# -*- coding: utf-8 -*-

"""

Created on Sun Dec 29 13:44:21 2019

@author: sairam.E

"""

import pandas as pd

from sklearn.model_selection import train_test_split

import numpy as np

from sklearn.pipeline import Pipeline

from sklearn.feature_extraction.text import CountVectorizer

from sklearn.feature_extraction.text import TfidfTransformer,TfidfVectorizer

from sklearn.svm import LinearSVC

from sklearn.naive_bayes import MultinomialNB

from sklearn.linear_model import SGDClassifier

from sklearn.naive_bayes import *

from sklearn.externals import joblib

#loading the input csv data into the pandas dataframe

public_griv_df = pd.read_csv("pg_complete_set_1.csv",

engine='python',error_bad_lines=False)

#print(public_griv_df.columns)

y = public_griv_df.org_name

X = public_griv_df.subject_content

#splitting the data into training and testing purposes

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)

#pipeline for Naive Bayes algorithm

txt_clf_NB=Pipeline([ ('vect',CountVectorizer()),

('clf',MultinomialNB())])

#pipeline for SVM classifier algorithm

text_clf_svm = Pipeline([('vect', CountVectorizer()),

])

#fitting and measuring accuracy for SVM model

text_clf_svm.fit(X_train, y_train)

#joblib.dump(text_clf_svm, 'model.pkl')

#text_clf_svm = joblib.load('model.pkl')

#print("Model dumped!")

predictions_svm = text_clf_svm.predict(X_test)

print(np.mean(predictions_svm == y_test))

svm_accuracy = np.mean(predictions_svm == y_test)

#fitting and measuring accuracy for Naive Bayes model

txt_clf_NB.fit(X_train, y_train)

predictions_NB = txt_clf_NB.predict(X_test)

print(np.mean(predictions_NB == y_test))

NB_accuracy = np.mean(predictions_NB == y_test)

if (int(svm_accuracy))< (int(NB_accuracy)):

print('Naive Bayes model has better accuracy and used for modelling')

joblib.dump(txt_clf_NB, 'model.pkl')

txt_clf_NB = joblib.load('model.pkl')

print("Model dumped!")

complete_df = pd.concat([y_test], axis=1) # features and actual

complete_df['Predicted'] = predictions_NB

print(complete_df)

else:

print('svm model has better accuracy and used for modelling')

joblib.dump(text_clf_svm, 'model.pkl')

text_clf_svm = joblib.load('model.pkl')

print("Model dumped!")

complete_df = pd.concat([y_test], axis=1) # features and actual

complete_df['Predicted'] = predictions_svm

print(complete_df)