TemporalConvNet¶

Defined in fynance.models.tcn

class TemporalConvNet(X, y, channels=None, kernel_size=2, drop=0., x_type=None, y_type=None)[source]

Bases: BaseNeuralNet

Causal dilated Temporal Convolutional Network.

A stack of residual blocks (_TemporalBlock) with dilation doubling at each level (1, 2, 4, …) followed by a linear read-out to the output dimension. Padding is causal (left-only, trimmed by _Chomp1d), so the prediction at time t never sees t + 1 — preserving the library’s no-lookahead invariant.

Configure the optimizer with BaseNeuralNet.set_optimizer (e.g. with fynance.models.loss.SharpeLoss).

Parameters:

X, yarray-like or int

If array-like, respectively the input and output data.
If an integer, respectively the input and output dimension.

channelslist of int, optional

Number of channels of each residual block (the depth is len(channels); dilation of block i is 2 ** i). Default [16, 16].

kernel_sizeint, optional

Convolution kernel size. Default 2.

dropfloat, optional

Dropout probability inside each block. Default 0..

Attributes:

tcntorch.nn.Sequential: The stack of temporal blocks.
lineartorch.nn.Linear: Final read-out from the last channel size to M outputs.

See also

fynance.models._base.BaseNeuralNet, fynance.models.lstm.LongShortTermMemory

Examples

>>> import torch
>>> from fynance.models.tcn import TemporalConvNet
>>> _ = torch.manual_seed(0)
>>> X = torch.randn(50, 3)
>>> y = torch.randn(50, 1)
>>> model = TemporalConvNet(X, y, channels=[8, 8], kernel_size=2)
>>> out = model(X)
>>> out.shape
torch.Size([50, 1])

forward(x)[source]

Forward pass.

Parameters:

xtorch.Tensor: Input window, shape (L, N) (time steps × features).

Returns:

torch.Tensor: Per-step output, shape (L, M).

load_model(path, load_optimizer=False)

Save the model with this weights and parameters.

Parameters:

pathstr or os.PathLike object: Path to load the model.
load_optimizerbool, optional: If True, then load also the optimizer.

predict(X)

Predicts outputs of neural network model.

Runs self.forward(X) under torch.no_grad, so no autograd graph is built. The returned tensor is detached and lives on the same device as the model parameters.

Parameters:

Xtorch.Tensor: Inputs to compute prediction. Same shape and dtype contract as train_on.

Returns:

torch.Tensor: Outputs prediction (detached, gradient-free).

save_model(path, save_optimizer=False)

Save the model with this weights and parameters.

Parameters:

pathstr or os.PathLike object: Path to save the model.
save_optimizerbool, optional: If True, then save also the optimizer.

set_data(X, y, x_type=None, y_type=None)

Set data inputs and outputs.

Coerces X and y to torch.Tensor and caches them as self.X / self.y. After the call the attributes self.T (number of observations), self.N (input columns) and self.M (output columns) are set.

Parameters:

X, yarray-like: Respectively input and output data. Accepted types: numpy.ndarray, torch.Tensor, polars.DataFrame. Shapes must be (T, N) and (T, M) respectively.
x_type, y_typetorch.dtype, optional: Target dtypes for the resulting tensors. Default is None, which preserves the input dtype.

Returns:

BaseNeuralNet: self, to allow chaining.

Raises:

ValueError: If self.N / self.M were already set and X / y do not match, or if X and y have different lengths.

set_lr_scheduler(lr_scheduler, **kwargs)

Set dynamic learning rate.

Parameters:

lr_schedulertorch.optim.lr_scheduler._LRScheduler: Method from torch.optim.lr_scheduler to wrap self.optimizer, cf module torch.optim.lr_scheduler in PyTorch documentation [2].
**kwargs: Keyword arguments to pass to the learning rate scheduler.

References

[2]

https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate

set_optimizer(criterion, optimizer, params=None, **kwargs)

Set the optimizer object.

Set optimizer object with specified criterion as loss function and any kwargs as optional parameters.

Parameters:

criterionCallabletorch.nn.modules.loss: A loss function.
optimizertorch.optim.Optimizer: An optimizer algorithm.
paramsobject or iterable object: Layer of parameters to optimize or dicts defining parameter groups. If set to None then all parameters of model will be optimized. Default is None.
**kwargs: Keyword arguments of optimizer, cf PyTorch documentation [1].

Returns:

BaseNeuralNet: Self object model.

References

[1]

https://pytorch.org/docs/stable/optim.html

set_seed(seed_torch=None, seed_numpy=None)

Set seed for PyTorch and NumPy random number generator.

Parameters:

seed_torch, seed_numpybool or int, optional: If seed is an int \(0 < seed < 2^32\) set respectively PyTorch and NumPy seed with the number. Otherwise if is True then choose a random number, else doesn’t set seed.

train_on(X, y)

Trains the neural network model on a single batch.

Runs one forward / backward / optimizer-step cycle on the batch (X, y). As a side effect, gradients of all parameters are zeroed before the forward pass and the optimizer state is advanced afterwards. If a learning-rate scheduler has been registered via set_lr_scheduler, its step is also called.

Parameters:

X, ytorch.Tensor: Respectively inputs and outputs to train model. Shapes must match what self.forward expects (see the class-level “Public API contract” section).

Returns:

torch.Tensor: The loss tensor produced by self.criterion(self(X), y), with gradient already consumed by loss.backward().

Raises:

AttributeError: If set_optimizer has not been called yet.