RapidMiner 9.7 is Now Available

Lots of amazing new improvements including true version control! Learn more about what's new here.

CLICK HERE TO DOWNLOAD

how to automatically set role operator

LeMarcLeMarc Member Posts: 72 Contributor II
Hello,

below is a random prediction model by using the titanic example set:

The label role for an attribute needs to be set two times in order to filter wrong predictions. My question is how can  the second "set role" operator be  automatically changed according to the first "set role" operator.

This seems somehow clear, however I camt seem to find a solution for that.
Thank you very much!


<?xml version="1.0" encoding="UTF-8"?><process version="9.7.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.7.000" 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="9.7.000" expanded="true" height="68" name="Retrieve Titanic" width="90" x="45" y="34">
        <parameter key="repository_entry" value="//Samples/data/Titanic"/>
      </operator>
      <operator activated="true" class="split_data" compatibility="9.7.000" expanded="true" height="103" name="Split Data" width="90" x="179" y="34">
        <enumeration key="partitions">
          <parameter key="ratio" value="0.7"/>
          <parameter key="ratio" value="0.3"/>
        </enumeration>
        <parameter key="sampling_type" value="automatic"/>
        <parameter key="use_local_random_seed" value="true"/>
        <parameter key="local_random_seed" value="1992"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="9.7.000" expanded="true" height="82" name="Set Role (2)" width="90" x="313" y="238">
        <parameter key="attribute_name" value="Survived"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="9.7.000" expanded="true" height="82" name="Set Role" width="90" x="313" y="34">
        <parameter key="attribute_name" value="Survived"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="multiply" compatibility="9.7.000" expanded="true" height="103" name="Multiply" width="90" x="447" y="34"/>
      <operator activated="true" class="concurrency:parallel_random_forest" compatibility="9.7.000" expanded="true" height="103" name="Random Forest" width="90" x="581" y="34">
        <parameter key="number_of_trees" value="100"/>
        <parameter key="criterion" value="gain_ratio"/>
        <parameter key="maximal_depth" value="10"/>
        <parameter key="apply_pruning" value="false"/>
        <parameter key="confidence" value="0.1"/>
        <parameter key="apply_prepruning" value="false"/>
        <parameter key="minimal_gain" value="0.01"/>
        <parameter key="minimal_leaf_size" value="2"/>
        <parameter key="minimal_size_for_split" value="4"/>
        <parameter key="number_of_prepruning_alternatives" value="3"/>
        <parameter key="random_splits" value="false"/>
        <parameter key="guess_subset_ratio" value="true"/>
        <parameter key="subset_ratio" value="0.2"/>
        <parameter key="voting_strategy" value="confidence vote"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <parameter key="enable_parallel_execution" value="true"/>
      </operator>
      <operator activated="true" class="apply_model" compatibility="9.7.000" expanded="true" height="82" name="Apply Model" width="90" x="447" y="238">
        <list key="application_parameters"/>
        <parameter key="create_view" value="false"/>
      </operator>
      <operator activated="true" class="filter_examples" compatibility="9.7.000" expanded="true" height="103" name="Filter Examples" width="90" x="581" y="238">
        <parameter key="parameter_expression" value=""/>
        <parameter key="condition_class" value="wrong_predictions"/>
        <parameter key="invert_filter" value="false"/>
        <list key="filters_list"/>
        <parameter key="filters_logic_and" value="true"/>
        <parameter key="filters_check_metadata" value="true"/>
      </operator>
      <connect from_op="Retrieve Titanic" from_port="output" to_op="Split Data" to_port="example set"/>
      <connect from_op="Split Data" from_port="partition 1" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Split Data" from_port="partition 2" to_op="Set Role (2)" to_port="example set input"/>
      <connect from_op="Set Role (2)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Multiply" to_port="input"/>
      <connect from_op="Multiply" from_port="output 1" to_op="Random Forest" to_port="training set"/>
      <connect from_op="Random Forest" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="Apply Model" from_port="labelled data" to_op="Filter Examples" to_port="example set input"/>
      <connect from_op="Filter Examples" from_port="example set output" 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"/>
    </process>
  </operator>
</process>


Best Answer

Answers

  • LeMarcLeMarc Member Posts: 72 Contributor II
    Yeah, I now used a macro :D. Thank you!
Sign In or Register to comment.