MMM.__init__

MMM.__init__(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.'))

Define the constructor method.