MLlib is Spark's machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. At a high level, it provides tools such as:
The MLlib RDD-based API is now in maintenance mode.
As of Spark 2.0, the RDD-based APIs in the spark.mllib package have entered maintenance mode. The primary Machine Learning API for Spark is now the DataFrame-based API in the spark.ml package.
What are the implications?
spark.mllib with bug fixes.Why is MLlib switching to the DataFrame-based API?
MLlib uses the linear algebra package Breeze, which depends on netlib-java for optimised numerical processing. If native libraries[^1] are not available at runtime, you will see a warning message and a pure JVM implementation will be used instead.
Due to licensing issues with runtime proprietary binaries, we do not include netlib-java‘s native proxies by default. To configure netlib-java / Breeze to use system optimised binaries, include com.github.fommil.netlib:all:1.1.2 (or build Spark with -Pnetlib-lgpl) as a dependency of your project and read the netlib-java documentation for your platform’s additional installation instructions.
To use MLlib in Python, you will need NumPy version 1.4 or newer.
[^1]: To learn more about the benefits and background of system optimised natives, you may wish to watch Sam Halliday's ScalaX talk on High Performance Linear Algebra in Scala.
MLlib is under active development. The APIs marked Experimental/DeveloperApi may change in future releases, and the migration guide below will explain all changes between releases.
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.
{% highlight scala %} 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 {% endhighlight %}
Refer to the MLUtils Scala docs for further detail.
{% highlight java %} import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.sql.Dataset;
// convert DataFrame columns Dataset convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF); Dataset 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(); {% endhighlight %}
Refer to the MLUtils Java docs for further detail.
{% highlight python %} from pyspark.mllib.util import MLUtils
convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF) convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
mlVec = mllibVec.asML() mlMat = mllibMat.asML() {% endhighlight %}
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:
setScoreCol in ml.evaluation.BinaryClassificationEvaluatorweights in LinearRegression and LogisticRegression in spark.mlsetMaxNumIterations in mllib.optimization.LBFGS (marked as DeveloperApi)treeReduce and treeAggregate in mllib.rdd.RDDFunctions (these functions are available on RDDs directly, and were marked as DeveloperApi)defaultStategy in mllib.tree.configuration.Strategybuild in mllib.tree.Nodemllib.util.MLUtilsA full list of breaking changes can be found at SPARK-14810.
Deprecations
Deprecations in the spark.mllib and spark.ml packages include:
spark.ml.regression.LinearRegressionSummary, the model field has been deprecated.spark.ml.regression.RandomForestRegressionModel and spark.ml.classification.RandomForestClassificationModel, the numTrees parameter has been deprecated in favor of getNumTrees method.spark.ml.param.Params, the validateParams method has been deprecated. We move all functionality in overridden methods to the corresponding transformSchema.spark.mllib package, LinearRegressionWithSGD, LassoWithSGD, RidgeRegressionWithSGD and LogisticRegressionWithSGD have been deprecated. We encourage users to use spark.ml.regression.LinearRegresson and spark.ml.classification.LogisticRegresson.spark.mllib.evaluation.MulticlassMetrics, the parameters precision, recall and fMeasure have been deprecated in favor of accuracy.spark.ml.util.MLReader and spark.ml.util.MLWriter, the context method has been deprecated in favor of session.spark.ml.feature.ChiSqSelectorModel, the setLabelCol method has been deprecated since it was not used by ChiSqSelectorModel.Changes of behavior
Changes of behavior in the spark.mllib and spark.ml packages include:
spark.mllib.classification.LogisticRegressionWithLBFGS directly calls spark.ml.classification.LogisticRegresson for binary classification now. This will introduce the following behavior changes for spark.mllib.classification.LogisticRegressionWithLBFGS:spark.ml.classification.LogisticRegresson, the default value of spark.mllib.classification.LogisticRegressionWithLBFGS: convergenceTol has been changed from 1E-4 to 1E-6.PowerIterationClustering which will likely change its result.LDA using the EM optimizer will keep the last checkpoint by default, if checkpointing is being used.Word2Vec now respects sentence boundaries. Previously, it did not handle them correctly.HashingTF uses MurmurHash3 as default hash algorithm in both spark.ml and spark.mllib.expectedType argument for PySpark Param was removed.Param values, which were mismatched between pipelines in Scala and Python, have been changed.QuantileDiscretizer now uses spark.sql.DataFrameStatFunctions.approxQuantile to find splits (previously used custom sampling logic). The output buckets will differ for same input data and params.Earlier migration guides are archived on this page.