TensorFlow implementation for adstock_process using loop/einsum.
meridian
.
backend
.
adstock_process
(
media
:
'_tf.Tensor'
,
weights
:
'_tf.Tensor'
,
n_times_output
:
int
)
->
'_tf.Tensor'
This function applies an adstock process to media spend data. It achieves
this by creating a windowed view of the media
tensor and then using tf.einsum
to efficiently compute the weighted sum based on the provided weights
. The weights
tensor defines the decay effect over a specific window_size
. The output is truncated to n_times_output
periods.
Args
Input media tensor. Expected shape is
(..., num_geos,
num_times_in, num_channels)
. The ...
represents optional batch
dimensions.Adstock weights tensor. Expected shape is
(..., num_channels,
window_size)
. The batch dimensions must be broadcast-compatible with
those in media
.
Returns
A tensor of shape
(..., num_geos, n_times_output, num_channels)
representing the adstocked media.