"Cost and Gamma SVM"

Hi Sven,

you can use a log operator and log C,gamma and performance. Than you can use standard charts on the log (in results view).

attached is a process on Iris. I use this optimize also as a building block.

Cheers,
Martin

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="6.4.000">
 <context>
   <input/>
   <output/>
   <macros/>
 </context>
 <operator activated="true" class="process" compatibility="6.4.000" expanded="true" name="Process">
   <process expanded="true">
     <operator activated="true" class="retrieve" compatibility="6.4.000" expanded="true" height="60" name="Retrieve Iris" width="90" x="112" y="120">
       <parameter key="repository_entry" value="//Samples/data/Iris"/>
     </operator>
     <operator activated="true" class="optimize_parameters_grid" compatibility="6.4.000" expanded="true" height="94" name="Optimize Parameters (Grid)" width="90" x="246" y="120">
       <list key="parameters">
         <parameter key="SVM.C" value="[1e-3;10;4;logarithmic]"/>
         <parameter key="SVM.gamma" value="[1e-3;10;4;logarithmic]"/>
       </list>
       <process expanded="true">
         <operator activated="true" class="x_validation" compatibility="5.1.002" expanded="true" height="112" name="Validation" width="90" x="45" y="30">
           <parameter key="sampling_type" value="2"/>
           <process expanded="true">
             <operator activated="true" class="support_vector_machine_libsvm" compatibility="6.4.000" expanded="true" height="76" name="SVM" width="90" x="112" y="30">
               <parameter key="gamma" value="10.0"/>
               <parameter key="C" value="10.0"/>
               <list key="class_weights"/>
             </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="6.4.000" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
               <list key="application_parameters"/>
             </operator>
             <operator activated="true" class="performance" compatibility="6.4.000" expanded="true" height="76" name="Performance" width="90" x="179" y="30"/>
             <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>
           <description align="center" color="transparent" colored="false" width="126">A cross-validation evaluating a decision tree model.</description>
         </operator>
         <operator activated="true" class="log" compatibility="6.4.000" expanded="true" height="76" name="Log" width="90" x="179" y="75">
           <list key="log">
             <parameter key="C" value="operator.SVM.parameter.C"/>
             <parameter key="gamma" value="operator.SVM.parameter.gamma"/>
             <parameter key="Performance" value="operator.Validation.value.performance"/>
           </list>
         </operator>
         <connect from_port="input 1" to_op="Validation" to_port="training"/>
         <connect from_op="Validation" from_port="averagable 1" to_op="Log" to_port="through 1"/>
         <connect from_op="Log" from_port="through 1" to_port="performance"/>
         <portSpacing port="source_input 1" spacing="0"/>
         <portSpacing port="source_input 2" spacing="0"/>
         <portSpacing port="sink_performance" spacing="0"/>
         <portSpacing port="sink_result 1" spacing="0"/>
       </process>
       <description align="center" color="transparent" colored="false" width="126">Optimize C and Gamma of a radial SVM using optimize by Grid</description>
     </operator>
     <connect from_op="Retrieve Iris" from_port="output" to_op="Optimize Parameters (Grid)" to_port="input 1"/>
     <connect from_op="Optimize Parameters (Grid)" 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"/>
   </process>
 </operator>
</process>

Dear Martin,
as usual, a clear and perfect solution.
Thanks, Have a nice weekend
Cheers
Sven

Hi,
I suppose the following chart is impossible to generate in RM?

http://scikit-learn.org/0.10/_images/plot_svm_parameters_selection_1.png
Cheers
Sven

Hi,

a similar chart is possbile. Marius did this once and told me how to do it. Sadly i forgot it

Cheers,
Martin

Hi Sven!
If you need this plot for your paper, i might programm a quick python snippet for it. Even though it might feel like code club: https://www.youtube.com/watch?v=a6FhAQsjRuk

Dear Martin,
This wound be very kind of you. I looked at: https://plot.ly/python/heatmaps/ but had to admit that I am only a anesthesiologist which means a human being with less brain volume compared to real data scientists!
Cheers
Sven 

Hi,

attached is a process doing it on iris. I do not know how to get rid of the blue background color... Might need to think about it. It is a bit strange for me to write this kind of code again.

Be sure to have the python extension and matplotlib installed. I personally use Anaconda which is available for windows and mac.
Please be careful running this on server, because it opens a dialogue and the process ends only if you close the dialogue

Cheers,
Martin

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="6.4.000">
 <context>
   <input/>
   <output/>
   <macros/>
 </context>
 <operator activated="true" class="process" compatibility="6.4.000" expanded="true" name="Process">
   <process expanded="true">
     <operator activated="true" class="retrieve" compatibility="6.4.000" expanded="true" height="60" name="Retrieve Iris" width="90" x="112" y="30">
       <parameter key="repository_entry" value="//Samples/data/Iris"/>
     </operator>
     <operator activated="true" class="optimize_parameters_grid" compatibility="6.4.000" expanded="true" height="112" name="Optimize Parameters (Grid)" width="90" x="246" y="30">
       <list key="parameters">
         <parameter key="SVM.C" value="[1e-3;10;4;logarithmic]"/>
         <parameter key="SVM.gamma" value="[1e-3;10;4;logarithmic]"/>
       </list>
       <process expanded="true">
         <operator activated="true" class="x_validation" compatibility="5.1.002" expanded="true" height="112" name="Validation" width="90" x="45" y="30">
           <parameter key="sampling_type" value="2"/>
           <process expanded="true">
             <operator activated="true" class="support_vector_machine_libsvm" compatibility="6.4.000" expanded="true" height="76" name="SVM" width="90" x="112" y="30">
               <parameter key="gamma" value="10.0"/>
               <parameter key="C" value="10.0"/>
               <list key="class_weights"/>
             </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="6.4.000" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
               <list key="application_parameters"/>
             </operator>
             <operator activated="true" class="performance" compatibility="6.4.000" expanded="true" height="76" name="Performance" width="90" x="179" y="30"/>
             <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>
           <description align="center" color="transparent" colored="false" width="126">A cross-validation evaluating a decision tree model.</description>
         </operator>
         <operator activated="true" class="log" compatibility="6.4.000" expanded="true" height="76" name="Log" width="90" x="179" y="75">
           <list key="log">
             <parameter key="C" value="operator.SVM.parameter.C"/>
             <parameter key="gamma" value="operator.SVM.parameter.gamma"/>
             <parameter key="Performance" value="operator.Validation.value.performance"/>
           </list>
         </operator>
         <connect from_port="input 1" to_op="Validation" to_port="training"/>
         <connect from_op="Validation" from_port="averagable 1" to_op="Log" to_port="through 1"/>
         <connect from_op="Log" from_port="through 1" to_port="performance"/>
         <portSpacing port="source_input 1" spacing="0"/>
         <portSpacing port="source_input 2" spacing="0"/>
         <portSpacing port="sink_performance" spacing="0"/>
         <portSpacing port="sink_result 1" spacing="0"/>
         <portSpacing port="sink_result 2" spacing="0"/>
       </process>
       <description align="center" color="transparent" colored="false" width="126">Optimize C and Gamma of a radial SVM using optimize by Grid</description>
     </operator>
     <operator activated="true" class="log_to_data" compatibility="6.4.000" expanded="true" height="94" name="Log to Data" width="90" x="380" y="30">
       <parameter key="log_name" value="Log"/>
     </operator>
     <operator activated="true" class="python_scripting:execute_python" compatibility="6.4.000" expanded="true" height="76" name="Execute Python" width="90" x="514" y="30">
       <parameter key="script" value="import pandas as pd&#10;import matplotlib.pyplot as plt&#10;import numpy as np&#10;&#10;def rm_main(data):&#10;&#10;&#10;    y =  np.log10(data.iloc[:][&quot;C&quot;])&#10;    x = np.log10(data.iloc[:][&quot;gamma&quot;])&#10;    z = data.iloc[:][&quot;Performance&quot;]&#10;&#10;     &#10;    plt.title(&quot;Radial SVM Performance&quot;,fontsize=25)&#10;    print x,y,z&#10;    plt.hist2d(x,y,weights=z,vmin=z.min(),vmax=z.max())&#10;    plt.colorbar()&#10;    plt.xlabel(&quot;log10(C)&quot;)&#10;    plt.ylabel(&quot;log10(gamma)&quot;)&#10;&#10;    plt.show()&#10;"/>
     </operator>
     <connect from_op="Retrieve Iris" from_port="output" to_op="Optimize Parameters (Grid)" to_port="input 1"/>
     <connect from_op="Optimize Parameters (Grid)" from_port="result 1" to_op="Log to Data" to_port="through 1"/>
     <connect from_op="Log to Data" from_port="exampleSet" to_op="Execute Python" to_port="input 1"/>
     <connect from_op="Execute Python" from_port="output 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"/>
   </process>
 </operator>
</process>

Hi,
Martin,
Works perfect with Iris. Now try my own log file.
You made my day!
Thanks
Sven

Nice!

You can also save yourself the Python by using Advanced Charts.

Set the Domain to : C
Set the Numerical Axis to : gamma
Set the Color dimension to : Performance

Click on domain dimension & set it to Logarithmic
Click on numerical axis & set it to Logarithmic

This will now give you little coloured dots, but really bigger dots would be nicer.
Click on Numerical Axis -> Series: gamma & then click on Format -> Configure
In this setting you can change from circle to square and then update the size of the square to whatever size looks good.

Hi,
I have a look. Nice to give alternatives!
Have nice day.
Sven

Hi,
Without the background if you define the number of bins : for iris number of bins = 5 plt.hist2d(x,y,bins=5,weights=z,vmin=z.min(),vmax=z.max())
Information from: http://matplotlib.org/api/pyplot_api.html

Cheers
Sven

So much regarding the

I am only a anesthesiologist

thing.

Well done, thanks for sharing. Since you can pass macros to python, you might automate it by counting the number of different C/gamma values

Hi,

the process attached extracts the xbins and ybins automatically from the process. Might be useful
The result is this:


<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="6.4.000">
 <context>
   <input/>
   <output/>
   <macros/>
 </context>
 <operator activated="true" class="process" compatibility="6.4.000" expanded="true" name="Process">
   <process expanded="true">
     <operator activated="true" class="retrieve" compatibility="6.4.000" expanded="true" height="60" name="Retrieve Iris" width="90" x="112" y="30">
       <parameter key="repository_entry" value="//Samples/data/Iris"/>
     </operator>
     <operator activated="true" class="optimize_parameters_grid" compatibility="6.4.000" expanded="true" height="112" name="Optimize Parameters (Grid)" width="90" x="246" y="30">
       <list key="parameters">
         <parameter key="SVM.C" value="[1e-3;10;20;logarithmic]"/>
         <parameter key="SVM.gamma" value="[1e-3;10;20;logarithmic]"/>
       </list>
       <process expanded="true">
         <operator activated="true" class="x_validation" compatibility="5.1.002" expanded="true" height="112" name="Validation" width="90" x="45" y="30">
           <parameter key="sampling_type" value="2"/>
           <process expanded="true">
             <operator activated="true" class="support_vector_machine_libsvm" compatibility="6.4.000" expanded="true" height="76" name="SVM" width="90" x="112" y="30">
               <parameter key="gamma" value="10.0"/>
               <parameter key="C" value="10.0"/>
               <list key="class_weights"/>
             </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="6.4.000" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
               <list key="application_parameters"/>
             </operator>
             <operator activated="true" class="performance" compatibility="6.4.000" expanded="true" height="76" name="Performance" width="90" x="179" y="30"/>
             <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>
           <description align="center" color="transparent" colored="false" width="126">A cross-validation evaluating a decision tree model.</description>
         </operator>
         <operator activated="true" class="log" compatibility="6.4.000" expanded="true" height="76" name="Log" width="90" x="179" y="75">
           <list key="log">
             <parameter key="C" value="operator.SVM.parameter.C"/>
             <parameter key="gamma" value="operator.SVM.parameter.gamma"/>
             <parameter key="Performance" value="operator.Validation.value.performance"/>
           </list>
         </operator>
         <connect from_port="input 1" to_op="Validation" to_port="training"/>
         <connect from_op="Validation" from_port="averagable 1" to_op="Log" to_port="through 1"/>
         <connect from_op="Log" from_port="through 1" to_port="performance"/>
         <portSpacing port="source_input 1" spacing="0"/>
         <portSpacing port="source_input 2" spacing="0"/>
         <portSpacing port="sink_performance" spacing="0"/>
         <portSpacing port="sink_result 1" spacing="0"/>
         <portSpacing port="sink_result 2" spacing="0"/>
       </process>
       <description align="center" color="transparent" colored="false" width="126">Optimize C and Gamma of a radial SVM using optimize by Grid</description>
     </operator>
     <operator activated="true" class="log_to_data" compatibility="6.4.000" expanded="true" height="94" name="Log to Data" width="90" x="380" y="30">
       <parameter key="log_name" value="Log"/>
     </operator>
     <operator activated="true" class="multiply" compatibility="6.4.000" expanded="true" height="112" name="Multiply" width="90" x="514" y="30"/>
     <operator activated="true" class="aggregate" compatibility="6.4.000" expanded="true" height="76" name="Aggregate (2)" width="90" x="648" y="165">
       <list key="aggregation_attributes">
         <parameter key="gamma" value="average"/>
       </list>
       <parameter key="group_by_attributes" value="gamma"/>
     </operator>
     <operator activated="true" class="extract_macro" compatibility="6.4.000" expanded="true" height="60" name="Extract Macro (2)" width="90" x="782" y="165">
       <parameter key="macro" value="xbins"/>
       <list key="additional_macros"/>
     </operator>
     <operator activated="true" class="aggregate" compatibility="6.4.000" expanded="true" height="76" name="Aggregate" width="90" x="648" y="75">
       <list key="aggregation_attributes">
         <parameter key="C" value="average"/>
       </list>
       <parameter key="group_by_attributes" value="C"/>
     </operator>
     <operator activated="true" class="extract_macro" compatibility="6.4.000" expanded="true" height="60" name="Extract Macro" width="90" x="782" y="75">
       <parameter key="macro" value="ybins"/>
       <list key="additional_macros"/>
     </operator>
     <operator activated="true" class="python_scripting:execute_python" compatibility="6.4.000" expanded="true" height="76" name="Execute Python" width="90" x="916" y="30">
       <parameter key="script" value="import pandas as pd&#10;import matplotlib.pyplot as plt&#10;import numpy as np&#10;&#10;def rm_main(data):&#10;&#10;&#10;    y =  np.log10(data.iloc[:][&quot;C&quot;])&#10;    x = np.log10(data.iloc[:][&quot;gamma&quot;])&#10;    z = data.iloc[:][&quot;Performance&quot;]&#10;    xbins = %{xbins} # From process&#10;    ybins = %{ybins} # From process&#10;&#10;    plt.title(&quot;Radial SVM Performance&quot;,fontsize=25)&#10;    print x,y,z&#10;    hist, xbins, ybins = np.histogram2d(x,y,weights=z,bins=[xbins,ybins])&#10;&#10;    #choose either none or gaussian for interpolation&#10;    plt.imshow(hist.T,interpolation=&quot;gaussian&quot;, origin='lower')&#10;    plt.colorbar()&#10;    plt.xlabel(&quot;log10(C)&quot;)&#10;    plt.ylabel(&quot;log10(gamma)&quot;)&#10;    plt.show()"/>
     </operator>
     <connect from_op="Retrieve Iris" from_port="output" to_op="Optimize Parameters (Grid)" to_port="input 1"/>
     <connect from_op="Optimize Parameters (Grid)" from_port="result 1" to_op="Log to Data" to_port="through 1"/>
     <connect from_op="Log to Data" from_port="exampleSet" to_op="Multiply" to_port="input"/>
     <connect from_op="Multiply" from_port="output 1" to_op="Execute Python" to_port="input 1"/>
     <connect from_op="Multiply" from_port="output 2" to_op="Aggregate" to_port="example set input"/>
     <connect from_op="Multiply" from_port="output 3" to_op="Aggregate (2)" to_port="example set input"/>
     <connect from_op="Aggregate (2)" from_port="example set output" to_op="Extract Macro (2)" to_port="example set"/>
     <connect from_op="Aggregate" from_port="example set output" to_op="Extract Macro" to_port="example set"/>
     <connect from_op="Execute Python" from_port="output 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"/>
   </process>
 </operator>
</process>

Hi,
Thanks Martin, worked excellent for me!
Cheers

Sven

Hi,
The axis in this chart might be the number of bins, not the log10 c or log 10 gamma?
I provide a link with the 2 charts I get using two processes provided by Martin.
Although I prefer the the smoothing of colors, I think the values are not the log10 c and log10 gamma values.
Any suggestions
Cheers
Sven
https://www.dropbox.com/s/knrrummnwztsxlu/SVMCgammacompare.docx?dl=0

Oh,

you are completly right. Sorry for that. The python script below exports the correct min and max values. For a scientific publication i would not use the gaussian interpolation. It simply adds information which might not be there.

Cheers,
Martin

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="6.4.000">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="6.4.000" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="retrieve" compatibility="6.4.000" expanded="true" height="60" name="Retrieve Iris" width="90" x="112" y="30">
        <parameter key="repository_entry" value="//Samples/data/Iris"/>
      </operator>
      <operator activated="true" class="optimize_parameters_grid" compatibility="6.4.000" expanded="true" height="112" name="Optimize Parameters (Grid)" width="90" x="246" y="30">
        <list key="parameters">
          <parameter key="SVM.C" value="[1e-3;10;20;logarithmic]"/>
          <parameter key="SVM.gamma" value="[1e-3;10;20;logarithmic]"/>
        </list>
        <process expanded="true">
          <operator activated="true" class="x_validation" compatibility="5.1.002" expanded="true" height="112" name="Validation" width="90" x="45" y="30">
            <parameter key="sampling_type" value="2"/>
            <process expanded="true">
              <operator activated="true" class="support_vector_machine_libsvm" compatibility="6.4.000" expanded="true" height="76" name="SVM" width="90" x="112" y="30">
                <parameter key="gamma" value="10.0"/>
                <parameter key="C" value="10.0"/>
                <list key="class_weights"/>
              </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="6.4.000" expanded="true" height="76" name="Apply Model" width="90" x="45" y="30">
                <list key="application_parameters"/>
              </operator>
              <operator activated="true" class="performance" compatibility="6.4.000" expanded="true" height="76" name="Performance" width="90" x="179" y="30"/>
              <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>
            <description align="center" color="transparent" colored="false" width="126">A cross-validation evaluating a decision tree model.</description>
          </operator>
          <operator activated="true" class="log" compatibility="6.4.000" expanded="true" height="76" name="Log" width="90" x="179" y="75">
            <list key="log">
              <parameter key="C" value="operator.SVM.parameter.C"/>
              <parameter key="gamma" value="operator.SVM.parameter.gamma"/>
              <parameter key="Performance" value="operator.Validation.value.performance"/>
            </list>
          </operator>
          <connect from_port="input 1" to_op="Validation" to_port="training"/>
          <connect from_op="Validation" from_port="averagable 1" to_op="Log" to_port="through 1"/>
          <connect from_op="Log" from_port="through 1" to_port="performance"/>
          <portSpacing port="source_input 1" spacing="0"/>
          <portSpacing port="source_input 2" spacing="0"/>
          <portSpacing port="sink_performance" spacing="0"/>
          <portSpacing port="sink_result 1" spacing="0"/>
          <portSpacing port="sink_result 2" spacing="0"/>
        </process>
        <description align="center" color="transparent" colored="false" width="126">Optimize C and Gamma of a radial SVM using optimize by Grid</description>
      </operator>
      <operator activated="true" class="log_to_data" compatibility="6.4.000" expanded="true" height="94" name="Log to Data" width="90" x="380" y="30">
        <parameter key="log_name" value="Log"/>
      </operator>
      <operator activated="true" class="multiply" compatibility="6.4.000" expanded="true" height="112" name="Multiply" width="90" x="514" y="30"/>
      <operator activated="true" class="aggregate" compatibility="6.4.000" expanded="true" height="76" name="Aggregate (2)" width="90" x="648" y="165">
        <list key="aggregation_attributes">
          <parameter key="gamma" value="average"/>
        </list>
        <parameter key="group_by_attributes" value="gamma"/>
      </operator>
      <operator activated="true" class="extract_macro" compatibility="6.4.000" expanded="true" height="60" name="Extract Macro (2)" width="90" x="782" y="165">
        <parameter key="macro" value="xbins"/>
        <list key="additional_macros"/>
      </operator>
      <operator activated="true" class="aggregate" compatibility="6.4.000" expanded="true" height="76" name="Aggregate" width="90" x="648" y="75">
        <list key="aggregation_attributes">
          <parameter key="C" value="average"/>
        </list>
        <parameter key="group_by_attributes" value="C"/>
      </operator>
      <operator activated="true" class="extract_macro" compatibility="6.4.000" expanded="true" height="60" name="Extract Macro" width="90" x="782" y="75">
        <parameter key="macro" value="ybins"/>
        <list key="additional_macros"/>
      </operator>
      <operator activated="true" class="python_scripting:execute_python" compatibility="6.4.000" expanded="true" height="76" name="Execute Python" width="90" x="916" y="30">
        <parameter key="script" value="import pandas as pd&#10;import matplotlib.pyplot as plt&#10;import numpy as np&#10;&#10;def rm_main(data):&#10;&#10;&#10;    y =  np.log10(data.iloc[:][&quot;C&quot;])&#10;    x = np.log10(data.iloc[:][&quot;gamma&quot;])&#10;    z = data.iloc[:][&quot;Performance&quot;]&#10;    xbins = %{xbins} # From process&#10;    ybins = %{ybins} # From process&#10;&#10;    plt.title(&quot;Radial SVM Performance&quot;,fontsize=25)&#10;    hist, xbins, ybins = np.histogram2d(x,y,weights=z,bins=[xbins,ybins])&#10;    #choose either none or gaussian for interpolation &#10;    plt.imshow( hist.T,&#10;                interpolation=&quot;gaussian&quot;, &#10;                origin='lower',&#10;                extent=[xbins.min(),xbins.max(),ybins.min(),ybins.max()])&#10;    plt.colorbar()&#10;    plt.xlabel(&quot;log10(C)&quot;)&#10;    plt.ylabel(&quot;log10(gamma)&quot;)&#10;    plt.show()"/>
      </operator>
      <connect from_op="Retrieve Iris" from_port="output" to_op="Optimize Parameters (Grid)" to_port="input 1"/>
      <connect from_op="Optimize Parameters (Grid)" from_port="result 1" to_op="Log to Data" to_port="through 1"/>
      <connect from_op="Log to Data" from_port="exampleSet" to_op="Multiply" to_port="input"/>
      <connect from_op="Multiply" from_port="output 1" to_op="Execute Python" to_port="input 1"/>
      <connect from_op="Multiply" from_port="output 2" to_op="Aggregate" to_port="example set input"/>
      <connect from_op="Multiply" from_port="output 3" to_op="Aggregate (2)" to_port="example set input"/>
      <connect from_op="Aggregate (2)" from_port="example set output" to_op="Extract Macro (2)" to_port="example set"/>
      <connect from_op="Aggregate" from_port="example set output" to_op="Extract Macro" to_port="example set"/>
      <connect from_op="Execute Python" from_port="output 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"/>
    </process>
  </operator>
</process>