bayesvalidrox.surrogate_models.meta_model.MetaModel¶

class bayesvalidrox.surrogate_models.meta_model.MetaModel(input_obj, meta_model_type='', bootstrap_method='fast', n_bootstrap_itrs=1, dim_red_method='no', is_gaussian=False, verbose=False, n_mc=1000, input_transform='user')¶

Bases: object

Meta (surrogate) model base class

This class describes the necessary functions and propoerties of a surrogate model in bayesvalidrox. It accepts an input object (input_obj) containing the specification of the distributions for uncertain parameters.

Attributes¶

input_objobj

Input object with the information on the model input parameters.

bootstrap_methodstr

Bootstraping method. Options are ‘normal’ and ‘fast’. The default is ‘fast’. It means that in each iteration except the first one, only the coefficent are recalculated with the ordinary least square method.

n_bootstrap_itrsint

Number of iterations for the bootstrap sampling. The default is 1.

dim_red_methodstr

Dimensionality reduction method for the output space. The available method is based on principal component analysis (PCA). The Default is ‘no’. There are two ways to select number of components: use percentage of the explainable variance threshold (between 0 and 100) (Option A) or direct prescription of components’ number (Option B):

>>> MetaModelOpts = MetaModel()
>>> MetaModelOpts.dim_red_method = 'PCA'
>>> MetaModelOpts.var_pca_threshold = 99.999  # Option A
>>> MetaModelOpts.n_pca_components = 12 # Option B

is_gaussianbool

Set to True if the surrogate model returns mean and stdev. The default is False.

verbosebool

Prints summary of the regression results. Default is False.

n_mcint

Number of Monte Carlo samples used for the calculation of the moments. Standard is 1000.

__init__(input_obj, meta_model_type='', bootstrap_method='fast', n_bootstrap_itrs=1, dim_red_method='no', is_gaussian=False, verbose=False, n_mc=1000, input_transform='user')¶

Methods

`__init__`(input_obj[, meta_model_type, ...])
`add_input_space`()	Instanciates experimental design object.
`build_metamodel`()	Builds the parts for the metamodel (polynomes,...) that are needed before fitting.
`calculate_moments`()	Computes the first two moments of the metamodel.
`check_is_gaussian`()	Check if the current surrogate will return both a mean and stdev as the output of being evaluated.
`copy_meta_model_opts`()	This method is a convinient function to copy the metamodel options.
`eval_metamodel`(samples)	Evaluates metamodel at the requested samples.
`fit`(X, y[, parallel, verbose])	Fits the surrogate to the given data (samples X, outputs y).
`pca_transformation`(target, n_pca_components)	Transforms the targets (outputs) via Principal Component Analysis.

class AutoVivification¶

Bases: dict

Implementation of perl’s AutoVivification feature.

Source: https://stackoverflow.com/a/651879/18082457

clear() → None. Remove all items from D.¶

copy() → a shallow copy of D¶

fromkeys(value=None, /)¶: Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)¶: Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items¶

keys() → a set-like object providing a view on D's keys¶

pop(k[, d]) → v, remove specified key and return the corresponding value.¶: If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()¶

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)¶

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from dict/iterable E and F.¶: If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values¶

add_input_space()¶: Instanciates experimental design object.

Returns¶

None.

build_metamodel() → None¶

Builds the parts for the metamodel (polynomes,…) that are needed before fitting.

This function should be extended and called in/before training the surrogate child classes!

Returns¶

None

calculate_moments()¶

Computes the first two moments of the metamodel.

Returns¶

means: dict: The first moment (mean) of the surrogate.
stds: dict: The second moment (standard deviation) of the surrogate.

check_is_gaussian() → bool¶

Check if the current surrogate will return both a mean and stdev as the output of being evaluated.

This function should be extended and applied in the constructor of all child classes!

Returns¶

bool: True if stdev is also returned.

copy_meta_model_opts()¶

This method is a convinient function to copy the metamodel options.

Returns¶

metamod_copyobject: The copied object.

eval_metamodel(samples)¶

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

Parameters¶

samplesarray of shape (n_samples, ndim), optional: Samples to evaluate metamodel at. The default is None.

Returns¶

mean_preddict: Mean of the predictions.
std_preddict: Standard deviatioon of the predictions. Return None if self.is_gaussian == False

fit(X: array, y: dict, parallel=False, verbose=False)¶

Fits the surrogate to the given data (samples X, outputs y). Note here that the samples X should be the transformed samples provided by the experimental design if the transformation is used there.

Parameters¶

X2D list or np.array of shape (#samples, #dim): The parameter value combinations that the model was evaluated at.
ydict of 2D lists or arrays of shape (#samples, #timesteps): The respective model evaluations.
parallelbool: Set to True to run the training in parallel for various keys. The default is False.
verbosebool: Set to True to obtain more information during runtime. The default is False.

Returns¶

None.

pca_transformation(target, n_pca_components)¶

Transforms the targets (outputs) via Principal Component Analysis. The number of features is set by self.n_pca_components. If this is not given, self.var_pca_threshold is used as a threshold.

ToDo: Check the inputs needed for this class, there is an error when PCA is used. ToDo: From the y_transformation() function, a dictionary is being sent instead of an array for target.

Parameters¶

targetarray of shape (n_samples,): Target values.

Returns¶

pcaobj: Fitted sklearnPCA object.
OutputMatrixarray of shape (n_samples,): Transformed target values.
n_pca_componentsint: Number of selected principal components.