def test_with_unigram_tfidf():
train_x, train_y, test_x, test_y = get_features('dbn')
train_x = np.array(train_x, dtype=np.float32)
# print(type(train_x))
train_y = np.array(train_y, dtype=np.int32)
test_x = np.array(test_x, dtype=np.float32)
test_y = np.array(test_y, dtype=np.int32)
print(type(train_x))
classifier = SupervisedDBNClassification(
hidden_layers_structure=[256, 256, 256],
learning_rate_rbm=0.05,
learning_rate=0.1,
n_epochs_rbm=10,
n_iter_backprop=100,
batch_size=32,
activation_function='relu',
dropout_p=0.2)
classifier.fit(train_x, train_y)
accuracies = []
f_measures = []
for i in range(1):
y_pred = classifier.predict(test_x)
accuracy = accuracy_score(test_y, y_pred)
f_measure = f1_score(test_y, y_pred)
accuracies.append(accuracy)
f_measures.append(f_measure)
print(accuracies)
print('Accuracy ', mean(accuracies))
print('F-measure', mean(f_measures))
return
def check_class(text, lexicon):
line = translator.translate(text, dest='hi').text
classifier = SupervisedDBNClassification.load('dbn.pkl')
predict_set = []
all_words = word_tokenize(line)
# all_words = [lemmatizer.lemmatize(i) for i in all_words]
features = np.zeros(len(lexicon))
for word in all_words:
if word in lexicon:
idx = lexicon.index(word)
features[idx] += 1
features = list(features)
predict_set.append(features)
predict_set = np.array(predict_set, dtype=np.float32)
predict_set = classifier.predict(predict_set)
# train_x = np.array(list(train_x))
train_x, train_y, test_x, test_y = get_features('dbn')
#train_x, train_y, test_x, test_y = create_feature_set_and_labels('pos_final.txt', 'neg_final.txt')
#print(type(train_x))
print(len(train_x), len(train_y), len(test_x), len(test_y))
train_x = np.array(train_x, dtype=np.float32)
#print(type(train_x))
train_y = np.array(train_y, dtype=np.int32)
test_x = np.array(test_x, dtype=np.float32)
test_y = np.array(test_y, dtype=np.int32)
print(type(train_x))
classifier = SupervisedDBNClassification(
hidden_layers_structure=[256, 256, 256],
learning_rate_rbm=0.05,
learning_rate=0.1,
n_epochs_rbm=10,
n_iter_backprop=100,
batch_size=32,
activation_function='relu',
dropout_p=0.2)
classifier.fit(train_x, train_y)
# classifier = SupervisedDBNClassification.load('model.pkl')
# classifier.save('model.pkl')
accuracies = []
f_measures = []
for i in range(1):
y_pred = classifier.predict(test_x)
accuracy = accuracy_score(test_y, y_pred)
f_measure = f1_score(test_y, y_pred)
accuracies.append(accuracy)
f_measures.append(f_measure)