mirror of
https://github.com/allaunthefox/Research-Stack.git
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291 lines
14 KiB
Python
Executable file
291 lines
14 KiB
Python
Executable file
#!/usr/bin/env python3
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"""Generate a domain graph for local Lean modules.
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This script inventories repo-local Lean files under 0-Core-Formalism/lean,
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excluding Lake package dependencies, and emits:
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* modules.jsonl: one record per Lean module
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* import_edges.csv: module import graph edges
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* domain_edges.csv: module -> domain membership edges
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* domains.csv: domain count summary
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* lean_module_domain_graph.graphml: import and domain graph
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* LEAN_MODULE_DOMAIN_GRAPH.md: readable audit report
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The classifier is intentionally conservative. Ambiguous files are marked HOLD.
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"""
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from __future__ import annotations
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import csv
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import hashlib
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import json
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import re
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import xml.sax.saxutils as xml_escape
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from collections import Counter, defaultdict
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[1]
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LEAN_ROOT = ROOT / "0-Core-Formalism" / "lean"
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DATA_OUT = ROOT / "shared-data" / "data" / "lean_module_graph"
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REPORT_OUT = ROOT / "6-Documentation" / "docs" / "reports" / "LEAN_MODULE_DOMAIN_GRAPH.md"
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IMPORT_RE = re.compile(r"^\s*import\s+([A-Za-z0-9_'.]+)")
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NAMESPACE_RE = re.compile(r"^\s*namespace\s+([A-Za-z0-9_'.]+)")
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DOMAIN_RULES = [
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("AncillaryHolding", ("Ancillary",)),
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("RuntimeEntrypoints", ("Main", "Server", "BenchmarkMain", "Generate")),
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("KernelCore", ("Semantics.lean", "Bind", "Canon", "Constitution", "MetricCore", "Transition", "Protocol", "Tape")),
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("InformationTheory", ("Information", "Shannon", "Entropy", "Metadata", "Overhead", "Efficiency", "Precision")),
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("CompressionGCCL", ("Compression", "GCCL", "Hutter", "LawfulLoss", "Landauer", "Substitution", "CandidateDictionary", "LadderLUT", "HexLogogramAtlas")),
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("RRCAndOmindirection", ("RRC", "Omindirection", "Logogram", "ManifoldBoundaryAtlas", "ProjectableGeometryCanonical")),
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("GenomicsBiology", ("Biology", "Bio", "Genetic", "Genome", "Genomic", "Codon", "Peptide", "Hachimoji", "Cancer", "Cellular", "Molecular", "Biomolecular")),
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("PhysicsPDE", ("Physics", "PDE", "Burgers", "KdV", "ColeHopf", "Hydrogenic", "Hamiltonian", "Thermo", "Thermodynamic", "Spectral", "Spectrum")),
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("GeometryTopology", ("Geometry", "Topology", "Manifold", "Braid", "Surface", "Torus", "Hyperbolic", "Curvature", "Voxel", "Betti", "Projection")),
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("QuantumInformation", ("Quantum", "Qubit", "Quandela", "Decoherence", "Entanglement")),
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("MathFormal", ("Math", "Functions", "ContinuedFraction", "Fibonacci", "GoldenAngle", "Prime", "Quaternion", "OrderedField", "BracketedCalculus")),
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("AgentsSwarm", ("Agent", "Agents", "Swarm", "MoE", "Gemma", "LaviGen", "AI_ML", "Cognitive", "Experience")),
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("HardwareSparkle", ("Hardware", "FPGA", "GPU", "NIC", "ASIC", "Sparkle", "Tang", "Blitter", "Backend", "IR")),
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("NetworkProtocol", ("Network", "Protocol", "WebRTC", "WebInteraction", "Connectors", "Bitcoin", "Github", "Forgejo")),
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("ENEIntegration", ("ENE", "Substrate", "JsonL", "Metadata", "PassiveComputation", "OTOMOntology")),
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("TestingVerification", ("Testing", "Verification", "Benchmark", "Testbench", "Harness", "Attestation")),
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("ControlRouting", ("Control", "Routing", "Route", "Orchestrate", "DynamicCanal", "FAMM", "AVMR", "AMMR")),
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("AddressingMemory", ("Address", "Memory", "Cache", "LUT", "Map", "Mapping")),
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("ExtensionScaffold", ("ExtensionScaffold",)),
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("LegacyQuarantine", ("legacy", "Quarantine", "archive")),
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("ExternalReference", ("external", "LeanGPT")),
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]
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def local_lean_files() -> list[Path]:
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files: list[Path] = []
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for path in LEAN_ROOT.rglob("*.lean"):
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parts = set(path.parts)
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if ".lake" in parts:
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continue
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files.append(path)
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return sorted(files)
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def module_name(path: Path) -> str:
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rel = path.relative_to(LEAN_ROOT)
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if rel.parts and rel.parts[0] == "Semantics":
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rel = Path(*rel.parts[1:])
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return ".".join(rel.with_suffix("").parts)
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def read_text(path: Path) -> str:
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return path.read_text(encoding="utf-8", errors="replace")
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def classify(path: Path, module: str, text: str) -> tuple[str, str, str, str]:
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rel = str(path.relative_to(ROOT))
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haystack = f"{rel} {module} {text[:1200]}"
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path_parts = set(path.relative_to(ROOT).parts)
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scores: Counter[str] = Counter()
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reasons: dict[str, str] = {}
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for domain, needles in DOMAIN_RULES:
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for needle in needles:
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if needle in haystack:
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scores[domain] += 1
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reasons.setdefault(domain, needle)
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if "ExtensionScaffold" in path_parts:
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scores["ExtensionScaffold"] += 4
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reasons.setdefault("ExtensionScaffold", "path")
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if "Ancillary" in path_parts:
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scores["AncillaryHolding"] += 6
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reasons.setdefault("AncillaryHolding", "path")
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if "Testing" in path_parts or "Verification" in path_parts or "Benchmarks" in path_parts:
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scores["TestingVerification"] += 4
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reasons.setdefault("TestingVerification", "path")
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if "external" in path_parts:
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scores["ExternalReference"] += 4
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reasons.setdefault("ExternalReference", "path")
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if "legacy" in path_parts or "Quarantine" in path_parts:
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scores["LegacyQuarantine"] += 4
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reasons.setdefault("LegacyQuarantine", "path")
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if not scores:
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return ("ReviewUnclassified", "0.20", "HOLD", "no classifier signal")
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best_score = max(scores.values())
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winners = sorted([domain for domain, score in scores.items() if score == best_score])
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# Stable precedence follows DOMAIN_RULES order. This gives every module a
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# primary graph edge while keeping ambiguous cases in HOLD review state.
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precedence = {domain: idx for idx, (domain, _) in enumerate(DOMAIN_RULES)}
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best = sorted(winners, key=lambda d: precedence.get(d, 999))[0]
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if len(winners) > 1:
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return (best, "0.45", "HOLD", "tie: " + ",".join(winners))
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total = sum(scores.values())
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confidence = min(0.98, 0.55 + (scores[best] / max(total, 1)) * 0.4)
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return (best, f"{confidence:.2f}", "ACCEPT", reasons.get(best, "classifier signal"))
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def parse_module(path: Path) -> dict:
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text = read_text(path)
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imports = [m.group(1) for line in text.splitlines() if (m := IMPORT_RE.match(line))]
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namespaces = [m.group(1) for line in text.splitlines() if (m := NAMESPACE_RE.match(line))]
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module = module_name(path)
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domain, confidence, review_status, reason = classify(path, module, text)
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return {
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"module": module,
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"path": str(path.relative_to(ROOT)),
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"domain": domain,
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"confidence": confidence,
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"review_status": review_status,
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"reason": reason,
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"imports": imports,
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"namespace": namespaces[0] if namespaces else "",
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"line_count": text.count("\n") + 1,
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"def_count": len(re.findall(r"^\s*def\s+", text, re.M)),
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"theorem_count": len(re.findall(r"^\s*(theorem|lemma|example)\s+", text, re.M)),
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"eval_count": len(re.findall(r"^\s*#eval\b", text, re.M)),
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"sorry_count": len(re.findall(r"\bsorry\b", text)),
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"sha256": hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest(),
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}
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def write_jsonl(path: Path, rows: list[dict]) -> None:
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with path.open("w", encoding="utf-8") as f:
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for row in rows:
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f.write(json.dumps(row, sort_keys=True) + "\n")
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def write_csv(path: Path, rows: list[dict], fieldnames: list[str]) -> None:
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with path.open("w", encoding="utf-8", newline="") as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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for row in rows:
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writer.writerow({key: row.get(key, "") for key in fieldnames})
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def graphml(modules: list[dict]) -> str:
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domain_names = sorted({m["domain"] for m in modules})
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module_ids = {m["module"]: "m" + hashlib.sha1(m["module"].encode()).hexdigest()[:12] for m in modules}
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domain_ids = {d: "d" + hashlib.sha1(d.encode()).hexdigest()[:12] for d in domain_names}
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known_modules = set(module_ids)
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lines = [
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'<?xml version="1.0" encoding="UTF-8"?>',
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'<graphml xmlns="http://graphml.graphdrawing.org/xmlns">',
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'<key id="kind" for="node" attr.name="kind" attr.type="string"/>',
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'<key id="domain" for="node" attr.name="domain" attr.type="string"/>',
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'<key id="path" for="node" attr.name="path" attr.type="string"/>',
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'<key id="relation" for="edge" attr.name="relation" attr.type="string"/>',
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'<graph id="LeanModuleDomainGraph" edgedefault="directed">',
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]
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for domain in domain_names:
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did = domain_ids[domain]
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lines.extend([
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f'<node id="{did}">',
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'<data key="kind">domain</data>',
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f'<data key="domain">{xml_escape.escape(domain)}</data>',
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'</node>',
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])
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for mod in modules:
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mid = module_ids[mod["module"]]
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lines.extend([
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f'<node id="{mid}">',
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'<data key="kind">module</data>',
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f'<data key="domain">{xml_escape.escape(mod["domain"])}</data>',
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f'<data key="path">{xml_escape.escape(mod["path"])}</data>',
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'</node>',
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])
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edge_id = 0
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for mod in modules:
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src = module_ids[mod["module"]]
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dst = domain_ids[mod["domain"]]
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lines.append(f'<edge id="e{edge_id}" source="{src}" target="{dst}"><data key="relation">belongs_to</data></edge>')
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edge_id += 1
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for imp in mod["imports"]:
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if imp in known_modules:
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lines.append(f'<edge id="e{edge_id}" source="{src}" target="{module_ids[imp]}"><data key="relation">imports</data></edge>')
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edge_id += 1
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lines.extend(["</graph>", "</graphml>"])
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return "\n".join(lines) + "\n"
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def report(modules: list[dict], import_edges: list[dict], domain_rows: list[dict]) -> str:
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hold = [m for m in modules if m["review_status"] == "HOLD"]
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sorry_total = sum(int(m["sorry_count"]) for m in modules)
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lines = [
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"# Lean Module Domain Graph",
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"",
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"Generated inventory of repo-local Lean modules under `0-Core-Formalism/lean`, excluding `.lake` package dependencies.",
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"",
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"## Outputs",
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"",
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"- `shared-data/data/lean_module_graph/modules.jsonl`",
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"- `shared-data/data/lean_module_graph/import_edges.csv`",
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"- `shared-data/data/lean_module_graph/domain_edges.csv`",
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"- `shared-data/data/lean_module_graph/domains.csv`",
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"- `shared-data/data/lean_module_graph/lean_module_domain_graph.graphml`",
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"",
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"## Summary",
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"",
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f"- Local Lean modules scanned: {len(modules)}",
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f"- Local import edges resolved: {len(import_edges)}",
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f"- Domain buckets: {len(domain_rows)}",
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f"- HOLD-review modules: {len(hold)}",
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f"- Total literal `sorry` occurrences: {sorry_total}",
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"",
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"## Domain Counts",
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"",
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"| Domain | Modules | HOLD? |",
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"|---|---:|---|",
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]
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for row in domain_rows:
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domain_hold = sum(1 for m in modules if m["domain"] == row["domain"] and m["review_status"] == "HOLD")
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lines.append(f"| {row['domain']} | {row['module_count']} | {domain_hold} review |")
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lines.extend([
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"",
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"## HOLD Samples",
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"",
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"HOLD means the classifier assigned a primary graph domain, but the module needs human review because the signal was missing or conflicting. It is not a build failure.",
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"",
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"| Module | Reason | Path |",
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"|---|---|---|",
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])
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for mod in hold[:50]:
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lines.append(f"| `{mod['module']}` | {mod['reason']} | `{mod['path']}` |")
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lines.extend([
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"",
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"## Claim Boundary",
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"",
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"This graph is an inventory and routing surface. It does not prove that each module is architecturally correct, imported by the aggregate build, or assigned to its final permanent domain. Ambiguous modules are deliberately marked HOLD for human review.",
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"",
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])
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return "\n".join(lines)
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def main() -> None:
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DATA_OUT.mkdir(parents=True, exist_ok=True)
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REPORT_OUT.parent.mkdir(parents=True, exist_ok=True)
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modules = [parse_module(path) for path in local_lean_files()]
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known = {m["module"] for m in modules}
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import_edges = [
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{"source": m["module"], "target": imp, "target_local": "true" if imp in known else "false"}
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for m in modules
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for imp in m["imports"]
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]
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domain_edges = [{"source": m["module"], "target": m["domain"], "confidence": m["confidence"], "review_status": m["review_status"], "reason": m["reason"]} for m in modules]
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counts = Counter(m["domain"] for m in modules)
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domain_rows = [
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{"domain": domain, "module_count": counts[domain]}
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for domain in sorted(counts, key=lambda d: (-counts[d], d))
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]
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write_jsonl(DATA_OUT / "modules.jsonl", modules)
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write_csv(DATA_OUT / "import_edges.csv", import_edges, ["source", "target", "target_local"])
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write_csv(DATA_OUT / "domain_edges.csv", domain_edges, ["source", "target", "confidence", "review_status", "reason"])
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write_csv(DATA_OUT / "domains.csv", domain_rows, ["domain", "module_count"])
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(DATA_OUT / "lean_module_domain_graph.graphml").write_text(graphml(modules), encoding="utf-8")
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REPORT_OUT.write_text(report(modules, [e for e in import_edges if e["target_local"] == "true"], domain_rows), encoding="utf-8")
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print(json.dumps({
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"modules": len(modules),
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"domain_count": len(domain_rows),
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"hold_count": sum(1 for m in modules if m["review_status"] == "HOLD"),
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"local_import_edges": sum(1 for e in import_edges if e["target_local"] == "true"),
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"report": str(REPORT_OUT.relative_to(ROOT)),
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"data_dir": str(DATA_OUT.relative_to(ROOT)),
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}, sort_keys=True))
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if __name__ == "__main__":
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main()
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