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))