LogisticSaturation#
- class pymc_marketing.mmm.components.saturation.LogisticSaturation(priors=None, prefix=None)[source]#
Wrapper around logistic saturation function.
Multiplies
pymc_marketing.mmm.transformers.logistic_saturation()by an extra scaling parameterbetaso the curve can reach an asymptote other than 1.- Parameters:
- lam
tensor Steepness of the curve, as in
logistic_saturation(). Default prior:Prior("Gamma", alpha=3, beta=1).- beta
tensor 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()
- lam
Methods
LogisticSaturation.__init__([priors, prefix])LogisticSaturation.apply(x, *[, core_dim, idx])Call within a model context.
Reconstruct a 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.
Plot samples from the curve.
Sample the curve of the saturation transformation given parameters.
LogisticSaturation.sample_prior([coords])Sample the priors for the transformation.
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.
Return a copy with default prior dims (dims=None) set to
dimsinstead.Return a copy with updated priors.
Attributes
Get the combined dims for all the parameters.
Get the priors for the function.
Mapping from variable name to prior for the model.
Get the priors for the function.
Whether the saturation must receive raw (unscaled) channel inputs.
Mapping from parameter name to variable name in the model.