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. The algorithm is described in Li et al., PFP: Parallel FP-Growth for Query Recommendation [LI2008]. 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 [HAN2000]
Note
null values in the feature column are ignored during fit().
Note
Internally transform collects and broadcasts association rules.
>>> 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| +----------+----------+----------+----+ | [t, s]| [y]| 1.0| 2.0| | [t, s]| [x]| 1.0| 1.5| | [t, s]| [z]| 1.0| 1.2| | [p]| [r]| 1.0| 2.0| | [p]| [z]| 1.0| 1.2| +----------+----------+----------+----+ only showing top 5 rows ... >>> new_data = spark.createDataFrame([(["t", "s"], )], ["items"]) >>> sorted(fpm.transform(new_data).first().newPrediction) ['x', 'y', 'z']
New in version 2.2.0.
Methods
Attributes
Methods Documentation
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clear
(param)¶ Clears a param from the param map if it has been explicitly set.
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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
extra – Extra parameters to copy to the new instance
- Returns
Copy of this instance
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explainParam
(param)¶ Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
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explainParams
()¶ Returns the documentation of all params with their optionally default values and user-supplied values.
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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
extra – extra param values
- Returns
merged param map
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fit
(dataset, params=None)¶ Fits a model to the input dataset with optional parameters.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – 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
fitted model(s)
New in version 1.3.0.
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fitMultiple
(dataset, paramMaps)¶ Fits a model to the input dataset for each param map in paramMaps.
- Parameters
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
.paramMaps – A Sequence of param maps.
- Returns
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.
New in version 2.3.0.
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getItemsCol
()¶ Gets the value of itemsCol or its default value.
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getMinConfidence
()¶ Gets the value of minConfidence or its default value.
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getMinSupport
()¶ Gets the value of minSupport or its default value.
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getNumPartitions
()¶ Gets the value of
numPartitions
or its default value.
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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.
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getParam
(paramName)¶ Gets a param by its name.
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getPredictionCol
()¶ Gets the value of predictionCol or its default value.
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hasDefault
(param)¶ Checks whether a param has a default value.
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hasParam
(paramName)¶ Tests whether this instance contains a param with a given (string) name.
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isDefined
(param)¶ Checks whether a param is explicitly set by user or has a default value.
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isSet
(param)¶ Checks whether a param is explicitly set by user.
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classmethod
load
(path)¶ Reads an ML instance from the input path, a shortcut of read().load(path).
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classmethod
read
()¶ Returns an MLReader instance for this class.
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save
(path)¶ Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
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set
(param, value)¶ Sets a parameter in the embedded param map.
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setMinConfidence
(value)[source]¶ Sets the value of
minConfidence
.
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setMinSupport
(value)[source]¶ Sets the value of
minSupport
.
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setNumPartitions
(value)[source]¶ Sets the value of
numPartitions
.
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setParams
(self, minSupport=0.3, minConfidence=0.8, itemsCol='items', predictionCol='prediction', numPartitions=None)[source]¶ New in version 2.2.0.
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setPredictionCol
(value)[source]¶ Sets the value of
predictionCol
.
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write
()¶ Returns an MLWriter instance for this ML instance.
Attributes Documentation
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itemsCol
= Param(parent='undefined', name='itemsCol', doc='items column name')¶
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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.')¶
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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.')¶
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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.')¶
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params
¶ Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
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predictionCol
= Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶
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