Comments (2)
SkLearn2PMML version 0.10.0 is not supported anymore.
A couple of leads:
- Does your Python script pass cleanly with the latest SkLearn2PMML version 0.17.4?
- You're showing me the Python exception, which does not contain any actionable information. What does the underlying Java exception say? To understand SkLearn2PMML exception hierarchy better, see this StackOverflow thread: http://stackoverflow.com/a/41701701
- Can you attach problematic Pickle files to this issue so that I could take a look at them myself? It's the ones that are mentioned right after
--pkl-estimator-input
and--pkl-mapper-input
command-line options - would beestimator-fwa91n.pkl.z
andmapper-kmzk1k.pkl.z
in the current case. - If the above Pickle files contain proprietary information and cannot be shared, then can you reproduce the exception with some publicly available dataset?
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Following are the answers to your questions
- Does your Python script pass cleanly with the latest SkLearn2PMML version 0.17.4?
Yes, It does work and Produce PMML 4.3 version, I am looking to create PMML 4.2 or older version.
Do you have any supported version through which I can download it and generate PMML 4.2 or older versions?. If so please let me know the paths. Thank You.
I am yet to work on the Other 3 questions.
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Related Issues (20)
- 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
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- 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
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- 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
- Storing LinearSVC as coefficients instead of support vectors HOT 1
- Reshape transformation results from 2-D to 1-D (column vectors) HOT 1
- sklearn2pmml does not work with sklearn version 1.3.0 and newer HOT 6
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