def main(options):
#take options from the yaml config
with open(options.config, 'r') as config_file:
config = yaml.load(config_file)
output_tag = config['output_tag']
mc_dir = config['mc_file_dir']
mc_fnames = config['mc_file_names']
#data not needed yet, could use this for validation later. keep for compat with class
data_dir = config['data_file_dir']
data_fnames = config['data_file_names']
train_vars = config['train_vars']
vars_to_add = config['vars_to_add']
presel = config['preselection']
proc_to_tree_name = config['proc_to_tree_name']
#sig_colour = 'forestgreen'
sig_colour = 'red'
#Data handling stuff#
sys.exit(1)
#load the mc dataframe for all years
if options.pt_reweight:
cr_selection = config['reweight_cr']
output_tag += '_pt_reweighted'
root_obj = ROOTHelpers(output_tag, mc_dir, mc_fnames, data_dir, data_fnames, proc_to_tree_name, train_vars, vars_to_add, cr_selection)
else: root_obj = ROOTHelpers(output_tag, mc_dir, mc_fnames, data_dir, data_fnames, proc_to_tree_name, train_vars, vars_to_add, presel)
for sig_obj in root_obj.sig_objects:
root_obj.load_mc(sig_obj, reload_samples=options.reload_samples)
for bkg_obj in root_obj.bkg_objects:
root_obj.load_mc(bkg_obj, bkg=True, reload_samples=options.reload_samples)
for data_obj in root_obj.data_objects:
root_obj.load_data(data_obj, reload_samples=options.reload_samples)
root_obj.concat()
if options.pt_reweight and options.reload_samples:
root_obj.apply_pt_rew('DYMC', presel)
#Plotter stuff#
#set up X, w and y, train-test
plotter = Plotter(root_obj, train_vars, sig_col=sig_colour, norm_to_data=True)
for var in train_vars:
plotter.plot_input(var, options.n_bins, output_tag, options.ratio_plot, norm_to_data=True)
def main(options):
#take options from the yaml config
with open(options.config, 'r') as config_file:
config = yaml.load(config_file)
output_tag = config['output_tag']
mc_dir = config['mc_file_dir']
mc_fnames = config['mc_file_names']
#data not needed yet, could use this for validation later. keep for compat with class
data_dir = config['data_file_dir']
data_fnames = config['data_file_names']
train_vars = config['train_vars']
vars_to_add = config['vars_to_add']
presel = config['preselection']
proc_to_tree_name = config['proc_to_tree_name']
#sig_colour = 'forestgreen'
sig_colour = 'red'
#Data handling stuff#
#load the mc dataframe for all years
if options.pt_reweight:
cr_selection = config['reweight_cr']
output_tag += '_pt_reweighted'
root_obj = ROOTHelpers(output_tag, mc_dir, mc_fnames, data_dir,
data_fnames, proc_to_tree_name, train_vars,
vars_to_add, cr_selection)
else:
root_obj = ROOTHelpers(output_tag, mc_dir, mc_fnames, data_dir,
data_fnames, proc_to_tree_name, train_vars,
vars_to_add, presel)
for sig_obj in root_obj.sig_objects:
root_obj.load_mc(sig_obj, reload_samples=options.reload_samples)
for bkg_obj in root_obj.bkg_objects:
root_obj.load_mc(bkg_obj,
bkg=True,
reload_samples=options.reload_samples)
for data_obj in root_obj.data_objects:
root_obj.load_data(data_obj, reload_samples=options.reload_samples)
root_obj.concat()
if options.pt_reweight and options.reload_samples:
root_obj.apply_pt_rew('DYMC', presel)
#load MVA
with open(options.mva_config, 'r') as mva_config_file:
config = yaml.load(mva_config_file)
model = config['models'][options.mva_proc]
boundaries = config['boundaries'][options.mva_proc]
#add DNN later
if isinstance(model, str):
print 'evaluating BDT: {}'.format(model)
clf = pickle.load(open('models/{}'.format(model), "rb"))
root_obj.mc_df_sig[
options.mva_proc + '_mva'] = clf.predict_proba(
root_obj.mc_df_sig[train_vars].values)[:, 1:].ravel()
root_obj.mc_df_bkg[
options.mva_proc + '_mva'] = clf.predict_proba(
root_obj.mc_df_bkg[train_vars].values)[:, 1:].ravel()
root_obj.data_df[
options.mva_proc + '_mva'] = clf.predict_proba(
root_obj.data_df[train_vars].values)[:, 1:].ravel()
else:
raise IOError(
'Did not get a classifier models in correct format in config'
)
#Plotter stuff#
plotter = Plotter(root_obj,
train_vars,
sig_col=sig_colour,
norm_to_data=True)
cat_counter = 0
for b in boundaries:
if cat_counter == 0:
extra_cuts = options.mva_proc + '_mva >' + str(
boundaries['tag_0'])
else:
extra_cuts = (options.mva_proc + '_mva <' + str(
boundaries['tag_' + str(cat_counter - 1)])) + ' and ' + (
options.mva_proc + '_mva >' +
str(boundaries['tag_' + str(cat_counter)]))
plotter.plot_input(options.mass_var_name,
options.n_bins,
output_tag,
options.ratio_plot,
norm_to_data=True,
extra_cuts=extra_cuts,
extra_tag=cat_counter,
blind=True)
cat_counter += 1