syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.learncurve.model_params module

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.learncurve.model_params.ISSModelParameters(gamma_is_one=False, **kwargs)[source]

Bases: MeanFunction

Maintains parameters of an ISSM of a particular power low decay form.

For each configuration, we have alpha < 0 and beta. These may depend on the input feature x (encoded configuration):

(alpha, beta) = F(x; params),

where params are the internal parameters to be learned.

There is also gamma > 0, which can be fixed to 1.

param_encoding_pairs()[source]

Returns list of tuples: (param_internal, encoding)

over all Gluon parameters maintained here.

Returns:: List [(param_internal, encoding)]

get_gamma()[source]

get_params()[source]

Return type:: Dict[str, Any]
Returns:: Dictionary with hyperparameter values

set_gamma(val)[source]

set_params(param_dict)[source]

Parameters:: param_dict (Dict[str, Any]) – Dictionary with new hyperparameter values
Returns:

get_issm_params(features)[source]

Given feature matrix X, returns ISSM parameters which configure the likelihood: alpha, beta vectors (size n), gamma scalar.

Parameters:: features – Feature matrix X, (n, d)
Return type:: Dict[str, Any]
Returns:: Dict with alpha, beta, gamma

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.learncurve.model_params.IndependentISSModelParameters(gamma_is_one=False, **kwargs)[source]

Bases: ISSModelParameters

Most basic implementation, where alpha, beta are scalars, independent of the configuration.