help='Number of tails (1 or 2)')
args, unknown = argparser.parse_known_args()
assert args.metric in ['loss', 'loglik'
], 'Metric must be one of ["loss", "loglik"].'
if args.pool:
args.ablation = True
ablations = None
basenames_to_pool = None
exps_outdirs = []
for path in args.config_paths:
p = Config(path)
models = filter_models(p.model_list, args.models)
cdr_models = [
x for x in models if (x.startswith('CDR') or x.startswith('DTSR'))
]
partitions = get_partition_list(args.partition)
partition_str = '-'.join(partitions)
if args.metric == 'loss':
file_name = 'losses_mse_%s.txt' % partition_str
else:
file_name = 'loglik_%s.txt' % partition_str
if args.twostep:
file_name = 'LM_2STEP_' + file_name
if args.ablation:
'Fit ablated models to data convolved using the ablated model. Otherwise fits ablated models to data convolved using the full model.'
)
argparser.add_argument(
'-f',
'--force',
action='store_true',
help=
'Refit and overwrite any previously trained models. Otherwise, previously trained models are skipped.'
)
args, unknown = argparser.parse_known_args()
for path in args.config_paths:
p = Config(path)
models = filter_models(p.model_list, args.models, cdr_only=True)
models = [
x for x in models if (x.startswith('CDR') or x.startswith('DTSR'))
]
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'
