MMM

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

Methods

MMM.__init__(date_column, channel_columns[, ...])	Initializes model configuration and sampler configuration for the model
MMM.build_model(X, y, **kwargs)	Creates an instance of pm.Model based on provided data and model_config, and attaches it to self.
MMM.compute_channel_contribution_original_scale()	rtype: DataArray
MMM.compute_channel_curve_optimization_parameters_original_scale([...])	Experimental: Estimate the parameters for the saturating function of each channel's contribution.
MMM.compute_mean_contributions_over_time([...])	Get the contributions of each channel over time.
MMM.fit(X[, y, progressbar, ...])	Fit a model using the data passed as a parameter.
MMM.get_params([deep])	Get all the model parameters needed to instantiate a copy of the model, not including training data.
MMM.get_target_transformer()	rtype: Pipeline
MMM.graphviz(**kwargs)
MMM.load(fname)	Creates a ModelBuilder instance from a file, Loads inference data for the model.
MMM.optimize_channel_budget_for_maximum_contribution(...)	Experimental: Optimize the allocation of a given total budget across multiple channels to maximize the expected contribution.
MMM.plot_budget_scenearios(*, base_data[, ...])	Experimental: Plots the budget and contribution bars side by side for multiple scenarios.
MMM.plot_channel_contribution_share_hdi([...])	rtype: Figure
MMM.plot_channel_parameter(param_name, ...)	rtype: Figure
MMM.plot_components_contributions(**plt_kwargs)	rtype: Figure
MMM.plot_direct_contribution_curves([...])	Plots the direct contribution curves for each marketing channel.
MMM.plot_grouped_contribution_breakdown_over_time([...])	Plot a time series area chart for all channel contributions.
MMM.plot_posterior_predictive([original_scale])	rtype: Figure
MMM.plot_prior_predictive([samples])	rtype: Figure
MMM.plot_waterfall_components_decomposition([...])	This function creates a waterfall plot.
MMM.predict(X_pred[, extend_idata])	Uses model to predict on unseen data and return point prediction of all the samples.
MMM.predict_posterior(X_pred[, ...])	Generate posterior predictive samples on unseen data.
MMM.predict_proba(X_pred[, extend_idata, ...])	Alias for predict_posterior, for consistency with scikit-learn probabilistic estimators.
MMM.preprocess(target, data)	Preprocess the provided data according to the specified target.
MMM.sample_posterior_predictive(X_pred[, ...])	Sample from the model's posterior predictive distribution.
MMM.sample_prior_predictive(X_pred[, ...])	Sample from the model's prior predictive distribution.
MMM.save(fname)	Save the model's inference data to a file.
MMM.set_idata_attrs([idata])	Set attributes on an InferenceData object.
MMM.set_params(**params)	Set all the model parameters needed to instantiate the model, not including training data.
MMM.validate(target, data)	Validates the input data based on the specified target type.
MMM.validate_channel_columns(data)	rtype: None
MMM.validate_date_col(data)	rtype: None
MMM.validate_target(data)	rtype: None

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 .
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 .
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
date_column
channel_columns