BaseGammaGammaModel

class pymc_marketing.clv.models.gamma_gamma.BaseGammaGammaModel(*, model_config=None, sampler_config=None, **kwargs)

Base class for Gamma-Gamma models.

Methods

BaseGammaGammaModel.__init__(*[, ...])	Initialize model configuration and sampler configuration for the model.
BaseGammaGammaModel.attrs_to_init_kwargs(attrs)	Convert the model configuration and sampler configuration from the attributes to keyword arguments.
BaseGammaGammaModel.build_from_idata(idata)	Build the model from the DataTree object.
BaseGammaGammaModel.build_model(**kwargs)	Create an instance of pm.Model based on provided data and model_config.
BaseGammaGammaModel.create_fit_data_group()	Build the fit_data group stored alongside the posterior.
BaseGammaGammaModel.create_idata_attrs()	Create attributes for the inference data.
BaseGammaGammaModel.distribution_customer_spend(data)	Posterior distribution of mean spend values for each customer.
BaseGammaGammaModel.distribution_new_customer_spend([...])	Posterior distribution of mean spend values for new customers.
BaseGammaGammaModel.expected_customer_lifetime_value(...)	Compute the average lifetime value for a group of one or more customers.
BaseGammaGammaModel.expected_customer_spend(data)	Compute the expected future mean spend value per customer.
BaseGammaGammaModel.expected_new_customer_spend()	Compute the expected mean spend value for a new customer.
BaseGammaGammaModel.fit([data, method, ...])	Infer the model posterior.
BaseGammaGammaModel.fit_summary(**kwargs)	Compute the summary of the fit result.
BaseGammaGammaModel.graphviz(**kwargs)	Get the graphviz representation of the model.
BaseGammaGammaModel.idata_to_init_kwargs(idata)	Create the model configuration and sampler configuration from the DataTree to keyword arguments.
BaseGammaGammaModel.load(fname[, check])	Create a ModelBuilder instance from a file.
BaseGammaGammaModel.load_from_idata(idata[, ...])	Create a ModelBuilder instance from a DataTree object.
BaseGammaGammaModel.post_sample_model_transformation()	Perform transformation on the model after sampling.
BaseGammaGammaModel.save(fname, **kwargs)	Save the model's inference data to a file.
BaseGammaGammaModel.set_idata_attrs([idata])	Set attributes on a DataTree object.
BaseGammaGammaModel.table(**model_table_kwargs)	Get the summary table of the model.
BaseGammaGammaModel.thin_fit_result(keep_every)	Return a copy of the model with a thinned fit result.

Attributes

default_model_config	Default model configuration.
default_sampler_config	Default sampler configuration.
fit_result	Get the posterior fit_result.
id	Generate a unique hash value for the model.
posterior	Access the 'posterior' group of the DataTree.
posterior_predictive	Access the 'posterior_predictive' group of the DataTree.
predictions	Access the 'predictions' group of the DataTree.
prior	Access the 'prior' group of the DataTree.
prior_predictive	Access the 'prior_predictive' group of the DataTree.
version
idata
sampler_config
model_config
model
is_fitted_