def transform(self, X):
mat = np.zeros((len(X), 1))
for i, (_, s) in enumerate(X.iterrows()):
idx = get_aligned_data().get((s.claimId, s.articleId))
if idx:
try:
claim_tok = get_tokenized_lemmas(s.claimHeadline)
article_tok = get_tokenized_lemmas(s.articleHeadline)
mat[i, 0] = self._sts(claim_tok, article_tok, idx)
except:
pass
return mat
def transform(self, X):
mat = np.zeros((len(X), 1))
for i, (_, s) in enumerate(X.iterrows()):
idx = get_aligned_data().get((s.claimId, s.articleId))
if idx:
try:
claim_tok = get_tokenized_lemmas(s.claimHeadline)
article_tok = get_tokenized_lemmas(s.articleHeadline)
mat[i, 0] = self._sts(claim_tok, article_tok, idx)
except:
pass
return mat
def transform(self, X):
mat = np.zeros((len(X), 1))
for i, (_, s) in enumerate(X.iterrows()):
idx = get_aligned_data().get((s.claimId, s.articleId))
f = 0
if idx:
claim_tok = get_tokenized_lemmas(s.claimHeadline)
article_tok = get_tokenized_lemmas(s.articleHeadline)
for x, y in idx:
if x > 0 and y == 0:
f = self._match(claim_tok[x-1])
elif x == 0 and y > 0:
f = self._match(article_tok[y-1])
elif [x-1, y-1] not in idx:
f = self._match(claim_tok[x-1]) or self._match(article_tok[y-1])
mat[i, 0] = f
return mat
def transform(self, X):
mat = np.zeros((len(X), 1))
for i, (_, s) in enumerate(X.iterrows()):
idx = get_aligned_data().get((s.claimId, s.articleId))
f = 0
if idx:
claim_tok = get_tokenized_lemmas(s.claimHeadline)
article_tok = get_tokenized_lemmas(s.articleHeadline)
for x, y in idx:
if x > 0 and y == 0:
f = self._match(claim_tok[x - 1])
elif x == 0 and y > 0:
f = self._match(article_tok[y - 1])
elif [x - 1, y - 1] not in idx:
f = self._match(claim_tok[x - 1]) or self._match(
article_tok[y - 1])
mat[i, 0] = f
return mat
