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  <h1>Source code for pyspark.ml.pipeline</h1><div class="highlight"><pre>
<span class="c">#</span>
<span class="c"># Licensed to the Apache Software Foundation (ASF) under one or more</span>
<span class="c"># contributor license agreements.  See the NOTICE file distributed with</span>
<span class="c"># this work for additional information regarding copyright ownership.</span>
<span class="c"># The ASF licenses this file to You under the Apache License, Version 2.0</span>
<span class="c"># (the &quot;License&quot;); you may not use this file except in compliance with</span>
<span class="c"># the License.  You may obtain a copy of the License at</span>
<span class="c">#</span>
<span class="c">#    http://www.apache.org/licenses/LICENSE-2.0</span>
<span class="c">#</span>
<span class="c"># Unless required by applicable law or agreed to in writing, software</span>
<span class="c"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
<span class="c"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
<span class="c"># See the License for the specific language governing permissions and</span>
<span class="c"># limitations under the License.</span>
<span class="c">#</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">from</span> <span class="nn">pyspark.ml.param</span> <span class="kn">import</span> <span class="n">Param</span><span class="p">,</span> <span class="n">Params</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.util</span> <span class="kn">import</span> <span class="n">keyword_only</span>
<span class="kn">from</span> <span class="nn">pyspark.mllib.common</span> <span class="kn">import</span> <span class="n">inherit_doc</span>


<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="Estimator"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.Estimator">[docs]</a><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="sd">&quot;&quot;&quot;</span>
<span class="sd">    Abstract class for estimators that fit models to data.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">__metaclass__</span> <span class="o">=</span> <span class="n">ABCMeta</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="sd">&quot;&quot;&quot;</span>
<span class="sd">        Fits a model to the input dataset. This is called by the</span>
<span class="sd">        default implementation of fit.</span>

<span class="sd">        :param dataset: input dataset, which is an instance of</span>
<span class="sd">                        :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd">        :returns: 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.fit"><a class="viewcode-back" href="../../../pyspark.ml.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">params</span><span class="o">=</span><span class="bp">None</span><span class="p">):</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">        :param dataset: input dataset, which is an instance of</span>
<span class="sd">                        :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd">        :param params: an optional param map that overrides embedded</span>
<span class="sd">                       params. If a list/tuple of param maps is given,</span>
<span class="sd">                       this calls fit on each param map and returns a</span>
<span class="sd">                       list of models.</span>
<span class="sd">        :returns: 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="bp">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="k">return</span> <span class="p">[</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="n">paramMap</span><span class="p">)</span> <span class="k">for</span> <span class="n">paramMap</span> <span class="ow">in</span> <span class="n">params</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">ValueError</span><span class="p">(</span><span class="s">&quot;Params must be either a param map or a list/tuple of param maps, &quot;</span>
                             <span class="s">&quot;but got </span><span class="si">%s</span><span class="s">.&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>
<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="Transformer"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.Transformer">[docs]</a><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="sd">&quot;&quot;&quot;</span>
<span class="sd">    Abstract class for transformers that transform one dataset into</span>
<span class="sd">    another.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">__metaclass__</span> <span class="o">=</span> <span class="n">ABCMeta</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="sd">&quot;&quot;&quot;</span>
<span class="sd">        Transforms the input dataset with optional parameters.</span>

<span class="sd">        :param dataset: input dataset, which is an instance of</span>
<span class="sd">                        :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd">        :returns: 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="../../../pyspark.ml.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">params</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Transforms the input dataset with optional parameters.</span>

<span class="sd">        :param dataset: input dataset, which is an instance of</span>
<span class="sd">                        :py:class:`pyspark.sql.DataFrame`</span>
<span class="sd">        :param params: an optional param map that overrides embedded</span>
<span class="sd">                       params.</span>
<span class="sd">        :returns: 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="bp">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">ValueError</span><span class="p">(</span><span class="s">&quot;Params must be either a param map but got </span><span class="si">%s</span><span class="s">.&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>
<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="Model"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.Model">[docs]</a><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="sd">&quot;&quot;&quot;</span>
<span class="sd">    Abstract class for models that are fitted by estimators.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">__metaclass__</span> <span class="o">=</span> <span class="n">ABCMeta</span>

</div>
<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="Pipeline"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.Pipeline">[docs]</a><span class="k">class</span> <span class="nc">Pipeline</span><span class="p">(</span><span class="n">Estimator</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    A simple pipeline, which acts as an estimator. A Pipeline consists</span>
<span class="sd">    of a sequence of stages, each of which is either an</span>
<span class="sd">    :py:class:`Estimator` or a :py:class:`Transformer`. When</span>
<span class="sd">    :py:meth:`Pipeline.fit` is called, the stages are executed in</span>
<span class="sd">    order. If a stage is an :py:class:`Estimator`, its</span>
<span class="sd">    :py:meth:`Estimator.fit` method will be called on the input</span>
<span class="sd">    dataset to fit a model. Then the model, which is a transformer,</span>
<span class="sd">    will be used to transform the dataset as the input to the next</span>
<span class="sd">    stage. If a stage is a :py:class:`Transformer`, its</span>
<span class="sd">    :py:meth:`Transformer.transform` method will be called to produce</span>
<span class="sd">    the dataset for the next stage. The fitted model from a</span>
<span class="sd">    :py:class:`Pipeline` is an :py:class:`PipelineModel`, which</span>
<span class="sd">    consists of fitted models and transformers, corresponding to the</span>
<span class="sd">    pipeline stages. If there are no stages, the pipeline acts as an</span>
<span class="sd">    identity transformer.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="nd">@keyword_only</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stages</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        __init__(self, stages=None)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">stages</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">stages</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Pipeline</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
        <span class="c">#: Param for pipeline stages.</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">stages</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s">&quot;stages&quot;</span><span class="p">,</span> <span class="s">&quot;pipeline stages&quot;</span><span class="p">)</span>
        <span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__init__</span><span class="o">.</span><span class="n">_input_kwargs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

<div class="viewcode-block" id="Pipeline.setStages"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.Pipeline.setStages">[docs]</a>    <span class="k">def</span> <span class="nf">setStages</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="sd">&quot;&quot;&quot;</span>
<span class="sd">        Set pipeline stages.</span>
<span class="sd">        :param value: a list of transformers or estimators</span>
<span class="sd">        :return: the pipeline instance</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">stages</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
        <span class="k">return</span> <span class="bp">self</span>
</div>
<div class="viewcode-block" id="Pipeline.getStages"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.Pipeline.getStages">[docs]</a>    <span class="k">def</span> <span class="nf">getStages</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Get pipeline stages.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">stages</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_paramMap</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">stages</span><span class="p">]</span>
</div>
    <span class="nd">@keyword_only</span>
<div class="viewcode-block" id="Pipeline.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.Pipeline.setParams">[docs]</a>    <span class="k">def</span> <span class="nf">setParams</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stages</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        setParams(self, stages=None)</span>
<span class="sd">        Sets params for Pipeline.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="n">stages</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">stages</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">setParams</span><span class="o">.</span><span class="n">_input_kwargs</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="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</div>
    <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">stages</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">getStages</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">stage</span> <span class="ow">in</span> <span class="n">stages</span><span class="p">:</span>
            <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">stage</span><span class="p">,</span> <span class="n">Estimator</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">stage</span><span class="p">,</span> <span class="n">Transformer</span><span class="p">)):</span>
                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
                    <span class="s">&quot;Cannot recognize a pipeline stage of type </span><span class="si">%s</span><span class="s">.&quot;</span> <span class="o">%</span> <span class="nb">type</span><span class="p">(</span><span class="n">stage</span><span class="p">))</span>
        <span class="n">indexOfLastEstimator</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">stage</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">stages</span><span class="p">):</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">stage</span><span class="p">,</span> <span class="n">Estimator</span><span class="p">):</span>
                <span class="n">indexOfLastEstimator</span> <span class="o">=</span> <span class="n">i</span>
        <span class="n">transformers</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">stage</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">stages</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;=</span> <span class="n">indexOfLastEstimator</span><span class="p">:</span>
                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">stage</span><span class="p">,</span> <span class="n">Transformer</span><span class="p">):</span>
                    <span class="n">transformers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">stage</span><span class="p">)</span>
                    <span class="n">dataset</span> <span class="o">=</span> <span class="n">stage</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="c"># must be an Estimator</span>
                    <span class="n">model</span> <span class="o">=</span> <span class="n">stage</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">transformers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
                    <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">indexOfLastEstimator</span><span class="p">:</span>
                        <span class="n">dataset</span> <span class="o">=</span> <span class="n">model</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="n">transformers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">stage</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">PipelineModel</span><span class="p">(</span><span class="n">transformers</span><span class="p">)</span>

<div class="viewcode-block" id="Pipeline.copy"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.Pipeline.copy">[docs]</a>    <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">extra</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">extra</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">extra</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
        <span class="n">that</span> <span class="o">=</span> <span class="n">Params</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">extra</span><span class="p">)</span>
        <span class="n">stages</span> <span class="o">=</span> <span class="p">[</span><span class="n">stage</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">extra</span><span class="p">)</span> <span class="k">for</span> <span class="n">stage</span> <span class="ow">in</span> <span class="n">that</span><span class="o">.</span><span class="n">getStages</span><span class="p">()]</span>
        <span class="k">return</span> <span class="n">that</span><span class="o">.</span><span class="n">setStages</span><span class="p">(</span><span class="n">stages</span><span class="p">)</span>

</div></div>
<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="PipelineModel"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.PipelineModel">[docs]</a><span class="k">class</span> <span class="nc">PipelineModel</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Represents a compiled pipeline with transformers and fitted models.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stages</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">PipelineModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">stages</span> <span class="o">=</span> <span class="n">stages</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="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">stages</span><span class="p">:</span>
            <span class="n">dataset</span> <span class="o">=</span> <span class="n">t</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">return</span> <span class="n">dataset</span>

<div class="viewcode-block" id="PipelineModel.copy"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.PipelineModel.copy">[docs]</a>    <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">extra</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">extra</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
            <span class="n">extra</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
        <span class="n">stages</span> <span class="o">=</span> <span class="p">[</span><span class="n">stage</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">extra</span><span class="p">)</span> <span class="k">for</span> <span class="n">stage</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">stages</span><span class="p">]</span>
        <span class="k">return</span> <span class="n">PipelineModel</span><span class="p">(</span><span class="n">stages</span><span class="p">)</span></div></div>
</pre></div>

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