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  <h1>Source code for pyspark.ml.evaluation</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">abstractmethod</span><span class="p">,</span> <span class="n">ABCMeta</span>

<span class="kn">from</span> <span class="nn">pyspark.ml.wrapper</span> <span class="kn">import</span> <span class="n">JavaWrapper</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.param.shared</span> <span class="kn">import</span> <span class="n">HasLabelCol</span><span class="p">,</span> <span class="n">HasPredictionCol</span><span class="p">,</span> <span class="n">HasRawPredictionCol</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="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;Evaluator&#39;</span><span class="p">,</span> <span class="s">&#39;BinaryClassificationEvaluator&#39;</span><span class="p">,</span> <span class="s">&#39;RegressionEvaluator&#39;</span><span class="p">,</span>
           <span class="s">&#39;MulticlassClassificationEvaluator&#39;</span><span class="p">]</span>


<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="Evaluator"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.Evaluator">[docs]</a><span class="k">class</span> <span class="nc">Evaluator</span><span class="p">(</span><span class="n">Params</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Base class for evaluators that compute metrics from predictions.</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">_evaluate</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">        Evaluates the output.</span>

<span class="sd">        :param dataset: a dataset that contains labels/observations and</span>
<span class="sd">               predictions</span>
<span class="sd">        :return: metric</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="Evaluator.evaluate"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.Evaluator.evaluate">[docs]</a>    <span class="k">def</span> <span class="nf">evaluate</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">        Evaluates the output with optional parameters.</span>

<span class="sd">        :param dataset: a dataset that contains labels/observations and</span>
<span class="sd">                        predictions</span>
<span class="sd">        :param params: an optional param map that overrides embedded</span>
<span class="sd">                       params</span>
<span class="sd">        :return: metric</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">_evaluate</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">_evaluate</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 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 class="viewcode-block" id="Evaluator.isLargerBetter"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.Evaluator.isLargerBetter">[docs]</a>    <span class="k">def</span> <span class="nf">isLargerBetter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Indicates whether the metric returned by :py:meth:`evaluate` should be maximized</span>
<span class="sd">        (True, default) or minimized (False).</span>
<span class="sd">        A given evaluator may support multiple metrics which may be maximized or minimized.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">True</span>

</div></div>
<span class="nd">@inherit_doc</span>
<span class="k">class</span> <span class="nc">JavaEvaluator</span><span class="p">(</span><span class="n">Evaluator</span><span class="p">,</span> <span class="n">JavaWrapper</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Base class for :py:class:`Evaluator`s that wrap Java/Scala</span>
<span class="sd">    implementations.</span>
<span class="sd">    &quot;&quot;&quot;</span>

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

    <span class="k">def</span> <span class="nf">_evaluate</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">        Evaluates the output.</span>
<span class="sd">        :param dataset: a dataset that contains labels/observations and predictions.</span>
<span class="sd">        :return: evaluation metric</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_transfer_params_to_java</span><span class="p">()</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_java_obj</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">_jdf</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">isLargerBetter</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_transfer_params_to_java</span><span class="p">()</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_java_obj</span><span class="o">.</span><span class="n">isLargerBetter</span><span class="p">()</span>


<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="BinaryClassificationEvaluator"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.BinaryClassificationEvaluator">[docs]</a><span class="k">class</span> <span class="nc">BinaryClassificationEvaluator</span><span class="p">(</span><span class="n">JavaEvaluator</span><span class="p">,</span> <span class="n">HasLabelCol</span><span class="p">,</span> <span class="n">HasRawPredictionCol</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Evaluator for binary classification, which expects two input</span>
<span class="sd">    columns: rawPrediction and label.</span>

<span class="sd">    &gt;&gt;&gt; from pyspark.mllib.linalg import Vectors</span>
<span class="sd">    &gt;&gt;&gt; scoreAndLabels = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1]),</span>
<span class="sd">    ...    [(0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)])</span>
<span class="sd">    &gt;&gt;&gt; dataset = sqlContext.createDataFrame(scoreAndLabels, [&quot;raw&quot;, &quot;label&quot;])</span>
<span class="sd">    ...</span>
<span class="sd">    &gt;&gt;&gt; evaluator = BinaryClassificationEvaluator(rawPredictionCol=&quot;raw&quot;)</span>
<span class="sd">    &gt;&gt;&gt; evaluator.evaluate(dataset)</span>
<span class="sd">    0.70...</span>
<span class="sd">    &gt;&gt;&gt; evaluator.evaluate(dataset, {evaluator.metricName: &quot;areaUnderPR&quot;})</span>
<span class="sd">    0.83...</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="c"># a placeholder to make it appear in the generated doc</span>
    <span class="n">metricName</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span> <span class="s">&quot;metricName&quot;</span><span class="p">,</span>
                       <span class="s">&quot;metric name in evaluation (areaUnderROC|areaUnderPR)&quot;</span><span class="p">)</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">rawPredictionCol</span><span class="o">=</span><span class="s">&quot;rawPrediction&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s">&quot;label&quot;</span><span class="p">,</span>
                 <span class="n">metricName</span><span class="o">=</span><span class="s">&quot;areaUnderROC&quot;</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        __init__(self, rawPredictionCol=&quot;rawPrediction&quot;, labelCol=&quot;label&quot;, \</span>
<span class="sd">                 metricName=&quot;areaUnderROC&quot;)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">BinaryClassificationEvaluator</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">_java_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_new_java_obj</span><span class="p">(</span>
            <span class="s">&quot;org.apache.spark.ml.evaluation.BinaryClassificationEvaluator&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">uid</span><span class="p">)</span>
        <span class="c">#: param for metric name in evaluation (areaUnderROC|areaUnderPR)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">metricName</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;metricName&quot;</span><span class="p">,</span>
                                <span class="s">&quot;metric name in evaluation (areaUnderROC|areaUnderPR)&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">rawPredictionCol</span><span class="o">=</span><span class="s">&quot;rawPrediction&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s">&quot;label&quot;</span><span class="p">,</span>
                         <span class="n">metricName</span><span class="o">=</span><span class="s">&quot;areaUnderROC&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">_set</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

<div class="viewcode-block" id="BinaryClassificationEvaluator.setMetricName"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.BinaryClassificationEvaluator.setMetricName">[docs]</a>    <span class="k">def</span> <span class="nf">setMetricName</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">        Sets the value of :py:attr:`metricName`.</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">metricName</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="BinaryClassificationEvaluator.getMetricName"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.BinaryClassificationEvaluator.getMetricName">[docs]</a>    <span class="k">def</span> <span class="nf">getMetricName</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Gets the value of metricName or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">metricName</span><span class="p">)</span>
</div>
    <span class="nd">@keyword_only</span>
<div class="viewcode-block" id="BinaryClassificationEvaluator.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.BinaryClassificationEvaluator.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">rawPredictionCol</span><span class="o">=</span><span class="s">&quot;rawPrediction&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s">&quot;label&quot;</span><span class="p">,</span>
                  <span class="n">metricName</span><span class="o">=</span><span class="s">&quot;areaUnderROC&quot;</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        setParams(self, rawPredictionCol=&quot;rawPrediction&quot;, labelCol=&quot;label&quot;, \</span>
<span class="sd">                  metricName=&quot;areaUnderROC&quot;)</span>
<span class="sd">        Sets params for binary classification evaluator.</span>
<span class="sd">        &quot;&quot;&quot;</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></div>
<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="RegressionEvaluator"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.RegressionEvaluator">[docs]</a><span class="k">class</span> <span class="nc">RegressionEvaluator</span><span class="p">(</span><span class="n">JavaEvaluator</span><span class="p">,</span> <span class="n">HasLabelCol</span><span class="p">,</span> <span class="n">HasPredictionCol</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Evaluator for Regression, which expects two input</span>
<span class="sd">    columns: prediction and label.</span>

<span class="sd">    &gt;&gt;&gt; scoreAndLabels = [(-28.98343821, -27.0), (20.21491975, 21.5),</span>
<span class="sd">    ...   (-25.98418959, -22.0), (30.69731842, 33.0), (74.69283752, 71.0)]</span>
<span class="sd">    &gt;&gt;&gt; dataset = sqlContext.createDataFrame(scoreAndLabels, [&quot;raw&quot;, &quot;label&quot;])</span>
<span class="sd">    ...</span>
<span class="sd">    &gt;&gt;&gt; evaluator = RegressionEvaluator(predictionCol=&quot;raw&quot;)</span>
<span class="sd">    &gt;&gt;&gt; evaluator.evaluate(dataset)</span>
<span class="sd">    2.842...</span>
<span class="sd">    &gt;&gt;&gt; evaluator.evaluate(dataset, {evaluator.metricName: &quot;r2&quot;})</span>
<span class="sd">    0.993...</span>
<span class="sd">    &gt;&gt;&gt; evaluator.evaluate(dataset, {evaluator.metricName: &quot;mae&quot;})</span>
<span class="sd">    2.649...</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c"># Because we will maximize evaluation value (ref: `CrossValidator`),</span>
    <span class="c"># when we evaluate a metric that is needed to minimize (e.g., `&quot;rmse&quot;`, `&quot;mse&quot;`, `&quot;mae&quot;`),</span>
    <span class="c"># we take and output the negative of this metric.</span>
    <span class="n">metricName</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span> <span class="s">&quot;metricName&quot;</span><span class="p">,</span>
                       <span class="s">&quot;metric name in evaluation (mse|rmse|r2|mae)&quot;</span><span class="p">)</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">predictionCol</span><span class="o">=</span><span class="s">&quot;prediction&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s">&quot;label&quot;</span><span class="p">,</span>
                 <span class="n">metricName</span><span class="o">=</span><span class="s">&quot;rmse&quot;</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        __init__(self, predictionCol=&quot;prediction&quot;, labelCol=&quot;label&quot;, \</span>
<span class="sd">                 metricName=&quot;rmse&quot;)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">RegressionEvaluator</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">_java_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_new_java_obj</span><span class="p">(</span>
            <span class="s">&quot;org.apache.spark.ml.evaluation.RegressionEvaluator&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">uid</span><span class="p">)</span>
        <span class="c">#: param for metric name in evaluation (mse|rmse|r2|mae)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">metricName</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;metricName&quot;</span><span class="p">,</span>
                                <span class="s">&quot;metric name in evaluation (mse|rmse|r2|mae)&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">predictionCol</span><span class="o">=</span><span class="s">&quot;prediction&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s">&quot;label&quot;</span><span class="p">,</span>
                         <span class="n">metricName</span><span class="o">=</span><span class="s">&quot;rmse&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">_set</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

<div class="viewcode-block" id="RegressionEvaluator.setMetricName"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.RegressionEvaluator.setMetricName">[docs]</a>    <span class="k">def</span> <span class="nf">setMetricName</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">        Sets the value of :py:attr:`metricName`.</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">metricName</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="RegressionEvaluator.getMetricName"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.RegressionEvaluator.getMetricName">[docs]</a>    <span class="k">def</span> <span class="nf">getMetricName</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Gets the value of metricName or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">metricName</span><span class="p">)</span>
</div>
    <span class="nd">@keyword_only</span>
<div class="viewcode-block" id="RegressionEvaluator.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.RegressionEvaluator.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">predictionCol</span><span class="o">=</span><span class="s">&quot;prediction&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s">&quot;label&quot;</span><span class="p">,</span>
                  <span class="n">metricName</span><span class="o">=</span><span class="s">&quot;rmse&quot;</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        setParams(self, predictionCol=&quot;prediction&quot;, labelCol=&quot;label&quot;, \</span>
<span class="sd">                  metricName=&quot;rmse&quot;)</span>
<span class="sd">        Sets params for regression evaluator.</span>
<span class="sd">        &quot;&quot;&quot;</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></div>
<span class="nd">@inherit_doc</span>
<div class="viewcode-block" id="MulticlassClassificationEvaluator"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.MulticlassClassificationEvaluator">[docs]</a><span class="k">class</span> <span class="nc">MulticlassClassificationEvaluator</span><span class="p">(</span><span class="n">JavaEvaluator</span><span class="p">,</span> <span class="n">HasLabelCol</span><span class="p">,</span> <span class="n">HasPredictionCol</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Evaluator for Multiclass Classification, which expects two input</span>
<span class="sd">    columns: prediction and label.</span>
<span class="sd">    &gt;&gt;&gt; scoreAndLabels = [(0.0, 0.0), (0.0, 1.0), (0.0, 0.0),</span>
<span class="sd">    ...     (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)]</span>
<span class="sd">    &gt;&gt;&gt; dataset = sqlContext.createDataFrame(scoreAndLabels, [&quot;prediction&quot;, &quot;label&quot;])</span>
<span class="sd">    ...</span>
<span class="sd">    &gt;&gt;&gt; evaluator = MulticlassClassificationEvaluator(predictionCol=&quot;prediction&quot;)</span>
<span class="sd">    &gt;&gt;&gt; evaluator.evaluate(dataset)</span>
<span class="sd">    0.66...</span>
<span class="sd">    &gt;&gt;&gt; evaluator.evaluate(dataset, {evaluator.metricName: &quot;precision&quot;})</span>
<span class="sd">    0.66...</span>
<span class="sd">    &gt;&gt;&gt; evaluator.evaluate(dataset, {evaluator.metricName: &quot;recall&quot;})</span>
<span class="sd">    0.66...</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c"># a placeholder to make it appear in the generated doc</span>
    <span class="n">metricName</span> <span class="o">=</span> <span class="n">Param</span><span class="p">(</span><span class="n">Params</span><span class="o">.</span><span class="n">_dummy</span><span class="p">(),</span> <span class="s">&quot;metricName&quot;</span><span class="p">,</span>
                       <span class="s">&quot;metric name in evaluation &quot;</span>
                       <span class="s">&quot;(f1|precision|recall|weightedPrecision|weightedRecall)&quot;</span><span class="p">)</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">predictionCol</span><span class="o">=</span><span class="s">&quot;prediction&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s">&quot;label&quot;</span><span class="p">,</span>
                 <span class="n">metricName</span><span class="o">=</span><span class="s">&quot;f1&quot;</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        __init__(self, predictionCol=&quot;prediction&quot;, labelCol=&quot;label&quot;, \</span>
<span class="sd">                 metricName=&quot;f1&quot;)</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">MulticlassClassificationEvaluator</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">_java_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_new_java_obj</span><span class="p">(</span>
            <span class="s">&quot;org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">uid</span><span class="p">)</span>
        <span class="c"># param for metric name in evaluation (f1|precision|recall|weightedPrecision|weightedRecall)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">metricName</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;metricName&quot;</span><span class="p">,</span>
                                <span class="s">&quot;metric name in evaluation&quot;</span>
                                <span class="s">&quot; (f1|precision|recall|weightedPrecision|weightedRecall)&quot;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_setDefault</span><span class="p">(</span><span class="n">predictionCol</span><span class="o">=</span><span class="s">&quot;prediction&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s">&quot;label&quot;</span><span class="p">,</span>
                         <span class="n">metricName</span><span class="o">=</span><span class="s">&quot;f1&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">_set</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

<div class="viewcode-block" id="MulticlassClassificationEvaluator.setMetricName"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.MulticlassClassificationEvaluator.setMetricName">[docs]</a>    <span class="k">def</span> <span class="nf">setMetricName</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">        Sets the value of :py:attr:`metricName`.</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">metricName</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="MulticlassClassificationEvaluator.getMetricName"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.MulticlassClassificationEvaluator.getMetricName">[docs]</a>    <span class="k">def</span> <span class="nf">getMetricName</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Gets the value of metricName or its default value.</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">getOrDefault</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">metricName</span><span class="p">)</span>
</div>
    <span class="nd">@keyword_only</span>
<div class="viewcode-block" id="MulticlassClassificationEvaluator.setParams"><a class="viewcode-back" href="../../../pyspark.ml.html#pyspark.ml.evaluation.MulticlassClassificationEvaluator.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">predictionCol</span><span class="o">=</span><span class="s">&quot;prediction&quot;</span><span class="p">,</span> <span class="n">labelCol</span><span class="o">=</span><span class="s">&quot;label&quot;</span><span class="p">,</span>
                  <span class="n">metricName</span><span class="o">=</span><span class="s">&quot;f1&quot;</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        setParams(self, predictionCol=&quot;prediction&quot;, labelCol=&quot;label&quot;, \</span>
<span class="sd">                  metricName=&quot;f1&quot;)</span>
<span class="sd">        Sets params for multiclass classification evaluator.</span>
<span class="sd">        &quot;&quot;&quot;</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></div>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">&quot;__main__&quot;</span><span class="p">:</span>
    <span class="kn">import</span> <span class="nn">doctest</span>
    <span class="kn">from</span> <span class="nn">pyspark.context</span> <span class="kn">import</span> <span class="n">SparkContext</span>
    <span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SQLContext</span>
    <span class="n">globs</span> <span class="o">=</span> <span class="nb">globals</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
    <span class="c"># The small batch size here ensures that we see multiple batches,</span>
    <span class="c"># even in these small test examples:</span>
    <span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="p">(</span><span class="s">&quot;local[2]&quot;</span><span class="p">,</span> <span class="s">&quot;ml.evaluation tests&quot;</span><span class="p">)</span>
    <span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>
    <span class="n">globs</span><span class="p">[</span><span class="s">&#39;sc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sc</span>
    <span class="n">globs</span><span class="p">[</span><span class="s">&#39;sqlContext&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sqlContext</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">sc</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
    <span class="k">if</span> <span class="n">failure_count</span><span class="p">:</span>
        <span class="nb">exit</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

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