| import json,math |
| from collections import Counter |
| import numpy as np |
| from sklearn.feature_extraction.text import TfidfVectorizer |
| from sklearn.linear_model import LogisticRegression |
| from sklearn.model_selection import StratifiedKFold |
| from sklearn.metrics import accuracy_score |
| rows=[json.loads(l) for l in open("data/texts.jsonl")] |
| rows=[r for r in rows if len(r.get("text","") or "")>=1] |
| print(len(rows),"texts total") |
| pc=Counter(r["persona"] for r in rows);keep={p for p,c in pc.items() if c>=25} |
| sub=[r for r in rows if r["persona"] in keep] |
| print(len(sub),"texts;",len(keep),"personas (>=25)") |
| vec=TfidfVectorizer(ngram_range=(1,2),analyzer="word",max_features=60000,sublinear_tf=True,token_pattern=r"[A-Za-z'][A-Za-z']+") |
| vc=TfidfVectorizer(ngram_range=(3,5),analyzer="char_wb",max_features=60000,sublinear_tf=True) |
| import scipy.sparse as sp |
| Xw=vec.fit_transform([r["text"]for r in sub]);Xc=vc.fit_transform([r["text"]for r in sub]);X=sp.hstack([Xw,Xc]).tocsr() |
| y=np.array([r["persona"]for r in sub]);chance=1/len(keep) |
| pred=np.empty(len(y),dtype=object) |
| for tr,te in StratifiedKFold(5,shuffle=True,random_state=0).split(X,y): |
| c=LogisticRegression(max_iter=300,C=2.0);c.fit(X[tr],y[tr]);pred[te]=c.predict(X[te]) |
| acc=accuracy_score(y,pred) |
| print(f"PERSONA recovery: {acc*100:.1f}% ({acc/chance:.0f}x chance {chance*100:.1f}%)") |
| SHORT={"gpt-oss":"gpt-oss","Qwen":"Qwen","GLM-4":"GLM-4","Mag":"Magistral","Gemma":"Gemma"} |
| for m in ["Gemma","Mag","gpt-oss","GLM-4","Qwen"]: |
| s=[r for r in sub if r["model"]==m];yy=np.array([r["persona"]for r in s]) |
| pcm=Counter(yy);kk={p for p,c in pcm.items() if c>=15} |
| s=[r for r in s if r["persona"] in kk];yy=np.array([r["persona"]for r in s]) |
| if len(set(yy))<3:continue |
| Xs=sp.hstack([vec.transform([r["text"]for r in s]),vc.transform([r["text"]for r in s])]).tocsr() |
| pr=np.empty(len(yy),dtype=object) |
| for tr,te in StratifiedKFold(3,shuffle=True,random_state=0).split(Xs,yy): |
| c=LogisticRegression(max_iter=300,C=2.0);c.fit(Xs[tr],yy[tr]);pr[te]=c.predict(Xs[te]) |
| a=accuracy_score(yy,pr);ch=1/len(set(yy));print(f" {m}: {a*100:.1f}% {a/ch:.0f}x ({len(set(yy))} personas)") |
|
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