class NPScorer(object):
def __init__(self, parser=None):
if parser is None:
self.nlp = SpacyInstance(
disable=["ner", "parser", "vectors", "textcat"]).parser
else:
self.nlp = parser
self.nlp.add_pipe(self.nlp.create_pipe("sentencizer"), first=True)
_path_to_model = path.join(chunker_local_path, chunker_model_file)
if not path.exists(chunker_local_path):
makedirs(chunker_local_path)
if not path.exists(_path_to_model):
logger.info(
"The pre-trained model to be downloaded for NLP Architect word"
" chunker model is licensed under Apache 2.0")
download_unlicensed_file(nlp_chunker_url, chunker_model_file,
_path_to_model)
_path_to_params = path.join(chunker_local_path, chunker_model_dat_file)
if not path.exists(_path_to_params):
download_unlicensed_file(nlp_chunker_url, chunker_model_dat_file,
_path_to_params)
self.nlp.add_pipe(NPAnnotator.load(_path_to_model, _path_to_params),
last=True)
def score_documents(self,
texts: list,
limit=-1,
return_all=False,
min_tf=5):
documents = []
assert len(texts) > 0, "texts should contain at least 1 document"
assert min_tf > 0, "min_tf should be at least 1"
with tqdm(total=len(texts),
desc="documents scoring progress",
unit="docs") as pbar:
for doc in self.nlp.pipe(texts, n_threads=-1):
if len(doc) > 0:
documents.append(doc)
pbar.update(1)
corpus = []
for doc in documents:
spans = get_noun_phrases(doc)
if len(spans) > 0:
corpus.append((doc, spans))
if len(corpus) < 1:
return []
documents, doc_phrases = list(zip(*corpus))
scorer = TextSpanScoring(documents=documents,
spans=doc_phrases,
min_tf=min_tf)
tfidf_scored_list = scorer.get_tfidf_scores()
if len(tfidf_scored_list) < 1:
return []
cvalue_scored_list = scorer.get_cvalue_scores()
freq_scored_list = scorer.get_freq_scores()
if limit > 0:
tf = {tuple(k[0]): k[1] for k in tfidf_scored_list}
cv = {tuple(k[0]): k[1] for k in cvalue_scored_list}
fr = {tuple(k[0]): k[1] for k in freq_scored_list}
tfidf_scored_list_limit = []
cvalue_scored_list_limit = []
freq_scored_list_limit = []
for phrase in list(zip(*tfidf_scored_list))[0][:limit]:
tfidf_scored_list_limit.append((phrase, tf[tuple(phrase)]))
cvalue_scored_list_limit.append((phrase, cv[tuple(phrase)]))
freq_scored_list_limit.append((phrase, fr[tuple(phrase)]))
tfidf_scored_list = tfidf_scored_list_limit
cvalue_scored_list = cvalue_scored_list_limit
freq_scored_list = freq_scored_list_limit
tfidf_scored_list = scorer.normalize_l2(tfidf_scored_list)
cvalue_scored_list = scorer.normalize_l2(cvalue_scored_list)
freq_scored_list = scorer.normalize_minmax(freq_scored_list,
invert=True)
tfidf_scored_list = scorer.normalize_minmax(tfidf_scored_list)
cvalue_scored_list = scorer.normalize_minmax(cvalue_scored_list)
if return_all:
tf = {tuple(k[0]): k[1] for k in tfidf_scored_list}
cv = {tuple(k[0]): k[1] for k in cvalue_scored_list}
fr = {tuple(k[0]): k[1] for k in freq_scored_list}
final_list = []
for phrases in tf.keys():
final_list.append(([p for p in phrases], tf[phrases],
cv[phrases], fr[phrases]))
return final_list
merged_list = scorer.interpolate_scores(
[tfidf_scored_list, cvalue_scored_list], [0.5, 0.5])
merged_list = scorer.multiply_scores([merged_list, freq_scored_list])
merged_list = scorer.normalize_minmax(merged_list)
final_list = []
for phrases, score in merged_list:
if any([len(p) > 1 for p in phrases]):
final_list.append(([p for p in phrases], score))
return final_list
_path_to_params)
logger.info('Done.')
nlp.add_pipe(NPAnnotator.load(_path_to_model, _path_to_params),
last=True)
else:
nlp = SpacyInstance(model='en_core_web_sm',
disable=['textcat', 'ner']).parser
logger.info('spacy loaded')
num_lines = sum(1 for line in corpus_file)
corpus_file.seek(0)
logger.info('%i lines in corpus', num_lines)
i = 0
with tqdm(total=num_lines) as pbar:
for doc in nlp.pipe(corpus_file, n_threads=-1):
if 'nlp_arch' in args.chunker:
spans = get_noun_phrases(doc)
else:
spans = list(doc.noun_chunks)
i += 1
if len(spans) > 0:
span = spans.pop(0)
else:
span = None
spanWritten = False
for token in doc:
if span is None:
if len(token.text.strip()) > 0:
marked_corpus_file.write(token.text + ' ')
else: