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Run after the experiment suites have finished::
python -m pimamba.report
"""
from __future__ import annotations
import json
import os
from pathlib import Path
from typing import Dict, List
import numpy as np
import pandas as pd
from . import analysis as A
ROOT = Path(os.environ.get("PIMAMBA_ROOT")
or Path(__file__).resolve().parents[2])
RUNS = ROOT / "results" / "runs"
TABLES = ROOT / "results" / "tables"
FIGS = ROOT / "results" / "figures"
MAIN_ORDER = ["mlp", "gru", "lstm", "transformer", "mamba", "mamba2", "pimamba"]
COLS = ["sigma_r2", "sigma_shape_r2", "sigma_level_corr", "macro_r2_primary",
"rho_total_r2", "twin_r2", "xi_FCC_r2", "peak_rel_err"]
def _pm(df: pd.DataFrame, key: str, col: str, nd: int = 3) -> str:
g = df[df[key[0]] == key[1]][col]
if not len(g):
return "n/a"
return A.fmt_pm(float(g.mean()), float(g.std()) if len(g) > 1 else float("nan"), nd)
def _maybe_csv(path: Path) -> "pd.DataFrame | None":
if path.exists():
return pd.read_csv(path)
return None
def table_main(df: pd.DataFrame) -> pd.DataFrame:
"""Per-fold mean+-sd *and* pooled out-of-fold metrics side by side."""
sub = df[df.suite == "main"].copy()
ps = _maybe_csv(TABLES / "tab_pooled_sigma.csv")
pt = _maybe_csv(TABLES / "tab_pooled_per_target.csv")
if ps is not None:
ps = ps.set_index("model")
if pt is not None:
pt = pt.set_index("model")
rows = []
for m in MAIN_ORDER:
g = sub[sub.model == m]
if not len(g):
continue
r = {"model": A.MODEL_LABEL[m], "n": len(g)}
r["应力 R²(逐折)"] = A.fmt_pm(float(g.sigma_r2.mean()), float(g.sigma_r2.std()))
r["形状 R²(逐折)"] = A.fmt_pm(float(g.sigma_shape_r2.mean()),
float(g.sigma_shape_r2.std()))
if ps is not None and m in ps.index:
r["池化应力 R²"] = f"{float(ps.loc[m, 'sigma_r2']):.3f}"
r["池化形状 R²"] = f"{float(ps.loc[m, 'shape_r2']):.3f}"
if pt is not None and m in pt.index:
r["池化主指标 R²"] = f"{float(pt.loc[m, 'primary_mean']):.3f}"
r["水平相关"] = A.fmt_pm(float(g.sigma_level_corr.mean()),
float(g.sigma_level_corr.std()))
# rare targets (twin: 6/33 labelled) have a meaningless per-fold R2 --
# a fold can contain a single labelled sample. Report pooled OOF.
if pt is not None and m in pt.index:
r["池化总位错 R²"] = f"{float(pt.loc[m, 'rho_total']):.3f}"
r["池化孪晶 R²"] = f"{float(pt.loc[m, 'twin']):.3f}"
r["池化FCC R²"] = f"{float(pt.loc[m, 'xi_FCC']):.3f}"
else:
r["总位错 R²"] = A.fmt_pm(float(g.rho_total_r2.mean()),
float(g.rho_total_r2.std()))
r["峰值相对误差"] = f"{g.peak_rel_err.mean():.3f}"
r["曲线 RMSE/GPa"] = A.fmt_pm(float(g.curve_rmse.mean()), float(g.curve_rmse.std()))
r["逐折宏平均 R²"] = A.fmt_pm(float(g.macro_r2_primary.mean()),
float(g.macro_r2_primary.std()))
r["参数量"] = f"{int(g.n_params.mean()):,}"
rows.append(r)
return pd.DataFrame(rows)
NEXT_ORDER = ["llama", "xlstm", "gdn", "hymba", "koopman", "node", "rssm",
"flow", "pimamba2"]
def table_next(df: pd.DataFrame) -> pd.DataFrame:
"""Next-generation backbones: per-fold + pooled OOF, sorted by R2."""
sub = df[df.suite == "next"].copy()
if not len(sub):
return pd.DataFrame()
ps = _maybe_csv(TABLES / "tab_pooled_next_sigma.csv")
pt = _maybe_csv(TABLES / "tab_pooled_next_per_target.csv")
if ps is not None:
ps = ps.set_index("model")
if pt is not None:
pt = pt.set_index("model")
rows = []
order = sub.groupby("model").sigma_r2.mean().sort_values(ascending=False).index
for m in order:
g = sub[sub.model == m]
r = {"模型": A.MODEL_LABEL.get(m, m), "n": len(g),
"应力 R²(逐折)": A.fmt_pm(float(g.sigma_r2.mean()),
float(g.sigma_r2.std())),
"形状 R²(逐折)": A.fmt_pm(float(g.sigma_shape_r2.mean()),
float(g.sigma_shape_r2.std()))}
if ps is not None and m in ps.index:
r["池化应力"] = f"{float(ps.loc[m, 'sigma_r2']):.3f}"
r["池化形状"] = f"{float(ps.loc[m, 'shape_r2']):.3f}"
if pt is not None and m in pt.index:
r["池化主指标"] = f"{float(pt.loc[m, 'primary_mean']):.3f}"
r["峰值误差"] = f"{g.peak_rel_err.mean():.3f}"
r["参数量"] = f"{int(g.n_params.mean()):,}"
r["耗时/run(s)"] = f"{g.wall_time_s.mean():.0f}"
rows.append(r)
return pd.DataFrame(rows)
def table_v2(df: pd.DataFrame) -> pd.DataFrame:
"""PI-Mamba v2 variants (Taylor coupling + peak loss)."""
sub = df[df.suite == "v2"].copy()
if not len(sub):
return pd.DataFrame()
ps = _maybe_csv(TABLES / "tab_pooled_v2_sigma.csv")
pt = _maybe_csv(TABLES / "tab_pooled_v2_per_target.csv")
if ps is not None:
ps = ps.set_index("model")
if pt is not None:
pt = pt.set_index("model")
rows = []
# reference = the main-suite PI-Mamba, so every variant is comparable to the
# baseline it is meant to improve (the v2 suite has no "pimamba" row).
bm = df[(df.suite == "main") & (df.model == "pimamba")].sigma_r2
base = float(bm.mean()) if len(bm) else float(sub.sigma_r2.max())
order = sub.groupby("variant").sigma_r2.mean().sort_values(ascending=False).index
for v in order:
g = sub[sub.variant == v]
r = {"变体": v, "n": len(g),
"应力 R²(逐折)": A.fmt_pm(float(g.sigma_r2.mean()),
float(g.sigma_r2.std())),
"形状 R²(逐折)": f"{g.sigma_shape_r2.mean():.3f}"}
if ps is not None and v in ps.index:
r["池化应力"] = f"{float(ps.loc[v, 'sigma_r2']):.3f}"
r["池化形状"] = f"{float(ps.loc[v, 'shape_r2']):.3f}"
if pt is not None and v in pt.index:
r["池化主指标"] = f"{float(pt.loc[v, 'primary_mean']):.3f}"
r["峰值误差"] = f"{g.peak_rel_err.mean():.3f}"
r["ΔR²(vs PI-Mamba)"] = f"{g.sigma_r2.mean() - base:+.3f}"
rows.append(r)
return pd.DataFrame(rows)
def table_loao(df: pd.DataFrame) -> pd.DataFrame:
sub = df[df.suite == "loao"].copy()
if not len(sub):
return pd.DataFrame()
rows = []
for al, g in sub.groupby("holdout"):
r = {"留出合金体系": al, "n": len(g)}
for m in ["mlp", "gru", "mamba", "pimamba"]:
gg = g[g.model == m]
r[A.MODEL_LABEL[m]] = (f"{gg.sigma_r2.mean():.3f}"
if len(gg) else "n/a")
rows.append(r)
rows.append({"留出合金体系": "**平均**", "n": len(sub),
**{A.MODEL_LABEL[m]: f"{sub[sub.model == m].sigma_r2.mean():.3f}"
for m in ["mlp", "gru", "mamba", "pimamba"]}})
return pd.DataFrame(rows)
def table_ablation(df: pd.DataFrame) -> pd.DataFrame:
sub = df[df.suite == "ablation"].copy()
if not len(sub):
return pd.DataFrame()
rows = []
base = sub[sub.variant == "pimamba"].sigma_r2.mean()
order = sub.groupby("variant").sigma_r2.mean().sort_values(ascending=False).index
for v in order:
g = sub[sub.variant == v]
rows.append({
"变体": A.MODEL_LABEL.get(v, v), "n": len(g),
"应力 R²": A.fmt_pm(float(g.sigma_r2.mean()), float(g.sigma_r2.std())),
"形状 R²": A.fmt_pm(float(g.sigma_shape_r2.mean()),
float(g.sigma_shape_r2.std())),
"总位错 R²": f"{g.rho_total_r2.mean():.3f}",
"ΔR²(应力)": f"{g.sigma_r2.mean() - base:+.3f}",
})
return pd.DataFrame(rows)
def table_dataeff(df: pd.DataFrame) -> pd.DataFrame:
sub = df[df.suite == "dataeff"].copy()
if not len(sub):
return pd.DataFrame()
def _frac(v):
f = float(str(v).split("@")[-1])
return f / 100.0 if f > 1.0 else f
sub["frac"] = sub.variant.map(_frac)
rows = []
for m, g in sub.groupby("model"):
r = {"模型": A.MODEL_LABEL.get(m, m)}
for fr, gg in sorted(g.groupby("frac")):
r[f"{int(fr*100)}%"] = f"{gg.sigma_r2.mean():.3f}"
r["n_train@100%"] = int(g[g.frac == 1.0].n_train.mean()) if len(g[g.frac == 1.0]) else np.nan
rows.append(r)
return pd.DataFrame(rows)
def table_per_target(tag: str) -> pd.DataFrame:
m = json.loads((RUNS / tag / "metrics.json").read_text())
rows = []
for k, v in m["per_target"].items():
if v["n"] == 0:
continue
rows.append({"目标": k, "组": v["group"], "n": v["n"],
"R²": round(v["r2"], 3) if np.isfinite(v["r2"]) else "nan",
"R²(log)": round(v["r2_log"], 3) if np.isfinite(v.get("r2_log", np.nan)) else "–",
"nMAE": round(v["nmae"], 3) if np.isfinite(v["nmae"]) else "nan",
"σ_y": f"{v['y_std']:.3g}", "单位": v["unit"]})
return pd.DataFrame(rows)
def rebuild_summary():
"""Reconstruct ``results/summary.csv`` from the individual run folders.
Safety net: the orchestrator only flushes the summary at the end of a suite,
so a crash would otherwise lose the table.
"""
from .run_experiments import _row
rows = []
for d in sorted(RUNS.iterdir()):
mp = d / "metrics.json"
if not mp.exists():
continue
res = json.loads(mp.read_text())
res["tag"] = d.name
n = d.name
if n.startswith("main_"):
suite, var = "main", res["config"]["model"]
elif n.startswith("abl_"):
suite, var = "ablation", "_".join(n.split("_")[1:-2])
elif n.startswith("loao_"):
suite, var = "loao", res["config"]["model"]
elif n.startswith("de_"):
frac = int(n.split("_")[2]) / 100.0
suite, var = "dataeff", f'{res["config"]["model"]}@{frac:.2f}'
elif n.startswith("next_"):
suite, var = "next", res["config"]["model"]
elif n.startswith("v2_"):
suite, var = "v2", "_".join(n.split("_")[1:-2])
else:
continue
try:
rows.append(_row(res, suite, var, res["config"].get("fold", -1),
res["config"].get("seed", 0)))
except Exception as e: # noqa: BLE001
print("skip", n, e)
if rows:
df = pd.DataFrame(rows).drop_duplicates(subset=["tag"], keep="last")
(ROOT / "results" / "summary.csv").parent.mkdir(parents=True, exist_ok=True)
df.to_csv(ROOT / "results" / "summary.csv", index=False)
print(f"rebuilt summary.csv: {len(df)} rows")
def main():
import sys
if "--rebuild" in sys.argv:
rebuild_summary()
TABLES.mkdir(parents=True, exist_ok=True)
FIGS.mkdir(parents=True, exist_ok=True)
df = A.load_summary()
out: Dict[str, str] = {}
tm = table_main(df)
if len(tm):
tm.to_csv(TABLES / "tab_main.csv", index=False)
out["main"] = A.markdown_table(tm, list(tm.columns))
A.fig_model_comparison(df)
tn = table_next(df)
if len(tn):
tn.to_csv(TABLES / "tab_next.csv", index=False)
out["next"] = A.markdown_table(tn, list(tn.columns))
A.fig_next(df)
tv = table_v2(df)
if len(tv):
tv.to_csv(TABLES / "tab_v2.csv", index=False)
out["v2"] = A.markdown_table(tv, list(tv.columns))
tl = table_loao(df)
if len(tl):
tl.to_csv(TABLES / "tab_loao.csv", index=False)
out["loao"] = A.markdown_table(tl, list(tl.columns))
ta = table_ablation(df)
if len(ta):
ta.to_csv(TABLES / "tab_ablation.csv", index=False)
out["ablation"] = A.markdown_table(ta, list(ta.columns))
A.fig_ablation(df)
td = table_dataeff(df)
if len(td):
td.to_csv(TABLES / "tab_dataeff.csv", index=False)
out["dataeff"] = A.markdown_table(td, list(td.columns))
A.fig_dataeff(df)
best = df[(df.suite == "main") & (df.model == "pimamba")].sort_values(
"sigma_r2", ascending=False)
if len(best):
tag = str(best.iloc[0]["tag"])
if (RUNS / tag / "predictions.npz").exists():
A.fig_curves(tag)
out["curve_tag"] = tag
tp = table_per_target(tag)
tp.to_csv(TABLES / "tab_per_target.csv", index=False)
out["per_target"] = A.markdown_table(tp, list(tp.columns))
out["per_target_tag"] = tag
lines = ["# 自动生成的结果汇总", "",
"> 口径说明：`(逐折)` 为 5 折 × 2 种子的 mean ± sd；`池化` 为把 5 折测试预测\n"
"> 拼成 33 个样本后统一计算的 out-of-fold 指标（更稳定，报告 §6 主用）。\n"
"> `逐折宏平均 R²` 仍包含 `rho_perfect` / `rho_112_111` 等退化目标，\n"
"> 数值大幅为负属正常，不可与池化主指标直接比较。", ""]
for k, title in [("main", "主对比"), ("next", "新一代架构（2024–2026）"),
("v2", "PI-Mamba v2 改进"), ("loao", "跨合金（LOAO）"),
("ablation", "消融"), ("dataeff", "数据效率")]:
if k in out:
lines += [f"## {title}", "", out[k], ""]
if "per_target" in out:
lines += [f"## 逐目标指标（{out['per_target_tag']}）", "", out["per_target"], ""]
(ROOT / "results" / "RESULTS.md").write_text("\n".join(lines), encoding="utf-8")
print("\n".join(lines))
if __name__ == "__main__":
main() |