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| <h1 class="title">Machine Learning Library (MLlib)</h1> |
| |
| <p>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.</p> |
| |
| <h1 id="dependencies">Dependencies</h1> |
| <p>MLlib uses the <a href="https://github.com/mikiobraun/jblas">jblas</a> linear algebra library, which itself |
| depends on native Fortran routines. You may need to install the |
| <a href="https://github.com/mikiobraun/jblas/wiki/Missing-Libraries">gfortran runtime library</a> |
| if it is not already present on your nodes. MLlib will throw a linking error if it cannot |
| detect these libraries automatically.</p> |
| |
| <h1 id="binary-classification">Binary Classification</h1> |
| |
| <p>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 <em>Algorithm</em> on a set of <em>labeled</em> examples, i.e., a set of entities |
| represented via (numerical) features along with underlying category labels. |
| The algorithm returns a trained <em>Model</em> that can predict the label for new |
| entities for which the underlying label is unknown. </p> |
| |
| <p>MLlib currently supports two standard model families for binary classification, |
| namely <a href="http://en.wikipedia.org/wiki/Support_vector_machine">Linear Support Vector Machines |
| (SVMs)</a> and <a href="http://en.wikipedia.org/wiki/Logistic_regression">Logistic |
| Regression</a>, along with <a href="http://en.wikipedia.org/wiki/Regularization_(mathematics)">L1 |
| and L2 regularized</a> |
| variants of each model family. The training algorithms all leverage an |
| underlying gradient descent primitive (described |
| <a href="#gradient-descent-primitive">below</a>), and take as input a regularization |
| parameter (<em>regParam</em>) along with various parameters associated with gradient |
| descent (<em>stepSize</em>, <em>numIterations</em>, <em>miniBatchFraction</em>). </p> |
| |
| <p>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.</p> |
| |
| <div class="highlight"><pre><code class="scala"><span class="k">import</span> <span class="nn">org.apache.spark.SparkContext</span> |
| <span class="k">import</span> <span class="nn">org.apache.spark.mllib.classification.SVMWithSGD</span> |
| <span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span> |
| |
| <span class="c1">// Load and parse the data file</span> |
| <span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="o">(</span><span class="s">"mllib/data/sample_svm_data.txt"</span><span class="o">)</span> |
| <span class="k">val</span> <span class="n">parsedData</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">line</span> <span class="k">=></span> |
| <span class="k">val</span> <span class="n">parts</span> <span class="k">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="sc">' '</span><span class="o">)</span> |
| <span class="nc">LabeledPoint</span><span class="o">(</span><span class="n">parts</span><span class="o">(</span><span class="mi">0</span><span class="o">).</span><span class="n">toDouble</span><span class="o">,</span> <span class="n">parts</span><span class="o">.</span><span class="n">tail</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">x</span> <span class="k">=></span> <span class="n">x</span><span class="o">.</span><span class="n">toDouble</span><span class="o">).</span><span class="n">toArray</span><span class="o">)</span> |
| <span class="o">}</span> |
| |
| <span class="c1">// Run training algorithm</span> |
| <span class="k">val</span> <span class="n">numIterations</span> <span class="k">=</span> <span class="mi">20</span> |
| <span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="nc">SVMWithSGD</span><span class="o">.</span><span class="n">train</span><span class="o">(</span><span class="n">parsedData</span><span class="o">,</span> <span class="n">numIterations</span><span class="o">)</span> |
| |
| <span class="c1">// Evaluate model on training examples and compute training error</span> |
| <span class="k">val</span> <span class="n">labelAndPreds</span> <span class="k">=</span> <span class="n">parsedData</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">point</span> <span class="k">=></span> |
| <span class="k">val</span> <span class="n">prediction</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="o">(</span><span class="n">point</span><span class="o">.</span><span class="n">features</span><span class="o">)</span> |
| <span class="o">(</span><span class="n">point</span><span class="o">.</span><span class="n">label</span><span class="o">,</span> <span class="n">prediction</span><span class="o">)</span> |
| <span class="o">}</span> |
| <span class="k">val</span> <span class="n">trainErr</span> <span class="k">=</span> <span class="n">labelAndPreds</span><span class="o">.</span><span class="n">filter</span><span class="o">(</span><span class="n">r</span> <span class="k">=></span> <span class="n">r</span><span class="o">.</span><span class="n">_1</span> <span class="o">!=</span> <span class="n">r</span><span class="o">.</span><span class="n">_2</span><span class="o">).</span><span class="n">count</span><span class="o">.</span><span class="n">toDouble</span> <span class="o">/</span> <span class="n">parsedData</span><span class="o">.</span><span class="n">count</span> |
| <span class="n">println</span><span class="o">(</span><span class="s">"trainError = "</span> <span class="o">+</span> <span class="n">trainErr</span><span class="o">)</span> |
| </code></pre></div> |
| |
| <p>The <code>SVMWithSGD.train()</code> method by default performs L2 regularization with the |
| regularization parameter set to 1.0. If we want to configure this algorithm, we |
| can customize <code>SVMWithSGD</code> 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. </p> |
| |
| <div class="highlight"><pre><code class="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.optimization.L1Updater</span> |
| |
| <span class="k">val</span> <span class="n">svmAlg</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SVMWithSGD</span><span class="o">()</span> |
| <span class="n">svmAlg</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">setNumIterations</span><span class="o">(</span><span class="mi">200</span><span class="o">)</span> |
| <span class="o">.</span><span class="n">setRegParam</span><span class="o">(</span><span class="mf">0.1</span><span class="o">)</span> |
| <span class="o">.</span><span class="n">setUpdater</span><span class="o">(</span><span class="k">new</span> <span class="n">L1Updater</span><span class="o">)</span> |
| <span class="k">val</span> <span class="n">modelL1</span> <span class="k">=</span> <span class="n">svmAlg</span><span class="o">.</span><span class="n">run</span><span class="o">(</span><span class="n">parsedData</span><span class="o">)</span> |
| </code></pre></div> |
| |
| <p>Both of the code snippets above can be executed in <code>spark-shell</code> to generate a |
| classifier for the provided dataset.</p> |
| |
| <p>Available algorithms for binary classification:</p> |
| |
| <ul> |
| <li><a href="api/mllib/index.html#org.apache.spark.mllib.classification.SVMWithSGD">SVMWithSGD</a></li> |
| <li><a href="api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD">LogisticRegressionWithSGD</a></li> |
| </ul> |
| |
| <h1 id="linear-regression">Linear Regression</h1> |
| |
| <p>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 |
| (<a href="http://en.wikipedia.org/wiki/Lasso_(statistics)#Lasso_method">lasso</a>) and L2 |
| (<a href="http://en.wikipedia.org/wiki/Ridge_regression">ridge</a>) regularized variants. |
| The regression algorithms in MLlib also leverage the underlying gradient |
| descent primitive (described <a href="#gradient-descent-primitive">below</a>), and have |
| the same parameters as the binary classification algorithms described above. </p> |
| |
| <p>Available algorithms for linear regression: </p> |
| |
| <ul> |
| <li><a href="api/mllib/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD">LinearRegressionWithSGD</a></li> |
| <li><a href="api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD">RidgeRegressionWithSGD</a></li> |
| <li><a href="api/mllib/index.html#org.apache.spark.mllib.regression.LassoWithSGD">LassoWithSGD</a></li> |
| </ul> |
| |
| <h1 id="clustering">Clustering</h1> |
| |
| <p>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 |
| <a href="http://en.wikipedia.org/wiki/K-means_clustering">k-means</a> clustering, arguably |
| the most commonly used clustering approach that clusters the data points into |
| <em>k</em> clusters. The MLlib implementation includes a parallelized |
| variant of the <a href="http://en.wikipedia.org/wiki/K-means%2B%2B">k-means++</a> method |
| called <a href="http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf">kmeans||</a>. |
| The implementation in MLlib has the following parameters: </p> |
| |
| <ul> |
| <li><em>k</em> is the number of clusters.</li> |
| <li><em>maxIterations</em> is the maximum number of iterations to run.</li> |
| <li><em>initializationMode</em> specifies either random initialization or |
| initialization via k-means||.</li> |
| <li><em>runs</em> 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).</li> |
| <li><em>initializiationSteps</em> determines the number of steps in the k-means|| algorithm.</li> |
| <li><em>epsilon</em> determines the distance threshold within which we consider k-means to have converged. </li> |
| </ul> |
| |
| <p>Available algorithms for clustering: </p> |
| |
| <ul> |
| <li><a href="api/mllib/index.html#org.apache.spark.mllib.clustering.KMeans">KMeans</a></li> |
| </ul> |
| |
| <h1 id="collaborative-filtering">Collaborative Filtering</h1> |
| |
| <p><a href="http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering">Collaborative filtering</a> |
| 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 <a href="http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf">alternating least squares |
| (ALS)</a> |
| algorithm to learn these latent factors. The implementation in MLlib has the |
| following parameters:</p> |
| |
| <ul> |
| <li><em>numBlocks</em> is the number of blacks used to parallelize computation (set to -1 to auto-configure). </li> |
| <li><em>rank</em> is the number of latent factors in our model.</li> |
| <li><em>iterations</em> is the number of iterations to run.</li> |
| <li><em>lambda</em> specifies the regularization parameter in ALS.</li> |
| <li><em>implicitPrefs</em> specifies whether to use the <em>explicit feedback</em> ALS variant or one adapted for <em>implicit feedback</em> data</li> |
| <li><em>alpha</em> is a parameter applicable to the implicit feedback variant of ALS that governs the <em>baseline</em> confidence in preference observations</li> |
| </ul> |
| |
| <h2 id="explicit-vs-implicit-feedback">Explicit vs Implicit Feedback</h2> |
| |
| <p>The standard approach to matrix factorization based collaborative filtering treats |
| the entries in the user-item matrix as <em>explicit</em> preferences given by the user to the item.</p> |
| |
| <p>It is common in many real-world use cases to only have access to <em>implicit feedback</em> |
| (e.g. views, clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with |
| such data is taken from |
| <a href="http://research.yahoo.com/pub/2433">Collaborative Filtering for Implicit Feedback Datasets</a>. |
| Essentially instead of trying to model the matrix of ratings directly, this approach treats the data as |
| a combination of binary preferences and <em>confidence values</em>. 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. </p> |
| |
| <p>Available algorithms for collaborative filtering: </p> |
| |
| <ul> |
| <li><a href="api/mllib/index.html#org.apache.spark.mllib.recommendation.ALS">ALS</a></li> |
| </ul> |
| |
| <h1 id="gradient-descent-primitive">Gradient Descent Primitive</h1> |
| |
| <p><a href="http://en.wikipedia.org/wiki/Gradient_descent">Gradient descent</a> (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:</p> |
| |
| <ul> |
| <li><em>gradient</em> 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. </li> |
| <li><em>updater</em> 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.</li> |
| <li><em>stepSize</em> 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). </li> |
| <li><em>numIterations</em> is the number of iterations to run.</li> |
| <li><em>regParam</em> is the regularization parameter when using L1 or L2 regularization.</li> |
| <li><em>miniBatchFraction</em> is the fraction of the data used to compute the gradient |
| at each iteration.</li> |
| </ul> |
| |
| <p>Available algorithms for gradient descent:</p> |
| |
| <ul> |
| <li><a href="api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent">GradientDescent</a></li> |
| </ul> |
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