From 5498dd7ca3233fa9dd516e4685c0c18e2ddcdaed Mon Sep 17 00:00:00 2001 From: ruv Date: Tue, 8 Sep 2026 23:12:09 -0400 Subject: [PATCH] feat(wiflow-dynamics): rung 3 heteroscedastic head -- EIG probing works once Sigma depends on z_t Verdict by the pre-registered conjunction: RUNG 3 DID NOT HELP. 2 of 3 criteria missed, and that verdict stands. But the criteria conflated two questions and rung 3 answers them oppositely: 1. beats rung-2 pose NLL FAIL (-102.83 vs -113.29) 2. coverage within +/-3pp all lvls FAIL (+7.46pp @50%, +3.20pp @80%) 3. EIG-greedy beats random >2 SE PASS decisively THE CENTRAL FINDING: the M4 negative at rung 2 was a property of the model class, not of information-gain probing. Once Sigma depends on z_t so the probe set can vary per sample, EIG-driven selection beats random at every budget (t = +3.11, +4.80, +5.04, +3.91 at K = 1, 3, 6, 9). Adaptivity is real: 19,653 distinct probe sets across 20,000 samples at K=9, where rung 2 had exactly 1 by construction. Point accuracy also jumps: PCK@20 0.9425 vs bar 0.7557 (+18.7 pts), against rung 2's ~0.8208 on the same pairs -- a nonlinear mean head is worth ~+12 pts. MPJPE 0.01073. Degeneracy guard passes (pred std 0.0208). Caveats recorded rather than hidden behind: - The NLL comparison is not like-for-like and that is this design's fault: rung 2 carries a full 30x30 pose covariance, rung 3 a diagonal one, and joint NLL strongly rewards cross-keypoint correlation. Still reported as a FAIL against the criterion as written; the fix is a low-rank+diagonal head, not abandoning heteroscedasticity. - Calibration got WORSE, not better (+7.46pp vs rung 2's +3.48pp at the 50% level), same direction: over-covered at the centre. Rung 3's uncertainty is less trustworthy than rung 2's and no calibrated- uncertainty claim may be made from it. - Rung 3's 0.9425 exceeding rung 2's 0.8568 target-peeking bound is not a contradiction: that bound held the linear-Gaussian predictor fixed and bounded probe policies within that model class only. Methodology change recorded: training length went from a fixed 12 epochs to early stopping on val NLL (patience 40, cap 400) after the first run was obviously undertrained -- 12 epochs took 16s with the loss still descending steeply. A convergence fix, not a change to any pre-registered criterion. Both runs kept; results/rung3_undertrained_12ep.json preserved. M1/M2 guards re-verified after all representation changes: 12/12 tests pass, validate still exits PASS on all 6 checks. Co-Authored-By: RuFlo Claude-Session: https://claude.ai/code/session_01JJnDvpYYk2rTvjkudQUoCk --- benchmarks/wiflow-dynamics/STATE.md | 84 +- .../wiflow-dynamics/harness/src/bin/rung3.rs | 437 ++++++++ .../wiflow-dynamics/harness/src/hetero.rs | 119 +++ benchmarks/wiflow-dynamics/harness/src/lib.rs | 1 + benchmarks/wiflow-dynamics/results/rung3.json | 978 ++++++++++++++++++ .../results/rung3_undertrained_12ep.json | 218 ++++ 6 files changed, 1836 insertions(+), 1 deletion(-) create mode 100644 benchmarks/wiflow-dynamics/harness/src/bin/rung3.rs create mode 100644 benchmarks/wiflow-dynamics/harness/src/hetero.rs create mode 100644 benchmarks/wiflow-dynamics/results/rung3.json create mode 100644 benchmarks/wiflow-dynamics/results/rung3_undertrained_12ep.json diff --git a/benchmarks/wiflow-dynamics/STATE.md b/benchmarks/wiflow-dynamics/STATE.md index ecfbe16d1..72a31687d 100644 --- a/benchmarks/wiflow-dynamics/STATE.md +++ b/benchmarks/wiflow-dynamics/STATE.md @@ -127,7 +127,8 @@ deviation. - [x] **M4 — Information-gain evaluation.** DONE 2026-09-08. EIG-greedy wins on information at every budget but **does NOT** convert that into pose accuracy — an honest negative, with an oracle bracket quantifying what adaptivity is worth. -- [ ] **M5 — Lineage/evidence recorded for every claim.** +- [x] **M5 — Lineage/evidence recorded for every claim.** STATE.md + `results/*.json` + + `fixtures/manifest.json` (sha256s). Every number states MEASURED or CITED. ## Open questions — ALL RESOLVED 2026-09-08 (MEASURED) @@ -264,6 +265,87 @@ optional; the charter is already satisfied by rungs 1–2. **75.4% mean-pose bar only**, with that non-comparability stated. - Nothing here is a causal intervention (see "Framing that must not drift"). +## Rung 3 — run log and one methodology change (recorded as it happened) + +**Run 1 (12 epochs, `results/rung3_undertrained_12ep.json`): self-reported +"RUNG 3 DID NOT HELP".** Criterion 1 failed (val NLL −81.24 vs rung-2 −113.29) and +criterion 2 failed (coverage); criterion 3 passed. **The run was undertrained, not +converged** — 12 epochs took 16 s and the loss was still descending steeply +(−68 → −81 over the final epochs). Two things in it are worth keeping regardless: +- PCK@20 **0.8357** vs bar 0.7557 (**+8.0 pts**) — already above rung 2's 0.8195, + from an undertrained model. +- **71 distinct probe sets** across the test pairs, modal set only 42% — + the probe set genuinely **varies per sample**. Rung 2 had exactly **1** distinct set + by construction. The capability rung 3 was built to add demonstrably exists. + +**Methodology change, stated plainly:** training length was raised from a fixed 12 +epochs to early stopping on val NLL (patience 40, cap 400). This is a *convergence* +fix for an obviously undertrained model, not a change to any pre-registered pass +criterion — the three criteria in the rung-3 pre-registration are untouched, and the +evaluation set, bar, and protocol are identical. Both runs are kept; the 12-epoch +result is preserved rather than discarded. + +### Run 2 (converged, early-stopped at epoch 163, best epoch 123) — `results/rung3.json` + +**Verdict by the pre-registered conjunction: "RUNG 3 DID NOT HELP" — 2 of 3 criteria +missed.** That is the headline verdict and it stands. But the criteria conflated two +questions, and rung 3 answers them oppositely: + +| Pre-registered criterion | Result | +|---|---| +| 1. beats rung-2 pose NLL | **FAIL** — −102.83 vs −113.29 | +| 2. coverage within ±3 pp at all four levels | **FAIL** — +7.46 pp @50%, +3.20 pp @80% (90/95 pass) | +| 3. EIG-greedy beats random by >2 SE | **PASS**, decisively — see below | + +**What rung 3 did do (MEASURED, same 20,000 test pairs as rung 2):** + +- **PCK@20 0.9425 vs bar 0.7557 → +18.7 pts.** Rung 2 on these same pairs was ~0.8208. + A nonlinear mean head is worth **~+12 pts** over the linear-Gaussian one. MPJPE + 0.01073 vs rung 2's ~0.0207. Degeneracy guard passes (pred std 0.0208). +- **EIG-driven probing now works, at every budget:** + + | K | greedy | random | fixed | greedy−random | SE | t | + |---|---|---|---|---|---|---| + | 1 | 0.9425 | 0.9417 | 0.9419 | +0.00078 | 0.00025 | **+3.11** | + | 3 | 0.9420 | 0.9404 | 0.9411 | +0.00166 | 0.00035 | **+4.80** | + | 6 | 0.9412 | 0.9390 | 0.9380 | +0.00221 | 0.00044 | **+5.04** | + | 9 | 0.9400 | 0.9383 | 0.9406 | +0.00176 | 0.00045 | **+3.91** | + +- **Adaptivity is real and large:** at K=9, **19,653 distinct probe sets across 20,000 + samples** (modal set ≈0%). Rung 2 had exactly **1**, by construction. + +**The central scientific finding of this work:** *the M4 negative at rung 2 was a +property of the model class, not of information-gain probing.* Once `Σ` depends on +`z_t`, so the probe set can vary per sample, EIG-driven selection beats random by +3–5 SE at every budget. Under a homoscedastic `Σ` it could not, because the +"EIG-optimal" set was frozen across all timesteps. + +**Two caveats I am not allowed to hide behind, and one clarification:** + +1. **The NLL comparison is not like-for-like, and that is my design's fault.** Rung 2 + carries a **full 30×30** pose covariance; my rung-3 head is **diagonal**. Joint + multivariate NLL strongly rewards modelling cross-keypoint correlation, which a + diagonal head structurally cannot. So −102.83 vs −113.29 does **not** show rung 3's + marginals are worse. It is still a **FAIL** against the criterion as written — I am + not rewriting the criterion — but the right next step is a low-rank + diagonal + covariance head, not abandoning heteroscedasticity. +2. **Calibration got worse, not better** (+7.46 pp at the 50% level vs rung 2's + +3.48 pp), in the same direction: over-covered at the centre, i.e. residuals more + peaked than Gaussian. A Student-t or low-rank predictive is the obvious fix. As it + stands, **rung 3's uncertainty is less trustworthy than rung 2's**, and no + calibrated-uncertainty claim may be made from it. +3. **Clarification, to pre-empt a false contradiction:** rung 3's 0.9425 exceeds rung + 2's target-peeking upper bound of 0.8568. That is not inconsistent. That bound + upper-bounded *probe-selection policies within the rung-2 model class*, holding the + linear-Gaussian predictor fixed. A better predictor moves the whole curve; the bound + was never a bound on all models. + +**Net:** the charter's M3/M4 primitive is demonstrated end-to-end — action-conditioned +prediction with per-sample adaptive, information-gain-driven probe selection that +measurably beats random and fixed at matched budget. The uncertainty is **not** +calibrated to the pre-registered standard, so the "calibrated uncertainty" half of M3 +is met only by rung 2 (3 of 4 levels), and by rung 3 not at all. + ## Reporting rules Every number states **MEASURED** (by this work, this run, command shown) or **CITED** diff --git a/benchmarks/wiflow-dynamics/harness/src/bin/rung3.rs b/benchmarks/wiflow-dynamics/harness/src/bin/rung3.rs new file mode 100644 index 000000000..9ef4e3b9f --- /dev/null +++ b/benchmarks/wiflow-dynamics/harness/src/bin/rung3.rs @@ -0,0 +1,437 @@ +//! Rung 3 driver: train the heteroscedastic head, then run it through the UNCHANGED +//! rung-2 evaluation (same 20,000 test pairs, same bar, same coverage protocol, same +//! policies and paired tests). +//! +//! Pass condition, pre-registered in `../STATE.md` before this file existed: +//! 1. beats rung-2 joint-Gaussian held-out pose NLL, AND +//! 2. coverage within +/-3 pp at ALL four levels, AND +//! 3. EIG-greedy beats random PCK@20 by more than 2 SE, paired, at some budget. +//! Miss any one -> "rung 3 did not help". + +use burn::backend::ndarray::{NdArray, NdArrayDevice}; +use burn::backend::Autodiff; +use burn::module::{AutodiffModule, Module}; +use burn::optim::{AdamConfig, GradientsParams, Optimizer}; +use burn::tensor::{Tensor, TensorData}; +use std::time::Instant; + +use wiflow_harness::bands::{BAND_DIM, NUM_PROBE_GROUPS}; +use wiflow_harness::gaussian::POSE_DIM; +use wiflow_harness::guards::{check_constant_pose, CorruptionMasks}; +use wiflow_harness::hetero::{expand_mask, hetero_nll, HeteroModel}; +use wiflow_harness::metrics::score; +use wiflow_harness::npy::Npy; + +type B = Autodiff>; +type BI = NdArray; + +const RESULTS: &str = + "/home/ruvultra/projects/ruview-worktrees/ruforecast-rust/benchmarks/wiflow-std/results"; +const COVERAGE_LEVELS: [(f64, f64); 4] = [ + (0.50, 0.6744897501960817), + (0.80, 1.2815515655446004), + (0.90, 1.6448536269514722), + (0.95, 1.959963984540054), +]; +/// CITED from results/m3_m4.json -- the rung-2 numbers rung 3 must beat. +const RUNG2_NLL: f64 = -113.29; +const RUNG2_BEST_PCK: f64 = 0.8208; + +struct Rng(u64); +impl Rng { + fn next(&mut self) -> u64 { + self.0 = self.0.wrapping_mul(6364136223846793005).wrapping_add(1442695040888963407); + self.0 + } + fn below(&mut self, n: usize) -> usize { + (self.next() >> 11) as usize % n + } +} + +fn subsample(items: &[usize], k: usize, seed: u64) -> Vec { + if items.len() <= k { + return items.to_vec(); + } + let mut rng = Rng(seed); + let mut pool = items.to_vec(); + for i in 0..k { + let j = i + rng.below(pool.len() - i); + pool.swap(i, j); + } + pool.truncate(k); + pool +} + +fn build_pairs(in_split: &[bool], masks: &CorruptionMasks, w2f: &[i64]) -> Vec { + (0..in_split.len() - 1) + .filter(|&t| in_split[t] && w2f[t] == w2f[t + 1] && !masks.is_corrupt(t) && !masks.is_corrupt(t + 1)) + .collect() +} + +fn random_groups(rng: &mut Rng, k: usize) -> Vec { + let mut pool: Vec = (0..NUM_PROBE_GROUPS).collect(); + for i in 0..k { + let j = i + rng.below(pool.len() - i); + pool.swap(i, j); + } + pool[..k].to_vec() +} + +/// Assemble one training/eval batch. +fn make_batch( + z: &[f32], + gt: &[f32], + pairs: &[usize], + groups_per: &[Vec], + device: &Bk::Device, +) -> (Tensor, Tensor, Tensor, Tensor) { + let n = pairs.len(); + let mut zt = Vec::with_capacity(n * BAND_DIM); + let mut mn = Vec::with_capacity(n * BAND_DIM); + let mut mk = Vec::with_capacity(n * BAND_DIM); + let mut yy = Vec::with_capacity(n * POSE_DIM); + for (i, &t) in pairs.iter().enumerate() { + let m = expand_mask(&groups_per[i]); + for d in 0..BAND_DIM { + zt.push(z[t * BAND_DIM + d]); + mn.push(m[d] * z[(t + 1) * BAND_DIM + d]); + mk.push(m[d]); + } + for d in 0..POSE_DIM { + yy.push(gt[(t + 1) * POSE_DIM + d]); + } + } + let t2 = |v: Vec, c: usize| { + Tensor::::from_data(TensorData::new(v, [n, c]).convert::(), device) + }; + (t2(zt, BAND_DIM), t2(mn, BAND_DIM), t2(mk, BAND_DIM), t2(yy, POSE_DIM)) +} + +fn main() { + let fx = concat!(env!("CARGO_MANIFEST_DIR"), "/../fixtures"); + let load = |f: &str| Npy::open(format!("{fx}/{f}")).unwrap_or_else(|e| panic!("{e}")); + let t0 = Instant::now(); + + let z_npy = load("band_features.npy"); + let z = z_npy.f32_slice().unwrap(); + let gt_npy = load("all_gt_clean.npy"); + let gt = gt_npy.f32_slice().unwrap(); + let w2f = load("window_to_file.npy").to_i64().unwrap(); + let n_windows = z_npy.shape[0]; + let masks = CorruptionMasks::load( + &format!("{RESULTS}/nan_windows_mask.npy"), + &format!("{RESULTS}/big_windows_mask.npy"), + ) + .unwrap(); + + let mut splits = Vec::new(); + for name in ["train_idx.npy", "val_idx.npy", "test_idx.npy"] { + let idx = load(name).to_i64().unwrap(); + let mut m = vec![false; n_windows]; + for &i in &idx { + m[i as usize] = true; + } + splits.push(m); + } + let train_pairs = subsample(&build_pairs(&splits[0], &masks, &w2f), 60_000, 42); + let val_pairs = subsample(&build_pairs(&splits[1], &masks, &w2f), 4_000, 7); + // IDENTICAL evaluation set to rung 2. + let test_pairs = subsample(&build_pairs(&splits[2], &masks, &w2f), 20_000, 999); + + let device = NdArrayDevice::default(); + let mut model: HeteroModel = HeteroModel::new(&device, 512, 256); + let mut opt = AdamConfig::new().init(); + let mut rng = Rng(2026); + + let epochs: usize = std::env::var("RUNG3_EPOCHS").ok().and_then(|v| v.parse().ok()).unwrap_or(400); + let batch = 512usize; + let lr = 1e-3; + let mut history = Vec::new(); + let mut best_val = f64::INFINITY; + let mut best_epoch = 0usize; + let mut best_model: Option> = None; + + for ep in 0..epochs { + // fresh shuffle each epoch + let order = subsample(&train_pairs, train_pairs.len(), 100 + ep as u64); + let mut running = 0.0f64; + let mut nb = 0usize; + for chunk in order.chunks(batch) { + // Random mask per sample per epoch: the model must learn p(. | z_t, S) + // for arbitrary S, not one fixed probing pattern. + let groups: Vec> = chunk + .iter() + .map(|_| { + let k = rng.below(NUM_PROBE_GROUPS / 2 + 1); + random_groups(&mut rng, k) + }) + .collect(); + let (zt, mn, mk, yy) = make_batch::(z, gt, chunk, &groups, &device); + let (mu, lv) = model.forward(zt, mn, mk); + let loss = hetero_nll(mu, lv, yy).mean(); + running += loss.clone().into_scalar() as f64; + nb += 1; + let grads = GradientsParams::from_grads(loss.backward(), &model); + model = opt.step(lr, model, grads); + } + let val = eval_nll(&model, z, gt, &val_pairs, &[], &device); + history.push(serde_json::json!({ "epoch": ep, "train_nll": running / nb as f64, "val_nll": val })); + if ep % 10 == 0 || ep + 1 == epochs { + eprintln!("epoch {ep}: train {:.3} val {:.3} ({:.0?})", running / nb as f64, val, t0.elapsed()); + } + // Early stopping on val NLL, patience 40 epochs. The first run stopped at a + // fixed 12 epochs while the loss was still descending steeply -- undertrained, + // not converged. + if val + 1e-6 < best_val { + best_val = val; + best_epoch = ep; + best_model = Some(model.clone()); + } else if ep - best_epoch >= 40 { + eprintln!("early stop at epoch {ep} (best {best_epoch}, val {best_val:.3})"); + break; + } + } + let model = best_model.unwrap_or(model); + + let inf = model.valid(); + + // ---- evaluation, reusing the rung-2 protocol unchanged ------------------- + let no_probe = evaluate(&inf, z, gt, &test_pairs, &vec![vec![]; test_pairs.len()], &device); + let bar = mean_pose_bar(gt, &test_pairs); + + let mut policies = Vec::new(); + for &k in &[1usize, 3, 6, 9] { + // EIG-greedy, now genuinely PER-SAMPLE because sigma depends on z_t. + let eig_sets = greedy_eig_per_sample(&inf, z, &test_pairs, k, &device); + let g = evaluate(&inf, z, gt, &test_pairs, &eig_sets, &device); + + let mut r = Rng(0xBEEF + k as u64); + let rnd_sets: Vec> = test_pairs.iter().map(|_| random_groups(&mut r, k)).collect(); + let rd = evaluate(&inf, z, gt, &test_pairs, &rnd_sets, &device); + + let fixed: Vec = (0..k).map(|i| i * NUM_PROBE_GROUPS / k).collect(); + let fx_e = evaluate(&inf, z, gt, &test_pairs, &vec![fixed.clone(); test_pairs.len()], &device); + + let (d, se, t) = paired(&g.per_sample, &rd.per_sample); + // How often does the per-sample EIG set actually differ from the modal one? + let mut counts = std::collections::HashMap::new(); + for s in &eig_sets { + *counts.entry(format!("{s:?}")).or_insert(0usize) += 1; + } + let modal = counts.values().copied().max().unwrap_or(0); + policies.push(serde_json::json!({ + "budget_k": k, + "pck@20": { "eig_greedy": g.pck20, "random": rd.pck20, "fixed": fx_e.pck20 }, + "pose_nll": { "eig_greedy": g.nll, "random": rd.nll, "fixed": fx_e.nll }, + "paired_greedy_minus_random": { "delta": d, "se": se, "t": t, "beats_by_2se": t > 2.0 }, + "adaptivity": { + "distinct_probe_sets": counts.len(), + "modal_set_fraction": modal as f64 / eig_sets.len() as f64, + "note": "rung 2 had exactly 1 distinct set by construction (homoscedastic)" + } + })); + } + + let cov_ok = no_probe + .coverage + .as_array() + .unwrap() + .iter() + .all(|c| c["error_pp"].as_f64().unwrap().abs() <= 3.0); + let nll_ok = no_probe.nll < RUNG2_NLL; + let eig_ok = policies + .iter() + .any(|p| p["paired_greedy_minus_random"]["beats_by_2se"].as_bool().unwrap_or(false)); + + let verdict = if nll_ok && cov_ok && eig_ok { + "PASS -- rung 3 helped on all three pre-registered criteria" + } else { + "RUNG 3 DID NOT HELP -- at least one pre-registered criterion was missed" + }; + + let report = serde_json::json!({ + "milestone": "Rung 3 -- neural heteroscedastic head. MEASURED by harness/src/bin/rung3.rs, this run.", + "backend": "burn-ndarray (CPU), by design -- see STATE.md rung-3 pre-registration", + "training": { "epochs": epochs, "batch": batch, "lr": lr, "train_pairs": train_pairs.len(), + "history": history }, + "no_probe": { "pose_nll": no_probe.nll, "pck@20": no_probe.pck20, + "mpjpe": no_probe.mpjpe, "coverage": no_probe.coverage }, + "honesty_bar_same_pairs": { "pck@20": bar.0, "mpjpe": bar.1 }, + "beats_bar": no_probe.pck20 > bar.0, + "degeneracy_guard": check_constant_pose(&no_probe.preds, test_pairs.len()), + "policies": policies, + "preregistered_criteria": { + "1_beats_rung2_nll": { "rung2": RUNG2_NLL, "rung3": no_probe.nll, "pass": nll_ok }, + "2_coverage_within_3pp_all_levels": cov_ok, + "3_eig_beats_random_by_2se": eig_ok, + "rung2_best_pck_for_reference": RUNG2_BEST_PCK + }, + "verdict": verdict + }); + println!("{}", serde_json::to_string_pretty(&report).unwrap()); + eprintln!("{verdict} ({:.0?})", t0.elapsed()); +} + +struct Ev { + nll: f64, + pck20: f64, + mpjpe: f64, + coverage: serde_json::Value, + per_sample: Vec, + preds: Vec, +} + +fn paired(a: &[f64], b: &[f64]) -> (f64, f64, f64) { + let n = a.len(); + let d: Vec = a.iter().zip(b).map(|(x, y)| x - y).collect(); + let m = d.iter().sum::() / n as f64; + let var = d.iter().map(|x| (x - m).powi(2)).sum::() / (n as f64 - 1.0); + let se = (var / n as f64).sqrt(); + (m, se, if se > 0.0 { m / se } else { 0.0 }) +} + +fn eval_nll( + model: &HeteroModel, + z: &[f32], + gt: &[f32], + pairs: &[usize], + groups: &[usize], + device: &NdArrayDevice, +) -> f64 { + let g = vec![groups.to_vec(); pairs.len()]; + let (zt, mn, mk, yy) = make_batch::(z, gt, pairs, &g, device); + let (mu, lv) = model.forward(zt, mn, mk); + hetero_nll(mu, lv, yy).mean().into_scalar() as f64 +} + +fn evaluate( + model: &HeteroModel, + z: &[f32], + gt: &[f32], + pairs: &[usize], + groups: &[Vec], + device: &NdArrayDevice, +) -> Ev { + let mut preds = Vec::with_capacity(pairs.len() * POSE_DIM); + let mut truths = Vec::with_capacity(pairs.len() * POSE_DIM); + let mut nll = 0.0f64; + let mut inside = [0usize; COVERAGE_LEVELS.len()]; + + for (chunk_i, chunk) in pairs.chunks(2048).enumerate() { + let gs: Vec> = (0..chunk.len()) + .map(|i| groups[chunk_i * 2048 + i].clone()) + .collect(); + let (zt, mn, mk, yy) = make_batch::(z, gt, chunk, &gs, device); + let (mu, lv) = model.forward(zt, mn, mk); + nll += hetero_nll(mu.clone(), lv.clone(), yy.clone()).sum().into_scalar() as f64; + let mud: Vec = mu.into_data().to_vec().unwrap(); + let lvd: Vec = lv.into_data().to_vec().unwrap(); + let yd: Vec = yy.into_data().to_vec().unwrap(); + for i in 0..chunk.len() { + for d in 0..POSE_DIM { + let j = i * POSE_DIM + d; + let sd = (lvd[j] as f64 * 0.5).exp(); + for (li, (_, zs)) in COVERAGE_LEVELS.iter().enumerate() { + if ((yd[j] - mud[j]) as f64).abs() <= zs * sd { + inside[li] += 1; + } + } + } + } + preds.extend_from_slice(&mud); + truths.extend_from_slice(&yd); + } + + let s = score(&preds, &truths, pairs.len()); + let per_sample: Vec = (0..pairs.len()) + .map(|i| { + let b = i * POSE_DIM; + score(&preds[b..b + POSE_DIM], &truths[b..b + POSE_DIM], 1).pck_at(0.2) + }) + .collect(); + let denom = (pairs.len() * POSE_DIM) as f64; + Ev { + nll: nll / pairs.len() as f64, + pck20: s.pck_at(0.2), + mpjpe: s.mpjpe, + per_sample, + preds, + coverage: serde_json::json!(COVERAGE_LEVELS + .iter() + .enumerate() + .map(|(i, (lvl, _))| serde_json::json!({ + "nominal": lvl, + "empirical": inside[i] as f64 / denom, + "error_pp": (inside[i] as f64 / denom - lvl) * 100.0 + })) + .collect::>()), + } +} + +/// Per-sample greedy EIG. Closed-form and peek-free: the variance head does not see +/// revealed values, so extending the mask gives the post-probe entropy directly. +fn greedy_eig_per_sample( + model: &HeteroModel, + z: &[f32], + pairs: &[usize], + k: usize, + device: &NdArrayDevice, +) -> Vec> { + let mut out = vec![Vec::new(); pairs.len()]; + for _step in 0..k { + // Score every candidate group for every sample in one batched pass per group. + let mut best: Vec<(f64, usize)> = vec![(f64::NEG_INFINITY, usize::MAX); pairs.len()]; + for cand in 0..NUM_PROBE_GROUPS { + let mut zt = Vec::with_capacity(pairs.len() * BAND_DIM); + let mut mk = Vec::with_capacity(pairs.len() * BAND_DIM); + for (i, &t) in pairs.iter().enumerate() { + let mut set = out[i].clone(); + if !set.contains(&cand) { + set.push(cand); + } + let m = expand_mask(&set); + for d in 0..BAND_DIM { + zt.push(z[t * BAND_DIM + d]); + mk.push(m[d]); + } + } + let n = pairs.len(); + let zt = Tensor::::from_data(TensorData::new(zt, [n, BAND_DIM]), device); + let mk = Tensor::::from_data(TensorData::new(mk, [n, BAND_DIM]), device); + let lv: Vec = model.log_var(zt, mk).into_data().to_vec().unwrap(); + for i in 0..n { + if out[i].contains(&cand) { + continue; + } + // Lower total log-variance == more information gained. + let s: f64 = -(0..POSE_DIM).map(|d| lv[i * POSE_DIM + d] as f64).sum::(); + if s > best[i].0 { + best[i] = (s, cand); + } + } + } + for i in 0..pairs.len() { + if best[i].1 != usize::MAX { + out[i].push(best[i].1); + } + } + } + out +} + +fn mean_pose_bar(gt: &[f32], pairs: &[usize]) -> (f64, f64) { + let n = pairs.len(); + let mut mu = vec![0.0f64; POSE_DIM]; + for &t in pairs { + for i in 0..POSE_DIM { + mu[i] += gt[(t + 1) * POSE_DIM + i] as f64; + } + } + let bar: Vec = mu.iter().map(|v| (v / n as f64) as f32).collect(); + let truths: Vec = pairs + .iter() + .flat_map(|&t| (0..POSE_DIM).map(move |i| gt[(t + 1) * POSE_DIM + i])) + .collect(); + let s = score(&bar.repeat(n), &truths, n); + (s.pck_at(0.2), s.mpjpe) +} diff --git a/benchmarks/wiflow-dynamics/harness/src/hetero.rs b/benchmarks/wiflow-dynamics/harness/src/hetero.rs new file mode 100644 index 000000000..b2f92f203 --- /dev/null +++ b/benchmarks/wiflow-dynamics/harness/src/hetero.rs @@ -0,0 +1,119 @@ +//! Rung 3: neural heteroscedastic action-conditioned dynamics head (Burn, CPU). +//! +//! See the rung-3 pre-registration in `../STATE.md`. The single question: +//! **does an input-dependent `Sigma(z_t, mask)` let EIG close any of the +//! target-peeking gap that rung 2 left open?** +//! +//! ## Why the variance head is blind to revealed values +//! +//! To score a *candidate* probe set we must know how much observing it would shrink +//! pose uncertainty -- before observing it. If `sigma` depended on the revealed +//! values, that would require sampling them (or peeking). By making the variance head +//! a function of `(z_t, mask)` only, EIG stays closed-form AND peek-free: +//! +//! ```text +//! EIG(S) = 0.5 * sum_i [ log sigma_i^2(z_t, M) - log sigma_i^2(z_t, M u S) ] +//! ``` +//! +//! `sigma` still depends on `z_t`, so the EIG-optimal set now **varies per sample** -- +//! precisely the capability rung 2 lacked. + +use burn::module::Module; +use burn::nn::{Linear, LinearConfig, Relu}; +use burn::tensor::backend::Backend; +use burn::tensor::Tensor; + +use crate::bands::{BAND_DIM, BANDS_PER_GROUP, NUM_PROBE_GROUPS}; +use crate::gaussian::POSE_DIM; + +/// Lower bound on predicted variance, in log space. Without a floor the NLL can be +/// driven to -inf by collapsing sigma on easy samples -- a Goodhart failure mode for +/// this loss, and one the coverage check would catch only after the fact. +pub const MIN_LOG_VAR: f64 = -14.0; +pub const MAX_LOG_VAR: f64 = 4.0; + +#[derive(Module, Debug)] +pub struct HeteroModel { + mean1: Linear, + mean2: Linear, + mean3: Linear, + var1: Linear, + var2: Linear, + var3: Linear, + act: Relu, +} + +impl HeteroModel { + pub fn new(device: &B::Device, hidden_mean: usize, hidden_var: usize) -> Self { + // mean head sees z_t, the masked next observation, and the mask + let mean_in = BAND_DIM * 3; + // variance head sees ONLY z_t and the mask (see module docs) + let var_in = BAND_DIM * 2; + Self { + mean1: LinearConfig::new(mean_in, hidden_mean).init(device), + mean2: LinearConfig::new(hidden_mean, hidden_mean / 2).init(device), + mean3: LinearConfig::new(hidden_mean / 2, POSE_DIM).init(device), + var1: LinearConfig::new(var_in, hidden_var).init(device), + var2: LinearConfig::new(hidden_var, hidden_var / 2).init(device), + var3: LinearConfig::new(hidden_var / 2, POSE_DIM).init(device), + act: Relu::new(), + } + } + + /// `z_t`: [batch, BAND_DIM]; `masked_next`: [batch, BAND_DIM] (revealed values, + /// zeros elsewhere); `mask`: [batch, BAND_DIM] of 1.0/0.0. + /// Returns `(mu, log_var)`, each [batch, POSE_DIM]. + pub fn forward( + &self, + z_t: Tensor, + masked_next: Tensor, + mask: Tensor, + ) -> (Tensor, Tensor) { + let mean_in = Tensor::cat(vec![z_t.clone(), masked_next, mask.clone()], 1); + let h = self.act.forward(self.mean1.forward(mean_in)); + let h = self.act.forward(self.mean2.forward(h)); + let mu = self.mean3.forward(h); + + let var_in = Tensor::cat(vec![z_t, mask], 1); + let g = self.act.forward(self.var1.forward(var_in)); + let g = self.act.forward(self.var2.forward(g)); + let log_var = self.var3.forward(g).clamp(MIN_LOG_VAR, MAX_LOG_VAR); + + (mu, log_var) + } + + /// Variance head alone -- all that EIG needs. + pub fn log_var(&self, z_t: Tensor, mask: Tensor) -> Tensor { + let var_in = Tensor::cat(vec![z_t, mask], 1); + let g = self.act.forward(self.var1.forward(var_in)); + let g = self.act.forward(self.var2.forward(g)); + self.var3.forward(g).clamp(MIN_LOG_VAR, MAX_LOG_VAR) + } +} + +/// Mean Gaussian NLL for a diagonal predictive, in nats per sample. +/// +/// `0.5 * sum_i [ (y_i - mu_i)^2 / sigma_i^2 + log sigma_i^2 + log(2*pi) ]` +pub fn hetero_nll( + mu: Tensor, + log_var: Tensor, + y: Tensor, +) -> Tensor { + let two_pi = (2.0 * std::f64::consts::PI).ln(); + let inv_var = log_var.clone().neg().exp(); + let sq = (y - mu).powf_scalar(2.0); + let per_dim = sq * inv_var + log_var + two_pi; + per_dim.sum_dim(1).squeeze::<1>() * 0.5 +} + +/// Expand a per-probe-group mask to the per-band feature mask the model consumes. +pub fn expand_mask(groups: &[usize]) -> Vec { + let mut m = vec![0.0f32; BAND_DIM]; + for &g in groups { + debug_assert!(g < NUM_PROBE_GROUPS); + for d in (g * BANDS_PER_GROUP)..((g + 1) * BANDS_PER_GROUP) { + m[d] = 1.0; + } + } + m +} diff --git a/benchmarks/wiflow-dynamics/harness/src/lib.rs b/benchmarks/wiflow-dynamics/harness/src/lib.rs index 8c9fe6795..386141e99 100644 --- a/benchmarks/wiflow-dynamics/harness/src/lib.rs +++ b/benchmarks/wiflow-dynamics/harness/src/lib.rs @@ -23,6 +23,7 @@ pub mod bands; pub mod gaussian; pub mod guards; +pub mod hetero; pub mod metrics; pub mod npy; diff --git a/benchmarks/wiflow-dynamics/results/rung3.json b/benchmarks/wiflow-dynamics/results/rung3.json new file mode 100644 index 000000000..32859d233 --- /dev/null +++ b/benchmarks/wiflow-dynamics/results/rung3.json @@ -0,0 +1,978 @@ +{ + "backend": "burn-ndarray (CPU), by design -- see STATE.md rung-3 pre-registration", + "beats_bar": true, + "degeneracy_guard": { + "degenerate": false, + "pred_std": 0.020772083145678934, + "threshold": 0.0001 + }, + "honesty_bar_same_pairs": { + "mpjpe": 0.02603940523099254, + "pck@20": 0.75565 + }, + "milestone": "Rung 3 -- neural heteroscedastic head. MEASURED by harness/src/bin/rung3.rs, this run.", + "no_probe": { + "coverage": [ + { + "empirical": 0.57465, + "error_pp": 7.465, + "nominal": 0.5 + }, + { + "empirical": 0.8319583333333334, + "error_pp": 3.195833333333331, + "nominal": 0.8 + }, + { + "empirical": 0.901975, + "error_pp": 0.1974999999999949, + "nominal": 0.9 + }, + { + "empirical": 0.9375966666666666, + "error_pp": -1.2403333333333322, + "nominal": 0.95 + } + ], + "mpjpe": 0.010727145804528966, + "pck@20": 0.9424633333333333, + "pose_nll": -102.8293953125 + }, + "policies": [ + { + "adaptivity": { + "distinct_probe_sets": 24, + "modal_set_fraction": 0.27215, + "note": "rung 2 had exactly 1 distinct set by construction (homoscedastic)" + }, + "budget_k": 1, + "paired_greedy_minus_random": { + "beats_by_2se": true, + "delta": 0.0007766666666666661, + "se": 0.000249411874400383, + "t": 3.1139923411179553 + }, + "pck@20": { + "eig_greedy": 0.94246, + "fixed": 0.9419066666666667, + "random": 0.9416833333333333 + }, + "pose_nll": { + "eig_greedy": -101.82346796875, + "fixed": -102.28751875, + "random": -102.50327109375 + } + }, + { + "adaptivity": { + "distinct_probe_sets": 1683, + "modal_set_fraction": 0.01575, + "note": "rung 2 had exactly 1 distinct set by construction (homoscedastic)" + }, + "budget_k": 3, + "paired_greedy_minus_random": { + "beats_by_2se": true, + "delta": 0.0016633333333333322, + "se": 0.0003468443663881694, + "t": 4.795618711222831 + }, + "pck@20": { + "eig_greedy": 0.9420133333333334, + "fixed": 0.9410733333333333, + "random": 0.94035 + }, + "pose_nll": { + "eig_greedy": -100.12109921875, + "fixed": -101.23589765625, + "random": -102.03474375 + } + }, + { + "adaptivity": { + "distinct_probe_sets": 17301, + "modal_set_fraction": 0.00085, + "note": "rung 2 had exactly 1 distinct set by construction (homoscedastic)" + }, + "budget_k": 6, + "paired_greedy_minus_random": { + "beats_by_2se": true, + "delta": 0.0022066666666666693, + "se": 0.0004378095774232015, + "t": 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MEASURED by harness/src/bin/rung3.rs, this run.", + "no_probe": { + "coverage": [ + { + "empirical": 0.6158283333333333, + "error_pp": 11.582833333333332, + "nominal": 0.5 + }, + { + "empirical": 0.8512266666666667, + "error_pp": 5.122666666666664, + "nominal": 0.8 + }, + { + "empirical": 0.90759, + "error_pp": 0.7589999999999986, + "nominal": 0.9 + }, + { + "empirical": 0.9363616666666666, + "error_pp": -1.3638333333333308, + "nominal": 0.95 + } + ], + "mpjpe": 0.019796433696580587, + "pck@20": 0.83574, + "pose_nll": -81.23594375 + }, + "policies": [ + { + "adaptivity": { + "distinct_probe_sets": 1, + "modal_set_fraction": 1.0, + "note": "rung 2 had exactly 1 distinct set by construction (homoscedastic)" + }, + "budget_k": 1, + "paired_greedy_minus_random": { + "beats_by_2se": false, + "delta": -0.004313333333333364, + "se": 0.00037483196895436315, + "t": -11.5073784804586 + }, + "pck@20": { + "eig_greedy": 0.8304933333333333, + "fixed": 0.8347033333333334, + "random": 0.8348066666666667 + }, + "pose_nll": { + "eig_greedy": -80.657655859375, + "fixed": -81.2702796875, + "random": -81.120386328125 + } + }, + { + "adaptivity": { + "distinct_probe_sets": 7, + "modal_set_fraction": 0.7363, + "note": "rung 2 had exactly 1 distinct set by construction (homoscedastic)" + }, + "budget_k": 3, + "paired_greedy_minus_random": { + "beats_by_2se": true, + "delta": 0.004560000000000032, + "se": 0.0005045303016857214, + "t": 9.03810927661688 + }, + "pck@20": { + "eig_greedy": 0.8352333333333334, + "fixed": 0.8325433333333333, + "random": 0.8306733333333334 + }, + "pose_nll": { + "eig_greedy": -80.389519921875, + "fixed": -80.92885546875, + "random": -80.7511015625 + } + }, + { + "adaptivity": { + "distinct_probe_sets": 18, + "modal_set_fraction": 0.6644, + "note": "rung 2 had exactly 1 distinct set by construction (homoscedastic)" + }, + "budget_k": 6, + "paired_greedy_minus_random": { + "beats_by_2se": false, + "delta": -0.002416666666666691, + "se": 0.0006109591591180738, + "t": -3.9555289917499166 + }, + "pck@20": { + "eig_greedy": 0.8206166666666667, + "fixed": 0.8236666666666667, + "random": 0.8230333333333333 + }, + "pose_nll": { + "eig_greedy": -78.4006375, + "fixed": -80.363200390625, + "random": -80.01705546875 + } + }, + { + "adaptivity": { + "distinct_probe_sets": 71, + "modal_set_fraction": 0.4242, + "note": "rung 2 had exactly 1 distinct set by construction (homoscedastic)" + }, + "budget_k": 9, + "paired_greedy_minus_random": { + "beats_by_2se": true, + "delta": 0.005983333333333409, + "se": 0.0006415411205105044, + "t": 9.32650011362045 + }, + "pck@20": { + "eig_greedy": 0.8231133333333334, + "fixed": 0.8358166666666667, + "random": 0.81713 + }, + "pose_nll": { + "eig_greedy": -76.970840234375, + "fixed": -79.6653265625, + "random": -79.440788671875 + } + } + ], + "preregistered_criteria": { + "1_beats_rung2_nll": { + "pass": false, + "rung2": -113.29, + "rung3": -81.23594375 + }, + "2_coverage_within_3pp_all_levels": false, + "3_eig_beats_random_by_2se": true, + "rung2_best_pck_for_reference": 0.8208 + }, + "training": { + "batch": 512, + "epochs": 12, + "history": [ + { + "epoch": 0, + "train_nll": -42.67732829158589, + "val_nll": -56.74852752685547 + }, + { + "epoch": 1, + "train_nll": -60.61488364914716, + "val_nll": -62.73134994506836 + }, + { + "epoch": 2, + "train_nll": -58.326725038431455, + "val_nll": -51.11528778076172 + }, + { + "epoch": 3, + "train_nll": -65.42834896152303, + "val_nll": -63.737789154052734 + }, + { + "epoch": 4, + "train_nll": -68.45023649829929, + "val_nll": -65.93348693847656 + }, + { + "epoch": 5, + "train_nll": -62.15681699979103, + "val_nll": -51.822471618652344 + }, + { + "epoch": 6, + "train_nll": -68.0091497776872, + "val_nll": -73.48114013671875 + }, + { + "epoch": 7, + "train_nll": -68.32070777375819, + "val_nll": -73.49523162841797 + }, + { + "epoch": 8, + "train_nll": -75.84418700913251, + "val_nll": -77.30884552001953 + }, + { + "epoch": 9, + "train_nll": -69.88452507277667, + "val_nll": -75.26997375488281 + }, + { + "epoch": 10, + "train_nll": -78.93434686175847, + "val_nll": -80.26214599609375 + }, + { + "epoch": 11, + "train_nll": -81.27487842107223, + "val_nll": -83.18534088134766 + } + ], + "lr": 0.001, + "train_pairs": 60000 + }, + "verdict": "RUNG 3 DID NOT HELP -- at least one pre-registered criterion was missed" +}