def print_auc_table_to_file(preds, target, preds_header=None, filename=None):
# prints a table of AUROCs and p-values
# also train the baseline model using df_mdl
# preds is a dictionary of predictions
if filename is None:
filename = 'auc_table.csv'
f = open(filename, 'w')
if preds_header is None:
preds_header = preds.keys()
P = len(preds_header)
y = target == 1
f.write('{}\t'.format(''))
# print header line
for p in range(P):
f.write('{}\t'.format(preds_header[p]))
f.write('\n')
for p in range(P):
f.write('{}\t'.format(preds_header[p]))
pname = preds_header[p]
for q in range(P):
qname = preds_header[q]
if pname not in preds:
f.write('{}\t'.format(
'')) # skip this as we do not have the prediction
elif p == q:
auc, ci = ru.calc_auc(preds[pname],
y,
with_ci=True,
alpha=0.05)
f.write('{:0.3f} [{:0.3f}, {:0.3f}]\t'.format(
auc, ci[0], ci[1]))
elif q > p:
#TODO: cronenback alpha
f.write('{}\t'.format(''))
else:
if qname not in preds:
f.write('{}\t'.format(
'')) # skip this as we do not have the prediction
else:
pval, ci = ru.test_auroc(preds[pname], preds[qname], y)
if pval > 0.001:
f.write('{:0.3f}{}\t'.format(pval, ''))
else:
f.write('< 0.001{}\t'.format(''))
f.write('\n')
f.close()
def print_auc_table(preds, target, preds_header, with_alpha=True):
# prints a table of AUROCs and p-values like what was presented in the sepsis 3 paper
y = target == 1
P = len(preds)
print('{:5s}'.format(''), end='\t')
for p in range(P):
print('{:20s}'.format(preds_header[p]), end='\t')
print('')
for p in range(P):
ppred = preds_header[p]
print('{:5s}'.format(ppred), end='\t')
for q in range(P):
qpred = preds_header[q]
if ppred not in preds:
print('{:20s}'.format(''),
end='\t') # skip this as we do not have the prediction
elif p == q:
auc, ci = ru.calc_auc(preds[ppred],
y,
with_ci=True,
alpha=0.05)
print('{:0.3f} [{:0.3f}, {:0.3f}]'.format(auc, ci[0], ci[1]),
end='\t')
elif qpred not in preds:
print('{:20s}'.format(''),
end='\t') # skip this as we do not have the prediction
elif q > p:
if with_alpha == False:
# skip printing cronbach alpha as requested by input
print('{:20s}'.format(''), end='\t')
else:
alpha, ci = cronbach_alpha_bootstrap(np.row_stack(
[preds[ppred], preds[qpred]]),
B=2000)
print('{:0.3f} [{:0.3f}, {:0.3f}]'.format(
alpha, ci[0], ci[1]),
end='\t')
else:
pval, ci = ru.test_auroc(preds[ppred], preds[qpred], y)
if pval > 0.001:
print('{:0.3f}{:15s}'.format(pval, ''), end='\t')
else:
print('< 0.001{:15s}'.format(''), end='\t')
print('')