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| <section id="rankingmetrics"> |
| <h1>RankingMetrics<a class="headerlink" href="#rankingmetrics" title="Permalink to this headline">¶</a></h1> |
| <dl class="py class"> |
| <dt id="pyspark.mllib.evaluation.RankingMetrics"> |
| <em class="property">class </em><code class="sig-prename descclassname">pyspark.mllib.evaluation.</code><code class="sig-name descname">RankingMetrics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">predictionAndLabels</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pyspark.rdd.RDD<span class="p">[</span>Tuple<span class="p">[</span>List<span class="p">[</span>T<span class="p">]</span><span class="p">, </span>List<span class="p">[</span>T<span class="p">]</span><span class="p">]</span><span class="p">]</span><span class="p">, </span>pyspark.rdd.RDD<span class="p">[</span>Tuple<span class="p">[</span>List<span class="p">[</span>T<span class="p">]</span><span class="p">, </span>List<span class="p">[</span>T<span class="p">]</span><span class="p">, </span>List<span class="p">[</span>float<span class="p">]</span><span class="p">]</span><span class="p">]</span><span class="p">]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/pyspark/mllib/evaluation.html#RankingMetrics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.evaluation.RankingMetrics" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Evaluator for ranking algorithms.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>predictionAndLabels</strong><span class="classifier"><a class="reference internal" href="pyspark.RDD.html#pyspark.RDD" title="pyspark.RDD"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyspark.RDD</span></code></a></span></dt><dd><p>an RDD of (predicted ranking, ground truth set) pairs |
| or (predicted ranking, ground truth set, |
| relevance value of ground truth set). |
| Since 3.4.0, it supports ndcg evaluation with relevance value.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <p class="rubric">Examples</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">predictionAndLabels</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([</span> |
| <span class="gp">... </span> <span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]),</span> |
| <span class="gp">... </span> <span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span> |
| <span class="gp">... </span> <span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[])])</span> |
| <span class="gp">>>> </span><span class="n">metrics</span> <span class="o">=</span> <span class="n">RankingMetrics</span><span class="p">(</span><span class="n">predictionAndLabels</span><span class="p">)</span> |
| <span class="gp">>>> </span><span class="n">metrics</span><span class="o">.</span><span class="n">precisionAt</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go">0.33...</span> |
| <span class="gp">>>> </span><span class="n">metrics</span><span class="o">.</span><span class="n">precisionAt</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> |
| <span class="go">0.26...</span> |
| <span class="gp">>>> </span><span class="n">metrics</span><span class="o">.</span><span class="n">precisionAt</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span> |
| <span class="go">0.17...</span> |
| <span class="gp">>>> </span><span class="n">metrics</span><span class="o">.</span><span class="n">meanAveragePrecision</span> |
| <span class="go">0.35...</span> |
| <span class="gp">>>> </span><span class="n">metrics</span><span class="o">.</span><span class="n">meanAveragePrecisionAt</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go">0.3333333333333333...</span> |
| <span class="gp">>>> </span><span class="n">metrics</span><span class="o">.</span><span class="n">meanAveragePrecisionAt</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> |
| <span class="go">0.25...</span> |
| <span class="gp">>>> </span><span class="n">metrics</span><span class="o">.</span><span class="n">ndcgAt</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> |
| <span class="go">0.33...</span> |
| <span class="gp">>>> </span><span class="n">metrics</span><span class="o">.</span><span class="n">ndcgAt</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> |
| <span class="go">0.48...</span> |
| <span class="gp">>>> </span><span class="n">metrics</span><span class="o">.</span><span class="n">recallAt</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> |
| <span class="go">0.06...</span> |
| <span class="gp">>>> </span><span class="n">metrics</span><span class="o">.</span><span class="n">recallAt</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> |
| <span class="go">0.35...</span> |
| <span class="gp">>>> </span><span class="n">metrics</span><span class="o">.</span><span class="n">recallAt</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span> |
| <span class="go">0.66...</span> |
| </pre></div> |
| </div> |
| <p class="rubric">Methods</p> |
| <table class="longtable table autosummary"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.evaluation.RankingMetrics.call" title="pyspark.mllib.evaluation.RankingMetrics.call"><code class="xref py py-obj docutils literal notranslate"><span class="pre">call</span></code></a>(name, *a)</p></td> |
| <td><p>Call method of java_model</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.evaluation.RankingMetrics.meanAveragePrecisionAt" title="pyspark.mllib.evaluation.RankingMetrics.meanAveragePrecisionAt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">meanAveragePrecisionAt</span></code></a>(k)</p></td> |
| <td><p>Returns the mean average precision (MAP) at first k ranking of all the queries.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.evaluation.RankingMetrics.ndcgAt" title="pyspark.mllib.evaluation.RankingMetrics.ndcgAt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndcgAt</span></code></a>(k)</p></td> |
| <td><p>Compute the average NDCG value of all the queries, truncated at ranking position k.</p></td> |
| </tr> |
| <tr class="row-even"><td><p><a class="reference internal" href="#pyspark.mllib.evaluation.RankingMetrics.precisionAt" title="pyspark.mllib.evaluation.RankingMetrics.precisionAt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">precisionAt</span></code></a>(k)</p></td> |
| <td><p>Compute the average precision of all the queries, truncated at ranking position k.</p></td> |
| </tr> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.evaluation.RankingMetrics.recallAt" title="pyspark.mllib.evaluation.RankingMetrics.recallAt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">recallAt</span></code></a>(k)</p></td> |
| <td><p>Compute the average recall of all the queries, truncated at ranking position k.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Attributes</p> |
| <table class="longtable table autosummary"> |
| <colgroup> |
| <col style="width: 10%" /> |
| <col style="width: 90%" /> |
| </colgroup> |
| <tbody> |
| <tr class="row-odd"><td><p><a class="reference internal" href="#pyspark.mllib.evaluation.RankingMetrics.meanAveragePrecision" title="pyspark.mllib.evaluation.RankingMetrics.meanAveragePrecision"><code class="xref py py-obj docutils literal notranslate"><span class="pre">meanAveragePrecision</span></code></a></p></td> |
| <td><p>Returns the mean average precision (MAP) of all the queries.</p></td> |
| </tr> |
| </tbody> |
| </table> |
| <p class="rubric">Methods Documentation</p> |
| <dl class="py method"> |
| <dt id="pyspark.mllib.evaluation.RankingMetrics.call"> |
| <code class="sig-name descname">call</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="o">*</span><span class="n">a</span><span class="p">:</span> <span class="n">Any</span></em><span class="sig-paren">)</span> → Any<a class="headerlink" href="#pyspark.mllib.evaluation.RankingMetrics.call" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Call method of java_model</p> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.evaluation.RankingMetrics.meanAveragePrecisionAt"> |
| <code class="sig-name descname">meanAveragePrecisionAt</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">k</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → float<a class="reference internal" href="../../_modules/pyspark/mllib/evaluation.html#RankingMetrics.meanAveragePrecisionAt"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.evaluation.RankingMetrics.meanAveragePrecisionAt" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the mean average precision (MAP) at first k ranking of all the queries. |
| If a query has an empty ground truth set, the average precision will be zero and |
| a log warning is generated.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 3.0.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.evaluation.RankingMetrics.ndcgAt"> |
| <code class="sig-name descname">ndcgAt</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">k</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → float<a class="reference internal" href="../../_modules/pyspark/mllib/evaluation.html#RankingMetrics.ndcgAt"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.evaluation.RankingMetrics.ndcgAt" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the average NDCG value of all the queries, truncated at ranking position k. |
| The discounted cumulative gain at position k is computed as: |
| sum,,i=1,,^k^ (2^{relevance of ‘’i’’th item}^ - 1) / log(i + 1), |
| and the NDCG is obtained by dividing the DCG value on the ground truth set. |
| In the current implementation, the relevance value is binary. |
| If a query has an empty ground truth set, zero will be used as NDCG together with |
| a log warning.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.evaluation.RankingMetrics.precisionAt"> |
| <code class="sig-name descname">precisionAt</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">k</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → float<a class="reference internal" href="../../_modules/pyspark/mllib/evaluation.html#RankingMetrics.precisionAt"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.evaluation.RankingMetrics.precisionAt" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the average precision of all the queries, truncated at ranking position k.</p> |
| <p>If for a query, the ranking algorithm returns n (n < k) results, the precision value |
| will be computed as #(relevant items retrieved) / k. This formula also applies when |
| the size of the ground truth set is less than k.</p> |
| <p>If a query has an empty ground truth set, zero will be used as precision together |
| with a log warning.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <dl class="py method"> |
| <dt id="pyspark.mllib.evaluation.RankingMetrics.recallAt"> |
| <code class="sig-name descname">recallAt</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">k</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → float<a class="reference internal" href="../../_modules/pyspark/mllib/evaluation.html#RankingMetrics.recallAt"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.mllib.evaluation.RankingMetrics.recallAt" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Compute the average recall of all the queries, truncated at ranking position k.</p> |
| <p>If for a query, the ranking algorithm returns n results, the recall value |
| will be computed as #(relevant items retrieved) / #(ground truth set). |
| This formula also applies when the size of the ground truth set is less than k.</p> |
| <p>If a query has an empty ground truth set, zero will be used as recall together |
| with a log warning.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 3.0.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| <p class="rubric">Attributes Documentation</p> |
| <dl class="py attribute"> |
| <dt id="pyspark.mllib.evaluation.RankingMetrics.meanAveragePrecision"> |
| <code class="sig-name descname">meanAveragePrecision</code><a class="headerlink" href="#pyspark.mllib.evaluation.RankingMetrics.meanAveragePrecision" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Returns the mean average precision (MAP) of all the queries. |
| If a query has an empty ground truth set, the average precision will be zero and |
| a log warning is generated.</p> |
| <div class="versionadded"> |
| <p><span class="versionmodified added">New in version 1.4.0.</span></p> |
| </div> |
| </dd></dl> |
| |
| </dd></dl> |
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