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In PySpark Estimator example use the model with validation_indicator (#8131)

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* use the validation_indicator model

* use the validation_indicator model for regression
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Praateek Mahajan 2022-08-02 22:57:41 -07:00 committed by GitHub
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demo/guide-python/spark_estimator_examples.py
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@ -48,7 +48,7 @@ diabetes_train_spark_df2 = diabetes_train_spark_df.withColumn(
# train xgboost regressor model with validation dataset
xgb_regressor2 = SparkXGBRegressor(max_depth=5, validation_indicator_col="validationIndicatorCol")
xgb_regressor_model2 = xgb_regressor.fit(diabetes_train_spark_df2)
xgb_regressor_model2 = xgb_regressor2.fit(diabetes_train_spark_df2)
transformed_diabetes_test_spark_df2 = xgb_regressor_model2.transform(diabetes_test_spark_df)
print(f"regressor2 rmse={regressor_evaluator.evaluate(transformed_diabetes_test_spark_df2)}")
@ -75,7 +75,7 @@ iris_train_spark_df2 = iris_train_spark_df.withColumn(
# train xgboost classifier model with validation dataset
xgb_classifier2 = SparkXGBClassifier(max_depth=5, validation_indicator_col="validationIndicatorCol")
xgb_classifier_model2 = xgb_classifier.fit(iris_train_spark_df2)
xgb_classifier_model2 = xgb_classifier2.fit(iris_train_spark_df2)
transformed_iris_test_spark_df2 = xgb_classifier_model2.transform(iris_test_spark_df)
print(f"classifier2 f1={classifier_evaluator.evaluate(transformed_iris_test_spark_df2)}")