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, '%')
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")
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, '%')
folds = np.array_split(data, 10) #make 10 folds in the dataset
test_set = 0 #define test_set
#for each folds treat one fold as test set and 9 fols at train set
for y in range(len(folds)):
X_train = pd.DataFrame()
#if not test-set append fold in the train set
for x in range(len(folds)):
if x == test_set:
y_test = folds[x]['class'].values
X_test = folds[x].drop(['class'], axis=1)
else:
X_train = X_train.append(folds[x])
y_train = X_train['class'].values
X_train = X_train.drop(['class'], axis=1)
nb = NaiveBayes() #initialize Naive Bayes Classifier
nb.fit(X_train, y_train) #train model with train data
y_pred = nb.predict(X_test) #test model with test set
#find error with respect to zero-one loss function
error = nb.zero_one_loss_function(y_test, y_pred)
printstr = "\nAccuracy of 0-1 loss for fold {0} ::: {1}".format(
y, (1 - error))
print_both(file, printstr)
accuracy_list.append((1 - error))
#get mean square error
acc, precision, recall = nb.confusion_matrix(y_test, y_pred)
printstr = "\nCF for fold {0} ::: acc:: {1} :: precision:: {2} :: recall :: {3}".format(
y, acc, precision, recall)
print_both(file, printstr)
CF_accuracy_list.append(acc)
CF_precision_list.append(precision)
