Model.py

import re
from string import punctuation
from konlpy.tag import Okt
from functools import reduce
import numpy as np
from gensim.models import Word2Vec
from sklearn.metrics import accuracy_score, classification_report
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib
import pickle




class tfidf_LR():
    def __init__(self):
        with open("./tfidf_model.pkl ", "rb") as fp:  #
            self.model = joblib.load(fp)
        with open("./tfidfv", "rb") as fp:  #
            self.tfidfv = pickle.load(fp)
        self.okt = Okt()


    def predict_article(self, news):
        data = [_[0] for _ in self.okt.pos(news) if _[1] == "Noun"]
        predicted = self.model.predict_proba(self.tfidfv.transform([' '.join(data)]))

        return predicted

    def predict_sentences(self, news):
        sentences = news.split('. ')

        data = []
        for s in sentences:
            temp = [_[0] for _ in self.okt.pos(s) if _[1] == "Noun"]
            if temp:
                data.append(temp)

        predicted = self.model.predict_proba(self.tfidfv.transform([' '.join(i) for i in data]))
        return [sentences[i].strip()+'. \n' for i in range(len(predicted)) if predicted[i][0] < 0.3 or predicted[i][0] > 0.7]

    def w2v_corpus(self, corpus):
        return [reduce(lambda x, y: x + y, [self.w2v_model[word] for word in doc if word in self.w2v_model]
                       , np.zeros(100)) for doc in corpus]