JPMML-SparkML

Java library and command-line application for converting Apache Spark ML pipelines to PMML.

Features

• Supported Spark ML PipelineStage types:
  • Feature extractors, transformers and selectors:
    • feature.Binarizer
    • feature.Bucketizer
    • feature.ChiSqSelectorModel (the result of fitting a feature.ChiSqSelector)
    • feature.ColumnPruner
    • feature.CountVectorizerModel (the result of fitting a feature.CountVectorizer)
    • feature.IDFModel (the result of fitting a feature.IDF)
    • feature.ImputerModel (the result of fitting a feature.Imputer)
    • feature.IndexToString
    • feature.Interaction
    • feature.MaxAbsScalerModel (the result of fitting a feature.MaxAbsScaler)
    • feature.MinMaxScalerModel (the result of fitting a feature.MinMaxScaler)
    • feature.NGram
    • feature.OneHotEncoder
    • feature.OneHotEncoderModel (the result of fitting a feature.OneHotEncoderEstimator)
    • feature.PCAModel (the result of fitting a feature.PCA)
    • feature.QuantileDiscretizer
    • feature.RegexTokenizer
    • feature.RFormulaModel (the result of fitting a feature.RFormula)
    • feature.SQLTransformer
    • feature.StandardScalerModel (the result of fitting a feature.StandardScaler)
    • feature.StopWordsRemover
    • feature.StringIndexerModel (the result of fitting a feature.StringIndexer)
    • feature.Tokenizer
    • feature.VectorAssembler
    • feature.VectorAttributeRewriter
    • feature.VectorIndexerModel (the result of fitting a feature.VectorIndexer)
    • feature.VectorSizeHint
    • feature.VectorSlicer
  • Prediction models:
    • classification.DecisionTreeClassificationModel
    • classification.GBTClassificationModel
    • classification.LinearSVCModel
    • classification.LogisticRegressionModel
    • classification.MultilayerPerceptronClassificationModel
    • classification.NaiveBayesModel
    • classification.RandomForestClassificationModel
    • clustering.KMeansModel
    • regression.DecisionTreeRegressionModel
    • regression.GBTRegressionModel
    • regression.GeneralizedLinearRegressionModel
    • regression.LinearRegressionModel
    • regression.RandomForestRegressionModel
  • Prediction model chains:
    • PipelineModel
    • Referencing the prediction column (HasPredictionCol#getPredictionCol()) of earlier clustering, classification and regression models.
    • Referencing the predicted probabilities column (HasProbabilityCol#getProbabilityCol()) of earlier classification models.
  • Hyperparameter selectors and tuners:
    • tuning.CrossValidatorModel
    • tuning.TrainValidationSplitModel
• Production quality:
  • Complete test coverage.
  • Fully compliant with the JPMML-Evaluator library.

Prerequisites

• Apache Spark version 1.5.X, 1.6.X, 2.0.X, 2.1.X, 2.2.X, 2.3.X or 2.4.X.

Installation

Library

JPMML-SparkML library JAR file (together with accompanying Java source and Javadocs JAR files) is released via Maven Central Repository.

The current version is 1.5.2 (7 April, 2019).

<dependency>
	<groupId>org.jpmml</groupId>
	<artifactId>jpmml-sparkml</artifactId>
	<version>1.5.2</version>
</dependency>

Compatibility matrix:

Apache Spark version	JPMML-SparkML branch	Status
1.5.X and 1.6.X	1.0.X	Archived
2.0.X	1.1.X	Archived
2.1.X	1.2.X	Archived
2.2.X	1.3.X	Active
2.3.X	1.4.X	Active
2.4.X	master	Active

JPMML-SparkML depends on the latest and greatest version of the JPMML-Model library, which is in conflict with the legacy version that is part of Apache Spark version 2.0.X, 2.1.X and 2.2.X distributions.

This conflict is documented in SPARK-15526. For possible resolutions, please switch from this README.md file to the README.md file of some earlier JPMML-SparkML development branch.

Example application

Enter the project root directory and build using Apache Maven:

mvn clean install

The build produces two JAR files:

• target/jpmml-sparkml-1.5-SNAPSHOT.jar - Library JAR file.
• target/jpmml-sparkml-executable-1.5-SNAPSHOT.jar - Example application JAR file.

Usage

Library

Fitting a Spark ML pipeline that only makes use of supported Transformer types:

DataFrame irisData = ...;

StructType schema = irisData.schema();

RFormula formula = new RFormula()
	.setFormula("Species ~ .");

DecisionTreeClassifier classifier = new DecisionTreeClassifier()
	.setLabelCol(formula.getLabelCol())
	.setFeaturesCol(formula.getFeaturesCol());

Pipeline pipeline = new Pipeline()
	.setStages(new PipelineStage[]{formula, classifier});

PipelineModel pipelineModel = pipeline.fit(irisData);

Converting the Spark ML pipeline to PMML using the org.jpmml.sparkml.PMMLBuilder builder class:

PMML pmml = new PMMLBuilder(schema, pipelineModel)
	.build();

// Viewing the result
JAXBUtil.marshalPMML(pmml, new StreamResult(System.out));

Please refer to the following resources for more ideas and code examples:

• Converting Apache Spark ML pipeline models to PMML

Example application

The example application JAR file contains an executable class org.jpmml.sparkml.Main, which can be used to convert a pair of serialized org.apache.spark.sql.types.StructType and org.apache.spark.ml.PipelineModel objects to PMML.

The example application JAR file does not include Apache Spark runtime libraries. Therefore, this executable class must be executed using Apache Spark's spark-submit helper script.

For example, converting a pair of Spark ML schema and pipeline serialization files src/test/resources/schema/Iris.json and src/test/resources/pipeline/DecisionTreeIris.zip, respectively, to a PMML file DecisionTreeIris.pmml:

spark-submit --master local --class org.jpmml.sparkml.Main target/jpmml-sparkml-executable-1.5-SNAPSHOT.jar --schema-input src/test/resources/schema/Iris.json --pipeline-input src/test/resources/pipeline/DecisionTreeIris.zip --pmml-output DecisionTreeIris.pmml

Getting help:

spark-submit --master local --class org.jpmml.sparkml.Main target/jpmml-sparkml-executable-1.5-SNAPSHOT.jar --help

License

JPMML-SparkML is dual-licensed under the GNU Affero General Public License (AGPL) version 3.0, and a commercial license.

Additional information

JPMML-SparkML is developed and maintained by Openscoring Ltd, Estonia.

Interested in using JPMML software in your application? Please contact [email protected]