class STANOptions(OptionReader):
init_only = get_opt('--init')
compile_only = get_opt('--compile')
joint = get_opt('--joint')
variant = get_opt('--var')
chains = get_opt('--chains', int)
iters = get_opt('--iters', int)
warmup = get_opt('--warmup', int)
input = get_opt('--input')
model = get_opt('MODEL')
data = get_opt('DATA')
@property
def basename(self):
v = self.variant
if v:
return f'{self.model}-{v}'
else:
return self.model
class TrainOptions(OptionReader):
data = get_opt('DATA')
algo = get_opt('ALGO')
drop_ratings = get_opt('--drop-ratings')
default = get_opt('--default')
name = get_opt('--name')
train_data = get_opt('--train-data')
@property
def algo_fn(self):
afn = self.name
if not afn:
afn = dt.afname(self.algo)
return afn
class EvalOptions(OptionReader):
data = get_opt('<dataset>')
algo = get_opt('<algorithm>')
drop_ratings = get_opt('--drop-ratings')
name = get_opt('--name')
n = get_opt('-n', int)
time = get_opt('--time')
default = get_opt('--default')
tuning = get_opt('--tuning-data')
pretrained = get_opt('--pretrained')
rerank = get_opt('--rerank')
output_format = get_opt('--output-format')
@property
def algo_fn(self):
afn = self.name
if not afn:
afn = dt.afname(self.algo)
return afn
class SplitOptions(OptionReader):
data = get_opt('DATASET')
test_users = get_opt('--test-users', int)
min_ratings = get_opt('--min-ratings', int)
subdir = get_opt('--subdir')
class SampleOptions(OptionReader):
data = get_opt('DATASET')
sample_size = get_opt('-n', int)
min_ratings = get_opt('--min', int)
class InspectOpts(OptionReader):
verbose = get_opt('--verbose')
path = get_opt('FILE', Path)