FPGrowth¶

class pyspark.ml.fpm.FPGrowth(*, minSupport: float = 0.3, minConfidence: float = 0.8, itemsCol: str = 'items', predictionCol: str = 'prediction', numPartitions: Optional[int] = None)¶

A parallel FP-growth algorithm to mine frequent itemsets.

Notes

The algorithm is described in Li et al., PFP: Parallel FP-Growth for Query Recommendation [1]. 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 [2]

NULL values in the feature column are ignored during fit().

Internally transform collects and broadcasts association rules.

1: Haoyuan Li, Yi Wang, Dong Zhang, Ming Zhang, and Edward Y. Chang. 2008. Pfp: parallel fp-growth for query recommendation. In Proceedings of the 2008 ACM conference on Recommender systems (RecSys ‘08). Association for Computing Machinery, New York, NY, USA, 107-114. DOI: https://doi.org/10.1145/1454008.1454027
2: Jiawei Han, Jian Pei, and Yiwen Yin. 2000. Mining frequent patterns without candidate generation. SIGMOD Rec. 29, 2 (June 2000), 1-12. DOI: https://doi.org/10.1145/335191.335372

Examples

>>> from pyspark.sql.functions import split
>>> data = (spark.read
...     .text("data/mllib/sample_fpgrowth.txt")
...     .select(split("value", "\s+").alias("items")))
>>> data.show(truncate=False)
+------------------------+
|items                   |
+------------------------+
|[r, z, h, k, p]         |
|[z, y, x, w, v, u, t, s]|
|[s, x, o, n, r]         |
|[x, z, y, m, t, s, q, e]|
|[z]                     |
|[x, z, y, r, q, t, p]   |
+------------------------+
...
>>> fp = FPGrowth(minSupport=0.2, minConfidence=0.7)
>>> fpm = fp.fit(data)
>>> fpm.setPredictionCol("newPrediction")
FPGrowthModel...
>>> fpm.freqItemsets.sort("items").show(5)
+---------+----+
|    items|freq|
+---------+----+
|      [p]|   2|
|   [p, r]|   2|
|[p, r, z]|   2|
|   [p, z]|   2|
|      [q]|   2|
+---------+----+
only showing top 5 rows
...
>>> fpm.associationRules.sort("antecedent", "consequent").show(5)
+----------+----------+----------+----+------------------+
|antecedent|consequent|confidence|lift|           support|
+----------+----------+----------+----+------------------+
|       [p]|       [r]|       1.0| 2.0|0.3333333333333333|
|       [p]|       [z]|       1.0| 1.2|0.3333333333333333|
|    [p, r]|       [z]|       1.0| 1.2|0.3333333333333333|
|    [p, z]|       [r]|       1.0| 2.0|0.3333333333333333|
|       [q]|       [t]|       1.0| 2.0|0.3333333333333333|
+----------+----------+----------+----+------------------+
only showing top 5 rows
...
>>> new_data = spark.createDataFrame([(["t", "s"], )], ["items"])
>>> sorted(fpm.transform(new_data).first().newPrediction)
['x', 'y', 'z']
>>> model_path = temp_path + "/fpm_model"
>>> fpm.save(model_path)
>>> model2 = FPGrowthModel.load(model_path)
>>> fpm.transform(data).take(1) == model2.transform(data).take(1)
True

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.
`fit`(dataset[, params])	Fits a model to the input dataset with optional parameters.
`fitMultiple`(dataset, paramMaps)	Fits a model to the input dataset for each param map in paramMaps.
`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`.
`setMinSupport`(value)	Sets the value of `minSupport`.
`setNumPartitions`(value)	Sets the value of `numPartitions`.
`setParams`(self, \*[, minSupport, …])
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`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

fit(dataset: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]¶

Fits a model to the input dataset with optional parameters.

Parameters

datasetpyspark.sql.DataFrame: input dataset.
paramsdict or list or tuple, optional: an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns

Transformer or a list of Transformer: fitted model(s)

fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]¶

Fits a model to the input dataset for each param map in paramMaps.

Parameters

datasetpyspark.sql.DataFrame: input dataset.
paramMapscollections.abc.Sequence: A Sequence of param maps.

Returns

_FitMultipleIterator: A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

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.FPGrowth ¶: Sets the value of itemsCol.

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

setMinSupport(value: float) → pyspark.ml.fpm.FPGrowth ¶: Sets the value of minSupport.

setNumPartitions(value: int) → pyspark.ml.fpm.FPGrowth ¶: Sets the value of numPartitions.

setParams(self, \*, minSupport=0.3, minConfidence=0.8, itemsCol="items", predictionCol="prediction", numPartitions=None)¶

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

write() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

itemsCol = Param(parent='undefined', name='itemsCol', doc='items column name')¶

minConfidence = 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 = 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 = 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.')¶

RankingEvaluator

FPGrowthModel