File size: 1,906 Bytes
ff4becd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | """Public data-loading entry point for the MacroLens unified API.
This module is a thin wrapper over :func:`whatif_bench.dataloader.load.load`.
The data layer (``dataloader/``) owns all IO and provenance bookkeeping;
this module exists only so that ``import macrolens as ml; ml.load(...)``
has a stable, lightweight surface.
"""
from __future__ import annotations
from typing import Any
from ..dataloader.load import load as _load
from ._types import LoadedData
def load(
task: str,
split: str,
*,
granularity: str = "daily",
lookback: int | None = None,
horizon: int | None = None,
setting: str | None = None,
) -> LoadedData:
"""Load canonical task data (delegates to ``dataloader.load.load``).
Parameters
----------
task
``"T1"`` .. ``"T7"``.
split
``"train"`` or ``"test"``.
granularity
``"daily"`` (default), ``"weekly"``, or ``"monthly"``.
lookback, horizon
Optional overrides; default to the first values from
``config.get_lookback_windows(granularity)`` /
``config.get_horizons(granularity)``.
setting
Optional ablation tier ``"A"``, ``"B"``, ``"C"``, ``"D"``, or
``"E"``. When set, projects the feature space to the named tier
(T1, T2, T4, T5 only). Setting ``"E"`` matches D's numeric
feature set; LLM methods consume filing-text excerpts at the
prompt layer in addition.
Returns
-------
:class:`LoadedData`
Sklearn-style ``(X, y, meta)`` NamedTuple.
"""
out: Any = _load(
task, split, granularity=granularity, lookback=lookback, horizon=horizon,
setting=setting,
)
# The data-layer module defines its own LoadedData NamedTuple; coerce
# to the public type so downstream callers see a single class identity.
return LoadedData(X=out.X, y=out.y, meta=out.meta)
__all__ = ["load"]
|