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)