diff options
| author | Patrick Wendell <pwendell@apache.org> | 2014-02-03 06:29:51 +0000 |
|---|---|---|
| committer | Patrick Wendell <pwendell@apache.org> | 2014-02-03 06:29:51 +0000 |
| commit | 61203830f49cf7f5ce82f70ead2a155e256c02ee (patch) | |
| tree | 67d83dfa860156196303eaa1f4873e2fefffe904 /site/docs/0.9.0/mllib-guide.html | |
| parent | 49ab3963af9b1d79c73f749ad200f7be80785f9f (diff) | |
| download | spark-website-61203830f49cf7f5ce82f70ead2a155e256c02ee.tar.gz spark-website-61203830f49cf7f5ce82f70ead2a155e256c02ee.tar.bz2 spark-website-61203830f49cf7f5ce82f70ead2a155e256c02ee.zip | |
Bumping version in docs.
Diffstat (limited to 'site/docs/0.9.0/mllib-guide.html')
| -rw-r--r-- | site/docs/0.9.0/mllib-guide.html | 659 |
1 files changed, 659 insertions, 0 deletions
diff --git a/site/docs/0.9.0/mllib-guide.html b/site/docs/0.9.0/mllib-guide.html new file mode 100644 index 000000000..42c4b390f --- /dev/null +++ b/site/docs/0.9.0/mllib-guide.html @@ -0,0 +1,659 @@ +<!DOCTYPE html> +<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]--> +<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]--> +<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]--> +<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]--> + <head> + <meta charset="utf-8"> + <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> + <title>Machine Learning Library (MLlib) - Spark 0.9.0 Documentation</title> + <meta name="description" content=""> + + <link rel="stylesheet" href="css/bootstrap.min.css"> + <style> + body { + padding-top: 60px; + padding-bottom: 40px; + } + </style> + <meta name="viewport" content="width=device-width"> + <link rel="stylesheet" href="css/bootstrap-responsive.min.css"> + <link rel="stylesheet" href="css/main.css"> + + <script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script> + + <link rel="stylesheet" href="css/pygments-default.css"> + + <!-- Google analytics script --> + <script type="text/javascript"> + /* + var _gaq = _gaq || []; + _gaq.push(['_setAccount', 'UA-32518208-1']); + _gaq.push(['_trackPageview']); + + (function() { + var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; + ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; + var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); + })(); + */ + </script> + + </head> + <body> + <!--[if lt IE 7]> + <p class="chromeframe">You are using an outdated browser. <a href="http://browsehappy.com/">Upgrade your browser today</a> or <a href="http://www.google.com/chromeframe/?redirect=true">install Google Chrome Frame</a> to better experience this site.</p> + <![endif]--> + + <!-- This code is taken from http://twitter.github.com/bootstrap/examples/hero.html --> + + <div class="navbar navbar-fixed-top" id="topbar"> + <div class="navbar-inner"> + <div class="container"> + <div class="brand"><a href="index.html"> + <img src="img/spark-logo-hd.png" style="height:50px;"/></a><span class="version">0.9.0</span> + </div> + <ul class="nav"> + <!--TODO(andyk): Add class="active" attribute to li some how.--> + <li><a href="index.html">Overview</a></li> + + <li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown">Programming Guides<b class="caret"></b></a> + <ul class="dropdown-menu"> + <li><a href="quick-start.html">Quick Start</a></li> + <li><a href="scala-programming-guide.html">Spark in Scala</a></li> + <li><a href="java-programming-guide.html">Spark in Java</a></li> + <li><a href="python-programming-guide.html">Spark in Python</a></li> + <li class="divider"></li> + <li><a href="streaming-programming-guide.html">Spark Streaming</a></li> + <li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li> + <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li> + <li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</a></li> + </ul> + </li> + + <li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown">API Docs<b class="caret"></b></a> + <ul class="dropdown-menu"> + <li><a href="api/core/index.html#org.apache.spark.package">Spark Core for Java/Scala</a></li> + <li><a href="api/pyspark/index.html">Spark Core for Python</a></li> + <li class="divider"></li> + <li><a href="api/streaming/index.html#org.apache.spark.streaming.package">Spark Streaming</a></li> + <li><a href="api/mllib/index.html#org.apache.spark.mllib.package">MLlib (Machine Learning)</a></li> + <li><a href="api/bagel/index.html#org.apache.spark.bagel.package">Bagel (Pregel on Spark)</a></li> + <li><a href="api/graphx/index.html#org.apache.spark.graphx.package">GraphX (Graph Processing)</a></li> + <li class="divider"></li> + <li class="dropdown-submenu"> + <a tabindex="-1" href="#">External Data Sources</a> + <ul class="dropdown-menu"> + <li><a href="api/external/kafka/index.html#org.apache.spark.streaming.kafka.KafkaUtils$">Kafka</a></li> + <li><a href="api/external/flume/index.html#org.apache.spark.streaming.flume.FlumeUtils$">Flume</a></li> + <li><a href="api/external/twitter/index.html#org.apache.spark.streaming.twitter.TwitterUtils$">Twitter</a></li> + <li><a href="api/external/zeromq/index.html#org.apache.spark.streaming.zeromq.ZeroMQUtils$">ZeroMQ</a></li> + <li><a href="api/external/mqtt/index.html#org.apache.spark.streaming.mqtt.MQTTUtils$">MQTT</a></li> + </ul> + </li> + </ul> + </li> + + <li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown">Deploying<b class="caret"></b></a> + <ul class="dropdown-menu"> + <li><a href="cluster-overview.html">Overview</a></li> + <li><a href="ec2-scripts.html">Amazon EC2</a></li> + <li><a href="spark-standalone.html">Standalone Mode</a></li> + <li><a href="running-on-mesos.html">Mesos</a></li> + <li><a href="running-on-yarn.html">YARN</a></li> + </ul> + </li> + + <li class="dropdown"> + <a href="api.html" class="dropdown-toggle" data-toggle="dropdown">More<b class="caret"></b></a> + <ul class="dropdown-menu"> + <li><a href="configuration.html">Configuration</a></li> + <li><a href="monitoring.html">Monitoring</a></li> + <li><a href="tuning.html">Tuning Guide</a></li> + <li><a href="hadoop-third-party-distributions.html">Running with CDH/HDP</a></li> + <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li> + <li><a href="job-scheduling.html">Job Scheduling</a></li> + <li class="divider"></li> + <li><a href="building-with-maven.html">Building Spark with Maven</a></li> + <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li> + </ul> + </li> + </ul> + <!--<p class="navbar-text pull-right"><span class="version-text">v0.9.0</span></p>--> + </div> + </div> + </div> + + <div class="container" id="content"> + <h1 class="title">Machine Learning Library (MLlib)</h1> + + <ul id="markdown-toc"> + <li><a href="#dependencies">Dependencies</a></li> + <li><a href="#binary-classification">Binary Classification</a></li> + <li><a href="#linear-regression">Linear Regression</a></li> + <li><a href="#clustering">Clustering</a></li> + <li><a href="#collaborative-filtering">Collaborative Filtering</a> <ul> + <li><a href="#explicit-vs-implicit-feedback">Explicit vs Implicit Feedback</a></li> + </ul> + </li> + <li><a href="#gradient-descent-primitive">Gradient Descent Primitive</a></li> + <li><a href="#using-mllib-in-scala">Using MLLib in Scala</a> <ul> + <li><a href="#binary-classification-1">Binary Classification</a></li> + <li><a href="#linear-regression-1">Linear Regression</a></li> + <li><a href="#clustering-1">Clustering</a></li> + <li><a href="#collaborative-filtering-1">Collaborative Filtering</a></li> + </ul> + </li> + <li><a href="#using-mllib-in-java">Using MLLib in Java</a></li> + <li><a href="#using-mllib-in-python">Using MLLib in Python</a> <ul> + <li><a href="#binary-classification-2">Binary Classification</a></li> + <li><a href="#linear-regression-2">Linear Regression</a></li> + <li><a href="#clustering-2">Clustering</a></li> + <li><a href="#collaborative-filtering-2">Collaborative Filtering</a></li> + </ul> + </li> +</ul> + +<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> + +<p>To use MLlib in Python, you will need <a href="http://www.numpy.org">NumPy</a> version 1.7 or newer +and Python 2.7.</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>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, one of +the most commonly used clustering algorithms that clusters the data points into +predfined number of 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 desired 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://www2.research.att.com/~yifanhu/PUB/cf.pdf">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> + +<h1 id="using-mllib-in-scala">Using MLLib in Scala</h1> + +<p>Following code snippets can be executed in <code>spark-shell</code>.</p> + +<h2 id="binary-classification-1">Binary Classification</h2> + +<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 to build the model</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">"Training Error = "</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> + +<h2 id="linear-regression-1">Linear Regression</h2> +<p>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 +<a href="http://en.wikipedia.org/wiki/Goodness_of_fit">goodness of fit</a></p> + +<div class="highlight"><pre><code class="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.LinearRegressionWithSGD</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span> + +<span class="c1">// Load and parse the data</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/ridge-data/lpsa.data"</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="mi">1</span><span class="o">).</span><span class="n">split</span><span class="o">(</span><span class="sc">' '</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">// Building the model</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">LinearRegressionWithSGD</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">valuesAndPreds</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="nc">MSE</span> <span class="k">=</span> <span class="n">valuesAndPreds</span><span class="o">.</span><span class="n">map</span><span class="o">{</span> <span class="k">case</span><span class="o">(</span><span class="n">v</span><span class="o">,</span> <span class="n">p</span><span class="o">)</span> <span class="k">=></span> <span class="n">math</span><span class="o">.</span><span class="n">pow</span><span class="o">((</span><span class="n">v</span> <span class="o">-</span> <span class="n">p</span><span class="o">),</span> <span class="mi">2</span><span class="o">)}.</span><span class="n">reduce</span><span class="o">(</span><span class="k">_</span> <span class="o">+</span> <span class="k">_</span><span class="o">)/</span><span class="n">valuesAndPreds</span><span class="o">.</span><span class="n">count</span> +<span class="n">println</span><span class="o">(</span><span class="s">"training Mean Squared Error = "</span> <span class="o">+</span> <span class="nc">MSE</span><span class="o">)</span> +</code></pre></div> + +<p>Similarly you can use RidgeRegressionWithSGD and LassoWithSGD and compare training +<a href="http://en.wikipedia.org/wiki/Mean_squared_error">Mean Squared Errors</a>.</p> + +<h2 id="clustering-1">Clustering</h2> +<p>In the following example after loading and parsing data, we use the KMeans object to cluster the data +into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within +Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing <em>k</em>. In fact the +optimal <em>k</em> is usually one where there is an “elbow” in the WSSSE graph.</p> + +<div class="highlight"><pre><code class="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.clustering.KMeans</span> + +<span class="c1">// Load and parse the data</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">"kmeans_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="k">_</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="sc">' '</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">toDouble</span><span class="o">))</span> + +<span class="c1">// Cluster the data into two classes using KMeans</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">numClusters</span> <span class="k">=</span> <span class="mi">2</span> +<span class="k">val</span> <span class="n">clusters</span> <span class="k">=</span> <span class="nc">KMeans</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">numClusters</span><span class="o">,</span> <span class="n">numIterations</span><span class="o">)</span> + +<span class="c1">// Evaluate clustering by computing Within Set Sum of Squared Errors</span> +<span class="k">val</span> <span class="nc">WSSSE</span> <span class="k">=</span> <span class="n">clusters</span><span class="o">.</span><span class="n">computeCost</span><span class="o">(</span><span class="n">parsedData</span><span class="o">)</span> +<span class="n">println</span><span class="o">(</span><span class="s">"Within Set Sum of Squared Errors = "</span> <span class="o">+</span> <span class="nc">WSSSE</span><span class="o">)</span> +</code></pre></div> + +<h2 id="collaborative-filtering-1">Collaborative Filtering</h2> +<p>In the following example we load rating data. Each row consists of a user, a product and a rating. +We use the default ALS.train() method which assumes ratings are explicit. We evaluate the recommendation +model by measuring the Mean Squared Error of rating prediction.</p> + +<div class="highlight"><pre><code class="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.recommendation.ALS</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.recommendation.Rating</span> + +<span class="c1">// Load and parse the data</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/als/test.data"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">ratings</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="k">_</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="sc">','</span><span class="o">)</span> <span class="k">match</span> <span class="o">{</span> + <span class="k">case</span> <span class="nc">Array</span><span class="o">(</span><span class="n">user</span><span class="o">,</span> <span class="n">item</span><span class="o">,</span> <span class="n">rate</span><span class="o">)</span> <span class="k">=></span> <span class="nc">Rating</span><span class="o">(</span><span class="n">user</span><span class="o">.</span><span class="n">toInt</span><span class="o">,</span> <span class="n">item</span><span class="o">.</span><span class="n">toInt</span><span class="o">,</span> <span class="n">rate</span><span class="o">.</span><span class="n">toDouble</span><span class="o">)</span> +<span class="o">})</span> + +<span class="c1">// Build the recommendation model using ALS</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">ALS</span><span class="o">.</span><span class="n">train</span><span class="o">(</span><span class="n">ratings</span><span class="o">,</span> <span class="mi">1</span><span class="o">,</span> <span class="mi">20</span><span class="o">,</span> <span class="mf">0.01</span><span class="o">)</span> + +<span class="c1">// Evaluate the model on rating data</span> +<span class="k">val</span> <span class="n">usersProducts</span> <span class="k">=</span> <span class="n">ratings</span><span class="o">.</span><span class="n">map</span><span class="o">{</span> <span class="k">case</span> <span class="nc">Rating</span><span class="o">(</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">,</span> <span class="n">rate</span><span class="o">)</span> <span class="k">=></span> <span class="o">(</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">)}</span> +<span class="k">val</span> <span class="n">predictions</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">usersProducts</span><span class="o">).</span><span class="n">map</span><span class="o">{</span> + <span class="k">case</span> <span class="nc">Rating</span><span class="o">(</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">,</span> <span class="n">rate</span><span class="o">)</span> <span class="k">=></span> <span class="o">((</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">),</span> <span class="n">rate</span><span class="o">)</span> +<span class="o">}</span> +<span class="k">val</span> <span class="n">ratesAndPreds</span> <span class="k">=</span> <span class="n">ratings</span><span class="o">.</span><span class="n">map</span><span class="o">{</span> + <span class="k">case</span> <span class="nc">Rating</span><span class="o">(</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">,</span> <span class="n">rate</span><span class="o">)</span> <span class="k">=></span> <span class="o">((</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">),</span> <span class="n">rate</span><span class="o">)</span> +<span class="o">}.</span><span class="n">join</span><span class="o">(</span><span class="n">predictions</span><span class="o">)</span> +<span class="k">val</span> <span class="nc">MSE</span> <span class="k">=</span> <span class="n">ratesAndPreds</span><span class="o">.</span><span class="n">map</span><span class="o">{</span> + <span class="k">case</span> <span class="o">((</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">),</span> <span class="o">(</span><span class="n">r1</span><span class="o">,</span> <span class="n">r2</span><span class="o">))</span> <span class="k">=></span> <span class="n">math</span><span class="o">.</span><span class="n">pow</span><span class="o">((</span><span class="n">r1</span><span class="o">-</span> <span class="n">r2</span><span class="o">),</span> <span class="mi">2</span><span class="o">)</span> +<span class="o">}.</span><span class="n">reduce</span><span class="o">(</span><span class="k">_</span> <span class="o">+</span> <span class="k">_</span><span class="o">)/</span><span class="n">ratesAndPreds</span><span class="o">.</span><span class="n">count</span> +<span class="n">println</span><span class="o">(</span><span class="s">"Mean Squared Error = "</span> <span class="o">+</span> <span class="nc">MSE</span><span class="o">)</span> +</code></pre></div> + +<p>If the rating matrix is derived from other source of information (i.e., it is inferred from +other signals), you can use the trainImplicit method to get better results.</p> + +<div class="highlight"><pre><code class="scala"><span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="nc">ALS</span><span class="o">.</span><span class="n">trainImplicit</span><span class="o">(</span><span class="n">ratings</span><span class="o">,</span> <span class="mi">1</span><span class="o">,</span> <span class="mi">20</span><span class="o">,</span> <span class="mf">0.01</span><span class="o">)</span> +</code></pre></div> + +<h1 id="using-mllib-in-java">Using MLLib in Java</h1> + +<p>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 <code>JavaRDD</code> class. You can convert a Java RDD to a Scala one by +calling <code>.rdd()</code> on your <code>JavaRDD</code> object.</p> + +<h1 id="using-mllib-in-python">Using MLLib in Python</h1> +<p>Following examples can be tested in the PySpark shell.</p> + +<h2 id="binary-classification-2">Binary Classification</h2> +<p>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.</p> + +<div class="highlight"><pre><code class="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.classification</span> <span class="kn">import</span> <span class="n">LogisticRegressionWithSGD</span> +<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">array</span> + +<span class="c"># Load and parse the data</span> +<span class="n">data</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="s">"mllib/data/sample_svm_data.txt"</span><span class="p">)</span> +<span class="n">parsedData</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">line</span><span class="p">:</span> <span class="n">array</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">' '</span><span class="p">)]))</span> +<span class="n">model</span> <span class="o">=</span> <span class="n">LogisticRegressionWithSGD</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">parsedData</span><span class="p">)</span> + +<span class="c"># Build the model</span> +<span class="n">labelsAndPreds</span> <span class="o">=</span> <span class="n">parsedData</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">point</span><span class="p">:</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">item</span><span class="p">(</span><span class="mi">0</span><span class="p">)),</span> + <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">point</span><span class="o">.</span><span class="n">size</span><span class="p">)))))</span> + +<span class="c"># Evaluating the model on training data</span> +<span class="n">trainErr</span> <span class="o">=</span> <span class="n">labelsAndPreds</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span> <span class="n">v</span> <span class="o">!=</span> <span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">parsedData</span><span class="o">.</span><span class="n">count</span><span class="p">())</span> +<span class="k">print</span><span class="p">(</span><span class="s">"Training Error = "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">trainErr</span><span class="p">))</span> +</code></pre></div> + +<h2 id="linear-regression-2">Linear Regression</h2> +<p>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 +<a href="http://en.wikipedia.org/wiki/Goodness_of_fit">goodness of fit</a></p> + +<div class="highlight"><pre><code class="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.regression</span> <span class="kn">import</span> <span class="n">LinearRegressionWithSGD</span> +<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">array</span> + +<span class="c"># Load and parse the data</span> +<span class="n">data</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="s">"mllib/data/ridge-data/lpsa.data"</span><span class="p">)</span> +<span class="n">parsedData</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">line</span><span class="p">:</span> <span class="n">array</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">line</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">','</span><span class="p">,</span> <span class="s">' '</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">' '</span><span class="p">)]))</span> + +<span class="c"># Build the model</span> +<span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegressionWithSGD</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">parsedData</span><span class="p">)</span> + +<span class="c"># Evaluate the model on training data</span> +<span class="n">valuesAndPreds</span> <span class="o">=</span> <span class="n">parsedData</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">point</span><span class="p">:</span> <span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">item</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> + <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">point</span><span class="o">.</span><span class="n">size</span><span class="p">)))))</span> +<span class="n">MSE</span> <span class="o">=</span> <span class="n">valuesAndPreds</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span> <span class="p">(</span><span class="n">v</span> <span class="o">-</span> <span class="n">p</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span><span class="o">/</span><span class="n">valuesAndPreds</span><span class="o">.</span><span class="n">count</span><span class="p">()</span> +<span class="k">print</span><span class="p">(</span><span class="s">"Mean Squared Error = "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">MSE</span><span class="p">))</span> +</code></pre></div> + +<h2 id="clustering-2">Clustering</h2> +<p>In the following example after loading and parsing data, we use the KMeans object to cluster the data +into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within +Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing <em>k</em>. In fact the +optimal <em>k</em> is usually one where there is an “elbow” in the WSSSE graph.</p> + +<div class="highlight"><pre><code class="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.clustering</span> <span class="kn">import</span> <span class="n">KMeans</span> +<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">array</span> +<span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">sqrt</span> + +<span class="c"># Load and parse the data</span> +<span class="n">data</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="s">"kmeans_data.txt"</span><span class="p">)</span> +<span class="n">parsedData</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">line</span><span class="p">:</span> <span class="n">array</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">' '</span><span class="p">)]))</span> + +<span class="c"># Build the model (cluster the data)</span> +<span class="n">clusters</span> <span class="o">=</span> <span class="n">KMeans</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">parsedData</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">maxIterations</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> + <span class="n">runs</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">initialization_mode</span><span class="o">=</span><span class="s">"random"</span><span class="p">)</span> + +<span class="c"># Evaluate clustering by computing Within Set Sum of Squared Errors</span> +<span class="k">def</span> <span class="nf">error</span><span class="p">(</span><span class="n">point</span><span class="p">):</span> + <span class="n">center</span> <span class="o">=</span> <span class="n">clusters</span><span class="o">.</span><span class="n">centers</span><span class="p">[</span><span class="n">clusters</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">point</span><span class="p">)]</span> + <span class="k">return</span> <span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">([</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">(</span><span class="n">point</span> <span class="o">-</span> <span class="n">center</span><span class="p">)]))</span> + +<span class="n">WSSSE</span> <span class="o">=</span> <span class="n">parsedData</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">point</span><span class="p">:</span> <span class="n">error</span><span class="p">(</span><span class="n">point</span><span class="p">))</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span> +<span class="k">print</span><span class="p">(</span><span class="s">"Within Set Sum of Squared Error = "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">WSSSE</span><span class="p">))</span> +</code></pre></div> + +<p>Similarly you can use RidgeRegressionWithSGD and LassoWithSGD and compare training Mean Squared +Errors.</p> + +<h2 id="collaborative-filtering-2">Collaborative Filtering</h2> +<p>In the following example we load rating data. Each row consists of a user, a product and a rating. +We use the default ALS.train() method which assumes ratings are explicit. We evaluate the +recommendation by measuring the Mean Squared Error of rating prediction.</p> + +<div class="highlight"><pre><code class="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.recommendation</span> <span class="kn">import</span> <span class="n">ALS</span> +<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">array</span> + +<span class="c"># Load and parse the data</span> +<span class="n">data</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="s">"mllib/data/als/test.data"</span><span class="p">)</span> +<span class="n">ratings</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">line</span><span class="p">:</span> <span class="n">array</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">','</span><span class="p">)]))</span> + +<span class="c"># Build the recommendation model using Alternating Least Squares</span> +<span class="n">model</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">ratings</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span> + +<span class="c"># Evaluate the model on training data</span> +<span class="n">testdata</span> <span class="o">=</span> <span class="n">ratings</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span> +<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predictAll</span><span class="p">(</span><span class="n">testdata</span><span class="p">)</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="p">((</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">r</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span> +<span class="n">ratesAndPreds</span> <span class="o">=</span> <span class="n">ratings</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="p">((</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">r</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span> +<span class="n">MSE</span> <span class="o">=</span> <span class="n">ratesAndPreds</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span><span class="o">/</span><span class="n">ratesAndPreds</span><span class="o">.</span><span class="n">count</span><span class="p">()</span> +<span class="k">print</span><span class="p">(</span><span class="s">"Mean Squared Error = "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">MSE</span><span class="p">))</span> +</code></pre></div> + +<p>If the rating matrix is derived from other source of information (i.e., it is inferred from other +signals), you can use the trainImplicit method to get better results.</p> + +<div class="highlight"><pre><code class="python"><span class="c"># Build the recommendation model using Alternating Least Squares based on implicit ratings</span> +<span class="n">model</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">trainImplicit</span><span class="p">(</span><span class="n">ratings</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span> +</code></pre></div> + + + <!-- Main hero unit for a primary marketing message or call to action --> + <!--<div class="hero-unit"> + <h1>Hello, world!</h1> + <p>This is a template for a simple marketing or informational website. It includes a large callout called the hero unit and three supporting pieces of content. Use it as a starting point to create something more unique.</p> + <p><a class="btn btn-primary btn-large">Learn more »</a></p> + </div>--> + + <!-- Example row of columns --> + <!--<div class="row"> + <div class="span4"> + <h2>Heading</h2> + <p>Donec id elit non mi porta gravida at eget metus. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet risus. Etiam porta sem malesuada magna mollis euismod. Donec sed odio dui. </p> + <p><a class="btn" href="#">View details »</a></p> + </div> + <div class="span4"> + <h2>Heading</h2> + <p>Donec id elit non mi porta gravida at eget metus. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet risus. Etiam porta sem malesuada magna mollis euismod. Donec sed odio dui. </p> + <p><a class="btn" href="#">View details »</a></p> + </div> + <div class="span4"> + <h2>Heading</h2> + <p>Donec sed odio dui. Cras justo odio, dapibus ac facilisis in, egestas eget quam. Vestibulum id ligula porta felis euismod semper. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet risus.</p> + <p><a class="btn" href="#">View details »</a></p> + </div> + </div> + + <hr>--> + + <footer> + <hr> + <p style="text-align: center; veritcal-align: middle; color: #999;"> + Apache Spark is an effort undergoing incubation at the Apache Software Foundation. + <a href="http://incubator.apache.org"> + <img style="margin-left: 20px;" src="img/incubator-logo.png" /> + </a> + </p> + </footer> + + </div> <!-- /container --> + + <script src="js/vendor/jquery-1.8.0.min.js"></script> + <script src="js/vendor/bootstrap.min.js"></script> + <script src="js/main.js"></script> + + <!-- A script to fix internal hash links because we have an overlapping top bar. + Based on https://github.com/twitter/bootstrap/issues/193#issuecomment-2281510 --> + <script> + $(function() { + function maybeScrollToHash() { + if (window.location.hash && $(window.location.hash).length) { + var newTop = $(window.location.hash).offset().top - $('#topbar').height() - 5; + $(window).scrollTop(newTop); + } + } + $(window).bind('hashchange', function() { + maybeScrollToHash(); + }); + // Scroll now too in case we had opened the page on a hash, but wait 1 ms because some browsers + // will try to do *their* initial scroll after running the onReady handler. + setTimeout(function() { maybeScrollToHash(); }, 1) + }) + </script> + + </body> +</html> |