| """Finding 2 redone on comments-per-post (CPP = in-degree/n_posts). |
| Decomposition (dy/tri/tet), LOMO (dy/tri), totals + absolute components, |
| and length-residualized per-model attractiveness for ALL models.""" |
| import sqlite3, json |
| from collections import defaultdict |
| from pathlib import Path |
| import numpy as np |
| 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): |
| out=[] |
| 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);nw=defaultdict(int) |
| 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;nw[u]+=len(cont.split()) |
| F=sorted(set(fam.values()));reg={2:"dyad",3:"tri",4:"tet"}.get(len(F)) |
| for u in fam: |
| if npost[u]>=2: |
| out.append({"run":db.stem,"model":fam[u],"persona":users.get(u,f"u{u}"),"cpp":ind[u]/npost[u], |
| "meanlen":nw[u]/npost[u],"net":F}) |
| return out |
| rows=load(RUNS.glob("sweep_*.db"))+load(TET.glob("tetra_s*_r*.db"),tet=True) |
| def dum(keys): |
| lv=sorted(set(map(str,keys)));idx={l:i for i,l in enumerate(lv)};D=np.zeros((len(keys),len(lv))) |
| for r,k in enumerate(keys):D[r,idx[str(k)]]=1 |
| return D[:,1:] |
| def r2(y,*B): |
| B=[b for b in B if b is not None and b.shape[1]>0];X=np.column_stack([np.ones(len(y))]+B) if B else np.ones((len(y),1)) |
| b,*_=np.linalg.lstsq(X,y,rcond=None);p=X@b;return 1-((y-p)**2).sum()/((y-y.mean())**2).sum() |
| def partner(r):return frozenset(m for m in r["net"] if m!=r["model"]) |
| print("=== Finding 2 on CPP: decomposition ===") |
| print(f"{'block':14s} {'dy':>7} {'tri':>7} {'tet':>7}") |
| def col(fn): |
| return [fn([r for r in rows if {'dyad':'dyad','tri':'tri','tet':'tet'}[r_reg:=({2:'dyad',3:'tri',4:'tet'}[len(r['net'])])]==m]) for m in ['dyad','tri','tet']] |
| def sub(mode):return [r for r in rows if {2:'dyad',3:'tri',4:'tet'}[len(r['net'])]==mode] |
| def line(lab,fn): |
| print(f"{lab:14s} "+" ".join(f"{fn(sub(m)):7.3f}" for m in ['dyad','tri','tet'])) |
| P=lambda s:dum([r['persona']for r in s]);M=lambda s:dum([r['model']for r in s]) |
| R=lambda s:dum([r['run']for r in s]);O=lambda s:np.array([[1.0 if r['model']=='gpt-oss' else 0]for r in s]) |
| SEL=lambda s:np.column_stack([M(s),dum([partner(r)for r in s])]) |
| Y=lambda s:np.array([r['cpp']for r in s]) |
| line("persona",lambda s:r2(Y(s),P(s))) |
| line("model(own)",lambda s:r2(Y(s),M(s))) |
| line("model-selection",lambda s:r2(Y(s),SEL(s))) |
| line("gpt-oss ind",lambda s:r2(Y(s),O(s))) |
| line("run",lambda s:r2(Y(s),R(s))) |
| line("persona+model",lambda s:r2(Y(s),P(s),M(s))) |
| line("persona+run",lambda s:r2(Y(s),P(s),R(s))) |
| line("full",lambda s:r2(Y(s),P(s),M(s),R(s))) |
| line("dR2_model",lambda s:r2(Y(s),P(s),M(s),R(s))-r2(Y(s),P(s),R(s))) |
| print("\ntotal CPP variance + absolute components (R^2 x total):") |
| for m in ['dyad','tri','tet']: |
| s=sub(m);y=Y(s);tot=y.var() |
| print(f" {m}: n={len(s)} totalvar={tot:.3f} persona={r2(y,P(s))*tot:.3f} model-sel={r2(y,SEL(s))*tot:.3f} oss-ind={r2(y,O(s))*tot:.3f}") |
| print("\n=== LOMO on CPP (dy/tri): persona / own / selection ===") |
| for excl in ["baseline","Qwen","gpt-oss","Mag","GLM-4","Gemma"]: |
| for m in ['dyad','tri']: |
| s=[r for r in sub(m) if excl=="baseline" or excl not in r['net']] |
| if len(s)<50:continue |
| y=Y(s);print(f" {excl:8s} {m:5s} n={len(s):4d} persona={r2(y,P(s))*100:5.1f}% own={r2(y,M(s))*100:5.1f}% sel={r2(y,SEL(s))*100:5.1f}%") |
| print("\n=== length-residualized CPP attractiveness, ALL models, by regime ===") |
| def wz(s,key): |
| o={};bd=defaultdict(list) |
| for r in s:bd[r['run']].append(r) |
| arr={} |
| vals=np.array([r[key]for r in s]);run=[r['run']for r in s] |
| z=np.zeros(len(s)) |
| for rr in set(run): |
| idx=[i for i,x in enumerate(run) if x==rr];v=vals[idx];m=v.mean();sd=v.std() or 1 |
| for i in idx:z[i]=(vals[i]-m)/sd |
| return z |
| for m in ['dyad','tri','tet']: |
| s=sub(m);cz=wz(s,'cpp');lz=wz(s,'meanlen');b=np.polyfit(lz,cz,1)[0];res=cz-b*lz |
| md=np.array([r['model']for r in s]) |
| print(f" {m} (length slope {b:+.2f}):") |
| for mod in ["gpt-oss","Qwen","GLM-4","Mag","Gemma"]: |
| mm=md==mod |
| if mm.sum():print(f" {mod:8s} raw {cz[mm].mean():+.2f} len-adj {res[mm].mean():+.2f}") |
|
|
