CLVModel

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

Methods

CLVModel.__init__(data, *[, model_config, ...])	Initializes model configuration and sampler configuration for the model
CLVModel.build_model(X, y, **kwargs)	Creates an instance of pm.Model based on provided data and model_config, and attaches it to self.
CLVModel.fit([fit_method])	Infer model posterior
CLVModel.fit_summary(**kwargs)
CLVModel.get_params([deep])	Get all the model parameters needed to instantiate a copy of the model, not including training data.
CLVModel.load(fname)	Creates a ModelBuilder instance from a file, Loads inference data for the model.
CLVModel.predict(X_pred[, extend_idata])	Uses model to predict on unseen data and return point prediction of all the samples.
CLVModel.predict_posterior(X_pred[, ...])	Generate posterior predictive samples on unseen data.
CLVModel.predict_proba(X_pred[, ...])	Alias for predict_posterior, for consistency with scikit-learn probabilistic estimators.
CLVModel.sample_posterior_predictive(X_pred)	Sample from the model's posterior predictive distribution.
CLVModel.sample_prior_predictive(X_pred[, ...])	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.set_params(**params)	Set all the model parameters needed to instantiate the model, not including training data.
CLVModel.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 Examples -------- >>> @classmethod >>> def default_model_config(self): >>> Return { >>> 'a' : { >>> 'loc': 7, >>> 'scale' : 3 >>> }, >>> 'b' : { >>> 'loc': 3, >>> 'scale': 5 >>> } >>> 'obs_error': 2 >>> }
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 Examples -------- >>> @classmethod >>> def default_sampler_config(self): >>> Return { >>> 'draws': 1_000, >>> 'tune': 1_000, >>> 'chains': 1, >>> 'target_accept': 0.95, >>> }
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