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support vector machine cannot handle polynominal attributes

nohanoha Member Posts: 10 Learner I
edited June 2020 in Help
At first i had an error in the nominal to numerical whenever i choose to transform the attribute prediction label it gave me an error and it doesn't include it in the output set then i checked the special attribute box it doesn't produce an error but it also doesn't include it on the output set and now i'm having the error of the support vector machine so here's the code and the screenshot.

<?xml version="1.0" encoding="UTF-8"?><process version="9.6.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.6.000" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="9.6.000" expanded="true" height="68" name="Retrieve final prediction" width="90" x="45" y="85">
        <parameter key="repository_entry" value="//Local Repository/naive bayes 1/final prediction"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="9.6.000" expanded="true" height="82" name="Set Role" width="90" x="179" y="85">
        <parameter key="attribute_name" value="prediction(label)"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="nominal_to_numerical" compatibility="9.6.000" expanded="true" height="103" name="Nominal to Numerical (2)" width="90" x="313" y="85">
        <parameter key="return_preprocessing_model" value="false"/>
        <parameter key="create_view" value="false"/>
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="prediction(label)"/>
        <parameter key="attributes" value=""/>
        <parameter key="use_except_expression" value="false"/>
        <parameter key="value_type" value="nominal"/>
        <parameter key="use_value_type_exception" value="false"/>
        <parameter key="except_value_type" value="file_path"/>
        <parameter key="block_type" value="single_value"/>
        <parameter key="use_block_type_exception" value="false"/>
        <parameter key="except_block_type" value="single_value"/>
        <parameter key="invert_selection" value="false"/>
        <parameter key="include_special_attributes" value="true"/>
        <parameter key="coding_type" value="dummy coding"/>
        <parameter key="use_comparison_groups" value="false"/>
        <list key="comparison_groups"/>
        <parameter key="unexpected_value_handling" value="all 0 and warning"/>
        <parameter key="use_underscore_in_name" value="false"/>
      </operator>
      <operator activated="true" class="support_vector_machine" compatibility="9.6.000" expanded="true" height="124" name="SVM" width="90" x="514" y="85">
        <parameter key="kernel_type" value="dot"/>
        <parameter key="kernel_gamma" value="1.0"/>
        <parameter key="kernel_sigma1" value="1.0"/>
        <parameter key="kernel_sigma2" value="0.0"/>
        <parameter key="kernel_sigma3" value="2.0"/>
        <parameter key="kernel_shift" value="1.0"/>
        <parameter key="kernel_degree" value="2.0"/>
        <parameter key="kernel_a" value="1.0"/>
        <parameter key="kernel_b" value="0.0"/>
        <parameter key="kernel_cache" value="200"/>
        <parameter key="C" value="0.0"/>
        <parameter key="convergence_epsilon" value="0.001"/>
        <parameter key="max_iterations" value="100000"/>
        <parameter key="scale" value="true"/>
        <parameter key="calculate_weights" value="true"/>
        <parameter key="return_optimization_performance" value="true"/>
        <parameter key="L_pos" value="1.0"/>
        <parameter key="L_neg" value="1.0"/>
        <parameter key="epsilon" value="0.0"/>
        <parameter key="epsilon_plus" value="0.0"/>
        <parameter key="epsilon_minus" value="0.0"/>
        <parameter key="balance_cost" value="false"/>
        <parameter key="quadratic_loss_pos" value="false"/>
        <parameter key="quadratic_loss_neg" value="false"/>
        <parameter key="estimate_performance" value="false"/>
      </operator>
      <operator activated="true" class="apply_model" compatibility="9.6.000" expanded="true" height="82" name="Apply Model" width="90" x="715" y="85">
        <list key="application_parameters"/>
        <parameter key="create_view" value="false"/>
      </operator>
      <operator activated="true" class="performance_classification" compatibility="9.6.000" expanded="true" height="82" name="Performance" width="90" x="983" y="187">
        <parameter key="main_criterion" value="first"/>
        <parameter key="accuracy" value="true"/>
        <parameter key="classification_error" value="false"/>
        <parameter key="kappa" value="false"/>
        <parameter key="weighted_mean_recall" value="false"/>
        <parameter key="weighted_mean_precision" value="false"/>
        <parameter key="spearman_rho" value="false"/>
        <parameter key="kendall_tau" value="false"/>
        <parameter key="absolute_error" value="false"/>
        <parameter key="relative_error" value="false"/>
        <parameter key="relative_error_lenient" value="false"/>
        <parameter key="relative_error_strict" value="false"/>
        <parameter key="normalized_absolute_error" value="false"/>
        <parameter key="root_mean_squared_error" value="false"/>
        <parameter key="root_relative_squared_error" value="false"/>
        <parameter key="squared_error" value="false"/>
        <parameter key="correlation" value="false"/>
        <parameter key="squared_correlation" value="false"/>
        <parameter key="cross-entropy" value="false"/>
        <parameter key="margin" value="false"/>
        <parameter key="soft_margin_loss" value="false"/>
        <parameter key="logistic_loss" value="false"/>
        <parameter key="skip_undefined_labels" value="true"/>
        <parameter key="use_example_weights" value="true"/>
        <list key="class_weights"/>
      </operator>
      <connect from_op="Retrieve final prediction" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Nominal to Numerical (2)" to_port="example set input"/>
      <connect from_op="Nominal to Numerical (2)" from_port="example set output" to_op="SVM" to_port="training set"/>
      <connect from_op="SVM" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="SVM" from_port="exampleSet" 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="Apply Model" from_port="model" to_port="result 1"/>
      <connect from_op="Performance" from_port="performance" to_port="result 2"/>
      <connect from_op="Performance" from_port="example set" 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>

Tagged:

Answers

  • Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,596   Unicorn
    Without the data it is not possible for anyone else to replicate the output you are seeing.
    However, the base SVM learner needs numerical attributes as inputs and a binominal or numerical label.  You can't have a polynominal label, nor can you have nominal attributes of any type.  Try changing your data types accordingly and see if that clears up your error.  
    Brian T.
    Lindon Ventures 
    Data Science Consulting from Certified RapidMiner Experts
  • nohanoha Member Posts: 10 Learner I
    the attributes of my data set are: Id(real), prediction label(polynomial) and text(polynomial). i get this error when i try to cast the label to be numerical
  • varunm1varunm1 Moderator, Member Posts: 1,207   Unicorn
    Hello @noha

    Just tick the "Include special Attributes" option.
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

  • nohanoha Member Posts: 10 Learner I
    hi @varunm1
    okay when is do this is till get the support vector machine error that it cannot handle polynomial attributes.
  • varunm1varunm1 Moderator, Member Posts: 1,207   Unicorn
    Hello @noha

    Did you convert all the polynominal attributes into numerical? You can check the type of attribute that are going into SVM operator by right clicking on SVM operator and then select "Break Point Before". Now run the process to see the data that is inputted to SVM, go to statistics tab and you can observe the type of each column.
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

  • nohanoha Member Posts: 10 Learner I
    hello @varunm1
    i just followed what you said and i got this error :) the data outputted was perfect. In set role i did define the label attribute 
  • varunm1varunm1 Moderator, Member Posts: 1,207   Unicorn
    Hello @noha

    Can you share data and your process here? You can export the process from the rapidminer studio using the FILE --> Export process and attach it here. We also need data to rerun the process, so attach that as well. If it is not possible to attach it here, you can send it in a private message option in the community.
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

  • nohanoha Member Posts: 10 Learner I
    @varunm1
    the data and the process. 
    Thank you
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