Custom Output

Back to custom extensions

Use output custom artifacts when the object is already a result, table, note, or diagnostic. Output functions do not run models or recompute metrics; they write named objects and record provenance.

write_artifacts

mf.output.write_artifacts(
    artifacts,
    output_dir,
    *,
    formats=("json", "csv"),
    manifest_format="json",
    include_provenance=True,
    provenance_fields=None,
    compression="none",
) -> mf.output.ArtifactManifest

Input

Input object	Written as	Metadata behavior
ForecastResult	forecast JSON plus forecast CSV	forecast metadata and stored-model sidecars recorded.
DataFrame	one file per requested format	attrs preserved in JSON and manifest records.
mapping	JSON	keys recorded in manifest metadata.
list/tuple	JSON	sequence length recorded.
scalar	JSON	object type recorded.

Example

manifest = mf.output.write_artifacts(
    {
        "forecast_result": result,
        "scores": scores,
        "custom_test": test.to_dict(),
        "custom_interpretation": interpretation,
        "run_notes": {"design": "local robustness check", "accepted": True},
    },
    "results/custom_flow",
)

Output

Manifest field	Meaning
artifacts	Mapping from written file name to path.
records	One ArtifactRecord per written object or stored-model sidecar.
provenance	Macroforecast version, Python/platform, git, and package versions unless disabled.
metadata_schema	{"kind": "artifact_manifest", "version": 1}.

bundle_outputs With Custom Extra

bundle = mf.output.bundle_outputs(
    forecasts=result,
    evaluation=report,
    metadata={"study": "custom"},
    extra={"custom_diagnostic": diagnostic_table},
)

manifest = mf.output.write_artifacts(bundle, "results/custom_bundle")

Use extra={...} for custom objects that should travel with the standard forecast/evaluation bundle.

Reporting Flow

If the custom object is a paper-facing table, format it before writing:

paper_table = mf.reporting.report_table(
    diagnostic_table,
    caption="Custom diagnostic",
    label="tab:custom_diagnostic",
)

mf.output.write_artifacts(
    {"custom_diagnostic": paper_table.data},
    "results/custom_tables",
)