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Loop Subsets of attributes and log result of each loop

JonesFoxJonesFox Member Posts: 5 Newbie
in Help
Hi,
I have a bunch of attributes / features in my dataset and want to find out which combination of features will train the best model.

Therefore I am using the LoopSubsets Operator. I specified it to "min number of attributes" = 2, so I will get at least 1 feature and the label in each loop. I use the branch operator to check, if the combination of features contains my label and then proceed in the "Then" Branch to train a model and log the performance using the "Log" operator. 

The results I get don't match my training results that I get when training the model separatly on a subset of the features, by factor 10 in Absolute Error. I have the assumption that  either the "Subset" Operator does something I don't expect or the log operator inside the loop doesn't work like it would outside of a loop.

How would you log the results (performance of the model or entire prediction example set) inside a loop?


This my log. I would expect absolute errors <0.1:


This is my process:

<?xml version="1.0" encoding="UTF-8"?><process version="9.10.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.10.001" 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="9.10.001" expanded="true" height="68" name="Retrieve df_90_Test_PVC" width="90" x="45" y="340">
        <parameter key="repository_entry" value="df_90_Test_PVC"/>
      </operator>
      <operator activated="true" class="retrieve" compatibility="9.10.001" expanded="true" height="68" name="Retrieve df_90_Training_PVC" width="90" x="45" y="136">
        <parameter key="repository_entry" value="df_90_Training_PVC"/>
      </operator>
      <operator activated="true" class="normalize" compatibility="9.10.001" expanded="true" height="103" name="Normalize" width="90" x="246" y="136">
        <parameter key="return_preprocessing_model" value="false"/>
        <parameter key="create_view" value="false"/>
        <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="numeric"/>
        <parameter key="use_value_type_exception" value="false"/>
        <parameter key="except_value_type" value="real"/>
        <parameter key="block_type" value="value_series"/>
        <parameter key="use_block_type_exception" value="false"/>
        <parameter key="except_block_type" value="value_series_end"/>
        <parameter key="invert_selection" value="false"/>
        <parameter key="include_special_attributes" value="false"/>
        <parameter key="method" value="Z-transformation"/>
        <parameter key="min" value="0.0"/>
        <parameter key="max" value="1.0"/>
        <parameter key="allow_negative_values" value="false"/>
      </operator>
      <operator activated="true" class="apply_model" compatibility="9.10.001" expanded="true" height="82" name="Apply Model (5)" width="90" x="246" y="340">
        <list key="application_parameters"/>
        <parameter key="create_view" value="false"/>
      </operator>
      <operator activated="true" class="append" compatibility="9.10.001" expanded="true" height="103" name="Append" width="90" x="447" y="136">
        <parameter key="datamanagement" value="double_array"/>
        <parameter key="data_management" value="auto"/>
        <parameter key="merge_type" value="all"/>
      </operator>
      <operator activated="true" class="select_attributes" compatibility="9.10.001" expanded="true" height="82" name="Select Attributes (2)" width="90" x="581" y="136">
        <parameter key="attribute_filter_type" value="subset"/>
        <parameter key="attribute" value="att1"/>
        <parameter key="attributes" value="F_angl[N]|F_anw[N]|T_Heiz[°C]|weld_factor|WS_Int[µVs]"/>
        <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="false"/>
      </operator>
      <operator activated="true" class="loop_attribute_subsets" compatibility="9.10.001" expanded="true" height="68" name="Loop Subsets" width="90" x="715" y="136">
        <parameter key="use_exact_number" value="false"/>
        <parameter key="exact_number_of_attributes" value="-1"/>
        <parameter key="min_number_of_attributes" value="2"/>
        <parameter key="limit_max_number" value="false"/>
        <parameter key="max_number_of_attributes" value="-1"/>
        <process expanded="true">
          <operator activated="true" class="multiply" compatibility="9.10.001" expanded="true" height="103" name="Multiply" width="90" x="45" y="34"/>
          <operator activated="true" class="branch" compatibility="9.10.001" expanded="true" height="103" name="Branch" width="90" x="313" y="34">
            <parameter key="condition_type" value="attribute_available"/>
            <parameter key="condition_value" value="weld_factor"/>
            <parameter key="expression" value=""/>
            <parameter key="io_object" value="ANOVAMatrix"/>
            <parameter key="return_inner_output" value="true"/>
            <process expanded="true">
              <operator activated="true" class="set_role" compatibility="9.10.001" expanded="true" height="82" name="Set Role" width="90" x="112" y="85">
                <parameter key="attribute_name" value="weld_factor"/>
                <parameter key="target_role" value="label"/>
                <list key="set_additional_roles"/>
              </operator>
              <operator activated="true" class="split_data" compatibility="9.10.001" expanded="true" height="103" name="Split Data" origin="GENERATED_TUTORIAL" width="90" x="246" y="85">
                <enumeration key="partitions">
                  <parameter key="ratio" value="0.8"/>
                  <parameter key="ratio" value="0.2"/>
                </enumeration>
                <parameter key="sampling_type" value="linear sampling"/>
                <parameter key="use_local_random_seed" value="false"/>
                <parameter key="local_random_seed" value="1992"/>
              </operator>
              <operator activated="true" class="linear_regression" compatibility="9.10.001" expanded="true" height="103" name="Linear Regression" width="90" x="447" y="85">
                <parameter key="feature_selection" value="M5 prime"/>
                <parameter key="alpha" value="0.05"/>
                <parameter key="max_iterations" value="10"/>
                <parameter key="forward_alpha" value="0.05"/>
                <parameter key="backward_alpha" value="0.05"/>
                <parameter key="eliminate_colinear_features" value="true"/>
                <parameter key="min_tolerance" value="0.05"/>
                <parameter key="use_bias" value="true"/>
                <parameter key="ridge" value="1.0E-8"/>
              </operator>
              <operator activated="true" class="apply_model" compatibility="9.10.001" expanded="true" height="82" name="Apply Model" origin="GENERATED_TUTORIAL" width="90" x="514" y="340">
                <list key="application_parameters"/>
                <parameter key="create_view" value="false"/>
              </operator>
              <operator activated="true" class="performance_regression" compatibility="9.10.001" expanded="true" height="82" name="Performance" width="90" x="648" y="340">
                <parameter key="main_criterion" value="root_mean_squared_error"/>
                <parameter key="root_mean_squared_error" value="true"/>
                <parameter key="absolute_error" value="true"/>
                <parameter key="relative_error" value="false"/>
                <parameter key="relative_error_lenient" value="false"/>
                <parameter key="relative_error_strict" value="false"/>
                <parameter key="normalized_absolute_error" value="false"/>
                <parameter key="root_relative_squared_error" value="false"/>
                <parameter key="squared_error" value="false"/>
                <parameter key="correlation" value="false"/>
                <parameter key="squared_correlation" value="false"/>
                <parameter key="prediction_average" value="false"/>
                <parameter key="spearman_rho" value="false"/>
                <parameter key="kendall_tau" value="false"/>
                <parameter key="skip_undefined_labels" value="true"/>
                <parameter key="use_example_weights" value="true"/>
              </operator>
              <operator activated="true" class="log" compatibility="9.10.001" expanded="true" height="82" name="Log" origin="GENERATED_TUTORIAL" width="90" x="648" y="187">
                <list key="log">
                  <parameter key="Features" value="operator.Loop Subsets.value.feature_names"/>
                  <parameter key="Testing MSE" value="operator.Performance.value.root_mean_squared_error"/>
                  <parameter key="Testing absolute Error" value="operator.Performance.value.absolute_error"/>
                </list>
                <parameter key="sorting_type" value="none"/>
                <parameter key="sorting_k" value="100"/>
                <parameter key="persistent" value="false"/>
              </operator>
              <connect from_port="input 1" to_op="Set Role" to_port="example set input"/>
              <connect from_op="Set Role" from_port="example set output" to_op="Split Data" to_port="example set"/>
              <connect from_op="Split Data" from_port="partition 1" to_op="Linear Regression" to_port="training set"/>
              <connect from_op="Split Data" from_port="partition 2" to_op="Apply Model" to_port="unlabelled data"/>
              <connect from_op="Linear Regression" from_port="model" to_op="Apply Model" to_port="model"/>
              <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
              <connect from_op="Performance" from_port="performance" to_op="Log" to_port="through 1"/>
              <connect from_op="Performance" from_port="example set" to_port="input 1"/>
              <portSpacing port="source_condition" spacing="0"/>
              <portSpacing port="source_input 1" spacing="0"/>
              <portSpacing port="source_input 2" spacing="0"/>
              <portSpacing port="sink_input 1" spacing="0"/>
              <portSpacing port="sink_input 2" spacing="0"/>
            </process>
            <process expanded="true">
              <connect from_port="condition" to_port="input 1"/>
              <portSpacing port="source_condition" spacing="0"/>
              <portSpacing port="source_input 1" spacing="0"/>
              <portSpacing port="source_input 2" spacing="0"/>
              <portSpacing port="sink_input 1" spacing="0"/>
              <portSpacing port="sink_input 2" spacing="0"/>
            </process>
          </operator>
          <connect from_port="example set" to_op="Multiply" to_port="input"/>
          <connect from_op="Multiply" from_port="output 1" to_op="Branch" to_port="condition"/>
          <connect from_op="Multiply" from_port="output 2" to_op="Branch" to_port="input 1"/>
          <portSpacing port="source_example set" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve df_90_Test_PVC" from_port="output" to_op="Apply Model (5)" to_port="unlabelled data"/>
      <connect from_op="Retrieve df_90_Training_PVC" from_port="output" to_op="Normalize" to_port="example set input"/>
      <connect from_op="Normalize" from_port="example set output" to_op="Append" to_port="example set 1"/>
      <connect from_op="Normalize" from_port="preprocessing model" to_op="Apply Model (5)" to_port="model"/>
      <connect from_op="Apply Model (5)" from_port="labelled data" to_op="Append" to_port="example set 2"/>
      <connect from_op="Append" from_port="merged set" to_op="Select Attributes (2)" to_port="example set input"/>
      <connect from_op="Select Attributes (2)" from_port="example set output" to_op="Loop Subsets" to_port="example set"/>
      <connect from_op="Loop Subsets" 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>

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Best Answer

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    MartinLiebigMartinLiebig Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,507 RM Data Scientist
    Solution Accepted
    Hi,
    did you check the Automatic Feature Engineering operator? That should be very close of what you want to do.

    BR,
    Martin
    - Sr. Director Data Solutions, Altair RapidMiner -
    Dortmund, Germany

Answers

  • Options
    JonesFoxJonesFox Member Posts: 5 Newbie
    The Automatic Feature Engineering Operator seems good for my purpose, thanks! However, it's not available in the free version...
  • Options
    MartinLiebigMartinLiebig Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,507 RM Data Scientist
    Did you check for our educational offers? Feels like you are eligible for it.

    - Sr. Director Data Solutions, Altair RapidMiner -
    Dortmund, Germany
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