bayesvalidrox.surrogate_models.engine.Engine

class bayesvalidrox.surrogate_models.engine.Engine(meta_model, model, exp_design, discrepancy=None)

Bases: object

This class is responsible for collecting and managing the experimental design, the model and the metamodel for training and evaluations.

Attributes

meta_modelobj

A bvr.MetaModel object. If no MetaModel should be trained and used, set this to None.

modelobj

A model interface of type bvr.PyLinkForwardModel that can be run.

exp_designobj

The experimental design that will be used to sample from the input space.

discrepancyobj, optional

A bvr.Discrepancy object that describes the model uncertainty, i.e. the diagonal entries of the variance matrix for a multivariate normal likelihood. This is used during active learning. The default is None.

__init__(meta_model, model, exp_design, discrepancy=None)

Methods

__init__(meta_model, model, exp_design[, ...])
eval_metamodel([samples, nsamples, ...])	Evaluates metamodel at the requested samples.
plot_adapt(y_mod, y_metamod, y_metamod_std)	Visualize the model and metamodel outputs
plot_posterior(posterior, par_names, key)	Plot the posterior of a specific key as a corner plot
start_engine()	Do all the preparations that need to be run before the actual training
train_normal([parallel, verbose, save])	Trains surrogate on static samples only.
train_sequential([parallel, verbose, plot])	Train the surrogate in a sequential manner.

eval_metamodel(samples=None, nsamples=None, sampling_method='random', return_samples=False, parallel=False)

Evaluates metamodel at the requested samples. One can also generate nsamples.

Parameters

samplesarray of shape (n_samples, n_params), optional

Samples to evaluate metamodel at. The default is None.

nsamplesint, optional

Number of samples to generate, if no samples is provided. The default is None.

sampling_methodstr, optional

Type of sampling, if no samples is provided. The default is ‘random’.

return_samplesbool, optional

Retun samples, if no samples is provided. The default is False.

parallelbool, optional

Set to True if the evaluations should be done in parallel. The default is False.

Returns

mean_preddict

Mean of the predictions.

std_preddict

Standard deviatioon of the predictions.

plot_adapt(y_mod, y_metamod, y_metamod_std, x_values=None, plot_ed=False, save=True)

Visualize the model and metamodel outputs

Parameters

y_moddict

Model outputs.

y_metamoddict

Mean MetaModel outputs.

y_metamod_stddict

Std of MetaModel outputs.

x_valueslist, optional

Values to write on the x-axis. The default is None

plot_edbool, optional

Toggles plotting the training samples as well. The default is False.

savebool, optional

Toggles saving the figure. The defautl is True.

plot_posterior(posterior, par_names, key)

Plot the posterior of a specific key as a corner plot

Parameters

posterior2d np.array

Samples of the posterior.

par_nameslist of strings

List of the parameter names.

keystring

Output key that this posterior belongs to.

start_engine() → None

Do all the preparations that need to be run before the actual training

Returns

None

train_normal(parallel=False, verbose=False, save=False) → None

Trains surrogate on static samples only. Samples are taken from the experimental design and the specified model is run on them. Alternatively the samples can be read in from a provided hdf5 file. save: bool determines whether the trained surrogate and the hdf5 file should be saved

Returns

None

train_sequential(parallel=False, verbose=False, plot=False) → None

Train the surrogate in a sequential manner. First build and train evereything on the static samples, then iterate choosing more samples and refitting the surrogate on them.

Parameters

parallelbool, optional

Toggles parallelization in the MetaModel training. The default is False

verbosebool, optional

Toggles verbose outputs during training. The default is False.

plotbool, optional

Toggles the generation of plots for each sequential iteration. The default is False.

Returns

None