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parameter Optimization Problem

can_yucebascan_yucebas Member Posts: 7 Contributor II
in Help
Hi to all,

I use parameter optimization (grid search) to decide the optimum minimal gain to be used in ID3 Tree. I ran the optimization, after it finished, in performance window it shows the optimum value for minimal gain is 4.107. But when I look to log file I can see that other values with higher perf values.

In addtion when I build ID3 Tree with same data set and parameters and for minimal gain I use 4.107, I can not get the same perf results that The paramater optimization perf vector shows.

So how can I trust the optimization?

Answers

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    SkirzynskiSkirzynski Member Posts: 164 Maven
    I can not reproduce you behavior. Here is my process setup: 

    <?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">
        <process expanded="true" height="431" width="748">
          <operator activated="true" class="retrieve" compatibility="5.3.000" expanded="true" height="60" name="Retrieve Iris" width="90" x="45" y="30">
            <parameter key="repository_entry" value="//Samples/data/Iris"/>
          </operator>
          <operator activated="true" class="optimize_parameters_grid" compatibility="5.3.000" expanded="true" height="94" name="Optimize Parameters (Grid)" width="90" x="179" y="30">
            <list key="parameters">
              <parameter key="Learner.minimal_gain" value="[0.1;20;100;linear]"/>
            </list>
            <process expanded="true" height="521" width="748">
              <operator activated="true" class="x_validation" compatibility="5.3.000" expanded="true" height="112" name="X-Validation" width="90" x="45" y="30">
                <parameter key="sampling_type" value="shuffled sampling"/>
                <process expanded="true" height="398" width="327">
                  <operator activated="true" class="decision_tree" compatibility="5.3.000" expanded="true" height="76" name="Learner" width="90" x="112" y="30">
                    <parameter key="minimal_gain" value="20.0"/>
                  </operator>
                  <connect from_port="training" to_op="Learner" to_port="training set"/>
                  <connect from_op="Learner" 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="398" width="327">
                  <operator activated="true" class="apply_model" compatibility="5.3.000" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
                    <list key="application_parameters"/>
                  </operator>
                  <operator activated="true" class="performance" compatibility="5.3.000" 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>
              <operator activated="true" class="performance_to_data" compatibility="5.3.000" expanded="true" height="76" name="Performance to Data" width="90" x="179" y="75"/>
              <operator activated="true" class="extract_log_value" compatibility="5.3.000" expanded="true" height="60" name="Extract Log Value" width="90" x="313" y="30">
                <parameter key="attribute_name" value="Value"/>
                <parameter key="example_index" value="1"/>
              </operator>
              <operator activated="true" class="log" compatibility="5.3.000" expanded="true" height="76" name="Log" width="90" x="447" y="120">
                <list key="log">
                  <parameter key="minimal_gain" value="operator.Learner.parameter.minimal_gain"/>
                  <parameter key="accuracy" value="operator.Extract Log Value.value.data_value"/>
                </list>
              </operator>
              <connect from_port="input 1" to_op="X-Validation" to_port="training"/>
              <connect from_op="X-Validation" from_port="averagable 1" to_op="Performance to Data" to_port="performance vector"/>
              <connect from_op="Performance to Data" from_port="example set" to_op="Extract Log Value" to_port="example set"/>
              <connect from_op="Performance to Data" from_port="performance vector" to_op="Log" to_port="through 1"/>
              <connect from_op="Log" from_port="through 1" to_port="performance"/>
              <portSpacing port="source_input 1" spacing="0"/>
              <portSpacing port="source_input 2" spacing="0"/>
              <portSpacing port="sink_performance" spacing="36"/>
              <portSpacing port="sink_result 1" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="set_parameters" compatibility="5.3.000" expanded="true" height="76" name="Set Parameters" width="90" x="313" y="75">
            <list key="name_map">
              <parameter key="Learner" value="OptimalLearner"/>
            </list>
          </operator>
          <operator activated="true" class="retrieve" compatibility="5.3.000" expanded="true" height="60" name="Retrieve Iris (2)" width="90" x="45" y="210">
            <parameter key="repository_entry" value="//Samples/data/Iris"/>
          </operator>
          <operator activated="true" class="decision_tree" compatibility="5.3.000" expanded="true" height="76" name="OptimalLearner" width="90" x="179" y="210"/>
          <connect from_op="Retrieve Iris" from_port="output" to_op="Optimize Parameters (Grid)" to_port="input 1"/>
          <connect from_op="Optimize Parameters (Grid)" from_port="parameter" to_op="Set Parameters" to_port="parameter set"/>
          <connect from_op="Set Parameters" from_port="parameter set" to_port="result 1"/>
          <connect from_op="Retrieve Iris (2)" from_port="output" to_op="OptimalLearner" to_port="training set"/>
          <connect from_op="OptimalLearner" from_port="model" 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>
    Can you provide a process with a minimal example of your problem?
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