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Evolutionary Parameter Optimization, bagging and W-RepTree

s-a-s-hs-a-s-h Member Posts: 4 Contributor I
edited November 2018 in Help
Hallo together,

I´ve tried to optimize of a bagging scheme combined with W-RepTree by a parameter optimization. Optimization regarding the Bagging (iterations, sample_ratio) works fine. Optimization of the W-RepTree unfortunately works not with the code below.

Does anybody have advice ?

Thank you,
Sascha
<operator name="Root" class="Process" expanded="yes">
    <operator name="ExcelExampleSource" class="ExcelExampleSource">
        <parameter key="create_label" value="true"/>
        <parameter key="excel_file" value="D:\Dissdaten\Literatur\Papers\ab08-08\DataMiningSoftware\RapidminerYALE\Trainingsdaten5.xls"/>
        <parameter key="first_row_as_names" value="true"/>
    </operator>
    <operator name="EvolutionaryParameterOptimization" class="EvolutionaryParameterOptimization" expanded="yes">
        <list key="parameters">
          <parameter key="Bagging2.iterations" value="[5.0;100.0]"/>
          <parameter key="Bagging2.sample_ratio" value="[0.0;1.0]"/>
          <parameter key="Training.M" value="[-1.0;5.0]"/>
        </list>
        <operator name="Validation" class="XValidation" expanded="yes">
            <parameter key="sampling_type" value="shuffled sampling"/>
            <operator name="Bagging2" class="Bagging" expanded="yes">
                <parameter key="iterations" value="64"/>
                <parameter key="sample_ratio" value="0.8633563476940593"/>
                <operator name="Training" class="W-REPTree">
                    <parameter key="L" value="-10.0"/>
                    <parameter key="M" value="2.387723832548856"/>
                    <parameter key="N" value="5.0"/>
                    <parameter key="P" value="true"/>
                    <parameter key="S" value="56.0"/>
                </operator>
            </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="absolute_error" value="true"/>
                    <parameter key="main_criterion" value="absolute_error"/>
                    <parameter key="normalized_absolute_error" value="true"/>
                    <parameter key="root_mean_squared_error" value="true"/>
                    <parameter key="squared_error" value="true"/>
                </operator>
            </operator>
        </operator>
        <operator name="Log (2)" class="ProcessLog">
            <parameter key="filename" value="paraopt.log"/>
            <list key="log">
              <parameter key="sample_ratio" value="operator.Bagging2.parameter.keep_example_set"/>
              <parameter key="M" value="operator.Training.parameter.M"/>
              <parameter key="V" value="operator.Training.parameter.V"/>
              <parameter key="N" value="operator.Training.parameter.N"/>
              <parameter key="S" value="operator.Training.parameter.S"/>
              <parameter key="P" value="operator.Training.parameter.P"/>
              <parameter key="L" value="operator.Training.parameter.L"/>
              <parameter key="absolute" value="operator.Evaluation.value.absolute_error"/>
              <parameter key="iterations" value="operator.Bagging2.parameter.iterations"/>
            </list>
        </operator>
    </operator>

Answers

  • s-a-s-hs-a-s-h Member Posts: 4 Contributor I
    an extension: the same problems appear with W-FT of the WEKA package...
  • landland RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 2,531   Unicorn
    Hi Sascha,
    the problem is within the weka learner W-REPTree. It defines the parameter M as real, but needs an integer. Unfortunatly we have no influence on the weka parameters.
    An solution would be to remove the M value from evoultionary parameter optimization and sourround the evolutionary optimization with a gridParameterOptimization. Then you could only insert integer values.

    Greetings,
      Sebastian
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