How to combine 2 pmml files into 1 file with 2 output? - modeling

Question

i have developed two models (classification and regression) and exported them to PMML exchange format via https://github.com/jpmml/jpmml-xgboost. Both models run fine when I call them in python. However, I would love to combine both into one file which returns two values, the class probability of the classification model AND the predicted value from the regression model.
I tried for hours now but failed to understand the PMML specification as good as necessary.
Does anyone have experience with this and could give me a hint how to combine files and flow the values though the file ? Both models require exactly the same input.
Thank you!
See two mini examples below:
regression model:
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<PMML xmlns="http://www.dmg.org/PMML-4_4" xmlns:data="http://jpmml.org/jpmml-model/InlineTable" version="4.4">
<Header>
<Application name="JPMML-XGBoost" version="1.5-SNAPSHOT"/>
<Timestamp>2021-07-27T11:55:26Z</Timestamp>
</Header>
<DataDictionary>
<DataField name="mpg" optype="continuous" dataType="float">
<Value value="NaN" property="missing"/>
</DataField>
<DataField name="IDVAR_REINIG" optype="continuous" dataType="float">
<Value value="NaN" property="missing"/>
</DataField>
</DataDictionary>
<MiningModel functionName="regression" algorithmName="XGBoost (GBTree)" x-mathContext="float">
<MiningSchema>
<MiningField name="mpg" usageType="target"/>
<MiningField name="IDVAR_REINIG"/>
</MiningSchema>
<Targets>
<Target field="mpg" rescaleConstant="0.5"/>
</Targets>
<Segmentation multipleModelMethod="sum">
<Segment id="1">
<True/>
<TreeModel functionName="regression" noTrueChildStrategy="returnLastPrediction" x-mathContext="float">
<MiningSchema>
<MiningField name="IDVAR_REINIG"/>
</MiningSchema>
<Output>
<OutputField name="mpg" optype="continuous" dataType="float" isFinalResult="false" rescaleConstant="0.5"/>
</Output>
<Node score="1.7433707">
<True/>
<Node score="6.1398296">
<SimplePredicate field="IDVAR_REINIG" operator="greaterOrEqual" value="6033.51"/>
</Node>
</Node>
</TreeModel>
</Segment>
</Segmentation>
</MiningModel>
</PMML>
classification model:
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<PMML xmlns="http://www.dmg.org/PMML-4_4" xmlns:data="http://jpmml.org/jpmml-model/InlineTable" version="4.4">
<Header>
<Application name="JPMML-XGBoost" version="1.5-SNAPSHOT"/>
<Timestamp>2021-07-27T11:54:45Z</Timestamp>
</Header>
<DataDictionary>
<DataField name="mpg" optype="categorical" dataType="integer">
<Value value="0"/>
<Value value="1"/>
</DataField>
<DataField name="IDVAR_REINIG" optype="continuous" dataType="float">
<Value value="NaN" property="missing"/>
</DataField>
</DataDictionary>
<MiningModel functionName="classification" algorithmName="XGBoost (GBTree)" x-mathContext="float">
<MiningSchema>
<MiningField name="mpg" usageType="target"/>
<MiningField name="IDVAR_REINIG"/>
</MiningSchema>
<Segmentation multipleModelMethod="modelChain" missingPredictionTreatment="returnMissing">
<Segment id="1">
<True/>
<MiningModel functionName="regression" x-mathContext="float">
<MiningSchema>
<MiningField name="IDVAR_REINIG"/>
</MiningSchema>
<Output>
<OutputField name="xgbValue" optype="continuous" dataType="float" isFinalResult="false"/>
</Output>
<Segmentation multipleModelMethod="sum">
<Segment id="1">
<True/>
<TreeModel functionName="regression" noTrueChildStrategy="returnLastPrediction" x-mathContext="float">
<MiningSchema>
<MiningField name="IDVAR_REINIG"/>
</MiningSchema>
<Node score="0.0070259375">
<True/>
<Node score="-0.030500757">
<SimplePredicate field="IDVAR_REINIG" operator="greaterOrEqual" value="2240.835"/>
</Node>
</Node>
</TreeModel>
</Segment>
</Segmentation>
</MiningModel>
</Segment>
<Segment id="2">
<True/>
<RegressionModel functionName="classification" normalizationMethod="logit" x-mathContext="float">
<MiningSchema>
<MiningField name="mpg" usageType="target"/>
<MiningField name="xgbValue"/>
</MiningSchema>
<Output>
<OutputField name="probability(0)" optype="continuous" dataType="float" feature="probability" value="0"/>
<OutputField name="probability(1)" optype="continuous" dataType="float" feature="probability" value="1"/>
</Output>
<RegressionTable intercept="0.0" targetCategory="1">
<NumericPredictor name="xgbValue" coefficient="1.0"/>
</RegressionTable>
<RegressionTable intercept="0.0" targetCategory="0"/>
</RegressionModel>
</Segment>
</Segmentation>
</MiningModel>
</PMML>

Answer 1

Create a parent MiningModel element that holds the two existing child model elements. Insert the classification model first, and the regression model second; execute them as a model chain.
By default, this model chain will only display the result fields of the last child model. However, you can export one or more result fields of the first model into "local variables", and then reflect their values wherever needed.
Sample PMML markup skeleton:
<MiningModel>
<Segmentation multipleModelMethod="modelChain">
<Segment id="classification">
<True/>
<MiningModel>
<Output>
<!-- Export the probability value to evaluation context -->
<OutputField name="probability(event)" feature="probability" value="event"/>
</Output>
</MiningModel>
</Segment>
<Segment id="regression">
<True/>
<MiningModel>
<MiningSchema>
<!-- Import the probability value from the evaluation context -->
<MiningField name="probability(event")/>
</MiningSchema>
<Output>
<!-- Re-export the probability value under a different name -->
<OutputField name="copy(probability(event))" feature="transformedValue">
<FieldRef field="probability(event)"/>
</OutputField>
</Output>
</MiningModel>
</Segment>
</Segmentation>
</MiningModel>

Answer 2

Alternatively, you may employ the "segment referencing" mechanism to access child model outputs from the parent output.
See the description of OutputField#segmentId attribute here.
Sample PMML markup skeleton:
<MiningModel>
<Segmentation multipleModelMethod="modelChain">
<Segment id="classification/>
<Segment id="regresion">
</Segmentation>
<Output>
<!-- Reflect the probability of the "event" class of the classification model -->
<OutputField name="probability(event)" segmentId="classification" feature="probability"/>
<!-- Reflect the predicted value of the regression model -->
<OutputField name="y" segmentId="regression" feature="predictedValue"/>
</Output>
</MiningModel>