plot_expected_purchases_ppc

pymc_marketing.clv.plotting.plot_expected_purchases_ppc(model, ppc='posterior', max_purchases=10, samples=1000, random_seed=45, ax=None, **kwargs)

Plot a prior or posterior predictive check for the customer purchase frequency distribution.

ParetoNBDModel, BetaGeoBetaBinomModel, BetaGeoModel and ModifiedBetaGeoModel are supported.

Adapted from legacy lifetimes library: CamDavidsonPilon/lifetimes

Parameters:

modelCLVModel

Prior predictive checks can be performed before or after a model is fit. Posterior predictive checks require a fitted model.

ppcstr, optional

Type of predictive check to perform. Options are ‘prior’ or ‘posterior’; defaults to ‘posterior’.

max_purchasesint, optional

Cutoff for bars of purchase counts to plot. Default is 10.

samplesint, optional

Number of samples to draw for prior predictive checks. This is not used for posterior predictive checks.

random_seedint, optional

Random seed to fix sampling results

axmatplotlib.Axes, optional

A matplotlib Axes instance. Creates new axes instance by default.

**kwargs

Additional arguments to pass into the pandas.DataFrame.plot command.

Returns:

axesmatplotlib.AxesSubplot