DataDerivedScaling

class pymc_marketing.mmm.scaling.DataDerivedScaling(**data)

Scale by a statistic of the data, computed at fit time.

Parameters:

method"max" | "mean"

The scaling method.

dimsstr or tuple of str

The dimensions to perform the operation through ("date" is always included implicitly).

Examples

Max-absolute scaling (default behaviour):

DataDerivedScaling(method="max", dims=())

Mean-absolute scaling across a custom dimension:

DataDerivedScaling(method="mean", dims=("country",))

Methods

DataDerivedScaling.__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
DataDerivedScaling.construct([_fields_set])
DataDerivedScaling.copy(*[, include, ...])	Returns a copy of the model.
DataDerivedScaling.dict(*[, include, ...])
DataDerivedScaling.from_dict(data)	Reconstruct from a dict via Pydantic model_validate.
DataDerivedScaling.from_orm(obj)
DataDerivedScaling.json(*[, include, ...])
DataDerivedScaling.model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
DataDerivedScaling.parse_file(path, *[, ...])
DataDerivedScaling.parse_obj(obj)
DataDerivedScaling.parse_raw(b, *[, ...])
DataDerivedScaling.scaling_description()	Human-readable summary of the scaling strategy.
DataDerivedScaling.schema([by_alias, ...])
DataDerivedScaling.schema_json(*[, ...])
DataDerivedScaling.to_dict([_orig])	Serialize to a dict via Pydantic model_dump.
DataDerivedScaling.update_forward_refs(**localns)
DataDerivedScaling.validate(value)

Attributes

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.
method
dims