import xgboost as xgb
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

from clearml import Task

task = Task.init(project_name='examples', task_name='xgboost metric auto reporting')

X, y = load_boston(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100)

dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)

params = {
    'objective': 'reg:squarederror',
    'eval_metric': 'rmse'
}

bst = xgb.train(
    params, dtrain, num_boost_round=100,
    evals=[(dtrain, 'train'), (dtest, 'test')],
    verbose_eval=0
)
