Scaling#

class pymc_marketing.mmm.scaling.Scaling(**data)[source]#

Scaling configuration for the MMM.

Parameters:

targetVariableScaling: Scaling configuration for the target (response) variable.
channelVariableScaling: Scaling configuration for the channel (media) variables.

Examples

Data-derived scaling:

Scaling(
    target=DataDerivedScaling(method="max", dims=()),
    channel=DataDerivedScaling(method="max", dims=()),
)

Fixed scaling for stable production refreshes:

Scaling(
    target=FixedScaling(dims=(), value=50_000.0),
    channel=FixedScaling(dims=(), value=10_000.0),
)

Methods

`Scaling.__init__`(**data)	Create a new model by parsing and validating input data from keyword arguments.
`Scaling.construct`([_fields_set])
`Scaling.copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`Scaling.dict`(*[, include, exclude, ...])
`Scaling.from_dict`(data)	Reconstruct from a dict, dispatching nested VariableScaling via __type__.
`Scaling.from_orm`(obj)
`Scaling.json`(*[, include, exclude, ...])
`Scaling.model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`Scaling.parse_file`(path, *[, content_type, ...])
`Scaling.parse_obj`(obj)
`Scaling.parse_raw`(b, *[, content_type, ...])
`Scaling.schema`([by_alias, ref_template])
`Scaling.schema_json`(*[, by_alias, ref_template])
`Scaling.to_dict`([_orig])	Serialize to a dict via Pydantic model_dump.
`Scaling.update_forward_refs`(**localns)
`Scaling.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.
`target`
`channel`