NoSaturation

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

Wrapper around linear saturation function.

Identity-like transformation that returns beta * x. Useful when a channel should not be saturated but still needs a learned coefficient.

Parameters:

betatensor

Slope of the linear response. Default prior: Prior("HalfNormal", sigma=1).

.. plot::

context:

close-figs

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

rng = np.random.default_rng(0)

saturation = NoSaturation() prior = saturation.sample_prior(random_seed=rng) curve = saturation.sample_curve(prior) saturation.plot_curve(curve, random_seed=rng) plt.show()

Methods

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