BaseGammaGammaModel

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

Base class for Gamma-Gamma models.

Methods

BaseGammaGammaModel.__init__(data, *[, ...])	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 model from the InferenceData object.
BaseGammaGammaModel.build_model(X, y, **kwargs)	Create an instance of pm.Model based on provided data and model_config.
BaseGammaGammaModel.create_fit_data(X, y)	Create the fit_data group based on the input data.
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([method, fit_method])	Infer model posterior.
BaseGammaGammaModel.fit_summary(**kwargs)	Compute the summary of the fit result.
BaseGammaGammaModel.graphviz(**kwargs)	Get the graphviz representation of the model.
BaseGammaGammaModel.load(fname)	Create a ModelBuilder instance from a file.
BaseGammaGammaModel.load_from_idata(idata)	Create a ModelBuilder instance from an InferenceData object.
BaseGammaGammaModel.post_sample_model_transformation()	Perform transformation on the model after sampling.
BaseGammaGammaModel.predict([X, extend_idata])	Use a model to predict on unseen data and return point prediction of all the samples.
BaseGammaGammaModel.predict_posterior([X, ...])	Generate posterior predictive samples on unseen data.
BaseGammaGammaModel.predict_proba([X, ...])	Alias for predict_posterior, for consistency with scikit-learn probabilistic estimators.
BaseGammaGammaModel.sample_posterior_predictive([...])	Sample from the model's posterior predictive distribution.
BaseGammaGammaModel.sample_prior_predictive([...])	Sample from the model's prior predictive distribution.
BaseGammaGammaModel.save(fname)	Save the model's inference data to a file.
BaseGammaGammaModel.set_idata_attrs([idata])	Set attributes on an InferenceData object.
BaseGammaGammaModel.thin_fit_result(keep_every)	Return a copy of the model with a thinned fit result.

Attributes

X
default_model_config	Return a class default configuration dictionary.
default_sampler_config	Default sampler configuration.
fit_result	Get the posterior fit_result.
id	Generate a unique hash value for the model.
output_var	Output variable of the model.
posterior
posterior_predictive
predictions
prior
prior_predictive
version
y