/reference/index.html This section is a hand-curated reference for the retained public Abacus API. It focuses on stable entry points that users are expected to import directly. It does not try to document every internal module under abacus.mmm.models, abacus.mmm.summarization, or abacus.pipeline.stages. For task-oriented workflows, use the main documentation sections first. Use this reference when you need the exact import path, object name, or the scope of a public surface. Main module groups Module Primary public surface abacus.mmm.panel PanelMMM abacus.mmm Adstock, saturation, Fourier, HSGP, and trend classes abacus.mmm.optimization PanelBudgetOptimizerWrapper and advanced optimisation helpers abacus.mmm.builders.yaml build_mmm_from_yaml(...) abacus.pipeline PipelineRunConfig, run_pipeline(...), PipelineRunResult abacus.scenario_planner Planner specs, ScenarioPlanner, ScenarioComparison abacus.data.idata MMMIDataWrapper, schema helpers, and idata utilities Pages PanelMMM: Core model import path, constructor surface, lifecycle methods, and bound properties. Post-Fit Facades: mmm.data, mmm.summary, mmm.diagnostics, mmm.plot, and direct factory imports. Transforms and Supporting Types: Adstock, saturation, Fourier, HSGP, trend, and scaling types. Optimisation API: PanelBudgetOptimizerWrapper and the exported advanced optimisation helpers. Builders and Pipeline: YAML builder, structured pipeline entry points, and run result objects. Scenario Planner API: Scenario spec classes, planner service objects, and the Dash app entry point. Additive Effects and Events: Advanced extension points for mu_effects and dated event modelling. Recommended starting points If you are building and fitting a model in code, start with PanelMMM. If you are reading fitted outputs, start with Post-Fit Facades. If you are building from YAML or running the structured runner, start with Builders and Pipeline. If you are comparing plans, start with Scenario Planner API. Hugo en-gb /reference/panel-mmm/index.html Mon, 01 Jan 0001 00:00:00 +0000 /reference/panel-mmm/index.html PanelMMM is the single retained public MMM model API in Abacus. Import it from: from abacus.mmm.panel import PanelMMM For conceptual guidance, see Model Overview. For data contracts, see Data Preparation. Constructor PanelMMM(...) is keyword-only. The main constructor arguments are: Argument Meaning date_column Name of the date column in X channel_columns Required media columns target_column Semantic target column name target_type "revenue" or "conversion" adstock An AdstockTransformation instance saturation A SaturationTransformation instance dims Optional panel dimensions such as ("geo",) control_columns Optional non-media regressors control_impacts Optional directional expectations for controls control_sign_policy "soft" or "strict" yearly_seasonality Number of yearly Fourier modes time_varying_intercept bool or an HSGPBase instance time_varying_media bool or an HSGPBase instance use_mundlak_cre Add Mundlak / correlated random effects terms scaling Scaling, a dict, or None model_config Prior and likelihood configuration sampler_config Default sampler settings adstock_first Whether adstock runs before saturation Core lifecycle methods The most commonly used methods are: /reference/transforms-and-supporting-types/index.html Mon, 01 Jan 0001 00:00:00 +0000 /reference/transforms-and-supporting-types/index.html Abacus keeps most reusable modelling primitives under abacus.mmm. This page lists the main import groups for transformations, seasonality and trend components, HSGP helpers, and scaling types. Top-level abacus.mmm re-exports Import these directly from abacus.mmm: from abacus.mmm import GeometricAdstock, LogisticSaturation, YearlyFourier Adstock transformations Top-level import path: /reference/post-fit-facades/index.html Mon, 01 Jan 0001 00:00:00 +0000 /reference/post-fit-facades/index.html After fitting, PanelMMM exposes most read and reporting operations through bound properties: mmm.data mmm.summary mmm.diagnostics mmm.plot 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 mmm.summary returns MMMSummaryFactory. /reference/optimisation-api/index.html Mon, 01 Jan 0001 00:00:00 +0000 /reference/optimisation-api/index.html Abacus exposes the retained optimisation surface through abacus.mmm.optimization. For workflow guidance and interpretation, see Optimisation. Primary wrapper Recommended import path: from abacus.mmm.optimization import PanelBudgetOptimizerWrapper PanelBudgetOptimizerWrapper adapts a fitted PanelMMM to the generic budget optimiser. Constructor: wrapper = PanelBudgetOptimizerWrapper( model=mmm, start_date="2025-03-03", end_date="2025-03-31", ) Main constructor arguments: Argument Meaning model Fitted PanelMMM start_date Optimisation window start date end_date Optimisation window end date compile_kwargs Optional compilation settings Useful attributes: /reference/scenario-planner-api/index.html Mon, 01 Jan 0001 00:00:00 +0000 /reference/scenario-planner-api/index.html The scenario planner API lives under abacus.scenario_planner. Use it when you want to compare current, manual, and fixed-budget optimised plans in total horizon spend units. For workflow guidance, see Scenario Planning. Main import path from abacus.scenario_planner import ( CurrentScenarioSpec, DataArraySpec, FixedBudgetOptimizedScenarioSpec, ManualAllocationScenarioSpec, ScenarioComparison, ScenarioPlanner, ScenarioResult, ) The package also exports shared base types: BaseScenarioSpec HistoricalReferenceScenarioSpec SimulatedScenarioSpec ScenarioSpec Scenario spec classes Main concrete spec types: /reference/builders-and-pipeline/index.html Mon, 01 Jan 0001 00:00:00 +0000 /reference/builders-and-pipeline/index.html Abacus exposes one public YAML builder and one structured pipeline runner. Use these surfaces when you want configuration-driven model construction or a staged run directory with machine-readable artefacts. YAML builder Import path: from abacus.mmm.builders.yaml import build_mmm_from_yaml Signature: model = build_mmm_from_yaml( config_path, X=X, y=y, model_kwargs=None, holidays_path=None, ) Main inputs: Argument Meaning config_path YAML file path X Optional pre-loaded feature data y Optional pre-loaded target data model_kwargs Model init overrides holidays_path Optional holiday CSV override It returns a built PanelMMM. /reference/additive-effects-and-events/index.html Mon, 01 Jan 0001 00:00:00 +0000 /reference/additive-effects-and-events/index.html Abacus supports advanced additive components through mu_effects and dated event surfaces. These are extension points rather than the default modelling path, but they are part of the retained public API. MuEffect protocol surface Import path: from abacus.mmm.additive_effect import MuEffect MuEffect is the abstract base class for additive components appended to mmm.mu_effects. Required methods: Method Purpose create_data(mmm) Register any required pm.Data inputs create_effect(mmm) Return the additive contribution tensor set_data(mmm, model, X) Update the effect for new prediction data Custom effects should inherit from MuEffect so they can participate in model serialization logic.