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Update XGBoost example (#524)

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* Change dataset (load_boston is deprecated)
* Black format
* Add save_model
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pollfly 2021-12-23 14:07:42 +02:00 committed by GitHub
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commit b036bbce2f
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examples/frameworks/xgboost/xgboost_metrics.py
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@ -1,24 +1,27 @@
import xgboost as xgb import xgboost as xgb
from sklearn.datasets import load_boston from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
from clearml import Task from clearml import Task
task = Task.init(project_name='examples', task_name='xgboost metric auto reporting') task = Task.init(project_name="examples", task_name="xgboost metric auto reporting")
X, y = load_boston(return_X_y=True) X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100) 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) dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test) dtest = xgb.DMatrix(X_test, label=y_test)
params = { params = {"objective": "reg:squarederror", "eval_metric": "rmse"}
'objective': 'reg:squarederror',
'eval_metric': 'rmse'
}
bst = xgb.train( bst = xgb.train(
params, dtrain, num_boost_round=100, params,
evals=[(dtrain, 'train'), (dtest, 'test')], dtrain,
verbose_eval=0 num_boost_round=100,
evals=[(dtrain, "train"), (dtest, "test")],
verbose_eval=0,
) )
bst.save_model("best_model")