finished running %s", program)
