bayesvalidrox.surrogate_models.engine.Engine¶
- class bayesvalidrox.surrogate_models.engine.Engine(MetaMod, Model, ExpDes)¶
Bases:
object- __init__(MetaMod, Model, ExpDes)¶
Methods
__init__(MetaMod, Model, ExpDes)eval_metamodel([samples, nsamples, ...])Evaluates metamodel at the requested samples.
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_seq_design([parallel, verbose])Starts the adaptive sequential design for refining the surrogate model by selecting training points in a sequential manner.
train_sequential([parallel, verbose])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.
- 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.
Returns¶
None