Source code for pyspark.ml.fpm

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import sys

from pyspark import keyword_only, since
from pyspark.sql import DataFrame
from pyspark.ml.util import JavaMLWritable, JavaMLReadable
from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaParams
from pyspark.ml.param.shared import HasPredictionCol, Param, TypeConverters, Params

__all__ = ["FPGrowth", "FPGrowthModel", "PrefixSpan"]


class _FPGrowthParams(HasPredictionCol):
    """
    Params for :py:class:`FPGrowth` and :py:class:`FPGrowthModel`.

    .. versionadded:: 3.0.0
    """

    itemsCol = Param(Params._dummy(), "itemsCol",
                     "items column name", typeConverter=TypeConverters.toString)
    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)
    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)
    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 __init__(self, *args):
        super(_FPGrowthParams, self).__init__(*args)
        self._setDefault(minSupport=0.3, minConfidence=0.8,
                         itemsCol="items", predictionCol="prediction")

    def getItemsCol(self):
        """
        Gets the value of itemsCol or its default value.
        """
        return self.getOrDefault(self.itemsCol)

    def getMinSupport(self):
        """
        Gets the value of minSupport or its default value.
        """
        return self.getOrDefault(self.minSupport)

    def getNumPartitions(self):
        """
        Gets the value of :py:attr:`numPartitions` or its default value.
        """
        return self.getOrDefault(self.numPartitions)

    def getMinConfidence(self):
        """
        Gets the value of minConfidence or its default value.
        """
        return self.getOrDefault(self.minConfidence)


[docs]class FPGrowthModel(JavaModel, _FPGrowthParams, JavaMLWritable, JavaMLReadable): """ Model fitted by FPGrowth. .. versionadded:: 2.2.0 """
[docs] @since("3.0.0") def setItemsCol(self, value): """ Sets the value of :py:attr:`itemsCol`. """ return self._set(itemsCol=value)
[docs] @since("3.0.0") def setMinConfidence(self, value): """ Sets the value of :py:attr:`minConfidence`. """ return self._set(minConfidence=value)
[docs] @since("3.0.0") def setPredictionCol(self, value): """ Sets the value of :py:attr:`predictionCol`. """ return self._set(predictionCol=value)
@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, _FPGrowthParams, JavaMLWritable, JavaMLReadable): r""" A parallel FP-growth algorithm to mine frequent itemsets. .. versionadded:: 2.2.0 Notes ----- The algorithm is described in Li et al., PFP: Parallel FP-Growth for Query Recommendation [1]_. 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 [2]_ NULL values in the feature column are ignored during `fit()`. Internally `transform` `collects` and `broadcasts` association rules. .. [1] Haoyuan Li, Yi Wang, Dong Zhang, Ming Zhang, and Edward Y. Chang. 2008. Pfp: parallel fp-growth for query recommendation. In Proceedings of the 2008 ACM conference on Recommender systems (RecSys '08). Association for Computing Machinery, New York, NY, USA, 107–114. DOI: https://doi.org/10.1145/1454008.1454027 .. [2] Jiawei Han, Jian Pei, and Yiwen Yin. 2000. Mining frequent patterns without candidate generation. SIGMOD Rec. 29, 2 (June 2000), 1–12. DOI: https://doi.org/10.1145/335191.335372 Examples -------- >>> 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| support| +----------+----------+----------+----+------------------+ | [t, s]| [y]| 1.0| 2.0|0.3333333333333333| | [t, s]| [x]| 1.0| 1.5|0.3333333333333333| | [t, s]| [z]| 1.0| 1.2|0.3333333333333333| | [p]| [r]| 1.0| 2.0|0.3333333333333333| | [p]| [z]| 1.0| 1.2|0.3333333333333333| +----------+----------+----------+----+------------------+ only showing top 5 rows ... >>> new_data = spark.createDataFrame([(["t", "s"], )], ["items"]) >>> sorted(fpm.transform(new_data).first().newPrediction) ['x', 'y', 'z'] >>> model_path = temp_path + "/fpm_model" >>> fpm.save(model_path) >>> model2 = FPGrowthModel.load(model_path) >>> fpm.transform(data).take(1) == model2.transform(data).take(1) True """ @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) 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)
[docs] def setItemsCol(self, value): """ Sets the value of :py:attr:`itemsCol`. """ return self._set(itemsCol=value)
[docs] def setMinSupport(self, value): """ Sets the value of :py:attr:`minSupport`. """ return self._set(minSupport=value)
[docs] def setNumPartitions(self, value): """ Sets the value of :py:attr:`numPartitions`. """ return self._set(numPartitions=value)
[docs] def setMinConfidence(self, value): """ Sets the value of :py:attr:`minConfidence`. """ return self._set(minConfidence=value)
[docs] def setPredictionCol(self, value): """ Sets the value of :py:attr:`predictionCol`. """ return self._set(predictionCol=value)
def _create_model(self, java_model): return FPGrowthModel(java_model)
[docs]class PrefixSpan(JavaParams): """ 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 <https://doi.org/10.1109/ICDE.2001.914830">`_). This class is not yet an Estimator/Transformer, use :py:func:`findFrequentSequentialPatterns` method to run the PrefixSpan algorithm. .. versionadded:: 2.4.0 Notes ----- See `Sequential Pattern Mining (Wikipedia) \ <https://en.wikipedia.org/wiki/Sequential_Pattern_Mining>`_ 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 | +----------+----+ ... """ 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("3.0.0") def setMinSupport(self, value): """ Sets the value of :py:attr:`minSupport`. """ return self._set(minSupport=value)
[docs] @since("3.0.0") def getMinSupport(self): """ Gets the value of minSupport or its default value. """ return self.getOrDefault(self.minSupport)
[docs] @since("3.0.0") def setMaxPatternLength(self, value): """ Sets the value of :py:attr:`maxPatternLength`. """ return self._set(maxPatternLength=value)
[docs] @since("3.0.0") def getMaxPatternLength(self): """ Gets the value of maxPatternLength or its default value. """ return self.getOrDefault(self.maxPatternLength)
[docs] @since("3.0.0") def setMaxLocalProjDBSize(self, value): """ Sets the value of :py:attr:`maxLocalProjDBSize`. """ return self._set(maxLocalProjDBSize=value)
[docs] @since("3.0.0") def getMaxLocalProjDBSize(self): """ Gets the value of maxLocalProjDBSize or its default value. """ return self.getOrDefault(self.maxLocalProjDBSize)
[docs] @since("3.0.0") def setSequenceCol(self, value): """ Sets the value of :py:attr:`sequenceCol`. """ return self._set(sequenceCol=value)
[docs] @since("3.0.0") def getSequenceCol(self): """ Gets the value of sequenceCol or its default value. """ return self.getOrDefault(self.sequenceCol)
[docs] def findFrequentSequentialPatterns(self, dataset): """ Finds the complete set of frequent sequential patterns in the input sequences of itemsets. .. versionadded:: 2.4.0 Parameters ---------- dataset : :py:class:`pyspark.sql.DataFrame` A dataframe containing a sequence column which is `ArrayType(ArrayType(T))` type, T is the item type for the input dataset. Returns ------- :py:class:`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` """ self._transfer_params_to_java() jdf = self._java_obj.findFrequentSequentialPatterns(dataset._jdf) return DataFrame(jdf, dataset.sql_ctx)
if __name__ == "__main__": import doctest import pyspark.ml.fpm from pyspark.sql import SparkSession globs = pyspark.ml.fpm.__dict__.copy() # The small batch size here ensures that we see multiple batches, # even in these small test examples: spark = SparkSession.builder\ .master("local[2]")\ .appName("ml.fpm tests")\ .getOrCreate() sc = spark.sparkContext globs['sc'] = sc globs['spark'] = spark import tempfile temp_path = tempfile.mkdtemp() globs['temp_path'] = temp_path try: (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) spark.stop() finally: from shutil import rmtree try: rmtree(temp_path) except OSError: pass if failure_count: sys.exit(-1)