Comments (1)
vruusmann
commented on July 19, 2024
when I use jpmml-converter and jpmml-sparkml，the following bugs arise:
The JPMML-SparkML library includes a correct version of the JPMML-Converter library automatically. You don't need to re-configure anything.
there the relationship between jpmml-converter and spark?
No, JPMML-Converter is completely independent of Apache Spark ML
Environment：spark-2.3.1
See the JPMML-SparkML readme:
https://github.com/jpmml/jpmml-sparkml/tree/1.4.X#installation
This is the corresponding JPMML-Converter transitive dependency declaration:
https://github.com/jpmml/jpmml-sparkml/blob/1.4.19/pom.xml#L71-L75
from jpmml-converter.
Related Issues (20)
- Support for `forecast::ets` models
- Request Support for 'survival::coxph' models HOT 1
- StackOverflowError HOT 15
- support tensorflow HOT 14
- PMML conversion : Casting float to decimal causing loss of Precision. HOT 7
- Support for more complex workflows HOT 1
- Constant elements should require a data type hint
- Can you provide a parameter to control the Separator of output result HOT 3
- Support for transformer-only pipelines HOT 3
- SVM's classificationMethod is always "OneAgainstOne" HOT 3
- Ability to show/hide default attribute values
- Option to choose the carrier data format (XML vs. JSON vs. YAML) in all end user-facing converter tools HOT 2
- Support for transforming labels
- Reusable visitor for (re-)generating score distributions from leaf elements
- PMML version in xmlns tag does not match version tag HOT 1
- Controlling scientific notation in PMML document HOT 2
- Failing to prune XGBoost tree models HOT 3
- Error on a pipeline with OneHotEncoder and xgboost HOT 2
- Streaming conversion mode
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