MMMSummaryFactory.contributions#

MMMSummaryFactory.contributions(hdi_probs=None, component='channel', frequency=None, output_format=None)[fuente]#

Create contribution summary DataFrame.

Computes mean, median, and HDI bounds for contribution samples for the specified component type.

Parameters:
hdi_probssequence of float, optional

HDI probability levels (default: uses factory default)

component{«channel», «control», «seasonality», «baseline»}, default «channel»

Which contribution component to summarize

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

  • channel/control: Component identifier

  • mean: Mean contribution

  • median: Median contribution

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

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

Notes

Expects validated data. Call data.validate_or_raise() if you’ve modified the underlying idata before calling this method.

Examples

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