Datasets:
File size: 4,071 Bytes
f02626b | 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 | """Canonical sample budgets per task.
Single source of truth. Every family runner reads from here so that
cross-method comparison on each task is fair (same N for every method,
same instances, same indices).
Normalization principle:
- Forecasting / regression-with-subsample tasks (T1, T4, T7):
N_eval = 1,000 / N_train = 10,000 (stratified subsample from larger pools)
- Ticker-holdout valuation tasks (T2, T5):
N_eval = 1,324 / N_train = 2,673 (full 30% holdout, no subsampling)
- Filing-level generation tasks (T3, T6):
N_eval = 1,058 (some holdout tickers lack complete XBRL); train varies
Sample sizes are intentionally conservative -- ~10x the median peer-benchmark
scale (CiK 125 / WIT 446 / EDINET 350 / SciTS 1,250) so reviewers cannot
claim small-sample noise, while keeping LLM eval (4 LLMs x 7 tasks x ~1K
samples = ~28K calls) tractable on 4xA100 within the wall-clock budget.
The values here are DEFAULTS; runners may override via the
`get_canonical_indices(task, split, n_eval=..., n_train=...)` keyword
arguments to regenerate (and re-cache) for a re-tune without rebuilding
any artifacts.
"""
from __future__ import annotations
from typing import Literal
Task = Literal["T1", "T2", "T3", "T4", "T5", "T6", "T7"]
# ── Canonical budgets ─────────────────────────────────────────────────────
EVAL_N_PER_TASK: dict[Task, int] = {
"T1": 1_000, # subsampled (full ~1.3M)
"T2": 1_324, # full 30% ticker holdout
"T3": 1_058, # filing-level holdout (subset of 1,324 with full XBRL)
"T4": 1_000, # subsampled (full ~3M scenario-ticker pairs)
"T5": 1_324, # full 30% ticker holdout
"T6": 1_058, # filing-level holdout
"T7": 1_000, # subsampled (full ~23K properties)
}
TRAIN_N_PER_TASK: dict[Task, int] = {
"T1": 10_000, # subsampled training windows, sector x mcap_q
"T2": 2_673, # latest snapshot per non-holdout ticker
"T3": 9_458, # prior fiscal years across non-holdout tickers
"T4": 10_000, # subsampled scenario-conditioned windows
"T5": 2_673, # latest snapshot per non-holdout ticker
"T6": 1_377, # prior fiscal years for filing-level holdout
"T7": 10_000, # subsampled training properties, property_type x state
}
# ── Seed + stratifier ─────────────────────────────────────────────────────
SEED: int = 42
# Bumped if the stratifier logic changes (forces cache invalidation
# without changing N values). Increment when:
# - the panel column used for stratification changes
# - the per-task stratifier columns change
# - the sampler's tie-breaking / fallback logic changes
STRATIFIER_VERSION: int = 1
# ── Cache key derivation ──────────────────────────────────────────────────
def cache_key(
*,
n_eval: dict[Task, int] | None = None,
n_train: dict[Task, int] | None = None,
seed: int | None = None,
stratifier_version: int | None = None,
) -> str:
"""Stable cache-directory name for the (budgets, seed, stratifier) tuple.
Defaults to the module-level canonical values. Override any subset to
generate a non-canonical cache (e.g. a re-tune at N_eval=2000 produces
its own cache dir leaving the canonical cache intact).
"""
ne = n_eval or EVAL_N_PER_TASK
nt = n_train or TRAIN_N_PER_TASK
s = SEED if seed is None else seed
sv = STRATIFIER_VERSION if stratifier_version is None else stratifier_version
# Compact, readable encoding -- avoids sha hashes so the directory
# contents are inspectable.
eval_str = "-".join(f"{t}={ne[t]}" for t in ("T1", "T2", "T3", "T4", "T5", "T6", "T7"))
train_str = "-".join(f"{t}={nt[t]}" for t in ("T1", "T2", "T3", "T4", "T5", "T6", "T7"))
return f"seed={s}_strat=v{sv}_eval[{eval_str}]_train[{train_str}]"
|