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_dir = config['data_file_dir']
data_fnames = config['data_file_names']
proc_to_tree_name = config['proc_to_tree_name']
object_vars = config['object_vars']
flat_obj_vars = [var for i_object in object_vars for var in i_object]
event_vars = config['event_vars']
vars_to_add = config['vars_to_add']
presel = config['preselection']
#Data handling stuff#
#load the mc dataframe for all years
root_obj = ROOTHelpers(output_tag, mc_dir, mc_fnames, data_dir, data_fnames, proc_to_tree_name, flat_obj_vars+event_vars, vars_to_add, presel)
for sig_obj in root_obj.sig_objects:
root_obj.load_mc(sig_obj, reload_samples=options.reload_samples)
if not options.data_as_bkg:
for bkg_obj in root_obj.bkg_objects:
root_obj.load_mc(bkg_obj, bkg=True, reload_samples=options.reload_samples)
else:
for data_obj in root_obj.data_objects:
root_obj.load_data(data_obj, reload_samples=options.reload_samples)
#overwrite background attribute, for compat with DNN class
root_obj.mc_df_bkg = root_obj.data_df
root_obj.concat()
#apply cut-based selection if not optimising BDT score (pred probs still evaluated for compatability w exisiting catOpt constructor).
if len(options.cut_based_str)>0:
root_obj.apply_more_cuts(options.cut_based_str)
# DNN evaluation stuff #
#load architecture and model weights
print 'loading DNN: {}'.format(options.model_architecture)
with open('{}'.format(options.model_architecture), 'r') as model_json:
model_architecture = model_json.read()
model = keras.models.model_from_json(model_architecture)
model.load_weights('{}'.format(options.model))
LSTM = LSTM_DNN(root_obj, object_vars, event_vars, 1.0, False, True)
# set up X and y Matrices. Log variables that have GeV units
LSTM.var_transform(do_data=False) #bkg=data here. This option is for plotting purposes
X_tot, y_tot = LSTM.create_X_y()
X_tot = X_tot[flat_obj_vars+event_vars] #filter unused vars
#scale X_vars to mean=0 and std=1. Use scaler fit during previous dnn training
LSTM.load_X_scaler(out_tag=output_tag)
X_tot = LSTM.X_scaler.transform(X_tot)
#make 2D vars for LSTM layers
X_tot = pd.DataFrame(X_tot, columns=flat_obj_vars+event_vars)
X_tot_high_level = X_tot[event_vars].values
X_tot_low_level = LSTM.join_objects(X_tot[flat_obj_vars])
#predict probs
pred_prob_tot = model.predict([X_tot_high_level, X_tot_low_level], batch_size=1024).flatten()
sig_weights = root_obj.mc_df_sig['weight'].values
sig_m_ee = root_obj.mc_df_sig['dielectronMass'].values
pred_prob_sig = pred_prob_tot[y_tot==1]
bkg_weights = root_obj.data_df['weight'].values
bkg_m_ee = root_obj.data_df['dielectronMass'].values
pred_prob_bkg = pred_prob_tot[y_tot==0]
#category optimisation stuff#
#set up optimiser ranges and no. categories to test if non-cut based
ranges = [ [0.3,1.] ]
names = ['{} score'.format(output_tag)] #arbitrary
print_str = ''
cats = [1,2,3,4]
AMS = []
#just to use class methods here
if len(options.cut_based_str)>0:
optimiser = CatOptim(sig_weights, sig_m_ee, [pred_prob_sig], bkg_weights, bkg_m_ee, [pred_prob_bkg], 0, ranges, names)
AMS = optimiser.cutBasedAMS()
print 'String for cut based optimimastion: {}'.format(options.cut_based_str)
print 'Cut-based optimimsation gives AMS = {:1.8f}'.format(AMS)
else:
for n_cats in cats:
optimiser = CatOptim(sig_weights, sig_m_ee, [pred_prob_sig], bkg_weights, bkg_m_ee, [pred_prob_bkg], n_cats, ranges, names)
optimiser.optimise(1, options.n_iters) #set lumi to 1 as already scaled when loading in
print_str += 'Results for {} categories : \n'.format(n_cats)
print_str += optimiser.getPrintableResult()
AMS.append(optimiser.bests.totSignif)
print '\n {}'.format(print_str)
#make nCat vs AMS plots
Plotter.cats_vs_ams(cats, AMS, output_tag)
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_dir = config['data_file_dir']
data_fnames = config['data_file_names']
proc_to_tree_name = config['proc_to_tree_name']
train_vars = config['train_vars']
vars_to_add = config['vars_to_add']
presel = config['preselection']
#load the mc dataframe for all years
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)
if not options.data_as_bkg:
for bkg_obj in root_obj.bkg_objects:
root_obj.load_mc(bkg_obj,
bkg=True,
reload_samples=options.reload_samples)
else:
for data_obj in root_obj.data_objects:
root_obj.load_data(data_obj,
reload_samples=options.reload_samples)
root_obj.concat()
print 'loading classifier: {}'.format(options.model)
clf = pickle.load(open("{}".format(options.model), "rb"))
#apply cut-based selection if not optimising BDT score (pred probs still evaluated for compatability w exisiting constructor).
if len(options.cut_based_str) > 0:
root_obj.apply_more_cuts(options.cut_based_str)
sig_weights = root_obj.mc_df_sig['weight'].values
sig_m_ee = root_obj.mc_df_sig['dielectronMass'].values
pred_prob_sig = clf.predict_proba(
root_obj.mc_df_sig[train_vars].values)[:, 1:].ravel()
if options.data_as_bkg:
bkg_weights = root_obj.data_df['weight'].values
bkg_m_ee = root_obj.data_df['dielectronMass'].values
pred_prob_bkg = clf.predict_proba(
root_obj.data_df[train_vars].values)[:, 1:].ravel()
else:
bkg_weights = root_obj.mc_df_bkg['weight'].values
bkg_m_ee = root_obj.mc_df_bkg['dielectronMass'].values
pred_prob_bkg = clf.predict_proba(
root_obj.mc_df_bkg[train_vars].values)[:, 1:].ravel()
#set up optimiser ranges and no. categories to test if non-cut based
ranges = [[0.15, 1.]]
names = ['{} score'.format(output_tag)] #arbitrary
print_str = ''
cats = [1, 2, 3, 4, 5]
AMS = []
#just to use class methods here
if len(options.cut_based_str) > 0:
optimiser = CatOptim(sig_weights, sig_m_ee, [pred_prob_sig],
bkg_weights, bkg_m_ee, [pred_prob_bkg], 0,
ranges, names)
AMS = optimiser.cutBasedAMS()
print 'String for cut based optimimastion: {}'.format(
options.cut_based_str)
print 'Cut-based optimimsation gives AMS = {:1.8f}'.format(AMS)
else:
for n_cats in cats:
optimiser = CatOptim(sig_weights, sig_m_ee, [pred_prob_sig],
bkg_weights, bkg_m_ee, [pred_prob_bkg],
n_cats, ranges, names)
optimiser.optimise(
1, options.n_iters
) #set lumi to 1 as already scaled when loading in
print_str += 'Results for {} categories : \n'.format(n_cats)
print_str += optimiser.getPrintableResult()
AMS.append(optimiser.bests.totSignif)
print '\n {}'.format(print_str)
#make nCat vs AMS plots
Plotter.cats_vs_ams(cats, AMS, output_tag)