MMMSummaryFactory.total_contribution#

MMMSummaryFactory.total_contribution(hdi_probs=None, frequency=None, output_format=None)[fuente]#

Create total contribution summary (all effects combined).

Summarizes contributions by component type (channel, control, etc.), summing across individual components within each type.

Parameters:
hdi_probssequence of float, optional

HDI probability levels (default: uses factory default)

frequency{«original», «weekly», «monthly», «quarterly», «yearly», «all_time»}, optional

Time aggregation period (default: None, no aggregation)

output_format{«pandas», «polars»}, optional

Output DataFrame format (default: uses factory default)

Returns:
pd.DataFrame or pl.DataFrame

Summary DataFrame with columns:

  • date: Time index

  • component: Effect type («channel», «control», «seasonality», «baseline»)

  • mean: Mean total contribution

  • median: Median total contribution

  • abs_error_{prob}_lower: HDI lower bound for each prob

  • abs_error_{prob}_upper: HDI upper bound for each prob

Ver también

contributions

For per-channel/control contributions

Examples

>>> df = mmm.summary.total_contribution()
>>> df = mmm.summary.total_contribution(frequency="monthly")
>>> df = mmm.summary.total_contribution(hdi_probs=[0.80, 0.94])