svm.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
Fake news detection
The SVM model
"""

from getEmbeddings import getEmbeddings
import numpy as np
from sklearn.svm import SVC
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 SVM for classification
clf = SVC()
clf.fit(xtr, ytr)
y_pred = clf.predict(xte)
m = yte.shape[0]
n = (yte != y_pred).sum()
print("Accuracy = " + format((m-n)/m*100, '.2f') + "%")   # 88.42%

# Draw the confusion matrix
plot_cmat(yte, y_pred)