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Machine Learning Library (MLlib) |
MLlib is a Spark implementation of some common machine learning (ML) functionality, as well associated tests and data generators. MLlib currently supports four common types of machine learning problem settings, namely, binary classification, regression, clustering and collaborative filtering, as well as an underlying gradient descent optimization primitive. This guide will outline the functionality supported in MLlib and also provides an example of invoking MLlib.
MLlib uses the jblas linear algebra library, which itself depends on native Fortran routines. You may need to install the gfortran runtime library if it is not already present on your nodes. MLlib will throw a linking error if it cannot detect these libraries automatically.
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).
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 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("trainError = " + 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 %}
Both of the code snippets above can be executed in spark-shell to generate a
classifier for the provided dataset.
Available algorithms for binary classification:
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:
Clustering is an unsupervised learning problem whereby we aim to group subsets of entities with one another based on some notion of similarity. Clustering is often used for exploratory analysis and/or as a component of a hierarchical supervised learning pipeline (in which distinct classifiers or regression models are trained for each cluster). MLlib supports k-means clustering, arguably the most commonly used clustering approach that clusters the data points into k clusters. The MLlib implementation includes a parallelized variant of the k-means++ method called kmeans||. The implementation in MLlib has the following parameters:
- k is the number of clusters.
- maxIterations is the maximum number of iterations to run.
- initializationMode specifies either random initialization or initialization via k-means||.
- runs is the number of times to run the k-means algorithm (k-means is not guaranteed to find a globally optimal solution, and when run multiple times on a given dataset, the algorithm returns the best clustering result).
- initializiationSteps determines the number of steps in the k-means|| algorithm.
- epsilon determines the distance threshold within which we consider k-means to have converged.
Available algorithms for clustering:
Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user-item association matrix. MLlib currently supports model-based collaborative filtering, in which users and products are described by a small set of latent factors that can be used to predict missing entries. In particular, we implement the alternating least squares (ALS) algorithm to learn these latent factors. The implementation in MLlib has the following parameters:
- numBlocks is the number of blacks used to parallelize computation (set to -1 to auto-configure).
- rank is the number of latent factors in our model.
- iterations is the number of iterations to run.
- lambda specifies the regularization parameter in ALS.
- implicitPrefs specifies whether to use the explicit feedback ALS variant or one adapted for implicit feedback data
- alpha is a parameter applicable to the implicit feedback variant of ALS that governs the baseline confidence in preference observations
The standard approach to matrix factorization based collaborative filtering treats the entries in the user-item matrix as explicit preferences given by the user to the item.
It is common in many real-world use cases to only have access to implicit feedback (e.g. views, clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with such data is taken from Collaborative Filtering for Implicit Feedback Datasets. Essentially instead of trying to model the matrix of ratings directly, this approach treats the data as a combination of binary preferences and confidence values. The ratings are then related to the level of confidence in observed user preferences, rather than explicit ratings given to items. The model then tries to find latent factors that can be used to predict the expected preference of a user for an item.
Available algorithms for collaborative filtering:
Gradient descent (along with stochastic variants thereof) are first-order optimization methods that are well-suited for large-scale and distributed computation. Gradient descent methods aim to find a local minimum of a function by iteratively taking steps in the direction of the negative gradient of the function at the current point, i.e., the current parameter value. Gradient descent is included as a low-level primitive in MLlib, upon which various ML algorithms are developed, and has the following parameters:
- gradient is a class that computes the stochastic gradient of the function being optimized, i.e., with respect to a single training example, at the current parameter value. MLlib includes gradient classes for common loss functions, e.g., hinge, logistic, least-squares. The gradient class takes as input a training example, its label, and the current parameter value.
- updater is a class that updates weights in each iteration of gradient descent. MLlib includes updaters for cases without regularization, as well as L1 and L2 regularizers.
- stepSize is a scalar value denoting the initial step size for gradient descent. All updaters in MLlib use a step size at the t-th step equal to stepSize / sqrt(t).
- numIterations is the number of iterations to run.
- regParam is the regularization parameter when using L1 or L2 regularization.
- miniBatchFraction is the fraction of the data used to compute the gradient at each iteration.
Available algorithms for gradient descent: