'C': 1.0,
'mu': 1.0,
'alpha': 1.0,
'h**o': 27.2114,
'lumo': 27.2114,
'gap': 27.2114,
'r2': 1.0,
'zpve': 27211.4,
'u0': 27.2114,
'u298': 27.2114,
'h298': 27.2114,
'g298': 27.2114,
'cv': 1.0,
'u0_atom': 27.2114,
'u298_atom': 27.2114,
'h298_atom': 27.2114,
'g298_atom': 27.2114,
'cv_atom': 1.0
}
for label in labels:
cf = conversion[label]
rloader = res.ResultsENN('smp-' + label, reps=[1, 2, 3, 4, 5])
results = rloader.get_all_predictions()
summary = met.evaluate_average(results,
metric=met.mae,
verbose=True,
select=3)
summary = [(cf * s[0], cf * s[1]) for s in summary]
print('%9s: %6.3f \pm %6.3f' % (label, *summary[2]))
import numpy as np
import atom3d.util.results as res
import atom3d.util.metrics as met
cutoff='06'
maxnumat='600'
for id_split in ['30','60']:
name = 'lba-id'+id_split+'-siamese_cutoff-'+cutoff+'_maxnumat-'+maxnumat
print(name)
rloader = res.ResultsENN(name, reps=[1,2,3])
results = rloader.get_all_predictions()
summary = met.evaluate_average(results, metric = met.rmse, verbose = False)
print('RMSE: %6.3f \pm %6.3f'%summary[2])
summary = met.evaluate_average(results, metric = met.pearson, verbose = False)
print('R_P: %6.3f \pm %6.3f'%summary[2])
summary = met.evaluate_average(results, metric = met.spearman, verbose = False)
print('R_S: %6.3f \pm %6.3f'%summary[2])