set(basenames_to_pool_cur))))
exps_outdirs.append(p.outdir)
else:
comparison_sets = {None: cdr_models}
if not args.pool:
for s in comparison_sets:
model_set = comparison_sets[s]
if len(model_set) > 1:
if s is not None:
stderr(
'Comparing models within ablation set "%s"...\n' %
s)
for i in range(len(model_set)):
m1 = model_set[i]
p.set_model(m1)
for j in range(i + 1, len(model_set)):
m2 = model_set[j]
is_nested = nested(m1, m2)
if is_nested or not args.ablation:
if is_nested:
if m1.count('!') > m2.count('!'):
a_model = m1
b_model = m2
else:
a_model = m2
b_model = m1
else:
a_model = m1
b_model = m2
partitions = get_partition_list(args.partition)
partition_str = '-'.join(partitions)
for m in models:
dir_path = p.outdir + '/' + m
if args.ablated_models:
data_path = dir_path + '/X_conv_' + partition_str + '.csv'
else:
data_path = p.outdir + '/' + m.split(
'!')[0] + '/X_conv_' + partition_str + '.csv'
stderr('Two-step analysis using data file %s\n' % data_path)
if os.path.exists(data_path):
p.set_model(m)
f = Formula(p['formula'])
model_form = f.to_lmer_formula_string(
z=args.zscore, correlated=not args.uncorrelated)
model_form = model_form.replace('-', '_')
is_lme = '|' in model_form
df = pd.read_csv(data_path, sep=' ', skipinitialspace=True)
for c in df.columns:
if df[c].dtype.name == 'object':
df[c] = df[c].astype(str)
new_cols = []
for c in df.columns:
new_cols.append(c.replace('-', '_'))