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)