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Loop Label and Expressions.

abcacabcac Member Posts: 4 Contributor I
edited November 2018 in Help
I have 10 attributes, 1 id, and 9 regular. I want to create 9 models, predicting each of the regular attributes with the other 8.

Loop Label seems appropriate, however models wont use special attributes as attributes. This means I need to change the role of the 7 other attributes to regular again.

How do you figure out if an attribute is the label?

Comments

  • abcacabcac Member Posts: 4 Contributor I
    If you name your variables with a number on the end, you can use the Loop operator and do it manually.

    Still I am not sure how to check if an attribute exists or not (in a branch).
  • MariusHelfMariusHelf RapidMiner Certified Expert, Member Posts: 1,869 Unicorn 
    Hi,

    you can use Loop Attributes for this task. Just leave the role of all attributes at "regular" before passing the data into the loop. Then you can do something like the process below. You surely want to modify the sample process such that you log the performance or something inside the loop.

    Best, Marius

    [code<?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="5.2.008">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="5.2.008" expanded="true" name="Process">
        <process expanded="true" height="190" width="547">
          <operator activated="true" class="generate_data" compatibility="5.2.008" expanded="true" height="60" name="Generate Data" width="90" x="45" y="30"/>
          <operator activated="true" class="loop_attributes" compatibility="5.2.008" expanded="true" height="60" name="Loop Attributes" width="90" x="179" y="30">
            <process expanded="true" height="511" width="598">
              <operator activated="true" class="set_role" compatibility="5.2.008" expanded="true" height="76" name="Set Role" width="90" x="45" y="30">
                <parameter key="name" value="%{loop_attribute}"/>
                <parameter key="target_role" value="label"/>
                <list key="set_additional_roles"/>
              </operator>
              <operator activated="true" class="support_vector_machine" compatibility="5.2.008" expanded="true" height="112" name="SVM" width="90" x="179" y="30"/>
              <operator activated="true" class="set_role" compatibility="5.2.008" expanded="true" height="76" name="Set Role (2)" width="90" x="313" y="30">
                <parameter key="name" value="%{loop_attribute}"/>
                <list key="set_additional_roles"/>
              </operator>
              <connect from_port="example set" to_op="Set Role" to_port="example set input"/>
              <connect from_op="Set Role" from_port="example set output" to_op="SVM" to_port="training set"/>
              <connect from_op="SVM" from_port="exampleSet" to_op="Set Role (2)" to_port="example set input"/>
              <connect from_op="Set Role (2)" from_port="example set output" to_port="example set"/>
              <portSpacing port="source_example set" spacing="0"/>
              <portSpacing port="sink_example set" spacing="0"/>
            </process>
          </operator>
          <connect from_op="Generate Data" from_port="output" to_op="Loop Attributes" to_port="example set"/>
          <connect from_op="Loop Attributes" from_port="example set" 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>
  • paldamopaldamo Member Posts: 3 Contributor I
    Hi, what exactly can I do with this code? copy it and paste where?, can a process be derived from this, please help
  • sgenzersgenzer Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,959 Community Manager
    hi @paldamo oof that is an OLD thread. :smile: But yes you copy the XML into RapidMiner. Here is a quick instruction guide on how to do this: https://community.rapidminer.com/discussion/37047

    FWIW here is a more up-to-date way to do this:

    <?xml version="1.0" encoding="UTF-8"?><process version="9.5.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.5.001" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="-1"/>
    <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" breakpoints="after" class="generate_data" compatibility="7.1.001" expanded="true" height="68" name="Generate Data" width="90" x="45" y="34">
        <parameter key="target_function" value="random"/>
        <parameter key="number_examples" value="100"/>
        <parameter key="number_of_attributes" value="5"/>
        <parameter key="attributes_lower_bound" value="-10.0"/>
        <parameter key="attributes_upper_bound" value="10.0"/>
        <parameter key="gaussian_standard_deviation" value="10.0"/>
        <parameter key="largest_radius" value="10.0"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <parameter key="datamanagement" value="double_array"/>
        <parameter key="data_management" value="auto"/>
      </operator>
      <operator activated="true" breakpoints="after" class="set_role" compatibility="9.5.001" expanded="true" height="82" name="Set Role (3)" width="90" x="179" y="34">
        <parameter key="attribute_name" value="label"/>
        <parameter key="target_role" value="regular"/>
        <list key="set_additional_roles"/>
        <description align="center" color="transparent" colored="false" width="126">this sets the label attribute to &amp;quot;regular&amp;quot;</description>
      </operator>
      <operator activated="true" class="time_series:multi_label_model_learner" compatibility="9.5.000" expanded="true" height="82" name="Multi Label Modeling" width="90" x="313" y="34">
        <parameter key="attribute_filter_type" value="all"/>
        <parameter key="attribute" value=""/>
        <parameter key="attributes" value=""/>
        <parameter key="use_except_expression" value="false"/>
        <parameter key="value_type" value="attribute_value"/>
        <parameter key="use_value_type_exception" value="false"/>
        <parameter key="except_value_type" value="time"/>
        <parameter key="block_type" value="attribute_block"/>
        <parameter key="use_block_type_exception" value="false"/>
        <parameter key="except_block_type" value="value_matrix_row_start"/>
        <parameter key="invert_selection" value="false"/>
        <parameter key="include_special_attributes" value="true"/>
        <parameter key="add_macros" value="false"/>
        <parameter key="current_label_name_macro" value="current_label_attribute"/>
        <parameter key="current_label_type_macro" value="current_label_type"/>
        <parameter key="enable_parallel_execution" value="true"/>
        <process expanded="true">
          <operator activated="true" class="support_vector_machine" compatibility="9.5.001" expanded="true" height="124" name="SVM (2)" width="90" x="45" y="34">
            <parameter key="kernel_type" value="dot"/>
            <parameter key="kernel_gamma" value="1.0"/>
            <parameter key="kernel_sigma1" value="1.0"/>
            <parameter key="kernel_sigma2" value="0.0"/>
            <parameter key="kernel_sigma3" value="2.0"/>
            <parameter key="kernel_shift" value="1.0"/>
            <parameter key="kernel_degree" value="2.0"/>
            <parameter key="kernel_a" value="1.0"/>
            <parameter key="kernel_b" value="0.0"/>
            <parameter key="kernel_cache" value="200"/>
            <parameter key="C" value="0.0"/>
            <parameter key="convergence_epsilon" value="0.001"/>
            <parameter key="max_iterations" value="100000"/>
            <parameter key="scale" value="true"/>
            <parameter key="calculate_weights" value="true"/>
            <parameter key="return_optimization_performance" value="true"/>
            <parameter key="L_pos" value="1.0"/>
            <parameter key="L_neg" value="1.0"/>
            <parameter key="epsilon" value="0.0"/>
            <parameter key="epsilon_plus" value="0.0"/>
            <parameter key="epsilon_minus" value="0.0"/>
            <parameter key="balance_cost" value="false"/>
            <parameter key="quadratic_loss_pos" value="false"/>
            <parameter key="quadratic_loss_neg" value="false"/>
            <parameter key="estimate_performance" value="false"/>
          </operator>
          <connect from_port="training set" to_op="SVM (2)" to_port="training set"/>
          <connect from_op="SVM (2)" from_port="model" to_port="model"/>
          <portSpacing port="source_training set" spacing="0"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="sink_model" spacing="0"/>
          <portSpacing port="sink_output 1" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Generate Data" from_port="output" to_op="Set Role (3)" to_port="example set input"/>
      <connect from_op="Set Role (3)" from_port="example set output" to_op="Multi Label Modeling" to_port="training set"/>
      <connect from_op="Multi Label Modeling" 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"/>
    </process>
  </operator>
</process>



    Scott
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