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Follows the approach presented in Rennie et.al.: Tackling the poor +assumptions of Naive Bayes Text classifiers, ICML 2003, http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf +</p></div><div class="toggleContainer block"> + <span class="toggle">Linear Supertypes</span> + <div class="superTypes hiddenContent"><span class="extype" name="org.apache.mahout.classifier.naivebayes.NaiveBayes">NaiveBayes</span>, <span class="extype" name="java.io.Serializable">Serializable</span>, <span class="extype" name="scala.AnyRef">AnyRef</span>, <span class="extype" name="scala.Any">Any</span></div> + </div></div> + + + <div id="mbrsel"> + <div id="textfilter"><span class="pre"></span><span class="input"><input id="mbrsel-input" type="text" accesskey="/" /></span><span class="post"></span></div> + <div id="order"> + <span class="filtertype">Ordering</span> + <ol> + + <li class="alpha in"><span>Alphabetic</span></li> + <li class="inherit out"><span>By inheritance</span></li> + </ol> + </div> + <div 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data-isabs="false" fullComment="yes" group="Ungrouped"> + <a id="extractLabelsAndAggregateObservations[K](stringKeyedObservations:org.apache.mahout.math.drm.DrmLike[K],cParser:String=>String)(implicitevidence$1:scala.reflect.ClassTag[K],implicitctx:org.apache.mahout.math.drm.DistributedContext):(scala.collection.mutable.HashMap[String,Integer],org.apache.mahout.math.drm.DrmLike[Int])"></a> + <a id="extractLabelsAndAggregateObservations[K](DrmLike[K],(String)âString)(ClassTag[K],DistributedContext):(HashMap[String,Integer],DrmLike[Int])"></a> + <h4 class="signature"> + <span class="modifier_kind"> + <span class="modifier"></span> + <span class="kind">def</span> + </span> + <span class="symbol"> + <span class="name">extractLabelsAndAggregateObservations</span><span class="tparams">[<span name="K">K</span>]</span><span class="params">(<span name="stringKeyedObservations">stringKeyedObservations: <span class="extype" name="org.apache.mahout.math.drm.DrmLike">DrmLike</span>[<span class="extype" name="org.apache.mahout.classifier.naivebayes.SparkNaiveBayes.extractLabelsAndAggregateObservations.K">K</span>]</span>, <span name="cParser">cParser: (<span class="extype" name="scala.Predef.String">String</span>) â <span class="extype" name="scala.Predef.String">String</span> = <span class="symbol">seq2SparseCategoryParser</span></span>)</span><span class="params">(<span class="implicit">implicit </span><span name="arg0">arg0: <span class="extype" name="scala.reflect.ClassTag">ClassTag</span>[<span class="extype" name="org.apache.mahout.classifier.naivebayes.SparkNaiveBayes.extractLabelsAndAggregateObservations.K">K</span>]</span>, <span name="ctx">ctx: <span class="extype" na me="org.apache.mahout.math.drm.DistributedContext">DistributedContext</span></span>)</span><span class="result">: (<span class="extype" name="scala.collection.mutable.HashMap">HashMap</span>[<span class="extype" name="scala.Predef.String">String</span>, <span class="extype" name="java.lang.Integer">Integer</span>], <span class="extype" name="org.apache.mahout.math.drm.DrmLike">DrmLike</span>[<span class="extype" name="scala.Int">Int</span>])</span> + </span> + </h4> + <p class="shortcomment cmt">Math-Scala Naive Bayes optimized for Spark.</p><div class="fullcomment"><div class="comment cmt"><p>Math-Scala Naive Bayes optimized for Spark.</p><p>Extract label Keys from raw TF or TF-IDF Matrix generated by seqdirectory/seq2sparse +and aggregate TF or TF-IDF values by their label +</p></div><dl class="paramcmts block"><dt class="param">stringKeyedObservations</dt><dd class="cmt"><p>DrmLike matrix; Output from seq2sparse + in form K = e.g./Category/document_title + V = TF or TF-IDF values per term</p></dd><dt class="param">cParser</dt><dd class="cmt"><p>a String => String function used to extract categories from + Keys of the stringKeyedObservations DRM. The default + CategoryParser will extract "Category" from: '/Category/document_id'</p></dd><dt>returns</dt><dd class="cmt"><p>(labelIndexMap, aggregatedByLabelObservationDrm) + labelIndexMap is a HashMap K = label row index + V = label + aggregatedByLabelObservationDrm is a DrmLike[Int] of aggregated + TF or TF-IDF counts per label +</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="" class="extype" name="org.apache.mahout.classifier.naivebayes.SparkNaiveBayes">SparkNaiveBayes</a> â NaiveBayes</dd></dl></div> + </li><li name="scala.AnyRef#finalize" visbl="prt" data-isabs="false" fullComment="yes" group="Ungrouped"> + <a id="finalize():Unit"></a> + <a id="finalize():Unit"></a> + <h4 class="signature"> + <span class="modifier_kind"> + <span class="modifier"></span> + <span class="kind">def</span> + </span> + <span class="symbol"> + <span class="name">finalize</span><span class="params">()</span><span class="result">: <span class="extype" 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</span> + </h4> + <p class="shortcomment cmt">Test a trained model with a labeled dataset</p><div class="fullcomment"><div class="comment cmt"><p>Test a trained model with a labeled dataset</p></div><dl class="paramcmts block"><dt class="tparam">K</dt><dd class="cmt"><p>implicitly determined Key type of test set DRM: String</p></dd><dt class="param">model</dt><dd class="cmt"><p>a trained NBModel</p></dd><dt class="param">testSet</dt><dd class="cmt"><p>a labeled testing set</p></dd><dt class="param">testComplementary</dt><dd class="cmt"><p>test using a complementary or a standard NB classifier</p></dd><dt class="param">cParser</dt><dd class="cmt"><p>a String => String function used to extract categories from + Keys of the testing set DRM. The default + CategoryParser will extract "Category" from: '/Category/document_id'</p></dd><dt>returns</dt><dd class="cmt"><p>a result analyzer with confusion matrix and accuracy statistics +</p></dd></dl><dl class="attributes block"> <dt>Definition Classes</dt><dd><a href="" class="extype" name="org.apache.mahout.classifier.naivebayes.SparkNaiveBayes">SparkNaiveBayes</a> â NaiveBayes</dd></dl></div> + </li><li name="scala.AnyRef#toString" visbl="pub" data-isabs="false" fullComment="yes" group="Ungrouped"> + <a id="toString():String"></a> + <a id="toString():String"></a> + <h4 class="signature"> + <span class="modifier_kind"> + <span class="modifier"></span> + <span class="kind">def</span> + </span> + <span class="symbol"> + <span class="name">toString</span><span class="params">()</span><span class="result">: <span class="extype" name="java.lang.String">String</span></span> + </span> + </h4> + <div class="fullcomment"><dl class="attributes block"> <dt>Definition Classes</dt><dd>AnyRef 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