AxModelManager

class optimas.utils.AxModelManager(source, varying_parameters=None, objectives=None, fit_out_of_design=False)

Class for building and exploring GP models using an AxClient.

Parameters:

sourceAxClient, str or DataFrame

Source data for the model. It can be either an existing AxClient with a GP model, a string with the path to a json file with a serialized AxClient, or a pandas DataFrame. When using a DataFrame, a list of objectives and varying parameters should also be provided.

objectiveslist of Objective, optional

Only needed if source is a pandas DataFrame. List of objectives for which a GP model should be built. The names and data of these objectives must be contained in the source DataFrame.

varying_parameterslist of VaryingParameter, optional

Only needed if source is a pandas DataFrame. List of parameters that were varied to scan the value of the objectives. The names and data of these parameters must be contained in the source DataFrame.

fit_out_of_designbool, optional

Whether to fit the surrogate model taking into account evaluations outside of the range of the varying parameters. This can be useful if the range of parameter has been reduced during the optimization. By default, False.

Methods

evaluate_model([sample, metric_name, ...])	Evaluate the model over the specified sample.
get_best_evaluation([metric_name, ...])	Get the best scoring point in the sample.
plot_contour([param_x, param_y, ...])	Plot a 2D slice of the surrogate model.
plot_cross_validation([metric_name, ...])	Make a cross-validation plot for the given metric.
plot_feature_importance([metric_name, ...])	Plot the importance of each varying parameter for the given metric.
plot_slice([param_name, metric_name, ...])	Plot a 1D slice of the surrogate model.