TanhSaturation

class pymc_marketing.mmm.components.saturation.TanhSaturation(priors=None, prefix=None)

Wrapper around tanh saturation function.

Calls pymc_marketing.mmm.transformers.tanh_saturation() directly. The saturation level is already exposed by the underlying function as b, so no extra scaling parameter is added at this layer.

Parameters:

btensor

Saturation point, the asymptote that the response approaches. Default prior: Prior("HalfNormal", sigma=1).

ctensor

Initial cost per user; larger values give a less efficient channel. Must be non-zero. Default prior: Prior("HalfNormal", sigma=1).

.. plot::

context:

close-figs

import matplotlib.pyplot as plt import numpy as np from pymc_marketing.mmm import TanhSaturation

rng = np.random.default_rng(0)

adstock = TanhSaturation() prior = adstock.sample_prior(random_seed=rng) curve = adstock.sample_curve(prior) adstock.plot_curve(curve, random_seed=rng) plt.show()

Methods

TanhSaturation.__init__([priors, prefix])
TanhSaturation.apply(x, *[, dims, core_dim, idx])	Call within a model context.
TanhSaturation.from_dict(data)	Reconstruct a saturation transformation from a dict.
TanhSaturation.function(x, b, c, *[, dim])	Tanh saturation function.
TanhSaturation.plot_curve(curve[, ...])	Plot curve HDI and samples.
TanhSaturation.plot_curve_hdi(curve[, ...])	Plot the HDI of the curve.
TanhSaturation.plot_curve_samples(curve[, ...])	Plot samples from the curve.
TanhSaturation.sample_curve([parameters, ...])	Sample the curve of the saturation transformation given parameters.
TanhSaturation.sample_prior([coords])	Sample the priors for the transformation.
TanhSaturation.set_dims_for_all_priors(dims)	Set the dims for all priors.
TanhSaturation.to_dict([_orig])	Convert the transformation to a dictionary.
TanhSaturation.update_priors(priors)	Update the priors for a function after initialization.
TanhSaturation.with_default_prior_dims(dims)	Return a copy with default prior dims (dims=None) set to dims instead.
TanhSaturation.with_updated_priors(priors)	Return a copy with updated priors.

Attributes

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