package playground;

import java.io.IOException;
import java.net.URISyntaxException;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.function.UnaryOperator;
import java.util.stream.Stream;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.DataModelBuilder;
import org.apache.mahout.cf.taste.eval.IRStatistics;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.eval.RecommenderIRStatsEvaluator;
import org.apache.mahout.cf.taste.impl.common.FastByIDMap;
import org.apache.mahout.cf.taste.impl.common.FastIDSet;
import org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator;
import org.apache.mahout.cf.taste.impl.model.GenericBooleanPrefDataModel;
import org.apache.mahout.cf.taste.impl.recommender.GenericBooleanPrefItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.PreferenceArray;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;
import org.junit.Assert;
import org.junit.Test;

public class IRStatisticsOnBooleanDataTest {

	private static final String RATINGS_DATA_FILE_RELATIVE_PATH = "ratings.csv";

	private final UnaryOperator<List<Long>> ascendingSorter = longs -> {
		List<Long> sorted = new ArrayList<>(longs);
		Collections.sort(sorted);
		return sorted;
	};

	private final UnaryOperator<List<Long>> descendingSorter = longs -> {
		List<Long> sorted = new ArrayList<>(longs);
		Collections.sort(sorted, Collections.reverseOrder());
		return sorted;
	};

	@Test
	public void testSamePrecisionRecall() throws IOException, URISyntaxException, TasteException {
		Map<Long, List<Long>> data = readData();

		GenericBooleanPrefDataModel dataModelAscending = buildGenericBooleanPrefDataModel(data, ascendingSorter);

		GenericBooleanPrefDataModel dataModelDescending = buildGenericBooleanPrefDataModel(data, descendingSorter);

		int at = 5;
		double evaluationPercentage = 1.0;
		Assert.assertTrue(
				samePrecisionRecall(
						evaluate(dataModelAscending, at, evaluationPercentage),
						evaluate(dataModelDescending, at, evaluationPercentage)));
	}

	private boolean samePrecisionRecall(IRStatistics s1, IRStatistics s2) {
		if (s1 == null && s2 != null) {
			return false;
		}
		if (s1 != null && s2 == null) {
			return false;
		}
		if (s1 == null && s2 == null) {
			return true;
		}
		return s1.getPrecision() == s2.getPrecision() &&
				s1.getRecall() == s2.getRecall();
	}

	private IRStatistics evaluate(DataModel dataModel, int at, double evaluationPercentage) throws TasteException {
		RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();

		RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {

			@Override
			public Recommender buildRecommender(DataModel dataModel) throws TasteException {
				ItemSimilarity itemSimilarity = new TanimotoCoefficientSimilarity(dataModel);
				return new GenericBooleanPrefItemBasedRecommender(dataModel, itemSimilarity);
			}
		};

		DataModelBuilder dataModelBuilder = new DataModelBuilder() {

			@Override
			public DataModel buildDataModel(FastByIDMap<PreferenceArray> trainingData) {
				return new GenericBooleanPrefDataModel(GenericBooleanPrefDataModel.toDataMap(trainingData));
			}
		};

		return evaluator.evaluate(
				recommenderBuilder,
				dataModelBuilder,
				dataModel,
				null, // IDRescorer
				at,
				GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD,
				evaluationPercentage);
	}

	/*
	 * Builds a GenericBooleanPrefDataModel using the data in the given Map<Long, List<Long>>,
	 * where keys are userIds, and values are the itemsIds associated with that userId.
	 * Uses the given UnaryOperator to sort itemIds associated to a userId.
	 */
	private GenericBooleanPrefDataModel buildGenericBooleanPrefDataModel(
			Map<Long, List<Long>> data,
			UnaryOperator<List<Long>> itemsSorter) {
		FastByIDMap<FastIDSet> userData = new FastByIDMap<>(data.size());
		for (Map.Entry<Long, List<Long>> entry : data.entrySet()) {
			long userId = entry.getKey();
			List<Long> itemIds = entry.getValue();
			List<Long> sortedItemIds = itemsSorter.apply(itemIds);
			FastIDSet userItems = new FastIDSet(sortedItemIds.size());
			for (Long itemId : sortedItemIds) {
				userItems.add(itemId);
			}
			userData.put(userId, userItems);
		}
		return new GenericBooleanPrefDataModel(userData);
	}

	/*
	 * Reads the "ratings.csv" file from the MovieLens dataset, and returns a Map<Long, List<Long>>
	 * where keys are userIds, and values are the itemsIds associated with that userId.
	 */
	private Map<Long, List<Long>> readData() throws IOException, URISyntaxException {
		try (
				Stream<String> lines = Files.lines(
						Paths.get(
								this.getClass()
										.getClassLoader()
										.getResource(RATINGS_DATA_FILE_RELATIVE_PATH)
										.toURI()))) {

			Map<Long, List<Long>> data = new HashMap<>();

			lines.skip(1)
					.forEach(line -> {
						String[] tokens = line.split(",");
						long userId = Long.parseLong(tokens[0]);
						long itemId = Long.parseLong(tokens[1]);
						if (!data.containsKey(userId)) {
							data.put(userId, new ArrayList<>());
						}
						data.get(userId).add(itemId);
					});

			return data;
		}
	}

}