dsipts.Base

class dsipts.Base(verbose: bool, past_steps: int, future_steps: int, past_channels: int, future_channels: int, out_channels: int, embs_past: List[int], embs_fut: List[int], n_classes: int = 0, persistence_weight: float = 0.0, loss_type: str = 'l1', quantiles: List[int] = [], reduction_mode: str = 'mean', use_classical_positional_encoder: bool = False, emb_dim: int = 16, optim: str | None = None, optim_config: dict = None, scheduler_config: dict = None)

This is the basic model, each model implemented must overwrite the init method and the forward method. The inference step is optional, by default it uses the forward method but for recurrent network you should implement your own method

Parameters:

  • verbose (bool) – Flag to enable verbose logging.

  • past_steps (int) – Number of past time steps to consider.

  • future_steps (int) – Number of future time steps to predict.

  • past_channels (int) – Number of channels in the past input data.

  • future_channels (int) – Number of channels in the future input data.

  • out_channels (int) – Number of output channels.

  • embs_past (List[int]) – List of embedding dimensions for past data.

  • embs_fut (List[int]) – List of embedding dimensions for future data.

  • n_classes (int, optional) – Number of classes for classification. Defaults to 0.

  • persistence_weight (float, optional) – Weight for persistence in loss calculation. Defaults to 0.0.

  • loss_type (str, optional) – Type of loss function to use (‘l1’ or ‘mse’). Defaults to ‘l1’.

  • quantiles (List[int], optional) – List of quantiles for quantile loss. Defaults to an empty list.

  • reduction_mode (str, optional) – Mode for reduction for categorical embedding layer (‘mean’, ‘sum’, ‘none’). Defaults to ‘mean’.

  • use_classical_positional_encoder (bool, optional) – Flag to use classical positional encoding or using embedding layer also for the positions. Defaults to False.

  • emb_dim (int, optional) – Dimension of categorical embeddings. Defaults to 16.

  • optim (Union[str, None], optional) – Optimizer type. Defaults to None.

  • optim_config (dict, optional) – Configuration for the optimizer. Defaults to None.

  • scheduler_config (dict, optional) – Configuration for the learning rate scheduler. Defaults to None.

Raises:

  • AssertionError – If the number of quantiles is not equal to 3 when quantiles are provided.

  • AssertionError – If the number of output channels is not 1 for classification tasks.

abstractmethod __init__(verbose: bool, past_steps: int, future_steps: int, past_channels: int, future_channels: int, out_channels: int, embs_past: List[int], embs_fut: List[int], n_classes: int = 0, persistence_weight: float = 0.0, loss_type: str = 'l1', quantiles: List[int] = [], reduction_mode: str = 'mean', use_classical_positional_encoder: bool = False, emb_dim: int = 16, optim: str | None = None, optim_config: dict = None, scheduler_config: dict = None)

This is the basic model, each model implemented must overwrite the init method and the forward method. The inference step is optional, by default it uses the forward method but for recurrent network you should implement your own method

Parameters:

  • verbose (bool) – Flag to enable verbose logging.

  • past_steps (int) – Number of past time steps to consider.

  • future_steps (int) – Number of future time steps to predict.

  • past_channels (int) – Number of channels in the past input data.

  • future_channels (int) – Number of channels in the future input data.

  • out_channels (int) – Number of output channels.

  • embs_past (List[int]) – List of embedding dimensions for past data.

  • embs_fut (List[int]) – List of embedding dimensions for future data.

  • n_classes (int, optional) – Number of classes for classification. Defaults to 0.

  • persistence_weight (float, optional) – Weight for persistence in loss calculation. Defaults to 0.0.

  • loss_type (str, optional) – Type of loss function to use (‘l1’ or ‘mse’). Defaults to ‘l1’.

  • quantiles (List[int], optional) – List of quantiles for quantile loss. Defaults to an empty list.

  • reduction_mode (str, optional) – Mode for reduction for categorical embedding layer (‘mean’, ‘sum’, ‘none’). Defaults to ‘mean’.

  • use_classical_positional_encoder (bool, optional) – Flag to use classical positional encoding or using embedding layer also for the positions. Defaults to False.

  • emb_dim (int, optional) – Dimension of categorical embeddings. Defaults to 16.

  • optim (Union[str, None], optional) – Optimizer type. Defaults to None.

  • optim_config (dict, optional) – Configuration for the optimizer. Defaults to None.

  • scheduler_config (dict, optional) – Configuration for the learning rate scheduler. Defaults to None.

Raises:

  • AssertionError – If the number of quantiles is not equal to 3 when quantiles are provided.

  • AssertionError – If the number of output channels is not 1 for classification tasks.

Methods

__init__(verbose, past_steps, future_steps, ...)

This is the basic model, each model implemented must overwrite the init method and the forward method.

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.

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().

compute_loss(batch, y_hat)

custom loss calculation

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()

Each model has optim_config and scheduler_config

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(batch)

Forlward method used during the training loop

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().

inference(batch)

Usually it is ok to return the output of the forward method but sometimes not (e.g. RNN).

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.

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()

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()

pythotrch lightening stuff

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()

pythotrch lightening stuff

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()

An iterable or collection of iterables specifying prediction samples.

predict_step(*args, **kwargs)

Step function called during predict().

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.

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(*args, **kwargs)

Operates on a single batch of data from the test set.

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

By default compiles the whole model to a 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)

pythotrch lightening stuff

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)

pythotrch lightening stuff

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

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.

description

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.

handle_categorical_variables

handle_future_covariates

handle_multivariate

handle_quantile_loss

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