Datasets:

eduzrh
/

training-model-material

"""Experiment orchestrator: main comparison / cross-alloy / ablations / data efficiency.
All runs use a fixed epoch budget with a full OneCycle anneal and no internal
validation split (``use_val=False``): with 33 samples a 15% hold-out is only
~4 trajectories, which makes early stopping pure noise (see report §5.1).
"""
from __future__ import annotations

import argparse
import json
import time
from pathlib import Path
from typing import Dict, List

import pandas as pd

from .data import PROC
from .train import Cfg, run

ROOT = Path(__file__).resolve().parents[2]
RUNS = ROOT / "results" / "runs"
SUMMARY = ROOT / "results" / "summary.csv"

MODELS = ["mlp", "gru", "lstm", "transformer", "mamba", "mamba2", "pimamba"]
MODELS_NEXT = ["llama", "xlstm", "gdn", "hymba", "koopman", "node", "rssm", "flow"]
# performance-first group: larger model, longer schedule, a fixed fold split so
# seeds differ only in initialisation (required for seed averaging / stacking).
MODELS_BEST = ["hymba", "gdn", "llama", "pimamba"]

ABLATIONS = [
    # name, cfg overrides
    ("pimamba", {}),
    ("pimamba_nocouple", {"coupling": False}),
    ("pimamba_phys", {"w_mono": 1.0, "w_init": 1.0, "w_smooth": 0.1}),
    ("pimamba_nogeom", {"drop_features": ("geom",)}),
    ("pimamba_noflags", {"drop_features": ("geom_flags",)}),
    ("pimamba_noensemble", {"drop_features": ("ensemble",)}),
    ("pimamba_noinit", {"drop_features": ("init_rho", "init_phase")}),
    ("pimamba_nonorm", {"norm_mode": "none"}),
]


def _row(res: Dict, suite: str, variant: str = "", fold: int = -1,
         seed: int = 0) -> Dict:
    cfg = res["config"]
    pt = res["per_target"]
    sig = pt.get("sigma", {})
    dec = res.get("decomp", {}) or {}
    row = {
        "suite": suite,
        "variant": variant or cfg["model"],
        "model": cfg["model"],
        "tag": Path(res.get("tag", "")).name or "",
        "fold": fold if fold >= 0 else cfg.get("fold", -1),
        "seed": seed,
        "holdout": cfg.get("holdout", ""),
        "n_train": res["n_train"],
        "n_test": res["n_test"],
        "n_params": res["n_params"],
        "epochs": res["epochs_run"],
        "wall_time_s": res["wall_time_s"],
        "macro_r2": res["summary"]["macro_r2"],
        "macro_r2_primary": res["summary"].get("macro_r2_primary"),
        "core_r2": res["summary"].get("core_r2"),
        "median_r2": res["summary"]["median_r2"],
        "macro_r2_log": res["summary"]["macro_r2_log"],
        "macro_nmae": res["summary"]["macro_nmae"],
        "sigma_r2": sig.get("r2"),
        "sigma_mae": sig.get("mae"),
        "sigma_nmae": sig.get("nmae"),
        "sigma_r2_log": sig.get("r2_log"),
        "sigma_shape_r2": dec.get("shape_r2"),
        "sigma_level_r2": dec.get("level_oracle_r2"),
        "sigma_level_corr": dec.get("level_corr"),
        "sigma_level_mae": dec.get("level_mae_GPa"),
        "xi_FCC_r2": pt.get("xi_FCC", {}).get("r2"),
        "rho_total_r2": pt.get("rho_total", {}).get("r2"),
        "rho_total_r2log": pt.get("rho_total", {}).get("r2_log"),
        "twin_r2": pt.get("twin", {}).get("r2"),
        "peak_rel_err": res["curve"].get("peak_rel_err_mean"),
        "curve_rmse": res["curve"].get("curve_rmse_mean"),
        "sig@0.05_rel_err": res["curve"].get("sig@0.05_rel_err_mean"),
    }
    return row


def _maybe_run(cfg: Cfg, suite: str, variant: str, tag: str,
               rows: List[Dict], force: bool = False):
    out = RUNS / tag / "metrics.json"
    if out.exists() and not force:
        r = json.loads(out.read_text())
        # a cached smoke run (fewer epochs than requested) must not satisfy the
        # request -- otherwise a 2-epoch debugging run silently blocks the real
        # 300-epoch experiment.
        if int(r.get("epochs_run", 0)) >= int(cfg.epochs):
            rows.append(_row(r, suite, variant, cfg.fold, cfg.seed))
            print(f"  cached {tag}")
            return
        print(f"  stale {tag}: {r.get('epochs_run')} < {cfg.epochs} epochs, rerun")
    cfg.tag = tag
    res = run(cfg, verbose=True)
    res["tag"] = tag
    rows.append(_row(res, suite, variant, cfg.fold, cfg.seed))


def suite_main(seeds, folds, models, force, epochs, lr=3e-4):
    rows = []
    for m in models:
        for f in folds:
            for s in seeds:
                cfg = Cfg(model=m, seed=s, split="kfold", fold=f, n_folds=5,
                          epochs=epochs, d_model=64, n_layers=2, use_val=False, lr=lr)
                _maybe_run(cfg, "main", m, f"main_{m}_f{f}_s{s}", rows, force)
    return rows


def suite_loao(seeds, models, force, epochs, lr=3e-4):
    meta = pd.read_csv(PROC / "samples_meta.csv")
    alloys = sorted(meta.alloy.unique())
    rows = []
    for m in models:
        for al in alloys:
            for s in seeds:
                cfg = Cfg(model=m, seed=s, split="loao", holdout=al,
                          epochs=epochs, d_model=64, n_layers=2, use_val=False, lr=lr)
                safe = "".join(ch if ch.isalnum() else "_" for ch in al)[:40]
                _maybe_run(cfg, "loao", m, f"loao_{m}_{safe}_s{s}", rows, force)
    return rows


def suite_ablation(seeds, folds, force, epochs, lr=3e-4):
    rows = []
    for name, over in ABLATIONS:
        for f in folds:
            for s in seeds:
                cfg = Cfg(model="pimamba", seed=s, split="kfold", fold=f,
                          n_folds=5, epochs=epochs, d_model=64, n_layers=2,
                          use_val=False, lr=lr, **over)
                _maybe_run(cfg, "ablation", name, f"abl_{name}_f{f}_s{s}", rows, force)
    return rows


def suite_dataeff(fractions, folds, models, force, epochs, lr=3e-4):
    rows = []
    for m in models:
        for fr in fractions:
            for f in folds:
                cfg = Cfg(model=m, seed=0, split="kfold", fold=f, n_folds=5,
                          epochs=epochs, d_model=64, n_layers=2, train_frac=fr,
                          use_val=False, lr=lr)
                _maybe_run(cfg, "dataeff", f"{m}@{fr:.2f}",
                           f"de_{m}_{int(fr*100):03d}_f{f}", rows, force)
    return rows


def suite_next(seeds, folds, models, force, epochs, lr=3e-4):
    """Next-generation backbones (2024-2026), same protocol as ``suite_main``."""
    rows = []
    for m in models:
        for f in folds:
            for s in seeds:
                cfg = Cfg(model=m, seed=s, split="kfold", fold=f, n_folds=5,
                          epochs=epochs, d_model=64, n_layers=2, use_val=False,
                          lr=lr)
                _maybe_run(cfg, "next", m, f"next_{m}_f{f}_s{s}", rows, force)
    return rows


def suite_best(seeds, folds, models, force, epochs, d_model=128, n_layers=4,
               lr=3e-4, split_seed=0):
    """Performance-first group: no equal-budget constraint.
    ``split_seed`` is pinned so every seed and architecture sees the same five
    folds; otherwise the OOF predictions cannot be averaged or stacked.
    """
    rows = []
    for m in models:
        for f in folds:
            for s in seeds:
                cfg = Cfg(model=m, seed=s, split="kfold", fold=f, n_folds=5,
                          epochs=epochs, d_model=d_model, n_layers=n_layers,
                          use_val=False, lr=lr, split_seed=split_seed)
                _maybe_run(cfg, "best", m, f"best_{m}_f{f}_s{s}", rows, force)
    return rows


def suite_v2(seeds, folds, force, epochs, lr=3e-4):
    """PI-Mamba v2: Taylor-form coupling and the curve-level peak loss."""
    variants = [
        ("pimamba2", {"model": "pimamba2"}),
        ("pimamba2_peak", {"model": "pimamba2", "w_peak": 0.5}),
        ("pimamba_peak", {"model": "pimamba", "w_peak": 0.5}),
        ("pimamba2_nocouple", {"model": "pimamba2", "coupling": False}),
    ]
    rows = []
    for name, over in variants:
        for f in folds:
            for s in seeds:
                cfg = Cfg(seed=s, split="kfold", fold=f, n_folds=5, epochs=epochs,
                          d_model=64, n_layers=2, use_val=False, lr=lr, **over)
                _maybe_run(cfg, "v2", name, f"v2_{name}_f{f}_s{s}", rows, force)
    return rows


def append_summary(rows: List[Dict]):
    if not rows:
        return
    df = pd.DataFrame(rows)
    if SUMMARY.exists():
        old = pd.read_csv(SUMMARY)
        df = pd.concat([old, df], ignore_index=True)
        df = df.drop_duplicates(subset=["tag"], keep="last")
    SUMMARY.parent.mkdir(parents=True, exist_ok=True)
    df.to_csv(SUMMARY, index=False)
    print(f"\nsummary -> {SUMMARY} ({len(df)} rows)")


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--suite", required=True,
                    choices=["main", "loao", "ablation", "dataeff", "next",
                             "v2", "best", "all"])
    ap.add_argument("--models", nargs="*", default=MODELS)
    ap.add_argument("--seeds", nargs="*", type=int, default=[0])
    ap.add_argument("--folds", nargs="*", type=int, default=[0, 1, 2, 3, 4])
    ap.add_argument("--fractions", nargs="*", type=float,
                    default=[0.25, 0.5, 0.75, 1.0])
    ap.add_argument("--epochs", type=int, default=None)
    ap.add_argument("--lr", type=float, default=3e-4)
    ap.add_argument("--d_model", type=int, default=None)
    ap.add_argument("--n_layers", type=int, default=None)
    ap.add_argument("--split_seed", type=int, default=0)
    ap.add_argument("--force", action="store_true")
    a = ap.parse_args()

    rows: List[Dict] = []
    ep = 300 if a.epochs is None else a.epochs      # equal-budget suites
    ep_best = 600 if a.epochs is None else a.epochs  # performance-first suite
    if a.suite in ("main", "all"):
        print("== main ==")
        rows += suite_main(a.seeds, a.folds, a.models, a.force, ep, a.lr)
    if a.suite in ("loao", "all"):
        print("== loao ==")
        rows += suite_loao(a.seeds, ["pimamba", "mamba", "gru", "mlp"], a.force, ep, a.lr)
    if a.suite in ("ablation", "all"):
        print("== ablation ==")
        rows += suite_ablation(a.seeds, a.folds, a.force, ep, a.lr)
    if a.suite in ("next", "all"):
        print("== next ==")
        ms = [m for m in a.models if m in MODELS_NEXT] or MODELS_NEXT
        rows += suite_next(a.seeds, a.folds, ms, a.force, ep, a.lr)
    if a.suite in ("v2", "all"):
        print("== v2 ==")
        rows += suite_v2(a.seeds, a.folds, a.force, ep, a.lr)
    if a.suite in ("best", "all"):
        print("== best ==")
        ms = [m for m in a.models if m in MODELS_BEST] or MODELS_BEST
        rows += suite_best(a.seeds, a.folds, ms, a.force, ep_best,
                           a.d_model or 128, a.n_layers or 4, a.lr, a.split_seed)
    if a.suite in ("dataeff", "all"):
        print("== dataeff ==")
        rows += suite_dataeff(a.fractions, a.folds,
                              ["pimamba", "mamba", "gru", "mlp"], a.force, ep, a.lr)
    append_summary(rows)


if __name__ == "__main__":
    main()

	"""Experiment orchestrator: main comparison / cross-alloy / ablations / data efficiency.

	All runs use a fixed epoch budget with a full OneCycle anneal and no internal
	validation split (``use_val=False``): with 33 samples a 15% hold-out is only
	~4 trajectories, which makes early stopping pure noise (see report §5.1).
	"""
	from __future__ import annotations

	import argparse
	import json
	import time
	from pathlib import Path
	from typing import Dict, List

	import pandas as pd

	from .data import PROC
	from .train import Cfg, run

	ROOT = Path(__file__).resolve().parents[2]
	RUNS = ROOT / "results" / "runs"
	SUMMARY = ROOT / "results" / "summary.csv"

	MODELS = ["mlp", "gru", "lstm", "transformer", "mamba", "mamba2", "pimamba"]
	MODELS_NEXT = ["llama", "xlstm", "gdn", "hymba", "koopman", "node", "rssm", "flow"]
	# performance-first group: larger model, longer schedule, a fixed fold split so
	# seeds differ only in initialisation (required for seed averaging / stacking).
	MODELS_BEST = ["hymba", "gdn", "llama", "pimamba"]

	ABLATIONS = [
	# name, cfg overrides
	("pimamba", {}),
	("pimamba_nocouple", {"coupling": False}),
	("pimamba_phys", {"w_mono": 1.0, "w_init": 1.0, "w_smooth": 0.1}),
	("pimamba_nogeom", {"drop_features": ("geom",)}),
	("pimamba_noflags", {"drop_features": ("geom_flags",)}),
	("pimamba_noensemble", {"drop_features": ("ensemble",)}),
	("pimamba_noinit", {"drop_features": ("init_rho", "init_phase")}),
	("pimamba_nonorm", {"norm_mode": "none"}),
	]


	def _row(res: Dict, suite: str, variant: str = "", fold: int = -1,
	seed: int = 0) -> Dict:
	cfg = res["config"]
	pt = res["per_target"]
	sig = pt.get("sigma", {})
	dec = res.get("decomp", {}) or {}
	row = {
	"suite": suite,
	"variant": variant or cfg["model"],
	"model": cfg["model"],
	"tag": Path(res.get("tag", "")).name or "",
	"fold": fold if fold >= 0 else cfg.get("fold", -1),
	"seed": seed,
	"holdout": cfg.get("holdout", ""),
	"n_train": res["n_train"],
	"n_test": res["n_test"],
	"n_params": res["n_params"],
	"epochs": res["epochs_run"],
	"wall_time_s": res["wall_time_s"],
	"macro_r2": res["summary"]["macro_r2"],
	"macro_r2_primary": res["summary"].get("macro_r2_primary"),
	"core_r2": res["summary"].get("core_r2"),
	"median_r2": res["summary"]["median_r2"],
	"macro_r2_log": res["summary"]["macro_r2_log"],
	"macro_nmae": res["summary"]["macro_nmae"],
	"sigma_r2": sig.get("r2"),
	"sigma_mae": sig.get("mae"),
	"sigma_nmae": sig.get("nmae"),
	"sigma_r2_log": sig.get("r2_log"),
	"sigma_shape_r2": dec.get("shape_r2"),
	"sigma_level_r2": dec.get("level_oracle_r2"),
	"sigma_level_corr": dec.get("level_corr"),
	"sigma_level_mae": dec.get("level_mae_GPa"),
	"xi_FCC_r2": pt.get("xi_FCC", {}).get("r2"),
	"rho_total_r2": pt.get("rho_total", {}).get("r2"),
	"rho_total_r2log": pt.get("rho_total", {}).get("r2_log"),
	"twin_r2": pt.get("twin", {}).get("r2"),
	"peak_rel_err": res["curve"].get("peak_rel_err_mean"),
	"curve_rmse": res["curve"].get("curve_rmse_mean"),
	"sig@0.05_rel_err": res["curve"].get("sig@0.05_rel_err_mean"),
	}
	return row


	def _maybe_run(cfg: Cfg, suite: str, variant: str, tag: str,
	rows: List[Dict], force: bool = False):
	out = RUNS / tag / "metrics.json"
	if out.exists() and not force:
	r = json.loads(out.read_text())
	# a cached smoke run (fewer epochs than requested) must not satisfy the
	# request -- otherwise a 2-epoch debugging run silently blocks the real
	# 300-epoch experiment.
	if int(r.get("epochs_run", 0)) >= int(cfg.epochs):
	rows.append(_row(r, suite, variant, cfg.fold, cfg.seed))
	print(f" cached {tag}")
	return
	print(f" stale {tag}: {r.get('epochs_run')} < {cfg.epochs} epochs, rerun")
	cfg.tag = tag
	res = run(cfg, verbose=True)
	res["tag"] = tag
	rows.append(_row(res, suite, variant, cfg.fold, cfg.seed))


	def suite_main(seeds, folds, models, force, epochs, lr=3e-4):
	rows = []
	for m in models:
	for f in folds:
	for s in seeds:
	cfg = Cfg(model=m, seed=s, split="kfold", fold=f, n_folds=5,
	epochs=epochs, d_model=64, n_layers=2, use_val=False, lr=lr)
	_maybe_run(cfg, "main", m, f"main_{m}_f{f}_s{s}", rows, force)
	return rows


	def suite_loao(seeds, models, force, epochs, lr=3e-4):
	meta = pd.read_csv(PROC / "samples_meta.csv")
	alloys = sorted(meta.alloy.unique())
	rows = []
	for m in models:
	for al in alloys:
	for s in seeds:
	cfg = Cfg(model=m, seed=s, split="loao", holdout=al,
	epochs=epochs, d_model=64, n_layers=2, use_val=False, lr=lr)
	safe = "".join(ch if ch.isalnum() else "_" for ch in al)[:40]
	_maybe_run(cfg, "loao", m, f"loao_{m}_{safe}_s{s}", rows, force)
	return rows


	def suite_ablation(seeds, folds, force, epochs, lr=3e-4):
	rows = []
	for name, over in ABLATIONS:
	for f in folds:
	for s in seeds:
	cfg = Cfg(model="pimamba", seed=s, split="kfold", fold=f,
	n_folds=5, epochs=epochs, d_model=64, n_layers=2,
	use_val=False, lr=lr, **over)
	_maybe_run(cfg, "ablation", name, f"abl_{name}_f{f}_s{s}", rows, force)
	return rows


	def suite_dataeff(fractions, folds, models, force, epochs, lr=3e-4):
	rows = []
	for m in models:
	for fr in fractions:
	for f in folds:
	cfg = Cfg(model=m, seed=0, split="kfold", fold=f, n_folds=5,
	epochs=epochs, d_model=64, n_layers=2, train_frac=fr,
	use_val=False, lr=lr)
	_maybe_run(cfg, "dataeff", f"{m}@{fr:.2f}",
	f"de_{m}_{int(fr*100):03d}_f{f}", rows, force)
	return rows


	def suite_next(seeds, folds, models, force, epochs, lr=3e-4):
	"""Next-generation backbones (2024-2026), same protocol as ``suite_main``."""
	rows = []
	for m in models:
	for f in folds:
	for s in seeds:
	cfg = Cfg(model=m, seed=s, split="kfold", fold=f, n_folds=5,
	epochs=epochs, d_model=64, n_layers=2, use_val=False,
	lr=lr)
	_maybe_run(cfg, "next", m, f"next_{m}_f{f}_s{s}", rows, force)
	return rows


	def suite_best(seeds, folds, models, force, epochs, d_model=128, n_layers=4,
	lr=3e-4, split_seed=0):
	"""Performance-first group: no equal-budget constraint.

	``split_seed`` is pinned so every seed and architecture sees the same five
	folds; otherwise the OOF predictions cannot be averaged or stacked.
	"""
	rows = []
	for m in models:
	for f in folds:
	for s in seeds:
	cfg = Cfg(model=m, seed=s, split="kfold", fold=f, n_folds=5,
	epochs=epochs, d_model=d_model, n_layers=n_layers,
	use_val=False, lr=lr, split_seed=split_seed)
	_maybe_run(cfg, "best", m, f"best_{m}_f{f}_s{s}", rows, force)
	return rows


	def suite_v2(seeds, folds, force, epochs, lr=3e-4):
	"""PI-Mamba v2: Taylor-form coupling and the curve-level peak loss."""
	variants = [
	("pimamba2", {"model": "pimamba2"}),
	("pimamba2_peak", {"model": "pimamba2", "w_peak": 0.5}),
	("pimamba_peak", {"model": "pimamba", "w_peak": 0.5}),
	("pimamba2_nocouple", {"model": "pimamba2", "coupling": False}),
	]
	rows = []
	for name, over in variants:
	for f in folds:
	for s in seeds:
	cfg = Cfg(seed=s, split="kfold", fold=f, n_folds=5, epochs=epochs,
	d_model=64, n_layers=2, use_val=False, lr=lr, **over)
	_maybe_run(cfg, "v2", name, f"v2_{name}_f{f}_s{s}", rows, force)
	return rows


	def append_summary(rows: List[Dict]):
	if not rows:
	return
	df = pd.DataFrame(rows)
	if SUMMARY.exists():
	old = pd.read_csv(SUMMARY)
	df = pd.concat([old, df], ignore_index=True)
	df = df.drop_duplicates(subset=["tag"], keep="last")
	SUMMARY.parent.mkdir(parents=True, exist_ok=True)
	df.to_csv(SUMMARY, index=False)
	print(f"\nsummary -> {SUMMARY} ({len(df)} rows)")


	def main():
	ap = argparse.ArgumentParser()
	ap.add_argument("--suite", required=True,
	choices=["main", "loao", "ablation", "dataeff", "next",
	"v2", "best", "all"])
	ap.add_argument("--models", nargs="*", default=MODELS)
	ap.add_argument("--seeds", nargs="*", type=int, default=[0])
	ap.add_argument("--folds", nargs="*", type=int, default=[0, 1, 2, 3, 4])
	ap.add_argument("--fractions", nargs="*", type=float,
	default=[0.25, 0.5, 0.75, 1.0])
	ap.add_argument("--epochs", type=int, default=None)
	ap.add_argument("--lr", type=float, default=3e-4)
	ap.add_argument("--d_model", type=int, default=None)
	ap.add_argument("--n_layers", type=int, default=None)
	ap.add_argument("--split_seed", type=int, default=0)
	ap.add_argument("--force", action="store_true")
	a = ap.parse_args()

	rows: List[Dict] = []
	ep = 300 if a.epochs is None else a.epochs # equal-budget suites
	ep_best = 600 if a.epochs is None else a.epochs # performance-first suite
	if a.suite in ("main", "all"):
	print("== main ==")
	rows += suite_main(a.seeds, a.folds, a.models, a.force, ep, a.lr)
	if a.suite in ("loao", "all"):
	print("== loao ==")
	rows += suite_loao(a.seeds, ["pimamba", "mamba", "gru", "mlp"], a.force, ep, a.lr)
	if a.suite in ("ablation", "all"):
	print("== ablation ==")
	rows += suite_ablation(a.seeds, a.folds, a.force, ep, a.lr)
	if a.suite in ("next", "all"):
	print("== next ==")
	ms = [m for m in a.models if m in MODELS_NEXT] or MODELS_NEXT
	rows += suite_next(a.seeds, a.folds, ms, a.force, ep, a.lr)
	if a.suite in ("v2", "all"):
	print("== v2 ==")
	rows += suite_v2(a.seeds, a.folds, a.force, ep, a.lr)
	if a.suite in ("best", "all"):
	print("== best ==")
	ms = [m for m in a.models if m in MODELS_BEST] or MODELS_BEST
	rows += suite_best(a.seeds, a.folds, ms, a.force, ep_best,
	a.d_model or 128, a.n_layers or 4, a.lr, a.split_seed)
	if a.suite in ("dataeff", "all"):
	print("== dataeff ==")
	rows += suite_dataeff(a.fractions, a.folds,
	["pimamba", "mamba", "gru", "mlp"], a.force, ep, a.lr)
	append_summary(rows)


	if __name__ == "__main__":
	main()