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<h2 title="Class RankingMetrics" class="title">Class RankingMetrics&lt;T&gt;</h2>
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<pre>public class <span class="strong">RankingMetrics&lt;T&gt;</span>
extends java.lang.Object
implements <a href="../../../../../org/apache/spark/Logging.html" title="interface in org.apache.spark">Logging</a>, scala.Serializable</pre>
<div class="block">::Experimental::
 Evaluator for ranking algorithms.
 <p>
 Java users should use <code>RankingMetrics$.of</code> to create a <a href="../../../../../org/apache/spark/mllib/evaluation/RankingMetrics.html" title="class in org.apache.spark.mllib.evaluation"><code>RankingMetrics</code></a> instance.
 <p>
 param:  predictionAndLabels an RDD of (predicted ranking, ground truth set) pairs.</div>
<dl><dt><span class="strong">See Also:</span></dt><dd><a href="../../../../../serialized-form.html#org.apache.spark.mllib.evaluation.RankingMetrics">Serialized Form</a></dd></dl>
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<td class="colOne"><code><strong><a href="../../../../../org/apache/spark/mllib/evaluation/RankingMetrics.html#RankingMetrics(org.apache.spark.rdd.RDD, scala.reflect.ClassTag)">RankingMetrics</a></strong>(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a>&lt;scala.Tuple2&lt;java.lang.Object,java.lang.Object&gt;&gt;&nbsp;predictionAndLabels,
              scala.reflect.ClassTag&lt;<a href="../../../../../org/apache/spark/mllib/evaluation/RankingMetrics.html" title="type parameter in RankingMetrics">T</a>&gt;&nbsp;evidence$1)</code>&nbsp;</td>
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<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/evaluation/RankingMetrics.html#meanAveragePrecision()">meanAveragePrecision</a></strong>()</code>
<div class="block">Returns the mean average precision (MAP) of all the queries.</div>
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<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/evaluation/RankingMetrics.html#ndcgAt(int)">ndcgAt</a></strong>(int&nbsp;k)</code>
<div class="block">Compute the average NDCG value of all the queries, truncated at ranking position k.</div>
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<td class="colFirst"><code>static &lt;E,T extends java.lang.Iterable&lt;E&gt;&gt;&nbsp;<br><a href="../../../../../org/apache/spark/mllib/evaluation/RankingMetrics.html" title="class in org.apache.spark.mllib.evaluation">RankingMetrics</a>&lt;E&gt;</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/evaluation/RankingMetrics.html#of(org.apache.spark.api.java.JavaRDD)">of</a></strong>(<a href="../../../../../org/apache/spark/api/java/JavaRDD.html" title="class in org.apache.spark.api.java">JavaRDD</a>&lt;scala.Tuple2&lt;T,T&gt;&gt;&nbsp;predictionAndLabels)</code>
<div class="block">Creates a <a href="../../../../../org/apache/spark/mllib/evaluation/RankingMetrics.html" title="class in org.apache.spark.mllib.evaluation"><code>RankingMetrics</code></a> instance (for Java users).</div>
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<td class="colFirst"><code>double</code></td>
<td class="colLast"><code><strong><a href="../../../../../org/apache/spark/mllib/evaluation/RankingMetrics.html#precisionAt(int)">precisionAt</a></strong>(int&nbsp;k)</code>
<div class="block">Compute the average precision of all the queries, truncated at ranking position k.</div>
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<code><a href="../../../../../org/apache/spark/Logging.html#initializeIfNecessary()">initializeIfNecessary</a>, <a href="../../../../../org/apache/spark/Logging.html#initializeLogging()">initializeLogging</a>, <a href="../../../../../org/apache/spark/Logging.html#isTraceEnabled()">isTraceEnabled</a>, <a href="../../../../../org/apache/spark/Logging.html#log_()">log_</a>, <a href="../../../../../org/apache/spark/Logging.html#log()">log</a>, <a href="../../../../../org/apache/spark/Logging.html#logDebug(scala.Function0)">logDebug</a>, <a href="../../../../../org/apache/spark/Logging.html#logDebug(scala.Function0, java.lang.Throwable)">logDebug</a>, <a href="../../../../../org/apache/spark/Logging.html#logError(scala.Function0)">logError</a>, <a href="../../../../../org/apache/spark/Logging.html#logError(scala.Function0, java.lang.Throwable)">logError</a>, <a href="../../../../../org/apache/spark/Logging.html#logInfo(scala.Function0)">logInfo</a>, <a href="../../../../../org/apache/spark/Logging.html#logInfo(scala.Function0, java.lang.Throwable)">logInfo</a>, <a href="../../../../../org/apache/spark/Logging.html#logName()">logName</a>, <a href="../../../../../org/apache/spark/Logging.html#logTrace(scala.Function0)">logTrace</a>, <a href="../../../../../org/apache/spark/Logging.html#logTrace(scala.Function0, java.lang.Throwable)">logTrace</a>, <a href="../../../../../org/apache/spark/Logging.html#logWarning(scala.Function0)">logWarning</a>, <a href="../../../../../org/apache/spark/Logging.html#logWarning(scala.Function0, java.lang.Throwable)">logWarning</a></code></li>
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<h4>RankingMetrics</h4>
<pre>public&nbsp;RankingMetrics(<a href="../../../../../org/apache/spark/rdd/RDD.html" title="class in org.apache.spark.rdd">RDD</a>&lt;scala.Tuple2&lt;java.lang.Object,java.lang.Object&gt;&gt;&nbsp;predictionAndLabels,
              scala.reflect.ClassTag&lt;<a href="../../../../../org/apache/spark/mllib/evaluation/RankingMetrics.html" title="type parameter in RankingMetrics">T</a>&gt;&nbsp;evidence$1)</pre>
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<pre>public static&nbsp;&lt;E,T extends java.lang.Iterable&lt;E&gt;&gt;&nbsp;<a href="../../../../../org/apache/spark/mllib/evaluation/RankingMetrics.html" title="class in org.apache.spark.mllib.evaluation">RankingMetrics</a>&lt;E&gt;&nbsp;of(<a href="../../../../../org/apache/spark/api/java/JavaRDD.html" title="class in org.apache.spark.api.java">JavaRDD</a>&lt;scala.Tuple2&lt;T,T&gt;&gt;&nbsp;predictionAndLabels)</pre>
<div class="block">Creates a <a href="../../../../../org/apache/spark/mllib/evaluation/RankingMetrics.html" title="class in org.apache.spark.mllib.evaluation"><code>RankingMetrics</code></a> instance (for Java users).</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>predictionAndLabels</code> - a JavaRDD of (predicted ranking, ground truth set) pairs</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>precisionAt</h4>
<pre>public&nbsp;double&nbsp;precisionAt(int&nbsp;k)</pre>
<div class="block">Compute the average precision of all the queries, truncated at ranking position k.
 <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>
 If a query has an empty ground truth set, zero will be used as precision together with
 a log warning.
 <p>
 See the following paper for detail:
 <p>
 IR evaluation methods for retrieving highly relevant documents. K. Jarvelin and J. Kekalainen
 <p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>k</code> - the position to compute the truncated precision, must be positive</dd>
<dt><span class="strong">Returns:</span></dt><dd>the average precision at the first k ranking positions</dd></dl>
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<h4>meanAveragePrecision</h4>
<pre>public&nbsp;double&nbsp;meanAveragePrecision()</pre>
<div class="block">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
 warining is generated.</div>
<dl><dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<h4>ndcgAt</h4>
<pre>public&nbsp;double&nbsp;ndcgAt(int&nbsp;k)</pre>
<div class="block">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.
 <p>
 If a query has an empty ground truth set, zero will be used as ndcg together with
 a log warning.
 <p>
 See the following paper for detail:
 <p>
 IR evaluation methods for retrieving highly relevant documents. K. Jarvelin and J. Kekalainen
 <p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>k</code> - the position to compute the truncated ndcg, must be positive</dd>
<dt><span class="strong">Returns:</span></dt><dd>the average ndcg at the first k ranking positions</dd></dl>
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