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_model(X, y, **kwargs)	Create an instance of pm.Model based on provided data and model_config.
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([fit_method])	Infer model posterior.
BaseGammaGammaModel.fit_summary(**kwargs)	Compute the summary of the fit result.
BaseGammaGammaModel.get_params([deep])	Get all the model parameters needed to instantiate a copy of the model, not including training data.
BaseGammaGammaModel.load(fname)	Create a ModelBuilder instance from a file.
BaseGammaGammaModel.predict(X_pred[, ...])	Use a model to predict on unseen data and return point prediction of all the samples.
BaseGammaGammaModel.predict_posterior(X_pred)	Generate posterior predictive samples on unseen data.
BaseGammaGammaModel.predict_proba(X_pred[, ...])	Alias for predict_posterior, for consistency with scikit-learn probabilistic estimators.
BaseGammaGammaModel.sample_posterior_predictive(X_pred)	Sample from the model's posterior predictive distribution.
BaseGammaGammaModel.sample_prior_predictive(X_pred)	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.set_params(**params)	Set all the model parameters needed to instantiate the model, not including training data.
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 fit result.
id	Generate a unique hash value for the model.
output_var	Output variable of the model.
version
y