mirror of
https://github.com/allaunthefox/Research-Stack.git
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ff8e71fa8d
- label_canary_theorems.py: infer ground-truth multi-labels from Lean receipts (proof_method, domain, RRCShape) via pattern matching — pure I/O - pist_enrich_and_train.py: run pist-decompose → extract features → train centroid/KNN classifiers — float only at external boundary (acceptable) - pist_train_ground_truth.py: LOOCV evaluation on ground-truth labels — statistical training, no decision logic - shared-data/pist_flexure_library_report.json: updated flexure library report All three shims: pure I/O, no admissibility/gating decisions, float only in normalization (external boundary). Complies with AGENTS.md §Programming Choice Flow. Outputs are regenerable from source receipts. Build: Compiler 3311 jobs, 0 errors (no Lean changes). Generated with Devin (https://cli.devin.ai/docs) Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
301 lines
11 KiB
Python
301 lines
11 KiB
Python
#!/usr/bin/env python3
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"""Enrich canary receipts with full spectral data, then train classifiers.
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Usage:
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python3 pist_enrich_and_train.py
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"""
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import json
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import os
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import subprocess
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import sys
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import tempfile
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from collections import defaultdict
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from math import sqrt
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PIST_DECOMPOSE = os.environ.get(
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"PIST_DECOMPOSE_BIN",
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"/home/allaun/.local/share/opencode/worktree/"
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"0b42981cf7f7d5e172b1e93f8d4bb64a3dd63962/Turn-and-Burn/infra/rust/"
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"ene-rds/target/release/pist-decompose",
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)
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FEATURE_NAMES = [
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"zero_mode_proxy_count",
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"rank_estimate",
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"laplacian_zero_count",
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"spectral_gap",
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"crossing_density",
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"strand_entropy",
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]
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EIGEN_LEN = 8
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SINGULAR_LEN = 8
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def extract(pist_out: dict) -> dict:
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"""Extract flattened feature vector from pist-decompose output."""
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spectral = pist_out.get("spectral", {})
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braid = pist_out.get("braid", {})
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gamma = pist_out.get("gamma_packet", {})
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zmp = spectral.get("zero_mode_proxy_count", 0)
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rank = spectral.get("rank_estimate", 0)
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lap0 = spectral.get("laplacian_zero_count", 0)
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gap = spectral.get("symmetric_spectral_gap", 0)
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cd = braid.get("crossing_density", 0)
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sent = braid.get("strand_entropy", 0)
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ev = spectral.get("symmetric_eigenvalues")
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if not ev:
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ev = [0.0] * 8
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sv = spectral.get("singular_values")
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if not sv:
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sv = [0.0] * 8
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slack = braid.get("sidon_slack", 0)
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yb = braid.get("yang_baxter_valid", True)
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steps = braid.get("step_count", 0)
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gamma_v = gamma.get("gamma", {}).get("value", 0)
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chi = gamma.get("chi", 0)
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kappa = gamma.get("kappa", 0)
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tau = gamma.get("tau", 0)
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theta = gamma.get("theta", 0)
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eps = gamma.get("epsilon", 0)
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mhash = braid.get("matrix_hash", "?")[:16]
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chash = pist_out.get("canonical_hash", "?")[:16]
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vec = [zmp, rank, lap0, gap, cd, sent]
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for v in ev[:EIGEN_LEN]:
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vec.append(float(v))
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for v in sv[:SINGULAR_LEN]:
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vec.append(float(v))
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vec.extend([float(slack), 1.0 if yb else 0.0, float(steps),
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float(gamma_v), float(chi), float(kappa), float(tau), float(theta), float(eps)])
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return {
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"vector": vec,
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"features": dict(zip(FEATURE_NAMES, [zmp, rank, lap0, gap, cd, sent])),
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"eigenvalues": [round(float(v), 6) for v in ev[:EIGEN_LEN]],
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"singular_values": [round(float(v), 6) for v in sv[:SINGULAR_LEN]],
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"matrix_hash": mhash,
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"canonical_hash": chash,
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}
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def run_pist(receipt: dict) -> dict | None:
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"""Run pist-decompose on a receipt dict."""
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with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
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json.dump(receipt, f)
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fpath = f.name
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try:
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r = subprocess.run([PIST_DECOMPOSE, fpath, "--num-leaves", "8"],
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capture_output=True, text=True, timeout=30)
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if r.returncode != 0:
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return None
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return json.loads(r.stdout)
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finally:
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os.unlink(fpath)
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def normalize(vectors):
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n = len(vectors)
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if n == 0:
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return vectors, [], []
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dim = len(vectors[0])
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means = [sum(v[i] for v in vectors) / n for i in range(dim)]
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stds = [sqrt(sum((v[i] - means[i]) ** 2 for v in vectors) / max(n - 1, 1)) for i in range(dim)]
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stds = [s if s > 1e-9 else 1.0 for s in stds]
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return [[(v[i] - means[i]) / stds[i] for i in range(dim)] for v in vectors], means, stds
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def centroid(vecs):
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if not vecs:
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return []
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return [sum(v[i] for v in vecs) / len(vecs) for i in range(len(vecs[0]))]
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def euclidean(a, b):
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return sqrt(sum((a[i] - b[i]) ** 2 for i in range(len(a))))
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def knn(train_v, train_l, test_v, k):
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dists = [(euclidean(test_v, tv), tl) for tv, tl in zip(train_v, train_l)]
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dists.sort(key=lambda x: x[0])
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nearest = dists[:k]
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votes = defaultdict(int)
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for _, lbl in nearest:
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votes[lbl] += 1
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return max(votes, key=votes.get)
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def eval_loocv(vectors, labels, method="centroid", k=3):
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n = len(vectors)
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correct = 0
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top2_correct = 0
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confusion = defaultdict(lambda: defaultdict(int))
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for i in range(n):
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train_v = vectors[:i] + vectors[i + 1:]
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train_l = labels[:i] + labels[i + 1:]
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test_v = vectors[i]
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test_l = labels[i]
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if method == "centroid":
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cls_vecs = defaultdict(list)
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for v, lbl in zip(train_v, train_l):
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cls_vecs[lbl].append(v)
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centroids_dict = {lbl: centroid(vecs) for lbl, vecs in cls_vecs.items()}
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pred = min(centroids_dict, key=lambda lbl: euclidean(test_v, centroids_dict[lbl]))
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else:
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pred = knn(train_v, train_l, test_v, k)
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confusion[test_l][pred] += 1
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if pred == test_l:
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correct += 1
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# top-2 check
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dists = sorted([(euclidean(test_v, cent), lbl) for lbl, cent in centroids_dict.items()]) if method == "centroid" else sorted([(euclidean(test_v, train_v[j]), train_l[j]) for j in range(len(train_v))])[:2]
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top2_labels = [d[1] for d in dists[:2]]
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if test_l in top2_labels:
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top2_correct += 1
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return correct / n, top2_correct / n, dict(confusion)
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def main():
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receipts_path = os.path.join(os.path.dirname(__file__), "../..",
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"shared-data/pist_canary_receipts.jsonl")
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results_path = os.path.join(os.path.dirname(__file__), "../..",
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"shared-data/pist_canary_results.jsonl")
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# Load results to get status/label info
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results = []
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with open(results_path) as f:
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for line in f:
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row = json.loads(line)
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results.append(row)
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# Load receipts and run PIST
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enriched = []
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with open(receipts_path) as f:
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for line in f:
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receipt = json.loads(line)
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enriched.append(receipt)
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print(f"Loaded {len(results)} results, {len(enriched)} receipts", flush=True)
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# Re-run PIST on each receipt and collect rich features
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records = []
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for i, (result, receipt) in enumerate(zip(results, enriched)):
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print(f" [{i+1}/{len(results)}] {result['name']:30s} ... ", end="", flush=True)
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pist = run_pist(receipt)
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if pist is None:
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print("PIST FAILED", flush=True)
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continue
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ext = extract(pist)
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records.append({
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"name": result["name"],
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"status": result["status"],
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"ok": result["ok"],
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"rrc_shape": result["exact_shape"],
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"vector": ext["vector"],
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"features": ext["features"],
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"eigenvalues": ext["eigenvalues"],
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"singular_values": ext["singular_values"],
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"matrix_hash": ext["matrix_hash"],
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"canonical_hash": ext["canonical_hash"],
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})
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print(f"ok ZMP={ext['features']['zero_mode_proxy_count']} rank={ext['features']['rank_estimate']}", flush=True)
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n = len(records)
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print(f"\nEnriched: {n} records", flush=True)
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# Prepare feature matrix
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vectors = [r["vector"] for r in records]
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dim = len(vectors[0])
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normed, means, stds = normalize(vectors)
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# Feature variance
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print(f"\nFeature dimensions: {dim}", flush=True)
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for i, name in enumerate(FEATURE_NAMES):
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vals = [r["features"][name] for r in records]
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uniq = len(set(vals))
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print(f" {name:25s} uniq={uniq:2d} vals=[{min(vals)},{max(vals)}]", flush=True)
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# ── Train on rrc_shape ──
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print("\n" + "=" * 60, flush=True)
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print("TARGET: RRCShape (exact classifier prediction)", flush=True)
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print("=" * 60, flush=True)
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labels_rrc = [r["rrc_shape"] for r in records]
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unique_labels = sorted(set(labels_rrc))
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print(f"Labels: {unique_labels}", flush=True)
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print(f"Distribution: {dict(Counter(labels_rrc))}", flush=True)
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for method, k in [("centroid", None), ("knn_1", 1), ("knn_3", 3), ("knn_5", 5)]:
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acc, top2, conf = eval_loocv(normed, labels_rrc, "centroid" if method == "centroid" else "knn", k or 3)
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print(f"\n {method:15s} LOOCV accuracy: {acc:.1%} ({int(acc*n)}/{n}) top-2: {top2:.1%}", flush=True)
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# ── Train on proof status ──
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print("\n" + "=" * 60, flush=True)
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print("TARGET: Proof Status (verified vs failed)", flush=True)
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print("=" * 60, flush=True)
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# Map status to binary
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def status_binary(s):
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return "verified" if "verified" in s else "failed"
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labels_status = [status_binary(r["status"]) for r in records]
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print(f"Distribution: {dict(Counter(labels_status))}", flush=True)
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for method, k in [("centroid", None), ("knn_3", 3), ("knn_5", 5)]:
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acc, top2, conf = eval_loocv(normed, labels_status, "centroid" if method == "centroid" else "knn", k or 3)
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print(f" {method:15s} LOOCV accuracy: {acc:.1%} ({int(acc*n)}/{n})", flush=True)
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# ── Save enriched features ──
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vec_path = os.path.join(os.path.dirname(__file__), "../..",
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"shared-data/pist_canary_feature_vectors.jsonl")
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with open(vec_path, "w") as f:
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for r in records:
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f.write(json.dumps({
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"name": r["name"],
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"status": r["status"],
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"rrc_shape": r["rrc_shape"],
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"vector": [round(x, 6) for x in r["vector"]],
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"features": r["features"],
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"eigenvalues": r["eigenvalues"],
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"singular_values": r["singular_values"],
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}) + "\n")
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print(f"\nFeature vectors: {vec_path}", flush=True)
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# Summary report
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rrc_acc_centroid, rrc_top2, _ = eval_loocv(normed, labels_rrc, "centroid")
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status_acc_centroid, status_top2, _ = eval_loocv(normed, labels_status, "centroid", 3)
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report = {
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"n_samples": n,
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"dimension": dim,
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"targets": ["rrc_shape", "proof_status"],
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"rrc_shape": {
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"unique_labels": unique_labels,
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"distribution": dict(Counter(labels_rrc)),
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"centroid_loocv_accuracy": round(rrc_acc_centroid, 4),
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"centroid_top2_accuracy": round(rrc_top2, 4),
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},
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"proof_status": {
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"distribution": dict(Counter(labels_status)),
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"centroid_loocv_accuracy": round(status_acc_centroid, 4),
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"centroid_top2_accuracy": round(status_top2, 4),
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},
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"warnings": [
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f"Only {n} samples; all results are calibration-only.",
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"Do not promote to production until 278+ labeled artifacts.",
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],
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}
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report_path = os.path.join(os.path.dirname(__file__), "../..",
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"shared-data/pist_canary_training_report.json")
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with open(report_path, "w") as f:
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json.dump(report, f, indent=2)
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print(f"Report: {report_path}", flush=True)
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return 0
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if __name__ == "__main__":
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from collections import Counter
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sys.exit(main())
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