def __init__(
self,
input_size: int,
num_layers: int,
num_cells: int,
cell_type: str,
history_length: int,
context_length: int,
prediction_length: int,
distr_output: DistributionOutput,
control_output: DistributionOutput,
dropout_rate: float,
cardinality: List[int],
embedding_dimension: List[int],
lags_seq: List[int],
scaling: bool = True,
dtype: np.dtype = np.float32,
) -> None:
super().__init__()
self.num_layers = num_layers
self.num_cells = num_cells
self.cell_type = cell_type
self.history_length = history_length
self.context_length = context_length
self.prediction_length = prediction_length
self.dropout_rate = dropout_rate
self.cardinality = cardinality
self.embedding_dimension = embedding_dimension
self.num_cat = len(cardinality)
self.scaling = scaling
self.dtype = dtype
self.lags_seq = lags_seq
self.distr_output = distr_output
self.control_output = control_output
rnn = {"LSTM": nn.LSTM, "GRU": nn.GRU}[self.cell_type]
self.rnn = rnn(
input_size=input_size,
hidden_size=num_cells,
num_layers=num_layers,
dropout=dropout_rate,
batch_first=True,
)
self.target_shape = distr_output.event_shape
self.proj_distr_args = distr_output.get_args_proj(num_cells + 1)
self.proj_control_args = control_output.get_args_proj(num_cells)
self.embedder = FeatureEmbedder(cardinalities=cardinality,
embedding_dims=embedding_dimension)
if scaling:
self.scaler = MeanScaler(keepdim=True)
self.control_scaler = NOPScaler(keepdim=True)
else:
self.scaler = NOPScaler(keepdim=True)
self.control_scaler = NOPScaler(keepdim=True)
def learn_distribution(
distr_output: DistributionOutput,
samples: torch.Tensor,
init_biases: List[np.ndarray] = None,
num_epochs: int = 5,
learning_rate: float = 1e-2,
):
arg_proj = distr_output.get_args_proj(in_features=1)
if init_biases is not None:
for param, bias in zip(arg_proj.proj, init_biases):
nn.init.constant_(param.bias, bias)
dummy_data = torch.ones((len(samples), 1, 1))
dataset = TensorDataset(dummy_data, samples)
train_data = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
optimizer = SGD(arg_proj.parameters(), lr=learning_rate)
for e in range(num_epochs):
cumulative_loss = 0
num_batches = 0
for i, (data, sample_label) in enumerate(train_data):
optimizer.zero_grad()
distr_args = arg_proj(data)
distr = distr_output.distribution(distr_args)
loss = -distr.log_prob(sample_label).mean()
loss.backward()
#clip_grad_norm_(arg_proj.parameters(), 10.0)
optimizer.step()
num_batches += 1
cumulative_loss += loss.item()
print("Epoch %s, loss: %s" % (e, cumulative_loss / num_batches))
sampling_dataloader = DataLoader(dataset,
batch_size=BATCH_SIZE,
shuffle=True)
i, (data, sample_label) = next(enumerate(sampling_dataloader))
distr_args = arg_proj(data)
distr = distr_output.distribution(distr_args)
samples = distr.sample((NUM_SAMPLES, ))
with torch.no_grad():
percentile_90 = distr.quantile_function(torch.ones((1, 1, 1)),
torch.ones((1, 1)) * 0.9)
percentile_10 = distr.quantile_function(torch.ones((1, 1, 1)),
torch.ones((1, 1)) * 0.1)
return samples.mean(), samples.std(), percentile_10.squeeze(
), percentile_90.squeeze()
def maximum_likelihood_estimate_sgd(
distr_output: DistributionOutput,
samples: torch.Tensor,
init_biases: List[np.ndarray] = None,
num_epochs: PositiveInt = PositiveInt(5),
learning_rate: PositiveFloat = PositiveFloat(1e-2),
):
arg_proj = distr_output.get_args_proj(in_features=1)
if init_biases is not None:
for param, bias in zip(arg_proj.proj, init_biases):
nn.init.constant_(param.bias, bias)
dummy_data = torch.ones((len(samples), 1))
dataset = TensorDataset(dummy_data, samples)
train_data = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
optimizer = SGD(arg_proj.parameters(), lr=learning_rate)
for e in range(num_epochs):
cumulative_loss = 0
num_batches = 0
for i, (data, sample_label) in enumerate(train_data):
optimizer.zero_grad()
distr_args = arg_proj(data)
distr = distr_output.distribution(distr_args)
loss = -distr.log_prob(sample_label).mean()
loss.backward()
clip_grad_norm_(arg_proj.parameters(), 10.0)
optimizer.step()
num_batches += 1
cumulative_loss += loss.item()
print("Epoch %s, loss: %s" % (e, cumulative_loss / num_batches))
if len(distr_args[0].shape) == 1:
return [
param.detach().numpy() for param in arg_proj(torch.ones((1, 1)))
]
return [
param[0].detach().numpy() for param in arg_proj(torch.ones((1, 1)))
]
def __init__(
self,
freq: str,
context_length: int,
prediction_length: int,
num_feat_dynamic_real: int,
num_feat_static_real: int,
num_feat_static_cat: int,
cardinality: List[int],
embedding_dimension: Optional[List[int]] = None,
num_layers: int = 2,
hidden_size: int = 40,
dropout_rate: float = 0.1,
distr_output: DistributionOutput = StudentTOutput(),
lags_seq: Optional[List[int]] = None,
scaling: bool = True,
num_parallel_samples: int = 100,
) -> None:
super().__init__()
self.context_length = context_length
self.prediction_length = prediction_length
self.distr_output = distr_output
self.param_proj = distr_output.get_args_proj(hidden_size)
self.target_shape = distr_output.event_shape
self.num_feat_dynamic_real = num_feat_dynamic_real
self.num_feat_static_cat = num_feat_static_cat
self.num_feat_static_real = num_feat_static_real
self.embedding_dimension = (
embedding_dimension
if embedding_dimension is not None or cardinality is None
else [min(50, (cat + 1) // 2) for cat in cardinality]
)
self.lags_seq = lags_seq or get_lags_for_frequency(freq_str=freq)
self.num_parallel_samples = num_parallel_samples
self.history_length = self.context_length + max(self.lags_seq)
self.embedder = FeatureEmbedder(
cardinalities=cardinality,
embedding_dims=self.embedding_dimension,
)
if scaling:
self.scaler = MeanScaler(dim=1, keepdim=True)
else:
self.scaler = NOPScaler(dim=1, keepdim=True)
self.lagged_rnn = LaggedLSTM(
input_size=1, # TODO fix
features_size=self._number_of_features,
num_layers=num_layers,
hidden_size=hidden_size,
dropout_rate=dropout_rate,
lags_seq=[lag - 1 for lag in self.lags_seq],
)
def __init__(
self,
input_size: int,
d_model: int,
num_heads: int,
act_type: str,
dropout_rate: float,
dim_feedforward_scale: int,
num_encoder_layers: int,
num_decoder_layers: int,
history_length: int,
context_length: int,
prediction_length: int,
distr_output: DistributionOutput,
cardinality: List[int],
embedding_dimension: List[int],
lags_seq: List[int],
scaling: bool = True,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.history_length = history_length
self.context_length = context_length
self.prediction_length = prediction_length
self.scaling = scaling
self.cardinality = cardinality
self.embedding_dimension = embedding_dimension
self.distr_output = distr_output
assert len(
set(lags_seq)) == len(lags_seq), "no duplicated lags allowed!"
lags_seq.sort()
self.lags_seq = lags_seq
self.target_shape = distr_output.event_shape
# [B, T, input_size] -> [B, T, d_model]
self.encoder_input = nn.Linear(input_size, d_model)
self.decoder_input = nn.Linear(input_size, d_model)
# [B, T, d_model] where d_model / num_heads is int
self.transformer = nn.Transformer(
d_model=d_model,
nhead=num_heads,
num_encoder_layers=num_encoder_layers,
num_decoder_layers=num_decoder_layers,
dim_feedforward=dim_feedforward_scale * d_model,
dropout=dropout_rate,
activation=act_type,
)
self.proj_dist_args = distr_output.get_args_proj(d_model)
self.embedder = FeatureEmbedder(
cardinalities=cardinality,
embedding_dims=embedding_dimension,
)
if scaling:
self.scaler = MeanScaler(keepdim=True)
else:
self.scaler = NOPScaler(keepdim=True)
# mask
self.register_buffer(
"tgt_mask",
self.transformer.generate_square_subsequent_mask(
prediction_length),
)
def __init__(
self,
input_size: int,
num_layers: int,
num_cells: int,
cell_type: str,
history_length: int,
context_length: int,
prediction_length: int,
distr_output: DistributionOutput,
dropout_rate: float,
lags_seq: List[int],
target_dim: int,
cardinality: List[int],
embedding_dimension: List[int],
scaling: bool = True,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.num_layers = num_layers
self.num_cells = num_cells
self.cell_type = cell_type
self.history_length = history_length
self.context_length = context_length
self.prediction_length = prediction_length
self.dropout_rate = dropout_rate
self.cardinality = cardinality
self.embedding_dimension = embedding_dimension
self.num_cat = len(cardinality)
self.target_dim = target_dim
self.scaling = scaling
self.target_dim_sample = target_dim
assert len(
set(lags_seq)) == len(lags_seq), "no duplicated lags allowed!"
lags_seq.sort()
self.lags_seq = lags_seq
self.distr_output = distr_output
self.target_dim = target_dim
rnn = {"LSTM": nn.LSTM, "GRU": nn.GRU}[self.cell_type]
self.rnn = rnn(
input_size=input_size,
hidden_size=num_cells,
num_layers=num_layers,
dropout=dropout_rate,
batch_first=True,
)
self.target_shape = distr_output.event_shape
self.proj_dist_args = distr_output.get_args_proj(num_cells)
self.embed = FeatureEmbedder(cardinalities=cardinality,
embedding_dims=embedding_dimension)
if scaling:
self.scaler = MeanScaler(keepdim=True)
else:
self.scaler = NOPScaler(keepdim=True)