Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package ml

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    Definition Classes
    spark
  • package fpm
    Definition Classes
    ml
  • FPGrowth
  • FPGrowthModel
  • PrefixSpan
c

org.apache.spark.ml.fpm

PrefixSpan

final class PrefixSpan extends Params

A parallel PrefixSpan algorithm to mine frequent sequential patterns. The PrefixSpan algorithm is described in J. Pei, et al., PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth (see here). This class is not yet an Estimator/Transformer, use findFrequentSequentialPatterns method to run the PrefixSpan algorithm.

Annotations
@Since( "2.4.0" )
Source
PrefixSpan.scala
See also

Sequential Pattern Mining (Wikipedia)

Linear Supertypes
Params, Serializable, Serializable, Identifiable, AnyRef, Any
Ordering
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Inherited
  1. PrefixSpan
  2. Params
  3. Serializable
  4. Serializable
  5. Identifiable
  6. AnyRef
  7. Any
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Visibility
  1. Public
  2. All

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. val maxLocalProjDBSize: LongParam

    Param for the maximum number of items (including delimiters used in the internal storage format) allowed in a projected database before local processing (default: 32000000).

    Param for the maximum number of items (including delimiters used in the internal storage format) allowed in a projected database before local processing (default: 32000000). If a projected database exceeds this size, another iteration of distributed prefix growth is run.

    Annotations
    @Since( "2.4.0" )
  2. val maxPatternLength: IntParam

    Param for the maximal pattern length (default: 10).

    Param for the maximal pattern length (default: 10).

    Annotations
    @Since( "2.4.0" )
  3. val minSupport: DoubleParam

    Param for the minimal support level (default: 0.1).

    Param for the minimal support level (default: 0.1). Sequential patterns that appear more than (minSupport * size-of-the-dataset) times are identified as frequent sequential patterns.

    Annotations
    @Since( "2.4.0" )
  4. val sequenceCol: Param[String]

    Param for the name of the sequence column in dataset (default "sequence"), rows with nulls in this column are ignored.

    Param for the name of the sequence column in dataset (default "sequence"), rows with nulls in this column are ignored.

    Annotations
    @Since( "2.4.0" )

Members

  1. final def clear(param: Param[_]): PrefixSpan.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  2. def copy(extra: ParamMap): PrefixSpan

    Creates a copy of this instance with the same UID and some extra params.

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Definition Classes
    PrefixSpanParams
    Annotations
    @Since( "2.4.0" )
  3. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  4. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  5. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  6. final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  7. def findFrequentSequentialPatterns(dataset: Dataset[_]): DataFrame

    Finds the complete set of frequent sequential patterns in the input sequences of itemsets.

    Finds the complete set of frequent sequential patterns in the input sequences of itemsets.

    dataset

    A dataset or a dataframe containing a sequence column which is

    ArrayType(ArrayType(T))
    returns

    A DataFrame that contains columns of sequence and corresponding frequency. The schema of it will be:

    • sequence: ArrayType(ArrayType(T)) (T is the item type)
    • freq: Long
    Annotations
    @Since( "2.4.0" )
  8. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  9. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  10. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  11. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  12. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  13. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  14. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  15. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  16. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  17. final def set[T](param: Param[T], value: T): PrefixSpan.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  18. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  19. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    PrefixSpanIdentifiable
    Annotations
    @Since( "2.4.0" )

Parameter setters

  1. def setMaxLocalProjDBSize(value: Long): PrefixSpan.this.type

    Annotations
    @Since( "2.4.0" )
  2. def setMaxPatternLength(value: Int): PrefixSpan.this.type

    Annotations
    @Since( "2.4.0" )
  3. def setMinSupport(value: Double): PrefixSpan.this.type

    Annotations
    @Since( "2.4.0" )
  4. def setSequenceCol(value: String): PrefixSpan.this.type

    Annotations
    @Since( "2.4.0" )

Parameter getters

  1. def getMaxLocalProjDBSize: Long

    Annotations
    @Since( "2.4.0" )
  2. def getMaxPatternLength: Int

    Annotations
    @Since( "2.4.0" )
  3. def getMinSupport: Double

    Annotations
    @Since( "2.4.0" )
  4. def getSequenceCol: String

    Annotations
    @Since( "2.4.0" )