calpit.nn.utils

Module Contents

Classes

RandomDataset	A custom dataset class to randomly select a data point.
EarlyStopping	Early stops the training if validation loss doesn't improve after a given patience.

Functions

count_parameters(→ int)	Count the number of trainable parameters in a model.
cde_loss(→ tuple)	Calculates conditional density estimation loss on holdout data.

class RandomDataset(x_data, y_data, oversample=1)

Bases: torch.utils.data.Dataset

A custom dataset class to randomly select a data point. The data point is prepended with a random value between 0 and 1 from a Uniform distribution (coverage parameter). The target value is 0 if Y value is less than or equal to the coverage parameter and 1 otherwise. The data set can be oversampled by a given factor.

Parameters:

• X (list or array-like) – The input features.

• Y (list or array-like) – The target values.

• oversample (float, optional) – The oversampling factor. Defaults to 1.

Returns:

A tuple containing the input feature and target value.

Return type:

tuple

__len__()

__getitem__(idx)

count_parameters(model: torch.nn.Module) → int

Count the number of trainable parameters in a model.

class EarlyStopping(patience=7, verbose=False, delta=0, path='checkpoint.pt', trace_func=print)

Early stops the training if validation loss doesn’t improve after a given patience. :param patience: How long to wait after last time validation loss improved.

Default: 7

Parameters:

• verbose (bool) – If True, prints a message for each validation loss improvement. Default: False

• delta (float) – Minimum change in the monitored quantity to qualify as an improvement. Default: 0

• path (str) – Path for the checkpoint to be saved to. Default: ‘checkpoint.pt’

• trace_func (function) – trace print function. Default: print

__call__(val_loss, model)

save_checkpoint(val_loss, model)

Saves model when validation loss decrease.

cde_loss(cde_estimates: torch.Tensor, y_grid: torch.Tensor, y_test: torch.Tensor) → tuple

Calculates conditional density estimation loss on holdout data. This is a PyTorch version of the original function.

Parameters:

• cde_estimates (torch.Tensor) – An array where each row is a density estimate on y_grid

• y_grid (torch.Tensor) – An array of the grid points at which cde_estimates is evaluated.

• y_test (torch.Tensor) – An array of the true y values corresponding to the rows of cde_estimates

Returns:

A tuple containing the loss and the standard error of the loss.

Return type:

tuple

Raises:

ValueError – If the dimensions of the input tensors are not compatible.