target_repl = (args.target_repl_coef > 0.0 and args.mode == 'train')
# Build readers, discretizers, normalizers
train_reader = MultitaskReader(dataset_dir='../../data/multitask/train/',
listfile='../../data/multitask/train_listfile.csv')
val_reader = MultitaskReader(dataset_dir='../../data/multitask/train/',
listfile='../../data/multitask/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('mult_ts%s.input_str:%s.start_time:zero.normalizer' % (args.timestep, args.imputation))
args_dict = dict(args._get_kwargs())
args_dict['header'] = discretizer_header
args_dict['ihm_pos'] = int(48.0 / args.timestep - 1e-6)
args_dict['target_repl'] = target_repl
# 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)
network = model # alias
target_repl = (args.target_repl_coef > 0.0 and args.mode == 'train')
# Build readers, discretizers, normalizers
train_reader = MultitaskReader(dataset_dir='../../data/multitask/train/',
listfile='../../data/multitask/train_listfile.csv')
val_reader = MultitaskReader(dataset_dir='../../data/multitask/train/',
listfile='../../data/multitask/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)["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('mult_ts%s.input_str:%s.start_time:zero.normalizer' % (args.timestep, args.imputation))
args_dict = dict(args._get_kwargs())
args_dict['header'] = discretizer_header
args_dict['ihm_pos'] = int(48.0 / args.timestep - 1e-6)
args_dict['target_repl'] = target_repl
# 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{}{}_partition={}_ihm={}_decomp={}_los={}_pheno={}".format(
if args.condensed:
experiment_name=experiment_name+'condensed_'
if args.small_part:
args.save_every = 2 ** 30
target_repl = (args.target_repl_coef > 0.0 and args.mode == 'train')
# Build readers, discretizers, normalizers
train_reader = MultitaskReader(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)
val_reader = MultitaskReader(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)
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)
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 which columns to standardize
normalizer_state = args.normalizer_state
if normalizer_state is None:
normalizer_state = 'mult_ts{}.input_str_{}.start_time_zero.normalizer'.format(args.timestep, args.imputation)
normalizer_state = os.path.join(os.path.dirname(__file__), normalizer_state)
# Build readers, discretizers, normalizers
train_reader = MultitaskReader(dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data,
'train_listfile.csv'))
val_reader = MultitaskReader(dataset_dir=os.path.join(args.data, 'train'),
listfile=os.path.join(args.data,
'val_listfile.csv'))
discretizer = Discretizer(timestep=args.timestep,
store_masks=True,
impute_strategy='previous',
start_time='zero')
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 which columns to standardize
normalizer_state = args.normalizer_state
if normalizer_state is None:
normalizer_state = 'mult_ts{}.input_str_{}.start_time_zero.normalizer'.format(
args.timestep, args.imputation)
normalizer_state = os.path.join(os.path.dirname(__file__),
normalizer_state)
normalizer.load_params(normalizer_state)
args_dict = dict(args._get_kwargs())