| """Exact absolute-variance decomposition of per-agent in-degree, by regime |
| (two-way / three-way from the 5-model pool; four-way from the tetradic pool). |
| Absolute component = single-block in-sample R^2 x total in-degree variance. |
| Keeps everything consistent with tab:ols_decomp and Fig variance_overview(a). |
| """ |
| 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 rows_from(dbiter, tet=False): |
| rows=[] |
| for db in dbiter: |
| 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 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) |
| for p,cu in coms: |
| o=owner.get(p) |
| if o in fam and o!=cu:ind[o]+=1 |
| net=tuple(sorted(set(fam.values()))) |
| mode="tet" if tet else ("tri" if len(net)==3 else "dyad" if len(net)==2 else "other") |
| for u,f in fam.items(): |
| rows.append({"run":db.stem,"model":f,"persona":users.get(u,f"u{u}"),"indeg":ind.get(u,0),"mode":mode,"net":net}) |
| return rows |
|
|
| def dummies(keys): |
| lv=sorted(set(keys),key=lambda x:str(x));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[k]]=1 |
| return D[:,1:] |
| def r2(y,*blocks): |
| blocks=[b for b in blocks if b is not None and b.shape[1]>0] |
| X=np.column_stack([np.ones(len(y))]+blocks) if blocks else np.ones((len(y),1)) |
| b,*_=np.linalg.lstsq(X,y,rcond=None);pred=X@b |
| return 1-((y-pred)**2).sum()/((y-y.mean())**2).sum() |
| def partner(r): return frozenset(m for m in r["net"] if m!=r["model"]) |
|
|
| allrows=rows_from(sorted(RUNS.glob("sweep_*.db")))+rows_from(sorted(TET.glob("tetra_s*_r*.db")),tet=True) |
| out=["# Absolute variance decomposition (per-agent in-degree)\n", |
| "| component | Two-way | Three-way | Four-way |","|---|---:|---:|---:|"] |
| comp={} |
| for mode in ["dyad","tri","tet"]: |
| rs=[r for r in allrows if r["mode"]==mode] |
| y=np.array([r["indeg"] for r in rs],float);tot=y.var() |
| P=dummies([r["persona"] for r in rs]);Mo=dummies([r["model"] for r in rs]) |
| Sel=np.column_stack([Mo,dummies([partner(r) for r in rs])]) if dummies([partner(r) for r in rs]).shape[1]>0 else Mo |
| oss=np.array([[1.0 if r["model"]=="gpt-oss" else 0.0] for r in rs]);Run=dummies([r["run"] for r in rs]) |
| comp[mode]={"n":len(rs),"total":tot,"persona":r2(y,P)*tot,"own":r2(y,Mo)*tot, |
| "selection":r2(y,Sel)*tot,"oss":r2(y,oss)*tot,"run":r2(y,Run)*tot, |
| "persona_r2":r2(y,P),"sel_r2":r2(y,Sel),"own_r2":r2(y,Mo),"oss_r2":r2(y,oss),"run_r2":r2(y,Run)} |
| def row(label,key): |
| return f"| {label} | "+" | ".join(f"{comp[m][key]:.0f}" for m in ["dyad","tri","tet"])+" |" |
| out+=[row("Total variance","total"),"|---|---|---|---|", |
| row("Persona","persona"),row("Model selection (own+partner)","selection"), |
| row("\\quad own model","own"),row("\\quad gpt-oss indicator","oss"),row("Run (baseline)","run")] |
| out+=["\n## underlying R^2 (for tab:ols_decomp consistency)\n", |
| "| block | Two-way | Three-way | Four-way |","|---|---:|---:|---:|"] |
| for lab,key in [("persona","persona_r2"),("model selection","sel_r2"),("own model","own_r2"),("gpt-oss ind","oss_r2"),("run","run_r2")]: |
| out.append(f"| {lab} | "+" | ".join(f"{comp[m][key]:.3f}" for m in ['dyad','tri','tet'])+" |") |
| out.append(f"\nn agents: dyad {comp['dyad']['n']}, tri {comp['tri']['n']}, tet {comp['tet']['n']}") |
| print("\n".join(out)) |
| Path("out/absolute_variance.md").write_text("\n".join(out)+"\n") |
|
|
