RegressionModelBuilder

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

ModelBuilder class providing an easy-to-use API similar to scikit-learn for regression models.

Training data is provided in the fit method and must follow the following convention:

• X: Matrix containing predictor variables

• y: Target variable array

Methods

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