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SearchSpace

Manages the variable space for Bayesian optimization, including real, integer, and categorical variables.

Class Reference

alchemist_core.data.search_space.SearchSpace()

Class for storing and managing the search space in a consistent way across backends. Provides methods for conversions to different formats required by different backends.

add_variable(name, var_type, **kwargs)

Add a variable to the search space.

Parameters:

Name Type Description Default
name str

Variable name

 required
var_type str

"real", "integer", or "categorical"

 required
**kwargs

Additional parameters (min, max, values)

 {}

from_dict(data)

Load search space from a list of dictionaries (used with JSON/CSV loading).

from_skopt(dimensions)

Load search space from skopt dimensions.

to_dict()

Convert search space to a list of dictionaries.

to_skopt()

Get skopt dimensions for scikit-learn.

to_ax_space()

Convert to Ax parameter format.

to_botorch_bounds()

Create bounds in BoTorch format.

get_variable_names()

Get list of all variable names.

get_categorical_variables()

Get list of categorical variable names.

get_integer_variables()

Get list of integer variable names.

save_to_json(filepath)

Save search space to a JSON file.

load_from_json(filepath)

Load search space from a JSON file.

from_json(filepath) classmethod

Class method to create a SearchSpace from a JSON file.

add_constraint(constraint_type, coefficients, rhs, name=None)

Add linear input constraint.

Parameters:

Name Type Description Default
constraint_type str

'inequality' (sum(coeff_i * x_i) <= rhs) or 'equality' (sum(coeff_i * x_i) == rhs)

 required
coefficients Dict[str, float]

{variable_name: coefficient} mapping

 required
rhs float

right-hand side value

 required
name Optional[str]

optional human-readable name

 None

get_constraints()

Return list of constraint dicts.

to_botorch_constraints(feature_names)

Convert to BoTorch format for optimize_acqf.

Each constraint is a tuple (indices_tensor, coefficients_tensor, rhs_float). BoTorch convention: inequality means sum(coeff_i * x_i) - rhs <= 0.

Parameters:

Name Type Description Default
feature_names List[str]

ordered list of feature column names matching model input

 required

Returns:

Type Description
Optional[List]

(inequality_constraints, equality_constraints) — each is a list of tuples
Optional[List]

or None if no constraints of that type exist.

See Also