Facing Typed check exception org.jpmml.evaluator.TypeCheckException: Expected FLOAT, but got DOUBLE (3.4)

[Purvihana]

While trying to read the pmml file created using sklearn2pmml using jpmml evaluator for prediction facing this error:

org.jpmml.evaluator.TypeCheckException: Expected FLOAT, but got DOUBLE (3.4)
	at org.jpmml.evaluator.TypeUtil.toFloat(TypeUtil.java:419)
	at org.jpmml.evaluator.TypeUtil.cast(TypeUtil.java:333)

I am using the version 1.3.5 of the evaluator. PFB the mapper used while creating the pmml file no transformation was specified

iris_pipeline = PMMLPipeline([
  ("mapper", DataFrameMapper([
    (["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"], [ContinuousDomain(), Imputer()])
  ])),
  ("classifier", RandomForestClassifier(n_estimators = 100))
])

[vruusmann]

There's something wrong with your environment (outdated sklearn2pmml package?).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>