feat(data): TRAIN + register the DataClass classifiers — specified→trained→reproduced (task #14)

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 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.
 - k-gram TF-IDF/LSA differential v0.1 — confirm stopword candidates by compositional scale (task #13): `tools/kgram_tfidf_differential.py` measures a candidate word's domain-specificity across n-gram ORDERS 3..7 (TF-IDF over domains + LSA/truncated-SVD top component) and takes the differential. Signal is discounted by intrinsic unigram specificity so a stopword embedded in a domain phrase ('the state machine') can't borrow the phrase's specificity. A true term PERSISTS across orders (confirmed-term = strongest un-stoplist proposal); a concentrated-but-diffuse word ('and') or a borrowed-specificity word ('the') stays unconfirmed. `validate-kgram-differential` teeth: set/class/state confirmed across 3..7; 'and' unconfirmed; 'the' stripped by the unigram discount. Closes the two-stage design (stopword deviation -> k-gram confirmation). Uses numpy.

Makefile

@@ -1,6 +1,6 @@
-.PHONY: validate validate-resource-contract validate-measurement validate-value-type validate-source-locator validate-sourceos-repo-manifest validate-mesh-action-registry validate-control-plane-examples validate-nlboot-examples validate-lattice-data-governai-examples validate-ops-history-examples validate-runtime-observability-examples validate-interpretability-examples validate-lifecycle-boundary-examples validate-svf-contracts validate-sync-cycle-receipts validate-onboarding-examples validate-runtime-causality-examples validate-agentic-os-examples validate-triparty-examples validate-labor-market-examples validate-supply-chain-risk-examples validate-reasoning-examples validate-mpcc-event-examples validate-knowledge-nugget-examples validate-semantic-action-examples validate-epistemic-kernel-examples validate-ab-update-examples validate-device-service-examples validate-duplicate-schema-ids validate-lawful-dispatch-receipt validate-architectural-building-block validate-agent-passport-examples validate-seam-definition-examples validate-agent-system-vocabulary validate-genesis-inception-examples validate-measurement validate-world-model-examples validate-eval-item-examples validate-ingestion-pipeline-examples validate-data-acquisition-examples validate-glossary-alignment-examples validate-data-class-examples validate-table-keys validate-dag-loop validate-vocab-currency-loop vocab-currency-dogfood validate-glossary-promotion validate-agreement validate-stopword-analysis stopword-analysis-live validate-kgram-differential validate-learned-dictionary
+.PHONY: validate validate-resource-contract validate-measurement validate-value-type validate-source-locator validate-sourceos-repo-manifest validate-mesh-action-registry validate-control-plane-examples validate-nlboot-examples validate-lattice-data-governai-examples validate-ops-history-examples validate-runtime-observability-examples validate-interpretability-examples validate-lifecycle-boundary-examples validate-svf-contracts validate-sync-cycle-receipts validate-onboarding-examples validate-runtime-causality-examples validate-agentic-os-examples validate-triparty-examples validate-labor-market-examples validate-supply-chain-risk-examples validate-reasoning-examples validate-mpcc-event-examples validate-knowledge-nugget-examples validate-semantic-action-examples validate-epistemic-kernel-examples validate-ab-update-examples validate-device-service-examples validate-duplicate-schema-ids validate-lawful-dispatch-receipt validate-architectural-building-block validate-agent-passport-examples validate-seam-definition-examples validate-agent-system-vocabulary validate-genesis-inception-examples validate-measurement validate-world-model-examples validate-eval-item-examples validate-ingestion-pipeline-examples validate-data-acquisition-examples validate-glossary-alignment-examples validate-data-class-examples validate-table-keys validate-dag-loop validate-vocab-currency-loop vocab-currency-dogfood validate-glossary-promotion validate-agreement validate-stopword-analysis stopword-analysis-live validate-kgram-differential validate-learned-dictionary train-dataclass-classifiers
 
-validate: validate-data-class-examples validate-glossary-alignment-examples validate-data-acquisition-examples validate-ingestion-pipeline-examples validate-control-plane-examples validate-nlboot-examples validate-lattice-data-governai-examples validate-ops-history-examples validate-runtime-observability-examples validate-interpretability-examples validate-lifecycle-boundary-examples validate-svf-contracts validate-sync-cycle-receipts validate-onboarding-examples validate-runtime-causality-examples validate-agentic-os-examples validate-triparty-examples validate-labor-market-examples validate-supply-chain-risk-examples validate-reasoning-examples validate-mpcc-event-examples validate-knowledge-nugget-examples validate-semantic-action-examples validate-epistemic-kernel-examples validate-ab-update-examples validate-device-service-examples validate-duplicate-schema-ids validate-value-type validate-source-locator validate-sourceos-repo-manifest validate-mesh-action-registry validate-lawful-dispatch-receipt validate-architectural-building-block validate-agent-passport-examples validate-seam-definition-examples validate-agent-system-vocabulary validate-genesis-inception-examples validate-measurement validate-world-model-examples validate-eval-item-examples validate-resource-contract validate-table-keys validate-dag-loop validate-vocab-currency-loop validate-glossary-promotion validate-agreement validate-stopword-analysis validate-kgram-differential validate-learned-dictionary
+validate: validate-data-class-examples validate-glossary-alignment-examples validate-data-acquisition-examples validate-ingestion-pipeline-examples validate-control-plane-examples validate-nlboot-examples validate-lattice-data-governai-examples validate-ops-history-examples validate-runtime-observability-examples validate-interpretability-examples validate-lifecycle-boundary-examples validate-svf-contracts validate-sync-cycle-receipts validate-onboarding-examples validate-runtime-causality-examples validate-agentic-os-examples validate-triparty-examples validate-labor-market-examples validate-supply-chain-risk-examples validate-reasoning-examples validate-mpcc-event-examples validate-knowledge-nugget-examples validate-semantic-action-examples validate-epistemic-kernel-examples validate-ab-update-examples validate-device-service-examples validate-duplicate-schema-ids validate-value-type validate-source-locator validate-sourceos-repo-manifest validate-mesh-action-registry validate-lawful-dispatch-receipt validate-architectural-building-block validate-agent-passport-examples validate-seam-definition-examples validate-agent-system-vocabulary validate-genesis-inception-examples validate-measurement validate-world-model-examples validate-eval-item-examples validate-resource-contract validate-table-keys validate-dag-loop validate-vocab-currency-loop validate-glossary-promotion validate-agreement validate-stopword-analysis validate-kgram-differential validate-learned-dictionary validate-trained-classifiers
 	@echo "OK: validate"
 
 validate-ingestion-pipeline-examples:
@@ -30,6 +30,15 @@ validate-learned-dictionary:
 	python3 -m pip install --user numpy >/dev/null
 	python3 tools/validate_learned_dictionary.py
 
+validate-trained-classifiers:
+	python3 -m pip install --user jsonschema numpy referencing >/dev/null
+	python3 tools/validate_trained_classifiers.py
+
+# On-demand: retrain + re-emit the DataClass classifier artifacts (deterministic).
+train-dataclass-classifiers:
+	python3 -m pip install --user numpy >/dev/null
+	python3 tools/train_dataclass_classifiers.py
+
 validate-stopword-analysis:
 	python3 tools/validate_stopword_analysis.py
 

examples/model-manifest.dataclass-assigner.json

@@ -0,0 +1,19 @@
+{
+  "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",
+  "format": "onnx",
+  "quantization": "none",
+  "license": "Apache-2.0",
+  "signature": {
+    "algorithm": "ed25519",
+    "keyId": "srcos-model-signing-2026",
+    "signatureDigest": "sha256:bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb"
+  },
+  "evidenceRefs": [
+    "urn:srcos:run:dataclass-currency-logistic-eval-001"
+  ]
+}

examples/run.dataclass-eval.json

@@ -0,0 +1,53 @@
+{
+  "id": "urn:srcos:run:dataclass-currency-logistic-eval-001",
+  "type": "RunRecord",
+  "specVersion": "2.0.0",
+  "workload": {
+    "workloadId": "urn:srcos:workload:dataclass-assigner-train",
+    "kind": "container",
+    "image": "ghcr.io/srcos/dataclass-trainer:v1",
+    "entrypoint": "train",
+    "params": {
+      "metric": "accuracy",
+      "value": 1.0,
+      "perClassAccuracy": {
+        "revenue": 1.0,
+        "cost": 1.0
+      }
+    }
+  },
+  "sphere": {
+    "sphereId": "urn:srcos:sphere:platform_curated",
+    "name": "Platform Curated Sphere",
+    "boundary": {
+      "zone": "curated",
+      "networkPolicy": null,
+      "storagePolicy": null
+    },
+    "controls": {
+      "accessEnforcer": "enforcer.v2",
+      "provenance": "hashes",
+      "containerAttestation": "basic"
+    }
+  },
+  "inputs": [
+    {
+      "refType": "dataset",
+      "id": "urn:srcos:dataset:dataclass-assigner-weights",
+      "fieldPaths": []
+    }
+  ],
+  "outputs": [
+    {
+      "refType": "dataset",
+      "id": "urn:srcos:dataset:dataclass-eval-report",
+      "fieldPaths": []
+    }
+  ],
+  "tokenRef": "tok_dataclass",
+  "status": "succeeded",
+  "time": {
+    "startedAt": "2026-08-03T02:00:00Z",
+    "endedAt": "2026-08-03T02:00:20Z"
+  }
+}

examples/run.dataclass-train.json

@@ -0,0 +1,53 @@
+{
+  "id": "urn:srcos:run:dataclass-assigner-train-001",
+  "type": "RunRecord",
+  "specVersion": "2.0.0",
+  "workload": {
+    "workloadId": "urn:srcos:workload:dataclass-assigner-train",
+    "kind": "container",
+    "image": "ghcr.io/srcos/dataclass-trainer:v1",
+    "entrypoint": "train",
+    "params": {
+      "epochs": 400,
+      "lr": 0.3,
+      "monotonicFeatures": [
+        "token_magnitude",
+        "decimal_places"
+      ]
+    }
+  },
+  "sphere": {
+    "sphereId": "urn:srcos:sphere:platform_curated",
+    "name": "Platform Curated Sphere",
+    "boundary": {
+      "zone": "curated",
+      "networkPolicy": null,
+      "storagePolicy": null
+    },
+    "controls": {
+      "accessEnforcer": "enforcer.v2",
+      "provenance": "hashes",
+      "containerAttestation": "basic"
+    }
+  },
+  "inputs": [
+    {
+      "refType": "dataset",
+      "id": "urn:srcos:dataset:dataclass-training",
+      "fieldPaths": []
+    }
+  ],
+  "outputs": [
+    {
+      "refType": "dataset",
+      "id": "urn:srcos:dataset:dataclass-assigner-weights",
+      "fieldPaths": []
+    }
+  ],
+  "tokenRef": "tok_dataclass",
+  "status": "succeeded",
+  "time": {
+    "startedAt": "2026-08-03T02:00:00Z",
+    "endedAt": "2026-08-03T02:00:20Z"
+  }
+}