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JPMML class model evaluator
There is a newer version: 1.6.8
/*
 * Copyright (c) 2017 Villu Ruusmann
 *
 * This file is part of JPMML-Evaluator
 *
 * JPMML-Evaluator 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-Evaluator 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-Evaluator.  If not, see .
 */
package org.jpmml.evaluator.mining;

import java.util.List;

import org.dmg.pmml.DataType;
import org.dmg.pmml.mining.Segmentation;
import org.jpmml.evaluator.EvaluatorUtil;
import org.jpmml.evaluator.HasProbability;
import org.jpmml.evaluator.ProbabilityAggregator;
import org.jpmml.evaluator.TypeCheckException;
import org.jpmml.evaluator.TypeUtil;
import org.jpmml.evaluator.Value;
import org.jpmml.evaluator.ValueAggregator;
import org.jpmml.evaluator.ValueFactory;
import org.jpmml.evaluator.ValueMap;
import org.jpmml.evaluator.VoteAggregator;

public class MiningModelUtil {

	private MiningModelUtil(){
	}

	static
	public  Value aggregateValues(ValueFactory valueFactory, Segmentation.MultipleModelMethod multipleModelMethod, List segmentResults){
		ValueAggregator aggregator;

		switch(multipleModelMethod){
			case AVERAGE:
			case SUM:
				aggregator = new ValueAggregator<>(valueFactory.newVector(0));
				break;
			case MEDIAN:
				aggregator = new ValueAggregator<>(valueFactory.newVector(segmentResults.size()));
				break;
			case WEIGHTED_AVERAGE:
			case WEIGHTED_SUM:
				aggregator = new ValueAggregator<>(valueFactory.newVector(0), valueFactory.newVector(0), valueFactory.newVector(0));
				break;
			case WEIGHTED_MEDIAN:
				aggregator = new ValueAggregator<>(valueFactory.newVector(segmentResults.size()), valueFactory.newVector(segmentResults.size()));
				break;
			default:
				throw new IllegalArgumentException();
		}

		for(SegmentResult segmentResult : segmentResults){
			Number value;

			try {
				Object targetValue = EvaluatorUtil.decode(segmentResult.getTargetValue());

				if(targetValue instanceof Number){
					value = (Number)targetValue;
				} else

				{
					value = (Double)TypeUtil.cast(DataType.DOUBLE, targetValue);
				}
			} catch(TypeCheckException tce){
				throw tce.ensureContext(segmentResult.getSegment());
			}

			switch(multipleModelMethod){
				case AVERAGE:
				case SUM:
				case MEDIAN:
					aggregator.add(value);
					break;
				case WEIGHTED_AVERAGE:
				case WEIGHTED_SUM:
				case WEIGHTED_MEDIAN:
					double weight = segmentResult.getWeight();

					aggregator.add(value, weight);
					break;
				default:
					throw new IllegalArgumentException();
			}
		}

		switch(multipleModelMethod){
			case AVERAGE:
				return aggregator.average();
			case WEIGHTED_AVERAGE:
				return aggregator.weightedAverage();
			case SUM:
				return aggregator.sum();
			case WEIGHTED_SUM:
				return aggregator.weightedSum();
			case MEDIAN:
				return aggregator.median();
			case WEIGHTED_MEDIAN:
				return aggregator.weightedMedian();
			default:
				throw new IllegalArgumentException();
		}
	}

	static
	public  ValueMap aggregateVotes(ValueFactory valueFactory, Segmentation.MultipleModelMethod multipleModelMethod, List segmentResults){
		VoteAggregator aggregator = new VoteAggregator(){

			@Override
			public ValueFactory getValueFactory(){
				return valueFactory;
			}
		};

		for(SegmentResult segmentResult : segmentResults){
			String key;

			try {
				Object targetValue = EvaluatorUtil.decode(segmentResult.getTargetValue());

				key = (String)TypeUtil.cast(DataType.STRING, targetValue);
			} catch(TypeCheckException tce){
				throw tce.ensureContext(segmentResult.getSegment());
			}

			switch(multipleModelMethod){
				case MAJORITY_VOTE:
					aggregator.add(key);
					break;
				case WEIGHTED_MAJORITY_VOTE:
					double weight = segmentResult.getWeight();

					aggregator.add(key, weight);
					break;
				default:
					throw new IllegalArgumentException();
			}
		}

		return aggregator.sumMap();
	}

	static
	public  ValueMap aggregateProbabilities(ValueFactory valueFactory, Segmentation.MultipleModelMethod multipleModelMethod, List categories, List segmentResults){
		ProbabilityAggregator aggregator;

		switch(multipleModelMethod){
			case AVERAGE:
				aggregator = new ProbabilityAggregator(0){

					@Override
					public ValueFactory getValueFactory(){
						return valueFactory;
					}
				};
				break;
			case WEIGHTED_AVERAGE:
				aggregator = new ProbabilityAggregator(0, valueFactory.newVector(0)){

					@Override
					public ValueFactory getValueFactory(){
						return valueFactory;
					}
				};
				break;
			case MEDIAN:
			case MAX:
				aggregator = new ProbabilityAggregator(segmentResults.size()){

					@Override
					public ValueFactory getValueFactory(){
						return valueFactory;
					}
				};
				break;
			default:
				throw new IllegalArgumentException();
		}

		for(SegmentResult segmentResult : segmentResults){
			HasProbability hasProbability;

			try {
				Object targetValue = segmentResult.getTargetValue();

				hasProbability = TypeUtil.cast(HasProbability.class, targetValue);
			} catch(TypeCheckException tce){
				throw tce.ensureContext(segmentResult.getSegment());
			}

			switch(multipleModelMethod){
				case AVERAGE:
				case MEDIAN:
				case MAX:
					aggregator.add(hasProbability);
					break;
				case WEIGHTED_AVERAGE:
					double weight = segmentResult.getWeight();

					aggregator.add(hasProbability, weight);
					break;
				default:
					throw new IllegalArgumentException();
			}
		}

		switch(multipleModelMethod){
			case AVERAGE:
				return aggregator.averageMap();
			case WEIGHTED_AVERAGE:
				return aggregator.weightedAverageMap();
			case MEDIAN:
				return aggregator.medianMap(categories);
			case MAX:
				return aggregator.maxMap(categories);
			default:
				throw new IllegalArgumentException();
		}
	}
}