Comments (2)
vruusmann
commented on August 20, 2024
it gives me this error "The JPMML-SkLearn conversion application has failed. The Java process should have printed more information about the failure into its standard output and/or error streams"
What did the Java process print to standard output and/or error streams?
is it because the pmml does not support the imbalanced-learn package?
The detailed description of the error condition is in the logfile. I don't have access to your logfiles, you do.
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vruusmann
commented on August 20, 2024
Possibly a duplicate of #23
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Related Issues (20)
- Unpickling error: invalid pickle opcode: 64 HOT 3
- Support for `numpy.datetime64` data type(s) HOT 23
- Failure to create a pmml file when using CountVectorizer with analyzer = 'char' HOT 1
- sklearn2pmml fails to create pmml for XGBoost model HOT 1
- Patching 0.51.1 for unknown categorical variables HOT 4
- Postprocessing the probabilities of an XGBoost Model HOT 3
- Link Estimator with prefit=True doesn't work
- Support for the `OneHotEncoder.infrequent_categories_` attribute HOT 1
- Revert PMML file to Python source code HOT 1
- Error when using `TargetEncoder` within `DataFrameMapper` within pipeline HOT 6
- Missing dependency `dill` from `setup.py`. HOT 7
- Support for Python power operator `**` HOT 1
- Assembling a pipeline from pre-fitted components HOT 3
- looking for support on quantile_forest for RandomForestQuantileRegressor HOT 2
- Failing to convert `CalibratedClassifierCV` plus `XGBClassifier` combination HOT 12
- Effectively debugging XGBoost pipelines (mis-matching predictions between Python and (J)PMML) HOT 14
- Revisit the missing value handling of custom transformers HOT 1
- Support for AgglomerativeClustering HOT 2
- `ExpressionTransformer` should try to rectify feature type information HOT 5
- `Alias` object has no attribute `transformer_` HOT 8
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