help="Optimized model type. It can be 'pupil' or optimizer or 'optimizer'. Default is 'optimizer'",
default='optimizer',
)
parser.add_argument(
"-v",
"--verbose",
help="Regulate verbosity",
action='store_true'
)
args = parser.parse_args()
abspath = os.path.abspath(__file__)
dname = os.path.dirname(abspath)
os.chdir(dname)
confs = parse_path_comb(args.confs)
eval_dirs = list()
for conf in confs:
base = '.'.join(conf.split('.')[:-1])
dirs = list()
if args.model == 'optimizer':
cand1 = os.path.join(base, 'evaluation')
cand2 = os.path.join(base, 'eval_fix')
if os.path.exists(cand1):
dirs.append(cand1)
if os.path.exists(cand2):
dirs.append(cand2)
elif args.model == 'pupil':
base = os.path.join(base, 'evaluation')
for eval_file in os.listdir(base):
if 'launch_log' not in eval_file:
)
parser.add_argument("-nl",
"--no_line",
help="Do not link dots with line. Default is True",
action='store_true')
parser.add_argument(
'-hpnf',
"--hp_names_file",
help=
"File with hyper parameter names. All available files are in the same directory with this script",
default='hp_plot_names_english.conf')
args = parser.parse_args()
abspath = os.path.abspath(__file__)
dname = os.path.dirname(abspath)
os.chdir(dname)
dirs = parse_path_comb(args.dirs)
labels = args.labels.split(',')
datasets = args.datasets.split(',')
lines_by_metrics = dict()
label_idx = 0
if args.x_select is None:
x_select = None
else:
x_select = parse_x_select(args.x_select)
for label, dir in zip(labels, dirs):
dir_contents = parse_1_line_dir(dir)
for metric, metric_data in dir_contents.items():
parser.add_argument(
'-on',
'--old_name',
help="old parameter names",
)
parser.add_argument(
'-nn',
'--new_name',
help='new parameter names',
)
parser.add_argument(
'-tp',
'--types',
help="Types of parameters. default is float for all",
default=None,
)
args = parser.parse_args()
transformations = args.transformation.split(',')
old_names = args.old_name.split(',')
new_names = args.new_name.split(',')
num_params = len(old_names)
res_files = parse_path_comb(args.eval_file)
for f in res_files:
perform_transformation(f, args.new_eval_dir, old_names, new_names,
args.types, transformations)
if args.x_select is None:
select['x_select'] = None
else:
select['x_select'] = parse_x_select(args.x_select)
if args.line_select is None:
select['line_select'] = None
else:
select['line_select'] = parse_line_select(args.line_select)
style = dict(
no_line=args.no_line,
error=args.error,
marker=args.marker,
)
eval_dirs = parse_path_comb(args.eval_dir)
metric_scales = parse_metric_scales_str(args.metric_scales)
hp_plot_order = args.hp_order.split(',')
# abspath = os.path.abspath(__file__)
# dname = os.path.dirname(abspath)
# os.chdir(dname)
plot_parameter_names = get_parameter_names(
os.path.join(*(os.path.split(os.path.abspath(__file__))[:-1] +
(args.hp_names_file, ))))
xscale = args.xscale
for eval_dir in eval_dirs:
print(eval_dir)
plot_dir = os.path.join(*list(os.path.split(eval_dir)[:-1]) +
[args.plot_dir])
"--verbose",
help="Regulate verbosity",
action='store_true')
parser.add_argument(
'-ng',
'--no_git',
help="don't add confs to git",
action='store_true',
)
args = parser.parse_args()
base_path = helmo.util.path_help.get_path_from_path_rel_to_repo_root(
os.path.join('helmo', 'experiments'))
confs = parse_path_comb(args.confs, filter_=False)
hp_names_with_types = args.hyper_parameters.split(':')
hp_names = list()
types = list()
for s in hp_names_with_types:
spl = s.split(',')
hp_names.append(spl[0])
types.append(spl[1])
span = args.span.split(':')
points = list()
for string in span:
points.append(get_points_from_range(string))
parser.add_argument('-o',
"--output",
help="Output file name. Path relative to this script.")
parser.add_argument(
'-i',
"--inter_path",
help="Path between model folder and results folder. default is 'test'",
default='test',
)
parser.add_argument("-v",
"--verbose",
help="Regulate verbosity",
action='store_true')
args = parser.parse_args()
model_paths = parse_path_comb(args.model_paths, filter_=False)
results = dict()
for model_path in model_paths:
results[model_path] = pupil_test_results_summarize(model_path,
args.inter_path)
string = stats_string_for_all_models(results, 2)
create_path(args.output, file_name_is_in_path=True)
with open(args.output, 'w') as f:
f.write(string)
if args.verbose: