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| <h1 class="title">Machine Learning Library (MLlib) Guide</h1> |
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| <p>MLlib is Spark’s machine learning (ML) library. |
| Its goal is to make practical machine learning scalable and easy. |
| At a high level, it provides tools such as:</p> |
| |
| <ul> |
| <li>ML Algorithms: common learning algorithms such as classification, regression, clustering, and collaborative filtering</li> |
| <li>Featurization: feature extraction, transformation, dimensionality reduction, and selection</li> |
| <li>Pipelines: tools for constructing, evaluating, and tuning ML Pipelines</li> |
| <li>Persistence: saving and load algorithms, models, and Pipelines</li> |
| <li>Utilities: linear algebra, statistics, data handling, etc.</li> |
| </ul> |
| |
| <h1 id="announcement-dataframe-based-api-is-primary-api">Announcement: DataFrame-based API is primary API</h1> |
| |
| <p><strong>The MLlib RDD-based API is now in maintenance mode.</strong></p> |
| |
| <p>As of Spark 2.0, the <a href="rdd-programming-guide.html#resilient-distributed-datasets-rdds">RDD</a>-based APIs in the <code>spark.mllib</code> package have entered maintenance mode. |
| The primary Machine Learning API for Spark is now the <a href="sql-programming-guide.html">DataFrame</a>-based API in the <code>spark.ml</code> package.</p> |
| |
| <p><em>What are the implications?</em></p> |
| |
| <ul> |
| <li>MLlib will still support the RDD-based API in <code>spark.mllib</code> with bug fixes.</li> |
| <li>MLlib will not add new features to the RDD-based API.</li> |
| <li>In the Spark 2.x releases, MLlib will add features to the DataFrames-based API to reach feature parity with the RDD-based API.</li> |
| <li>After reaching feature parity (roughly estimated for Spark 2.3), the RDD-based API will be deprecated.</li> |
| <li>The RDD-based API is expected to be removed in Spark 3.0.</li> |
| </ul> |
| |
| <p><em>Why is MLlib switching to the DataFrame-based API?</em></p> |
| |
| <ul> |
| <li>DataFrames provide a more user-friendly API than RDDs. The many benefits of DataFrames include Spark Datasources, SQL/DataFrame queries, Tungsten and Catalyst optimizations, and uniform APIs across languages.</li> |
| <li>The DataFrame-based API for MLlib provides a uniform API across ML algorithms and across multiple languages.</li> |
| <li>DataFrames facilitate practical ML Pipelines, particularly feature transformations. See the <a href="ml-pipeline.html">Pipelines guide</a> for details.</li> |
| </ul> |
| |
| <p><em>What is “Spark ML”?</em></p> |
| |
| <ul> |
| <li>“Spark ML” is not an official name but occasionally used to refer to the MLlib DataFrame-based API. |
| This is majorly due to the <code>org.apache.spark.ml</code> Scala package name used by the DataFrame-based API, |
| and the “Spark ML Pipelines” term we used initially to emphasize the pipeline concept.</li> |
| </ul> |
| |
| <p><em>Is MLlib deprecated?</em></p> |
| |
| <ul> |
| <li>No. MLlib includes both the RDD-based API and the DataFrame-based API. |
| The RDD-based API is now in maintenance mode. |
| But neither API is deprecated, nor MLlib as a whole.</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 native 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="highlights-in-22">Highlights in 2.2</h1> |
| |
| <p>The list below highlights some of the new features and enhancements added to MLlib in the <code>2.2</code> |
| release of Spark:</p> |
| |
| <ul> |
| <li><a href="ml-collaborative-filtering.html"><code>ALS</code></a> methods for <em>top-k</em> recommendations for all |
| users or items, matching the functionality in <code>mllib</code> |
| (<a href="https://issues.apache.org/jira/browse/SPARK-19535">SPARK-19535</a>). |
| Performance was also improved for both <code>ml</code> and <code>mllib</code> |
| (<a href="https://issues.apache.org/jira/browse/SPARK-11968">SPARK-11968</a> and |
| <a href="https://issues.apache.org/jira/browse/SPARK-20587">SPARK-20587</a>)</li> |
| <li><a href="ml-statistics.html#correlation"><code>Correlation</code></a> and |
| <a href="ml-statistics.html#hypothesis-testing"><code>ChiSquareTest</code></a> stats functions for <code>DataFrames</code> |
| (<a href="https://issues.apache.org/jira/browse/SPARK-19636">SPARK-19636</a> and |
| <a href="https://issues.apache.org/jira/browse/SPARK-19635">SPARK-19635</a>)</li> |
| <li><a href="ml-frequent-pattern-mining.html#fp-growth"><code>FPGrowth</code></a> algorithm for frequent pattern mining |
| (<a href="https://issues.apache.org/jira/browse/SPARK-14503">SPARK-14503</a>)</li> |
| <li><code>GLM</code> now supports the full <code>Tweedie</code> family |
| (<a href="https://issues.apache.org/jira/browse/SPARK-18929">SPARK-18929</a>)</li> |
| <li><a href="ml-features.html#imputer"><code>Imputer</code></a> feature transformer to impute missing values in a dataset |
| (<a href="https://issues.apache.org/jira/browse/SPARK-13568">SPARK-13568</a>)</li> |
| <li><a href="ml-classification-regression.html#linear-support-vector-machine"><code>LinearSVC</code></a> |
| for linear Support Vector Machine classification |
| (<a href="https://issues.apache.org/jira/browse/SPARK-14709">SPARK-14709</a>)</li> |
| <li>Logistic regression now supports constraints on the coefficients during training |
| (<a href="https://issues.apache.org/jira/browse/SPARK-20047">SPARK-20047</a>)</li> |
| </ul> |
| |
| <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-21-to-22">From 2.1 to 2.2</h2> |
| |
| <h3 id="breaking-changes">Breaking changes</h3> |
| |
| <p>There are no breaking changes.</p> |
| |
| <h3 id="deprecations-and-changes-of-behavior">Deprecations and changes of behavior</h3> |
| |
| <p><strong>Deprecations</strong></p> |
| |
| <p>There are no deprecations.</p> |
| |
| <p><strong>Changes of behavior</strong></p> |
| |
| <ul> |
| <li><a href="https://issues.apache.org/jira/browse/SPARK-19787">SPARK-19787</a>: |
| Default value of <code>regParam</code> changed from <code>1.0</code> to <code>0.1</code> for <code>ALS.train</code> method (marked <code>DeveloperApi</code>). |
| <strong>Note</strong> this does <em>not affect</em> the <code>ALS</code> Estimator or Model, nor MLlib’s <code>ALS</code> class.</li> |
| <li><a href="https://issues.apache.org/jira/browse/SPARK-14772">SPARK-14772</a>: |
| Fixed inconsistency between Python and Scala APIs for <code>Param.copy</code> method.</li> |
| <li><a href="https://issues.apache.org/jira/browse/SPARK-11569">SPARK-11569</a>: |
| <code>StringIndexer</code> now handles <code>NULL</code> values in the same way as unseen values. Previously an exception |
| would always be thrown regardless of the setting of the <code>handleInvalid</code> parameter.</li> |
| </ul> |
| |
| <h2 id="previous-spark-versions">Previous Spark versions</h2> |
| |
| <p>Earlier migration guides are archived <a href="ml-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> |
| |
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