| """Does agent style cluster more by model or by persona? Fair test via permutation- |
| adjusted variance-explained (controls for differing group counts: 5 models vs 43 personas). |
| Style space = SVD(30), axis 1 (language) dropped.""" |
| import sqlite3, json |
| from collections import defaultdict |
| from pathlib import Path |
| import numpy as np |
| from sklearn.feature_extraction.text import TfidfVectorizer |
| from sklearn.decomposition import TruncatedSVD |
| RUNS=Path("runs") |
| SHORT={"Qwen/Qwen3-32B":"Qwen","openai/gpt-oss-20b":"gpt-oss","mistralai/Magistral-Small-2509":"Mag","zai-org/GLM-4-32B-0414":"GLM-4","google/gemma-4-31B-it":"Gemma"} |
| ag=[] |
| for db in sorted(RUNS.glob("sweep_*.db")): |
| if any(x in db.name for x in ["archive","olm","hermes","glm47","pilot_","snapshot","qwq"]):continue |
| mp=str(db).replace(".db",".metadata.json") |
| if not Path(mp).exists():continue |
| try:amm=json.load(open(mp)).get("agent_model_map",{}) |
| except:continue |
| if not amm:continue |
| fam={int(k)+1:SHORT.get(v["model"]) for k,v in amm.items()} |
| if any(v is None for v in fam.values()):continue |
| try: |
| c=sqlite3.connect(db);posts=c.execute("SELECT user_id,content FROM post").fetchall();users=dict(c.execute("SELECT user_id,user_name FROM user").fetchall());coms=c.execute("SELECT 1 FROM comment").fetchall();c.close() |
| except:continue |
| if len(coms)<200:continue |
| txt=defaultdict(list) |
| for u,cont in posts: |
| if u in fam and cont:txt[u].append(cont) |
| for u,tl in txt.items(): |
| if tl:ag.append({"model":fam[u],"persona":users.get(u,f"u{u}"),"text":" ".join(tl)}) |
| n=len(ag);print(n,"agents") |
| vc=TfidfVectorizer(ngram_range=(1,2),analyzer="word",min_df=10,max_features=20000,sublinear_tf=True,norm="l2",stop_words="english",token_pattern=r"[A-Za-z'][A-Za-z']+") |
| X=vc.fit_transform([a["text"]for a in ag]) |
| S=TruncatedSVD(30,random_state=0).fit_transform(X) |
| Z=((S-S.mean(0))/(S.std(0)+1e-9))[:,1:] |
| model=np.array([a["model"]for a in ag]);persona=np.array([a["persona"]for a in ag]) |
| tot=((Z-Z.mean(0))**2).sum() |
| def eta2(lab): |
| w=0.0 |
| for g in set(lab): |
| Zi=Z[lab==g];w+=((Zi-Zi.mean(0))**2).sum() |
| return 1-w/tot |
| rng=np.random.default_rng(0) |
| def null(lab,B=200): |
| lab=lab.copy();vals=[] |
| for _ in range(B): |
| rng.shuffle(lab);vals.append(eta2(lab)) |
| return np.mean(vals) |
| em,ep=eta2(model),eta2(persona) |
| nm,np_=null(model),null(persona) |
| print(f"{'group':8s} {'#groups':>7} {'eta2':>7} {'null':>7} {'excess':>7}") |
| print(f"{'model':8s} {len(set(model)):7d} {em:7.3f} {nm:7.3f} {em-nm:7.3f}") |
| print(f"{'persona':8s} {len(set(persona)):7d} {ep:7.3f} {np_:7.3f} {ep-np_:7.3f}") |
| print(f"\nratio of excess (model/persona): {(em-nm)/(ep-np_):.2f}") |
| |
| def tight(lab): |
| cs=[] |
| for g in set(lab): |
| Zi=Z[lab==g] |
| if len(Zi)<3:continue |
| c=Zi.mean(0);cs.append(np.mean([float(zi@c/((np.linalg.norm(zi)*np.linalg.norm(c))+1e-9)) for zi in Zi])) |
| return np.mean(cs) |
| print(f"\nmean within-group cosine-to-centroid: model={tight(model):.3f} persona={tight(persona):.3f} (higher=tighter)") |
|
|
