LegacyMMMPlotSuite

class pymc_marketing.mmm.legacy_plot.LegacyMMMPlotSuite(idata)

Legacy matplotlib-based MMM plotting suite.

Deprecated since version 0.18.0: This class will be removed in v0.20.0. Use MMMPlotSuite with mmm_plot_config[“plot.use_v2”] = True for the new arviz_plots-based suite.

This class is maintained for backward compatibility but will be removed in a future release. The new MMMPlotSuite supports multiple backends (matplotlib, plotly, bokeh) and returns PlotCollection objects.

Provides methods for visualizing the posterior predictive distribution, contributions over time, and saturation curves for a Media Mix Model.

Methods

LegacyMMMPlotSuite.__init__(idata)
LegacyMMMPlotSuite.allocated_contribution_by_channel_over_time(samples)	Plot the allocated contribution by channel with uncertainty intervals.
LegacyMMMPlotSuite.budget_allocation(samples)	Plot the budget allocation and channel contributions.
LegacyMMMPlotSuite.contributions_over_time(var)	Plot the time-series contributions for each variable in var.
LegacyMMMPlotSuite.marginal_curve([...])	Plot precomputed marginal effects stored under idata.sensitivity_analysis['marginal_effects'].
LegacyMMMPlotSuite.posterior_predictive([...])	Plot time series from the posterior predictive distribution.
LegacyMMMPlotSuite.saturation_curves(curve)	Overlay saturation‑curve scatter‑plots with posterior‑predictive sample curves and HDI bands.
LegacyMMMPlotSuite.saturation_curves_scatter([...])	Plot scatter plots of channel contributions vs.
LegacyMMMPlotSuite.saturation_scatterplot([...])	Plot the saturation curves for each channel.
LegacyMMMPlotSuite.sensitivity_analysis([...])	Plot sensitivity analysis results.
LegacyMMMPlotSuite.uplift_curve([hdi_prob, ...])	Plot precomputed uplift curves stored under idata.sensitivity_analysis['uplift_curve'].