org.apache.mahout.classifier.df.split.OptIgSplit Maven / Gradle / Ivy
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* http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.mahout.classifier.df.split;
import org.apache.commons.math3.stat.descriptive.rank.Percentile;
import org.apache.mahout.classifier.df.data.Data;
import org.apache.mahout.classifier.df.data.DataUtils;
import org.apache.mahout.classifier.df.data.Dataset;
import org.apache.mahout.classifier.df.data.Instance;
import java.util.Arrays;
import java.util.Collection;
import java.util.Iterator;
import java.util.TreeSet;
/**
* Optimized implementation of IgSplit.
* This class can be used when the criterion variable is the categorical attribute.
*
* This code was changed in MAHOUT-1419 to deal in sampled splits among numeric
* features to fix a performance problem. To generate some synthetic data that exercises
* the issue, try for example generating 4 features of Normal(0,1) values with a random
* boolean 0/1 categorical feature. In Scala:
*
* {@code
* val r = new scala.util.Random()
* val pw = new java.io.PrintWriter("random.csv")
* (1 to 10000000).foreach(e =>
* pw.println(r.nextDouble() + "," +
* r.nextDouble() + "," +
* r.nextDouble() + "," +
* r.nextDouble() + "," +
* (if (r.nextBoolean()) 1 else 0))
* )
* pw.close()
* }
*/
public class OptIgSplit extends IgSplit {
private static final int MAX_NUMERIC_SPLITS = 16;
@Override
public Split computeSplit(Data data, int attr) {
if (data.getDataset().isNumerical(attr)) {
return numericalSplit(data, attr);
} else {
return categoricalSplit(data, attr);
}
}
/**
* Computes the split for a CATEGORICAL attribute
*/
private static Split categoricalSplit(Data data, int attr) {
double[] values = data.values(attr).clone();
double[] splitPoints = chooseCategoricalSplitPoints(values);
int numLabels = data.getDataset().nblabels();
int[][] counts = new int[splitPoints.length][numLabels];
int[] countAll = new int[numLabels];
computeFrequencies(data, attr, splitPoints, counts, countAll);
int size = data.size();
double hy = entropy(countAll, size); // H(Y)
double hyx = 0.0; // H(Y|X)
double invDataSize = 1.0 / size;
for (int index = 0; index < splitPoints.length; index++) {
size = DataUtils.sum(counts[index]);
hyx += size * invDataSize * entropy(counts[index], size);
}
double ig = hy - hyx;
return new Split(attr, ig);
}
static void computeFrequencies(Data data,
int attr,
double[] splitPoints,
int[][] counts,
int[] countAll) {
Dataset dataset = data.getDataset();
for (int index = 0; index < data.size(); index++) {
Instance instance = data.get(index);
int label = (int) dataset.getLabel(instance);
double value = instance.get(attr);
int split = 0;
while (split < splitPoints.length && value > splitPoints[split]) {
split++;
}
if (split < splitPoints.length) {
counts[split][label]++;
} // Otherwise it's in the last split, which we don't need to count
countAll[label]++;
}
}
/**
* Computes the best split for a NUMERICAL attribute
*/
static Split numericalSplit(Data data, int attr) {
double[] values = data.values(attr).clone();
Arrays.sort(values);
double[] splitPoints = chooseNumericSplitPoints(values);
int numLabels = data.getDataset().nblabels();
int[][] counts = new int[splitPoints.length][numLabels];
int[] countAll = new int[numLabels];
int[] countLess = new int[numLabels];
computeFrequencies(data, attr, splitPoints, counts, countAll);
int size = data.size();
double hy = entropy(countAll, size);
double invDataSize = 1.0 / size;
int best = -1;
double bestIg = -1.0;
// try each possible split value
for (int index = 0; index < splitPoints.length; index++) {
double ig = hy;
DataUtils.add(countLess, counts[index]);
DataUtils.dec(countAll, counts[index]);
// instance with attribute value < values[index]
size = DataUtils.sum(countLess);
ig -= size * invDataSize * entropy(countLess, size);
// instance with attribute value >= values[index]
size = DataUtils.sum(countAll);
ig -= size * invDataSize * entropy(countAll, size);
if (ig > bestIg) {
bestIg = ig;
best = index;
}
}
if (best == -1) {
throw new IllegalStateException("no best split found !");
}
return new Split(attr, bestIg, splitPoints[best]);
}
/**
* @return an array of values to split the numeric feature's values on when
* building candidate splits. When input size is <= MAX_NUMERIC_SPLITS + 1, it will
* return the averages between success values as split points. When larger, it will
* return MAX_NUMERIC_SPLITS approximate percentiles through the data.
*/
private static double[] chooseNumericSplitPoints(double[] values) {
if (values.length <= 1) {
return values;
}
if (values.length <= MAX_NUMERIC_SPLITS + 1) {
double[] splitPoints = new double[values.length - 1];
for (int i = 1; i < values.length; i++) {
splitPoints[i-1] = (values[i] + values[i-1]) / 2.0;
}
return splitPoints;
}
Percentile distribution = new Percentile();
distribution.setData(values);
double[] percentiles = new double[MAX_NUMERIC_SPLITS];
for (int i = 0 ; i < percentiles.length; i++) {
double p = 100.0 * ((i + 1.0) / (MAX_NUMERIC_SPLITS + 1.0));
percentiles[i] = distribution.evaluate(p);
}
return percentiles;
}
private static double[] chooseCategoricalSplitPoints(double[] values) {
// There is no great reason to believe that categorical value order matters,
// but the original code worked this way, and it's not terrible in the absence
// of more sophisticated analysis
Collection uniqueOrderedCategories = new TreeSet();
for (double v : values) {
uniqueOrderedCategories.add(v);
}
double[] uniqueValues = new double[uniqueOrderedCategories.size()];
Iterator it = uniqueOrderedCategories.iterator();
for (int i = 0; i < uniqueValues.length; i++) {
uniqueValues[i] = it.next();
}
return uniqueValues;
}
/**
* Computes the Entropy
*
* @param counts counts[i] = numInstances with label i
* @param dataSize numInstances
*/
private static double entropy(int[] counts, int dataSize) {
if (dataSize == 0) {
return 0.0;
}
double entropy = 0.0;
for (int count : counts) {
if (count > 0) {
double p = count / (double) dataSize;
entropy -= p * Math.log(p);
}
}
return entropy / LOG2;
}
}