Linear Methods - RDD-based API

2020-01-21

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Mathematical formulation

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 spark.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).

Loss functions

The following table summarizes the loss functions and their gradients or sub-gradients for the methods spark.mllib supports:

loss function $L(\wv; \x, y)$ gradient or sub-gradient
hinge loss $\max \{0, 1-y \wv^T \x \}, \quad y \in \{-1, +1\}$ $\begin{cases}-y \cdot \x & \text{if$y \wv^T \x <1$}, \\ 0 & \text{otherwise}.\end{cases}$
logistic loss $\log(1+\exp( -y \wv^T \x)), \quad y \in \{-1, +1\}$ $-y \left(1-\frac1{1+\exp(-y \wv^T \x)} \right) \cdot \x$
squared loss $\frac{1}{2} (\wv^T \x - y)^2, \quad y \in \R$ $(\wv^T \x - y) \cdot \x$

Note that, in the mathematical formulation above, a binary 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 spark.mllib instead of $-1$, to be consistent with multiclass labeling.

Regularizers

The purpose of the regularizer is to encourage simple models and avoid overfitting. We support the following regularizers in spark.mllib:

regularizer $R(\wv)$ gradient or sub-gradient
zero (unregularized) 0 $\0$
L2 $\frac{1}{2}\|\wv\|_2^2$ $\wv$
L1 $\|\wv\|_1$ $\mathrm{sign}(\wv)$
elastic net $\alpha \|\wv\|_1 + (1-\alpha)\frac{1}{2}\|\wv\|_2^2$ $\alpha \mathrm{sign}(\wv) + (1-\alpha) \wv$

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. Elastic net is a combination of L1 and L2 regularization. It is not recommended to train models without any regularization, especially when the number of training examples is small.

Optimization

Under the hood, linear methods use convex optimization methods to optimize the objective functions. spark.mllib uses two methods, SGD and L-BFGS, described in the optimization section. Currently, most algorithm APIs support Stochastic Gradient Descent (SGD), and a few support L-BFGS. Refer to this optimization section for guidelines on choosing between optimization methods.

Classification

Classification aims to divide items into categories. The most common classification type is binary classification, where there are two categories, usually named positive and negative. If there are more than two categories, it is called multiclass classification. spark.mllib supports two linear methods for classification: linear Support Vector Machines (SVMs) and logistic regression. Linear SVMs supports only binary classification, while logistic regression supports both binary and multiclass classification problems. For both methods, spark.mllib supports L1 and L2 regularized variants. The training data set is represented by an RDD of LabeledPoint in MLlib, where labels are class indices starting from zero: $0, 1, 2, \ldots$.

Linear Support Vector Machines (SVMs)

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.

Examples

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. Refer to the [SVMWithSGD Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.SVMWithSGD) and [SVMModel Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.SVMModel) for details on the API. {% include_example scala/org/apache/spark/examples/mllib/SVMWithSGDExample.scala %} 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 spark.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 %}
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. A self-contained application example that is equivalent to the provided example in Scala is given below: Refer to the [SVMWithSGD Java docs](api/java/org/apache/spark/mllib/classification/SVMWithSGD.html) and [SVMModel Java docs](api/java/org/apache/spark/mllib/classification/SVMModel.html) for details on the API. {% include_example java/org/apache/spark/examples/mllib/JavaSVMWithSGDExample.java %} 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 spark.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()); SVMModel modelL1 = svmAlg.run(training.rdd()); {% endhighlight %} In order to run the above application, follow the instructions provided in the [Self-Contained Applications](quick-start.html#self-contained-applications) section of the Spark quick-start guide. Be sure to also include *spark-mllib* to your build file as a dependency.
The following example shows how to load a sample dataset, build SVM model, and make predictions with the resulting model to compute the training error. Refer to the [SVMWithSGD Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.SVMWithSGD) and [SVMModel Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.SVMModel) for more details on the API. {% include_example python/mllib/svm_with_sgd_example.py %}

Logistic regression

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: $L(\wv;\x,y) := \log(1+\exp( -y \wv^T \x)).$

For binary classification problems, the algorithm outputs a binary 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).

Binary logistic regression can be generalized into multinomial logistic regression to train and predict multiclass classification problems. For example, for $K$ possible outcomes, one of the outcomes can be chosen as a "pivot", and the other $K - 1$ outcomes can be separately regressed against the pivot outcome. In spark.mllib, the first class $0$ is chosen as the "pivot" class. See Section 4.4 of The Elements of Statistical Learning for references. Here is a detailed mathematical derivation.

For multiclass classification problems, the algorithm will output a multinomial logistic regression model, which contains $K - 1$ binary logistic regression models regressed against the first class. Given a new data points, $K - 1$ models will be run, and the class with largest probability will be chosen as the predicted class.

We implemented two algorithms to solve logistic regression: mini-batch gradient descent and L-BFGS. We recommend L-BFGS over mini-batch gradient descent for faster convergence.

Examples

The following code illustrates how to load a sample multiclass dataset, split it into train and test, and use [LogisticRegressionWithLBFGS](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS) to fit a logistic regression model. Then the model is evaluated against the test dataset and saved to disk. Refer to the [LogisticRegressionWithLBFGS Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS) and [LogisticRegressionModel Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionModel) for details on the API. {% include_example scala/org/apache/spark/examples/mllib/LogisticRegressionWithLBFGSExample.scala %}
The following code illustrates how to load a sample multiclass dataset, split it into train and test, and use [LogisticRegressionWithLBFGS](api/java/org/apache/spark/mllib/classification/LogisticRegressionWithLBFGS.html) to fit a logistic regression model. Then the model is evaluated against the test dataset and saved to disk. Refer to the [LogisticRegressionWithLBFGS Java docs](api/java/org/apache/spark/mllib/classification/LogisticRegressionWithLBFGS.html) and [LogisticRegressionModel Java docs](api/java/org/apache/spark/mllib/classification/LogisticRegressionModel.html) for details on the API. {% include_example java/org/apache/spark/examples/mllib/JavaLogisticRegressionWithLBFGSExample.java %}
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. Note that the Python API does not yet support multiclass classification and model save/load but will in the future. Refer to the [LogisticRegressionWithLBFGS Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.LogisticRegressionWithLBFGS) and [LogisticRegressionModel Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.LogisticRegressionModel) for more details on the API. {% include_example python/mllib/logistic_regression_with_lbfgs_example.py %}

Regression

Linear least squares, Lasso, and ridge regression

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.

Streaming linear regression

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. spark.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.

Examples

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. Then we make input streams for training and testing data. We assume a StreamingContext ssc has already been created, see [Spark Streaming Programming Guide](streaming-programming-guide.html#initializing) 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. We create our model by initializing the weights to zero and register the streams for training and testing then start the job. Printing predictions alongside true labels lets us easily see the result. Finally, we can 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 args(0) the model will update. Anytime a text file is placed in args(1) you will see predictions. As you feed more data to the training directory, the predictions will get better! Here is a complete example: {% include_example scala/org/apache/spark/examples/mllib/StreamingLinearRegressionExample.scala %}
First, we import the necessary classes for parsing our input data and creating the model. Then we make input streams for training and testing data. We assume a StreamingContext ssc has already been created, see [Spark Streaming Programming Guide](streaming-programming-guide.html#initializing) 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. We create our model by initializing the weights to 0. Now we register the streams for training and testing and start the job. 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 sys.argv the model will update. Anytime a text file is placed in sys.argv you will see predictions. As you feed more data to the training directory, the predictions will get better! Here a complete example: {% include_example python/mllib/streaming_linear_regression_example.py %}

Implementation (developer)

Behind the scene, spark.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).

For Logistic Regression, L-BFGS version is implemented under LogisticRegressionWithLBFGS, and this version supports both binary and multinomial Logistic Regression while SGD version only supports binary Logistic Regression. However, L-BFGS version doesn't support L1 regularization but SGD one supports L1 regularization. When L1 regularization is not required, L-BFGS version is strongly recommended since it converges faster and more accurately compared to SGD by approximating the inverse Hessian matrix using quasi-Newton method.

Algorithms are all implemented in Scala: