450 lines
17 KiB
Python
450 lines
17 KiB
Python
#!/usr/bin/env python3
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"""Analyze the uncensored 300-second phase-aware matched pilot."""
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from __future__ import annotations
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import argparse
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import hashlib
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import importlib.util
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import json
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import math
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from collections import defaultdict
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from pathlib import Path
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from typing import Any, Iterable, Mapping
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HERE = Path(__file__).resolve().parent
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P1_PATH = HERE.parent / "intervention-response-v0" / "analyze_p1.py"
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SCHEMA = "intervention-response-phase-aware-pilot-analysis-v2"
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EXPECTED_ACTION_PAIRS = 9
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EXPECTED_REPEAT_PAIRS = 12
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MIN_EFFICACY_CLASS = 3
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def _load_p1():
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spec = importlib.util.spec_from_file_location("intervention_response_p1", P1_PATH)
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module = importlib.util.module_from_spec(spec)
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assert spec.loader is not None
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spec.loader.exec_module(module)
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return module
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P1 = _load_p1()
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def sha256_file(path: Path) -> str:
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digest = hashlib.sha256()
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with path.open("rb") as source:
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for chunk in iter(lambda: source.read(1 << 20), b""):
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digest.update(chunk)
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return digest.hexdigest()
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def numeric(values: Iterable[float | int]) -> dict[str, Any]:
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return P1.V0.numeric(values)
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def _trial_record(
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*,
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run_root: Path,
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session: Mapping[str, Any],
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level: str,
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result: Mapping[str, Any],
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result_path: Path,
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requests_path: Path,
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state: dict[str, float],
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outcome: dict[str, float],
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) -> dict[str, Any]:
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return {
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"trial_id": str(result_path.relative_to(run_root)),
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"cell": str(result["cell"]),
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"tp": int(result["tp"]),
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"mns": int(result["mns"]),
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"level": level,
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"replicate": int(session["replicate"]),
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"offered_rate_per_gpu": float(
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result["selection"]["offered_req_s_per_gpu"]
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),
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"request_hash": str(result["selection"]["request_id_order_sha256"]),
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"request_count": int(result["selection"]["count"]),
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"result_sha256": sha256_file(result_path),
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"requests_sha256": sha256_file(requests_path),
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"full_pass_rate": float(result["pass_rate"]),
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"full_feasible": bool(result["feasible"]),
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"early_stopped": bool(result["early_stopped"]),
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"state": state,
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"outcome": outcome,
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}
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def validate_result_against_manifest(
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*,
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result: Mapping[str, Any],
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selection: Mapping[str, Any],
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session: Mapping[str, Any],
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level: str,
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expected_duration_s: float,
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) -> None:
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identity = f"{session['session']}:{level}"
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if int(result["mns"]) != int(session["mns"]) or int(result["tp"]) != 4:
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raise ValueError(f"config mismatch: {identity}")
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if bool(result["early_stopped"]):
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raise ValueError(f"early-stopped measured result: {identity}")
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if result.get("slo_early_stop_disabled") is not True:
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raise ValueError(f"SLO early stop was enabled: {identity}")
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if float(result["interval"]["elapsed_s"]) + 1e-9 < expected_duration_s:
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raise ValueError(f"result does not cover full arrival window: {identity}")
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if int(result["selection"]["count"]) != int(selection["selected_count"]):
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raise ValueError(f"selection count mismatch: {identity}")
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for result_key, manifest_key in (
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("request_id_order_sha256", "request_id_order_sha256"),
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("arrival_order_sha256", "arrival_order_sha256"),
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("raw_length_order_sha256", "input_length_order_sha256"),
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):
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if result["selection"][result_key] != selection[manifest_key]:
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raise ValueError(f"selection hash mismatch {result_key}: {identity}")
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if int(result["observed_count"]) != int(selection["selected_count"]):
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raise ValueError(f"request accounting mismatch: {identity}")
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def load_interval_trials(
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*,
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run_root: Path,
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manifest: Mapping[str, Any],
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intervals_s: tuple[tuple[float, float], ...],
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) -> tuple[dict[tuple[float, float], list[dict[str, Any]]], list[dict[str, Any]]]:
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by_interval = {interval: [] for interval in intervals_s}
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streams = []
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duration_s = float(manifest["engine"]["duration_s"])
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for session in manifest["sessions"]:
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session_root = run_root / "sessions" / str(session["session"])
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stream_paths = sorted((session_root / "opprof").glob("*.jsonl"))
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if len(stream_paths) != 1:
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raise ValueError(f"{session_root}: expected one Layer-1 stream")
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stream_path = stream_paths[0]
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stream = P1.load_jsonl(stream_path)
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streams.append(
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{
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"session": str(session["session"]),
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"path": str(stream_path.resolve()),
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"sha256": sha256_file(stream_path),
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"bytes": stream_path.stat().st_size,
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}
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)
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repetition = manifest["repetitions"][str(session["replicate"])]
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for level, selection in repetition["selections"].items():
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result_path = session_root / level / "result.json"
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requests_path = session_root / level / "requests.jsonl"
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result = json.loads(result_path.read_text(encoding="utf-8"))
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requests = P1.load_jsonl(requests_path)
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validate_result_against_manifest(
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result=result,
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selection=selection,
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session=session,
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level=level,
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expected_duration_s=duration_s,
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)
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start_ns = int(result["interval"]["start_mono_ns"])
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for interval in intervals_s:
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start_s, end_s = interval
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state = P1.V0.flatten_state(
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P1.summarize_engine(
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stream,
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start_ns=start_ns + int(start_s * 1e9),
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end_ns=start_ns + int(end_s * 1e9),
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request_count=int(result["selection"]["count"]),
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)
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)
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outcome = P1._prefix_outcome(result, requests, end_s)
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by_interval[interval].append(
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_trial_record(
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run_root=run_root,
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session=session,
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level=level,
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result=result,
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result_path=result_path,
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requests_path=requests_path,
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state=state,
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outcome=outcome,
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)
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)
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return by_interval, streams
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def analyze_window(
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trials: list[dict[str, Any]], *, start_s: float, end_s: float, fraction: float
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) -> dict[str, Any]:
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actions, repeats = P1.build_pairs(trials)
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response = P1.V0.response_statistics(actions, repeats)
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response_qualifying = sorted(
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feature for feature, item in response.items() if item["qualifies"]
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)
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labels = [int(pair["full_action_efficacy"]) for pair in actions]
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cross_validation_possible = all(
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set(
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int(pair["full_action_efficacy"])
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for pair in actions
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if pair["group"]["replicate"] != held_out
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)
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== {0, 1}
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for held_out in (1, 2, 3)
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)
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if cross_validation_possible:
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outcome_cv = P1.one_feature_leave_repeat_out(
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actions, delta_key="delta_outcome", features=P1.OUTCOME_FEATURES
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)
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telemetry_cv = P1.one_feature_leave_repeat_out(
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actions, delta_key="delta_state", features=P1.V0.GATE_FEATURES
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)
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outcome_best = float(outcome_cv["best_balanced_accuracy"])
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efficacy_qualifying = sorted(
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feature
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for feature, item in telemetry_cv["features"].items()
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if item["balanced_accuracy"] >= P1.MIN_EFFICACY_BALANCED_ACCURACY
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and item["balanced_accuracy"]
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>= outcome_best + P1.MIN_EFFICACY_DELTA_OVER_OUTCOME
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)
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else:
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unavailable = {
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"status": "UNAVAILABLE",
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"reason": "each leave-one-repetition-out train fold needs both classes",
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}
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outcome_cv = unavailable
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telemetry_cv = unavailable
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efficacy_qualifying = []
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transitions = defaultdict(int)
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for pair in actions:
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transitions[pair["full_feasibility_transition"]] += 1
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admitted = [float(trial["outcome"]["admitted_fraction"]) for trial in trials]
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completed = [
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float(trial["outcome"]["admitted_fraction"])
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* float(trial["outcome"]["completed_over_admitted"])
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for trial in trials
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]
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action_metadata = [
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{
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"group": pair["group"],
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"source": pair["source"],
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"target": pair["target"],
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"full_action_efficacy": pair["full_action_efficacy"],
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"full_feasibility_transition": pair["full_feasibility_transition"],
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"delta_state": pair["delta_state"],
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"delta_outcome": pair["delta_outcome"],
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}
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for pair in actions
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]
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invariants = {
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"expected_action_pair_count": len(actions) == EXPECTED_ACTION_PAIRS,
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"expected_repeat_pair_count": len(repeats) == EXPECTED_REPEAT_PAIRS,
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"finite_deltas": all(
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math.isfinite(value)
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for pair in [*actions, *repeats]
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for key in ("delta_state", "delta_outcome")
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for value in pair[key].values()
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),
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"all_results_uncensored": all(not trial["early_stopped"] for trial in trials),
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}
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return {
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"start_s": start_s,
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"end_s": end_s,
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"end_fraction": fraction,
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"coverage_at_end": {
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"admitted_fraction_of_total": numeric(admitted),
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"completed_fraction_of_total": numeric(completed),
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},
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"action_pairs": len(actions),
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"repeat_pairs": len(repeats),
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"actions": action_metadata,
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"response_statistics": response,
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"qualifying_response_features": response_qualifying,
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"efficacy": {
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"labels": numeric(labels),
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"positive": sum(labels),
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"negative": len(labels) - sum(labels),
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"label_balance_sufficient": (
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sum(labels) >= MIN_EFFICACY_CLASS
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and len(labels) - sum(labels) >= MIN_EFFICACY_CLASS
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),
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"cross_validation_possible": cross_validation_possible,
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"transitions": dict(sorted(transitions.items())),
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"outcome_delta": outcome_cv,
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"telemetry_delta": telemetry_cv,
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"telemetry_qualifying_features": efficacy_qualifying,
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},
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"sanity": {
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"trials": len(trials),
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"invariants": invariants,
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"red_flags": [name for name, passed in invariants.items() if not passed],
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},
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}
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def stable_adjacent_features(windows: list[dict[str, Any]]) -> dict[str, list[str]]:
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result = {}
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for left, right in zip(windows, windows[1:], strict=False):
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key = f"{left['end_fraction']:.2f}->{right['end_fraction']:.2f}"
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result[key] = sorted(
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set(left["qualifying_response_features"])
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& set(right["qualifying_response_features"])
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)
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return result
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def consistent_load_regimes(
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windows: list[dict[str, Any]], stable: dict[str, list[str]]
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) -> dict[str, Any]:
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by_end = {float(window["end_fraction"]): window for window in windows}
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result = {}
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for transition, features in stable.items():
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_left_text, right_text = transition.split("->")
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window = by_end[float(right_text)]
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for feature in features:
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deltas_by_level: dict[str, list[float]] = defaultdict(list)
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all_deltas = []
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for action in window["actions"]:
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value = float(action["delta_state"][feature])
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deltas_by_level[str(action["group"]["level"])].append(value)
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all_deltas.append(value)
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positive = sum(value > 1e-12 for value in all_deltas)
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negative = sum(value < -1e-12 for value in all_deltas)
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direction = 1 if positive >= negative else -1
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consistent = []
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for level, values in sorted(deltas_by_level.items()):
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matching = sum(direction * value > 1e-12 for value in values)
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nonzero = sum(abs(value) > 1e-12 for value in values)
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if nonzero and matching / nonzero >= 2.0 / 3.0:
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consistent.append(level)
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result[f"{transition}:{feature}"] = {
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"direction": direction,
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"consistent_load_regimes": consistent,
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"passes_two_regimes": len(consistent) >= 2,
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}
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return result
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def audit(*, run_root: Path, manifest_path: Path, output_path: Path) -> dict[str, Any]:
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manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
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if manifest.get("schema") != "intervention-response-phase-aware-pilot-manifest-v2":
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raise ValueError("unexpected phase-aware pilot manifest schema")
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fractions = [float(value) for value in manifest["checkpoints"]["fractions"]]
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seconds = [float(value) for value in manifest["checkpoints"]["seconds"]]
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cumulative_intervals = tuple((0.0, end_s) for end_s in seconds)
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quarter_intervals = ((0.0, 75.0), (75.0, 150.0), (150.0, 225.0), (225.0, 300.0))
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intervals = tuple(dict.fromkeys([*cumulative_intervals, *quarter_intervals]))
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trials_by_interval, streams = load_interval_trials(
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run_root=run_root, manifest=manifest, intervals_s=intervals
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)
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cumulative = [
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analyze_window(
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trials_by_interval[interval],
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start_s=interval[0],
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end_s=interval[1],
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fraction=fraction,
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)
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for fraction, interval in zip(fractions, cumulative_intervals, strict=True)
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]
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quarter_blocks = [
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analyze_window(
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trials_by_interval[interval],
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start_s=interval[0],
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end_s=interval[1],
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fraction=interval[1] / 300.0,
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)
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for interval in quarter_intervals
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]
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stable = stable_adjacent_features(cumulative)
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load_consistency = consistent_load_regimes(cumulative, stable)
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mechanism_features = sorted(
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{
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key.split(":", 1)[1]
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for key, item in load_consistency.items()
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if item["passes_two_regimes"]
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}
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)
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full = cumulative[-1]
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efficacy_features = sorted(
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set.intersection(
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*(
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set(window["efficacy"]["telemetry_qualifying_features"])
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for window in cumulative
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if window["end_fraction"] >= 0.25
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)
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)
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)
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red_flags = sorted(
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{
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flag
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for window in [*cumulative, *quarter_blocks]
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for flag in window["sanity"]["red_flags"]
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}
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)
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if red_flags:
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decision = "STOP_DATA_INVALID"
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elif not mechanism_features:
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decision = "STOP_NO_PHASE_STABLE_RESPONSE"
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elif not full["efficacy"]["label_balance_sufficient"]:
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decision = "MECHANISM_ONLY_NO_LABEL_BALANCE"
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elif not efficacy_features:
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decision = "STOP_NO_INCREMENTAL_TUNING_SIGNAL"
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else:
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decision = "OPEN_E2E_POLICY_TEST"
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payload = {
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"schema": SCHEMA,
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"status": "COMPLETE",
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"decision": decision,
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"claim_boundary": "Development mechanism pilot; not a held-out paper claim.",
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"mechanism_features": mechanism_features,
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"stable_adjacent_features": stable,
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"load_consistency": load_consistency,
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"stable_incremental_efficacy_features": efficacy_features,
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"cumulative": cumulative,
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"quarter_blocks": quarter_blocks,
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"provenance": {
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"analysis_script": str(Path(__file__).resolve()),
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"analysis_script_sha256": sha256_file(Path(__file__).resolve()),
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"manifest": str(manifest_path.resolve()),
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"manifest_sha256": sha256_file(manifest_path),
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"run_root": str(run_root.resolve()),
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"streams": streams,
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},
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"sanity": {
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"streams": len(streams),
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"stream_bytes": numeric(item["bytes"] for item in streams),
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"red_flags": red_flags,
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},
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}
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output_path.parent.mkdir(parents=True, exist_ok=True)
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output_path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
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return payload
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--run-root", type=Path, required=True)
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parser.add_argument("--manifest", type=Path, required=True)
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parser.add_argument("--output", type=Path, required=True)
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args = parser.parse_args()
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payload = audit(
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run_root=args.run_root,
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manifest_path=args.manifest,
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output_path=args.output,
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)
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print(
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json.dumps(
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{
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"decision": payload["decision"],
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"mechanism_features": payload["mechanism_features"],
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"stable_incremental_efficacy_features": payload[
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"stable_incremental_efficacy_features"
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],
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"sanity": payload["sanity"],
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},
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indent=2,
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sort_keys=True,
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)
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)
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if __name__ == "__main__":
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main()
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