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"One class Svm Error"

udaykumarudaykumar Member Posts: 6 Contributor II
edited June 2019 in Help
Lib svm caused error : one-class SVM probability output not supported yet.


The process is as follows


<?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">
    <process expanded="true">
      <operator activated="true" class="read_csv" compatibility="5.3.015" expanded="true" height="60" name="Read CSV" width="90" x="45" y="30">
        <parameter key="csv_file" value="/home/user/Desktop/sys_logSlot 3_new_oneclass.csv"/>
        <parameter key="first_row_as_names" value="false"/>
        <list key="annotations">
          <parameter key="0" value="Name"/>
        </list>
        <parameter key="encoding" value="UTF-8"/>
        <list key="data_set_meta_data_information">
          <parameter key="0" value="date.false.polynominal.attribute"/>
          <parameter key="1" value="os.false.binominal.attribute"/>
          <parameter key="2" value="type.false.binominal.attribute"/>
          <parameter key="3" value="msg.true.integer.attribute"/>
          <parameter key="4" value="Slot.false.integer.attribute"/>
          <parameter key="5" value="rea.true.polynominal.attribute"/>
          <parameter key="6" value="date1.false.polynominal.attribute"/>
          <parameter key="7" value="os1.false.binominal.attribute"/>
          <parameter key="8" value="type1.false.binominal.attribute"/>
          <parameter key="9" value="msg1.true.integer.attribute"/>
          <parameter key="10" value="Slot1.false.integer.attribute"/>
          <parameter key="11" value="rea1.true.polynominal.attribute"/>
          <parameter key="12" value="date2.false.polynominal.attribute"/>
          <parameter key="13" value="os2.false.binominal.attribute"/>
          <parameter key="14" value="type2.false.binominal.attribute"/>
          <parameter key="15" value="msg2.true.integer.attribute"/>
          <parameter key="16" value="Slot2.false.integer.attribute"/>
          <parameter key="17" value="rea2.true.polynominal.attribute"/>
          <parameter key="18" value="date3.false.polynominal.attribute"/>
          <parameter key="19" value="os3.false.binominal.attribute"/>
          <parameter key="20" value="type3.false.binominal.attribute"/>
          <parameter key="21" value="msg3.true.integer.attribute"/>
          <parameter key="22" value="Slot3.false.integer.attribute"/>
          <parameter key="23" value="rea3.true.polynominal.attribute"/>
          <parameter key="24" value="date4.false.polynominal.attribute"/>
          <parameter key="25" value="os4.false.binominal.attribute"/>
          <parameter key="26" value="type4.false.binominal.attribute"/>
          <parameter key="27" value="msg4.true.integer.attribute"/>
          <parameter key="28" value="Slot4.false.integer.attribute"/>
          <parameter key="29" value="rea4.true.polynominal.attribute"/>
          <parameter key="30" value="Error.true.binominal.label"/>
          <parameter key="31" value="P(Error).true.attribute_value.prediction"/>
        </list>
      </operator>
      <operator activated="true" class="nominal_to_numerical" compatibility="5.3.015" expanded="true" height="94" name="Nominal to Numerical" width="90" x="45" y="120">
        <list key="comparison_groups"/>
      </operator>
      <operator activated="true" class="set_role" compatibility="5.3.015" expanded="true" height="76" name="Set Role" width="90" x="179" y="210">
        <parameter key="attribute_name" value="Error"/>
        <parameter key="target_role" value="label"/>
        <list key="set_additional_roles"/>
      </operator>
      <operator activated="true" class="x_validation" compatibility="5.1.002" expanded="true" height="112" name="Validation (2)" width="90" x="246" y="30">
        <description>A cross-validation evaluating a linear regression model.</description>
        <process expanded="true">
          <operator activated="true" class="support_vector_machine_libsvm" compatibility="5.3.015" expanded="true" height="76" name="SVM" width="90" x="45" y="30">
            <parameter key="svm_type" value="one-class"/>
            <parameter key="kernel_type" value="poly"/>
            <parameter key="degree" value="5"/>
            <list key="class_weights"/>
            <parameter key="calculate_confidences" value="true"/>
          </operator>
          <connect from_port="training" to_op="SVM" to_port="training set"/>
          <connect from_op="SVM" 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 (2)" width="90" x="45" y="30">
            <list key="application_parameters"/>
          </operator>
          <operator activated="true" class="write_csv" compatibility="5.3.015" expanded="true" height="76" name="Write CSV" width="90" x="45" y="165">
            <parameter key="csv_file" value="/home/user/Desktop/feb_18/output_model_csv.csv"/>
          </operator>
          <operator activated="true" class="performance_classification" compatibility="5.3.015" expanded="true" height="76" name="Performance" width="90" x="112" y="255">
            <list key="class_weights"/>
          </operator>
          <connect from_port="model" to_op="Apply Model (2)" to_port="model"/>
          <connect from_port="test set" to_op="Apply Model (2)" to_port="unlabelled data"/>
          <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Write CSV" to_port="input"/>
          <connect from_op="Write CSV" from_port="through" 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="Read CSV" from_port="output" to_op="Nominal to Numerical" to_port="example set input"/>
      <connect from_op="Nominal to Numerical" 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="Validation (2)" to_port="training"/>
      <connect from_op="Validation (2)" from_port="model" to_port="result 1"/>
      <connect from_op="Validation (2)" from_port="averagable 1" to_port="result 2"/>
      <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>


Please help to solve this issue

1) tell me how to use one class svm for poynomial and binomial attributes


Thank you
Tagged:

Answers

  • Options
    MartinLiebigMartinLiebig Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,507 RM Data Scientist
    Hi,

    a SVM is simply not supporting nominal values by design. You can use Nominal to Numiercial to change the type. Then you would most likely create a dummy coding.

    cheers,

    martin
    - Sr. Director Data Solutions, Altair RapidMiner -
    Dortmund, Germany
  • Options
    udaykumarudaykumar Member Posts: 6 Contributor II
    Hello,

    I have used nominal to numerical operator but after that also it showing same errror.

    My data looks like this

    a1      a2              a3            a4                                        a5
    date  polynomial  binomial  error(one class label)        prediction

    i am predicting error from this but it is showing

    one class svm not supported yet
    so
    please help me regarding this.

    I have already sent process xml file
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