Comments (1)
I don't see support for missing values in mojo-pmml conversion.
Where did you look? Did you see this?
https://github.com/jpmml/jpmml-h2o/blob/1.1.4/src/main/java/org/jpmml/h2o/GlmMojoModelBaseConverter.java#L86-L111
"Exception in thread "main" org.jpmml.evaluator.InvalidResultException : Field "X" cannot accept input value NaN".
According to PMML conventions, NaN
is an invalid value, not a missing value.
If you want to signal to a model that a field value is missing, then you should be using Java's null
reference value.
Could the missing value handling be added to this jpmml-h2o package?
I believe you're simply confusing invalid and missing value handling.
To resolve the situation, please provide a reproducible code example (what you're aiming to do, what is expected to happen, what really happens).
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Related Issues (15)
- openscoring can't read output .pmml HOT 1
- Score mismatch between PMML file and H2O Mojo prediction HOT 1
- Support for `quantile` distribution in GBM
- Output PMML file too big in size HOT 2
- Support of categorical variables HOT 2
- Use H2O.ai node identifiers (aka "nodeNumbers")
- Could this be used for models built with older version of H2O? HOT 6
- Throws error when I change the max_depth > 5 and ntrees = 100 for a GBM HOT 3
- Throws and error when converting a poisson GBM to PMML HOT 3
- Detect feature promotion from (high cardinality-) categorical to pseudo-numeric HOT 4
- Missing number of values in the array HOT 2
- Capturing variable importances
- release 1.0.10 is not compatible with h2o-3.28.x HOT 2
- Downgrading PMML 4.4 to PMML 4.2 HOT 4
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