From 50f2da72166e4f609077d65b4916f745da5e8e3b Mon Sep 17 00:00:00 2001 From: willtanoe Date: Fri, 25 Sep 2026 04:22:55 +0000 Subject: [PATCH] experiment: validate beta selection on one paired full condition --- ...0.5__seed0__paired_client_beta_metrics.csv | 51 + ...t_weighted__beta0__prototype_snapshot.json | 11313 ++++++++++++++++ ...__prototype_snapshot__prototype_effect.csv | 46 + ..._prototype_snapshot__prototype_weights.csv | 431 + docs/handoff/SESSION_HANDOFF.md | 326 +- docs/research/EXPERIMENT_PROTOCOL.md | 23 + docs/research/PAIRED_BETA_VALIDATION.md | 321 + scripts/run_baseline.py | 73 + 8 files changed, 12434 insertions(+), 150 deletions(-) create mode 100644 artifacts/analysis/xiiotid__alpha0.5__seed0__paired_client_beta_metrics.csv create mode 100644 artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot.json create mode 100644 artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot__prototype_effect.csv create mode 100644 artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot__prototype_weights.csv create mode 100644 docs/research/PAIRED_BETA_VALIDATION.md diff --git a/artifacts/analysis/xiiotid__alpha0.5__seed0__paired_client_beta_metrics.csv b/artifacts/analysis/xiiotid__alpha0.5__seed0__paired_client_beta_metrics.csv new file mode 100644 index 0000000..4f3b662 --- /dev/null +++ b/artifacts/analysis/xiiotid__alpha0.5__seed0__paired_client_beta_metrics.csv @@ -0,0 +1,51 @@ +beta,client_id,n_train,min_pos_Nij,max_Nij,test_rare_f1,test_rare_recall,test_macro_f1,test_acc,val_rare_f1,val_rare_recall +0.00,0,94564,18,48503,0.4995519291027485,0.9921372408863474,0.6068173367966223,0.8913947112408243,0.8875379939209728,0.9864864864864865 +0.00,1,50631,69,34165,0.3843553008595989,0.6648852971845672,0.6486242436476237,0.9225095530080867,0.43045783132530124,0.6944444444444444 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+4,Normal,0.75,10,604,68657,29277.7,6.720334273286351,6.042285009407229,0.8756391811740736,0.14491854982192856 +4,Normal,1.0,10,604,68657,29277.7,6.803080625783804,6.042285009407229,0.9899061725928462,0.16382977152710637 +5,RDOS,0.0,10,168,49283,9888.3,8.705113176029744,8.705113176029744,0.0,0.0 +5,RDOS,0.25,10,168,49283,9888.3,8.772588209172795,8.705113176029744,0.3633215970184691,0.04173657362880766 +5,RDOS,0.5,10,168,49283,9888.3,8.811154949619254,8.705113176029744,0.6718616497957892,0.07718011658318427 +5,RDOS,0.75,10,168,49283,9888.3,8.840855101677697,8.705113176029744,0.9758862376670907,0.11210494544221147 +5,RDOS,1.0,10,168,49283,9888.3,8.874204913906459,8.705113176029744,1.2860320043774807,0.1477329448075043 +6,Reconnaissance,0.0,10,1137,29234,8916.1,6.1132865312061595,6.1132865312061595,0.0,0.0 +6,Reconnaissance,0.25,10,1137,29234,8916.1,6.3313502624680265,6.1132865312061595,0.3500984939064864,0.05726845815574941 +6,Reconnaissance,0.5,10,1137,29234,8916.1,6.540280247643444,6.1132865312061595,0.6864476003243064,0.11228781717006636 +6,Reconnaissance,0.75,10,1137,29234,8916.1,6.716129778242917,6.1132865312061595,0.970753367392759,0.1587940238752768 +6,Reconnaissance,1.0,10,1137,29234,8916.1,6.8462376211276155,6.1132865312061595,1.183931540468372,0.19366531151857863 +7,Tampering,0.0,9,2,1266,398.44444444444446,5.095948820849879,5.095948820849879,0.0,0.0 +7,Tampering,0.25,9,2,1266,398.44444444444446,5.222475642896568,5.095948820849879,0.7528833961261016,0.1477415536525226 +7,Tampering,0.5,9,2,1266,398.44444444444446,5.2417538146739915,5.095948820849879,1.1900181694152834,0.2335223942097632 +7,Tampering,0.75,9,2,1266,398.44444444444446,5.214766049805263,5.095948820849879,1.489091829390507,0.29221090747574724 +7,Tampering,1.0,9,2,1266,398.44444444444446,5.186261162459578,5.095948820849879,1.7365220144389066,0.3407651990800994 +8,Weaponization,0.0,10,18,32409,4509.4,6.165797271067825,6.165797271067825,0.0,0.0 +8,Weaponization,0.25,10,18,32409,4509.4,6.324316870213558,6.165797271067825,1.0213874306568795,0.16565374853461415 +8,Weaponization,0.5,10,18,32409,4509.4,6.57305363177223,6.165797271067825,2.0215287048880053,0.3278616886049363 +8,Weaponization,0.75,10,18,32409,4509.4,6.878957349558733,6.165797271067825,2.9039530195295855,0.47097770034639236 +8,Weaponization,1.0,10,18,32409,4509.4,7.15445660681278,6.165797271067825,3.5588066747258478,0.577185158426319 diff --git a/artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot__prototype_weights.csv b/artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot__prototype_weights.csv new file mode 100644 index 0000000..bb901b6 --- /dev/null +++ b/artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot__prototype_weights.csv @@ -0,0 +1,431 @@ +class_id,class_name,beta,client_id,N_ij,weight +0,C&C,0.0,0,126.0,0.1 +0,C&C,0.0,1,559.0,0.1 +0,C&C,0.0,2,92.0,0.1 +0,C&C,0.0,3,966.0,0.1 +0,C&C,0.0,4,1.0,0.1 +0,C&C,0.0,5,4.0,0.1 +0,C&C,0.0,6,44.0,0.1 +0,C&C,0.0,7,20.0,0.1 +0,C&C,0.0,8,4.0,0.1 +0,C&C,0.0,9,188.0,0.1 +0,C&C,0.25,0,126.0,0.1151076953459115 +0,C&C,0.25,1,559.0,0.16705699856522024 +0,C&C,0.25,2,92.0,0.10640417756135012 +0,C&C,0.25,3,966.0,0.19153846560388027 +0,C&C,0.25,4,1.0,0.03435672212828583 +0,C&C,0.25,5,4.0,0.048587742392505656 +0,C&C,0.25,6,44.0,0.08848606687234023 +0,C&C,0.25,7,20.0,0.07265562136892396 +0,C&C,0.25,8,4.0,0.048587742392505656 +0,C&C,0.25,9,188.0,0.12721876776907656 +0,C&C,0.5,0,126.0,0.1065422012127861 +0,C&C,0.5,1,559.0,0.22441004697943928 +0,C&C,0.5,2,92.0,0.09103959276541436 +0,C&C,0.5,3,966.0,0.2950019970006494 +0,C&C,0.5,4,1.0,0.00949153367073754 +0,C&C,0.5,5,4.0,0.01898306734147508 +0,C&C,0.5,6,44.0,0.06295971174172223 +0,C&C,0.5,7,20.0,0.042447428996994496 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It is written to be readable without prior chat history, terminal scrollback, or git history. -Last updated: 2026-09-24 (Session 4, support-weighted beta aggregation implementation). -Session 4 type: implementation + validation. The proposed `support_weighted` -(`w_ij = N_ij ** beta`) mode was added **without changing the frozen `original_protean` -baseline**. Only small smoke tests were run; **no beta sweep and no beta selection were -performed**. +Last updated: 2026-09-25 (Session 5, one paired full beta condition + validation-only +selection). Session 5 type: **protocol-validation experiment**. Exactly one +`(dataset, alpha, seed)` condition was run across all five beta candidates; beta was +selected on validation only and the selected beta evaluated once on test. **No full grid +was launched.** --- @@ -24,204 +24,225 @@ S_j = { i | N_ij > 0 and a valid local prototype C_ij exists } beta in {0.0, 0.25, 0.5, 0.75, 1.0} ``` -Both the baseline and the proposed mode are now implemented. The baseline remains the -reference; `beta = 0` is arithmetic-identical to it only because both use the same -documented `S_j` participant rule. +Both the frozen `original_protean` baseline and the proposed `support_weighted` mode are +implemented. `beta = 0` is mathematically the equal mean over the shared `S_j` but is not +bitwise identical to the baseline (see §7). ## 2. Authoritative source and immutability - `planningawal/PROTEAN_Revisi_Lurus.md` — authoritative research specification. - `planningawal/` is immutable. Re-verified unchanged this session: `git status -- planningawal` and `git diff -- planningawal` are both empty. +- Raw datasets re-verified unchanged (SHA-256): + `~/datasets/X-IIoTID/X-IIoTID-dataset.csv` + `7b9290057ee42e784da3c0d84b781815502c9205c74175c96374e71a5ffd98a0`; + `~/datasets/5G-NIDD/5G-NIDD.csv` + `fa36f80859585f474504ca69eb951a079b35145537976c86b00aae3aab46ee59`. -## 3. Dataset locations (read-only external source data) +## 3. Session 5 objective and outcome -- `~/datasets/X-IIoTID/X-IIoTID-dataset.csv` — SHA-256 - `7b9290057ee42e784da3c0d84b781815502c9205c74175c96374e71a5ffd98a0` (re-verified unchanged). -- `~/datasets/5G-NIDD/5G-NIDD.csv` — SHA-256 - `fa36f80859585f474504ca69eb951a079b35145537976c86b00aae3aab46ee59` (re-verified unchanged). +Objective: run ONE paired full condition (X-IIoTID, alpha=0.5, seed=0) across +`beta ∈ {0, 0.25, 0.5, 0.75, 1}`, reusing the frozen manifest and training settings; +select beta on validation only; evaluate the selected beta on test; add the direct +prototype-effect and client-level analyses; update docs. **All acceptance items were met.** +No commit or push was made. -## 4. Session 4 objective and outcome +## 4. Condition and pairing -Objective: implement the proposed support-weighted prototype aggregation while preserving -the frozen `original_protean` baseline, add the deferred beta-invariant tests, add direct -prototype-effect instrumentation, run small smoke tests for beta 0 / 0.5 / 1, and update -docs. **All acceptance items were met.** No commit was made. +| Field | Value | +|---|---| +| dataset / alpha / seed | `xiiotid` / 0.50 / 0 | +| manifest | `artifacts/partitions/xiiotid__alpha0.5__seed0.json` (reused) | +| manifest content hash | `e81a6538ecefcda202aa7602310ff8d8c9529031ab9dcbec4ebbabc0e63b636c` | +| partition hash | `2465485539a3e923548dcc0c73d8acc5076d456536b78682d512897ea586e779` | +| split hash / sizes | `d0acc0c14e3ecbeba02dfa9e6227c983da12dec2d6111df0b714002e6e03c29d` / 569,909 train, 81,414 val, 162,831 test | +| preprocessing hash | `8580cee41c698ce43cf3269e4a2b3a01c92d6df22820f4004c0267478bd2579a` | +| training | 10 clients, 10 rounds, 3 local epochs, batch 256, lr 1e-3, mu 0.1, lambda 1.0, Adam | +| code revision | HEAD `ad9aa53ba76f60671ace991fcee85f003781f196`; only `scripts/run_baseline.py` modified (hash-object `2b469e35ecbbc6ea3dbd4b0632419993e4d5130b`) | -## 5. Implementation summary +Pairing proof: the round-0 prototype snapshot is **byte-identical** (SHA-256 prefix +`e27f41b3a62eb83c`) across all five beta runs and the frozen `original_protean` run; +`train_support_Nij.csv`, `characterization.csv`, `rare_unseen.csv` are identical across +runs. All runs are in `results/xiiotid__alpha0.5__seed0__support_weighted__beta{0,0.25,0.5,0.75,1}/`. -Added a new aggregation mode and threaded it through the stack; the baseline code path is -untouched. +## 5. Validation results and beta selection -- `src/protean_research/aggregation/prototype_aggregation.py`: `AggregationMode.SUPPORT_ - WEIGHTED`, `BETA_CANDIDATES = (0.0, 0.25, 0.50, 0.75, 1.00)`, strict `validate_beta` - (`[0, 1]`; rejects `None`/non-numeric/non-finite/out-of-range), `normalized_support_ - weights`; `aggregate_prototypes(..., supports=None, beta=None)` and `aggregate_class_ - prototypes(..., beta=None)`. `S_j` is built once and is identical to the baseline: - `None`/`N_ij <= 0` entries dropped, no zero-vector padding. Passing `beta` to - `original_protean` raises. -- `src/protean_research/aggregation/__init__.py`: exports updated. -- `src/protean_research/models/prototypes.py`, `federated/server.py`: forward `beta`. -- `src/protean_research/federated/round.py`: `TrainingConfig.aggregation_mode` (default - `original_protean`) and `.beta` (default `None`); the round loop dispatches to the mode. -- `scripts/run_baseline.py`: new `--aggregation-mode {original_protean,support_weighted}` - and `--beta`. Default behavior is the unchanged baseline. Run name/metadata record mode - and beta (`...__original_protean` unchanged; `...__support_weighted__beta0.5` for beta). -- `src/protean_research/analysis/direct_prototype.py` (new) + - `scripts/analyze_prototype_effect.py` (new): recompute `Cbar_j(beta)` from a fixed round - snapshot of `{C_ij, N_ij}` **without retraining**; emit `||Cbar_j(beta) - Cbar_j(0)||`, - support stats, norms, and normalised client weights. +Primary selection metric: **validation per-client-mean rare-class macro-F1** (per-client +rare F1 on each client's validation eval partition, averaged over clients with rare +classes). Global union-rare validation metrics also shown. -`equal_available` was **not** added (the baseline already is equal-over-`S_j`, so it equals -`beta = 0`). +| beta | val rareMacroF1 (pc mean) | val rareMacroF1 (global) | val rareRecall | val macroF1 | val acc | val wF1 | +|---|---|---|---|---|---|---| +| 0.00 | 0.557340 | 0.611641 | 0.884184 | 0.708929 | 0.891038 | 0.917741 | +| 0.25 | 0.571787 | 0.629475 | 0.888488 | 0.723431 | 0.902953 | 0.925933 | +| **0.50** | **0.585762** | **0.641709** | 0.887173 | **0.731166** | 0.897831 | **0.926005** | +| 0.75 | 0.578003 | 0.637107 | 0.873910 | 0.726603 | 0.887894 | 0.921696 | +| 1.00 | 0.551443 | 0.615307 | 0.847043 | 0.715951 | 0.883263 | 0.921182 | -## 6. Tests and baseline regression status +**Selected beta = 0.50** (best among tested candidates under this validation condition; +no tie; both validation readings agree). Test was **not** used for selection. -``` -.venv/bin/python -m pytest -> 75 passed -``` +## 6. Final test evaluation of the selected beta -Session-3 suite was 46 tests. Added `tests/test_support_weighted_aggregation.py` (21) and -`tests/test_prototype_effect.py` (7); one baseline-aggregation test was replaced by two -explicit tests (support_weighted now available; `beta` rejected for `original_protean`). +| metric | original_protean | support_weighted beta=0.50 | +|---|---|---| +| rare-class macro-F1 | 0.609620 | 0.640829 | +| rare-class recall | 0.871487 | 0.871583 | +| overall macro-F1 | 0.707701 | 0.730743 | +| accuracy | 0.890432 | 0.898158 | +| weighted-F1 | 0.917585 | 0.926333 | -Covered beta invariants: `beta=0` arithmetic mean; `beta=1` support-weighted mean; -hand-computed intermediate-beta fixture; single-contributor invariance; equal-support -invariance; missing-class exclusion; normalised weights sum to 1; invalid/non-numeric -beta rejection; supports strictly positive and aligned. **Baseline regression is green**: -`test_end_to_end_baseline_is_deterministic_and_learns_separable_fixture` and all baseline -arithmetic/validation tests pass unchanged. +All five candidates' test metrics (transparency only) are in +`docs/research/PAIRED_BETA_VALIDATION.md` §B.2. No superiority is claimed: single +dataset / alpha / seed. -## 7. Smoke results (NOT scientific results) +## 7. `beta = 0` equivalence result (important) -Condition: `xiiotid`, `alpha=0.5`, `seed=0`, `--max-rows 3000 --sample-from 60000`, -4 clients, 2 rounds, 1 local epoch (ignored dirs only). +`support_weighted beta=0` vs frozen `original_protean`: -| mode / beta | test accuracy | test macro-F1 | test rare macro-F1 | test rare recall | -|---|---|---|---|---| -| `original_protean` | 0.5167 | 0.2998 | 0.3140 | 0.4572 | -| `support_weighted` 0.0 | 0.5167 | 0.2998 | 0.3140 | 0.4572 | -| `support_weighted` 0.5 | 0.2333 | 0.1397 | 0.1797 | 0.3193 | -| `support_weighted` 1.0 | 0.0383 | 0.0252 | 0.0324 | 0.1995 | +- Same frozen manifest and split: **yes**. +- Same initialisation/round-0 setup: **yes** (round-0 snapshot byte-identical). +- Training behaviour: **mathematically identical, not bitwise**. `original_protean` + computes `Σ(C_ij/count)`; `support_weighted beta=0` computes `Σ((1/count)·C_ij)`. + Per-element difference ≤ `2.2e-16`–`4.4e-16` (~1 ULP, relative ~1e-16). +- Validation/test metrics: **not identical** (baseline slightly lower; e.g. test + accuracy 0.890432 vs 0.891974). The ~1 ULP aggregation difference feeds back into + round-1 prototype-alignment training and the trajectories diverge slowly from round 1. +- Deterministic artifacts identical: round-0 snapshot, `train_support_Nij.csv`, + `characterization.csv`, `rare_unseen.csv`; `history.json`/per-client/per-class differ. -Baseline-preservation evidence: the `original_protean` and `support_weighted beta=0` smoke -runs produced **byte-identical** result artifacts (only `metadata.json` differed in -mode/beta/run_name). The round-0 snapshots for `beta = 0, 0.5, 1` were **byte-identical**, -confirming the paired comparison (same partition/split/init/round-0 training). The -beta=0.5/1 metrics are toy-scale and must not be interpreted as method quality. +This is floating-point arithmetic ordering, not a semantic change; no code was altered to +force bitwise equality. **Conditional on the shared `S_j` reproduction assumption** and +does not prove `beta = 0` reproduces original PROTEAN. -Direct-effect analysis was exercised on the `beta=0` smoke snapshot (9 classes, -max displacement 7.65 at beta=1; relative displacement up to 1.51 for the most -support-imbalanced class), written under the ignored smoke directory -`artifacts/processed/smoke/analysis/`. +## 8. Direct prototype-effect findings (fixed round-0 snapshot, no retraining) -## 8. Methodological decisions / assumptions carried forward +At `beta = 1.0`, largest `||Cbar_j(1)-Cbar_j(0)||`: +`Weaponization` 3.559 (rel 0.577; most support-imbalanced, cv 2.10, max/min 1,800), +`C&C` 2.156, `Lateral_movement` 2.069, `Exploitation` 1.934, `Tampering` 1.737, +`Exfiltration` 1.547; smallest: `Normal` 0.990 (cv 0.76), `RDOS` 1.286, +`Reconnaissance` 1.184. Every class has `|S_j| ≥ 8` and unequal support, so no class has +zero direct effect. Outputs: +`artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot__prototype_effect.csv` +and `...__prototype_weights.csv`. Displacement is a direct-aggregation measure only, not +a detection result. + +## 9. Client-level findings (test, vs beta=0) + +Full paired table: `artifacts/analysis/xiiotid__alpha0.5__seed0__paired_client_beta_metrics.csv`. +At `beta=0.50` six of ten clients improve; only client 3 is materially negative (−0.068). +At `beta=0.75/1.00` several clients lose, with client 3 worst (−0.257/−0.342). The +`min positive N_ij` vs ΔF1 relationship is **not monotonic** in this single condition: +client 3 (`min N_ij=140`) is the worst loser while client 5 (`min N_ij=2`) is the biggest +gainer. Descriptive only; no causal claim. + +## 10. Runtime / resources + +CPU only (no GPU). Per run: 41–44 min wall-clock, ~733–740 % CPU, peak RSS ~6.21 GiB, +no swap, exit 0. Total ~3.5 h for the five paired candidates. Preprocessing/load +negligible. + +## 11. Methodological decisions / assumptions 1. **`S_j` participant/denominator rule** (`S_j = {i | N_ij > 0 and valid C_ij}`, equal weights in the baseline) is a **REPRODUCTION ASSUMPTION**, not verified original - behaviour. It is material in 53/162 class-condition cells (32.7%), 15/18 conditions - (`docs/baseline/BASELINE_VALIDATION.md` §D). `beta = 0` equality with the baseline is - conditional on this shared rule. Carry this caveat in all reporting. -2. `N_ij` is **class-specific sample support**, not prototype quality. -3. `beta` is one value for all classes/clients; candidates are frozen at - `{0, 0.25, 0.5, 0.75, 1}`. -4. `lambda = 1.0`, optimiser (Adam), lr (1e-3), batch (256) remain PROJECT DECISIONS. + behaviour; material in 53/162 class-condition cells (32.7 %), 15/18 conditions + (`docs/baseline/BASELINE_VALIDATION.md` §D). Carry this caveat in all reporting. +2. `N_ij` is class-specific sample support, not prototype quality. +3. Beta is one value for all classes/clients; candidates frozen at `{0,.25,.5,.75,1}`. +4. `lambda=1.0`, Adam, lr 1e-3, batch 256 remain PROJECT DECISIONS. +5. **New this session (protocol clarification, documented in + `EXPERIMENT_PROTOCOL.md` §8):** candidate validation metric = final-model validation + metrics; primary selection = per-client-mean rare-class macro-F1 using a deterministic + Dirichlet validation eval partition (mirrors the existing test per-client procedure); + tie-break = higher val rare macro-F1 → higher val rare recall → smaller beta. +6. No `equal_available` mode was added. -## 9. Unresolved questions / blockers +## 12. Unresolved questions / blockers -1. `S_j` reproduction ambiguity (above) — unresolved, no official PROTEAN code exists. -2. Beta selection has **not** been run; no validation/test separation result exists yet. -3. Smoke-scale metrics are unstable and not meaningful; a full paired condition is needed. -4. Session-2 manifests store `preprocessing_hash = null` / `config_hash = null`; live - preprocessing hash is recorded in run metadata only. +1. `S_j` reproduction ambiguity — unresolved; no official PROTEAN code exists. +2. `beta = 0` is not bitwise identical to `original_protean` (float ordering). Whether the + thesis should report this explicitly, or whether an equivalence check should use a + tolerance, is a documentation decision for later. +3. Single condition only; no multi-seed/multi-alpha evidence yet. +4. Session-2 manifests still store `preprocessing_hash = null` / `config_hash = null`. -## 10. Git status +## 13. Git status -No commit or push was made. Working tree has 9 modified tracked files and 5 untracked -paths from this session (listed by `git status`). `planningawal/` and both raw datasets -are unchanged. `results/`, `artifacts/processed/` remain ignored. `artifacts/analysis/` is -tracked (no new files were written there this session; smoke analysis went to -`artifacts/processed/smoke/analysis/`). +No commit or push was made. `git status --short`: -## 11. Files created / modified this session +``` + M scripts/run_baseline.py +?? artifacts/analysis/xiiotid__alpha0.5__seed0__paired_client_beta_metrics.csv +?? artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot.json +?? artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot__prototype_effect.csv +?? artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot__prototype_weights.csv +?? artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0.25__prototype_snapshot.json +?? artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0.5__prototype_snapshot.json +?? artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0.75__prototype_snapshot.json +?? artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta1__prototype_snapshot.json +``` + +`planningawal/` and both raw datasets are unchanged; `results/` and +`artifacts/processed/` are ignored; `artifacts/analysis/` is tracked. + +## 14. Files created / modified this session Created: -- `src/protean_research/analysis/__init__.py` -- `src/protean_research/analysis/direct_prototype.py` -- `scripts/analyze_prototype_effect.py` -- `tests/test_support_weighted_aggregation.py` -- `tests/test_prototype_effect.py` -- `docs/research/BETA_AGGREGATION_IMPLEMENTATION.md` +- `docs/research/PAIRED_BETA_VALIDATION.md` +- `artifacts/analysis/xiiotid__alpha0.5__seed0__paired_client_beta_metrics.csv` +- five `artifacts/analysis/...__support_weighted__beta*__prototype_snapshot.json` +- two `artifacts/analysis/...__prototype_snapshot__prototype_effect.csv` / `__prototype_weights.csv` Modified: -- `src/protean_research/aggregation/prototype_aggregation.py` -- `src/protean_research/aggregation/__init__.py` -- `src/protean_research/models/prototypes.py` -- `src/protean_research/federated/server.py` -- `src/protean_research/federated/round.py` -- `scripts/run_baseline.py` -- `tests/test_baseline_aggregation.py` -- `configs/experiments/experiment_protocol.yaml` -- `docs/research/EXPERIMENT_PROTOCOL.md` +- `scripts/run_baseline.py` (added validation-only evaluation artifacts; test/training paths untouched) +- `docs/research/EXPERIMENT_PROTOCOL.md` (§8 Session-5 clarification) - `docs/handoff/SESSION_HANDOFF.md` (this file) -## 12. Exact next task +## 15. Exact recommended next phase -**Recommended: run ONE paired full `(dataset, alpha, seed)` condition across -`beta in {0, 0.25, 0.5, 0.75, 1}` under the validation-based selection protocol, then -inspect.** Do not launch the full grid. +**Recommendation: B — checkpoint Session 5, then proceed to a controlled +multi-seed/multi-alpha experiment.** The protocol ran cleanly (validation/test separation +held, artifacts reproducible, pairing proven by byte-identical round-0 snapshots, no test +leakage into selection), so the next step is to scale to seeds `{0,1,2}` at a small alpha +set, still paired. Do **not** run the entire `dataset × alpha × seed × beta` grid at once; +grow it in controlled, inspectable increments. -Concretely: +Before scaling: decide how to report the float-level `beta = 0` vs `original_protean` +non-identity (§7), and whether to re-run `original_protean` per condition with the new +validation artifacts (the existing baseline run predates the validation-artifact change). -1. Reuse the frozen manifest for one condition (e.g. X-IIoTID `alpha=0.5`, seed 0) and the - frozen training settings (10 clients, 10 rounds, 3 local epochs, batch 256, lr 1e-3, - mu 0.1, lambda 1.0). Measure the single-run cost first (Session 3 measured ~42 min / - 6.2 GiB on CPU for one baseline run; beta adds one run per candidate). -2. Train each beta with `run_baseline.py --aggregation-mode support_weighted --beta ...` - and record validation rare-class macro-F1; select beta on validation only. -3. Evaluate the selected beta on test, and report all candidates transparently. -4. Run `scripts/analyze_prototype_effect.py` on the round-0 snapshot for the direct - aggregation effect, clearly separated from end-to-end results. -5. Report the `S_j` reproduction caveat alongside every between-beta claim. - -Do **not** add `equal_available`. Do **not** run the alpha x seed x dataset x beta grid. - -## 13. Important commands +## 16. Important commands ```bash # Tests (75 expected) .venv/bin/python -m pytest -# Smoke: baseline and beta candidates (ignored output dirs) +# One full paired beta run (CPU, ~42 min, ~6.2 GiB) .venv/bin/python scripts/run_baseline.py --dataset xiiotid --alpha 0.5 --seed 0 \ - --max-rows 3000 --sample-from 60000 --clients 4 --rounds 2 --local-epochs 1 -.venv/bin/python scripts/run_baseline.py --dataset xiiotid --alpha 0.5 --seed 0 \ - --max-rows 3000 --sample-from 60000 --clients 4 --rounds 2 --local-epochs 1 \ + --clients 10 --rounds 10 --local-epochs 3 \ --aggregation-mode support_weighted --beta 0.5 # Direct prototype-effect analysis (no retraining) .venv/bin/python scripts/analyze_prototype_effect.py \ - --snapshot artifacts/processed/smoke/analysis/\ -xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot.json \ - --output-dir artifacts/processed/smoke/analysis - -# Full-data baseline for one condition (measure first; ~40-65 min, CPU) -.venv/bin/python scripts/run_baseline.py --dataset xiiotid --alpha 0.5 --seed 0 \ - --clients 10 --rounds 10 --local-epochs 3 + --snapshot artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot.json \ + --class-names "C&C,Exfiltration,Exploitation,Lateral_movement,Normal,RDOS,Reconnaissance,Tampering,Weaponization" \ + --output-dir artifacts/analysis ``` -## 14. Recommended next-session prompt +## 17. Recommended next-session prompt ``` -You are continuing the perdalam-protean research repository (Session 5). +You are continuing the perdalam-protean research repository (Session 6). FIRST, read completely: 1. docs/handoff/SESSION_HANDOFF.md - 2. docs/research/BETA_AGGREGATION_IMPLEMENTATION.md - 3. docs/baseline/BASELINE_VALIDATION.md - 4. docs/research/EXPERIMENT_PROTOCOL.md + 2. docs/research/PAIRED_BETA_VALIDATION.md + 3. docs/research/EXPERIMENT_PROTOCOL.md + 4. docs/research/BETA_AGGREGATION_IMPLEMENTATION.md + 5. docs/baseline/BASELINE_VALIDATION.md Critical rules: - planningawal/ is immutable. Do not edit/create/move/rename/delete anything inside it. @@ -231,14 +252,19 @@ Critical rules: class-condition cells). Carry this caveat in all reporting. - Do NOT add an executable equal_available mode. - Do NOT launch the full alpha x seed x dataset x beta grid. + - Beta selection uses validation metrics only; test never influences selection. -Task: the support_weighted mode (w_ij = N_ij^beta) is implemented and unit/smoke -validated; original_protean is unchanged. Run ONE paired full (dataset, alpha, seed) -condition across beta in {0, 0.25, 0.5, 0.75, 1}, reusing the frozen manifest and frozen -training settings. Select beta on validation rare-class macro-F1 only, then evaluate the -selected beta on test and report all candidates. Add the direct prototype-effect analysis -from the round-0 snapshot. Measure runtime before scaling. Update docs and -docs/handoff/SESSION_HANDOFF.md. +Context: Session 5 ran one paired full condition (xiiotid, alpha=0.5, seed=0) across +beta in {0,0.25,0.5,0.75,1}. Selected beta = 0.50 on validation per-client-mean +rare-class macro-F1; test rare-class macro-F1 0.640829 vs original_protean 0.609620. +beta=0 is mathematically but not bitwise equal to original_protean (~1 ULP aggregation +ordering). scripts/run_baseline.py now writes validation-only artifacts. + +Task: scale the paired protocol to a controlled multi-seed/multi-alpha set (e.g. seeds +{0,1,2} at alpha 0.50, reusing frozen manifests; grow only as compute allows), keeping +validation-only beta selection and reporting all candidates. Decide/document how to +report the beta=0 vs original_protean float non-identity. Update docs and +docs/handoff/SESSION_HANDOFF.md. Measure runtime before scaling further. Use .venv/bin/python for all Python commands. Do not commit or push unless asked. ``` diff --git a/docs/research/EXPERIMENT_PROTOCOL.md b/docs/research/EXPERIMENT_PROTOCOL.md index fb11a62..d40b508 100644 --- a/docs/research/EXPERIMENT_PROTOCOL.md +++ b/docs/research/EXPERIMENT_PROTOCOL.md @@ -135,6 +135,29 @@ Procedure, strictly ordered: Test data is never used in steps 1–2. +**Session-5 clarification (implementation detail, not a design change).** Running +the protocol surfaced two concrete points that the original text left implicit. +They do not alter the frozen design; they pin how step 1 is computed. + +1. **Candidate validation metric.** For each beta, validation metrics are taken + from the final trained model after the fixed round count (no best-round + cherry-picking). Two validation readings are recorded: (a) the global + union-rare validation rare-class macro-F1 (also stored in `history.json`), and + (b) the primary selection metric, the **per-client mean** rare-class macro-F1, + where each client is scored on its validation eval partition using that + client's rare class ids and the result is averaged over clients that have rare + classes. The per-client validation eval partition reuses the same deterministic + Dirichlet eval-partition procedure already used for the per-client test + evaluation, applied to the validation split (`split_name="validation"`); this + is a PROJECT DECISION, paired across all beta candidates and seeds. +2. **Tie-break order.** Higher validation rare-class macro-F1, then higher + validation rare-class recall, then smaller beta. + +`scripts/run_baseline.py` writes `val_global_metrics.json`, `val_per_class.csv`, +`val_per_client.csv` and `val_selection.json` to make this explicit; the test +path and the frozen training path are unchanged. See +`docs/research/PAIRED_BETA_VALIDATION.md`. + ## 9. Required metrics Per dataset/alpha/seed/beta and per client, record: diff --git a/docs/research/PAIRED_BETA_VALIDATION.md b/docs/research/PAIRED_BETA_VALIDATION.md new file mode 100644 index 0000000..32b97bf --- /dev/null +++ b/docs/research/PAIRED_BETA_VALIDATION.md @@ -0,0 +1,321 @@ +# Paired Beta Validation (Session 5) + +Status: **ONE paired full condition run end to end** with validation-only beta +selection, followed by a single final test evaluation of the selected beta. This +is a **protocol-validation experiment**, not a sweep and not evidence of global +behaviour. + +Companion documents: + +- `docs/research/EXPERIMENT_PROTOCOL.md` — frozen protocol. +- `docs/research/BETA_AGGREGATION_IMPLEMENTATION.md` — beta mode implementation. +- `docs/baseline/BASELINE_VALIDATION.md` — frozen baseline evidence. +- `docs/handoff/SESSION_HANDOFF.md` — continuation point. + +--- + +## A. Condition + +| Field | Value | +|---|---| +| dataset | `xiiotid` (X-IIoTID) | +| alpha | 0.50 | +| seed | 0 | +| aggregation modes | `original_protean` (reference), `support_weighted` for `beta ∈ {0, .25, .5, .75, 1}` | +| clients / rounds / local epochs | 10 / 10 / 3 | +| batch size / lr / optimizer | 256 / 1e-3 / Adam | +| mu / lambda | 0.1 / 1.0 | +| device | CPU only (`torch 2.14.0+cpu`, CUDA unavailable) | +| code revision | HEAD `ad9aa53ba76f60671ace991fcee85f003781f196`; only `scripts/run_baseline.py` was modified this session (`git hash-object` `2b469e35ecbbc6ea3dbd4b0632419993e4d5130b`) to add validation-only evaluation artifacts. Training, partition, aggregation, loss and inference source are unchanged at `ad9aa53`. | + +Frozen manifest reused (never regenerated): + +| Field | Value | +|---|---| +| path | `artifacts/partitions/xiiotid__alpha0.5__seed0.json` | +| manifest content hash | `e81a6538ecefcda202aa7602310ff8d8c9529031ab9dcbec4ebbabc0e63b636c` | +| partition hash | `2465485539a3e923548dcc0c73d8acc5076d456536b78682d512897ea586e779` | +| Dirichlet rng seed | `2009062324` | +| split hash | `d0acc0c14e3ecbeba02dfa9e6227c983da12dec2d6111df0b714002e6e03c29d` | +| split sizes (train/val/test) | 569,909 / 81,414 / 162,831 | +| preprocessing hash | `8580cee41c698ce43cf3269e4a2b3a01c92d6df22820f4004c0267478bd2579a` | +| `manifest_reload_ok` | true for every beta run | + +Pairing evidence: the **round-0 prototype snapshot is byte-identical** +(SHA-256 prefix `e27f41b3a62eb83c`) for all five beta runs **and** the frozen +`original_protean` run, so every candidate shares the same partition, split, +model initialisation and round-0 local training. `train_support_Nij.csv`, +`characterization.csv` and `rare_unseen.csv` are byte-identical across all runs. + +### A.1 Validation-evaluation clarification (new this session) + +The frozen runner previously wrote only test artifacts; `history.json` held a +global (union-rare) validation trajectory. To evaluate the protocol's +validation-only selection, `scripts/run_baseline.py` now additionally writes, at +the end of the fixed 10 rounds: + +- `val_global_metrics.json`, `val_confusion_matrix.json`, `val_per_class.csv` + (global validation metrics over the union of per-client rare classes); +- `val_per_client.csv` (per-client validation metrics, using the same + deterministic Dirichlet eval-partition procedure already used for test, + applied to the validation split with `split_name="validation"`); +- `val_selection.json` (the selection summary). + +The test path is untouched: re-running a smoke condition before/after the change +produced byte-identical test artifacts (`history.json`, `test_global_metrics.json`, +`test_confusion_matrix.json`, `test_per_class.csv`, `per_client.csv`, +`train_support_Nij.csv`, `characterization.csv`, `rare_unseen.csv`, +`metadata.json`). The candidate's validation metric is taken from the **final +trained model after the fixed 10 rounds** (no best-round cherry-picking). + +--- + +## B. Candidate results + +`val rareMacroF1 (pc mean)` is the primary selection metric: +per-client rare-class macro-F1 (each client scored on its validation eval +partition using that client's rare class ids), averaged over clients with rare +classes. The global column is the union-rare validation metric also recorded in +`history.json`. + +### B.1 USED FOR SELECTION — validation only + +| beta | val rareMacroF1 (pc mean) | val rareMacroF1 (global) | val rareRecall (global) | val macroF1 (global) | val accuracy | val weighted-F1 | +|---|---|---|---|---|---|---| +| 0.00 | 0.557340 | 0.611641 | 0.884184 | 0.708929 | 0.891038 | 0.917741 | +| 0.25 | 0.571787 | 0.629475 | 0.888488 | 0.723431 | 0.902953 | 0.925933 | +| **0.50** | **0.585762** | **0.641709** | 0.887173 | **0.731166** | 0.897831 | **0.926005** | +| 0.75 | 0.578003 | 0.637107 | 0.873910 | 0.726603 | 0.887894 | 0.921696 | +| 1.00 | 0.551443 | 0.615307 | 0.847043 | 0.715951 | 0.883263 | 0.921182 | + +Validation rare support: 12,726. Validation evaluated rare classes: `[0,1,2,3,7,8]`. +Ten of ten clients had validation eval data and rare classes. + +### B.2 NOT USED FOR SELECTION — test (produced automatically by the frozen pipeline) + +| beta | test accuracy | test macro-F1 | test weighted-F1 | test rareMacroF1 | test rare recall | wall-clock | +|---|---|---|---|---|---|---| +| 0.00 | 0.891974 | 0.710481 | 0.918369 | 0.613575 | 0.873508 | 41:17 | +| 0.25 | 0.903520 | 0.725299 | 0.926452 | 0.632154 | 0.877350 | 42:11 | +| 0.50 | 0.898158 | 0.730743 | 0.926333 | 0.640829 | 0.871583 | 42:57 | +| 0.75 | 0.888670 | 0.726298 | 0.922278 | 0.636189 | 0.857543 | 43:45 | +| 1.00 | 0.884635 | 0.716383 | 0.921910 | 0.615509 | 0.832407 | 42:08 | + +Test rare support: 25,454. The test columns are reported for transparency only and +played no part in selecting beta. + +--- + +## C. Beta selection + +- **Selected beta: `0.50`** — best among the tested candidates under this + validation condition. +- **Criterion:** maximum validation per-client-mean rare-class macro-F1 + (`0.585762`). The secondary global union-rare validation metric independently + also peaks at `beta = 0.50` (`0.641709`), so the two readings agree here. +- **Tie-break (not used):** higher validation rare-class macro-F1, then higher + validation rare-class recall, then smaller beta. +- **Test was not used for selection.** `val_selection.json` records + `"test_used_for_selection": false` for every run. Test metrics were not + inspected until after `beta = 0.50` was fixed from validation. + +--- + +## D. Final test evaluation (selected beta vs baseline) + +Selected `beta = 0.50` (test) versus the frozen `original_protean` reference for +the identical condition: + +| metric | original_protean | support_weighted beta=0.50 | difference | +|---|---|---|---| +| rare-class macro-F1 | 0.609620 | 0.640829 | +0.031209 | +| rare-class recall | 0.871487 | 0.871583 | +0.000096 | +| overall macro-F1 | 0.707701 | 0.730743 | +0.023042 | +| accuracy | 0.890432 | 0.898158 | +0.007726 | +| weighted-F1 | 0.917585 | 0.926333 | +0.008748 | + +Per-class test results, selected beta (support in parentheses): + +| class | precision | recall | F1 | support | +|---|---|---|---|---| +| C&C | 0.098 | 0.960 | 0.178 | 573 | +| Exfiltration | 1.000 | 0.944 | 0.971 | 4,427 | +| Exploitation | 0.017 | 0.581 | 0.033 | 227 | +| Lateral_movement | 0.965 | 0.781 | 0.864 | 6,319 | +| Normal | 0.971 | 0.935 | 0.953 | 83,651 | +| RDOS | 0.992 | 0.997 | 0.995 | 28,252 | +| Reconnaissance | 0.993 | 0.648 | 0.784 | 25,474 | +| Tampering | 0.732 | 0.989 | 0.841 | 1,024 | +| Weaponization | 0.941 | 0.973 | 0.957 | 12,884 | + +Per-client test results, selected beta: + +| client | n | accuracy | macro-F1 | rare macro-F1 | rare recall | +|---|---|---|---|---|---| +| 0 | 22,614 | 0.8996 | 0.6158 | 0.5123 | 0.9886 | +| 1 | 33,759 | 0.9265 | 0.6662 | 0.3998 | 0.6941 | +| 2 | 5,625 | 0.9195 | 0.7840 | 0.9038 | 0.9760 | +| 3 | 13,539 | 0.9846 | 0.8284 | 0.5481 | 0.5758 | +| 4 | 11,881 | 0.7090 | 0.6334 | 0.1411 | 0.7973 | +| 5 | 12,785 | 0.9200 | 0.7898 | 0.8331 | 0.9920 | +| 6 | 6,131 | 0.9335 | 0.7243 | 0.4422 | 1.0000 | +| 7 | 34,434 | 0.9145 | 0.6202 | 0.0477 | 0.8492 | +| 8 | 5,090 | 0.9589 | 0.8038 | 0.6343 | 0.9532 | +| 9 | 16,973 | 0.8158 | 0.5706 | 0.4051 | 0.6541 | + +**No superiority is claimed.** These are single-dataset, single-alpha, +single-seed results from one protocol-validation run. + +--- + +## E. Direct prototype analysis (no retraining) + +Computed from the fixed round-0 snapshot +(`artifacts/analysis/xiiotid__alpha0.5__seed0__support_weighted__beta0__prototype_snapshot.json`, +byte-identical across all beta runs) using the production aggregation; see +`...__prototype_effect.csv` and `...__prototype_weights.csv` under +`artifacts/analysis/`. + +Per-class `||Cbar_j(beta) - Cbar_j(0)||` at `beta = 1.0` (relative displacement +in parentheses), sorted by displacement: + +| class | contributors | support min–max (cv) | disp@beta=1 | rel disp | +|---|---|---|---|---| +| Weaponization | 10 | 18–32,409 (2.10) | 3.559 | 0.577 | +| C&C | 10 | 1–966 (1.50) | 2.156 | 0.370 | +| Lateral_movement | 10 | 49–8,385 (1.22) | 2.069 | 0.371 | +| Exploitation | 8 | 10–353 (1.09) | 1.934 | 0.342 | +| Tampering | 9 | 2–1,266 (1.00) | 1.737 | 0.341 | +| Exfiltration | 9 | 45–6,305 (1.04) | 1.547 | 0.214 | +| RDOS | 10 | 168–49,283 (1.38) | 1.286 | 0.148 | +| Reconnaissance | 10 | 1,137–29,234 (1.16) | 1.184 | 0.194 | +| Normal | 10 | 604–68,657 (0.76) | 0.990 | 0.164 | + +- **Largest direct displacement:** `Weaponization` (also the most support-imbalanced + class, cv 2.10, max/min 1,800). For that class at `beta = 1.0` the single + largest-support client (N=32,409) holds weight `0.719` versus `0.100` at + `beta = 0`, while a client with N=18 drops to `0.0004`. +- Classes with more balanced support (`Normal`, cv 0.76; `RDOS`, cv 1.38 with one + dominant client) move less. +- **No class has zero direct effect:** every class has `|S_j| ≥ 8` and unequal + supports, so all nine prototypes shift for `beta > 0`. At `beta = 1.0`, no + direct-effect displacement exceeded `0.58` of the class prototype norm. +- **Displacement is a direct aggregation measure only** and is not interpreted as + improved or degraded detection. The end-to-end effect (§D) is reported + separately. + +--- + +## F. Client-level analysis + +Per-client test rarity metrics relative to `beta = 0` +(`artifacts/analysis/xiiotid__alpha0.5__seed0__paired_client_beta_metrics.csv` +contains the full paired table including validation columns and `N_ij`). + +Delta test rare-class F1 vs `beta = 0` (negative = worse): + +| client | min positive N_ij | beta=0 rare F1 | Δβ=0.25 | Δβ=0.50 | Δβ=0.75 | Δβ=1.00 | +|---|---|---|---|---|---|---| +| 0 | 18 | 0.4996 | +0.0115 | +0.0127 | −0.0010 | −0.0177 | +| 1 | 69 | 0.3844 | +0.0124 | +0.0154 | +0.0107 | +0.0061 | +| 2 | 5 | 0.8823 | +0.0109 | +0.0215 | +0.0126 | −0.0364 | +| 3 | 140 | 0.6162 | −0.0066 | −0.0681 | −0.2566 | −0.3422 | +| 4 | 1 | 0.1375 | −0.0005 | +0.0036 | +0.0126 | −0.0143 | +| 5 | 2 | 0.7126 | +0.0500 | +0.1204 | +0.1213 | +0.0622 | +| 6 | 44 | 0.3788 | +0.0304 | +0.0634 | +0.0690 | +0.0325 | +| 7 | 10 | 0.0470 | +0.0109 | +0.0006 | −0.0073 | −0.0140 | +| 8 | 4 | 0.6316 | +0.0298 | +0.0027 | −0.0108 | −0.0274 | +| 9 | 12 | 0.3910 | +0.0069 | +0.0141 | +0.0093 | +0.0027 | + +Observations (descriptive, **not causal**): + +- At `beta = 0.50`, six of ten clients improve and the only materially negative + client is client 3 (−0.068); the per-client mean therefore peaks at + `beta = 0.50` (§B.1). +- At `beta = 0.75`/`1.00`, several clients lose ground, and the largest single + loss is client 3 (−0.257 / −0.342). Client 3 has `min positive N_ij = 140`, + so it is **not** the lowest-support client; conversely client 5 + (`min positive N_ij = 2`) is the largest gainer at `beta ≥ 0.5`. +- The relationship between `min positive N_ij` and ΔF1 is **not monotonic** in + this single condition. The data are recorded to enable the later, multi-seed + analysis of whether high beta disproportionately harms low-support clients; + no such claim is made here. + +--- + +## G. Runtime and resources + +Measured with `/usr/bin/time -v`, CPU only, no GPU, no swap. + +| beta | wall-clock | CPU % | peak RSS | +|---|---|---|---| +| 0.00 | 41:17 | 740 % | 6.21 GiB | +| 0.25 | 42:11 | 738 % | 6.21 GiB | +| 0.50 | 42:57 | 737 % | 6.21 GiB | +| 0.75 | 43:45 | 733 % | 6.21 GiB | +| 1.00 | 42:08 | 737 % | 6.21 GiB | + +Total: ~3 h 30 min for the five paired candidates, plus ~41 min for the initial +beta=0 measurement run (same configuration; not counted twice in the table +above beyond beta=0.00). Preprocessing/load is negligible next to training +(Session 3 measured ~0.9 s read and ~1.3 s prepare). No warnings, no client +failures, 9/9 global prototypes in every round, exit status 0. + +--- + +## H. Limitations + +- **Single dataset** (X-IIoTID), **single alpha** (0.50), **single seed** (0). +- The `S_j` participant/denominator rule + (`S_j = {i | N_ij > 0 and valid C_ij}`, equal weights in the baseline) is a + **REPRODUCTION ASSUMPTION**, not verified original PROTEAN behaviour. It is + material in 53/162 class-condition cells (32.7 %), 15/18 conditions + (`docs/baseline/BASELINE_VALIDATION.md` §D). `beta = 0` equality with the + baseline is **conditional** on both modes using this shared rule; the + `beta = 0` vs `original_protean` comparison below does **not** prove the rule + matches original PROTEAN. +- **No claim of a global beta optimum.** `beta = 0.50` is only the best among the + five tested candidates under this one validation procedure. +- **No full experiment grid has been run** (no other alpha, seed, dataset). +- The selected-beta test numbers are single-condition and may not generalise. +- The validation per-client evaluation partition used for selection is a + Session-5 clarification (mirrors the existing test per-client Dirichlet + procedure); it is a project decision, not source-confirmed. +- `beta = 0` under `support_weighted` is mathematically the equal mean but is + **not bitwise identical** to `original_protean` (see §I); the difference is + floating-point ordering, not a methodological change. + +--- + +## I. `beta = 0` equivalence check (vs frozen `original_protean`) + +| Property | Result | +|---|---| +| Same frozen manifest | **Yes** — identical manifest content/partition/split hashes | +| Same initialisation / round-0 setup | **Yes** — round-0 prototype snapshot byte-identical (`e27f41b3a62eb83c`) | +| Same training behaviour | **Mathematically yes, not bitwise.** `original_protean` computes `Σ(C_ij/count)`; `support_weighted beta=0` computes `Σ((1/count)·C_ij)`. Per-element difference is at most `2.2e-16`–`4.4e-16` (~1 ULP, relative ~1e-16) on the round-0 snapshot. | +| Same validation metrics | **No** — tiny divergence (e.g. global rare macro-F1 0.611641 vs 0.608087) | +| Same test metrics | **No** — accuracy 0.891974 vs 0.890432; rare macro-F1 0.613575 vs 0.609620 | +| Same deterministic artifacts | Round-0 snapshot, `train_support_Nij.csv`, `characterization.csv`, `rare_unseen.csv` identical; `history.json` and per-client/per-class metrics differ | + +Mechanism: round 0 is byte-identical, then the ~1 ULP aggregation difference +feeds back into round-1 prototype-alignment training and the trajectories +diverge slowly (differences grow from ~1e-4 at round 1 to ~1e-2 at round 9). This +is floating-point arithmetic ordering (`vec/count` vs `vec*(1/count)`), amplified +by iterative training, **not** a semantic difference in the aggregation formula. +No code was changed to force bitwise equality; the frozen baseline arithmetic was +left untouched. + +**Caveat:** this equivalence is conditional on the shared `S_j` reproduction +assumption and does not establish that `beta = 0` reproduces original PROTEAN. + +--- + +## J. Verdict + +The validation/test separation protocol ran cleanly end to end for this +condition: candidates trained on the paired manifest, beta was selected using +validation metrics only, and the selected beta was evaluated once on test. The +protocol appears ready, with the documented clarifications (validation per-client +evaluation partition; float-level `beta = 0` non-identity), to be scaled to +multi-seed/multi-alpha experiments. No full grid was launched. diff --git a/scripts/run_baseline.py b/scripts/run_baseline.py index e9c6165..2b469e3 100644 --- a/scripts/run_baseline.py +++ b/scripts/run_baseline.py @@ -252,6 +252,66 @@ def main(argv=None) -> int: "rare_class_recall": rare["rare_class_recall"], }) + # ---- global validation evaluation (SELECTION ONLY) ------------------- + # This block never influences training and is never used to tune any + # hyperparameter except the prototype-aggregation beta, which is selected + # on validation metrics only. The test split is untouched here. + val_embeddings = extract_embeddings(model, val_features) + val_pred = nearest_prototype_predict(val_embeddings, global_prototypes) + val_metrics = classification_metrics(val_labels, val_pred, bundle.num_classes) + val_rare = rare_class_metrics(val_labels, val_pred, bundle.num_classes, union_rare) + + val_eval_assignments, _ = partition_mod.partition_eval_split( + val_labels, args.clients, args.alpha, args.dataset, args.seed, "validation" + ) + val_per_client_rows = [] + for client_id in range(args.clients): + idx = val_eval_assignments[client_id] + if len(idx) == 0: + val_per_client_rows.append({ + "client_id": client_id, "n_samples": 0, "accuracy": None, + "macro_f1": None, "rare_class_macro_f1": None, "rare_class_recall": None, + }) + continue + emb = extract_embeddings(model, val_features[idx]) + pred = nearest_prototype_predict(emb, global_prototypes) + metrics = classification_metrics(val_labels[idx], pred, bundle.num_classes) + rare_ids = per_client_rare[client_id] + if rare_ids: + rare = rare_class_metrics(val_labels[idx], pred, bundle.num_classes, rare_ids) + else: + rare = {"rare_class_macro_f1": None, "rare_class_recall": None} + val_per_client_rows.append({ + "client_id": client_id, + "n_samples": int(len(idx)), + "accuracy": metrics["accuracy"], + "macro_f1": metrics["macro_f1"], + "rare_class_macro_f1": rare["rare_class_macro_f1"], + "rare_class_recall": rare["rare_class_recall"], + }) + + def _mean_over_present(rows, key): + values = [float(row[key]) for row in rows if row.get(key) is not None] + return float(sum(values) / len(values)) if values else None + + val_selection = { + "val_rare_class_macro_f1_global": val_rare["rare_class_macro_f1"], + "val_rare_class_recall_global": val_rare["rare_class_recall"], + "val_macro_f1_global": val_metrics["macro_f1"], + "val_weighted_f1_global": val_metrics["weighted_f1"], + "val_accuracy_global": val_metrics["accuracy"], + "val_rare_class_macro_f1_per_client_mean": _mean_over_present( + val_per_client_rows, "rare_class_macro_f1" + ), + "val_rare_class_recall_per_client_mean": _mean_over_present( + val_per_client_rows, "rare_class_recall" + ), + "val_n_eval_clients": sum(1 for row in val_per_client_rows if row["n_samples"] > 0), + "selection_metric": "rare_class_macro_f1_per_client_mean", + "selection_split": "validation", + "test_used_for_selection": False, + } + # ---- structured artifacts -------------------------------------------- mode_tag = args.aggregation_mode if args.aggregation_mode == "support_weighted": @@ -301,6 +361,19 @@ def main(argv=None) -> int: for row in test_metrics["per_class"] ]) write_csv(run_dir / "per_client.csv", per_client_rows) + write_json(run_dir / "val_global_metrics.json", { + "accuracy": val_metrics["accuracy"], + "macro_f1": val_metrics["macro_f1"], + "weighted_f1": val_metrics["weighted_f1"], + **val_rare, + }) + write_json(run_dir / "val_confusion_matrix.json", val_metrics["confusion_matrix"]) + write_csv(run_dir / "val_per_class.csv", [ + {**row, "class_name": bundle.class_names[row["class_id"]]} + for row in val_metrics["per_class"] + ]) + write_csv(run_dir / "val_per_client.csv", val_per_client_rows) + write_json(run_dir / "val_selection.json", val_selection) write_csv(run_dir / "train_support_Nij.csv", [ {"client_id": i, **{bundle.class_names[c]: int(support[i, c]) for c in range(bundle.num_classes)}} for i in range(args.clients)