hypertools.reduce.autoencoders.SparseAutoencoder

class hypertools.reduce.autoencoders.SparseAutoencoder(n_components=2, sparsity_weight=0.001, epochs=100, batch_size=64, lr=0.001, hidden_dims=None, device='auto', random_state=None, verbose=False)

A single-hidden-layer autoencoder with an L1 penalty on the hidden activation (sparse coding), encouraging a sparser, more interpretable intermediate representation.

Parameters:

n_componentsint

Latent (bottleneck) dimensionality (default: 2).

sparsity_weightfloat

Weight of the L1 penalty on the mean absolute hidden-layer activation, added to the MSE reconstruction loss (default: 1e-3).

epochs, batch_size, lr, hidden_dims, device, random_state, verbose

See Autoencoder.

Attributes:

net_torch.nn.Module or None

The fitted network.

Methods

fit(X[, y])	Fit the autoencoder on X (see fit_transform); returns self.
fit_transform(X[, y])	Standardize X, train a fresh network on it, and return the fitted latent codes.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
inverse_transform(Z)	Decode latent codes Z back to (standardization-undone) feature space.
set_inverse_transform_request(*[, Z])	Configure whether metadata should be requested to be passed to the inverse_transform method.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Apply the already-fitted network to (new) X, without refitting.

__init__(n_components=2, sparsity_weight=0.001, epochs=100, batch_size=64, lr=0.001, hidden_dims=None, device='auto', random_state=None, verbose=False)

Methods

__init__([n_components, sparsity_weight, ...])
fit(X[, y])	Fit the autoencoder on X (see fit_transform); returns self.
fit_transform(X[, y])	Standardize X, train a fresh network on it, and return the fitted latent codes.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
inverse_transform(Z)	Decode latent codes Z back to (standardization-undone) feature space.
set_inverse_transform_request(*[, Z])	Configure whether metadata should be requested to be passed to the inverse_transform method.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Apply the already-fitted network to (new) X, without refitting.

Attributes

is_fitted

Whether fit/fit_transform has already been run.