MMM.add_cost_per_target_calibration#

MMM.add_cost_per_target_calibration(data, calibration_data, cpt_variable_name='cost_per_target', name_prefix='cpt_calibration')[source]#

Calibrate cost-per-target using constraints via pm.Potential.

This adds a deterministic cpt_variable_name computed as channel_data_spend / channel_contribution_original_scale and creates per-row penalty terms based on calibration_data using a quadratic penalty:

penalty = - |cpt_mean - target|^2 / (2 * sigma^2).

Parameters:

datapd.DataFrame

Feature-like DataFrame with columns matching training X but with channel values representing spend (original units). Must include the same date and any model dims columns.

calibration_datapd.DataFrame

DataFrame with rows specifying calibration targets. Must include:

channel: channel name in self.channel_columns
cost_per_target: desired CPT value
sigma: accepted deviation; larger => weaker penalty

and one column per dimension in self.dims.

cpt_variable_namestr

Name for the cost-per-target Deterministic in the model.

name_prefixstr

Prefix to use for generated potential names.

Examples

Build a model and calibrate CPT for selected (dims, channel):

# spend data in original scale with the same structure as X
spend_df = X.copy()
# e.g., if X contains impressions, replace with monetary spend
# spend_df[channels] = ...

calibration_df = pd.DataFrame(
    {
        "channel": ["C1", "C2"],
        "geo": ["US", "US"],  # dims columns as needed
        "cost_per_target": [30.0, 45.0],
        "sigma": [2.0, 3.0],
    }
)

mmm.add_cost_per_target_calibration(
    data=spend_df,
    calibration_data=calibration_df,
    cpt_variable_name="cost_per_target",
    name_prefix="cpt_calibration",
)