pytorch_forecasting.models.tide._tide_dsipts._tide_v2.TIDE#

class pytorch_forecasting.models.tide._tide_dsipts._tide_v2.TIDE(metadata: dict, loss: Module, hidden_size: int, d_model: int, n_add_enc: int, n_add_dec: int, dropout_rate: float, activation: str = '', embs: list[int] = [], persistence_weight: float = 0.0, optim: str | None = None, optim_config: dict | None = None, scheduler_config: dict | None = None, **kwargs)[source]#

Long-term Forecasting with TiDE: Time-series Dense Encoder https://arxiv.org/abs/2304.08424

This NN uses as subnet the ResidualBlocks, which is composed by skip connection and activation+dropout. Every encoder and decoder head is composed by one Residual Block, like the temporal decoder and the feature projection for covariates.

Initialise the model.

Parameters:

  • metadata (dict) – Metadata for the model from EncoderDecoderDataModule. This can include information about the dataset, such as the number of time steps, number of features, etc. It is used to initialize the model and ensure it is compatible with the data being used.

  • loss (nn.Module) – Loss function module (e.g., MSELoss, QuantileLoss).

  • hidden_size (int) – Dimensionality of hidden layers in projections (R).

  • d_model (int) – Dimensionality of model projections after feature projection (R̃).

  • n_add_enc (int) – Number of additional encoder residual blocks (after the first).

  • n_add_dec (int) – Number of additional decoder residual blocks (after the first).

  • dropout_rate (float) – Dropout probability applied in residual blocks.

  • activation (str, optional) – Name of activation function to use (e.g., "relu").

  • embs (list of int, optional) – List specifying embedding sizes for categorical variables.

  • persistence_weight (float, optional) – Weight for the persistence (autoregressive) component.

  • optim (str or None, optional) – Name of optimizer (e.g., "adam"), or None to use default.

  • optim_config (dict or None, optional) – Optimizer configuration dictionary.

  • scheduler_config (dict or None, optional) – Scheduler configuration dictionary.

  • **kwargs – Additional keyword arguments passed to BaseModel.

__init__(metadata: dict, loss: Module, hidden_size: int, d_model: int, n_add_enc: int, n_add_dec: int, dropout_rate: float, activation: str = '', embs: list[int] = [], persistence_weight: float = 0.0, optim: str | None = None, optim_config: dict | None = None, scheduler_config: dict | None = None, **kwargs) → None[source]#

Initialise the model.

Parameters:

  • metadata (dict) – Metadata for the model from EncoderDecoderDataModule. This can include information about the dataset, such as the number of time steps, number of features, etc. It is used to initialize the model and ensure it is compatible with the data being used.

  • loss (nn.Module) – Loss function module (e.g., MSELoss, QuantileLoss).

  • hidden_size (int) – Dimensionality of hidden layers in projections (R).

  • d_model (int) – Dimensionality of model projections after feature projection (R̃).

  • n_add_enc (int) – Number of additional encoder residual blocks (after the first).

  • n_add_dec (int) – Number of additional decoder residual blocks (after the first).

  • dropout_rate (float) – Dropout probability applied in residual blocks.

  • activation (str, optional) – Name of activation function to use (e.g., "relu").

  • embs (list of int, optional) – List specifying embedding sizes for categorical variables.

  • persistence_weight (float, optional) – Weight for the persistence (autoregressive) component.

  • optim (str or None, optional) – Name of optimizer (e.g., "adam"), or None to use default.

  • optim_config (dict or None, optional) – Optimizer configuration dictionary.

  • scheduler_config (dict or None, optional) – Scheduler configuration dictionary.

  • **kwargs – Additional keyword arguments passed to BaseModel.

Methods

__call__(*args, **kwargs)

Call self as a function.

__delattr__(name)

Implement delattr(self, name).

__dir__()

Default dir() implementation.

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattr__(name)

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init_subclass__

This method is called when a class is subclassed.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__ne__(value, /)

Return self!=value.

__new__(*args, **kwargs)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value)

Implement setattr(self, name, value).

__setstate__(state)

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

__subclasshook__

Abstract classes can override this to customize issubclass().

_apply(fn[, recurse])

_apply_batch_transfer_handler(batch[, ...])

_call_batch_hook(hook_name, *args)

_call_impl(*args, **kwargs)

_get_backward_hooks()

Return the backward hooks for use in the call function.

_get_backward_pre_hooks()

_get_name()

_get_optimizer()

Get the optimizer based on the specified optimizer name and parameters.

_get_scheduler(optimizer)

Get the lr scheduler based on the specified scheduler name and params.

_load_from_state_dict(state_dict, prefix, ...)

Copy parameters and buffers from state_dict into only this module, but not its descendants.

_log_dict_through_fabric(dictionary[, logger])

_maybe_warn_non_full_backward_hook(inputs, ...)

_named_members(get_members_fn[, prefix, ...])

Help yield various names + members of modules.

_on_before_batch_transfer(batch[, ...])

_pkg()

Package containing the model.

_register_load_state_dict_pre_hook(hook[, ...])

See register_load_state_dict_pre_hook() for details.

_register_state_dict_hook(hook)

Register a post-hook for the state_dict() method.

_replicate_for_data_parallel()

_save_to_state_dict(destination, prefix, ...)

Save module state to the destination dictionary.

_set_hparams(hp)

_slow_forward(*input, **kwargs)

_to_hparams_dict(hp)

_verify_is_manual_optimization(fn_name)

_wrapped_call_impl(*args, **kwargs)

add_module(name, module)

Add a child module to the current module.

all_gather(data[, group, sync_grads])

Gather tensors or collections of tensors from multiple processes.

apply(fn)

Apply fn recursively to every submodule (as returned by .children()) as well as self.

backward(loss, *args, **kwargs)

Called to perform backward on the loss returned in training_step().

bfloat16()

Casts all floating point parameters and buffers to bfloat16 datatype.

buffers([recurse])

Return an iterator over module buffers.

cat_categorical_vars(batch)

Extracting categorical context about past and future

children()

Return an iterator over immediate children modules.

clip_gradients(optimizer[, ...])

Handles gradient clipping internally.

compile(*args, **kwargs)

Compile this Module's forward using torch.compile().

configure_callbacks()

Configure model-specific callbacks.

configure_gradient_clipping(optimizer[, ...])

Perform gradient clipping for the optimizer parameters.

configure_model()

Hook to create modules in a strategy and precision aware context.

configure_optimizers()

Configure the optimizer and learning rate scheduler.

configure_sharded_model()

Deprecated.

cpu()

See torch.nn.Module.cpu().

cuda([device])

Moves all model parameters and buffers to the GPU.

double()

See torch.nn.Module.double().

eval()

Set the module in evaluation mode.

extra_repr()

Return the extra representation of the module.

float()

See torch.nn.Module.float().

forward(X)

training process of the diffusion network

freeze()

Freeze all params for inference.

get_buffer(target)

Return the buffer given by target if it exists, otherwise throw an error.

get_extra_state()

Return any extra state to include in the module's state_dict.

get_parameter(target)

Return the parameter given by target if it exists, otherwise throw an error.

get_submodule(target)

Return the submodule given by target if it exists, otherwise throw an error.

half()

See torch.nn.Module.half().

ipu([device])

Move all model parameters and buffers to the IPU.

load_from_checkpoint(checkpoint_path[, ...])

Primary way of loading a model from a checkpoint.

load_state_dict(state_dict[, strict, assign])

Copy parameters and buffers from state_dict into this module and its descendants.

log(name, value[, prog_bar, logger, ...])

Log a key, value pair.

log_dict(dictionary[, prog_bar, logger, ...])

Log a dictionary of values at once.

log_metrics(y_hat, y[, prefix])

Log additional metrics during training, validation, or testing.

lr_scheduler_step(scheduler, metric)

Override this method to adjust the default way the Trainer calls each scheduler.

lr_schedulers()

Returns the learning rate scheduler(s) that are being used during training.

manual_backward(loss, *args, **kwargs)

Call this directly from your training_step() when doing optimizations manually.

modules([remove_duplicate])

Return an iterator over all modules in the network.

mtia([device])

Move all model parameters and buffers to the MTIA.

named_buffers([prefix, recurse, ...])

Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

named_children()

Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

named_modules([memo, prefix, remove_duplicate])

Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

named_parameters([prefix, recurse, ...])

Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

on_after_backward()

Called after loss.backward() and before optimizers are stepped.

on_after_batch_transfer(batch, dataloader_idx)

Override to alter or apply batch augmentations to your batch after it is transferred to the device.

on_before_backward(loss)

Called before loss.backward().

on_before_batch_transfer(batch, dataloader_idx)

Override to alter or apply batch augmentations to your batch before it is transferred to the device.

on_before_optimizer_step(optimizer)

Called before optimizer.step().

on_before_zero_grad(optimizer)

Called after training_step() and before optimizer.zero_grad().

on_fit_end()

Called at the very end of fit.

on_fit_start()

Called at the very beginning of fit.

on_load_checkpoint(checkpoint)

Called by Lightning to restore your model.

on_predict_batch_end(outputs, batch, batch_idx)

Called in the predict loop after the batch.

on_predict_batch_start(batch, batch_idx[, ...])

Called in the predict loop before anything happens for that batch.

on_predict_end()

Called at the end of predicting.

on_predict_epoch_end()

Called at the end of predicting.

on_predict_epoch_start()

Called at the beginning of predicting.

on_predict_model_eval()

Called when the predict loop starts.

on_predict_start()

Called at the beginning of predicting.

on_save_checkpoint(checkpoint)

Called by Lightning when saving a checkpoint to give you a chance to store anything else you might want to save.

on_test_batch_end(outputs, batch, batch_idx)

Called in the test loop after the batch.

on_test_batch_start(batch, batch_idx[, ...])

Called in the test loop before anything happens for that batch.

on_test_end()

Called at the end of testing.

on_test_epoch_end()

Called in the test loop at the very end of the epoch.

on_test_epoch_start()

Called in the test loop at the very beginning of the epoch.

on_test_model_eval()

Called when the test loop starts.

on_test_model_train()

Called when the test loop ends.

on_test_start()

Called at the beginning of testing.

on_train_batch_end(outputs, batch, batch_idx)

Called in the training loop after the batch.

on_train_batch_start(batch, batch_idx)

Called in the training loop before anything happens for that batch.

on_train_end()

Called at the end of training before logger experiment is closed.

on_train_epoch_end()

Called in the training loop at the very end of the epoch.

on_train_epoch_start()

Called in the training loop at the very beginning of the epoch.

on_train_start()

Called at the beginning of training after sanity check.

on_validation_batch_end(outputs, batch, ...)

Called in the validation loop after the batch.

on_validation_batch_start(batch, batch_idx)

Called in the validation loop before anything happens for that batch.

on_validation_end()

Called at the end of validation.

on_validation_epoch_end()

Called in the validation loop at the very end of the epoch.

on_validation_epoch_start()

Called in the validation loop at the very beginning of the epoch.

on_validation_model_eval()

Called when the validation loop starts.

on_validation_model_train()

Called when the validation loop ends.

on_validation_model_zero_grad()

Called by the training loop to release gradients before entering the validation loop.

on_validation_start()

Called at the beginning of validation.

optimizer_step(epoch, batch_idx, optimizer)

Override this method to adjust the default way the Trainer calls the optimizer.

optimizer_zero_grad(epoch, batch_idx, optimizer)

Override this method to change the default behaviour of optimizer.zero_grad().

optimizers([use_pl_optimizer])

Returns the optimizer(s) that are being used during training.

parameters([recurse])

Return an iterator over module parameters.

predict(dataloader[, mode, return_info, ...])

Generate predictions for new data using the lightning.Trainer.

predict_dataloader()

An iterable or collection of iterables specifying prediction samples.

predict_step(batch, batch_idx[, dataloader_idx])

Prediction step for the model.

prepare_data()

Use this to download and prepare data.

print(*args, **kwargs)

Prints only from process 0.

register_backward_hook(hook)

Register a backward hook on the module.

register_buffer(name, tensor[, persistent])

Add a buffer to the module.

register_forward_hook(hook, *[, prepend, ...])

Register a forward hook on the module.

register_forward_pre_hook(hook, *[, ...])

Register a forward pre-hook on the module.

register_full_backward_hook(hook[, prepend])

Register a backward hook on the module.

register_full_backward_pre_hook(hook[, prepend])

Register a backward pre-hook on the module.

register_load_state_dict_post_hook(hook)

Register a post-hook to be run after module's load_state_dict() is called.

register_load_state_dict_pre_hook(hook)

Register a pre-hook to be run before module's load_state_dict() is called.

register_module(name, module)

Alias for add_module().

register_parameter(name, param)

Add a parameter to the module.

register_state_dict_post_hook(hook)

Register a post-hook for the state_dict() method.

register_state_dict_pre_hook(hook)

Register a pre-hook for the state_dict() method.

remove_ignored_hparams(ignore_list)

Remove ignored hyperparameters from the stored state.

remove_var(tensor, indexes_to_exclude, dimension)

Function to remove variables from tensors in chosen dimension and position

requires_grad_([requires_grad])

Change if autograd should record operations on parameters in this module.

save_hyperparameters(*args[, ignore, frame, ...])

Save arguments to hparams attribute.

set_extra_state(state)

Set extra state contained in the loaded state_dict.

set_submodule(target, module[, strict])

Set the submodule given by target if it exists, otherwise throw an error.

setup(stage)

Called at the beginning of fit (train + validate), validate, test, or predict.

share_memory()

See torch.Tensor.share_memory_().

state_dict(*args[, destination, prefix, ...])

Return a dictionary containing references to the whole state of the module.

teardown(stage)

Called at the end of fit (train + validate), validate, test, or predict.

test_dataloader()

An iterable or collection of iterables specifying test samples.

test_step(batch, batch_idx)

Test step for the model.

to(*args, **kwargs)

See torch.nn.Module.to().

to_empty(*, device[, recurse])

Move the parameters and buffers to the specified device without copying storage.

to_onnx([file_path, input_sample])

Saves the model in ONNX format.

to_prediction(out, **kwargs)

Converts raw model output to point forecasts.

to_quantiles(out, **kwargs)

Converts raw model output to quantile forecasts.

to_tensorrt([file_path, input_sample, ir, ...])

Export the model to ScriptModule or GraphModule using TensorRT compile backend.

to_torchscript([file_path, method, ...])

By default compiles the whole model to a torch.jit.ScriptModule.

toggle_optimizer(optimizer)

Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.

toggled_optimizer(optimizer)

Makes sure only the gradients of the current optimizer's parameters are calculated in the training step to prevent dangling gradients in multiple-optimizer setup.

train([mode])

Set the module in training mode.

train_dataloader()

An iterable or collection of iterables specifying training samples.

training_step(batch, batch_idx)

Training step for the model.

transfer_batch_to_device(batch, device, ...)

Override this hook if your DataLoader returns tensors wrapped in a custom data structure.

type(dst_type)

See torch.nn.Module.type().

unfreeze()

Unfreeze all parameters for training.

untoggle_optimizer(optimizer)

Resets the state of required gradients that were toggled with toggle_optimizer().

val_dataloader()

An iterable or collection of iterables specifying validation samples.

validation_step(batch, batch_idx)

Validation step for the model.

xpu([device])

Move all model parameters and buffers to the XPU.

zero_grad([set_to_none])

Reset gradients of all model parameters.

Attributes

CHECKPOINT_HYPER_PARAMS_KEY

CHECKPOINT_HYPER_PARAMS_NAME

CHECKPOINT_HYPER_PARAMS_TYPE

T_destination

_OPTIMIZER_REGISTRY

_SCHEDULER_REGISTRY

__annotations__

__dict__

__doc__

__jit_unused_properties__

__module__

__weakref__

list of weak references to the object

_compiled_call_impl

_jit_is_scripting

_version

This allows better BC support for load_state_dict().

automatic_optimization

If set to False you are responsible for calling .backward(), .step(), .zero_grad().

call_super_init

current_epoch

The current epoch in the Trainer, or 0 if not attached.

device

device_mesh

Strategies like ModelParallelStrategy will create a device mesh that can be accessed in the configure_model() hook to parallelize the LightningModule.

dtype

dump_patches

example_input_array

The example input array is a specification of what the module can consume in the forward() method.

fabric

global_rank

The index of the current process across all nodes and devices.

global_step

Total training batches seen across all epochs.

hparams

The collection of hyperparameters saved with save_hyperparameters().

hparams_initial

The collection of hyperparameters saved with save_hyperparameters().

local_rank

The index of the current process within a single node.

logger

Reference to the logger object in the Trainer.

loggers

Reference to the list of loggers in the Trainer.

on_gpu

Returns True if this model is currently located on a GPU.

strict_loading

Determines how Lightning loads this model using .load_state_dict(..., strict=model.strict_loading).

trainer

training

_parameters

_buffers

_non_persistent_buffers_set

_backward_pre_hooks

_backward_hooks

_is_full_backward_hook

_forward_hooks

_forward_hooks_with_kwargs

_forward_hooks_always_called

_forward_pre_hooks

_forward_pre_hooks_with_kwargs

_state_dict_hooks

_load_state_dict_pre_hooks

_state_dict_pre_hooks

_load_state_dict_post_hooks

_modules