Add story-to-pack research and a structured situation review
Research toward building a content pack from a story corpus, kept on its own branch and independent of the game. Records the selection experiments against blind labels, and settles selection as gate G2 followed by a human review: review.py writes REVIEW.md and a review.json form, apply_review.py checks the filled form and writes situations.json for the next stage. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01C6UDQ9o6L6Ey173U7XVou6
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JesseMarkowitz
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Claude Opus 5
Claude Opus 5
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d84ae495f4
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"""Recall recovery: baseline gate and three methods on the seed-13 partition (SELECTION.md).
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python3 recover.py
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Writes:
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recover-map.json sheet id -> member chunks and which methods produced it (do not read before labelling)
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sheet-recover.txt blind sheet: member summaries only, shuffled
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labels-recover.json empty label template (only if absent)
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Prints counts per method only.
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"""
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import collections, json, math, pathlib, random, re, sys
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SEED, K = 13, 60
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MIN_GATE_SIZE, GROW_CAP = 13, 30
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sys.argv = ['measure.py', 'summary_embeddings.json']
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exec(pathlib.Path('measure.py').read_text(encoding='utf-8').split("print(f'{path.name}")[0])
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# defined: chunks, V, n, dim, story, dot, norm, kmeans (on V), describe, MIN_SIZE, MIN_STORIES, MAX_DOMINANT
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frozen = json.loads(pathlib.Path('rule-frozen.json').read_text())
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CUT = {s: t for s, _, t in frozen['terms']}
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S1_CUT, S6_CUT = CUT['S1'], CUT['S6']
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summaries = json.loads(pathlib.Path('summaries_clean.json').read_text(encoding='utf-8'))['summaries']
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text = {x['chunk']: x['summary'] for x in summaries}
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# Identical to signals.py.
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STOP = set("""a an the and or but of to in on at for with by from as into over under about after before
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while during than then that this these those it its his her hers him he she they them their theirs we our
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you your i me my is are was were be been being has have had do does did not no nor so such who whom whose
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which what when where why how all any each both either neither one two some other others another more most
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less least very just only also even yet still now up down out off away back again once own same too can
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could will would shall should may might must upon despite toward towards between among amid amidst
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someone someone's someone’s""".split())
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docs = [set(re.findall(r"[a-z][a-z'’]+", text[i].lower())) - STOP for i in range(n)]
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df = collections.Counter(w for d in docs for w in d)
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def s1(m):
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tot, cnt = 0.0, 0
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for x in range(len(m)):
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for y in range(x + 1, len(m)):
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if story[m[x]] != story[m[y]]:
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tot += dot(V[m[x]], V[m[y]]); cnt += 1
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return tot / cnt if cnt else 0.0
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def s6(m):
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size = len(m)
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cw = collections.Counter(w for i in m for w in docs[i])
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best = None
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for w, c in cw.items():
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if c < 2: continue
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score = math.log((c + .5) / (size - c + .5)) - math.log((df[w] + .5) / (n - df[w] + .5))
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if best is None or score > best[0]: best = (score, c)
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return best[1] / size if best else 0.0
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def eligible(m):
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size, nst, dom, _ = describe(m)
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return size >= MIN_SIZE and nst >= MIN_STORIES and dom <= MAX_DOMINANT
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def passes(m):
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return eligible(m) and s1(m) >= S1_CUT and s6(m) <= S6_CUT
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# Guard against drift from signals.py: recompute S1/S6 for the dev candidates.
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dev_sig = json.loads(pathlib.Path('signals-dev.json').read_text())
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dev_cand = json.loads(pathlib.Path('candidates-dev.json').read_text())
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for cid, m in dev_cand.items():
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assert abs(s1(m) - dev_sig[cid]['S1']) < 1e-3 and abs(s6(m) - dev_sig[cid]['S6']) < 1e-3, cid
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def kmeans_on(vecs, k, seed, iters=15):
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"""Same algorithm as measure.kmeans, over arbitrary unit vectors; returns index groups."""
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m, d = len(vecs), len(vecs[0])
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rnd = random.Random(seed)
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cent = [vecs[i] for i in rnd.sample(range(m), k)]
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assign = [-1] * m
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for _ in range(iters):
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moved = 0
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for i, v in enumerate(vecs):
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best, bs = 0, -2.0
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for j, c in enumerate(cent):
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s = sum(a * b for a, b in zip(v, c))
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if s > bs: bs, best = s, j
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if assign[i] != best: assign[i] = best; moved += 1
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if moved == 0: break
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groups = collections.defaultdict(list)
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for i, j in enumerate(assign): groups[j].append(i)
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for j, mem in groups.items():
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cent[j] = norm([sum(vecs[i][x] for i in mem) / len(mem) for x in range(d)])
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groups = collections.defaultdict(list)
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for i, j in enumerate(assign): groups[j].append(i)
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return list(groups.values())
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def centroid(m):
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return norm([sum(V[i][x] for i in m) / len(m) for x in range(dim)])
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# Baseline
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partition = [sorted(c) for c in kmeans(K, SEED)]
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candidates = [c for c in partition if eligible(c)]
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B = [c for c in candidates if passes(c)]
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print(f'seed {SEED}: {len(candidates)} candidates; B accepts {len(B)}', flush=True)
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# M1: re-cluster everything outside B's accepted clusters
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in_B = {i for c in B for i in c}
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pool = [i for i in range(n) if i not in in_B]
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k1 = max(2, round(len(pool) / 20))
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M1 = [list(c) for c in B]
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for g in kmeans_on([V[i] for i in pool], k1, SEED):
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m = sorted(pool[x] for x in g)
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if passes(m): M1.append(m)
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print(f'M1: pool {len(pool)}, k={k1}; accepts {len(M1)} ({len(M1) - len(B)} new)', flush=True)
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# M2: consensus clustering over co-association rows
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co = json.loads(pathlib.Path('coassoc.json').read_text())
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rows = [norm([float(x) for x in co[i]]) for i in range(n)]
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M2 = [sorted(g) for g in kmeans_on(rows, K, SEED) if passes(sorted(g))]
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print(f'M2: accepts {len(M2)}', flush=True)
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# M3: prune failing candidates to a passing core, then grow every passing cluster
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def prune(m):
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m = list(m)
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while not passes(m):
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if len(m) <= MIN_GATE_SIZE: return None
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def cross_mean(x):
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others = [y for y in m if story[y] != story[x]]
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return sum(dot(V[x], V[y]) for y in others) / len(others) if others else -1.0
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m.remove(min(m, key=cross_mean))
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return m
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def grow(m):
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m = list(m)
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while len(m) < GROW_CAP:
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cen = centroid(m)
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members = set(m)
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j = max((i for i in range(n) if i not in members), key=lambda i: dot(V[i], cen))
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if not passes(m + [j]): break
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m.append(j)
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return sorted(m)
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M3, pruned_ok = [], 0
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for c in candidates:
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core = c if passes(c) else prune(c)
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if core is None: continue
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if core is not c: pruned_ok += 1
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M3.append(grow(core))
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cover3 = collections.Counter(i for c in M3 for i in c)
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print(f'M3: accepts {len(M3)} ({pruned_ok} recovered by pruning); scenes in >1 cluster: {sum(v > 1 for v in cover3.values())}', flush=True)
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# Pool for blind labelling
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methods = {'B': B, 'M1': M1, 'M2': M2, 'M3': M3}
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union = {}
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for name, clusters in methods.items():
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for c in clusters:
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union.setdefault(tuple(sorted(c)), []).append(name)
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keys = list(union)
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rnd = random.Random(1307)
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rnd.shuffle(keys)
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mapping = {f'R{i + 1:03d}': {'members': list(k), 'methods': union[k]} for i, k in enumerate(keys)}
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pathlib.Path('recover-map.json').write_text(json.dumps(mapping, indent=1))
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lines = []
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for rid, entry in mapping.items():
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shown = entry['members'][:]
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rnd.shuffle(shown)
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lines.append(f'=== {rid}')
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lines.extend(f' - {text[i]}' for i in shown)
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lines.append('')
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pathlib.Path('sheet-recover.txt').write_text('\n'.join(lines), encoding='utf-8')
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lab = pathlib.Path('labels-recover.json')
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if not lab.exists():
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lab.write_text(json.dumps({rid: {'label': None, 'name': ''} for rid in mapping}, indent=1))
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print(f'blind sheet: {len(mapping)} distinct clusters, {sum(len(e["members"]) for e in mapping.values())} lines')
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