[SOLVED] Unable to get X-validation working. Average port constantly gives error
invictus_champ
New Altair Community Member
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??
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>
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0
Answers
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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>0 -
Thank you very much!0