cross-validation in Attribute Selection Mode

[Mosaic Han]

In the "Select attributes" tag of the Weka Explorer (GUI), there are two options under the "Attribute Selection Mode", i.e. (i) Use full training set; (ii) Cross-validation

I think the code shown on https://fracpete.github.io/python-weka-wrapper/api.html
>>> search = ASSearch(classname="weka.attributeSelection.BestFirst", options=["-D", "1", "-N", "5"])
>>> evaluator = ASEvaluation(classname="weka.attributeSelection.CfsSubsetEval", options=["-P", "1", "-E", "1"])
>>> attsel = AttributeSelection()
>>> attsel.search(search)
>>> attsel.evaluator(evaluator)
>>> attsel.select_attributes(data)

fits for the full training set, i.e. option (i). How can I modify the above for the cross-validation mode in attribute selection. At the same time, how could I display the outcome too?
Any help is greatly appreciated.

[Peter Reutemann]

Simply set the flag for cross-validation:
https://fracpete.github.io/python-weka-wrapper3/weka.html#weka.attribute_selection.AttributeSelection.crossvalidation

And retrieve the results from the results_string property:
https://fracpete.github.io/python-weka-wrapper3/weka.html#weka.attribute_selection.AttributeSelection.results_string

This is demonstrated in this example:
https://github.com/fracpete/python-weka-wrapper3-examples/blob/master/src/wekaexamples/attribute_selection/attribute_selection.py

Cheers, Peter
--
Peter Reutemann
Dept. of Computer Science
University of Waikato, Hamilton, NZ
Mobile +64 22 190 2375
https://www.cs.waikato.ac.nz/~fracpete/
http://www.data-mining.co.nz/

[Mosaic Han]

Again, good pointer.

The example shown is quite useful and explains all.

Thanks