JPMML-LightGBM

Java library and command-line application for converting LightGBM models to PMML.

Prerequisites

• LightGBM 2.0.0 or newer.
• Java 1.8 or newer.

Installation

Enter the project root directory and build using Apache Maven:

mvn clean install

The build produces a library JAR file pmml-lightgbm/target/pmml-lightgbm-1.5-SNAPSHOT.jar, and an executable uber-JAR file pmml-lightgbm-example/target/pmml-lightgbm-example-executable-1.5-SNAPSHOT.jar.

Usage

A typical workflow can be summarized as follows:

1. Use LightGBM to train a model.
2. Save the model to a text file in a local filesystem.
3. Use the JPMML-LightGBM command-line converter application to turn this text file to a PMML file.

The LightGBM side of operations

Training a binary classification model using the `Audit.csv dataset.

R language

library("lightgbm")

df = read.csv("Audit.csv", stringsAsFactors = TRUE)

# Three continuous features, followed by five categorical features
X = df[c("Age", "Hours", "Income", "Education", "Employment", "Gender", "Marital", "Occupation")]
y = df[["Adjusted"]]

cat_cols = c("Education", "Employment", "Gender", "Marital", "Occupation")
for(cat_col in cat_cols){
	X[[cat_col]] = as.numeric(X[[cat_col]])
}

audit.matrix = as.matrix(X)
audit.ds = lgb.Dataset(data = audit.matrix, label = y, categorical_feature = cat_cols)

audit.lgbm = lgb.train(params = list(objective = "binary"), data = audit.ds, nrounds = 131)
lgb.save(audit.lgbm, "LightGBMAudit.txt")

Python language

import lightgbm
import pandas

df = pandas.read_csv("Audit.csv")

# Three continuous features, followed by five categorical features
X = df[["Age", "Hours", "Income", "Education", "Employment", "Gender", "Marital", "Occupation"]]
y = df["Adjusted"]

cat_cols = ["Education", "Employment", "Gender", "Marital", "Occupation"]

for cat_col in cat_cols:
	X[cat_col] = X[cat_col].astype("category")

audit_ds = lightgbm.Dataset(data = X, label = y, categorical_feature = cat_cols)

audit_booster = lightgbm.train({"objective" : "binary", "num_iterations" : 131}, audit_ds)
audit_booster.save_model("LightGBMAudit.txt")

The JPMML-LightGBM side of operations

Converting the text file LightGBMAudit.txt to a PMML file LightGBMAudit.pmml:

java -jar pmml-lightgbm-example/target/pmml-lightgbm-example-executable-1.5-SNAPSHOT.jar --lgbm-input LightGBMAudit.txt --pmml-output LightGBMAudit.pmml

Getting help:

java -jar pmml-lightgbm-example/target/pmml-lightgbm-example-executable-1.5-SNAPSHOT.jar  --help

Documentation

• Stacking Scikit-Learn, LightGBM and XGBoost models
• Deploying LightGBM models on Java/JVM platform
• Extending Scikit-Learn with GBDT+LR ensemble models (Using LightGBM models on the GBDT side of GBDT+LR ensemble)
• Converting Scikit-Learn LightGBM pipelines to PMML

License

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

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

Additional information

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

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