RDataFrame and TMVA with KFolding


I am trying to use:

import ROOT

def add_bdt(df, xmlpath):
    ROOT.gInterpreter.ProcessLine('''TMVA::Experimental::RReader model("{}");'''.format(xmlpath))
    nvars = ROOT.model.GetVariableNames().size()
    ROOT.gInterpreter.ProcessLine('''auto computeModel = TMVA::Experimental::Compute<{}, float>(model);'''.format(nvars))

    l_expr = ROOT.model.GetVariableNames()
    l_varn = ROOT.std.vector['std::string']()
    for i_expr, expr in enumerate(l_expr):
        varname = 'v_{}'.format(i_expr)

        df=df.Define(varname, '(float)({})'.format(expr) )
    df = df.Define('mva', ROOT.computeModel, l_varn)

    return df

df=ROOT.RDataFrame('tree', filepath)
df=add_bdt(df, xmlpath)

c = ROOT.TCanvas('c', '', 600, 600)
h = df.Histo1D('bdt')

In order to calculate the mva classifier column for the tree tree in the file file.root using the weights from mva_0.xml.

Now, the problem is that each entry in the tree has a branch called fold, which is an integer between 0 and 4. At the same time I have 5 xml files (mva_0, 1,2,3,4.xml) such that each entry of the tree needs to be assigned an MVA score from the evaluation of those xml files.

Question: How do I modify the code above such that the computeModel switches between different model objects depending on the fold value?


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_ROOT Version:6.22/06

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Anyway, I did it in a less elegant way, but it seems to work. However, please look at what I did in the code above. I created a function that takes the XML file, the data frame and just adds a BDT score. All the other nasty stuff (declaring variables, taking care of types, etc) is hidden. As analyzers, we do not need to know any of that. It can be nicely hidden in such a way that we only interact with a small function, our lives are already hard dealing with Physics and we do not want to have to also deal with code.

ROOT code should focus on solving problems, what is the problem here? We have an MVA score in the XML file, we want to put it in the tree/dataframe. We only need one small function for that and ideally we would not have to write it ourselves.


Hi @rooter_03 ,
I think we need @moneta 's help with your first post, I don’t know enough about TMVA and reading weights from XMLs.

Thank you for following up with a second post. If this is a common problem we might definitely provide a helper function, @moneta what do you think?


If I have understood you well, you would like a better integration of the TMVA model prediction with the RDataFrame to avoid writing functions as the one above.
We are working on this and we are aiming to have something for the next release.
Thank you for your feedback



Thanks for your reply. I think something like this:

df.addMVA('mva_1.xml', 'mva_1')
df.addMVA('mva_2.xml', 'mva_2')

would nicely wrap all into one function. This way we could compare multiple classifiers or use them together.

For k-folding maybe:

if True:
    d_fold[0] = 'mva_0.xml'
    d_fold[1] = 'mva_1.xml'
    d_fold[2] = 'mva_2.xml'

df.addMVAFolds(d_fold, 'fold', 'mva')

where d_fold contains the correspondence between the fold and the XML file, fold is a column that should exist in the data frame (which allows picking the score fromt the right XML) and mva is the new column with the score.

I am trying to write my code this way and I guess many people are writting the same code that I write, for each analysis. Which is wasteful, because we spend time writting hundreds of lines of the same bad or at most barely acceptable (given that many of the coders are students who are still learning) code instead of having that code, written once and well as part of ROOT.

Code like this would remove hundreds of lines from many analyses code bases and would make us faster and less prone to bugs. This is only my view and it would be good to have people who actually do data analysis giving further feedback.


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