lfxai.models.time_series module

class AutoencoderCNN(embedding_dim: int = 64, name: str = 'model', loss_f: callable = L1Loss())

Bases: Module

_backward_hooks: Dict[int, Callable]

_buffers: Dict[str, Optional[Tensor]]

_forward_hooks: Dict[int, Callable]

_forward_pre_hooks: Dict[int, Callable]

_is_full_backward_hook: Optional[bool]

_load_state_dict_post_hooks: Dict[int, Callable]

_load_state_dict_pre_hooks: Dict[int, Callable]

_modules: Dict[str, Optional[Module]]

_non_persistent_buffers_set: Set[str]

_parameters: Dict[str, Optional[Parameter]]

_state_dict_hooks: Dict[int, Callable]

fit(device: device, train_loader: DataLoader, test_loader: DataLoader, save_dir: Path, n_epoch: int = 30, patience: int = 10, checkpoint_interval: int = -1) → None

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

save(directory: Path) → None

Save a model and corresponding metadata. Parameters: ———- directory : pathlib.Path

Path to the directory where to save the data.

test_epoch(device: device, dataloader: DataLoader)

train_epoch(device: device, dataloader: DataLoader, optimizer: Optimizer) → ndarray

training: bool

class Decoder(seq_len: int, n_features: int = 1, input_dim: int = 64)

Bases: Module

_backward_hooks: Dict[int, Callable]

_buffers: Dict[str, Optional[Tensor]]

_forward_hooks: Dict[int, Callable]

_forward_pre_hooks: Dict[int, Callable]

_is_full_backward_hook: Optional[bool]

_load_state_dict_post_hooks: Dict[int, Callable]

_load_state_dict_pre_hooks: Dict[int, Callable]

_modules: Dict[str, Optional[Module]]

_non_persistent_buffers_set: Set[str]

_parameters: Dict[str, Optional[Parameter]]

_state_dict_hooks: Dict[int, Callable]

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class DecoderCNN(encoded_space_dim)

Bases: Module

_backward_hooks: Dict[int, Callable]

_buffers: Dict[str, Optional[Tensor]]

_forward_hooks: Dict[int, Callable]

_forward_pre_hooks: Dict[int, Callable]

_is_full_backward_hook: Optional[bool]

_load_state_dict_post_hooks: Dict[int, Callable]

_load_state_dict_pre_hooks: Dict[int, Callable]

_modules: Dict[str, Optional[Module]]

_non_persistent_buffers_set: Set[str]

_parameters: Dict[str, Optional[Parameter]]

_state_dict_hooks: Dict[int, Callable]

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class Encoder(seq_len: int, n_features: int = 1, embedding_dim: int = 64)

Bases: Module

_backward_hooks: Dict[int, Callable]

_buffers: Dict[str, Optional[Tensor]]

_forward_hooks: Dict[int, Callable]

_forward_pre_hooks: Dict[int, Callable]

_is_full_backward_hook: Optional[bool]

_load_state_dict_post_hooks: Dict[int, Callable]

_load_state_dict_pre_hooks: Dict[int, Callable]

_modules: Dict[str, Optional[Module]]

_non_persistent_buffers_set: Set[str]

_parameters: Dict[str, Optional[Parameter]]

_state_dict_hooks: Dict[int, Callable]

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class EncoderCNN(encoded_space_dim)

Bases: Module

_backward_hooks: Dict[int, Callable]

_buffers: Dict[str, Optional[Tensor]]

_forward_hooks: Dict[int, Callable]

_forward_pre_hooks: Dict[int, Callable]

_is_full_backward_hook: Optional[bool]

_load_state_dict_post_hooks: Dict[int, Callable]

_load_state_dict_pre_hooks: Dict[int, Callable]

_modules: Dict[str, Optional[Module]]

_non_persistent_buffers_set: Set[str]

_parameters: Dict[str, Optional[Parameter]]

_state_dict_hooks: Dict[int, Callable]

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class RecurrentAutoencoder(seq_len: int, n_features: int, embedding_dim: int = 64, name: str = 'model', loss_f: callable = L1Loss())

Bases: Module

_backward_hooks: Dict[int, Callable]

_buffers: Dict[str, Optional[Tensor]]

_forward_hooks: Dict[int, Callable]

_forward_pre_hooks: Dict[int, Callable]

_is_full_backward_hook: Optional[bool]

_load_state_dict_post_hooks: Dict[int, Callable]

_load_state_dict_pre_hooks: Dict[int, Callable]

_modules: Dict[str, Optional[Module]]

_non_persistent_buffers_set: Set[str]

_parameters: Dict[str, Optional[Parameter]]

_state_dict_hooks: Dict[int, Callable]

fit(device: device, train_loader: DataLoader, test_loader: DataLoader, save_dir: Path, n_epoch: int = 30, patience: int = 10, checkpoint_interval: int = -1) → None

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

save(directory: Path) → None

Save a model and corresponding metadata. Parameters: ———- directory : pathlib.Path

Path to the directory where to save the data.

test_epoch(device: device, dataloader: DataLoader)

train_epoch(device: device, dataloader: DataLoader, optimizer: Optimizer) → ndarray

training: bool