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allaun
670e7617c3
- Rename python/build_corpus250.py → python/build_manifold.py - Rename emitCorpus250 → emitManifold, corpus250 → allFixtures - Schema: avm_rrc_corpus250_v1 → avm_rrc_manifold_v1 - Classify.lean now delegates to ClassifyN (generic n-dim module) - ClassifyN.hashTable8: extracted 119-entry hash table from old Classify - build_manifold.py now emits ClassifyN.classifyProxy hashTable8 + classifyExact 8 - build_pist_matrices_250.py: added pistMatrixDim constant - SilverSight docs/AGENTS.md updated for renames - Research Stack AGENTS.md updated for toolchain references - Authentik deployed on neon-64gb (port 30001, working) - cross_domain_significance.py: statistical significance test (all phases <6σ with n=3) - setup_authentik.sh: fixed image, password, port, key sharing Build: 3307 jobs, 0 errors (lake build)
258 lines
10 KiB
Python
258 lines
10 KiB
Python
#!/usr/bin/env python3
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# /// script
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# requires-python = ">=3.10"
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# dependencies = []
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# ///
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"""
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Build formal/SilverSight/RRC/Q16_16Manifold.lean from
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archive/experimental-shim-probes/rrc_equation_classifier_receipt.json,
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merged with 8×8 braid adjacency matrices from
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shared-data/rrc_pist_predictions_250_v1.json.
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Python's role:
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- read raw features from the classifier receipt
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- merge matrices by invariant_receipt.object_id
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- emit deterministic Lean source
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Lean's role:
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- PIST classification (classifyProxy/classifyExact) from the matrix
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- alignment gate via determineAlignment
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- receipt stamping and all admissibility/promotion decisions
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Usage:
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python3 python/build_manifold.py
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python3 python/build_manifold.py \
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--receipt /path/to/rrc_equation_classifier_receipt.json \
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--predictions /path/to/rrc_pist_predictions_250_v1.json \
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--out-lean formal/SilverSight/RRC/Q16_16Manifold.lean
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"""
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from __future__ import annotations
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import argparse, hashlib, json, sys
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from pathlib import Path
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# classifier JSON shape name → SilverSight.RRCLogogramProjection.RRCShape constructor
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SHAPE_MAP = {
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"CognitiveLoadField": ".cognitiveLoadField",
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"SignalShapedRouteCompiler": ".signalShapedRouteCompiler",
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"ProjectableGeometryTopology": ".projectableGeometryTopology",
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"CadForceProbeReceipt": ".cadForceProbeReceipt",
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"LogogramProjection": ".logogramProjection",
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"HoldForUnlawfulOrUnderspecifiedShape": ".holdForUnlawfulOrUnderspecifiedShape",
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}
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def template_key(rrc_kind: str, status: str) -> str:
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"""Map rrc_kind + classifier status to a page-generator template key."""
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if status == "HOLD":
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return "hold"
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kind_map = {
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"cognitive_field_receipt": "definition",
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"compression_route_prior": "master_equation",
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"geometry_topology_receipt": "definition",
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"cad_force_receipt": "gate",
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"logogram_projection": "receipt",
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"negative_control": "hold",
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}
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return kind_map.get(rrc_kind, "definition")
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def operator_tokens(er: dict) -> list[str]:
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"""Derive operator/domain tokens from route_hint, rrc_kind, and equation text."""
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tokens = []
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rh = (er.get("route_hint_non_authoritative") or "").strip()
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rk = (er.get("rrc_kind") or "").strip()
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if rh and rh != "unclassified_equation":
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tokens.append(rh)
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if rk:
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tokens.append(rk)
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eq_text = (er.get("equation") or "").lower()
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for op in ["exp(", "log(", "max(", "min(", "sum(", "integral", "derivative",
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"laplacian", "nabla", "div(", "curl(", "sigmoid", "softmax",
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"tanh(", "relu(", "norm(", "dot(", "cross("]:
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if op in eq_text:
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tokens.append(op.rstrip("("))
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return list(dict.fromkeys(tokens))
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def lean_str(s: str) -> str:
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s = s.replace("\\", "\\\\").replace('"', '\\"')
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return f'"{s}"'
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def lean_opt(s: str | None) -> str:
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return "none" if s is None else f"some {lean_str(s)}"
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def lean_q16_16(val: float) -> str:
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"""Convert a float in [0,1] to a Q16_16 Lean literal via exact rational."""
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if val == 0.0:
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return "Q16_16.zero"
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from fractions import Fraction
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f = Fraction(val).limit_denominator(100)
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return f"Q16_16.ofRatio {f.numerator} {f.denominator}"
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def lean_str_list(xs: list[str]) -> str:
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return "[" + ", ".join(lean_str(x) for x in xs) + "]"
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def load_matrices(path: Path) -> dict[str, list[list[int]]]:
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"""Load predictions JSON into equation_id → matrix lookup."""
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if not path.exists():
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return {}
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data = json.loads(path.read_text())
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return {
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p.get("equation_id", ""): p.get("matrix_8x8", [])
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for p in data.get("predictions", [])
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if p.get("equation_id")
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}
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def main() -> int:
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parser = argparse.ArgumentParser(description="Generate SilverSight RRC Q16_16Manifold.lean")
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parser.add_argument(
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"--receipt",
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type=Path,
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default=Path("/home/allaun/Research Stack/archive/experimental-shim-probes/rrc_equation_classifier_receipt.json"),
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help="Path to rrc_equation_classifier_receipt.json",
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)
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parser.add_argument(
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"--predictions",
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type=Path,
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default=Path("/home/allaun/Research Stack/shared-data/rrc_pist_predictions_250_v1.json"),
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help="Path to rrc_pist_predictions_250_v1.json",
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)
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parser.add_argument(
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"--out-lean",
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type=Path,
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default=Path("formal/SilverSight/RRC/Q16_16Manifold.lean"),
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help="Output Lean module path",
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)
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args = parser.parse_args()
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receipt = json.loads(args.receipt.read_text())
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eqs = receipt.get("compiled_equations", [])
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matrices = load_matrices(args.predictions)
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print(f"Loaded {len(eqs)} equations and {len(matrices)} matrices", file=sys.stderr)
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# Deterministic order by equation_id.
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eqs_sorted = sorted(eqs, key=lambda eq: eq.get("invariant_receipt", {}).get("object_id", ""))
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# Content hash over the raw corpus data for reproducibility.
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content_blob = json.dumps(
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[
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{
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"object_id": eq.get("invariant_receipt", {}).get("object_id"),
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"name": eq.get("equation_record", {}).get("name"),
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"shape": eq.get("invariant_receipt", {}).get("shape"),
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"status": eq.get("invariant_receipt", {}).get("status"),
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"matrix": matrices.get(eq.get("invariant_receipt", {}).get("object_id", "")),
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}
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for eq in eqs_sorted
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],
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sort_keys=True,
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separators=(",", ":"),
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).encode("utf-8")
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content_hash = hashlib.sha256(content_blob).hexdigest()
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rows: list[str] = []
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for eq in eqs_sorted:
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er = eq["equation_record"]
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ir = eq["invariant_receipt"]
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tw = eq["type_witness"]
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eq_id = ir.get("object_id", "")
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name = er.get("name", "")
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shape_str = ir.get("shape", "HoldForUnlawfulOrUnderspecifiedShape")
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lean_shape = SHAPE_MAP.get(shape_str, ".holdForUnlawfulOrUnderspecifiedShape")
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status_str = ir.get("status", "HOLD")
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lean_status = ".candidate" if status_str == "CANDIDATE" else ".hold"
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rrc_kind = er.get("rrc_kind", "")
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weak_cnt = len(tw.get("missing_or_weak_axes") or [])
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# Manifold coordinates for negative control witness (Q16_16)
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coords = eq.get("manifold_projection", {}).get("coordinates", {})
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nc_strength = coords.get("negative_control_strength", 0.0)
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residual_risk = coords.get("residual_risk", 0.0)
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scale_band = coords.get("scale_band_declared", 0.0)
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weak_axes = tw.get("missing_or_weak_axes") or []
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op_tokens = operator_tokens(er)
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inv_declared = (er.get("domain_type") or "unknown").strip() or "unknown"
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bound_conds = (er.get("bind_class") or "unknown").strip() or "unknown"
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t_key = template_key(rrc_kind, status_str)
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route_hint = er.get("route_hint_non_authoritative") or "unclassified_equation"
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t_params = f"route={route_hint};shape={shape_str}"
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arxiv_pid = (er.get("arxiv_paper_id") or "").strip() or None
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rows.append(
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f" {{ equationId := {lean_str(eq_id)}\n"
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f" name := {lean_str(name)}\n"
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f" shape := {lean_shape}\n"
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f" status := {lean_status}\n"
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f" rrcKind := {lean_str(rrc_kind)}\n"
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f" weakAxesCnt := {weak_cnt}\n"
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f" pistProxyLabel := Option.bind (findMatrix {lean_str(eq_id)}) (SilverSight.PIST.ClassifyN.classifyProxy SilverSight.PIST.ClassifyN.hashTable8)\n"
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f" pistExactLabel := Option.bind (findMatrix {lean_str(eq_id)}) (SilverSight.PIST.ClassifyN.classifyExact 8)\n"
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f" arxivPaperId := {lean_opt(arxiv_pid)}\n"
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f" ncObserved := {lean_q16_16(nc_strength)}\n"
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f" residualRisk := {lean_q16_16(residual_risk)}\n"
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f" scaleBandDeclared := {lean_q16_16(scale_band)}\n"
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f" weakAxesNames := {lean_str_list(weak_axes)}\n"
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f" operatorTokens := {lean_str_list(op_tokens)}\n"
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f" invariantsDeclared := {lean_str(inv_declared)}\n"
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f" boundaryConds := {lean_str(bound_conds)}\n"
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f" templateKey := {lean_str(t_key)}\n"
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f" templateParams := {lean_str(t_params)} }}"
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)
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lines = [
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"-- SilverSight.RRC.Q16_16Manifold — AUTO-GENERATED by python/build_manifold.py",
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"-- DO NOT EDIT BY HAND. Regenerate with:",
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"-- python3 python/build_manifold.py",
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"--",
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"-- Python role: raw feature extraction + matrix merge.",
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"-- Lean role: PIST classification, alignment gate (determineAlignment),",
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"-- receipt stamping, and all admissibility/promotion decisions.",
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"--",
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"-- Source: rrc_equation_classifier_receipt.json",
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"-- Matrices: rrc_pist_predictions_250_v1.json",
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f"-- Content hash (SHA-256): {content_hash}",
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f"-- Equation count: {len(rows)}",
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"--",
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"import SilverSight.RRC.Emit",
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"import SilverSight.FixedPoint",
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"import SilverSight.PIST.Classify",
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"import SilverSight.PIST.ClassifyN",
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"import SilverSight.PIST.Matrices250",
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"",
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"namespace SilverSight.RRC.Q16_16Manifold",
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"",
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"open SilverSight.RRC.Emit",
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"open SilverSight.FixedPoint",
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"open SilverSight.RRCLogogramProjection",
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"open SilverSight.ReceiptCore",
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"open SilverSight.PIST.Matrices250",
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"",
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"/-- Full 250-equation manifold from rrc_equation_classifier_receipt.json,",
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" merged with 8×8 braid adjacency matrices from",
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" rrc_pist_predictions_250_v1.json.",
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" Each row carries raw features only; the alignment gate in",
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" SilverSight.RRC.Emit.emitCorpus makes all admissibility decisions. -/",
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"def allFixtures : List FixtureRow := [",
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",\n".join(rows),
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"]",
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"",
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"end SilverSight.RRC.Q16_16Manifold",
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"",
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]
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args.out_lean.parent.mkdir(parents=True, exist_ok=True)
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args.out_lean.write_text("\n".join(lines))
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print(f"Wrote {args.out_lean} ({len(rows)} rows)", file=sys.stderr)
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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