def __getitem__(self, query):
"""Get similarities of document `query` to all documents in the corpus.
**or**
If `query` is a corpus (iterable of documents), return a matrix of similarities
of all query documents vs. all corpus document. This batch query is more
efficient than computing the similarities one document after another.
"""
self.close_shard() # no-op if no documents added to index since last query
# reset num_best and normalize parameters, in case they were changed dynamically
for shard in self.shards:
shard.num_best = self.num_best
shard.normalize = self.normalize
# there are 4 distinct code paths, depending on whether input `query` is
# a corpus (or numpy/scipy matrix) or a single document, and whether the
# similarity result should be a full array or only num_best most similar
# documents.
pool, shard_results = self.query_shards(query)
if self.num_best is None:
# user asked for all documents => just stack the sub-results into a single matrix
# (works for both corpus / single doc query)
result = numpy.hstack(shard_results)
else:
# the following uses a lot of lazy evaluation and (optionally) parallel
# processing, to improve query latency and minimize memory footprint.
offsets = numpy.cumsum([0] + [len(shard) for shard in self.shards])
convert = lambda doc, shard_no: [(doc_index + offsets[shard_no], sim)
for doc_index, sim in doc]
is_corpus, query = utils.is_corpus(query)
is_corpus = is_corpus or hasattr(query, 'ndim') and query.ndim > 1 and query.shape[0] > 1
if not is_corpus:
# user asked for num_best most similar and query is a single doc
results = (convert(result, shard_no) for shard_no, result in enumerate(shard_results))
result = heapq.nlargest(self.num_best, itertools.chain(*results), key=lambda item: item[1])
else:
# the trickiest combination: returning num_best results when query was a corpus
results = []
for shard_no, result in enumerate(shard_results):
shard_result = [convert(doc, shard_no) for doc in result]
results.append(shard_result)
result = []
for parts in izip(*results):
merged = heapq.nlargest(self.num_best, itertools.chain(*parts), key=lambda item: item[1])
result.append(merged)
if pool:
# gc doesn't seem to collect the Pools, eventually leading to
# "IOError 24: too many open files". so let's terminate it manually.
pool.terminate()
return result
def compactify(self):
"""
Assign new word ids to all words.
This is done to make the ids more compact, e.g. after some tokens have
been removed via :func:`filter_tokens` and there are gaps in the id series.
Calling this method will remove the gaps.
"""
logger.debug("rebuilding dictionary, shrinking gaps")
# build mapping from old id -> new id
idmap = dict(
izip(itervalues(self.token2id), xrange(len(self.token2id))))
# reassign mappings to new ids
self.token2id = dict((token, idmap[tokenid])
for token, tokenid in iteritems(self.token2id))
self.id2token = {}
self.dfs = dict(
(idmap[tokenid], freq) for tokenid, freq in iteritems(self.dfs))
def compactify(self):
"""
Assign new word ids to all words.
This is done to make the ids more compact, e.g. after some tokens have
been removed via :func:`filter_tokens` and there are gaps in the id series.
Calling this method will remove the gaps.
"""
logger.debug("rebuilding dictionary, shrinking gaps")
# build mapping from old id -> new id
idmap = dict(izip(itervalues(self.token2id),
xrange(len(self.token2id))))
# reassign mappings to new ids
self.token2id = dict((token, idmap[tokenid])
for token, tokenid in iteritems(self.token2id))
self.id2token = {}
self.dfs = dict((idmap[tokenid], freq)
for tokenid, freq in iteritems(self.dfs))
def __init__(self, fname, id2word=None, line2words=split_on_space):
"""
Initialize the corpus from a file.
`id2word` and `line2words` are optional parameters.
If provided, `id2word` is a dictionary mapping between word_ids (integers)
and words (strings). If not provided, the mapping is constructed from
the documents.
`line2words` is a function which converts lines into tokens. Defaults to
simple splitting on spaces.
"""
IndexedCorpus.__init__(self, fname)
logger.info("loading corpus from %s" % fname)
self.fname = fname # input file, see class doc for format
self.line2words = line2words # how to translate lines into words (simply split on space by default)
self.num_docs = self._calculate_num_docs()
if not id2word:
# build a list of all word types in the corpus (distinct words)
logger.info("extracting vocabulary from the corpus")
all_terms = set()
self.use_wordids = False # return documents as (word, wordCount) 2-tuples
for doc in self:
all_terms.update(word for word, wordCnt in doc)
all_terms = sorted(
all_terms
) # sort the list of all words; rank in that list = word's integer id
self.id2word = dict(izip(
xrange(len(all_terms)),
all_terms)) # build a mapping of word id(int) -> word (string)
else:
logger.info("using provided word mapping (%i ids)" % len(id2word))
self.id2word = id2word
self.word2id = dict((v, k) for k, v in iteritems(self.id2word))
self.num_terms = len(self.word2id)
self.use_wordids = True # return documents as (wordIndex, wordCount) 2-tuples
logger.info("loaded corpus with %i documents and %i terms from %s" %
(self.num_docs, self.num_terms, fname))
def __init__(self, fname, id2word=None, line2words=split_on_space):
"""
Initialize the corpus from a file.
`id2word` and `line2words` are optional parameters.
If provided, `id2word` is a dictionary mapping between word_ids (integers)
and words (strings). If not provided, the mapping is constructed from
the documents.
`line2words` is a function which converts lines into tokens. Defaults to
simple splitting on spaces.
"""
IndexedCorpus.__init__(self, fname)
logger.info("loading corpus from %s" % fname)
self.fname = fname # input file, see class doc for format
self.line2words = line2words # how to translate lines into words (simply split on space by default)
self.num_docs = self._calculate_num_docs()
if not id2word:
# build a list of all word types in the corpus (distinct words)
logger.info("extracting vocabulary from the corpus")
all_terms = set()
self.use_wordids = False # return documents as (word, wordCount) 2-tuples
for doc in self:
all_terms.update(word for word, wordCnt in doc)
all_terms = sorted(all_terms) # sort the list of all words; rank in that list = word's integer id
self.id2word = dict(
izip(xrange(len(all_terms)), all_terms)
) # build a mapping of word id(int) -> word (string)
else:
logger.info("using provided word mapping (%i ids)" % len(id2word))
self.id2word = id2word
self.word2id = dict((v, k) for k, v in iteritems(self.id2word))
self.num_terms = len(self.word2id)
self.use_wordids = True # return documents as (wordIndex, wordCount) 2-tuples
logger.info("loaded corpus with %i documents and %i terms from %s" % (self.num_docs, self.num_terms, fname))
def __getitem__(self, query):
"""Get similarities of document `query` to all documents in the corpus.
**or**
If `query` is a corpus (iterable of documents), return a matrix of similarities
of all query documents vs. all corpus document. This batch query is more
efficient than computing the similarities one document after another.
"""
self.close_shard(
) # no-op if no documents added to index since last query
# reset num_best and normalize parameters, in case they were changed dynamically
for shard in self.shards:
shard.num_best = self.num_best
shard.normalize = self.normalize
# there are 4 distinct code paths, depending on whether input `query` is
# a corpus (or numpy/scipy matrix) or a single document, and whether the
# similarity result should be a full array or only num_best most similar
# documents.
pool, shard_results = self.query_shards(query)
if self.num_best is None:
# user asked for all documents => just stack the sub-results into a single matrix
# (works for both corpus / single doc query)
result = numpy.hstack(shard_results)
else:
# the following uses a lot of lazy evaluation and (optionally) parallel
# processing, to improve query latency and minimize memory footprint.
offsets = numpy.cumsum([0] + [len(shard) for shard in self.shards])
convert = lambda doc, shard_no: [
(doc_index + offsets[shard_no], sim) for doc_index, sim in doc
]
is_corpus, query = utils.is_corpus(query)
is_corpus = is_corpus or hasattr(
query, 'ndim') and query.ndim > 1 and query.shape[0] > 1
if not is_corpus:
# user asked for num_best most similar and query is a single doc
results = (convert(result, shard_no)
for shard_no, result in enumerate(shard_results))
result = heapq.nlargest(self.num_best,
itertools.chain(*results),
key=lambda item: item[1])
else:
# the trickiest combination: returning num_best results when query was a corpus
results = []
for shard_no, result in enumerate(shard_results):
shard_result = [convert(doc, shard_no) for doc in result]
results.append(shard_result)
result = []
for parts in izip(*results):
merged = heapq.nlargest(self.num_best,
itertools.chain(*parts),
key=lambda item: item[1])
result.append(merged)
if pool:
# gc doesn't seem to collect the Pools, eventually leading to
# "IOError 24: too many open files". so let's terminate it manually.
pool.terminate()
return result
def __iter__(self):
for indprev, indnow in izip(self.sparse.indptr, self.sparse.indptr[1:]):
yield zip(self.sparse.indices[indprev:indnow], self.sparse.data[indprev:indnow])
