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diff --git a/docs/ml-ensembles.md b/docs/ml-ensembles.md new file mode 100644 index 0000000000..9ff50e95fc --- /dev/null +++ b/docs/ml-ensembles.md @@ -0,0 +1,129 @@ +--- +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> diff --git a/docs/ml-guide.md b/docs/ml-guide.md index cac705683c..c5f50ed799 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -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 |