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"saved XML output bug"

labratlabrat Member Posts: 10 Contributor II
edited May 2019 in Help
Hi all,

the XML that is exported out of Rapidminer when you save results is currently invalid because the closing object-stream tag is omitted from the bottom of the file.

Cheers

Stuart
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Answers

  • fischerfischer Member Posts: 439 Maven
    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
  • labratlabrat Member Posts: 10 Contributor II
    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
  • fischerfischer Member Posts: 439 Maven
    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
  • labratlabrat Member Posts: 10 Contributor II
    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

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