PMML evaluator library for the Apache Spark cluster computing system (http://spark.apache.org/)
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README.md

JPMML-Evaluator-Spark Build Status

PMML evaluator library for the Apache Spark cluster computing system (http://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 or 2.3.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 1.2.0 (1 September, 2018).

<dependency>
	<groupId>org.jpmml</groupId>
	<artifactId>jpmml-evaluator-spark</artifactId>
	<version>1.2.0</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 document in local filesystem:

File pmmlFile = ...;

Evaluator evaluator = EvaluatorUtil.createEvaluator(pmmlFile);

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:

DataFrame input = ...;
DataFrame output = pmmlTransformer.transform(input);

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 licensed under the GNU Affero General Public License (AGPL) version 3.0. Other licenses are available on request.

Additional information

Please contact info@openscoring.io