| """Surface the agent-level TF-IDF -> SVD style axes: per-axis top +/- terms (from V^T) |
| and explained-variance share. Matches the finding3 representation.""" |
| 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 |
| RUNS=Path("runs") |
| OUT=Path("out/svd_axes.md") |
| 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"} |
| K=30 |
| docs=[];models=[] |
| 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 user_id,content FROM post").fetchall();coms=c.execute("SELECT 1 FROM comment").fetchall();c.close() |
| except:continue |
| if len(coms)<200:continue |
| txt=defaultdict(list) |
| for u,cont in posts: |
| if u in fam and cont:txt[u].append(cont) |
| for u,tl in txt.items(): |
| if tl:docs.append(" ".join(tl));models.append(fam[u]) |
| print(len(docs),"agent docs") |
| vc=TfidfVectorizer(ngram_range=(1,2),analyzer="word",min_df=10,max_features=20000,sublinear_tf=True,norm="l2",stop_words="english",token_pattern=r"[A-Za-z'][A-Za-z']+") |
| X=vc.fit_transform(docs);vocab=np.array(vc.get_feature_names_out()) |
| svd=TruncatedSVD(K,random_state=0);S=svd.fit_transform(X) |
| V=svd.components_ |
| evr=svd.explained_variance_ratio_ |
| models=np.array(models) |
| o=[f"# SVD style axes ({len(docs)} agent docs, {X.shape[1]} terms, K={K})\n", |
| f"Cumulative explained variance (top {K}): {evr.sum()*100:.1f}%\n", |
| "| axis | EV% | top +terms | top -terms | model loading (mean axis score, high->low) |","|---|---|---|---|---|"] |
| for k in range(12): |
| w=V[k];pos=vocab[np.argsort(w)[::-1][:6]];neg=vocab[np.argsort(w)[:6]] |
| ml={m:S[models==m,k].mean() for m in ["gpt-oss","Qwen","GLM-4","Mag","Gemma"]} |
| order=sorted(ml,key=ml.get,reverse=True) |
| mls=", ".join(f"{m}{ml[m]:+.2f}" for m in order) |
| o.append(f"| {k+1} | {evr[k]*100:.1f} | {', '.join(pos)} | {', '.join(neg)} | {mls} |") |
| OUT.write_text("\n".join(o)+"\n");print("\n".join(o));print("wrote",OUT) |
|
|
