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Updated DT examples
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@jkbradley
jkbradley committed Aug 20, 2014
1 parent 57eee9f commit b9bee04
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73 changes: 55 additions & 18 deletions docs/mllib-decision-tree.md
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Original file line number Diff line number Diff line change
Expand Up @@ -162,47 +162,64 @@ val labelAndPreds = data.map { point =>
}
val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / data.count
println("Training Error = " + trainErr)
println("Learned classification tree model:\n" + model)
{% endhighlight %}
</div>

<div data-lang="java">
{% highlight java %}
import java.util.HashMap;
import scala.Tuple2;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.DecisionTree;
import org.apache.spark.mllib.tree.model.DecisionTreeModel;

JavaRDD<LabeledPoint> data = ... // data set

// Train a DecisionTree model.
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.SparkConf;

SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTree");
JavaSparkContext sc = new JavaSparkContext(sparkConf);

String datapath = "data/mllib/sample_libsvm_data.txt";
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD();
// Compute the number of classes from the data.
Integer numClasses = data.map(new Function<LabeledPoint, Double>() {
@Override public Double call(LabeledPoint p) {
return p.label();
}
}).countByValue().size();

// Set parameters.
// Empty categoricalFeaturesInfo indicates all features are continuous.
Integer numClasses = ... // number of classes
HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
String impurity = "gini";
Integer maxDepth = 5;
Integer maxBins = 100;

// Train a DecisionTree model for classification.
final DecisionTreeModel model = DecisionTree.trainClassifier(data, numClasses,
categoricalFeaturesInfo, impurity, maxDepth, maxBins);

// Evaluate model on training instances and compute training error
JavaPairRDD<Double, Double> predictionAndLabel =
JavaPairRDD<Double, Double> predictionAndLabel =
data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
@Override public Tuple2<Double, Double> call(LabeledPoint p) {
return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
}
});
Double trainErr = 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
Double trainErr =
1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
@Override public Boolean call(Tuple2<Double, Double> pl) {
return !pl._1().equals(pl._2());
}
}).count() / data.count();
System.out.print("Training error: " + trainErr);
System.out.print("Learned model:\n" + model);
System.out.println("Training error: " + trainErr);
System.out.println("Learned classification tree model:\n" + model);
{% endhighlight %}
</div>

Expand All @@ -225,6 +242,8 @@ predictions = model.predict(data.map(lambda x: x.features))
labelsAndPredictions = data.map(lambda lp: lp.label).zip(predictions)
trainErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(data.count())
print('Training Error = ' + str(trainErr))
print('Learned classification tree model:')
print(model)
{% endhighlight %}

Note: When making predictions for a dataset, it is more efficient to do batch prediction rather
Expand Down Expand Up @@ -268,47 +287,63 @@ val labelsAndPredictions = data.map { point =>
}
val trainMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean()
println("Training Mean Squared Error = " + trainMSE)
println("Learned regression tree model:\n" + model)
{% endhighlight %}
</div>

<div data-lang="java">
{% highlight java %}
import java.util.HashMap;
import scala.Tuple2;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.mllib.tree.DecisionTree;
import org.apache.spark.mllib.tree.model.DecisionTreeModel;
import scala.Tuple2;
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.SparkConf;

JavaRDD<LabeledPoint> data = ... // data set
String datapath = "data/mllib/sample_libsvm_data.txt";
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD();

// Train a DecisionTree model.
SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTree");
JavaSparkContext sc = new JavaSparkContext(sparkConf);

// Set parameters.
// Empty categoricalFeaturesInfo indicates all features are continuous.
HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
String impurity = "variance";
Integer maxDepth = 5;
Integer maxBins = 100;

// Train a DecisionTree model.
final DecisionTreeModel model = DecisionTree.trainRegressor(data,
categoricalFeaturesInfo, impurity, maxDepth, maxBins);

// Evaluate model on training instances and compute training error
JavaPairRDD<Double, Double> predictionAndLabel =
JavaPairRDD<Double, Double> predictionAndLabel =
data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
@Override public Tuple2<Double, Double> call(LabeledPoint p) {
return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
}
});
Double trainMSE = predictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
Double trainMSE =
predictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
@Override public Double call(Tuple2<Double, Double> pl) {
Double diff = pl._1() - pl._2();
Double diff = pl._1() - pl._2();
return diff * diff;
}
}).sum() / data.count();
System.out.print("Training Mean Squared Error: " + trainMSE);
System.out.print("Learned model:\n" + model);
}).reduce(new Function2<Double, Double, Double>() {
@Override public Double call(Double a, Double b) {
return a + b;
}
}) / data.count();
System.out.println("Training Mean Squared Error: " + trainMSE);
System.out.println("Learned regression tree model:\n" + model);
{% endhighlight %}
</div>

Expand All @@ -331,6 +366,8 @@ predictions = model.predict(data.map(lambda x: x.features))
labelsAndPredictions = data.map(lambda lp: lp.label).zip(predictions)
trainMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum() / float(data.count())
print('Training Mean Squared Error = ' + str(trainMSE))
print('Learned regression tree model:')
print(model)
{% endhighlight %}

Note: When making predictions for a dataset, it is more efficient to do batch prediction rather
Expand Down
25 changes: 13 additions & 12 deletions examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java
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Original file line number Diff line number Diff line change
Expand Up @@ -19,11 +19,10 @@

import java.util.HashMap;

import scala.reflect.ClassTag;
import scala.Tuple2;

import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
Expand All @@ -34,30 +33,33 @@
import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.SparkConf;


/**
* Classification and regression using decision trees.
*/
public final class JavaDecisionTree {

public static void main(String[] args) {
if (args.length != 1) {
String datapath = "data/mllib/sample_libsvm_data.txt";
if (args.length == 1) {
datapath = args[0];
} else if (args.length > 1) {
System.err.println("Usage: JavaDecisionTree <libsvm format data file>");
System.exit(1);
}
SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTree");
JavaSparkContext sc = new JavaSparkContext(sparkConf);
String datapath = args[0];

JavaRDD<LabeledPoint> data = JavaRDD.fromRDD(MLUtils.loadLibSVMFile(sc.sc(), datapath));
JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD();

// Compute the number of classes from the data.
Integer numClasses = data.map(new Function<LabeledPoint, Double>() {
@Override public Double call(LabeledPoint p) {
return p.label();
}
}).countByValue().size();
// Empty categoricalFeaturesInfo indicates all features are continuous.

// Set parameters.
// Empty categoricalFeaturesInfo indicates all features are continuous.
HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
String impurity = "gini";
Integer maxDepth = 5;
Expand All @@ -80,12 +82,11 @@ public static void main(String[] args) {
return !pl._1().equals(pl._2());
}
}).count() / data.count();
System.out.print("Training error: " + trainErr);
System.out.print("Learned classification tree model:\n" + model);
System.out.println("Training error: " + trainErr);
System.out.println("Learned classification tree model:\n" + model);

// Train a DecisionTree model for regression.
impurity = "variance";

final DecisionTreeModel regressionModel = DecisionTree.trainRegressor(data,
categoricalFeaturesInfo, impurity, maxDepth, maxBins);

Expand All @@ -107,8 +108,8 @@ public static void main(String[] args) {
return a + b;
}
}) / data.count();
System.out.print("Training Mean Squared Error: " + trainMSE);
System.out.print("Learned regression tree model:\n" + regressionModel);
System.out.println("Training Mean Squared Error: " + trainMSE);
System.out.println("Learned regression tree model:\n" + regressionModel);

sc.stop();
}
Expand Down

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