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| author | Michael Armbrust <marmbrus@apache.org> | 2016-01-04 17:53:21 +0000 |
|---|---|---|
| committer | Michael Armbrust <marmbrus@apache.org> | 2016-01-04 17:53:21 +0000 |
| commit | 0d824b0442707b6842ef19a589cb8a2af997273b (patch) | |
| tree | 4ac1758b478ee6cb74382733fd3eb3157b6c3161 /site/docs/1.6.0/mllib-guide.html | |
| parent | ebcd167cbcb4ba33d8926b66850eff0cce12d365 (diff) | |
| download | spark-website-0d824b0442707b6842ef19a589cb8a2af997273b.tar.gz spark-website-0d824b0442707b6842ef19a589cb8a2af997273b.tar.bz2 spark-website-0d824b0442707b6842ef19a589cb8a2af997273b.zip | |
Add Spark 1.6.0 docs
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| -rw-r--r-- | site/docs/1.6.0/mllib-guide.html | 500 |
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diff --git a/site/docs/1.6.0/mllib-guide.html b/site/docs/1.6.0/mllib-guide.html new file mode 100644 index 000000000..cc9e2f240 --- /dev/null +++ b/site/docs/1.6.0/mllib-guide.html @@ -0,0 +1,500 @@ + +<!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 1.6.0 Documentation</title> + + <meta name="description" content="MLlib machine learning library overview for Spark 1.6.0"> + + + + + <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 ? 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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, Datasets 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 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<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 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">v1.6.0</span></p>--> + </div> + </div> + </div> + + <div class="container-wrapper"> + + + <div class="left-menu-wrapper"> + <div class="left-menu"> + <h3><a href="ml-guide.html">spark.ml package</a></h3> + +<ul> + + <li> + <a href="ml-guide.html"> + + Overview: estimators, transformers and pipelines + + </a> + </li> + + + <li> + <a href="ml-features.html"> + + Extracting, transforming and selecting features + + </a> + </li> + + + <li> + <a href="ml-classification-regression.html"> + + Classification and Regression + + </a> + </li> + + + <li> + <a href="ml-clustering.html"> + + Clustering + + </a> + </li> + + + <li> + <a href="ml-advanced.html"> + + Advanced topics + + </a> + </li> + + +</ul> + + <h3><a href="mllib-guide.html">spark.mllib package</a></h3> + +<ul> + + <li> + <a href="mllib-data-types.html"> + + Data types + + </a> + </li> + + + <li> + <a href="mllib-statistics.html"> + + Basic statistics + + </a> + </li> + + + <li> + <a href="mllib-classification-regression.html"> + + Classification and regression + + </a> + </li> + + + <li> + <a href="mllib-collaborative-filtering.html"> + + Collaborative filtering + + </a> + </li> + + + <li> + <a href="mllib-clustering.html"> + + Clustering + + </a> + </li> + + + <li> + <a href="mllib-dimensionality-reduction.html"> + + Dimensionality reduction + + </a> + </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> + </li> + + + <li> + <a href="mllib-evaluation-metrics.html"> + + Evaluation metrics + + </a> + </li> + + + <li> + <a href="mllib-pmml-model-export.html"> + + PMML model export + + </a> + </li> + + + <li> + <a href="mllib-optimization.html"> + + Optimization (developer) + + </a> + </li> + + +</ul> + + </div> +</div> + <input id="nav-trigger" class="nav-trigger" checked type="checkbox"> + <label for="nav-trigger"></label> + <div class="content-with-sidebar" id="content"> + + <h1 class="title">Machine Learning Library (MLlib) Guide</h1> + + + <p>MLlib is Spark’s machine learning (ML) library. +Its goal is to make practical machine learning scalable and easy. +It consists of common learning algorithms and utilities, including classification, regression, +clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization +primitives and higher-level pipeline APIs.</p> + +<p>It divides into two packages:</p> + +<ul> + <li><a href="mllib-guide.html#data-types-algorithms-and-utilities"><code>spark.mllib</code></a> contains the original API +built on top of <a href="programming-guide.html#resilient-distributed-datasets-rdds">RDDs</a>.</li> + <li><a href="ml-guide.html"><code>spark.ml</code></a> provides higher-level API +built on top of <a href="sql-programming-guide.html#dataframes">DataFrames</a> for constructing ML pipelines.</li> +</ul> + +<p>Using <code>spark.ml</code> is recommended because with DataFrames the API is more versatile and flexible. +But we will keep supporting <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.ml</code> if they fit the ML pipeline concept well, +e.g., feature extractors and transformers.</p> + +<p>We list major functionality from both below, with links to detailed guides.</p> + +<h1 id="sparkmllib-data-types-algorithms-and-utilities">spark.mllib: data types, algorithms, and utilities</h1> + +<ul> + <li><a href="mllib-data-types.html">Data types</a></li> + <li><a href="mllib-statistics.html">Basic statistics</a> + <ul> + <li><a href="mllib-statistics.html#summary-statistics">summary statistics</a></li> + <li><a href="mllib-statistics.html#correlations">correlations</a></li> + <li><a href="mllib-statistics.html#stratified-sampling">stratified sampling</a></li> + <li><a href="mllib-statistics.html#hypothesis-testing">hypothesis testing</a></li> + <li><a href="mllib-statistics.html#streaming-significance-testing">streaming significance testing</a></li> + <li><a href="mllib-statistics.html#random-data-generation">random data generation</a></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 (Random Forests and Gradient-Boosted Trees)</a></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><a href="mllib-collaborative-filtering.html#collaborative-filtering">alternating least squares (ALS)</a></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#bisecting-kmeans">bisecting k-means</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><a href="mllib-dimensionality-reduction.html#singular-value-decomposition-svd">singular value decomposition (SVD)</a></li> + <li><a href="mllib-dimensionality-reduction.html#principal-component-analysis-pca">principal component analysis (PCA)</a></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><a href="mllib-frequent-pattern-mining.html#fp-growth">FP-growth</a></li> + <li><a href="mllib-frequent-pattern-mining.html#association-rules">association rules</a></li> + <li><a href="mllib-frequent-pattern-mining.html#prefix-span">PrefixSpan</a></li> + </ul> + </li> + <li><a href="mllib-evaluation-metrics.html">Evaluation metrics</a></li> + <li><a href="mllib-pmml-model-export.html">PMML model export</a></li> + <li><a href="mllib-optimization.html">Optimization (developer)</a> + <ul> + <li><a href="mllib-optimization.html#stochastic-gradient-descent-sgd">stochastic gradient descent</a></li> + <li><a href="mllib-optimization.html#limited-memory-bfgs-l-bfgs">limited-memory BFGS (L-BFGS)</a></li> + </ul> + </li> +</ul> + +<h1 id="sparkml-high-level-apis-for-ml-pipelines">spark.ml: high-level APIs for ML pipelines</h1> + +<ul> + <li><a href="ml-guide.html">Overview: estimators, transformers and pipelines</a></li> + <li><a href="ml-features.html">Extracting, transforming and selecting features</a></li> + <li><a href="ml-classification-regression.html">Classification and regression</a></li> + <li><a href="ml-clustering.html">Clustering</a></li> + <li><a href="ml-advanced.html">Advanced topics</a></li> +</ul> + +<p>Some techniques are not available yet in spark.ml, most notably dimensionality reduction +Users can seamlessly combine the implementation of these techniques found in <code>spark.mllib</code> with the rest of the algorithms found in <code>spark.ml</code>.</p> + +<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 libraries<sup id="fnref:1"><a href="#fn:1" class="footnote">1</a></sup> are not available at runtime, you will see a warning message and a pure JVM +implementation will be used instead.</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> + +<h1 id="migration-guide">Migration guide</h1> + +<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> + +<h2 id="from-15-to-16">From 1.5 to 1.6</h2> + +<p>There are no breaking API changes in the <code>spark.mllib</code> or <code>spark.ml</code> packages, but there are +deprecations and changes of behavior.</p> + +<p>Deprecations:</p> + +<ul> + <li><a href="https://issues.apache.org/jira/browse/SPARK-11358">SPARK-11358</a>: + In <code>spark.mllib.clustering.KMeans</code>, the <code>runs</code> parameter has been deprecated.</li> + <li><a href="https://issues.apache.org/jira/browse/SPARK-10592">SPARK-10592</a>: + In <code>spark.ml.classification.LogisticRegressionModel</code> and + <code>spark.ml.regression.LinearRegressionModel</code>, the <code>weights</code> field has been deprecated in favor of + the new name <code>coefficients</code>. This helps disambiguate from instance (row) “weights” given to + algorithms.</li> +</ul> + +<p>Changes of behavior:</p> + +<ul> + <li><a href="https://issues.apache.org/jira/browse/SPARK-7770">SPARK-7770</a>: + <code>spark.mllib.tree.GradientBoostedTrees</code>: <code>validationTol</code> has changed semantics in 1.6. + Previously, it was a threshold for absolute change in error. Now, it resembles the behavior of + <code>GradientDescent</code>’s <code>convergenceTol</code>: For large errors, it uses relative error (relative to the + previous error); for small errors (<code>< 0.01</code>), it uses absolute error.</li> + <li><a href="https://issues.apache.org/jira/browse/SPARK-11069">SPARK-11069</a>: + <code>spark.ml.feature.RegexTokenizer</code>: Previously, it did not convert strings to lowercase before + tokenizing. Now, it converts to lowercase by default, with an option not to. This matches the + behavior of the simpler <code>Tokenizer</code> transformer.</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> + +<hr /> +<div class="footnotes"> + <ol> + <li id="fn:1"> + <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>. <a href="#fnref:1" class="reversefootnote">↩</a></p> + </li> + </ol> +</div> + + + </div> + + <!-- /container --> + </div> + + <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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