Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/2063#discussion_r16503947
--- Diff: docs/mllib-decision-tree.md ---
@@ -114,35 +135,122 @@ perform classification using a decision tree using
Gini impurity as an impurity
maximum tree depth of 5. The training error is calculated to measure the
algorithm accuracy.
<div class="codetabs">
+
<div data-lang="scala">
{% highlight scala %}
-import org.apache.spark.SparkContext
import org.apache.spark.mllib.tree.DecisionTree
-import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.tree.configuration.Algo._
-import org.apache.spark.mllib.tree.impurity.Gini
+import org.apache.spark.mllib.util.MLUtils
// Load and parse the data file
-val data = sc.textFile("data/mllib/sample_tree_data.csv")
-val parsedData = data.map { line =>
- val parts = line.split(',').map(_.toDouble)
- LabeledPoint(parts(0), Vectors.dense(parts.tail))
-}
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-// Run training algorithm to build the model
+// Train a DecisionTree model.
+// Empty categoricalFeaturesInfo indicates all features are continuous.
+val numClasses = 2
+val categoricalFeaturesInfo = Map[Int, Int]()
+val impurity = "gini"
val maxDepth = 5
-val model = DecisionTree.train(parsedData, Classification, Gini, maxDepth)
+val maxBins = 100
+
+val model = DecisionTree.trainClassifier(data, numClasses,
categoricalFeaturesInfo, impurity,
+ maxDepth, maxBins)
-// Evaluate model on training examples and compute training error
-val labelAndPreds = parsedData.map { point =>
+// Evaluate model on training instances and compute training error
+val labelAndPreds = data.map { point =>
val prediction = model.predict(point.features)
(point.label, prediction)
}
-val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble /
parsedData.count
+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;
+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();
--- End diff --
We cached the binned features in training. But in this example, we visit
the raw features twice. Since it is reading from disk, it should help if we
cache the data.
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