Campaign

class obsidian.campaign.campaign.Campaign(X_space: ParamSpace, target: Target | list[Target], constraints: Output_Constraint | list[Output_Constraint] | None = None, optimizer: Optimizer | None = None, designer: ExpDesigner | None = None, objective: Objective | None = None, seed: int | None = None)

Bases: object

Base class for tracking optimization progress and other metrics over multiple iterations.

X_space

The parameter space for the campaign.

Type:

ParamSpace

data

The data collected during the campaign.

Type:

pd.DataFrame

optimizer

The optimizer used for optimization.

Type:

Optimizer

designer

The experimental designer used for experiment design.

Type:

ExpDesigner

iter

The current iteration number.

Type:

int

seed

The seed for random number generation.

Type:

int

Properties:

m_exp (int): The number of observations in campaign.data y (pd.Series): The response data in campaign.data y_names (list): The names of the response data columns f (pd.Series): The transformed response data o (pd.Series): The objective function evaluated on f o_names (list): The names of the objective function columns X (pd.DataFrame): The input features of campaign.data response_max (float | pd.Series): The maximum for each response target (Target | list[Target]): The target(s) for optimization. objective (Objective, optional): The objective of the optimization campaign

__init__(X_space: ParamSpace, target: Target | list[Target], constraints: Output_Constraint | list[Output_Constraint] | None = None, optimizer: Optimizer | None = None, designer: ExpDesigner | None = None, objective: Objective | None = None, seed: int | None = None)

Methods

__init__(X_space, target[, constraints, ...])
add_data(df)	Adds data to the campaign.
clear_data()	Clears campaign data
clear_objective()	Clears the campaign objective function
clear_output_constraints()	Clears output constraints
constrain_outputs(constraints)	Sets optional output constraints for the campaign.
evaluate(X_suggest)	Maps Optimizer.evaluate method
fit()	Maps Optimizer.fit method
initialize(**design_kwargs)	Maps ExpDesigner.initialize method
load_state(obj_dict)	Loads the state of the campaign from a dictionary.
save_state()	Saves the state of the Campaign object as a dictionary.
set_X_space(X_space)	Sets the campaign ParamSpace
set_designer(designer)	Sets the campaign experiment designer
set_objective(objective)	(Re)sets the campaign objective function
set_optimizer(optimizer)	Sets the campaign optimizer
set_target(target)	Sets the experimental target context for the campaign.
suggest(**optim_kwargs)	Maps Optimizer.suggest method

Attributes

X	Feature columns of the training data
X_best	Best performing X values
X_space	Campaign ParamSpace
designer	Campaign Experimental Designer
f	Experimental response data, in transformed space
m_exp	Number of observations in training data
o	Objective function evaluated on f
objective	Campaign Objective function
optimizer	Campaign Optimizer
out	Returns the objective function as appropriate, else the response data
response_max	Maximum response data in training set
target	Campaign experimental target(s)
y	Experimental response data

property X: DataFrame

Feature columns of the training data

property X_best: DataFrame

Best performing X values

property X_space: ParamSpace

Campaign ParamSpace

add_data(df: DataFrame)

Adds data to the campaign.

Parameters:

Z_i (pd.DataFrame) – The data to be added to the campaign.

Raises:

• KeyError – If all X_names are not in the dataset

• KeyError – If all y_names are not in the dataset

clear_data()

Clears campaign data

clear_objective()

Clears the campaign objective function

clear_output_constraints()

Clears output constraints

constrain_outputs(constraints: Output_Constraint | list[Output_Constraint] | None) → None

Sets optional output constraints for the campaign.

property designer: ExpDesigner

Campaign Experimental Designer

evaluate(X_suggest: DataFrame)

Maps Optimizer.evaluate method

property f: Series | DataFrame

Experimental response data, in transformed space

fit()

Maps Optimizer.fit method

Raises:

ValueError – If no data has been registered to the campaign

initialize(**design_kwargs)

Maps ExpDesigner.initialize method

classmethod load_state(obj_dict: dict)

Loads the state of the campaign from a dictionary.

Parameters:

• cls (Campaign) – The class object.

• obj_dict (dict) – A dictionary containing the campaign state.

Returns:

A new campaign object with the loaded state.

Return type:

Campaign

property m_exp: int

Number of observations in training data

property o: Series | DataFrame

Objective function evaluated on f

property objective: Objective | None

Campaign Objective function

property optimizer: Optimizer

Campaign Optimizer

property out: Series | DataFrame

Returns the objective function as appropriate, else the response data

property response_max: float | Series

Maximum response data in training set

save_state() → dict

Saves the state of the Campaign object as a dictionary.

Returns:

A dictionary containing the saved state of the Campaign object.

Return type:

dict

set_X_space(X_space: ParamSpace)

Sets the campaign ParamSpace

set_designer(designer: ExpDesigner)

Sets the campaign experiment designer

set_objective(objective: Objective | None)

(Re)sets the campaign objective function

set_optimizer(optimizer: Optimizer)

Sets the campaign optimizer

set_target(target: Target | list[Target])

Sets the experimental target context for the campaign.

Parameters:

target (Target | list[Target] | None) – The target or list of targets to set.

suggest(**optim_kwargs)

Maps Optimizer.suggest method

property target

Campaign experimental target(s)

property y: Series | DataFrame

Experimental response data