org.apache.spark.mllib.util

Class MLUtils

Object

org.apache.spark.mllib.util.MLUtils

public class MLUtils
extends Object

Helper methods to load, save and pre-process data used in MLLib.

Constructor Summary

Constructors

Constructor and Description
MLUtils()

Method Summary

Modifier and Type	Method and Description
static Vector	appendBias(Vector vector) Returns a new vector with 1.0 (bias) appended to the input vector.
static Dataset<Row>	convertMatrixColumnsFromML(Dataset<?> dataset, scala.collection.Seq<String> cols) Converts matrix columns in an input Dataset to the Matrix type from the new Matrix type under the spark.ml package.
static Dataset<Row>	convertMatrixColumnsFromML(Dataset<?> dataset, String... cols) Converts matrix columns in an input Dataset to the Matrix type from the new Matrix type under the spark.ml package.
static Dataset<Row>	convertMatrixColumnsToML(Dataset<?> dataset, scala.collection.Seq<String> cols) Converts Matrix columns in an input Dataset from the Matrix type to the new Matrix type under the spark.ml package.
static Dataset<Row>	convertMatrixColumnsToML(Dataset<?> dataset, String... cols) Converts Matrix columns in an input Dataset from the Matrix type to the new Matrix type under the spark.ml package.
static Dataset<Row>	convertVectorColumnsFromML(Dataset<?> dataset, scala.collection.Seq<String> cols) Converts vector columns in an input Dataset to the Vector type from the new Vector type under the spark.ml package.
static Dataset<Row>	convertVectorColumnsFromML(Dataset<?> dataset, String... cols) Converts vector columns in an input Dataset to the Vector type from the new Vector type under the spark.ml package.
static Dataset<Row>	convertVectorColumnsToML(Dataset<?> dataset, scala.collection.Seq<String> cols) Converts vector columns in an input Dataset from the Vector type to the new Vector type under the spark.ml package.
static Dataset<Row>	convertVectorColumnsToML(Dataset<?> dataset, String... cols) Converts vector columns in an input Dataset from the Vector type to the new Vector type under the spark.ml package.
static scala.Tuple2<RDD<Row>,RDD<Row>>[]	kFold(Dataset<Row> df, int numFolds, String foldColName) Version of kFold() taking a fold column name.
static <T> scala.Tuple2<RDD<T>,RDD<T>>[]	kFold(RDD<T> rdd, int numFolds, int seed, scala.reflect.ClassTag<T> evidence$1) Return a k element array of pairs of RDDs with the first element of each pair containing the training data, a complement of the validation data and the second element, the validation data, containing a unique 1/kth of the data.
static <T> scala.Tuple2<RDD<T>,RDD<T>>[]	kFold(RDD<T> rdd, int numFolds, long seed, scala.reflect.ClassTag<T> evidence$2) Version of kFold() taking a Long seed.
static RDD<LabeledPoint>	loadLabeledPoints(SparkContext sc, String dir) Loads labeled points saved using RDD[LabeledPoint].saveAsTextFile with the default number of partitions.
static RDD<LabeledPoint>	loadLabeledPoints(SparkContext sc, String path, int minPartitions) Loads labeled points saved using RDD[LabeledPoint].saveAsTextFile.
static RDD<LabeledPoint>	loadLibSVMFile(SparkContext sc, String path) Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], with number of features determined automatically and the default number of partitions.
static RDD<LabeledPoint>	loadLibSVMFile(SparkContext sc, String path, int numFeatures) Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of partitions.
static RDD<LabeledPoint>	loadLibSVMFile(SparkContext sc, String path, int numFeatures, int minPartitions) Loads labeled data in the LIBSVM format into an RDD[LabeledPoint].
static RDD<Vector>	loadVectors(SparkContext sc, String path) Loads vectors saved using RDD[Vector].saveAsTextFile with the default number of partitions.
static RDD<Vector>	loadVectors(SparkContext sc, String path, int minPartitions) Loads vectors saved using RDD[Vector].saveAsTextFile.
static void	org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)
static org.slf4j.Logger	org$apache$spark$internal$Logging$$log_()
static void	saveAsLibSVMFile(RDD<LabeledPoint> data, String dir) Save labeled data in LIBSVM format.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

MLUtils

public MLUtils()

Method Detail

convertVectorColumnsToML

public static Dataset<Row> convertVectorColumnsToML(Dataset<?> dataset,
                                                    String... cols)

Converts vector columns in an input Dataset from the Vector type to the new Vector type under the spark.ml package.

Parameters:

dataset - input dataset

cols - a list of vector columns to be converted. New vector columns will be ignored. If unspecified, all old vector columns will be converted except nested ones.

Returns:

the input DataFrame with old vector columns converted to the new vector type

convertVectorColumnsFromML

public static Dataset<Row> convertVectorColumnsFromML(Dataset<?> dataset,
                                                      String... cols)

Converts vector columns in an input Dataset to the Vector type from the new Vector type under the spark.ml package.

Parameters:

dataset - input dataset

cols - a list of vector columns to be converted. Old vector columns will be ignored. If unspecified, all new vector columns will be converted except nested ones.

Returns:

the input DataFrame with new vector columns converted to the old vector type

convertMatrixColumnsToML

public static Dataset<Row> convertMatrixColumnsToML(Dataset<?> dataset,
                                                    String... cols)

Converts Matrix columns in an input Dataset from the Matrix type to the new Matrix type under the spark.ml package.

Parameters:

dataset - input dataset

cols - a list of matrix columns to be converted. New matrix columns will be ignored. If unspecified, all old matrix columns will be converted except nested ones.

Returns:

the input DataFrame with old matrix columns converted to the new matrix type

convertMatrixColumnsFromML

public static Dataset<Row> convertMatrixColumnsFromML(Dataset<?> dataset,
                                                      String... cols)

Converts matrix columns in an input Dataset to the Matrix type from the new Matrix type under the spark.ml package.

Parameters:

dataset - input dataset

cols - a list of matrix columns to be converted. Old matrix columns will be ignored. If unspecified, all new matrix columns will be converted except nested ones.

Returns:

the input DataFrame with new matrix columns converted to the old matrix type

loadLibSVMFile

public static RDD<LabeledPoint> loadLibSVMFile(SparkContext sc,
                                               String path,
                                               int numFeatures,
                                               int minPartitions)

Loads labeled data in the LIBSVM format into an RDD[LabeledPoint]. The LIBSVM format is a text-based format used by LIBSVM and LIBLINEAR. Each line represents a labeled sparse feature vector using the following format:

label index1:value1 index2:value2 ...

where the indices are one-based and in ascending order. This method parses each line into a {@link org.apache.spark.mllib.regression.LabeledPoint}, where the feature indices are converted to zero-based. @param sc Spark context @param path file or directory path in any Hadoop-supported file system URI @param numFeatures number of features, which will be determined from the input data if a nonpositive value is given. This is useful when the dataset is already split into multiple files and you want to load them separately, because some features may not present in certain files, which leads to inconsistent feature dimensions. @param minPartitions min number of partitions @return labeled data stored as an RDD[LabeledPoint]

Parameters:

sc - (undocumented)

path - (undocumented)

numFeatures - (undocumented)

minPartitions - (undocumented)

Returns:

(undocumented)

loadLibSVMFile

public static RDD<LabeledPoint> loadLibSVMFile(SparkContext sc,
                                               String path,
                                               int numFeatures)

Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of partitions.

Parameters:

sc - (undocumented)

path - (undocumented)

numFeatures - (undocumented)

Returns:

(undocumented)

loadLibSVMFile

public static RDD<LabeledPoint> loadLibSVMFile(SparkContext sc,
                                               String path)

Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], with number of features determined automatically and the default number of partitions.

Parameters:

sc - (undocumented)

path - (undocumented)

Returns:

(undocumented)

saveAsLibSVMFile

public static void saveAsLibSVMFile(RDD<LabeledPoint> data,
                                    String dir)

Save labeled data in LIBSVM format.

Parameters:

data - an RDD of LabeledPoint to be saved

dir - directory to save the data

See Also:

org.apache.spark.mllib.util.MLUtils.loadLibSVMFile

loadVectors

public static RDD<Vector> loadVectors(SparkContext sc,
                                      String path,
                                      int minPartitions)

Loads vectors saved using RDD[Vector].saveAsTextFile.

Parameters:

sc - Spark context

path - file or directory path in any Hadoop-supported file system URI

minPartitions - min number of partitions

Returns:

vectors stored as an RDD[Vector]

loadVectors

public static RDD<Vector> loadVectors(SparkContext sc,
                                      String path)

Loads vectors saved using RDD[Vector].saveAsTextFile with the default number of partitions.

Parameters:

sc - (undocumented)

path - (undocumented)

Returns:

(undocumented)

loadLabeledPoints

public static RDD<LabeledPoint> loadLabeledPoints(SparkContext sc,
                                                  String path,
                                                  int minPartitions)

Loads labeled points saved using RDD[LabeledPoint].saveAsTextFile.

Parameters:

sc - Spark context

path - file or directory path in any Hadoop-supported file system URI

minPartitions - min number of partitions

Returns:

labeled points stored as an RDD[LabeledPoint]

loadLabeledPoints

public static RDD<LabeledPoint> loadLabeledPoints(SparkContext sc,
                                                  String dir)

Loads labeled points saved using RDD[LabeledPoint].saveAsTextFile with the default number of partitions.

Parameters:

sc - (undocumented)

dir - (undocumented)

Returns:

(undocumented)

kFold

public static <T> scala.Tuple2<RDD<T>,RDD<T>>[] kFold(RDD<T> rdd,
                                                      int numFolds,
                                                      int seed,
                                                      scala.reflect.ClassTag<T> evidence$1)

Return a k element array of pairs of RDDs with the first element of each pair containing the training data, a complement of the validation data and the second element, the validation data, containing a unique 1/kth of the data. Where k=numFolds.

Parameters:

rdd - (undocumented)

numFolds - (undocumented)

seed - (undocumented)

evidence$1 - (undocumented)

Returns:

(undocumented)

kFold

public static <T> scala.Tuple2<RDD<T>,RDD<T>>[] kFold(RDD<T> rdd,
                                                      int numFolds,
                                                      long seed,
                                                      scala.reflect.ClassTag<T> evidence$2)

Version of kFold() taking a Long seed.

Parameters:

rdd - (undocumented)

numFolds - (undocumented)

seed - (undocumented)

evidence$2 - (undocumented)

Returns:

(undocumented)

kFold

public static scala.Tuple2<RDD<Row>,RDD<Row>>[] kFold(Dataset<Row> df,
                                                      int numFolds,
                                                      String foldColName)

Version of kFold() taking a fold column name.

Parameters:

df - (undocumented)

numFolds - (undocumented)

foldColName - (undocumented)

Returns:

(undocumented)

appendBias

public static Vector appendBias(Vector vector)

Returns a new vector with 1.0 (bias) appended to the input vector.

Parameters:

vector - (undocumented)

Returns:

(undocumented)

convertVectorColumnsToML

public static Dataset<Row> convertVectorColumnsToML(Dataset<?> dataset,
                                                    scala.collection.Seq<String> cols)

Converts vector columns in an input Dataset from the Vector type to the new Vector type under the spark.ml package.

Parameters:

dataset - input dataset

cols - a list of vector columns to be converted. New vector columns will be ignored. If unspecified, all old vector columns will be converted except nested ones.

Returns:

the input DataFrame with old vector columns converted to the new vector type

convertVectorColumnsFromML

public static Dataset<Row> convertVectorColumnsFromML(Dataset<?> dataset,
                                                      scala.collection.Seq<String> cols)

Converts vector columns in an input Dataset to the Vector type from the new Vector type under the spark.ml package.

Parameters:

dataset - input dataset

cols - a list of vector columns to be converted. Old vector columns will be ignored. If unspecified, all new vector columns will be converted except nested ones.

Returns:

the input DataFrame with new vector columns converted to the old vector type

convertMatrixColumnsToML

public static Dataset<Row> convertMatrixColumnsToML(Dataset<?> dataset,
                                                    scala.collection.Seq<String> cols)

Converts Matrix columns in an input Dataset from the Matrix type to the new Matrix type under the spark.ml package.

Parameters:

dataset - input dataset

cols - a list of matrix columns to be converted. New matrix columns will be ignored. If unspecified, all old matrix columns will be converted except nested ones.

Returns:

the input DataFrame with old matrix columns converted to the new matrix type

convertMatrixColumnsFromML

public static Dataset<Row> convertMatrixColumnsFromML(Dataset<?> dataset,
                                                      scala.collection.Seq<String> cols)

Converts matrix columns in an input Dataset to the Matrix type from the new Matrix type under the spark.ml package.

Parameters:

dataset - input dataset

cols - a list of matrix columns to be converted. Old matrix columns will be ignored. If unspecified, all new matrix columns will be converted except nested ones.

Returns:

the input DataFrame with new matrix columns converted to the old matrix type

org$apache$spark$internal$Logging$$log_

public static org.slf4j.Logger org$apache$spark$internal$Logging$$log_()

org$apache$spark$internal$Logging$$log__$eq

public static void org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)