parser.add_argument('--verbose', dest="verbose", default=False, action="store_true",
help="The format of the log")
args = parser.parse_args()
list_of_directory = os.listdir(args.folder)
group = {}
for directory in list_of_directory:
if(args.fls not in directory and args.fls != "*"):
# list_of_directory.remove(list_of_directory)
pass
else:
try:
logger_object = logger.JSONLogger(os.path.join(args.folder,directory,"log.json"), mod="continue")
dct_key = logger_object["dataset"]+"-"+str(logger_object["size"])+"D"
if(args.verbose):
print("Folder \""+directory+"\" embedding size->"+str(logger_object["size"]))
if(dct_key not in group):
group[dct_key] = {}
# euclidean classifier getter
ll = [logger_object["unsupervised_eval"]["nmi"]]
if(ll[0] == 0.):
print("0 error in ", directory, " better ignore it please ")
if("nmi" not in group[dct_key]):
group[dct_key]["nmi"] = []
group[dct_key]["nmi"] += ll
help=
"Precision at to evaluate (e.g --precision 1 3 5 | evaluate precision at 1, 3 and 5) "
)
parser.add_argument('--cuda', dest="cuda", action="store_true")
args = parser.parse_args()
torch.set_default_tensor_type(torch.DoubleTensor)
if (args.folder == "" and args.id == ""):
print("Please give arguments for --folder or --id")
quit()
if args.folder == "" and args.id != "":
global_config = config.ConfigurationFile("./DATA/config.conf")
saving_folder = global_config["save_folder"]
args.folder = os.path.join(saving_folder, args.id)
log_in = logger.JSONLogger(os.path.join(args.folder, "log.json"),
mod="continue")
dataset_name = log_in["dataset"]
n_gaussian = log_in["n_gaussian"]
print("EVALUATE SUPERVISED CLUSTERING ON ")
print("\t Dataset -> ", dataset_name)
print("\t Number of communities -> ", n_gaussian)
size = log_in["size"]
# print("Loading Corpus ")
X, Y = data_loader.load_corpus(dataset_name)
representations_init = torch.load(
os.path.join(args.folder, "embeddings_init.t7"))
representations = torch.load(os.path.join(args.folder, "embeddings.t7"))[0]
help="Do force relaunch experiment evaluation")
args = parser.parse_args()
nb_communities_dict = {
"dblp": 5,
"flickr": 195,
"blogCatalog": 39,
"wikipedia": 40
}
list_of_directory = os.listdir(args.folder)
for directory in list_of_directory:
try:
path_to_experiment = os.path.join(args.folder, directory)
logger_object = logger.JSONLogger(os.path.join(path_to_experiment,
"log.json"),
mod="continue")
# if the experiment has already been evaluated we go to the next one
if ("unsupervised_eval" in logger_object
and "supervised_eval" in logger_object and not args.force):
print(directory, " has already been evaluated")
# continue
else:
pass
if (0 == 1):
pass
else:
print(directory)
# set info vars
print('Loading Corpus')