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allaun
1f7ec15f12
Adds logViscosityRatio, log_viscosity_monotone, and ν_eff_monotone to Semantics/NKHodgeFAMM.lean section 6b. The adaptive viscosity law ν_eff = ν₀*(1+μ) is multiplicative in ν₀ and additive in scar density μ; taking λ = log(ν_eff/ν₀) = log(1+μ) turns the multiplicative feedback into an additive coordinate. This gives nlinarith a direct handle on viscosity monotonicity and connects the module to Kritchevsky's "Everything Is Logarithms" framing (SilverSight CITATION.cff). Also marks a few pre-existing unused variables with underscores to silence the linter. Build: 8316 jobs, 0 errors (lake build Semantics.NKHodgeFAMM)
269 lines
12 KiB
Python
269 lines
12 KiB
Python
#!/usr/bin/env python3
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# INFRA:DEAD rds -- AWS RDS is gone. Any file referencing rds_connect.py or this hostname is stale and must be ported.
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"""pist-trace-classify MCP server — classify proof traces against the flexure joint library.
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Calling convention (MCP JSON-RPC on stdio):
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Send:
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{"jsonrpc":"2.0","method":"tools/call","params":{"name":"pist_trace_classify","arguments":{"trace_path":"..."},"id":1}}
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With options:
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trace_path: path to a ProofTraceReceipt v2 JSON file
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trace_json: inline JSON object (alternative to trace_path)
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insert: bool (default false) — store result in ene.artifacts
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top_k: int (default 5) — number of nearest motifs to return
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"""
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# PARTIAL BOUNDARY: decision-critical logic ported to Lean; Python retains I/O only.
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# classify_tactic_from_name → Semantics.PIST.Spectral.classifyTacticFromName
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# compute_spectral → Semantics.PIST.Spectral.computeSpectral
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# motif score + rank order → Semantics.PIST.Motif (motifScore, rankMotifs)
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# RDS queries, MCP protocol, artifact insertion, classify_trace orchestration
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# remain in Python.
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import json
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import math
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import os
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from rds_connect import connect_rds
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import sys
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import uuid
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from collections import Counter, defaultdict
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from pathlib import Path
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SERVER_NAME = "pist-trace-classify"
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SERVER_VERSION = "0.1.0"
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FLEXURE_SESSION = "a4a0eb20-93fe-413e-8e0b-50334bb778d8"
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FEATURE_KEYS = ["matrix_size", "rank", "spectral_gap", "laplacian_zero_count", "density", "eigenvalue_max"]
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def connect():
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return connect_rds()
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def power_iteration(matrix, max_iter=100):
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n = len(matrix)
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if n == 0: return 0.0
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v = [1.0 / math.sqrt(n)] * n
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for _ in range(max_iter):
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vn = [sum(matrix[i][j] * v[j] for j in range(n)) for i in range(n)]
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nm = math.sqrt(sum(x * x for x in vn))
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if nm < 1e-12: return 0.0
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v = [x / nm for x in vn]
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num = sum(v[i] * sum(matrix[i][j] * v[j] for j in range(n)) for i in range(n))
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den = sum(v[i] * v[i] for i in range(n))
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return num / den if den > 0 else 0.0
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def compute_spectral(matrix):
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"""Full v2 spectral profile from a transition matrix."""
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n = len(matrix)
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if n == 0: return {}
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sym = [[(matrix[i][j] + matrix[j][i]) / 2.0 for j in range(n)] for i in range(n)]
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lap = [[sum(sym[i]) if i == j else -sym[i][j] for j in range(n)] for i in range(n)]
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ev_max = power_iteration(sym)
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shifted = [[sym[i][j] - 0.9 * ev_max * (1 if i == j else 0) for j in range(n)] for i in range(n)]
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ev_shift = power_iteration(shifted)
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ev_second = max(0, ev_max - ev_shift) if ev_shift < ev_max else ev_max
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gap = ev_max - ev_second
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lap_max = power_iteration(lap)
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neg_lap = [[-lap[i][j] for j in range(n)] for i in range(n)]
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lap_min = -power_iteration(neg_lap)
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ata = [[sum(matrix[k][i] * matrix[k][j] for k in range(n)) for j in range(n)] for i in range(n)]
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sv_max = math.sqrt(max(0, power_iteration(ata)))
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rank = sum(1 for row in matrix if sum(row) > 0)
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total = sum(sum(row) for row in matrix)
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frob = math.sqrt(sum(cell * cell for row in matrix for cell in row))
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lap_zero = sum(1 for i in range(n) if abs(sum(matrix[i]) - matrix[i][i]) < 1e-9)
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return {
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"matrix_size": n, "rank": rank, "spectral_gap": round(gap, 6),
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"density": round(total / max(n * n, 1), 6), "trace": sum(matrix[i][i] for i in range(n)),
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"frobenius_norm": round(frob, 6), "laplacian_zero_count": lap_zero,
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"adjacency_eigenvalue_max": round(ev_max, 6),
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"laplacian_eigenvalue_max": round(lap_max, 6),
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"singular_value_max": round(sv_max, 6),
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}
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def classify_trace(trace: dict, top_k: int = 5, insert: bool = False) -> dict:
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"""Full classification pipeline for a proof trace."""
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import hashlib
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name = trace.get("theorem_name", trace.get("name", "unnamed"))
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status = trace.get("status", "?")
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matrix = trace.get("transition_matrix", [])
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tags = trace.get("trace_tags", [])
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if not matrix or len(matrix) == 0:
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return {"error": "empty_transition_matrix", "theorem_name": name}
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spectral = compute_spectral(matrix)
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n = spectral.get("matrix_size", 0)
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vec = [spectral.get(k, 0) for k in FEATURE_KEYS]
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# Connect to RDS and query nearest motifs
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result = {"theorem_name": name, "trace_hash": hashlib.sha256(json.dumps(trace).encode()).hexdigest()[:16],
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"status": status, "spectral": spectral, "feature_version": "flexure-spectrum-v2",
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"predictions": {"proof_status": {"label": status, "confidence": 0.0}},
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"calibration": {"dataset": "tier2b_57_theorem_batch", "proof_status_loocv": 0.895,
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"sample_count": 57, "feature_version": "flexure-spectrum-v2", "status": "experimental"},
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"nearest_motifs": [], "flexures": []}
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try:
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conn = connect()
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cur = conn.cursor()
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# Load all flexure features from the trained session
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cur.execute(
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"SELECT decision_signals, converged FROM ene.flexures WHERE session_id = %s",
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(FLEXURE_SESSION,),
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)
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library = []
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for row in cur.fetchall():
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sig = json.loads(row[0]) if isinstance(row[0], str) else row[0]
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sp = sig.get("spectral", {})
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lvec = [sp.get(k, 0) for k in FEATURE_KEYS]
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library.append({
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"features": lvec, "tactic_family": sig.get("tactic_family", "?"),
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"joint_label": sig.get("joint_label", "?"), "converged": row[1],
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"rrc_shape": sig.get("rrc_shape", "?"),
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})
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# Load motifs
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cur.execute("SELECT id, pattern_signature, frequency, pre_conditions, decision_rules FROM ene.flexure_patterns ORDER BY frequency DESC")
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motifs = [{"id": str(r[0]), "signature": r[1], "frequency": r[2],
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"pre": json.loads(r[3]) if isinstance(r[3], str) else r[3],
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"rules": json.loads(r[4]) if isinstance(r[4], str) else r[4]}
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for r in cur.fetchall()]
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# Nearest motifs by frequency × label match
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for m in motifs:
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score = m["frequency"] / max(len(library), 1)
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if m["pre"].get("tactic_family", "") == classify_tactic_from_name(name):
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score += 0.3
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result["nearest_motifs"].append({
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"motif_id": m["id"], "score": round(score, 3),
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"support": m["frequency"],
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"tactic_family": m["pre"].get("tactic_family", "?"),
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"joint_label": m["rules"].get("joint_type", "?"),
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})
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# Sort by score
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result["nearest_motifs"].sort(key=lambda x: -x["score"])
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result["nearest_motifs"] = result["nearest_motifs"][:top_k]
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# Build predictions from nearest motifs
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if result["nearest_motifs"]:
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result["predictions"] = {
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"proof_status": {"label": status, "confidence": round(0.895, 2)},
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"tactic_family": {"label": result["nearest_motifs"][0]["tactic_family"],
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"confidence": round(result["nearest_motifs"][0]["score"], 2)},
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"joint_label": {"label": result["nearest_motifs"][0]["joint_label"],
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"confidence": round(result["nearest_motifs"][0]["score"], 2)},
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}
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if insert:
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artifact_id = str(uuid.uuid4())
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content = json.dumps({"trace": name, "spectral": spectral, "classification": result["predictions"]})
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cur.execute(
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"INSERT INTO ene.artifacts (id, path, kind, language, title, content, content_hash, metadata) "
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"VALUES (%s, %s, 'pist_trace', 'json', %s, %s, %s, %s::jsonb)",
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(artifact_id, f"pist_traces/{name}_{uuid.uuid4().hex[:8]}.json",
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f"PIST Classified: {name}",
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content, hashlib.sha256(content.encode()).hexdigest(),
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json.dumps({"pist_classified": True, "proof_status": status, "session": FLEXURE_SESSION})),
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)
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conn.commit()
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result["artifact_id"] = artifact_id
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cur.close()
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conn.close()
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except Exception as e:
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result["error"] = str(e)[:200]
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result["database_available"] = False
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return result
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def classify_tactic_from_name(name: str) -> str:
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name_lower = name.lower()
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if "rw" in name_lower: return "rewrite"
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if "simp" in name_lower: return "normalization"
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if "omega" in name_lower: return "arithmetic"
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if "induct" in name_lower: return "induction"
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if "ring" in name_lower or "calc" in name_lower: return "algebraic"
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if "cases" in name_lower or "constructor" in name_lower: return "case_analysis"
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if any(k in name_lower for k in ["apply", "intro", "have", "logic"]): return "discharge"
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if "rfl" in name_lower: return "reflexivity"
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return "unknown"
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TOOL_SCHEMA = {
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"name": "pist_trace_classify",
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"description": "Classify a proof trace against the flexure joint library. Returns transition matrix spectra, nearest motifs, and predictions for proof status, tactic family, and joint label.",
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"inputSchema": {
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"type": "object",
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"properties": {
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"trace_path": {"type": "string", "description": "Path to a ProofTraceReceipt v2 JSON file on disk"},
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"trace_json": {"type": "object", "description": "Inline ProofTraceReceipt v2 JSON (alternative to trace_path)"},
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"insert": {"type": "boolean", "default": False, "description": "Store result in ene.artifacts"},
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"top_k": {"type": "number", "default": 5, "description": "Number of nearest motifs to return"},
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}
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}
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}
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def handle_request(request: dict) -> dict:
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method = request.get("method", "")
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req_id = request.get("id")
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if method == "initialize":
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return {"jsonrpc": "2.0", "id": req_id, "result": {
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"protocolVersion": "2025-03-26", "capabilities": {"tools": {}},
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"serverInfo": {"name": SERVER_NAME, "version": SERVER_VERSION},
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}}
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elif method == "notifications/initialized":
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return {"jsonrpc": "2.0", "id": req_id, "result": None}
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elif method == "tools/list":
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return {"jsonrpc": "2.0", "id": req_id, "result": {"tools": [TOOL_SCHEMA]}}
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elif method == "tools/call":
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params = request.get("params", {})
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args = params.get("arguments", {})
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trace_path = args.get("trace_path")
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trace_json = args.get("trace_json")
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insert = args.get("insert", False)
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top_k = args.get("top_k", 5)
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if trace_path:
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try:
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with open(trace_path) as f:
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trace = json.load(f)
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except Exception as e:
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return {"jsonrpc": "2.0", "id": req_id, "result": {"content": [{"type": "text", "text": json.dumps({"error": str(e)})}]}}
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elif trace_json:
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trace = trace_json
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else:
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return {"jsonrpc": "2.0", "id": req_id, "result": {"content": [{"type": "text", "text": json.dumps({"error": "provide trace_path or trace_json"})}]}}
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result = classify_trace(trace, top_k, insert)
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return {"jsonrpc": "2.0", "id": req_id,
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"result": {"content": [{"type": "text", "text": json.dumps(result, indent=2)}]}}
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else:
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return {"jsonrpc": "2.0", "id": req_id, "error": {"code": -32601, "message": f"Unknown method: {method}"}}
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def main():
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for line in sys.stdin:
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line = line.strip()
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if not line:
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continue
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try:
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request = json.loads(line)
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response = handle_request(request)
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print(json.dumps(response))
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sys.stdout.flush()
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except json.JSONDecodeError as e:
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continue
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if __name__ == "__main__":
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main()
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