ModelBuilder

class pymc_marketing.model_builder.ModelBuilder(model_config=None, sampler_config=None)

Base class for building models with PyMC-Marketing.

It provides an easy-to-use API (similar to scikit-learn) for models and help with deployment.

Methods

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

Attributes

X
default_model_config	Return a class default configuration dictionary.
default_sampler_config	Return a class default sampler configuration dictionary.
fit_result	Get the posterior fit_result.
id	Generate a unique hash value for the model.
output_var	Returns the name of the output variable of the model.
posterior
posterior_predictive
predictions
prior
prior_predictive
version
y