BaseMMM

class pymc_marketing.mmm.delayed_saturated_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=Union[str, Annotated[AdstockTransformation, InstanceOf]], required=True, description='Type of adstock transformation to apply.'), saturation=FieldInfo(annotation=Union[str, Annotated[SaturationTransformation, InstanceOf]], required=True, description='Type of saturation transformation to apply.'), adstock_max_lag=FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='Number of lags to consider in the adstock transformation. Defaults to the max lag of the adstock transformation.', metadata=[Gt(gt=0)]), 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.'))

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, ...])	Constructor method.
BaseMMM.attrs_to_init_kwargs(attrs)
BaseMMM.build_model(X, y, **kwargs)	Builds 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()	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_idata_attrs()
BaseMMM.fit(X[, y, progressbar, ...])	Fit a model using the data passed as a parameter.
BaseMMM.forward_pass(x)	Transforms channel input into target contributions of each channel.
BaseMMM.get_errors([original_scale])	Get model errors posterior distribution.
BaseMMM.get_params([deep])	Get all the model parameters needed to instantiate a copy of the model, not including training data.
BaseMMM.get_target_transformer()	Return the target transformer pipeline used for preprocessing the target variable.
BaseMMM.graphviz(**kwargs)
BaseMMM.load(fname)	Creates a ModelBuilder instance from a file,
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 in the scaled space.
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 posterior distribution from the model fit.
BaseMMM.plot_prior_predictive(**plt_kwargs)
BaseMMM.plot_waterfall_components_decomposition([...])	This function creates a waterfall plot.
BaseMMM.predict(X_pred[, extend_idata])	Uses model to predict on unseen data and return point prediction of all the samples.
BaseMMM.predict_posterior(X_pred[, ...])	Generate posterior predictive samples on unseen data.
BaseMMM.predict_proba(X_pred[, ...])	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_pred)	Sample from the model's posterior predictive distribution.
BaseMMM.sample_prior_predictive(X_pred[, ...])	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.set_params(**params)	Set all the model parameters needed to instantiate the model, not including training data.
BaseMMM.validate(target, data)	Validates the input data based on the specified target type.
BaseMMM.validate_channel_columns(data)
BaseMMM.validate_date_col(data)
BaseMMM.validate_target(data)

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 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	Defines target variable for 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