BaseMMM

class pymc_marketing.mmm.mmm.BaseMMM(date_column=FieldInfo(annotation=str, required=True, description='Column name of the date variable.'), channel_columns=FieldInfo(annotation=list[str], required=True, description='Column names of the media channel variables.', metadata=[MinLen(min_length=1)]), adstock=FieldInfo(annotation=AdstockTransformation, required=True, description='Type of adstock transformation to apply.', metadata=[InstanceOf()]), saturation=FieldInfo(annotation=SaturationTransformation, required=True, description='Type of saturation transformation to apply.', metadata=[InstanceOf()]), time_varying_intercept=FieldInfo(annotation=bool, required=False, default=False, description='Whether to consider time-varying intercept.'), time_varying_media=FieldInfo(annotation=bool, required=False, default=False, description='Whether to consider time-varying media contributions.'), model_config=FieldInfo(annotation=Union[dict, NoneType], required=False, default=None, description='Model configuration.'), sampler_config=FieldInfo(annotation=Union[dict, NoneType], required=False, default=None, description='Sampler configuration.'), validate_data=FieldInfo(annotation=bool, required=False, default=True, description='Whether to validate the data before fitting to model'), control_columns=None, yearly_seasonality=None, adstock_first=FieldInfo(annotation=bool, required=False, default=True, description='Whether to apply adstock first.'), dag=FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Optional DAG provided as a string Dot format for causal identification.'), treatment_nodes=FieldInfo(annotation=Union[list[str], tuple[str], NoneType], required=False, default=None, description='Column names of the variables of interest to identify causal effects on outcome.'), outcome_node=FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Name of the outcome variable.'))

Base class for a media mix model using Delayed Adstock and Logistic Saturation (see [1]).

References

[1]

Jin, Yuxue, et al. “Bayesian methods for media mix modeling with carryover and shape effects.” (2017).

Methods

BaseMMM.__init__([date_column, ...])	Define the constructor method.
BaseMMM.attrs_to_init_kwargs(attrs)	Convert attributes to initialization kwargs.
BaseMMM.build_from_idata(idata)	Build model from the InferenceData object.
BaseMMM.build_model(X, y, **kwargs)	Build a probabilistic model using PyMC for marketing mix modeling.
BaseMMM.channel_contributions_forward_pass(...)	Evaluate the channel contribution for a given channel data and a fitted model, ie.
BaseMMM.compute_channel_contribution_original_scale([prior])	Compute the channel contributions in the original scale of the target variable.
BaseMMM.compute_mean_contributions_over_time([...])	Get the contributions of each channel over time.
BaseMMM.create_fit_data(X, y)	Create the fit_data group based on the input data.
BaseMMM.create_idata_attrs()	Create attributes for the inference data.
BaseMMM.fit(X[, y, progressbar, random_seed])	Fit a model using the data passed as a parameter.
BaseMMM.forward_pass(x)	Transform channel input into target contributions of each channel.
BaseMMM.get_channel_contributions_share_samples([prior])	Get the share of channel contributions in the original scale of the target variable.
BaseMMM.get_errors([original_scale])	Get model errors posterior distribution.
BaseMMM.get_target_transformer()	Return the target transformer pipeline used for preprocessing the target variable.
BaseMMM.graphviz(**kwargs)	Get the graphviz representation of the model.
BaseMMM.load(fname)	Create a ModelBuilder instance from a file.
BaseMMM.load_from_idata(idata)	Create a ModelBuilder instance from an InferenceData object.
BaseMMM.plot_channel_contribution_share_hdi([...])	Plot the share of channel contributions in a forest plot.
BaseMMM.plot_components_contributions(...)	Plot the target variable and the posterior predictive model components.
BaseMMM.plot_errors([original_scale, ax])	Plot model errors by taking the difference between true values and predicted.
BaseMMM.plot_grouped_contribution_breakdown_over_time([...])	Plot a time series area chart for all channel contributions.
BaseMMM.plot_posterior_predictive([...])	Plot the posterior predictive distribution from the model fit.
BaseMMM.plot_prior_predictive([...])	Plot the prior predictive distribution from the model fit.
BaseMMM.plot_prior_vs_posterior(var_name[, ...])	Plot the prior vs posterior distribution for a specified variable in a 3 columngrid layout.
BaseMMM.plot_waterfall_components_decomposition([...])	Create a waterfall plot.
BaseMMM.post_sample_model_transformation()	Post-sample model transformation in order to store the HSGP state from fit.
BaseMMM.predict([X, extend_idata])	Use a model to predict on unseen data and return point prediction of all the samples.
BaseMMM.predict_posterior([X, extend_idata, ...])	Generate posterior predictive samples on unseen data.
BaseMMM.predict_proba([X, extend_idata, ...])	Alias for predict_posterior, for consistency with scikit-learn probabilistic estimators.
BaseMMM.preprocess(target, data)	Preprocess the provided data according to the specified target.
BaseMMM.sample_posterior_predictive([X, ...])	Sample from the model's posterior predictive distribution.
BaseMMM.sample_prior_predictive([X, y, ...])	Sample from the model's prior predictive distribution.
BaseMMM.save(fname)	Save the model's inference data to a file.
BaseMMM.set_idata_attrs([idata])	Set attributes on an InferenceData object.
BaseMMM.validate(target, data)	Validate the input data based on the specified target type.
BaseMMM.validate_channel_columns(data)	Validate the channel columns.
BaseMMM.validate_date_col(data)	Validate the date column.
BaseMMM.validate_target(data)	Validate the target column.

Attributes

X
default_model_config	Define the default model configuration.
default_sampler_config	Default sampler configuration for the model.
fit_result	Get the posterior fit_result.
id	Generate a unique hash value for the model.
methods	Get all methods of the object.
output_var	Define target variable for the model.
posterior
posterior_predictive
predictions
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