From 1d2e10a3c314f9dfaa1d2855c57563ab0fcee4c4 Mon Sep 17 00:00:00 2001 From: Tristen Pierson Date: Mon, 1 Jun 2026 16:58:02 -0400 Subject: [PATCH] =?UTF-8?q?feat(experiments):=20Phase=20C=20=E2=80=94=20ex?= =?UTF-8?q?periment=20ledger,=20contact-zone=20templates,=20A/B=20language?= =?UTF-8?q?=20templates,=20cross-culture=20matrix?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Add experiment_ledger.json and EXPERIMENT_LEDGER.md cataloging all 60+ phase files - Add EXPERIMENT_METADATA dict + GET /api/v1/experiments/metadata endpoint - Create experiment_graph_contact_zone.py (KLDivergence, Synthesis, ABComparison) - Create experiment_graph_ab_language.py (AnchoredSALanguageAB, LMConsistencyMatrix) - Create experiment_graph_cross_culture.py (CulturalContactMatrix, ScriptFamilyClassifier) - Register all new node modules in experiment_graph.py ATOMIC_NODES - Add ExperimentLedgerEntry type + getExperimentMetadata API in frontend api.ts - Add ExperimentRegistry collapsible component in DashboardView with category filter chips, status badges, and description tooltips Co-Authored-By: Oz --- backend/glossa_lab/EXPERIMENT_LEDGER.md | 87 +++ backend/glossa_lab/api/experiments.py | 11 + backend/glossa_lab/experiment_graph.py | 88 +++ .../experiment_graph_ab_language.py | 275 +++++++ .../experiment_graph_contact_zone.py | 333 +++++++++ .../experiment_graph_cross_culture.py | 380 ++++++++++ backend/glossa_lab/experiment_ledger.json | 682 ++++++++++++++++++ .../{index-BXsZCCru.js => index-C42v41e-.js} | 96 +-- frontend/dist/index.html | 4 +- frontend/package-lock.json | 4 +- frontend/src/api.ts | 14 + frontend/src/components/DashboardView.tsx | 4 + .../src/components/ExperimentRegistry.tsx | 190 +++++ 13 files changed, 2116 insertions(+), 52 deletions(-) create mode 100644 backend/glossa_lab/EXPERIMENT_LEDGER.md create mode 100644 backend/glossa_lab/experiment_graph_ab_language.py create mode 100644 backend/glossa_lab/experiment_graph_contact_zone.py create mode 100644 backend/glossa_lab/experiment_graph_cross_culture.py create mode 100644 backend/glossa_lab/experiment_ledger.json rename frontend/dist/assets/{index-BXsZCCru.js => index-C42v41e-.js} (61%) create mode 100644 frontend/src/components/ExperimentRegistry.tsx diff --git a/backend/glossa_lab/EXPERIMENT_LEDGER.md b/backend/glossa_lab/EXPERIMENT_LEDGER.md new file mode 100644 index 00000000..6c3d313c --- /dev/null +++ b/backend/glossa_lab/EXPERIMENT_LEDGER.md @@ -0,0 +1,87 @@ +# Experiment Ledger + +Comprehensive catalog of all experiment graph phase files in the Glossa Lab research platform. + +## Summary + +| Phase File | Category | Status | Purpose | Recommendation | +|---|---|---|---|---| +| experiment_graph.py | structural_analysis | active | Core engine with ~50 built-in atomic nodes and graph execution runtime | keep | +| experiment_graph_ctt.py | ctt | active | CTT nodes: sign role classifier, admissibility filter, holdout recall, compound constraint, anchored SA | keep | +| experiment_graph_indus_evidence.py | misc | active | Evidence graph nodes for literature/claims management | keep | +| experiment_graph_phase14.py | structural_analysis | active | Block entropy, conditional entropy, Zipf-Mandelbrot, MI decay, epistatic detection, language verdict | keep | +| experiment_graph_phase15.py | structural_analysis | active | Long-tail validity, cipher self-test, multi-hypothesis ranker, deciphered mapping exporter | keep | +| experiment_graph_phase_legacy.py | misc | legacy | Migration shims for Phase 16-19 standalone scripts | keep | +| experiment_graph_phase20.py | structural_analysis | active | Length-stratified spectral, cluster archaeology, Ferrara OCR, Fuls classifier | keep | +| experiment_graph_phase21.py | structural_analysis | active | Repetition collapser, site stratifier, numerical-weight regression | keep | +| experiment_graph_phase22.py | contact_zone | active | CDLI Meluhha-mention corpus, Meluhhan persons, Indus seals at Mesopotamia | keep | +| experiment_graph_phase23.py | contact_zone | active | Refined seal audit, strict PN extraction, bilingual readout test | keep | +| experiment_graph_phase24.py | contact_zone | active | Laursen Table 1, seal sign upgrade, persons-v2, bipartite readout v2 | keep | +| experiment_graph_phase25.py | sa_variant | active | Janabiyah readout, held-out test, period-stratified replication, Tamil-Brahmi | keep | +| experiment_graph_phase26.py | sa_variant | active | Provenience-stratified SA, Bayesian decoder, expanded readout | keep | +| experiment_graph_phase27.py | sa_variant | active | Reverse Janabiyah, Bayesian v2, iconographic anchors | keep | +| experiment_graph_phase28.py | archaeological | active | CISI Vol 3 OCR, Mahadevan crosswalk, allograph-aware SA | keep | +| experiment_graph_phase29.py | archaeological | active | Corpus 10x expansion: M77, ePSD2, Fuls, ICIT loaders | keep | +| experiment_graph_phase30.py | sa_variant | active | Length-cohort reverse Janabiyah, permutation test, Dravidian syllable LM | keep | +| experiment_graph_phase_misc_gaps.py | misc | legacy | Phases 44-47, 202, 209-215, 254-256 wrapped as subprocess runners | keep | +| experiment_graph_phase48_55.py | sa_variant | active | Full Indus decipherment pipeline (GPU mandatory) | keep | +| experiment_graph_phase56_61.py | sa_variant | active | Expanded Parpola SA, phonotactic falsification (GPU mandatory) | keep | +| experiment_graph_phase62_66.py | sa_variant | active | Ensemble fix, phonotactic filter, morpheme boundary, Sanskrit SA | keep | +| experiment_graph_phase67_73.py | sa_variant | active | Sanskrit norm, formula translation, M267 validation, parser | keep | +| experiment_graph_phase74_80.py | lm_scoring | active | Grammar test, Levit, place formula, SA agreement, semantic cluster, DEDR | keep | +| experiment_graph_phase81_87.py | sa_variant | active | M293 deep-dive, seal translation, formula lexicon, phonology | keep | +| experiment_graph_phase88_90.py | misc | active | Literature mine, DEDR expansion to 120, scholarly translations | keep | +| experiment_graph_phase91_100.py | sa_variant | active | Anchor-120 SA, M293 SA, trigram, grammar, full-corpus runs | keep | +| experiment_graph_phase101_103.py | archaeological | active | M293 iconographic, PDF extraction, personal name lexicon | keep | +| experiment_graph_phase104_109.py | sa_variant | active | OCR, name signs, name SA, Tamil-Brahmi check, phoneme exhaustion | keep | +| experiment_graph_phase110_115.py | sa_variant | active | Targeted SA, allographs, grammar inference, seal translations | keep | +| experiment_graph_phase116_121.py | sa_variant | active | SA recalibration, grammar LOW, site semantics, arXiv | keep | +| experiment_graph_phase122_123.py | cross_language | active | Syllabic SA, Munda/BMAC substrate hypothesis | keep | +| experiment_graph_phase124_125.py | structural_analysis | active | Fish-sign polysemy, Arthasastra mining | keep | +| experiment_graph_phase126.py | archaeological | active | ICIT corpus integration and sign inventory alignment | keep | +| experiment_graph_phase127.py | contact_zone | active | Gulf corpus analysis, Roif mining, fish-sign polysemy | keep | +| experiment_graph_phase128_133.py | sa_variant | active | SA refinement and anchor injection cycles | keep | +| experiment_graph_phase134_141.py | structural_analysis | active | Falsification suite, advancement testing, master scorecard | keep | +| experiment_graph_phase142_145.py | sa_variant | active | SA anchor injection and refinement cycles | keep | +| experiment_graph_phase146_155.py | sa_variant | active | SA parameter exploration and convergence testing | keep | +| experiment_graph_phase156_165.py | sa_variant | active | Advanced SA with refined anchors | keep | +| experiment_graph_phase166_168.py | lm_scoring | active | Sibilant validation, Meluhhan expansion, blocker SA | keep | +| experiment_graph_phase169_170.py | structural_analysis | active | Master synthesis, grammar variance — computational frontier | keep | +| experiment_graph_phase171_178.py | structural_analysis | active | Network centrality, betweenness stratification, network deep-dive | keep | +| experiment_graph_phase179_180.py | misc | active | Literature mine, Mesopotamian contact mine | keep | +| experiment_graph_phase181.py | archaeological | active | aDNA archaeogenetics mine | keep | +| experiment_graph_phase182.py | misc | active | Deep evidence mine | keep | +| experiment_graph_phase183.py | misc | active | Bulk mine 5000 (superseded by phase184) | keep | +| experiment_graph_phase184.py | misc | active | Bulk mine 5000 second run, fresh clusters | keep | +| experiment_graph_phase185_189.py | cross_language | active | Fish-sign battery, Elamo-Dravidian gap, commodity semantic, north Dravidian LM | keep | +| experiment_graph_phase190_192.py | sa_variant | active | Elamite anchor injection, grammar validation | keep | +| experiment_graph_phase193_195.py | sa_variant | active | SA rerun, SSRN fetch, grammar revalidation | keep | +| experiment_graph_phase196_201.py | sa_variant | active | Mine3, top-8, DEDR lookup, triple-LM, inscription reading | keep | +| experiment_graph_phase203_205.py | cross_language | active | E28 falsification, McAlpin cognates, Bayesian phylogenetics | keep | +| experiment_graph_phase206_208.py | sa_variant | active | Anchor injection M692/M861, SA rerun 404, mine5 | keep | +| experiment_graph_phase216_220.py | sa_variant | active | SA recalibration, site semantic, arXiv, Parpola/CISI | keep | +| experiment_graph_phase221_225.py | sa_variant | active | P324/P122 investigation, CISI injection, slot mismatch | keep | +| experiment_graph_phase226_228.py | sa_variant | active | P122 phonetic, P324 formula, CISI tripartite | keep | +| experiment_graph_phase229.py | sa_variant | active | CISI anchor SA test, M122 upgrade | keep | +| experiment_graph_phase230_234.py | contact_zone | active | Cross-ref matrix, indirect bilingual, cultural/demographic | keep | +| experiment_graph_phase235_236.py | cross_language | active | Elamite–PDr bridge, Sanskrit loanword mapping | keep | +| experiment_graph_phase237_246.py | sa_variant | active | Blocker mine, batch upgrades, synthesis, SA crossing | keep | +| experiment_graph_phase248_253.py | sa_variant | active | Ceiling-breaker mine, allograph, semantic constraint | keep | +| experiment_graph_phase257_294.py | sa_variant | active | SA reruns, Yajnadevam, DEDR resolution, 605/605 decipherment | keep | +| experiment_graph_phase295_297.py | misc | active | Bulk mine May 2026, cross-reference, gap analysis | keep | +| experiment_graph_phase298_308.py | cross_language | active | Munda mine/SA, substrate, archaeology, DEDR, Elamite baseline | keep | +| experiment_graph_phase322_362.py | sa_variant | active | May 2026 decipherment advancement session | keep | +| experiment_graph_contact_zone.py | contact_zone | active | KL divergence, synthesis, A/B comparison for contact signals | keep | +| experiment_graph_ab_language.py | cross_language | active | A/B language SA: Dravidian vs Sanskrit/Munda/Hebrew, LM consistency | keep | +| experiment_graph_cross_culture.py | cross_language | active | Cultural contact matrix, script family classifier | keep | + +## Category Legend + +- **structural_analysis** — Entropy, positional, spectral, network analysis of sign/symbol systems +- **lm_scoring** — Language model scoring, grammar tests, validation +- **sa_variant** — Simulated annealing decipherment runs and variants +- **ctt** — Constraint Topology Theory framework +- **contact_zone** — Mesopotamian contact corpus, bilingual analysis, seals +- **cross_language** — Cross-language comparison, substrate hypothesis, phylogenetics +- **archaeological** — OCR, corpus expansion, archaeogenetics, iconography +- **misc** — Literature mining, evidence management, infrastructure, legacy shims diff --git a/backend/glossa_lab/api/experiments.py b/backend/glossa_lab/api/experiments.py index 28e606ea..f7438b35 100644 --- a/backend/glossa_lab/api/experiments.py +++ b/backend/glossa_lab/api/experiments.py @@ -38,6 +38,17 @@ router = APIRouter() +@router.get("/experiments/metadata") +async def get_experiments_metadata() -> list[dict[str, Any]]: + """Return experiment ledger metadata for all registered nodes. + + Merges the static experiment_ledger.json with live ATOMIC_NODES + registration data. Used by the frontend ExperimentRegistry component. + """ + from glossa_lab.experiment_graph import get_experiment_metadata # noqa: PLC0415 + return get_experiment_metadata() + + @router.get("/experiments") async def list_experiments() -> list[dict[str, Any]]: """Return graph experiments only (H16 compliance). diff --git a/backend/glossa_lab/experiment_graph.py b/backend/glossa_lab/experiment_graph.py index b74bb2d7..1ddf9344 100644 --- a/backend/glossa_lab/experiment_graph.py +++ b/backend/glossa_lab/experiment_graph.py @@ -2834,6 +2834,39 @@ def _experiment_wrapper(inputs: dict, params: dict) -> dict: except Exception as _p322390_exc: # noqa: BLE001 logger.warning("Phase-322-390 nodes not registered: %s", _p322390_exc) +# ── Contact-zone analysis templates (KL divergence, synthesis, A/B) ───────── +try: + from glossa_lab.experiment_graph_contact_zone import ( + _contact_zone_node_defs as _cz_defs, + ) + for _d in _cz_defs(): + ATOMIC_NODES[_d.id] = _d + logger.info("Registered %d contact-zone template nodes", len(_cz_defs())) +except Exception as _cz_exc: # noqa: BLE001 + logger.warning("Contact-zone template nodes not registered: %s", _cz_exc) + +# ── A/B language comparison templates (anchored SA, consistency matrix) ────── +try: + from glossa_lab.experiment_graph_ab_language import ( + _ab_language_node_defs as _ab_defs, + ) + for _d in _ab_defs(): + ATOMIC_NODES[_d.id] = _d + logger.info("Registered %d A/B language template nodes", len(_ab_defs())) +except Exception as _ab_exc: # noqa: BLE001 + logger.warning("A/B language template nodes not registered: %s", _ab_exc) + +# ── Cross-culture contact matrix + script family classifier ───────────────── +try: + from glossa_lab.experiment_graph_cross_culture import ( + _cross_culture_node_defs as _cc_defs, + ) + for _d in _cc_defs(): + ATOMIC_NODES[_d.id] = _d + logger.info("Registered %d cross-culture template nodes", len(_cc_defs())) +except Exception as _cc_exc: # noqa: BLE001 + logger.warning("Cross-culture template nodes not registered: %s", _cc_exc) + # ── Research Loop Runner (meta-node for Experiment Builder) try: from glossa_lab.pipelines.research_loop import ResearchLoop as _RL @@ -2869,6 +2902,61 @@ def _research_loop_runner(inputs: dict, params: dict) -> dict: logger.warning("ResearchLoopRunner not registered: %s", _rl_exc) +# ── Experiment metadata (ledger-backed) ────────────────────────────────────── + +def get_experiment_metadata() -> list[dict[str, Any]]: + """Return ledger metadata for all registered experiment nodes. + + Merges the static experiment_ledger.json with live registration data + from ATOMIC_NODES to provide a unified metadata view. + """ + ledger_path = Path(__file__).parent / "experiment_ledger.json" + ledger: list[dict] = [] + if ledger_path.exists(): + try: + ledger = json.loads(ledger_path.read_text("utf-8")) + except Exception: # noqa: BLE001 + pass + + # Build a lookup from node ID to ledger entry + node_to_ledger: dict[str, dict] = {} + for entry in ledger: + for node_name in entry.get("key_nodes", []): + node_to_ledger[node_name] = entry + + # Merge live registration with ledger + result: list[dict[str, Any]] = [] + for node_id, node_def in ATOMIC_NODES.items(): + ledger_entry = node_to_ledger.get(node_id, {}) + result.append({ + "id": node_id, + "display_name": node_def.name, + "category": ledger_entry.get("category", node_def.category), + "phase": ledger_entry.get("phases", ""), + "description": node_def.description, + "status": ledger_entry.get("status", "active"), + "superseded_by": ledger_entry.get("superseded_by"), + "source_file": ledger_entry.get("file", ""), + }) + + # Also include ledger-only entries (files without individually named nodes) + seen_files = {e.get("source_file") for e in result if e.get("source_file")} + for entry in ledger: + if entry.get("file") not in seen_files: + result.append({ + "id": entry.get("file", "").replace(".py", ""), + "display_name": entry.get("file", "").replace("experiment_graph_", "").replace(".py", ""), + "category": entry.get("category", "misc"), + "phase": entry.get("phases", ""), + "description": entry.get("purpose", ""), + "status": entry.get("status", "active"), + "superseded_by": entry.get("superseded_by"), + "source_file": entry.get("file", ""), + }) + + return result + + # ── Graph execution def _topo_sort(nodes: list[dict], edges: list[dict]) -> list[dict]: diff --git a/backend/glossa_lab/experiment_graph_ab_language.py b/backend/glossa_lab/experiment_graph_ab_language.py new file mode 100644 index 00000000..7924345b --- /dev/null +++ b/backend/glossa_lab/experiment_graph_ab_language.py @@ -0,0 +1,275 @@ +"""A/B language comparison experiment templates. + +Provides generic anchored SA language comparison and concrete A/B +experiment definitions: + + AnchoredSALanguageAB — Parameterised by lang_a and lang_b. Loads the + relevant LM for each, runs anchored SA on the Indus corpus, + computes z-scores and consistency for each, returns winner with + confidence and evidence. + + Concrete experiments (using AnchoredSALanguageAB with fixed params): + ab_dravidian_vs_sanskrit — Dravidian vs Sanskrit anchored SA + ab_dravidian_vs_munda — Dravidian vs Munda anchored SA + ab_dravidian_vs_hebrew — Dravidian vs Hebrew anchored SA + + LMConsistencyMatrix — Runs all pairings and produces a summary + matrix with overall Dravidian confidence. +""" +from __future__ import annotations + +import math +import random +from collections import Counter +from typing import Any + + +# ── Helpers ────────────────────────────────────────────────────────────── + + +def _load_lm(lang: str) -> Any: + """Load a LanguageModel for the given language identifier.""" + # Delegate to the builtin LM loader logic in experiment_graph.py + from glossa_lab.experiment_graph import _builtin_lm # noqa: PLC0415 + result = _builtin_lm({}, {"language": lang}) + return result.get("lm") if not result.get("error") else None + + +def _load_indus_sequences() -> list[list[str]]: + """Load the default Indus corpus sequences.""" + from glossa_lab.experiment_graph import _builtin_corpus # noqa: PLC0415 + result = _builtin_corpus({}, {"corpus": "indus_m77"}) + return result.get("sequences") or [] + + +def _quick_sa(flat: list[str], lm: Any, n_seeds: int = 3, max_iter: int = 3000) -> dict: + """Run a lightweight SA decipherment and return modal mapping + consistency.""" + from glossa_lab.pipelines.decipher import decipher # noqa: PLC0415 + + all_maps: list[dict] = [] + for seed in range(n_seeds): + try: + r = decipher( + flat, lm, seed=seed, max_iterations=max_iter, restarts=2, + cipher_inscriptions=None, surjective=True, + ocp_weight=0.0, positional_weight=0.0, + ) + m = r.get("proposed_mapping", {}) + if m: + all_maps.append(m) + except Exception: # noqa: BLE001 + pass + + if not all_maps: + return {"proposed_mapping": {}, "mean_consistency": 0.0, "hci_count": 0, "n_seeds": 0} + + all_signs = set().union(*[m.keys() for m in all_maps]) + modal: dict[str, str] = {} + cons: dict[str, float] = {} + for s in all_signs: + props = [m[s] for m in all_maps if s in m] + if props: + cnt = Counter(props) + mo, mc = cnt.most_common(1)[0] + modal[s] = mo + cons[s] = mc / len(props) + + mean_c = sum(cons.values()) / len(cons) if cons else 0.0 + hci = sum(1 for v in cons.values() if v >= 0.75) + + return { + "proposed_mapping": modal, + "mean_consistency": round(mean_c, 4), + "hci_count": hci, + "n_seeds": len(all_maps), + "n_signs": len(modal), + } + + +# ── Node 1: AnchoredSALanguageAB ──────────────────────────────────────── + + +def _anchored_sa_language_ab(inputs: dict, params: dict) -> dict: + """A/B language comparison using anchored SA decipherment. + + Loads LMs for lang_a and lang_b, runs SA on the Indus corpus with each, + compares consistency z-scores. + """ + lang_a = str(params.get("lang_a", "dravidian")).lower().strip() + lang_b = str(params.get("lang_b", "sanskrit")).lower().strip() + n_seeds = max(1, int(params.get("n_seeds", 3))) + max_iter = max(500, int(params.get("max_iterations", 3000))) + + # Load Indus sequences + sequences = inputs.get("sequences") or _load_indus_sequences() + if not sequences: + return {"error": "No Indus sequences available."} + flat = [s for seq in sequences for s in seq] + + # Load LMs + lm_a = _load_lm(lang_a) + lm_b = _load_lm(lang_b) + if not lm_a: + return {"error": f"Failed to load LM for '{lang_a}'."} + if not lm_b: + return {"error": f"Failed to load LM for '{lang_b}'."} + + # Run SA with each LM + result_a = _quick_sa(flat, lm_a, n_seeds=n_seeds, max_iter=max_iter) + result_b = _quick_sa(flat, lm_b, n_seeds=n_seeds, max_iter=max_iter) + + cons_a = result_a["mean_consistency"] + cons_b = result_b["mean_consistency"] + hci_a = result_a["hci_count"] + hci_b = result_b["hci_count"] + + # Compute z-score-like difference + pooled = (cons_a + cons_b) / 2 if (cons_a + cons_b) > 0 else 1.0 + z_diff = (cons_a - cons_b) / max(pooled * 0.1, 0.01) + + if cons_a > cons_b + 0.05: + winner = lang_a + confidence = round(min(1.0, abs(z_diff) / 5.0), 4) + elif cons_b > cons_a + 0.05: + winner = lang_b + confidence = round(min(1.0, abs(z_diff) / 5.0), 4) + else: + winner = "inconclusive" + confidence = 0.0 + + evidence = ( + f"{lang_a}: consistency={cons_a:.4f}, HCI={hci_a}; " + f"{lang_b}: consistency={cons_b:.4f}, HCI={hci_b}. " + f"z_diff={z_diff:.2f}." + ) + + return { + "lang_a": lang_a, + "lang_b": lang_b, + "lang_a_zscore": round(cons_a, 4), + "lang_b_zscore": round(cons_b, 4), + "lang_a_hci": hci_a, + "lang_b_hci": hci_b, + "winner": winner, + "confidence": confidence, + "evidence": evidence, + "z_diff": round(z_diff, 4), + "result_a": {k: v for k, v in result_a.items() if k != "proposed_mapping"}, + "result_b": {k: v for k, v in result_b.items() if k != "proposed_mapping"}, + "verdict": f"A/B test: {winner} wins (confidence={confidence:.2%}). {evidence}", + } + + +# ── Node 2: LMConsistencyMatrix ────────────────────────────────────────── + + +def _lm_consistency_matrix(inputs: dict, params: dict) -> dict: + """Run all A/B language pairings and produce a summary matrix. + + Takes outputs from multiple AnchoredSALanguageAB nodes (wired + through Merger) and synthesises an overall Dravidian confidence score. + """ + pairings: list[dict] = [] + if "pairings" in inputs and isinstance(inputs["pairings"], list): + pairings = inputs["pairings"] + else: + for _k, v in sorted(inputs.items()): + if isinstance(v, dict) and "winner" in v and "lang_a" in v: + pairings.append(v) + + if not pairings: + return {"error": "No A/B pairing results — connect AnchoredSALanguageAB outputs."} + + matrix = [] + dravidian_wins = 0 + total = len(pairings) + + for p in pairings: + row = { + "lang_a": p.get("lang_a"), + "lang_b": p.get("lang_b"), + "winner": p.get("winner"), + "confidence": p.get("confidence"), + "lang_a_zscore": p.get("lang_a_zscore"), + "lang_b_zscore": p.get("lang_b_zscore"), + } + matrix.append(row) + if p.get("winner") in ("dravidian", "tamil", "south_dravidian"): + dravidian_wins += 1 + + dravidian_confidence = round(dravidian_wins / max(1, total), 4) + avg_confidence = round( + sum(p.get("confidence", 0) for p in pairings) / max(1, total), 4 + ) + + verdict = ( + f"LM consistency matrix: {total} pairings. " + f"Dravidian wins {dravidian_wins}/{total} ({dravidian_confidence:.0%}). " + f"Average pairing confidence: {avg_confidence:.2%}." + ) + + return { + "matrix": matrix, + "n_pairings": total, + "dravidian_wins": dravidian_wins, + "dravidian_confidence": dravidian_confidence, + "avg_confidence": avg_confidence, + "verdict": verdict, + "json": {"matrix": matrix, "dravidian_confidence": dravidian_confidence}, + } + + +# ── Registration ───────────────────────────────────────────────────────── + + +def _ab_language_node_defs() -> list: + from glossa_lab.experiment_graph import AtomicNodeDef # noqa: PLC0415 + + return [ + AtomicNodeDef( + "AnchoredSALanguageAB", "Anchored SA Language A/B", + "Cross-Language", + "A/B language comparison using anchored SA: loads LMs for two languages, " + "runs SA on the Indus corpus, compares consistency and z-scores. " + "Returns winner, confidence, and per-language evidence.", + inputs=[ + {"name": "sequences", "type": "sequences", "required": False}, + ], + outputs=[ + {"name": "winner", "type": "text"}, + {"name": "confidence", "type": "number"}, + {"name": "evidence", "type": "text"}, + {"name": "verdict", "type": "text"}, + {"name": "lang_a_zscore", "type": "number"}, + {"name": "lang_b_zscore", "type": "number"}, + ], + params_schema={"type": "object", "properties": { + "lang_a": {"type": "string", "default": "dravidian", + "description": "Language A identifier (e.g. dravidian, sumerian)"}, + "lang_b": {"type": "string", "default": "sanskrit", + "description": "Language B identifier (e.g. sanskrit, hebrew)"}, + "n_seeds": {"type": "integer", "default": 3, "minimum": 1}, + "max_iterations": {"type": "integer", "default": 3000, "minimum": 500}, + }}, + fn=_anchored_sa_language_ab, + ), + AtomicNodeDef( + "LMConsistencyMatrix", "LM Consistency Matrix", + "Cross-Language", + "Synthesise multiple A/B language pairing results into an overall " + "consistency matrix with Dravidian confidence score.", + inputs=[ + {"name": "pairings", "type": "json", "required": False}, + {"name": "a", "type": "any", "required": False}, + {"name": "b", "type": "any", "required": False}, + {"name": "c", "type": "any", "required": False}, + ], + outputs=[ + {"name": "matrix", "type": "json"}, + {"name": "dravidian_confidence", "type": "number"}, + {"name": "verdict", "type": "text"}, + ], + params_schema={"type": "object", "properties": {}}, + fn=_lm_consistency_matrix, + ), + ] diff --git a/backend/glossa_lab/experiment_graph_contact_zone.py b/backend/glossa_lab/experiment_graph_contact_zone.py new file mode 100644 index 00000000..98dadfd5 --- /dev/null +++ b/backend/glossa_lab/experiment_graph_contact_zone.py @@ -0,0 +1,333 @@ +"""Contact-zone analysis experiment templates. + +Provides three atomic nodes for quantifying inter-corpus contact signals: + + ContactZoneKLDivergence — KL/JS divergence between two corpus symbol + distributions, top-N overlapping symbols, and + a contact-zone verdict. + ContactZoneSynthesis — Synthesises outputs from multiple KL comparisons + into a ranked summary of language-family contact. + ContactZoneABComparison — Runs two corpora A and B against Indus and + reports which has the stronger contact signal. + +Registered templates: + contact_zone_kl_v1, contact_zone_synthesis_v1, contact_zone_ab_v1 +""" +from __future__ import annotations + +import math +from collections import Counter +from typing import Any + + +# ── Helpers ────────────────────────────────────────────────────────────── + + +def _freq_map_from_sequences(sequences: list[list[str]]) -> dict[str, int]: + c: Counter[str] = Counter() + for seq in sequences: + c.update(seq) + return dict(c) + + +def _kl_js(p_map: dict[str, int], q_map: dict[str, int], top_n: int = 20) -> dict[str, Any]: + """Compute KL(P||Q) and JS divergence, plus top-N overlapping symbols.""" + total_p = sum(p_map.values()) or 1 + total_q = sum(q_map.values()) or 1 + all_keys = set(p_map) | set(q_map) + + kl = 0.0 + js = 0.0 + overlap_scores: list[tuple[str, float]] = [] + + for k in all_keys: + pk = p_map.get(k, 0) / total_p + qk = q_map.get(k, 0) / total_q + if pk > 0 and qk > 0: + kl += pk * math.log2(pk / qk) + # Score overlap by harmonic mean of the two probabilities + overlap_scores.append((k, 2 * pk * qk / (pk + qk))) + m = (pk + qk) / 2 + if pk > 0 and m > 0: + js += 0.5 * pk * math.log2(pk / m) + if qk > 0 and m > 0: + js += 0.5 * qk * math.log2(qk / m) + + overlap_scores.sort(key=lambda x: -x[1]) + top_overlap = [{"symbol": s, "score": round(sc, 6)} for s, sc in overlap_scores[:top_n]] + n_shared = sum(1 for k in all_keys if k in p_map and k in q_map) + + return { + "kl_divergence": round(kl, 6), + "js_divergence": round(js, 6), + "n_symbols_p": len(p_map), + "n_symbols_q": len(q_map), + "n_shared": n_shared, + "overlap_ratio": round(n_shared / max(1, len(all_keys)), 4), + "top_overlap": top_overlap, + } + + +# ── Node 1: ContactZoneKLDivergence ────────────────────────────────────── + + +def _contact_zone_kl_divergence(inputs: dict, params: dict) -> dict: + """Compute KL divergence between two corpus token sequences. + + Inputs: + sequences_a — list[list[str]] (corpus A, e.g. Indus) + sequences_b — list[list[str]] (corpus B, e.g. Sumerian) + OR freq_map_a / freq_map_b — pre-computed frequency maps + + Returns divergence score, top-N overlapping symbols, and a + contact-zone verdict based on JS divergence thresholds. + """ + seq_a = inputs.get("sequences_a") or inputs.get("sequences") or [] + seq_b = inputs.get("sequences_b") or [] + fm_a = inputs.get("freq_map_a") or inputs.get("freq_map") or ( + _freq_map_from_sequences(seq_a) if seq_a else {} + ) + fm_b = inputs.get("freq_map_b") or ( + _freq_map_from_sequences(seq_b) if seq_b else {} + ) + + label_a = str(params.get("label_a", "corpus_a")) + label_b = str(params.get("label_b", "corpus_b")) + top_n = int(params.get("top_n", 20)) + + if not fm_a or not fm_b: + return {"error": "Need two corpora: connect sequences_a + sequences_b or freq_map_a + freq_map_b."} + + result = _kl_js(fm_a, fm_b, top_n=top_n) + + # Verdict based on JS divergence + js = result["js_divergence"] + if js < 0.3: + verdict = "STRONG_CONTACT" + verdict_text = ( + f"Strong contact signal between {label_a} and {label_b}: " + f"JS divergence {js:.4f} < 0.3 with {result['n_shared']} shared symbols." + ) + elif js < 0.6: + verdict = "MODERATE_CONTACT" + verdict_text = ( + f"Moderate contact signal between {label_a} and {label_b}: " + f"JS divergence {js:.4f}. {result['n_shared']} shared symbols." + ) + else: + verdict = "WEAK_CONTACT" + verdict_text = ( + f"Weak/no contact signal between {label_a} and {label_b}: " + f"JS divergence {js:.4f} >= 0.6." + ) + + return { + **result, + "label_a": label_a, + "label_b": label_b, + "verdict": verdict, + "verdict_text": verdict_text, + "number": round(js, 6), + } + + +# ── Node 2: ContactZoneSynthesis ───────────────────────────────────────── + + +def _contact_zone_synthesis(inputs: dict, params: dict) -> dict: + """Synthesise outputs from multiple KL comparisons. + + Takes a list of KL divergence results (wired from multiple + ContactZoneKLDivergence nodes via Merger) and ranks which + language families show the highest contact signal with the + target corpus. + """ + # Collect all upstream KL results. They may arrive as individual + # keys (a, b, c, …) from a Merger or as a list under "comparisons". + comparisons: list[dict] = [] + if "comparisons" in inputs and isinstance(inputs["comparisons"], list): + comparisons = inputs["comparisons"] + else: + for _k, v in sorted(inputs.items()): + if isinstance(v, dict) and "js_divergence" in v: + comparisons.append(v) + + if not comparisons: + return {"error": "No KL comparison results — connect ContactZoneKLDivergence outputs."} + + # Rank by JS divergence (lower = stronger contact) + ranked = sorted(comparisons, key=lambda c: c.get("js_divergence", 999)) + top_n = int(params.get("top_n", 10)) + + summary = [] + for i, c in enumerate(ranked[:top_n]): + summary.append({ + "rank": i + 1, + "label_a": c.get("label_a", "?"), + "label_b": c.get("label_b", "?"), + "js_divergence": c.get("js_divergence"), + "kl_divergence": c.get("kl_divergence"), + "n_shared": c.get("n_shared"), + "overlap_ratio": c.get("overlap_ratio"), + "verdict": c.get("verdict"), + }) + + strongest = ranked[0] if ranked else {} + verdict_text = ( + f"Contact synthesis: {len(comparisons)} comparisons ranked. " + f"Strongest contact: {strongest.get('label_b', '?')} " + f"(JS={strongest.get('js_divergence', '?'):.4f}, " + f"{strongest.get('n_shared', 0)} shared symbols)." + ) if strongest else "No comparisons available." + + return { + "ranked_summary": summary, + "n_comparisons": len(comparisons), + "strongest_contact": strongest.get("label_b", ""), + "strongest_js": strongest.get("js_divergence"), + "verdict": verdict_text, + "json": {"ranked_summary": summary}, + } + + +# ── Node 3: ContactZoneABComparison ────────────────────────────────────── + + +def _contact_zone_ab_comparison(inputs: dict, params: dict) -> dict: + """Compare two corpora A and B against Indus, return which has stronger contact signal. + + Inputs: + sequences_indus — Indus corpus sequences + sequences_a — corpus A sequences (e.g. Dravidian) + sequences_b — corpus B sequences (e.g. Sumerian) + + Returns which corpus shows stronger contact with Indus. + """ + seq_indus = inputs.get("sequences_indus") or inputs.get("sequences") or [] + seq_a = inputs.get("sequences_a") or [] + seq_b = inputs.get("sequences_b") or [] + + label_a = str(params.get("label_a", "corpus_a")) + label_b = str(params.get("label_b", "corpus_b")) + top_n = int(params.get("top_n", 15)) + + if not seq_indus: + return {"error": "No Indus sequences — connect CorpusReader or BuiltinCorpus."} + if not seq_a and not seq_b: + return {"error": "Need at least one comparison corpus (sequences_a or sequences_b)."} + + fm_indus = _freq_map_from_sequences(seq_indus) + + result_a = _kl_js(fm_indus, _freq_map_from_sequences(seq_a), top_n) if seq_a else None + result_b = _kl_js(fm_indus, _freq_map_from_sequences(seq_b), top_n) if seq_b else None + + js_a = result_a["js_divergence"] if result_a else 999.0 + js_b = result_b["js_divergence"] if result_b else 999.0 + + if js_a < js_b: + winner = label_a + confidence = round(1.0 - js_a / max(js_b, 0.001), 4) + elif js_b < js_a: + winner = label_b + confidence = round(1.0 - js_b / max(js_a, 0.001), 4) + else: + winner = "tie" + confidence = 0.0 + + verdict_text = ( + f"A/B contact comparison: {label_a} JS={js_a:.4f} vs {label_b} JS={js_b:.4f}. " + f"Winner: {winner} (confidence={confidence:.2%})." + ) + + return { + "winner": winner, + "confidence": confidence, + "label_a": label_a, + "label_b": label_b, + "js_a": js_a, + "js_b": js_b, + "result_a": result_a, + "result_b": result_b, + "verdict": verdict_text, + } + + +# ── Registration ───────────────────────────────────────────────────────── + + +def _contact_zone_node_defs() -> list: + from glossa_lab.experiment_graph import AtomicNodeDef # noqa: PLC0415 + + return [ + AtomicNodeDef( + "ContactZoneKLDivergence", "Contact Zone KL Divergence", + "Contact Zone", + "Compute KL and JS divergence between two corpus symbol distributions. " + "Returns divergence score, top-N overlapping symbols, and contact verdict.", + inputs=[ + {"name": "sequences_a", "type": "sequences", "required": False}, + {"name": "sequences_b", "type": "sequences", "required": False}, + {"name": "freq_map_a", "type": "freq_map", "required": False}, + {"name": "freq_map_b", "type": "freq_map", "required": False}, + ], + outputs=[ + {"name": "kl_divergence", "type": "number"}, + {"name": "js_divergence", "type": "number"}, + {"name": "top_overlap", "type": "json"}, + {"name": "verdict", "type": "text"}, + {"name": "number", "type": "number"}, + ], + params_schema={"type": "object", "properties": { + "label_a": {"type": "string", "default": "corpus_a"}, + "label_b": {"type": "string", "default": "corpus_b"}, + "top_n": {"type": "integer", "default": 20, "minimum": 1}, + }}, + fn=_contact_zone_kl_divergence, + ), + AtomicNodeDef( + "ContactZoneSynthesis", "Contact Zone Synthesis", + "Contact Zone", + "Synthesise multiple KL divergence comparisons into a ranked summary " + "of which language families show highest contact signal with the target corpus.", + inputs=[ + {"name": "comparisons", "type": "json", "required": False}, + {"name": "a", "type": "any", "required": False}, + {"name": "b", "type": "any", "required": False}, + {"name": "c", "type": "any", "required": False}, + {"name": "d", "type": "any", "required": False}, + ], + outputs=[ + {"name": "ranked_summary", "type": "json"}, + {"name": "strongest_contact", "type": "text"}, + {"name": "verdict", "type": "text"}, + ], + params_schema={"type": "object", "properties": { + "top_n": {"type": "integer", "default": 10, "minimum": 1}, + }}, + fn=_contact_zone_synthesis, + ), + AtomicNodeDef( + "ContactZoneABComparison", "Contact Zone A/B Comparison", + "Contact Zone", + "Compare two corpora A and B against Indus to determine which shows " + "stronger contact signal. Returns winner, confidence, and per-corpus divergence.", + inputs=[ + {"name": "sequences_indus", "type": "sequences", "required": True}, + {"name": "sequences_a", "type": "sequences", "required": False}, + {"name": "sequences_b", "type": "sequences", "required": False}, + ], + outputs=[ + {"name": "winner", "type": "text"}, + {"name": "confidence", "type": "number"}, + {"name": "verdict", "type": "text"}, + {"name": "result_a", "type": "json"}, + {"name": "result_b", "type": "json"}, + ], + params_schema={"type": "object", "properties": { + "label_a": {"type": "string", "default": "corpus_a"}, + "label_b": {"type": "string", "default": "corpus_b"}, + "top_n": {"type": "integer", "default": 15, "minimum": 1}, + }}, + fn=_contact_zone_ab_comparison, + ), + ] diff --git a/backend/glossa_lab/experiment_graph_cross_culture.py b/backend/glossa_lab/experiment_graph_cross_culture.py new file mode 100644 index 00000000..83584611 --- /dev/null +++ b/backend/glossa_lab/experiment_graph_cross_culture.py @@ -0,0 +1,380 @@ +"""Cross-culture contact matrix and script family classifier. + + CulturalContactMatrix — Given a list of corpus IDs, computes pairwise + contact scores using entropy signature overlap and KL-divergence. + Returns a matrix and top-N ranking of which cultures show strongest + contact signals with Indus. + + ScriptFamilyClassifier — Applies a simple feature-based classifier + (bigram entropy, type-token ratio, Zipf alpha, positional bias) + to classify a script into families: syllabary, logographic, abjad, + alphabet, undetermined. Returns classification with confidence. + +Registered templates: + cross_culture_contact_matrix_v1, script_family_classifier_v1 +""" +from __future__ import annotations + +import math +from collections import Counter +from typing import Any + + +# ── Helpers ────────────────────────────────────────────────────────────── + + +def _freq_map(sequences: list[list[str]]) -> dict[str, int]: + c: Counter[str] = Counter() + for seq in sequences: + c.update(seq) + return dict(c) + + +def _h1(freq: dict[str, int]) -> float: + total = sum(freq.values()) + if total <= 0: + return 0.0 + return -sum((c / total) * math.log2(c / total) for c in freq.values() if c > 0) + + +def _bigram_h(sequences: list[list[str]]) -> float: + bg: Counter[tuple[str, str]] = Counter() + for seq in sequences: + for i in range(len(seq) - 1): + bg[(seq[i], seq[i + 1])] += 1 + total = sum(bg.values()) + if total <= 0: + return 0.0 + return -sum((c / total) * math.log2(c / total) for c in bg.values() if c > 0) + + +def _zipf_alpha(freq: dict[str, int]) -> float: + ranked = sorted(freq.values(), reverse=True) + n = len(ranked) + if n < 2: + return 0.0 + lrs = [math.log(r + 1) for r in range(n)] + lfs = [math.log(f) if f > 0 else 0 for f in ranked] + mr = sum(lrs) / n + mf = sum(lfs) / n + num = sum((lrs[i] - mr) * (lfs[i] - mf) for i in range(n)) + den = sum((lr - mr) ** 2 for lr in lrs) + return round(-num / den, 4) if den else 0.0 + + +def _type_token_ratio(freq: dict[str, int]) -> float: + total = sum(freq.values()) + return round(len(freq) / max(1, total), 6) + + +def _positional_bias(sequences: list[list[str]]) -> float: + """Compute average positional bias: how concentrated signs are in initial/terminal positions.""" + if not sequences: + return 0.0 + tc: Counter[str] = Counter(s for seq in sequences for s in seq) + te: Counter[str] = Counter(seq[-1] for seq in sequences if seq) + ic: Counter[str] = Counter(seq[0] for seq in sequences if seq) + biases = [] + for sym, n in tc.items(): + if n < 3: + continue + t_rate = te[sym] / n + i_rate = ic[sym] / n + biases.append(max(t_rate, i_rate)) + return round(sum(biases) / max(1, len(biases)), 4) if biases else 0.0 + + +def _js_divergence(p_map: dict[str, int], q_map: dict[str, int]) -> float: + total_p = sum(p_map.values()) or 1 + total_q = sum(q_map.values()) or 1 + all_k = set(p_map) | set(q_map) + js = 0.0 + for k in all_k: + pk = p_map.get(k, 0) / total_p + qk = q_map.get(k, 0) / total_q + m = (pk + qk) / 2 + if pk > 0 and m > 0: + js += 0.5 * pk * math.log2(pk / m) + if qk > 0 and m > 0: + js += 0.5 * qk * math.log2(qk / m) + return round(js, 6) + + +# ── Node 1: CulturalContactMatrix ──────────────────────────────────────── + + +def _cultural_contact_matrix(inputs: dict, params: dict) -> dict: + """Compute pairwise contact scores between multiple corpora. + + Inputs: + corpora — dict mapping corpus_name to list[list[str]] sequences, + OR multiple named inputs (a, b, c, ...) each being + {sequences: [...], label: "..."} + + Returns a pairwise JS-divergence matrix and a ranking of which + cultures show strongest contact signals. + """ + corpora: dict[str, list[list[str]]] = {} + + # Try structured input + if "corpora" in inputs and isinstance(inputs["corpora"], dict): + for name, seqs in inputs["corpora"].items(): + if isinstance(seqs, list): + corpora[name] = seqs + else: + # Try named inputs (a, b, c, ...) + for k, v in sorted(inputs.items()): + if isinstance(v, dict): + label = v.get("label") or v.get("corpus") or k + seqs = v.get("sequences") or [] + if seqs: + corpora[str(label)] = seqs + elif isinstance(v, list) and v and isinstance(v[0], list): + corpora[k] = v + + if len(corpora) < 2: + return {"error": "Need at least 2 corpora for pairwise comparison."} + + names = sorted(corpora.keys()) + freq_maps = {n: _freq_map(corpora[n]) for n in names} + + # Compute entropy signatures for overlap scoring + signatures: dict[str, dict] = {} + for n in names: + fm = freq_maps[n] + seqs = corpora[n] + signatures[n] = { + "h1": round(_h1(fm), 4), + "bigram_h": round(_bigram_h(seqs), 4), + "zipf_alpha": _zipf_alpha(fm), + "n_types": len(fm), + "n_tokens": sum(fm.values()), + "ttr": _type_token_ratio(fm), + } + + # Pairwise JS divergence matrix + matrix: list[dict] = [] + for i, a in enumerate(names): + for j, b in enumerate(names): + if j <= i: + continue + js = _js_divergence(freq_maps[a], freq_maps[b]) + # Entropy signature similarity + sig_a = signatures[a] + sig_b = signatures[b] + h1_diff = abs(sig_a["h1"] - sig_b["h1"]) + zipf_diff = abs(sig_a["zipf_alpha"] - sig_b["zipf_alpha"]) + # Combined contact score (lower = stronger contact) + contact_score = round(js * 0.6 + h1_diff * 0.1 + zipf_diff * 0.3, 6) + matrix.append({ + "corpus_a": a, + "corpus_b": b, + "js_divergence": js, + "h1_diff": round(h1_diff, 4), + "zipf_diff": round(zipf_diff, 4), + "contact_score": contact_score, + }) + + # Rank by contact score + matrix.sort(key=lambda x: x["contact_score"]) + top_n = int(params.get("top_n", 10)) + + verdict = ( + f"Cultural contact matrix: {len(names)} corpora, " + f"{len(matrix)} pairwise comparisons. " + f"Strongest contact: {matrix[0]['corpus_a']} ↔ {matrix[0]['corpus_b']} " + f"(score={matrix[0]['contact_score']:.4f})." + ) if matrix else "No comparisons computed." + + return { + "matrix": matrix[:top_n], + "full_matrix": matrix, + "signatures": signatures, + "n_corpora": len(names), + "n_pairs": len(matrix), + "corpus_names": names, + "verdict": verdict, + "json": {"matrix": matrix[:top_n], "signatures": signatures}, + } + + +# ── Node 2: ScriptFamilyClassifier ─────────────────────────────────────── + + +def _script_family_classifier(inputs: dict, params: dict) -> dict: + """Classify a script into writing system families using structural features. + + Features used: + - Bigram entropy (H2) + - Type-token ratio (TTR) + - Zipf alpha exponent + - Positional bias (max(initial_rate, terminal_rate) averaged) + - Vocabulary size (number of distinct symbols) + + Families: syllabary, logographic, abjad, alphabet, undetermined + """ + sequences = inputs.get("sequences") or [] + if not sequences: + return {"error": "No sequences — connect a corpus."} + + label = str(params.get("label", "unknown")) + + fm = _freq_map(sequences) + h1_val = _h1(fm) + h2_val = _bigram_h(sequences) + ttr = _type_token_ratio(fm) + zipf = _zipf_alpha(fm) + pos_bias = _positional_bias(sequences) + n_types = len(fm) + n_tokens = sum(fm.values()) + avg_len = round(sum(len(s) for s in sequences) / max(1, len(sequences)), 2) + + # Rule-based classification + scores: dict[str, float] = { + "syllabary": 0.0, + "logographic": 0.0, + "abjad": 0.0, + "alphabet": 0.0, + "undetermined": 0.0, + } + + # Vocabulary size signal + if n_types > 200: + scores["logographic"] += 3.0 + elif n_types > 80: + scores["syllabary"] += 2.0 + scores["logographic"] += 1.0 + elif n_types > 40: + scores["syllabary"] += 2.0 + elif n_types > 20: + scores["abjad"] += 2.0 + scores["alphabet"] += 1.5 + else: + scores["abjad"] += 3.0 + + # H1 signal + if h1_val > 8.0: + scores["logographic"] += 2.0 + elif h1_val > 5.5: + scores["syllabary"] += 2.0 + elif h1_val > 4.0: + scores["abjad"] += 1.5 + scores["alphabet"] += 1.0 + else: + scores["abjad"] += 2.0 + + # Zipf alpha signal + if zipf > 1.5: + scores["logographic"] += 1.0 + elif zipf > 1.0: + scores["syllabary"] += 0.5 + else: + scores["abjad"] += 0.5 + scores["alphabet"] += 0.5 + + # Positional bias signal (high = more structured/restricted) + if pos_bias > 0.4: + scores["logographic"] += 1.0 # logograms often position-specific + elif pos_bias > 0.25: + scores["syllabary"] += 0.5 + + # Average word length + if avg_len > 5: + scores["syllabary"] += 1.0 + elif avg_len < 3: + scores["abjad"] += 0.5 + + # Find winner + ranked = sorted(scores.items(), key=lambda x: -x[1]) + best_family = ranked[0][0] + best_score = ranked[0][1] + total_score = sum(scores.values()) + confidence = round(best_score / max(1.0, total_score), 4) + + # Low confidence → undetermined + if confidence < 0.3 or best_score < 2.0: + best_family = "undetermined" + confidence = round(confidence * 0.5, 4) + + verdict = ( + f"Script family classifier ({label}): {best_family} " + f"(confidence={confidence:.2%}). " + f"Features: H1={h1_val:.2f}, H2={h2_val:.2f}, TTR={ttr:.6f}, " + f"Zipf={zipf:.2f}, pos_bias={pos_bias:.3f}, " + f"vocab={n_types}, avg_len={avg_len}." + ) + + return { + "label": label, + "classification": best_family, + "confidence": confidence, + "scores": {k: round(v, 2) for k, v in ranked}, + "features": { + "h1": round(h1_val, 4), + "h2": round(h2_val, 4), + "ttr": ttr, + "zipf_alpha": zipf, + "positional_bias": pos_bias, + "n_types": n_types, + "n_tokens": n_tokens, + "avg_word_length": avg_len, + }, + "verdict": verdict, + "text": verdict, + } + + +# ── Registration ───────────────────────────────────────────────────────── + + +def _cross_culture_node_defs() -> list: + from glossa_lab.experiment_graph import AtomicNodeDef # noqa: PLC0415 + + return [ + AtomicNodeDef( + "CulturalContactMatrix", "Cultural Contact Matrix", + "Cross-Language", + "Compute pairwise contact scores between multiple corpora using " + "entropy signature overlap and KL-divergence. Returns a ranked matrix " + "of which cultures show strongest contact signals.", + inputs=[ + {"name": "corpora", "type": "json", "required": False}, + {"name": "a", "type": "any", "required": False}, + {"name": "b", "type": "any", "required": False}, + {"name": "c", "type": "any", "required": False}, + {"name": "d", "type": "any", "required": False}, + {"name": "e", "type": "any", "required": False}, + ], + outputs=[ + {"name": "matrix", "type": "json"}, + {"name": "signatures", "type": "json"}, + {"name": "verdict", "type": "text"}, + ], + params_schema={"type": "object", "properties": { + "top_n": {"type": "integer", "default": 10, "minimum": 1}, + }}, + fn=_cultural_contact_matrix, + ), + AtomicNodeDef( + "ScriptFamilyClassifier", "Script Family Classifier", + "Cross-Language", + "Classify a script into writing system families (syllabary, logographic, " + "abjad, alphabet, undetermined) using bigram entropy, type-token ratio, " + "Zipf alpha, and positional bias features.", + inputs=[ + {"name": "sequences", "type": "sequences", "required": True}, + ], + outputs=[ + {"name": "classification", "type": "text"}, + {"name": "confidence", "type": "number"}, + {"name": "features", "type": "json"}, + {"name": "scores", "type": "json"}, + {"name": "verdict", "type": "text"}, + ], + params_schema={"type": "object", "properties": { + "label": {"type": "string", "default": "unknown", + "description": "Label for the corpus being classified"}, + }}, + fn=_script_family_classifier, + ), + ] diff --git a/backend/glossa_lab/experiment_ledger.json b/backend/glossa_lab/experiment_ledger.json new file mode 100644 index 00000000..9e8a20c9 --- /dev/null +++ b/backend/glossa_lab/experiment_ledger.json @@ -0,0 +1,682 @@ +[ + { + "file": "experiment_graph.py", + "phases": "core", + "category": "structural_analysis", + "purpose": "Core experiment graph engine with ~50 built-in atomic nodes (CorpusReader, FreqCounter, PositionalProfiler, EntropyCalc, SADecipher, etc.) and graph 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    `+We+" + -
    +
    diff --git a/frontend/package-lock.json b/frontend/package-lock.json index bb001ba5..643ad8f3 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -1273,7 +1273,7 @@ "version": "19.2.14", "resolved": "https://registry.npmjs.org/@types/react/-/react-19.2.14.tgz", "integrity": "sha512-ilcTH/UniCkMdtexkoCN0bI7pMcJDvmQFPvuPvmEaYA/NSfFTAgdUSLAoVjaRJm7+6PvcM+q1zYOwS4wTYMF9w==", - "devOptional": true, + "dev": true, "license": "MIT", "dependencies": { "csstype": "^3.2.2" @@ -1427,7 +1427,7 @@ "version": "3.2.3", "resolved": "https://registry.npmjs.org/csstype/-/csstype-3.2.3.tgz", "integrity": "sha512-z1HGKcYy2xA8AGQfwrn0PAy+PB7X/GSj3UVJW9qKyn43xWa+gl5nXmU4qqLMRzWVLFC8KusUX8T/0kCiOYpAIQ==", - "devOptional": true, + "dev": true, "license": "MIT" }, "node_modules/d3-color": { diff --git a/frontend/src/api.ts b/frontend/src/api.ts index d84a51dc..cd574cf8 100644 --- a/frontend/src/api.ts +++ b/frontend/src/api.ts @@ -701,6 +701,20 @@ export interface ExperimentMeta { export const getNodeSchema = (nodeType: string, refId: string): Promise> => request("GET", `/node-registry/${nodeType}/${refId}`); +export interface ExperimentLedgerEntry { + id: string; + display_name: string; + category: string; + phase: string; + description: string; + status: "active" | "superseded" | "legacy" | "scaffold"; + superseded_by: string | null; + source_file: string; +} + +export const getExperimentMetadata = (): Promise => + request("GET", "/experiments/metadata"); + export const listExperiments = (): Promise => request("GET", "/experiments"); diff --git a/frontend/src/components/DashboardView.tsx b/frontend/src/components/DashboardView.tsx index 9b5bfaee..a42e0533 100644 --- a/frontend/src/components/DashboardView.tsx +++ b/frontend/src/components/DashboardView.tsx @@ -44,6 +44,7 @@ import { useAIChat } from "../hooks/useAIChat"; import { useProject } from "../hooks/useProject"; import { useToast } from "../hooks/useToast"; import { DeciphermentPanel } from "./DeciphermentPanel"; +import ExperimentRegistry from "./ExperimentRegistry"; import { ResearchLoopPanel } from "./ResearchLoopPanel"; // ── Insight persistence ────────────────────────────────────────────────── @@ -769,6 +770,9 @@ export function DashboardView() { marginBottom: 12 }}>{error} )} + {/* Experiment Registry */} + + {/* Decipherment Progress Panel */} applyAction({ diff --git a/frontend/src/components/ExperimentRegistry.tsx b/frontend/src/components/ExperimentRegistry.tsx new file mode 100644 index 00000000..f1a2d349 --- /dev/null +++ b/frontend/src/components/ExperimentRegistry.tsx @@ -0,0 +1,190 @@ +import { useCallback, useEffect, useState } from "react"; +import type { ExperimentLedgerEntry } from "../api"; +import { getExperimentMetadata } from "../api"; + +const CATEGORIES = [ + "structural_analysis", "lm_scoring", "sa_variant", "ctt", + "contact_zone", "cross_language", "archaeological", "misc", +] as const; + +const CATEGORY_LABELS: Record = { + structural_analysis: "Structural", + lm_scoring: "LM Scoring", + sa_variant: "SA Variant", + ctt: "CTT", + contact_zone: "Contact Zone", + cross_language: "Cross-Language", + archaeological: "Archaeological", + misc: "Misc", +}; + +const STATUS_COLORS: Record = { + active: { bg: "#dcfce7", fg: "#166534" }, + superseded: { bg: "#fef9c3", fg: "#854d0e" }, + legacy: { bg: "#f3f4f6", fg: "#6b7280" }, + scaffold: { bg: "#ede9fe", fg: "#6d28d9" }, +}; + +export default function ExperimentRegistry() { + const [entries, setEntries] = useState([]); + const [loading, setLoading] = useState(true); + const [open, setOpen] = useState(false); + const [activeFilter, setActiveFilter] = useState(null); + const [showAll, setShowAll] = useState(false); + + const load = useCallback(async () => { + try { + const data = await getExperimentMetadata(); + setEntries(data); + } catch { /* ignore */ } + setLoading(false); + }, []); + + useEffect(() => { void load(); }, [load]); + + const filtered = entries.filter((e) => { + if (activeFilter && e.category !== activeFilter) return false; + if (!showAll && e.status !== "active") return false; + return true; + }); + + const byCategory = new Map(); + for (const e of filtered) { + const cat = e.category || "misc"; + if (!byCategory.has(cat)) byCategory.set(cat, []); + byCategory.get(cat)!.push(e); + } + + const totalActive = entries.filter((e) => e.status === "active").length; + + return ( +
    +
    setOpen(!open)} + > + 🧪 + + Experiment Registry ({totalActive}) + + + {open ? "▾" : "▸"} + +
    + + {open && ( +
    + {/* Category filter chips */} +
    + + {CATEGORIES.map((cat) => { + const count = entries.filter( + (e) => e.category === cat && (showAll || e.status === "active") + ).length; + if (count === 0) return null; + return ( + + ); + })} + + +
    + + {loading &&
    Loading…
    } + + {!loading && filtered.length === 0 && ( +
    No experiments match the filter.
    + )} + + {/* Experiments grouped by category */} + {Array.from(byCategory.entries()).map(([cat, items]) => ( +
    +
    + {CATEGORY_LABELS[cat] || cat} ({items.length}) +
    +
    + {items.slice(0, 20).map((e) => { + const sc = STATUS_COLORS[e.status] || STATUS_COLORS.active; + return ( +
    + + {e.status} + + {e.display_name} + {e.phase && ( + + ph {e.phase} + + )} + {e.superseded_by && ( + + → {e.superseded_by} + + )} +
    + ); + })} + {items.length > 20 && ( +
    + + {items.length - 20} more +
    + )} +
    +
    + ))} +
    + )} +
    + ); +}