ModelBuilder

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

ModelBuilder that takes data at initialization.

Methods

ModelBuilder.__init__(data[, 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_fit_data()	Create the fit_data group based on the input data.
ModelBuilder.create_idata_attrs()	Create attributes for the inference data.
ModelBuilder.fit([progressbar, random_seed])	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)	Convert 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.

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