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- import Forums.Classifier.transformer
- import pickle, re
-
-
- class Transformer:
- def __init__(self):
- self.ngram_list = ['res', 'vir', 'att', 'tta', 'tac', 'web', 'cat', 'rse', 'cra', 'rac', 'nso', 'omw', 'mwa', 'tec', 'boo', 'adv', 'abl', 'can', 'mmi', 'cke', 'bot', 'oks', 'ick', 'eak', 'whe', 'val', 'acc', 'mon', 'dvi', 'nto', 'phi', 'deo', 'hao', 'aos', 'pst', 'ddo', 'dos', 'iru', 'kit', 'jac']
-
- self.ngram_index_dict = self.get_ngram_index()
-
- def get_ngram_index(self):
- ngram_index = {}
- index = 0
- for ngram in self.ngram_list:
- if ngram not in ngram_index:
- ngram_index[ngram] = index
- index += 1
- return ngram_index
-
- def binary_vector(self, text):
- vec = [0] * len(self.ngram_index_dict)
- for ngram, index in self.ngram_index_dict.items():
- if ngram in text:
- vec[index] = 1
- return vec
-
- def frequency_vector(self, text):
- vec = [0] * len(self.ngram_index_dict)
- for ngram, index in self.ngram_index_dict.items():
- vec[index] = text.count(ngram)
- return vec
-
- def relative_frequency_vector(self, freq_vec):
- total = 0
- for count in freq_vec:
- total += count
-
- vec = [0] * len(freq_vec)
- if total > 0:
- for i, count in enumerate(freq_vec):
- vec[i] = count / total
- return vec
-
- def transform(self, text):
- clean_sent = re.sub(r'[^a-zA-Z ]', '', text).lower()
-
- bin_vec = self.binary_vector(clean_sent)
-
- return bin_vec
-
-
- def load(file_path):
- with open(file_path, 'rb') as f:
- return pickle.load(f)
-
-
- def fix(fname):
- t = load(fname)
- new_t = Forums.Classifier.transformer.Transformer()
- new_t.ngram_list = t.ngram_list
- new_t.ngram_index_dict = t.ngram_index_dict
- Forums.Classifier.transformer.save(new_t, fname + ".new")
-
-
- fix("topic_title_transformer.pickle")
- # fix("title_transformer.pickle")
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