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from pyspark import keyword_only, since
from pyspark.sql import DataFrame
from pyspark.ml.util import *
from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaParams, _jvm
from pyspark.ml.param.shared import *
__all__ = ["FPGrowth", "FPGrowthModel", "PrefixSpan"]
class HasMinSupport(Params):
"""
Mixin for param minSupport.
"""
minSupport = Param(
Params._dummy(),
"minSupport",
"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.",
typeConverter=TypeConverters.toFloat)
def setMinSupport(self, value):
"""
Sets the value of :py:attr:`minSupport`.
"""
return self._set(minSupport=value)
def getMinSupport(self):
"""
Gets the value of minSupport or its default value.
"""
return self.getOrDefault(self.minSupport)
class HasNumPartitions(Params):
"""
Mixin for param numPartitions: Number of partitions (at least 1) used by parallel FP-growth.
"""
numPartitions = Param(
Params._dummy(),
"numPartitions",
"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.",
typeConverter=TypeConverters.toInt)
def setNumPartitions(self, value):
"""
Sets the value of :py:attr:`numPartitions`.
"""
return self._set(numPartitions=value)
def getNumPartitions(self):
"""
Gets the value of :py:attr:`numPartitions` or its default value.
"""
return self.getOrDefault(self.numPartitions)
class HasMinConfidence(Params):
"""
Mixin for param minConfidence.
"""
minConfidence = Param(
Params._dummy(),
"minConfidence",
"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.",
typeConverter=TypeConverters.toFloat)
def setMinConfidence(self, value):
"""
Sets the value of :py:attr:`minConfidence`.
"""
return self._set(minConfidence=value)
def getMinConfidence(self):
"""
Gets the value of minConfidence or its default value.
"""
return self.getOrDefault(self.minConfidence)
class HasItemsCol(Params):
"""
Mixin for param itemsCol: items column name.
"""
itemsCol = Param(Params._dummy(), "itemsCol",
"items column name", typeConverter=TypeConverters.toString)
def setItemsCol(self, value):
"""
Sets the value of :py:attr:`itemsCol`.
"""
return self._set(itemsCol=value)
def getItemsCol(self):
"""
Gets the value of itemsCol or its default value.
"""
return self.getOrDefault(self.itemsCol)
[docs]class FPGrowthModel(JavaModel, JavaMLWritable, JavaMLReadable):
"""
.. note:: Experimental
Model fitted by FPGrowth.
.. versionadded:: 2.2.0
"""
@property
@since("2.2.0")
def freqItemsets(self):
"""
DataFrame with two columns:
* `items` - Itemset of the same type as the input column.
* `freq` - Frequency of the itemset (`LongType`).
"""
return self._call_java("freqItemsets")
@property
@since("2.2.0")
def associationRules(self):
"""
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`).
"""
return self._call_java("associationRules")
[docs]class FPGrowth(JavaEstimator, HasItemsCol, HasPredictionCol,
HasMinSupport, HasNumPartitions, HasMinConfidence,
JavaMLWritable, JavaMLReadable):
r"""
.. note:: Experimental
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]_
.. [LI2008] http://dx.doi.org/10.1145/1454008.1454027
.. [HAN2000] http://dx.doi.org/10.1145/335191.335372
.. 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.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|
+----------+----------+----------+
| [t, s]| [y]| 1.0|
| [t, s]| [x]| 1.0|
| [t, s]| [z]| 1.0|
| [p]| [r]| 1.0|
| [p]| [z]| 1.0|
+----------+----------+----------+
only showing top 5 rows
>>> new_data = spark.createDataFrame([(["t", "s"], )], ["items"])
>>> sorted(fpm.transform(new_data).first().prediction)
['x', 'y', 'z']
.. versionadded:: 2.2.0
"""
@keyword_only
def __init__(self, minSupport=0.3, minConfidence=0.8, itemsCol="items",
predictionCol="prediction", numPartitions=None):
"""
__init__(self, minSupport=0.3, minConfidence=0.8, itemsCol="items", \
predictionCol="prediction", numPartitions=None)
"""
super(FPGrowth, self).__init__()
self._java_obj = self._new_java_obj("org.apache.spark.ml.fpm.FPGrowth", self.uid)
self._setDefault(minSupport=0.3, minConfidence=0.8,
itemsCol="items", predictionCol="prediction")
kwargs = self._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
@since("2.2.0")
def setParams(self, minSupport=0.3, minConfidence=0.8, itemsCol="items",
predictionCol="prediction", numPartitions=None):
"""
setParams(self, minSupport=0.3, minConfidence=0.8, itemsCol="items", \
predictionCol="prediction", numPartitions=None)
"""
kwargs = self._input_kwargs
return self._set(**kwargs)
def _create_model(self, java_model):
return FPGrowthModel(java_model)
[docs]class PrefixSpan(JavaParams):
"""
.. note:: Experimental
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 <a href="http://doi.org/10.1109/ICDE.2001.914830">here</a>).
This class is not yet an Estimator/Transformer, use :py:func:`findFrequentSequentialPatterns`
method to run the PrefixSpan algorithm.
@see <a href="https://en.wikipedia.org/wiki/Sequential_Pattern_Mining">Sequential Pattern Mining
(Wikipedia)</a>
.. versionadded:: 2.4.0
"""
minSupport = Param(Params._dummy(), "minSupport", "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.",
typeConverter=TypeConverters.toFloat)
maxPatternLength = Param(Params._dummy(), "maxPatternLength",
"The maximal length of the sequential pattern. Must be > 0.",
typeConverter=TypeConverters.toInt)
maxLocalProjDBSize = Param(Params._dummy(), "maxLocalProjDBSize",
"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.",
typeConverter=TypeConverters.toInt)
sequenceCol = Param(Params._dummy(), "sequenceCol", "The name of the sequence column in " +
"dataset, rows with nulls in this column are ignored.",
typeConverter=TypeConverters.toString)
@keyword_only
def __init__(self, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000,
sequenceCol="sequence"):
"""
__init__(self, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000, \
sequenceCol="sequence")
"""
super(PrefixSpan, self).__init__()
self._java_obj = self._new_java_obj("org.apache.spark.ml.fpm.PrefixSpan", self.uid)
self._setDefault(minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000,
sequenceCol="sequence")
kwargs = self._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
@since("2.4.0")
def setParams(self, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000,
sequenceCol="sequence"):
"""
setParams(self, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000, \
sequenceCol="sequence")
"""
kwargs = self._input_kwargs
return self._set(**kwargs)
[docs] @since("2.4.0")
def findFrequentSequentialPatterns(self, dataset):
"""
.. note:: Experimental
Finds the complete set of frequent sequential patterns in the input sequences of itemsets.
:param dataset: A dataframe containing a sequence column which is
`ArrayType(ArrayType(T))` type, T is the item type for the input dataset.
:return: 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`
>>> 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(minSupport=0.5, maxPatternLength=5)
>>> prefixSpan.findFrequentSequentialPatterns(df).sort("sequence").show(truncate=False)
+----------+----+
|sequence |freq|
+----------+----+
|[[1]] |3 |
|[[1], [3]]|2 |
|[[1, 2]] |3 |
|[[2]] |3 |
|[[3]] |2 |
+----------+----+
.. versionadded:: 2.4.0
"""
self._transfer_params_to_java()
jdf = self._java_obj.findFrequentSequentialPatterns(dataset._jdf)
return DataFrame(jdf, dataset.sql_ctx)
