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

class FPGrowth extends Estimator[FPGrowthModel] with FPGrowthParams with DefaultParamsWritable

A parallel FP-growth algorithm to mine frequent itemsets. The algorithm is described in Li et al., PFP: Parallel FP-Growth for Query Recommendation. PFP distributes computation in such a way that each worker executes an independent group of mining tasks. The FP-Growth algorithm is described in Han et al., Mining frequent patterns without candidate generation. Note null values in the itemsCol column are ignored during fit().

Annotations
@Since( "2.2.0" )
Source
FPGrowth.scala
See also

Association rule learning (Wikipedia)

Linear Supertypes
DefaultParamsWritable, MLWritable, FPGrowthParams, HasPredictionCol, Estimator[FPGrowthModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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  3. By Inheritance
Inherited
  1. FPGrowth
  2. DefaultParamsWritable
  3. MLWritable
  4. FPGrowthParams
  5. HasPredictionCol
  6. Estimator
  7. PipelineStage
  8. Logging
  9. Params
  10. Serializable
  11. Serializable
  12. Identifiable
  13. AnyRef
  14. 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 itemsCol: Param[String]

    Items column name.

    Items column name. Default: "items"

    Definition Classes
    FPGrowthParams
    Annotations
    @Since( "2.2.0" )
  2. val minConfidence: DoubleParam

    Minimal confidence for generating Association Rule.

    Minimal confidence for generating Association Rule. minConfidence will not affect the mining for frequent itemsets, but will affect the association rules generation. Default: 0.8

    Definition Classes
    FPGrowthParams
    Annotations
    @Since( "2.2.0" )
  3. val minSupport: DoubleParam

    Minimal support level of the frequent pattern.

    Minimal support level of the frequent pattern. [0.0, 1.0]. Any pattern that appears more than (minSupport * size-of-the-dataset) times will be output in the frequent itemsets. Default: 0.3

    Definition Classes
    FPGrowthParams
    Annotations
    @Since( "2.2.0" )
  4. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol

Members

  1. final def clear(param: Param[_]): FPGrowth.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): FPGrowth

    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
    FPGrowthEstimatorPipelineStageParams
    Annotations
    @Since( "2.2.0" )
  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 fit(dataset: Dataset[_]): FPGrowthModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    FPGrowthEstimator
    Annotations
    @Since( "2.2.0" )
  8. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[FPGrowthModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  9. def fit(dataset: Dataset[_], paramMap: ParamMap): FPGrowthModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  10. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): FPGrowthModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  11. 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
  12. 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
  13. 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
  14. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  15. 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
  16. 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
  17. 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
  18. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  19. 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.

  20. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  21. final def set[T](param: Param[T], value: T): FPGrowth.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  22. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  23. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes
    FPGrowthPipelineStage
    Annotations
    @Since( "2.2.0" )
  24. 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
    FPGrowthIdentifiable
    Annotations
    @Since( "2.2.0" )
  25. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritableMLWritable

Parameter setters

  1. def setItemsCol(value: String): FPGrowth.this.type

    Annotations
    @Since( "2.2.0" )
  2. def setMinConfidence(value: Double): FPGrowth.this.type

    Annotations
    @Since( "2.2.0" )
  3. def setMinSupport(value: Double): FPGrowth.this.type

    Annotations
    @Since( "2.2.0" )
  4. def setPredictionCol(value: String): FPGrowth.this.type

    Annotations
    @Since( "2.2.0" )

Parameter getters

  1. def getItemsCol: String

    Definition Classes
    FPGrowthParams
    Annotations
    @Since( "2.2.0" )
  2. def getMinConfidence: Double

    Definition Classes
    FPGrowthParams
    Annotations
    @Since( "2.2.0" )
  3. def getMinSupport: Double

    Definition Classes
    FPGrowthParams
    Annotations
    @Since( "2.2.0" )
  4. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol

(expert-only) Parameters

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

  1. val numPartitions: IntParam

    Number of partitions (at least 1) used by parallel FP-growth.

    Number of partitions (at least 1) used by parallel FP-growth. By default the param is not set, and partition number of the input dataset is used.

    Definition Classes
    FPGrowthParams
    Annotations
    @Since( "2.2.0" )

(expert-only) Parameter setters

  1. def setNumPartitions(value: Int): FPGrowth.this.type

    Annotations
    @Since( "2.2.0" )

(expert-only) Parameter getters

  1. def getNumPartitions: Int

    Definition Classes
    FPGrowthParams
    Annotations
    @Since( "2.2.0" )