package org.apache.flink.examples.java.clustering;

/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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.
 */

import java.io.Serializable;
import java.util.ArrayList;
import java.util.Collection;
import java.util.List;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.functions.FunctionAnnotation.ForwardedFields;
import org.apache.flink.api.java.operators.IterativeDataSet;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.examples.java.clustering.util.KMeansOutlierDetectionData;;

/**
 * This example implements a basic K-Means clustering algorithm.
 * 
 * <p>
 * K-Means is an iterative clustering algorithm and works as follows:<br>
 * K-Means is given a set of data points to be clustered and an initial set of
 * <i>K</i> cluster centers. In each iteration, the algorithm computes the
 * distance of each data point to each cluster center. Each point is assigned to
 * the cluster center which is closest to it. Subsequently, each cluster center
 * is moved to the center (<i>mean</i>) of all points that have been assigned to
 * it. The moved cluster centers are fed into the next iteration. The algorithm
 * terminates after a fixed number of iterations (as in this implementation) or
 * if cluster centers do not (significantly) move in an iteration.<br>
 * This is the Wikipedia entry for the
 * <a href="http://en.wikipedia.org/wiki/K-means_clustering">K-Means Clustering
 * algorithm</a>.
 * 
 * <p>
 * This implementation works on two-dimensional data points. <br>
 * It computes an assignment of data points to cluster centers, i.e., each data
 * point is annotated with the id of the final cluster (center) it belongs to.
 * 
 * <p>
 * Input files are plain text files and must be formatted as follows:
 * <ul>
 * <li>Data points are represented as two double values separated by a blank
 * character. Data points are separated by newline characters.<br>
 * For example <code>"1.2 2.3\n5.3 7.2\n"</code> gives two data points (x=1.2,
 * y=2.3) and (x=5.3, y=7.2).
 * <li>Cluster centers are represented by an integer id and a point value.<br>
 * For example <code>"1 6.2 3.2\n2 2.9 5.7\n"</code> gives two centers (id=1,
 * x=6.2, y=3.2) and (id=2, x=2.9, y=5.7).
 * </ul>
 * 
 * <p>
 * Usage:
 * <code>KMeans &lt;points path&gt; &lt;centers path&gt; &lt;result path&gt; &lt;num iterations&gt;</code>
 * <br>
 * If no parameters are provided, the program is run with default data from
 * {@link org.apache.flink.examples.java.clustering.util.KMeansData} and 10
 * iterations.
 * 
 * <p>
 * This example shows how to use:
 * <ul>
 * <li>Bulk iterations
 * <li>Broadcast variables in bulk iterations
 * <li>Custom Java objects (PoJos)
 * </ul>
 */
@SuppressWarnings("serial")
public class KMeansOutlierDetection {

	// *************************************************************************
	// PROGRAM
	// *************************************************************************

	public static void main(String[] args) throws Exception {

		if (!parseParameters(args)) {
			return;
		}

		// set up execution environment
		ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

		// get input data
		DataSet<Point> points = getPointDataSet(env);
		DataSet<Centroid> centroids = getCentroidDataSet(env);

		// set number of bulk iterations for KMeans algorithm
		IterativeDataSet<Centroid> loop = centroids.iterate(numIterations);

		DataSet<Centroid> newCentroids = points
				// compute closest centroid for each point
				.map(new SelectNearestCenter()).withBroadcastSet(loop, "centroids")
				// count and sum point coordinates for each centroid
				.map(new CountAppender()).groupBy(0).reduce(new CentroidAccumulator())
				// compute new centroids from point counts and coordinate sums
				.map(new CentroidAverager());

		// feed new centroids back into next iteration
		DataSet<Centroid> finalCentroids = loop.closeWith(newCentroids);

		DataSet<Tuple2<Integer, Point>> clusteredPoints = points
				// assign points to final clusters
				.map(new SelectNearestCenter()).withBroadcastSet(finalCentroids, "centroids");
		DataSet<Tuple3> fElements = env.fromCollection(findOutliers(clusteredPoints, finalCentroids));
		// emit result
		if (fileOutput) {
			fElements.writeAsCsv(outputPath, "\n", " ");
			// clusteredPoints.writeAsCsv(outputPath, "\n", " ");
			// since file sinks are lazy, we trigger the execution explicitly
			env.execute("KMeans Example");
		} else {
			fElements.print();
		}
	}

	@SuppressWarnings("rawtypes")
	public static List<Tuple3> findOutliers(DataSet<Tuple2<Integer, Point>> clusteredPoints,
			DataSet<Centroid> centroids) throws Exception {
		List<Tuple3> finalElements = new ArrayList<Tuple3>();
		List<Tuple2<Integer, Point>> elements = clusteredPoints.collect();
		List<Centroid> centroidList = centroids.collect();
		List<Tuple3<Centroid, Tuple2<Integer, Point>, Double>> elementsWithDistance = new ArrayList<Tuple3<Centroid, Tuple2<Integer, Point>, Double>>();
		for (Centroid centroid : centroidList) {
			elementsWithDistance = new ArrayList<Tuple3<Centroid, Tuple2<Integer, Point>, Double>>();
			double totalDistance = 0;
			int elementsCount = 0;
			for (Tuple2<Integer, Point> e : elements) {
				// compute distance
				if (e.f0 == centroid.id) {

					Tuple3<Centroid, Tuple2<Integer, Point>, Double> newElement = new Tuple3<Centroid, Tuple2<Integer, Point>, Double>();
					double distance = e.f1.euclideanDistance(centroid);
					totalDistance += distance;
					newElement.setFields(centroid, e, distance);
					elementsWithDistance.add(newElement);
					elementsCount++;
				}
			}
			// finding mean
			double mean = totalDistance / elementsCount;
			double sdTotalDistanceSquare = 0;
			for (Tuple3<Centroid, Tuple2<Integer, Point>, Double> elementWithDistance : elementsWithDistance) {
				double distanceSquare = Math.pow(mean - elementWithDistance.f2, 2);
				sdTotalDistanceSquare += distanceSquare;
			}
			double sd = Math.sqrt(sdTotalDistanceSquare / elementsCount);
			double upperlimit = mean + 2 * sd;
			double lowerlimit = mean - 2 * sd;
			Tuple3<Integer, Point, Boolean> newElement = new Tuple3<Integer, Point, Boolean>();// true
			// =
			// outlier
			for (Tuple3<Centroid, Tuple2<Integer, Point>, Double> elementWithDistance : elementsWithDistance) {
				newElement = new Tuple3<Integer, Point, Boolean>();
				if (elementWithDistance.f2 < lowerlimit || elementWithDistance.f2 > upperlimit) {
					// set as outlier
					newElement.setFields(elementWithDistance.f1.f0, elementWithDistance.f1.f1, true);
				} else {
					newElement.setFields(elementWithDistance.f1.f0, elementWithDistance.f1.f1, false);
				}
				finalElements.add(newElement);
			}
		}
		return finalElements;
	}

	// *************************************************************************
	// DATA TYPES
	// *************************************************************************

	/**
	 * A simple two-dimensional point.
	 */
	public static class Point implements Serializable {

		public double x, y;

		public Point() {
		}

		public Point(double x, double y) {
			this.x = x;
			this.y = y;
		}

		public Point add(Point other) {
			x += other.x;
			y += other.y;
			return this;
		}

		public Point div(long val) {
			x /= val;
			y /= val;
			return this;
		}

		public double euclideanDistance(Point other) {
			return Math.sqrt((x - other.x) * (x - other.x) + (y - other.y) * (y - other.y));
		}

		public void clear() {
			x = y = 0.0;
		}

		@Override
		public String toString() {
			return x + " " + y;
		}
	}

	/**
	 * A simple two-dimensional centroid, basically a point with an ID.
	 */
	public static class Centroid extends Point {

		public int id;

		public Centroid() {
		}

		public Centroid(int id, double x, double y) {
			super(x, y);
			this.id = id;
		}

		public Centroid(int id, Point p) {
			super(p.x, p.y);
			this.id = id;
		}

		@Override
		public String toString() {
			return id + " " + super.toString();
		}
	}

	// *************************************************************************
	// USER FUNCTIONS
	// *************************************************************************

	/** Converts a {@code Tuple2<Double,Double>} into a Point. */
	@ForwardedFields("0->x; 1->y")
	public static final class TuplePointConverter implements MapFunction<Tuple2<Double, Double>, Point> {

		@Override
		public Point map(Tuple2<Double, Double> t) throws Exception {
			return new Point(t.f0, t.f1);
		}
	}

	/** Converts a {@code Tuple3<Integer, Double,Double>} into a Centroid. */
	@ForwardedFields("0->id; 1->x; 2->y")
	public static final class TupleCentroidConverter implements MapFunction<Tuple3<Integer, Double, Double>, Centroid> {

		@Override
		public Centroid map(Tuple3<Integer, Double, Double> t) throws Exception {
			return new Centroid(t.f0, t.f1, t.f2);
		}
	}

	/** Determines the closest cluster center for a data point. */
	@ForwardedFields("*->1")
	public static final class SelectNearestCenter extends RichMapFunction<Point, Tuple2<Integer, Point>> {
		private Collection<Centroid> centroids;

		/**
		 * Reads the centroid values from a broadcast variable into a
		 * collection.
		 */
		@Override
		public void open(Configuration parameters) throws Exception {
			this.centroids = getRuntimeContext().getBroadcastVariable("centroids");
		}

		@Override
		public Tuple2<Integer, Point> map(Point p) throws Exception {

			double minDistance = Double.MAX_VALUE;
			int closestCentroidId = -1;

			// check all cluster centers
			for (Centroid centroid : centroids) {
				// compute distance
				double distance = p.euclideanDistance(centroid);

				// update nearest cluster if necessary
				if (distance < minDistance) {
					minDistance = distance;
					closestCentroidId = centroid.id;
				}
			}

			// emit a new record with the center id and the data point.
			return new Tuple2<Integer, Point>(closestCentroidId, p);
		}
	}

	/** Appends a count variable to the tuple. */
	@ForwardedFields("f0;f1")
	public static final class CountAppender
			implements MapFunction<Tuple2<Integer, Point>, Tuple3<Integer, Point, Long>> {

		@Override
		public Tuple3<Integer, Point, Long> map(Tuple2<Integer, Point> t) {
			return new Tuple3<Integer, Point, Long>(t.f0, t.f1, 1L);
		}
	}

	/** Sums and counts point coordinates. */
	@ForwardedFields("0")
	public static final class CentroidAccumulator implements ReduceFunction<Tuple3<Integer, Point, Long>> {

		@Override
		public Tuple3<Integer, Point, Long> reduce(Tuple3<Integer, Point, Long> val1,
				Tuple3<Integer, Point, Long> val2) {
			return new Tuple3<Integer, Point, Long>(val1.f0, val1.f1.add(val2.f1), val1.f2 + val2.f2);
		}
	}

	/** Computes new centroid from coordinate sum and count of points. */
	@ForwardedFields("0->id")
	public static final class CentroidAverager implements MapFunction<Tuple3<Integer, Point, Long>, Centroid> {

		@Override
		public Centroid map(Tuple3<Integer, Point, Long> value) {
			return new Centroid(value.f0, value.f1.div(value.f2));
		}
	}

	// *************************************************************************
	// UTIL METHODS
	// *************************************************************************

	private static boolean fileOutput = false;
	private static String pointsPath = null;
	private static String centersPath = null;
	private static String outputPath = null;
	private static int numIterations = 10;

	private static boolean parseParameters(String[] programArguments) {

		if (programArguments.length > 0) {
			// parse input arguments
			fileOutput = true;
			if (programArguments.length == 4) {
				pointsPath = programArguments[0];
				centersPath = programArguments[1];
				outputPath = programArguments[2];
				numIterations = Integer.parseInt(programArguments[3]);
			} else {
				System.err.println("Usage: KMeans <points path> <centers path> <result path> <num iterations>");
				return false;
			}
		} else {
			System.out.println("Executing K-Means example with default parameters and built-in default data.");
			System.out.println("  Provide parameters to read input data from files.");
			System.out.println("  See the documentation for the correct format of input files.");
			System.out.println("  We provide a data generator to create synthetic input files for this program.");
			System.out.println("  Usage: KMeans <points path> <centers path> <result path> <num iterations>");
		}
		return true;
	}

	private static DataSet<Point> getPointDataSet(ExecutionEnvironment env) {
		if (fileOutput) {
			// read points from CSV file
			return env.readCsvFile(pointsPath).fieldDelimiter(" ").includeFields(true, true)
					.types(Double.class, Double.class).map(new TuplePointConverter());
		} else {
			return KMeansOutlierDetectionData.getDefaultPointDataSet(env);
		}
	}

	private static DataSet<Centroid> getCentroidDataSet(ExecutionEnvironment env) {
		if (fileOutput) {
			return env.readCsvFile(centersPath).fieldDelimiter(" ").includeFields(true, true, true)
					.types(Integer.class, Double.class, Double.class).map(new TupleCentroidConverter());
		} else {
			return KMeansOutlierDetectionData.getDefaultCentroidDataSet(env);
		}
	}
}