Source code for pyspark.ml.fpm

#
# 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.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)