[SOLVED] Unable to get X-validation working. Average port constantly gives error

gordion

Hi All,

I am having an issue with the crossvalidation. I have a labeled (binomial) data set; which transformed and fed to X-validation. Before trying x-validation, I managed to get a SVM model with the same data.

When I remove SVM, then insert x-validation, and in sub-process, I include SVM and Apply Model. On the testing RHS, therthere is Validation.averagable 1 (averagable 1) port, which is constantly red the moment I load xvalidation. When I try running I got the error;

"wrong data of type 'data table' was delivered at port 'averagable 1' expected data of type 'average vector'

How can I get this x-validation work??

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.3.000" expanded="true" name="Root">
    <description>For many learning tasks, Support Vector Machines are among the best suited learning schemes.They adapt the idea of structural risk minimization and allows for non-linear generalizations with help of kernel functions.</description>
    <process expanded="true" height="584" width="962">
      <operator activated="true" class="retrieve" compatibility="5.3.000" expanded="true" height="60" name="Retrieve Fraud-All-Labeled" width="90" x="45" y="165">
        <parameter key="repository_entry" value="//RapidLocalRepository/Fraud/NewTests/Fraud-All-Labeled"/>
      </operator>
      <operator activated="true" class="nominal_to_numerical" compatibility="5.3.000" expanded="true" height="94" name="Nominal to Numerical" width="90" x="179" y="30">
        <list key="comparison_groups"/>
      </operator>
      <operator activated="true" class="x_validation" compatibility="5.3.000" expanded="true" height="130" name="Validation" width="90" x="313" y="30">
        <process expanded="true" height="434" width="351">
          <operator activated="true" class="support_vector_machine" compatibility="5.3.000" expanded="true" height="112" name="SVM" width="90" x="112" y="75"/>
          <connect from_port="training" to_op="SVM" to_port="training set"/>
          <connect from_op="SVM" from_port="model" to_port="model"/>
          <connect from_op="SVM" from_port="weights" to_port="through 1"/>
          <portSpacing port="source_training" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
          <portSpacing port="sink_through 2" spacing="0"/>
        </process>
        <process expanded="true" height="434" width="351">
          <operator activated="true" class="apply_model" compatibility="5.3.000" expanded="true" height="76" name="Apply Model" width="90" x="45" y="75">
            <list key="application_parameters"/>
          </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_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="source_through 2" spacing="0"/>
          <portSpacing port="sink_averagable 1" spacing="0"/>
          <portSpacing port="sink_averagable 2" spacing="0"/>
          <portSpacing port="sink_averagable 3" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve Fraud-All-Labeled" from_port="output" to_op="Nominal to Numerical" to_port="example set input"/>
      <connect from_op="Nominal to Numerical" from_port="example set output" to_op="Validation" to_port="training"/>
      <connect from_op="Validation" from_port="averagable 1" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>

earmijo

Try this:

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.2.008">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.2.008" expanded="true" name="Root">
    <description>For many learning tasks, Support Vector Machines are among the best suited learning schemes.They adapt the idea of structural risk minimization and allows for non-linear generalizations with help of kernel functions.</description>
    <process expanded="true" height="467" width="567">
      <operator activated="true" class="retrieve" compatibility="5.2.008" expanded="true" height="60" name="Retrieve Fraud-All-Labeled" width="90" x="45" y="165">
        <parameter key="repository_entry" value="//RapidLocalRepository/Fraud/NewTests/Fraud-All-Labeled"/>
      </operator>
      <operator activated="true" class="nominal_to_numerical" compatibility="5.2.008" expanded="true" height="94" name="Nominal to Numerical" width="90" x="179" y="30">
        <list key="comparison_groups"/>
      </operator>
      <operator activated="true" class="x_validation" compatibility="5.0.000" expanded="true" height="112" name="Validation" width="90" x="447" y="30">
        <description>A cross-validation evaluating a decision tree model.</description>
        <process expanded="true" height="654" width="466">
          <operator activated="true" class="support_vector_machine" compatibility="5.2.008" expanded="true" height="112" name="SVM" width="90" x="188" y="30"/>
          <connect from_port="training" to_op="SVM" to_port="training set"/>
          <connect from_op="SVM" 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="654" width="466">
          <operator activated="true" class="apply_model" compatibility="5.0.000" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="performance" compatibility="5.0.000" expanded="true" height="76" name="Performance" width="90" x="179" y="30"/>
          <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 Fraud-All-Labeled" from_port="output" to_op="Nominal to Numerical" to_port="example set input"/>
      <connect from_op="Nominal to Numerical" from_port="example set output" to_op="Validation" to_port="training"/>
      <connect from_op="Validation" from_port="model" to_port="result 1"/>
      <connect from_op="Validation" from_port="averagable 1" to_port="result 2"/>
      <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"/>
    </process>
  </operator>
</process>

gordion

Thank you very much!