Python _is_str_match Beispiele

Beispiel #1

Datei: feature_first_last_ngram.py Projekt: amsqr/Kaggle_HomeDepot

 def _get_match_count(self, obs, target, idx):
     cnt = 0
     if (len(obs) != 0) and (len(target) != 0):
         for word in target:
             if dist_utils._is_str_match(word, obs[idx], self.str_match_threshold):
                 cnt += 1
     return cnt

Beispiel #2

 def _get_match_count(self, obs, target, idx):
     cnt = 0
     if (len(obs) != 0) and (len(target) != 0):
         for word in target:
             if dist_utils._is_str_match(word, obs[idx],
                                         self.str_match_threshold):
                 cnt += 1
     return cnt

Beispiel #3

Datei: feature_intersect_count.py Projekt: MrSnark/Kaggle_HomeDepot

 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))

Beispiel #4

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

Beispiel #5

 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.
                 break
     return s

Beispiel #6

 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

Beispiel #7

Datei: feature_stat_cooc_tfidf.py Projekt: amsqr/Kaggle_HomeDepot

 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

Beispiel #8

 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)
     K = self.k1 * (1 - self.b + self.b * np_utils._try_divide(len(target_ngrams), self.avg_ngram_doc_len))
     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.
         bm25 = s * self._get_idf(w1) * np_utils._try_divide(1 + self.k1, s + K)
         val_list.append(bm25)
     if len(val_list) == 0:
         val_list = [config.MISSING_VALUE_NUMERIC]
     return val_list

Beispiel #9

Datei: feature_stat_cooc_tfidf.py Projekt: amsqr/Kaggle_HomeDepot

 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)
     K = self.k1 * (1 - self.b + self.b * np_utils._try_divide(len(target_ngrams), self.avg_ngram_doc_len))
     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.
         bm25 = s * self._get_idf(w1) * np_utils._try_divide(1 + self.k1, s + K)
         val_list.append(bm25)
     if len(val_list) == 0:
         val_list = [config.MISSING_VALUE_NUMERIC]
     return val_list