"saved XML output bug"

This is strange. The XML output is handled by a library, so this is hard to track down. Can you post an example?

Cheers,
Simon

Using RM 4.5 and using the SVM/Xval example in the tutorial if you do the analysis:

<operator name="Root" class="Process" expanded="yes">
    <operator name="Input" class="ExampleSource">
        <parameter key="attributes" value="../data/polynomial.aml"/>
    </operator>
    <operator name="XVal" class="XValidation" expanded="yes">
        <parameter key="sampling_type" value="shuffled sampling"/>
        <operator name="Training" class="LibSVMLearner">
            <parameter key="svm_type" value="epsilon-SVR"/>
            <parameter key="kernel_type" value="poly"/>
            <parameter key="C" value="1000.0"/>
            <list key="class_weights">
            </list>
        </operator>
        <operator name="ApplierChain" class="OperatorChain" expanded="yes">
            <operator name="Test" class="ModelApplier">
                <list key="application_parameters">
                </list>
            </operator>
            <operator name="Evaluation" class="RegressionPerformance">
                <parameter key="root_mean_squared_error" value="true"/>
                <parameter key="absolute_error" value="true"/>
                <parameter key="relative_error" value="true"/>
                <parameter key="normalized_absolute_error" value="true"/>
                <parameter key="root_relative_squared_error" value="true"/>
                <parameter key="squared_error" value="true"/>
                <parameter key="correlation" value="true"/>
            </operator>
        </operator>
    </operator>
</operator>

if you save the performance file *.per you get this;

<object-stream>
  <PerformanceVector id="1">
    <currentValues id="2">
      <entry>
        <string>root_mean_squared_error</string>
        <double>7.271397088254498</double>
      </entry>
      <entry>
        <string>relative_error</string>
        <double>0.4261726449515895</double>
      </entry>
      <entry>
        <string>correlation</string>
        <double>0.9990774750706919</double>
      </entry>
      <entry>
        <string>normalized_absolute_error</string>
        <double>0.04030556352101554</double>
      </entry>
      <entry>
        <string>absolute_error</string>
        <double>5.107471175794692</double>
      </entry>
      <entry>
        <string>squared_error</string>
        <double>54.826375982674925</double>
      </entry>
      <entry>
        <string>root_relative_squared_error</string>
        <double>0.04407058437419177</double>
      </entry>
    </currentValues>
    <comparator class="com.rapidminer.operator.performance.PerformanceVector$DefaultComparator" id="3"/>
    <mainCriterion>first</mainCriterion>
    <averagesList id="4">
      <root__mean__squared__error id="5">
        <sum>10965.275196534985</sum>
        <squaresSum>3960036.9527361454</squaresSum>
        <exampleCount>200.0</exampleCount>
        <predictedAttribute class="NumericalAttribute" id="6">
          <attributeDescription id="7">
            <name>prediction(label)</name>
            <valueType>4</valueType>
            <blockType>1</blockType>
            <defaultValue>0.0</defaultValue>
            <index>6</index>
          </attributeDescription>
          <transformations id="8"/>
          <statistics class="linked-list" id="9">
            <NumericalStatistics id="10">
              <sum>0.0</sum>
              <squaredSum>0.0</squaredSum>
              <valueCounter>0</valueCounter>
            </NumericalStatistics>
            <WeightedNumericalStatistics id="11">
              <sum>0.0</sum>
              <squaredSum>0.0</squaredSum>
              <totalWeight>0.0</totalWeight>
              <count>0.0</count>
            </WeightedNumericalStatistics>
            <com.rapidminer.example.MinMaxStatistics id="12">
              <minimum>Infinity</minimum>
              <maximum>-Infinity</maximum>
            </com.rapidminer.example.MinMaxStatistics>
            <UnknownStatistics id="13">
              <unknownCounter>0</unknownCounter>
            </UnknownStatistics>
          </statistics>
          <constructionDescription>prediction(label)</constructionDescription>
        </predictedAttribute>
        <labelAttribute class="NumericalAttribute" id="14">
          <attributeDescription id="15">
            <name>label</name>
            <valueType>4</valueType>
            <blockType>1</blockType>
            <defaultValue>0.0</defaultValue>
            <index>5</index>
          </attributeDescription>
          <transformations id="16"/>
          <statistics class="linked-list" id="17">
            <NumericalStatistics id="18">
              <sum>0.0</sum>
              <squaredSum>0.0</squaredSum>
              <valueCounter>0</valueCounter>
            </NumericalStatistics>
            <WeightedNumericalStatistics id="19">
              <sum>0.0</sum>
              <squaredSum>0.0</squaredSum>
              <totalWeight>0.0</totalWeight>
              <count>0.0</count>
            </WeightedNumericalStatistics>
            <com.rapidminer.example.MinMaxStatistics id="20">
              <minimum>Infinity</minimum>
              <maximum>-Infinity</maximum>
            </com.rapidminer.example.MinMaxStatistics>
            <UnknownStatistics id="21">
              <unknownCounter>0</unknownCounter>
            </UnknownStatistics>
          </statistics>
          <constructionDescription>label</constructionDescription>
        </labelAttribute>
        <meanSum>72.71397088254498</meanSum>
        <meanSquaredSum>548.2637598267493</meanSquaredSum>
        <averageCount>10</averageCount>
      </root__mean__squared__error>
      <absolute__error id="22">
        <sum>1021.4942351589382</sum>
        <squaresSum>10965.275196534985</squaresSum>
        <exampleCount>200.0</exampleCount>
        <predictedAttribute class="NumericalAttribute" reference="6"/>
        <labelAttribute class="NumericalAttribute" reference="14"/>
        <meanSum>51.07471175794692</meanSum>
        <meanSquaredSum>269.8618246507336</meanSquaredSum>
        <averageCount>10</averageCount>
      </absolute__error>
      <relative__error id="23">
        <sum>85.2345289903179</sum>
        <squaresSum>1012.762540663155</squaresSum>
        <exampleCount>200.0</exampleCount>
        <predictedAttribute class="NumericalAttribute" reference="6"/>
        <labelAttribute class="NumericalAttribute" reference="14"/>
        <meanSum>4.261726449515895</meanSum>
        <meanSquaredSum>3.142985588188072</meanSquaredSum>
        <averageCount>10</averageCount>
      </relative__error>
      <normalized__absolute__error id="24">
        <predictedAttribute class="NumericalAttribute" reference="6"/>
        <labelAttribute class="NumericalAttribute" reference="14"/>
        <deviationSum>1021.4942351589382</deviationSum>
        <relativeSum>27075.057565148352</relativeSum>
        <trueLabelSum>4078.1396808612185</trueLabelSum>
        <exampleCounter>20.0</exampleCounter>
        <meanSum>0.40305563521015536</meanSum>
        <meanSquaredSum>0.018255354969483512</meanSquaredSum>
        <averageCount>10</averageCount>
      </normalized__absolute__error>
      <root__relative__squared__error id="25">
        <predictedAttribute class="NumericalAttribute" reference="6"/>
        <labelAttribute class="NumericalAttribute" reference="14"/>
        <deviationSum>10965.275196534985</deviationSum>
        <relativeSum>6475981.792977156</relativeSum>
        <trueLabelSum>4078.1396808612185</trueLabelSum>
        <exampleCounter>20.0</exampleCounter>
        <meanSum>0.4407058437419177</meanSum>
        <meanSquaredSum>0.021258629086615133</meanSquaredSum>
        <averageCount>10</averageCount>
      </root__relative__squared__error>
      <squared__error id="26">
        <sum>10965.275196534985</sum>
        <squaresSum>3960036.9527361454</squaresSum>
        <exampleCount>200.0</exampleCount>
        <predictedAttribute class="NumericalAttribute" reference="6"/>
        <labelAttribute class="NumericalAttribute" reference="14"/>
        <meanSum>548.2637598267493</meanSum>
        <meanSquaredSum>34173.58564301037</meanSquaredSum>
        <averageCount>10</averageCount>
      </squared__error>
      <correlation id="27">
        <labelAttribute class="NumericalAttribute" reference="14"/>
        <predictedLabelAttribute class="NumericalAttribute" reference="6"/>
        <exampleCount>200.0</exampleCount>
        <sumLabel>36083.680010339376</sumLabel>
        <sumPredict>36280.64884722099</sumPredict>
        <sumLabelPredict>1.3344662294616919E7</sumLabelPredict>
        <sumLabelSqr>1.3277723556890765E7</sumLabelSqr>
        <sumPredictSqr>1.34225663075396E7</sumPredictSqr>
        <meanSum>9.990774750706919</meanSum>
        <meanSquaredSum>9.981562312225481</meanSquaredSum>
        <averageCount>10</averageCount>
      </correlation>
    </averagesList>
    <source>Evaluation</source>
  </PerformanceVector>


as you see you are missing the "</object-stream>" tag.  This is also the same for the *.RES files too


Stuart

Confirmed. However, that does not prevent RM from reading the file back in, does it? At least not for me.

This is in fact a problem with xstream. It was simple to fix from our side, although I think this is a flaw in the implementation of xstream. It requires us to close the stream after every object which now prevents us to send several XML streams in a row.

Cheers,
Simon

Correct RM can read is able to read it back, however some programs (like EXCEL)  can be very fussy about having correctly constructed XML.

Well i glad i could help