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org.apache.spark.mllib.util

MLUtils

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object MLUtils extends Logging

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

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@Since( "0.8.0" )
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  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def appendBias(vector: Vector): Vector

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

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

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    @Since( "1.0.0" )
  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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  7. def convertMatrixColumnsFromML(dataset: Dataset[_], cols: String*): DataFrame

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

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

    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

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    @Since( "2.0.0" ) @varargs()
  8. def convertMatrixColumnsToML(dataset: Dataset[_], cols: String*): DataFrame

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

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

    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

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    @Since( "2.0.0" ) @varargs()
  9. def convertVectorColumnsFromML(dataset: Dataset[_], cols: String*): DataFrame

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

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

    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

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    @Since( "2.0.0" ) @varargs()
  10. def convertVectorColumnsToML(dataset: Dataset[_], cols: String*): DataFrame

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

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

    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

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    @Since( "2.0.0" ) @varargs()
  11. final def eq(arg0: AnyRef): Boolean

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  12. def equals(arg0: Any): Boolean

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  13. def finalize(): Unit

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  14. final def getClass(): Class[_]

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  15. def hashCode(): Int

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  16. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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  17. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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  18. final def isInstanceOf[T0]: Boolean

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  19. def isTraceEnabled(): Boolean

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  20. def kFold[T](rdd: RDD[T], numFolds: Int, seed: Long)(implicit arg0: ClassTag[T]): Array[(RDD[T], RDD[T])]

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    Version of kFold() taking a Long seed.

    Version of kFold() taking a Long seed.

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    @Since( "2.0.0" )
  21. def kFold[T](rdd: RDD[T], numFolds: Int, seed: Int)(implicit arg0: ClassTag[T]): Array[(RDD[T], RDD[T])]

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    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.

    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.

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    @Since( "1.0.0" )
  22. def loadLabeledPoints(sc: SparkContext, dir: String): RDD[LabeledPoint]

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

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

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    @Since( "1.1.0" )
  23. def loadLabeledPoints(sc: SparkContext, path: String, minPartitions: Int): RDD[LabeledPoint]

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

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

    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]

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    @Since( "1.1.0" )
  24. def loadLibSVMFile(sc: SparkContext, path: String): RDD[LabeledPoint]

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

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

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    @Since( "1.0.0" )
  25. def loadLibSVMFile(sc: SparkContext, path: String, numFeatures: Int): RDD[LabeledPoint]

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

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

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    @Since( "1.0.0" )
  26. def loadLibSVMFile(sc: SparkContext, path: String, numFeatures: Int, minPartitions: Int): RDD[LabeledPoint]

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    Loads labeled data in the LIBSVM format into an RDD[LabeledPoint].

    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 org.apache.spark.mllib.regression.LabeledPoint, where the feature indices are converted to zero-based.

    sc

    Spark context

    path

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

    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.

    minPartitions

    min number of partitions

    returns

    labeled data stored as an RDD[LabeledPoint]

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    @Since( "1.0.0" )
  27. def loadVectors(sc: SparkContext, path: String): RDD[Vector]

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

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

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    @Since( "1.1.0" )
  28. def loadVectors(sc: SparkContext, path: String, minPartitions: Int): RDD[Vector]

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    Loads vectors saved using RDD[Vector].saveAsTextFile.

    Loads vectors saved using RDD[Vector].saveAsTextFile.

    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]

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    @Since( "1.1.0" )
  29. def log: Logger

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  30. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  31. def logDebug(msg: ⇒ String): Unit

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  32. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  33. def logError(msg: ⇒ String): Unit

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  34. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  35. def logInfo(msg: ⇒ String): Unit

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  36. def logName: String

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  37. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  38. def logTrace(msg: ⇒ String): Unit

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  39. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  40. def logWarning(msg: ⇒ String): Unit

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  41. final def ne(arg0: AnyRef): Boolean

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  42. final def notify(): Unit

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  43. final def notifyAll(): Unit

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  44. def saveAsLibSVMFile(data: RDD[LabeledPoint], dir: String): Unit

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    Save labeled data in LIBSVM format.

    Save labeled data in LIBSVM format.

    data

    an RDD of LabeledPoint to be saved

    dir

    directory to save the data

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    @Since( "1.0.0" )
    See also

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

  45. final def synchronized[T0](arg0: ⇒ T0): T0

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  46. def toString(): String

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  47. final def wait(): Unit

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  48. final def wait(arg0: Long, arg1: Int): Unit

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  49. final def wait(arg0: Long): Unit

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