CLVModel

class pymc_marketing.clv.models.basic.CLVModel(data, *, model_config=None, sampler_config=None, non_distributions=None)

CLV Model base class.

Methods

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