FPGrowthModel¶

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

Model fitted by FPGrowth.

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.

setMinConfidence(value: float) → pyspark.ml.fpm.FPGrowthModel ¶: Sets the value of minConfidence.

setPredictionCol(value: str) → pyspark.ml.fpm.FPGrowthModel ¶: Sets the value of predictionCol.

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

Transforms the input dataset with optional parameters.

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).

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

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

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