Comments (3)
The example file was not working for me either. However, I was able to locate two problems:
One is related to the java implementation. The corresponding issue can be found in the waikato/meka repository: Waikato/meka#1
Upon fitting the classifier, the python wrapper calls the java commands providing no test data. Updating your java-meka to the nightly snapshot solved the problem for me.
Another problem arises when you try to do predictions. The parser in the python wrapper is unable to deal with samples for which no label has been predicted by meka (which meka frequently does).
A possible hackaround:
Change file meka.py
I've attached the version I am currently using to run the example correctly. Basically, you have to change the way the wrapper parses the meka output, the changes are commented.
However, I have not checked if this breaks other parts of the program. Especially the "verbosity==6" case is not most certainly broken (->see code)
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@brodin, @wenkph it is true, scikit-multilearn requires at least meka 1.9.1, I was talking to @jmread about this in the Maestra summer school in Macedonia last semptember, I think we need to wait a moment for their relaese, in the mean time I've updated the docs in eb478e5
@wenkph I've added support for the no-label assigned case and removed the nonworking verbosity=6 in f701d1f - thank you for your input and help!
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@niedakh Thanks! 👍
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