Coverage for pyspark/ml/fpm.py : 85%
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# # 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. #
""" Params for :py:class:`FPGrowth` and :py:class:`FPGrowthModel`.
.. versionadded:: 3.0.0 """
"items column name", typeConverter=TypeConverters.toString) 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) 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) 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)
itemsCol="items", predictionCol="prediction")
""" Gets the value of itemsCol or its default value. """ return self.getOrDefault(self.itemsCol)
""" Gets the value of minSupport or its default value. """ return self.getOrDefault(self.minSupport)
""" Gets the value of :py:attr:`numPartitions` or its default value. """ return self.getOrDefault(self.numPartitions)
""" Gets the value of minConfidence or its default value. """ return self.getOrDefault(self.minConfidence)
""" Model fitted by FPGrowth.
.. versionadded:: 2.2.0 """
def setItemsCol(self, value): """ Sets the value of :py:attr:`itemsCol`. """ return self._set(itemsCol=value)
def setMinConfidence(self, value): """ Sets the value of :py:attr:`minConfidence`. """ return self._set(minConfidence=value)
def setPredictionCol(self, value): """ Sets the value of :py:attr:`predictionCol`. """
def freqItemsets(self): """ DataFrame with two columns: * `items` - Itemset of the same type as the input column. * `freq` - Frequency of the itemset (`LongType`). """
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`). """
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 """ predictionCol="prediction", numPartitions=None): """ __init__(self, \\*, minSupport=0.3, minConfidence=0.8, itemsCol="items", \ predictionCol="prediction", numPartitions=None) """
predictionCol="prediction", numPartitions=None): """ setParams(self, \\*, minSupport=0.3, minConfidence=0.8, itemsCol="items", \ predictionCol="prediction", numPartitions=None) """
""" Sets the value of :py:attr:`itemsCol`. """ return self._set(itemsCol=value)
""" Sets the value of :py:attr:`minSupport`. """ return self._set(minSupport=value)
""" Sets the value of :py:attr:`numPartitions`. """ return self._set(numPartitions=value)
""" Sets the value of :py:attr:`minConfidence`. """ return self._set(minConfidence=value)
""" Sets the value of :py:attr:`predictionCol`. """ return self._set(predictionCol=value)
""" 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 | +----------+----+ ... """
"sequential pattern. Sequential pattern that appears more than " + "(minSupport * size-of-the-dataset) times will be output. Must be >= 0.", typeConverter=TypeConverters.toFloat)
"The maximal length of the sequential pattern. Must be > 0.", typeConverter=TypeConverters.toInt)
"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)
"dataset, rows with nulls in this column are ignored.", typeConverter=TypeConverters.toString)
sequenceCol="sequence"): """ __init__(self, \\*, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000, \ sequenceCol="sequence") """ sequenceCol="sequence")
sequenceCol="sequence"): """ setParams(self, \\*, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000, \ sequenceCol="sequence") """
def setMinSupport(self, value): """ Sets the value of :py:attr:`minSupport`. """
def getMinSupport(self): """ Gets the value of minSupport or its default value. """ return self.getOrDefault(self.minSupport)
def setMaxPatternLength(self, value): """ Sets the value of :py:attr:`maxPatternLength`. """
def getMaxPatternLength(self): """ Gets the value of maxPatternLength or its default value. """ return self.getOrDefault(self.maxPatternLength)
def setMaxLocalProjDBSize(self, value): """ Sets the value of :py:attr:`maxLocalProjDBSize`. """ return self._set(maxLocalProjDBSize=value)
def getMaxLocalProjDBSize(self): """ Gets the value of maxLocalProjDBSize or its default value. """
def setSequenceCol(self, value): """ Sets the value of :py:attr:`sequenceCol`. """ return self._set(sequenceCol=value)
def getSequenceCol(self): """ Gets the value of sequenceCol or its default value. """
""" 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` """
# The small batch size here ensures that we see multiple batches, # even in these small test examples: .master("local[2]")\ .appName("ml.fpm tests")\ .getOrCreate() finally: except OSError: pass sys.exit(-1) |