AdstockTransformation

class pymc_marketing.mmm.components.adstock.AdstockTransformation(l_max=FieldInfo(annotation=int, required=True, description='Maximum lag for the adstock transformation.', metadata=[Gt(gt=0)]), normalize=FieldInfo(annotation=bool, required=False, default=True, description='Whether to normalize the adstock values.'), mode=FieldInfo(annotation=ConvMode, required=False, default=<ConvMode.After: 'After'>, description='Convolution mode.'), priors=FieldInfo(annotation=Union[dict[str, Union[Annotated[Prior, InstanceOf], float, Annotated[TensorVariable, InstanceOf], Annotated[VariableFactory, InstanceOf]]], NoneType], required=False, default=None, description='Priors for the parameters.'), prefix=FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Prefix for the parameters.'))

Subclass for all adstock functions.

In order to use a custom saturation function, inherit from this class and define:

• function: a function that takes x to adstock x

• default_priors: dictionary with priors for every parameter in function

Consider the predefined subclasses as examples.

Methods

AdstockTransformation.__init__([l_max, ...])
AdstockTransformation.apply(x[, dims])	Call within a model context.
AdstockTransformation.plot_curve(curve[, ...])	Plot curve HDI and samples.
AdstockTransformation.plot_curve_hdi(curve)	Plot the HDI of the curve.
AdstockTransformation.plot_curve_samples(curve)	Plot samples from the curve.
AdstockTransformation.sample_curve(parameters)	Sample the adstock transformation given parameters.
AdstockTransformation.sample_prior([coords])	Sample the priors for the transformation.
AdstockTransformation.set_dims_for_all_priors(dims)	Set the dims for all priors.
AdstockTransformation.to_dict()	Convert the adstock transformation to a dictionary.
AdstockTransformation.update_priors(priors)	Update the priors for a function after initialization.

Attributes

combined_dims	Get the combined dims for all the parameters.
function_priors	Get the priors for the function.
model_config	Mapping from variable name to prior for the model.
prefix
variable_mapping	Mapping from parameter name to variable name in the model.
lookup_name
default_priors
function