def get_data_df(self):
doc = self.doc
i_0 = self.first_i
predicates = doc._.lex_matches
n = len(predicates)
t = Timer()
t.start()
data = [{
'i': i_0 + tok.i,
'sent_i': i_0 + tok.sent.start,
't': i_0 + tok._.subsent_root.i,
'neg': tok._.negated,
'lemma': tok.lemma_,
'text': tok.text,
'R_agent': agent.root.text if agent else None,
'R_patient': patient.root.text if patient else None,
**{('L_' + doc.vocab[cat].text): 1.0
for cat in tok._.lex},
} for tok in predicates for agent in (tok._.agents or [None])
for patient in (tok._.patients or [None])]
table = pd.DataFrame(data).sort_values('i')
predicate_cols = [c for c in list(table.columns) if c.startswith('L_')]
table[predicate_cols] = table[predicate_cols].fillna(0)
t.stop()
logger.debug('%d predicates (%d distinct) [%s]', len(table.index), n,
t)
return table
def get_entities_df(self):
t = Timer()
t.start()
ent_cls = proc_ent.entity_classifier(self.doc.vocab)
df = pd.DataFrame(ent_cls(self.doc))
t.stop()
logger.debug('%d entities [%s]', len(df.index), t)
return df
