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
- Grouped
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- FPGrowth
- DefaultParamsWritable
- MLWritable
- FPGrowthParams
- HasPredictionCol
- Estimator
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
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Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
-
val
itemsCol: Param[String]
Items column name.
Items column name. Default: "items"
- Definition Classes
- FPGrowthParams
- Annotations
- @Since( "2.2.0" )
-
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" )
-
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" )
-
final
val
predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
Members
-
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
-
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
- FPGrowth → Estimator → PipelineStage → Params
- Annotations
- @Since( "2.2.0" )
-
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
-
def
explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam()
.- Definition Classes
- Params
-
final
def
extractParamMap(): ParamMap
extractParamMap
with no extra values.extractParamMap
with no extra values.- Definition Classes
- Params
-
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
-
def
fit(dataset: Dataset[_]): FPGrowthModel
Fits a model to the input data.
-
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" )
-
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" )
-
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()
-
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
-
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
-
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
-
def
getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
-
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
-
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
-
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
-
final
def
isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
-
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.
-
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( ... )
-
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
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
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 byParam.validate()
.Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- FPGrowth → PipelineStage
- Annotations
- @Since( "2.2.0" )
-
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
- FPGrowth → Identifiable
- Annotations
- @Since( "2.2.0" )
-
def
write: MLWriter
Returns an
MLWriter
instance for this ML instance.Returns an
MLWriter
instance for this ML instance.- Definition Classes
- DefaultParamsWritable → MLWritable
Parameter setters
-
def
setItemsCol(value: String): FPGrowth.this.type
- Annotations
- @Since( "2.2.0" )
-
def
setMinConfidence(value: Double): FPGrowth.this.type
- Annotations
- @Since( "2.2.0" )
-
def
setMinSupport(value: Double): FPGrowth.this.type
- Annotations
- @Since( "2.2.0" )
-
def
setPredictionCol(value: String): FPGrowth.this.type
- Annotations
- @Since( "2.2.0" )
Parameter getters
-
def
getItemsCol: String
- Definition Classes
- FPGrowthParams
- Annotations
- @Since( "2.2.0" )
-
def
getMinConfidence: Double
- Definition Classes
- FPGrowthParams
- Annotations
- @Since( "2.2.0" )
-
def
getMinSupport: Double
- Definition Classes
- FPGrowthParams
- Annotations
- @Since( "2.2.0" )
-
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.
(expert-only) Parameter setters
(expert-only) Parameter getters
-
def
getNumPartitions: Int
- Definition Classes
- FPGrowthParams
- Annotations
- @Since( "2.2.0" )