pre-refactor 041426
This commit is contained in:
rpotter6298
@@ -0,0 +1,197 @@
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"""
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Dispatch phase 3 experiment runs to the distributed job server.
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Reads experiment_grid.json, checks which runs already have complete 10x5 results,
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and submits the rest via submit-cv. Skips runs marked needs_implementation.
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Usage:
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python -m v3.scripts.main.phase3.dispatch_phase3 \
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--server http://hades:8765 --token hypertower
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# Dry run (print what would be submitted, don't actually submit):
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python -m v3.scripts.main.phase3.dispatch_phase3 \
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--server http://hades:8765 --token hypertower --dry-run
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# Override number of reps (default 10):
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python -m v3.scripts.main.phase3.dispatch_phase3 \
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--server http://hades:8765 --token hypertower --reps 4
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import sys
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from pathlib import Path
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import requests
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# Allow running as `python v3/scripts/main/phase3/dispatch_phase3.py`
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sys.path.insert(0, str(Path(__file__).resolve().parents[4]))
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GRID_PATH = Path(__file__).parent / "experiment_grid.json"
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RESULTS_ROOT = Path(__file__).resolve().parents[4] / "v3" / "results"
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MODULE = "v3.scripts.main.run_cv"
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OUTPUT_DIR = "v3/results"
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REP_SEED_START = 100
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REP_SEED_STEP = 100
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# ── Completion check ──────────────────────────────────────────────────────────
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def _completed_reps(run_name: str, reps: int) -> list[int]:
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"""Return list of rep indices that already have a summary.json."""
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done = []
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for i in range(reps):
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summary = RESULTS_ROOT / run_name / f"rep{i:02d}" / "binary" / "single" / "summary.json"
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if summary.exists():
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done.append(i)
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return done
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# ── Server API ────────────────────────────────────────────────────────────────
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class _API:
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def __init__(self, base_url: str, token: str):
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self.base_url = base_url.rstrip("/")
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self._h = {"x-token": token}
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def get(self, path: str, **params) -> object:
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r = requests.get(f"{self.base_url}{path}", headers=self._h, params=params, timeout=10)
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r.raise_for_status()
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return r.json()
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def post(self, path: str, body: dict) -> dict:
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r = requests.post(f"{self.base_url}{path}", headers=self._h, json=body, timeout=10)
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r.raise_for_status()
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return r.json()
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def _queued_reps(jobs: list[dict], run_name: str) -> set[int]:
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"""Return rep indices already pending or running in the server queue."""
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active = set()
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for job in jobs:
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if job["run_name"] != run_name:
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continue
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if job["state"] not in ("pending", "running"):
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continue
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# Extract --rep-index from job args
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try:
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args = job["args"] if isinstance(job["args"], list) else json.loads(job["args"])
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if "--rep-index" in args:
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active.add(int(args[args.index("--rep-index") + 1]))
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except Exception:
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pass
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return active
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def _submit_cv(api: _API, run_name: str, run_args: list[str],
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reps: int, missing: list[int], dry_run: bool):
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"""Submit one job per missing rep."""
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for i in missing:
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seed = REP_SEED_START + i * REP_SEED_STEP
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rep_args = run_args + [
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"--run-name", run_name,
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"--reps", "1",
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"--rep-seed-start", str(seed),
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"--rep-index", str(i),
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]
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body = {
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"run_name": run_name,
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"module": MODULE,
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"args": rep_args,
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"output_dir": OUTPUT_DIR,
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"priority": 0,
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}
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if dry_run:
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print(f" [dry-run] would queue rep{i:02d} seed={seed}")
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else:
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resp = api.post("/jobs", body)
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print(f" queued rep{i:02d} seed={seed} job_id={resp['job_id']}")
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# ── Main ──────────────────────────────────────────────────────────────────────
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def main():
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ap = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("--server", default=os.environ.get("HT_SERVER", ""),
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help="Server URL (or set HT_SERVER)")
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ap.add_argument("--token", default=os.environ.get("HT_TOKEN", ""),
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help="Shared secret (or set HT_TOKEN)")
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ap.add_argument("--reps", type=int, default=10,
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help="Expected number of reps per run (default: 10)")
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ap.add_argument("--grid", type=Path, default=GRID_PATH,
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help="Path to experiment grid JSON (default: experiment_grid.json)")
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ap.add_argument("--dry-run", action="store_true",
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help="Print what would be submitted without actually submitting")
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args = ap.parse_args()
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if not args.dry_run:
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if not args.server:
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ap.error("--server is required (or set HT_SERVER)")
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if not args.token:
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ap.error("--token is required (or set HT_TOKEN)")
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elif not args.server or not args.token:
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print("[dry-run] no --server/--token provided — skipping queue check, showing disk state only")
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grid = json.loads(args.grid.read_text())
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common_args = grid["common_args"]
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api = _API(args.server, args.token) if (args.server and args.token) else None
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# Fetch current server queue once (pending + running)
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server_jobs: list[dict] = []
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if api:
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try:
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all_jobs = api.get("/jobs")
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server_jobs = [j for j in all_jobs if j["state"] in ("pending", "running")]
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print(f"[server] {len(server_jobs)} job(s) currently pending/running in queue")
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except Exception as e:
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print(f"[warn] could not fetch server queue: {e}")
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# Collect all runs: baseline + every group's runs
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all_runs = [grid["baseline"]]
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for group in grid["groups"]:
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if group.get("needs_implementation"):
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print(f"\n[skip] group '{group['name']}' — {group['needs_implementation']}")
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continue
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all_runs.extend(group["runs"])
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submitted_total = 0
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skipped_total = 0
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for run in all_runs:
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run_name = run["run_name"]
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run_args = common_args + run.get("extra_args", [])
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done = set(_completed_reps(run_name, args.reps))
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queued = _queued_reps(server_jobs, run_name)
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accounted = done | queued
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missing = [i for i in range(args.reps) if i not in accounted]
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if not missing:
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if len(done) == args.reps:
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print(f"\n[done] {run_name} ({args.reps}/{args.reps} reps complete)")
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else:
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in_q = sorted(queued - done)
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print(f"\n[skip] {run_name} ({len(done)} done, {len(in_q)} queued: {[f'rep{i:02d}' for i in in_q]})")
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skipped_total += 1
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continue
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parts = []
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if done: parts.append(f"{len(done)} done")
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if queued: parts.append(f"{len(queued - done)} queued")
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status = ", ".join(parts) if parts else "not started"
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print(f"\n[queue] {run_name} ({status}) — submitting {len(missing)} rep(s)")
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_submit_cv(api, run_name, run_args, args.reps, missing, args.dry_run)
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submitted_total += len(missing)
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print(f"\n{'='*50}")
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print(f"Submitted: {submitted_total} jobs | Already accounted for: {skipped_total} runs")
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if grid.get("groups"):
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needs_impl = sum(1 for g in grid["groups"] if g.get("needs_implementation"))
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if needs_impl:
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print(f"Skipped (needs implementation): {needs_impl} group(s)")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,66 @@
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{
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"_notes": [
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"Epoch length sensitivity experiments.",
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"All other settings match the phase3 baseline (fused bridge, BCD p=0.5, refugelike, etc.).",
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"common_args are prepended to every run's args list."
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],
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"common_args": [
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"--eval-mode", "binary",
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"--tower-mode", "single",
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"--in-memory-cache",
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"--augment",
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"--tune-binary-threshold",
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"--backbone", "refugelike",
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"--output-root", "v3/results"
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],
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"_common_args_implicit_defaults": {
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"--bridge-mode": "fused",
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"--tower-loss-mode": "bcd",
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"--bcd-prob": "0.5",
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"--warmup-cd-epochs": "40",
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"--single-warmup-tower-epochs": "3",
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"--single-warmup-fused-epochs": "3"
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},
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"baseline": {
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"run_name": "phase3/epochs_30",
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"description": "30-epoch run — same as phase3 baseline, included here for direct comparison.",
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"extra_args": ["--epochs", "30"]
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},
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"groups": [
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{
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"name": "epoch_length",
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"description": "Test sensitivity to total training epochs (warmup epochs unchanged).",
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"runs": [
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{
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"run_name": "phase3/epochs_1",
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"description": "1 epoch total — effectively pure warmup output with a single main-phase step.",
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"extra_args": ["--epochs", "1"]
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},
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{
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"run_name": "phase3/epochs_5",
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"description": "5 epochs total.",
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"extra_args": ["--epochs", "5"]
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},
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{
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"run_name": "phase3/epochs_10",
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"description": "10 epochs total.",
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"extra_args": ["--epochs", "10"]
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},
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{
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"run_name": "phase3/epochs_20",
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"description": "20 epochs total.",
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"extra_args": ["--epochs", "20"]
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},
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{
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"run_name": "phase3/epochs_50",
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"description": "50 epochs total.",
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"extra_args": ["--epochs", "50"]
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}
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]
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}
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]
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}
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@@ -0,0 +1,362 @@
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{
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"_notes": [
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"All runs use single-eye tower mode, binary eval, 10x5 rep-CV.",
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"common_args are prepended to every run's args list.",
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"Entries marked 'needs_implementation' require small code changes before running (noted inline).",
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"Bridge fusion uses elementwise product of projected image/clinical features.",
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"SE infrastructure exists in Bridge/ImageTower/ClinicalTower but use_se is hardcoded False",
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" in SingleEyeHT \u2014 add --se-img-tower / --se-cd-tower / --se-bridge flags to wire through.",
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"Dropout is hardcoded: Bridge classifier=0.5, ClinicalTower=0.1 \u2014 add --bridge-dropout /",
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" --cd-dropout flags to make configurable."
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],
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"common_args": [
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"--eval-mode",
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"binary",
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"--tower-mode",
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"single",
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"--epochs",
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"30",
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"--in-memory-cache",
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"--augment",
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"--tune-binary-threshold",
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"--backbone",
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"refugelike",
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"--output-root",
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"v3/results"
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],
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"_common_args_implicit_defaults": {
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"--bridge-mode": "fused",
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"--tower-loss-mode": "bcd",
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"--bcd-prob": "0.5",
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"--warmup-cd-epochs": "40",
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"--single-warmup-tower-epochs": "3",
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"--single-warmup-fused-epochs": "3"
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},
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"baseline": {
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"run_name": "phase3/baseline",
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"description": "Fused bridge (image+clinical), BCD p=0.5, cd_warmup=40, tower/fused warmup=3/3, no SE, no IOP correction.",
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"extra_args": []
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},
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"groups": [
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{
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"name": "loss_function",
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"description": "Test BCD loss variants vs cross-entropy baseline.",
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"runs": [
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{
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"run_name": "phase3/loss_all",
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"description": "All-losses mode (cross-entropy on all three heads every step).",
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"extra_args": [
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"--tower-loss-mode",
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"all"
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]
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},
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{
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"run_name": "phase3/loss_bcd_p03",
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"description": "BCD with lower switching probability (more CE, less BCD).",
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"extra_args": [
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"--tower-loss-mode",
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"bcd",
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"--bcd-prob",
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"0.3"
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]
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},
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{
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"run_name": "phase3/loss_bcd_p07",
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"description": "BCD with higher switching probability (more BCD, less CE).",
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"extra_args": [
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"--tower-loss-mode",
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"bcd",
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"--bcd-prob",
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"0.7"
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]
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}
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]
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},
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{
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"name": "se_attention",
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"description": "Squeeze-and-excitation gates at different points in the network.",
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"runs": [
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{
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"run_name": "phase3/se_bridge",
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"description": "SE gate on fused vector inside the bridge only.",
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"extra_args": [
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"--se-bridge"
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]
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},
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{
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"run_name": "phase3/se_img_tower",
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"description": "SE gate on image tower output features.",
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"extra_args": [
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"--se-img-tower"
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]
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},
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{
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"run_name": "phase3/se_cd_tower",
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"description": "SE gate on clinical tower output features.",
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"extra_args": [
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"--se-cd-tower"
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]
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},
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{
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"run_name": "phase3/se_all",
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"description": "SE gates on image tower, clinical tower, and bridge.",
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"extra_args": [
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"--se-img-tower",
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"--se-cd-tower",
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"--se-bridge"
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]
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}
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]
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},
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{
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"name": "iop_correction",
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"description": "Test different IOP measurement correction strategies (default: no correction).",
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"runs": [
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{
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"run_name": "phase3/iop_ratio",
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"description": "IOP correction via Perkins\u2192Pneumatic ratio scaling.",
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"extra_args": [
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"--iop-corr-method",
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"ratio"
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]
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},
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{
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"run_name": "phase3/iop_ols",
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"description": "IOP correction via OLS regression.",
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"extra_args": [
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"--iop-corr-method",
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"ols"
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]
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},
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{
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"run_name": "phase3/iop_lad",
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"description": "IOP correction via LAD (robust to outliers) regression.",
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"extra_args": [
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"--iop-corr-method",
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"lad"
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]
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},
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{
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"run_name": "phase3/iop_multi",
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"description": "IOP correction via multivariate regression including pachymetry.",
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"extra_args": [
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"--iop-corr-method",
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"multi"
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]
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},
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{
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"run_name": "phase3/iop_ratio_drop_raw",
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"description": "Ratio correction + drop raw IOP (only corrected IOP seen by model).",
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"extra_args": [
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"--iop-corr-method",
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"ratio",
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"--iop-drop-raw"
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]
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}
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]
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},
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{
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"name": "feature_ablation",
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"description": "Exclude individual clinical features to measure each one's contribution.",
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"runs": [
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{
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"run_name": "phase3/excl_iop",
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"description": "No IOP features \u2014 tests how much intraocular pressure contributes.",
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"extra_args": [
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"--exclude-cols",
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"IOP",
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"Pachymetry"
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]
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},
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{
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"run_name": "phase3/excl_age",
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"description": "No age feature.",
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"extra_args": [
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"--exclude-cols",
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"Age"
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]
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||||
},
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{
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"run_name": "phase3/excl_axial_length",
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"description": "No axial length feature.",
|
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"extra_args": [
|
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"--exclude-cols",
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"Axial_Length"
|
||||
]
|
||||
},
|
||||
{
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"run_name": "phase3/excl_refractive",
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"description": "No refractive defect feature.",
|
||||
"extra_args": [
|
||||
"--exclude-cols",
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"Refractive_Defect"
|
||||
]
|
||||
}
|
||||
]
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||||
},
|
||||
{
|
||||
"name": "network_dims",
|
||||
"description": "Test sensitivity to clinical tower and bridge fusion dimensionality.",
|
||||
"runs": [
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||||
{
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||||
"run_name": "phase3/cd_hidden_64",
|
||||
"description": "Smaller clinical tower (64 hidden units vs default 128).",
|
||||
"extra_args": [
|
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"--cd-hidden-dim",
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"64"
|
||||
]
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||||
},
|
||||
{
|
||||
"run_name": "phase3/cd_hidden_256",
|
||||
"description": "Larger clinical tower (256 hidden units vs default 128).",
|
||||
"extra_args": [
|
||||
"--cd-hidden-dim",
|
||||
"256"
|
||||
]
|
||||
},
|
||||
{
|
||||
"run_name": "phase3/fusion_dim_128",
|
||||
"description": "Smaller fusion space (128 vs default 256).",
|
||||
"extra_args": [
|
||||
"--fusion-dim",
|
||||
"128"
|
||||
]
|
||||
},
|
||||
{
|
||||
"run_name": "phase3/fusion_dim_512",
|
||||
"description": "Larger fusion space (512 vs default 256).",
|
||||
"extra_args": [
|
||||
"--fusion-dim",
|
||||
"512"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "backbone_freezing",
|
||||
"description": "Partial backbone freezing to reduce overfitting and speed training.",
|
||||
"runs": [
|
||||
{
|
||||
"run_name": "phase3/freeze_25",
|
||||
"description": "Freeze earliest 25% of backbone blocks.",
|
||||
"extra_args": [
|
||||
"--freeze-ratio",
|
||||
"0.25"
|
||||
]
|
||||
},
|
||||
{
|
||||
"run_name": "phase3/freeze_50",
|
||||
"description": "Freeze earliest 50% of backbone blocks.",
|
||||
"extra_args": [
|
||||
"--freeze-ratio",
|
||||
"0.50"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "learning_rate",
|
||||
"description": "Test LR sensitivity (default 1e-4).",
|
||||
"runs": [
|
||||
{
|
||||
"run_name": "phase3/lr_1e3",
|
||||
"description": "Higher learning rate 1e-3.",
|
||||
"extra_args": [
|
||||
"--lr",
|
||||
"1e-3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"run_name": "phase3/lr_3e4",
|
||||
"description": "Intermediate learning rate 3e-4.",
|
||||
"extra_args": [
|
||||
"--lr",
|
||||
"3e-4"
|
||||
]
|
||||
},
|
||||
{
|
||||
"run_name": "phase3/lr_1e5",
|
||||
"description": "Lower learning rate 1e-5.",
|
||||
"extra_args": [
|
||||
"--lr",
|
||||
"1e-5"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "dropout",
|
||||
"description": "Test bridge classifier and clinical tower dropout rates.",
|
||||
"runs": [
|
||||
{
|
||||
"run_name": "phase3/bridge_dropout_03",
|
||||
"description": "Reduce bridge classifier dropout from 0.5 to 0.3.",
|
||||
"extra_args": [
|
||||
"--bridge-dropout",
|
||||
"0.3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"run_name": "phase3/bridge_dropout_07",
|
||||
"description": "Increase bridge classifier dropout to 0.7.",
|
||||
"extra_args": [
|
||||
"--bridge-dropout",
|
||||
"0.7"
|
||||
]
|
||||
},
|
||||
{
|
||||
"run_name": "phase3/cd_dropout_03",
|
||||
"description": "Increase clinical tower dropout from 0.1 to 0.3.",
|
||||
"extra_args": [
|
||||
"--cd-dropout",
|
||||
"0.3"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "warmup",
|
||||
"description": "Test warmup ablations vs default (cd=40, tower/fused=3/3).",
|
||||
"runs": [
|
||||
{
|
||||
"run_name": "phase3/warmup_no_cd",
|
||||
"description": "No CD warmup (cd=0) \u2014 tests whether the 40-epoch CD warmup is necessary.",
|
||||
"extra_args": [
|
||||
"--warmup-cd-epochs",
|
||||
"0"
|
||||
]
|
||||
},
|
||||
{
|
||||
"run_name": "phase3/warmup_tower5_fused5",
|
||||
"description": "Extended tower/fused warmup (5/5 vs default 3/3).",
|
||||
"extra_args": [
|
||||
"--single-warmup-tower-epochs",
|
||||
"5",
|
||||
"--single-warmup-fused-epochs",
|
||||
"5"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "sampling_augmentation",
|
||||
"description": "Test data sampling and augmentation choices.",
|
||||
"runs": [
|
||||
{
|
||||
"run_name": "phase3/no_augment",
|
||||
"description": "No augmentation \u2014 baseline images only.",
|
||||
"extra_args": [
|
||||
"--no-augment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"run_name": "phase3/balanced_sampling",
|
||||
"description": "Weighted balanced sampler to counter class imbalance.",
|
||||
"extra_args": [
|
||||
"--balanced-sampling"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,54 @@
|
||||
{
|
||||
"_notes": [
|
||||
"Phase 3.5 — confirmation and BCD tuning.",
|
||||
"All runs use best settings from phase 3: refugelike backbone, ratio IOP correction, drop raw IOP, exclude axial length.",
|
||||
"Baseline here is phase3/iop_ratio_drop_raw (0.8685 ± 0.011) — already complete, not re-run.",
|
||||
"common_args are prepended to every run's args list."
|
||||
],
|
||||
|
||||
"common_args": [
|
||||
"--eval-mode", "binary",
|
||||
"--tower-mode", "single",
|
||||
"--epochs", "30",
|
||||
"--in-memory-cache",
|
||||
"--augment",
|
||||
"--tune-binary-threshold",
|
||||
"--backbone", "refugelike",
|
||||
"--iop-corr-method", "ratio",
|
||||
"--iop-drop-raw",
|
||||
"--exclude-cols", "Axial_Length",
|
||||
"--output-root", "v3/results"
|
||||
],
|
||||
|
||||
"_common_args_implicit_defaults": {
|
||||
"--bridge-mode": "fused",
|
||||
"--warmup-cd-epochs": "40",
|
||||
"--single-warmup-tower-epochs": "3",
|
||||
"--single-warmup-fused-epochs": "3"
|
||||
},
|
||||
|
||||
"baseline": {
|
||||
"run_name": "phase35/iop_bcd_p07",
|
||||
"description": "Best IOP preprocessing + best BCD prob from phase 3 combined.",
|
||||
"extra_args": ["--tower-loss-mode", "bcd", "--bcd-prob", "0.7"]
|
||||
},
|
||||
|
||||
"groups": [
|
||||
{
|
||||
"name": "bcd_tuning",
|
||||
"description": "Extended BCD probability sweep with best IOP settings.",
|
||||
"runs": [
|
||||
{
|
||||
"run_name": "phase35/iop_bcd_p08",
|
||||
"description": "BCD p=0.8 with ratio IOP + drop raw.",
|
||||
"extra_args": ["--tower-loss-mode", "bcd", "--bcd-prob", "0.8"]
|
||||
},
|
||||
{
|
||||
"run_name": "phase35/iop_bcd_p09",
|
||||
"description": "BCD p=0.9 with ratio IOP + drop raw.",
|
||||
"extra_args": ["--tower-loss-mode", "bcd", "--bcd-prob", "0.9"]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user