InverseScaledLogisticSaturation

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

Wrapper around inverse scaled logistic saturation function.

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

Parameters:

lamtensor

Half-saturation point of the curve (when eps keeps its default value). Default prior: Prior("Gamma", alpha=0.5, 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 InverseScaledLogisticSaturation

rng = np.random.default_rng(0)

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

Methods

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