iuh

2026-09-13 10:52:28 +08:00
parent 2c63610f12
commit 78e4420a3e
25 changed files with 903 additions and 215 deletions
@@ -1,7 +1,9 @@
-"""Build vector figure sources, complete source listings, and a compact support ZIP.
+"""Build vector figure sources, comparison tables, source listings, and a support ZIP.

 Formal figures use the audited snapshot only. Geometric figures use declared
 algorithm parameters, not fabricated observations or hidden simulator data.
+The paired optimal-versus-baseline tables are generated from project/results/*.json
+so that no number in the comparison section is hand copied.
 """
 from __future__ import annotations

@@ -9,11 +11,12 @@ import argparse
 import hashlib
 import json
 import zipfile
-from decimal import Decimal
+from decimal import Decimal, ROUND_HALF_UP
 from pathlib import Path

 ROOT = Path(__file__).resolve().parents[1]
 PAPER = ROOT / "docs/paper"
+PROJECT_RESULTS = ROOT / "project/results"

 # Display copies only: executable Python files remain byte-for-byte unchanged.
 ESCAPE_OPEN = "(" + "*@"
@@ -46,28 +49,6 @@ def digest(data: bytes) -> str:
 def figures(rows: list[dict]) -> None:
    out = PAPER / "figures"
    out.mkdir(parents=True, exist_ok=True)
-    plan = json.loads((ROOT / "src/mcm26b/guarantee/plans/q4_mincover_v2.json").read_text("utf-8"))
-    lines = [
-        r"\begin{figure}[htbp]\centering",
-        r"\begin{tikzpicture}[x=0.00145cm,y=0.00145cm,font=\small]",
-        r"\fill[inkblue!5] (0,0) circle (1800);",
-        r"\draw[thick] (0,0) circle (1800);",
-        r"\draw[->,warmgray] (-2900,0)--(2950,0) node[right] {$x$/m};",
-        r"\draw[->,warmgray] (0,-2800)--(0,2850) node[above] {$y$/m};",
-    ]
-    for x in (-2000, -1000, 1000, 2000):
-        lines += [fr"\draw ({x},-35)--({x},35);",
-                  fr"\node[below,font=\scriptsize] at ({x},-50) {{{x}}};"]
-    for x, y in plan["points"]:
-        lines.append(fr"\fill[inkblue] ({x},{y}) circle (33);")
-    lines += [
-        r"\node[anchor=west,align=left] at (3250,900) {圆域：源位置先验\\蓝点：31个检测站};",
-        r"\node[anchor=west,align=left] at (3250,-700) {部分站点在圆域外\\用于接收向外发射的源};",
-        r"\end{tikzpicture}",
-        r"\caption{第四问31点计划的几何分布。站点取自计划文件，不代表正式运行轨迹；离散验证不等同于连续覆盖证明。}",
-        r"\label{fig:q4plan}\end{figure}",
-    ]
-    (out / "q4-plan.tex").write_text("\n".join(lines) + "\n", "utf-8")
    colors = ["inkblue", "inkblue!50", "warmgray!70", "black!18"]
    bars = [
        r"\begin{figure}[htbp]\centering",
@@ -101,6 +82,164 @@ def figures(rows: list[dict]) -> None:
    (out / "formal-time-bars.tex").write_text("\n".join(bars) + "\n", "utf-8")


+def fixed(value: float | None, digits: int = 3) -> str:
+    if value is None:
+        return "--"
+    return str(Decimal(str(value)).quantize(Decimal(1).scaleb(-digits), rounding=ROUND_HALF_UP))
+
+
+def percent(value: float | None, digits: int = 1) -> str:
+    if value is None:
+        return "--"
+    return fixed(100 * value, digits) + r"\%"
+
+
+def flag(value: bool | None) -> str:
+    return "--" if value is None else ("是" if value else "否")
+
+
+def project_tables() -> None:
+    """Generate the optimal-versus-baseline comparison tables from project/results."""
+    out = PAPER / "data"
+    out.mkdir(parents=True, exist_ok=True)
+    data = {name: json.loads((PROJECT_RESULTS / f"{name}.json").read_text("utf-8"))
+            for name in ("q1", "q2", "q3", "q4")}
+    examples, random = data["q1"]["aggregate_examples"], data["q1"]["aggregate_random"]
+    two = data["q2"]["aggregate"]
+    three, paired3 = data["q3"]["aggregate"]["variants"], data["q3"]["aggregate"]["paired"]
+    four, paired4 = data["q4"]["aggregate"]["variants"], data["q4"]["aggregate"]["paired"]
+    head = "% Generated by scripts/build_paper_support.py from project/results/*.json."
+
+    macros = [
+        ("ProjQOneExampleCases", str(examples["cases"])),
+        ("ProjQOneRandomCases", str(random["cases"])),
+        ("ProjQOneFixedStatusBase", percent(examples["status_accuracy_baseline"])),
+        ("ProjQOneFixedMaeBase", fixed(examples["diameter_mae_baseline_m"])),
+        ("ProjQOneFixedMaeMaxBase", fixed(examples["diameter_max_err_baseline_m"])),
+        ("ProjQOneFixedCoverBase", percent(examples["coverage_accuracy_baseline"])),
+        ("ProjQOneFixedMsOpt", fixed(examples["runtime_optimal_mean_ms"])),
+        ("ProjQOneFixedMsBase", fixed(examples["runtime_baseline_mean_ms"])),
+        ("ProjQOneRandStatusBase", percent(random["status_accuracy_baseline"])),
+        ("ProjQOneRandMaeBase", fixed(random["diameter_mae_baseline_m"])),
+        ("ProjQOneRandMaeMaxBase", fixed(random["diameter_max_err_baseline_m"])),
+        ("ProjQOneRandCoverBase", percent(random["coverage_accuracy_baseline"])),
+        ("ProjQOneRandMsOpt", fixed(random["runtime_optimal_mean_ms"])),
+        ("ProjQOneRandMsBase", fixed(random["runtime_baseline_mean_ms"])),
+        ("ProjQTwoCases", str(two["optimal"]["cases"])),
+        ("ProjQTwoInitialRadius", fixed(two["_initial_radius_mean_m"])),
+        ("ProjQTwoGuaranteeOpt", percent(two["optimal"]["guaranteed_rate"])),
+        ("ProjQTwoGuaranteeMec", percent(two["baseline_mec"]["guaranteed_rate"])),
+        ("ProjQTwoGuaranteePerp", percent(two["baseline_perp"]["guaranteed_rate"])),
+        ("ProjQTwoWorstOpt", fixed(two["optimal"]["worst_posterior_mean_m"])),
+        ("ProjQTwoWorstRef", fixed(two["reference"]["worst_posterior_mean_m"])),
+        ("ProjQTwoRatioOpt", fixed(two["optimal"]["final_ratio_mean"])),
+        ("ProjQTwoRatioMec", fixed(two["baseline_mec"]["final_ratio_mean"])),
+        ("ProjQTwoWorstPerp", fixed(two["baseline_perp"]["worst_posterior_mean_m"])),
+        ("ProjQTwoWorstMec", fixed(two["baseline_mec"]["worst_posterior_mean_m"])),
+        ("ProjQThreeScenes", str(three["optimal"]["episodes"])),
+        ("ProjQThreeMeanOpt", fixed(three["optimal"]["virtual_time_mean_s"])),
+        ("ProjQThreeMeanSeven", fixed(three["seven"]["virtual_time_mean_s"])),
+        ("ProjQThreeMeanBase", fixed(three["baseline"]["virtual_time_mean_s"])),
+        ("ProjQThreeDropOpt", percent(-paired3["optimal_vs_baseline"]["mean_relative_change"])),
+        ("ProjQThreeDropMedianOpt",
+         percent(-paired3["optimal_vs_baseline"]["median_relative_change"])),
+        ("ProjQThreeDropSeven", percent(-paired3["seven_vs_baseline"]["mean_relative_change"])),
+        ("ProjQThreeFasterOpt",
+         f"{paired3['optimal_vs_baseline']['faster_scenes']}/{paired3['optimal_vs_baseline']['compared']}"),
+        ("ProjQThreeMeasuresOpt", fixed(three["optimal"]["measures_mean"])),
+        ("ProjQThreeMeasuresBase", fixed(three["baseline"]["measures_mean"])),
+        ("ProjQThreeMoveOpt", fixed(three["optimal"]["move_distance_mean_m"] / 1000)),
+        ("ProjQThreeMoveBase", fixed(three["baseline"]["move_distance_mean_m"] / 1000)),
+        ("ProjQThreeWallOpt", fixed(three["optimal"]["compute_wall_mean_s"])),
+        ("ProjQFourScenes", str(four["optimal"]["episodes"])),
+        ("ProjQFourMeanOpt", fixed(four["optimal"]["virtual_time_mean_s"])),
+        ("ProjQFourMeanGrid", fixed(four["baseline_grid"]["virtual_time_mean_s"])),
+        ("ProjQFourMeanBase", fixed(four["baseline"]["virtual_time_mean_s"])),
+        ("ProjQFourClearOpt", percent(four["optimal"]["cleared_ratio_mean"])),
+        ("ProjQFourClearGrid", percent(four["baseline_grid"]["cleared_ratio_mean"])),
+        ("ProjQFourClearBase", percent(four["baseline"]["cleared_ratio_mean"])),
+        ("ProjQFourAbsentBase", str(four["baseline"]["absent_misjudged_total"])),
+        ("ProjQFourMeasuresOpt", fixed(four["optimal"]["measures_mean"])),
+        ("ProjQFourMeasuresBase", fixed(four["baseline"]["measures_mean"])),
+        ("ProjQFourFallbackOpt", fixed(four["optimal"]["fallback_clear_attempts_mean"])),
+        ("ProjQFourValidOpt",
+         f"{four['optimal']['valid_episodes']}/{four['optimal']['episodes']}"),
+        ("ProjQFourValidGrid",
+         f"{four['baseline_grid']['valid_episodes']}/{four['baseline_grid']['episodes']}"),
+        ("ProjQFourRiseOpt", percent(paired4["optimal_vs_baseline"]["mean_relative_change"])),
+        ("ProjQFourMoveOpt", fixed(four["optimal"]["move_distance_mean_m"] / 1000)),
+        ("ProjQFourMoveBase", fixed(four["baseline"]["move_distance_mean_m"] / 1000)),
+    ]
+    lines = [head] + [f"\\newcommand{{\\{name}}}{{{value}}}" for name, value in macros]
+    (out / "project-macros.tex").write_text("\n".join(lines) + "\n", "utf-8")
+
+    def write(stem: str, rows: list[str]) -> None:
+        (out / stem).write_text("\n".join([head, *rows]) + "\n", "utf-8")
+
+    write("project-overview-rows.tex", [
+        "问题 & 核心指标 & 最优实现 & 基线实现 " + r"\\",
+        f"1 & 直径平均绝对误差 / 覆盖判定准确率 & {fixed(random['diameter_mae_optimal_m'])} m / "
+        f"{percent(random['coverage_accuracy_optimal'])} & {fixed(random['diameter_mae_baseline_m'])} m / "
+        f"{percent(random['coverage_accuracy_baseline'])} " + r"\\",
+        f"2 & 保证接收率 / 最坏后验半径均值 & {percent(two['optimal']['guaranteed_rate'])} / "
+        f"{fixed(two['optimal']['worst_posterior_mean_m'])} m & {percent(two['baseline_mec']['guaranteed_rate'])} / "
+        f"{fixed(two['baseline_mec']['worst_posterior_mean_m'])} m " + r"\\",
+        f"3 & 全清率 / 平均虚拟耗时 & {percent(three['optimal']['cleared_ratio_mean'])} / "
+        f"{fixed(three['optimal']['virtual_time_mean_s'])} s & {percent(three['baseline']['cleared_ratio_mean'])} / "
+        f"{fixed(three['baseline']['virtual_time_mean_s'])} s " + r"\\",
+        f"4 & 全清率 / 缺席误判总数 & {percent(four['optimal']['cleared_ratio_mean'])} / "
+        f"{four['optimal']['absent_misjudged_total']} & {percent(four['baseline']['cleared_ratio_mean'])} / "
+        f"{four['baseline']['absent_misjudged_total']} " + r"\\",
+    ])
+
+    write("project-q1-example-rows.tex", [
+        f"{row['name']} & {fixed(row['diameter_ref_m'])} & {fixed(row['diameter_opt_m'])} & "
+        f"{fixed(row['diameter_base_m'])} & {fixed(row['abs_err_base_m'])} & "
+        f"{flag(row['covers_ref'])} & {flag(row['covers_opt'])} & {flag(row['covers_base'])} " + r"\\"
+        for row in data["q1"]["examples"]
+    ])
+
+    write("project-q1-summary-rows.tex", [
+        f"最优（固定 {examples['cases']} 例） & {percent(examples['status_accuracy_optimal'])} & "
+        f"{fixed(examples['diameter_mae_optimal_m'])} & {fixed(examples['diameter_max_err_optimal_m'])} & "
+        f"{percent(examples['coverage_accuracy_optimal'])} & {fixed(examples['runtime_optimal_mean_ms'])} " + r"\\",
+        f"最优（随机 {random['cases']} 例） & {percent(random['status_accuracy_optimal'])} & "
+        f"{fixed(random['diameter_mae_optimal_m'])} & {fixed(random['diameter_max_err_optimal_m'])} & "
+        f"{percent(random['coverage_accuracy_optimal'])} & {fixed(random['runtime_optimal_mean_ms'])} " + r"\\",
+        f"基线（固定 {examples['cases']} 例） & {percent(examples['status_accuracy_baseline'])} & "
+        f"{fixed(examples['diameter_mae_baseline_m'])} & {fixed(examples['diameter_max_err_baseline_m'])} & "
+        f"{percent(examples['coverage_accuracy_baseline'])} & {fixed(examples['runtime_baseline_mean_ms'])} " + r"\\",
+        f"基线（随机 {random['cases']} 例） & {percent(random['status_accuracy_baseline'])} & "
+        f"{fixed(random['diameter_mae_baseline_m'])} & {fixed(random['diameter_max_err_baseline_m'])} & "
+        f"{percent(random['coverage_accuracy_baseline'])} & {fixed(random['runtime_baseline_mean_ms'])} " + r"\\",
+    ])
+
+    write("project-q2-rows.tex", [
+        f"{label} & {percent(two[key]['guaranteed_rate'])} & {fixed(two[key]['worst_posterior_mean_m'])} & "
+        f"{fixed(two[key]['worst_posterior_median_m'])} & {fixed(two[key]['improvement_mean_m'])} & "
+        f"{fixed(two[key]['final_ratio_mean'])} & {fixed(two[key]['move_cost_mean_s'], 2)} " + r"\\"
+        for key, label in (("optimal", "算法2（默认选点）"), ("reference", "参考：盘内网格枚举"),
+                           ("baseline_perp", "基线：垂直 1000 m"), ("baseline_mec", "基线：候选圆心"))
+    ])
+
+    def variant_rows(table: dict, mapping: list[tuple[str, str]]) -> list[str]:
+        return [
+            f"{label} & {table[key]['valid_episodes']}/{table[key]['episodes']} & "
+            f"{percent(table[key]['cleared_ratio_mean'])} & {table[key]['absent_misjudged_total']} & "
+            f"{fixed(table[key]['virtual_time_mean_s'])} & {fixed(table[key]['virtual_time_median_s'])} & "
+            f"{fixed(table[key]['virtual_time_max_s'])} & {fixed(table[key]['measures_mean'], 1)} & "
+            f"{fixed(table[key]['move_distance_mean_m'] / 1000)} & {fixed(table[key]['fallback_clear_attempts_mean'], 1)} "
+            + r"\\"
+            for key, label in mapping
+        ]
+
+    write("project-q3-rows.tex", variant_rows(three, [
+        ("optimal", "bayes（默认策略）"), ("seven", "seven（七点批量）"), ("baseline", "grid（49 点网格）")]))
+    write("project-q4-rows.tex", variant_rows(four, [
+        ("optimal", "directional（默认策略）"), ("baseline_grid", "grid（问题三网格）"),
+        ("baseline", "bayes（问题三策略）")]))
+
+
 def listings(rows: list[dict]) -> None:
    data = PAPER / "data"
    files = sorted((ROOT / "src/mcm26b").rglob("*.py"))
@@ -190,6 +329,11 @@ def package(rows: list[dict]) -> Path:
    selected.update((PAPER / "data").rglob("*"))
    selected.update((PAPER / "figures").glob("*.tex"))
    selected.update((PAPER / "sections").glob("*.tex"))
+    selected.update((ROOT / "docs/figures").glob("*.png"))
+    selected.update((ROOT / "docs/figures").glob("*.json"))
+    selected.add(ROOT / "docs/figures/README.md")
+    selected.update(path for path in (ROOT / "project").rglob("*")
+                    if path.is_file() and "__pycache__" not in path.parts)
    selected.update([PAPER / "main.tex", PAPER / "mcm26b-paper.cls",
                     PAPER / "README.md", PAPER / "REWRITE_NOTES.md",
                     ROOT / "scripts/audit_paper_formal.py",
@@ -222,6 +366,7 @@ def main() -> None:
    args = parser.parse_args()
    rows = json.loads((PAPER / "data/formal-results.json").read_text("utf-8"))["records"]
    figures(rows)
+    project_tables()
    listings(rows)
    if args.package:
        package(rows)