PySpark2PMML

Python library for converting Apache Spark ML pipelines to PMML.

Features

This package provides Python wrapper classes and functions for the JPMML-SparkML library. For the full list of supported Apache Spark ML Estimator and Transformer types, please refer to JPMML-SparkML documentation.

Prerequisites

Apache Spark 2.0.X, 2.1.X, 2.2.X, 2.3.X, 2.4.X or 3.0.X.
Python 2.7, 3.4 or newer.

Installation

Install a release version from PyPI:

pip install pyspark2pmml

Alternatively, install the latest snapshot version from GitHub:

pip install --upgrade git+https://github.com/jpmml/pyspark2pmml.git

Configuration and usage

PySpark2PMML must be paired with JPMML-SparkML based on the following compatibility matrix:

Apache Spark version	JPMML-SparkML branch	JPMML-SparkML uber-JAR file
2.0.X	`1.1.X` (Archived)	1.1.23
2.1.X	`1.2.X` (Archived)	1.2.15
2.2.X	`1.3.X` (Archived)	1.3.15
2.3.X	`1.4.X`	1.4.18
2.4.X	`1.5.X`	1.5.11
3.0.X	`master`	1.6.3

Launch PySpark; use the --jars command-line option to specify the location of the JPMML-SparkML uber-JAR file:

pyspark --jars /path/to/jpmml-sparkml-executable-${version}.jar

Fitting a Spark ML pipeline:

from pyspark.ml import Pipeline
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.feature import RFormula

df = spark.read.csv("Iris.csv", header = True, inferSchema = True)

formula = RFormula(formula = "Species ~ .")
classifier = DecisionTreeClassifier()
pipeline = Pipeline(stages = [formula, classifier])
pipelineModel = pipeline.fit(df)

Exporting the fitted Spark ML pipeline to a PMML file:

from pyspark2pmml import PMMLBuilder

pmmlBuilder = PMMLBuilder(sc, df, pipelineModel)

pmmlBuilder.buildFile("DecisionTreeIris.pmml")

The representation of individual Spark ML pipeline stages can be customized via conversion options:

from pyspark2pmml import PMMLBuilder

classifierModel = pipelineModel.stages[1]

pmmlBuilder = PMMLBuilder(sc, df, pipelineModel) \
	.putOption(classifierModel, "compact", False) \
	.putOption(classifierModel, "estimate_featureImportances", True)

pmmlBuilder.buildFile("DecisionTreeIris.pmml")

License

PySpark2PMML is licensed under the terms and conditions of the GNU Affero General Public License, Version 3.0.

If you would like to use PySpark2PMML in a proprietary software project, then it is possible to enter into a licensing agreement which makes PySpark2PMML available under the terms and conditions of the BSD 3-Clause License instead.

Additional information

PySpark2PMML is developed and maintained by Openscoring Ltd, Estonia.

Interested in using Java PMML API software in your company? Please contact info@openscoring.io

yuzisun / pyspark2pmml