fix(data): adversarial hardening of trained DataClass classifiers (self-review)

CHANGELOG.md

@@ -9,6 +9,7 @@ The format follows [Keep a Changelog](https://keepachangelog.com/en/1.0.0/). Thi
 ## [Unreleased]
 
 ### Added
+- Trained DataClass classifiers — adversarial hardening (follow-up to #264): stronger fixture where BOTH monotone features (token_magnitude, decimal_places) genuinely drive the label so the monotone constraint actually BINDS (was vacuous — decimal_places held constant); standardiser fit on the TRAIN split only (no test leakage); ModelManifest named honestly `monotone-logistic` (a realisation of the declared tf-lattice-wide-and-deep contract, not a full lattice). Validator now checks BOTH per-class heads monotone AND asserts the constraint binds (non-vacuous), and compares reproduced accuracy at the stored 4-decimal precision (fixes a latent tolerance bug the trivial 1.0-accuracy fixture had masked). 8 teeth.
 - Trained DataClass classifiers v0.1 — specified→trained→registered→reproduced (closes the program's last owed thread): `tools/train_dataclass_classifiers.py` trains real per-class LOGISTIC (one-vs-rest) + per-table SOFTMAX heads, MONOTONE-constrained in `monotonicFeatures` (weight >=0 projection = TF-Lattice essence), deterministically; emits trained weights + a conformant `ModelManifest` (id=DataClass modelRef) + train/eval `RunRecord`s (ids=runRef/evalRunRef) so the previously-dangling DataClass classifier references now RESOLVE. `validate-trained-classifiers` (recompute-don't-trust): digest matches weight bytes; eval accuracy REPRODUCED from weights+fixture (inflated claim fails); model verified genuinely monotone (perturb up → score never drops); refs resolve; artifacts schema-conform. Teeth-verified (tampered weights refused). Uses numpy.
 - Learned spell-correction + user dictionary v0.1 — from context, not dictionary matching (task #13): `tools/learned_dictionary.py` decides each UNKNOWN token from a count-based skip-gram word-sense predictor (PPMI + truncated SVD; SGNS≈PPMI-SVD) instead of a static wordlist. LEARN a token that recurs with a coherent word-sense (a real domain term the dictionary hadn't seen); CORRECT a rare token to the known word its SENSE matches (skip-gram cosine picks the target, not edit distance alone — a near-spelled but different-sense token is not auto-corrected); leave the rest UNKNOWN (fail-closed, human-admitted). Every decision is a proposal, never a silent rewrite. `validate-learned-dictionary` teeth: epistemiclevel learned, reciept->receipt by sense, qwzptl unknown, learned term never auto-corrected. Uses numpy.
 - Stopword deviation analysis v0.1 — the dropped words are governed vocabulary too (task #13): `tools/stopword_analysis.py` audits the loop's stoplist ACROSS domains using two signals — cross-domain deviation (concentration) AND compositional density (repeated-collocation rate) — because frequency alone can't tell a domain term from a stylistic quirk. Surfaces `term-candidate` (concentrated + compositional = a domain term hiding in the stoplist, propose un-stoplisting) vs `stylistic` (concentrated by style only) vs `noise` (uniform). `validate-stopword-analysis` teeth: domain terms surfaced; a stylistically-concentrated word ('and') is NOT wrongly promoted; a uniform word ('the') is noise. `stopword-analysis-live` audits the shipped stoplist over specs/*.md. Compositional density is the bigram floor of the k-gram TF-IDF/LSA differential (orders 3..7) to follow.

examples/model-manifest.dataclass-assigner.json

@@ -2,9 +2,9 @@
   "id": "urn:srcos:model-manifest:dataclass-assigner-lattice-v1",
   "type": "ModelManifest",
   "specVersion": "2.1.0",
-  "modelDigest": "sha256:6092cf3788d3cad23c5639dd5e10e92d74a652ccc56ef6250542a91beb452f04",
-  "displayName": "dataclass-assigner (monotone wide-and-deep)",
-  "architecture": "monotone-logistic-wide-and-deep",
+  "modelDigest": "sha256:d8774347e7f8c65488402e06d09043969f32930d196aae11d748ac6caab3b5b3",
+  "displayName": "dataclass-assigner (monotone logistic)",
+  "architecture": "monotone-logistic",
   "format": "onnx",
   "quantization": "none",
   "license": "Apache-2.0",

examples/run.dataclass-eval.json

@@ -12,7 +12,7 @@
       "value": 1.0,
       "perClassAccuracy": {
         "revenue": 1.0,
-        "cost": 1.0
+        "cost": 0.7857
       }
     }
   },