Model Validation

A ValidationSession object stores a Dataset object that serves as a basis for validate your models. Then, the user can append as many simulation results as he/she want to the same ValidationSession object that automatically computes validation metrics and store the results in the validation_results attribute.

Warning

It is discouraged to change a Dataset object once it is an attribute of a ValidationSession object. This because the validation results will be jeopardized.

If you want to change Dataset, then consider to create a new ValidationSession object.

ValidationSession class

Constructor

ValidationSession(name, validation_dataset)

The ValidationSession class is used to validate models against a given dataset.

Attributes

ValidationSession.name	The validation session name.
ValidationSession.Dataset	The reference Dataset class object.
ValidationSession.auto_correlation	The auto-correlation tensors.
ValidationSession.cross_correlation	The cross-correlation tensors.
ValidationSession.validation_results	The validation results.

Methods

ValidationSession.append_simulation(...[, ...])	Append simulation results.
ValidationSession.drop_simulation(*sims)	Drop simulation results from the validation session.
ValidationSession.plot_simulations([...])	Plot the stored simulation results.
ValidationSession.plot_residuals([...])	Plot the residuals.
ValidationSession.simulations_names()	Return a list of names of the stored simulations.
ValidationSession.simulation_signals_list(...)	Return the signal name list of a given simulation result.
ValidationSession.clear()	Clear all the stored simulation results.

Functions

acorr_norm(Rxx[, l_norm, matrix_norm])	Return the norm of the auto-correlation tensor.
rsquared(x, y)	Return the \(R^2\) value of two signals.
xcorr(X, Y)	Return the normalized cross-correlation of two MIMO signals.
xcorr_norm(Rxy[, l_norm, matrix_norm])	Return the norm of the cross-correlation tensor.