FPGrowth

class pyspark.ml.fpm.FPGrowth(*, minSupport=0.3, minConfidence=0.8, itemsCol='items', predictionCol='prediction', numPartitions=None)[source]

A parallel FP-growth algorithm to mine frequent itemsets.

New in version 2.2.0.

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.show(5)
+---------+----+
|    items|freq|
+---------+----+
|      [s]|   3|
|   [s, x]|   3|
|[s, x, z]|   2|
|   [s, z]|   2|
|      [r]|   3|
+---------+----+
only showing top 5 rows
...
>>> fpm.associationRules.show(5)
+----------+----------+----------+----+------------------+
|antecedent|consequent|confidence|lift|           support|
+----------+----------+----------+----+------------------+
|    [t, s]|       [y]|       1.0| 2.0|0.3333333333333333|
|    [t, s]|       [x]|       1.0| 1.5|0.3333333333333333|
|    [t, s]|       [z]|       1.0| 1.2|0.3333333333333333|
|       [p]|       [r]|       1.0| 2.0|0.3333333333333333|
|       [p]|       [z]|       1.0| 1.2|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, …])

New in version 2.2.0.

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)

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

copy(extra=None)

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)

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=None)

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, params=None)

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

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, paramMaps)

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

New in version 2.3.0.

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()

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. Raises an error if neither is set.

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.

classmethod load(path)

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

classmethod 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)[source]

Sets the value of itemsCol.

setMinConfidence(value)[source]

Sets the value of minConfidence.

setMinSupport(value)[source]

Sets the value of minSupport.

setNumPartitions(value)[source]

Sets the value of numPartitions.

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

New in version 2.2.0.

setPredictionCol(value)[source]

Sets the value of predictionCol.

write()

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.')