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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. |
| 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> |
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| <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> |
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| <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> |
| |
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