Java interface for the SHAP (SHapley Additive exPlanations) library for tree
ensembles (TreeExplainer). Note that shap4j is not a pure Java port of SHAP. Rather, it utilizes
JavaCPP to provide a Java-Native Interface (JNI) on top of the
fast C++ implementation of TreeExplainer.
In this sense, shap4j leverages the same underlying native code that powers the
Python version of TreeExplainer,
to ensure validity and efficiency.
Current supported platforms:
macosx-x86_64macosx-arm64linux-x86_64
shap4j enables lean SHAP integration in JVM projects, i.e. a project can import shap4j as the sole dependency,
without having to depend on heavier third-party tree ensemble libraries,
e.g. xgboost4j.
In order for shap4j to explain a tree ensemble model, that model must be provided in a .shap4j data file, which can
be generated from model dumps (pickle files) of XGBoost/LightGBM/CatBoost/sklearn using the companion Python library
shap4j-data-converter.
<dependency>
<groupId>io.github.xydrolase</groupId>
<artifactId>shap4j-platform</artifactId>
<version>0.0.3</version>
</dependency>libraryDependencies += "io.github.xydrolase" % "shap4j-platform" % "0.0.3"
package examples;
import java.nio.file.Files;
import java.io.File;
import shap4j.TreeExplainer;
class ExampleApp {
public static void main(String[] args) throws Exception {
byte[] data = Files.readAllBytes(new File("boston.shap4j").toPath());
TreeExplainer explainer = new TreeExplainer(data);
double[] x = {
6.320e-03, 1.800e+01, 2.310e+00, 0.000e+00, 5.380e-01, 6.575e+00,
6.520e+01, 4.090e+00, 1.000e+00, 2.960e+02, 1.530e+01, 3.969e+02,
4.980e+00
};
double[] shapValues = explainer.shapValues(x, false);
System.out.println("SHAP values: " + Arrays.toString(shapValues));
}
}The data file boston.shap4j can be found here.
Or, you can follow the examples in shap4j-data-converter to
generate it yourself.
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