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WEKA learners + xvalidation

fritmorefritmore Member Posts: 90 Contributor II
edited November 2018 in Help
Hi
is there a way to 'mix' weka learners with validation and optimization operators?

So far it seems Weka operators dont really work with validation,performance or optimization operators.
thx

Answers

  • wesselwessel Member Posts: 537 Maven
    What you mean it does not work?

    Attached below is a process that uses a Bayesian Network inside x-validation.
    It loads the famous play tennis data and builds a Bayesian Network (using -- -P 22 to get a real network).
    I double checked that the results look the same in Weka as they do in Rapid Miner.
    The only problem I can find is that you can not turn off the "use AD Tree" in Rapid Miner.

    Correctly Classified Instances          9              64.2857 %
    Incorrectly Classified Instances        5              35.7143 %

    accuracy: 60.00% +/- 43.59% (mikro: 64.29%)

    Best regards,

    Wessel



    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="5.2.006">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="5.2.006" expanded="true" name="Process">
        <parameter key="logverbosity" value="status"/>
        <process expanded="true" height="392" width="300">
          <operator activated="true" class="retrieve" compatibility="5.2.006" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30">
            <parameter key="repository_entry" value="//Samples/data/Golf"/>
          </operator>
          <operator activated="true" class="x_validation" compatibility="5.2.006" expanded="true" height="112" name="Validation" width="90" x="180" y="30">
            <process expanded="true" height="392" width="165">
              <operator activated="true" class="weka:W-BayesNet" compatibility="5.1.001" expanded="true" height="76" name="W-BayesNet" width="90" x="45" y="30">
                <parameter key="Q" value="weka.classifiers.bayes.net.search.local.K2 -- -P 22 -S BAYES"/>
                <parameter key="E" value="weka.classifiers.bayes.net.estimate.SimpleEstimator -- -A 0.5"/>
              </operator>
              <connect from_port="training" to_op="W-BayesNet" to_port="training set"/>
              <connect from_op="W-BayesNet" from_port="model" to_port="model"/>
              <portSpacing port="source_training" spacing="0"/>
              <portSpacing port="sink_model" spacing="0"/>
              <portSpacing port="sink_through 1" spacing="0"/>
            </process>
            <process expanded="true" height="392" width="165">
              <operator activated="true" class="apply_model" compatibility="5.2.006" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance_classification" compatibility="5.2.006" expanded="true" height="76" name="Performance" width="90" x="45" y="120">
                <list key="class_weights"/>
              </operator>
              <connect from_port="model" to_op="Apply Model" to_port="model"/>
              <connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
              <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
              <connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
              <portSpacing port="source_model" spacing="0"/>
              <portSpacing port="source_test set" spacing="0"/>
              <portSpacing port="source_through 1" spacing="0"/>
              <portSpacing port="sink_averagable 1" spacing="0"/>
              <portSpacing port="sink_averagable 2" spacing="0"/>
            </process>
          </operator>
          <connect from_op="Retrieve" from_port="output" to_op="Validation" to_port="training"/>
          <connect from_op="Validation" from_port="model" to_port="result 1"/>
          <connect from_op="Validation" from_port="training" to_port="result 2"/>
          <connect from_op="Validation" from_port="averagable 1" to_port="result 3"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
          <portSpacing port="sink_result 2" spacing="0"/>
          <portSpacing port="sink_result 3" spacing="0"/>
          <portSpacing port="sink_result 4" spacing="0"/>
        </process>
      </operator>
    </process>
  • fritmorefritmore Member Posts: 90 Contributor II
    i wish i knew...
    was getting butt load of wild errors with any weka learner in validation wrapped in param opt(worked fine with e.g. NN).

    will look into it...
  • wesselwessel Member Posts: 537 Maven
    Send the process XML?

    Weka learners can sometimes be a bit counter intuitive to get working, but they should work.
  • fritmorefritmore Member Posts: 90 Contributor II
    Standard param optimization design as in Sample RM folder.

    Now am testing some simple flows using xval,boostin... and all is fine.

    maybe tomorrow ill try optimization again and will see.
  • MariusHelfMariusHelf RapidMiner Certified Expert, Member Posts: 1,869 Unicorn
    As Wessel said: without the process XML we can't help you much. Often it's the small things which cause the problems and are not caught in "Standard param optimization as in Sample folder".

    Best, Marius
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