the RandomForest PyMVA method in this example is not found:
Thanks for your help
It looks like the tutorial in the AFS folder comes from another ROOT version. If you try
it should work.
thanks for your reply.
I must be doing something wrong, but including:
if (Use[“RandomForest”]) // Allow Using random forest
factory->BookMethod( dataloader, TMVA::Types::kPyRandomForest, “PyRandomForest”, “!V:NEstimators=100:Criterion=gini:MaxFeatures=auto:MaxDepth=6:MinSamplesLeaf=1:MinWeightFractionLeaf=0:Bootstrap=kTRUE” );
on the example you pointed gives the same error:
Hm, perhaps @moneta can help?
If you have built wROOT with support for PyMVA, you need also to call at the beginning
thanks, I get another error now:
ImportError: No module named sklearn.ensemble
: Failed to run python code
not sure if I should install and link something myself, but was assuming this was already done, since:
cxx11 asimage builtin_afterimage builtin_clang builtin_davix builtin_ftgl builtin_gl2ps builtin_glew builtin_llvm builtin_lz4 builtin_lzma builtin_pcre builtin_tbb builtin_unuran builtin_vdt builtin_veccore builtin_xrootd builtin_xxhash builtin_zlib clad dataframe davix exceptions fftw3 fitsio fortran gdml http imt mathmore mlp minuit2 mysql opengl pgsql pyroot roofit runtime_cxxmodules shadowpw shared soversion sqlite ssl tmva tmva-cpu tmva-pymva spectrum unuran vmc vdt veccore x11 xml xrootd
You need also to have sklearn installed in your python environment.
So if you do:
>>> import sklearn
it should work. If not, you need to install it (e.g. by doing
pip install sklearn)
in case other people might need it, posting here a recipe that works for me on lxplus (despite inconsistency in architecture):
It would be great to have access to RMVA bindings as well in the future!
@omazapa might know more if the R packages needed for RMVA are available in cvmfs