HillSaturationSigmoid#
- class pymc_marketing.mmm.components.saturation.HillSaturationSigmoid(priors=None, prefix=None)[source]#
Wrapper around Hill saturation sigmoid function.
Calls
pymc_marketing.mmm.transformers.hill_saturation_sigmoid()directly. The saturation level is exposed by the underlying function assigma, so no extra scaling parameter is added at this layer. Note thatbetahere is the slope of the sigmoid, not a scaling factor.- Parameters:
- sigma
tensor Upper-asymptote parameter (approximate; the true maximum is
sigma * (1 - 1 / (1 + exp(beta * lam))), seehill_saturation_sigmoid()). Default prior:Prior("HalfNormal", sigma=1.5).- beta
tensor Slope of the sigmoid, controlling the steepness of the transition. Default prior:
Prior("HalfNormal", sigma=1.5).- lam
tensor Midpoint of the transition on the input axis. Default prior:
Prior("HalfNormal", sigma=1.5).- .. plot::
- context:
close-figs
import matplotlib.pyplot as plt import numpy as np from pymc_marketing.mmm import HillSaturationSigmoid
rng = np.random.default_rng(0)
adstock = HillSaturationSigmoid() prior = adstock.sample_prior(random_seed=rng) curve = adstock.sample_curve(prior) adstock.plot_curve(curve, random_seed=rng) plt.show()
- sigma
Methods
HillSaturationSigmoid.__init__([priors, prefix])HillSaturationSigmoid.apply(x, *[, ...])Call within a model context.
Reconstruct a transformation from a dict.
HillSaturationSigmoid.function(x, sigma, ...)Hill sigmoid function.
HillSaturationSigmoid.plot_curve(curve[, ...])Plot curve HDI and samples.
Plot the HDI of the curve.
Plot samples from the curve.
Sample the curve of the saturation transformation given parameters.
HillSaturationSigmoid.sample_prior([coords])Sample the priors for the transformation.
Set the dims for all priors.
HillSaturationSigmoid.to_dict([_orig])Convert the transformation to a dictionary.
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.