63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
|
"""
|
||
|
|
This package contains algorithms for extracting document representations from their raw
|
||
|
|
bag-of-word counts.
|
||
|
|
"""
|
||
|
|
|
||
|
|
# bring model classes directly into package namespace, to save some typing
|
||
|
|
from .coherencemodel import CoherenceModel # noqa:F401
|
||
|
|
from .hdpmodel import HdpModel # noqa:F401
|
||
|
|
from .ldamodel import LdaModel # noqa:F401
|
||
|
|
from .lsimodel import LsiModel # noqa:F401
|
||
|
|
from .tfidfmodel import TfidfModel # noqa:F401
|
||
|
|
from .rpmodel import RpModel # noqa:F401
|
||
|
|
from .logentropy_model import LogEntropyModel # noqa:F401
|
||
|
|
from .word2vec import Word2Vec # noqa:F401
|
||
|
|
from .doc2vec import Doc2Vec # noqa:F401
|
||
|
|
from .keyedvectors import KeyedVectors # noqa:F401
|
||
|
|
from .ldamulticore import LdaMulticore # noqa:F401
|
||
|
|
from .phrases import Phrases # noqa:F401
|
||
|
|
from .normmodel import NormModel # noqa:F401
|
||
|
|
from .atmodel import AuthorTopicModel # noqa:F401
|
||
|
|
from .ldaseqmodel import LdaSeqModel # noqa:F401
|
||
|
|
from .fasttext import FastText # noqa:F401
|
||
|
|
from .translation_matrix import TranslationMatrix, BackMappingTranslationMatrix # noqa:F401
|
||
|
|
|
||
|
|
from . import wrappers # noqa:F401
|
||
|
|
from . import deprecated # noqa:F401
|
||
|
|
|
||
|
|
from gensim import interfaces, utils
|
||
|
|
|
||
|
|
|
||
|
|
class VocabTransform(interfaces.TransformationABC):
|
||
|
|
"""
|
||
|
|
Remap feature ids to new values.
|
||
|
|
|
||
|
|
Given a mapping between old ids and new ids (some old ids may be missing = these
|
||
|
|
features are to be discarded), this will wrap a corpus so that iterating over
|
||
|
|
`VocabTransform[corpus]` returns the same vectors but with the new ids.
|
||
|
|
|
||
|
|
Old features that have no counterpart in the new ids are discarded. This
|
||
|
|
can be used to filter vocabulary of a corpus "online"::
|
||
|
|
|
||
|
|
>>> old2new = {oldid: newid for newid, oldid in enumerate(ids_you_want_to_keep)}
|
||
|
|
>>> vt = VocabTransform(old2new)
|
||
|
|
>>> for vec_with_new_ids in vt[corpus_with_old_ids]:
|
||
|
|
>>> ...
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, old2new, id2token=None):
|
||
|
|
self.old2new = old2new
|
||
|
|
self.id2token = id2token
|
||
|
|
|
||
|
|
def __getitem__(self, bow):
|
||
|
|
"""
|
||
|
|
Return representation with the ids transformed.
|
||
|
|
"""
|
||
|
|
# if the input vector is in fact a corpus, return a transformed corpus as a result
|
||
|
|
is_corpus, bow = utils.is_corpus(bow)
|
||
|
|
if is_corpus:
|
||
|
|
return self._apply(bow)
|
||
|
|
|
||
|
|
return sorted((self.old2new[oldid], weight) for oldid, weight in bow if oldid in self.old2new)
|