Old Migration Guides - MLlib

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

From 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

• SPARK-19787: Default value of regParam changed from 1.0 to 0.1 for ALS.train method (marked DeveloperApi). Note this does not affect the ALS Estimator or Model, nor MLlib’s ALS class.
• SPARK-14772: Fixed inconsistency between Python and Scala APIs for Param.copy method.
• SPARK-11569: StringIndexer now handles NULL values in the same way as unseen values. Previously an exception would always be thrown regardless of the setting of the handleInvalid parameter.

From 2.0 to 2.1

Breaking changes

Deprecated methods removed

• setLabelCol in feature.ChiSqSelectorModel
• numTrees in classification.RandomForestClassificationModel (This now refers to the Param called numTrees)
• numTrees in regression.RandomForestRegressionModel (This now refers to the Param called numTrees)
• model in regression.LinearRegressionSummary
• validateParams in PipelineStage
• validateParams in Evaluator

Deprecations and changes of behavior

Deprecations

• SPARK-18592: Deprecate all Param setter methods except for input/output column Params for DecisionTreeClassificationModel, GBTClassificationModel, RandomForestClassificationModel, DecisionTreeRegressionModel, GBTRegressionModel and RandomForestRegressionModel

Changes of behavior

• SPARK-17870: Fix a bug of ChiSqSelector which will likely change its result. Now ChiSquareSelector use pValue rather than raw statistic to select a fixed number of top features.
• SPARK-3261: KMeans returns potentially fewer than k cluster centers in cases where k distinct centroids aren’t available or aren’t selected.
• SPARK-17389: KMeans reduces the default number of steps from 5 to 2 for the k-means|| initialization mode.

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.

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

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();

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()

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.

From 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:

• SPARK-11358: In spark.mllib.clustering.KMeans, the runs parameter has been deprecated.
• SPARK-10592: In spark.ml.classification.LogisticRegressionModel and spark.ml.regression.LinearRegressionModel, the weights field has been deprecated in favor of the new name coefficients. This helps disambiguate from instance (row) “weights” given to algorithms.

Changes of behavior:

• SPARK-7770: spark.mllib.tree.GradientBoostedTrees: validationTol has changed semantics in 1.6. Previously, it was a threshold for absolute change in error. Now, it resembles the behavior of GradientDescent’s convergenceTol: For large errors, it uses relative error (relative to the previous error); for small errors (< 0.01), it uses absolute error.
• SPARK-11069: spark.ml.feature.RegexTokenizer: Previously, it did not convert strings to lowercase before tokenizing. Now, it converts to lowercase by default, with an option not to. This matches the behavior of the simpler Tokenizer transformer.

From 1.4 to 1.5

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

• SPARK-9005: RegressionMetrics.explainedVariance returns the average regression sum of squares.
• SPARK-8600: NaiveBayesModel.labels become sorted.
• SPARK-3382: GradientDescent has a default convergence tolerance 1e-3, and hence iterations might end earlier than 1.4.

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

• SPARK-9268: Java’s varargs support is removed from Params.setDefault due to a Scala compiler bug.
• SPARK-10097: Evaluator.isLargerBetter is added to indicate metric ordering. Metrics like RMSE no longer flip signs as in 1.4.

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.