Custom Extensions#

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macroforecast supports custom work by accepting ordinary Python callables at the stage that owns the behavior. There is no separate custom registry and no YAML wrapper requirement. A custom function must return the same object shape that the next stage already expects.

Use this section when a built-in loader, cleaning step, feature transform, model, search rule, diagnostic, or artifact is not enough.

Pages#

Page	Use it for
Custom Flow	End-to-end custom workflow and stage-local design rule.
custom_dataset	In-memory panels, custom CSV/Parquet loaders, frequency and metadata contracts.
custom_preprocess	Direct preprocessing callables and runner-safe custom preprocessing steps.
custom_features	Direct custom feature transforms and fitted feature-spec steps.
custom_model	User estimators, fit return objects, model specs, model ensembles, and search spaces.
custom_window_selection_forecasting	Custom sample policies, hyperparameter search algorithms, and forecast combinations.
custom_evaluation_tests	Custom scalar metrics, custom forecast-comparison tests, and custom evaluation slices.
custom_interpretation_analysis	Custom model interpretation, feature diagnostics, and forecast diagnostics.
custom_output	Saving custom tables, dictionaries, notes, diagnostics, and manifests.

Flow Contract#

Stage	Custom entry point	Must return
Data	`mf.data.custom_dataset`, `mf.data.load_custom_csv`, `mf.data.load_custom_parquet`	`DataBundle` with canonical panel and metadata.
Preprocessing	`mf.preprocessing.custom_preprocess`, `mf.preprocessing.custom_preprocess_step`	`PreprocessedData` or a preprocessing step dictionary.
Feature engineering	`mf.feature_engineering.custom_features`, `mf.feature_engineering.custom_step`	Feature `DataFrame` or feature step dictionary.
Models	`mf.models.custom_model`, `mf.model_ensemble.custom_model_ensemble`	`ModelSpec`.
Windows and selection	`mf.window.custom_stage_policy`, `mf.model_selection.custom_search`	`StagePolicy` or `SearchSpec`.
Forecasting	`mf.forecasting.custom_combination`	`CombinationSpec`.
Evaluation and tests	callable metric, `mf.tests.custom_test`, custom aggregation mapping	Metric columns, `TestResult`, or `EvaluationReport` tables.
Interpretation and analysis	`mf.interpretation.custom_interpretation`, `mf.feature_analysis.custom_feature_diagnostic`, `mf.forecast_analysis.custom_forecast_diagnostic`	Schema-tagged `DataFrame`.
Output	`mf.output.write_artifacts({...})`	Files plus `ArtifactManifest`.