def echo_summary_table_row(model, epoch, hidden):
translate = {
'inference-features': 'W',
'forward-selection': 'F'
}
data = get_data(model, epoch, hidden)
auc = np.array(data['auc'])
low, high, _ = ci(auc)
print r'& %s %s hidden %s iter & \T\B %.04f & %.06f & [%.04f,%.04f]\\\cline{2-5}' % (translate[model], hidden, epoch, auc.mean(), auc.std(ddof=1), low, high)
def echo_result(model, epoch, num_hidden):
data = get_data(model, epoch, num_hidden)
auc = np.array(data['auc'])
low, high, _ = ci(auc)
title = get_title(model, epoch, num_hidden)
print r'\subsection{%s}' % (title,)
echo_auc_table(auc, title)
echo_statistics(auc)
print ''
print ''
def echo_logreg_rows():
datafiles = [
('W', 'sessions/17-recreating-winning-entry/data/inference-features-for-report.json'),
('F top 3', 'sessions/18-forward-selection/data/top-3-features-for-report.json'),
('F all feat', 'sessions/18-forward-selection/data/all-features-for-report.json')
]
for title, path in datafiles:
data = json.load(open(path))
auc = np.array(data['auc'])
low, high, _ = ci(auc)
print r'& %s & \T\B %.04f & %.06f & [%.04f,%.04f]\\\cline{2-5}' % (title, auc.mean(), auc.std(ddof=1), low, high)
def echo_statistics(auc):
m = auc.mean()
std = auc.std(ddof=1)
low, high, _ = ci(auc)
print r'''\subsubsection{Statistics on the AUC-score}
First the sample mean and sample standard deviation are calculated
\[
m = %.04f \quad\quad\text{and}\quad\quad s = %.06f
\]
which gives the 95\%% confidence interval
\[
\CI{%.04f}{%.04f}
\]''' % (m, std, low, high)
