def transform_one(self, obs, target, id):
     obs_tokens = nlp_utils._tokenize(obs, token_pattern)
     target_tokens = nlp_utils._tokenize(target, token_pattern)
     obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram)
     target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram)
     pos_list = _inter_norm_pos_list(obs_ngrams, target_ngrams)
     return pos_list

 def transform_one(self, obs, target, id):
     obs_tokens = nlp_utils._tokenize(obs, token_pattern)
     target_tokens = nlp_utils._tokenize(target, token_pattern)
     obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram)
     target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram)
     s = 0.
     for w1 in obs_ngrams:
         for w2 in target_ngrams:
             if dist_utils._is_str_match(w1, w2, self.str_match_threshold):
                 s += 1.
     return np_utils._try_divide(s, len(obs_ngrams)*len(target_ngrams))

 def transform(self):
     # ngrams
     obs_ngrams = list(map(lambda x: ngram_utils._ngrams(x.split(" "), self.obs_ngram, "_"), self.obs_corpus))
     target_ngrams = list(map(lambda x: ngram_utils._ngrams(x.split(" "), self.target_ngram, "_"), self.target_corpus))
     # cooccurrence ngrams
     cooc_terms = list(map(lambda lst1,lst2: self._get_cooc_terms(lst1, lst2, "X"), obs_ngrams, target_ngrams))
     ## tfidf
     tfidf = self._init_word_ngram_tfidf(ngram=1)
     X = tfidf.fit_transform(cooc_terms)
     ## svd
     svd = TruncatedSVD(n_components=self.svd_dim, n_iter=self.svd_n_iter, random_state=config.RANDOM_SEED)
     return svd.fit_transform(X)

 def transform_one(self, obs, target, id):
     obs_tokens = nlp_utils._tokenize(obs, token_pattern)
     target_tokens = nlp_utils._tokenize(target, token_pattern)
     obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram)
     target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram)
     val_list = []
     for w1 in obs_ngrams:
         s = 0.
         for w2 in target_ngrams:
             if dist_utils._is_str_match(w1, w2, self.str_match_threshold):
                 s += 1.
         val_list.append(np_utils._try_divide(s, len(target_ngrams)))
     if len(val_list) == 0:
         val_list = [config.MISSING_VALUE_NUMERIC]
     return val_list

 def transform_one(self, obs, target, id):
     df = self.dfTrain[self.dfTrain["search_term"] == obs].copy()
     val_list = [config.MISSING_VALUE_NUMERIC]
     if df is not None:
         df = df[df["id"] != id].copy()
         df = df[df["relevance"] == self.relevance].copy()
         if df is not None and df.shape[0] > 0:
             target_tokens = nlp_utils._tokenize(target, token_pattern)
             target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram)
             val_list = []
             for x in df[self.target_field]:
                 x_tokens = nlp_utils._tokenize(x, token_pattern)
                 x_ngrams = ngram_utils._ngrams(x_tokens, self.ngram)
                 val_list.append(dist_utils._jaccard_coef(x_ngrams, target_ngrams))
     return val_list

 def _get_avg_ngram_doc_len(self):
     lst = []
     for target in self.target_corpus:
         target_tokens = nlp_utils._tokenize(target, token_pattern)
         target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram)
         lst.append(len(target_ngrams))
     return np.mean(lst)

 def transform_one(self, obs, target, id):
     obs_tokens = nlp_utils._tokenize(obs, token_pattern)
     target_tokens = nlp_utils._tokenize(target, token_pattern)
     obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram)
     target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram)
     val_list = []
     for w1 in obs_ngrams:
         _val_list = []
         for w2 in target_ngrams:
             s = dist_utils._edit_dist(w1, w2)
             _val_list.append(s)
         if len(_val_list) == 0:
             _val_list = [ config.MISSING_VALUE_NUMERIC ]
         val_list.append( _val_list )
     if len(val_list) == 0:
         val_list = [ [config.MISSING_VALUE_NUMERIC] ]
     return val_list

 def _get_df_dict(self):
     # smoothing
     d = defaultdict(lambda : 1)
     for target in self.target_corpus:
         target_tokens = nlp_utils._tokenize(target, token_pattern)
         target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram)
         for w in set(target_ngrams):
             d[w] += 1
     return d

 def _ngram(self, text):
     tokens = text.split(" ")
     tokens = [token for token in tokens if token not in self.stopwords]
     return ngram_utils._ngrams(tokens, self.ngram, " ")

 def transform_one(self, obs, target, id):
     obs_tokens = nlp_utils._tokenize(obs, token_pattern)
     obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram)
     return np_utils._try_divide(len(set(obs_ngrams)), len(obs_ngrams))

 def transform_one(self, obs, target, id):
     obs_tokens = nlp_utils._tokenize(obs, token_pattern)
     target_tokens = nlp_utils._tokenize(target, token_pattern)
     obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram)
     target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram)
     return np_utils._try_divide(self._get_match_count(obs_ngrams, target_ngrams, self.idx), len(target_ngrams))