MultiLayerPerceptron

Defined in fynance.models.mlp

class MultiLayerPerceptron(X, y, layers=[], activation=None, drop=None, x_type=None, y_type=None, bias=True, activation_kwargs={})

Bases: BaseNeuralNet

Neural network with MultiLayer Perceptron architecture.

Refered as vanilla neural network model, with n hidden layers s.t n \(\geq\) 1, with each one a specified number of neurons.

Each hidden layer is a torch.nn.Linear followed by an optional dropout and the configured activation function. The MLP is the standard baseline for tabular and sliding-window features in finance — useful for non-linear regression on engineered features (technical indicators, volatility, sentiment scores). For time-ordered sequence input, prefer fynance.models.lstm.LongShortTermMemory or attention-based architectures.

Configure the optimizer with BaseNeuralNet.set_optimizer and wrap with fynance.models.rolling.RollMultiLayerPerceptron for walk-forward training.

Parameters:

X, yarray-like or int

• If it’s an array-like, respectively inputs and outputs data.

• If it’s an integer, respectively dimension of inputs and outputs.

layerslist of int

List of number of neurons in each hidden layer.

activationtorch.nn.Module

Activation function of layers.

dropfloat, optional

Probability of an element to be zeroed.

Attributes:

criteriontorch.nn.modules.loss

A loss function.

optimizertorch.optim

An optimizer algorithm.

nint

Number of hidden layers.

layerslist of int

List with the number of neurons for each hidden layer.

ftorch.nn.Module

Activation function.

See also

fynance.models._base.BaseNeuralNet

fynance.models.rolling.RollMultiLayerPerceptron

forward(x)

Forward computation.

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, pandas.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.