def test1():
tokenizer = RegexpTokenizer(r'\w+')
spams = []
hams = []
load_data(hams, spams, 'res/test1.txt')
spam_words = []
ham_words = []
for spam in spams:
spam_words += tokenizer.tokenize(spam)
for ham in hams:
ham_words += tokenizer.tokenize(ham)
naive_bayes = NaiveBayes()
naive_bayes.load(ham_words, spam_words)
test_spams = []
test_hams = []
load_data(test_hams, test_spams, 'res/test1_check.txt')
spam_correct = 0
spam_incorrect = 0
for word in test_spams:
result = naive_bayes.is_positive(tokenizer.tokenize(word))
if result:
spam_incorrect += 1
else:
spam_correct += 1
print('spam:', 'correct', spam_correct, 'incorrect', spam_incorrect)
print('spam:', (spam_correct / (spam_incorrect + spam_correct)) * 100, '%')
ham_correct = 0
ham_incorrect = 0
for word in test_hams:
result = naive_bayes.is_positive(tokenizer.tokenize(word))
if result:
ham_correct += 1
else:
ham_incorrect += 1
print('ham:', 'correct', ham_correct, 'incorrect', ham_incorrect)
print('ham:', (ham_correct / (ham_incorrect + ham_correct)) * 100, '%')
def test2(is_from_begginning=True, training_percent=70):
tokenizer = RegexpTokenizer(r'\w+')
data = get_data('res/SMSSpamCollection.txt')
training_data_length = int((len(data) * training_percent) / 100)
if is_from_begginning:
training_data = data[:training_data_length]
test_data_length = len(data) - training_data_length
test_data = data[-test_data_length:]
else:
training_data = data[-training_data_length:]
test_data_length = len(data) - training_data_length
test_data = data[test_data_length:]
training_hams = []
training_spams = []
divide_data(training_data, training_hams, training_spams)
training_spam_words = []
training_ham_words = []
for ham in training_hams:
training_ham_words += tokenizer.tokenize(ham)
for spam in training_spams:
training_spam_words += tokenizer.tokenize(spam)
naive_bayes = NaiveBayes()
naive_bayes.load(training_ham_words, training_spam_words)
test_hams = []
test_spams = []
divide_data(test_data, test_hams, test_spams)
spam_correct = 0
spam_incorrect = 0
for word in test_spams:
result = naive_bayes.is_positive(tokenizer.tokenize(word))
if result:
spam_incorrect += 1
else:
spam_correct += 1
print('spam:', 'correct', spam_correct, 'incorrect', spam_incorrect)
print('spam:', (spam_correct / (spam_incorrect + spam_correct)) * 100, '%')
ham_correct = 0
ham_incorrect = 0
for word in test_hams:
result = naive_bayes.is_positive(tokenizer.tokenize(word))
if result:
ham_correct += 1
else:
ham_incorrect += 1
print('ham:', 'correct', ham_correct, 'incorrect', ham_incorrect)
print('ham:', (ham_correct / (ham_incorrect + ham_correct)) * 100, '%')
from classifier import NaiveBayes
from util import load_data
tokenizer = RegexpTokenizer(r'\w+')
spams = []
hams = []
load_data(hams, spams, 'res/SMSSpamCollection.txt')
spam_words = []
ham_words = []
for spam in spams:
spam_words += tokenizer.tokenize(spam)
for ham in hams:
ham_words += tokenizer.tokenize(ham)
naive_bayes = NaiveBayes()
naive_bayes.load(ham_words, spam_words)
message = ""
while message != "stop":
message = input("Enter your SMS:")
if naive_bayes.is_positive(tokenizer.tokenize(message)):
print("ham")
else:
print("spam")
