LogisticSaturation

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

Wrapper around logistic saturation function.

Multiplies pymc_marketing.mmm.transformers.logistic_saturation() by an extra scaling parameter beta so the curve can reach an asymptote other than 1.

Parameters:

lamtensor

Steepness of the curve, as in logistic_saturation(). Default prior: Prior("Gamma", alpha=3, beta=1).

betatensor

Asymptote that the saturated response approaches as the input grows. Default prior: Prior("HalfNormal", sigma=2).

.. plot::

context:

close-figs

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

rng = np.random.default_rng(0)

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

Methods

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