122c8999cb
### What changes were proposed in this pull request?
Changes
pyspark.sql.dataframe.DataFrame
to
:py:class:`pyspark.sql.DataFrame`
### Why are the changes needed?
Consistency (see https://github.com/apache/spark/pull/30285#pullrequestreview-526764104).
### Does this PR introduce _any_ user-facing change?
User will see shorter reference with a link.
### How was this patch tested?
`dev/lint-python` and manual check of the rendered docs.
Closes #30313 from zero323/SPARK-33251-FOLLOW-UP.
Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: Huaxin Gao <huaxing@us.ibm.com>
487 lines
17 KiB
Python
487 lines
17 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import sys
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from pyspark import keyword_only, since
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from pyspark.sql import DataFrame
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from pyspark.ml.util import JavaMLWritable, JavaMLReadable
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from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaParams
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from pyspark.ml.param.shared import HasPredictionCol, Param, TypeConverters, Params
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__all__ = ["FPGrowth", "FPGrowthModel", "PrefixSpan"]
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class _FPGrowthParams(HasPredictionCol):
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"""
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Params for :py:class:`FPGrowth` and :py:class:`FPGrowthModel`.
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.. versionadded:: 3.0.0
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"""
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itemsCol = Param(Params._dummy(), "itemsCol",
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"items column name", typeConverter=TypeConverters.toString)
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minSupport = Param(
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Params._dummy(),
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"minSupport",
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"Minimal support level of the frequent pattern. [0.0, 1.0]. " +
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"Any pattern that appears more than (minSupport * size-of-the-dataset) " +
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"times will be output in the frequent itemsets.",
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typeConverter=TypeConverters.toFloat)
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numPartitions = Param(
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Params._dummy(),
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"numPartitions",
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"Number of partitions (at least 1) used by parallel FP-growth. " +
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"By default the param is not set, " +
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"and partition number of the input dataset is used.",
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typeConverter=TypeConverters.toInt)
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minConfidence = Param(
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Params._dummy(),
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"minConfidence",
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"Minimal confidence for generating Association Rule. [0.0, 1.0]. " +
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"minConfidence will not affect the mining for frequent itemsets, " +
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"but will affect the association rules generation.",
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typeConverter=TypeConverters.toFloat)
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def __init__(self, *args):
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super(_FPGrowthParams, self).__init__(*args)
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self._setDefault(minSupport=0.3, minConfidence=0.8,
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itemsCol="items", predictionCol="prediction")
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def getItemsCol(self):
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"""
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Gets the value of itemsCol or its default value.
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"""
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return self.getOrDefault(self.itemsCol)
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def getMinSupport(self):
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"""
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Gets the value of minSupport or its default value.
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"""
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return self.getOrDefault(self.minSupport)
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def getNumPartitions(self):
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"""
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Gets the value of :py:attr:`numPartitions` or its default value.
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"""
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return self.getOrDefault(self.numPartitions)
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def getMinConfidence(self):
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"""
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Gets the value of minConfidence or its default value.
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"""
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return self.getOrDefault(self.minConfidence)
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class FPGrowthModel(JavaModel, _FPGrowthParams, JavaMLWritable, JavaMLReadable):
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"""
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Model fitted by FPGrowth.
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.. versionadded:: 2.2.0
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"""
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@since("3.0.0")
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def setItemsCol(self, value):
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"""
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Sets the value of :py:attr:`itemsCol`.
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"""
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return self._set(itemsCol=value)
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@since("3.0.0")
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def setMinConfidence(self, value):
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"""
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Sets the value of :py:attr:`minConfidence`.
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"""
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return self._set(minConfidence=value)
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@since("3.0.0")
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def setPredictionCol(self, value):
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"""
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Sets the value of :py:attr:`predictionCol`.
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"""
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return self._set(predictionCol=value)
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@property
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@since("2.2.0")
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def freqItemsets(self):
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"""
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DataFrame with two columns:
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* `items` - Itemset of the same type as the input column.
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* `freq` - Frequency of the itemset (`LongType`).
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"""
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return self._call_java("freqItemsets")
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@property
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@since("2.2.0")
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def associationRules(self):
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"""
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DataFrame with four columns:
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* `antecedent` - Array of the same type as the input column.
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* `consequent` - Array of the same type as the input column.
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* `confidence` - Confidence for the rule (`DoubleType`).
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* `lift` - Lift for the rule (`DoubleType`).
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"""
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return self._call_java("associationRules")
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class FPGrowth(JavaEstimator, _FPGrowthParams, JavaMLWritable, JavaMLReadable):
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r"""
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A parallel FP-growth algorithm to mine frequent itemsets.
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.. versionadded:: 2.2.0
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Notes
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-----
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The algorithm is described in
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Li et al., PFP: Parallel FP-Growth for Query Recommendation [1]_.
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PFP distributes computation in such a way that each worker executes an
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independent group of mining tasks. The FP-Growth algorithm is described in
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Han et al., Mining frequent patterns without candidate generation [2]_
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NULL values in the feature column are ignored during `fit()`.
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Internally `transform` `collects` and `broadcasts` association rules.
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.. [1] Haoyuan Li, Yi Wang, Dong Zhang, Ming Zhang, and Edward Y. Chang. 2008.
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Pfp: parallel fp-growth for query recommendation.
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In Proceedings of the 2008 ACM conference on Recommender systems (RecSys '08).
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Association for Computing Machinery, New York, NY, USA, 107–114.
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DOI: https://doi.org/10.1145/1454008.1454027
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.. [2] Jiawei Han, Jian Pei, and Yiwen Yin. 2000.
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Mining frequent patterns without candidate generation.
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SIGMOD Rec. 29, 2 (June 2000), 1–12.
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DOI: https://doi.org/10.1145/335191.335372
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Examples
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--------
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>>> from pyspark.sql.functions import split
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>>> data = (spark.read
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... .text("data/mllib/sample_fpgrowth.txt")
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... .select(split("value", "\s+").alias("items")))
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>>> data.show(truncate=False)
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+------------------------+
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|items |
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+------------------------+
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|[r, z, h, k, p] |
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|[z, y, x, w, v, u, t, s]|
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|[s, x, o, n, r] |
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|[x, z, y, m, t, s, q, e]|
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|[z] |
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|[x, z, y, r, q, t, p] |
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+------------------------+
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...
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>>> fp = FPGrowth(minSupport=0.2, minConfidence=0.7)
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>>> fpm = fp.fit(data)
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>>> fpm.setPredictionCol("newPrediction")
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FPGrowthModel...
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>>> fpm.freqItemsets.show(5)
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+---------+----+
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| items|freq|
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+---------+----+
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| [s]| 3|
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| [s, x]| 3|
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|[s, x, z]| 2|
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| [s, z]| 2|
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| [r]| 3|
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+---------+----+
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only showing top 5 rows
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...
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>>> fpm.associationRules.show(5)
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+----------+----------+----------+----+------------------+
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|antecedent|consequent|confidence|lift| support|
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+----------+----------+----------+----+------------------+
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| [t, s]| [y]| 1.0| 2.0|0.3333333333333333|
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| [t, s]| [x]| 1.0| 1.5|0.3333333333333333|
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| [t, s]| [z]| 1.0| 1.2|0.3333333333333333|
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| [p]| [r]| 1.0| 2.0|0.3333333333333333|
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| [p]| [z]| 1.0| 1.2|0.3333333333333333|
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+----------+----------+----------+----+------------------+
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only showing top 5 rows
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...
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>>> new_data = spark.createDataFrame([(["t", "s"], )], ["items"])
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>>> sorted(fpm.transform(new_data).first().newPrediction)
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['x', 'y', 'z']
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>>> model_path = temp_path + "/fpm_model"
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>>> fpm.save(model_path)
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>>> model2 = FPGrowthModel.load(model_path)
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>>> fpm.transform(data).take(1) == model2.transform(data).take(1)
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True
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"""
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@keyword_only
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def __init__(self, *, minSupport=0.3, minConfidence=0.8, itemsCol="items",
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predictionCol="prediction", numPartitions=None):
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"""
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__init__(self, \\*, minSupport=0.3, minConfidence=0.8, itemsCol="items", \
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predictionCol="prediction", numPartitions=None)
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"""
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super(FPGrowth, self).__init__()
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self._java_obj = self._new_java_obj("org.apache.spark.ml.fpm.FPGrowth", self.uid)
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kwargs = self._input_kwargs
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self.setParams(**kwargs)
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@keyword_only
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@since("2.2.0")
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def setParams(self, *, minSupport=0.3, minConfidence=0.8, itemsCol="items",
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predictionCol="prediction", numPartitions=None):
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"""
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setParams(self, \\*, minSupport=0.3, minConfidence=0.8, itemsCol="items", \
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predictionCol="prediction", numPartitions=None)
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"""
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kwargs = self._input_kwargs
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return self._set(**kwargs)
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def setItemsCol(self, value):
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"""
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Sets the value of :py:attr:`itemsCol`.
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"""
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return self._set(itemsCol=value)
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def setMinSupport(self, value):
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"""
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Sets the value of :py:attr:`minSupport`.
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"""
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return self._set(minSupport=value)
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def setNumPartitions(self, value):
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"""
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Sets the value of :py:attr:`numPartitions`.
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"""
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return self._set(numPartitions=value)
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def setMinConfidence(self, value):
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"""
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Sets the value of :py:attr:`minConfidence`.
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"""
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return self._set(minConfidence=value)
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def setPredictionCol(self, value):
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"""
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Sets the value of :py:attr:`predictionCol`.
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"""
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return self._set(predictionCol=value)
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def _create_model(self, java_model):
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return FPGrowthModel(java_model)
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class PrefixSpan(JavaParams):
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"""
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A parallel PrefixSpan algorithm to mine frequent sequential patterns.
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The PrefixSpan algorithm is described in J. Pei, et al., PrefixSpan: Mining Sequential Patterns
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Efficiently by Prefix-Projected Pattern Growth
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(see `here <https://doi.org/10.1109/ICDE.2001.914830">`_).
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This class is not yet an Estimator/Transformer, use :py:func:`findFrequentSequentialPatterns`
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method to run the PrefixSpan algorithm.
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.. versionadded:: 2.4.0
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Notes
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-----
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See `Sequential Pattern Mining (Wikipedia) \
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<https://en.wikipedia.org/wiki/Sequential_Pattern_Mining>`_
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Examples
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--------
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>>> from pyspark.ml.fpm import PrefixSpan
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>>> from pyspark.sql import Row
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>>> df = sc.parallelize([Row(sequence=[[1, 2], [3]]),
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... Row(sequence=[[1], [3, 2], [1, 2]]),
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... Row(sequence=[[1, 2], [5]]),
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... Row(sequence=[[6]])]).toDF()
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>>> prefixSpan = PrefixSpan()
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>>> prefixSpan.getMaxLocalProjDBSize()
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32000000
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>>> prefixSpan.getSequenceCol()
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'sequence'
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>>> prefixSpan.setMinSupport(0.5)
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PrefixSpan...
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>>> prefixSpan.setMaxPatternLength(5)
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PrefixSpan...
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>>> prefixSpan.findFrequentSequentialPatterns(df).sort("sequence").show(truncate=False)
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+----------+----+
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|sequence |freq|
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+----------+----+
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|[[1]] |3 |
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|[[1], [3]]|2 |
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|[[2]] |3 |
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|[[2, 1]] |3 |
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|[[3]] |2 |
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+----------+----+
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...
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"""
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minSupport = Param(Params._dummy(), "minSupport", "The minimal support level of the " +
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"sequential pattern. Sequential pattern that appears more than " +
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"(minSupport * size-of-the-dataset) times will be output. Must be >= 0.",
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typeConverter=TypeConverters.toFloat)
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maxPatternLength = Param(Params._dummy(), "maxPatternLength",
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"The maximal length of the sequential pattern. Must be > 0.",
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typeConverter=TypeConverters.toInt)
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maxLocalProjDBSize = Param(Params._dummy(), "maxLocalProjDBSize",
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"The maximum number of items (including delimiters used in the " +
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"internal storage format) allowed in a projected database before " +
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"local processing. If a projected database exceeds this size, " +
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"another iteration of distributed prefix growth is run. " +
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"Must be > 0.",
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typeConverter=TypeConverters.toInt)
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sequenceCol = Param(Params._dummy(), "sequenceCol", "The name of the sequence column in " +
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"dataset, rows with nulls in this column are ignored.",
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typeConverter=TypeConverters.toString)
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@keyword_only
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def __init__(self, *, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000,
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sequenceCol="sequence"):
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"""
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__init__(self, \\*, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000, \
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sequenceCol="sequence")
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"""
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super(PrefixSpan, self).__init__()
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self._java_obj = self._new_java_obj("org.apache.spark.ml.fpm.PrefixSpan", self.uid)
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self._setDefault(minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000,
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sequenceCol="sequence")
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kwargs = self._input_kwargs
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self.setParams(**kwargs)
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@keyword_only
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@since("2.4.0")
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def setParams(self, *, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000,
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sequenceCol="sequence"):
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"""
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setParams(self, \\*, minSupport=0.1, maxPatternLength=10, maxLocalProjDBSize=32000000, \
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sequenceCol="sequence")
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"""
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kwargs = self._input_kwargs
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return self._set(**kwargs)
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@since("3.0.0")
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def setMinSupport(self, value):
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"""
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Sets the value of :py:attr:`minSupport`.
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"""
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return self._set(minSupport=value)
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@since("3.0.0")
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def getMinSupport(self):
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"""
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Gets the value of minSupport or its default value.
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"""
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return self.getOrDefault(self.minSupport)
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@since("3.0.0")
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def setMaxPatternLength(self, value):
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"""
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Sets the value of :py:attr:`maxPatternLength`.
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"""
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return self._set(maxPatternLength=value)
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@since("3.0.0")
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def getMaxPatternLength(self):
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"""
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Gets the value of maxPatternLength or its default value.
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"""
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return self.getOrDefault(self.maxPatternLength)
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@since("3.0.0")
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def setMaxLocalProjDBSize(self, value):
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"""
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Sets the value of :py:attr:`maxLocalProjDBSize`.
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"""
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return self._set(maxLocalProjDBSize=value)
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@since("3.0.0")
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def getMaxLocalProjDBSize(self):
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"""
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Gets the value of maxLocalProjDBSize or its default value.
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"""
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return self.getOrDefault(self.maxLocalProjDBSize)
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@since("3.0.0")
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def setSequenceCol(self, value):
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"""
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Sets the value of :py:attr:`sequenceCol`.
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"""
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return self._set(sequenceCol=value)
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@since("3.0.0")
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def getSequenceCol(self):
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"""
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Gets the value of sequenceCol or its default value.
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"""
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return self.getOrDefault(self.sequenceCol)
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def findFrequentSequentialPatterns(self, dataset):
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"""
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Finds the complete set of frequent sequential patterns in the input sequences of itemsets.
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.. versionadded:: 2.4.0
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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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A dataframe containing a sequence column which is
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`ArrayType(ArrayType(T))` type, T is the item type for the input dataset.
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Returns
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-------
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:py:class:`pyspark.sql.DataFrame`
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A `DataFrame` that contains columns of sequence and corresponding frequency.
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The schema of it will be:
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- `sequence: ArrayType(ArrayType(T))` (T is the item type)
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- `freq: Long`
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"""
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self._transfer_params_to_java()
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jdf = self._java_obj.findFrequentSequentialPatterns(dataset._jdf)
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return DataFrame(jdf, dataset.sql_ctx)
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if __name__ == "__main__":
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import doctest
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import pyspark.ml.fpm
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from pyspark.sql import SparkSession
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globs = pyspark.ml.fpm.__dict__.copy()
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# The small batch size here ensures that we see multiple batches,
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# even in these small test examples:
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spark = SparkSession.builder\
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.master("local[2]")\
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.appName("ml.fpm tests")\
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.getOrCreate()
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sc = spark.sparkContext
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globs['sc'] = sc
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globs['spark'] = spark
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import tempfile
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temp_path = tempfile.mkdtemp()
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globs['temp_path'] = temp_path
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try:
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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spark.stop()
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finally:
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from shutil import rmtree
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try:
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rmtree(temp_path)
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except OSError:
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pass
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if failure_count:
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sys.exit(-1)
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