Ada boost issue

Hi Guys-

Looks like the ada boost operator has an error. I can run the process without issue by using the Weka Ada boost and Weka decision stump.

Below is the code and then the error message.

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
  <context>
    <input>
      <location/>
    </input>
    <output>
      <location/>
      <location/>
      <location/>
    </output>
    <macros/>
  </context>
  <operator activated="true" class="process" expanded="true" name="Process">
    <process expanded="true" height="546" width="386">
      <operator activated="true" class="read_csv" expanded="true" height="60" name="Read CSV" width="90" x="45" y="30">
        <parameter key="file_name" value="C:\Documents and Settings\aiufh35\Desktop\misc\PLAT\PLAT.csv"/>
      </operator>
      <operator activated="true" class="set_role" expanded="true" height="76" name="Set Role" width="90" x="112" y="30">
        <parameter key="name" value="PLAT"/>
        <parameter key="target_role" value="label"/>
      </operator>
      <operator activated="true" class="numerical_to_binominal" expanded="true" height="76" name="Numerical to Binominal" width="90" x="271" y="-12">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="PLAT"/>
        <parameter key="include_special_attributes" value="true"/>
      </operator>
      <operator activated="true" class="read_csv" expanded="true" height="60" name="Read CSV (2)" width="90" x="45" y="210">
        <parameter key="file_name" value="C:\Documents and Settings\aiufh35\Desktop\misc\PLAT\PLAT_VAL.csv"/>
      </operator>
      <operator activated="true" class="set_role" expanded="true" height="76" name="Set Role (2)" width="90" x="45" y="300">
        <parameter key="name" value="PLAT"/>
        <parameter key="target_role" value="label"/>
      </operator>
      <operator activated="true" class="numerical_to_binominal" expanded="true" height="76" name="Numerical to Binominal (2)" width="90" x="45" y="390">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="PLAT"/>
        <parameter key="include_special_attributes" value="true"/>
      </operator>
      <operator activated="true" class="work_on_subset" expanded="true" height="76" name="Work on Subset" width="90" x="45" y="120">
        <parameter key="attribute_filter_type" value="single"/>
        <parameter key="attribute" value="weight"/>
        <parameter key="attributes" value="torder_dum|sq_DAYSFROMAPPTOORD|SQ_SKUSPURCHASED|SQ_RCSPONORED|SQ_FULLCADENCE|RCSPONORED2_1|PRNT_PERSONALCAREREV|PRNT_NUTRITIONREV|PRNT_HOMECAREREV|PRNT_DURABLESREV|PRNT_BEAUTYREV|PLAT|PERSONALCAREREV|Ord2_1|NUTRITIONREV|LN_TOTALORDERS|LN_GROSSIBOPRICE|IBOSPONORED2_1|IBOSPONORED|HOMECAREREV|DaysIBO90|DURABLESREV|Cadence2_1|BEAUTYREV"/>
        <parameter key="invert_selection" value="true"/>
        <parameter key="include_special_attributes" value="true"/>
        <process expanded="true">
          <connect from_port="exampleSet" to_port="example set"/>
          <portSpacing port="source_exampleSet" spacing="0"/>
          <portSpacing port="sink_example set" spacing="0"/>
          <portSpacing port="sink_through 1" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="adaboost" expanded="true" height="76" name="AdaBoost" width="90" x="179" y="120">
        <process expanded="true" height="368" width="368">
          <operator activated="true" class="decision_stump" expanded="true" height="76" name="Decision Stump" width="90" x="65" y="42"/>
          <connect from_port="training set" to_op="Decision Stump" to_port="training set"/>
          <connect from_op="Decision Stump" 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" expanded="true" height="76" name="Apply Model" width="90" x="196" y="243">
        <list key="application_parameters"/>
      </operator>
      <operator activated="true" class="performance" expanded="true" height="76" name="Performance" width="90" x="313" y="120"/>
      <connect from_op="Read CSV" from_port="output" to_op="Set Role" to_port="example set input"/>
      <connect from_op="Set Role" from_port="example set output" to_op="Numerical to Binominal" to_port="example set input"/>
      <connect from_op="Numerical to Binominal" from_port="example set output" to_op="Work on Subset" to_port="example set"/>
      <connect from_op="Read CSV (2)" from_port="output" to_op="Set Role (2)" to_port="example set input"/>
      <connect from_op="Set Role (2)" from_port="example set output" to_op="Numerical to Binominal (2)" to_port="example set input"/>
      <connect from_op="Numerical to Binominal (2)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="Work on Subset" from_port="example set" to_op="AdaBoost" to_port="training set"/>
      <connect from_op="AdaBoost" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="AdaBoost" from_port="example set" to_port="result 2"/>
      <connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
      <connect from_op="Performance" from_port="performance" 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>

error:

Message: Cannot clone com.rapidminer.example.set.RemappedExampleSet: java.lang.reflect.InvocationTargetException. Target: java.lang.UnsupportedOperationException: The method getNominalMapping() is not supported by numerical attributes! You probably tried to execute an operator on a numerical data which is only able to handle nominal values. You could use one of the discretization operators before this application.. Cause: java.lang.UnsupportedOperationException: The method getNominalMapping() is not supported by numerical attributes! You probably tried to execute an operator on a numerical data which is only able to handle nominal values. You could use one of the discretization operators before this application..