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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 | """Data loading functions for MacroLens benchmark."""
from __future__ import annotations
from typing import Any
import pandas as pd
from ._compat import WhatIfTSFDataset, ValuationDataset, config
_TASK_ALIASES: dict[str, str] = {
# Numeric aliases (Task 1 = TSF, use load_tsf() instead)
"2": "A",
"3": "B",
"4": "C",
"5": "D",
"6": "E",
"7": "F",
# Letter aliases (legacy)
"a": "A",
"b": "B",
"c": "C",
"d": "D",
"e": "E",
"f": "F",
# Name aliases → Task 2 (Val-PT)
"valuation": "A",
"val-pt": "A",
"val_pt": "A",
"valuation_accuracy": "A",
# Name aliases → Task 3 (Stmt-Gen)
"statement_generation": "B",
"stmt-gen": "B",
"stmt_gen": "B",
# Name aliases → Task 4 (Scen-Ret)
"scenario_forecast": "C",
"scen-ret": "C",
"scen_ret": "C",
# Name aliases → Task 5 (Priv-Val)
"private_valuation": "D",
"priv-val": "D",
"priv_val": "D",
"pe_valuation": "D",
# Name aliases → Task 6 (Gen-Eval)
"generator_evaluation": "E",
"gen-eval": "E",
"gen_eval": "E",
# Name aliases → Task 7 (RE-Val)
"real_estate_valuation": "F",
"re-val": "F",
"re_val": "F",
}
def load_tsf(
split: str = "test",
horizon: int = 21,
lookback: int | None = None,
granularity: str = "daily",
target_col: str = "close",
load_text: bool = False,
) -> WhatIfTSFDataset:
"""Load the time-series forecasting dataset.
Parameters
----------
split : str
``"train"`` or ``"test"`` (default: ``"test"``).
horizon : int
Forecast horizon in periods. Must be one of the benchmark's
defined horizons (daily: 5, 21, 63). Default: 21.
lookback : int, optional
Lookback window length. Default: first benchmark lookback (63 for daily).
granularity : str
``"daily"`` (default), ``"weekly"``, or ``"monthly"``.
target_col : str
Target column name (default: ``"close"``).
load_text : bool
Whether to load filing text into context. Default: ``False``
(faster loading; set ``True`` for LLM/RAG experiments).
Returns
-------
WhatIfTSFDataset
Iterable dataset with ``__len__`` and ``__getitem__`` support.
Example
-------
>>> tsf = macrolens.load_tsf(split="test", horizon=21)
>>> len(tsf)
1303243
>>> sample = tsf[0]
>>> sample["lookback"].shape
(63, 103)
>>> sample["target"].shape
(21,)
"""
valid_horizons = config.get_horizons(granularity)
if horizon not in valid_horizons:
raise ValueError(
f"Invalid horizon {horizon} for granularity '{granularity}'. "
f"Valid horizons: {valid_horizons}"
)
if split not in ("train", "test"):
raise ValueError(f"split must be 'train' or 'test', got '{split}'")
if lookback is None:
lookback = config.get_lookback_windows(granularity)[0]
return WhatIfTSFDataset(
split=split,
lookback=lookback,
horizon=horizon,
granularity=granularity,
target_col=target_col,
load_text=load_text,
)
def load_task(
task: str,
granularity: str = "daily",
) -> ValuationDataset:
"""Load a valuation-task dataset (Task 2, 3, or 4).
Parameters
----------
task : str
Task identifier. Accepts: ``"2"``, ``"3"``, ``"4"`` (or legacy ``"A"``, ``"B"``, ``"C"``),
or full names like ``"valuation"``, ``"statement_generation"``, ``"scenario_forecast"``.
granularity : str
``"daily"`` (default), ``"weekly"``, or ``"monthly"``.
Returns
-------
ValuationDataset
Dataset with ``inputs``, ``ground_truth``, and ``holdout_tickers``
attributes.
Example
-------
>>> task_2 = macrolens.load_task("2")
>>> len(task_2)
11839
>>> task_2.inputs.columns.tolist()[:5]
['ticker', 'date', 'sector', 'industry', 'derived_market_cap']
"""
canonical = _TASK_ALIASES.get(task.lower(), task.upper())
if canonical not in ("A", "B", "C", "D", "E", "F"):
raise ValueError(
f"Unknown task '{task}'. Valid: '2' (valuation), "
"'3' (statement generation), '4' (scenario forecast), "
"'5' (private valuation), '6' (generator eval), '7' (RE valuation)"
)
return ValuationDataset(task=canonical, granularity=granularity)
def load_scenarios(granularity: str = "daily") -> pd.DataFrame:
"""Load raw scenario ground-truth data.
Returns the full ``scenario_forecast_ground_truth.parquet`` as a DataFrame.
Parameters
----------
granularity : str
``"daily"`` (default).
Returns
-------
pd.DataFrame
Columns include ``scenario_id``, ``ticker``, ``event_type``,
``event_date``, ``actual_return_pct``.
Example
-------
>>> sc = macrolens.load_scenarios()
>>> sc.shape
(3100000, 7)
>>> sc["event_type"].nunique()
50
"""
bench_dir = config.get_benchmark_dir(granularity)
path = bench_dir / "scenario_forecast_ground_truth.parquet"
if not path.exists():
raise FileNotFoundError(
f"Scenario ground truth not found at {path}. "
"Run benchmark assembly first."
)
return pd.read_parquet(path)
def load_panel(
split: str = "train",
granularity: str = "daily",
columns: list[str] | None = None,
) -> pd.DataFrame:
"""Load the raw panel data as a DataFrame.
Useful for custom feature engineering or analysis. For standard
benchmark evaluation, prefer :func:`load_tsf` or :func:`load_task`.
Parameters
----------
split : str
``"train"`` or ``"test"``.
granularity : str
``"daily"`` (default).
columns : list[str], optional
Subset of columns to load (faster for large panels).
Returns
-------
pd.DataFrame
The benchmark panel with all features.
Example
-------
>>> panel = macrolens.load_panel("train", columns=["ticker", "date", "close"])
>>> panel.shape
(2160000, 3)
"""
if split not in ("train", "test"):
raise ValueError(f"split must be 'train' or 'test', got '{split}'")
bench_dir = config.get_benchmark_dir(granularity)
path = bench_dir / f"panel_{split}.parquet"
if not path.exists():
raise FileNotFoundError(f"Panel not found at {path}.")
kwargs: dict[str, Any] = {}
if columns is not None:
kwargs["columns"] = columns
return pd.read_parquet(path, **kwargs)
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