| |
| """Merge keyword coding + manual judgements into the publishable CSV and the private verification file. |
| |
| Usage: |
| python3 -I scripts/build_dataset.py CODED.jsonl OUT_PUBLIC.csv OUT_PRIVATE.csv [--all] [JUDGEMENT.csv ...] |
| JUDGEMENT.csv rows are keyed by `url`, or by `idx` = row number in raw/coded_kw.jsonl (pilot file). |
| Files are merged field by field in the order given: a non-empty value in a later file wins. |
| Pass manual/judgement_v4.csv last: it holds the relevance (yes/no) and thread_type under rules v4. |
| The judgement files call the topic column `problem_category` (its name before 2026-10-30); it is read as `topic`. |
| A judged topic `other` predates the topics added 2026-10-30 (agent_behavior, observability, |
| performance_scaling): when the keywords give one of those, the keyword value is kept (same for an agent_* |
| error_type over a judged other_unknown). |
| All judgements so far are by the AI assistant (Claude), not by a person (column judged_by). |
| --all : write every thread with a `relevance` column (pilot.csv); default: only relevant == yes. |
| Published file: NO text, NO author info (no title, no excerpt, no author hash). |
| Private file : url, title, 30-word excerpt, codes — local verification only, never published. |
| """ |
| import csv, json, os, sys |
| from datetime import date |
|
|
| ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) |
| args = [a for a in sys.argv[1:] if a != "--all"] |
| ALL = "--all" in sys.argv |
| coded_path, out_pub, out_priv, judg_paths = args[0], args[1], args[2], args[3:] |
| coded = [json.loads(l) for l in open(coded_path)] |
| pilot = [json.loads(l) for l in open(os.path.join(ROOT, "raw", "coded_kw.jsonl"))] |
|
|
| manual = {} |
| for jp in judg_paths: |
| for r in csv.DictReader(open(jp)): |
| url = r.get("url") or pilot[int(r["idx"])]["url"] |
| m = manual.setdefault(url, {}) |
| for k, v in r.items(): |
| if k == "problem_category": |
| k = "topic" |
| if v not in ("", None): |
| m[k] = v |
| coded_urls = {c["url"] for c in coded} |
| for url, m in manual.items(): |
| if url in coded_urls and m.get("relevant") not in ("yes", "no"): |
| sys.exit(f"{url}: relevance must be yes/no under rules v4 (got {m.get('relevant')!r}); re-judge it in manual/judgement_v4.csv") |
|
|
| CODES = ["target_host", "app_type", "framework", "model_provider", "topic", "error_type", |
| "resolution_type", "ai_generated_app", "ai_tool"] |
| HAND = ["target_host", "app_type", "framework", "model_provider", "topic", "error_type", "resolution_type"] |
| PUB = ["url", "source", "repo", "repo_group", "created_at", "closed_at", "state", "state_reason", "n_comments", |
| "n_reactions", "maintainer_replied", "time_to_first_response_h", "time_to_close_h", "resolved", "thread_type"] + \ |
| CODES[:3] + ["stack"] + CODES[3:] + ["coding_method", "coding_confidence", "relevance_method", "collected_on"] |
| if ALL: |
| PUB.insert(1, "relevance") |
| PRIV = ["url", "title", "excerpt_private", "relevance", "relevance_method", "kw_relevant", "kw_unsure", "manual_note", |
| "thread_type", "kw_thread_type"] + CODES + ["coding_method"] |
|
|
| NEW_TOPICS = {"agent_behavior", "observability", "performance_scaling"} |
| today = date.today().isoformat() |
| pub_rows, priv_rows = [], [] |
| for c in coded: |
| m = manual.get(c["url"]) |
| rel = m["relevant"] if m else c["kw_relevant"] |
| row = {k: c.get(k) for k in ["url", "source", "repo", "created_at", "closed_at", "state", "state_reason", |
| "n_comments", "n_reactions", "maintainer_replied", "time_to_first_response_h", |
| "time_to_close_h", "resolved", "coding_confidence"]} |
| row["repo_group"] = c["group"] |
| row["stack"] = c.get("kw_stack") |
| for f in CODES: |
| row[f] = c.get("kw_" + f) |
| hand = bool(m and any(m.get(f) for f in HAND)) |
| if m: |
| for f in CODES: |
| if m.get(f): |
| if f == "topic" and m[f] == "other" and row[f] in NEW_TOPICS: |
| continue |
| if f == "error_type" and m[f] == "other_unknown" and (row[f] or "").startswith("agent_"): |
| continue |
| row[f] = m[f] |
| row["thread_type"] = (m or {}).get("thread_type") or c.get("kw_thread_type") |
| row["coding_method"] = "keyword+agent_reviewed" if hand else "keyword" |
| row["relevance"] = rel |
| row["relevance_method"] = "agent_reviewed" if m else "keyword" |
| row["collected_on"] = c.get("collected_on", today) |
| if ALL or rel == "yes": |
| pub_rows.append({k: row.get(k) for k in PUB}) |
| priv_rows.append({**{k: row.get(k) for k in PRIV}, "title": c["title"], "excerpt_private": c.get("excerpt_private"), |
| "kw_relevant": c["kw_relevant"], "kw_unsure": c.get("kw_unsure"), "kw_thread_type": c.get("kw_thread_type"), |
| "manual_note": (m or {}).get("note")}) |
|
|
| for path, rows, cols in ((out_pub, pub_rows, PUB), (out_priv, priv_rows, PRIV)): |
| os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True) |
| with open(path, "w", newline="") as f: |
| w = csv.DictWriter(f, fieldnames=cols) |
| w.writeheader() |
| w.writerows(rows) |
| print(f"{out_pub}: {len(pub_rows)} rows; {out_priv}: {len(priv_rows)} rows") |
|
|
