def analyze_data_folds(filedir,filename, models, tr_files, label, variables, sigmodel,cut_values,sub_dir,debug=False):
data, X = read_data_apply(filedir+filename, tr_files, label, variables, sigmodel)
if len(X)==0: return
#print(len(data),len(X))
pred_fold, proba_fold = calculate_pred_fold(models,data,X,cut_values)
save_file(data, pred_fold, proba_fold, filename, sigmodel, sub_dir)
if debug:
for i in range(len(data['EventNumber'])):
print (data['EventNumber'][i], proba_fold[i][0])
def analyze_data_folds(filedir,
filename,
models,
tr_files,
label,
variables,
phys_model,
cut_values,
sub_dir,
syst_var,
mass_points,
debug=False):
prob_files = get_prob_files(len(models), phys_model, sub_dir)
use_app_randomlabel = False
if os.path.isfile(('OutputModel/' + sub_dir + 'use_bkg_randomlabel')):
use_app_randomlabel = True
#print()
#print(use_app_randomlabel)
#print()
data, X = read_data_apply(filedir + filename,
tr_files,
label,
variables,
prob_files,
syst_var,
mass_points=mass_points,
use_app_randomlabel=use_app_randomlabel)
if len(X) == 0: return
#print(len(data),len(X))
pred_fold, proba_fold = calculate_pred_fold(models, data, X, cut_values)
save_file(data, pred_fold, proba_fold, filename, phys_model, sub_dir,
syst_var)
if debug:
for i in range(len(data['EventNumber'])):
print(data['EventNumber'][i], proba_fold[i][0])
def analyze_data(filedir, filename, model, X_mean, X_dev, label, variables,
sigmodel):
data, X = read_data_apply(filedir + filename, X_mean, X_dev, label,
variables, sigmodel)
pred, proba = calculate_pred(model, X)
save_file(data, pred, proba, filename, sigmodel)
def analyze_data(filedir,filename, model, X_mean, X_dev, label, variables, sigmodel,cut_value,sub_dir):
data, X = read_data_apply(filedir+filename, X_mean, X_dev, label, variables, sigmodel)
if len(X)==0: return
pred, proba = calculate_pred(model,X,cut_value)
save_file(data, pred, proba, filename, sigmodel, sub_dir)