Migration Guide

Migration Guide: MLlib (Machine Learning)

Note that this migration guide describes the items specific to MLlib. Many items of SQL migration can be applied when migrating MLlib to higher versions for DataFrame-based APIs. Please refer Migration Guide: SQL, Datasets and DataFrame.

Upgrading from MLlib 2.4 to 3.0

Breaking changes

Deprecations and changes of behavior

Deprecations

Changes of behavior

Upgrading from MLlib 2.2 to 2.3

Breaking changes

Deprecations and changes of behavior

Deprecations

Changes of behavior

Upgrading from MLlib 2.1 to 2.2

Breaking changes

There are no breaking changes.

Deprecations and changes of behavior

Deprecations

There are no deprecations.

Changes of behavior

Upgrading from MLlib 2.0 to 2.1

Breaking changes

Deprecated methods removed

Deprecations and changes of behavior

Deprecations

Changes of behavior

Upgrading from MLlib 1.6 to 2.0

Breaking changes

There were several breaking changes in Spark 2.0, which are outlined below.

Linear algebra classes for DataFrame-based APIs

Spark’s linear algebra dependencies were moved to a new project, mllib-local (see SPARK-13944). As part of this change, the linear algebra classes were copied to a new package, spark.ml.linalg. The DataFrame-based APIs in spark.ml now depend on the spark.ml.linalg classes, leading to a few breaking changes, predominantly in various model classes (see SPARK-14810 for a full list).

Note: the RDD-based APIs in spark.mllib continue to depend on the previous package spark.mllib.linalg.

Converting vectors and matrices

While most pipeline components support backward compatibility for loading, some existing DataFrames and pipelines in Spark versions prior to 2.0, that contain vector or matrix columns, may need to be migrated to the new spark.ml vector and matrix types. Utilities for converting DataFrame columns from spark.mllib.linalg to spark.ml.linalg types (and vice versa) can be found in spark.mllib.util.MLUtils.

There are also utility methods available for converting single instances of vectors and matrices. Use the asML method on a mllib.linalg.Vector / mllib.linalg.Matrix for converting to ml.linalg types, and mllib.linalg.Vectors.fromML / mllib.linalg.Matrices.fromML for converting to mllib.linalg types.

import org.apache.spark.mllib.util.MLUtils

// convert DataFrame columns
val convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF)
val convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
// convert a single vector or matrix
val mlVec: org.apache.spark.ml.linalg.Vector = mllibVec.asML
val mlMat: org.apache.spark.ml.linalg.Matrix = mllibMat.asML

Refer to the MLUtils Scala docs for further detail.

import org.apache.spark.mllib.util.MLUtils;
import org.apache.spark.sql.Dataset;

// convert DataFrame columns
Dataset<Row> convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF);
Dataset<Row> convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF);
// convert a single vector or matrix
org.apache.spark.ml.linalg.Vector mlVec = mllibVec.asML();
org.apache.spark.ml.linalg.Matrix mlMat = mllibMat.asML();

Refer to the MLUtils Java docs for further detail.

from pyspark.mllib.util import MLUtils

# convert DataFrame columns
convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF)
convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
# convert a single vector or matrix
mlVec = mllibVec.asML()
mlMat = mllibMat.asML()

Refer to the MLUtils Python docs for further detail.

Deprecated methods removed

Several deprecated methods were removed in the spark.mllib and spark.ml packages:

A full list of breaking changes can be found at SPARK-14810.

Deprecations and changes of behavior

Deprecations

Deprecations in the spark.mllib and spark.ml packages include:

Changes of behavior

Changes of behavior in the spark.mllib and spark.ml packages include:

Upgrading from MLlib 1.5 to 1.6

There are no breaking API changes in the spark.mllib or spark.ml packages, but there are deprecations and changes of behavior.

Deprecations:

Changes of behavior:

Upgrading from MLlib 1.4 to 1.5

In the spark.mllib package, there are no breaking API changes but several behavior changes:

In the spark.ml package, there exists one breaking API change and one behavior change:

Upgrading from MLlib 1.3 to 1.4

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

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

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.

Upgrading from MLlib 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.

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

Other changes were in LogisticRegression:

Upgrading from MLlib 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.

Upgrading from MLlib 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.

Upgrading from MLlib 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.