Java library and command-line application for converting Scikit-Learn pipelines to PMML.

• Features
  • Overview
  • Supported packages
• Prerequisites
  • The Python side of operations
  • The JPMML-SkLearn side of operations
• Installation
• Usage
  • The Python side of operations
  • The JPMML-SkLearn side of operations
• Documentation
• License
• Additional information

• Functionality:
  • Three times more supported Python packages, transformers and estimators than all the competitors combined!
  • Thorough collection, analysis and encoding of feature information:
    • Names.
    • Data and operational types.
    • Valid, invalid and missing value spaces.
    • Descriptive statistics.
  • Pipeline extensions:
    • Pruning.
    • Decision engineering (prediction post-processing).
    • Model verification.
  • Conversion options.
• Extensibility:
  • Rich Java APIs for developing custom converters.
  • Automatic discovery and registration of custom converters based on META-INF/sklearn2pmml.properties resource files.
  • Direct interfacing with other JPMML conversion libraries such as JPMML-H2O, JPMML-LightGBM, JPMML-StatsModels and JPMML-XGBoost.
• Production quality:
  • Complete test coverage.
  • Fully compliant with the JPMML-Evaluator library.

For a full list of supported transformer and estimator classes see the features.md file.

• Python 2.7, 3.4 or newer.
• Scikit-Learn 0.16.0 or newer. This is not a typo - all Scikit-Learn version from the past 10 years (2015 or newer) should work equally fine.

• Java 11 or newer.

Enter the project root directory and build using Apache Maven:

mvn clean install

The build produces a library JAR file pmml-sklearn/target/pmml-sklearn-1.9-SNAPSHOT.jar, and an executable uber-JAR file pmml-sklearn-example/target/pmml-sklearn-example-executable-1.9-SNAPSHOT.jar.

A typical workflow can be summarized as follows:

1. Use Scikit-Learn to assemble and fit a pipeline.
2. Serialize this pipeline in pickle data format to a file in a local filesystem.
3. Use the JPMML-SkLearn command-line application to convert this pickle file to a PMML file.

Assembling and fitting a pipeline:

from sklearn.compose import ColumnTransformer
from sklearn.datasets import load_iris
#from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler

iris_X, iris_y = load_iris(return_X_y = True, as_frame = True)
iris_X.columns = [col.rstrip(" (cm)") for col in iris_X.columns]

pipeline = Pipeline([
    # Column-oriented feature engineering
    ("transformer", ColumnTransformer([
        ("scaler", StandardScaler(), [0, 1, 2, 3])
    ], remainder = "drop")),
    # Table-oriented feature engineering
    #("pca", PCA(n_components = 3)),
    # Final model
    ("classifier", LogisticRegression())
])
pipeline.fit(iris_X, iris_y)

Serializing the pipeline in Joblib-flavoured pickle data format:

import joblib

joblib.dump(pipeline, "pipeline.pkl")

Please see the test script file main.py for more classification (binary and multi-class) and regression workflows.

Converting a pickle file to a PMML file:

java -jar pmml-sklearn-example/target/pmml-sklearn-example-executable-1.9-SNAPSHOT.jar --pkl-input pipeline.pkl --pmml-output pipeline.pmml

Getting help:

java -jar pmml-sklearn-example/target/pmml-sklearn-example-executable-1.9-SNAPSHOT.jar --help

Integrations:

• Training Scikit-Learn GridSearchCV StatsModels pipelines
• Converting Scikit-Learn H2O.ai pipelines to PMML
• Converting customized Scikit-Learn estimators to PMML
• Training Scikit-Learn StatsModels pipelines
• Upgrading Scikit-Learn XGBoost pipelines
• Training Python-based XGBoost accelerated failure time models
• Converting Scikit-Learn PyCaret 3 pipelines to PMML
• Training Scikit-Learn H2O.ai pipelines
• One-hot encoding categorical features in Scikit-Learn XGBoost pipelines
• Training Scikit-Learn TF(-IDF) plus XGBoost pipelines
• Converting Scikit-Learn TF(-IDF) pipelines to PMML
• Converting Scikit-Learn Imbalanced-Learn pipelines to PMML
• Converting logistic regression models to PMML
• Stacking Scikit-Learn, LightGBM and XGBoost models
• Converting Scikit-Learn GridSearchCV pipelines to PMML
• Converting Scikit-Learn TPOT pipelines to PMML
• Converting Scikit-Learn LightGBM pipelines to PMML

Extensions:

• Extending Scikit-Learn with feature cross-references
• Extending Scikit-Learn with UDF expression transformer
• Extending Scikit-Learn with CHAID models
• Extending Scikit-Learn with prediction post-processing
• Extending Scikit-Learn with outlier detector transformer
• Extending Scikit-Learn with date and datetime features
• Extending Scikit-Learn with feature specifications
• Extending Scikit-Learn with GBDT+LR ensemble models
• Extending Scikit-Learn with business rules model

Miscellaneous:

• Upgrading Scikit-Learn decision tree models
• Measuring the memory consumption of Scikit-Learn models
• Benchmarking Scikit-Learn against JPMML-Evaluator
• Analyzing Scikit-Learn feature importances via PMML

Archived:

• Converting Scikit-Learn to PMML

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

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

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

Interested in using Java PMML API software in your company? Please contact [email protected]