Using Mahout For Linear Regression

The goal here is to create the most simple of probability monitoring.

	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));
	}
	}
	}

view raw Mahout LR Example hosted with ❤ by GitHub