CLVModel#
- class pymc_marketing.clv.models.basic.CLVModel(data, *, model_config=None, sampler_config=None)[source]#
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.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 .
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