com.insightml.evaluation.simulation.SplitSimulation.java Source code

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Here is the source code for com.insightml.evaluation.simulation.SplitSimulation.java

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/*
 * Copyright (C) 2016 Stefan Hen
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package com.insightml.evaluation.simulation;

import java.util.LinkedList;
import java.util.List;
import java.util.Random;

import org.apache.commons.math3.util.Pair;

import com.insightml.data.samples.Sample;
import com.insightml.utils.Utils;

public final class SplitSimulation<I extends Sample> extends AbstractSimulation<I> {

    private static final long serialVersionUID = -2994345013519681199L;

    private final double trainFraction;

    public SplitSimulation(final double trainFraction, final SimulationResultConsumer database) {
        super((int) (trainFraction * 100) + "/" + (int) ((1 - trainFraction) * 100) + " Split", database);
        this.trainFraction = trainFraction;
    }

    @Override
    public String getDescription() {
        return "Trains on " + trainFraction + " and evaluates on " + (1 - trainFraction);
    }

    @Override
    public <E, P> ISimulationResults<E, P>[] run(final Iterable<I> train, final SimulationSetup<I, E, P> setup) {
        final Pair<Iterable<I>, List<I>> split = split(train, trainFraction, Utils.random());
        return run(split.getFirst(), split.getSecond(), setup);
    }

    public static <S extends Sample> Pair<Iterable<S>, List<S>> split(final Iterable<S> instances,
            final double trainFraction, final Random random) {
        if (trainFraction == 1.0) {
            return new Pair<>(instances, null);
        }
        final List<S> train = new LinkedList<>();
        final List<S> test = new LinkedList<>();
        for (final S sample : instances) {
            if (random.nextDouble() < trainFraction) {
                train.add(sample);
            } else {
                test.add(sample);
            }
        }
        return new Pair<>(train, test);
    }
}