import pandas as pd

import numpy as np

import warnings

from sklearn.model_selection import train_test_split

from sklearn.feature_extraction.text import CountVectorizer

from sklearn.linear_model import LogisticRegression

class NLP:

def __init__(self):

warnings.filterwarnings("ignore")

self.model = {'model':'model'}

def training(self,input_list):

for item in input_list:

print(item)

status=input("Positive/Negative(1?0):")

f = open("model","a")

f.write(item+"\t"+status+"\n")

f.close()

def getModel(self):

df_list = []

for source, filepath in self.model.items():

df = pd.read_csv(filepath, names=['sentence','label'], sep='\t')

df['source'] = source

df_list.append(df)

df= pd.concat(df_list)

df_model = df[df['source'] == 'model']

self.sentences = df_model['sentence'].values

self.label = df_model['label'].values

def match(self, test):

test_train = np.ones(len(test), dtype=np.int)

vectorizer = CountVectorizer()

vectorizer.fit(self.sentences)

train = vectorizer.transform(self.sentences)

test = vectorizer.transform(test)

classifier = LogisticRegression()

classifier.fit(train, self.label)

score = classifier.score(test, test_train)

return score