meridian.model.adstock_hill.AdstockTransformer

Class to compute the Adstock transformation of media.

Inherits From: AdstockHillTransformer

meridian.model.adstock_hill.AdstockTransformer(
    alpha: meridian.backend.Tensor,
    max_lag: int,
    n_times_output: int,
    decay_functions: (str | Sequence[str]) = constants.GEOMETRIC_DECAY
)

Args
alpha	Tensor of alpha parameters taking values in [0, 1] with dimensions [..., n_media_channels]. Batch dimensions (...) are optional. Note that alpha = 0 is allowed, so it is possible to put a point mass prior at zero (effectively no Adstock).
max_lag	Integer indicating the maximum number of lag periods (≥ 0) to include in the Adstock calculation.
n_times_output	Integer indicating the number of time periods to include in the output tensor. Cannot exceed the number of time periods of the media argument, for example, media.shape[-2]. The output time periods correspond to the most recent time periods of the media argument. For example, media[..., -n_times_output:, :] represents the media execution of the output weeks.
decay_functions	String or list of strings indicating the decay function(s) to use for the Adstock calculation for each channel. Default is geometric decay for all channels.

Methods

forward

View source

forward(
    media: meridian.backend.Tensor
) -> meridian.backend.Tensor

Computes the Adstock transformation of a given media tensor.

For geo g, time period t, and media channel m, Adstock is calculated as adstock_{g,t,m} = sum_{i=0}^max_lag media_{g,t-i,m} alpha^i.

Note: The Hill function can be applied before or after Adstock. If Hill is applied first, then the Adstock media input can contain batch dimensions because the transformed media tensor will be different for each posterior sample.

Args
media	Tensor of media values with dimensions [..., n_geos, n_media_times, n_media_channels]. Batch dimensions (...) are optional, but if batch dimensions are included, they must match the batch dimensions of alpha. Media is not required to have batch dimensions even if alpha contains batch dimensions.

Returns
Tensor with dimensions [..., n_geos, n_times_output, n_media_channels] representing Adstock transformed media.