def predict(t_net: DaRnnNet, t_dat: TrainData, train_size: int, batch_size: int, T: int, on_train=False):
out_size = t_dat.targs.shape[1]
if on_train:
y_pred = np.zeros((train_size - T + 1, out_size))
else:
y_pred = np.zeros((t_dat.feats.shape[0] - train_size, out_size))
for y_i in range(0, len(y_pred), batch_size):
y_slc = slice(y_i, y_i + batch_size)
batch_idx = range(len(y_pred))[y_slc]
b_len = len(batch_idx)
X = np.zeros((b_len, T - 1, t_dat.feats.shape[1]))
y_history = np.zeros((b_len, T - 1, t_dat.targs.shape[1]))
for b_i, b_idx in enumerate(batch_idx):
if on_train:
idx = range(b_idx, b_idx + T - 1)
else:
idx = range(b_idx + train_size - T, b_idx + train_size - 1)
X[b_i, :, :] = t_dat.feats[idx, :]
y_history[b_i, :] = t_dat.targs[idx]
y_history = numpy_to_tvar(y_history)
_, input_encoded = t_net.encoder(numpy_to_tvar(X))
y_pred[y_slc] = t_net.decoder(input_encoded, y_history).cpu().data.numpy()
return y_pred
def train_iteration(t_net: DaRnnNet, loss_func: typing.Callable, X, y_history, y_target):
t_net.enc_opt.zero_grad()
t_net.dec_opt.zero_grad()
input_weighted, input_encoded = t_net.encoder(numpy_to_tvar(X))
y_pred = t_net.decoder(input_encoded, numpy_to_tvar(y_history))
y_true = numpy_to_tvar(y_target)
loss = loss_func(y_pred, y_true)
loss.backward()
t_net.enc_opt.step()
t_net.dec_opt.step()
return loss.item()
def predict(t_net: DaRnnNet,
t_dat: TrainData,
train_size: int,
batch_size: int,
T: int,
on_train=False,
eval=False):
out_size = t_dat.targs.shape[1]
if on_train:
y_pred = np.zeros((train_size - T + 1, out_size))
else:
y_pred = np.zeros((t_dat.feats.shape[0] - train_size, out_size))
#y_pred = np.zeros((VALI_SIZE, out_size))
if eval:
y_pred = np.zeros((VALI_SIZE, out_size))
for y_i in range(0, len(y_pred), batch_size):
y_slc = slice(y_i, y_i + batch_size)
batch_idx = range(len(y_pred))[y_slc]
b_len = len(batch_idx)
X = np.zeros((b_len, T - 1, t_dat.feats.shape[1]))
y_history = np.zeros((b_len, T - 1, t_dat.targs.shape[1]))
for b_i, b_idx in enumerate(batch_idx):
if on_train:
idx = range(b_idx, b_idx + T - 1)
else:
# ANDREA --> The validation set is chosen at random
# b_idx = np.random.randint(0, len(t_dat.feats)-train_size)
idx = range(b_idx + train_size - T, b_idx + train_size - 1)
X[b_i, :, :] = t_dat.feats[idx, :]
## Leave it zeros
# y_history[b_i, :] = t_dat.targs[idx]
y_history = numpy_to_tvar(y_history)
_, input_encoded = t_net.encoder(numpy_to_tvar(X))
y_pred[y_slc] = t_net.decoder(input_encoded,
y_history).cpu().data.numpy()
return y_pred
def train_iteration(t_net: DaRnnNet, loss_func: typing.Callable, X, y_history,
y_target):
t_net.enc_opt.zero_grad()
t_net.dec_opt.zero_grad()
X = Variable(torch.from_numpy(X).type(torch.FloatTensor).to(device))
y_history = Variable(
torch.from_numpy(y_history).type(torch.FloatTensor).to(device))
y_target = Variable(
torch.from_numpy(y_target).type(torch.FloatTensor).to(device))
input_weighted, input_encoded = t_net.encoder(X)
y_pred = t_net.decoder(input_encoded, y_history)
y_true = y_target
loss = loss_func(y_pred, y_true)
loss.backward()
t_net.enc_opt.step()
t_net.dec_opt.step()
return loss.item()