Simulator

class obsidian.experiment.simulator.Simulator(X_space: ParamSpace, response_function: Callable, name: str | list[str] = 'Response', eps: float | list[float] = 0.0, **kwargs)

Bases: object

Simulator class for generating in-silico responses to requested experiments.

This class provides functionality to simulate responses to a set of experiments based on a given response function. The simulated responses can be subject to error, which is controlled by the eps parameter.

X_space

The ParamSpace object representing the allowable space for optimization.

Type:

ParamSpace

response_function

The callable function used to convert experiments to responses.

Type:

Callable

name

Name of the simulated output(s). Default is Response.

Type:

str or list[str]

eps

The simulated error to apply, as the standard deviation of the Standard Normal distribution. Default is 0.

Type:

float or list[float]

kwargs

Optional hyperparameters for the response function.

Type:

dict

Raises:

• TypeError – If response_function is not a callable function.

• TypeError – If X_space is not an obsidian ParamSpace object.

__init__(X_space: ParamSpace, response_function: Callable, name: str | list[str] = 'Response', eps: float | list[float] = 0.0, **kwargs)

Methods

__init__(X_space, response_function[, name, eps])
simulate(X_prop)	Generates a response to a set of experiments.

simulate(X_prop: DataFrame) → ndarray

Generates a response to a set of experiments.

Currently, response function only handles strictly numeric values and categories are manually penalized.

Parameters:

X_prop (pd.DataFrame) – Proposed experiments to evaluate.

Returns:

Array of response values to experiments.

Return type:

np.ndarray