syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.learncurve.likelihood module

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.learncurve.likelihood.GaussAdditiveMarginalLikelihood(kernel, res_model, mean=None, initial_noise_variance=None, encoding_type=None, **kwargs)

Bases: MarginalLikelihood

Marginal likelihood of joint learning curve model, where each curve is modelled as sum of a Gaussian process over x (for the value at r_max) and a Gaussian model over r.

The latter res_model is either an ISSM or another Gaussian process with exponential decay covariance function.

Parameters:

• kernel (KernelFunction) – Kernel function k(x, x’)

• res_model (Union[ISSModelParameters, ExponentialDecayBaseKernelFunction]) – Gaussian model over r

• mean (Optional[MeanFunction]) – Mean function mu(x)

• initial_noise_variance – A scalar to initialize the value of the residual noise variance

get_posterior_state(data)

Return type:

PosteriorState

forward(data)

Overrides to implement forward computation using NDArray. Only accepts positional arguments. Parameters ———- *args : list of NDArray

Input tensors.

param_encoding_pairs()

Return a list of tuples with the Gluon parameters of the likelihood and their respective encodings

Return type:

List[tuple]

get_noise_variance(as_ndarray=False)

get_params()

Return type:

Dict[str, Any]

set_params(param_dict)

data_precomputations(data, overwrite=False)

Some models require precomputations based on data. Precomputed variables are appended to data. This is done only if not already included in data, unless overwrite is True.

Parameters:

• data (Dict[str, Any]) –

• overwrite (bool) –

on_fit_start(data)

Called at the beginning of fit.

Parameters:

data (Dict[str, Any]) – Argument passed to fit