# PrefixSpan¶

class pyspark.ml.fpm.PrefixSpan(*, minSupport: float = 0.1, maxPatternLength: int = 10, maxLocalProjDBSize: int = 32000000, sequenceCol: str = 'sequence')

A parallel PrefixSpan algorithm to mine frequent sequential patterns. The PrefixSpan algorithm is described in J. Pei, et al., PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth (see here). This class is not yet an Estimator/Transformer, use findFrequentSequentialPatterns() method to run the PrefixSpan algorithm.

Notes

Examples

>>> from pyspark.ml.fpm import PrefixSpan
>>> from pyspark.sql import Row
>>> df = sc.parallelize([Row(sequence=[[1, 2], [3]]),
...                      Row(sequence=[[1], [3, 2], [1, 2]]),
...                      Row(sequence=[[1, 2], [5]]),
...                      Row(sequence=[[6]])]).toDF()
>>> prefixSpan = PrefixSpan()
>>> prefixSpan.getMaxLocalProjDBSize()
32000000
>>> prefixSpan.getSequenceCol()
'sequence'
>>> prefixSpan.setMinSupport(0.5)
PrefixSpan...
>>> prefixSpan.setMaxPatternLength(5)
PrefixSpan...
>>> prefixSpan.findFrequentSequentialPatterns(df).sort("sequence").show(truncate=False)
+----------+----+
|sequence  |freq|
+----------+----+
|[[1]]     |3   |
|[[1], [3]]|2   |
|[[2]]     |3   |
|[[2, 1]]  |3   |
|[[3]]     |2   |
+----------+----+
...


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. 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. Finds the complete set of frequent sequential patterns in the input sequences of itemsets. Gets the value of maxLocalProjDBSize or its default value. Gets the value of maxPatternLength or its default value. Gets the value of minSupport 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. Gets the value of sequenceCol 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. set(param, value) Sets a parameter in the embedded param map. Sets the value of maxLocalProjDBSize. Sets the value of maxPatternLength. setMinSupport(value) Sets the value of minSupport. setParams(self, \*[, minSupport, …]) setSequenceCol(value) Sets the value of sequenceCol.

Attributes

 maxLocalProjDBSize maxPatternLength minSupport params Returns all params ordered by name. sequenceCol

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

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

extra param values

Returns
dict

merged param map

findFrequentSequentialPatterns(dataset: pyspark.sql.dataframe.DataFrame) → pyspark.sql.dataframe.DataFrame

Finds the complete set of frequent sequential patterns in the input sequences of itemsets.

Parameters
datasetpyspark.sql.DataFrame

A dataframe containing a sequence column which is ArrayType(ArrayType(T)) type, T is the item type for the input dataset.

Returns
pyspark.sql.DataFrame

A DataFrame that contains columns of sequence and corresponding frequency. The schema of it will be:

• sequence: ArrayType(ArrayType(T)) (T is the item type)

• freq: Long

getMaxLocalProjDBSize() → int

Gets the value of maxLocalProjDBSize or its default value.

getMaxPatternLength() → int

Gets the value of maxPatternLength or its default value.

getMinSupport() → float

Gets the value of minSupport 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.

getSequenceCol() → str

Gets the value of sequenceCol 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.

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

Sets a parameter in the embedded param map.

setMaxLocalProjDBSize(value: int)pyspark.ml.fpm.PrefixSpan

Sets the value of maxLocalProjDBSize.

setMaxPatternLength(value: int)pyspark.ml.fpm.PrefixSpan

Sets the value of maxPatternLength.

setMinSupport(value: float)pyspark.ml.fpm.PrefixSpan

Sets the value of minSupport.

setParams(self, \*, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000, sequenceCol="sequence")
setSequenceCol(value: str)pyspark.ml.fpm.PrefixSpan

Sets the value of sequenceCol.

Attributes Documentation

maxLocalProjDBSize: pyspark.ml.param.Param[int] = Param(parent='undefined', name='maxLocalProjDBSize', doc='The maximum number of items (including delimiters used in the internal storage format) allowed in a projected database before local processing. If a projected database exceeds this size, another iteration of distributed prefix growth is run. Must be > 0.')
maxPatternLength: pyspark.ml.param.Param[int] = Param(parent='undefined', name='maxPatternLength', doc='The maximal length of the sequential pattern. Must be > 0.')
minSupport: pyspark.ml.param.Param[float] = Param(parent='undefined', name='minSupport', doc='The minimal support level of the sequential pattern. Sequential pattern that appears more than (minSupport * size-of-the-dataset) times will be output. Must be >= 0.')
params

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

sequenceCol: pyspark.ml.param.Param[str] = Param(parent='undefined', name='sequenceCol', doc='The name of the sequence column in dataset, rows with nulls in this column are ignored.')