class FPGrowthModel extends Model[FPGrowthModel] with FPGrowthParams with MLWritable
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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.
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val
itemsCol: Param[String]
Items column name.
Items column name. Default: "items"
- Definition Classes
- FPGrowthParams
- Annotations
- @Since( "2.2.0" )
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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" )
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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" )
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final
val
predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
Members
-
def
associationRules: DataFrame
Get association rules fitted using the minConfidence.
Get association rules fitted using the minConfidence. Returns a dataframe with five fields, "antecedent", "consequent", "confidence", "lift" and "support", where "antecedent" and "consequent" are Array[T], whereas "confidence", "lift" and "support" are Double.
- Annotations
- @Since( "2.2.0" ) @transient()
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final
def
clear(param: Param[_]): FPGrowthModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
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def
copy(extra: ParamMap): FPGrowthModel
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
- FPGrowthModel → Model → Transformer → 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
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final
def
extractParamMap(): ParamMap
extractParamMapwith no extra values.extractParamMapwith no extra values.- Definition Classes
- Params
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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
-
val
freqItemsets: DataFrame
- Annotations
- @Since( "2.2.0" )
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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
-
def
hasParent: Boolean
Indicates whether this Model has a corresponding parent.
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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.
-
var
parent: Estimator[FPGrowthModel]
The parent estimator that produced this model.
The parent estimator that produced this model.
- Definition Classes
- Model
- Note
For ensembles' component Models, this value can be null.
-
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( ... )
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final
def
set[T](param: Param[T], value: T): FPGrowthModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
def
setParent(parent: Estimator[FPGrowthModel]): FPGrowthModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
- Definition Classes
- Model
-
def
toString(): String
- Definition Classes
- FPGrowthModel → Identifiable → AnyRef → Any
- Annotations
- @Since( "3.0.0" )
-
def
transform(dataset: Dataset[_]): DataFrame
The transform method first generates the association rules according to the frequent itemsets.
The transform method first generates the association rules according to the frequent itemsets. Then for each transaction in itemsCol, the transform method will compare its items against the antecedents of each association rule. If the record contains all the antecedents of a specific association rule, the rule will be considered as applicable and its consequents will be added to the prediction result. The transform method will summarize the consequents from all the applicable rules as prediction. The prediction column has the same data type as the input column(Array[T]) and will not contain existing items in the input column. The null values in the itemsCol columns are treated as empty sets. WARNING: internally it collects association rules to the driver and uses broadcast for efficiency. This may bring pressure to driver memory for large set of association rules.
- Definition Classes
- FPGrowthModel → Transformer
- Annotations
- @Since( "2.2.0" )
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
- dataset
input dataset
- paramMap
additional parameters, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
- dataset
input dataset
- firstParamPair
the first param pair, overwrite embedded params
- otherParamPairs
other param pairs, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
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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
transformSchemaand 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
- FPGrowthModel → PipelineStage
- Annotations
- @Since( "2.2.0" )
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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
- FPGrowthModel → Identifiable
- Annotations
- @Since( "2.2.0" )
-
def
write: MLWriter
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- FPGrowthModel → MLWritable
- Annotations
- @Since( "2.2.0" )
Parameter setters
-
def
setItemsCol(value: String): FPGrowthModel.this.type
- Annotations
- @Since( "2.2.0" )
-
def
setMinConfidence(value: Double): FPGrowthModel.this.type
- Annotations
- @Since( "2.2.0" )
-
def
setPredictionCol(value: String): FPGrowthModel.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 getters
-
def
getNumPartitions: Int
- Definition Classes
- FPGrowthParams
- Annotations
- @Since( "2.2.0" )