K.set_session(sess)
if args.small_part:
args.save_every = 2**30
# Build readers, discretizers, normalizers
train_reader = DecompensationReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'))
val_reader = DecompensationReader(dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(
args.data, 'val_listfile.csv'))
discretizer = OneHotEncoder(impute_strategy=args.imputation)
discretizer_header = discretizer.transform(
train_reader.read_example(0)["X"])[2].split(',')
cont_channels = [
i for (i, x) in enumerate(discretizer_header) if x.find("->") == -1
]
normalizer = Normalizer(
fields=cont_channels) # choose here which columns to standardize
normalizer_state = args.normalizer_state
if normalizer_state is None:
normalizer_state = 'decomp_onehotenc_n:{}.normalizer'.format(
args.n_samples)
normalizer_state = os.path.join(os.path.dirname(__file__),
normalizer_state)
normalizer.load_params(normalizer_state)
train_reader = DecompensationReader(
dataset_dir='../../data/decompensation/train/',
listfile='../../data/decompensation/train_listfile.csv')
val_reader = DecompensationReader(
dataset_dir='../../data/decompensation/train/',
listfile='../../data/decompensation/val_listfile.csv')
discretizer = Discretizer(timestep=args.timestep,
store_masks=True,
imput_strategy='previous',
start_time='zero')
discretizer_header = discretizer.transform(
train_reader.read_example(0)[0])[1].split(',')
cont_channels = [
i for (i, x) in enumerate(discretizer_header) if x.find("->") == -1
]
normalizer = Normalizer(fields=cont_channels) # choose here onlycont vs all
normalizer.load_params(
'decomp_ts{}.input_str:previous.n1e5.start_time:zero.normalizer'.format(
args.timestep))
args_dict = dict(args._get_kwargs())
args_dict['header'] = discretizer_header
# init class
print "==> using network %s" % args.network
network_module = importlib.import_module("networks." + args.network)
small_part=args.small_part)
else:
train_reader = DecompensationReader(dataset_dir='../../data/decompensation/train/',
listfile='../../data/decompensation/train_listfile.csv')
val_reader = DecompensationReader(dataset_dir='../../data/decompensation/train/',
listfile='../../data/decompensation/val_listfile.csv')
discretizer = Discretizer(timestep=args.timestep,
store_masks=True,
imput_strategy='previous',
start_time='zero')
if args.deep_supervision:
discretizer_header = discretizer.transform(train_data_loader._data["X"][0])[1].split(',')
else:
discretizer_header = discretizer.transform(train_reader.read_example(0)["X"])[1].split(',')
cont_channels = [i for (i, x) in enumerate(discretizer_header) if x.find("->") == -1]
normalizer = Normalizer(fields=cont_channels) # choose here onlycont vs all
normalizer.load_params('decomp_ts{}.input_str:previous.n1e5.start_time:zero.normalizer'.format(args.timestep))
args_dict = dict(args._get_kwargs())
args_dict['header'] = discretizer_header
args_dict['task'] = 'decomp'
# Build the model
print "==> using model {}".format(args.network)
model_module = imp.load_source(os.path.basename(args.network), args.network)
model = model_module.Network(**args_dict)
suffix = "{}.bs{}{}{}.ts{}".format("" if not args.deep_supervision else ".dsup",
parser.set_defaults(small_part=False)
args = parser.parse_args()
print args
train_reader = DecompensationReader(dataset_dir='../../data/decompensation/train/',
listfile='../../data/decompensation/train_listfile.csv')
val_reader = DecompensationReader(dataset_dir='../../data/decompensation/train/',
listfile='../../data/decompensation/val_listfile.csv')
discretizer = Discretizer(timestep=args.timestep,
store_masks=True,
imput_strategy='previous',
start_time='zero')
discretizer_header = discretizer.transform(train_reader.read_example(0)[0])[1].split(',')
cont_channels = [i for (i, x) in enumerate(discretizer_header) if x.find("->") == -1]
normalizer = Normalizer(fields=cont_channels) # choose here onlycont vs all
normalizer.load_params('decomp_ts0.8.input_str:previous.n1e5.start_time:zero.normalizer')
args_dict = dict(args._get_kwargs())
# init class
print "==> using network %s" % args.network
network_module = importlib.import_module("networks." + args.network)
network = network_module.Network(**args_dict)
time_step_suffix = ".ts%.2f" % args.timestep
network_name = args.prefix + network.say_name() + time_step_suffix
print "==> network_name:", network_name
condensed=args.condensed)
val_reader = DecompensationReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'val_listfile.csv'),
sources=sources,
timesteps=args.timesteps,
condensed=args.condensed)
train_reader = DecompensationReader(
dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data, 'train_listfile.csv'),
sources=sources,
timesteps=args.timesteps,
condensed=args.condensed)
reader_header = train_reader.read_example(0)['header']
n_bins = len(train_reader.read_example(0))
discretizer = Discretizer(timestep=args.timestep,
store_masks=True,
impute_strategy='previous',
start_time='zero',
header=reader_header,
sources=sources)
if args.deep_supervision:
discretizer_header = discretizer.transform(
train_data_loader._data["X"][0])[1].split(',')
else:
discretizer_header = discretizer.transform(
train_reader.read_example(0)["X"])[1].split(',')