ts = ret["t"]
train_y = ret["y"]
train_names = ret["name"]
diseases_list = get_diseases(train_names, f'{data_dir}/')
diseases_embedding = disease_embedding(embeddings, word_indices, diseases_list)
d, discretizer_header, begin_pos, end_pos = discretizer.transform_reg(data[0])
discretizer_header = discretizer_header.split(',')
cont_channels = [
i for (i, x) in enumerate(discretizer_header) if x.find("->") == -1
]
da = [
discretizer.transform_end_t_hours_reg(X, los=t)[1]
for (X, t) in zip(data, ts)
]
mask = [column_sum(x) for x in da]
#train_set=[]
d = [
discretizer.transform_end_t_hours_reg(X, los=t)[0]
for (X, t) in zip(data, ts)
]
idx_features_train = [
logit(X, cont_channels, begin_pos, end_pos)[0] for X in d
]
features_train = [logit(X, cont_channels, begin_pos, end_pos)[1] for X in d]
train_y = ret["y"]
train_names = ret["name"]
diseases_list = get_diseases(train_names, dataset_subject_dir)
diseases_embedding = disease_embedding(embeddings, word_indices, diseases_list)
feature_cols = ['LOS', 'Hos_LOS', 'Num_Prev_Hos_Adm']
additional_features = get_additional_features(train_names)
# additional_features_list = normalize_standard(additional_features, feature_cols).values.tolist()
d, discretizer_header, begin_pos, end_pos = discretizer.transform_reg(data[0])
discretizer_header = discretizer_header.split(',')
cont_channels = [i for (i, x) in enumerate(discretizer_header) if x.find("->") == -1]
da = [discretizer.transform_end_t_hours_reg(X, los=t)[1] for (X, t) in zip(data, ts)]
mask = [column_sum(x) for x in da]
# train_set=[]
d = [discretizer.transform_end_t_hours_reg(X, los=t)[0] for (X, t) in zip(data, ts)]
idx_features_train = [logit(X, cont_channels, begin_pos, end_pos)[0] for X in d]
features_train = [logit(X, cont_channels, begin_pos, end_pos)[1] for X in d]
features_train_extended = [np.hstack([X, d]) for (X, d) in zip(features_train, additional_features.values.tolist())]
train_X = features_train_extended
scaler = MinMaxScaler()
scaler.fit(train_X)
train_X = scaler.transform(train_X)