def addTMVAResponse ( dataset ,
inputs ,
weights_files ,
prefix = '' ,
suffix = '' ,
aux = 0.9 ) :
"""
Helper function to add TMVA responce into dataset
>>> tar_file = trainer.tar_file
>>> dataset = ...
>>> inputs = [ 'var1' , 'var2' , 'var2' ]
>>> dataset.addTMVAResponce ( inputs , tar_file , prefix = 'tmva_' )
"""
from ostap.core.core import cpp, std, Ostap
PP = std.pair ( 'std::string', 'std::string' )
VP = std.vector ( PP )
_inputs = _inputs2map_ ( inputs )
_weights = _weights2map_ ( weights_files )
sc = Ostap.TMVA.addResponse ( dataset ,
_inputs ,
_weights ,
prefix ,
suffix ,
aux )
if sc.isFailure() :
logger.error ( 'Error from Ostap::TMVA::addResponse %s' % sc )
return sc
logger.info('Dataset:%s ' % dataset)
models = set()
# =============================================================================
signal1 = Models.Gauss_pdf('Gx', xvar=m_x)
signal2 = Models.Gauss_pdf('Gy', xvar=m_y)
signal2s = signal1.clone(name='GyS', xvar=m_y)
signal1.mean = m.value()
signal1.sigma = m.error()
signal2.mean = m.value()
signal2.sigma = m.error()
knots = std.vector('double')()
knots.push_back(m_x.xmin())
knots.push_back(0.5 * (m_x.xmin() + m_x.xmax()))
knots.push_back(m_x.xmax())
spline1 = Ostap.Math.BSpline(knots, 2)
# =============================================================================
## gauss as signal, expo times 1st order polynomial as background
# =============================================================================
def test_model_14():
logger = getLogger('test_model_14')
logger.info(
'Non-factorazeable background component (spline): ( Gauss + P1 ) (x) ( Gauss + P1 ) + Spline2D'
)
def addChoppingResponse(dataset,
chopper,
N,
inputs,
weights_files,
category_name='chopping',
prefix='tmva_',
suffix='_response',
aux=0.9):
"""
Helper function to add TMVA response into dataset
>>> tar_file = trainer.tar_file
>>> dataset = ...
>>> inputs = [ 'var1' , 'var2' , 'var2' ]
>>> dataset.addTMVAResponce ( inputs , tar_file , prefix = 'tmva_' )
"""
assert isinstance(N, int) and 1 < N < 10000, 'Invalid "N" %s' % N
if isinstance(chopper, str):
if chopper in dataset:
chopper = getattr(varset, chopper)
else:
varset = dataset.get()
varlist = ROOT.RooArgList()
for v in varset:
varlist.add(v)
chopper = ROOT.RooFormulaVar('chopping', chopper, varlist)
logger.debug('Create chopping function %s' % chopper)
assert isinstance(chopper,
ROOT.RooAbsReal), 'Invalid choper type %s' % chopper
category = ROOT.RooCategory(
category_name, 'Chopping category: (%s)%%%d' % (chopper.GetTitle(), N))
for i in range(N):
if N < 10: cn = category_name + '_%d' % i
if N < 100: cn = category_name + '_%02d' % i
elif N < 1000: cn = category_name + '_%03d' % i
elif N < 10000: cn = category_name + '_%04d' % i
else: cn = category_name + '_%d' % i
category.defineType(cn, i)
## decode inputs&weights
from ostap.tools.tmva import _inputs2map_, _weights2map_
_inputs = _inputs2map_(inputs)
files = WeightsFiles(weights_files).files
files_ = [_weights2map_(f) for f in files]
from ostap.core.core import cpp, std, Ostap
MAP = std.map('std::string', 'std::string')
MAPS = std.vector(MAP)
_maps = MAPS()
for m in files_:
_maps.push_back(m)
sc = Ostap.TMVA.addChoppingResponse(dataset, chopper, category, N, _inputs,
_maps, prefix, suffix, aux)
if sc.isFailure():
logger.error('Error from Ostap::TMVA::addChoppingResponse %s' % sc)
return sc