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svm

[Deleted User][Deleted User] Posts: 0 Learner III
Hi
how can we use svm in polynominal label ?

Best Answers

  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,077  RM Data Scientist
    Solution Accepted
    Hi,
    i would recommend to use the operator Polynominal by Binominal Classification for this.

    Best,
    Martin
    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
    yyhuang[Deleted User]
  • varunm1varunm1 Moderator, Member Posts: 1,207   Unicorn
    edited April 2019 Solution Accepted
    There are two options, you can either classify with a variant of SVM operator called LibSVM (you can find in operators) that can handle multiple classes or use a polynomial to the binomial operator that divides classes and do one on all classification. 

    You can take a look at the operators and in help, you can find a tutorial which shows how you can apply these operators.

    Thanks
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

  • varunm1varunm1 Moderator, Member Posts: 1,207   Unicorn
    Solution Accepted
    @mbs

    Provide @Noel with your XML code if possible. You can copy and paste that from View --> Show Panel --> XML or export your process and attach it here so that he can import. This would help more compared to screenshots :smile:
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

    Noel
  • varunm1varunm1 Moderator, Member Posts: 1,207   Unicorn
    Solution Accepted
    Don't go crazy seeing bad results. First, we should understand how machine learning works. You don't get good results with every model or algorithm. A model is as good as its data. SVM is doing bad because this data set might not be the type SVM can deal with, or the features might not be suitable for SVM. GBT does better because it is a sophisticated sequential learning algorithm. This means that it builds trees one after the other and learns from the mistakes in a prior tree and improves itself. You try playing with SVM parameters in case they might change your performance, but you should have a good idea on how the parameters in SVM work. Your dataset is highly imbalanced which makes it difficult for many models to predict, also for this kind of imbalanced dataset accuracy is a bad parameter to consider. My suggestion, use kappa as your performance metric rather than accuracy. To understand more about kappa try searching google or see my explanation in some other thread related to @mbs . Finally, don't expect all the models to do well on your data. I made some minor changes in your model.
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

    sgenzerNoel

Answers

  • varunm1varunm1 Moderator, Member Posts: 1,207   Unicorn
    SVM(libSVM) does work on polynomial labels
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

  • [Deleted User][Deleted User] Posts: 0 Learner III
    plz explain it more  :)
    Thank you
  • [Deleted User][Deleted User] Posts: 0 Learner III
    edited April 2019
    Thank you every body.
    I will try all of your suggestions. :)
  • NoelNoel Member Posts: 82  Maven
    Hi Martin-

    I found myself in a similar situation and, as you suggested, I used the Nominal to Binominal operator. I still get an error, though, "Insufficient capability... SVM does not have sufficient capabilities for the given data set: binominal attributes not supported".

    Any thots (process and data attached, if helpful)?

    Thanks,
    Noel
  • [Deleted User][Deleted User] Posts: 0 Learner III
    edited April 2019
    Hi @ Noel 
    If you click on the yellow triangle it can help you 
    Noel
  • [Deleted User][Deleted User] Posts: 0 Learner III
    edited April 2019
    this is the solution with your excel

    sgenzerNoel
  • [Deleted User][Deleted User] Posts: 0 Learner III
    And this is one example with your data

    sgenzerNoel
  • NoelNoel Member Posts: 82  Maven
    @mbs- Thanks for the replies. Much appreciated! Hopefully I’ll be able to replicate your solution later this afternoon.
  • NoelNoel Member Posts: 82  Maven
    @mbs- Thanks again. Unfortunately, I'm a newbie and am still having some trouble. Which flavors of Discretize and Performance did you use? What parameters did you use for the Nominal to Numerical and Discretize? (Would you be opposed to posting the process?)
  • [Deleted User][Deleted User] Posts: 0 Learner III
    @varunm1
    sure I will do it  :)B)
    Noel
  • [Deleted User][Deleted User] Posts: 0 Learner III
    edited April 2019
    @Noel
    sorry because of delay. please look at the XML :)
    <?xml version="1.0" encoding="UTF-8"?><process version="9.2.001">
      <context>
        <input/>
        <output/>
        <macros/>
      </context>
      <operator activated="true" class="process" compatibility="9.2.001" 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="read_excel" compatibility="9.2.001" expanded="true" height="68" name="Read Excel" width="90" x="45" y="34">
            <parameter key="excel_file" value="C:\Users\asus\Desktop\123\source_data_TFEM_cdx_gbt_base_moves.xlsx"/>
            <parameter key="sheet_selection" value="sheet number"/>
            <parameter key="sheet_number" value="1"/>
            <parameter key="imported_cell_range" value="A1"/>
            <parameter key="encoding" value="SYSTEM"/>
            <parameter key="first_row_as_names" value="true"/>
            <list key="annotations"/>
            <parameter key="date_format" value=""/>
            <parameter key="time_zone" value="SYSTEM"/>
            <parameter key="locale" value="English (United States)"/>
            <parameter key="read_all_values_as_polynominal" value="false"/>
            <list key="data_set_meta_data_information">
              <parameter key="0" value="anch_dt.true.date_time.attribute"/>
              <parameter key="1" value="cmbx6_bb_sprd.true.real.attribute"/>
              <parameter key="2" value="cmbx_bbb_sprd.true.real.attribute"/>
              <parameter key="3" value="all_loans_stm.true.real.attribute"/>
              <parameter key="4" value="bb_loans_stm.true.real.attribute"/>
              <parameter key="5" value="b_loans_stm.true.real.attribute"/>
              <parameter key="6" value="ccc_loans_stm.true.real.attribute"/>
              <parameter key="7" value="ccc_bonds_stw.true.real.attribute"/>
              <parameter key="8" value="b_bonds_stw.true.real.attribute"/>
              <parameter key="9" value="bb_bonds_stw.true.real.attribute"/>
              <parameter key="10" value="hy_bonds_stw.true.real.attribute"/>
              <parameter key="11" value="bbb_bonds_stw.true.real.attribute"/>
              <parameter key="12" value="clo_bbb_dm.true.real.attribute"/>
              <parameter key="13" value="clo_bb_dm.true.real.attribute"/>
              <parameter key="14" value="clo_b_dm.true.real.attribute"/>
              <parameter key="15" value="cdx_hy_sprd.true.real.attribute"/>
              <parameter key="16" value="dod_chg.true.real.attribute"/>
              <parameter key="17" value="five_day_chg.true.real.attribute"/>
              <parameter key="18" value="wider_ten_over5.true.polynominal.attribute"/>
              <parameter key="19" value="wider_twenty_over5.true.polynominal.attribute"/>
              <parameter key="20" value="wider_thirty_over5.true.polynominal.attribute"/>
              <parameter key="21" value="tighter_ten_over5.true.polynominal.attribute"/>
              <parameter key="22" value="tighter_twenty_over5.true.polynominal.attribute"/>
              <parameter key="23" value="tighter_thirty_over5.true.polynominal.attribute"/>
              <parameter key="24" value="move_ten_over5.true.polynominal.attribute"/>
              <parameter key="25" value="move_twenty_over5.true.polynominal.attribute"/>
              <parameter key="26" value="move_thirty_over5.true.polynominal.attribute"/>
              <parameter key="27" value="spy_px.true.real.attribute"/>
              <parameter key="28" value="jnk_px.true.real.attribute"/>
              <parameter key="29" value="ndq_px.true.real.attribute"/>
              <parameter key="30" value="hyg_px.true.real.attribute"/>
              <parameter key="31" value="bkln_px.true.real.attribute"/>
              <parameter key="32" value="spx_px.true.real.attribute"/>
              <parameter key="33" value="oneq_px.true.real.attribute"/>
            </list>
            <parameter key="read_not_matching_values_as_missings" value="false"/>
            <parameter key="datamanagement" value="double_array"/>
            <parameter key="data_management" value="auto"/>
          </operator>
          <operator activated="true" class="set_role" compatibility="9.2.001" expanded="true" height="82" name="Set Role" width="90" x="179" y="34">
            <parameter key="attribute_name" value="oneq_px"/>
            <parameter key="target_role" value="label"/>
            <list key="set_additional_roles"/>
          </operator>
          <operator activated="true" class="split_data" compatibility="9.2.001" expanded="true" height="82" name="Split Data" width="90" x="313" y="34">
            <enumeration key="partitions">
              <parameter key="ratio" value="0.7"/>
              <parameter key="ratio" value="0.3"/>
            </enumeration>
            <parameter key="sampling_type" value="automatic"/>
            <parameter key="use_local_random_seed" value="false"/>
            <parameter key="local_random_seed" value="1992"/>
          </operator>
          <operator activated="true" class="nominal_to_numerical" compatibility="9.2.001" expanded="true" height="103" name="Nominal to Numerical" width="90" x="447" y="34">
            <parameter key="return_preprocessing_model" value="false"/>
            <parameter key="create_view" value="false"/>
            <parameter key="attribute_filter_type" value="all"/>
            <parameter key="attribute" value=""/>
            <parameter key="attributes" value=""/>
            <parameter key="use_except_expression" value="false"/>
            <parameter key="value_type" value="nominal"/>
            <parameter key="use_value_type_exception" value="false"/>
            <parameter key="except_value_type" value="file_path"/>
            <parameter key="block_type" value="single_value"/>
            <parameter key="use_block_type_exception" value="false"/>
            <parameter key="except_block_type" value="single_value"/>
            <parameter key="invert_selection" value="false"/>
            <parameter key="include_special_attributes" value="false"/>
            <parameter key="coding_type" value="dummy coding"/>
            <parameter key="use_comparison_groups" value="false"/>
            <list key="comparison_groups"/>
            <parameter key="unexpected_value_handling" value="all 0 and warning"/>
            <parameter key="use_underscore_in_name" value="false"/>
          </operator>
          <operator activated="true" class="set_role" compatibility="9.2.001" expanded="true" height="82" name="Set Role (2)" width="90" x="581" y="34">
            <parameter key="attribute_name" value="bbb_bonds_stw"/>
            <parameter key="target_role" value="label"/>
            <list key="set_additional_roles"/>
          </operator>
          <operator activated="true" class="discretize_by_size" compatibility="9.2.001" expanded="true" height="103" name="Discretize" width="90" x="715" y="34">
            <parameter key="return_preprocessing_model" value="false"/>
            <parameter key="create_view" value="false"/>
            <parameter key="attribute_filter_type" value="regular_expression"/>
            <parameter key="attribute" value=""/>
            <parameter key="attributes" value=""/>
            <parameter key="regular_expression" value="bbb_bonds_stw"/>
            <parameter key="use_except_expression" value="false"/>
            <parameter key="value_type" value="numeric"/>
            <parameter key="use_value_type_exception" value="false"/>
            <parameter key="except_value_type" value="real"/>
            <parameter key="block_type" value="value_series"/>
            <parameter key="use_block_type_exception" value="false"/>
            <parameter key="except_block_type" value="value_series_end"/>
            <parameter key="invert_selection" value="false"/>
            <parameter key="include_special_attributes" value="true"/>
            <parameter key="size_of_bins" value="10"/>
            <parameter key="sorting_direction" value="decreasing"/>
            <parameter key="range_name_type" value="long"/>
            <parameter key="automatic_number_of_digits" value="true"/>
            <parameter key="number_of_digits" value="-1"/>
          </operator>
          <operator activated="true" class="support_vector_machine_libsvm" compatibility="9.2.001" expanded="true" height="82" name="SVM" width="90" x="849" y="34">
            <parameter key="svm_type" value="C-SVC"/>
            <parameter key="kernel_type" value="rbf"/>
            <parameter key="degree" value="3"/>
            <parameter key="gamma" value="0.0"/>
            <parameter key="coef0" value="0.0"/>
            <parameter key="C" value="0.0"/>
            <parameter key="nu" value="0.5"/>
            <parameter key="cache_size" value="80"/>
            <parameter key="epsilon" value="0.001"/>
            <parameter key="p" value="0.1"/>
            <list key="class_weights"/>
            <parameter key="shrinking" value="true"/>
            <parameter key="calculate_confidences" value="false"/>
            <parameter key="confidence_for_multiclass" value="true"/>
          </operator>
          <operator activated="true" class="performance_support_vector_count" compatibility="9.2.001" expanded="true" height="82" name="Performance" width="90" x="983" y="34">
            <parameter key="optimization_direction" value="minimization"/>
          </operator>
          <connect from_port="input 1" to_op="Read Excel" to_port="file"/>
          <connect from_op="Read Excel" from_port="output" to_op="Set Role" to_port="example set input"/>
          <connect from_op="Set Role" from_port="example set output" to_op="Split Data" to_port="example set"/>
          <connect from_op="Split Data" from_port="partition 1" 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 (2)" to_port="example set input"/>
          <connect from_op="Set Role (2)" from_port="example set output" to_op="Discretize" to_port="example set input"/>
          <connect from_op="Discretize" from_port="example set output" to_op="SVM" to_port="training set"/>
          <connect from_op="SVM" from_port="model" to_op="Performance" to_port="model"/>
          <connect from_op="Performance" from_port="model" to_port="result 1"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="source_input 2" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
          <portSpacing port="sink_result 2" spacing="0"/>
        </process>
      </operator>
    </process>

    Noel
  • NoelNoel Member Posts: 82  Maven
    @mbs- Thanks for posting the XML. Very much appreciated!
    [Deleted User]
  • NoelNoel Member Posts: 82  Maven
    varunm1, @mschmitz -

    Hi- I seem to be caught in a bind here. I literally have a binary classification problem i.e. TRUE or FALSE. SVM (LibSVM) and the standard core version can't handle nominal attributes. When I convert to numeric, however, the SVM models will run, but I can't find a performance operator that will give me a standard confusion matrix (I'm most interested in precision). The vanilla Performance operator seems to only give me RMSE and squared error. The Binominal Classification and Classification performance operators only take nominal input. It seems as if the Support Vector Count performance operator only give you very specific feedback, however.

    Any suggestions?

    Best,
    Noel
  • varunm1varunm1 Moderator, Member Posts: 1,207   Unicorn
    edited April 2019
    Hi @Noel

    If you have a label column with only two classes (True or False), you can change the column type by right-clicking near the dataset and selecting Edit. Now you can see your data editor where you can right click on the attribute and click modify attribute. Once you get the options in attribute type select Binomial the click ok and click on the save Icon of the data editor. Once you are done saving, now drag and drop the dataset again into your process. 

    You can use performance (Binomial Classification) to get the required perfromances. See highlighted parts in below image and follow my instructions above.

    If you don't want to do this, you can also use Performance (Classification) operator that gives you accuracy, weighted mean recall, and precisions. You can see the confusion matrix as well.



    Hope this helps.
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

    Noel
  • NoelNoel Member Posts: 82  Maven
    Hi @varunm1 I appreciate your help, but I'm still I'm clearly doing something wrong (I've attached the process and input data).

    When I run the data with GBT, I get reasonable results (top in screen cap below). When I run with SVM, I'm getting a head-scratcher result:


  • NoelNoel Member Posts: 82  Maven
    Whoops forgot the attachments...
  • [Deleted User][Deleted User] Posts: 0 Learner III
    edited May 2019
    varunm1 
    Hi 
    I have the same problem and Im not able to calculate accuracy for my data. with svm 
    but according to your suggestion Im still working on my thesis and when I show the result to my professor he accepted the result and I explained kappa for him o:):)
    varunm1
  • [Deleted User][Deleted User] Posts: 0 Learner III
    edited May 2019
    Reason 3: A kappa value can be between -1 to 1. A positive kappa value between 0 and 1 with higher the better. A negative kappa value between -1 and 0 represent your algorithm is predicting exactly opposite classes for data. For example, if you have 20 samples with 10 labeled as male and 10 labeled as female. A kappa value of zero means, your algorithm is predicting all 20 samples as either male or female. A negative kappa value means, your algorithms are predicting opposite classes, this means male samples are predicted as female and female samples are predicted as male. A positive kappa value means it is trying to predict correct classes for the given samples. Higher kappa means better predictions.
          varunm1 about kappa. :)
  • NoelNoel Member Posts: 82  Maven
    @varunm1-

    Thank you very much for detailed response. I also appreciate your patience -- dealing with newbies must be frustrating. I'd never seen results like the ones from that SVM model before.

    Thanks again,
    Noel
  • varunm1varunm1 Moderator, Member Posts: 1,207   Unicorn
    @Noel

    I am also a newbie before becoming Unicorn(which I still feel is a bit overhyped for me). SVM or any algorithm can perform poorly as it depends on your data, parameters, etc.
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

    sgenzer
  • sgenzersgenzer 12Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,959  Community Manager
    @varunm1 I think you deserve the 'unicorn' rank but I can downgrade you if you like :wink:
    varunm1
  • varunm1varunm1 Moderator, Member Posts: 1,207   Unicorn
    edited May 2019
    @sgenzer as you say my lord, you are the boss here with all access :wink:
    Regards,
    Varun
    https://www.varunmandalapu.com/

    Be Safe. Follow precautions and Maintain Social Distancing

    sgenzer
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