DNN

class obsidian.surrogates.custom_torch.DNN(train_X: Tensor, train_Y: Tensor, p_dropout: float = 0.2, h_width: int = 16, h_layers: int = 2, num_outputs: int = 1)

Bases: EnsembleModel, FantasizeMixin

__init__(train_X: Tensor, train_Y: Tensor, p_dropout: float = 0.2, h_width: int = 16, h_layers: int = 2, num_outputs: int = 1)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__(train_X, train_Y[, p_dropout, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
condition_on_observations(X, Y)	Condition the model to new observations, returning a fantasy model
construct_inputs(training_data)	Construct Model keyword arguments from a SupervisedDataset.
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Puts the model in eval mode and sets the transformed inputs.
extra_repr()	Set the extra representation of the module.
fantasize(X)	Construct a fantasy model.
float()	Casts all floating point parameters and buffers to float datatype.
forward(x)	Compute the (ensemble) model output at X.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules()	Return an iterator over all modules in the network.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
posterior(X[, n_sample, output_indices, ...])	Calculates the posterior distribution of the model at X
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post hook to be run after module's load_state_dict is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
subset_output(idcs)	Subset the model along the output dimension.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Put the model in train mode.
transform_inputs(X[, input_transform])	Transform inputs.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
batch_shape	The batch shape of the model.
call_super_init
dtypes_of_buffers
dump_patches
num_outputs	Number of outputs of the model
training

condition_on_observations(X: Tensor, Y: Tensor) → TFantasizeMixin

Condition the model to new observations, returning a fantasy model

fantasize(X: Tensor) → Model

Construct a fantasy model.

Constructs a fantasy model in the following fashion: (1) compute the model posterior at X, including observation noise. If observation_noise is a Tensor, use it directly as the observation noise to add. (2) sample from this posterior (using sampler) to generate “fake” observations. (3) condition the model on the new fake observations.

Parameters:

• X – A batch_shape x n’ x d-dim Tensor, where d is the dimension of the feature space, n’ is the number of points per batch, and batch_shape is the batch shape (must be compatible with the batch shape of the model).

• sampler – The sampler used for sampling from the posterior at X.

• observation_noise – A model_batch_shape x 1 x m-dim tensor or a model_batch_shape x n’ x m-dim tensor containing the average noise for each batch and output, where m is the number of outputs. noise must be in the outcome-transformed space if an outcome transform is used. If None and using an inferred noise likelihood, the noise will be the inferred noise level. If using a fixed noise likelihood, the mean across the observation noise in the training data is used as observation noise.

• kwargs – Will be passed to model.condition_on_observations

Returns:

The constructed fantasy model.

forward(x: Tensor) → Tensor

Compute the (ensemble) model output at X.

Parameters:

X – A batch_shape x n x d-dim input tensor X.

Returns:

A batch_shape x s x n x m-dimensional output tensor where s is the size of the ensemble.

property num_outputs: int

Number of outputs of the model

posterior(X: Tensor, n_sample: int = 512, output_indices: list[int] | None = None, observation_noise: bool | Tensor = False) → Posterior

Calculates the posterior distribution of the model at X

transform_inputs(X: Tensor, input_transform: Module | None = None) → Tensor

Transform inputs.

Parameters:

• X – A tensor of inputs

• input_transform – A Module that performs the input transformation.

Returns:

A tensor of transformed inputs