| """(A) Held-out 5-fold CV R^2 for persona-only / model-only / full (addresses the |
| 43-vs-5 parameter-count confound in in-sample R^2). (B) Per-model engagement effect |
| (within-run-z CPP) raw vs. after controlling for post length -> attractor/repeller survive.""" |
| import sqlite3, json |
| from collections import defaultdict |
| from pathlib import Path |
| import numpy as np |
| import pandas as pd |
| from sklearn.linear_model import LinearRegression |
| from sklearn.model_selection import KFold |
| from sklearn.metrics import r2_score |
| RUNS=Path("runs");TET=RUNS/"tetratic" |
| 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"} |
| def load(globpat,tet=False): |
| rows=[] |
| for db in sorted(globpat): |
| if not tet and any(x in db.name for x in ["archive","olm","hermes","glm47","pilot_","snapshot","qwq"]):continue |
| if tet and (TET/f"{db.stem}_checkpoint.json").exists():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 |
| |
| |
| |
| umin=min(int(k) for k in amm) |
| fam={int(k)+umin: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 post_id,user_id,content FROM post").fetchall() |
| coms=c.execute("SELECT post_id,user_id FROM comment").fetchall();users=dict(c.execute("SELECT user_id,user_name FROM user").fetchall());c.close() |
| except:continue |
| if len(coms)<(100 if tet else 200):continue |
| owner={p:u for p,u,_ in posts};ind=defaultdict(int);npost=defaultdict(int);loglen=defaultdict(list) |
| for p,cu in coms: |
| o=owner.get(p) |
| if o in fam and o!=cu:ind[o]+=1 |
| for p,u,cont in posts: |
| if u in fam:npost[u]+=1;loglen[u].append(np.log(max(1,len(cont or "")))) |
| reg={2:"dyad",3:"triad",4:"tetrad"}[len(set(fam.values()))] |
| for u in fam: |
| if npost[u]>=2: |
| rows.append({"run":db.stem,"reg":reg,"model":fam[u],"persona":users.get(u,f"u{u}"), |
| "cpp":ind[u]/npost[u],"loglen":np.mean(loglen[u])}) |
| return rows |
| rows=load(RUNS.glob("sweep_*.db"))+load(TET.glob("tetra_s*_r*.db"),tet=True) |
| df=pd.DataFrame(rows) |
| |
| df["z"]=df.groupby("run")["cpp"].transform(lambda v:(v-v.mean())/(v.std() or 1)) |
| df["zlen"]=df.groupby("run")["loglen"].transform(lambda v:(v-v.mean())/(v.std() or 1)) |
| def cvr2(sub,cols): |
| y=sub["z"].values;X=pd.get_dummies(sub[cols],columns=cols,drop_first=False).astype(float).values |
| kf=KFold(5,shuffle=True,random_state=0);sc=[] |
| for tr,te in kf.split(X): |
| m=LinearRegression().fit(X[tr],y[tr]);sc.append(r2_score(y[te],m.predict(X[te]))) |
| return np.mean(sc) |
| print("=== (A) HELD-OUT 5-fold CV R^2 (within-run z CPP) ===") |
| print(f"{'regime':10s}{'persona':>10}{'model':>9}{'full(p+m)':>11} (#personas / #models)") |
| for reg in ["dyad","triad","tetrad"]: |
| s=df[df.reg==reg] |
| pe=cvr2(s,["persona"]);mo=cvr2(s,["model"]);fu=cvr2(s,["persona","model"]) |
| print(f"{reg:10s}{pe:>10.3f}{mo:>9.3f}{fu:>11.3f} ({s.persona.nunique()} / {s.model.nunique()}), n={len(s)}") |
| print("\n=== (B) per-model engagement (z CPP) RAW vs LENGTH-CONTROLLED ===") |
| for reg in ["dyad","triad","tetrad"]: |
| s=df[df.reg==reg].copy() |
| |
| b=np.polyfit(s["zlen"],s["z"],1);s["zadj"]=s["z"]-(b[0]*s["zlen"]+b[1]) |
| print(f"-- {reg} (length slope b={b[0]:+.2f}) --") |
| for m in ["gpt-oss","Mag","GLM-4","Qwen","Gemma"]: |
| sm=s[s.model==m] |
| if len(sm)<5:continue |
| print(f" {m:8s} raw z={sm['z'].mean():+.2f} length-adj z={sm['zadj'].mean():+.2f} (n={len(sm)}, mean loglen z={sm['zlen'].mean():+.2f})") |
|
|
