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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 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | """MacroLens — public unified API (v0.2).
The 10-line workflow::
import macrolens as ml
X_train, y_train, meta_train = ml.load("T1", "train", granularity="daily")
X_test, y_test, meta_test = ml.load("T1", "test")
model = ml.methods.LightGBMRegressor(task="T1")
model.fit(X_train, y_train, seed=42)
y_pred = model.predict(X_test)
metrics = ml.score("T1", y_test, y_pred,
cluster_keys=meta_test["ticker"].values)
print(metrics["mse"].value, metrics["mse"].ci_lo, metrics["mse"].ci_hi)
Public surface
--------------
* :func:`load` — sklearn-style ``(X, y, meta)`` data layer.
* :func:`score` / :func:`compare_methods` — eval layer.
* :func:`info` / :func:`features` — benchmark metadata.
* :func:`list_methods` — registered method names (filterable by family / task).
* :data:`methods` — sub-namespace; ``ml.methods.<ClassName>(task=...)``.
* :class:`LoadedData`, :class:`MetricValue`, :class:`RunRecord` — types.
Legacy v0.1 entry points (``load_tsf``, ``to_arrays``, ``evaluate``,
``ask_lumina``, ...) remain importable during the v0.1 → v0.2 transition;
they will be removed in Phase 7.
"""
from __future__ import annotations
# ── v0.2 unified API (primary surface) ────────────────────────────────────
from . import methods # noqa: F401 (sub-namespace; ml.methods.<Name>)
from ._types import LoadedData, MetricValue, RunRecord
from .data import load
from .eval import compare_methods, score
from .meta import BENCHMARK_NAME, __version__, features, info
from .methods import ALL_METHODS, list_methods
# ── Legacy v0.1 entry points (transitional) ───────────────────────────────
# These are imported lazily below so the new public surface stays usable
# even when the legacy modules grow new dependencies. Failures during the
# transitional period are captured and surfaced as ImportError on first
# attribute access (rather than crashing every ``import macrolens`` call).
def _import_legacy() -> dict[str, object]:
out: dict[str, object] = {}
try:
from ._evaluate import evaluate, format_submission
out["evaluate"] = evaluate
out["format_submission"] = format_submission
except Exception: # pragma: no cover -- legacy module surface drift
pass
try:
from ._fast import TSFTorchDataset, load_torch, to_arrays
out["TSFTorchDataset"] = TSFTorchDataset
out["load_torch"] = load_torch
out["to_arrays"] = to_arrays
# legacy `features` function on _fast shadowed by meta.features in
# the v0.2 surface; expose under a private alias for back-compat.
from ._fast import features as _legacy_features
out["_legacy_features"] = _legacy_features
except Exception: # pragma: no cover
pass
try:
from ._loaders import load_panel, load_scenarios, load_task, load_tsf
out["load_panel"] = load_panel
out["load_scenarios"] = load_scenarios
out["load_task"] = load_task
out["load_tsf"] = load_tsf
except Exception: # pragma: no cover
pass
try:
from ._meta import BENCHMARK_VERSION
out["BENCHMARK_VERSION"] = BENCHMARK_VERSION
except Exception: # pragma: no cover
pass
try:
from ._types import (
BenchmarkInfo,
GenerationMetrics,
REValuationMetrics,
ScenarioMetrics,
TaskSample,
TSFMetrics,
TSFSample,
ValuationMetrics,
)
out["BenchmarkInfo"] = BenchmarkInfo
out["GenerationMetrics"] = GenerationMetrics
out["REValuationMetrics"] = REValuationMetrics
out["ScenarioMetrics"] = ScenarioMetrics
out["TaskSample"] = TaskSample
out["TSFMetrics"] = TSFMetrics
out["TSFSample"] = TSFSample
out["ValuationMetrics"] = ValuationMetrics
except Exception: # pragma: no cover
pass
return out
_LEGACY = _import_legacy()
def __getattr__(name: str):
"""Resolve legacy attributes lazily (and ``ask_lumina`` even more so)."""
if name in _LEGACY:
return _LEGACY[name]
if name == "ask_lumina":
# The lumina agent imports openrouter / vector store deps that may
# not be installed in CPU-only paper-scope environments. Defer the
# import to first call.
from ..agents.lumina import ask as ask_lumina
return ask_lumina
if name == "lakehouse":
def _lakehouse(tag: str = "macrolens-v1.0"):
from ..lakehouse import Client
return Client.from_release(tag)
return _lakehouse
raise AttributeError(f"module 'macrolens' has no attribute {name!r}")
__all__ = [
# v0.2 unified API
"load",
"score",
"compare_methods",
"info",
"features",
"list_methods",
"methods",
"ALL_METHODS",
"LoadedData",
"MetricValue",
"RunRecord",
"BENCHMARK_NAME",
"__version__",
# legacy (lazy)
"evaluate",
"format_submission",
"TSFTorchDataset",
"load_torch",
"to_arrays",
"load_panel",
"load_scenarios",
"load_task",
"load_tsf",
"ask_lumina",
"lakehouse",
"BENCHMARK_VERSION",
"BenchmarkInfo",
"GenerationMetrics",
"REValuationMetrics",
"ScenarioMetrics",
"TaskSample",
"TSFMetrics",
"TSFSample",
"ValuationMetrics",
]
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