lags_i = np.arange(0, 120, 10)
list_of_fc = [
fcev(
path_data=ERA_q65tail,
precur_aggr=precur_aggr,
use_fold=use_fold,
start_end_TVdate=None,
stat_model=(
'logitCV',
{
'Cs': 10, #np.logspace(-4,1,10)
'class_weight': {
0: 1,
1: 1
},
'scoring': 'neg_brier_score',
'penalty': 'l2',
'solver': 'lbfgs',
'max_iter': 100,
'kfold': 5,
'seed': 2
}),
kwrgs_pp={
'add_autocorr': False,
'normalize': 'datesRV'
},
dataset='',
keys_d='CPPA+sm'),
fcev(
path_data=ERA_q65tail,
precur_aggr=precur_aggr,
kwrgs_events = kwrgs_events
precur_aggr = 15
add_autocorr = False
use_fold = None
n_boot = 5
lags_i = np.array([0, 10, 15, 20, 25, 30])
start_end_TVdate = None # ('7-04', '8-22')
list_of_fc = [
fcev(path_data=ERA_data,
precur_aggr=precur_aggr,
use_fold=use_fold,
start_end_TVdate=None,
stat_model=logitCV,
kwrgs_pp={
'add_autocorr': add_autocorr,
'normalize': 'datesRV'
},
dataset=f'CPPA vs PEP',
keys_d='PEP',
n_cpu=n_cpu)
]
# fcev(path_data=ERA_data, precur_aggr=precur_aggr,
# use_fold=use_fold, start_end_TVdate=None,
# stat_model=logitCV,
# kwrgs_pp={'add_autocorr':add_autocorr, 'normalize':'datesRV'},
# dataset=f'CPPA vs PEP',
# keys_d='CPPA',
# n_cpu=n_cpu),
# fcev(path_data=ERA_data, precur_aggr=precur_aggr,
# use_fold=use_fold, start_end_TVdate=None,
import wrapper_PCMCI as wPCMCI
except ImportError as e:
print('Not able to load in Tigramite modules, to enable causal inference '
'features, install Tigramite from '
'https://github.com/jakobrunge/tigramite/')
# remove created output folders
shutil.rmtree(rg.path_outsub1)
shutil.rmtree(os.path.join(main_dir, 'data', 'preprocessed'))
raise(e)
from class_fc import fcev
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}
ERA_data = data_dir + '/CPPA_ERA5_14-05-20_08hr_lag_0_c378f.h5'
kwrgs_events = {'event_percentile': 'std'} # = mean + std, see class_RV.Ev_threshold
kwrgs_events = kwrgs_events
precur_aggr = 1
use_fold = None
n_boot = 2000
lags_i = np.array([0, 10, 15, 20 , 25, 30])
start_end_TVdate = ('6-24', '8-22')
list_of_fc = [fcev(path_data=ERA_data, precur_aggr=precur_aggr,
use_fold=use_fold, start_end_TVdate=start_end_TVdate,
stat_model=logitCV,
kwrgs_pp={'add_autocorr':False, 'normalize':'datesRV'},
dataset=f'ERA-5',
keys_d='PEP', n_cpu=n_cpu),
fcev(path_data=ERA_data, precur_aggr=precur_aggr,
use_fold=use_fold, start_end_TVdate=start_end_TVdate,
stat_model=logitCV,
kwrgs_pp={'add_autocorr':False, 'normalize':'datesRV'},
dataset=f'ERA-5',
keys_d='CPPA', n_cpu=n_cpu),
fcev(path_data=EC_data, precur_aggr=precur_aggr,
use_fold=use_fold, start_end_TVdate=start_end_TVdate,
stat_model=logitCV,
kwrgs_pp={'add_autocorr':False, 'normalize':'datesRV'},
dataset=f'EC-Earth',
keys_d='PEP', n_cpu=n_cpu),
fcev(path_data=EC_data, precur_aggr=precur_aggr,