MLlib - Old Migration Guides

The migration guide for the current Spark version is kept on the MLlib Programming Guide main page.

From 1.3 to 1.4

In the spark.mllib package, there were several breaking changes, but all in DeveloperApi or Experimental APIs:

• Gradient-Boosted Trees
  • (Breaking change) The signature of the Loss.gradient method was changed. This is only an issues for users who wrote their own losses for GBTs.
  • (Breaking change) The apply and copy methods for the case class BoostingStrategy have been changed because of a modification to the case class fields. This could be an issue for users who use BoostingStrategy to set GBT parameters.
• (Breaking change) The return value of LDA.run has changed. It now returns an abstract class LDAModel instead of the concrete class DistributedLDAModel. The object of type LDAModel can still be cast to the appropriate concrete type, which depends on the optimization algorithm.

In the spark.ml package, several major API changes occurred, including:

• Param and other APIs for specifying parameters
• uid unique IDs for Pipeline components
• Reorganization of certain classes

Since the spark.ml API was an alpha component in Spark 1.3, we do not list all changes here. However, since 1.4 spark.ml is no longer an alpha component, we will provide details on any API changes for future releases.

From 1.2 to 1.3

In the spark.mllib package, there were several breaking changes. The first change (in ALS) is the only one in a component not marked as Alpha or Experimental.

• (Breaking change) In ALS, the extraneous method solveLeastSquares has been removed. The DeveloperApi method analyzeBlocks was also removed.
• (Breaking change) StandardScalerModel remains an Alpha component. In it, the variance method has been replaced with the std method. To compute the column variance values returned by the original variance method, simply square the standard deviation values returned by std.
• (Breaking change) StreamingLinearRegressionWithSGD remains an Experimental component. In it, there were two changes:
  • The constructor taking arguments was removed in favor of a builder pattern using the default constructor plus parameter setter methods.
  • Variable model is no longer public.
• (Breaking change) DecisionTree remains an Experimental component. In it and its associated classes, there were several changes:
  • In DecisionTree, the deprecated class method train has been removed. (The object/static train methods remain.)
  • In Strategy, the checkpointDir parameter has been removed. Checkpointing is still supported, but the checkpoint directory must be set before calling tree and tree ensemble training.
• PythonMLlibAPI (the interface between Scala/Java and Python for MLlib) was a public API but is now private, declared private[python]. This was never meant for external use.
• 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.

In the spark.ml package, the main API changes are from Spark SQL. We list the most important changes here:

• The old SchemaRDD has been replaced with DataFrame with a somewhat modified API. All algorithms in Spark ML which used to use SchemaRDD now use DataFrame.
• In Spark 1.2, we used implicit conversions from RDDs of LabeledPoint into SchemaRDDs by calling import sqlContext._ where sqlContext was an instance of SQLContext. These implicits have been moved, so we now call import sqlContext.implicits._.
• Java APIs for SQL have also changed accordingly. Please see the examples above and the Spark SQL Programming Guide for details.

Other changes were in LogisticRegression:

• The scoreCol output column (with default value “score”) was renamed to be probabilityCol (with default value “probability”). The type was originally Double (for the probability of class 1.0), but it is now Vector (for the probability of each class, to support multiclass classification in the future).
• In Spark 1.2, LogisticRegressionModel 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 spark.mllib.LogisticRegressionWithLBFGS. The option to use an intercept will be added in the future.

From 1.1 to 1.2

The only API changes in MLlib v1.2 are in DecisionTree, which continues to be an experimental API in MLlib 1.2:

1. (Breaking change) The Scala API for classification takes a named argument specifying the number of classes. In MLlib v1.1, this argument was called numClasses in Python and numClassesForClassification in Scala. In MLlib v1.2, the names are both set to numClasses. This numClasses parameter is specified either via Strategy or via DecisionTree static trainClassifier and trainRegressor methods.

2. (Breaking change) The API for Node has changed. This should generally not affect user code, unless the user manually constructs decision trees (instead of using the trainClassifier or trainRegressor methods). The tree Node now includes more information, including the probability of the predicted label (for classification).

3. Printing methods’ output has changed. The toString (Scala/Java) and __repr__ (Python) methods used to print the full model; they now print a summary. For the full model, use toDebugString.

Examples in the Spark distribution and examples in the Decision Trees Guide have been updated accordingly.

From 1.0 to 1.1

The only API changes in MLlib v1.1 are in DecisionTree, which continues to be an experimental API in MLlib 1.1:

1. (Breaking change) The meaning of tree depth has been changed by 1 in order to match the implementations of trees in scikit-learn and in rpart. 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 maxDepth parameter in Strategy or via DecisionTree static trainClassifier and trainRegressor methods.

2. (Non-breaking change) We recommend using the newly added trainClassifier and trainRegressor methods to build a DecisionTree, rather than using the old parameter class Strategy. These new training methods explicitly separate classification and regression, and they replace specialized parameter types with simple String types.

Examples of the new, recommended trainClassifier and trainRegressor are given in the Decision Trees Guide.

From 0.9 to 1.0

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.