[SOLVED] Access to IOObject

memvis70

Hello,

I want to access to the entries of a ConfusionMatrix, so I can visualize the Matrix in my application,
I was trying to get access through the IOObject of the resulting IOContainer, but I didn't find a method.

Can I get access through the IOObject or is there another way to do that?


Thanks a lot for any response in advance!

Regards

Marco_Boeck

Hi,

the following code snippet might help. You can have a look inside the ConfusionMatrixViewer class and check the ConfusionMatrixViewerTable.

MultiClassificationPerformance performance = (MultiClassificationPerformance) ioObject;
ConfusionMatrixViewer viewer = new ConfusionMatrixViewer(performance.getName(), performance.getTitle(), performance.getClassNames(), performance.getCounter());

Obligatory legal advice:
Please note that if you are using RapidMiner 5 in your application, it must be released under the AGPL v3. If that is not an option, please contact us for an OEM license.
The usage of RapidMiner Studio 6 inside your own application always requires an OEM license.

Regards,
Marco

memvis70

Hi,

First of all thank you for your response.

However I get the following exception:

java.lang.ClassCastException: com.rapidminer.operator.performance.PerformanceVector cannot be cast to com.rapidminer.operator.performance.MultiClassificationPerformance

How can I handle this problem?

Best Regards

memvis70

Is there a way to handle it or any other hint?

Thanks for any response in advance.

Marco_Boeck

Hi,

can you please post your process xml?

Regards,
Marco

memvis70

Hi Marco,

the process looks like this:

<?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 A" width="90" x="112" y="75">
        <parameter key="repository_entry" value="//Local Repository/A"/>
      </operator>
      <operator activated="true" class="retrieve" compatibility="5.3.015" expanded="true" height="60" name="Retrieve B" width="90" x="112" y="210">
        <parameter key="repository_entry" value="//Local Repository/B"/>
      </operator>
      <operator activated="true" class="join" compatibility="5.3.015" expanded="true" height="76" name="Join" width="90" x="313" y="120">
        <parameter key="remove_double_attributes" value="true"/>
        <parameter key="join_type" value="inner"/>
        <parameter key="use_id_attribute_as_key" value="true"/>
        <list key="key_attributes"/>
        <parameter key="keep_both_join_attributes" value="false"/>
      </operator>
      <operator activated="true" class="x_validation" compatibility="5.3.015" expanded="true" height="112" name="Validation" width="90" x="514" 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="10"/>
        <parameter key="sampling_type" value="stratified sampling"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <process expanded="true">
          <operator activated="true" class="decision_tree" compatibility="5.3.015" expanded="true" height="76" name="Decision Tree" width="90" x="179" y="165">
            <parameter key="criterion" value="accuracy"/>
            <parameter key="minimal_size_for_split" value="4"/>
            <parameter key="minimal_leaf_size" value="2"/>
            <parameter key="minimal_gain" value="0.1"/>
            <parameter key="maximal_depth" value="20"/>
            <parameter key="confidence" value="0.25"/>
            <parameter key="number_of_prepruning_alternatives" value="3"/>
            <parameter key="no_pre_pruning" value="false"/>
            <parameter key="no_pruning" value="false"/>
          </operator>
          <connect from_port="training" to_op="Decision Tree" to_port="training set"/>
          <connect from_op="Decision Tree" 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="112" y="75">
            <list key="application_parameters"/>
            <parameter key="create_view" value="false"/>
          </operator>
          <operator activated="true" class="performance" compatibility="5.3.015" expanded="true" height="76" name="Performance" width="90" x="112" y="300">
            <parameter key="use_example_weights" value="true"/>
          </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="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 A" from_port="output" to_op="Join" to_port="left"/>
      <connect from_op="Retrieve B" from_port="output" to_op="Join" to_port="right"/>
      <connect from_op="Join" from_port="join" to_op="Validation" to_port="training"/>
      <connect from_op="Validation" from_port="model" to_port="result 1"/>
      <connect from_op="Validation" from_port="training" to_port="result 2"/>
      <connect from_op="Validation" from_port="averagable 1" to_port="result 3"/>
      <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"/>
      <portSpacing port="sink_result 4" spacing="0"/>
    </process>
  </operator>
</process>

Marco_Boeck

Hi,

you can get the PerformanceVector by doing this:

PerformanceVector performance = (PerformanceVector) ioObject;

You can also have a look at the PerformanceVectorRenderer class in the createReportable() method which calls the performanceVector.getCriterion() methods.

Regards,
Marco