Comments (2)
Duplicate of jpmml/jpmml-sklearn#44
Will add a sample transformer class com.mycompany.PowerFunction
into the SkLearn2PMML-Plugin project later today (or tomorrow).
There is no need to create a special-purpose Java-backed UDF for this. PMML provides a built-in function pow
for raising numbers to arbitrary powers:
<Apply function="pow">
<FieldRef field="number"/>
<Constant dataType="integer">3</Constant>
</Apply>
from sklearn2pmml.
Thanks for adding the function. For my use case, I need to know how to pass the argument to a custom transformer function, from the PMMLPipeline mapper. To be more specific, how do I create a Data Frame mapper which enables me to pass an argument for the respective custom transformer function, so that it creates a <Constant>
markup carrying my argument in the PMML file for my custom transformer.
from sklearn2pmml.
Related Issues (20)
- 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
- 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
- sklearn2pmml does not work with xgboost >= 2.0.0 HOT 9
- LookupTransformer TypeError when default value is not exactly the type from the mapping HOT 2
- xgb 生成的pkl 转pmml 失败 HOT 1
- Can sklearn2pmml just for variable transformations be used? HOT 1
- PyPMML is making systematically off predictions with XGBoost PMML documents? HOT 16
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from sklearn2pmml.