Deploying models with TL2cgen4J


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TL2cgen4J is the Java runtime for TL2cgen. This tutorial will show how to use TL2cgen4J to deploy decision tree models to Java applications.

Build and install the Java runtime

Check out the copy of TL2cgen by running

git clone

Navigate to the java_runtime directory and run Maven to install the Java runtime:

cd java_runtime/tl2cgen4j
mvn install

Specify TL2cgen4J as a dependency for your Java application

Add the following entry to your pom.xml:


Load compiled model

Locate the compiled model (dll/so/dylib) in the local filesystem. We load the compiled model by creating a Predictor object:


Predictor predictor = new Predictor("path/to/", -1, true);

The second argument is set to -1, to utilize all CPU cores available.

Query the model

Once the compiled model is loaded, we can query it:

// Get the input dimension, i.e. the number of feature values in the input vector
int num_feature = predictor.GetNumFeature();

// Get the number of classes.
// This number is 1 for tasks other than multi-class classification.
// For multi-class classification task, the number is equal to the number of classes.
int num_class = predictor.GetNumClass();

Predict with a data matrix

For predicting with a batch of inputs, we create a list of DataPoint objects. Each DataPoint object consists of feature values and corresponding feature indices.

Let us look at an example. Consider the following 4-by-6 data matrix

\[\begin{split}\left[ \begin{array}{cccccc} 10 & 20 & \cdot & \cdot & \cdot & \cdot\\ \cdot & 30 & \cdot & 40 & \cdot & \cdot\\ \cdot & \cdot & 50 & 60 & 70 & \cdot\\ \cdot & \cdot & \cdot & \cdot & \cdot & 80 \end{array} \right]\end{split}\]

where the dot (.) indicates the missing value. The matrix consists of 4 data points (instances), each with 6 feature values. Since not all feature values are present, we need to store feature indices as well as feature values:

import ml.dmlc.tl2cgen4j.DataPoint;

// Create a list consisting of 4 data points
List<DataPoint> data_list = new ArrayList<DataPoint>() {
    //                feature indices     feature values
    add(new DataPoint(new int[]{0, 1},    new float[]{10f, 20f}));
    add(new DataPoint(new int[]{1, 3},    new float[]{30f, 40f}));
    add(new DataPoint(new int[]{2, 3, 4}, new float[]{50f, 60f, 70f}));
    add(new DataPoint(new int[]{5},       new float[]{80f}));

Once the list is created, we then convert it into a DMatrix object. We use sparse representation here because significant portion of the data matrix consists of missing values.


// Convert data point list into DMatrix object
DMatrix dmat = DMatrixBuilder.createSparseCSRDMatrix(data_list.iterator());

Now invoke the dmat prediction function using the DMatrix object:

// verbose=true, pred_margin=false
float[][] result = predictor.predict(dmat, true, false).toFloatMatrix();

The returned array is a two-dimensional array where the array result[i] represents the prediction for the i-th data point. For most applications, each result[i] has length 1. Multi-class classification task is specical, in that for that task result[i] contains class probabilities, so the array is as long as the number of target classes.

For your convenience, we also provide a convenience method to load a data text file in the LIBSVM format:

List<DataPoint> data_list = DMatrixBuilder.LoadDatasetFromLibSVM("path/to/");
DMatrix dmat = DMatrixBuilder.createSparseCSRDMatrix(data_list.iterator());