spark-instrumented-optimizer/docs/mllib-classification-regression.md

layout	title
global	MLlib - Classification and Regression

• Table of contents {:toc}

Binary Classification

Binary classification is a supervised learning problem in which we want to classify entities into one of two distinct categories or labels, e.g., predicting whether or not emails are spam. This problem involves executing a learning Algorithm on a set of labeled examples, i.e., a set of entities represented via (numerical) features along with underlying category labels. The algorithm returns a trained Model that can predict the label for new entities for which the underlying label is unknown.

MLlib currently supports two standard model families for binary classification, namely Linear Support Vector Machines (SVMs) and Logistic Regression, along with L1 and L2 regularized variants of each model family. The training algorithms all leverage an underlying gradient descent primitive (described below), and take as input a regularization parameter (regParam) along with various parameters associated with gradient descent (stepSize, numIterations, miniBatchFraction).

Available algorithms for binary classification:

• SVMWithSGD
• LogisticRegressionWithSGD

Linear Regression

Linear regression is another classical supervised learning setting. In this problem, each entity is associated with a real-valued label (as opposed to a binary label as in binary classification), and we want to predict labels as closely as possible given numerical features representing entities. MLlib supports linear regression as well as L1 (lasso) and L2 (ridge) regularized variants. The regression algorithms in MLlib also leverage the underlying gradient descent primitive (described below), and have the same parameters as the binary classification algorithms described above.

Available algorithms for linear regression:

• LinearRegressionWithSGD
• RidgeRegressionWithSGD
• LassoWithSGD

Behind the scenes, all above methods use the SGD implementation from the gradient descent primitive in MLlib, see the optimization part:

• GradientDescent

Usage in Scala

Following code snippets can be executed in spark-shell.

Binary Classification

The following code snippet illustrates how to load a sample dataset, execute a training algorithm on this training data using a static method in the algorithm object, and make predictions with the resulting model to compute the training error.

{% highlight scala %} import org.apache.spark.SparkContext import org.apache.spark.mllib.classification.SVMWithSGD import org.apache.spark.mllib.regression.LabeledPoint

// Load and parse the data file val data = sc.textFile("mllib/data/sample_svm_data.txt") val parsedData = data.map { line => val parts = line.split(' ') LabeledPoint(parts(0).toDouble, parts.tail.map(x => x.toDouble).toArray) }

// Run training algorithm to build the model val numIterations = 20 val model = SVMWithSGD.train(parsedData, numIterations)

// Evaluate model on training examples and compute training error val labelAndPreds = parsedData.map { point => val prediction = model.predict(point.features) (point.label, prediction) } val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / parsedData.count println("Training Error = " + trainErr) {% endhighlight %}

The SVMWithSGD.train() method by default performs L2 regularization with the regularization parameter set to 1.0. If we want to configure this algorithm, we can customize SVMWithSGD further by creating a new object directly and calling setter methods. All other MLlib algorithms support customization in this way as well. For example, the following code produces an L1 regularized variant of SVMs with regularization parameter set to 0.1, and runs the training algorithm for 200 iterations.

{% highlight scala %} import org.apache.spark.mllib.optimization.L1Updater

val svmAlg = new SVMWithSGD() svmAlg.optimizer.setNumIterations(200) .setRegParam(0.1) .setUpdater(new L1Updater) val modelL1 = svmAlg.run(parsedData) {% endhighlight %}

Linear Regression

The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint. The example then uses LinearRegressionWithSGD to build a simple linear model to predict label values. We compute the Mean Squared Error at the end to evaluate goodness of fit

{% highlight scala %} import org.apache.spark.mllib.regression.LinearRegressionWithSGD import org.apache.spark.mllib.regression.LabeledPoint

// Load and parse the data val data = sc.textFile("mllib/data/ridge-data/lpsa.data") val parsedData = data.map { line => val parts = line.split(',') LabeledPoint(parts(0).toDouble, parts(1).split(' ').map(x => x.toDouble).toArray) }

// Building the model val numIterations = 20 val model = LinearRegressionWithSGD.train(parsedData, numIterations)

// Evaluate model on training examples and compute training error val valuesAndPreds = parsedData.map { point => val prediction = model.predict(point.features) (point.label, prediction) } val MSE = valuesAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.reduce(_ + _)/valuesAndPreds.count println("training Mean Squared Error = " + MSE) {% endhighlight %}

Similarly you can use RidgeRegressionWithSGD and LassoWithSGD and compare training Mean Squared Errors.

Usage in Java

All of MLlib's methods use Java-friendly types, so you can import and call them there the same way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the Spark Java API uses a separate JavaRDD class. You can convert a Java RDD to a Scala one by calling .rdd() on your JavaRDD object.

Usage in Python

Following examples can be tested in the PySpark shell.

Binary Classification

The following example shows how to load a sample dataset, build Logistic Regression model, and make predictions with the resulting model to compute the training error.

{% highlight python %} from pyspark.mllib.classification import LogisticRegressionWithSGD from numpy import array

Load and parse the data

data = sc.textFile("mllib/data/sample_svm_data.txt") parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')])) model = LogisticRegressionWithSGD.train(parsedData)

Build the model

labelsAndPreds = parsedData.map(lambda point: (int(point.item(0)), model.predict(point.take(range(1, point.size)))))

Evaluating the model on training data

trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count()) print("Training Error = " + str(trainErr)) {% endhighlight %}

Linear Regression

The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint. The example then uses LinearRegressionWithSGD to build a simple linear model to predict label values. We compute the Mean Squared Error at the end to evaluate goodness of fit

{% highlight python %} from pyspark.mllib.regression import LinearRegressionWithSGD from numpy import array

Load and parse the data

data = sc.textFile("mllib/data/ridge-data/lpsa.data") parsedData = data.map(lambda line: array([float(x) for x in line.replace(',', ' ').split(' ')]))

Build the model

model = LinearRegressionWithSGD.train(parsedData)

Evaluate the model on training data

valuesAndPreds = parsedData.map(lambda point: (point.item(0), model.predict(point.take(range(1, point.size))))) MSE = valuesAndPreds.map(lambda (v, p): (v - p)**2).reduce(lambda x, y: x + y)/valuesAndPreds.count() print("Mean Squared Error = " + str(MSE)) {% endhighlight %}