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[SOLVED] NPE on Apply Model

sambo1972sambo1972 Member Posts: 6 Contributor II
edited November 2018 in Help
Hi,

I keep getting an NPE when I try to apply my model to by test set. The model works fine when I run it through a validator for example to get performance info but not otherwise.

It seems to happen when using meta-learners - I've running 5.3.013 on Ubuntu 10.0.4 LTS.

I'm sure I've done something wrong - or I misunderstand, but I've lost all afternoon trying different combinations to get this to work.

Any help would be fantastic.

The log dump is:

Aug 19, 2013 8:38:41 PM INFO: Loading initial data.
Aug 19, 2013 8:38:45 PM SEVERE: Process failed: operator cannot be executed. Check the log messages...
Aug 19, 2013 8:38:45 PM SEVERE: Here:           Process[1] (Process)
          subprocess 'Main Process'
            +- Training[1] (Retrieve)
            +- Scrub Training[1] (Subprocess)
          subprocess 'Nested Chain'
            |     +- SurvivedToBinominal[1] (Numerical to Binominal)
            |     +- Select Attributes[1] (Select Attributes)
            |     +- Set Roles (2)[1] (Set Role)
            |     +- Set Missing P Class[1] (Replace Missing Values)
            |     +- Replace Missing Values (3)[1] (Replace Missing Values)
            +- Retrieve Titanic_Testing[1] (Retrieve)
            +- Scrub Test[1] (Subprocess)
          subprocess 'Nested Chain'
            |     +- Select Attributes (2)[1] (Select Attributes)
            |     +- Set Roles[1] (Set Role)
            |     +- Set Missing P Class (2)[1] (Replace Missing Values)
            |     +- Replace Missing Values (5)[1] (Replace Missing Values)
            +- Bagging[1] (Bagging)
          subprocess 'Learning Process'
            |     +- Stacking (2)[10] (Stacking)
          subprocess 'Base Learner'
            |        |  +- AdaBoost (3)[10] (AdaBoost)
          subprocess 'Learning Process'
            |        |  |     +- Random Forest (2)[32] (Random Forest)
            |        |  +- AdaBoost (4)[10] (AdaBoost)
          subprocess 'Learning Process'
            |        |        +- Naive Bayes (2)[58] (Naive Bayes (Kernel))
          subprocess 'Stacking Model Learner'
            |           +- Naive Bayes (3)[10] (Naive Bayes)
      ==>   +- Apply Model[1] (Apply Model)
Aug 19, 2013 8:38:45 PM SEVERE: java.lang.NullPointerException

And my process is:
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.013">
 <context>
   <input/>
   <output/>
   <macros/>
 </context>
 <operator activated="true" class="process" compatibility="5.3.013" expanded="true" name="Process">
   <process expanded="true">
     <operator activated="true" class="retrieve" compatibility="5.3.013" expanded="true" height="60" name="Training" width="90" x="45" y="75">
       <parameter key="repository_entry" value="//Local Repository/data/Titanic_Training"/>
     </operator>
     <operator activated="true" class="subprocess" compatibility="5.3.013" expanded="true" height="76" name="Scrub Training" width="90" x="179" y="75">
       <process expanded="true">
         <operator activated="true" class="numerical_to_binominal" compatibility="5.3.013" expanded="true" height="76" name="SurvivedToBinominal" width="90" x="45" y="30">
           <parameter key="attribute_filter_type" value="single"/>
           <parameter key="attribute" value="Survived"/>
         </operator>
         <operator activated="true" class="select_attributes" compatibility="5.3.013" expanded="true" height="76" name="Select Attributes" width="90" x="179" y="30">
           <parameter key="attribute_filter_type" value="subset"/>
           <parameter key="attributes" value="Age|PassengerId|Pclass|Sex|Survived"/>
         </operator>
         <operator activated="true" class="set_role" compatibility="5.3.013" expanded="true" height="76" name="Set Roles (2)" width="90" x="313" y="30">
           <parameter key="attribute_name" value="PassengerId"/>
           <parameter key="target_role" value="id"/>
           <list key="set_additional_roles">
             <parameter key="Survived" value="label"/>
           </list>
         </operator>
         <operator activated="true" class="replace_missing_values" compatibility="5.3.013" expanded="true" height="94" name="Set Missing P Class" width="90" x="112" y="165">
           <parameter key="attribute_filter_type" value="single"/>
           <parameter key="attribute" value="Pclass"/>
           <parameter key="default" value="minimum"/>
           <list key="columns"/>
         </operator>
         <operator activated="true" class="replace_missing_values" compatibility="5.3.013" expanded="true" height="94" name="Replace Missing Values (3)" width="90" x="313" y="165">
           <parameter key="attribute_filter_type" value="single"/>
           <parameter key="attribute" value="Age"/>
           <list key="columns"/>
           <parameter key="replenishment_value" value="S"/>
         </operator>
         <connect from_port="in 1" to_op="SurvivedToBinominal" to_port="example set input"/>
         <connect from_op="SurvivedToBinominal" from_port="example set output" to_op="Select Attributes" to_port="example set input"/>
         <connect from_op="Select Attributes" from_port="example set output" to_op="Set Roles (2)" to_port="example set input"/>
         <connect from_op="Set Roles (2)" from_port="example set output" to_op="Set Missing P Class" to_port="example set input"/>
         <connect from_op="Set Missing P Class" from_port="example set output" to_op="Replace Missing Values (3)" to_port="example set input"/>
         <connect from_op="Replace Missing Values (3)" from_port="example set output" to_port="out 1"/>
         <portSpacing port="source_in 1" spacing="0"/>
         <portSpacing port="source_in 2" spacing="0"/>
         <portSpacing port="sink_out 1" spacing="0"/>
         <portSpacing port="sink_out 2" spacing="0"/>
       </process>
     </operator>
     <operator activated="true" class="retrieve" compatibility="5.3.013" expanded="true" height="60" name="Retrieve Titanic_Testing" width="90" x="45" y="390">
       <parameter key="repository_entry" value="../data/Titanic_Testing"/>
     </operator>
     <operator activated="true" class="subprocess" compatibility="5.3.013" expanded="true" height="76" name="Scrub Test" width="90" x="179" y="390">
       <process expanded="true">
         <operator activated="true" class="select_attributes" compatibility="5.3.013" expanded="true" height="76" name="Select Attributes (2)" width="90" x="45" y="30">
           <parameter key="attribute_filter_type" value="subset"/>
           <parameter key="attributes" value="Age|PassengerId|Pclass|Sex"/>
         </operator>
         <operator activated="true" class="set_role" compatibility="5.3.013" expanded="true" height="76" name="Set Roles" width="90" x="179" y="75">
           <parameter key="attribute_name" value="PassengerId"/>
           <parameter key="target_role" value="id"/>
           <list key="set_additional_roles"/>
         </operator>
         <operator activated="true" class="replace_missing_values" compatibility="5.3.013" expanded="true" height="94" name="Set Missing P Class (2)" width="90" x="313" y="210">
           <parameter key="attribute_filter_type" value="single"/>
           <parameter key="attribute" value="Pclass"/>
           <parameter key="default" value="minimum"/>
           <list key="columns"/>
         </operator>
         <operator activated="true" class="replace_missing_values" compatibility="5.3.013" expanded="true" height="94" name="Replace Missing Values (5)" width="90" x="447" y="165">
           <parameter key="attribute_filter_type" value="single"/>
           <parameter key="attribute" value="Age"/>
           <list key="columns"/>
           <parameter key="replenishment_value" value="S"/>
         </operator>
         <connect from_port="in 1" to_op="Select Attributes (2)" to_port="example set input"/>
         <connect from_op="Select Attributes (2)" from_port="example set output" to_op="Set Roles" to_port="example set input"/>
         <connect from_op="Set Roles" from_port="example set output" to_op="Set Missing P Class (2)" to_port="example set input"/>
         <connect from_op="Set Missing P Class (2)" from_port="example set output" to_op="Replace Missing Values (5)" to_port="example set input"/>
         <connect from_op="Replace Missing Values (5)" from_port="example set output" to_port="out 1"/>
         <portSpacing port="source_in 1" spacing="0"/>
         <portSpacing port="source_in 2" spacing="0"/>
         <portSpacing port="sink_out 1" spacing="0"/>
         <portSpacing port="sink_out 2" spacing="0"/>
       </process>
     </operator>
     <operator activated="true" class="bagging" compatibility="5.3.013" expanded="true" height="76" name="Bagging" width="90" x="112" y="255">
       <process expanded="true">
         <operator activated="true" class="stacking" compatibility="5.3.013" expanded="true" height="60" name="Stacking (2)" width="90" x="246" y="30">
           <process expanded="true">
             <operator activated="true" class="adaboost" compatibility="5.3.013" expanded="true" name="AdaBoost (3)">
               <process expanded="true">
                 <operator activated="true" class="random_forest" compatibility="5.3.013" expanded="true" name="Random Forest (2)"/>
                 <connect from_port="training set" to_op="Random Forest (2)" to_port="training set"/>
                 <connect from_op="Random Forest (2)" from_port="model" to_port="model"/>
                 <portSpacing port="source_training set" spacing="0"/>
                 <portSpacing port="sink_model" spacing="0"/>
               </process>
             </operator>
             <operator activated="true" class="adaboost" compatibility="5.3.013" expanded="true" name="AdaBoost (4)">
               <process expanded="true">
                 <operator activated="true" class="naive_bayes_kernel" compatibility="5.3.013" expanded="true" name="Naive Bayes (2)"/>
                 <connect from_port="training set" to_op="Naive Bayes (2)" to_port="training set"/>
                 <connect from_op="Naive Bayes (2)" from_port="model" to_port="model"/>
                 <portSpacing port="source_training set" spacing="0"/>
                 <portSpacing port="sink_model" spacing="0"/>
               </process>
             </operator>
             <connect from_port="training set 1" to_op="AdaBoost (3)" to_port="training set"/>
             <connect from_port="training set 2" to_op="AdaBoost (4)" to_port="training set"/>
             <connect from_op="AdaBoost (3)" from_port="model" to_port="base model 1"/>
             <connect from_op="AdaBoost (4)" from_port="model" to_port="base model 2"/>
             <portSpacing port="source_training set 1" spacing="0"/>
             <portSpacing port="source_training set 2" spacing="0"/>
             <portSpacing port="source_training set 3" spacing="0"/>
             <portSpacing port="sink_base model 1" spacing="0"/>
             <portSpacing port="sink_base model 2" spacing="0"/>
             <portSpacing port="sink_base model 3" spacing="0"/>
           </process>
           <process expanded="true">
             <operator activated="true" class="naive_bayes" compatibility="5.3.013" expanded="true" name="Naive Bayes (3)"/>
             <connect from_port="stacking examples" to_op="Naive Bayes (3)" to_port="training set"/>
             <connect from_op="Naive Bayes (3)" from_port="model" to_port="stacking model"/>
             <portSpacing port="source_stacking examples" spacing="0"/>
             <portSpacing port="sink_stacking model" spacing="0"/>
           </process>
         </operator>
         <connect from_port="training set" to_op="Stacking (2)" to_port="training set"/>
         <connect from_op="Stacking (2)" from_port="model" to_port="model"/>
         <portSpacing port="source_training set" spacing="0"/>
         <portSpacing port="sink_model" spacing="0"/>
       </process>
     </operator>
     <operator activated="true" class="apply_model" compatibility="5.3.013" expanded="true" height="76" name="Apply Model" width="90" x="380" y="300">
       <list key="application_parameters"/>
     </operator>
     <connect from_op="Training" from_port="output" to_op="Scrub Training" to_port="in 1"/>
     <connect from_op="Scrub Training" from_port="out 1" to_op="Bagging" to_port="training set"/>
     <connect from_op="Retrieve Titanic_Testing" from_port="output" to_op="Scrub Test" to_port="in 1"/>
     <connect from_op="Scrub Test" from_port="out 1" to_op="Apply Model" to_port="unlabelled data"/>
     <connect from_op="Bagging" from_port="model" to_op="Apply Model" to_port="model"/>
     <connect from_op="Apply Model" from_port="labelled data" 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>
Thanks

Sam

PS - the data sets are from Kaggle's competition - if that helps. I can put links to these if necessary.

Answers

  • Options
    sambo1972sambo1972 Member Posts: 6 Contributor II
    More info to hand, I simplified my process drastically. The learner is now an AdaBoosted Decision tree and the error still occurs.

    If I just use a decision tree it works fine.

    Log Output:

    Aug 20, 2013 1:09:01 PM SEVERE: Process failed: operator cannot be executed. Check the log messages...
    Aug 20, 2013 1:09:01 PM SEVERE: Here:          Process[1] (Process)
              subprocess 'Main Process'
                +- Training[1] (Retrieve)
                +- Scrub Training[1] (Subprocess)
              subprocess 'Nested Chain'
                |    +- SurvivedToBinominal[1] (Numerical to Binominal)
                |    +- Select Attributes[1] (Select Attributes)
                |    +- Set Roles (2)[1] (Set Role)
                |    +- Set Missing P Class[1] (Replace Missing Values)
                |    +- Replace Missing Values (3)[1] (Replace Missing Values)
                +- AdaBoost[1] (AdaBoost)
              subprocess 'Learning Process'
                |    +- Decision Tree[5] (Decision Tree)
                +- Retrieve Titanic_Testing[1] (Retrieve)
                +- Scrub Test[1] (Subprocess)
              subprocess 'Nested Chain'
                |    +- Select Attributes (2)[1] (Select Attributes)
                |    +- Set Roles[1] (Set Role)
                |    +- Set Missing P Class (2)[1] (Replace Missing Values)
                |    +- Replace Missing Values (5)[1] (Replace Missing Values)
          ==>  +- Apply Model[1] (Apply Model)
    Aug 20, 2013 1:09:01 PM SEVERE: java.lang.NullPointerException


    Simplified process:
    <?xml version="1.0" encoding="UTF-8" standalone="no"?>
    <process version="5.3.013">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="5.3.013" expanded="true" name="Process">
        <process expanded="true">
          <operator activated="true" class="retrieve" compatibility="5.3.013" expanded="true" height="60" name="Training" width="90" x="45" y="75">
            <parameter key="repository_entry" value="//Local Repository/data/Titanic_Training"/>
          </operator>
          <operator activated="true" class="subprocess" compatibility="5.3.013" expanded="true" height="94" name="Scrub Training" width="90" x="179" y="75">
            <process expanded="true">
              <operator activated="true" class="numerical_to_binominal" compatibility="5.3.013" expanded="true" height="76" name="SurvivedToBinominal" width="90" x="45" y="30">
                <parameter key="attribute_filter_type" value="single"/>
                <parameter key="attribute" value="Survived"/>
              </operator>
              <operator activated="true" class="select_attributes" compatibility="5.3.013" expanded="true" height="76" name="Select Attributes" width="90" x="179" y="30">
                <parameter key="attribute_filter_type" value="subset"/>
                <parameter key="attributes" value="Age|PassengerId|Pclass|Sex|Survived"/>
              </operator>
              <operator activated="true" class="set_role" compatibility="5.3.013" expanded="true" height="76" name="Set Roles (2)" width="90" x="313" y="30">
                <parameter key="attribute_name" value="PassengerId"/>
                <parameter key="target_role" value="id"/>
                <list key="set_additional_roles">
                  <parameter key="Survived" value="label"/>
                </list>
              </operator>
              <operator activated="true" class="replace_missing_values" compatibility="5.3.013" expanded="true" height="94" name="Set Missing P Class" width="90" x="112" y="165">
                <parameter key="attribute_filter_type" value="single"/>
                <parameter key="attribute" value="Pclass"/>
                <parameter key="default" value="minimum"/>
                <list key="columns"/>
              </operator>
              <operator activated="true" class="replace_missing_values" compatibility="5.3.013" expanded="true" height="94" name="Replace Missing Values (3)" width="90" x="313" y="165">
                <parameter key="attribute_filter_type" value="single"/>
                <parameter key="attribute" value="Age"/>
                <list key="columns"/>
                <parameter key="replenishment_value" value="S"/>
              </operator>
              <connect from_port="in 1" to_op="SurvivedToBinominal" to_port="example set input"/>
              <connect from_op="SurvivedToBinominal" from_port="example set output" to_op="Select Attributes" to_port="example set input"/>
              <connect from_op="Select Attributes" from_port="example set output" to_op="Set Roles (2)" to_port="example set input"/>
              <connect from_op="Set Roles (2)" from_port="example set output" to_op="Set Missing P Class" to_port="example set input"/>
              <connect from_op="Set Missing P Class" from_port="example set output" to_op="Replace Missing Values (3)" to_port="example set input"/>
              <connect from_op="Replace Missing Values (3)" from_port="example set output" to_port="out 1"/>
              <portSpacing port="source_in 1" spacing="0"/>
              <portSpacing port="source_in 2" spacing="0"/>
              <portSpacing port="sink_out 1" spacing="0"/>
              <portSpacing port="sink_out 2" spacing="0"/>
              <portSpacing port="sink_out 3" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="adaboost" compatibility="5.3.013" expanded="true" height="76" name="AdaBoost" width="90" x="246" y="255">
            <process expanded="true">
              <operator activated="true" class="decision_tree" compatibility="5.3.013" expanded="true" height="76" name="Decision Tree" width="90" x="313" y="75"/>
              <connect from_port="training set" to_op="Decision Tree" to_port="training set"/>
              <connect from_op="Decision Tree" from_port="model" to_port="model"/>
              <portSpacing port="source_training set" spacing="0"/>
              <portSpacing port="sink_model" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="retrieve" compatibility="5.3.013" expanded="true" height="60" name="Retrieve Titanic_Testing" width="90" x="45" y="480">
            <parameter key="repository_entry" value="../data/Titanic_Testing"/>
          </operator>
          <operator activated="true" class="subprocess" compatibility="5.3.013" expanded="true" height="76" name="Scrub Test" width="90" x="246" y="435">
            <process expanded="true">
              <operator activated="true" class="select_attributes" compatibility="5.3.013" expanded="true" height="76" name="Select Attributes (2)" width="90" x="45" y="30">
                <parameter key="attribute_filter_type" value="subset"/>
                <parameter key="attributes" value="Age|PassengerId|Pclass|Sex"/>
              </operator>
              <operator activated="true" class="set_role" compatibility="5.3.013" expanded="true" height="76" name="Set Roles" width="90" x="179" y="75">
                <parameter key="attribute_name" value="PassengerId"/>
                <parameter key="target_role" value="id"/>
                <list key="set_additional_roles"/>
              </operator>
              <operator activated="true" class="replace_missing_values" compatibility="5.3.013" expanded="true" height="94" name="Set Missing P Class (2)" width="90" x="313" y="210">
                <parameter key="attribute_filter_type" value="single"/>
                <parameter key="attribute" value="Pclass"/>
                <parameter key="default" value="minimum"/>
                <list key="columns"/>
              </operator>
              <operator activated="true" class="replace_missing_values" compatibility="5.3.013" expanded="true" height="94" name="Replace Missing Values (5)" width="90" x="447" y="165">
                <parameter key="attribute_filter_type" value="single"/>
                <parameter key="attribute" value="Age"/>
                <list key="columns"/>
                <parameter key="replenishment_value" value="S"/>
              </operator>
              <connect from_port="in 1" to_op="Select Attributes (2)" to_port="example set input"/>
              <connect from_op="Select Attributes (2)" from_port="example set output" to_op="Set Roles" to_port="example set input"/>
              <connect from_op="Set Roles" from_port="example set output" to_op="Set Missing P Class (2)" to_port="example set input"/>
              <connect from_op="Set Missing P Class (2)" from_port="example set output" to_op="Replace Missing Values (5)" to_port="example set input"/>
              <connect from_op="Replace Missing Values (5)" from_port="example set output" to_port="out 1"/>
              <portSpacing port="source_in 1" spacing="0"/>
              <portSpacing port="source_in 2" spacing="0"/>
              <portSpacing port="sink_out 1" spacing="0"/>
              <portSpacing port="sink_out 2" spacing="0"/>
            </process>
          </operator>
          <operator activated="true" class="apply_model" compatibility="5.3.013" expanded="true" height="76" name="Apply Model" width="90" x="447" y="300">
            <list key="application_parameters"/>
          </operator>
          <connect from_op="Training" from_port="output" to_op="Scrub Training" to_port="in 1"/>
          <connect from_op="Scrub Training" from_port="out 1" to_op="AdaBoost" to_port="training set"/>
          <connect from_op="Scrub Training" from_port="out 2" to_port="result 2"/>
          <connect from_op="AdaBoost" from_port="model" to_op="Apply Model" to_port="model"/>
          <connect from_op="Retrieve Titanic_Testing" from_port="output" to_op="Scrub Test" to_port="in 1"/>
          <connect from_op="Scrub Test" from_port="out 1" to_op="Apply Model" to_port="unlabelled data"/>
          <connect from_op="Apply Model" from_port="labelled data" 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"/>
          <portSpacing port="sink_result 3" spacing="0"/>
        </process>
      </operator>
    </process>
  • Options
    Nils_WoehlerNils_Woehler Member Posts: 463 Maven
    Hi,

    thank you for the bug report. Could you provide us with the data somehow, so we can reproduce the error?
    If this is not possible, would you be so kind to reproduce the error with data from the samples repository or with data generated by 'Generate Data' operators?

    Best,
    Nils
  • Options
    sambo1972sambo1972 Member Posts: 6 Contributor II
    Hi Nils,

    Wow - I don't want to be the guy who found a bug! I'm hoping it's just me - or something I've done with the data  ;)

    Anyway, I've put the training data here:

    http://jav0.binu.net/BiNuGeneratorWeb/train.csv

    and the test data here:

    http://jav0.binu.net/BiNuGeneratorWeb/test.csv

    Thanks for your reply,

    Regards

    Sam
  • Options
    Nils_WoehlerNils_Woehler Member Posts: 463 Maven
    Thanks for the report! Unfortunately this seems to be a bug. I've filed a ticket for it.

    Best,
    Nils
  • Options
    sambo1972sambo1972 Member Posts: 6 Contributor II
    Hey Nils,

    That's bad news : I can't believe no one's tried to boost a d-tree before. It's gotta be a recent issue then.

    I've had this with both boosting and stacking learners - although bagging seems to be ok (unless I try bagging a boosted learner).

    Thanks for your reply - I'd be keen on any other info you can tell me too.

    Cheers

    Sam
  • Options
    sambo1972sambo1972 Member Posts: 6 Contributor II
    Hi Nils,

    OK - now I understand from stepping though the source - it's looking for a label attribute in the test set rather than a predicted label when handling special attributes. Of course there isn't one in a test set, hence the NPE.

    It also explains why the error didn't happen in cross validation training and only when applying the model.

    I fixed this in my local copy and now it works fine.

    Thanks again

    Sam

  • Options
    Nils_WoehlerNils_Woehler Member Posts: 463 Maven
    Hi Sam,

    cool you could fix the error yourself. We didn't have the time to fix the issue yet.
    Could you provide us with a patch so we can add the fix with the next release? We would really appreciate it :-)

    Best,
    Nils

  • Options
    sambo1972sambo1972 Member Posts: 6 Contributor II
    Hi Nils,

    No problem - the patch file is simple so I've just copied it in-line below.

    Cheers

    Sam
    ### Eclipse Workspace Patch 1.0
    #P RapidMiner_Unuk
    Index: src/com/rapidminer/operator/learner/meta/AdaBoostModel.java
    ===================================================================
    --- src/com/rapidminer/operator/learner/meta/AdaBoostModel.java (revision 867)
    +++ src/com/rapidminer/operator/learner/meta/AdaBoostModel.java (working copy)

    }

    private void evaluateSpecialAttributes(ExampleSet exampleSet, Attribute[] specialAttributes) {
    - Attribute label = exampleSet.getAttributes().getLabel();
    Attribute predictedLabel = exampleSet.getAttributes().getPredictedLabel();
    Iterator<Example> reader = exampleSet.iterator();
    while (reader.hasNext()) {

    bestLabel = n;
    }
    }
    -
    - example.setValue(predictedLabel, label.getMapping().mapString(this.getLabel().getMapping().mapIndex(bestLabel)));
    -
    +
    + example.setValue(predictedLabel, predictedLabel.getMapping().mapString(this.getLabel().getMapping().mapIndex(bestLabel)));
    +
    for (int n = 0; n < confidences.length; n++) {
    confidences = Math.exp(confidences - bestConf);
    // remember for normalization:
  • Options
    Marco_BoeckMarco_Boeck Administrator, Moderator, Employee, Member, University Professor Posts: 1,996 RM Engineering
    Hi,

    thank you for pointing out the location! We have fixed the issue in the latest development version.

    Regards,
    Marco
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