FPGrowthModel

class pyspark.ml.fpm.FPGrowthModel(java_model: Optional[JavaObject] = None)

Model fitted by FPGrowth.

New in version 2.2.0.

Methods

clear(param)	Clears a param from the param map if it has been explicitly set.
copy([extra])	Creates a copy of this instance with the same uid and some extra params.
explainParam(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams()	Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([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 < user-supplied values < extra.
getItemsCol()	Gets the value of itemsCol or its default value.
getMinConfidence()	Gets the value of minConfidence or its default value.
getMinSupport()	Gets the value of minSupport or its default value.
getNumPartitions()	Gets the value of numPartitions or its default value.
getOrDefault(param)	Gets the value of a param in the user-supplied param map or its default value.
getParam(paramName)	Gets a param by its name.
getPredictionCol()	Gets the value of predictionCol or its default value.
hasDefault(param)	Checks whether a param has a default value.
hasParam(paramName)	Tests whether this instance contains a param with a given (string) name.
isDefined(param)	Checks whether a param is explicitly set by user or has a default value.
isSet(param)	Checks whether a param is explicitly set by user.
load(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
read()	Returns an MLReader instance for this class.
save(path)	Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set(param, value)	Sets a parameter in the embedded param map.
setItemsCol(value)	Sets the value of itemsCol.
setMinConfidence(value)	Sets the value of minConfidence.
setPredictionCol(value)	Sets the value of predictionCol.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

associationRules	DataFrame with four columns: * antecedent - Array of the same type as the input column.
freqItemsets	DataFrame with two columns: * items - Itemset of the same type as the input column.
itemsCol
minConfidence
minSupport
numPartitions
params	Returns all params ordered by name.
predictionCol

Methods Documentation

clear(param: pyspark.ml.param.Param) → None

Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

extradict, optional

Extra parameters to copy to the new instance

Returns

JavaParams

Copy of this instance

explainParam(param: Union[str, pyspark.ml.param.Param]) → str

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → 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 < user-supplied values < extra.

Parameters

extradict, optional

extra param values

Returns

dict

merged param map

getItemsCol() → str

Gets the value of itemsCol or its default value.

getMinConfidence() → float

Gets the value of minConfidence or its default value.

getMinSupport() → float

Gets the value of minSupport or its default value.

getNumPartitions() → int

Gets the value of numPartitions or its default value.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getParam(paramName: str) → pyspark.ml.param.Param

Gets a param by its name.

getPredictionCol() → str

Gets the value of predictionCol or its default value.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param has a default value.

hasParam(paramName: str) → bool

Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

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

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL

Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read() → pyspark.ml.util.JavaMLReader[RL]

Returns an MLReader instance for this class.

save(path: str) → None

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None

Sets a parameter in the embedded param map.

setItemsCol(value: str) → pyspark.ml.fpm.FPGrowthModel

Sets the value of itemsCol.

New in version 3.0.0.

setMinConfidence(value: float) → pyspark.ml.fpm.FPGrowthModel

Sets the value of minConfidence.

New in version 3.0.0.

setPredictionCol(value: str) → pyspark.ml.fpm.FPGrowthModel

Sets the value of predictionCol.

New in version 3.0.0.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame

input dataset

paramsdict, optional

an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame

transformed dataset

write() → pyspark.ml.util.JavaMLWriter

Returns an MLWriter instance for this ML instance.

Attributes Documentation

associationRules

DataFrame with four columns: * antecedent - Array of the same type as the input column. * consequent - Array of the same type as the input column. * confidence - Confidence for the rule (DoubleType). * lift - Lift for the rule (DoubleType).

New in version 2.2.0.

freqItemsets

DataFrame with two columns: * items - Itemset of the same type as the input column. * freq - Frequency of the itemset (LongType).

New in version 2.2.0.

itemsCol: pyspark.ml.param.Param[str] = Param(parent='undefined', name='itemsCol', doc='items column name')

minConfidence: pyspark.ml.param.Param[float] = Param(parent='undefined', name='minConfidence', doc='Minimal confidence for generating Association Rule. [0.0, 1.0]. minConfidence will not affect the mining for frequent itemsets, but will affect the association rules generation.')

minSupport: pyspark.ml.param.Param[float] = Param(parent='undefined', name='minSupport', doc='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.')

numPartitions: pyspark.ml.param.Param[int] = Param(parent='undefined', name='numPartitions', doc='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.')

params

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')

FPGrowth PrefixSpan