58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
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# Amazon revievs search engine
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import pandas as pd
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import numpy as np
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from sklearn.feature_extraction.text import TfidfVectorizer
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from vectorizer_idf import VectorizerIdf
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from vectorizer_tf import VectorizerTf
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def get_answers_array():
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d = pd.read_csv('answers.csv')
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answers = d["AnswerText"]
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answers = answers.dropna()
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return np.array(answers)
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def okapi_mb25(query, tf, idf, a_len, documents):
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k = 1.6
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b = 0.75
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scores = []
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for document in documents:
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v_tf = VectorizerTf([document])
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tf_for_doc = v_tf.get_tf_for_document(query)
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s = 0
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tf_for_document = v_tf.tf_matrix.toarray() * tf_for_doc[0]
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for idx, val in enumerate(tf_for_doc[0]):
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licznik = val * (k + 1)
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mianownik = val + k * (1 - b + b * (len(tf_for_doc) / a_len))
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idf_for_word = idf.get_idf_for_word(v_tf.feature_names[idx])
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s += idf_for_word * (licznik / mianownik)
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scores.append(s)
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return scores
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if __name__ == "__main__":
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# data = get_answers_array()
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data = ['Ala ma kota', 'Maciej i Ala ma psa i Ala Ala Ala', 'Ala ma żółwia', 'Maciej ma psa, żółwia i kota',
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'Ola ma psa, żółwia i kota, masło']
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average_lens = []
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for doc in data:
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words = doc.split()
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average_lens.append(sum(len(word) for word in words) / len(words))
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average_doc_len = sum(average_lens) / len(average_lens)
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# print('Doc len', average_doc_len)
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#
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vectorizer_tf = VectorizerTf(data)
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# print('tf', vectorizer_tf.get_tf_for_document('Ala ma psa'))
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vectorizer_idf = VectorizerIdf(data)
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score = okapi_mb25('Ala ma kota', vectorizer_tf, vectorizer_idf, average_doc_len, data)
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print('Score ', score)
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score = okapi_mb25('Ala', vectorizer_tf, vectorizer_idf, average_doc_len, data)
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print('Score 2', score)
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