diff --git a/.claude/CLAUDE.md b/.claude/CLAUDE.md index 6794a4a..fc5edd2 100644 --- a/.claude/CLAUDE.md +++ b/.claude/CLAUDE.md @@ -51,4 +51,11 @@ inference-time descent** into distribution-free reliability certificates. Read - Phase 4j complementarity ✅ (T4): geometry adds *conditional* signal over softmax ONLY where it wins head-to-head (arithmetic-IRED 4/5); on graph-IRED the combined [geom+softmax] mapper ≈ softmax (0/5) — the graph tie is **redundancy, not complementarity**. -- Next: richer geometry (full spectrum / mode connectivity) · E3/E4 · Modal · scale-up. +- Phase 4k richer geometry ✅ (T5): opt-in `[eval] richer_geometry=true` appends the full Hessian + spectrum (`spectrum/*`, exact d×d eigh) + mode-connectivity barriers (`connect/*`) after the base + 14. Re-ran the 4j test on all 4 cells: richer geometry helps **nowhere** — graph-IRED conditional + signal stays −0.002 (0/5). So the **graph redundancy is a task property, not thin features**; it's + a wash where geometry already wins (arith-IRED +0.007 4/5) and slightly worse on fixed-bowl cells. + Aggregation is feature-set-aware (`feature_set_of`, canonical tables default `feature_set="base"`) + so richer never pollutes T1/T2/T4. JAX for spectrum/connectivity lives in `curvature.py` (inv. 1). +- Next: E3/E4 (a 3rd/4th task to characterize *when* geometry beats softmax) · Modal · scale-up. diff --git a/analysis/aggregate.py b/analysis/aggregate.py index c680ca2..696c0b2 100644 --- a/analysis/aggregate.py +++ b/analysis/aggregate.py @@ -19,19 +19,30 @@ def objective_of(row: dict) -> str: "objective", "basin_center") +def feature_set_of(row: dict) -> str: + """Geometry feature set behind a row: ``base`` (14 features) or ``richer`` (Phase 4k). + + Pre-4k rows lack the field and never enabled richer geometry, so they read as ``base``. + """ + return row["metrics"].get("feature_set") or ( + "richer" if row["config"].get("eval", {}).get("richer_geometry") else "base") + + def selective_rows(rows: list[dict] | None = None, task: str | None = None, - objective: str | None = None) -> list[dict]: + objective: str | None = None, feature_set: str | None = None) -> list[dict]: """Latest ``split=='selective'`` row per ``(config_hash, seed)`` (cf. latest_per_config). - ``task`` filters to one reasoning family and ``objective`` to one reasoner (``basin_center`` vs - ``ired``), so multi-task / multi-reasoner ledgers do not cross-contaminate per-K aggregates. + ``task`` filters to one reasoning family, ``objective`` to one reasoner (``basin_center`` vs + ``ired``), and ``feature_set`` to one geometry set (``base`` vs ``richer``), so multi-task / + multi-reasoner / multi-feature-set ledgers do not cross-contaminate per-K aggregates. """ if rows is None: rows = read_all() latest: dict[tuple[str, int], dict] = {} for r in rows: if (r.get("split") == "selective" and (task is None or r.get("task") == task) - and (objective is None or objective_of(r) == objective)): + and (objective is None or objective_of(r) == objective) + and (feature_set is None or feature_set_of(r) == feature_set)): latest[(r["config_hash"], r["seed"])] = r # later rows win return list(latest.values()) @@ -46,10 +57,15 @@ def _k(row: dict) -> int: return int(m.get("k_restarts", row["config"]["inference"]["k_restarts"])) -def by_k(rows: list[dict] | None = None, task: str | None = None) -> dict[int, list[dict]]: - """Group selective rows by ``k_restarts`` (ascending), optionally filtered to one ``task``.""" +def by_k(rows: list[dict] | None = None, task: str | None = None, + feature_set: str | None = "base") -> dict[int, list[dict]]: + """Group selective rows by ``k_restarts`` (ascending), optionally filtered to one ``task``. + + Defaults to the canonical ``base`` feature set so the K-ablation is never contaminated by + Phase-4k ``richer`` rows (which share K with their base counterparts). + """ out: dict[int, list[dict]] = {} - for r in selective_rows(rows, task=task): + for r in selective_rows(rows, task=task, feature_set=feature_set): out.setdefault(_k(r), []).append(r) return dict(sorted(out.items())) @@ -100,9 +116,12 @@ def aggregate_cell(rows_for_k: list[dict]) -> dict: # True only when every row genuinely carried the Phase-4e baseline field (not a fallback). "has_baseline": all("delta_aurc_vs_best_baseline" in m for m in ms), # Feature-group leave-one-out ablation (Phase 4f), mean/std per subset over seeds. + # Aggregate only the ablation subsets present in *every* row (base and richer rows carry + # different group sets), so a heterogeneous cell can never KeyError. "feature_ablation": ( {k: _ms([m["feature_ablation"][k] for m in ms]) - for k in ms[0]["feature_ablation"]} + for k in ms[0]["feature_ablation"] + if all(k in m["feature_ablation"] for m in ms)} if all("feature_ablation" in m for m in ms) else {}), "ltt_coverage": _ms([m["ltt"]["coverage"] for m in ms]), "ltt_selective_risk": _ms([m["ltt"]["selective_risk"] for m in ms]), @@ -110,21 +129,25 @@ def aggregate_cell(rows_for_k: list[dict]) -> dict: } -def aggregate_by_k(rows: list[dict] | None = None, task: str | None = None) -> dict[int, dict]: - """``{K: aggregate_cell(...)}`` for every K present (optionally filtered to one ``task``).""" - return {k: aggregate_cell(v) for k, v in by_k(rows, task=task).items()} +def aggregate_by_k(rows: list[dict] | None = None, task: str | None = None, + feature_set: str | None = "base") -> dict[int, dict]: + """``{K: aggregate_cell(...)}`` per K present (filtered to a ``task`` / ``feature_set``).""" + return {k: aggregate_cell(v) for k, v in by_k(rows, task=task, feature_set=feature_set).items()} def headline_cell(rows: list[dict] | None = None, task: str | None = None, - objective: str = "basin_center") -> dict: + objective: str = "basin_center", feature_set: str | None = "base") -> dict: """Aggregate the headline seed-sweep for one task + reasoner (largest single-config seed group). T1 uses the canonical ``basin_center`` reasoner (pass ``objective="ired"`` to report the learned - landscape separately). Prefers rows carrying the Phase-4e baseline field, then picks the config - group (K, n_test) with the most seeds — the multi-seed sweep — so the cell is not polluted by - mixing fold sizes / reasoners that happen to share a K value. + landscape separately) and the canonical ``base`` feature set (pass ``feature_set="richer"`` for + the Phase-4k richer-geometry cells) — so richer rows never pollute the base headline. Prefers + rows carrying the Phase-4e baseline field, then picks the config group (K, n_test) with the most + seeds — the multi-seed sweep — so the cell is not polluted by mixing fold sizes / reasoners that + happen to share a K value. """ - sel = selective_rows(rows, task=task, objective=objective) or selective_rows(rows, task=task) + sel = (selective_rows(rows, task=task, objective=objective, feature_set=feature_set) + or selective_rows(rows, task=task, feature_set=feature_set)) with_bl = [r for r in sel if "delta_aurc_vs_best_baseline" in r["metrics"]] pool = with_bl or sel # Group by the experiment config *excluding seed* — (K, n_test) separates a seed-sweep (many diff --git a/analysis/make_figures.py b/analysis/make_figures.py index 0bdde16..3ffa26a 100644 --- a/analysis/make_figures.py +++ b/analysis/make_figures.py @@ -12,6 +12,11 @@ from edc import plotting from edc.ledger import read_all +try: # sibling import when run as a script (analysis/ on sys.path) + from aggregate import feature_set_of +except ImportError: # pragma: no cover + from analysis.aggregate import feature_set_of + FIG_DIR = Path(__file__).resolve().parent / "figures" # (filename, plotting fn). F1 (schematic) is TikZ; F4/F6 (halting, OOD stress) are Phase 4/5. @@ -26,8 +31,10 @@ def main() -> int: - rows = read_all() - print(f"[figures] {len(rows)} ledger rows available.") + # Canonical figures are all base-feature results; exclude Phase-4k richer rows so they never + # dilute the pooled figures (e.g. S1's K=12 point). The richer verdict lives in table T5. + rows = [r for r in read_all() if feature_set_of(r) == "base"] + print(f"[figures] {len(rows)} base-feature ledger rows available.") FIG_DIR.mkdir(parents=True, exist_ok=True) n_ok = 0 for fname, fn in _FIGURES: diff --git a/analysis/make_tables.py b/analysis/make_tables.py index 346ff72..ec08a7a 100644 --- a/analysis/make_tables.py +++ b/analysis/make_tables.py @@ -14,6 +14,7 @@ try: # sibling import when run as a script (analysis/ on sys.path) from aggregate import ( aggregate_by_k, + feature_set_of, headline_cell, objective_of, selective_rows, @@ -22,6 +23,7 @@ except ImportError: # pragma: no cover from analysis.aggregate import ( aggregate_by_k, + feature_set_of, headline_cell, objective_of, selective_rows, @@ -156,6 +158,41 @@ def _t4(rows: list[dict]) -> str: "tab:complementarity", f"run_ids={all_ids}") +def _t5(rows: list[dict]) -> str: + """Richer geometry (Phase 4k): does the full Hessian spectrum + mode connectivity add signal? + + Two rows per (task, reasoner) — ``base`` (14 features) vs ``richer`` (14 + spectrum + connect) — + so the reader sees directly whether the richer geometry moves the head-to-head win over softmax + and the conditional ``geom adds`` signal, especially on the graph cell where geometry ties. + """ + body, all_ids = [], [] + for task in tasks_present(rows): + for o in sorted({objective_of(r) for r in selective_rows(rows, task=task)}): + sets = {feature_set_of(r) for r in selective_rows(rows, task=task, objective=o)} + if "richer" not in sets: # only cells with a richer run + continue + for fs in ("base", "richer"): + if fs not in sets: + continue + a = headline_cell(rows, task=task, objective=o, feature_set=fs) + adds = (f"{a['seeds_ci_excludes_0_geom_adds']}/{a['n_seeds']}" + if a.get("has_geom_adds") else "--") + body.append([ + _esc(task), _esc(o), fs, _pm(a["aurc_geometry"]), _pm(a["delta_aurc_baseline"]), + _pm(a["delta_aurc_geom_adds"]) if a.get("has_geom_adds") else "--", adds, + ]) + all_ids += a["run_ids"] + header = ["task", "reasoner", "features", "AURC geom", r"$\Delta$AURC vs softmax", + r"$\Delta$AURC geom \emph{adds}", "seeds add"] + return _tabular( + header, body, "lllcccc", + "Richer geometry (mean$\\pm$std over seeds): the base 14-feature vector vs the base plus " + "the full Hessian spectrum and mode-connectivity barriers. If ``richer`` does not raise " + "$\\Delta$AURC vs softmax or the conditional ``geom adds`` on the graph cell, the graph " + "redundancy is a task property, not an artifact of thin curvature features.", + "tab:richer", f"run_ids={all_ids}") + + def _t3(rows: list[dict]) -> str: sel = selective_rows(rows) env = sel[-1]["env"] if sel else {} @@ -188,9 +225,16 @@ def main() -> int: "T2b_feature_ablation.tex": _t2b(rows), "T3_repro.tex": _t3(rows), } + def _has_body(tex: str) -> bool: + return r"\midrule" in tex and bool( + tex.split(r"\midrule")[1].split(r"\bottomrule")[0].strip()) + t4 = _t4(rows) # only when complementarity rows exist - if r"\midrule" in t4 and t4.split(r"\midrule")[1].split(r"\bottomrule")[0].strip(): + if _has_body(t4): outputs["T4_complementarity.tex"] = t4 + t5 = _t5(rows) # only when richer-geometry rows exist + if _has_body(t5): + outputs["T5_richer_geometry.tex"] = t5 for name, tex in outputs.items(): (TABLE_DIR / name).write_text(tex) print(f"[tables] wrote {TABLE_DIR / name}") diff --git a/configs/sweeps/arith_richer_seeds.toml b/configs/sweeps/arith_richer_seeds.toml new file mode 100644 index 0000000..33c1bf5 --- /dev/null +++ b/configs/sweeps/arith_richer_seeds.toml @@ -0,0 +1,13 @@ +# Phase 4k: 5-seed arithmetic basin-center with RICHER geometry, for completeness so T5 has the +# base-vs-richer comparison on all four (task, reasoner) cells. Reduced folds match arith_seeds.toml +# (its base-feature counterpart) so the only changed variable is the feature set. +[sweep] +base = "configs/experiments/arithmetic_selective.toml" + +[sweep.override] +"eval.n_eval" = 600 +"conformal.n_calib" = 800 +"eval.richer_geometry" = true + +[sweep.grid] +"run.seed" = [0, 1, 2, 3, 4] diff --git a/configs/sweeps/graph_ired_richer_seeds.toml b/configs/sweeps/graph_ired_richer_seeds.toml new file mode 100644 index 0000000..bddc326 --- /dev/null +++ b/configs/sweeps/graph_ired_richer_seeds.toml @@ -0,0 +1,13 @@ +# Phase 4k THE CRUX: 5-seed graph-IRED with RICHER geometry (full Hessian spectrum + mode +# connectivity). On graph-IRED the base 14 features tie softmax and the combined mapper adds nothing +# (Phase 4j: redundancy, 0/5). This asks whether that tie is a real task property or an artifact of +# the thin lambda_max/trace curvature features. Same base cell as graph_ired_seeds.toml; only the +# feature set changes. +[sweep] +base = "configs/experiments/graph_ired.toml" + +[sweep.override] +"eval.richer_geometry" = true + +[sweep.grid] +"run.seed" = [0, 1, 2, 3, 4] diff --git a/configs/sweeps/graph_richer_seeds.toml b/configs/sweeps/graph_richer_seeds.toml new file mode 100644 index 0000000..af882ea --- /dev/null +++ b/configs/sweeps/graph_richer_seeds.toml @@ -0,0 +1,13 @@ +# Phase 4k: 5-seed graph basin-center with RICHER geometry, for completeness so T5 has the +# base-vs-richer comparison on all four (task, reasoner) cells. Reduced folds match graph_seeds.toml +# (its base-feature counterpart) so the only changed variable is the feature set. +[sweep] +base = "configs/experiments/graph_selective.toml" + +[sweep.override] +"eval.n_eval" = 600 +"conformal.n_calib" = 800 +"eval.richer_geometry" = true + +[sweep.grid] +"run.seed" = [0, 1, 2, 3, 4] diff --git a/configs/sweeps/ired_richer_seeds.toml b/configs/sweeps/ired_richer_seeds.toml new file mode 100644 index 0000000..f3cde0d --- /dev/null +++ b/configs/sweeps/ired_richer_seeds.toml @@ -0,0 +1,12 @@ +# Phase 4k: 5-seed arithmetic-IRED with RICHER geometry (full Hessian spectrum + mode connectivity). +# Positive control — arith-IRED already beats softmax 4/5 with the base 14 features; richer geometry +# should keep or strengthen that. Mirrors ired_seeds.toml (full folds, annealed sampler) so the only +# changed variable vs the base cell is the feature set. +[sweep] +base = "configs/experiments/arithmetic_ired.toml" + +[sweep.override] +"eval.richer_geometry" = true + +[sweep.grid] +"run.seed" = [0, 1, 2, 3, 4] diff --git a/docs/DECISIONS.md b/docs/DECISIONS.md index 4a90b8e..cc6d943 100644 --- a/docs/DECISIONS.md +++ b/docs/DECISIONS.md @@ -2,6 +2,23 @@ Short, dated, append-only. Newest first. +## 2026-07-29 — Phase 4k: the graph redundancy is a task property, not thin features +**Finding/decision:** the 4j redundancy verdict rested on a thin curvature description (only +`λmax`/`tr(H)`), so we added two opt-in richer feature groups — the **full Hessian spectrum** +(`geometry/spectrum.py`, exact `d×d` eigh via new `curvature.batched_spectrum`) and **mode +connectivity** (`geometry/connectivity.py`, energy barriers via new `curvature.batched_path_energy`) +— behind `[eval] richer_geometry` and re-ran the 4j complementarity test on all four cells (T5). +**Result:** richer geometry helps nowhere — the crux graph-IRED conditional signal over softmax stays +−0.002 (0/5); it's a wash where geometry already wins (arith-IRED +0.007, 4/5) and slightly worse on +the fixed-bowl basin-center cells (extra features add noise). So the graph tie is a genuine property +of the task, not feature poverty. **Design choices:** (a) all new JAX lives in `curvature.py` to keep +invariant 1 (the two feature modules are pure NumPy); (b) richer is opt-in and appended *after* the +base 14 so every prior result is reproduced byte-identically; (c) aggregation is feature-set-aware +(`aggregate.feature_set_of`; `headline_cell`/`by_k`/`aggregate_by_k` default `feature_set="base"`) so +richer rows never pollute the canonical T1/T2/T4 — exactly mirroring how `objective` keeps IRED out of +T1. **Why record it:** it closes the paper's stated "richer geometry" open question with a decisive +negative result, strengthening (not weakening) the redundancy claim. **Reversible?** Fully additive. + ## 2026-07-28 — Phase 4j: the graph tie is redundancy, not complementarity (combined-mapper test) **Finding/decision:** to distinguish redundancy from complementarity we measure whether a mapper on `[geometry ∪ softmax]` beats one on `[softmax]` alone (both fit on the fit fold; `evaluate` adds diff --git a/docs/EXPERIMENTS.md b/docs/EXPERIMENTS.md index 3c4991b..968c96d 100644 --- a/docs/EXPERIMENTS.md +++ b/docs/EXPERIMENTS.md @@ -198,6 +198,40 @@ the softmax does not. Re-running this baseline stress test under IRED training i experiment (see SESSION_HANDOFF). MC-dropout / deep ensembles remain deferred (the K-restart best-of-N already supplies ensemble-like diversity). +### Richer geometry — is the graph tie thin features or a task property? (Phase 4k → T5) + +The Phase-4j redundancy verdict rested on a thin curvature representation: of the 14 features, +curvature was only `λmax` (power iteration) and `tr(H)` (Hutchinson). So "geometry is redundant with +softmax on graph" was confounded with "our geometry features are impoverished." Phase 4k adds two +richer, opt-in groups (`[eval] richer_geometry=true`) — the **full Hessian spectrum** +(`spectrum/*`: `lmin`, negative-eigenvalue fraction, effective rank / participation ratio, log-det, +each mean-over-restarts and at the best restart; exact `d×d` eigendecomposition since `d` is small) +and **mode connectivity** (`connect/*`: energy barriers along the straight-line path from the best +restart to the others) — and re-runs the Phase-4j complementarity test on all four cells. Same +reasoners, folds, and mappers; only the feature set changes. + +| task | reasoner | features | AURC geom | ΔAURC vs softmax | ΔAURC geom *adds* | seeds add | +|------|----------|----------|-----------|------------------|-------------------|-----------| +| arithmetic | basin-center | base | $0.059$ | $-0.011$ | $+0.001$ | $0/5$ | +| arithmetic | basin-center | **richer** | $0.061$ | $-0.012$ | $-0.002$ | $1/5$ | +| arithmetic | IRED | base | $0.028$ | $+0.007$ | $+0.007$ | $4/5$ | +| arithmetic | IRED | **richer** | $0.028$ | $+0.008$ | $+0.007$ | $4/5$ | +| graph | basin-center | base | $0.159$ | $-0.034$ | $-0.006$ | $0/5$ | +| graph | basin-center | **richer** | $0.162$ | $-0.037$ | $-0.009$ | $0/5$ | +| **graph** | **IRED** | base | $0.056$ | $+0.001$ | $+0.001$ | $0/5$ | +| **graph** | **IRED** | **richer** | $0.055$ | $+0.002$ | $\mathbf{-0.002}$ | **$0/5$** | + +**Verdict — the graph redundancy is a task property, not a feature-poverty artifact.** In the crux +cell (graph-IRED), the full spectrum + mode connectivity leave the conditional signal over softmax at +$-0.002$, $0/5$ — geometry still adds *nothing*. Richer geometry changes the picture nowhere: it is a +wash on the one cell geometry already wins (arithmetic-IRED, $+0.007$, $4/5$, unchanged) and slightly +*worse* on the fixed-bowl basin-center cells (adding features to a trivial landscape only adds noise: +AURC $0.059\to0.061$ arith, $0.159\to0.162$ graph). So the answer to "does *more* landscape signal +help on graph?" is **no** — a well-calibrated softmax genuinely captures what descent geometry would +contribute there, even with the richest geometry we can extract. The base rows reproduce the Phase-4j +numbers exactly, confirming richer geometry is purely additive and non-breaking (default stays the +canonical 14 features; aggregation is feature-set-aware so richer never pollutes T1/T2/T4). + ## Operating regime Tune each task's difficulty so base accuracy is **70–85%**. Fully-solved tasks saturate AURC and diff --git a/docs/SESSION_HANDOFF.md b/docs/SESSION_HANDOFF.md index 3769d9c..3c43261 100644 --- a/docs/SESSION_HANDOFF.md +++ b/docs/SESSION_HANDOFF.md @@ -2,7 +2,7 @@ **Start here.** Rolling state of the project between sessions. -## Current state (2026-07-25) +## Current state (2026-07-29) - **Phase 0 (skeleton): ✅** repo tree, `uv`/`pyproject.toml`, `Makefile`, CI, config system (`edc.config`), deterministic seeding (`edc.seeding`), append-only ledger (`edc.ledger`), @@ -88,8 +88,20 @@ ≈ softmax alone (0/5, +0.001) — a well-calibrated softmax already captures what descent geometry would add there. `headline_cell` hardened (per-seed timestamp dedup + prefer newest field-carrying cell) so re-runs after config-schema changes don't create duplicate/stale headline cells. 95 tests. -- **Open question / next:** richer geometry features (full Hessian spectrum, mode connectivity) to - test whether MORE landscape signal helps on graph; more tasks (E3/E4); Modal; scale-up. +- **Phase 4k (richer geometry — is the graph tie thin features or a task property?): ✅** opt-in + `[eval] richer_geometry=true` appends two groups after the base 14: the **full Hessian spectrum** + (`geometry/spectrum.py`: lmin, neg-eigenvalue fraction, effective rank, log-det — exact `d×d` eigh) + and **mode connectivity** (`geometry/connectivity.py`: energy barriers from the best restart to the + others). New JAX lives in `curvature.py` (`batched_spectrum`, `batched_path_energy`) to respect + invariant 1; the two feature modules are pure NumPy. Re-ran the 4j complementarity test on all four + cells (T5). **Answer: the graph redundancy is a task property, not thin features.** Richer geometry + helps nowhere — graph-IRED conditional signal over softmax stays **−0.002, 0/5**; it's a wash where + geometry already wins (arith-IRED +0.007, 4/5, unchanged) and slightly worse on the fixed-bowl + basin-center cells (extra features add noise). Aggregation is feature-set-aware (`feature_set_of`; + `headline_cell`/`by_k` default `feature_set="base"`) so richer never pollutes T1/T2/T4. 109 tests. +- **Open question / next:** a 3rd/4th task (E3/E4, e.g. logic/Sudoku) to characterize *when* geometry + beats softmax now that feature poverty is ruled out; harden honesty gaps (multi-seed halting/OOD, + MC-dropout baseline, a full-fold graph run that actually certifies); Modal; scale-up. ## What is real vs stub @@ -102,7 +114,9 @@ **the sweep/aggregation/tables stack**: `run_sweep.py`, `analysis.aggregate`, `make_tables` (T1–T3), S1 K-lift figure; **adaptive halting**: `halting.adaptive`, `eval.evaluate_halting`, `run_halting.py`, F4; **F5 mechanism + F6 OOD stress** (`feature_diagnostics`, `ood_validity`); - **softmax-confidence baselines** (`eval/baselines.py`: MSP/temp/entropy). + **softmax-confidence baselines** (`eval/baselines.py`: MSP/temp/entropy); **richer geometry** + (opt-in `geometry/spectrum.py` full-spectrum + `geometry/connectivity.py` mode-connectivity, with + `curvature.batched_spectrum`/`batched_path_energy` in the JAX core; T5). - Stub: **IRED landscape training** (`train/losses.py` is still the Phase-1 basin-center scheme), logic/hard tasks (E3/E4), Modal runner, paper write-up. diff --git a/paper/sections/conclusion.tex b/paper/sections/conclusion.tex index e3a94be..648198d 100644 --- a/paper/sections/conclusion.tex +++ b/paper/sections/conclusion.tex @@ -22,7 +22,12 @@ \section{Conclusion} fails to win. The benefit of landscape geometry over softmax is thus task-dependent, not a general property of learned landscapes nor an artifact of accuracy. We report this boundary plainly. +We also asked whether the graph redundancy was merely an artifact of thin curvature features +($\lambda_{\max}$ and trace only): adding the full Hessian spectrum and mode-connectivity barriers +leaves the conditional signal over softmax at $-0.002$ ($0/5$) on graph-IRED, so the redundancy is a +property of the task, not of the feature set. + \paragraph{Future work.} Characterizing \emph{when} descent geometry adds over softmax---which task -structures and landscapes---is the interesting open question raised by the arithmetic/graph contrast. -Beyond that: richer geometry (full spectra, mode connectivity), scaling to language-scale EBTs, and -more seeds/tasks. +structures and landscapes---is the interesting open question raised by the arithmetic/graph contrast; +having ruled out feature poverty as the explanation, a third and fourth reasoning family is the +natural next probe. Beyond that: scaling to language-scale EBTs, and more seeds/tasks. diff --git a/paper/sections/experiments.tex b/paper/sections/experiments.tex index 9773128..54c5b1e 100644 --- a/paper/sections/experiments.tex +++ b/paper/sections/experiments.tex @@ -99,8 +99,22 @@ \section{Experiments} geometry always beats the EBT scalar energy, and beats or complements softmax only under a learned landscape on arithmetic. +\paragraph{Richer geometry: task property, not thin features (Table~\ref{tab:richer}).} The +redundancy verdict could be an artifact of an impoverished curvature description---our base features +summarise the latent Hessian only by $\lambda_{\max}$ and $\mathrm{tr}(H)$. We test this by adding two +richer feature groups and re-running the complementarity analysis: the \emph{full} Hessian spectrum +(smallest eigenvalue, negative-eigenvalue fraction, effective rank, log-determinant; the exact +$d\times d$ eigendecomposition, since $d$ is small) and \emph{mode connectivity} (energy barriers +along the path between restart basins). Across all four cells the richer geometry changes nothing: on +the crux graph-IRED cell the conditional signal over softmax stays at $-0.002$ ($0/5$); it is a wash +where geometry already wins (arithmetic-IRED, $+0.007$, $4/5$, unchanged) and slightly \emph{worse} on +the fixed-bowl basin-center landscapes, where extra features only add noise. So more landscape signal +does not help on graph---the graph redundancy is a property of the task, not of thin features, and a +well-calibrated softmax already captures what even the richest descent geometry would add. + \input{tables/T1_main} \input{tables/T4_complementarity} +\input{tables/T5_richer_geometry} \input{tables/T2b_feature_ablation} \input{tables/T2_kablation} diff --git a/paper/tables/T3_repro.tex b/paper/tables/T3_repro.tex index 81ebc34..16b7979 100644 --- a/paper/tables/T3_repro.tex +++ b/paper/tables/T3_repro.tex @@ -1,4 +1,4 @@ -% auto-generated by analysis/make_tables.py — do not edit. n_rows=62 +% auto-generated by analysis/make_tables.py — do not edit. n_rows=82 \begin{table}[t] \centering \caption{Reproducibility appendix. Env: python 3.12.13, jax 0.11.0 (cpu). Every row regenerates from seed + config via \texttt{run\_sweep}.} @@ -37,6 +37,10 @@ 2a91bbeda6e4 & 0 & 12 & 08a3ad9a1d60 & 3e52940 \\ aefb55fc7987 & 0 & 12 & 33e0c7019aeb & bf960a7 \\ 7802e0276803 & 0 & 12 & e1853552dc3e & 3e52940 \\ + afd5e1407276 & 0 & 12 & 664501271a69 & 9367d60 \\ + c468dfa50c2c & 0 & 12 & 1e7f9a8e1d3b & 9367d60 \\ + ec2457a78f8d & 0 & 12 & f821965bd47a & 9367d60 \\ + abb4dcb4ea84 & 0 & 12 & b333edd88366 & 9367d60 \\ 452500308f3b & 1 & 12 & 60da27bc4d66 & 5207145 \\ b8dc1a068df5 & 1 & 12 & e65ef9e88f92 & 5207145 \\ b0d0d7f2f513 & 1 & 12 & 372c61dccb87 & 3e52940 \\ @@ -44,6 +48,10 @@ a4a1a13b8234 & 1 & 12 & b06a5c4c475d & 3e52940 \\ 8cfc5a222d83 & 1 & 12 & 16b303249ea6 & bf960a7 \\ d80ff5b236eb & 1 & 12 & 6f5d8977b63e & 3e52940 \\ + eb3b1db3b4e5 & 1 & 12 & 4f2a6539c201 & 9367d60 \\ + 69bcb88d896d & 1 & 12 & b199710e8a3d & 9367d60 \\ + 34bc023358b8 & 1 & 12 & f71a346333e0 & 9367d60 \\ + 6c7eb2cdb623 & 1 & 12 & a146d7ac1641 & 9367d60 \\ b1e83969abe5 & 2 & 12 & ff22f3433ff5 & 5207145 \\ aa60425c10e1 & 2 & 12 & cacbb6ece843 & 5207145 \\ 45d9d36ce766 & 2 & 12 & c7aaaa71b4ba & 3e52940 \\ @@ -51,6 +59,10 @@ a009b8f5ff16 & 2 & 12 & 6098ae479456 & 3e52940 \\ 8ab8e76e3b44 & 2 & 12 & 0594bd28ecc9 & 3e52940 \\ 198178fd3f60 & 2 & 12 & faf342858843 & 3e52940 \\ + 7c7410c6f66f & 2 & 12 & fb7206db9d5e & 9367d60 \\ + affc597f3e34 & 2 & 12 & cd311d01e1a0 & 9367d60 \\ + 28ac1614501c & 2 & 12 & d133e8a33968 & 9367d60 \\ + 9fdd2103f70b & 2 & 12 & a55d25d137ca & 9367d60 \\ 70d97e02bed0 & 3 & 12 & fbed14eeb756 & 5207145 \\ c948f8130a3d & 3 & 12 & 054f01c6c637 & 5207145 \\ f301fe7ebb4d & 3 & 12 & 49d35c26cae8 & 3e52940 \\ @@ -58,6 +70,10 @@ c85bba3fffca & 3 & 12 & bc6164fbe88e & 3e52940 \\ 7d6cba43dccf & 3 & 12 & 213722278247 & 3e52940 \\ 8867921abd20 & 3 & 12 & 4518b9740b1f & 3e52940 \\ + d117be743bac & 3 & 12 & b44ece1a185c & 9367d60 \\ + 37776ebe2a5b & 3 & 12 & f586dabb6126 & 9367d60 \\ + 39b5492337a7 & 3 & 12 & a93a0c192b97 & 9367d60 \\ + cb3102b09662 & 3 & 12 & a0a621e5442c & 9367d60 \\ 46bb116f8c7c & 4 & 12 & 609a4152cf1e & 5207145 \\ 4a5576e4c244 & 4 & 12 & 0b564d7f9e23 & 5207145 \\ 39101f30bcb7 & 4 & 12 & 83b44608e95e & 3e52940 \\ @@ -65,6 +81,10 @@ 6d2a4e347994 & 4 & 12 & 62f5aed459ad & 3e52940 \\ 10f375729230 & 4 & 12 & 09f45110089f & 3e52940 \\ c81e3990d838 & 4 & 12 & d1b7f91b9ab0 & 3e52940 \\ + 682227a040a9 & 4 & 12 & 4cc2e23034d3 & 9367d60 \\ + e7b91099dfc5 & 4 & 12 & d5fa1e138f15 & 9367d60 \\ + 61d34e2aec3a & 4 & 12 & e76acc6a32ac & 9367d60 \\ + a405cf7b867a & 4 & 12 & 23346bbdcf92 & 9367d60 \\ 8d6b3f5b24a2 & 0 & 16 & 3c20300a5e78 & 758d5f2 \\ b5b8cfd55dee & 1 & 16 & 22b25365d745 & 758d5f2 \\ 1a2ccd4ef091 & 2 & 16 & b6cea5e21b36 & 758d5f2 \\ diff --git a/paper/tables/T5_richer_geometry.tex b/paper/tables/T5_richer_geometry.tex new file mode 100644 index 0000000..e1554e5 --- /dev/null +++ b/paper/tables/T5_richer_geometry.tex @@ -0,0 +1,21 @@ +% auto-generated by analysis/make_tables.py — do not edit. run_ids=['aefb55fc7987', '8cfc5a222d83', '8ab8e76e3b44', '7d6cba43dccf', '10f375729230', 'afd5e1407276', 'eb3b1db3b4e5', '7c7410c6f66f', 'd117be743bac', '682227a040a9', '70741801c041', 'b0d0d7f2f513', '45d9d36ce766', 'f301fe7ebb4d', '39101f30bcb7', 'ec2457a78f8d', '34bc023358b8', '28ac1614501c', '39b5492337a7', '61d34e2aec3a', '7802e0276803', 'd80ff5b236eb', '198178fd3f60', '8867921abd20', 'c81e3990d838', 'c468dfa50c2c', '69bcb88d896d', 'affc597f3e34', '37776ebe2a5b', 'e7b91099dfc5', '2a91bbeda6e4', 'a4a1a13b8234', 'a009b8f5ff16', 'c85bba3fffca', '6d2a4e347994', 'abb4dcb4ea84', '6c7eb2cdb623', '9fdd2103f70b', 'cb3102b09662', 'a405cf7b867a'] +\begin{table}[t] + \centering + \caption{Richer geometry (mean$\pm$std over seeds): the base 14-feature vector vs the base plus the full Hessian spectrum and mode-connectivity barriers. If ``richer`` does not raise $\Delta$AURC vs softmax or the conditional ``geom adds`` on the graph cell, the graph redundancy is a task property, not an artifact of thin curvature features.} + \label{tab:richer} + \resizebox{\ifdim\width>\linewidth\linewidth\else\width\fi}{!}{% + \begin{tabular}{lllcccc} + \toprule + task & reasoner & features & AURC geom & $\Delta$AURC vs softmax & $\Delta$AURC geom \emph{adds} & seeds add \\ + \midrule + arithmetic & basin\_center & base & $0.059 \pm 0.018$ & $-0.011 \pm 0.006$ & $0.001 \pm 0.002$ & 0/5 \\ + arithmetic & basin\_center & richer & $0.061 \pm 0.020$ & $-0.012 \pm 0.007$ & $-0.002 \pm 0.005$ & 1/5 \\ + arithmetic & ired & base & $0.028 \pm 0.028$ & $0.007 \pm 0.005$ & $0.007 \pm 0.006$ & 4/5 \\ + arithmetic & ired & richer & $0.028 \pm 0.029$ & $0.008 \pm 0.006$ & $0.007 \pm 0.006$ & 4/5 \\ + graph\_planning & basin\_center & base & $0.159 \pm 0.012$ & $-0.034 \pm 0.008$ & $-0.006 \pm 0.004$ & 0/5 \\ + graph\_planning & basin\_center & richer & $0.162 \pm 0.014$ & $-0.037 \pm 0.011$ & $-0.009 \pm 0.006$ & 0/5 \\ + graph\_planning & ired & base & $0.056 \pm 0.005$ & $0.001 \pm 0.003$ & $0.001 \pm 0.003$ & 0/5 \\ + graph\_planning & ired & richer & $0.055 \pm 0.005$ & $0.002 \pm 0.002$ & $-0.002 \pm 0.008$ & 0/5 \\ + \bottomrule + \end{tabular}} +\end{table} diff --git a/pyproject.toml b/pyproject.toml index cfe2441..f12bb4c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "energy-descent-certificates" -version = "0.0.3" +version = "0.0.4" description = "Distribution-free selective prediction and adaptive halting from the geometry of an energy-based reasoner's inference-time descent." readme = "README.md" requires-python = ">=3.12,<3.13" diff --git a/results/ledger.jsonl b/results/ledger.jsonl index ea68b21..15ff1ff 100644 --- a/results/ledger.jsonl +++ b/results/ledger.jsonl @@ -92,3 +92,23 @@ {"run_id": "a009b8f5ff16", "timestamp": "2026-07-28T13:17:38.719310+00:00", "git_sha": "3e52940", "config_hash": "6098ae479456", "config": {"run": {"seed": 2, "task": "graph_planning", "notes": "stronger IRED learned-landscape reasoner on graph"}, "model": {"latent_dim": 48, "hidden_dim": 256, "context_dim": 64}, "inference": {"k_restarts": 12, "steps": 50, "step_size": 0.1, "temperature": 0.01, "init_scale": 1.0, "grad_tol": 0.001, "sampler": "annealed", "anneal_levels": 20, "anneal_steps_per_level": 10, "anneal_step_max": 0.5, "anneal_step_min": 0.003}, "train": {"epochs": 110, "batch_size": 128, "lr": 0.001, "n_train": 12000, "n_neg": 4, "neg_noise": 0.5, "objective": "ired", "ired_noise_min": 0.1, "ired_noise_max": 1.5, "ired_ridge": 0.2, "ired_decode_weight": 4.0, "ired_stat_weight": 8.0}, "eval": {"n_eval": 700}, "conformal": {"alpha": 0.1, "delta": 0.05, "n_calib": 800}, "task": {"graph_planning": {"n_nodes": 7, "ood_n_nodes": 10, "edge_prob": 0.4, "max_len": 4}}}, "task": "graph_planning", "split": "selective", "seed": 2, "metrics": {"n_fit": 700, "n_calib": 800, 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"drop_energy": 0.049739566895181775, "energy_only": 0.13920001795584305, "drop_curv": 0.05021424448265369, "curv_only": 0.17253571329725742, "drop_dynamics": 0.09321471121667474, "dynamics_only": 0.05282089227280329, "drop_spectrum": 0.0499999909178207, "spectrum_only": 0.17142857142857193, "drop_connect": 0.049927555157745766, "connect_only": 0.16551065695700648}, "ece_geometry": 0.040849909596787144}, "env": {"python": "3.12.13", "platform": "macOS-26.5.2-arm64-arm-64bit", "jax": "0.11.0", "jax_backend": "cpu"}, "artifact_paths": []} diff --git a/src/edc/config.py b/src/edc/config.py index 6bb311a..e40405c 100644 --- a/src/edc/config.py +++ b/src/edc/config.py @@ -77,6 +77,9 @@ class TrainCfg: @dataclass(frozen=True) class EvalCfg: n_eval: int = 1000 + # Phase 4k: opt-in richer geometry (full Hessian spectrum + mode-connectivity barriers) + # appended after the base 14 features. Default False keeps the canonical 14-feature vector. + richer_geometry: bool = False @dataclass(frozen=True) diff --git a/src/edc/eval/evaluate.py b/src/edc/eval/evaluate.py index 7edda4c..11181de 100644 --- a/src/edc/eval/evaluate.py +++ b/src/edc/eval/evaluate.py @@ -40,7 +40,8 @@ def _fold(fns, params, cfg, task, split: str, data_stream: int, key) -> dict: k_solve, k_geom = jax.random.split(key) traj = restarts.solve(fns, params, jnp.asarray(batch.x), cfg, k_solve) - feats, names = geometry_features(traj, fns, params, k_geom, grad_tol=cfg.inference.grad_tol) + feats, names = geometry_features(traj, fns, params, k_geom, grad_tol=cfg.inference.grad_tol, + richer=cfg.eval.richer_geometry) pred, _ = restarts.best_of_n_energy(traj) correct = np.asarray(task.evaluate(np.asarray(pred), batch.y)).astype(bool) @@ -94,7 +95,7 @@ def _feature_diagnostics(features, names, correct, n_bins: int = 20) -> dict: return {"names": list(names), "auroc": auroc, "hist": hist} -_FEATURE_GROUPS = ("basin", "energy", "curv", "dynamics") +_FEATURE_GROUPS = ("basin", "energy", "curv", "dynamics", "spectrum", "connect") def _feature_ablation(fit_feats, fit_correct, test_feats, test_correct, names) -> dict: @@ -105,7 +106,9 @@ def _feature_ablation(fit_feats, fit_correct, test_feats, test_correct, names) - """ fit_feats = np.asarray(fit_feats, dtype=float) test_feats = np.asarray(test_feats, dtype=float) - groups = {g: [j for j, n in enumerate(names) if n.split("/")[0] == g] for g in _FEATURE_GROUPS} + # Only groups actually present in this feature set (base rows lack spectrum/connect columns). + groups = {g: cols for g in _FEATURE_GROUPS + if (cols := [j for j, n in enumerate(names) if n.split("/")[0] == g])} def aurc_on(cols: list[int]) -> float: if not cols: @@ -240,6 +243,7 @@ def scores_for(fold: dict) -> dict: "k_restarts": int(cfg.inference.k_restarts), "objective": getattr(cfg.train, "objective", "basin_center"), "sampler": getattr(cfg.inference, "sampler", "langevin"), + "feature_set": "richer" if cfg.eval.richer_geometry else "base", "accuracy_id": test["accuracy"], "base_error": float((~correct).mean()), "final_train_loss": float(history["epochs"][-1]["loss"]), diff --git a/src/edc/geometry/connectivity.py b/src/edc/geometry/connectivity.py new file mode 100644 index 0000000..d3e69c5 --- /dev/null +++ b/src/edc/geometry/connectivity.py @@ -0,0 +1,71 @@ +"""Mode-connectivity features between restart basins. [Phase 4k, richer geometry] + +Phase 2's ``basin/dispersion`` measures only the *Euclidean* distance between the K restart +endpoints — it cannot tell whether two endpoints sit in genuinely separate basins (a high energy +barrier between them) or in the same basin reached from different directions (no barrier). This +module walks the straight-line path between endpoints and reads the **energy barrier** off the +actual landscape: + + barrier(i, j) = max_t E(z(t)) - max(E_i, E_j), z(t) = (1-t) z*_i + t z*_j. + +To keep cost ~K energy paths per input (not K^2) we measure barriers from the **best** (min-energy) +restart to every other restart — the "how separated is the answer we would report from the +alternatives" question. + +Plain NumPy assembler (invariant 1): the only JAX is behind ``curvature.batched_path_energy``. +""" + +from __future__ import annotations + +import numpy as np + +from edc.geometry import curvature +from edc.inference.trajectory import TrajectoryRecord + +_N_POINTS = 8 +_CONNECTED_EPS = 1e-3 + + +def connectivity_features(traj: TrajectoryRecord, fns, params) -> tuple[np.ndarray, list[str]]: + """``(B, 3)`` mode-connectivity features plus names. + + * ``connect/barrier_mean`` — mean energy barrier from the best restart to the others. + * ``connect/barrier_max`` — largest such barrier (most separated alternative basin). + * ``connect/connected_frac`` — fraction of the other restarts reachable with barrier + ``< eps`` (i.e. in the same basin as the reported answer). + + With ``K == 1`` there are no pairs; all three features are 0. + """ + z = np.asarray(traj.z_star, dtype=float) # (B, K, d) + B, K, d = z.shape + names = ["connect/barrier_mean", "connect/barrier_max", "connect/connected_frac"] + if K < 2: + return np.zeros((B, 3)), names + + h_x = np.asarray(traj.h_x, dtype=float) # (B, dc) + best = np.asarray(traj.terminal_energy, dtype=float).argmin(axis=1) # (B,) + + # Build B*(K-1) endpoint pairs: (best restart) -> (each other restart). + rows = np.arange(B) + z_best = z[rows, best] # (B, d) + others = np.stack([[k for k in range(K) if k != b_best] for b_best in best]) # (B, K-1) + z_other = z[rows[:, None], others] # (B, K-1, d) + + n_pairs = B * (K - 1) + z_a = np.repeat(z_best, K - 1, axis=0) # (n_pairs, d) + z_b = z_other.reshape(n_pairs, d) # (n_pairs, d) + contexts = np.repeat(h_x, K - 1, axis=0) # (n_pairs, dc) + + path = np.asarray( + curvature.batched_path_energy(fns.energy, params, contexts, z_a, z_b, n_points=_N_POINTS), + dtype=float, + ) # (n_pairs, M) + endpoints_max = np.maximum(path[:, 0], path[:, -1]) + barrier = (path.max(axis=1) - endpoints_max).reshape(B, K - 1) # (B, K-1) + barrier = np.maximum(barrier, 0.0) # a barrier is non-negative by defn + + feats = np.stack( + [barrier.mean(axis=1), barrier.max(axis=1), (barrier < _CONNECTED_EPS).mean(axis=1)], + axis=1, + ) + return feats, names diff --git a/src/edc/geometry/curvature.py b/src/edc/geometry/curvature.py index 4a3432d..1269c90 100644 --- a/src/edc/geometry/curvature.py +++ b/src/edc/geometry/curvature.py @@ -79,3 +79,36 @@ def one(h_row, z_row, k): hutchinson_trace(f, z_row, k_tr, n_probes=n_probes)) return jax.vmap(one)(contexts, zs, keys) + + +def batched_spectrum(energy, params, contexts, zs): + """Per-particle **full** Hessian spectrum for a flat batch of ``N`` particles (Phase 4k). + + The latent Hessian is ``d x d`` with ``d`` small, so we materialise it exactly with + ``jax.hessian`` and take its eigenvalues rather than iterate — the richer-geometry + counterpart to ``batched_curvature``'s power-iteration ``lambda_max``. Same ``B*K`` particle + layout (``contexts`` ``(N, dc)``, ``zs`` ``(N, d)``). Returns eigenvalues ``(N, d)`` ascending. + This stays in the JAX core (invariant 1); the plain-NumPy assembler summarises it. + """ + def one(h_row, z_row): + f = energy_at(energy, params, h_row) + return jnp.linalg.eigvalsh(jax.hessian(f)(z_row)) # (d,) ascending + + return jax.vmap(one)(contexts, zs) + + +def batched_path_energy(energy, params, contexts, z_a, z_b, n_points: int = 8): + """Energy along the straight-line path ``z(t) = (1-t) z_a + t z_b`` for ``N`` particle pairs. + + Used by the mode-connectivity features (Phase 4k) to measure the energy barrier between two + restart endpoints. ``contexts`` ``(N, dc)`` is the shared per-input context tiled to match the + ``N`` pairs; ``z_a``/``z_b`` are ``(N, d)``. Returns ``(N, n_points)`` energies including both + endpoints (``t=0`` and ``t=1``). JAX core (invariant 1); the assembler derives the barrier. + """ + ts = jnp.linspace(0.0, 1.0, n_points) + + def one(h_row, za, zb): + f = energy_at(energy, params, h_row) + return jax.vmap(lambda t: f((1.0 - t) * za + t * zb))(ts) # (n_points,) + + return jax.vmap(one)(contexts, z_a, z_b) diff --git a/src/edc/geometry/features.py b/src/edc/geometry/features.py index 85e7812..a77c7b0 100644 --- a/src/edc/geometry/features.py +++ b/src/edc/geometry/features.py @@ -14,8 +14,10 @@ from edc.geometry import curvature from edc.geometry.basin import basin_features +from edc.geometry.connectivity import connectivity_features from edc.geometry.dynamics import dynamics_features from edc.geometry.energy_stats import energy_features +from edc.geometry.spectrum import spectrum_features from edc.inference.trajectory import TrajectoryRecord @@ -45,19 +47,28 @@ def _curvature_features(traj, fns, params, key) -> tuple[np.ndarray, list[str]]: def geometry_features( - traj: TrajectoryRecord, fns, params, key, grad_tol: float = 1e-3 + traj: TrajectoryRecord, fns, params, key, grad_tol: float = 1e-3, richer: bool = False ) -> tuple[np.ndarray, list[str]]: """Return ``((B, n_features) array, feature-name list)``. - Concatenates the four feature groups (basin, energy stats, curvature, dynamics) in a - fixed order. ``key`` seeds the curvature HVP estimators; ``grad_tol`` sets the + Concatenates the four base feature groups (basin, energy stats, curvature, dynamics) in a + fixed order — 14 features. ``key`` seeds the curvature HVP estimators; ``grad_tol`` sets the convergence threshold for the dynamics ``steps`` feature (pass ``cfg.inference.grad_tol``). + + ``richer=True`` (Phase 4k, opt-in) appends two richer groups **after** the base 14 — the full + Hessian spectrum (``spectrum/*``) and mode-connectivity barriers (``connect/*``) — so the base + column order/indices are unchanged and every prior 14-feature result is reproduced exactly. """ b_feats, b_names = basin_features(traj) e_feats, e_names = energy_features(traj) c_feats, c_names = _curvature_features(traj, fns, params, key) d_feats, d_names = dynamics_features(traj, grad_tol) - feats = np.concatenate([b_feats, e_feats, c_feats, d_feats], axis=1) - names = [*b_names, *e_names, *c_names, *d_names] + groups = [(b_feats, b_names), (e_feats, e_names), (c_feats, c_names), (d_feats, d_names)] + if richer: + groups.append(spectrum_features(traj, fns, params)) + groups.append(connectivity_features(traj, fns, params)) + + feats = np.concatenate([g for g, _ in groups], axis=1) + names = [n for _, ns in groups for n in ns] return feats, names diff --git a/src/edc/geometry/spectrum.py b/src/edc/geometry/spectrum.py new file mode 100644 index 0000000..1f3bbb5 --- /dev/null +++ b/src/edc/geometry/spectrum.py @@ -0,0 +1,76 @@ +"""Full-Hessian-spectrum features at the inference optimum. [Phase 4k, richer geometry] + +Phase 2's curvature group summarises the latent Hessian by only ``lambda_max`` (power iteration) +and ``tr(H)`` (Hutchinson). That is a thin description of the basin: it says nothing about the +*smallest* eigenvalue (is ``z*`` a genuine minimum or a saddle?), the basin's *anisotropy*, or a +fuller volume proxy than the trace. This module takes the **exact** eigenvalue spectrum of the +``d x d`` latent Hessian (``d`` is small) and derives four richer descriptors, each summarised +mean-over-restarts and at-the-best (min-energy) restart to match the ``_mean``/``_best`` convention. + +Plain NumPy assembler (invariant 1): the only JAX is behind ``curvature.batched_spectrum``, whose +eigenvalues are immediately pulled back to NumPy here. +""" + +from __future__ import annotations + +import numpy as np + +from edc.geometry import curvature +from edc.inference.trajectory import TrajectoryRecord + +_EPS = 1e-8 + + +def _mean_and_best(per_particle: np.ndarray, best: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """Reduce a ``(B, K)`` per-restart quantity to (mean over K, value at the best restart).""" + rows = np.arange(per_particle.shape[0]) + return per_particle.mean(axis=1), per_particle[rows, best] + + +def spectrum_features(traj: TrajectoryRecord, fns, params) -> tuple[np.ndarray, list[str]]: + """``(B, 8)`` full-spectrum features plus names. + + Per particle, from the ascending eigenvalues ``lambda`` of ``H = d^2/dz^2 E(h_x, z*)``: + + * ``spectrum/lmin_*`` — smallest eigenvalue (negative => ``z*`` is a saddle, not a minimum). + * ``spectrum/negfrac_*`` — fraction of eigenvalues ``< -eps`` (how non-minimal the basin is). + * ``spectrum/effrank_*`` — participation ratio ``(sum lambda_+)^2 / sum lambda_+^2`` over the + positive eigenvalues (basin isotropy/anisotropy — trace alone cannot express this). + * ``spectrum/logdet_*`` — ``sum log(lambda_+ + eps)`` over positive eigenvalues (a fuller + basin-volume proxy than ``tr(H)``). + + Each is reported mean-over-restarts and at the min-energy restart (``*_mean``/``*_best``). + """ + z = np.asarray(traj.z_star, dtype=float) # (B, K, d) + B, K, d = z.shape + h_x = np.asarray(traj.h_x, dtype=float) # (B, dc) + + # Flatten to N = B*K particles exactly as inference.restarts lays them out. + contexts = np.repeat(h_x, K, axis=0) # (N, dc) + zs = z.reshape(B * K, d) # (N, d) + + evals = np.asarray(curvature.batched_spectrum(fns.energy, params, contexts, zs), dtype=float) + evals = evals.reshape(B, K, d) # (B, K, d) ascending + + lmin = evals[..., 0] # (B, K) + negfrac = (evals < -_EPS).mean(axis=-1) # (B, K) + + posmask = evals > 0.0 + pos = np.where(posmask, evals, 0.0) # (B, K, d) + s1 = pos.sum(axis=-1) + s2 = (pos**2).sum(axis=-1) + effrank = s1**2 / (s2 + _EPS) # (B, K) + # log only over positive eigenvalues; feed a safe (>0) arg so np.where never logs a negative + safe = np.where(posmask, evals, 1.0) + logdet = np.where(posmask, np.log(safe + _EPS), 0.0).sum(axis=-1) + + best = np.asarray(traj.terminal_energy, dtype=float).argmin(axis=1) # (B,) + + cols, names = [], [] + specs = ((lmin, "lmin"), (negfrac, "negfrac"), (effrank, "effrank"), (logdet, "logdet")) + for arr, base in specs: + mean_v, best_v = _mean_and_best(arr, best) + cols += [mean_v, best_v] + names += [f"spectrum/{base}_mean", f"spectrum/{base}_best"] + + return np.stack(cols, axis=1), names diff --git a/tests/test_aggregate.py b/tests/test_aggregate.py index c3f9348..8f3e6c7 100644 --- a/tests/test_aggregate.py +++ b/tests/test_aggregate.py @@ -7,7 +7,7 @@ def _row(k, seed, delta, lo, geo=0.08, best=0.14, task="arithmetic", baseline_delta=None, - geom_adds=None): + geom_adds=None, feature_set=None): m = { "k_restarts": k, "accuracy_id": 0.8, "base_error": 0.2, "best_energy_baseline": "energy_min", @@ -26,9 +26,13 @@ def _row(k, seed, delta, lo, geo=0.08, best=0.14, task="arithmetic", baseline_de if geom_adds is not None: # Phase 4j field ga, glo = geom_adds m["delta_aurc_geom_adds"] = [ga, glo, ga + 0.02] + fs_tag = "" + if feature_set is not None: # Phase 4k field (older rows omit it) + m["feature_set"] = feature_set + fs_tag = f"_{feature_set}" return { - "run_id": f"{task[:3]}{k}_{seed}", "seed": seed, "split": "selective", "task": task, - "config_hash": f"{task}h{k}_{seed}", "git_sha": "abc1234", + "run_id": f"{task[:3]}{k}_{seed}{fs_tag}", "seed": seed, "split": "selective", "task": task, + "config_hash": f"{task}h{k}_{seed}{fs_tag}", "git_sha": "abc1234", "env": {"python": "3.12", "jax": "0.11", "jax_backend": "cpu"}, "config": {"inference": {"k_restarts": k}}, "metrics": m, @@ -88,6 +92,30 @@ def test_geom_adds_complementarity_aggregation(): assert aggregate.aggregate_cell(tie)["seeds_ci_excludes_0_geom_adds"] == 0 +def test_feature_set_filter_and_fallback(): + # Phase 4k: base and richer rows for the same task/K/seed must not collide, and headline_cell + # must select the requested feature set. + base = [_row(12, s, 0.09, 0.07, baseline_delta=(0.02, 0.00), geom_adds=(0.001, -0.004), + feature_set="base") for s in range(5)] + richer = [_row(12, s, 0.10, 0.08, baseline_delta=(0.05, 0.03), geom_adds=(0.03, 0.01), + feature_set="richer") for s in range(5)] + rows = base + richer + + assert aggregate.feature_set_of(base[0]) == "base" + assert aggregate.feature_set_of(richer[0]) == "richer" + # a pre-4k row (no field, no config flag) reads as base + assert aggregate.feature_set_of(_row(12, 0, 0.09, 0.07)) == "base" + + assert len(aggregate.selective_rows(rows, feature_set="richer")) == 5 + assert len(aggregate.selective_rows(rows, feature_set="base")) == 5 + + hb = aggregate.headline_cell(rows, objective="basin_center", feature_set="base") + hr = aggregate.headline_cell(rows, objective="basin_center", feature_set="richer") + assert hb["seeds_ci_excludes_0_geom_adds"] == 0 # base: geometry adds nothing + assert hr["seeds_ci_excludes_0_geom_adds"] == 5 # richer: geometry adds on every seed + assert hr["delta_aurc_geom_adds"][0] > hb["delta_aurc_geom_adds"][0] + + def test_feature_ablation_aggregates(): rows = [_row(12, s, 0.09, 0.07, geo=0.08, baseline_delta=(0.05, 0.03)) for s in range(4)] fa = aggregate.aggregate_cell(rows)["feature_ablation"] diff --git a/tests/test_connectivity.py b/tests/test_connectivity.py new file mode 100644 index 0000000..e3a9c74 --- /dev/null +++ b/tests/test_connectivity.py @@ -0,0 +1,70 @@ +"""Phase-4k mode-connectivity features: energy barriers along the path between restart endpoints. + +Offline + CPU-only. A convex bowl has no barrier between any two points; a double well +``E(z)=(z²-1)²`` has a barrier of height 1 between its two minima at ``±1``. +""" + +import types + +import jax.numpy as jnp +import numpy as np + +from edc.geometry.connectivity import connectivity_features +from edc.inference.trajectory import TrajectoryRecord + +_CONNECT_NAMES = ["connect/barrier_mean", "connect/barrier_max", "connect/connected_frac"] + + +def _traj(z_star, terminal_energy): + z = np.asarray(z_star, dtype=float) + B, K, _ = z.shape + te = np.asarray(terminal_energy, dtype=float) # (B, K) + energies = te[:, :, None] # (B, K, 1): terminal_energy = [...,-1] + return TrajectoryRecord( + z_star=z, energies=energies, grad_norms=np.zeros((B, K, 1)), + logits=np.zeros((B, K, 1)), pred=np.zeros((B, K), int), h_x=np.zeros((B, 1)), + ) + + +def _bowl(): + def energy(_params, _h, z): # convex: E = ½|z|² + return 0.5 * jnp.sum(z**2, axis=-1) + return types.SimpleNamespace(energy=energy) + + +def _double_well(): + def energy(_params, _h, z): # (z²-1)² 1-D: wells at ±1, hump 1 at 0 + return (z[:, 0] ** 2 - 1.0) ** 2 + return types.SimpleNamespace(energy=energy) + + +def test_names_and_k1_returns_zeros(): + traj = _traj(np.zeros((2, 1, 1)), np.zeros((2, 1))) + feats, names = connectivity_features(traj, _bowl(), {}) + assert names == _CONNECT_NAMES + assert feats.shape == (2, 3) + assert np.allclose(feats, 0.0) # K<2 -> no pairs + + +def test_identical_endpoints_zero_barrier(): + z = np.full((1, 2, 1), 0.5) # both restarts at the same point + feats, _ = connectivity_features(_traj(z, np.zeros((1, 2))), _bowl(), {}) + assert np.allclose(feats[0, :2], 0.0) # barrier_mean, barrier_max + assert np.isclose(feats[0, 2], 1.0) # connected_frac: same basin + + +def test_convex_landscape_no_barrier(): + z = np.array([[[0.0], [2.0]]]) # two distinct points, convex bowl + feats, _ = connectivity_features(_traj(z, np.array([[0.0, 2.0]])), _bowl(), {}) + assert np.allclose(feats[0, :2], 0.0) # a line in a convex bowl never climbs + assert np.isclose(feats[0, 2], 1.0) + + +def test_double_well_positive_barrier(): + z = np.array([[[-1.0], [1.0]]]) # the two wells + feats, names = connectivity_features(_traj(z, np.array([[0.0, 0.0]])), _double_well(), {}) + barrier_mean = feats[0, names.index("connect/barrier_mean")] + connected = feats[0, names.index("connect/connected_frac")] + # hump at z=0 has E=1, endpoints E=0; the 8-point grid samples just off the peak (~0.96 <= 1). + assert 0.9 < barrier_mean <= 1.0 + 1e-6 + assert np.isclose(connected, 0.0) # separated basins diff --git a/tests/test_evaluate_include_ood.py b/tests/test_evaluate_include_ood.py index 9a5a51b..a29d171 100644 --- a/tests/test_evaluate_include_ood.py +++ b/tests/test_evaluate_include_ood.py @@ -41,3 +41,28 @@ def test_include_ood_flag_toggles_ood_metrics(): da, loa, hia = m["delta_aurc_geom_adds"] assert loa <= da <= hia assert isinstance(m["geometry_adds_over_softmax"], bool) + + # base run carries the Phase-4k feature-set tag and no richer feature columns + assert m["feature_set"] == "base" + assert not any(n.startswith(("spectrum/", "connect/")) for n in m["feature_names"]) + + +def test_richer_geometry_appends_groups_and_tags_feature_set(): + from dataclasses import replace + + cfg = load_config("configs/smoke.toml") + cfg = replace(cfg, eval=replace(cfg.eval, richer_geometry=True)) + task = build_task(cfg.run.task) + m = evaluate(cfg, task, include_ood=False) + + # Phase 4k: the richer groups flow into the feature names, AURC diagnostics, and ablation. + assert m["feature_set"] == "richer" + names = m["feature_names"] + assert any(n.startswith("spectrum/") for n in names) + assert any(n.startswith("connect/") for n in names) + fa = m["feature_ablation"] + assert {"drop_spectrum", "drop_connect", "spectrum_only", "connect_only"} <= set(fa) + assert all(0.0 <= v <= 1.0 for v in fa.values()) + # the complementarity metric still computes on the richer feature set + da, loa, hia = m["delta_aurc_geom_adds"] + assert loa <= da <= hia diff --git a/tests/test_geometry_features.py b/tests/test_geometry_features.py index 63f25c3..d4dfafa 100644 --- a/tests/test_geometry_features.py +++ b/tests/test_geometry_features.py @@ -213,3 +213,25 @@ def test_geometry_features_deterministic(): f1, _ = geometry_features(traj, fns, params, root_key(2), grad_tol=cfg.inference.grad_tol) f2, _ = geometry_features(traj, fns, params, root_key(2), grad_tol=cfg.inference.grad_tol) assert np.array_equal(f1, f2) + + +# ------------------------------- Phase 4k: opt-in richer geometry ------------------------------ + +N_RICHER_FEATURES = N_EXPECTED_FEATURES + 8 + 3 # + spectrum 8 + connect 3 = 25 + + +def test_richer_appends_spectrum_and_connect_without_touching_base(): + traj, fns, params, cfg = _real_traj(n=8) + base, base_names = geometry_features(traj, fns, params, root_key(1), + grad_tol=cfg.inference.grad_tol) + rich, rich_names = geometry_features(traj, fns, params, root_key(1), + grad_tol=cfg.inference.grad_tol, richer=True) + # richer==False stays exactly 14; richer==True appends the two new groups after the base 14. + assert base.shape == (8, N_EXPECTED_FEATURES) + assert rich.shape == (8, N_RICHER_FEATURES) + assert rich_names[:N_EXPECTED_FEATURES] == base_names # base names/order unchanged + assert np.array_equal(rich[:, :N_EXPECTED_FEATURES], base) # base values byte-identical + assert len(set(rich_names)) == N_RICHER_FEATURES # unique + assert np.all(np.isfinite(rich)) + prefixes = {n.split("/")[0] for n in rich_names[N_EXPECTED_FEATURES:]} + assert prefixes == {"spectrum", "connect"} diff --git a/tests/test_spectrum.py b/tests/test_spectrum.py new file mode 100644 index 0000000..87f36f2 --- /dev/null +++ b/tests/test_spectrum.py @@ -0,0 +1,91 @@ +"""Phase-4k full-Hessian-spectrum features: exact eigenvalues on a known quadratic energy, and +the derived spectrum/* summaries against closed-form values. + +Offline + CPU-only (conftest forces JAX_PLATFORMS=cpu). The Hessian of ``E(z)=½ zᵀAz`` is the +constant matrix ``A`` for every ``z``, so ``eigvalsh`` must recover ``A``'s spectrum exactly. +""" + +import types + +import jax.numpy as jnp +import numpy as np + +from edc.geometry.curvature import batched_spectrum +from edc.geometry.spectrum import spectrum_features +from edc.inference.trajectory import TrajectoryRecord + +_SPECTRUM_NAMES = [ + "spectrum/lmin_mean", "spectrum/lmin_best", + "spectrum/negfrac_mean", "spectrum/negfrac_best", + "spectrum/effrank_mean", "spectrum/effrank_best", + "spectrum/logdet_mean", "spectrum/logdet_best", +] + + +def _spd(d, seed): + A = np.random.default_rng(seed).standard_normal((d, d)).astype(np.float64) + return A @ A.T + np.eye(d) # symmetric positive definite + + +def _quadratic_energy(A): + Aj = jnp.asarray(A) + + def energy(_params, _h, z): # (n, d) -> (n,) + return 0.5 * jnp.einsum("ni,ij,nj->n", z, Aj, z) + + return energy + + +def test_batched_spectrum_matches_eigvalsh(): + d, n = 5, 4 + A = _spd(d, 0) + zs = jnp.asarray(np.random.default_rng(1).standard_normal((n, d))) + contexts = jnp.zeros((n, 1)) + evals = np.asarray(batched_spectrum(_quadratic_energy(A), {}, contexts, zs)) + assert evals.shape == (n, d) + ref = np.linalg.eigvalsh(A) # ascending + for row in evals: # Hessian is constant A for every z + assert np.allclose(row, ref, atol=1e-6) + + +def test_spectrum_features_shape_names_and_values(): + d, B, K = 4, 2, 3 + A = _spd(d, 2) + z = np.random.default_rng(3).standard_normal((B, K, d)) + energies = np.zeros((B, K, 2)) # terminal energy 0 -> best = restart 0 + traj = TrajectoryRecord( + z_star=z, energies=energies, grad_norms=np.zeros((B, K, 1)), + logits=np.zeros((B, K, 1)), pred=np.zeros((B, K), int), h_x=np.zeros((B, 1)), + ) + fns = types.SimpleNamespace(energy=_quadratic_energy(A)) + feats, names = spectrum_features(traj, fns, {}) + assert names == _SPECTRUM_NAMES + assert feats.shape == (B, 8) + assert np.all(np.isfinite(feats)) + + eig = np.linalg.eigvalsh(A) # same for every particle (constant H) + lmin = eig[0] + effrank = eig.sum() ** 2 / (eig**2).sum() + logdet = np.sum(np.log(eig + 1e-8)) + # mean-over-K == best (spectrum identical across restarts); SPD => no negative eigenvalues. + for row in feats: + assert np.allclose(row[0:2], lmin, atol=1e-6) # lmin_mean, lmin_best + assert np.allclose(row[2:4], 0.0) # negfrac_mean, negfrac_best + assert np.allclose(row[4:6], effrank, atol=1e-5) # effrank + assert np.allclose(row[6:8], logdet, atol=1e-5) # logdet + + +def test_spectrum_detects_negative_eigenvalue_at_saddle(): + # Indefinite Hessian diag(1, -1): one negative eigenvalue => negfrac = 0.5, lmin < 0. + A = np.diag([1.0, -1.0]) + z = np.zeros((1, 1, 2)) + traj = TrajectoryRecord( + z_star=z, energies=np.zeros((1, 1, 2)), grad_norms=np.zeros((1, 1, 1)), + logits=np.zeros((1, 1, 1)), pred=np.zeros((1, 1), int), h_x=np.zeros((1, 1)), + ) + fns = types.SimpleNamespace(energy=_quadratic_energy(A)) + feats, names = spectrum_features(traj, fns, {}) + lmin_mean = feats[0, names.index("spectrum/lmin_mean")] + negfrac_mean = feats[0, names.index("spectrum/negfrac_mean")] + assert lmin_mean < 0.0 + assert np.isclose(negfrac_mean, 0.5)