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66 lines
2.0 KiB
66 lines
2.0 KiB
import MarketPlaces.Classifier.transformer
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import pickle, re
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class Transformer:
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def __init__(self):
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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']
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self.ngram_index_dict = self.get_ngram_index()
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def get_ngram_index(self):
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ngram_index = {}
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index = 0
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for ngram in self.ngram_list:
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if ngram not in ngram_index:
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ngram_index[ngram] = index
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index += 1
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return ngram_index
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def binary_vector(self, text):
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vec = [0] * len(self.ngram_index_dict)
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for ngram, index in self.ngram_index_dict.items():
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if ngram in text:
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vec[index] = 1
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return vec
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def frequency_vector(self, text):
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vec = [0] * len(self.ngram_index_dict)
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for ngram, index in self.ngram_index_dict.items():
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vec[index] = text.count(ngram)
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return vec
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def relative_frequency_vector(self, freq_vec):
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total = 0
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for count in freq_vec:
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total += count
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vec = [0] * len(freq_vec)
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if total > 0:
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for i, count in enumerate(freq_vec):
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vec[i] = count / total
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return vec
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def transform(self, text):
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clean_sent = re.sub(r'[^a-zA-Z ]', '', text).lower()
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bin_vec = self.binary_vector(clean_sent)
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return bin_vec
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def load(file_path):
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with open(file_path, 'rb') as f:
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return pickle.load(f)
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def fix(fname):
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t = load(fname)
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new_t = MarketPlaces.Classifier.transformer.Transformer()
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new_t.ngram_list = t.ngram_list
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new_t.ngram_index_dict = t.ngram_index_dict
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MarketPlaces.Classifier.transformer.save(new_t, fname + ".new")
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# fix("desc_transformer.pickle")
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fix("product_title_transformer.pickle")
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