# Amazon revievs search engine
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from vectorizer_idf import VectorizerIdf
from vectorizer_tf import VectorizerTf


def get_answers_array():
    d = pd.read_csv('answers.csv')
    answers = d["AnswerText"]
    answers = answers.dropna()

    return np.array(answers)


def okapi_mb25(query, tf, idf, a_len, documents):
    k = 1.6
    b = 0.75
    scores = []
    for document in documents:
        v_tf = VectorizerTf([document])
        tf_for_doc = v_tf.get_tf_for_document(query)
        s = 0
        tf_for_document = v_tf.tf_matrix.toarray() * tf_for_doc[0]
        for idx, val in enumerate(tf_for_doc[0]):

            licznik = val * (k + 1)
            mianownik = val + k * (1 - b + b * (len(tf_for_doc) / a_len))
            idf_for_word = idf.get_idf_for_word(v_tf.feature_names[idx])
            s += idf_for_word * (licznik / mianownik)
        scores.append(s)
    return scores


if __name__ == "__main__":
    # data = get_answers_array()
    data = ['Ala ma kota', 'Maciej i Ala ma psa i Ala Ala Ala', 'Ala ma żółwia', 'Maciej ma psa, żółwia i kota',
            'Ola ma psa, żółwia i kota, masło']

    average_lens = []
    for doc in data:
        words = doc.split()
        average_lens.append(sum(len(word) for word in words) / len(words))
    average_doc_len = sum(average_lens) / len(average_lens)
    # print('Doc len', average_doc_len)
    #
    vectorizer_tf = VectorizerTf(data)
    # print('tf', vectorizer_tf.get_tf_for_document('Ala ma psa'))

    vectorizer_idf = VectorizerIdf(data)


    score = okapi_mb25('Ala ma kota', vectorizer_tf, vectorizer_idf, average_doc_len, data)
    print('Score ', score)
    score = okapi_mb25('Ala', vectorizer_tf, vectorizer_idf, average_doc_len, data)
    print('Score 2', score)