def calc_all_ic(pred_dict_all, label, date_col="datetime", dropna=False, n_jobs=-1):
"""calc_all_ic.
Parameters
----------
pred_dict_all :
A dict like {<method_name>: <prediction>}
label:
A pd.Series of label values
Returns
-------
{'Q2+IND_z': {'ic': <ic series like>
2016-01-04 -0.057407
...
2020-05-28 0.183470
2020-05-29 0.171393
'ric': <rank ic series like>
2016-01-04 -0.040888
...
2020-05-28 0.236665
2020-05-29 0.183886
}
...}
"""
pred_all_ics = {}
for k, pred in pred_dict_all.items():
pred_all_ics[k] = DelayedDict(["ic", "ric"], delayed(calc_ic)(pred, label, date_col=date_col, dropna=dropna))
pred_all_ics = complex_parallel(Parallel(n_jobs=n_jobs, verbose=10), pred_all_ics)
return pred_all_ics
def pred_autocorr_all(pred_dict, n_jobs=-1, **kwargs):
"""
calculate auto correlation for pred_dict
Parameters
----------
pred_dict : dict
A dict like {<method_name>: <prediction>}
kwargs :
all these arguments will be passed into pred_autocorr
"""
ac_dict = {}
for k, pred in pred_dict.items():
ac_dict[k] = delayed(pred_autocorr)(pred, **kwargs)
return complex_parallel(Parallel(n_jobs=n_jobs, verbose=10), ac_dict)