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| <h1 class="title">Old Migration Guides - spark.mllib</h1> |
| |
| |
| <p>The migration guide for the current Spark version is kept on the <a href="mllib-guide.html#migration-guide">MLlib Programming Guide main page</a>.</p> |
| |
| <h2 id="from-14-to-15">From 1.4 to 1.5</h2> |
| |
| <p>In the <code>spark.mllib</code> package, there are no breaking API changes but several behavior changes:</p> |
| |
| <ul> |
| <li><a href="https://issues.apache.org/jira/browse/SPARK-9005">SPARK-9005</a>: |
| <code>RegressionMetrics.explainedVariance</code> returns the average regression sum of squares.</li> |
| <li><a href="https://issues.apache.org/jira/browse/SPARK-8600">SPARK-8600</a>: <code>NaiveBayesModel.labels</code> become |
| sorted.</li> |
| <li><a href="https://issues.apache.org/jira/browse/SPARK-3382">SPARK-3382</a>: <code>GradientDescent</code> has a default |
| convergence tolerance <code>1e-3</code>, and hence iterations might end earlier than 1.4.</li> |
| </ul> |
| |
| <p>In the <code>spark.ml</code> package, there exists one breaking API change and one behavior change:</p> |
| |
| <ul> |
| <li><a href="https://issues.apache.org/jira/browse/SPARK-9268">SPARK-9268</a>: Java’s varargs support is removed |
| from <code>Params.setDefault</code> due to a |
| <a href="https://issues.scala-lang.org/browse/SI-9013">Scala compiler bug</a>.</li> |
| <li><a href="https://issues.apache.org/jira/browse/SPARK-10097">SPARK-10097</a>: <code>Evaluator.isLargerBetter</code> is |
| added to indicate metric ordering. Metrics like RMSE no longer flip signs as in 1.4.</li> |
| </ul> |
| |
| <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> |
| |
| <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> |
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