def reload_data(data_paths):
"""
Iterate through the data folder and organize each experiment into a list, with their progress data, hyper-parameters
and also analyze all the curves and give the distinct hyper-parameters.
:param data_path: Path of the folder storing all the data
:return [exps_data, plottable_keys, distinct_params]
exps_data: A list of the progress data for each curve. Each curve is an AttrDict with the key
'progress': A dictionary of plottable keys. The val of each key is an ndarray representing the
values of the key during training, or one column in the progress.txt file.
'params'/'flat_params': A dictionary of all hyperparameters recorded in 'variants.json' file.
plottable_keys: A list of strings representing all the keys that can be plotted.
distinct_params: A list of hyper-parameters which have different values among all the curves. This can be used
to split the graph into multiple figures. Each element is a tuple (param, list_of_values_to_take).
"""
exps_data = copy.copy(
core.load_exps_data(data_paths,
disable_variant=False,
ignore_missing_keys=True))
plottable_keys = copy.copy(
sorted(
list(set(flatten(list(exp.progress.keys())
for exp in exps_data)))))
distinct_params = copy.copy(sorted(
core.extract_distinct_params(exps_data)))
return exps_data, plottable_keys, distinct_params
def reload_data():
global exps_data
global plottable_keys
global distinct_params
exps_data = core.load_exps_data(args.data_paths, args.disable_variant)
plottable_keys = sorted(list(
set(flatten(list(exp.progress.keys()) for exp in exps_data))))
distinct_params = sorted(core.extract_distinct_params(exps_data))
def reload_data():
global exps_data
global plottable_keys
global distinct_params
exps_data = core.load_exps_data(args.data_paths,args.disable_variant)
plottable_keys = sorted(list(
set(flatten(list(exp.progress.keys()) for exp in exps_data))))
distinct_params = sorted(core.extract_distinct_params(exps_data))
def reload_data():
global exps_data
global plottable_keys
global distinct_params
exps_data = core.load_exps_data(args.data_path)
plottable_keys = list(
set(flatten(exp.progress.keys() for exp in exps_data)))
distinct_params = core.extract_distinct_params(exps_data)
def reload_data():
global exps_data
global plottable_keys
global distinct_params
exps_data = core.load_exps_data(args.data_paths,args.disable_variant)
plottable_keys = list(
set(flatten(list(exp.progress.keys()) for exp in exps_data)))
plottable_keys = sorted([k for k in plottable_keys if k is not None])
# distinct_params = sorted(core.extract_distinct_params(exps_data))
distinct_params = [] # todo: to get normal plots at all
def reload_data():
global exps_data
global plottable_keys
global distinct_params
args.disable_variant = False
exps_data = core.load_exps_data(args.data_paths, args.disable_variant)
plottable_keys = list(
set(flatten(list(exp.progress.keys()) for exp in exps_data)))
plottable_keys = sorted([k for k in plottable_keys if k is not None])
distinct_params = sorted(core.extract_distinct_params(exps_data))
print("\n\n distinct_params:{} \n\n".format(distinct_params))
