[SPARK-7574] [ML] [DOC] User guide for OneVsRest

Including Iris Dataset (after shuffling and relabeling 3 -> 0 to confirm to 0 -> numClasses-1 labeling). Could not find an existing dataset in data/mllib for multiclass classification.

Author: Ram Sriharsha <rsriharsha@hw11853.local>

Closes #6296 from harsha2010/SPARK-7574 and squashes the following commits:

645427c [Ram Sriharsha] cleanup
46c41b1 [Ram Sriharsha] cleanup
2f76295 [Ram Sriharsha] Code Review Fixes
ebdf103 [Ram Sriharsha] Java Example
c026613 [Ram Sriharsha] Code Review fixes
4b7d1a6 [Ram Sriharsha] minor cleanup
13bed9c [Ram Sriharsha] add wikipedia link
bb9dbfa [Ram Sriharsha] Clean up naming
6f90db1 [Ram Sriharsha] [SPARK-7574][ml][doc] User guide for OneVsRest

1 files changed, 129 insertions, 0 deletions

diff --git a/docs/ml-ensembles.md b/docs/ml-ensembles.md
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+---
+layout: global
+title: Ensembles
+displayTitle: <a href="ml-guide.html">ML</a> - Ensembles
+---
+
+**Table of Contents**
+
+* This will become a table of contents (this text will be scraped).
+{:toc}
+
+An [ensemble method](http://en.wikipedia.org/wiki/Ensemble_learning)
+is a learning algorithm which creates a model composed of a set of other base models.
+The Pipelines API supports the following ensemble algorithms: [`OneVsRest`](api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest)
+
+## OneVsRest
+
+[OneVsRest](http://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest) is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently.
+
+`OneVsRest` is implemented as an `Estimator`. For the base classifier it takes instances of `Classifier` and creates a binary classification problem for each of the k classes. The classifier for class i is trained to predict whether the label is i or not, distinguishing class i from all other classes.
+
+Predictions are done by evaluating each binary classifier and the index of the most confident classifier is output as label.
+
+### Example
+
+The example below demonstrates how to load the
+[Iris dataset](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/iris.scale), parse it as a DataFrame and perform multiclass classification using `OneVsRest`. The test error is calculated to measure the algorithm accuracy.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest}
+import org.apache.spark.mllib.evaluation.MulticlassMetrics
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.sql.{Row, SQLContext}
+
+val sqlContext = new SQLContext(sc)
+
+// parse data into dataframe
+val data = MLUtils.loadLibSVMFile(sc, 
+  "data/mllib/sample_multiclass_classification_data.txt")
+val Array(train, test) = data.toDF().randomSplit(Array(0.7, 0.3))
+
+// instantiate multiclass learner and train
+val ovr = new OneVsRest().setClassifier(new LogisticRegression)
+
+val ovrModel = ovr.fit(train)
+
+// score model on test data
+val predictions = ovrModel.transform(test).select("prediction", "label")
+val predictionsAndLabels = predictions.map {case Row(p: Double, l: Double) => (p, l)}
+
+// compute confusion matrix
+val metrics = new MulticlassMetrics(predictionsAndLabels)
+println(metrics.confusionMatrix)
+
+// the Iris DataSet has three classes
+val numClasses = 3
+
+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">
+{% highlight java %}
+
+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.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");
+
+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
+OneVsRestModel ovrModel = ovr.fit(train.cache());
+
+// score the model on test data
+DataFrame predictions = ovrModel
+  .transform(test)
+  .select("prediction", "label");
+
+// obtain metrics
+MulticlassMetrics metrics = new MulticlassMetrics(predictions);
+Matrix confusionMatrix = metrics.confusionMatrix();
+
+// output the Confusion Matrix
+System.out.println("Confusion Matrix");
+System.out.println(confusionMatrix);
+
+// compute the false positive rate per label
+System.out.println();
+System.out.println("label\tfpr\n");
+
+// the Iris DataSet has three classes
+int numClasses = 3;
+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();
+}
+{% endhighlight %}
+</div>
+</div>