BinomialAdstock#

class pymc_marketing.mmm.components.adstock.BinomialAdstock(l_max=FieldInfo(annotation=NoneType, required=True, description='Maximum lag for the adstock transformation.', metadata=[Gt(gt=0)]), normalize=FieldInfo(annotation=NoneType, required=False, default=True, description='Whether to normalize the adstock values.'), mode=FieldInfo(annotation=NoneType, required=False, default=<ConvMode.After: 'After'>, description='Convolution mode.'), priors=FieldInfo(annotation=NoneType, required=False, default=None, description='Priors for the parameters.'), prefix=FieldInfo(annotation=NoneType, required=False, default=None, description='Prefix for the parameters.'))[source]#

Wrapper around the binomial adstock function.

Calls pymc_marketing.mmm.transformers.binomial_adstock() with the wrapper’s l_max, normalize and mode settings.

Parameters:

alphatensor

Retention rate of the ad effect; must be between 0 and 1. Default prior: Prior("Beta", alpha=1, beta=3).

.. plot::

context:: close-figs

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

rng = np.random.default_rng(0)

adstock = BinomialAdstock(l_max=10) prior = adstock.sample_prior(random_seed=rng) curve = adstock.sample_curve(prior) adstock.plot_curve(curve, random_seed=rng) plt.show()

Methods

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