def spamHamtoyExample() -> None:
'''
Trains a naive bayes classifier using a folder with spam/ham emails
Checks quality of classifier by using the model to predict the emails from the 'test' folder
Different feature numbers are used to check how many features gives the best classification score (1-50)
Plots the classification - x-axis = number of features, y-axis = classification accuracy
'''
filedir = '../data/emails/'
naivebay = NaiveBayes()
naivebay.train(os.path.join(filedir, 'train/'))
numOfItemsToPrint = 4
naivebay.printMostPopularHamWords(numOfItemsToPrint)
naivebay.printMostPopularSpamWords(numOfItemsToPrint)
naivebay.printMostindicativeHamWords(numOfItemsToPrint)
naivebay.printMostindicativeSpamWords(numOfItemsToPrint)
print('Model logPrior: {}'.format(naivebay.logPrior))
features = [1, 2, 5, 10, 20, 30, 40, 50]
accuracy = []
for i in features:
acc = naivebay.classifyAndEvaluateAllInFolder(
os.path.join(filedir, 'test/'), i)
accuracy.append(acc)
print(i, "features, classification score:", acc)
plt.figure("Naive results: #features vs classification error rate")
plt.plot(features, accuracy)
plt.grid(True)
plt.xlabel('Number of Features')
plt.ylabel('Classification Score')
plt.show()
