Post-Fit Facades

After fitting, PanelMMM exposes most read and reporting operations through bound properties:

These are the preferred entry points when you already have a fitted model.

`mmm.data`

mmm.data returns MMMIDataWrapper.

Direct import path:

from abacus.data.idata import MMMIDataWrapper

You can also create it explicitly with:

wrapper = MMMIDataWrapper.from_mmm(mmm)

Main methods:

Method	Purpose
`get_target(original_scale=True)`	Return observed target data
`get_channel_spend()`	Return observed channel spend
`get_posterior_predictive(original_scale=True)`	Return posterior predictive samples
`get_errors(original_scale=True)`	Return residual samples
`get_channel_contributions(original_scale=True)`	Return media contribution samples
`get_contributions(...)`	Return channels, baseline, controls, seasonality, and events
`get_elementwise_roas(original_scale=True)`	Contribution-over-spend ratios
`get_elementwise_cost_per_target(original_scale=True)`	Spend-over-contribution ratios
`get_channel_scale()`	Return stored channel scaling factors
`get_target_scale()`	Return stored target scaling factors
`to_original_scale(...)`	Convert a posterior variable or array to original scale
`to_scaled(...)`	Convert an original-scale array back to model scale

mmm.summary returns MMMSummaryFactory.

Direct import path:

from abacus.mmm.summary import MMMSummaryFactory

If you instantiate it manually, pass model=mmm when you need transform-backed curve summaries:

summary = MMMSummaryFactory(mmm.data, model=mmm)

Main methods:

Method	Purpose
`posterior_predictive(...)`	Predictive summary table with observed target
`contributions(...)`	Tidy contribution summaries
`mean_contributions_over_time(...)`	Wide decomposition table
`roas(...)`	ROAS summary
`cost_per_target(...)`	Cost-per-target summary
`efficiency(...)`	Target-type-aware efficiency summary
`channel_spend(...)`	Raw spend table
`saturation_curves(...)`	Saturation curve summary table
`adstock_curves(...)`	Adstock curve summary table
`total_contribution(...)`	Totals by component type
`change_over_time(...)`	Percentage change in channel contributions

MMMSummaryFactory also exposes:

mmm.diagnostics returns MMMDiagnosticsFactory.

Direct import path:

from abacus.mmm.diagnostics.factory import MMMDiagnosticsFactory

Main methods:

Method	Purpose
`design_summary(X, ...)`	Per-variable design checks
`design_report(X, ...)`	Machine-readable design report
`mcmc_summary(...)`	Parameter-level MCMC diagnostics
`mcmc_report(...)`	Machine-readable MCMC report
`predictive_summary(...)`	Aggregate predictive metrics
`predictive_report(...)`	Machine-readable predictive report

The report methods return typed dataclass objects with to_dict().

mmm.plot returns MMMPlotSuite.

Direct import path:

from abacus.mmm.plot import MMMPlotSuite

PanelMMM binds this automatically to the model’s idata, but the class also supports compatible custom InferenceData objects.

Main methods:

Method	Purpose
`posterior_predictive(...)`	Plot fitted or sampled predictive series
`prior_predictive(...)`	Plot prior predictive series
`residuals_over_time(...)`	Plot residual trajectories
`residuals_posterior_distribution(...)`	Plot residual posterior distributions
`contributions_over_time(...)`	Plot time-series contributions
`posterior_distribution(...)`	Plot posterior violin distributions
`channel_parameter(...)`	Plot channel-level parameter posteriors
`prior_vs_posterior(...)`	Compare prior and posterior distributions
`saturation_scatterplot(...)`	Plot spend-versus-contribution scatter views
`saturation_curves(...)`	Plot sampled saturation curves
`waterfall_components_decomposition(...)`	Plot waterfall decompositions
`media_contribution_over_time(...)`	Plot stacked media contributions
`channel_contribution_share_hdi(...)`	Plot contribution share intervals
`budget_allocation(...)`	Plot optimisation allocation outputs
`allocated_contribution_by_channel_over_time(...)`	Plot simulated allocation contributions

The abacus.data.idata package also exports schema and utility helpers:

Import	Purpose
`MMMIdataSchema`	Expected structure for retained MMM `InferenceData`
`VariableSchema`	Variable-level schema helper
`InferenceDataGroupSchema`	Group-level schema helper
`filter_idata_by_dates(...)`	Filter idata on a date window
`filter_idata_by_dims(...)`	Filter idata on non-date dimensions
`aggregate_idata_time(...)`	Aggregate idata over time
`aggregate_idata_dims(...)`	Aggregate idata over non-time dims
`subsample_draws(...)`	Subsample posterior draws