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

There is a newer version: 1.8.7

/*
 * Copyright (c) 2016 Villu Ruusmann
 *
 * This file is part of JPMML-XGBoost
 *
 * JPMML-XGBoost is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Affero General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * JPMML-XGBoost is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Affero General Public License for more details.
 *
 * You should have received a copy of the GNU Affero General Public License
 * along with JPMML-XGBoost.  If not, see .
 */
package org.jpmml.xgboost;

import java.util.ArrayList;
import java.util.List;

import org.dmg.pmml.DataType;
import org.dmg.pmml.OpType;
import org.dmg.pmml.mining.MiningModel;
import org.dmg.pmml.regression.RegressionModel;
import org.jpmml.converter.CMatrixUtil;
import org.jpmml.converter.CategoricalLabel;
import org.jpmml.converter.FieldNameUtil;
import org.jpmml.converter.ModelUtil;
import org.jpmml.converter.Schema;
import org.jpmml.converter.mining.MiningModelUtil;

public class MultinomialLogisticRegression extends Classification {

	public MultinomialLogisticRegression(String name, int num_class){
		super(name, num_class);

		if(num_class < 2){
			throw new IllegalArgumentException("Multi-class classification requires two or more target categories");
		}
	}

	@Override
	public MiningModel encodeMiningModel(List trees, List weights, float base_score, Integer ntreeLimit, boolean numeric, Schema schema){
		Schema segmentSchema = schema.toAnonymousRegressorSchema(DataType.FLOAT);

		List miningModels = new ArrayList<>();

		CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel();

		for(int i = 0, columns = categoricalLabel.size(), rows = (trees.size() / columns); i < columns; i++){
			MiningModel miningModel = createMiningModel(CMatrixUtil.getColumn(trees, rows, columns, i), (weights != null) ? CMatrixUtil.getColumn(weights, rows, columns, i) : null, base_score, ntreeLimit, numeric, segmentSchema)
				.setOutput(ModelUtil.createPredictedOutput(FieldNameUtil.create("xgbValue", categoricalLabel.getValue(i)), OpType.CONTINUOUS, DataType.FLOAT));

			miningModels.add(miningModel);
		}

		return MiningModelUtil.createClassification(miningModels, RegressionModel.NormalizationMethod.SOFTMAX, true, schema);
	}
}