"Cost and Gamma SVM"
DocMusher
New Altair Community Member
Answers
-
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>
0 -
Dear Martin,
as usual, a clear and perfect solution.
Thanks, Have a nice weekend
Cheers
Sven0 -
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
Sven0 -
Hi,
a similar chart is possbile. Marius did this once and told me how to do it. Sadly i forgot it
Cheers,
Martin0 -
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=a6FhAQsjRuk0 -
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
Sven0 -
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 import matplotlib.pyplot as plt import numpy as np def rm_main(data): y = np.log10(data.iloc[:]["C"]) x = np.log10(data.iloc[:]["gamma"]) z = data.iloc[:]["Performance"] plt.title("Radial SVM Performance",fontsize=25) print x,y,z plt.hist2d(x,y,weights=z,vmin=z.min(),vmax=z.max()) plt.colorbar() plt.xlabel("log10(C)") plt.ylabel("log10(gamma)") 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="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>
0 -
Hi,
Martin,
Works perfect with Iris. Now try my own log file.
You made my day!
Thanks
Sven
0 -
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.0 -
Hi,
I have a look. Nice to give alternatives!
Have nice day.
Sven0 -
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
Sven0 -
So much regarding the
thing.
I am only a anesthesiologist
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 0 -
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 import matplotlib.pyplot as plt import numpy as np def rm_main(data): y = np.log10(data.iloc[:]["C"]) x = np.log10(data.iloc[:]["gamma"]) z = data.iloc[:]["Performance"] xbins = %{xbins} # From process ybins = %{ybins} # From process plt.title("Radial SVM Performance",fontsize=25) print x,y,z hist, xbins, ybins = np.histogram2d(x,y,weights=z,bins=[xbins,ybins]) #choose either none or gaussian for interpolation plt.imshow(hist.T,interpolation="gaussian", origin='lower') plt.colorbar() plt.xlabel("log10(C)") plt.ylabel("log10(gamma)") 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>
0 -
Hi,
Thanks Martin, worked excellent for me!
Cheers
Sven0 -
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=00 -
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 import matplotlib.pyplot as plt import numpy as np def rm_main(data): y = np.log10(data.iloc[:]["C"]) x = np.log10(data.iloc[:]["gamma"]) z = data.iloc[:]["Performance"] xbins = %{xbins} # From process ybins = %{ybins} # From process plt.title("Radial SVM Performance",fontsize=25) hist, xbins, ybins = np.histogram2d(x,y,weights=z,bins=[xbins,ybins]) #choose either none or gaussian for interpolation plt.imshow( hist.T, interpolation="gaussian", origin='lower', extent=[xbins.min(),xbins.max(),ybins.min(),ybins.max()]) plt.colorbar() plt.xlabel("log10(C)") plt.ylabel("log10(gamma)") 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>
0
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