Java Evaluator API for Predictive Model Markup Language (PMML).
JPMML-Evaluator is de facto the reference implementation of the PMML specification for the Java platform:
- Pre-processing of active fields according to the [DataDictionary] (http://www.dmg.org/v4-2-1/DataDictionary.html) and [MiningSchema] (http://www.dmg.org/v4-2-1/MiningSchema.html) elements:
- Complete data type system
- Complete operational type system.
- Treatment of outlier, missing and/or invalid values.
- Model evaluation:
- [Association rules] (http://www.dmg.org/v4-2-1/AssociationRules.html)
- [Cluster model] (http://www.dmg.org/v4-2-1/ClusteringModel.html)
- [General regression] (http://www.dmg.org/v4-2-1/GeneralRegression.html)
- [Naive Bayes] (http://www.dmg.org/v4-2-1/NaiveBayes.html)
- [k-Nearest neighbors] (http://www.dmg.org/v4-2-1/KNN.html)
- [Neural network] (http://www.dmg.org/v4-2-1/NeuralNetwork.html)
- [Regression] (http://www.dmg.org/v4-2-1/Regression.html)
- [Rule set] (http://www.dmg.org/v4-2-1/RuleSet.html)
- [Scorecard] (http://www.dmg.org/v4-2-1/Scorecard.html)
- [Support Vector Machine] (http://www.dmg.org/v4-2-1/SupportVectorMachine.html)
- [Tree model] (http://www.dmg.org/v4-2-1/TreeModel.html)
- [Ensemble model] (http://www.dmg.org/v4-2-1/MultipleModels.html)
- Post-processing of target fields according to the [Targets] (http://www.dmg.org/v4-2-1/Targets.html) element:
- Rescaling and/or casting regression results.
- Replacing a missing regression result with the default value.
- Replacing a missing classification result with the map of prior probabilities.
- Calculation of auxiliary output fields according to the [Output] (http://www.dmg.org/v4-2-1/Output.html) element:
- Over 20 different result feature types.
- Model verification according to the [ModelVerification] (http://www.dmg.org/v4-2-1/ModelVerification.html) element.
For more information please see the [features.md] (https://github.com/jpmml/jpmml-evaluator/blob/master/features.md) file.
JPMML-Evaluator has been tested with popular open-source PMML producer software:
- [R] (http://www.r-project.org/) and [Rattle] (http://rattle.togaware.com/)
- [KNIME] (http://www.knime.com/)
- [RapidMiner] (http://rapidminer.com/products/rapidminer-studio/)
JPMML-Evaluator is thread safe and can easily deliver over one million scorings per second (on a single quad-core CPU) when working with simpler models.
- Java 1.7 or newer
JPMML-Evaluator library JAR files (together with accompanying Java source and Javadocs JAR files) are released via [Maven Central Repository] (http://repo1.maven.org/maven2/org/jpmml/). Please join the [JPMML mailing list] (https://groups.google.com/forum/#!forum/jpmml) for release announcements.
The current version is 1.2.6 (4 November, 2015).
<dependency>
<groupId>org.jpmml</groupId>
<artifactId>pmml-evaluator</artifactId>
<version>1.2.6</version>
</dependency>JPMML-Evaluator depends on the [JPMML-Model] (https://github.com/jpmml/jpmml-model) library for PMML class model.
Loading a PMML schema version 3.X or 4.X document into an org.dmg.pmml.PMML instance:
PMML pmml;
InputStream is = ...;
try {
Source transformedSource = ImportFilter.apply(new InputSource(is));
pmml = JAXBUtil.unmarshalPMML(transformedSource);
} finally {
is.close();
}
If the model type is known, then it is possible to instantiate the corresponding subclass of org.jpmml.evaluator.ModelEvaluator directly:
PMML pmml = ...;
ModelEvaluator<TreeModel> modelEvaluator = new TreeModelEvaluator(pmml);Otherwise, if the model type is unknown, then the model evaluator instantiation work should be delegated to an instance of class org.jpmml.evaluator.ModelEvaluatorFactory:
PMML pmml = ...;
ModelEvaluatorFactory modelEvaluatorFactory = ModelEvaluatorFactory.newInstance();
ModelEvaluator<?> modelEvaluator = modelEvaluatorFactory.newModelManager(pmml);Model evaluator classes follow functional programming principles and are completely thread safe.
Model evaluator instances are fairly lightweight, which makes them cheap to create and destroy. Nevertheless, long-running applications should maintain a one-to-one mapping between PMML and ModelEvaluator instances for better performance.
It is advisable for application code to work against the org.jpmml.evaluator.Evaluator interface:
Evaluator evaluator = (Evaluator)modelEvaluator;An evaluator instance can be queried for the definition of active (ie. independent), target (ie. primary dependent) and output (ie. secondary dependent) fields:
List<FieldName> activeFields = evaluator.getActiveFields();
List<FieldName> targetFields = evaluator.getTargetFields();
List<FieldName> outputFields = evaluator.getOutputFields();The PMML scoring operation must be invoked with valid arguments. Otherwise, the behaviour of the model evaluator class is unspecified.
The preparation of field values:
Map<FieldName, FieldValue> arguments = new LinkedHashMap<FieldName, FieldValue>();
List<FieldName> activeFields = evaluator.getActiveFields();
for(FieldName activeField : activeFields){
// The raw (ie. user-supplied) value could be any Java primitive value
Object rawValue = ...;
// The raw value is passed through: 1) outlier treatment, 2) missing value treatment, 3) invalid value treatment and 4) type conversion
FieldValue activeValue = evaluator.prepare(activeField, rawValue);
arguments.put(activeField, activeValue);
}The scoring:
Map<FieldName, ?> results = evaluator.evaluate(arguments);Typically, a model has exactly one target field:
FieldName targetName = evaluator.getTargetField();
Object targetValue = results.get(targetName);The target value is either a Java primitive value (as a wrapper object) or an instance of org.jpmml.evaluator.Computable:
if(targetValue instanceof Computable){
Computable computable = (Computable)targetValue;
Object primitiveValue = computable.getResult();
}The target value may implement interfaces that descend from interface org.jpmml.evaluator.ResultFeature:
// Test for "entityId" result feature
if(targetValue instanceof HasEntityId){
HasEntityId hasEntityId = (HasEntityId)targetValue;
HasEntityRegistry<?> hasEntityRegistry = (HasEntityRegistry<?>)evaluator;
BiMap<String, ? extends Entity> entities = hasEntityRegistry.getEntityRegistry();
Entity winner = entities.get(hasEntityId.getEntityId());
// Test for "probability" result feature
if(targetValue instanceof HasProbability){
HasProbability hasProbability = (HasProbability)targetValue;
Double winnerProbability = hasProbability.getProbability(winner.getId());
}
}Module pmml-evaluator-example exemplifies the use of the JPMML-Evaluator library.
This module can be built using [Apache Maven] (http://maven.apache.org/):
mvn clean install
The resulting uber-JAR file target/example-1.2-SNAPSHOT.jar contains the following command-line applications:
org.jpmml.evaluator.EvaluationExample[(source)] (https://github.com/jpmml/jpmml-evaluator/blob/master/pmml-evaluator-example/src/main/java/org/jpmml/evaluator/EvaluationExample.java). Evaluates a PMML model using data records from a TSV or CSV file.org.jpmml.evaluator.EnhancementExample. Enhances a PMML model with a ModelVerification element using data records from a TSV or CSV file.org.jpmml.evaluator.OptimizationExample. Optimizes a PMML model.
Evaluating model model.pmml by loading input data records from input.tsv and storing output data records to output.tsv:
java -cp target/example-1.2-SNAPSHOT.jar org.jpmml.evaluator.EvaluationExample --model model.pmml --input input.tsv --output output.tsv
Optimizing model model.pmml by appling a list of four Visitor classes to it. The JVM option javaagent loads the [JPMML agent] (https://github.com/jpmml/jpmml-model/tree/master/pmml-agent), which provides memory usage measurement functionality:
java -javaagent:pmml-agent-1.2-SNAPSHOT.jar -cp target/example-1.2-SNAPSHOT.jar org.jpmml.evaluator.OptimizationExample --model model.pmml --visitor-classes org.jpmml.model.visitors.LocatorNullifier,org.jpmml.model.visitors.ArrayListOptimizer,org.jpmml.model.visitors.StringInterner,org.jpmml.evaluator.visitors.PredicateInterner
The [Openscoring.io blog] (http://openscoring.io/blog/) contains fully worked out examples about using JPMML-Model and JPMML-Evaluator libraries.
Recommended reading:
- [Preparing arguments for evaluation] (http://openscoring.io/blog/2014/05/15/jpmml_evaluator_api_prepare_evaluate/)
- [Testing PMML Applications] (http://openscoring.io/blog/2014/05/12/testing_pmml_applications/)
JPMML-Evaluator is dual-licensed under the [GNU Affero General Public License (AGPL) version 3.0] (http://www.gnu.org/licenses/agpl-3.0.html) and a commercial license.
Please contact [[email protected]] (mailto:[email protected])