cleanup

Author: Ram Sriharsha

docs/ml-ensembles.md

@@ -56,8 +56,11 @@ println(metrics.confusionMatrix)
 // the Iris DataSet has three classes
 val numClasses = 3
 
-val fprs = (0 until numClasses).map(label => label + "\t" + metrics.falsePositiveRate(label.toDouble)).mkString("\n")
-println("label\tfpr\n" + fprs)
+println("label\tfpr\n")
+(0 until numClasses).foreach { index =>
+  val label = index.toDouble
+  println(label + "\t" + metrics.falsePositiveRate(label))
+}
 {% endhighlight %}
 </div>
 <div data-lang="java" markdown="1">
@@ -67,38 +70,37 @@ import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.ml.classification.OneVsRest;
+import org.apache.spark.ml.classification.OneVsRestModel;
 import org.apache.spark.mllib.evaluation.MulticlassMetrics;
 import org.apache.spark.mllib.linalg.Matrix;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
-import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SQLContext;
 
 SparkConf conf = new SparkConf().setAppName("JavaOneVsRestExample");
 JavaSparkContext jsc = new JavaSparkContext(conf);
 SQLContext jsql = new SQLContext(jsc);
-    
+
 RDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(),
   "data/mllib/sample_multiclass_classification_data.txt");
 
-RDD<LabeledPoint>[] split = data.randomSplit(new double[]{0.7, 0.3}, 12345);
-RDD<LabeledPoint> train = split[0];
-RDD<LabeledPoint> test = split[1];
+DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+DataFrame[] splits = dataFrame.randomSplit(new double[]{0.7, 0.3}, 12345);
+DataFrame train = splits[0];
+DataFrame test = splits[1];
 
 // instantiate the One Vs Rest Classifier
 OneVsRest ovr = new OneVsRest().setClassifier(new LogisticRegression());
-    
+
 // train the multiclass model
-DataFrame trainingDataFrame = jsql.createDataFrame(train, LabeledPoint.class);    
-OneVsRestModel ovrModel = ovr.fit(trainingDataFrame.cache());
+OneVsRestModel ovrModel = ovr.fit(train.cache());
 
 // score the model on test data
-DataFrame testDataFrame = jsql.createDataFrame(test, LabeledPoint.class);
 DataFrame predictions = ovrModel
-    .transform(testDataFrame.cache())
-    .select("prediction", "label");
+  .transform(test)
+  .select("prediction", "label");
 
 // obtain metrics
 MulticlassMetrics metrics = new MulticlassMetrics(predictions);
@@ -109,20 +111,19 @@ System.out.println("Confusion Matrix");
 System.out.println(confusionMatrix);
 
 // compute the false positive rate per label
-StringBuilder results = new StringBuilder();
-results.append("label\tfpr\n");
+System.out.println();
+System.out.println("label\tfpr\n");
 
 // the Iris DataSet has three classes
 int numClasses = 3;
-
-for (int label = 0; label < numClasses; label++) {
-  results.append(label);
-  results.append("\t");
-  results.append(metrics.falsePositiveRate((double) label));
-  results.append("\n");
+for (int index = 0; index < numClasses; index++) {
+  double label = (double) index;
+  System.out.print(label);
+  System.out.print("\t");
+  System.out.print(metrics.falsePositiveRate(label));
+  System.out.println();
 }
-System.out.println();
-System.out.println(results);
+
 {% endhighlight %}
 </div>
 </div>