Implementing majority votes

t_artierest_artieres Member Posts: 1 Contributor I
edited November 2018 in Help

I want to learn a bunch of classifiers that each work on a random subset of features and then make them vote. it is quite like a bagging feature approach.

i wrote a process that learns all these classifiers on a training set, and that computes the predictions of all classifier on the test set.

Next i want to use this collection of every classifier outputs to make them vote and decide the class of each test sample.

Yet the result of this loop is a collection of predictions, one per classifier, on the test set. This is a IOO collection with as many object as there are classifiers. The object for classifier i is an exampleset with one atribute which is the decision of the classifier on the test set.

I suppose that to compute majority vote decisions i should first transform the IOO collection in an exampleset with as manuy attributes as there are classifiers.

But I get two problems. Firsrt is transforming this IOO in an example set on which i excpect i compute majority voting decision. Second, assuming i a can transform the IOO in an example set, how to compute majority vote decisions ?

Some body could help ?

here is the code. note that it could be any dataset with real valued attributes.

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.2.008">
 <operator activated="true" class="process" compatibility="5.2.008" expanded="true" name="Process">
   <process expanded="true" height="611" width="882">
     <operator activated="true" class="retrieve" compatibility="5.2.008" expanded="true" height="60" name="Retrieve (2)" width="90" x="45" y="300">
       <parameter key="repository_entry" value="../Datas/Datas_PAN_2012"/>
     <operator activated="true" class="normalize" compatibility="5.2.008" expanded="true" height="94" name="Normalize" width="90" x="45" y="165"/>
     <operator activated="true" class="shuffle" compatibility="5.2.008" expanded="true" height="76" name="Shuffle" width="90" x="45" y="30">
       <parameter key="use_local_random_seed" value="true"/>
     <operator activated="true" class="multiply" compatibility="5.2.008" expanded="true" height="94" name="Multiply" width="90" x="179" y="30"/>
     <operator activated="true" class="loop" compatibility="5.2.008" expanded="true" height="94" name="Loop" width="90" x="380" y="75">
       <parameter key="set_iteration_macro" value="true"/>
       <parameter key="iterations" value="2"/>
       <process expanded="true" height="585" width="835">
         <operator activated="true" class="select_by_random" compatibility="5.2.008" expanded="true" height="76" name="Select by Random (2)" width="90" x="45" y="30">
           <parameter key="use_fixed_number_of_attributes" value="true"/>
           <parameter key="number_of_attributes" value="300"/>
         <operator activated="true" class="split_data" compatibility="5.2.008" expanded="true" height="94" name="Split Data (3)" width="90" x="179" y="30">
           <enumeration key="partitions">
             <parameter key="ratio" value="0.7"/>
             <parameter key="ratio" value="0.3"/>
           <parameter key="sampling_type" value="linear sampling"/>
         <operator activated="true" class="support_vector_machine_libsvm" compatibility="5.2.008" expanded="true" height="76" name="SVM (3)" width="90" x="313" y="30">
           <list key="class_weights"/>
         <operator activated="true" class="apply_model" compatibility="5.2.008" expanded="true" height="76" name="Apply Model (3)" width="90" x="246" y="210">
           <list key="application_parameters"/>
         <operator activated="true" class="multiply" compatibility="5.2.008" expanded="true" height="94" name="Multiply (2)" width="90" x="380" y="210"/>
         <operator activated="true" class="select_attributes" compatibility="5.2.008" expanded="true" height="76" name="Select Attributes (2)" width="90" x="581" y="255">
           <parameter key="attribute_filter_type" value="single"/>
           <parameter key="attribute" value="prediction(gensym2998)"/>
           <parameter key="include_special_attributes" value="true"/>
         <operator activated="true" class="rename" compatibility="5.2.008" expanded="true" height="76" name="Rename" width="90" x="715" y="210">
           <parameter key="old_name" value="prediction(gensym2998)"/>
           <parameter key="new_name" value="prediction_%{iteration}"/>
           <list key="rename_additional_attributes"/>
         <operator activated="true" class="performance_classification" compatibility="5.2.008" expanded="true" height="76" name="Performance (3)" width="90" x="648" y="30">
           <list key="class_weights"/>
         <connect from_port="input 1" to_op="Select by Random (2)" to_port="example set input"/>
         <connect from_op="Select by Random (2)" from_port="example set output" to_op="Split Data (3)" to_port="example set"/>
         <connect from_op="Split Data (3)" from_port="partition 1" to_op="SVM (3)" to_port="training set"/>
         <connect from_op="Split Data (3)" from_port="partition 2" to_op="Apply Model (3)" to_port="unlabelled data"/>
         <connect from_op="SVM (3)" from_port="model" to_op="Apply Model (3)" to_port="model"/>
         <connect from_op="Apply Model (3)" from_port="labelled data" to_op="Multiply (2)" to_port="input"/>
         <connect from_op="Multiply (2)" from_port="output 1" to_op="Performance (3)" to_port="labelled data"/>
         <connect from_op="Multiply (2)" from_port="output 2" to_op="Select Attributes (2)" to_port="example set input"/>
         <connect from_op="Select Attributes (2)" from_port="example set output" to_op="Rename" to_port="example set input"/>
         <connect from_op="Rename" from_port="example set output" to_port="output 2"/>
         <connect from_op="Performance (3)" from_port="performance" to_port="output 1"/>
         <portSpacing port="source_input 1" spacing="0"/>
         <portSpacing port="source_input 2" spacing="0"/>
         <portSpacing port="sink_output 1" spacing="0"/>
         <portSpacing port="sink_output 2" spacing="0"/>
         <portSpacing port="sink_output 3" spacing="0"/>
     <connect from_op="Retrieve (2)" from_port="output" to_op="Normalize" to_port="example set input"/>
     <connect from_op="Normalize" from_port="example set output" to_op="Shuffle" to_port="example set input"/>
     <connect from_op="Shuffle" from_port="example set output" to_op="Multiply" to_port="input"/>
     <connect from_op="Multiply" from_port="output 1" to_port="result 1"/>
     <connect from_op="Multiply" from_port="output 2" to_op="Loop" to_port="input 1"/>
     <connect from_op="Loop" from_port="output 1" to_port="result 2"/>
     <connect from_op="Loop" from_port="output 2" 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"/>


  • MariusHelfMariusHelf RapidMiner Certified Expert, Member Posts: 1,869 Unicorn

    I admit that I did not open your process, but maybe the Vote operator could help you. The attached process demonstrates that you can create composed models which perform a majority vote. I created 3 different models on the inside, and of course you can also perform different preprocessing etc. for the creation of each model.

    Best regards,
    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="5.3.005">
      <operator activated="true" class="process" compatibility="5.3.005" expanded="true" name="Process">
        <process expanded="true">
          <operator activated="true" class="retrieve" compatibility="5.3.005" expanded="true" height="60" name="Retrieve Sonar" width="90" x="112" y="75">
            <parameter key="repository_entry" value="//Samples/data/Sonar"/>
          <operator activated="true" class="vote" compatibility="5.3.005" expanded="true" height="60" name="Vote" width="90" x="313" y="75">
            <process expanded="true">
              <operator activated="true" class="naive_bayes" compatibility="5.3.005" expanded="true" height="76" name="Naive Bayes" width="90" x="179" y="30"/>
              <operator activated="true" class="decision_tree" compatibility="5.3.005" expanded="true" height="76" name="Decision Tree" width="90" x="179" y="165"/>
              <operator activated="true" class="support_vector_machine" compatibility="5.3.005" expanded="true" height="112" name="SVM" width="90" x="179" y="300"/>
              <connect from_port="training set 1" to_op="Naive Bayes" to_port="training set"/>
              <connect from_port="training set 2" to_op="Decision Tree" to_port="training set"/>
              <connect from_port="training set 3" to_op="SVM" to_port="training set"/>
              <connect from_op="Naive Bayes" from_port="model" to_port="base model 1"/>
              <connect from_op="Decision Tree" from_port="model" to_port="base model 2"/>
              <connect from_op="SVM" from_port="model" to_port="base model 3"/>
              <portSpacing port="source_training set 1" spacing="0"/>
              <portSpacing port="source_training set 2" spacing="0"/>
              <portSpacing port="source_training set 3" spacing="234"/>
              <portSpacing port="source_training set 4" spacing="0"/>
              <portSpacing port="sink_base model 1" spacing="0"/>
              <portSpacing port="sink_base model 2" spacing="126"/>
              <portSpacing port="sink_base model 3" spacing="108"/>
              <portSpacing port="sink_base model 4" spacing="0"/>
          <connect from_op="Retrieve Sonar" from_port="output" to_op="Vote" to_port="training set"/>
          <connect from_op="Vote" from_port="model" 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"/>
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