| """Is the low text->engagement R^2 a ceiling or an artifact of SVD/linearization? |
| Predict per-agent within-run-z CPP from: full TF-IDF (Ridge), SVD-30/100/500 (Ridge), |
| and a nonlinear model (HistGBT on SVD-100). 5-model pool, posts-only agent text.""" |
| 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 |
| from sklearn.linear_model import Ridge |
| from sklearn.ensemble import HistGradientBoostingRegressor |
| from sklearn.model_selection import KFold |
| 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 post_id,user_id,content FROM post").fetchall();coms=c.execute("SELECT post_id,user_id FROM comment").fetchall();c.close() |
| except:continue |
| if len(coms)<200:continue |
| owner={p:u for p,u,_ in posts};ind=defaultdict(int);npost=defaultdict(int);txt=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 and cont:npost[u]+=1;txt[u].append(cont) |
| for u in fam: |
| if npost[u]>=2:ag.append({"run":db.stem,"cpp":ind[u]/npost[u],"text":" ".join(txt[u])}) |
| n=len(ag);print(n,"agents") |
| runs=np.array([a["run"]for a in ag]) |
| y=np.zeros(n);bd=defaultdict(list) |
| for i,a in enumerate(ag):bd[a["run"]].append(i) |
| for r,idx in bd.items(): |
| v=np.array([ag[i]["cpp"]for i in idx],float);m=v.mean();sd=v.std() or 1 |
| for j,i in enumerate(idx):y[i]=(v[j]-m)/sd |
| vc=TfidfVectorizer(ngram_range=(1,2),analyzer="word",min_df=10,max_features=40000,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]);print("TF-IDF dims:",X.shape[1]) |
| def cvr2(Xf,est_fn): |
| kf=KFold(5,shuffle=True,random_state=0);sse=sst=0 |
| for tr,te in kf.split(Xf if not hasattr(Xf,'tocsr') else np.arange(n)): |
| e=est_fn();e.fit(Xf[tr],y[tr]);pp=e.predict(Xf[te]) |
| sse+=((y[te]-pp)**2).sum();sst+=((y[te]-y[tr].mean())**2).sum() |
| return 1-sse/sst |
| |
| print(f"full TF-IDF Ridge(alpha=5): R2={cvr2(X,lambda:Ridge(alpha=5.0)):.3f}",flush=True) |
| for K in [30,100]: |
| S=TruncatedSVD(K,random_state=0).fit_transform(X) |
| print(f"SVD-{K} Ridge: R2={cvr2(S,lambda:Ridge(alpha=1.0)):.3f}",flush=True) |
| |
| S100=TruncatedSVD(100,random_state=0).fit_transform(X) |
| print(f"SVD-100 HistGBT (nonlinear): R2={cvr2(S100,lambda:HistGradientBoostingRegressor(max_iter=300,learning_rate=0.05)):.3f}") |
|
|
