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Many standard machine learning methods can be formulated as a convex optimization problem, i.e. the task of finding a minimizer of a convex function $f$ that depends on a variable vector $\wv$ (called weights in the code), which has $d$ entries. Formally, we can write this as the optimization problem $\min_{\wv \in\R^d} \; f(\wv)$, where the objective function is of the form \begin{equation} f(\wv) := \lambda\, R(\wv) + \frac1n \sum_{i=1}^n L(\wv;\x_i,y_i) \label{eq:regPrimal} \ . \end{equation} Here the vectors $\x_i\in\R^d$ are the training data examples, for $1\le i\le n$, and $y_i\in\R$ are their corresponding labels, which we want to predict. We call the method linear if $L(\wv; \x, y)$ can be expressed as a function of $\wv^T x$ and $y$. Several of MLlib's classification and regression algorithms fall into this category, and are discussed here.
The objective function $f$ has two parts: the regularizer that controls the complexity of the model, and the loss that measures the error of the model on the training data. The loss function $L(\wv;.)$ is typically a convex function in $\wv$. The fixed regularization parameter $\lambda \ge 0$ (regParam in the code) defines the trade-off between the two goals of minimizing the loss (i.e., training error) and minimizing model complexity (i.e., to avoid overfitting).
The following table summarizes the loss functions and their gradients or sub-gradients for the methods MLlib supports:
The purpose of the regularizer is to encourage simple models and avoid overfitting. We support the following regularizers in MLlib:
Here $\mathrm{sign}(\wv)$ is the vector consisting of the signs ($\pm1$) of all the entries of $\wv$.
L2-regularized problems are generally easier to solve than L1-regularized due to smoothness. However, L1 regularization can help promote sparsity in weights leading to smaller and more interpretable models, the latter of which can be useful for feature selection. It is not recommended to train models without any regularization, especially when the number of training examples is small.
Binary classification aims to divide items into two categories: positive and negative. MLlib supports two linear methods for binary classification: linear support vector machines (SVMs) and logistic regression. For both methods, MLlib supports L1 and L2 regularized variants. The training data set is represented by an RDD of LabeledPoint in MLlib. Note that, in the mathematical formulation in this guide, a training label $y$ is denoted as either $+1$ (positive) or $-1$ (negative), which is convenient for the formulation. However, the negative label is represented by $0$ in MLlib instead of $-1$, to be consistent with multiclass labeling.
The linear SVM is a standard method for large-scale classification tasks. It is a linear method as described above in equation $\eqref{eq:regPrimal}$, with the loss function in the formulation given by the hinge loss:
\[ L(\wv;\x,y) := \max \{0, 1-y \wv^T \x \}. \] By default, linear SVMs are trained with an L2 regularization. We also support alternative L1 regularization. In this case, the problem becomes a linear program.
The linear SVMs algorithm outputs an SVM model. Given a new data point, denoted by $\x$, the model makes predictions based on the value of $\wv^T \x$. By the default, if $\wv^T \x \geq 0$ then the outcome is positive, and negative otherwise.
Logistic regression is widely used to predict a binary response. It is a linear method as described above in equation $\eqref{eq:regPrimal}$, with the loss function in the formulation given by the logistic loss: \[ L(\wv;\x,y) := \log(1+\exp( -y \wv^T \x)). \]
The logistic regression algorithm outputs a logistic regression model. Given a new data point, denoted by $\x$, the model makes predictions by applying the logistic function \[ \mathrm{f}(z) = \frac{1}{1 + e^{-z}} \] where $z = \wv^T \x$. By default, if $\mathrm{f}(\wv^T x) > 0.5$, the outcome is positive, or negative otherwise, though unlike linear SVMs, the raw output of the logistic regression model, $\mathrm{f}(z)$, has a probabilistic interpretation (i.e., the probability that $\x$ is positive).
MLlib supports common evaluation metrics for binary classification (not available in PySpark). This includes precision, recall, F-measure, receiver operating characteristic (ROC), precision-recall curve, and area under the curves (AUC). AUC is commonly used to compare the performance of various models while precision/recall/F-measure can help determine the appropriate threshold to use for prediction purposes.
{% highlight scala %} import org.apache.spark.SparkContext import org.apache.spark.mllib.classification.SVMWithSGD import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.MLUtils
// Load training data in LIBSVM format. val data = MLUtils.loadLibSVMFile(sc, “data/mllib/sample_libsvm_data.txt”)
// Split data into training (60%) and test (40%). val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L) val training = splits(0).cache() val test = splits(1)
// Run training algorithm to build the model val numIterations = 100 val model = SVMWithSGD.train(training, numIterations)
// Clear the default threshold. model.clearThreshold()
// Compute raw scores on the test set. val scoreAndLabels = test.map { point => val score = model.predict(point.features) (score, point.label) }
// Get evaluation metrics. val metrics = new BinaryClassificationMetrics(scoreAndLabels) val auROC = metrics.areaUnderROC()
println("Area under ROC = " + auROC) {% 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(training) {% endhighlight %}
LogisticRegressionWithSGD can be used in a similar fashion as SVMWithSGD.
{% highlight java %} import java.util.Random;
import scala.Tuple2;
import org.apache.spark.api.java.; import org.apache.spark.api.java.function.Function; import org.apache.spark.mllib.classification.; import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.SparkConf; import org.apache.spark.SparkContext;
public class SVMClassifier { public static void main(String[] args) { SparkConf conf = new SparkConf().setAppName(“SVM Classifier Example”); SparkContext sc = new SparkContext(conf); String path = “data/mllib/sample_libsvm_data.txt”; JavaRDD data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
// Split initial RDD into two... [60% training data, 40% testing data].
JavaRDD<LabeledPoint> training = data.sample(false, 0.6, 11L);
training.cache();
JavaRDD<LabeledPoint> test = data.subtract(training);
// Run training algorithm to build the model.
int numIterations = 100;
final SVMModel model = SVMWithSGD.train(training.rdd(), numIterations);
// Clear the default threshold.
model.clearThreshold();
// Compute raw scores on the test set.
JavaRDD<Tuple2<Object, Object>> scoreAndLabels = test.map(
new Function<LabeledPoint, Tuple2<Object, Object>>() {
public Tuple2<Object, Object> call(LabeledPoint p) {
Double score = model.predict(p.features());
return new Tuple2<Object, Object>(score, p.label());
}
}
);
// Get evaluation metrics.
BinaryClassificationMetrics metrics =
new BinaryClassificationMetrics(JavaRDD.toRDD(scoreAndLabels));
double auROC = metrics.areaUnderROC();
System.out.println("Area under ROC = " + auROC);
} } {% 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 java %} import org.apache.spark.mllib.optimization.L1Updater;
SVMWithSGD svmAlg = new SVMWithSGD(); svmAlg.optimizer() .setNumIterations(200) .setRegParam(0.1) .setUpdater(new L1Updater()); final SVMModel modelL1 = svmAlg.run(training.rdd()); {% endhighlight %}
In order to run the above standalone application, follow the instructions provided in the Standalone Applications section of the Spark quick-start guide. Be sure to also include spark-mllib to your build file as a dependency.
{% highlight python %} from pyspark.mllib.classification import LogisticRegressionWithSGD from pyspark.mllib.regression import LabeledPoint from numpy import array
def parsePoint(line): values = [float(x) for x in line.split(' ')] return LabeledPoint(values[0], values[1:])
data = sc.textFile(“data/mllib/sample_svm_data.txt”) parsedData = data.map(parsePoint)
model = LogisticRegressionWithSGD.train(parsedData)
labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features))) trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count()) print("Training Error = " + str(trainErr)) {% endhighlight %}
Linear least squares is the most common formulation for regression problems. It is a linear method as described above in equation $\eqref{eq:regPrimal}$, with the loss function in the formulation given by the squared loss: \[ L(\wv;\x,y) := \frac{1}{2} (\wv^T \x - y)^2. \]
Various related regression methods are derived by using different types of regularization: ordinary least squares or linear least squares uses no regularization; ridge regression uses L2 regularization; and Lasso uses L1 regularization. For all of these models, the average loss or training error, $\frac{1}{n} \sum_{i=1}^n (\wv^T x_i - y_i)^2$, is known as the mean squared error.
{% highlight scala %} import org.apache.spark.mllib.regression.LinearRegressionWithSGD import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.linalg.Vectors
// Load and parse the data val data = sc.textFile(“data/mllib/ridge-data/lpsa.data”) val parsedData = data.map { line => val parts = line.split(‘,’) LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble))) }
// Building the model val numIterations = 100 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)}.mean() println("training Mean Squared Error = " + MSE) {% endhighlight %}
RidgeRegressionWithSGD and LassoWithSGD can be used in a similar fashion as LinearRegressionWithSGD.
{% highlight java %} import scala.Tuple2;
import org.apache.spark.api.java.*; import org.apache.spark.api.java.function.Function; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.regression.LinearRegressionModel; import org.apache.spark.mllib.regression.LinearRegressionWithSGD; import org.apache.spark.SparkConf;
public class LinearRegression { public static void main(String[] args) { SparkConf conf = new SparkConf().setAppName(“Linear Regression Example”); JavaSparkContext sc = new JavaSparkContext(conf);
// Load and parse the data
String path = "data/mllib/ridge-data/lpsa.data";
JavaRDD<String> data = sc.textFile(path);
JavaRDD<LabeledPoint> parsedData = data.map(
new Function<String, LabeledPoint>() {
public LabeledPoint call(String line) {
String[] parts = line.split(",");
String[] features = parts[1].split(" ");
double[] v = new double[features.length];
for (int i = 0; i < features.length - 1; i++)
v[i] = Double.parseDouble(features[i]);
return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(v));
}
}
);
// Building the model
int numIterations = 100;
final LinearRegressionModel model =
LinearRegressionWithSGD.train(JavaRDD.toRDD(parsedData), numIterations);
// Evaluate model on training examples and compute training error
JavaRDD<Tuple2<Double, Double>> valuesAndPreds = parsedData.map(
new Function<LabeledPoint, Tuple2<Double, Double>>() {
public Tuple2<Double, Double> call(LabeledPoint point) {
double prediction = model.predict(point.features());
return new Tuple2<Double, Double>(prediction, point.label());
}
}
);
JavaRDD<Object> MSE = new JavaDoubleRDD(valuesAndPreds.map(
new Function<Tuple2<Double, Double>, Object>() {
public Object call(Tuple2<Double, Double> pair) {
return Math.pow(pair._1() - pair._2(), 2.0);
}
}
).rdd()).mean();
System.out.println("training Mean Squared Error = " + MSE);
} } {% endhighlight %}
In order to run the above standalone application, follow the instructions provided in the Standalone Applications section of the Spark quick-start guide. Be sure to also include spark-mllib to your build file as a dependency.
{% highlight python %} from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD from numpy import array
def parsePoint(line): values = [float(x) for x in line.replace(‘,’, ' ‘).split(’ ')] return LabeledPoint(values[0], values[1:])
data = sc.textFile(“data/mllib/ridge-data/lpsa.data”) parsedData = data.map(parsePoint)
model = LinearRegressionWithSGD.train(parsedData)
valuesAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features))) MSE = valuesAndPreds.map(lambda (v, p): (v - p)**2).reduce(lambda x, y: x + y) / valuesAndPreds.count() print("Mean Squared Error = " + str(MSE)) {% endhighlight %}
When data arrive in a streaming fashion, it is useful to fit regression models online, updating the parameters of the model as new data arrives. MLlib currently supports streaming linear regression using ordinary least squares. The fitting is similar to that performed offline, except fitting occurs on each batch of data, so that the model continually updates to reflect the data from the stream.
The following example demonstrates how to load training and testing data from two different input streams of text files, parse the streams as labeled points, fit a linear regression model online to the first stream, and make predictions on the second stream.
First, we import the necessary classes for parsing our input data and creating the model.
{% highlight scala %}
import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD
{% endhighlight %}
Then we make input streams for training and testing data. We assume a StreamingContext ssc has already been created, see Spark Streaming Programming Guide for more info. For this example, we use labeled points in training and testing streams, but in practice you will likely want to use unlabeled vectors for test data.
{% highlight scala %}
val trainingData = ssc.textFileStream(‘/training/data/dir’).map(LabeledPoint.parse) val testData = ssc.textFileStream(‘/testing/data/dir’).map(LabeledPoint.parse)
{% endhighlight %}
We create our model by initializing the weights to 0
{% highlight scala %}
val numFeatures = 3 val model = new StreamingLinearRegressionWithSGD() .setInitialWeights(Vectors.zeros(numFeatures))
{% endhighlight %}
Now we register the streams for training and testing and start the job. Printing predictions alongside true labels lets us easily see the result.
{% highlight scala %}
model.trainOn(trainingData) model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print()
ssc.start() ssc.awaitTermination()
{% endhighlight %}
We can now save text files with data to the training or testing folders. Each line should be a data point formatted as (y,[x1,x2,x3]) where y is the label and x1,x2,x3 are the features. Anytime a text file is placed in /training/data/dir the model will update. Anytime a text file is placed in /testing/data/dir you will see predictions. As you feed more data to the training directory, the predictions will get better!
Behind the scene, MLlib implements a simple distributed version of stochastic gradient descent (SGD), building on the underlying gradient descent primitive (as described in the optimization section). All provided algorithms take as input a regularization parameter (regParam) along with various parameters associated with stochastic gradient descent (stepSize, numIterations, miniBatchFraction). For each of them, we support all three possible regularizations (none, L1 or L2).
Algorithms are all implemented in Scala:
Python calls the Scala implementation via PythonMLLibAPI.