sensitivity_uplift#
- pymc_marketing.mmm.summary.sensitivity.sensitivity_uplift(data, hdi_probs=(0.94,), dims=None, aggregation=None, x_sweep_axis='relative', apply_cost_per_unit=True, output_format='pandas')[source]#
Summarize uplift curves (
sensitivity_analysis['uplift_curve']).- Parameters:
- data
MMMIDataWrapper Fitted model data wrapper with uplift curve results.
- hdi_probssequence of
float, default (0.94,) HDI probability levels.
- dims
dict, optional Dimension filters.
- aggregation
dict, optional Aggregation spec before summarization.
- x_sweep_axis{“relative”, “absolute”}, default “relative”
Sweep axis interpretation for
sweep_xcolumn.- apply_cost_per_unitbool, default
True Use spend for absolute x-axis when applicable.
- output_format{“pandas”, “polars”}, default “pandas”
Output DataFrame format.
- data
- Returns:
pd.DataFrameorpl.DataFrameSummary with sweep, mean, median, HDI, and
sweep_xcolumns.