add_cost_per_target_potentials#

pymc_marketing.mmm.lift_test.add_cost_per_target_potentials(calibration_df, *, model=None, cpt_variable_name='cost_per_target', name_prefix='cpt_calibration', get_indices=<function exact_row_indices>)[source]#

Add pm.Potential penalties to calibrate cost-per-target.

For each row, we compute the mean of cpt_variable_name across the date dimension for the specified (dims, channel) slice and add a soft quadratic penalty:

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

Parameters:

calibration_dfpd.DataFrame: Must include columns channel, sigma, and a target column. By default the target column is assumed to be cost_per_target; if a column matching cpt_variable_name is present it will be used instead. The DataFrame must also include one column per model dimension found in the CPT variable (excluding date).
modelpm.Model, optional: Model containing the cpt_variable_name Deterministic with dims (“date”, *dims, “channel”). If None, uses the current model context.
cpt_variable_namestr: Name of the cost-per-target Deterministic variable.
name_prefixstr: Prefix for created potential names.
get_indicesCallable[[pd.DataFrame, pm.Model], Indices]: Alignment function mapping rows to model coordinate indices.

Examples

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

add_cost_per_target_potentials(
    calibration_df=calibration_df,
    model=mmm.model,
    cpt_variable_name="cost_per_target",
    name_prefix="cpt_calibration",
)