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df01031cfe
* Prepare for release Co-authored-by: Moe Kayali <t-moekayali@microsoft.com> * bug fix * improve doc and code quality Co-authored-by: Qingyun Wu
17 lines
521 B
Python
17 lines
521 B
Python
from flaml.data import load_openml_dataset
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from flaml.default import LGBMRegressor
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from flaml.ml import sklearn_metric_loss_score
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X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=537, data_dir="./")
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lgbm = LGBMRegressor()
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hyperparams, estimator_name, X_transformed, y_transformed = lgbm.suggest_hyperparams(
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X_train, y_train
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)
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print(hyperparams)
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lgbm.fit(X_train, y_train)
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y_pred = lgbm.predict(X_test)
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print("flamlized lgbm r2 =", 1 - sklearn_metric_loss_score("r2", y_pred, y_test))
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print(lgbm)
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