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Determine multiple comparisons correction after evolutionary feature selection

bsegalbsegal Member, University Professor Posts: 7 University Professor
edited February 2020 in Help

I have been trying to employ Ingo's method from his recent posts on evolutionary feature selection.  I have large dimension data sets that this has helped me with a lot.  I now have performance about where I think it is close to optimized, based on previous work with more manual feature selection methods previously published.  I'm interested in determining the P value for the classification the model is yielding.  I can calculate the raw value from the binomial distribuition, but my feeling is I need to correct this for the multiple comparisons that go into the feature selection and cross validation steps.  Is the log file, which lists only the number of rows, the number of different trials leading to the final result?  Is there another way to know this if not?

 

Here is my xml:

<?xml version="1.0" encoding="UTF-8"?><process version="8.0.001">
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    <process expanded="true">
      <operator activated="true" class="read_excel" compatibility="8.0.001" expanded="true" height="68" name="Read Excel" width="90" x="45" y="34">
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        <parameter key="imported_cell_range" value="A1:DZ81"/>
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        <parameter key="first_row_as_names" value="false"/>
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        <parameter key="locale" value="English (United States)"/>
        <list key="data_set_meta_data_information">
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          <parameter key="3" value="Height (cm).true.integer.attribute"/>
          <parameter key="4" value="Weight (kg).true.numeric.attribute"/>
          <parameter key="5" value="BMI (kg/m2).true.numeric.attribute"/>
          <parameter key="6" value="Mallampati Class.true.numeric.attribute"/>
          <parameter key="7" value="Thyromental Distance (FBs).true.numeric.attribute"/>
          <parameter key="8" value="Brow Ridge - high/low.true.numeric.attribute"/>
          <parameter key="9" value="Brow Ridge - high/low ^2.true.numeric.attribute"/>
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          <parameter key="11" value="Brow Ridge Inner - down/up ^2.true.numeric.attribute"/>
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          <parameter key="32" value="Chin - small/large.true.real.attribute"/>
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          <parameter key="72" value="Jaw - neck slope high/low.true.real.attribute"/>
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          <parameter key="74" value="Jawline - concave/convex.true.numeric.attribute"/>
          <parameter key="75" value="Jawline - concave/convex ^2.true.numeric.attribute"/>
          <parameter key="76" value="Mouth - drawn/pursed.true.numeric.attribute"/>
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          <parameter key="89" value="Mouth - protruding/retracted ^2.true.real.attribute"/>
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          <parameter key="128" value="Temples - thin/wide.true.real.attribute"/>
          <parameter key="129" value="Temples - thin/wide ^2.true.real.attribute"/>
        </list>
        <parameter key="read_not_matching_values_as_missings" value="true"/>
        <parameter key="datamanagement" value="double_array"/>
        <parameter key="data_management" value="auto"/>
      </operator>
      <operator activated="true" class="optimize_selection_evolutionary" compatibility="8.0.001" expanded="true" height="103" name="Optimize Selection (Evolutionary)" width="90" x="313" y="34">
        <parameter key="use_exact_number_of_attributes" value="false"/>
        <parameter key="restrict_maximum" value="true"/>
        <parameter key="min_number_of_attributes" value="1"/>
        <parameter key="max_number_of_attributes" value="20"/>
        <parameter key="exact_number_of_attributes" value="1"/>
        <parameter key="initialize_with_input_weights" value="false"/>
        <parameter key="population_size" value="5"/>
        <parameter key="maximum_number_of_generations" value="30"/>
        <parameter key="use_early_stopping" value="false"/>
        <parameter key="generations_without_improval" value="2"/>
        <parameter key="normalize_weights" value="true"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <parameter key="user_result_individual_selection" value="false"/>
        <parameter key="show_population_plotter" value="false"/>
        <parameter key="plot_generations" value="10"/>
        <parameter key="constraint_draw_range" value="false"/>
        <parameter key="draw_dominated_points" value="true"/>
        <parameter key="maximal_fitness" value="Infinity"/>
        <parameter key="selection_scheme" value="tournament"/>
        <parameter key="tournament_size" value="0.25"/>
        <parameter key="start_temperature" value="1.0"/>
        <parameter key="dynamic_selection_pressure" value="true"/>
        <parameter key="keep_best_individual" value="false"/>
        <parameter key="save_intermediate_weights" value="false"/>
        <parameter key="intermediate_weights_generations" value="10"/>
        <parameter key="p_initialize" value="0.5"/>
        <parameter key="p_mutation" value="-1.0"/>
        <parameter key="p_crossover" value="0.5"/>
        <parameter key="crossover_type" value="uniform"/>
        <process expanded="true">
          <operator activated="true" class="concurrency:cross_validation" compatibility="8.0.001" expanded="true" height="145" name="Cross Validation" width="90" x="112" y="34">
            <parameter key="split_on_batch_attribute" value="false"/>
            <parameter key="leave_one_out" value="false"/>
            <parameter key="number_of_folds" value="20"/>
            <parameter key="sampling_type" value="automatic"/>
            <parameter key="use_local_random_seed" value="false"/>
            <parameter key="local_random_seed" value="1992"/>
            <parameter key="enable_parallel_execution" value="true"/>
            <process expanded="true">
              <operator activated="true" class="naive_bayes" compatibility="8.0.001" expanded="true" height="82" name="Naive Bayes" width="90" x="179" y="187">
                <parameter key="laplace_correction" value="true"/>
              </operator>
              <connect from_port="training set" to_op="Naive Bayes" to_port="training set"/>
              <connect from_op="Naive Bayes" from_port="model" to_port="model"/>
              <portSpacing port="source_training set" spacing="0"/>
              <portSpacing port="sink_model" spacing="0"/>
              <portSpacing port="sink_through 1" spacing="0"/>
            </process>
            <process expanded="true">
              <operator activated="true" class="apply_model" compatibility="8.0.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
                <list key="application_parameters"/>
                <parameter key="create_view" value="false"/>
              </operator>
              <operator activated="true" class="performance_binominal_classification" compatibility="8.0.001" expanded="true" height="82" name="Performance" width="90" x="246" y="34">
                <parameter key="main_criterion" value="first"/>
                <parameter key="accuracy" value="true"/>
                <parameter key="classification_error" value="false"/>
                <parameter key="kappa" value="false"/>
                <parameter key="AUC (optimistic)" value="false"/>
                <parameter key="AUC" value="false"/>
                <parameter key="AUC (pessimistic)" value="false"/>
                <parameter key="precision" value="false"/>
                <parameter key="recall" value="false"/>
                <parameter key="lift" value="false"/>
                <parameter key="fallout" value="false"/>
                <parameter key="f_measure" value="false"/>
                <parameter key="false_positive" value="false"/>
                <parameter key="false_negative" value="false"/>
                <parameter key="true_positive" value="false"/>
                <parameter key="true_negative" value="false"/>
                <parameter key="sensitivity" value="false"/>
                <parameter key="specificity" value="false"/>
                <parameter key="youden" value="false"/>
                <parameter key="positive_predictive_value" value="false"/>
                <parameter key="negative_predictive_value" value="false"/>
                <parameter key="psep" value="false"/>
                <parameter key="skip_undefined_labels" value="true"/>
                <parameter key="use_example_weights" value="true"/>
              </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="performance 1"/>
              <connect from_op="Performance" from_port="example set" to_port="test set results"/>
              <portSpacing port="source_model" spacing="0"/>
              <portSpacing port="source_test set" spacing="0"/>
              <portSpacing port="source_through 1" spacing="0"/>
              <portSpacing port="sink_test set results" spacing="0"/>
              <portSpacing port="sink_performance 1" spacing="0"/>
              <portSpacing port="sink_performance 2" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="log" compatibility="8.0.001" expanded="true" height="82" name="Log" width="90" x="380" y="136">
            <list key="log"/>
            <parameter key="sorting_type" value="none"/>
            <parameter key="sorting_k" value="100"/>
            <parameter key="persistent" value="false"/>
          </operator>
          <connect from_port="example set" to_op="Cross Validation" to_port="example set"/>
          <connect from_op="Cross Validation" from_port="performance 1" to_op="Log" to_port="through 1"/>
          <connect from_op="Log" from_port="through 1" to_port="performance"/>
          <portSpacing port="source_example set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="sink_performance" spacing="0"/>
        </process>
      </operator>
      <connect from_port="input 1" to_op="Read Excel" to_port="file"/>
      <connect from_op="Read Excel" from_port="output" to_op="Optimize Selection (Evolutionary)" to_port="example set in"/>
      <connect from_op="Optimize Selection (Evolutionary)" from_port="example set out" to_port="result 3"/>
      <connect from_op="Optimize Selection (Evolutionary)" from_port="weights" to_port="result 1"/>
      <connect from_op="Optimize Selection (Evolutionary)" from_port="performance" to_port="result 2"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="source_input 2" 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"/>
      <portSpacing port="sink_result 5" spacing="0"/>
    </process>
  </operator>
</process>

One of my data files is also attached.  Thanks in advance! 

Tagged:

Answers

  • Options
    IngoRMIngoRM Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, Community Manager, RMResearcher, Member, University Professor Posts: 1,751 RM Founder

    Hi,

     

    I guess that ultimately you want to know how often a certain attribute was used?  If yes, you could try to also log the used feature names changing the settings of the Log Operator.  The resulting data set would need some post-processing but you can probably figure out the count for each attribute from it.  Below is a quick and dirty process which shows this.

     

    However, the way the log operator is used here means that only the best attribute set so far is logged.  If you want to log ALL variants which have been tried, you would need to manually extract the feature names and store them.  That process is much more complicated though...

     

    Anyway, I hope this helps already,

    Ingo

     

    <?xml version="1.0" encoding="UTF-8"?><process version="8.0.001">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="8.0.001" expanded="true" name="Process">
        <process expanded="true">
          <operator activated="true" class="retrieve" compatibility="8.0.001" expanded="true" height="68" name="Retrieve Sonar" width="90" x="45" y="34">
            <parameter key="repository_entry" value="../data/Sonar"/>
          </operator>
          <operator activated="true" class="optimize_selection_evolutionary" compatibility="8.0.001" expanded="true" height="103" name="Optimize Selection (Evolutionary)" width="90" x="179" y="34">
            <parameter key="population_size" value="20"/>
            <parameter key="generations_without_improval" value="10"/>
            <parameter key="keep_best_individual" value="true"/>
            <parameter key="p_initialize" value="0.9"/>
            <parameter key="p_crossover" value="1.0"/>
            <process expanded="true">
              <operator activated="true" class="concurrency:cross_validation" compatibility="8.0.001" expanded="true" height="145" name="Validation" width="90" x="45" y="34">
                <parameter key="number_of_folds" value="5"/>
                <parameter key="sampling_type" value="stratified sampling"/>
                <parameter key="use_local_random_seed" value="true"/>
                <process expanded="true">
                  <operator activated="true" class="naive_bayes" compatibility="8.0.001" expanded="true" height="82" name="Naive Bayes" width="90" x="45" y="34"/>
                  <connect from_port="training set" to_op="Naive Bayes" to_port="training set"/>
                  <connect from_op="Naive Bayes" from_port="model" to_port="model"/>
                  <portSpacing port="source_training set" spacing="0"/>
                  <portSpacing port="sink_model" spacing="0"/>
                  <portSpacing port="sink_through 1" spacing="0"/>
                  <description align="left" color="green" colored="true" height="80" resized="false" width="248" x="37" y="137">In the training phase, a model is built on the current training data set. (90 % of data by default, 10 times)</description>
                </process>
                <process expanded="true">
                  <operator activated="true" class="apply_model" compatibility="8.0.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
                    <list key="application_parameters"/>
                  </operator>
                  <operator activated="true" class="performance" compatibility="8.0.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34"/>
                  <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="performance 1"/>
                  <connect from_op="Performance" from_port="example set" to_port="test set results"/>
                  <portSpacing port="source_model" spacing="0"/>
                  <portSpacing port="source_test set" spacing="0"/>
                  <portSpacing port="source_through 1" spacing="0"/>
                  <portSpacing port="sink_test set results" spacing="0"/>
                  <portSpacing port="sink_performance 1" spacing="0"/>
                  <portSpacing port="sink_performance 2" spacing="0"/>
                  <description align="left" color="blue" colored="true" height="103" resized="false" width="315" x="38" y="137">The model created in the Training step is applied to the current test set (10 %).&lt;br/&gt;The performance is evaluated and sent to the operator results.</description>
                </process>
                <description align="center" color="transparent" colored="false" width="126">A cross-validation evaluating a decision tree model.</description>
              </operator>
              <operator activated="true" class="log" compatibility="8.0.001" expanded="true" height="82" name="Log" width="90" x="179" y="34">
                <list key="log">
                  <parameter key="Generation" value="operator.Optimize Selection (Evolutionary).value.generation"/>
                  <parameter key="Accuracy" value="operator.Optimize Selection (Evolutionary).value.best"/>
                  <parameter key="Features" value="operator.Optimize Selection (Evolutionary).value.feature_names"/>
                </list>
              </operator>
              <connect from_port="example set" to_op="Validation" to_port="example set"/>
              <connect from_op="Validation" from_port="performance 1" to_op="Log" to_port="through 1"/>
              <connect from_op="Log" from_port="through 1" to_port="performance"/>
              <portSpacing port="source_example set" spacing="0"/>
              <portSpacing port="source_through 1" spacing="0"/>
              <portSpacing port="sink_performance" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="log_to_data" compatibility="8.0.001" expanded="true" height="82" name="Log to Data" width="90" x="380" y="136">
            <parameter key="log_name" value="Log"/>
          </operator>
          <connect from_op="Retrieve Sonar" from_port="output" to_op="Optimize Selection (Evolutionary)" to_port="example set in"/>
          <connect from_op="Optimize Selection (Evolutionary)" from_port="example set out" to_port="result 1"/>
          <connect from_op="Optimize Selection (Evolutionary)" from_port="weights" to_port="result 2"/>
          <connect from_op="Optimize Selection (Evolutionary)" from_port="performance" to_port="result 3"/>
          <connect from_op="Log to Data" from_port="exampleSet" to_port="result 4"/>
          <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"/>
          <portSpacing port="sink_result 5" spacing="0"/>
        </process>
      </operator>
    </process>
  • Options
    bsegalbsegal Member, University Professor Posts: 7 University Professor

    Thanks, I'll give this a try!

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