def train_n_classes(self):
l = LoadData()
stopWords = l.loadStopWords()
train_sizes = [100, 200, 300] # size per class
for train_size in train_sizes:
print('Training size:',
math.floor(train_size * 0.75) * 5, 'Test size:',
math.ceil(train_size * 0.25) * 5)
self.loadData(train_size)
vect = TfidfVectorizer(stop_words=stopWords)
self.train_and_test_split(0.75)
classes = {}
x = 0
for i in self.data['class_name']:
if i not in classes:
classes[i] = x
x += 1
X_train = vect.fit_transform(self.train_data['data'])
Y_train = [classes[i] for i in self.train_data['class_name']]
X_test = vect.transform(self.test_data['data'])
Y_test = [classes[i] for i in self.test_data['class_name']]
nb = MultinomialNB()
Y_pred = nb.fit(X_train, Y_train).predict(X_test)
self.metric(Y_test, Y_pred)
print('---------------------------------------------------')
def train(self):
l = LoadData()
stopWords = l.loadStopWords()
self.loadDataCSV('bbc-text.csv')
vect = TfidfVectorizer(stop_words=stopWords)
self.train_and_test_split(0.75)
X_train = vect.fit_transform(self.train_data['data'])
Y_train = self.train_data['class_name']
X_test = vect.transform(self.test_data['data'])
Y_test = self.test_data['class_name']
nb = MultinomialNB()
Y_pred = nb.fit(X_train, Y_train).predict(X_test)
self.metric(Y_test, Y_pred)
def train_1_class(self):
l = LoadData()
stopWords = l.loadStopWords()
train_sizes = [100, 200] # size per class
for train_size in train_sizes:
print('Training size:',
math.floor(train_size * 0.75) * 2, 'Test size:',
math.ceil(train_size * 0.25) * 2)
self.loadData(train_size)
vect = TfidfVectorizer(stop_words=stopWords)
# balance classes
temp_class = self.data['class_name'][train_size:]
temp_data = self.data['data'][train_size:]
idx = random.choices(range(len(temp_class)), k=train_size)
temp_class = [temp_class[i] for i in idx]
temp_data = [temp_data[i] for i in idx]
del self.data['data'][train_size:]
del self.data['class_name'][train_size:]
self.data['class_name'].extend(temp_class)
self.data['data'].extend(temp_data)
self.train_and_test_split(0.75)
X_train = vect.fit_transform(self.train_data['data'])
Y_train = [
1 if i == 'business' else 0
for i in self.train_data['class_name']
]
X_test = vect.transform(self.test_data['data'])
Y_test = [
1 if i == 'business' else 0
for i in self.test_data['class_name']
]
nb = MultinomialNB()
Y_pred = nb.fit(X_train, Y_train).predict(X_test)
self.metric(Y_test, Y_pred)
print('---------------------------------------------------')
