ETC = (int(np.mean(times) * total_n_runs)) print(f'Time elapsed single run in {single_run_time} sec\t' f'ETC {int(ETC/60)} min \t Progress {int(100*(time()-t00)/ETC)}% ') # In[8]: working_folder, filename = list_of_fc[0]._print_sett(list_of_fc=list_of_fc) store = False if __name__ == "__main__": filename = list_of_fc[0].filename store = True import valid_plots as dfplots import functions_pp dict_all = dfplots.merge_valid_info(list_of_fc, store=store) if store: dict_merge_all = functions_pp.load_hdf5(filename + '.h5') lag_rel = 50 kwrgs = { 'wspace': 0.16, 'hspace': .25, 'col_wrap': 2, 'skip_redundant_title': True, 'lags_relcurve': [lag_rel], 'fontbase': 14, 'figaspect': 2 } #kwrgs = {'wspace':0.25, 'col_wrap':3, 'threshold_bin':fc.threshold_pred} met = ['AUC-ROC', 'AUC-PR', 'Precision', 'BSS', 'Accuracy', 'Rel. Curve']
import valid_plots as dfplots if __name__ == '__main__': fc = fcev(path_data=path_df_data, n_cpu=1, causal=True) fc.get_TV(kwrgs_events=None) fc.fit_models(lead_max=35) dict_experiments = {} fc.perform_validation(n_boot=100, blocksize='auto', threshold_pred=(1.5, 'times_clim')) dict_experiments['test'] = fc.dict_sum working_folder, filename = fc._print_sett(list_of_fc=[fc]) store=True dict_all = dfplots.merge_valid_info([fc], store=store) if store: dict_merge_all = functions_pp.load_hdf5(filename) kwrgs = {'wspace':0.25, 'col_wrap':3} #, 'threshold_bin':fc.threshold_pred} met = ['AUC-ROC', 'AUC-PR', 'BSS', 'Rel. Curve', 'Precision', 'Accuracy'] expers = list(dict_experiments.keys()) line_dim = 'model' fig = dfplots.valid_figures(dict_merge_all, line_dim=line_dim, group_line_by=None, lines_legend=None, met=met, **kwrgs)