/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 *   *  Redistributions of source code must retain the above copyright notice,
 *      this list of conditions and the following disclaimer.
 *
 *   *  Redistributions in binary form must reproduce the above copyright notice,
 *      this list of conditions and the following disclaimer in the documentation
 *      and/or other materials provided with the distribution.
 *
 *   *  Neither the name of the University of Southampton nor the names of its
 *      contributors may be used to endorse or promote products derived from this
 *      software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
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 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.experiment.dataset.sampling;

import java.util.List;
import java.util.Map;

import org.openimaj.data.RandomData;
import org.openimaj.data.dataset.GroupedDataset;
import org.openimaj.data.dataset.ListBackedDataset;
import org.openimaj.data.dataset.ListDataset;
import org.openimaj.data.dataset.MapBackedDataset;
import org.openimaj.experiment.dataset.util.DatasetAdaptors;
import org.openimaj.util.list.AcceptingListView;
import org.openimaj.util.list.SkippingListView;

/**
 * A stratified uniformly random sampling scheme for grouped datasets. Both
 * sampling with and without replacement are supported. The sampler returns a
 * dataset that selects a predefined fraction of the input data. Specifically,
 * the given percentage of data is selected from each group independently, thus
 * ensuring that the distribution of relative group sizes before and after
 * sampling remains (approximately) constant.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 * 
 * @param <KEY>
 *            Type of groups
 * @param <INSTANCE>
 *            Type of instances
 */
public class StratifiedGroupedUniformRandomisedSampler<KEY, INSTANCE>
                implements
                Sampler<GroupedDataset<KEY, ListDataset<INSTANCE>, INSTANCE>>
{
        private boolean withReplacement = false;

        // this is overloaded to hold either a percentage or number of instances.
        // Percentages are stored in the range 0..1; numbers are stored as -number.
        private double percentage;

        /**
         * Construct a {@link StratifiedGroupedUniformRandomisedSampler}
         * with the given percentage of instances to select. By default, the
         * sampling is without replacement (i.e. an instance can only be selected
         * once).
         * 
         * @param percentage
         *            percentage of instances to select
         */
        public StratifiedGroupedUniformRandomisedSampler(double percentage) {
                if (percentage < 0 || percentage > 1)
                        throw new IllegalArgumentException("percentage of sample instances must be between 0 and 1");

                this.percentage = percentage;
        }

        /**
         * Construct a {@link StratifiedGroupedUniformRandomisedSampler}
         * with the given percentage of instances to select, using with
         * with-replacement or without-replacement sampling.
         * 
         * @param percentage
         *            percentage of instances to select
         * @param withReplacement
         *            should the sampling be performed with replacement (true); or
         *            without replacement (false).
         */
        public StratifiedGroupedUniformRandomisedSampler(
                        double percentage, boolean withReplacement)
        {
                this(percentage);
                this.withReplacement = withReplacement;
        }

        /**
         * Construct a {@link StratifiedGroupedUniformRandomisedSampler}
         * with the given number of instances to select. By default, the sampling is
         * without replacement (i.e. an instance can only be selected once).
         * 
         * @param number
         *            number of instances to select
         */
        public StratifiedGroupedUniformRandomisedSampler(int number) {
                if (number < 1)
                        throw new IllegalArgumentException("number of sample instances must be greater than 1");

                this.percentage = -number;
        }

        /**
         * Construct a {@link StratifiedGroupedUniformRandomisedSampler}
         * with the given number of instances to select, using with with-replacement
         * or without-replacement sampling.
         * 
         * @param number
         *            number of instances to select
         * @param withReplacement
         *            should the sampling be performed with replacement (true); or
         *            without replacement (false).
         */
        public StratifiedGroupedUniformRandomisedSampler(
                        int number, boolean withReplacement)
        {
                this(number);
                this.withReplacement = withReplacement;
        }

        @Override
        public GroupedDataset<KEY, ListDataset<INSTANCE>, INSTANCE> sample(
                        GroupedDataset<KEY, ListDataset<INSTANCE>, INSTANCE> dataset)
        {
                final MapBackedDataset<KEY, ListDataset<INSTANCE>, INSTANCE> sample = new MapBackedDataset<KEY, ListDataset<INSTANCE>, INSTANCE>();
                final Map<KEY, ListDataset<INSTANCE>> map = sample.getMap();

                for (final KEY key : dataset.getGroups()) {
                        final List<INSTANCE> list = DatasetAdaptors.asList(dataset
                                        .getInstances(key));
                        final int size = list.size();

                        final boolean skip;
                        final int N;
                        if (percentage >= 0) {
                                // if we want more than 50%, it's better to select 1-percentage
                                // indexes to skip
                                skip = percentage > 0.5;
                                final double per = skip ? 1.0 - percentage : percentage;

                                N = (int) Math.round(size * per);
                        } else {
                                N = (int) -percentage;
                                skip = N > (size / 2);
                        }

                        int[] selectedIds;
                        if (withReplacement) {
                                selectedIds = RandomData.getRandomIntArray(N, 0, size);
                        } else {
                                selectedIds = RandomData.getUniqueRandomInts(N, 0, size);
                        }

                        if (!skip) {
                                map.put(key, new ListBackedDataset<INSTANCE>(
                                                new AcceptingListView<INSTANCE>(list, selectedIds)));
                        } else {
                                map.put(key, new ListBackedDataset<INSTANCE>(
                                                new SkippingListView<INSTANCE>(list, selectedIds)));
                        }
                }

                return sample;
        }
}