MMM#

class pymc_marketing.mmm.multidimensional.MMM(date_column=FieldInfo(annotation=NoneType, required=True, description='Column name of the date variable.'), channel_columns=FieldInfo(annotation=NoneType, required=True, description='Column names of the media channel variables.', metadata=[MinLen(min_length=1)]), target_column=FieldInfo(annotation=NoneType, required=True, description='The name of the target column.'), adstock=FieldInfo(annotation=NoneType, required=True, description='Type of adstock transformation to apply.'), saturation=FieldInfo(annotation=NoneType, required=True, description='The saturation transformation to apply to the channel data.'), time_varying_intercept=False, time_varying_media=False, dims=FieldInfo(annotation=NoneType, required=False, default=None, description='Additional dimensions for the model.'), scaling=FieldInfo(annotation=NoneType, required=False, default=None, description='Scaling configuration for the model.'), model_config=FieldInfo(annotation=NoneType, required=False, default=None, description='Configuration settings for the model.'), sampler_config=FieldInfo(annotation=NoneType, required=False, default=None, description='Configuration settings for the sampler.'), control_columns=None, yearly_seasonality=None, adstock_first=True)[source]#

Marketing Mix Model class for estimating the impact of marketing channels on a target variable.

This class implements the core functionality of a Marketing Mix Model (MMM), allowing for the specification of various marketing channels, adstock transformations, saturation effects, and time-varying parameters. It provides methods for fitting the model to data, making predictions, and visualizing the results.

Attributes:

date_columnstr: The name of the column representing the date in the dataset.
channel_columnslist[str]: A list of columns representing the marketing channels.
target_columnstr: The name of the column representing the target variable to be predicted.
adstockAdstockTransformation: The adstock transformation to apply to the channel data.
saturationSaturationTransformation: The saturation transformation to apply to the channel data.
time_varying_interceptbool: Whether to use a time-varying intercept in the model.
time_varying_mediabool: Whether to use time-varying effects for media channels.
dimstuple | None: Additional dimensions for the model.
scalingScaling | dict | None: Scaling methods to be used for the target variable and the marketing channels. Defaults to max scaling for both.
model_configdict | None: Configuration settings for the model.
sampler_configdict | None: Configuration settings for the sampler.
control_columnslist[str] | None: A list of control variables to include in the model.
yearly_seasonalityint | None: The number of yearly seasonalities to include in the model.
adstock_firstbool: Whether to apply adstock transformations before saturation.

Methods

`MMM.__init__`([date_column, channel_columns, ...])	Define the constructor method.
`MMM.add_events`(df_events, prefix, effect)	Add event effects to the model.
`MMM.add_lift_test_measurements`(df_lift_test)	Add lift tests to the model.
`MMM.add_original_scale_contribution_variable`(var)	Add a pm.Deterministic variable to the model that multiplies by the scaler.
`MMM.attrs_to_init_kwargs`(attrs)	Convert the idata attributes to the model initialization kwargs.
`MMM.build_from_idata`(idata)	Build model from the InferenceData object.
`MMM.build_model`(X, y, **kwargs)	Build a probabilistic model using PyMC for marketing mix modeling.
`MMM.create_fit_data`(X, y)	Create the fit_data group based on the input data.
`MMM.create_idata_attrs`()	Return the idata attributes for the model.
`MMM.fit`(X[, y, progressbar, random_seed])	Fit a model using the data passed as a parameter.
`MMM.forward_pass`(x, dims)	Transform channel input into target contributions of each channel.
`MMM.get_scales_as_xarray`()	Return the saved scaling factors as xarray DataArrays.
`MMM.graphviz`(**kwargs)	Get the graphviz representation of the model.
`MMM.load`(fname)	Create a ModelBuilder instance from a file.
`MMM.load_from_idata`(idata)	Create a ModelBuilder instance from an InferenceData object.
`MMM.post_sample_model_transformation`()	Post-sample model transformation in order to store the HSGP state from fit.
`MMM.predict`([X, extend_idata])	Use a model to predict on unseen data and return point prediction of all the samples.
`MMM.predict_posterior`([X, extend_idata, ...])	Generate posterior predictive samples on unseen data.
`MMM.predict_proba`([X, extend_idata, combined])	Alias for `predict_posterior`, for consistency with scikit-learn probabilistic estimators.
`MMM.sample_posterior_predictive`([X, ...])	Sample from the model's posterior predictive distribution.
`MMM.sample_prior_predictive`([X, y, samples, ...])	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.table`(**model_table_kwargs)	Get the summary table of the model.

Attributes

`X`
`default_model_config`	Define the default model configuration.
`default_sampler_config`	Default sampler configuration.
`fit_result`	Get the posterior fit_result.
`id`	Generate a unique hash value for the model.
`output_var`	Define target variable for the model.
`plot`	Use the MMMPlotSuite to plot the results.
`posterior`
`posterior_predictive`
`predictions`
`prior`
`prior_predictive`
`version`
`y`