delayed_adstock#

pymc_marketing.mmm.transformers.delayed_adstock(x, alpha=0.0, theta=0, l_max=12, normalize=False, axis=0, mode=ConvMode.After)[source]#

Delayed adstock transformation.

This transformation is similar to geometric adstock transformation, but it allows for a delayed peak of the effect. The peak is assumed to occur at theta.

(Source code, png, hires.png, pdf)

../../_images/pymc_marketing-mmm-transformers-delayed_adstock-1.png

Parameters:

xtensor

Input tensor.

alphafloat, by default 0.0

Retention rate of ad effect. Must be between 0 and 1.

thetafloat, by default 0

Delay of the peak effect. Must be between 0 and l_max - 1.

l_maxint, by default 12

Maximum duration of carryover effect.

normalizebool, by default False

Whether to normalize the weights.

axisint

The axis of x along witch to apply the convolution

modeConvMode, optional

The convolution mode determines how the convolution is applied at the boundaries of the input signal, denoted as “x.” The default mode is ConvMode.After.

ConvMode.After: Applies the convolution with the “Adstock” effect, resulting in a trailing decay effect.
ConvMode.Before: Applies the convolution with the “Excitement” effect, creating a leading effect
similar to the wow factor.
ConvMode.Overlap: Applies the convolution with both “Pull-Forward” and “Pull-Backward” effects,
where the effect overlaps with both preceding and succeeding elements.

Returns:

tensor: Transformed tensor.

References

[1]

Jin, Yuxue, et al. “Bayesian methods for media mix modeling with carryover and shape effects.” (2017).