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 # 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
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 #    http://www.apache.org/licenses/LICENSE-2.0
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 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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 from pyspark import keyword_only, since
 from pyspark.rdd import ignore_unicode_prefix
 from pyspark.sql import DataFrame
 from pyspark.ml.util import *
 from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaParams
 from pyspark.ml.param.shared import *
 
 __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 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)
 
 
 class FPGrowthModel(JavaModel, _FPGrowthParams, JavaMLWritable, JavaMLReadable):
     """
     Model fitted by FPGrowth.
 
     .. versionadded:: 2.2.0
     """
 
     @since("3.0.0")
     def setItemsCol(self, value):
         """
         Sets the value of :py:attr:`itemsCol`.
         """
         return self._set(itemsCol=value)
 
     @since("3.0.0")
     def setMinConfidence(self, value):
         """
         Sets the value of :py:attr:`minConfidence`.
         """
         return self._set(minConfidence=value)
 
     @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")
 
 
 @ignore_unicode_prefix
 class FPGrowth(JavaEstimator, _FPGrowthParams, JavaMLWritable, JavaMLReadable):
     r"""
     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] https://doi.org/10.1145/1454008.1454027
     .. [HAN2000] https://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.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|
     +----------+----------+----------+----+
     |    [t, s]|       [y]|       1.0| 2.0|
     |    [t, s]|       [x]|       1.0| 1.5|
     |    [t, s]|       [z]|       1.0| 1.2|
     |       [p]|       [r]|       1.0| 2.0|
     |       [p]|       [z]|       1.0| 1.2|
     +----------+----------+----------+----+
     only showing top 5 rows
     ...
     >>> new_data = spark.createDataFrame([(["t", "s"], )], ["items"])
     >>> sorted(fpm.transform(new_data).first().newPrediction)
     [u'x', u'y', u'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 setItemsCol(self, value):
         """
         Sets the value of :py:attr:`itemsCol`.
         """
         return self._set(itemsCol=value)
 
     def setMinSupport(self, value):
         """
         Sets the value of :py:attr:`minSupport`.
         """
         return self._set(minSupport=value)
 
     def setNumPartitions(self, value):
         """
         Sets the value of :py:attr:`numPartitions`.
         """
         return self._set(numPartitions=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`.
         """
         return self._set(predictionCol=value)
 
     def _create_model(self, java_model):
         return FPGrowthModel(java_model)
 
 
 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 <a href="https://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>
 
     >>> 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   |
     +----------+----+
     ...
 
     .. 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)
 
     @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)
 
     @since("3.0.0")
     def setMinSupport(self, value):
         """
         Sets the value of :py:attr:`minSupport`.
         """
         return self._set(minSupport=value)
 
     @since("3.0.0")
     def getMinSupport(self):
         """
         Gets the value of minSupport or its default value.
         """
         return self.getOrDefault(self.minSupport)
 
     @since("3.0.0")
     def setMaxPatternLength(self, value):
         """
         Sets the value of :py:attr:`maxPatternLength`.
         """
         return self._set(maxPatternLength=value)
 
     @since("3.0.0")
     def getMaxPatternLength(self):
         """
         Gets the value of maxPatternLength or its default value.
         """
         return self.getOrDefault(self.maxPatternLength)
 
     @since("3.0.0")
     def setMaxLocalProjDBSize(self, value):
         """
         Sets the value of :py:attr:`maxLocalProjDBSize`.
         """
         return self._set(maxLocalProjDBSize=value)
 
     @since("3.0.0")
     def getMaxLocalProjDBSize(self):
         """
         Gets the value of maxLocalProjDBSize or its default value.
         """
         return self.getOrDefault(self.maxLocalProjDBSize)
 
     @since("3.0.0")
     def setSequenceCol(self, value):
         """
         Sets the value of :py:attr:`sequenceCol`.
         """
         return self._set(sequenceCol=value)
 
     @since("3.0.0")
     def getSequenceCol(self):
         """
         Gets the value of sequenceCol or its default value.
         """
         return self.getOrDefault(self.sequenceCol)
 
     @since("2.4.0")
     def findFrequentSequentialPatterns(self, dataset):
         """
         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`
 
         .. versionadded:: 2.4.0
         """
 
         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)

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