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| author | Reynold Xin <rxin@apache.org> | 2015-09-17 22:07:42 +0000 |
|---|---|---|
| committer | Reynold Xin <rxin@apache.org> | 2015-09-17 22:07:42 +0000 |
| commit | ee9ffe89d608e7640a2487406b618d27e58026d6 (patch) | |
| tree | 50ec819abb41a9a769d7f64eed1f0ab2084aa6ff /site/docs/1.5.0/mllib-migration-guides.html | |
| parent | c7104724b279f09486ea62f4a24252e8d06f5c96 (diff) | |
| download | spark-website-ee9ffe89d608e7640a2487406b618d27e58026d6.tar.gz spark-website-ee9ffe89d608e7640a2487406b618d27e58026d6.tar.bz2 spark-website-ee9ffe89d608e7640a2487406b618d27e58026d6.zip | |
delete 1.5.0
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diff --git a/site/docs/1.5.0/mllib-migration-guides.html b/site/docs/1.5.0/mllib-migration-guides.html deleted file mode 100644 index 60ccc3af8..000000000 --- a/site/docs/1.5.0/mllib-migration-guides.html +++ /dev/null @@ -1,304 +0,0 @@ -<!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>Old Migration Guides - MLlib - Spark 1.5.0 Documentation</title> - - <meta name="description" content="MLlib migration guides from before Spark 1.5.0"> - - - - - <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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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> - -<p>In the <code>spark.ml</code> package, several major API changes occurred, including:</p> - -<ul> - <li><code>Param</code> and other APIs for specifying parameters</li> - <li><code>uid</code> unique IDs for Pipeline components</li> - <li>Reorganization of certain classes</li> -</ul> - -<p>Since the <code>spark.ml</code> API was an alpha component in Spark 1.3, we do not list all changes here. -However, since 1.4 <code>spark.ml</code> is no longer an alpha component, we will provide details on any API -changes for future releases.</p> - -<h2 id="from-12-to-13">From 1.2 to 1.3</h2> - -<p>In the <code>spark.mllib</code> package, there were several breaking changes. The first change (in <code>ALS</code>) is the only one in a component not marked as Alpha or Experimental.</p> - -<ul> - <li><em>(Breaking change)</em> In <a href="api/scala/index.html#org.apache.spark.mllib.recommendation.ALS"><code>ALS</code></a>, the extraneous method <code>solveLeastSquares</code> has been removed. The <code>DeveloperApi</code> method <code>analyzeBlocks</code> was also removed.</li> - <li><em>(Breaking change)</em> <a href="api/scala/index.html#org.apache.spark.mllib.feature.StandardScalerModel"><code>StandardScalerModel</code></a> remains an Alpha component. In it, the <code>variance</code> method has been replaced with the <code>std</code> method. To compute the column variance values returned by the original <code>variance</code> method, simply square the standard deviation values returned by <code>std</code>.</li> - <li><em>(Breaking change)</em> <a href="api/scala/index.html#org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD"><code>StreamingLinearRegressionWithSGD</code></a> remains an Experimental component. In it, there were two changes: - <ul> - <li>The constructor taking arguments was removed in favor of a builder pattern using the default constructor plus parameter setter methods.</li> - <li>Variable <code>model</code> is no longer public.</li> - </ul> - </li> - <li><em>(Breaking change)</em> <a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a> remains an Experimental component. In it and its associated classes, there were several changes: - <ul> - <li>In <code>DecisionTree</code>, the deprecated class method <code>train</code> has been removed. (The object/static <code>train</code> methods remain.)</li> - <li>In <code>Strategy</code>, the <code>checkpointDir</code> parameter has been removed. Checkpointing is still supported, but the checkpoint directory must be set before calling tree and tree ensemble training.</li> - </ul> - </li> - <li><code>PythonMLlibAPI</code> (the interface between Scala/Java and Python for MLlib) was a public API but is now private, declared <code>private[python]</code>. This was never meant for external use.</li> - <li>In linear regression (including Lasso and ridge regression), the squared loss is now divided by 2. -So in order to produce the same result as in 1.2, the regularization parameter needs to be divided by 2 and the step size needs to be multiplied by 2.</li> -</ul> - -<p>In the <code>spark.ml</code> package, the main API changes are from Spark SQL. We list the most important changes here:</p> - -<ul> - <li>The old <a href="http://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.sql.SchemaRDD">SchemaRDD</a> has been replaced with <a href="api/scala/index.html#org.apache.spark.sql.DataFrame">DataFrame</a> with a somewhat modified API. All algorithms in Spark ML which used to use SchemaRDD now use DataFrame.</li> - <li>In Spark 1.2, we used implicit conversions from <code>RDD</code>s of <code>LabeledPoint</code> into <code>SchemaRDD</code>s by calling <code>import sqlContext._</code> where <code>sqlContext</code> was an instance of <code>SQLContext</code>. These implicits have been moved, so we now call <code>import sqlContext.implicits._</code>.</li> - <li>Java APIs for SQL have also changed accordingly. Please see the examples above and the <a href="sql-programming-guide.html">Spark SQL Programming Guide</a> for details.</li> -</ul> - -<p>Other changes were in <code>LogisticRegression</code>:</p> - -<ul> - <li>The <code>scoreCol</code> output column (with default value “score”) was renamed to be <code>probabilityCol</code> (with default value “probability”). The type was originally <code>Double</code> (for the probability of class 1.0), but it is now <code>Vector</code> (for the probability of each class, to support multiclass classification in the future).</li> - <li>In Spark 1.2, <code>LogisticRegressionModel</code> did not include an intercept. In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it uses the default settings for <a href="api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS">spark.mllib.LogisticRegressionWithLBFGS</a>. The option to use an intercept will be added in the future.</li> -</ul> - -<h2 id="from-11-to-12">From 1.1 to 1.2</h2> - -<p>The only API changes in MLlib v1.2 are in -<a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>, -which continues to be an experimental API in MLlib 1.2:</p> - -<ol> - <li> - <p><em>(Breaking change)</em> The Scala API for classification takes a named argument specifying the number -of classes. In MLlib v1.1, this argument was called <code>numClasses</code> in Python and -<code>numClassesForClassification</code> in Scala. In MLlib v1.2, the names are both set to <code>numClasses</code>. -This <code>numClasses</code> parameter is specified either via -<a href="api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy"><code>Strategy</code></a> -or via <a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a> -static <code>trainClassifier</code> and <code>trainRegressor</code> methods.</p> - </li> - <li> - <p><em>(Breaking change)</em> The API for -<a href="api/scala/index.html#org.apache.spark.mllib.tree.model.Node"><code>Node</code></a> has changed. -This should generally not affect user code, unless the user manually constructs decision trees -(instead of using the <code>trainClassifier</code> or <code>trainRegressor</code> methods). -The tree <code>Node</code> now includes more information, including the probability of the predicted label -(for classification).</p> - </li> - <li> - <p>Printing methods’ output has changed. The <code>toString</code> (Scala/Java) and <code>__repr__</code> (Python) methods used to print the full model; they now print a summary. For the full model, use <code>toDebugString</code>.</p> - </li> -</ol> - -<p>Examples in the Spark distribution and examples in the -<a href="mllib-decision-tree.html#examples">Decision Trees Guide</a> have been updated accordingly.</p> - -<h2 id="from-10-to-11">From 1.0 to 1.1</h2> - -<p>The only API changes in MLlib v1.1 are in -<a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>, -which continues to be an experimental API in MLlib 1.1:</p> - -<ol> - <li> - <p><em>(Breaking change)</em> The meaning of tree depth has been changed by 1 in order to match -the implementations of trees in -<a href="http://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree">scikit-learn</a> -and in <a href="http://cran.r-project.org/web/packages/rpart/index.html">rpart</a>. -In MLlib v1.0, a depth-1 tree had 1 leaf node, and a depth-2 tree had 1 root node and 2 leaf nodes. -In MLlib v1.1, a depth-0 tree has 1 leaf node, and a depth-1 tree has 1 root node and 2 leaf nodes. -This depth is specified by the <code>maxDepth</code> parameter in -<a href="api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy"><code>Strategy</code></a> -or via <a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a> -static <code>trainClassifier</code> and <code>trainRegressor</code> methods.</p> - </li> - <li> - <p><em>(Non-breaking change)</em> We recommend using the newly added <code>trainClassifier</code> and <code>trainRegressor</code> -methods to build a <a href="api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree"><code>DecisionTree</code></a>, -rather than using the old parameter class <code>Strategy</code>. These new training methods explicitly -separate classification and regression, and they replace specialized parameter types with -simple <code>String</code> types.</p> - </li> -</ol> - -<p>Examples of the new, recommended <code>trainClassifier</code> and <code>trainRegressor</code> are given in the -<a href="mllib-decision-tree.html#examples">Decision Trees Guide</a>.</p> - -<h2 id="from-09-to-10">From 0.9 to 1.0</h2> - -<p>In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few -breaking changes. If your data is sparse, please store it in a sparse format instead of dense to -take advantage of sparsity in both storage and computation. Details are described below.</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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