java.lang.IllegalArgumentException: reg:squarederror

[matigurten]

I create and fit a pipeline with the following model:

XGBRF_Regressor_pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('reg', xgb.XGBRFRegressor(n_estimators= 5, criterion="mse", max_depth=5, random_state=0, objective ='reg:squarederror'))])

Because when I don't use " objective ='reg:squarederror' " a warning rises saying:

[22:05:01] WARNING: C:/Jenkins/workspace/xgboost-win64_release_0.90/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.

Converting it to PMML gives the following error:

Standard output is empty
Standard error:
 20, 2019 9:58:25 PM org.jpmml.sklearn.Main run
INFO: Parsing PKL..
 20, 2019 9:58:25 PM org.jpmml.sklearn.Main run
INFO: Parsed PKL in 38 ms.
 20, 2019 9:58:25 PM org.jpmml.sklearn.Main run
INFO: Converting..
 20, 2019 9:58:26 PM org.jpmml.sklearn.Main run
SEVERE: Failed to convert
java.lang.IllegalArgumentException: reg:squarederror
	at org.jpmml.xgboost.Learner.load(Learner.java:97)
	at org.jpmml.xgboost.XGBoostUtil.loadLearner(XGBoostUtil.java:53)
	at xgboost.sklearn.Booster.loadLearner(Booster.java:52)
	at xgboost.sklearn.Booster.getLearner(Booster.java:42)
	at xgboost.sklearn.BoosterUtil.getLearner(BoosterUtil.java:72)
	at xgboost.sklearn.BoosterUtil.getNumberOfFeatures(BoosterUtil.java:40)
	at xgboost.sklearn.XGBRegressor.getNumberOfFeatures(XGBRegressor.java:35)
	at sklearn2pmml.pipeline.PMMLPipeline.encodePMML(PMMLPipeline.java:222)
	at org.jpmml.sklearn.Main.run(Main.java:145)
	at org.jpmml.sklearn.Main.main(Main.java:94)

Exception in thread "main" java.lang.IllegalArgumentException: reg:squarederror
	at org.jpmml.xgboost.Learner.load(Learner.java:97)
	at org.jpmml.xgboost.XGBoostUtil.loadLearner(XGBoostUtil.java:53)
	at xgboost.sklearn.Booster.loadLearner(Booster.java:52)
	at xgboost.sklearn.Booster.getLearner(Booster.java:42)
	at xgboost.sklearn.BoosterUtil.getLearner(BoosterUtil.java:72)
	at xgboost.sklearn.BoosterUtil.getNumberOfFeatures(BoosterUtil.java:40)
	at xgboost.sklearn.XGBRegressor.getNumberOfFeatures(XGBRegressor.java:35)
	at sklearn2pmml.pipeline.PMMLPipeline.encodePMML(PMMLPipeline.java:222)
	at org.jpmml.sklearn.Main.run(Main.java:145)
	at org.jpmml.sklearn.Main.main(Main.java:94)

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-18-94a7e51504f7> in <module>()
----> 1 pmml_export(my_model, X_train[normalization_features], model_name, use_nyoka = False)

C:\Barak\Python\V9\A_Utils.ipynb in pmml_export(my_model, X_train, filename, pmml_path, downgrade, save_pickle, use_nyoka)
    674    "cell_type": "code",
    675    "execution_count": 18,
--> 676    "metadata": {
    677     "ExecuteTime": {
    678      "end_time": "2019-10-18T01:22:31.104164Z",

c:\users\mati\appdata\local\programs\python\python36\lib\site-packages\sklearn2pmml\__init__.py in sklearn2pmml(pipeline, pmml, user_classpath, with_repr, debug, java_encoding)
    251                                 print("Standard error is empty")
    252                 if retcode:
--> 253                         raise RuntimeError("The JPMML-SkLearn conversion application has failed. The Java executable should have printed more information about the failure into its standard output and/or standard error streams")
    254         finally:
    255                 if debug:

RuntimeError: The JPMML-SkLearn conversion application has failed. The Java executable should have printed more information about the failure into its standard output and/or standard error streams

[vruusmann]

Duplicate of jpmml/jpmml-xgboost#43

Has got nothing to do with the JPMML-Evaluator library.