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"""Summarize the three-model masked-entry imputation experiment."""
from __future__ import annotations
import argparse
import csv
import json
from pathlib import Path
import numpy as np
METHOD_LABELS = {
"timae": "Ti-MAE-based",
"lobgrid_timae": "LOBGrid--Ti-MAE reference",
"simlob2026": "SimLOB-based",
}
WEIGHTS = (0.0, 2.0)
SEEDS = (2026, 2027, 2028)
METRICS = {
"masked_mse": lambda row: row["test_lob_metrics"]["region_mse"],
"masked_price_mae": lambda row: row["test_lob_metrics"]["region_raw_price_mae"],
"masked_volume_mae": lambda row: row["test_lob_metrics"]["region_raw_volume_mae"],
"completed_endpoint_mse": lambda row: row["test_lob_metrics"]["endpoint_mse"],
"spread_validity": lambda row: row["test_lob_metrics"]["tick_spread_valid_rate"],
"joint_validity": lambda row: row["test_lob_metrics"][
"joint_five_constraint_snapshot_valid_rate"
],
"violation_ticks": lambda row: row["test_lob_metrics"][
"mean_price_constraint_violation_ticks"
],
}
def stock_interval(values: list[float], seed: int = 20260802) -> list[float]:
array = np.asarray(values, dtype=np.float64)
rng = np.random.default_rng(seed)
draws = rng.choice(array, size=(20_000, len(array)), replace=True).mean(axis=1)
return [float(value) for value in np.quantile(draws, (0.025, 0.975))]
def write_csv(path: Path, rows: list[dict[str, object]]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", newline="", encoding="utf-8") as handle:
writer = csv.DictWriter(handle, fieldnames=list(rows[0]))
writer.writeheader()
writer.writerows(rows)
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--results-dir", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
args = parser.parse_args()
rows = []
for path in sorted(args.results_dir.glob("*.json")):
row = json.loads(path.read_text(encoding="utf-8"))
if row.get("task") == "imputation":
rows.append(row)
stocks = sorted({row["stock"] for row in rows})
expected = {
(stock, method, seed, weight)
for stock in stocks
for method in METHOD_LABELS
for seed in SEEDS
for weight in WEIGHTS
}
keys = {
(
row["stock"],
row["method"],
int(row["seed"]),
float(row["structure_weight"]),
)
for row in rows
}
if len(stocks) != 5 or keys != expected or len(rows) != len(expected):
raise ValueError(
f"imputation matrix contains {len(rows)} cases over {len(stocks)} stocks; "
f"expected 90 cases over five stocks"
)
index = {
(
row["stock"],
row["method"],
int(row["seed"]),
float(row["structure_weight"]),
): row
for row in rows
}
summary_rows: list[dict[str, object]] = []
payload: dict[str, object] = {
"design": {
"stocks": stocks,
"methods": list(METHOD_LABELS),
"seeds": list(SEEDS),
"structure_weights": list(WEIGHTS),
"mask_ratio": 0.2,
"mask_unit": "individual price and volume entries",
"aggregation": "seeds within stock, followed by five stock means",
},
"results": {},
}
for method, label in METHOD_LABELS.items():
method_payload: dict[str, object] = {}
for weight in WEIGHTS:
metric_payload: dict[str, object] = {}
flat: dict[str, object] = {
"method": method,
"model": label,
"lambda": weight,
}
for metric, extractor in METRICS.items():
by_stock = {
stock: float(
np.mean(
[
extractor(index[(stock, method, seed, weight)])
for seed in SEEDS
]
)
)
for stock in stocks
}
values = list(by_stock.values())
absolute = float(np.mean(values))
interval = stock_interval(values)
if weight == 0.0:
changes = {stock: 0.0 for stock in stocks}
else:
changes = {
stock: float(
np.mean(
[
extractor(index[(stock, method, seed, weight)])
- extractor(index[(stock, method, seed, 0.0)])
for seed in SEEDS
]
)
)
for stock in stocks
}
change_values = list(changes.values())
change = float(np.mean(change_values))
change_interval = stock_interval(change_values, seed=20260803)
metric_payload[metric] = {
"mean": absolute,
"stock_resampling_interval_95": interval,
"stock_means": by_stock,
"mean_change_from_lambda_0": change,
"change_interval_95": change_interval,
"stock_changes": changes,
}
flat[f"{metric}_mean"] = absolute
flat[f"{metric}_change_from_lambda_0"] = change
method_payload[str(weight)] = metric_payload
summary_rows.append(flat)
payload["results"][method] = method_payload
case_rows: list[dict[str, object]] = []
for row in sorted(
rows,
key=lambda item: (
item["method"],
item["stock"],
int(item["seed"]),
float(item["structure_weight"]),
),
):
case_rows.append(
{
"stock": row["stock"],
"method": row["method"],
"seed": row["seed"],
"lambda": row["structure_weight"],
"best_epoch": row["best_epoch"],
**{name: extractor(row) for name, extractor in METRICS.items()},
}
)
args.output_dir.mkdir(parents=True, exist_ok=True)
(args.output_dir / "imputation_structural_summary.json").write_text(
json.dumps(payload, indent=2), encoding="utf-8"
)
write_csv(args.output_dir / "imputation_structural_summary.csv", summary_rows)
write_csv(args.output_dir / "imputation_structural_cases.csv", case_rows)
lines = [
"# Masked-entry imputation and structural-loss results",
"",
(
"The experiment masks 20% of individual price and volume entries. "
"Three seeds are averaged within each stock before the five stock means."
),
"",
"| Model | lambda | Masked MSE | Price MAE | Spread valid | Joint valid | Violation ticks |",
"|---|---:|---:|---:|---:|---:|---:|",
]
for row in summary_rows:
lines.append(
f"| {row['model']} | {float(row['lambda']):g} | "
f"{float(row['masked_mse_mean']):.4f} | "
f"{float(row['masked_price_mae_mean']):.4f} | "
f"{100 * float(row['spread_validity_mean']):.1f}% | "
f"{100 * float(row['joint_validity_mean']):.1f}% | "
f"{float(row['violation_ticks_mean']):.3f} |"
)
(args.output_dir / "imputation_structural_summary.md").write_text(
"\n".join(lines) + "\n", encoding="utf-8"
)
if __name__ == "__main__":
main()
|