BudgetOptimizer#

class pymc_marketing.mmm.budget_optimizer.BudgetOptimizer(**data)[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:

adstockAdstockTransformation: The adstock class.
saturationSaturationTransformation: The saturation class.
num_periodsint: The number of time units.
parametersdict: A dictionary of parameters for each channel.
adstock_firstbool, optional: Whether to apply adstock transformation first or saturation transformation first. Default is True.

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 the total budget, budget bounds, and custom constraints.
`BudgetOptimizer.construct`([_fields_set])
`BudgetOptimizer.copy`(*[, include, exclude, ...])	Returns a copy of the model.
`BudgetOptimizer.dict`(*[, include, exclude, ...])
`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/2.9/concepts/serialization/#model_copy
`BudgetOptimizer.model_dump`(*[, mode, ...])	Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
`BudgetOptimizer.model_dump_json`(*[, indent, ...])	Usage docs: https://docs.pydantic.dev/2.9/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/2.9/concepts/json/#json-parsing
`BudgetOptimizer.model_validate_strings`(obj, *)	Validate the given object with string data against the Pydantic model.
`BudgetOptimizer.objective`(budgets)	Calculate the total response during a period of time given the budgets.
`BudgetOptimizer.parse_file`(path, *[, ...])
`BudgetOptimizer.parse_obj`(obj)
`BudgetOptimizer.parse_raw`(b, *[, ...])
`BudgetOptimizer.schema`([by_alias, ref_template])
`BudgetOptimizer.schema_json`(*[, by_alias, ...])
`BudgetOptimizer.update_forward_refs`(**localns)
`BudgetOptimizer.validate`(value)

Attributes

`model_computed_fields`	A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects.
`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`	Metadata about the fields defined on the model, mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects.
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.
`adstock`
`saturation`
`num_periods`
`parameters`
`scales`
`adstock_first`