139da116f1
## What changes were proposed in this pull request? Expose numPartitions (expert) param of PySpark FPGrowth. ## How was this patch tested? + [x] Pass all unit tests. Author: Yan Facai (颜发才) <facai.yan@gmail.com> Closes #18058 from facaiy/ENH/pyspark_fpg_add_num_partition.
246 lines
8 KiB
Python
246 lines
8 KiB
Python
#
|
|
# Licensed to the Apache Software Foundation (ASF) under one or more
|
|
# contributor license agreements. See the NOTICE file distributed with
|
|
# this work for additional information regarding copyright ownership.
|
|
# The ASF licenses this file to You under the Apache License, Version 2.0
|
|
# (the "License"); you may not use this file except in compliance with
|
|
# the License. You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
|
|
from pyspark import keyword_only, since
|
|
from pyspark.ml.util import *
|
|
from pyspark.ml.wrapper import JavaEstimator, JavaModel
|
|
from pyspark.ml.param.shared import *
|
|
|
|
__all__ = ["FPGrowth", "FPGrowthModel"]
|
|
|
|
|
|
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)
|
|
|
|
|
|
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):
|
|
"""
|
|
Data with three 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`).
|
|
"""
|
|
return self._call_java("associationRules")
|
|
|
|
|
|
class FPGrowth(JavaEstimator, HasItemsCol, HasPredictionCol,
|
|
HasMinSupport, HasNumPartitions, HasMinConfidence,
|
|
JavaMLWritable, JavaMLReadable):
|
|
|
|
"""
|
|
.. 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)
|
|
|
|
@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)
|