Mahout LR Example

package com.claytantor.mahout.test.app;

import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import org.apache.mahout.classifier.sgd.L1;
import org.apache.mahout.classifier.sgd.OnlineLogisticRegression;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.google.common.collect.Lists;

public class OnlineLROne {

	public static class Point {
		public int x;
		public int y;

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

		@Override
		public boolean equals(Object arg0) {
			Point p = (Point) arg0;
			return ((this.x == p.x) && (this.y == p.y));
		}

		@Override
		public String toString() {
			// TODO Auto-generated method stub
			return this.x + " , " + this.y;
		}
	}

	public static Vector getVector(Point point) {
		Vector v = new DenseVector(3);
		v.set(0, point.x);
		v.set(1, point.y);
		v.set(2, 1);
		return v;
	}

	/**
	 * @param args
	 */
	public static void main(String[] args) {
		
		Logger logger = LoggerFactory.getLogger(ReccomenderOne.class);
	    logger.info("Hello World");

		Map<Point, Integer> points = new HashMap<Point, Integer>();

		points.put(new Point(0, 0), 0);
		points.put(new Point(1, 1), 0);
		points.put(new Point(1, 0), 0);
		points.put(new Point(0, 1), 0);
		points.put(new Point(2, 2), 0);

		points.put(new Point(8, 8), 1);
		points.put(new Point(8, 9), 1);
		points.put(new Point(9, 8), 1);
		points.put(new Point(9, 9), 1);

		OnlineLogisticRegression learningAlgo = new OnlineLogisticRegression(2,
				3, new L1());
		// this is a really big value which will make the model very cautious
		// for lambda = 0.1, the first example below should be about .83 certain
		// for lambda = 0.01, the first example below should be about 0.98
		// certain
		learningAlgo.lambda(0.1);
		learningAlgo.learningRate(4);

		System.out.println("training model  \n");
		final List<Point> keys = Lists.newArrayList(points.keySet());
		// 200 times through the training data is probably over-kill. It doesn't
		// matter
		// for tiny data. The key here is total number of points seen, not
		// number of passes.
		for (int i = 0; i < 200; i++) {
			// randomize training data on each iteration
			Collections.shuffle(keys);
			for (Point point : keys) {
				Vector v = getVector(point);
				learningAlgo.train(points.get(point), v);
			}
		}
		learningAlgo.close();

		// now classify real data
		Vector v = new RandomAccessSparseVector(3);
		v.set(0, 0.5);
		v.set(1, 0.5);
		v.set(2, 1);

		Vector r = learningAlgo.classifyFull(v);
		System.out.println(r);

		System.out.println("ans = ");
		System.out.printf("no of categories = %d\n",
				learningAlgo.numCategories());
		System.out.printf("no of features = %d\n", learningAlgo.numFeatures());
		System.out.printf("Probability of cluster 0 = %.3f\n", r.get(0));
		System.out.printf("Probability of cluster 1 = %.3f\n", r.get(1));

		v.set(0, 4.5);
		v.set(1, 6.5);
		v.set(2, 1);

		r = learningAlgo.classifyFull(v);

		System.out.println("ans = ");
		System.out.printf("no of categories = %d\n",
				learningAlgo.numCategories());
		System.out.printf("no of features = %d\n", learningAlgo.numFeatures());
		System.out.printf("Probability of cluster 0 = %.3f\n", r.get(0));
		System.out.printf("Probability of cluster 1 = %.3f\n", r.get(1));

		// show how the score varies along a line from 0,0 to 1,1
		System.out.printf("\nx\tscore\n");
		for (int i = 0; i < 100; i++) {
			final double x = 0.0 + i / 10.0;
			v.set(0, x);
			v.set(1, x);
			v.set(2, 1);

			r = learningAlgo.classifyFull(v);

			System.out.printf("%.2f\t%.3f\n", x, r.get(1));
		}

	}

}