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[SPARK-7574] [ML] [DOC] User guide for OneVsRest
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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 <[email protected]>

Closes apache#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
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Ram Sriharsha authored and nemccarthy committed Jun 19, 2015
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150 changes: 150 additions & 0 deletions data/mllib/sample_multiclass_classification_data.txt
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129 changes: 129 additions & 0 deletions 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>
3 changes: 2 additions & 1 deletion docs/ml-guide.md
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Expand Up @@ -150,11 +150,12 @@ This is useful if there are two algorithms with the `maxIter` parameter in a `Pi

# Algorithm Guides

There are now several algorithms in the Pipelines API which are not in the lower-level MLlib API, so we link to documentation for them here. These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines.
There are now several algorithms in the Pipelines API which are not in the lower-level MLlib API, so we link to documentation for them here. These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines, and ensembles, which fit naturally into the `Estimator` abstraction in the Pipelines.

**Pipelines API Algorithm Guides**

* [Feature Extraction, Transformation, and Selection](ml-features.html)
* [Ensembles](ml-ensembles.html)


# Code Examples
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