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<h1>Source code for pyspark.ml.base</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 &quot;License&quot;); 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 &quot;AS IS&quot; 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">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">import</span> <span class="nn">copy</span>
<span class="kn">import</span> <span class="nn">threading</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">Any</span><span class="p">,</span>
<span class="n">Callable</span><span class="p">,</span>
<span class="n">Generic</span><span class="p">,</span>
<span class="n">Iterator</span><span class="p">,</span>
<span class="n">List</span><span class="p">,</span>
<span class="n">Optional</span><span class="p">,</span>
<span class="n">Sequence</span><span class="p">,</span>
<span class="n">Tuple</span><span class="p">,</span>
<span class="n">TypeVar</span><span class="p">,</span>
<span class="n">Union</span><span class="p">,</span>
<span class="n">cast</span><span class="p">,</span>
<span class="n">overload</span><span class="p">,</span>
<span class="n">TYPE_CHECKING</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">since</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.param</span> <span class="kn">import</span> <span class="n">P</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.common</span> <span class="kn">import</span> <span class="n">inherit_doc</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.param.shared</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">HasInputCol</span><span class="p">,</span>
<span class="n">HasOutputCol</span><span class="p">,</span>
<span class="n">HasLabelCol</span><span class="p">,</span>
<span class="n">HasFeaturesCol</span><span class="p">,</span>
<span class="n">HasPredictionCol</span><span class="p">,</span>
<span class="n">Params</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.dataframe</span> <span class="kn">import</span> <span class="n">DataFrame</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="kn">import</span> <span class="n">udf</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.types</span> <span class="kn">import</span> <span class="n">DataType</span><span class="p">,</span> <span class="n">StructField</span><span class="p">,</span> <span class="n">StructType</span>
<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">pyspark.ml._typing</span> <span class="kn">import</span> <span class="n">ParamMap</span>
<span class="n">T</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">&quot;T&quot;</span><span class="p">)</span>
<span class="n">M</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">&quot;M&quot;</span><span class="p">,</span> <span class="n">bound</span><span class="o">=</span><span class="s2">&quot;Transformer&quot;</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">_FitMultipleIterator</span><span class="p">(</span><span class="n">Generic</span><span class="p">[</span><span class="n">M</span><span class="p">]):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Used by default implementation of Estimator.fitMultiple to produce models in a thread safe</span>
<span class="sd"> iterator. This class handles the simple case of fitMultiple where each param map should be</span>
<span class="sd"> fit independently.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> fitSingleModel : function</span>
<span class="sd"> Callable[[int], Transformer] which fits an estimator to a dataset.</span>
<span class="sd"> `fitSingleModel` may be called up to `numModels` times, with a unique index each time.</span>
<span class="sd"> Each call to `fitSingleModel` with an index should return the Model associated with</span>
<span class="sd"> that index.</span>
<span class="sd"> numModel : int</span>
<span class="sd"> Number of models this iterator should produce.</span>
<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> See :py:meth:`Estimator.fitMultiple` for more info.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fitSingleModel</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="nb">int</span><span class="p">],</span> <span class="n">M</span><span class="p">],</span> <span class="n">numModels</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot; &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">fitSingleModel</span> <span class="o">=</span> <span class="n">fitSingleModel</span>
<span class="bp">self</span><span class="o">.</span><span class="n">numModel</span> <span class="o">=</span> <span class="n">numModels</span>
<span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">=</span> <span class="mi">0</span>
<span class="bp">self</span><span class="o">.</span><span class="n">lock</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Lock</span><span class="p">()</span>
<span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterator</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">M</span><span class="p">]]:</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="fm">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">M</span><span class="p">]:</span>
<span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">lock</span><span class="p">:</span>
<span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">counter</span>
<span class="k">if</span> <span class="n">index</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">numModel</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">StopIteration</span><span class="p">(</span><span class="s2">&quot;No models remaining.&quot;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">index</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fitSingleModel</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">next</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">M</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;For python2 compatibility.&quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__next__</span><span class="p">()</span>
<div class="viewcode-block" id="Estimator"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.Estimator.html#pyspark.ml.Estimator">[docs]</a><span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">Estimator</span><span class="p">(</span><span class="n">Params</span><span class="p">,</span> <span class="n">Generic</span><span class="p">[</span><span class="n">M</span><span class="p">],</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class for estimators that fit models to data.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">M</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits a model to the input dataset. This is called by the default implementation of fit.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> dataset : :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> input dataset</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :class:`Transformer`</span>
<span class="sd"> fitted model</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
<div class="viewcode-block" id="Estimator.fitMultiple"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.Estimator.html#pyspark.ml.Estimator.fitMultiple">[docs]</a> <span class="k">def</span> <span class="nf">fitMultiple</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">paramMaps</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="s2">&quot;ParamMap&quot;</span><span class="p">]</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Iterator</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">M</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits a model to the input dataset for each param map in `paramMaps`.</span>
<span class="sd"> .. versionadded:: 2.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> dataset : :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> input dataset.</span>
<span class="sd"> paramMaps : :py:class:`collections.abc.Sequence`</span>
<span class="sd"> A Sequence of param maps.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`_FitMultipleIterator`</span>
<span class="sd"> A thread safe iterable which contains one model for each param map. Each</span>
<span class="sd"> call to `next(modelIterator)` will return `(index, model)` where model was fit</span>
<span class="sd"> using `paramMaps[index]`. `index` values may not be sequential.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">fitSingleModel</span><span class="p">(</span><span class="n">index</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">M</span><span class="p">:</span>
<span class="k">return</span> <span class="n">estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">paramMaps</span><span class="p">[</span><span class="n">index</span><span class="p">])</span>
<span class="k">return</span> <span class="n">_FitMultipleIterator</span><span class="p">(</span><span class="n">fitSingleModel</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">paramMaps</span><span class="p">))</span></div>
<span class="nd">@overload</span>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="s2">&quot;ParamMap&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="o">...</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">M</span><span class="p">:</span>
<span class="o">...</span>
<span class="nd">@overload</span>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">params</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="s2">&quot;ParamMap&quot;</span><span class="p">],</span> <span class="n">Tuple</span><span class="p">[</span><span class="s2">&quot;ParamMap&quot;</span><span class="p">]]</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="n">M</span><span class="p">]:</span>
<span class="o">...</span>
<div class="viewcode-block" id="Estimator.fit"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.Estimator.html#pyspark.ml.Estimator.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span>
<span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span>
<span class="n">params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="s2">&quot;ParamMap&quot;</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="s2">&quot;ParamMap&quot;</span><span class="p">],</span> <span class="n">Tuple</span><span class="p">[</span><span class="s2">&quot;ParamMap&quot;</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">M</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="n">M</span><span class="p">]]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits a model to the input dataset with optional parameters.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> dataset : :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> input dataset.</span>
<span class="sd"> params : dict or list or tuple, optional</span>
<span class="sd"> an optional param map that overrides embedded params. If a list/tuple of</span>
<span class="sd"> param maps is given, this calls fit on each param map and returns a list of</span>
<span class="sd"> models.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`Transformer` or a list of :py:class:`Transformer`</span>
<span class="sd"> fitted model(s)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
<span class="n">models</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Optional</span><span class="p">[</span><span class="n">M</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">fitMultiple</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="n">models</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span>
<span class="k">return</span> <span class="n">cast</span><span class="p">(</span><span class="n">List</span><span class="p">[</span><span class="n">M</span><span class="p">],</span> <span class="n">models</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
<span class="k">if</span> <span class="n">params</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">_fit</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fit</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
<span class="s2">&quot;Params must be either a param map or a list/tuple of param maps, &quot;</span>
<span class="s2">&quot;but got </span><span class="si">%s</span><span class="s2">.&quot;</span> <span class="o">%</span> <span class="nb">type</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
<span class="p">)</span></div></div>
<div class="viewcode-block" id="Transformer"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.Transformer.html#pyspark.ml.Transformer">[docs]</a><span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">Transformer</span><span class="p">(</span><span class="n">Params</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class for transformers that transform one dataset into another.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Transforms the input dataset.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> dataset : :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> input dataset.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> transformed dataset</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
<div class="viewcode-block" id="Transformer.transform"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.Transformer.html#pyspark.ml.Transformer.transform">[docs]</a> <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">,</span> <span class="n">params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="s2">&quot;ParamMap&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Transforms the input dataset with optional parameters.</span>
<span class="sd"> .. versionadded:: 1.3.0</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> dataset : :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> input dataset</span>
<span class="sd"> params : dict, optional</span>
<span class="sd"> an optional param map that overrides embedded params.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd"> transformed dataset</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">params</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
<span class="k">if</span> <span class="n">params</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">params</span><span class="p">)</span><span class="o">.</span><span class="n">_transform</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Params must be a param map but got </span><span class="si">%s</span><span class="s2">.&quot;</span> <span class="o">%</span> <span class="nb">type</span><span class="p">(</span><span class="n">params</span><span class="p">))</span></div></div>
<div class="viewcode-block" id="Model"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.Model.html#pyspark.ml.Model">[docs]</a><span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">Transformer</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class for models that are fitted by estimators.</span>
<span class="sd"> .. versionadded:: 1.4.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">pass</span></div>
<div class="viewcode-block" id="UnaryTransformer"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.UnaryTransformer.html#pyspark.ml.UnaryTransformer">[docs]</a><span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">UnaryTransformer</span><span class="p">(</span><span class="n">HasInputCol</span><span class="p">,</span> <span class="n">HasOutputCol</span><span class="p">,</span> <span class="n">Transformer</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class for transformers that take one input column, apply transformation,</span>
<span class="sd"> and output the result as a new column.</span>
<span class="sd"> .. versionadded:: 2.3.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<div class="viewcode-block" id="UnaryTransformer.setInputCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.UnaryTransformer.html#pyspark.ml.UnaryTransformer.setInputCol">[docs]</a> <span class="k">def</span> <span class="nf">setInputCol</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="n">P</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">P</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`inputCol`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="UnaryTransformer.setOutputCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.UnaryTransformer.html#pyspark.ml.UnaryTransformer.setOutputCol">[docs]</a> <span class="k">def</span> <span class="nf">setOutputCol</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="n">P</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">P</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`outputCol`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="n">outputCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="UnaryTransformer.createTransformFunc"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.UnaryTransformer.html#pyspark.ml.UnaryTransformer.createTransformFunc">[docs]</a> <span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">createTransformFunc</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates the transform function using the given param map. The input param map already takes</span>
<span class="sd"> account of the embedded param map. So the param values should be determined</span>
<span class="sd"> solely by the input param map.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
<div class="viewcode-block" id="UnaryTransformer.outputDataType"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.UnaryTransformer.html#pyspark.ml.UnaryTransformer.outputDataType">[docs]</a> <span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">outputDataType</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataType</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the data type of the output column.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
<div class="viewcode-block" id="UnaryTransformer.validateInputType"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.UnaryTransformer.html#pyspark.ml.UnaryTransformer.validateInputType">[docs]</a> <span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">validateInputType</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputType</span><span class="p">:</span> <span class="n">DataType</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Validates the input type. Throw an exception if it is invalid.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
<div class="viewcode-block" id="UnaryTransformer.transformSchema"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.UnaryTransformer.html#pyspark.ml.UnaryTransformer.transformSchema">[docs]</a> <span class="k">def</span> <span class="nf">transformSchema</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">schema</span><span class="p">:</span> <span class="n">StructType</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">StructType</span><span class="p">:</span>
<span class="n">inputType</span> <span class="o">=</span> <span class="n">schema</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">getInputCol</span><span class="p">()]</span><span class="o">.</span><span class="n">dataType</span>
<span class="bp">self</span><span class="o">.</span><span class="n">validateInputType</span><span class="p">(</span><span class="n">inputType</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOutputCol</span><span class="p">()</span> <span class="ow">in</span> <span class="n">schema</span><span class="o">.</span><span class="n">names</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Output column </span><span class="si">%s</span><span class="s2"> already exists.&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">getOutputCol</span><span class="p">())</span>
<span class="n">outputFields</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">schema</span><span class="o">.</span><span class="n">fields</span><span class="p">)</span>
<span class="n">outputFields</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">StructField</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">getOutputCol</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputDataType</span><span class="p">(),</span> <span class="n">nullable</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
<span class="k">return</span> <span class="n">StructType</span><span class="p">(</span><span class="n">outputFields</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrame</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">transformSchema</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">schema</span><span class="p">)</span>
<span class="n">transformUDF</span> <span class="o">=</span> <span class="n">udf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">createTransformFunc</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">outputDataType</span><span class="p">())</span>
<span class="n">transformedDataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">withColumn</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">getOutputCol</span><span class="p">(),</span> <span class="n">transformUDF</span><span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">getInputCol</span><span class="p">()])</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">transformedDataset</span></div>
<span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">_PredictorParams</span><span class="p">(</span><span class="n">HasLabelCol</span><span class="p">,</span> <span class="n">HasFeaturesCol</span><span class="p">,</span> <span class="n">HasPredictionCol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Params for :py:class:`Predictor` and :py:class:`PredictorModel`.</span>
<span class="sd"> .. versionadded:: 3.0.0</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">pass</span>
<div class="viewcode-block" id="Predictor"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.Predictor.html#pyspark.ml.Predictor">[docs]</a><span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">Predictor</span><span class="p">(</span><span class="n">Estimator</span><span class="p">[</span><span class="n">M</span><span class="p">],</span> <span class="n">_PredictorParams</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Estimator for prediction tasks (regression and classification).</span>
<span class="sd"> &quot;&quot;&quot;</span>
<div class="viewcode-block" id="Predictor.setLabelCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.Predictor.html#pyspark.ml.Predictor.setLabelCol">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setLabelCol</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="n">P</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">P</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`labelCol`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="n">labelCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="Predictor.setFeaturesCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.Predictor.html#pyspark.ml.Predictor.setFeaturesCol">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setFeaturesCol</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="n">P</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">P</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`featuresCol`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="n">featuresCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="Predictor.setPredictionCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.Predictor.html#pyspark.ml.Predictor.setPredictionCol">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setPredictionCol</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="n">P</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">P</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`predictionCol`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="n">predictionCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div></div>
<div class="viewcode-block" id="PredictionModel"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.PredictionModel.html#pyspark.ml.PredictionModel">[docs]</a><span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">PredictionModel</span><span class="p">(</span><span class="n">Model</span><span class="p">,</span> <span class="n">_PredictorParams</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="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Model for prediction tasks (regression and classification).</span>
<span class="sd"> &quot;&quot;&quot;</span>
<div class="viewcode-block" id="PredictionModel.setFeaturesCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.PredictionModel.html#pyspark.ml.PredictionModel.setFeaturesCol">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setFeaturesCol</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="n">P</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">P</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`featuresCol`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="n">featuresCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<div class="viewcode-block" id="PredictionModel.setPredictionCol"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.PredictionModel.html#pyspark.ml.PredictionModel.setPredictionCol">[docs]</a> <span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">setPredictionCol</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="n">P</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">P</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Sets the value of :py:attr:`predictionCol`.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_set</span><span class="p">(</span><span class="n">predictionCol</span><span class="o">=</span><span class="n">value</span><span class="p">)</span></div>
<span class="nd">@property</span>
<span class="nd">@abstractmethod</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;2.1.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">numFeatures</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of features the model was trained on. If unknown, returns -1</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span>
<div class="viewcode-block" id="PredictionModel.predict"><a class="viewcode-back" href="../../../reference/api/pyspark.ml.PredictionModel.html#pyspark.ml.PredictionModel.predict">[docs]</a> <span class="nd">@abstractmethod</span>
<span class="nd">@since</span><span class="p">(</span><span class="s2">&quot;3.0.0&quot;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">:</span> <span class="n">T</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predict label for the given features.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div></div>
</pre></div>
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