spark-instrumented-optimizer/docs/mllib-guide.md

layout	title	displayTitle	description
global	MLlib	Machine Learning Library (MLlib) Guide	MLlib machine learning library overview for Spark SPARK_VERSION_SHORT

MLlib is Spark's machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.

It divides into two packages:

• spark.mllib contains the original API built on top of RDDs.
• spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.

Using spark.ml is recommended because with DataFrames the API is more versatile and flexible. But we will keep supporting spark.mllib along with the development of spark.ml. Users should be comfortable using spark.mllib features and expect more features coming. Developers should contribute new algorithms to spark.ml if they fit the ML pipeline concept well, e.g., feature extractors and transformers.

We list major functionality from both below, with links to detailed guides.

spark.mllib: data types, algorithms, and utilities

• Data types
• Basic statistics
  • summary statistics
  • correlations
  • stratified sampling
  • hypothesis testing
  • streaming significance testing
  • random data generation
• Classification and regression
  • linear models (SVMs, logistic regression, linear regression)
  • naive Bayes
  • decision trees
  • ensembles of trees (Random Forests and Gradient-Boosted Trees)
  • isotonic regression
• Collaborative filtering
  • alternating least squares (ALS)
• Clustering
  • k-means
  • Gaussian mixture
  • power iteration clustering (PIC)
  • latent Dirichlet allocation (LDA)
  • bisecting k-means
  • streaming k-means
• Dimensionality reduction
  • singular value decomposition (SVD)
  • principal component analysis (PCA)
• Feature extraction and transformation
• Frequent pattern mining
  • FP-growth
  • association rules
  • PrefixSpan
• Evaluation metrics
• PMML model export
• Optimization (developer)
  • stochastic gradient descent
  • limited-memory BFGS (L-BFGS)

spark.ml: high-level APIs for ML pipelines

• Overview: estimators, transformers and pipelines
• Extracting, transforming and selecting features
• Classification and regression
• Clustering
• Collaborative filtering
• Advanced topics

Some techniques are not available yet in spark.ml, most notably dimensionality reduction Users can seamlessly combine the implementation of these techniques found in spark.mllib with the rest of the algorithms found in spark.ml.

Dependencies

MLlib uses the linear algebra package Breeze, which depends on netlib-java for optimised numerical processing. If natives libraries¹ 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.

Migration guide

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.

From 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.

{% 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

convert DataFrame columns

convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF) convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)

convert a single vector or matrix

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.BinaryClassificationEvaluator
• weights in LinearRegression and LogisticRegression in spark.ml
• setMaxNumIterations 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.Strategy
• build in mllib.tree.Node
• libsvm loaders for multiclass and load/save labeledData methods in mllib.util.MLUtils

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:

• SPARK-14984: In spark.ml.regression.LinearRegressionSummary, the model field has been deprecated.
• SPARK-13784: In spark.ml.regression.RandomForestRegressionModel and spark.ml.classification.RandomForestClassificationModel, the numTrees parameter has been deprecated in favor of getNumTrees method.
• SPARK-13761: In spark.ml.param.Params, the validateParams method has been deprecated. We move all functionality in overridden methods to the corresponding transformSchema.
• SPARK-14829: In 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-14900: In spark.mllib.evaluation.MulticlassMetrics, the parameters precision, recall and fMeasure have been deprecated in favor of accuracy.
• SPARK-15644: In spark.ml.util.MLReader and spark.ml.util.MLWriter, the context method has been deprecated in favor of session.
• In 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-7780: 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:
  • The intercept will not be regularized when training binary classification model with L1/L2 Updater.
  • If users set without regularization, training with or without feature scaling will return the same solution by the same convergence rate.
• SPARK-13429: In order to provide better and consistent result with spark.ml.classification.LogisticRegresson, the default value of spark.mllib.classification.LogisticRegressionWithLBFGS: convergenceTol has been changed from 1E-4 to 1E-6.
• SPARK-12363: Fix a bug of PowerIterationClustering which will likely change its result.
• SPARK-13048: LDA using the EM optimizer will keep the last checkpoint by default, if checkpointing is being used.
• SPARK-12153: Word2Vec now respects sentence boundaries. Previously, it did not handle them correctly.
• SPARK-10574: HashingTF uses MurmurHash3 as default hash algorithm in both spark.ml and spark.mllib.
• SPARK-14768: The expectedType argument for PySpark Param was removed.
• SPARK-14931: Some default Param values, which were mismatched between pipelines in Scala and Python, have been changed.
• SPARK-13600: 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.

Previous Spark versions

Earlier migration guides are archived on this page.

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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. ↩︎