FixedScaling#

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

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).

valuefloat or dict[str, float] or xarray.DataArray

Fixed scaling constant(s). A single float applies uniformly.

A dict maps coordinate labels along the single remaining dimension after reducing over date and dims (see the multidimensional MMM). If more than one non-reduced dimension remains, use an xarray.DataArray whose dimensions broadcast to that grid (e.g. a vector over country when the media grid is country × channel). All values must be positive; NaNs are not allowed.

Examples

Fixed scalar scaling for production stability:

FixedScaling(dims=(), value=10_000.0)

Per-dimension fixed scaling (multidimensional MMM):

FixedScaling(
    dims=("country",),
    value={"US": 50_000, "UK": 30_000},
)

Multi-dimensional fixed scale (e.g. country × channel) with xarray:

import xarray as xr

FixedScaling(
    dims=(),
    value=xr.DataArray(
        [[1e3, 2e3], [3e3, 4e3]],
        dims=("country", "channel"),
        coords={"country": ["US", "UK"], "channel": ["tv", "search"]},
    ),
)

Long-format table via from_long_dataframe():

FixedScaling.from_long_dataframe(
    dims=(),
    df=long_df,
    value_col="scale",
    dim_cols=["country", "channel"],
)

Methods

`FixedScaling.__init__`(**data)	Create a new model by parsing and validating input data from keyword arguments.
`FixedScaling.construct`([_fields_set])
`FixedScaling.copy`(*[, include, exclude, ...])	Returns a copy of the model.
`FixedScaling.dict`(*[, include, exclude, ...])
`FixedScaling.from_dict`(data)	Deserialize; restores encoded `xarray.DataArray` values.
`FixedScaling.from_long_dataframe`(dims, df, ...)	Build fixed scaling from a long table (one row per coordinate combination).
`FixedScaling.from_orm`(obj)
`FixedScaling.json`(*[, include, exclude, ...])
`FixedScaling.model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`FixedScaling.parse_file`(path, *[, ...])
`FixedScaling.parse_obj`(obj)
`FixedScaling.parse_raw`(b, *[, content_type, ...])
`FixedScaling.scaling_description`()	Human-readable summary of the scaling strategy.
`FixedScaling.schema`([by_alias, ref_template])
`FixedScaling.schema_json`(*[, by_alias, ...])
`FixedScaling.to_dict`([_orig])	Serialize to a dict via Pydantic model_dump.
`FixedScaling.update_forward_refs`(**localns)
`FixedScaling.validate`(value)

Attributes

`method`	Return the scaling method name.
`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.
`value`
`dims`