emukit.core.interfaces package¶

Submodules¶

class emukit.core.interfaces.models.IModel¶

Bases: object

predict(X)¶

Predict mean and variance values for given points

Parameters: X (ndarray) – array of shape (n_points x n_inputs) of points to run prediction for
Return type: Tuple[ndarray, ndarray]
Returns: Tuple of mean and variance which are 2d arrays of shape (n_points x n_outputs)

set_data(X, Y)¶

Sets training data in model

Parameters

Return type

None

optimize()¶

Optimize hyper-parameters of model

class emukit.core.interfaces.models.IDifferentiable¶

get_prediction_gradients(X)¶

Computes and returns model gradients of mean and variance at given points

class emukit.core.interfaces.models.IJointlyDifferentiable¶

predict_with_full_covariance(X)¶

Parameters: X (ndarray) – (n_points x n_dimensions) array containing locations at which to get predictions
Return type: Tuple[ndarray, ndarray]
Returns: (mean, variance) Arrays of size n_points x 1 and n_points x n_points of the predictive mean and variance at each input location

get_joint_prediction_gradients(X)¶

Computes and returns model gradients of mean and full covariance matrix at given points

Parameters: X (ndarray) – points to compute gradients at, nd array of shape (q, d)
Return type: Tuple[ndarray, ndarray]
Returns: Tuple with first item being gradient of the mean of shape (q) at X with respect to X (return shape is (q, q, d)). The second item is the gradient of the full covariance matrix of shape (q, q) at X with respect to X (return shape is (q, q, q, d)).

class emukit.core.interfaces.models.IPriorHyperparameters¶

generate_hyperparameters_samples(n_samples, n_burnin, subsample_interval, step_size, leapfrog_steps)¶

Generates the samples from the hyper-parameters of the model, and returns them.

Parameters

n_samples (int) – Number of hyper-parameter samples
n_burnin (int) – Number of initial samples not used.
subsample_interval (int) – Interval of subsampling from HMC samples.
step_size (float) – Size of the gradient steps in the HMC sampler.
leapfrog_steps (int) – Number of gradient steps before each Metropolis Hasting step.

Return type

ndarray

fix_model_hyperparameters(sample_hyperparameters)¶

Fixes the model hyper-parameters to certain values (which can be taken from samples).

Parameters: sample_hyperparameters (ndarray) – np.ndarray whose rows contain each hyper-parameters set.
Return type: None

class emukit.core.interfaces.models.IModelWithNoise¶

predict_noiseless(X)¶

For given points X, predict mean and variance of the output without observation noise.

Parameters: X (ndarray) – array of shape (n_points x n_inputs) of points to run prediction for
Return type: Tuple[ndarray, ndarray]
Returns: Tuple of mean and variance which are 2d arrays of shape (n_points x n_outputs)