def parse_config_json(config_path, lastfm):
''' Parse a JSON configuration file into a handy Namespace.
Parameters
-----------
config_path: str
The path to the .json file, or the directory where it is saved.
lastfm: str, LastFm, LastFm2Pandas
Instance of the tags database. If a string is passed, try to instantiate the tags database from the (string as a) path.
Returns
-------
config: argparse.Namespace
'''
if not isinstance(lastfm, (LastFm, LastFm2Pandas)):
lastfm = LastFm(os.path.expanduser(lastfm))
# if config_path is a folder, assume the folder contains a config.json
if os.path.isdir(os.path.expanduser(config_path)):
config_path = os.path.join(os.path.abspath(os.path.expanduser(config_path)), 'config.json')
else:
config_path = os.path.expanduser(config_path)
# load json
with open(config_path, 'r') as f:
config_dict = json.loads(f.read())
# create config namespace
config = argparse.Namespace(**config_dict['model'], **config_dict['model-training'], **config_dict['tfrecords'])
config.path = os.path.abspath(config_path)
# update config (optimizer will be instantiated with tf.get_optimizer using {"class_name": config.optimizer_name, "config": config.optimizer})
config.optimizer_name = config.optimizer.pop('name')
# read tags from popularity dataframe
top = config_dict['tags']['top']
if (top is not None) and (top != config.n_tags):
top_tags = lastfm.popularity()['tag'][:top].tolist()
tags = set(top_tags)
else:
tags = None
# update tags according to 'with' (to be added) and 'without' (to be discarded)
if tags is not None:
if config_dict['tags']['with']:
tags.update(config_dict['tags']['with'])
if config_dict['tags']['without']:
tags.difference_update(config_dict['tags']['without'])
tags = list(tags)
else:
raise ValueError("parameter 'with' is inconsistent to parameter 'top'")
# write final tags
config.tags = np.sort(lastfm.tag_to_tag_num(tags)) if tags is not None else None # sorting is necessary to aviod unexpected behaviour
# write final tags to merge together
config.tags_to_merge = lastfm.tag_to_tag_num(config_dict['tags']['merge']) if config_dict['tags']['merge'] is not None else None
# count number of classes
config.n_output_neurons = len(tags) if tags is not None else config.n_tags
return config
