Comments (1)
There's something wrong with your environment (outdated sklearn2pmml
package?). Or you have manually modified the PMML file, thereby making it invalid.
I executed your Python script, converted the fitted pipeline to PMML (using JPMML-SkLearn version 1.2.10), and scored the Iris dataset (using JPMML-Evaluator version 1.3.5). The scoring was successful, there were no exceptions being thrown.
If you open a random forest classifier's PMML file in text editor, then it's possible to see that it's fully type safe:
First, input fields are of double
data type:
<DataDictionary>
<DataField name="Sepal.Length" optype="continuous" dataType="double">
<Interval closure="closedClosed" leftMargin="4.3" rightMargin="7.9"/>
</DataField>
</DataDictionary>
Second, input fields are cast from double
to float
data type:
<TransformationDictionary>
<DerivedField name="float(Sepal.Length)" optype="continuous" dataType="float">
<FieldRef field="Sepal.Length"/>
</DerivedField>
</TransformationDictionary>
Finally, the random forest model accesses derived float
values:
<Node id="11" score="virginica" recordCount="3.0">
<SimplePredicate field="float(Sepal.Length)" operator="lessOrEqual" value="6.2"/>
<ScoreDistribution value="setosa" recordCount="0.0"/>
<ScoreDistribution value="versicolor" recordCount="0.0"/>
<ScoreDistribution value="virginica" recordCount="3.0"/>
</Node>
from jpmml-evaluator.
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from jpmml-evaluator.