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"""Rule-based heuristic baseline agent.
Deterministic decision tree — no API key required. Bit-exact reproducible.
Usage:
python baseline_heuristic.py [--url http://localhost:7860]
"""
from __future__ import annotations
import argparse
import json
import sys
from ml_training_debugger.models import MLTrainingAction
from server.environment import MLTrainingEnvironment
ALL_TASKS = [
"task_001",
"task_002",
"task_003",
"task_004",
"task_005",
"task_006",
"task_007",
]
def run_heuristic_episode(task_id: str, seed: int = 42) -> float:
"""Run one heuristic baseline episode. Returns grader score."""
env = MLTrainingEnvironment()
obs = env.reset(seed=seed, episode_id=f"baseline_{task_id}", task_id=task_id)
# Step 1: inspect_gradients
obs = env.step(MLTrainingAction(action_type="inspect_gradients"))
if obs.gradient_stats:
# Check exploding
if any(g.is_exploding for g in obs.gradient_stats):
obs = env.step(
MLTrainingAction(
action_type="modify_config",
target="learning_rate",
value=0.001,
)
)
obs = env.step(MLTrainingAction(action_type="restart_run"))
obs = env.step(
MLTrainingAction(
action_type="mark_diagnosed",
diagnosis="lr_too_high",
)
)
session = env._get_session()
return session.last_score if session and session.last_score is not None else 0.0
# Check vanishing
if any(g.is_vanishing for g in obs.gradient_stats):
obs = env.step(
MLTrainingAction(
action_type="modify_config",
target="learning_rate",
value=0.01,
)
)
obs = env.step(MLTrainingAction(action_type="restart_run"))
obs = env.step(
MLTrainingAction(
action_type="mark_diagnosed",
diagnosis="vanishing_gradients",
)
)
session = env._get_session()
return session.last_score if session and session.last_score is not None else 0.0
# Step 2: inspect_data_batch
obs = env.step(MLTrainingAction(action_type="inspect_data_batch"))
if obs.data_batch_stats and obs.data_batch_stats.class_overlap_score > 0.5:
obs = env.step(MLTrainingAction(action_type="patch_data_loader"))
obs = env.step(MLTrainingAction(action_type="restart_run"))
obs = env.step(
MLTrainingAction(
action_type="mark_diagnosed",
diagnosis="data_leakage",
)
)
session = env._get_session()
return session.last_score if session and session.last_score is not None else 0.0
# Detect overfitting pattern (used later, after ruling out code bugs)
_looks_like_overfitting = False
if obs.val_loss_history and obs.training_loss_history and len(obs.val_loss_history) >= 10:
early_train = sum(obs.training_loss_history[:5]) / 5
late_train = sum(obs.training_loss_history[-5:]) / 5
early_val = sum(obs.val_loss_history[:5]) / 5
late_val = sum(obs.val_loss_history[-5:]) / 5
train_dropped = late_train < early_train * 0.5
train_loss_low = late_train < 0.15
val_not_improving = late_val >= early_val * 0.95
gap_widening = (late_val - late_train) > (early_val - early_train)
if (
(train_dropped or train_loss_low)
and (val_not_improving or gap_widening)
and obs.data_batch_stats
and obs.data_batch_stats.class_overlap_score < 0.3
):
_looks_like_overfitting = True
# Step 3: inspect_model_modes
obs = env.step(MLTrainingAction(action_type="inspect_model_modes"))
if obs.model_mode_info:
has_eval = any(v == "eval" for v in obs.model_mode_info.values())
if has_eval:
obs = env.step(MLTrainingAction(action_type="fix_model_mode"))
obs = env.step(MLTrainingAction(action_type="restart_run"))
obs = env.step(
MLTrainingAction(
action_type="mark_diagnosed",
diagnosis="batchnorm_eval_mode",
)
)
session = env._get_session()
return session.last_score if session and session.last_score is not None else 0.0
# Step 4: inspect_code
obs = env.step(MLTrainingAction(action_type="inspect_code"))
if obs.code_snippet:
code = obs.code_snippet.code
if "model.eval()" in code and "model.train()" not in code:
obs = env.step(
MLTrainingAction(
action_type="fix_code",
line=5,
replacement="model.train()",
)
)
elif ".detach()" in code:
obs = env.step(
MLTrainingAction(
action_type="fix_code",
line=14,
replacement=" loss = criterion(output, batch_y)",
)
)
elif "inplace=True" in code:
obs = env.step(
MLTrainingAction(
action_type="fix_code",
line=15,
replacement=" output = F.relu(output)",
)
)
elif "optimizer.zero_grad()" not in code and "optimizer.step()" in code:
obs = env.step(
MLTrainingAction(
action_type="fix_code",
line=11,
replacement=" optimizer.zero_grad()",
)
)
if obs.episode_state.fix_action_taken:
obs = env.step(MLTrainingAction(action_type="restart_run"))
obs = env.step(
MLTrainingAction(
action_type="mark_diagnosed",
diagnosis="code_bug",
)
)
session = env._get_session()
return session.last_score if session and session.last_score is not None else 0.0
# Step 5: Check for scheduler issue (loss stagnates after initial progress)
if obs.training_loss_history and len(obs.training_loss_history) >= 10:
early_loss = sum(obs.training_loss_history[:3]) / 3
mid_loss = sum(obs.training_loss_history[5:8]) / 3
late_loss = sum(v for v in obs.training_loss_history[-3:] if v != float("inf")) / 3
improving_then_stuck = early_loss > mid_loss and abs(late_loss - mid_loss) < 0.3
if improving_then_stuck and obs.current_config.learning_rate < 0.01:
obs = env.step(
MLTrainingAction(
action_type="modify_config",
target="learning_rate",
value=0.001,
)
)
obs = env.step(MLTrainingAction(action_type="restart_run"))
obs = env.step(
MLTrainingAction(
action_type="mark_diagnosed",
diagnosis="scheduler_misconfigured",
)
)
session = env._get_session()
return session.last_score if session and session.last_score is not None else 0.0
# Overfitting fallback — only if code inspection didn't find a bug
if _looks_like_overfitting:
obs = env.step(
MLTrainingAction(
action_type="modify_config",
target="weight_decay",
value=0.01,
)
)
obs = env.step(MLTrainingAction(action_type="restart_run"))
obs = env.step(
MLTrainingAction(
action_type="mark_diagnosed",
diagnosis="overfitting",
)
)
session = env._get_session()
return session.last_score if session and session.last_score is not None else 0.0
# Final fallback
obs = env.step(
MLTrainingAction(
action_type="mark_diagnosed",
diagnosis="overfitting",
)
)
session = env._get_session()
return session.last_score if session and session.last_score is not None else 0.0
def main() -> None:
parser = argparse.ArgumentParser(description="Rule-based baseline agent")
parser.add_argument("--url", default="http://localhost:7860")
args = parser.parse_args()
scores: dict[str, float] = {}
for task_id in ALL_TASKS:
score = run_heuristic_episode(task_id)
scores[task_id] = round(score, 4)
print(json.dumps(scores, indent=2))
if __name__ == "__main__":
main()
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