Optimizing Parameters with RM 5
Hi,
Before Rapidminer 5, the parameters would change in the Learner. Now, it seems that we have to use a Set Parameter object. This doesn't seem to work below. Will I need two learners, or am I naming the objects wrong in SetParameter. Thanks in advance!
Before Rapidminer 5, the parameters would change in the Learner. Now, it seems that we have to use a Set Parameter object. This doesn't seem to work below. Will I need two learners, or am I naming the objects wrong in SetParameter. Thanks in advance!
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" expanded="true" name="Root">
<description>This process tries to find the best selection threshold for the weights provided by a SVM learner. The weights and the example set are given to a parameter optimization. The parameter &quot;weight&quot; of the Selection operator is optimized with a grid search. The performance of this threshold is evaluated with the cross validation building block. Please refer to the meta sample processes for further details regarding the parameter optimization operators.</description>
<process expanded="true" height="604" width="846">
<operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve" width="90" x="45" y="30">
<parameter key="repository_entry" value="../../data/Weighting"/>
</operator>
<operator activated="true" class="support_vector_machine" expanded="true" height="112" name="InitialWeights" width="90" x="179" y="30">
<parameter key="scale" value="false"/>
</operator>
<operator activated="true" class="optimize_parameters_grid" expanded="true" height="112" name="GridParameterOptimization" width="90" x="380" y="30">
<list key="parameters">
<parameter key="Selection.weight" value="0.5,0.25,0.2,0.0"/>
<parameter key="JMySVMLearner.C" value="[1.0;100.0;10;linear]"/>
</list>
<process expanded="true" height="604" width="846">
<operator activated="true" class="select_by_weights" expanded="true" height="94" name="Selection" width="90" x="45" y="30">
<parameter key="weight" value="0.0"/>
</operator>
<operator activated="true" class="x_validation" expanded="true" height="130" name="XValidation" width="90" x="179" y="30">
<process expanded="true">
<operator activated="true" class="support_vector_machine" expanded="true" name="JMySVMLearner">
<parameter key="C" value="90.10000000000001"/>
</operator>
<connect from_port="training" to_op="JMySVMLearner" to_port="training set"/>
<connect from_op="JMySVMLearner" from_port="model" to_port="model"/>
<connect from_op="JMySVMLearner" 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">
<operator activated="true" class="apply_model" expanded="true" name="ModelApplier">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_classification" expanded="true" name="ClassificationPerformance">
<parameter key="classification_error" value="true"/>
<list key="class_weights"/>
</operator>
<connect from_port="model" to_op="ModelApplier" to_port="model"/>
<connect from_port="test set" to_op="ModelApplier" to_port="unlabelled data"/>
<connect from_port="through 1" to_port="averagable 2"/>
<connect from_op="ModelApplier" from_port="labelled data" to_op="ClassificationPerformance" to_port="labelled data"/>
<connect from_op="ClassificationPerformance" 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="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_port="input 1" to_op="Selection" to_port="example set input"/>
<connect from_port="input 2" to_op="Selection" to_port="weights"/>
<connect from_op="Selection" from_port="example set output" to_op="XValidation" to_port="training"/>
<connect from_op="XValidation" from_port="averagable 1" to_port="performance"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="source_input 2" spacing="0"/>
<portSpacing port="source_input 3" spacing="0"/>
<portSpacing port="sink_performance" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="set_parameters" expanded="true" height="60" name="Set Parameters" width="90" x="581" y="120">
<list key="name_map">
<parameter key="JMySVMLearner.C" value="JMySVMLearner"/>
</list>
</operator>
<connect from_op="Retrieve" from_port="output" to_op="InitialWeights" to_port="training set"/>
<connect from_op="InitialWeights" from_port="weights" to_op="GridParameterOptimization" to_port="input 2"/>
<connect from_op="InitialWeights" from_port="exampleSet" to_op="GridParameterOptimization" to_port="input 1"/>
<connect from_op="GridParameterOptimization" from_port="performance" to_port="result 1"/>
<connect from_op="GridParameterOptimization" from_port="parameter" to_op="Set Parameters" to_port="parameter set"/>
<connect from_op="GridParameterOptimization" from_port="result 1" to_port="result 3"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 3" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>