Append-Operator in Testing Phase of X-Validation changes confusion mattrix

MuhammadMuhammad Member Posts: 2 Contributor I
edited November 2018 in Help
Hi,

I am working on a classification problem where I have 3 classes [good (180), mediocre (4535), bad (183)]. (#number of examples in that class)

In my rapidminer process I only learn a model for "good" and "bad" and in the testing phase I want to modify the prediction depending on the confidence of my classifier. So I am filtering out all examples with low confidence and assign them to the "default class" "mediocre".
In order to do this reassignment I use a "filter example" operator together with a "replace" operator.

My problem is:
If I run my process without my reassignment step (i.e. filtering and replacing) I get the expected values for true good (180), true mediocre(4535) and true bad (183) in my confusion matrix. However, if I do the reassignment my confusion matrix yields unexpected values for true good, mediocre and bad.
Why is that happening?
My process as follows:

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.015">
 <context>
   <input/>
   <output/>
   <macros/>
 </context>
 <operator activated="true" class="process" compatibility="5.3.015" 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="5.3.015" expanded="true" height="60" name="Retrieve DataSet-WhiteWine" width="90" x="45" y="30">
       <parameter key="repository_entry" value="//Local Repository/data/GroupProject_WineQuality_White"/>
     </operator>
     <operator activated="true" class="set_role" compatibility="5.3.015" expanded="true" height="76" name="Set Role" width="90" x="179" y="30">
       <parameter key="attribute_name" value="quality"/>
       <parameter key="target_role" value="label"/>
       <list key="set_additional_roles"/>
     </operator>
     <operator activated="true" class="generate_id" compatibility="5.3.015" expanded="true" height="76" name="Generate ID" width="90" x="313" y="30">
       <parameter key="create_nominal_ids" value="false"/>
       <parameter key="offset" value="0"/>
     </operator>
     <operator activated="true" class="normalize" compatibility="5.3.015" expanded="true" height="94" name="Normalize" width="90" x="447" y="30">
       <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="range transformation"/>
       <parameter key="min" value="0.0"/>
       <parameter key="max" value="1.0"/>
     </operator>
     <operator activated="true" class="discretize_by_user_specification" compatibility="5.3.015" expanded="true" height="94" name="Discretize" width="90" x="581" y="30">
       <parameter key="return_preprocessing_model" value="false"/>
       <parameter key="create_view" value="false"/>
       <parameter key="attribute_filter_type" value="subset"/>
       <parameter key="attribute" value="quality"/>
       <parameter key="attributes" value="quality"/>
       <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="true"/>
       <parameter key="attribute_type" value="nominal"/>
       <list key="classes">
         <parameter key="bad" value="4.0"/>
         <parameter key="mediocre" value="7.0"/>
         <parameter key="good" value="10.0"/>
       </list>
     </operator>
     <operator activated="true" class="x_validation" compatibility="5.3.015" expanded="true" height="112" name="Validation" width="90" x="715" y="30">
       <parameter key="create_complete_model" value="false"/>
       <parameter key="average_performances_only" value="true"/>
       <parameter key="leave_one_out" value="false"/>
       <parameter key="number_of_validations" value="30"/>
       <parameter key="sampling_type" value="shuffled sampling"/>
       <parameter key="use_local_random_seed" value="false"/>
       <parameter key="local_random_seed" value="1985"/>
       <process expanded="true">
         <operator activated="true" class="filter_examples" compatibility="5.3.015" expanded="true" height="76" name="Filter Examples" width="90" x="45" y="30">
           <parameter key="condition_class" value="attribute_value_filter"/>
           <parameter key="parameter_string" value="quality != mediocre"/>
           <parameter key="invert_filter" value="false"/>
         </operator>
         <operator activated="true" class="naive_bayes" compatibility="5.3.015" expanded="true" height="76" name="Naive Bayes" width="90" x="179" y="30">
           <parameter key="laplace_correction" value="true"/>
         </operator>
         <connect from_port="training" to_op="Filter Examples" to_port="example set input"/>
         <connect from_op="Filter Examples" from_port="example set output" to_op="Naive Bayes" to_port="training set"/>
         <connect from_op="Naive Bayes" 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">
         <operator activated="true" class="apply_model" compatibility="5.3.015" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
           <list key="application_parameters"/>
           <parameter key="create_view" value="false"/>
         </operator>
         <operator activated="true" class="multiply" compatibility="5.3.015" expanded="true" height="76" name="Multiply" width="90" x="179" y="30"/>
         <operator activated="true" class="filter_examples" compatibility="5.3.015" expanded="true" height="76" name="Filter Examples (3)" width="90" x="112" y="210">
           <parameter key="condition_class" value="attribute_value_filter"/>
           <parameter key="parameter_string" value="confidence(bad)&lt;0.999 &amp;&amp; confidence(good)&lt;0.99"/>
           <parameter key="invert_filter" value="false"/>
         </operator>
         <operator activated="true" breakpoints="after" class="filter_examples" compatibility="5.3.015" expanded="true" height="76" name="Filter Examples (2)" width="90" x="246" y="255">
           <parameter key="condition_class" value="attribute_value_filter"/>
           <parameter key="parameter_string" value="confidence(bad)&lt;0.999 &amp;&amp; confidence(good)&lt;0.99"/>
           <parameter key="invert_filter" value="true"/>
         </operator>
         <operator activated="true" breakpoints="after" class="replace" compatibility="5.3.015" expanded="true" height="76" name="Replace (3)" width="90" x="246" y="165">
           <parameter key="attribute_filter_type" value="single"/>
           <parameter key="attribute" value="prediction(quality)"/>
           <parameter key="attributes" value=""/>
           <parameter key="use_except_expression" value="false"/>
           <parameter key="value_type" value="nominal"/>
           <parameter key="use_value_type_exception" value="false"/>
           <parameter key="except_value_type" value="file_path"/>
           <parameter key="block_type" value="single_value"/>
           <parameter key="use_block_type_exception" value="false"/>
           <parameter key="except_block_type" value="single_value"/>
           <parameter key="invert_selection" value="false"/>
           <parameter key="include_special_attributes" value="true"/>
           <parameter key="replace_what" value="bad|good"/>
           <parameter key="replace_by" value="mediocre"/>
         </operator>
         <operator activated="true" class="union" compatibility="5.3.015" expanded="true" height="76" name="Union" width="90" x="447" y="210"/>
         <operator activated="true" class="select_attributes" compatibility="5.3.015" expanded="true" height="76" name="Select Attributes" width="90" x="380" y="30">
           <parameter key="attribute_filter_type" value="subset"/>
           <parameter key="attribute" value=""/>
           <parameter key="attributes" value="|quality|prediction(quality)"/>
           <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="performance_classification" compatibility="5.3.015" expanded="true" height="76" name="Performance" width="90" x="514" y="30">
           <parameter key="main_criterion" value="first"/>
           <parameter key="accuracy" value="true"/>
           <parameter key="classification_error" value="false"/>
           <parameter key="kappa" value="false"/>
           <parameter key="weighted_mean_recall" value="false"/>
           <parameter key="weighted_mean_precision" value="false"/>
           <parameter key="spearman_rho" value="false"/>
           <parameter key="kendall_tau" value="false"/>
           <parameter key="absolute_error" value="false"/>
           <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_mean_squared_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="cross-entropy" value="false"/>
           <parameter key="margin" value="false"/>
           <parameter key="soft_margin_loss" value="false"/>
           <parameter key="logistic_loss" value="false"/>
           <parameter key="skip_undefined_labels" value="true"/>
           <parameter key="use_example_weights" value="true"/>
           <list key="class_weights"/>
         </operator>
         <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="Multiply" to_port="input"/>
         <connect from_op="Multiply" from_port="output 1" to_op="Filter Examples (3)" to_port="example set input"/>
         <connect from_op="Filter Examples (3)" from_port="example set output" to_op="Replace (3)" to_port="example set input"/>
         <connect from_op="Filter Examples (3)" from_port="original" to_op="Filter Examples (2)" to_port="example set input"/>
         <connect from_op="Filter Examples (2)" from_port="example set output" to_op="Union" to_port="example set 2"/>
         <connect from_op="Replace (3)" from_port="example set output" to_op="Union" to_port="example set 1"/>
         <connect from_op="Union" from_port="union" to_op="Select Attributes" to_port="example set input"/>
         <connect from_op="Select Attributes" from_port="example set output" 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>
     <connect from_op="Retrieve DataSet-WhiteWine" from_port="output" to_op="Set Role" to_port="example set input"/>
     <connect from_op="Set Role" from_port="example set output" to_op="Generate ID" to_port="example set input"/>
     <connect from_op="Generate ID" from_port="example set output" to_op="Normalize" to_port="example set input"/>
     <connect from_op="Normalize" from_port="example set output" to_op="Discretize" to_port="example set input"/>
     <connect from_op="Discretize" from_port="example set output" to_op="Validation" to_port="training"/>
     <connect from_op="Validation" from_port="averagable 1" 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>
Through a bit of debugging the operators I found out that if you just add an "Append" operator with only one input (the actual output of "apply model" nothing else) in the testing phase of X-Validation the confusion matrix yields wrong values for true <classname>.
In the above process I first used "Append" and then changed it to the "Union" operator, however I am still having the same problem.

Am I doing anything wrong?

Thanks in advance for your help!!!
Tagged:

Answers

  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 2,022  RM Data Scientist
    Hello Muhammad,

    I've created an example process with the iris data set where i learn on two classes and assign the "unsure" predictions (between 0.3 and 0.7) to the third

    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="6.1.000">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="6.1.000" expanded="true" name="Process">
        <process expanded="true">
          <operator activated="true" class="retrieve" compatibility="6.1.000" 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="x_validation" compatibility="5.0.000" expanded="true" height="112" name="Validation" width="90" x="246" y="30">
            <description>A cross-validation evaluating a decision tree model.</description>
            <process expanded="true">
              <operator activated="true" class="filter_examples" compatibility="6.1.000" expanded="true" height="94" name="Filter Examples" width="90" x="45" y="30">
                <list key="filters_list">
                  <parameter key="filters_entry_key" value="label.does_not_equal.Iris-versicolor"/>
                </list>
              </operator>
              <operator activated="true" class="random_forest" compatibility="6.1.000" expanded="true" height="76" name="Random Forest" width="90" x="179" y="30">
                <parameter key="number_of_trees" value="25"/>
              </operator>
              <connect from_port="training" to_op="Filter Examples" to_port="example set input"/>
              <connect from_op="Filter Examples" from_port="example set output" to_op="Random Forest" to_port="training set"/>
              <connect from_op="Random Forest" 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">
              <operator activated="true" class="apply_model" compatibility="5.0.000" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="rename_by_replacing" compatibility="6.1.000" expanded="true" height="76" name="Rename by Replacing" width="90" x="179" y="165">
                <parameter key="include_special_attributes" value="true"/>
                <parameter key="replace_what" value="\(|\)|-"/>
              </operator>
              <operator activated="true" class="generate_attributes" compatibility="6.1.000" expanded="true" height="76" name="Generate Attributes" width="90" x="313" y="165">
                <list key="function_descriptions">
                  <parameter key="predictionlabel" value="if((confidenceIrissetosa &gt; 0.2 &amp;&amp; confidenceIrissetosa &lt;0.8),&quot;Iris-versicolor&quot;,predictionlabel)"/>
                </list>
              </operator>
              <operator activated="true" class="rename_by_replacing" compatibility="6.1.000" expanded="true" height="76" name="Rename by Replacing (2)" width="90" x="447" y="165">
                <parameter key="include_special_attributes" value="true"/>
                <parameter key="replace_what" value="predictionlabel"/>
                <parameter key="replace_by" value="prediction(label)"/>
              </operator>
              <operator activated="true" class="performance" compatibility="5.0.000" expanded="true" height="76" name="Performance" width="90" x="581" 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="Rename by Replacing" to_port="example set input"/>
              <connect from_op="Rename by Replacing" from_port="example set output" to_op="Generate Attributes" to_port="example set input"/>
              <connect from_op="Generate Attributes" from_port="example set output" to_op="Rename by Replacing (2)" to_port="example set input"/>
              <connect from_op="Rename by Replacing (2)" from_port="example set output" 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>
          <connect from_op="Retrieve Iris" from_port="output" to_op="Validation" to_port="training"/>
          <connect from_op="Validation" from_port="averagable 1" 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>

    This works for me quite well. I hope you can use this as a template


    Best,

    Martin
    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
  • MuhammadMuhammad Member Posts: 2 Contributor I
    Hi Martin,

    thanks for your reply. Could you please elaborate on your process, i.e. why is at necessary to rename the attributes which where generated by RapidMiner itself?

    Also, I tried to adopt your approach to my problem. However, I get same issue.

    I found out, that it somehow is related to the "Append" operator.

    I created an example using the Weighting data., If you look at this process, please:
    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="5.3.015">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="5.3.015" expanded="true" name="Process">
        <process expanded="true">
          <operator activated="true" class="retrieve" compatibility="5.3.015" expanded="true" height="60" name="Retrieve Weighting" width="90" x="45" y="120">
            <parameter key="repository_entry" value="//Samples/data/Weighting"/>
          </operator>
          <operator activated="true" class="x_validation" compatibility="5.3.015" expanded="true" height="112" name="Validation" width="90" x="179" y="120">
            <process expanded="true">
              <operator activated="true" class="naive_bayes" compatibility="5.3.015" expanded="true" height="76" name="Naive Bayes" width="90" x="45" y="30"/>
              <connect from_port="training" to_op="Naive Bayes" to_port="training set"/>
              <connect from_op="Naive Bayes" 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">
              <operator activated="true" class="apply_model" compatibility="5.3.015" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="append" compatibility="5.3.015" expanded="true" height="76" name="Append" width="90" x="179" y="75"/>
              <operator activated="true" class="performance_classification" compatibility="5.3.015" expanded="true" height="76" name="Performance" width="90" x="313" y="120">
                <list key="class_weights"/>
              </operator>
              <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="Append" to_port="example set 1"/>
              <connect from_op="Append" from_port="merged set" 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>
          <connect from_op="Retrieve Weighting" from_port="output" to_op="Validation" to_port="training"/>
          <connect from_op="Validation" from_port="averagable 1" 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>
    You will see an "Append"-Operator in the Training-Phase which only has one input - hence it shouldn't do anything. However, if you compare the confusion matrix of the process with and without the "Append"-Operator you will notice a difference.
    The correct confusion matrix (in terms of the amount of true positives and true negatives ) is the one of the process without the "Append"-Operator. The other one yields a wrong number of total true positives and true negatives.

    Any idea why? Also, what do I need to do to use the Append-Operator on a data set with in total about 5000 data points?

    Thanks,
    Muhammad
  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 2,022  RM Data Scientist
    Hi,

    the Append operator is modifing the meta data.. Thus there are some changes - but i am currently not sure how it effects the performance operator

    Regarding my process:
    Generate attributes can not handle attributes with brackets, minus,plus or whitespaces, because they are interpreted as part of the formula, thus i needed to replace them.
    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
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