BaseGammaGammaModel

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

Methods

BaseGammaGammaModel.__init__(data, *[, ...])	Initializes model configuration and sampler configuration for the model
BaseGammaGammaModel.build_model(X, y, **kwargs)	Creates an instance of pm.Model based on provided data and model_config, and attaches it to self.
BaseGammaGammaModel.distribution_customer_spend(...)	Posterior distribution of transaction value per customer
BaseGammaGammaModel.distribution_new_customer_spend([...])	Posterior distribution of transaction value for new customers
BaseGammaGammaModel.expected_customer_lifetime_value(...)	Expected customer lifetime value.
BaseGammaGammaModel.expected_customer_spend(...)	Expected transaction value per customer
BaseGammaGammaModel.expected_new_customer_spend()	Expected transaction value for a new customer
BaseGammaGammaModel.fit([fit_method])	Infer model posterior
BaseGammaGammaModel.fit_summary(**kwargs)
BaseGammaGammaModel.get_params([deep])	Get all the model parameters needed to instantiate a copy of the model, not including training data.
BaseGammaGammaModel.load(fname)	Creates a ModelBuilder instance from a file, Loads inference data for the model.
BaseGammaGammaModel.predict(X_pred[, ...])	Uses 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	Returns a class default config dict for model builder if no model_config is provided on class initialization Useful for understanding structure of required model_config to allow its customization by users .
default_sampler_config	Returns a class default sampler dict for model builder if no sampler_config is provided on class initialization Useful for understanding structure of required sampler_config to allow its customization by users .
fit_result
id	Generate a unique hash value for the model.
output_var	Returns the name of the output variable of the model.
version
y