| |
| """Replay labelled turns through the decision engine and score it. |
| |
| Reads training rows (rows.py). For every row the engine answers the question |
| set the row's turn was asked (one option per loadable tool and scored |
| instruction unit it offered, plus the kind choice) and a skill ranking, and |
| the answers are compared with the labels: |
| |
| tools load precision, recall, F1 at load_at (micro, over every option), |
| macro recall (the mean of per-tool recall, so a rare tool counts |
| as much as a common one), calibration error, loads per turn, and |
| the share of turns whose every used tool was loaded (no round trip) |
| guides omission precision (an omitted unit was unneeded), omission rate, |
| and recall of needed units; unknown labels are skipped |
| kind accuracy at the confidence floor, abstention rate |
| skills over the corpus's skill cards, the text the engine ranks: top-1 |
| hit rate against the skill the coordinator read first, the share |
| of turns whose first-read skill the pruned roster listed (score at |
| or above list_at within roster_max), and against judged |
| skill_scores the precision of preloads (the top skill clears |
| preload_at and the judge scored it 3 or more), the preload rate, |
| and the share of turns whose roster lists a judged skill |
| requests for each request_tools need: the share of the tools the turn |
| then used that its spelled-out names already load, and over the |
| rest, which unit-rank ranks, the share it loads (score at or |
| above request.load_at, at most max_loads, or the nearest_loads |
| closest when none reaches load_at), ranked loads per |
| need, the share of those judged relevant (3 or more), and the |
| mean reciprocal rank of the first used tool |
| bytes prompt bytes the decision saved per turn: omitted unit bodies plus |
| unloaded tool schemas, against loading everything |
| latency engine time per turn, p50 and p95 |
| state the share of turns whose request reached the catalog's |
| user_text_chars bound, so truncation is a measured cost |
| |
| Results print overall and broken down by host, surface, language, project, |
| and the model that drove the session, and per pack of origin when |
| --holdout-pack names packs whose units were held out of training. The engine |
| is a pw-decide launcher, from --engine or LYCAON_DECIDE_ENGINE. |
| |
| Usage: replay_eval.py --corpus FILE --examples FILE [--engine PATH] [--limit N] |
| [--holdout-pack ID ...] [--json OUT] |
| """ |
|
|
| import argparse |
| import collections |
| import json |
| import os |
| import statistics |
| import subprocess |
| import sys |
| import time |
|
|
| sys.path.insert(0, os.path.dirname(__file__)) |
| import rows as rowfile |
| from corpus import Corpus |
|
|
|
|
| class Engine: |
| def __init__(self, binary): |
| self.proc = subprocess.Popen([binary], stdin=subprocess.PIPE, stdout=subprocess.PIPE, text=True, bufsize=1) |
| self.seq = 0 |
| |
| |
| self.answers = {} |
| self.hello = self.call({"method": "hello"}) |
|
|
| def call(self, req): |
| key = json.dumps(req, sort_keys=True, ensure_ascii=False) |
| if key not in self.answers: |
| self.answers[key] = self.ask(dict(req)) |
| return self.answers[key] |
|
|
| def ask(self, req): |
| self.seq += 1 |
| req["id"] = self.seq |
| self.proc.stdin.write(json.dumps(req, ensure_ascii=False) + "\n") |
| self.proc.stdin.flush() |
| while True: |
| line = self.proc.stdout.readline() |
| if not line: |
| raise RuntimeError("engine exited") |
| resp = json.loads(line) |
| if resp.get("id") == self.seq: |
| if resp.get("error"): |
| raise RuntimeError(resp["error"]) |
| return resp |
|
|
| def close(self): |
| self.proc.stdin.close() |
| self.proc.wait(timeout=10) |
|
|
|
|
| def option_answer(answers, qid, name): |
| """One option's (probability, certainty) out of a multi answer; absent when the engine did not answer.""" |
| probs = (answers.get(qid) or {}).get("probabilities") or {} |
| if name not in probs: |
| return None, None |
| p = float(probs[name]) |
| return p, max(p, 1.0 - p) |
|
|
|
|
| def ece(pairs, bins=10): |
| """Expected calibration error over (probability, outcome) pairs.""" |
| if not pairs: |
| return 0.0 |
| buckets = collections.defaultdict(list) |
| for p, y in pairs: |
| buckets[min(int(p * bins), bins - 1)].append((p, y)) |
| total = 0.0 |
| for items in buckets.values(): |
| conf = sum(p for p, _ in items) / len(items) |
| acc = sum(y for _, y in items) / len(items) |
| total += abs(conf - acc) * len(items) / len(pairs) |
| return total |
|
|
|
|
| def prf(tp, fp, fn): |
| prec = tp / (tp + fp) if tp + fp else 0.0 |
| rec = tp / (tp + fn) if tp + fn else 0.0 |
| f1 = 2 * prec * rec / (prec + rec) if prec + rec else 0.0 |
| return round(prec, 3), round(rec, 3), round(f1, 3) |
|
|
|
|
| def score(examples, engine, corpus, schema_bytes, unit_bytes, preload_at, timed=True): |
| q = corpus.spec |
| load_at = float(q["tools"]["load_at"]) |
| omit_below = float(q["guides"]["omit_below"]) |
| conf_floor = float(q["guides"]["confidence_floor"]) |
| kind_floor = float(q["kind"]["confidence_floor"]) |
| tool_stats = collections.defaultdict(collections.Counter) |
| tool_calib = collections.defaultdict(list) |
| guide_stats = collections.defaultdict(collections.Counter) |
| kind_hits = kind_total = kind_abstain = 0 |
| skill_hits = skill_total = skill_listed = 0 |
| preloads = preload_hits = judged_total = judged_listed = 0 |
| roster_sizes = [] |
| list_at = float(q.get("skills", {}).get("list_at", 1.0)) |
| roster_max = int(q.get("skills", {}).get("roster_max", 6)) |
| if preload_at is None: |
| preload_at = float(q.get("skills", {}).get("preload_at", 3.0)) |
| saved_turns = eligible_turns = 0 |
| bytes_saved = [] |
| latencies = [] |
| cards = corpus.skill_cards() |
| roster = sorted(cards) |
| tool_cards = corpus.tool_cards() |
| req_load_at = float(corpus.data.get("request", {}).get("load_at", 2.5)) |
| req_max = int(corpus.data.get("request", {}).get("max_loads", 12)) |
| req_nearest = int(corpus.data.get("request", {}).get("nearest_loads", 0)) |
| req_needs = req_used = req_hit = req_loads = req_rel = 0 |
| req_named = req_named_total = 0 |
| req_rr = [] |
| loads_per_turn = [] |
| truncated = 0 |
| user_chars = int(corpus.state_spec.get("user_text_chars", 900)) |
| for ex in examples: |
| loadable = [t for t in ex["offered"]["loadable"] if t in tool_cards] |
| for request in ex["labels"].get("requests") or []: |
| exact = set(request.get("exact") or []) |
| rest = [t for t in loadable if t not in exact] |
| after = [t for t in request.get("after") or [] if t in loadable] |
| req_named_total += len(after) |
| req_named += sum(1 for t in after if t in exact) |
| used = [t for t in after if t not in exact] |
| if not rest or not used: |
| continue |
| scores = engine.call({"method": "rank", "head": "unit-rank", "task": request["need"], "candidates": [tool_cards[t] for t in rest]})["scores"] |
| order = sorted(range(len(rest)), key=lambda i: -scores[i]) |
| loaded = {rest[i] for i in order[:req_max] if scores[i] >= req_load_at} |
| if not loaded: |
| loaded = {rest[i] for i in order[:req_nearest]} |
| judged = request.get("scores") or {} |
| req_needs += 1 |
| req_used += len(used) |
| req_hit += sum(1 for t in used if t in loaded) |
| req_loads += len(loaded) |
| req_rel += sum(1 for t in loaded if judged.get(t, 0) >= 3) |
| positions = {rest[i]: n + 1 for n, i in enumerate(order)} |
| req_rr.append(1 / min(positions[t] for t in used)) |
| if ex["partial"]: |
| continue |
| truncated += len(ex["state"].get("user", "")) >= user_chars |
| qs = corpus.row_questions(ex) |
| started = time.perf_counter() |
| answers = engine.call({"method": "decide", "head": "turn-load", "state": ex["state"], "questions": qs})["answers"] |
| latencies.append((time.perf_counter() - started) * 1000) |
| kind = answers.get("kind", {}) |
| vetoed = float(kind.get("confidence", 0.0)) >= kind_floor and kind.get("choice") == "answer_only" |
| truth_tools = rowfile.truth_tools(ex) |
| loaded = set() |
| saved = 0 |
| for name in qs.get("tools", {}).get("options", {}): |
| p, _ = option_answer(answers, "tools", name) |
| p = 0.0 if p is None else p |
| hit = name in truth_tools |
| tool_calib[name].append((p, 1 if hit else 0)) |
| if p >= load_at and not vetoed: |
| loaded.add(name) |
| tool_stats[name]["tp" if hit else "fp"] += 1 |
| else: |
| tool_stats[name]["fn" if hit else "tn"] += 1 |
| saved += schema_bytes.get(name, 0) |
| loads_per_turn.append(len(loaded)) |
| if truth_tools: |
| eligible_turns += 1 |
| if truth_tools <= loaded: |
| saved_turns += 1 |
| guide_truth = ex["labels"]["guides"] |
| for uid in qs.get("guides", {}).get("options", {}): |
| p, conf = option_answer(answers, "guides", uid) |
| p, conf = (0.0, 0.0) if p is None else (p, conf) |
| omitted = p < omit_below and conf >= conf_floor |
| needed = guide_truth.get(uid) |
| if omitted: |
| saved += unit_bytes.get(uid, 0) |
| if needed is None: |
| guide_stats[uid]["unknown"] += 1 |
| continue |
| if omitted and needed: |
| guide_stats[uid]["omitted_needed"] += 1 |
| elif omitted: |
| guide_stats[uid]["omitted_unneeded"] += 1 |
| elif needed: |
| guide_stats[uid]["kept_needed"] += 1 |
| else: |
| guide_stats[uid]["kept_unneeded"] += 1 |
| bytes_saved.append(saved) |
| kind_total += 1 |
| if float(kind.get("confidence", 0.0)) < kind_floor: |
| kind_abstain += 1 |
| elif kind.get("choice") == ex["labels"]["kind"]: |
| kind_hits += 1 |
| read = [name for name in ex["labels"].get("skills") or [] if name in cards] |
| judged = {name for name, v in (ex["labels"].get("skill_scores") or {}).items() if name in cards and v >= 3} |
| if roster and (read or ex["labels"].get("skill_scores")): |
| scores = engine.call({"method": "rank", "head": "unit-rank", "task": ex["state"]["user"], "candidates": [cards[name] for name in roster]})["scores"] |
| best = max(range(len(scores)), key=lambda i: scores[i]) |
| order = sorted(range(len(scores)), key=lambda i: -scores[i]) |
| listed = [roster[i] for i in order[:roster_max] if scores[i] >= list_at] |
| roster_sizes.append(len(listed)) |
| if read: |
| skill_total += 1 |
| if scores[best] >= preload_at and roster[best] == read[0]: |
| skill_hits += 1 |
| if read[0] in listed: |
| skill_listed += 1 |
| if ex["labels"].get("skill_scores"): |
| judged_total += 1 |
| if scores[best] >= preload_at: |
| preloads += 1 |
| preload_hits += roster[best] in judged |
| if judged and judged & set(listed): |
| judged_listed += 1 |
| report = {"turns": len(examples), "tools": {}, "guides": {}, "kind": {}, "skills": {}, "requests": {}, "savings": {}, "latency_ms": {}} |
| tp = fp = fn = 0 |
| for name, c in sorted(tool_stats.items()): |
| tp += c["tp"] |
| fp += c["fp"] |
| fn += c["fn"] |
| prec, rec, f1 = prf(c["tp"], c["fp"], c["fn"]) |
| report["tools"][name] = {"precision": prec, "recall": rec, "f1": f1, "support": c["tp"] + c["fn"], "ece": round(ece(tool_calib[name]), 3)} |
| prec, rec, f1 = prf(tp, fp, fn) |
| recalls = [v["recall"] for v in report["tools"].values() if v["support"]] |
| report["tools"]["_all"] = {"precision": prec, "recall": rec, "f1": f1, "support": tp + fn, |
| "macro_recall": round(statistics.mean(recalls), 3) if recalls else None, |
| "loads_per_turn": round(statistics.mean(loads_per_turn), 2) if loads_per_turn else 0.0} |
| omitted_unneeded = omitted_needed = kept_needed = 0 |
| for uid, c in sorted(guide_stats.items()): |
| omitted = c["omitted_needed"] + c["omitted_unneeded"] |
| report["guides"][uid] = { |
| "omission_precision": round(c["omitted_unneeded"] / omitted, 3) if omitted else None, |
| "omission_rate": round(omitted / max(sum(c.values()) - c["unknown"], 1), 3), |
| "recall": round(c["kept_needed"] / max(c["kept_needed"] + c["omitted_needed"], 1), 3), |
| "unknown": c["unknown"], |
| } |
| omitted_unneeded += c["omitted_unneeded"] |
| omitted_needed += c["omitted_needed"] |
| kept_needed += c["kept_needed"] |
| omitted = omitted_unneeded + omitted_needed |
| report["guides"]["_all"] = { |
| "omission_precision": round(omitted_unneeded / omitted, 3) if omitted else None, |
| "recall": round(kept_needed / max(kept_needed + omitted_needed, 1), 3), |
| } |
| report["kind"] = {"accuracy": round(kind_hits / kind_total, 3) if kind_total else 0.0, |
| "abstained": round(kind_abstain / kind_total, 3) if kind_total else 0.0} |
| report["skills"] = {"top1": round(skill_hits / skill_total, 3) if skill_total else 0.0, |
| "listed": round(skill_listed / skill_total, 3) if skill_total else 0.0, |
| "roster_size": round(statistics.mean(roster_sizes), 1) if roster_sizes else 0, |
| "roster_max": roster_max, "list_at": list_at, "preload_at": preload_at, "support": skill_total, |
| "judged": {"preload_precision": round(preload_hits / preloads, 3) if preloads else None, |
| "preload_rate": round(preloads / judged_total, 3) if judged_total else 0.0, |
| "listed_relevant": round(judged_listed / judged_total, 3) if judged_total else 0.0, |
| "support": judged_total}} |
| report["requests"] = {"used_named": round(req_named / req_named_total, 3) if req_named_total else None, |
| "ranked_needs": req_needs, "used_loaded": round(req_hit / req_used, 3) if req_used else None, |
| "loads_per_need": round(req_loads / req_needs, 2) if req_needs else None, |
| "loads_judged_relevant": round(req_rel / req_loads, 3) if req_loads else None, |
| "mrr": round(statistics.mean(req_rr), 3) if req_rr else None, "load_at": req_load_at} |
| report["savings"] = {"turns_fully_preloaded": saved_turns, "turns_needing_loads": eligible_turns, |
| "bytes_saved_per_turn": round(statistics.mean(bytes_saved)) if bytes_saved else 0} |
| report["state"] = {"truncated_share": round(truncated / max(len(loads_per_turn), 1), 3), "user_text_chars": user_chars} |
| if timed and latencies: |
| latencies.sort() |
| report["latency_ms"] = {"p50": round(statistics.median(latencies)), "p95": round(latencies[min(len(latencies) - 1, int(len(latencies) * 0.95))])} |
| return report |
|
|
|
|
| def load_sizes(corpus): |
| """Bytes each tool schema and unit body would add to a prompt.""" |
| root = corpus_root() |
| schemas = root / "lycaon/config/packs/painted-wolf/platform/tools/schemas" |
| schema_bytes = {name: (schemas / (name + ".yaml")).stat().st_size for name in corpus.tools if (schemas / (name + ".yaml")).exists()} |
| unit_bytes = {} |
| for uid, unit in corpus.units.items(): |
| for pack_dir in (root / "lycaon/config/packs/painted-wolf").iterdir(): |
| path = pack_dir / "shared/units" / (uid + ".md") |
| if path.exists(): |
| unit_bytes[uid] = path.stat().st_size |
| return schema_bytes, unit_bytes |
|
|
|
|
| def corpus_root(): |
| from pathlib import Path |
| return Path(__file__).resolve().parents[2] |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--corpus", required=True) |
| ap.add_argument("--examples", required=True) |
| ap.add_argument("--engine", default=os.environ.get("LYCAON_DECIDE_ENGINE"), help="a pw-decide launcher: `pw-decide serve --model ... --head turn-load=...` (default $LYCAON_DECIDE_ENGINE)") |
| ap.add_argument("--limit", type=int, default=0) |
| ap.add_argument("--preload-at", type=float, default=None, help="skill preload threshold (default: the corpus's turn.skills.preload_at)") |
| ap.add_argument("--holdout-pack", action="append", default=[], help="pack ids whose units were held out of training; reported separately") |
| ap.add_argument("--json", default="") |
| args = ap.parse_args() |
| if not args.engine: |
| ap.error("--engine (or LYCAON_DECIDE_ENGINE) must name a pw-decide launcher") |
|
|
| corpus = Corpus.load(args.corpus) |
| examples = rowfile.load(args.examples) |
| if args.limit: |
| examples = examples[: args.limit] |
| schema_bytes, unit_bytes = load_sizes(corpus) |
| engine = Engine(args.engine) |
| try: |
| report = {"engine": engine.hello.get("engine"), "corpus": corpus.revision, |
| "overall": score(examples, engine, corpus, schema_bytes, unit_bytes, args.preload_at)} |
| for key, field in (("by_host", "host"), ("by_surface", "surface"), ("by_lang", "lang"), ("by_project", "project"), ("by_model", "model")): |
| groups = collections.defaultdict(list) |
| for ex in examples: |
| groups[ex[field]].append(ex) |
| if len(groups) > 1: |
| report[key] = {name: score(exs, engine, corpus, schema_bytes, unit_bytes, args.preload_at, timed=False) |
| for name, exs in sorted(groups.items())} |
| if args.holdout_pack: |
| held = {uid for uid, u in corpus.units.items() if u["pack_id"] in args.holdout_pack} |
| report["holdout"] = {"packs": args.holdout_pack, "units": sorted(held), |
| "guides": {uid: v for uid, v in report["overall"]["guides"].items() if uid in held}} |
| finally: |
| engine.close() |
| text = json.dumps(report, indent=2, ensure_ascii=False) |
| if args.json: |
| open(args.json, "w", encoding="utf-8").write(text + "\n") |
| print(text) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
