MMMModelBuilder

class pymc_marketing.mmm.base.MMMModelBuilder(date_column, channel_columns, model_config=None, sampler_config=None, **kwargs)

Methods

MMMModelBuilder.__init__(date_column, ...[, ...])	Initializes model configuration and sampler configuration for the model
MMMModelBuilder.build_model(X, y, **kwargs)	Creates an instance of pm.Model based on provided data and model_config, and attaches it to self.
MMMModelBuilder.compute_channel_contribution_original_scale()	Compute the channel contributions in the original scale of the target variable.
MMMModelBuilder.compute_mean_contributions_over_time([...])	Get the contributions of each channel over time.
MMMModelBuilder.fit(X[, y, progressbar, ...])	Fit a model using the data passed as a parameter.
MMMModelBuilder.get_errors([original_scale])	Get model errors posterior distribution.
MMMModelBuilder.get_params([deep])	Get all the model parameters needed to instantiate a copy of the model, not including training data.
MMMModelBuilder.get_target_transformer()	Return the target transformer pipeline used for preprocessing the target variable.
MMMModelBuilder.graphviz(**kwargs)
MMMModelBuilder.load(fname)	Creates a ModelBuilder instance from a file, Loads inference data for the model.
MMMModelBuilder.plot_channel_contribution_share_hdi([...])	Plot the share of channel contributions in a forest plot.
MMMModelBuilder.plot_components_contributions(...)	Plot the target variable and the posterior predictive model components in the scaled space.
MMMModelBuilder.plot_errors([original_scale, ax])	Plot model errors by taking the difference between true values and predicted.
MMMModelBuilder.plot_grouped_contribution_breakdown_over_time([...])	Plot a time series area chart for all channel contributions.
MMMModelBuilder.plot_posterior_predictive([...])	Plot posterior distribution from the model fit.
MMMModelBuilder.plot_prior_predictive(...)
MMMModelBuilder.plot_waterfall_components_decomposition([...])	This function creates a waterfall plot.
MMMModelBuilder.predict(X_pred[, extend_idata])	Uses model to predict on unseen data and return point prediction of all the samples.
MMMModelBuilder.predict_posterior(X_pred[, ...])	Generate posterior predictive samples on unseen data.
MMMModelBuilder.predict_proba(X_pred[, ...])	Alias for predict_posterior, for consistency with scikit-learn probabilistic estimators.
MMMModelBuilder.preprocess(target, data)	Preprocess the provided data according to the specified target.
MMMModelBuilder.sample_posterior_predictive(X_pred)	Sample from the model's posterior predictive distribution.
MMMModelBuilder.sample_prior_predictive(X_pred)	Sample from the model's prior predictive distribution.
MMMModelBuilder.save(fname)	Save the model's inference data to a file.
MMMModelBuilder.set_idata_attrs([idata])	Set attributes on an InferenceData object.
MMMModelBuilder.set_params(**params)	Set all the model parameters needed to instantiate the model, not including training data.
MMMModelBuilder.validate(target, data)	Validates the input data based on the specified target type.

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 Examples -------- >>> @classmethod >>> def default_model_config(self): >>> Return { >>> 'a' : { >>> 'loc': 7, >>> 'scale' : 3 >>> }, >>> 'b' : { >>> 'loc': 3, >>> 'scale': 5 >>> } >>> 'obs_error': 2 >>> }
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 Examples -------- >>> @classmethod >>> def default_sampler_config(self): >>> Return { >>> 'draws': 1_000, >>> 'tune': 1_000, >>> 'chains': 1, >>> 'target_accept': 0.95, >>> }
fit_result
id	Generate a unique hash value for the model.
methods
output_var	Returns the name of the output variable of the model.
posterior_predictive
preprocessing_methods	A property that provides preprocessing methods for features ("X") and the target variable ("y").
prior
prior_predictive
validation_methods	A property that provides validation methods for features ("X") and the target variable ("y").
version
y
model