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naive-bayes.py
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naive-bayes.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Fake news detection
The naive bayes model
"""
from getEmbeddings import getEmbeddings
from sklearn.naive_bayes import GaussianNB
import numpy as np
import matplotlib.pyplot as plt
import scikitplot.plotters as skplt
import os
def plot_cmat(yte, ypred):
'''Plotting confusion matrix'''
skplt.plot_confusion_matrix(yte,ypred)
plt.show()
# Read the data
if not os.path.isfile('./xtr.npy') or \
not os.path.isfile('./xte.npy') or \
not os.path.isfile('./ytr.npy') or \
not os.path.isfile('./yte.npy'):
xtr,xte,ytr,yte = getEmbeddings("datasets/train.csv")
np.save('./xtr', xtr)
np.save('./xte', xte)
np.save('./ytr', ytr)
np.save('./yte', yte)
xtr = np.load('./xtr.npy')
xte = np.load('./xte.npy')
ytr = np.load('./ytr.npy')
yte = np.load('./yte.npy')
# Use the built-in Naive Bayes classifier
gnb = GaussianNB()
gnb.fit(xtr,ytr)
y_pred = gnb.predict(xte)
m = yte.shape[0]
n = (yte != y_pred).sum()
print("Accuracy = " + format((m-n)/m*100, '.2f') + "%") # 72.94%
# Draw the confusion matrix
plot_cmat(yte, y_pred)