def variable_time_collate_fn3(
batch,
args,
device=torch.device("cpu"),
data_type="train",
data_min=None,
data_max=None,
):
"""
Expects a batch of time series data in the form of (record_id, tt, vals, mask, labels) where
- record_id is a patient id
- tt is a 1-dimensional tensor containing T time values of observations.
- vals is a (T, D) tensor containing observed values for D variables.
- mask is a (T, D) tensor containing 1 where values were observed and 0 otherwise.
- labels is a list of labels for the current patient, if labels are available. Otherwise None.
Returns:
combined_tt: The union of all time observations.
combined_vals: (M, T, D) tensor containing the observed values.
combined_mask: (M, T, D) tensor containing 1 where values were observed and 0 otherwise.
"""
D = batch[0][2].shape[1]
len_tt = [ex[1].size(0) for ex in batch]
maxlen = np.max(len_tt)
enc_combined_tt = torch.zeros([len(batch), maxlen]).to(device)
enc_combined_vals = torch.zeros([len(batch), maxlen, D]).to(device)
enc_combined_mask = torch.zeros([len(batch), maxlen, D]).to(device)
for b, (record_id, tt, vals, mask, labels) in enumerate(batch):
currlen = tt.size(0)
enc_combined_tt[b, :currlen] = tt.to(device)
enc_combined_vals[b, :currlen] = vals.to(device)
enc_combined_mask[b, :currlen] = mask.to(device)
enc_combined_vals, _, _ = utils.normalize_masked_data(
enc_combined_vals, enc_combined_mask, att_min=data_min, att_max=data_max
)
if torch.max(enc_combined_tt) != 0.0:
enc_combined_tt = enc_combined_tt / torch.max(enc_combined_tt)
data_dict = {
"observed_data": enc_combined_vals,
"observed_tp": enc_combined_tt,
"observed_mask": enc_combined_mask,
}
return data_dict
def variable_time_collate_fn2(
batch,
args,
device=torch.device("cpu"),
data_type="train",
data_min=None,
data_max=None,
):
"""
Expects a batch of time series data in the form of (record_id, tt, vals, mask, labels) where
- record_id is a patient id
- tt is a 1-dimensional tensor containing T time values of observations.
- vals is a (T, D) tensor containing observed values for D variables.
- mask is a (T, D) tensor containing 1 where values were observed and 0 otherwise.
- labels is a list of labels for the current patient, if labels are available. Otherwise None.
Returns:
combined_tt: The union of all time observations.
combined_vals: (M, T, D) tensor containing the observed values.
combined_mask: (M, T, D) tensor containing 1 where values were observed and 0 otherwise.
"""
D = batch[0][2].shape[1]
len_tt = [ex[1].size(0) for ex in batch]
maxlen = np.max(len_tt)
enc_combined_tt = torch.zeros([len(batch), maxlen]).to(device)
enc_combined_vals = torch.zeros([len(batch), maxlen, D]).to(device)
enc_combined_mask = torch.zeros([len(batch), maxlen, D]).to(device)
for b, (record_id, tt, vals, mask, labels) in enumerate(batch):
currlen = tt.size(0)
enc_combined_tt[b, :currlen] = tt.to(device)
enc_combined_vals[b, :currlen] = vals.to(device)
enc_combined_mask[b, :currlen] = mask.to(device)
combined_tt, inverse_indices = torch.unique(
torch.cat([ex[1] for ex in batch]), sorted=True, return_inverse=True
)
combined_tt = combined_tt.to(device)
offset = 0
combined_vals = torch.zeros([len(batch), len(combined_tt), D]).to(device)
combined_mask = torch.zeros([len(batch), len(combined_tt), D]).to(device)
combined_labels = None
N_labels = 1
combined_labels = torch.zeros(len(batch), N_labels) + torch.tensor(float("nan"))
combined_labels = combined_labels.to(device=device)
for b, (record_id, tt, vals, mask, labels) in enumerate(batch):
tt = tt.to(device)
vals = vals.to(device)
mask = mask.to(device)
if labels is not None:
labels = labels.to(device)
indices = inverse_indices[offset : offset + len(tt)]
offset += len(tt)
combined_vals[b, indices] = vals
combined_mask[b, indices] = mask
if labels is not None:
combined_labels[b] = labels
combined_vals, _, _ = utils.normalize_masked_data(
combined_vals, combined_mask, att_min=data_min, att_max=data_max
)
enc_combined_vals, _, _ = utils.normalize_masked_data(
enc_combined_vals, enc_combined_mask, att_min=data_min, att_max=data_max
)
if torch.max(combined_tt) != 0.0:
combined_tt = combined_tt / torch.max(combined_tt)
enc_combined_tt = enc_combined_tt / torch.max(enc_combined_tt)
data_dict = {
"enc_data": enc_combined_vals,
"enc_mask": enc_combined_mask,
"enc_time_steps": enc_combined_tt,
"data": combined_vals,
"time_steps": combined_tt,
"mask": combined_mask,
"labels": combined_labels,
}
data_dict = utils.split_and_subsample_batch(data_dict, args, data_type=data_type)
return data_dict