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https://github.com/jpmml/jpmml-evaluator

Java Evaluator API for PMML
https://github.com/jpmml/jpmml-evaluator
Last synced: about 1 month ago
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Java Evaluator API for PMML
Host: GitHub
URL: https://github.com/jpmml/jpmml-evaluator
Owner: jpmml
License: agpl-3.0
Created: 2014-02-25T21:07:42.000Z (almost 11 years ago)
Default Branch: master
Last Pushed: 2024-07-07T20:41:22.000Z (5 months ago)
Last Synced: 2024-08-01T15:07:52.360Z (4 months ago)
Language: Java
Size: 6.85 MB
Stars: 888
Watchers: 61
Forks: 256
Open Issues: 17
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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awesome - jpmml-evaluator - Java Evaluator API for PMML (Java)
README

        JPMML-Evaluator [![Build Status](https://github.com/jpmml/jpmml-evaluator/workflows/maven/badge.svg)](https://github.com/jpmml/jpmml-evaluator/actions?query=workflow%3A%22maven%22)

===============

Java Evaluator API for Predictive Model Markup Language (PMML).

# Table of Contents #

- [Features](#features)

- [Prerequisites](#prerequisites)

- [Installation](#installation)

- [API](#api)

- [Basic usage](#basic-usage)

- [Advanced usage](#advanced-usage)

    + [Loading models](#loading-models)

    + [Querying the "data schema" of models](#querying-the-data-schema-of-models)

    + [Evaluating models](#evaluating-models)

- [Example applications](#example-applications)

- [Documentation](#documentation)

- [Support](#support)

- [License](#license)

- [Additional information](#additional-information)

# Features #

JPMML-Evaluator is *de facto* the reference implementation of the PMML specification versions 3.0, 3.1, 3.2, 4.0, 4.1, 4.2, 4.3 and 4.4 for the Java/JVM platform:

* Pre-processing of input fields according to the [DataDictionary](http://www.dmg.org/pmml/v4-4-1/DataDictionary.html) and [MiningSchema](http://www.dmg.org/pmml/v4-4-1/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/pmml/v4-4-1/AssociationRules.html)

  * [Cluster model](http://www.dmg.org/pmml/v4-4-1/ClusteringModel.html)

  * [General regression](http://www.dmg.org/pmml/v4-4-1/GeneralRegression.html)

  * [Naive Bayes](http://www.dmg.org/pmml/v4-4-1/NaiveBayes.html)

  * [k-Nearest neighbors](http://www.dmg.org/pmml/v4-4-1/KNN.html)

  * [Neural network](http://www.dmg.org/pmml/v4-4-1/NeuralNetwork.html)

  * [Regression](http://www.dmg.org/pmml/v4-4-1/Regression.html)

  * [Rule set](http://www.dmg.org/pmml/v4-4-1/RuleSet.html)

  * [Scorecard](http://www.dmg.org/pmml/v4-4-1/Scorecard.html)

  * [Support Vector Machine](http://www.dmg.org/pmml/v4-4-1/SupportVectorMachine.html)

  * [Tree model](http://www.dmg.org/pmml/v4-4-1/TreeModel.html)

  * [Ensemble model](http://www.dmg.org/pmml/v4-4-1/MultipleModels.html)

* Post-processing of target fields according to the [Targets](http://www.dmg.org/pmml/v4-4-1/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/pmml/v4-4-1/Output.html) element:

  * Over 20 different result feature types.

* Model verification according to the [ModelVerification](http://www.dmg.org/pmml/v4-4-1/ModelVerification.html) element.

* Vendor extensions:

  * Memory and security sandboxing.

  * Java-backed model, expression and predicate types - integrate any 3rd party Java library into PMML data flow.

  * MathML prediction reports.

For more information please see the [features.md](https://github.com/jpmml/jpmml-evaluator/blob/master/features.md) file.

JPMML-Evaluator is interoperable with most popular statistics and data mining software:

* [R](https://www.r-project.org/) and [Rattle](https://rattle.togaware.com/):

  * [JPMML-R](https://github.com/jpmml/jpmml-r) library.

  * [`r2pmml`](https://github.com/jpmml/r2pmml) package.

  * [`pmml`](https://cran.r-project.org/package=pmml) and [`pmmlTransformations`](https://CRAN.R-project.org/package=pmmlTransformations) packages.

* [Python](https://www.python.org/) and [Scikit-Learn](https://scikit-learn.org/):

  * [JPMML-SkLearn](https://github.com/jpmml/jpmml-sklearn) library.

  * [`sklearn2pmml`](https://github.com/jpmml/sklearn2pmml) package.

* [Apache Spark](https://spark.apache.org/):

  * [JPMML-SparkML](https://github.com/jpmml/jpmml-sparkml) library.

  * [`pyspark2pmml`](https://github.com/pyspark2pmml) and [`sparklyr2pmml`](https://github.com/jpmml/sparklyr2pmml) packages.

  * [`mllib.pmml.PMMLExportable`](https://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/pmml/PMMLExportable.html) interface.

* [H2O.ai](https://www.h2o.ai/):

  * [JPMML-H2O](https://github.com/jpmml/jpmml-h2o) library.

* [XGBoost](https://github.com/dmlc/xgboost):

  * [JPMML-XGBoost](https://github.com/jpmml/jpmml-xgboost) library.

* [LightGBM](https://github.com/Microsoft/LightGBM):

  * [JPMML-LightGBM](https://github.com/jpmml/jpmml-lightgbm) library.

* [TensorFlow](https://tensorflow.org):

  * [JPMML-TensorFlow](https://github.com/jpmml/jpmml-tensorflow) library.

* [KNIME](https://www.knime.com/)

* [RapidMiner](https://rapidminer.com/products/rapidminer-studio/)

* [SAS](https://www.sas.com/en_us/software/analytics/enterprise-miner.html)

* [SPSS](https://www-01.ibm.com/software/analytics/spss/)

JPMML-Evaluator is fast and memory efficient. It can deliver one million scorings per second already on a desktop computer.

# Prerequisites #

* Java Platform, Standard Edition 8 or newer.

# Installation #

JPMML-Evaluator library JAR files (together with accompanying Java source and Javadocs JAR files) are released via [Maven Central Repository](https://repo1.maven.org/maven2/org/jpmml/).

The current version is **1.6.6** (13 October, 2024).

The main component of JPMML-Evaluator is `org.jpmml:pmml-evaluator`.

However, in most application scenarios, this component is not included directly, but via a data format-specific runtime component(s) `org.jpmml:pmml-evaluator-${runtime}` that handle the loading and storage of PMML class model objects.

The recommended data format for PMML documents is XML, and the recommended implementation is [Jakarta XML Binding](https://eclipse-ee4j.github.io/jaxb-ri/) via the [Glassfish Metro](https://metro.java.net) JAXB runtime:

```xml

	org.jpmml

	pmml-evaluator-metro

	1.6.6

```

Available components:

| Component | Data format(s) |

| --- | --- |

| `org.jpmml:pmml-evaluator` | [Java serialization](https://docs.oracle.com/javase/8/docs/api/java/io/Serializable.html) |

| `org.jpmml:pmml-evaluator-jackson`| JSON, YAML, TOML etc. via the [FasterXML Jackson](https://github.com/FasterXML/jackson) suite |

| `org.jpmml:pmml-evaluator-kryo` | [Kryo serialization](https://github.com/EsotericSoftware/kryo) |

| `org.jpmml:pmml-evaluator-metro` | XML via the [GlassFish Metro](https://metro.java.net) JAXB runtime |

| `org.jpmml:pmml-evaluator-moxy` | JSON and XML via the [EclipseLink MOXy](https://www.eclipse.org/eclipselink) JAXB runtime |

# API #

Core types:

* Interface `org.jpmml.evaluator.EvaluatorBuilder`

  * Class `org.jpmml.evaluator.ModelEvaluatorBuilder` - Builds a `ModelEvaluator` instance based on an `org.dmg.pmml.PMML` instance

    * Class `org.jpmml.evaluator.LoadingModelEvaluatorBuilder` - Builds a `ModelEvaluator` instance from a PMML byte stream or a PMML file

    * Class `org.jpmml.evaluator.ServiceLoadingModelEvaluatorBuilder` - Builds a `ModelEvaluator` instance from a PMML service provider JAR file

* Interface `org.jpmml.evaluator.Evaluator`

  * Abstract class `org.jpmml.evaluator.ModelEvaluator` - Implements model evaluator functionality based on an `org.dmg.pmml.Model` instance

    * Classes `org.jpmml.evaluator.Evaluator` (`GeneralRegressionModelEvaluator`, `MiningModelEvaluator`, `NeuralNetworkEvaluator`, `RegressionEvaluator`, `TreeModelEvaluator`, `SupportVectorMachineEvaluator` etc.)

* Abstract class `org.jpmml.evaluator.ModelField`

  * Abstract class `org.jpmml.evaluator.InputField` - Describes a model input field

  * Abstract class `org.jpmml.evaluator.ResultField`

    * Class `org.jpmml.evaluator.TargetField` - Describes a primary model result field

    * Class `org.jpmml.evaluator.OutputField` - Describes a secondary model result field

* Abstract class `org.jpmml.evaluator.FieldValue`

  * Class `org.jpmml.evaluator.CollectionValue`

  * Abstract class `org.jpmml.evaluator.ScalarValue`

    * Class `org.jpmml.evaluator.ContinuousValue`

    * Abstract class `org.jpmml.evaluator.DiscreteValue`

      * Class `org.jpmml.evaluator.CategoricalValue`

      * Class `org.jpmml.evaluator.OrdinalValue`

* Utility class `org.jpmml.evaluator.EvaluatorUtil`

* Utility class `org.jpmml.evaluator.FieldValueUtil`

Core methods:

* `EvaluatorBuilder`

  * `#build()`

* `Evaluator`

  * `#verify()`

  * `#getInputFields()`

  * `#getTargetFields()`

  * `#getOutputFields()`

  * `#evaluate(Map)`

* `InputField`

  * `#prepare(Object)`

Target value types:

* Interface `org.jpmml.evaluator.Computable`

  * Abstract class `org.jpmml.evaluator.AbstractComputable`

    * Class `org.jpmml.evaluator.Classification`

    * Class `org.jpmml.evaluator.Regression`

    * Class `org.jpmml.evaluator.Vote`

* Interface `org.jpmml.evaluator.ResultFeature`

  * Interface `org.jpmml.evaluator.HasCategoricalResult`

    * Interface `org.jpmml.evaluator.HasAffinity`

      * Interface `org.jpmml.evaluator.HasAffinityRanking`

    * Interface `org.jpmml.evaluator.HasConfidence`

    * Interface `org.jpmml.evaluator.HasProbability`

  * Interface `org.jpmml.evaluator.HasDisplayValue`

  * Interface `org.jpmml.evaluator.HasEntityId`

    * Interface `org.jpmml.evaluator.HasEntityAffinity`

    * Interface `org.jpmml.evaluator.HasEntityIdRanking`

  * Interface `org.jpmml.evaluator.HasPrediction`

  * Interface `org.jpmml.evaluator.HasReasonCodeRanking`

  * Interface `org.jpmml.evaluator.HasRuleValues`

  * Interface `org.jpmml.evaluator.mining.HasSegmentResults`

  * Interface `org.jpmml.evaluator.scorecard.HasPartialScores`

  * Interface `org.jpmml.evaluator.tree.HasDecisionPath`

* Abstract class `org.jpmml.evaluator.Report`

* Utility class `org.jpmml.evaluator.ReportUtil`

Target value methods:

* `Computable`

  * `#getResult()`

* `HasProbability`

  * `#getProbability(String)`

  * `#getProbabilityReport(String)`

* `HasPrediction`

  * `#getPrediction()`

  * `#getPredictionReport()`

Exception types:

* Abstract class `org.jpmml.model.PMMLException`

  * Abstract class `org.jpmml.model.MarkupException`

    * Abstract class `org.jpmml.model.InvalidMarkupException`

    * Abstract class `org.jpmml.model.MissingMarkupException`

    * Abstract class `org.jpmml.model.UnsupportedMarkupException`

  * Abstract class `org.jpmml.evaluator.EvaluationException`

# Basic usage #

```java

// Building a model evaluator from a PMML file

Evaluator evaluator = new LoadingModelEvaluatorBuilder()

	.load(new File("model.pmml"))

	.build();

// Perforing the self-check

evaluator.verify();

// Printing input (x1, x2, .., xn) fields

List inputFields = evaluator.getInputFields();

System.out.println("Input fields: " + inputFields);

// Printing primary result (y) field(s)

List targetFields = evaluator.getTargetFields();

System.out.println("Target field(s): " + targetFields);

// Printing secondary result (eg. probability(y), decision(y)) fields

List outputFields = evaluator.getOutputFields();

System.out.println("Output fields: " + outputFields);

// Iterating through columnar data (eg. a CSV file, an SQL result set)

while(true){

	// Reading a record from the data source

	Map arguments = readRecord();

	if(arguments == null){

		break;

	}

	// Evaluating the model

	Map results = evaluator.evaluate(arguments);

	// Decoupling results from the JPMML-Evaluator runtime environment

	results = EvaluatorUtil.decodeAll(results);

	// Writing a record to the data sink

	writeRecord(results);

}

// Making the model evaluator eligible for garbage collection

evaluator = null;

```

# Advanced usage #

### Loading models ###

The PMML standard defines large number of model types.

The evaluation logic for each model type is encapsulated into a corresponding `ModelEvaluator` subclass.

Even though `ModelEvaluator` subclasses can be instantiated and configured directly, the recommended approach is to follow the Builder design pattern as implemented by the `ModelEvaluatorBuilder` builder class.

A model evaluator builder provides configuration and loading services.

The default configuration corresponds to most common needs.

It can be overriden to customize the behaviour of model evaluators for more specific needs.

A model evaluator is given a copy of the configuration that was effective when the `ModelEvaluatorBuilder#build()` method was invoked. It is not affected by later configuration changes.

For example, creating two differently configured model evaluators from a `PMML` instance:

```java

import org.jpmml.evaluator.reporting.ReportingValueFactoryFactory

PMML pmml = ...;

ModelEvaluatorBuilder modelEvaluatorBuilder = new ModelEvaluatorBuilder(pmml);

Evaluator evaluator = modelEvaluatorBuilder.build();

// Activate the generation of MathML prediction reports

modelEvaluatorBuilder.setValueFactoryFactory(ReportingValueFactoryFactory.newInstance());

Evaluator reportingEvaluator = modelEvaluatorBuilder.build();

```

Configurations and model evaluators are fairly lightweight, which makes them cheap to create and destroy.

However, for maximum performance, it is advisable to maintain a one-to-one mapping between `PMML`, `ModelEvaluatorBuilder` and `ModelEvaluator` instances (ie. an application should load a PMML byte stream or file exactly once, and then maintain and reuse the resulting model evaluator as long as needed).

Some `ModelEvaluator` subclasses contain static caches that are lazily populated on a `PMML` instance basis.

This may cause the first `ModelEvaluator#evaluate(Map)` method invocation to take somewhat longer to complete (relative to all the subsequent method invocations).

If the model contains model verification data, then this "warm-up cost" is paid once and for all during the initial `ModelEvaluator#verify()` method invocation.

### Thread safety ###

The `ModelEvaluatorBuilder` base class is thread safe.

It is permitted to construct and configure a central `ModelEvaluatorBuilder` instance, and invoke its `ModelEvaluatorBuilder#build()` method concurrently.

Some `ModelEvaluatorBuilder` subclasses may extend the base class with functionality that is not thread safe.

The case in point are all sorts of "loading" implementations, which modify the value of `ModelEvaluatorBuilder#pmml` and/or `ModelEvaluatorBuilder#model` fields.

The `ModelEvaluator` base class and all its subclasses are completely thread safe.

It is permitted to share a central `ModelEvaluator` instance between any number of threads, and invoke its `ModelEvaluator#evaluate(Map)` method concurrently.

The JPMML-Evaluator library follow functional programming principles.

In a multi-threaded environment, its data throughput capabilities should scale linearly with respect to the number of threads.

### Querying the "data schema" of models ###

The model evaluator can be queried for the list of input (ie. independent), target (ie. primary dependent) and output (ie. secondary dependent) field definitions, which provide information about field name, data type, operational type, value domain etc.

Querying and analyzing input fields:

```java

List extends InputField> inputFields = evaluator.getInputFields();

for(InputField inputField : inputFields){

	org.dmg.pmml.DataField pmmlDataField = (org.dmg.pmml.DataField)inputField.getField();

	org.dmg.pmml.MiningField pmmlMiningField = inputField.getMiningField();

	org.dmg.pmml.DataType dataType = inputField.getDataType();

	org.dmg.pmml.OpType opType = inputField.getOpType();

	switch(opType){

		case CONTINUOUS:

			com.google.common.collect.RangeSet validInputRanges = inputField.getContinuousDomain();

			break;

		case CATEGORICAL:

		case ORDINAL:

			List> validInputValues = inputField.getDiscreteDomain();

			break;

		default:

			break;

	}

}

```

Querying and analyzing target fields:

```java

List extends TargetField> targetFields = evaluator.getTargetFields();

for(TargetField targetField : targetFields){

	org.dmg.pmml.DataField pmmlDataField = targetField.getField();

	org.dmg.pmml.MiningField pmmlMiningField = targetField.getMiningField(); // Could be null

	org.dmg.pmml.Target pmmlTarget = targetField.getTarget(); // Could be null

	org.dmg.pmml.DataType dataType = targetField.getDataType();

	org.dmg.pmml.OpType opType = targetField.getOpType();

	switch(opType){

		case CONTINUOUS:

			break;

		case CATEGORICAL:

		case ORDINAL:

			List> validTargetValues = targetField.getDiscreteDomain();

			// The list of target category values for querying HasCategoricalResults subinterfaces (HasProbability, HasConfidence etc).

			// The default element type is String.

			// If the PMML instance is pre-parsed, then the element type changes to the appropriate Java primitive type

			List> categories = targetField.getCategories();

			break;

		default:

			break;

	}

}

```

Querying and analyzing output fields:

```java

List extends OutputField> outputFields = evaluator.getOutputFields();

for(OutputField outputField : outputFields){

	org.dmg.pmml.OutputField pmmlOutputField = outputField.getOutputField();

	org.dmg.pmml.DataType dataType = outputField.getDataType(); // Could be null

	org.dmg.pmml.OpType opType = outputField.getOpType(); // Could be null

	boolean finalResult = outputField.isFinalResult();

	if(!finalResult){

		continue;

	}

}

```

### Evaluating models ###

A model may contain verification data, which is a small but representative set of data records (inputs plus expected outputs) for ensuring that the model evaluator is behaving correctly in this deployment configuration (JPMML-Evaluator version, Java/JVM version and vendor etc. variables).

The model evaluator should be verified once, before putting it into actual use.

Performing the self-check:

```java

evaluator.verify();

```

During scoring, the application code should iterate over data records (eg. rows of a table), and apply the following encode-evaluate-decode sequence of operations to each one of them.

The processing of the first data record will be significantly slower than the processing of all subsequent data records, because the model evaluator needs to lookup, validate and pre-parse model content.

If the model contains verification data, then this warm-up cost is borne during the self-check.

Preparing the argument map:

```java

Map inputDataRecord = ...;

Map arguments = new LinkedHashMap<>();

List inputFields = evaluator.getInputFields();

for(InputField inputField : inputFields){

	String inputName = inputField.getName();

	Object rawValue = inputDataRecord.get(inputName);

	// Transforming an arbitrary user-supplied value to a known-good PMML value

	// The user-supplied value is passed through: 1) outlier treatment, 2) missing value treatment, 3) invalid value treatment and 4) type conversion

	FieldValue inputValue = inputField.prepare(rawValue);

	arguments.put(inputName, inputValue);

}

```

Performing the evaluation:

```java

Map results = evaluator.evaluate(arguments);

```

Extracting primary results from the result map:

```java

List targetFields = evaluator.getTargetFields();

for(TargetField targetField : targetFields){

	String targetName = targetField.getName();

	Object targetValue = results.get(targetName);

}

```

The target value is either a Java primitive value (as a wrapper object) or a complex value as a `Computable` instance.

A complex target value may expose additional information about the prediction by implementing appropriate `ResultFeature` subinterfaces:

```java

// Test for "entityId" result feature

if(targetValue instanceof HasEntityId){

	HasEntityId hasEntityId = (HasEntityId)targetValue;

	HasEntityRegistry> hasEntityRegistry = (HasEntityRegistry>)evaluator;

	BiMap entities = hasEntityRegistry.getEntityRegistry();

	Entity winner = entities.get(hasEntityId.getEntityId());

}

// Test for "probability" result feature

if(targetValue instanceof HasProbability){

	HasProbability hasProbability = (HasProbability)targetValue;

	Set> categories = hasProbability.getCategories();

	for(Object category : categories){

		Double categoryProbability = hasProbability.getProbability(category);

	}

}

```

A complex target value may hold a reference to the model evaluator that created it. It is adisable to decode it to a Java primitive value (ie. decoupling from the JPMML-Evaluator runtime environment) as soon as all the additional information has been retrieved:

```java

if(targetValue instanceof Computable){

	Computable computable = (Computable)targetValue;

	targetValue = computable.getResult();

}

```

Extracting secondary results from the result map:

```java

List outputFields = evaluator.getOutputFields();

for(OutputField outputField : outputFields){

	String outputName = outputField.getName();

	Object outputValue = results.get(outputName);

}

```

The output value is always a Java primitive value (as a wrapper object).

# Example applications #

Module `pmml-evaluator-example` exemplifies the use of the JPMML-Evaluator library.

This module can be built using [Apache Maven](https://maven.apache.org/):

```

mvn clean install

```

The resulting uber-JAR file `target/pmml-evaluator-example-executable-1.6-SNAPSHOT.jar` contains the following command-line applications:

* `org.jpmml.evaluator.example.EvaluationExample` [(source)](https://github.com/jpmml/jpmml-evaluator/blob/master/pmml-evaluator-example/src/main/java/org/jpmml/evaluator/example/EvaluationExample.java).

* `org.jpmml.evaluator.example.RecordCountingExample` [(source)](https://github.com/jpmml/jpmml-evaluator/blob/master/pmml-evaluator-example/src/main/java/org/jpmml/evaluator/example/RecordCountingExample.java).

* `org.jpmml.evaluator.example.TestingExample` [(source)](https://github.com/jpmml/jpmml-evaluator/blob/master/pmml-evaluator-example/src/main/java/org/jpmml/evaluator/example/TestingExample.java).

Evaluating model `model.pmml` with data records from `input.csv`. The predictions are stored to `output.csv`:

```

java -cp target/pmml-evaluator-example-executable-1.6-SNAPSHOT.jar org.jpmml.evaluator.example.EvaluationExample --model model.pmml --input input.csv --output output.csv

```

Evaluating model `model.pmml` with data records from `input.csv`. The predictions are verified against data records from `expected-output.csv`:

```

java -cp target/pmml-evaluator-example-executable-1.6-SNAPSHOT.jar org.jpmml.evaluator.example.TestingExample --model model.pmml --input input.csv --expected-output expected-output.csv

```

Enhancing model `model.pmml` with verification data records from `input_expected-output.csv`:

```

java -cp target/pmml-evaluator-example-executable-1.6-SNAPSHOT.jar org.jpmml.evaluator.example.EnhancementExample --model model.pmml --verification input_expected_output.csv

```

Getting help:

```

java -cp target/pmml-evaluator-example-executable-1.6-SNAPSHOT.jar  --help

```

# Documentation #

Up-to-date:

* [Benchmarking Scikit-Learn against JPMML-Evaluator in Java and Python environments](https://openscoring.io/blog/2021/08/04/benchmarking_sklearn_jpmml_evaluator/)

* [Tracing and reporting ML model predictions](https://openscoring.io/blog/2019/02/26/jpmml_evaluator_api_tracing_reporting_predictions/)

* [Upgrading from the Factory pattern to the Builder pattern](https://openscoring.io/blog/2018/12/06/jpmml_evaluator_api_builder_pattern/)

Slightly outdated:

* [Testing PMML applications](https://openscoring.io/blog/2014/05/12/testing_pmml_applications/)

# Support #

Limited public support is available via the [JPMML mailing list](https://groups.google.com/forum/#!forum/jpmml).

# License #

JPMML-Evaluator is licensed under the terms and conditions of the [GNU Affero General Public License, Version 3.0](https://www.gnu.org/licenses/agpl-3.0.html).

For a quick summary of your rights ("Can") and obligations ("Cannot" and "Must") under AGPLv3, please refer to [TLDRLegal](https://tldrlegal.com/license/gnu-affero-general-public-license-v3-(agpl-3.0)).

If you would like to use JPMML-Evaluator in a proprietary software project, then it is possible to enter into a licensing agreement which makes JPMML-Evaluator available under the terms and conditions of the [BSD 3-Clause License](https://opensource.org/licenses/BSD-3-Clause) instead.

# Additional information #

JPMML-Evaluator is developed and maintained by Openscoring OÜ, Estonia.

Interested in using [Java PMML API](https://github.com/jpmml) software in your company? Please contact [[email protected]](mailto:[email protected])