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| <h1>Source code for pyspark.mllib.fpm</h1><div class="highlight"><pre> |
| <span></span><span class="c1">#</span> |
| <span class="c1"># Licensed to the Apache Software Foundation (ASF) under one or more</span> |
| <span class="c1"># contributor license agreements. See the NOTICE file distributed with</span> |
| <span class="c1"># this work for additional information regarding copyright ownership.</span> |
| <span class="c1"># The ASF licenses this file to You under the Apache License, Version 2.0</span> |
| <span class="c1"># (the "License"); you may not use this file except in compliance with</span> |
| <span class="c1"># the License. You may obtain a copy of the License at</span> |
| <span class="c1">#</span> |
| <span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span> |
| <span class="c1">#</span> |
| <span class="c1"># Unless required by applicable law or agreed to in writing, software</span> |
| <span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span> |
| <span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span> |
| <span class="c1"># See the License for the specific language governing permissions and</span> |
| <span class="c1"># limitations under the License.</span> |
| <span class="c1">#</span> |
| |
| <span class="kn">import</span> <span class="nn">sys</span> |
| |
| <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Generic</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">NamedTuple</span><span class="p">,</span> <span class="n">TypeVar</span> |
| |
| <span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">since</span><span class="p">,</span> <span class="n">SparkContext</span> |
| <span class="kn">from</span> <span class="nn">pyspark.mllib.common</span> <span class="kn">import</span> <span class="n">JavaModelWrapper</span><span class="p">,</span> <span class="n">callMLlibFunc</span> |
| <span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">JavaSaveable</span><span class="p">,</span> <span class="n">JavaLoader</span><span class="p">,</span> <span class="n">inherit_doc</span> |
| <span class="kn">from</span> <span class="nn">pyspark.core.rdd</span> <span class="kn">import</span> <span class="n">RDD</span> |
| |
| <span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"FPGrowth"</span><span class="p">,</span> <span class="s2">"FPGrowthModel"</span><span class="p">,</span> <span class="s2">"PrefixSpan"</span><span class="p">,</span> <span class="s2">"PrefixSpanModel"</span><span class="p">]</span> |
| |
| <span class="n">T</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">"T"</span><span class="p">)</span> |
| |
| |
| <div class="viewcode-block" id="FPGrowthModel"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.fpm.FPGrowthModel.html#pyspark.mllib.fpm.FPGrowthModel">[docs]</a><span class="nd">@inherit_doc</span> |
| <span class="k">class</span> <span class="nc">FPGrowthModel</span><span class="p">(</span><span class="n">JavaModelWrapper</span><span class="p">,</span> <span class="n">JavaSaveable</span><span class="p">,</span> <span class="n">JavaLoader</span><span class="p">[</span><span class="s2">"FPGrowthModel"</span><span class="p">]):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> A FP-Growth model for mining frequent itemsets</span> |
| <span class="sd"> using the Parallel FP-Growth algorithm.</span> |
| |
| <span class="sd"> .. versionadded:: 1.4.0</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> data = [["a", "b", "c"], ["a", "b", "d", "e"], ["a", "c", "e"], ["a", "c", "f"]]</span> |
| <span class="sd"> >>> rdd = sc.parallelize(data, 2)</span> |
| <span class="sd"> >>> model = FPGrowth.train(rdd, 0.6, 2)</span> |
| <span class="sd"> >>> sorted(model.freqItemsets().collect())</span> |
| <span class="sd"> [FreqItemset(items=['a'], freq=4), FreqItemset(items=['c'], freq=3), ...</span> |
| <span class="sd"> >>> model_path = temp_path + "/fpm"</span> |
| <span class="sd"> >>> model.save(sc, model_path)</span> |
| <span class="sd"> >>> sameModel = FPGrowthModel.load(sc, model_path)</span> |
| <span class="sd"> >>> sorted(model.freqItemsets().collect()) == sorted(sameModel.freqItemsets().collect())</span> |
| <span class="sd"> True</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="FPGrowthModel.freqItemsets"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.fpm.FPGrowthModel.html#pyspark.mllib.fpm.FPGrowthModel.freqItemsets">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">"1.4.0"</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">freqItemsets</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">RDD</span><span class="p">[</span><span class="s2">"FPGrowth.FreqItemset"</span><span class="p">]:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Returns the frequent itemsets of this model.</span> |
| <span class="sd"> """</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">call</span><span class="p">(</span><span class="s2">"getFreqItemsets"</span><span class="p">)</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">FPGrowth</span><span class="o">.</span><span class="n">FreqItemset</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span></div> |
| |
| <div class="viewcode-block" id="FPGrowthModel.load"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.fpm.FPGrowthModel.html#pyspark.mllib.fpm.FPGrowthModel.load">[docs]</a> <span class="nd">@classmethod</span> |
| <span class="nd">@since</span><span class="p">(</span><span class="s2">"2.0.0"</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">sc</span><span class="p">:</span> <span class="n">SparkContext</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="s2">"FPGrowthModel"</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Load a model from the given path.</span> |
| <span class="sd"> """</span> |
| <span class="n">model</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">_load_java</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> |
| <span class="n">wrapper</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">_jvm</span><span class="o">.</span><span class="n">org</span><span class="o">.</span><span class="n">apache</span><span class="o">.</span><span class="n">spark</span><span class="o">.</span><span class="n">mllib</span><span class="o">.</span><span class="n">api</span><span class="o">.</span><span class="n">python</span><span class="o">.</span><span class="n">FPGrowthModelWrapper</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> |
| <span class="k">return</span> <span class="n">FPGrowthModel</span><span class="p">(</span><span class="n">wrapper</span><span class="p">)</span></div></div> |
| |
| |
| <div class="viewcode-block" id="FPGrowth"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.fpm.FPGrowth.html#pyspark.mllib.fpm.FPGrowth">[docs]</a><span class="k">class</span> <span class="nc">FPGrowth</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> A Parallel FP-growth algorithm to mine frequent itemsets.</span> |
| |
| <span class="sd"> .. versionadded:: 1.4.0</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="FPGrowth.train"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.fpm.FPGrowth.html#pyspark.mllib.fpm.FPGrowth.train">[docs]</a> <span class="nd">@classmethod</span> |
| <span class="k">def</span> <span class="nf">train</span><span class="p">(</span> |
| <span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">T</span><span class="p">]],</span> <span class="n">minSupport</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">numPartitions</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="s2">"FPGrowthModel"</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Computes an FP-Growth model that contains frequent itemsets.</span> |
| |
| <span class="sd"> .. versionadded:: 1.4.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> data : :py:class:`pyspark.RDD`</span> |
| <span class="sd"> The input data set, each element contains a transaction.</span> |
| <span class="sd"> minSupport : float, optional</span> |
| <span class="sd"> The minimal support level.</span> |
| <span class="sd"> (default: 0.3)</span> |
| <span class="sd"> numPartitions : int, optional</span> |
| <span class="sd"> The number of partitions used by parallel FP-growth. A value</span> |
| <span class="sd"> of -1 will use the same number as input data.</span> |
| <span class="sd"> (default: -1)</span> |
| <span class="sd"> """</span> |
| <span class="n">model</span> <span class="o">=</span> <span class="n">callMLlibFunc</span><span class="p">(</span><span class="s2">"trainFPGrowthModel"</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">minSupport</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">numPartitions</span><span class="p">))</span> |
| <span class="k">return</span> <span class="n">FPGrowthModel</span><span class="p">(</span><span class="n">model</span><span class="p">)</span></div> |
| |
| <span class="k">class</span> <span class="nc">FreqItemset</span><span class="p">(</span><span class="n">NamedTuple</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Represents an (items, freq) tuple.</span> |
| |
| <span class="sd"> .. versionadded:: 1.4.0</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">items</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> |
| <span class="n">freq</span><span class="p">:</span> <span class="nb">int</span></div> |
| |
| |
| <div class="viewcode-block" id="PrefixSpanModel"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.fpm.PrefixSpanModel.html#pyspark.mllib.fpm.PrefixSpanModel">[docs]</a><span class="nd">@inherit_doc</span> |
| <span class="k">class</span> <span class="nc">PrefixSpanModel</span><span class="p">(</span><span class="n">JavaModelWrapper</span><span class="p">,</span> <span class="n">Generic</span><span class="p">[</span><span class="n">T</span><span class="p">]):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Model fitted by PrefixSpan</span> |
| |
| <span class="sd"> .. versionadded:: 1.6.0</span> |
| |
| <span class="sd"> Examples</span> |
| <span class="sd"> --------</span> |
| <span class="sd"> >>> data = [</span> |
| <span class="sd"> ... [["a", "b"], ["c"]],</span> |
| <span class="sd"> ... [["a"], ["c", "b"], ["a", "b"]],</span> |
| <span class="sd"> ... [["a", "b"], ["e"]],</span> |
| <span class="sd"> ... [["f"]]]</span> |
| <span class="sd"> >>> rdd = sc.parallelize(data, 2)</span> |
| <span class="sd"> >>> model = PrefixSpan.train(rdd)</span> |
| <span class="sd"> >>> sorted(model.freqSequences().collect())</span> |
| <span class="sd"> [FreqSequence(sequence=[['a']], freq=3), FreqSequence(sequence=[['a'], ['a']], freq=1), ...</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="PrefixSpanModel.freqSequences"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.fpm.PrefixSpanModel.html#pyspark.mllib.fpm.PrefixSpanModel.freqSequences">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">"1.6.0"</span><span class="p">)</span> |
| <span class="k">def</span> <span class="nf">freqSequences</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">RDD</span><span class="p">[</span><span class="s2">"PrefixSpan.FreqSequence"</span><span class="p">]:</span> |
| <span class="w"> </span><span class="sd">"""Gets frequent sequences"""</span> |
| <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">call</span><span class="p">(</span><span class="s2">"getFreqSequences"</span><span class="p">)</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">PrefixSpan</span><span class="o">.</span><span class="n">FreqSequence</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span></div></div> |
| |
| |
| <div class="viewcode-block" id="PrefixSpan"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.fpm.PrefixSpan.html#pyspark.mllib.fpm.PrefixSpan">[docs]</a><span class="k">class</span> <span class="nc">PrefixSpan</span><span class="p">:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> A parallel PrefixSpan algorithm to mine frequent sequential patterns.</span> |
| <span class="sd"> The PrefixSpan algorithm is described in Jian Pei et al (2001) [1]_</span> |
| |
| <span class="sd"> .. versionadded:: 1.6.0</span> |
| |
| <span class="sd"> .. [1] Jian Pei et al.,</span> |
| <span class="sd"> "PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth,"</span> |
| <span class="sd"> Proceedings 17th International Conference on Data Engineering, Heidelberg,</span> |
| <span class="sd"> Germany, 2001, pp. 215-224,</span> |
| <span class="sd"> doi: https://doi.org/10.1109/ICDE.2001.914830</span> |
| <span class="sd"> """</span> |
| |
| <div class="viewcode-block" id="PrefixSpan.train"><a class="viewcode-back" href="../../../reference/api/pyspark.mllib.fpm.PrefixSpan.html#pyspark.mllib.fpm.PrefixSpan.train">[docs]</a> <span class="nd">@classmethod</span> |
| <span class="k">def</span> <span class="nf">train</span><span class="p">(</span> |
| <span class="bp">cls</span><span class="p">,</span> |
| <span class="n">data</span><span class="p">:</span> <span class="n">RDD</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">T</span><span class="p">]]],</span> |
| <span class="n">minSupport</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> |
| <span class="n">maxPatternLength</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> |
| <span class="n">maxLocalProjDBSize</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32000000</span><span class="p">,</span> |
| <span class="p">)</span> <span class="o">-></span> <span class="n">PrefixSpanModel</span><span class="p">[</span><span class="n">T</span><span class="p">]:</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Finds the complete set of frequent sequential patterns in the</span> |
| <span class="sd"> input sequences of itemsets.</span> |
| |
| <span class="sd"> .. versionadded:: 1.6.0</span> |
| |
| <span class="sd"> Parameters</span> |
| <span class="sd"> ----------</span> |
| <span class="sd"> data : :py:class:`pyspark.RDD`</span> |
| <span class="sd"> The input data set, each element contains a sequence of</span> |
| <span class="sd"> itemsets.</span> |
| <span class="sd"> minSupport : float, optional</span> |
| <span class="sd"> The minimal support level of the sequential pattern, any</span> |
| <span class="sd"> pattern that appears more than (minSupport *</span> |
| <span class="sd"> size-of-the-dataset) times will be output.</span> |
| <span class="sd"> (default: 0.1)</span> |
| <span class="sd"> maxPatternLength : int, optional</span> |
| <span class="sd"> The maximal length of the sequential pattern, any pattern</span> |
| <span class="sd"> that appears less than maxPatternLength will be output.</span> |
| <span class="sd"> (default: 10)</span> |
| <span class="sd"> maxLocalProjDBSize : int, optional</span> |
| <span class="sd"> The maximum number of items (including delimiters used in the</span> |
| <span class="sd"> internal storage format) allowed in a projected database before</span> |
| <span class="sd"> local processing. If a projected database exceeds this size,</span> |
| <span class="sd"> another iteration of distributed prefix growth is run.</span> |
| <span class="sd"> (default: 32000000)</span> |
| <span class="sd"> """</span> |
| <span class="n">model</span> <span class="o">=</span> <span class="n">callMLlibFunc</span><span class="p">(</span> |
| <span class="s2">"trainPrefixSpanModel"</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">minSupport</span><span class="p">,</span> <span class="n">maxPatternLength</span><span class="p">,</span> <span class="n">maxLocalProjDBSize</span> |
| <span class="p">)</span> |
| <span class="k">return</span> <span class="n">PrefixSpanModel</span><span class="p">(</span><span class="n">model</span><span class="p">)</span></div> |
| |
| <span class="k">class</span> <span class="nc">FreqSequence</span><span class="p">(</span><span class="n">NamedTuple</span><span class="p">):</span> |
| <span class="w"> </span><span class="sd">"""</span> |
| <span class="sd"> Represents a (sequence, freq) tuple.</span> |
| |
| <span class="sd"> .. versionadded:: 1.6.0</span> |
| <span class="sd"> """</span> |
| |
| <span class="n">sequence</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Any</span><span class="p">]]</span> |
| <span class="n">freq</span><span class="p">:</span> <span class="nb">int</span></div> |
| |
| |
| <span class="k">def</span> <span class="nf">_test</span><span class="p">()</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span> |
| <span class="kn">import</span> <span class="nn">doctest</span> |
| <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SparkSession</span> |
| <span class="kn">import</span> <span class="nn">pyspark.mllib.fpm</span> |
| |
| <span class="n">globs</span> <span class="o">=</span> <span class="n">pyspark</span><span class="o">.</span><span class="n">mllib</span><span class="o">.</span><span class="n">fpm</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> |
| <span class="n">spark</span> <span class="o">=</span> <span class="n">SparkSession</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">master</span><span class="p">(</span><span class="s2">"local[4]"</span><span class="p">)</span><span class="o">.</span><span class="n">appName</span><span class="p">(</span><span class="s2">"mllib.fpm tests"</span><span class="p">)</span><span class="o">.</span><span class="n">getOrCreate</span><span class="p">()</span> |
| <span class="n">globs</span><span class="p">[</span><span class="s2">"sc"</span><span class="p">]</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">sparkContext</span> |
| <span class="kn">import</span> <span class="nn">tempfile</span> |
| |
| <span class="n">temp_path</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span><span class="p">()</span> |
| <span class="n">globs</span><span class="p">[</span><span class="s2">"temp_path"</span><span class="p">]</span> <span class="o">=</span> <span class="n">temp_path</span> |
| <span class="k">try</span><span class="p">:</span> |
| <span class="p">(</span><span class="n">failure_count</span><span class="p">,</span> <span class="n">test_count</span><span class="p">)</span> <span class="o">=</span> <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">(</span><span class="n">globs</span><span class="o">=</span><span class="n">globs</span><span class="p">,</span> <span class="n">optionflags</span><span class="o">=</span><span class="n">doctest</span><span class="o">.</span><span class="n">ELLIPSIS</span><span class="p">)</span> |
| <span class="n">spark</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span> |
| <span class="k">finally</span><span class="p">:</span> |
| <span class="kn">from</span> <span class="nn">shutil</span> <span class="kn">import</span> <span class="n">rmtree</span> |
| |
| <span class="k">try</span><span class="p">:</span> |
| <span class="n">rmtree</span><span class="p">(</span><span class="n">temp_path</span><span class="p">)</span> |
| <span class="k">except</span> <span class="ne">OSError</span><span class="p">:</span> |
| <span class="k">pass</span> |
| <span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span> |
| <span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> |
| |
| |
| <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span> |
| <span class="n">_test</span><span class="p">()</span> |
| </pre></div> |
| |
| </article> |
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