def Classify(X, Y, cls, rep, k=5000):
# Start moment
Start_moment = time.time()
title = 'Classificando com {} e {} k={}'.format(cls, rep, k)
print(title)
# Creating the K-fold cross validator
if 'w2v' in rep:
train_x = load(open('w2v_rep/{}_train_x.pkl'.format(rep), 'rb'))
train_y = load(open('w2v_rep/{}_train_y.pkl'.format(rep), 'rb'))
test_x = load(open('w2v_rep/{}_test_x.pkl'.format(rep), 'rb'))
test_y = load(open('w2v_rep/{}_test_y.pkl'.format(rep), 'rb'))
else:
X_train, X_test, y_train, y_test = train_test_split(X,
Y,
test_size=0.2,
random_state=123,
stratify=Y)
train_x, train_y, test_x, test_y = Representations(
).get_representation(rep=rep,
train_x=X_train,
train_y=y_train,
test_x=X_test,
test_y=y_test,
k=k,
cat=None)
sm = SMOTE(sampling_strategy='minority', random_state=None)
train_x, train_y = sm.fit_sample(train_x, train_y)
# dump(train_x, open('w2v_rep/{}_train_x.pkl'.format(rep), 'wb'))
# dump(train_y, open('w2v_rep/{}_train_y.pkl'.format(rep), 'wb'))
# dump(test_x, open('w2v_rep/{}_test_x.pkl'.format(rep), 'wb'))
# dump(test_y, open('w2v_rep/{}_test_y.pkl'.format(rep), 'wb'))
# return
classifier = Models().get_classifier(cls)
classifier.fit(train_x, train_y)
# Train_Classifier(classifier, X_train, Y_train)
pred = classifier.predict(test_x)
# report = classification_report(test_labels, test_pred, target_names=['Contrário', 'Favorável'] if plb =='polaridade' else ['neutro', 'opiniao'])
report = classification_report(test_y, pred, target_names=['no', 'yes'])
print(report)
Finish_moment = time.time()
tm = "It took " + str((Finish_moment - Start_moment)) + " seconds"
print(tm)
def Classify(X, Y, cls, rep, k=5000):
# Start moment
Start_moment = time.time()
title = 'Classificando com {} e {} k={}'.format(cls, rep, k)
print(title)
# Creating the K-fold cross validator
K_fold = KFold(n_splits=10, shuffle=True)
# Labels
test_labels = np.array([], 'int32')
test_pred = np.array([], 'int32')
# Confusion Matrix
confusion = np.array([[0, 0], [0, 0]])
# The test
for train_indices, test_indices in K_fold.split(X):
print('Running .... =)')
X_train = [X[i] for i in train_indices]
Y_train = [Y[i] for i in train_indices]
X_test = [X[i] for i in test_indices]
Y_test = [Y[i] for i in test_indices]
train_x, train_y, test_x, test_y = Representations(
).get_representation(rep=rep,
train_x=X_train,
train_y=Y_train,
test_x=X_test,
test_y=Y_test,
k=k,
cat=None)
# c = Counter(Y_train)
# print(Counter(train_y))
# print({1:c.most_common(1)[0][1], 0:c.most_common(1)[0][1], 2:c.most_common(1)[0][1]})
sm = SMOTE(sampling_strategy='minority', random_state=None)
# sm = SMOTE(sampling_strategy={1:c.most_common(1)[0][1], 0:c.most_common(1)[0][1], 2:c.most_common(1)[0][1]}, random_state=None)
# print(len(train_y))
train_x, train_y = sm.fit_sample(train_x, train_y)
# print(Counter(train_y))
test_labels = np.append(test_labels, Y_test)
classifier = Models().get_classifier(cls)
classifier.fit(train_x, train_y)
# Train_Classifier(classifier, X_train, Y_train)
pred = classifier.predict(test_x)
test_pred = np.append(test_pred, pred)
# print(test_y)
# print(pred)
confusion += confusion_matrix(test_y, pred)
# report = classification_report(test_labels, test_pred, target_names=['Contrário', 'Favorável'] if plb =='polaridade' else ['neutro', 'opiniao'])
report = classification_report(test_labels,
test_pred,
target_names=['no', 'yes'])
print(report)
print("Confusion matrix:")
print(confusion)
Finish_moment = time.time()
tm = "It took " + str((Finish_moment - Start_moment)) + " seconds"
print(tm)
