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| author | Patrick Wendell <pwendell@apache.org> | 2015-07-15 04:17:01 +0000 |
|---|---|---|
| committer | Patrick Wendell <pwendell@apache.org> | 2015-07-15 04:17:01 +0000 |
| commit | 8c2ee8d4aa3eb7fdc66536019d86793e1db2b135 (patch) | |
| tree | 07beeb27472248193736185dfa7567fc078426fa /site/docs/1.4.1/mllib-guide.html | |
| parent | 7378439da1198903bbb855218de3d050c611a057 (diff) | |
| download | spark-website-8c2ee8d4aa3eb7fdc66536019d86793e1db2b135.tar.gz spark-website-8c2ee8d4aa3eb7fdc66536019d86793e1db2b135.tar.bz2 spark-website-8c2ee8d4aa3eb7fdc66536019d86793e1db2b135.zip | |
Spark 1.4.1 docs
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| -rw-r--r-- | site/docs/1.4.1/mllib-guide.html | 313 |
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diff --git a/site/docs/1.4.1/mllib-guide.html b/site/docs/1.4.1/mllib-guide.html new file mode 100644 index 000000000..884963a4c --- /dev/null +++ b/site/docs/1.4.1/mllib-guide.html @@ -0,0 +1,313 @@ +<!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>MLlib - Spark 1.4.1 Documentation</title> + + <meta name="description" content="MLlib machine learning library overview for Spark 1.4.1"> + + + + + <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-32518208-2']); + _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">1.4.1</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="programming-guide.html">Spark Programming Guide</a></li> + <li class="divider"></li> + <li><a href="streaming-programming-guide.html">Spark Streaming</a></li> + <li><a href="sql-programming-guide.html">DataFrames and SQL</a></li> + <li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li> + <li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</a></li> + <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li> + <li><a href="sparkr.html">SparkR (R on Spark)</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/scala/index.html#org.apache.spark.package">Scala</a></li> + <li><a href="api/java/index.html">Java</a></li> + <li><a href="api/python/index.html">Python</a></li> + <li><a href="api/R/index.html">R</a></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="submitting-applications.html">Submitting Applications</a></li> + <li class="divider"></li> + <li><a href="spark-standalone.html">Spark Standalone</a></li> + <li><a href="running-on-mesos.html">Mesos</a></li> + <li><a href="running-on-yarn.html">YARN</a></li> + <li class="divider"></li> + <li><a href="ec2-scripts.html">Amazon EC2</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="job-scheduling.html">Job Scheduling</a></li> + <li><a href="security.html">Security</a></li> + <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li> + <li><a href="hadoop-third-party-distributions.html">3<sup>rd</sup>-Party Hadoop Distros</a></li> + <li class="divider"></li> + <li><a href="building-spark.html">Building Spark</a></li> + <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li> + <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Supplemental+Spark+Projects">Supplemental Projects</a></li> + </ul> + </li> + </ul> + <!--<p class="navbar-text pull-right"><span class="version-text">v1.4.1</span></p>--> + </div> + </div> + </div> + + <div class="container" id="content"> + + <h1 class="title">Machine Learning Library (MLlib) Guide</h1> + + + <p>MLlib is Spark’s scalable machine learning library consisting of common learning algorithms and utilities, +including classification, regression, clustering, collaborative +filtering, dimensionality reduction, as well as underlying optimization primitives. +Guides for individual algorithms are listed below.</p> + +<p>The API is divided into 2 parts:</p> + +<ul> + <li><a href="mllib-guide.html#mllib-types-algorithms-and-utilities">The original <code>spark.mllib</code> API</a> is the primary API.</li> + <li><a href="mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines">The “Pipelines” <code>spark.ml</code> API</a> is a higher-level API for constructing ML workflows.</li> +</ul> + +<p>We list major functionality from both below, with links to detailed guides.</p> + +<h1 id="mllib-types-algorithms-and-utilities">MLlib types, algorithms and utilities</h1> + +<p>This lists functionality included in <code>spark.mllib</code>, the main MLlib API.</p> + +<ul> + <li><a href="mllib-data-types.html">Data types</a></li> + <li><a href="mllib-statistics.html">Basic statistics</a> + <ul> + <li>summary statistics</li> + <li>correlations</li> + <li>stratified sampling</li> + <li>hypothesis testing</li> + <li>random data generation</li> + </ul> + </li> + <li><a href="mllib-classification-regression.html">Classification and regression</a> + <ul> + <li><a href="mllib-linear-methods.html">linear models (SVMs, logistic regression, linear regression)</a></li> + <li><a href="mllib-naive-bayes.html">naive Bayes</a></li> + <li><a href="mllib-decision-tree.html">decision trees</a></li> + <li><a href="mllib-ensembles.html">ensembles of trees</a> (Random Forests and Gradient-Boosted Trees)</li> + <li><a href="mllib-isotonic-regression.html">isotonic regression</a></li> + </ul> + </li> + <li><a href="mllib-collaborative-filtering.html">Collaborative filtering</a> + <ul> + <li>alternating least squares (ALS)</li> + </ul> + </li> + <li><a href="mllib-clustering.html">Clustering</a> + <ul> + <li><a href="mllib-clustering.html#k-means">k-means</a></li> + <li><a href="mllib-clustering.html#gaussian-mixture">Gaussian mixture</a></li> + <li><a href="mllib-clustering.html#power-iteration-clustering-pic">power iteration clustering (PIC)</a></li> + <li><a href="mllib-clustering.html#latent-dirichlet-allocation-lda">latent Dirichlet allocation (LDA)</a></li> + <li><a href="mllib-clustering.html#streaming-k-means">streaming k-means</a></li> + </ul> + </li> + <li><a href="mllib-dimensionality-reduction.html">Dimensionality reduction</a> + <ul> + <li>singular value decomposition (SVD)</li> + <li>principal component analysis (PCA)</li> + </ul> + </li> + <li><a href="mllib-feature-extraction.html">Feature extraction and transformation</a></li> + <li><a href="mllib-frequent-pattern-mining.html">Frequent pattern mining</a> + <ul> + <li>FP-growth</li> + </ul> + </li> + <li><a href="mllib-optimization.html">Optimization (developer)</a> + <ul> + <li>stochastic gradient descent</li> + <li>limited-memory BFGS (L-BFGS)</li> + </ul> + </li> + <li><a href="mllib-pmml-model-export.html">PMML model export</a></li> +</ul> + +<p>MLlib is under active development. +The APIs marked <code>Experimental</code>/<code>DeveloperApi</code> may change in future releases, +and the migration guide below will explain all changes between releases.</p> + +<h1 id="sparkml-high-level-apis-for-ml-pipelines">spark.ml: high-level APIs for ML pipelines</h1> + +<p>Spark 1.2 introduced a new package called <code>spark.ml</code>, which aims to provide a uniform set of +high-level APIs that help users create and tune practical machine learning pipelines.</p> + +<p><em>Graduated from Alpha!</em> The Pipelines API is no longer an alpha component, although many elements of it are still <code>Experimental</code> or <code>DeveloperApi</code>.</p> + +<p>Note that we will keep supporting and adding features to <code>spark.mllib</code> along with the +development of <code>spark.ml</code>. +Users should be comfortable using <code>spark.mllib</code> features and expect more features coming. +Developers should contribute new algorithms to <code>spark.mllib</code> and can optionally contribute +to <code>spark.ml</code>.</p> + +<p>More detailed guides for <code>spark.ml</code> include:</p> + +<ul> + <li><strong><a href="ml-guide.html">spark.ml programming guide</a></strong>: overview of the Pipelines API and major concepts</li> + <li><a href="ml-features.html">Feature transformers</a>: Details on transformers supported in the Pipelines API, including a few not in the lower-level <code>spark.mllib</code> API</li> + <li><a href="ml-ensembles.html">Ensembles</a>: Details on ensemble learning methods in the Pipelines API</li> +</ul> + +<h1 id="dependencies">Dependencies</h1> + +<p>MLlib uses the linear algebra package +<a href="http://www.scalanlp.org/">Breeze</a>, which depends on +<a href="https://github.com/fommil/netlib-java">netlib-java</a> for optimised +numerical processing. If natives are not available at runtime, you +will see a warning message and a pure JVM implementation will be used +instead.</p> + +<p>To learn more about the benefits and background of system optimised +natives, you may wish to watch Sam Halliday’s ScalaX talk on +<a href="http://fommil.github.io/scalax14/#/">High Performance Linear Algebra in Scala</a>).</p> + +<p>Due to licensing issues with runtime proprietary binaries, we do not +include <code>netlib-java</code>’s native proxies by default. To configure +<code>netlib-java</code> / Breeze to use system optimised binaries, include +<code>com.github.fommil.netlib:all:1.1.2</code> (or build Spark with +<code>-Pnetlib-lgpl</code>) as a dependency of your project and read the +<a href="https://github.com/fommil/netlib-java">netlib-java</a> documentation for +your platform’s additional installation instructions.</p> + +<p>To use MLlib in Python, you will need <a href="http://www.numpy.org">NumPy</a> +version 1.4 or newer.</p> + +<hr /> + +<h1 id="migration-guide">Migration Guide</h1> + +<p>For the <code>spark.ml</code> package, please see the <a href="ml-guide.html#migration-guide">spark.ml Migration Guide</a>.</p> + +<h2 id="from-13-to-14">From 1.3 to 1.4</h2> + +<p>In the <code>spark.mllib</code> package, there were several breaking changes, but all in <code>DeveloperApi</code> or <code>Experimental</code> APIs:</p> + +<ul> + <li>Gradient-Boosted Trees + <ul> + <li><em>(Breaking change)</em> The signature of the <a href="api/scala/index.html#org.apache.spark.mllib.tree.loss.Loss"><code>Loss.gradient</code></a> method was changed. This is only an issues for users who wrote their own losses for GBTs.</li> + <li><em>(Breaking change)</em> The <code>apply</code> and <code>copy</code> methods for the case class <a href="api/scala/index.html#org.apache.spark.mllib.tree.configuration.BoostingStrategy"><code>BoostingStrategy</code></a> have been changed because of a modification to the case class fields. This could be an issue for users who use <code>BoostingStrategy</code> to set GBT parameters.</li> + </ul> + </li> + <li><em>(Breaking change)</em> The return value of <a href="api/scala/index.html#org.apache.spark.mllib.clustering.LDA"><code>LDA.run</code></a> has changed. It now returns an abstract class <code>LDAModel</code> instead of the concrete class <code>DistributedLDAModel</code>. The object of type <code>LDAModel</code> can still be cast to the appropriate concrete type, which depends on the optimization algorithm.</li> +</ul> + +<h2 id="previous-spark-versions">Previous Spark Versions</h2> + +<p>Earlier migration guides are archived <a href="mllib-migration-guides.html">on this page</a>.</p> + + + </div> <!-- /container --> + + <script src="js/vendor/jquery-1.8.0.min.js"></script> + <script src="js/vendor/bootstrap.min.js"></script> + <script src="js/vendor/anchor.min.js"></script> + <script src="js/main.js"></script> + + <!-- MathJax Section --> + <script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + TeX: { equationNumbers: { autoNumber: "AMS" } } + }); + </script> + <script> + // Note that we load MathJax this way to work with local file (file://), HTTP and HTTPS. + // We could use "//cdn.mathjax...", but that won't support "file://". + (function(d, script) { + script = d.createElement('script'); + script.type = 'text/javascript'; + script.async = true; + script.onload = function(){ + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ], + displayMath: [ ["$$","$$"], ["\\[", "\\]"] ], + processEscapes: true, + skipTags: ['script', 'noscript', 'style', 'textarea', 'pre'] + } + }); + }; + script.src = ('https:' == document.location.protocol ? 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