IOObject is not giving the same result in java as in Rapidminer

ameer2014

I am trying to use .rpm file in java to get some classification results in my application. The problem is my java program is not giving the same result as in Rapidminer GUI. Instead it turns out that it is only giving attribute names from the result. Following is my   my process:

<?xml version="1.0" encoding="UTF-8"?><process version="8.1.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="8.1.001" 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="8.1.001" expanded="true" height="68" name="Retrieve livestock data(animal att)" width="90" x="45" y="85">
        <parameter key="repository_entry" value="//Local Repository/livestock data(animal att)"/>
      </operator>
      <operator activated="true" class="concurrency:cross_validation" compatibility="8.1.001" expanded="true" height="145" name="Cross Validation" width="90" x="380" y="187">
        <parameter key="split_on_batch_attribute" value="false"/>
        <parameter key="leave_one_out" value="true"/>
        <parameter key="number_of_folds" value="2"/>
        <parameter key="sampling_type" value="shuffled sampling"/>
        <parameter key="use_local_random_seed" value="false"/>
        <parameter key="local_random_seed" value="1992"/>
        <parameter key="enable_parallel_execution" value="true"/>
        <process expanded="true">
          <operator activated="true" class="naive_bayes" compatibility="8.1.001" expanded="true" height="82" name="Naive Bayes" width="90" x="45" y="34">
            <parameter key="laplace_correction" value="true"/>
          </operator>
          <connect from_port="training set" to_op="Naive Bayes" to_port="training set"/>
          <connect from_op="Naive Bayes" from_port="model" to_port="model"/>
          <portSpacing port="source_training set" 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="8.1.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
            <list key="application_parameters"/>
            <parameter key="create_view" value="false"/>
          </operator>
          <operator activated="true" class="performance_classification" compatibility="8.1.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34">
            <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="Performance" to_port="labelled data"/>
          <connect from_op="Performance" from_port="performance" to_port="performance 1"/>
          <portSpacing port="source_model" spacing="0"/>
          <portSpacing port="source_test set" spacing="0"/>
          <portSpacing port="source_through 1" spacing="0"/>
          <portSpacing port="sink_test set results" spacing="0"/>
          <portSpacing port="sink_performance 1" spacing="0"/>
          <portSpacing port="sink_performance 2" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Retrieve livestock data(animal att)" from_port="output" to_op="Cross Validation" to_port="example set"/>
      <connect from_op="Cross Validation" from_port="performance 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"/>
      <description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="247" y="334">Type your comment</description>
    </process>
  </operator>
</process>

And this is my java code:

RapidMiner.setExecutionMode(RapidMiner.ExecutionMode.COMMAND_LINE);
RapidMiner.init();
Process pr = new Process(new File("C:\\Users\\Ameer Abdullah\\.RapidMiner\\repositories\\Local Repository\\naivebayesmodel.rmp"));
Operator op = pr.getOperator("Read CSV");
IOContainer container = pr.run();
for (int i = 0; i < container.size(); i++) {
    IOObject ioObject = container.getElementAt(i);   
    System.out.println(i + ioObject.toString());
 }

How can i get the confidence values of each possible label in java?

ameer2014

Sir @Marco_Boeck

Please respond to this.

Marco_Boeck

Hi,

 

check what type the result is. IOObject is just the generic interface, your result is likely of the type PerformanceVector.

Cast it to that, then see what methods you have access to. You can find out the results you desire that way.

 

Regards,

Marco