ModelBuilder

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

Base class for building PyMC-Marketing models.

Child classes must implement the following methods:

• default_model_config: Returns a dictionary for default model configuration.

• default_sampler_config: Returns a dictionary for default sampler configuration.

• build_model: Builds the model based on the provided data and model configuration.

• build_from_idata: Builds the model from an InferenceData object. Needed for loading models.

• fit: Fits the model based on the provided data and sampler configurations.

• attrs_to_init_kwargs: Override to add additional init keyword arguments.

• _serializable_model_config: Needed for saving and loading the model.

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 the model from the InferenceData object.
ModelBuilder.build_model(**kwargs)	Create an instance of pm.Model based on provided data and model_config.
ModelBuilder.create_idata_attrs()	Create attributes for the inference data.
ModelBuilder.fit(**kwargs)	Fit a model using the data passed as a parameter.
ModelBuilder.graphviz(**kwargs)	Get the graphviz representation of the model.
ModelBuilder.idata_to_init_kwargs(idata)	Create the model configuration and sampler configuration from the InferenceData to keyword arguments.
ModelBuilder.load(fname[, check])	Create a ModelBuilder instance from a file.
ModelBuilder.load_from_idata(idata[, check])	Create a ModelBuilder instance from an InferenceData object.
ModelBuilder.save(fname, **kwargs)	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

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.
posterior
posterior_predictive
predictions
prior
prior_predictive
version
idata
sampler_config
model_config