JarCastingJPMML-Evaluator-Spark 
PMML evaluator library for the Apache Spark cluster computing system (https://spark.apache.org/).
Features
- Full support for PMML specification versions 3.0 through 4.3. The evaluation is handled by the JPMML-Evaluator library.
Prerequisites
- Apache Spark version 2.0.X, 2.1.X, 2.2.X, 2.3.X or 2.4.X.
Installation
The JPMML-Evaluator-Spark library JAR file (together with accompanying Java source and Javadocs JAR files) is released via Maven Central Repository.
The current version is 1.2.2 (16 January, 2019).
<dependency>
<groupId>org.jpmml</groupId>
<artifactId>jpmml-evaluator-spark</artifactId>
<version>1.2.2</version>
</dependency>
A note about building and packaging JPMML-Evaluator-Spark applications. The JPMML-Evaluator library depends on JPMML-Model and Google Guava library versions that are in conflict with the ones that are bundled with Apache Spark and/or Apache Hadoop. This conflict can be easily solved by relocating JPMML-Evaluator library dependencies to a different namespace using the Apache Maven Shade Plugin.
Usage
Building a generic transformer based on a PMML byte stream:
InputStream pmmlIs = ...;
EvaluatorBuilder evaluatorBuilder = new LoadingModelEvaluatorBuilder()
.setLocatable(false)
.setVisitors(new DefaultVisitorBattery())
.load(pmmlIs);
Evaluator evaluator = evaluatorBuilder.build();
// Performing a self-check (duplicates as a warm-up)
evaluator.verify();
TransformerBuilder pmmlTransformerBuilder = new TransformerBuilder(evaluator)
.withTargetCols()
.withOutputCols()
.exploded(false);
Transformer pmmlTransformer = pmmlTransformerBuilder.build();
Building an Apache Spark ML-style regressor when the PMML document is known to contain a regression model (eg. auto-mpg dataset):
TransformerBuilder pmmlTransformerBuilder = new TransformerBuilder(evaluator)
.withLabelCol("MPG") // Double column
.exploded(true);
Building an Apache Spark ML-style classifier when the PMML document is known to contain a classification model (eg. iris-species dataset):
TransformerBuilder pmmlTransformerBuilder = new TransformerBuilder(evaluator)
.withLabelCol("Species") // String column
.withProbabilityCol("Species_probability", Arrays.asList("setosa", "versicolor", "virginica")) // Vector column
.exploded(true);
Scoring data:
Dataset<?> inputDs = ...;
Dataset<?> resultDs = pmmlTransformer.transform(inputDs);
In default mode, the transformation appends an intermediary "pmml" column to the data frame, which contains all the requested result columns:
root
|-- Sepal_Length: double (nullable = true)
|-- Sepal_Width: double (nullable = true)
|-- Petal_Length: double (nullable = true)
|-- Petal_Width: double (nullable = true)
|-- pmml: struct (nullable = true)
| |-- Species: string (nullable = false)
| |-- Species_probability: vector (nullable = false)
In exploded mode, the transformation appends all the requested result columns to the data frame:
root
|-- Sepal_Length: double (nullable = true)
|-- Sepal_Width: double (nullable = true)
|-- Petal_Length: double (nullable = true)
|-- Petal_Width: double (nullable = true)
|-- Species: string (nullable = false)
|-- Species_probability: vector (nullable = false)
License
JPMML-Evaluator-Spark is dual-licensed under the GNU Affero General Public License (AGPL) version 3.0, and a commercial license.
Additional information
JPMML-Evaluator-Spark is developed and maintained by Openscoring Ltd, Estonia.
Interested in using JPMML software in your application? Please contact [email protected]
