y_train, \
y_val = train_test_split(X, y, test_size=0.3, random_state=0)
# xlearn
# param:
# 0. binary classification
# 1. model scale: 0.1
# 2. epoch number: 10 (auto early-stop)
# 3. learning rate: 0.1
# 4. regular lambda: 1.0
# 5. use sgd optimization method
linear_model = xl.LRModel(task='binary', init=0.1,
epoch=10, lr=0.1,
reg_lambda=1.0, opt='sgd')
print(datetime.datetime.now())
# Start to train
linear_model.fit(X_train, y_train,
eval_set=[X_val, y_val],
is_lock_free=False)
# print model weights
# print(linear_model.weights)
# Generate predictions
y_pred = linear_model.predict(X_val)
print(datetime.datetime.now())
# sklearn
lr = LogisticRegression()
lr.fit(X_train, y_train)
print('{0}, val mean acc:{1}'.format(lr.__str__(), lr.score(X_val, y_val)))
print(datetime.datetime.now())
