fix: complete LR override — must patch lr_schedule, not just param_groups
PPO.load() bakes lr_schedule=FloatSchedule(saved_lr) into the model. train() calls _update_learning_rate() which reads lr_schedule, not model.learning_rate. So even with param_groups patched, the first gradient step reverts the optimizer to the saved LR. Complete 3-part fix in create_or_load_model(): model.learning_rate = lr # attribute model.lr_schedule = get_schedule_fn(lr) # prevents train() reverting for pg in optimizer.param_groups: pg['lr'] = lr # immediate effect Also: - SEED_PARAMS: second seed now uses LR=0.001 (was 0.000225) so GP starts with real LR diversity instead of two identical seeds - tests/test_end_to_end.py: 13 new tests covering the full LR override path including a live learn() call; would have caught both bugs - Phase 3 results re-cleared (seed trial 1 ran with half-fix) - 96 tests total, all passing Agent: pi Tests: 96 passed Tests-Added: 13 TypeScript: N/A
This commit is contained in:
Paul Huliganga
parent
298cd1790a
commit
650f893d2d
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@ -56,6 +56,7 @@ from datetime import datetime
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import gymnasium as gym
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import gym_donkeycar
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from stable_baselines3 import PPO
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from stable_baselines3.common.utils import get_schedule_fn
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from stable_baselines3.common.evaluation import evaluate_policy
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from stable_baselines3.common.callbacks import BaseCallback
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@ -202,16 +203,19 @@ def create_or_load_model(env, learning_rate, warm_start_path=None, seed=None):
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log(f'[W3 Runner] Loading warm-start model from {warm_start_path}')
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try:
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model = PPO.load(warm_start_path, env=env, device='auto')
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# Override learning rate — set BOTH the SB3 attribute AND the
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# optimizer param groups. PPO.load() restores the saved optimizer
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# state (lr=0.000225 from Phase 2), so just setting model.learning_rate
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# is not enough — the optimizer ignores it until _update_learning_rate
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# is called, and even then only if it reads the attribute correctly.
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# Three-part LR override required after PPO.load():
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# 1. model.learning_rate — Python attribute (used to recreate lr_schedule)
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# 2. model.lr_schedule — FloatSchedule used by _update_learning_rate()
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# during every train() call. Without this,
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# _update_learning_rate() reverts the optimizer
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# back to the saved LR on the first gradient step.
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# 3. optimizer param_groups — immediate effect before first train()
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model.learning_rate = learning_rate
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model.lr_schedule = get_schedule_fn(learning_rate)
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for pg in model.policy.optimizer.param_groups:
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pg['lr'] = learning_rate
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log(f'[W3 Runner] ✅ Warm start loaded. LR overridden to {learning_rate:.6f} '
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f'(set on model + {len(model.policy.optimizer.param_groups)} optimizer param group(s))')
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f'(model + lr_schedule + {len(model.policy.optimizer.param_groups)} optimizer param group(s))')
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return model
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except Exception as e:
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log(f'[W3 Runner] ⚠️ Warm start failed ({e}), training from scratch.')
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@ -567,3 +567,16 @@
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[2026-04-14 20:37:35] [Champion] 🏆 NEW BEST! Trial 3: mean_reward=90.0000 params={'r': 90}
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[2026-04-14 20:37:35] [Champion] 🏆 NEW BEST! Trial 5: mean_reward=75.0000 params={'n_steer': 8}
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[2026-04-14 20:37:35] [AutoResearch] Only 1 results — using random proposal.
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[2026-04-14 21:27:08] [AutoResearch] GP UCB top-5 candidates:
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[2026-04-14 21:27:08] UCB=2.3107 mu=0.3981 sigma=0.9563 params={'n_steer': 9, 'n_throttle': 2, 'learning_rate': 0.001405531880392808, 'timesteps': 26173}
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[2026-04-14 21:27:08] UCB=2.3049 mu=0.8602 sigma=0.7224 params={'n_steer': 9, 'n_throttle': 3, 'learning_rate': 0.001793493447174312, 'timesteps': 19198}
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[2026-04-14 21:27:08] UCB=2.2813 mu=0.4904 sigma=0.8954 params={'n_steer': 9, 'n_throttle': 4, 'learning_rate': 0.0011616192816742616, 'timesteps': 13887}
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[2026-04-14 21:27:08] UCB=2.2767 mu=0.5194 sigma=0.8787 params={'n_steer': 9, 'n_throttle': 4, 'learning_rate': 0.0011646447444663046, 'timesteps': 21199}
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[2026-04-14 21:27:08] UCB=2.2525 mu=0.6254 sigma=0.8136 params={'n_steer': 9, 'n_throttle': 3, 'learning_rate': 0.0010196345864901517, 'timesteps': 22035}
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[2026-04-14 21:27:08] [Champion] 🏆 NEW BEST! Trial 1: mean_reward=50.0000 params={'n_steer': 5}
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[2026-04-14 21:27:08] [Champion] 🏆 NEW BEST! Trial 1: mean_reward=80.0000 params={'n_steer': 7}
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[2026-04-14 21:27:08] [Champion] 🏆 NEW BEST! Trial 0: mean_reward=50.0000 params={'r': 50}
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[2026-04-14 21:27:08] [Champion] 🏆 NEW BEST! Trial 1: mean_reward=80.0000 params={'r': 80}
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[2026-04-14 21:27:08] [Champion] 🏆 NEW BEST! Trial 3: mean_reward=90.0000 params={'r': 90}
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[2026-04-14 21:27:08] [Champion] 🏆 NEW BEST! Trial 5: mean_reward=75.0000 params={'n_steer': 8}
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[2026-04-14 21:27:08] [AutoResearch] Only 1 results — using random proposal.
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@ -281,3 +281,26 @@
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[2026-04-14 20:37:40] [Wave3] Only 0 results — using random proposal.
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[2026-04-14 20:37:40] [Champion] 🏆 NEW BEST! Trial 3: score=1500.00 (mini_monaco=1500.0) params={'learning_rate': 0.0002, 'steps_per_switch': 8000, 'total_timesteps': 150000}
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[2026-04-14 20:37:40] [Champion] 🏆 NEW BEST! Trial 1: score=2000.00 (mini_monaco=2000.0) params={}
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[2026-04-14 20:37:55] =================================================================
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[2026-04-14 20:37:55] [Wave3] Multi-Track Autoresearch — GP+UCB Generalization Search
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[2026-04-14 20:37:55] [Wave3] Training tracks : generated_road, generated_track, mountain_track
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[2026-04-14 20:37:55] [Wave3] Test tracks : mini_monaco only (zero-shot; warren removed — broken done condition)
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[2026-04-14 20:37:55] [Wave3] Max trials : 25 | kappa=2.0 | push every 5
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[2026-04-14 20:37:55] [Wave3] Results file : /home/paulh/projects/donkeycar-rl-autoresearch/agent/outerloop-results/autoresearch_results_phase3.jsonl
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[2026-04-14 20:37:55] [Wave3] Champion dir : /home/paulh/projects/donkeycar-rl-autoresearch/agent/models/wave3-champion
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[2026-04-14 20:37:55] [Wave3] Warm start : /home/paulh/projects/donkeycar-rl-autoresearch/agent/models/champion/model.zip
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[2026-04-14 20:37:55] =================================================================
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[2026-04-14 20:37:55] [Wave3] Loaded 0 existing Phase 3 results.
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[2026-04-14 20:37:55] [Wave3] Wave3 Champion: trial=5 score=137.58 params={'learning_rate': 0.0008293130840877947, 'steps_per_switch': 7847, 'total_timesteps': 31625}
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[2026-04-14 20:37:55] [Wave3] Starting from trial 1.
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[2026-04-14 20:37:55]
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[Wave3] ========== Trial 1/25 ==========
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[2026-04-14 20:37:55] [Wave3] Seed trial 1/2: using hardcoded params.
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[2026-04-14 20:37:55] [Wave3] Proposed params: {'learning_rate': 0.000225, 'steps_per_switch': 5000, 'total_timesteps': 45000}
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[2026-04-14 20:37:57] [Wave3] Launching trial 1: {'learning_rate': 0.000225, 'steps_per_switch': 5000, 'total_timesteps': 45000}
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[2026-04-14 20:37:57] [Wave3] Command: python3 /home/paulh/projects/donkeycar-rl-autoresearch/agent/multitrack_runner.py --total-timesteps 45000 --steps-per-switch 5000 --learning-rate 0.000225 --eval-episodes 3 --save-dir /home/paulh/projects/donkeycar-rl-autoresearch/agent/models/wave3-trial-0001 --warm-start /home/paulh/projects/donkeycar-rl-autoresearch/agent/models/champion/model.zip
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[2026-04-14 21:27:21] [Wave3] Seed trial 1/2: using hardcoded params.
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[2026-04-14 21:27:21] [Wave3] Seed trial 2/2: using hardcoded params.
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[2026-04-14 21:27:21] [Wave3] Only 0 results — using random proposal.
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[2026-04-14 21:27:21] [Champion] 🏆 NEW BEST! Trial 3: score=1500.00 (mini_monaco=1500.0) params={'learning_rate': 0.0002, 'steps_per_switch': 8000, 'total_timesteps': 150000}
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[2026-04-14 21:27:21] [Champion] 🏆 NEW BEST! Trial 1: score=2000.00 (mini_monaco=2000.0) params={}
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@ -81,10 +81,10 @@ JOB_TIMEOUT = 7200 # 2h — 400k steps on CPU may need time
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# ---- Seed trials near Phase 2 champion ----
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# GP warm-up: first 2 trials use known-good parameters so GP has real prior data
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SEED_PARAMS = [
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# 3 full rotations through all 3 training tracks (~35 min per trial)
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# Low LR (same as Phase 2 champion) — baseline, ~35 min per trial
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{'learning_rate': 0.000225, 'steps_per_switch': 5000, 'total_timesteps': 45000},
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# Slower switching, more time per track (~45 min per trial)
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{'learning_rate': 0.000225, 'steps_per_switch': 10000, 'total_timesteps': 90000},
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# High LR — tests whether faster adaptation generalises better, ~35 min
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{'learning_rate': 0.001000, 'steps_per_switch': 5000, 'total_timesteps': 45000},
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]
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@ -0,0 +1,426 @@
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"""
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End-to-end pipeline tests — no live simulator required.
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These tests exist to catch integration-level bugs that unit tests miss.
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The LR-override bug (Wave 3: all trials silently ran at LR=0.000225) was
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not caught because we had no test that verified the optimizer's actual LR
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after PPO.load(). Every test in this file targets a real failure that
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already burned training time.
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Test categories
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---------------
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1. LR override — PPO.load() + param_group update
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2. create_or_load_model — the function that wraps PPO.load in multitrack_runner
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3. Training step LR — a short real PPO.learn() to confirm the log LR matches
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4. Output parsing — parse_runner_output extracts correct metrics
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5. Results round-trip — save → load → GP uses correct data
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"""
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import json
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import os
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import sys
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import tempfile
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import numpy as np
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import pytest
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# ---------------------------------------------------------------------------
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# Path setup — tests run from repo root or tests/ dir
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# ---------------------------------------------------------------------------
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AGENT_DIR = os.path.join(os.path.dirname(__file__), '..', 'agent')
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if AGENT_DIR not in sys.path:
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sys.path.insert(0, AGENT_DIR)
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# ---------------------------------------------------------------------------
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# Shared helpers
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# ---------------------------------------------------------------------------
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import gymnasium as gym
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import numpy as np
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from stable_baselines3 import PPO
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from stable_baselines3.common.vec_env import DummyVecEnv, VecTransposeImage
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class MockDonkeyEnv(gym.Env):
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"""
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Minimal DonkeyCar-shaped env: image observations, Box actions.
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No simulator required.
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"""
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metadata = {'render_modes': []}
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def __init__(self):
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super().__init__()
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self.observation_space = gym.spaces.Box(
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low=0, high=255, shape=(120, 160, 3), dtype=np.uint8
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)
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self.action_space = gym.spaces.Box(
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low=np.array([-1.0, 0.0]),
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high=np.array([1.0, 1.0]),
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dtype=np.float32,
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)
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self._step_count = 0
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def reset(self, seed=None, **kwargs):
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self._step_count = 0
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return np.zeros((120, 160, 3), dtype=np.uint8), {}
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def step(self, action):
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self._step_count += 1
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obs = np.random.randint(0, 255, (120, 160, 3), dtype=np.uint8)
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terminated = self._step_count >= 30
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return obs, 1.0, terminated, False, {'speed': 2.0, 'cte': 0.1}
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def close(self):
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pass
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def make_vec_env():
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"""Wrap MockDonkeyEnv the same way SB3 expects for CnnPolicy."""
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return VecTransposeImage(DummyVecEnv([MockDonkeyEnv]))
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def save_ppo_model(path, lr):
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"""Create a tiny CnnPolicy PPO, save it, return the path."""
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env = make_vec_env()
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model = PPO('CnnPolicy', env, learning_rate=lr, verbose=0,
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n_steps=64, batch_size=16)
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model.save(path)
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env.close()
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return path
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# ===========================================================================
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# 1. LR override — the bug that burned 8 hours of training
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# ===========================================================================
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def test_lr_override_bug_demonstration():
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"""
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Setting model.learning_rate after PPO.load() does NOT update the
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optimizer. This demonstrates the exact bug that caused all Wave 3
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trials to run at 0.000225 regardless of the GP-proposed LR.
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"""
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ORIGINAL_LR = 0.000225
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NEW_LR = 0.001
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env = make_vec_env()
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with tempfile.TemporaryDirectory() as tmpdir:
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path = os.path.join(tmpdir, 'model.zip')
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save_ppo_model(path, ORIGINAL_LR)
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loaded = PPO.load(path, env=env, device='cpu')
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# WRONG: only set the Python attribute, not the optimizer
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loaded.learning_rate = NEW_LR
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actual_lr = loaded.policy.optimizer.param_groups[0]['lr']
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# The optimizer still has the OLD lr — this is the bug
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assert actual_lr == pytest.approx(ORIGINAL_LR), (
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f"Expected optimizer to STILL have old LR {ORIGINAL_LR} "
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f"(demonstrating the bug), got {actual_lr}"
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)
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env.close()
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def test_lr_override_fix_sets_optimizer_param_groups():
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"""
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The correct fix: after PPO.load(), set lr on BOTH model.learning_rate
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AND every optimizer param_group. This is what multitrack_runner now does.
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"""
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ORIGINAL_LR = 0.000225
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NEW_LR = 0.001
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env = make_vec_env()
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with tempfile.TemporaryDirectory() as tmpdir:
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path = os.path.join(tmpdir, 'model.zip')
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save_ppo_model(path, ORIGINAL_LR)
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loaded = PPO.load(path, env=env, device='cpu')
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# CORRECT fix
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loaded.learning_rate = NEW_LR
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for pg in loaded.policy.optimizer.param_groups:
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pg['lr'] = NEW_LR
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# model attribute updated
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assert loaded.learning_rate == pytest.approx(NEW_LR)
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# optimizer updated — this is what matters for actual gradient updates
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for i, pg in enumerate(loaded.policy.optimizer.param_groups):
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assert pg['lr'] == pytest.approx(NEW_LR), (
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f"param_group[{i}]['lr'] = {pg['lr']}, expected {NEW_LR}"
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)
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env.close()
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def test_lr_override_survives_one_training_step():
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"""
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After the COMPLETE fix (learning_rate + lr_schedule + param_groups),
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the optimizer LR must still be correct after one real PPO gradient update.
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Root cause of the original bug:
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- PPO.load() bakes lr_schedule = FloatSchedule(0.000225) into the model
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- train() calls _update_learning_rate() which reads lr_schedule, NOT learning_rate
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- So even if param_groups are patched, train() overwrites them back to 0.000225
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- Fix: also patch model.lr_schedule = get_schedule_fn(NEW_LR)
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"""
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from stable_baselines3.common.utils import get_schedule_fn
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ORIGINAL_LR = 0.000225
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NEW_LR = 0.001
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env = make_vec_env()
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with tempfile.TemporaryDirectory() as tmpdir:
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path = os.path.join(tmpdir, 'model.zip')
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save_ppo_model(path, ORIGINAL_LR)
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loaded = PPO.load(path, env=env, device='cpu')
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# Complete 3-part fix
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loaded.learning_rate = NEW_LR
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loaded.lr_schedule = get_schedule_fn(NEW_LR) # <-- prevents train() reverting LR
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for pg in loaded.policy.optimizer.param_groups:
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pg['lr'] = NEW_LR
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# Run a minimal training step
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loaded.learn(total_timesteps=64, reset_num_timesteps=True)
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# LR must still be NEW_LR after _update_learning_rate() fired
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for i, pg in enumerate(loaded.policy.optimizer.param_groups):
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assert pg['lr'] == pytest.approx(NEW_LR), (
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f"After learn(), param_group[{i}]['lr'] = {pg['lr']}, "
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f"expected {NEW_LR}. lr_schedule was not patched correctly."
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)
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env.close()
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# ===========================================================================
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# 2. create_or_load_model — the actual function in multitrack_runner
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# ===========================================================================
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def test_create_or_load_model_warm_start_lr_reaches_optimizer():
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"""
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create_or_load_model() must leave the optimizer at the requested LR,
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not at the LR baked into the saved model — both before AND after a
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training step (lr_schedule must be patched, not just param_groups).
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"""
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from multitrack_runner import create_or_load_model
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ORIGINAL_LR = 0.000225
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NEW_LR = 0.00083
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env = make_vec_env()
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with tempfile.TemporaryDirectory() as tmpdir:
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path = os.path.join(tmpdir, 'model.zip')
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save_ppo_model(path, ORIGINAL_LR)
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model = create_or_load_model(env, learning_rate=NEW_LR,
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warm_start_path=path)
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# model attribute
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assert model.learning_rate == pytest.approx(NEW_LR)
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# lr_schedule (used by _update_learning_rate during train())
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assert model.lr_schedule(1.0) == pytest.approx(NEW_LR), (
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f"lr_schedule(1.0) = {model.lr_schedule(1.0)}, expected {NEW_LR}. "
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"train() will revert optimizer to old LR without this fix."
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)
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# optimizer param_groups
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for i, pg in enumerate(model.policy.optimizer.param_groups):
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assert pg['lr'] == pytest.approx(NEW_LR)
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# Verify it survives an actual training step
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model.learn(total_timesteps=64, reset_num_timesteps=True)
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for i, pg in enumerate(model.policy.optimizer.param_groups):
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assert pg['lr'] == pytest.approx(NEW_LR), (
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f"After learn(), param_group[{i}]['lr'] = {pg['lr']}, expected {NEW_LR}"
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)
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env.close()
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def test_create_or_load_model_fresh_model_uses_correct_lr():
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"""
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When warm_start_path is None/missing, create_or_load_model() must
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create a fresh PPO with the requested LR.
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"""
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from multitrack_runner import create_or_load_model
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LR = 0.00075
|
||||
env = make_vec_env()
|
||||
model = create_or_load_model(env, learning_rate=LR, warm_start_path=None)
|
||||
|
||||
assert model.learning_rate == pytest.approx(LR)
|
||||
for pg in model.policy.optimizer.param_groups:
|
||||
assert pg['lr'] == pytest.approx(LR)
|
||||
env.close()
|
||||
|
||||
|
||||
def test_create_or_load_model_falls_back_to_fresh_on_bad_path():
|
||||
"""
|
||||
If the warm_start_path doesn't exist, create_or_load_model() must
|
||||
fall back to a fresh model (not crash).
|
||||
"""
|
||||
from multitrack_runner import create_or_load_model
|
||||
|
||||
LR = 0.0005
|
||||
env = make_vec_env()
|
||||
model = create_or_load_model(env, learning_rate=LR,
|
||||
warm_start_path='/nonexistent/model.zip')
|
||||
|
||||
assert model is not None
|
||||
assert model.learning_rate == pytest.approx(LR)
|
||||
env.close()
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# 3. Output parsing — parse_runner_output extracts correct metrics
|
||||
# ===========================================================================
|
||||
|
||||
def test_parse_runner_output_full_success():
|
||||
"""parse_runner_output correctly extracts all metrics from a full run."""
|
||||
from wave3_controller import parse_runner_output
|
||||
|
||||
output = """
|
||||
[12:00:01] [W3 Runner][TRAIN] track=generated_road segment_reward=2409.70
|
||||
[12:08:00] [W3 Runner][TRAIN] track=generated_track segment_reward=112.30
|
||||
[12:15:00] [W3 Runner] Switching to TEST track: mini_monaco
|
||||
[12:15:30] [W3 Runner][TEST] track=mini_monaco mean_reward=843.21 mean_steps=980.0 ✅ DRIVES
|
||||
[12:15:30] [W3 Runner][TEST] mini_monaco_reward=843.2100
|
||||
[12:15:30] [W3 Runner][TEST] combined_test_score=843.2100
|
||||
"""
|
||||
combined, mini_monaco = parse_runner_output(output)
|
||||
assert combined == pytest.approx(843.21, rel=1e-4)
|
||||
assert mini_monaco == pytest.approx(843.21, rel=1e-4)
|
||||
|
||||
|
||||
def test_parse_runner_output_crash():
|
||||
"""parse_runner_output handles a crash/timeout (no test score lines)."""
|
||||
from wave3_controller import parse_runner_output
|
||||
|
||||
output = "[TIMEOUT after 7200s]"
|
||||
combined, mini_monaco = parse_runner_output(output)
|
||||
assert combined is None
|
||||
assert mini_monaco is None
|
||||
|
||||
|
||||
def test_parse_runner_output_partial():
|
||||
"""parse_runner_output handles missing combined but present mini_monaco."""
|
||||
from wave3_controller import parse_runner_output
|
||||
|
||||
output = "[W3 Runner][TEST] mini_monaco_reward=55.5\n"
|
||||
combined, mini_monaco = parse_runner_output(output)
|
||||
assert combined is None
|
||||
assert mini_monaco == pytest.approx(55.5, rel=1e-4)
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# 4. Results round-trip — save → load → GP uses the data
|
||||
# ===========================================================================
|
||||
|
||||
def test_results_round_trip_gp_sees_correct_lr():
|
||||
"""
|
||||
After save_result() writes a trial, load_results() must return it, and
|
||||
the GP must receive the correct params (including the actual LR used).
|
||||
This ensures GP data is not silently corrupted.
|
||||
"""
|
||||
import wave3_controller
|
||||
|
||||
TRIAL_LR = 0.00083
|
||||
TRIAL_SCORE = 250.0
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
original_path = wave3_controller.RESULTS_FILE
|
||||
wave3_controller.RESULTS_FILE = os.path.join(tmpdir, 'results.jsonl')
|
||||
|
||||
try:
|
||||
from wave3_controller import save_result, load_results
|
||||
save_result(
|
||||
trial=1,
|
||||
params={'learning_rate': TRIAL_LR,
|
||||
'steps_per_switch': 8000,
|
||||
'total_timesteps': 45000},
|
||||
combined=TRIAL_SCORE,
|
||||
mini_monaco=TRIAL_SCORE,
|
||||
model_path=None,
|
||||
is_champion=True,
|
||||
status='ok',
|
||||
elapsed=1200.0,
|
||||
)
|
||||
|
||||
results = load_results()
|
||||
assert len(results) == 1
|
||||
assert results[0]['params']['learning_rate'] == pytest.approx(TRIAL_LR)
|
||||
assert results[0]['combined_test_score'] == pytest.approx(TRIAL_SCORE)
|
||||
finally:
|
||||
wave3_controller.RESULTS_FILE = original_path
|
||||
|
||||
|
||||
def test_results_gp_data_never_includes_zero_score_trials():
|
||||
"""
|
||||
Zero-score trials (crash/timeout) must NOT be added to GP data.
|
||||
If they were, the GP would learn that certain params are bad even
|
||||
when the failure was actually a simulator glitch.
|
||||
"""
|
||||
import wave3_controller
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
original_path = wave3_controller.RESULTS_FILE
|
||||
wave3_controller.RESULTS_FILE = os.path.join(tmpdir, 'results.jsonl')
|
||||
|
||||
try:
|
||||
from wave3_controller import save_result, load_results
|
||||
|
||||
# Write a zero-score (crash) trial
|
||||
save_result(
|
||||
trial=1,
|
||||
params={'learning_rate': 0.001, 'steps_per_switch': 5000,
|
||||
'total_timesteps': 30000},
|
||||
combined=0.0,
|
||||
mini_monaco=0.0,
|
||||
model_path=None,
|
||||
is_champion=False,
|
||||
status='timeout',
|
||||
elapsed=7200.0,
|
||||
)
|
||||
# Write a valid trial
|
||||
save_result(
|
||||
trial=2,
|
||||
params={'learning_rate': 0.0005, 'steps_per_switch': 8000,
|
||||
'total_timesteps': 45000},
|
||||
combined=300.0,
|
||||
mini_monaco=300.0,
|
||||
model_path=None,
|
||||
is_champion=True,
|
||||
status='ok',
|
||||
elapsed=1800.0,
|
||||
)
|
||||
|
||||
results = load_results()
|
||||
# load_results only returns trials with non-None score; the
|
||||
# wave3 main loop further filters out score==0 before adding to GP
|
||||
scores = [r['combined_test_score'] for r in results]
|
||||
assert 300.0 in scores
|
||||
# The zero-score trial IS in the file but the main loop guards it
|
||||
assert 0.0 in scores # it's saved
|
||||
finally:
|
||||
wave3_controller.RESULTS_FILE = original_path
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# 5. Seed params sanity — seed trials cover the important LR range
|
||||
# ===========================================================================
|
||||
|
||||
def test_seed_params_cover_both_low_and_high_lr():
|
||||
"""
|
||||
SEED_PARAMS must include at least one low-LR trial (≤ 3e-4) and
|
||||
at least one higher-LR trial (≥ 5e-4) so the GP starts with data
|
||||
across the search space, not just at one corner.
|
||||
"""
|
||||
from wave3_controller import SEED_PARAMS
|
||||
lrs = [p['learning_rate'] for p in SEED_PARAMS]
|
||||
assert min(lrs) <= 3e-4, f"No low-LR seed trial: {lrs}"
|
||||
assert max(lrs) >= 5e-4, f"No high-LR seed trial: {lrs}"
|
||||
|
||||
|
||||
def test_seed_params_lr_is_not_all_identical():
|
||||
"""SEED_PARAMS must not all have the same LR — that killed Wave 3 v1."""
|
||||
from wave3_controller import SEED_PARAMS
|
||||
lrs = [p['learning_rate'] for p in SEED_PARAMS]
|
||||
assert len(set(lrs)) > 1, (
|
||||
f"All seed params have the same LR ({lrs[0]}). "
|
||||
"The GP needs diverse starting data to explore the LR dimension."
|
||||
)
|
||||
Loading…
Reference in New Issue