bayesvalidrox.post_processing.post_processing.PostProcessing¶

class bayesvalidrox.post_processing.post_processing.PostProcessing(engine, name='calib')¶

Bases: object

This class provides many helper functions to post-process the trained meta-model.

Attributes¶

MetaModelobj: MetaModel object to do postprocessing on.
namestr: Type of the anaylsis. The default is ‘calib’. If a validation is expected to be performed change this to ‘valid’.

__init__(engine, name='calib')¶

Methods

`__init__`(engine[, name])
`check_accuracy`([n_samples, samples, outputs])	Checks accuracy of the metamodel by computing the root mean square error and validation error for all outputs.
`check_reg_quality`([n_samples, samples])	Checks the quality of the metamodel for single output models based on: https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685
`compute_pce_moments`()	Computes the first two moments using the PCE-based meta-model.
`eval_pce_model_3d`()
`plot_moments`([xlabel, plot_type])	Plots the moments in a pdf format in the directory Outputs_PostProcessing.
`plot_seq_design_diagnostics`([ref_BME_KLD])	Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence (KLD) for the sequential design.
`sobol_indices`([xlabel, plot_type])	Provides Sobol indices as a sensitivity measure to infer the importance of the input parameters.
`valid_metamodel`([n_samples, samples, ...])	Evaluates and plots the meta model and the PCEModel outputs for the given number of samples or the given samples.

check_accuracy(n_samples=None, samples=None, outputs=None)¶

Checks accuracy of the metamodel by computing the root mean square error and validation error for all outputs.

Parameters¶

n_samplesint, optional: Number of samples. The default is None.
samplesarray of shape (n_samples, n_params), optional: Parameter sets to be checked. The default is None.
outputsdict, optional: Output dictionary with model outputs for all given output types in Model.Output.names. The default is None.

Raises¶

Exception: When neither n_samples nor samples are provided.

Returns¶

rmse: dict: Root mean squared error for each output.
valid_errordict: Validation error for each output.

check_reg_quality(n_samples=1000, samples=None)¶

Checks the quality of the metamodel for single output models based on: https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685

Parameters¶

n_samplesint, optional: Number of parameter sets to use for the check. The default is 1000.
samplesarray of shape (n_samples, n_params), optional: Parameter sets to use for the check. The default is None.

Returns¶

None.

compute_pce_moments()¶

Computes the first two moments using the PCE-based meta-model.

Returns¶

pce_means: dict: The first moments (mean) of outpust.
pce_means: dict: The first moments (mean) of outpust.

plot_moments(xlabel='Time [s]', plot_type=None)¶

Plots the moments in a pdf format in the directory Outputs_PostProcessing.

Parameters¶

xlabelstr, optional: String to be displayed as x-label. The default is ‘Time [s]’.
plot_typestr, optional: Options: bar or line. The default is None.

Returns¶

pce_means: dict: Mean of the model outputs.
pce_means: dict: Standard deviation of the model outputs.

plot_seq_design_diagnostics(ref_BME_KLD=None)¶

Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence (KLD) for the sequential design.

Parameters¶

ref_BME_KLDarray, optional: Reference BME and KLD . The default is None.

Returns¶

None.

sobol_indices(xlabel='Time [s]', plot_type=None)¶

Provides Sobol indices as a sensitivity measure to infer the importance of the input parameters. See Eq. 27 in [1] for more details. For the case with Principal component analysis refer to [2].

[1] Global sensitivity analysis: A flexible and efficient framework with an example from stochastic hydrogeology S. Oladyshkin, F.P. de Barros, W. Nowak https://doi.org/10.1016/j.advwatres.2011.11.001

[2] Nagel, J.B., Rieckermann, J. and Sudret, B., 2020. Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: Application to urban drainage simulation. Reliability Engineering & System Safety, 195, p.106737.

Parameters¶

xlabelstr, optional: Label of the x-axis. The default is ‘Time [s]’.
plot_typestr, optional: Plot type. The default is None. This corresponds to line plot. Bar chart can be selected by bar.

Returns¶

sobol_cell: dict: Sobol indices.
total_sobol: dict: Total Sobol indices.

valid_metamodel(n_samples=1, samples=None, model_out_dict=None, x_axis='Time [s]')¶

Evaluates and plots the meta model and the PCEModel outputs for the given number of samples or the given samples.

Parameters¶

n_samplesint, optional: Number of samples to be evaluated. The default is 1.
samplesarray of shape (n_samples, n_params), optional: Samples to be evaluated. The default is None.
model_out_dict: dict: The model runs using the samples provided.
x_axisstr, optional: Label of x axis. The default is ‘Time [s]’.

Returns¶

None.