VariableScaling

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

Abstract base for scaling a variable.

The scaling through the dimension of 'date' is assumed and doesn’t need to be specified.

Concrete subclasses:

• DataDerivedScaling – scale by a statistic of the data ("max" or "mean"), computed at fit time.

• FixedScaling – use a user-supplied constant that stays the same across model refreshes.

Parameters:

dimsstr or tuple of str

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

Methods

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