BudgetOptimizer#

class pymc_marketing.mmm.budget_optimizer.BudgetOptimizer(*, num_periods: ~typing.Annotated[int, ~annotated_types.Gt(gt=0)], model: ~pymc_marketing.mmm.budget_optimizer.Annotated[~pymc_marketing.mmm.budget_optimizer.OptimizerCompatibleModelWrapper, ~pydantic.functional_validators.InstanceOf()], response_variable: str = 'total_contributions', utility_function: ~collections.abc.Callable[[~pytensor.tensor.variable.TensorVariable, ~pytensor.tensor.variable.TensorVariable], float] = <function average_response>, budgets_to_optimize: ~xarray.core.dataarray.DataArray | None = None, custom_constraints: ~collections.abc.Sequence[~pymc_marketing.mmm.constraints.Constraint] = (), default_constraints: bool = True)[source]#

A class for optimizing budget allocation in a marketing mix model.

The goal of this optimization is to maximize the total expected response by allocating the given budget across different marketing channels. The optimization is performed using the Sequential Least Squares Quadratic Programming (SLSQP) method, which is a gradient-based optimization algorithm suitable for solving constrained optimization problems.

For more information on the SLSQP algorithm, refer to the documentation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html

Parameters:

model: MMMModel: The marketing mix model to optimize.
utility_functionUtilityFunctionType, optional: The utility function to maximize. Default is the mean of the response distribution.

Methods

`BudgetOptimizer.__init__`(**data)	Create a new model by parsing and validating input data from keyword arguments.
`BudgetOptimizer.allocate_budget`(total_budget)	Allocate the budget based on `total_budget`, optional `budget_bounds`, and custom constraints.
`BudgetOptimizer.construct`([_fields_set])
`BudgetOptimizer.copy`(*[, include, exclude, ...])	Returns a copy of the model.
`BudgetOptimizer.dict`(*[, include, exclude, ...])
`BudgetOptimizer.extract_response_distribution`(...)	Extract the response distribution graph, conditioned on posterior parameters.
`BudgetOptimizer.from_orm`(obj)
`BudgetOptimizer.json`(*[, include, exclude, ...])
`BudgetOptimizer.model_construct`([_fields_set])	Creates a new instance of the `Model` class with validated data.
`BudgetOptimizer.model_copy`(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy
`BudgetOptimizer.model_dump`(*[, mode, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump
`BudgetOptimizer.model_dump_json`(*[, indent, ...])	Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_dump_json
`BudgetOptimizer.model_json_schema`([...])	Generates a JSON schema for a model class.
`BudgetOptimizer.model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`BudgetOptimizer.model_post_init`(...)	Override this method to perform additional initialization after `__init__` and `model_construct`.
`BudgetOptimizer.model_rebuild`(*[, force, ...])	Try to rebuild the pydantic-core schema for the model.
`BudgetOptimizer.model_validate`(obj, *[, ...])	Validate a pydantic model instance.
`BudgetOptimizer.model_validate_json`(json_data, *)	Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#json-parsing
`BudgetOptimizer.model_validate_strings`(obj, *)	Validate the given object with string data against the Pydantic model.
`BudgetOptimizer.parse_file`(path, *[, ...])
`BudgetOptimizer.parse_obj`(obj)
`BudgetOptimizer.parse_raw`(b, *[, ...])
`BudgetOptimizer.schema`([by_alias, ref_template])
`BudgetOptimizer.schema_json`(*[, by_alias, ...])
`BudgetOptimizer.set_constraints`(constraints)	Set constraints for the optimizer.
`BudgetOptimizer.update_forward_refs`(**localns)
`BudgetOptimizer.validate`(value)

Attributes

`DEFAULT_MINIMIZE_KWARGS`
`model_computed_fields`
`model_config`	Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
`model_extra`	Get extra fields set during validation.
`model_fields`
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.
`num_periods`
`mmm_model`
`response_variable`
`utility_function`
`budgets_to_optimize`
`custom_constraints`
`default_constraints`