def __call__(self, corpus: Corpus) -> Corpus:
"""
Preprocess corpus. Should be extended when inherited and
invoke _preprocess method on a document or token(s).
:param corpus: Corpus
:return: Corpus
Preprocessed corpus.
"""
ids = corpus.ids
corpus = corpus.copy()
corpus.ids = ids
corpus.used_preprocessor = self
return corpus
def __call__(self, corpus: Corpus, copy: bool = True,
processed_callback=None) -> Corpus:
"""Adds matrix of document embeddings to a corpus.
Parameters
----------
corpus : Corpus
Corpus on which transform is performed.
copy : bool
If set to True, a copy of corpus is made.
Returns
-------
Corpus
Corpus (original or a copy) with new features added.
Raises
------
ValueError
If corpus is not instance of Corpus.
RuntimeError
If document in corpus is larger than
50 KB after compression.
"""
if not isinstance(corpus, Corpus):
raise ValueError("Input should be instance of Corpus.")
corpus = corpus.copy() if copy else corpus
embs = self._embedder.embedd_data(
list(corpus.ngrams),
processed_callback=processed_callback)
dim = None
send_warning = False
for emb in embs: # find embedding dimension
if emb is not None:
dim = len(emb)
break
# Check if some documents in corpus in weren't embedded
# for some reason. This is a very rare case.
inds = list()
for i, emb in enumerate(embs):
if emb is not None:
inds.append(i)
else:
embs[i] = np.zeros(dim) * np.nan
send_warning = True
variable_attrs = {
'hidden': True,
'skip-normalization': True,
'embedding-feature': True
}
embs = np.array(embs)
new_corpus = corpus[inds]
if len(inds) > 0:
# if at least one embedding is not None,
# extend attributes
new_corpus.extend_attributes(
np.array(embs[inds]),
['Dim{}'.format(i + 1) for i in range(dim)],
var_attrs=variable_attrs)
if send_warning:
warnings.warn(("Some documents were not embedded for " +
"unknown reason. Those documents " +
"are skipped."),
RuntimeWarning)
return new_corpus
