MMMPlotSuite.allocated_contribution_by_channel_over_time#

MMMPlotSuite.allocated_contribution_by_channel_over_time(samples, hdi_prob=0.85, backend=None)[source]#

Plot channel contributions over time from budget allocation optimization.

Visualizes how contributions from each channel evolve over time given an optimized budget allocation. Shows mean contribution lines per channel with HDI uncertainty bands.

Parameters:

samplesxr.Dataset

Dataset from budget allocation optimization containing channel contributions over time. Required dimensions: - ‘channel’: Channel names - ‘date’: Time dimension - ‘sample’: MCMC samples

Required variables: - Variable containing ‘channel_contribution’ (e.g., ‘channel_contribution’

or ‘channel_contribution_original_scale’)

Typically obtained from: mmm.allocate_budget_to_maximize_response(...)

hdi_probfloat, default 0.85

Probability mass for HDI interval (between 0 and 1).

backendstr | None, optional

Backend to use for plotting. If None, uses global backend configuration.

Returns:

PlotCollection

arviz_plots PlotCollection object containing the plot.

Use .show() to display or .save("filename") to save. Unlike the legacy suite which returned (Figure, Axes), this provides a unified interface across all backends.

Raises:

ValueError: If required dimensions (‘channel’, ‘date’, ‘sample’) not found in samples.
ValueError: If no variable containing ‘channel_contribution’ found in samples.

See also

budget_allocation_roas: Plot ROI distributions from same allocation results
LegacyMMMPlotSuite.allocated_contribution_by_channel_over_time: Legacy implementation

Notes

Breaking changes from legacy implementation:

Returns PlotCollection instead of (Figure, Axes)
Lost scale_factor, lower_quantile, upper_quantile, figsize, ax parameters
Now uses HDI instead of quantiles for uncertainty
Automatic handling of extra dimensions (creates subplots)

Examples

Basic usage with budget optimization results:

allocation_results = mmm.allocate_budget_to_maximize_response(
    total_budget=100_000, budget_bounds={"lower": 0.5, "upper": 2.0}
)
pc = mmm.plot.allocated_contribution_by_channel_over_time(
    allocation_results
)
pc.show()

Custom HDI probability:

pc = mmm.plot.allocated_contribution_by_channel_over_time(
    allocation_results, hdi_prob=0.94
)
pc.show()

Use different backend:

pc = mmm.plot.allocated_contribution_by_channel_over_time(
    allocation_results, backend="plotly"
)
pc.show()