Bayes Learner and Text Classification - Index Out of Bounds

B_
B_ New Altair Community Member
edited November 5 in Community Q&A
I am trying to classify text with W-Naive Bayes Multinomial, but keep getting an error 263 Array Index Out of Bounds error.

I use a sample of 200 text records from a database as a training set, save the model, then read 2000 text records to classify.  I know there are additional terms in the full set that are not in the test set.  Is this causing the index out of bounds error?

What will correct this?

Thanks

B.

training code 


<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.0.0" expanded="true" name="Root">
    <description>Using a simple Naive Bayes classifier.</description>
    <process expanded="true" height="584" width="962">
      <operator activated="true" class="retrieve" compatibility="5.0.10" expanded="true" height="60" name="Retrieve" width="90" x="45" y="75">
        <parameter key="repository_entry" value="//RMRepository/train_source"/>
      </operator>
      <operator activated="true" class="select_attributes" compatibility="5.0.10" expanded="true" height="76" name="Select Attributes" width="90" x="45" y="165">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="trainentry"/>
        <parameter key="invert_selection" value="true"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="5.0.10" expanded="true" height="76" name="Set Role" width="90" x="45" y="300">
        <parameter key="name" value="trainlabel"/>
        <parameter key="target_role" value="label"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="5.0.10" expanded="true" height="76" name="Set Role (2)" width="90" x="45" y="390">
        <parameter key="name" value="id"/>
        <parameter key="target_role" value="id"/>
      </operator>
      <operator activated="true" class="nominal_to_text" compatibility="5.0.10" expanded="true" height="76" name="Nominal to Text" width="90" x="246" y="345">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="posttitle"/>
      </operator>
      <operator activated="true" class="text:process_document_from_data" compatibility="5.0.6" expanded="true" height="76" name="Process Documents from Data" width="90" x="380" y="255">
        <list key="specify_weights"/>
        <process expanded="true" height="565" width="827">
          <operator activated="true" class="text:transform_cases" compatibility="5.0.6" expanded="true" height="60" name="Transform Cases" width="90" x="179" y="165"/>
          <operator activated="true" class="text:tokenize" compatibility="5.0.6" expanded="true" height="60" name="Tokenize" width="90" x="313" y="120"/>
          <connect from_port="document" to_op="Transform Cases" to_port="document"/>
          <connect from_op="Transform Cases" from_port="document" to_op="Tokenize" to_port="document"/>
          <connect from_op="Tokenize" from_port="document" to_port="document 1"/>
          <portSpacing port="source_document" spacing="0"/>
          <portSpacing port="sink_document 1" spacing="0"/>
          <portSpacing port="sink_document 2" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="weka:W-NaiveBayesMultinomialUpdateable" compatibility="5.0.1" expanded="true" height="76" name="W-NaiveBayesMultinomialUpdateable" width="90" x="447" y="120">
        <parameter key="D" value="true"/>
      </operator>
      <operator activated="true" class="write_model" compatibility="5.0.10" expanded="true" height="60" name="Write Model" width="90" x="455" y="30">
        <parameter key="model_file" value="M:\RM\mdl_test_01.mod"/>
        <parameter key="output_type" value="XML"/>
      </operator>
      <connect from_op="Retrieve" from_port="output" to_op="Select Attributes" to_port="example set input"/>
      <connect from_op="Select Attributes" from_port="example set output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Set Role (2)" to_port="example set input"/>
      <connect from_op="Set Role (2)" from_port="example set output" to_op="Nominal to Text" to_port="example set input"/>
      <connect from_op="Nominal to Text" from_port="example set output" to_op="Process Documents from Data" to_port="example set"/>
      <connect from_op="Process Documents from Data" from_port="example set" to_op="W-NaiveBayesMultinomialUpdateable" to_port="training set"/>
      <connect from_op="W-NaiveBayesMultinomialUpdateable" from_port="model" to_op="Write Model" to_port="input"/>
      <connect from_op="W-NaiveBayesMultinomialUpdateable" from_port="exampleSet" to_port="result 2"/>
      <connect from_op="Write Model" from_port="through" 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>
model applier 




<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="5.0.10" expanded="true" name="Process">
    <process expanded="true" height="341" width="681">
      <operator activated="true" class="read_model" compatibility="5.0.10" expanded="true" height="60" name="Read Model" width="90" x="376" y="34">
        <parameter key="model_file" value="M:\RM\mdl_test_01.mod"/>
      </operator>
      <operator activated="true" class="retrieve" compatibility="5.0.10" expanded="true" height="60" name="Retrieve" width="90" x="44" y="22">
        <parameter key="repository_entry" value="assignlabel_01"/>
      </operator>
      <operator activated="true" class="select_attributes" compatibility="5.0.10" expanded="true" height="76" name="Select Attributes" width="90" x="45" y="120">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="entrylabel"/>
        <parameter key="invert_selection" value="true"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="5.0.10" expanded="true" height="76" name="Set Role" width="90" x="45" y="255">
        <parameter key="name" value="id"/>
        <parameter key="target_role" value="id"/>
      </operator>
      <operator activated="true" class="nominal_to_text" compatibility="5.0.10" expanded="true" height="76" name="Nominal to Text" width="90" x="179" y="75">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="posttitle"/>
      </operator>
      <operator activated="true" class="text:process_document_from_data" compatibility="5.0.6" expanded="true" height="76" name="Process Documents from Data" width="90" x="313" y="165">
        <list key="specify_weights"/>
        <process expanded="true" height="583" width="845">
          <operator activated="true" class="text:transform_cases" compatibility="5.0.6" expanded="true" height="60" name="Transform Cases" width="90" x="112" y="51"/>
          <operator activated="true" class="text:tokenize" compatibility="5.0.6" expanded="true" height="60" name="Tokenize" width="90" x="313" y="75"/>
          <connect from_port="document" to_op="Transform Cases" to_port="document"/>
          <connect from_op="Transform Cases" from_port="document" to_op="Tokenize" to_port="document"/>
          <connect from_op="Tokenize" from_port="document" to_port="document 1"/>
          <portSpacing port="source_document" spacing="0"/>
          <portSpacing port="sink_document 1" spacing="0"/>
          <portSpacing port="sink_document 2" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="apply_model" compatibility="5.0.10" expanded="true" height="76" name="Apply Model" width="90" x="581" y="75">
        <list key="application_parameters"/>
      </operator>
      <connect from_op="Read Model" from_port="output" to_op="Apply Model" to_port="model"/>
      <connect from_op="Retrieve" from_port="output" to_op="Select Attributes" to_port="example set input"/>
      <connect from_op="Select Attributes" from_port="example set output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Nominal to Text" to_port="example set input"/>
      <connect from_op="Nominal to Text" from_port="example set output" to_op="Process Documents from Data" to_port="example set"/>
      <connect from_op="Process Documents from Data" from_port="example set" 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"/>
    </process>
  </operator>
</process>

Tagged:

Answers

  • fischer
    fischer New Altair Community Member
    Hi,

    could you paste the stack trace from the log?

    Best,
    Simon
  • B_
    B_ New Altair Community Member
    Simon

    the W-Naive Bayes did not show a stack trace, only the error messages in the log below.
    W-NaiveBayesMultinomialUpdateable: Exception occured while classifying example:263 [class java.lang.ArrayIndexOutOfBoundsException]



    This is from using the Naive Bayes process to create a model.

    Exception: java.lang.ArrayIndexOutOfBoundsException
    Message: 262
    Stack trace:

      com.rapidminer.operator.learner.bayes.SimpleDistributionModel.performPrediction(SimpleDistributionModel.java:384)
      com.rapidminer.operator.learner.PredictionModel.apply(PredictionModel.java:76)
      com.rapidminer.operator.ModelApplier.doWork(ModelApplier.java:100)
      com.rapidminer.operator.Operator.execute(Operator.java:771)
      com.rapidminer.operator.execution.SimpleUnitExecutor.execute(SimpleUnitExecutor.java:51)
      com.rapidminer.operator.ExecutionUnit.execute(ExecutionUnit.java:709)
      com.rapidminer.operator.OperatorChain.doWork(OperatorChain.java:368)
      com.rapidminer.operator.Operator.execute(Operator.java:771)
      com.rapidminer.Process.run(Process.java:899)
      com.rapidminer.Process.run(Process.java:795)
      com.rapidminer.Process.run(Process.java:790)
      com.rapidminer.Process.run(Process.java:780)
      com.rapidminer.gui.ProcessThread.run(ProcessThread.java:62)
  • fischer
    fischer New Altair Community Member
    Hi,

    I opened a bug report. You can help by attaching a process and data to it.

    http://bugs.rapid-i.com/show_bug.cgi?id=403

    Best,
    Simon
  • B_
    B_ New Altair Community Member
    Is there a known good Bayes in an earlier version of RM to use while the bug is fixed?
  • fischer
    fischer New Altair Community Member
    Hi,

    Problem is closed due to invalid classification process. The word list was missing and so the example set was incompatible with the model.

    Best,
    Simon
  • B_
    B_ New Altair Community Member
    What do you mean by the word list was missing?

    Can you provide a simple example of how to set up Bayes text classification?  there isn't one in the samples.

    Thanks

    B.
  • B_
    B_ New Altair Community Member
    Add:  searched the forum but didn't find any examples of how to correctly set up Bayes
  • fischer
    fischer New Altair Community Member
    Hi,

    the Naive Bayes operator can be used like any other learning algorithm. Try with an example without text first.

    The problem is not the Naive Bayes operator. The problem is that the classification process does not have the same word list as the training process. For that reason, the example sets have different attributes during training and application. The whole process does not make sense, it won't give correct results for any learner. In order to fix the setup, you must store the word list generated during training and feed it back into the classification step. The document processing operator has an input for that. Consider the word list as a part of the model.

    Best,
    Simon
  • B_
    B_ New Altair Community Member
    "the classification process does not have the same word list as the training process. For that reason, the example sets have different attributes during training and application"

    How to use word input wasn't clear - I assumed the word attributes were stored in the model.  Now it's working.

    thanks Simon

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