def __init__(
self,
parse: bool = True,
rerank_model: PathLike = None,
asp_thresh: int = 3,
op_thresh: int = 2,
max_iter: int = 3,
):
self.acquire_lexicon = AcquireTerms(asp_thresh, op_thresh, max_iter)
if parse:
from nlp_architect.pipelines.spacy_bist import SpacyBISTParser
self.parser = SpacyBISTParser()
else:
self.parser = None
if not rerank_model:
print("using pre-trained reranking model")
rerank_model = _download_pretrained_rerank_model(
RERANK_MODEL_DEFAULT_PATH)
download_unzip(*EMBEDDING_URL,
EMBEDDING_PATH,
license_msg="Glove word embeddings.")
self.rerank = RerankTerms(vector_cache=True,
rerank_model=rerank_model,
emb_model_path=EMBEDDING_PATH)
class TrainSentiment:
def __init__(self,
parse: bool = True,
rerank_model: PathLike = None,
asp_thresh: int = 3,
op_thresh: int = 2,
max_iter: int = 3):
self.acquire_lexicon = AcquireTerms(asp_thresh, op_thresh, max_iter)
if parse:
from nlp_architect.pipelines.spacy_bist import SpacyBISTParser
self.parser = SpacyBISTParser()
else:
self.parser = None
if not rerank_model:
print('using pre-trained reranking model')
rerank_model = _download_pretrained_rerank_model(
RERANK_MODEL_DEFAULT_PATH)
download_unzip(*EMBEDDING_URL,
EMBEDDING_PATH,
license_msg="Glove word embeddings.")
self.rerank = RerankTerms(vector_cache=True,
rerank_model=rerank_model,
emb_model_path=EMBEDDING_PATH)
def run(self,
data: Union[str, PathLike] = None,
parsed_data: Union[str, PathLike] = None,
out_dir: Union[str, PathLike] = TRAIN_OUT):
if not parsed_data:
if not self.parser:
raise RuntimeError(
"Parser not initialized (try parse=True at init)")
parsed_dir = Path(out_dir) / 'parsed' / Path(data).stem
parsed_data = self.parse_data(data, parsed_dir)
generated_aspect_lex = self.acquire_lexicon.acquire_lexicons(
parsed_data)
_write_aspect_lex(parsed_data, generated_aspect_lex, LEXICONS_OUT)
generated_opinion_lex_reranked = \
self.rerank.predict(AcquireTerms.acquired_opinion_terms_path,
AcquireTerms.generic_opinion_lex_path)
_write_opinion_lex(parsed_data, generated_opinion_lex_reranked,
LEXICONS_OUT)
return generated_opinion_lex_reranked, generated_aspect_lex
def parse_data(self, data: PathLike or PosixPath, parsed_dir: PathLike
or PosixPath):
_, data_size = parse_docs(self.parser, data, out_dir=parsed_dir)
if data_size < 1000:
raise ValueError(
'The data contains only {0} sentences. A minimum of 1000 '
'sentences is required for training.'.format(data_size))
return parsed_dir