LOBench-A-share-processed / code /experiments /tbd_revision /scripts /summarize_imputation_structural.py
| #!/usr/bin/env python3 | |
| """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() | |