File size: 4,498 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 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 | """Benchmark metadata and ``info()`` display."""
from __future__ import annotations
import json
from typing import Any
from ._compat import config
BENCHMARK_NAME = "MacroLens"
BENCHMARK_VERSION = "1.0"
_TASK_DESCRIPTIONS: dict[str, dict[str, str]] = {
"TSF": {
"name": "Time-Series Forecasting",
"target": "Close price",
"metrics": "MSE, RMSE, MAE, DA",
"horizons": "5, 21, 63 (daily) | 4, 13, 26 (weekly)",
},
"A": {
"name": "Equity Valuation (Val-PT)",
"target": "Market capitalization",
"metrics": "MAPE, MedAPE, Spearman rho",
},
"B": {
"name": "Statement Generation (Stmt-Gen)",
"target": "XBRL financial fields",
"metrics": "Per-field MAPE, Balance-equation accuracy",
},
"C": {
"name": "Scenario-Conditioned Return (Scen-Ret)",
"target": "Post-event return (%)",
"metrics": "MAE (%), DA",
},
"D": {
"name": "Private Company Valuation (Priv-Val)",
"target": "Market capitalization (from financials + sector only)",
"metrics": "MAPE, MedAPE, Spearman rho",
"note": "PE simulation: no price-derived inputs",
},
"E": {
"name": "Generator Evaluation (Gen-Eval)",
"target": "Financial statement fields (Generator output vs XBRL actual)",
"metrics": "Per-field MAPE, Balance-equation accuracy",
},
"F": {
"name": "Real Estate Valuation (RE-Val)",
"target": "Property rent and price",
"metrics": "Rent MAPE, Price MAPE",
},
}
def _load_metadata(granularity: str = "daily") -> dict[str, Any]:
"""Load benchmark metadata from disk."""
bench_dir = config.get_benchmark_dir(granularity)
meta: dict[str, Any] = {"granularity": granularity}
for name in ("metadata.json", "task_definition.json", "valuation_tasks.json"):
p = bench_dir / name
if p.exists():
meta[name.replace(".json", "")] = json.loads(p.read_text())
# Panel sizes
for split in ("train", "test"):
p = bench_dir / f"panel_{split}.parquet"
if p.exists():
try:
import pandas as pd
meta[f"panel_{split}_rows"] = len(pd.read_parquet(p, columns=["date"]))
except Exception:
pass
# Scenario count
sc_path = bench_dir / "scenario_forecast_ground_truth.parquet"
if sc_path.exists():
try:
import pandas as pd
sc = pd.read_parquet(sc_path, columns=["scenario_id"])
meta["n_scenarios"] = int(sc["scenario_id"].nunique())
meta["n_scenario_rows"] = len(sc)
except Exception:
pass
return meta
def info(granularity: str = "daily") -> dict[str, Any]:
"""Print and return a summary of the MacroLens benchmark.
Parameters
----------
granularity : str
``"daily"`` (default), ``"weekly"``, or ``"monthly"``.
Returns
-------
dict
Benchmark metadata.
Example
-------
>>> import macrolens
>>> macrolens.info()
MacroLens v1.0 — Multi-Task Financial Forecasting Benchmark
...
"""
meta = _load_metadata(granularity)
horizons = config.get_horizons(granularity)
lookbacks = config.get_lookback_windows(granularity)
# ── Pretty-print ──
print(f"\n{'='*60}")
print(f" {BENCHMARK_NAME} v{BENCHMARK_VERSION}")
print(f" Multi-Task Financial Forecasting Benchmark")
print(f"{'='*60}")
print(f" Granularity : {granularity}")
print(f" Horizons : {horizons}")
print(f" Lookbacks : {lookbacks}")
train_rows = meta.get("panel_train_rows")
test_rows = meta.get("panel_test_rows")
if train_rows or test_rows:
print(f" Panel : {train_rows:,} train / {test_rows:,} test rows")
n_sc = meta.get("n_scenarios")
if n_sc:
print(f" Scenarios : {n_sc} unique events, {meta.get('n_scenario_rows', 0):,} GT rows")
print(f"\n Tasks:")
for task_id, desc in _TASK_DESCRIPTIONS.items():
print(f" {task_id:>3} {desc['name']}")
print(f" Target : {desc['target']}")
print(f" Metrics: {desc['metrics']}")
print(f"{'='*60}\n")
return {
"name": BENCHMARK_NAME,
"version": BENCHMARK_VERSION,
"granularity": granularity,
"horizons": horizons,
"lookbacks": lookbacks,
"tasks": _TASK_DESCRIPTIONS,
**meta,
}
|