syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.posterior_state module

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.posterior_state.PosteriorState

Bases: object

Interface for posterior state of Gaussian-linear model.

property num_data

property num_features

property num_fantasies

neg_log_likelihood()

Return type:

ndarray

Returns:

Negative log marginal likelihood

predict(test_features)

Computes marginal statistics (means, variances) for a number of test features.

Parameters:

test_features (ndarray) – Features for test configs

Return type:

Tuple[ndarray, ndarray]

Returns:

posterior_means, posterior_variances

sample_marginals(test_features, num_samples=1, random_state=None)

See comments of predict.

Parameters:

• test_features (ndarray) – Input points for test configs

• num_samples (int) – Number of samples

• random_state (Optional[RandomState]) – PRNG

Return type:

ndarray

Returns:

Marginal samples, (num_test, num_samples)

backward_gradient(input, head_gradients, mean_data, std_data)

Implements Predictor.backward_gradient, see comments there. This is for a single posterior state. If the Predictor uses MCMC, have to call this for every sample.

Parameters:

• input (ndarray) – Single input point x, shape (d,)

• head_gradients (Dict[str, ndarray]) – See Predictor.backward_gradient

• mean_data (float) – Mean used to normalize targets

• std_data (float) – Stddev used to normalize targets

Return type:

ndarray

Returns:

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.posterior_state.PosteriorStateWithSampleJoint

Bases: PosteriorState

sample_joint(test_features, num_samples=1, random_state=None)

See comments of predict.

Parameters:

• test_features (ndarray) – Input points for test configs

• num_samples (int) – Number of samples

• random_state (Optional[RandomState]) – PRNG

Return type:

ndarray

Returns:

Joint samples, (num_test, num_samples)

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.posterior_state.GaussProcPosteriorState(features, targets, mean, kernel, noise_variance, debug_log=False, **kwargs)

Bases: PosteriorStateWithSampleJoint

Represent posterior state for Gaussian process regression model. Note that members are immutable. If the posterior state is to be updated, a new object is created and returned.

property num_data

property num_features

property num_fantasies

neg_log_likelihood()

Works only if fantasy samples are not used (single targets vector).

Return type:

ndarray

predict(test_features)

Computes marginal statistics (means, variances) for a number of test features.

Parameters:

test_features (ndarray) – Features for test configs

Return type:

Tuple[ndarray, ndarray]

Returns:

posterior_means, posterior_variances

sample_marginals(test_features, num_samples=1, random_state=None)

See comments of predict.

Parameters:

• test_features (ndarray) – Input points for test configs

• num_samples (int) – Number of samples

• random_state (Optional[RandomState]) – PRNG

Return type:

ndarray

Returns:

Marginal samples, (num_test, num_samples)

backward_gradient(input, head_gradients, mean_data, std_data)

Implements Predictor.backward_gradient, see comments there. This is for a single posterior state. If the Predictor uses MCMC, have to call this for every sample.

The posterior represented here is based on normalized data, while the acquisition function is based on the de-normalized predictive distribution, which is why we need ‘mean_data’, ‘std_data’ here.

Parameters:

• input (ndarray) – Single input point x, shape (d,)

• head_gradients (Dict[str, ndarray]) – See Predictor.backward_gradient

• mean_data (float) – Mean used to normalize targets

• std_data (float) – Stddev used to normalize targets

Return type:

ndarray

Returns:

sample_joint(test_features, num_samples=1, random_state=None)

See comments of predict.

Parameters:

• test_features (ndarray) – Input points for test configs

• num_samples (int) – Number of samples

• random_state (Optional[RandomState]) – PRNG

Return type:

ndarray

Returns:

Joint samples, (num_test, num_samples)

syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.posterior_state.backward_gradient_given_predict(predict_func, input, head_gradients, mean_data, std_data)

Implements Predictor.backward_gradient, see comments there. This is for a single posterior state. If the Predictor uses MCMC, have to call this for every sample.

The posterior represented here is based on normalized data, while the acquisition function is based on the de-normalized predictive distribution, which is why we need ‘mean_data’, ‘std_data’ here.

Parameters:

• predict_func (Callable[[ndarray], Tuple[ndarray, ndarray]]) – Function mapping input x to mean, variance

• input (ndarray) – Single input point x, shape (d,)

• head_gradients (Dict[str, ndarray]) – See Predictor.backward_gradient

• mean_data (float) – Mean used to normalize targets

• std_data (float) – Stddev used to normalize targets

Return type:

ndarray

Returns:

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.posterior_state.IncrementalUpdateGPPosteriorState(features, targets, mean, kernel, noise_variance, **kwargs)

Bases: GaussProcPosteriorState

Extension of GaussProcPosteriorState which allows for incremental updating, given that a single data case is appended to the training set.

In order to not mutate members, “the update method returns a new object.”

update(feature, target)

Parameters:

• feature (ndarray) – Additional input xstar, shape (1, d)

• target (ndarray) – Additional target ystar, shape (1, m)

Return type:

IncrementalUpdateGPPosteriorState

Returns:

Posterior state for increased data set

sample_and_update(feature, mean_impute_mask=None, random_state=None)

Draw target(s), shape (1, m), from current posterior state, then update state based on these. The main computation of lvec is shared among the two. If mean_impute_mask is given, it is a boolean vector of size m (number columns of pred_mat). Columns j of target, where mean_impute_ mask[j] is true, are set to the predictive mean (instead of being sampled).

Parameters:

• feature (ndarray) – Additional input xstar, shape (1, d)

• mean_impute_mask – See above

• random_state (Optional[RandomState]) – PRN generator

Return type:

(ndarray, IncrementalUpdateGPPosteriorState)

Returns:

target, poster_state_new

expand_fantasies(num_fantasies)

If this posterior has been created with a single targets vector, shape (n, 1), use this to duplicate this vector m = num_fantasies times. Call this method before fantasy targets are appended by update.

Parameters:

num_fantasies (int) – Number m of fantasy samples

Return type:

IncrementalUpdateGPPosteriorState

Returns:

New state with targets duplicated m times