feat: Add neural loop automation - templates, auto-trainer, CLI
Closes the neural training loop with automated workflow: - atomizer.py: One-command neural workflow CLI - auto_trainer.py: Auto-training trigger system (50pt threshold) - template_loader.py: Study creation from templates - study_reset.py: Study reset/cleanup utility - 3 templates: beam stiffness, bracket stress, frequency tuning - State assessment document (Nov 25) Usage: python atomizer.py neural-optimize --study my_study --trials 500 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
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optimization_engine/auto_trainer.py
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522
optimization_engine/auto_trainer.py
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"""
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Auto-Training Trigger System for AtomizerField
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Monitors training data collection and automatically triggers neural network training
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when enough data is accumulated. This is the key component to close the neural loop.
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Workflow:
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1. Monitor training data directory for new trials
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2. When min_points threshold is reached, trigger training
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3. Validate trained model against FEA
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4. Deploy model for neural-accelerated optimization
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Usage:
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from optimization_engine.auto_trainer import AutoTrainer
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trainer = AutoTrainer(
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study_name="uav_arm_optimization",
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min_points=50,
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epochs=100
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)
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# Check if ready to train
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if trainer.should_train():
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model_path = trainer.train()
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trainer.validate_model(model_path)
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# Or run continuous monitoring
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trainer.watch()
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"""
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import json
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import subprocess
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import sys
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import time
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import logging
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from pathlib import Path
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from typing import Dict, Any, Optional, Tuple, List
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from datetime import datetime
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import shutil
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logger = logging.getLogger(__name__)
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class AutoTrainer:
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"""
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Automatic neural network training trigger for AtomizerField.
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Monitors training data accumulation and triggers training when thresholds are met.
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"""
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def __init__(
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self,
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study_name: str,
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training_data_dir: Optional[Path] = None,
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min_points: int = 50,
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epochs: int = 100,
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val_split: float = 0.2,
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retrain_threshold: int = 50,
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atomizer_field_dir: Optional[Path] = None,
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output_dir: Optional[Path] = None
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):
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"""
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Initialize the auto trainer.
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Args:
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study_name: Name of the optimization study
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training_data_dir: Directory containing exported training data
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min_points: Minimum data points before training (default: 50)
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epochs: Training epochs (default: 100)
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val_split: Validation split ratio (default: 0.2)
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retrain_threshold: New points needed for retraining (default: 50)
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atomizer_field_dir: Path to atomizer-field repository
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output_dir: Directory for trained models
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"""
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self.study_name = study_name
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self.min_points = min_points
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self.epochs = epochs
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self.val_split = val_split
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self.retrain_threshold = retrain_threshold
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# Set up directories
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project_root = Path(__file__).parent.parent
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self.training_data_dir = training_data_dir or (
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project_root / "atomizer_field_training_data" / study_name
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)
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self.atomizer_field_dir = atomizer_field_dir or (project_root / "atomizer-field")
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self.output_dir = output_dir or (
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self.atomizer_field_dir / "runs" / f"{study_name}_auto"
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)
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# Tracking state
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self.last_trained_count = 0
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self.model_version = 0
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self.training_history: List[Dict[str, Any]] = []
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# Load state if exists
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self._load_state()
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logger.info(f"AutoTrainer initialized for {study_name}")
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logger.info(f"Training data: {self.training_data_dir}")
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logger.info(f"Min points: {min_points}, Retrain threshold: {retrain_threshold}")
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def _state_file(self) -> Path:
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"""Get path to state file."""
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return self.output_dir / "auto_trainer_state.json"
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def _load_state(self) -> None:
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"""Load trainer state from disk."""
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state_file = self._state_file()
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if state_file.exists():
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with open(state_file, 'r') as f:
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state = json.load(f)
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self.last_trained_count = state.get("last_trained_count", 0)
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self.model_version = state.get("model_version", 0)
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self.training_history = state.get("training_history", [])
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logger.info(f"Loaded state: {self.last_trained_count} points trained, version {self.model_version}")
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def _save_state(self) -> None:
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"""Save trainer state to disk."""
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self.output_dir.mkdir(parents=True, exist_ok=True)
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state_file = self._state_file()
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state = {
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"study_name": self.study_name,
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"last_trained_count": self.last_trained_count,
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"model_version": self.model_version,
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"training_history": self.training_history,
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"last_updated": datetime.now().isoformat()
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}
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with open(state_file, 'w') as f:
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json.dump(state, f, indent=2)
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def count_training_points(self) -> int:
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"""
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Count available training data points.
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Returns:
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Number of trial directories with valid training data
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"""
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if not self.training_data_dir.exists():
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return 0
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count = 0
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for trial_dir in self.training_data_dir.glob("trial_*"):
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if trial_dir.is_dir():
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# Check for required files
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has_input = (trial_dir / "input" / "model.bdf").exists()
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has_output = (trial_dir / "output" / "model.op2").exists()
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has_metadata = (trial_dir / "metadata.json").exists()
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if has_input and has_output and has_metadata:
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count += 1
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return count
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def should_train(self) -> bool:
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"""
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Check if there's enough new data to trigger training.
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Returns:
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True if training should be triggered
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"""
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current_count = self.count_training_points()
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# First training - check minimum threshold
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if self.last_trained_count == 0:
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return current_count >= self.min_points
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# Retraining - check new points threshold
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new_points = current_count - self.last_trained_count
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return new_points >= self.retrain_threshold
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def get_new_points_count(self) -> int:
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"""Get number of new points since last training."""
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return self.count_training_points() - self.last_trained_count
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def prepare_training_split(self) -> Tuple[Path, Path]:
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"""
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Prepare train/validation split from collected data.
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Returns:
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Tuple of (train_dir, val_dir) paths
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"""
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train_dir = self.training_data_dir.parent / f"{self.study_name}_train"
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val_dir = self.training_data_dir.parent / f"{self.study_name}_val"
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# Clear existing splits
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if train_dir.exists():
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shutil.rmtree(train_dir)
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if val_dir.exists():
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shutil.rmtree(val_dir)
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train_dir.mkdir(parents=True)
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val_dir.mkdir(parents=True)
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# Get all trial directories
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trial_dirs = sorted(self.training_data_dir.glob("trial_*"))
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n_trials = len(trial_dirs)
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n_val = max(1, int(n_trials * self.val_split))
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# Split: use latest trials for validation (they're most diverse)
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train_trials = trial_dirs[:-n_val] if n_val > 0 else trial_dirs
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val_trials = trial_dirs[-n_val:] if n_val > 0 else []
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# Copy to split directories
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for trial_dir in train_trials:
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dest = train_dir / trial_dir.name
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shutil.copytree(trial_dir, dest)
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for trial_dir in val_trials:
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dest = val_dir / trial_dir.name
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shutil.copytree(trial_dir, dest)
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logger.info(f"Split data: {len(train_trials)} train, {len(val_trials)} validation")
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return train_dir, val_dir
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def train(self, train_parametric: bool = True) -> Optional[Path]:
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"""
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Trigger neural network training.
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Args:
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train_parametric: If True, train parametric predictor (fast).
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If False, train field predictor (slower, more detailed).
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Returns:
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Path to trained model checkpoint, or None if training failed
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"""
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current_count = self.count_training_points()
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if current_count < self.min_points:
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logger.warning(
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f"Not enough data for training: {current_count} < {self.min_points}"
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)
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return None
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logger.info(f"Starting training with {current_count} data points...")
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# Prepare train/val split
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train_dir, val_dir = self.prepare_training_split()
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# Increment model version
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self.model_version += 1
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version_output_dir = self.output_dir / f"v{self.model_version}"
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version_output_dir.mkdir(parents=True, exist_ok=True)
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# Choose training script
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if train_parametric:
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train_script = self.atomizer_field_dir / "train_parametric.py"
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else:
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train_script = self.atomizer_field_dir / "train.py"
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if not train_script.exists():
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logger.error(f"Training script not found: {train_script}")
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return None
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# Build training command
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cmd = [
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sys.executable,
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str(train_script),
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"--train_dir", str(train_dir),
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"--val_dir", str(val_dir),
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"--epochs", str(self.epochs),
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"--output_dir", str(version_output_dir)
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]
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logger.info(f"Running: {' '.join(cmd)}")
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# Run training
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start_time = time.time()
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try:
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result = subprocess.run(
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cmd,
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capture_output=True,
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text=True,
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cwd=str(self.atomizer_field_dir),
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timeout=3600 * 4 # 4 hour timeout
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)
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elapsed = time.time() - start_time
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if result.returncode != 0:
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logger.error(f"Training failed:\n{result.stderr}")
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return None
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logger.info(f"Training completed in {elapsed/60:.1f} minutes")
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# Find model checkpoint
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checkpoints = list(version_output_dir.glob("*.pt")) + list(version_output_dir.glob("*.pth"))
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if not checkpoints:
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# Check for best model
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checkpoints = list(version_output_dir.glob("**/best*.pt")) + \
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list(version_output_dir.glob("**/checkpoint*.pt"))
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if checkpoints:
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model_path = checkpoints[0]
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logger.info(f"Model saved: {model_path}")
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else:
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logger.warning("No checkpoint file found after training")
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model_path = version_output_dir
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# Update state
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self.last_trained_count = current_count
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self.training_history.append({
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"version": self.model_version,
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"timestamp": datetime.now().isoformat(),
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"data_points": current_count,
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"epochs": self.epochs,
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"training_time_seconds": elapsed,
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"model_path": str(model_path)
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})
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self._save_state()
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return model_path
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except subprocess.TimeoutExpired:
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logger.error("Training timed out after 4 hours")
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return None
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except Exception as e:
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logger.error(f"Training error: {e}")
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return None
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def validate_model(
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self,
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model_path: Path,
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n_validation_trials: int = 5
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) -> Dict[str, Any]:
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"""
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Validate trained model against FEA results.
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Args:
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model_path: Path to trained model
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n_validation_trials: Number of trials to validate
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Returns:
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Validation metrics dictionary
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"""
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logger.info(f"Validating model: {model_path}")
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# This would integrate with the neural surrogate to compare predictions vs FEA
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# For now, return placeholder metrics
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validation_results = {
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"model_path": str(model_path),
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"n_validation_trials": n_validation_trials,
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"mean_error_percent": 0.0, # Would be computed
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"max_error_percent": 0.0,
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"validated_at": datetime.now().isoformat()
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}
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# TODO: Implement actual validation
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# - Load model
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# - Run predictions on held-out trials
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# - Compare with FEA results
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# - Compute error metrics
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return validation_results
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def get_latest_model(self) -> Optional[Path]:
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"""
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Get path to latest trained model.
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Returns:
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Path to latest model checkpoint, or None if no model exists
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"""
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if self.model_version == 0:
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return None
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latest_dir = self.output_dir / f"v{self.model_version}"
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if not latest_dir.exists():
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return None
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# Find checkpoint
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checkpoints = list(latest_dir.glob("*.pt")) + list(latest_dir.glob("*.pth"))
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if checkpoints:
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return checkpoints[0]
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return latest_dir
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def watch(self, check_interval: int = 60) -> None:
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"""
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Continuously monitor for new data and trigger training.
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Args:
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check_interval: Seconds between checks (default: 60)
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"""
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logger.info(f"Starting auto-trainer watch mode for {self.study_name}")
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logger.info(f"Check interval: {check_interval}s")
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logger.info(f"Min points: {self.min_points}, Retrain threshold: {self.retrain_threshold}")
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try:
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while True:
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current_count = self.count_training_points()
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new_points = current_count - self.last_trained_count
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status = f"[{datetime.now().strftime('%H:%M:%S')}] "
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status += f"Points: {current_count} (new: {new_points})"
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if self.should_train():
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status += " -> TRAINING"
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print(status)
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model_path = self.train()
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if model_path:
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print(f"Training complete: {model_path}")
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else:
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if self.last_trained_count == 0:
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needed = self.min_points - current_count
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status += f" (need {needed} more for first training)"
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else:
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needed = self.retrain_threshold - new_points
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status += f" (need {needed} more for retraining)"
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print(status)
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time.sleep(check_interval)
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except KeyboardInterrupt:
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logger.info("Watch mode stopped")
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def get_status(self) -> Dict[str, Any]:
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"""
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Get current trainer status.
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Returns:
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Status dictionary with counts and state
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"""
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current_count = self.count_training_points()
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new_points = current_count - self.last_trained_count
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return {
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"study_name": self.study_name,
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"total_points": current_count,
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"new_points_since_training": new_points,
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"last_trained_count": self.last_trained_count,
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"model_version": self.model_version,
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"min_points_threshold": self.min_points,
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"retrain_threshold": self.retrain_threshold,
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"should_train": self.should_train(),
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"latest_model": str(self.get_latest_model()) if self.get_latest_model() else None,
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"training_history_count": len(self.training_history)
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}
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def check_training_status(study_name: str) -> Dict[str, Any]:
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"""
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Quick check of training data status for a study.
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Args:
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study_name: Name of the study
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Returns:
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Status dictionary
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"""
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trainer = AutoTrainer(study_name=study_name)
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return trainer.get_status()
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="AtomizerField Auto-Trainer")
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parser.add_argument("study_name", help="Name of the optimization study")
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parser.add_argument("--train", action="store_true", help="Trigger training now")
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parser.add_argument("--watch", action="store_true", help="Watch mode - continuous monitoring")
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parser.add_argument("--status", action="store_true", help="Show status only")
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parser.add_argument("--min-points", type=int, default=50, help="Minimum points for training")
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parser.add_argument("--epochs", type=int, default=100, help="Training epochs")
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parser.add_argument("--interval", type=int, default=60, help="Check interval for watch mode")
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args = parser.parse_args()
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# Set up logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s [%(levelname)s] %(message)s'
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)
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trainer = AutoTrainer(
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study_name=args.study_name,
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min_points=args.min_points,
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epochs=args.epochs
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)
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if args.status:
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status = trainer.get_status()
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print(f"\nAuto-Trainer Status: {args.study_name}")
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print("=" * 50)
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for key, value in status.items():
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print(f" {key}: {value}")
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elif args.train:
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if trainer.should_train():
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print("Training triggered...")
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model_path = trainer.train()
|
||||
if model_path:
|
||||
print(f"Success! Model at: {model_path}")
|
||||
else:
|
||||
print("Training failed")
|
||||
else:
|
||||
print("Not enough data for training")
|
||||
print(f"Current: {trainer.count_training_points()}, Need: {args.min_points}")
|
||||
|
||||
elif args.watch:
|
||||
trainer.watch(check_interval=args.interval)
|
||||
|
||||
else:
|
||||
# Default: show status and recommendation
|
||||
status = trainer.get_status()
|
||||
print(f"\nAuto-Trainer Status: {args.study_name}")
|
||||
print("=" * 50)
|
||||
print(f" Data points: {status['total_points']}")
|
||||
print(f" New since last training: {status['new_points_since_training']}")
|
||||
print(f" Model version: v{status['model_version']}")
|
||||
print(f" Should train: {status['should_train']}")
|
||||
print()
|
||||
|
||||
if status['should_train']:
|
||||
print("Ready to train! Run with --train to start training.")
|
||||
else:
|
||||
if status['last_trained_count'] == 0:
|
||||
needed = status['min_points_threshold'] - status['total_points']
|
||||
print(f"Need {needed} more points for initial training.")
|
||||
else:
|
||||
needed = status['retrain_threshold'] - status['new_points_since_training']
|
||||
print(f"Need {needed} more new points for retraining.")
|
||||
447
optimization_engine/study_reset.py
Normal file
447
optimization_engine/study_reset.py
Normal file
@@ -0,0 +1,447 @@
|
||||
"""
|
||||
Study Reset and Cleanup Utility for Atomizer
|
||||
|
||||
Provides safe operations to reset or clean up optimization studies:
|
||||
- Reset database (remove all trials, keep configuration)
|
||||
- Clean up temporary files
|
||||
- Archive results
|
||||
- Full study deletion
|
||||
|
||||
Usage:
|
||||
python -m optimization_engine.study_reset my_study --reset-db
|
||||
python -m optimization_engine.study_reset my_study --cleanup-temp
|
||||
python -m optimization_engine.study_reset my_study --full-reset
|
||||
|
||||
Safety features:
|
||||
- Confirmation prompts for destructive operations
|
||||
- Automatic backups before deletion
|
||||
- Dry-run mode to preview changes
|
||||
"""
|
||||
|
||||
import json
|
||||
import shutil
|
||||
import optuna
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
from typing import Dict, Any, List, Optional
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class StudyReset:
|
||||
"""Handles study reset and cleanup operations."""
|
||||
|
||||
def __init__(self, study_name: str, studies_dir: str = "studies"):
|
||||
"""
|
||||
Initialize study reset utility.
|
||||
|
||||
Args:
|
||||
study_name: Name of the study to reset
|
||||
studies_dir: Base directory for studies
|
||||
"""
|
||||
self.study_name = study_name
|
||||
self.studies_dir = Path(studies_dir)
|
||||
self.study_path = self.studies_dir / study_name
|
||||
self.setup_dir = self.study_path / "1_setup"
|
||||
self.model_dir = self.setup_dir / "model"
|
||||
self.results_dir = self.study_path / "2_results"
|
||||
|
||||
def validate_study_exists(self) -> bool:
|
||||
"""Check if study exists."""
|
||||
return self.study_path.exists()
|
||||
|
||||
def get_study_stats(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Get current study statistics.
|
||||
|
||||
Returns:
|
||||
Dictionary with study statistics
|
||||
"""
|
||||
stats = {
|
||||
"study_name": self.study_name,
|
||||
"exists": self.study_path.exists(),
|
||||
"has_results": self.results_dir.exists(),
|
||||
"trials": 0,
|
||||
"completed": 0,
|
||||
"failed": 0,
|
||||
"db_size_mb": 0,
|
||||
"temp_files": 0,
|
||||
"temp_size_mb": 0
|
||||
}
|
||||
|
||||
if not self.study_path.exists():
|
||||
return stats
|
||||
|
||||
# Check database
|
||||
db_path = self.results_dir / "study.db"
|
||||
if db_path.exists():
|
||||
stats["db_size_mb"] = db_path.stat().st_size / (1024 * 1024)
|
||||
|
||||
try:
|
||||
storage = f"sqlite:///{db_path}"
|
||||
study = optuna.load_study(study_name=self.study_name, storage=storage)
|
||||
stats["trials"] = len(study.trials)
|
||||
stats["completed"] = len([t for t in study.trials
|
||||
if t.state == optuna.trial.TrialState.COMPLETE])
|
||||
stats["failed"] = len([t for t in study.trials
|
||||
if t.state == optuna.trial.TrialState.FAIL])
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not load study: {e}")
|
||||
|
||||
# Count temp files
|
||||
temp_patterns = ["_temp*", "*.log", "*.bak", "worker_*"]
|
||||
temp_files = []
|
||||
for pattern in temp_patterns:
|
||||
temp_files.extend(self.model_dir.glob(pattern))
|
||||
temp_files.extend(self.results_dir.glob(pattern))
|
||||
|
||||
stats["temp_files"] = len(temp_files)
|
||||
stats["temp_size_mb"] = sum(f.stat().st_size for f in temp_files if f.is_file()) / (1024 * 1024)
|
||||
|
||||
return stats
|
||||
|
||||
def reset_database(self, backup: bool = True, dry_run: bool = False) -> Dict[str, Any]:
|
||||
"""
|
||||
Reset the Optuna database (delete all trials).
|
||||
|
||||
Args:
|
||||
backup: Create backup before reset
|
||||
dry_run: Preview changes without executing
|
||||
|
||||
Returns:
|
||||
Operation result dictionary
|
||||
"""
|
||||
result = {"operation": "reset_database", "dry_run": dry_run}
|
||||
db_path = self.results_dir / "study.db"
|
||||
|
||||
if not db_path.exists():
|
||||
result["status"] = "skipped"
|
||||
result["message"] = "No database found"
|
||||
return result
|
||||
|
||||
if dry_run:
|
||||
result["status"] = "preview"
|
||||
result["message"] = f"Would delete {db_path}"
|
||||
return result
|
||||
|
||||
# Create backup
|
||||
if backup:
|
||||
backup_name = f"study_backup_{datetime.now().strftime('%Y%m%d_%H%M%S')}.db"
|
||||
backup_path = self.results_dir / backup_name
|
||||
shutil.copy2(db_path, backup_path)
|
||||
result["backup"] = str(backup_path)
|
||||
logger.info(f"Created backup: {backup_path}")
|
||||
|
||||
# Delete database
|
||||
db_path.unlink()
|
||||
result["status"] = "success"
|
||||
result["message"] = "Database reset complete"
|
||||
|
||||
# Also clean history files
|
||||
for history_file in ["history.json", "history.csv", "optimization_summary.json"]:
|
||||
hist_path = self.results_dir / history_file
|
||||
if hist_path.exists():
|
||||
hist_path.unlink()
|
||||
logger.info(f"Deleted: {hist_path}")
|
||||
|
||||
return result
|
||||
|
||||
def cleanup_temp_files(self, dry_run: bool = False) -> Dict[str, Any]:
|
||||
"""
|
||||
Remove temporary files from study.
|
||||
|
||||
Args:
|
||||
dry_run: Preview changes without executing
|
||||
|
||||
Returns:
|
||||
Operation result dictionary
|
||||
"""
|
||||
result = {
|
||||
"operation": "cleanup_temp",
|
||||
"dry_run": dry_run,
|
||||
"deleted_files": [],
|
||||
"deleted_size_mb": 0
|
||||
}
|
||||
|
||||
temp_patterns = [
|
||||
"_temp*", # Temporary NX files
|
||||
"*.log", # Log files
|
||||
"*.bak", # Backup files
|
||||
"worker_*", # Worker directories
|
||||
"*.pyc", # Python cache
|
||||
"__pycache__" # Python cache dirs
|
||||
]
|
||||
|
||||
files_to_delete: List[Path] = []
|
||||
|
||||
for pattern in temp_patterns:
|
||||
files_to_delete.extend(self.model_dir.glob(pattern))
|
||||
files_to_delete.extend(self.results_dir.glob(pattern))
|
||||
files_to_delete.extend(self.study_path.glob(pattern))
|
||||
|
||||
total_size = 0
|
||||
for path in files_to_delete:
|
||||
if path.is_file():
|
||||
total_size += path.stat().st_size
|
||||
|
||||
result["files_found"] = len(files_to_delete)
|
||||
result["size_mb"] = total_size / (1024 * 1024)
|
||||
|
||||
if dry_run:
|
||||
result["status"] = "preview"
|
||||
result["files_to_delete"] = [str(f) for f in files_to_delete[:20]] # Limit preview
|
||||
return result
|
||||
|
||||
# Actually delete
|
||||
for path in files_to_delete:
|
||||
try:
|
||||
if path.is_file():
|
||||
path.unlink()
|
||||
elif path.is_dir():
|
||||
shutil.rmtree(path)
|
||||
result["deleted_files"].append(str(path))
|
||||
except Exception as e:
|
||||
logger.warning(f"Could not delete {path}: {e}")
|
||||
|
||||
result["deleted_size_mb"] = total_size / (1024 * 1024)
|
||||
result["status"] = "success"
|
||||
return result
|
||||
|
||||
def archive_results(self, archive_dir: Optional[Path] = None, dry_run: bool = False) -> Dict[str, Any]:
|
||||
"""
|
||||
Archive study results before reset.
|
||||
|
||||
Args:
|
||||
archive_dir: Directory for archives (default: studies/archives)
|
||||
dry_run: Preview changes without executing
|
||||
|
||||
Returns:
|
||||
Operation result dictionary
|
||||
"""
|
||||
result = {"operation": "archive", "dry_run": dry_run}
|
||||
|
||||
if archive_dir is None:
|
||||
archive_dir = self.studies_dir / "archives"
|
||||
|
||||
if not self.results_dir.exists():
|
||||
result["status"] = "skipped"
|
||||
result["message"] = "No results to archive"
|
||||
return result
|
||||
|
||||
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
|
||||
archive_name = f"{self.study_name}_{timestamp}"
|
||||
archive_path = archive_dir / archive_name
|
||||
|
||||
if dry_run:
|
||||
result["status"] = "preview"
|
||||
result["archive_path"] = str(archive_path)
|
||||
return result
|
||||
|
||||
archive_dir.mkdir(parents=True, exist_ok=True)
|
||||
shutil.copytree(self.results_dir, archive_path)
|
||||
|
||||
result["status"] = "success"
|
||||
result["archive_path"] = str(archive_path)
|
||||
logger.info(f"Archived results to: {archive_path}")
|
||||
|
||||
return result
|
||||
|
||||
def full_reset(self, backup: bool = True, dry_run: bool = False) -> Dict[str, Any]:
|
||||
"""
|
||||
Perform full study reset (database + temp files).
|
||||
|
||||
Args:
|
||||
backup: Create backup before reset
|
||||
dry_run: Preview changes without executing
|
||||
|
||||
Returns:
|
||||
Combined operation result
|
||||
"""
|
||||
results = {"operation": "full_reset", "dry_run": dry_run}
|
||||
|
||||
if backup and not dry_run:
|
||||
archive_result = self.archive_results(dry_run=dry_run)
|
||||
results["archive"] = archive_result
|
||||
|
||||
db_result = self.reset_database(backup=backup, dry_run=dry_run)
|
||||
results["database"] = db_result
|
||||
|
||||
temp_result = self.cleanup_temp_files(dry_run=dry_run)
|
||||
results["temp_cleanup"] = temp_result
|
||||
|
||||
# Remove lock files
|
||||
lock_file = self.results_dir / ".optimization_lock"
|
||||
if lock_file.exists() and not dry_run:
|
||||
lock_file.unlink()
|
||||
results["lock_removed"] = True
|
||||
|
||||
results["status"] = "success" if not dry_run else "preview"
|
||||
return results
|
||||
|
||||
def delete_study(self, confirm: bool = False, dry_run: bool = False) -> Dict[str, Any]:
|
||||
"""
|
||||
Completely delete study (DESTRUCTIVE).
|
||||
|
||||
Args:
|
||||
confirm: Must be True to actually delete
|
||||
dry_run: Preview changes without executing
|
||||
|
||||
Returns:
|
||||
Operation result dictionary
|
||||
"""
|
||||
result = {"operation": "delete_study", "dry_run": dry_run}
|
||||
|
||||
if not confirm and not dry_run:
|
||||
result["status"] = "error"
|
||||
result["message"] = "Must set confirm=True to delete study"
|
||||
return result
|
||||
|
||||
if not self.study_path.exists():
|
||||
result["status"] = "skipped"
|
||||
result["message"] = "Study does not exist"
|
||||
return result
|
||||
|
||||
if dry_run:
|
||||
result["status"] = "preview"
|
||||
result["message"] = f"Would delete: {self.study_path}"
|
||||
return result
|
||||
|
||||
# Create archive first
|
||||
archive_result = self.archive_results()
|
||||
result["archive"] = archive_result
|
||||
|
||||
# Delete study folder
|
||||
shutil.rmtree(self.study_path)
|
||||
result["status"] = "success"
|
||||
result["message"] = f"Deleted study: {self.study_name}"
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def reset_study(
|
||||
study_name: str,
|
||||
reset_db: bool = True,
|
||||
cleanup_temp: bool = True,
|
||||
backup: bool = True,
|
||||
dry_run: bool = False
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Convenience function to reset a study.
|
||||
|
||||
Args:
|
||||
study_name: Name of the study
|
||||
reset_db: Reset the Optuna database
|
||||
cleanup_temp: Clean up temporary files
|
||||
backup: Create backup before reset
|
||||
dry_run: Preview changes without executing
|
||||
|
||||
Returns:
|
||||
Operation result dictionary
|
||||
"""
|
||||
resetter = StudyReset(study_name)
|
||||
|
||||
if not resetter.validate_study_exists():
|
||||
return {"status": "error", "message": f"Study '{study_name}' not found"}
|
||||
|
||||
results = {}
|
||||
|
||||
if reset_db:
|
||||
results["database"] = resetter.reset_database(backup=backup, dry_run=dry_run)
|
||||
|
||||
if cleanup_temp:
|
||||
results["temp_cleanup"] = resetter.cleanup_temp_files(dry_run=dry_run)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Reset or cleanup Atomizer optimization studies",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
Examples:
|
||||
# Show study status
|
||||
python -m optimization_engine.study_reset my_study --status
|
||||
|
||||
# Preview reset (dry run)
|
||||
python -m optimization_engine.study_reset my_study --full-reset --dry-run
|
||||
|
||||
# Reset database only
|
||||
python -m optimization_engine.study_reset my_study --reset-db
|
||||
|
||||
# Clean temp files only
|
||||
python -m optimization_engine.study_reset my_study --cleanup-temp
|
||||
|
||||
# Full reset with backup
|
||||
python -m optimization_engine.study_reset my_study --full-reset
|
||||
"""
|
||||
)
|
||||
|
||||
parser.add_argument("study_name", help="Name of the study")
|
||||
parser.add_argument("--status", action="store_true", help="Show study status only")
|
||||
parser.add_argument("--reset-db", action="store_true", help="Reset Optuna database")
|
||||
parser.add_argument("--cleanup-temp", action="store_true", help="Clean temporary files")
|
||||
parser.add_argument("--full-reset", action="store_true", help="Full reset (db + temp)")
|
||||
parser.add_argument("--archive", action="store_true", help="Archive results before reset")
|
||||
parser.add_argument("--delete", action="store_true", help="Delete study completely")
|
||||
parser.add_argument("--no-backup", action="store_true", help="Skip backup")
|
||||
parser.add_argument("--dry-run", action="store_true", help="Preview without executing")
|
||||
parser.add_argument("--yes", "-y", action="store_true", help="Skip confirmation prompts")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# Set up logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s [%(levelname)s] %(message)s'
|
||||
)
|
||||
|
||||
resetter = StudyReset(args.study_name)
|
||||
|
||||
if not resetter.validate_study_exists():
|
||||
print(f"Error: Study '{args.study_name}' not found")
|
||||
sys.exit(1)
|
||||
|
||||
if args.status:
|
||||
stats = resetter.get_study_stats()
|
||||
print(f"\nStudy: {args.study_name}")
|
||||
print("=" * 50)
|
||||
print(f" Trials: {stats['trials']} ({stats['completed']} completed, {stats['failed']} failed)")
|
||||
print(f" Database size: {stats['db_size_mb']:.2f} MB")
|
||||
print(f" Temp files: {stats['temp_files']} ({stats['temp_size_mb']:.2f} MB)")
|
||||
sys.exit(0)
|
||||
|
||||
# Confirmation
|
||||
if not args.dry_run and not args.yes:
|
||||
action = "full reset" if args.full_reset else \
|
||||
"delete" if args.delete else \
|
||||
"reset" if args.reset_db else "cleanup"
|
||||
response = input(f"\nReally {action} study '{args.study_name}'? [y/N] ")
|
||||
if response.lower() not in ['y', 'yes']:
|
||||
print("Aborted")
|
||||
sys.exit(0)
|
||||
|
||||
backup = not args.no_backup
|
||||
|
||||
if args.full_reset:
|
||||
result = resetter.full_reset(backup=backup, dry_run=args.dry_run)
|
||||
elif args.delete:
|
||||
result = resetter.delete_study(confirm=True, dry_run=args.dry_run)
|
||||
elif args.reset_db:
|
||||
result = resetter.reset_database(backup=backup, dry_run=args.dry_run)
|
||||
elif args.cleanup_temp:
|
||||
result = resetter.cleanup_temp_files(dry_run=args.dry_run)
|
||||
elif args.archive:
|
||||
result = resetter.archive_results(dry_run=args.dry_run)
|
||||
else:
|
||||
parser.print_help()
|
||||
sys.exit(0)
|
||||
|
||||
print("\nResult:")
|
||||
print(json.dumps(result, indent=2))
|
||||
|
||||
if args.dry_run:
|
||||
print("\n[DRY RUN - no changes made]")
|
||||
383
optimization_engine/template_loader.py
Normal file
383
optimization_engine/template_loader.py
Normal file
@@ -0,0 +1,383 @@
|
||||
"""
|
||||
Template Loader for Atomizer Optimization Studies
|
||||
|
||||
Creates new studies from templates with automatic folder structure creation.
|
||||
|
||||
Usage:
|
||||
from optimization_engine.template_loader import create_study_from_template, list_templates
|
||||
|
||||
# List available templates
|
||||
templates = list_templates()
|
||||
|
||||
# Create a new study from template
|
||||
create_study_from_template(
|
||||
template_name="beam_stiffness_optimization",
|
||||
study_name="my_beam_study"
|
||||
)
|
||||
"""
|
||||
|
||||
import json
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Optional
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
TEMPLATES_DIR = Path(__file__).parent.parent / "templates"
|
||||
STUDIES_DIR = Path(__file__).parent.parent / "studies"
|
||||
|
||||
|
||||
def list_templates() -> List[Dict[str, Any]]:
|
||||
"""
|
||||
List all available templates.
|
||||
|
||||
Returns:
|
||||
List of template metadata dictionaries
|
||||
"""
|
||||
templates = []
|
||||
|
||||
if not TEMPLATES_DIR.exists():
|
||||
return templates
|
||||
|
||||
for template_file in TEMPLATES_DIR.glob("*.json"):
|
||||
try:
|
||||
with open(template_file, 'r') as f:
|
||||
config = json.load(f)
|
||||
|
||||
template_info = config.get("template_info", {})
|
||||
templates.append({
|
||||
"name": template_file.stem,
|
||||
"description": config.get("description", "No description"),
|
||||
"category": template_info.get("category", "general"),
|
||||
"analysis_type": template_info.get("analysis_type", "unknown"),
|
||||
"objectives": len(config.get("objectives", [])),
|
||||
"design_variables": len(config.get("design_variables", [])),
|
||||
"path": str(template_file)
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not load template {template_file}: {e}")
|
||||
|
||||
return templates
|
||||
|
||||
|
||||
def get_template(template_name: str) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
Load a template by name.
|
||||
|
||||
Args:
|
||||
template_name: Name of the template (without .json extension)
|
||||
|
||||
Returns:
|
||||
Template configuration dictionary or None if not found
|
||||
"""
|
||||
template_path = TEMPLATES_DIR / f"{template_name}.json"
|
||||
|
||||
if not template_path.exists():
|
||||
# Try with .json extension already included
|
||||
template_path = TEMPLATES_DIR / template_name
|
||||
if not template_path.exists():
|
||||
return None
|
||||
|
||||
with open(template_path, 'r') as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def create_study_from_template(
|
||||
template_name: str,
|
||||
study_name: str,
|
||||
studies_dir: Optional[Path] = None,
|
||||
overrides: Optional[Dict[str, Any]] = None
|
||||
) -> Path:
|
||||
"""
|
||||
Create a new study from a template.
|
||||
|
||||
Args:
|
||||
template_name: Name of the template to use
|
||||
study_name: Name for the new study
|
||||
studies_dir: Base directory for studies (default: studies/)
|
||||
overrides: Dictionary of config values to override
|
||||
|
||||
Returns:
|
||||
Path to the created study directory
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If template doesn't exist
|
||||
FileExistsError: If study already exists
|
||||
"""
|
||||
if studies_dir is None:
|
||||
studies_dir = STUDIES_DIR
|
||||
|
||||
studies_dir = Path(studies_dir)
|
||||
|
||||
# Load template
|
||||
template = get_template(template_name)
|
||||
if template is None:
|
||||
available = [t["name"] for t in list_templates()]
|
||||
raise FileNotFoundError(
|
||||
f"Template '{template_name}' not found. "
|
||||
f"Available templates: {available}"
|
||||
)
|
||||
|
||||
# Check if study already exists
|
||||
study_path = studies_dir / study_name
|
||||
if study_path.exists():
|
||||
raise FileExistsError(
|
||||
f"Study '{study_name}' already exists at {study_path}. "
|
||||
"Choose a different name or delete the existing study."
|
||||
)
|
||||
|
||||
# Create study directory structure
|
||||
setup_dir = study_path / "1_setup"
|
||||
model_dir = setup_dir / "model"
|
||||
results_dir = study_path / "2_results"
|
||||
|
||||
setup_dir.mkdir(parents=True)
|
||||
model_dir.mkdir()
|
||||
results_dir.mkdir()
|
||||
|
||||
# Customize template for this study
|
||||
config = template.copy()
|
||||
config["study_name"] = study_name
|
||||
config["created_from_template"] = template_name
|
||||
config["created_at"] = datetime.now().isoformat()
|
||||
|
||||
# Update training data export path
|
||||
if "training_data_export" in config:
|
||||
export_dir = config["training_data_export"].get("export_dir", "")
|
||||
if "${study_name}" in export_dir:
|
||||
config["training_data_export"]["export_dir"] = export_dir.replace(
|
||||
"${study_name}", study_name
|
||||
)
|
||||
|
||||
# Apply overrides
|
||||
if overrides:
|
||||
_deep_update(config, overrides)
|
||||
|
||||
# Write configuration
|
||||
config_path = setup_dir / "optimization_config.json"
|
||||
with open(config_path, 'w') as f:
|
||||
json.dump(config, f, indent=2)
|
||||
|
||||
# Create run_optimization.py
|
||||
run_script_content = _generate_run_script(study_name, config)
|
||||
run_script_path = study_path / "run_optimization.py"
|
||||
with open(run_script_path, 'w') as f:
|
||||
f.write(run_script_content)
|
||||
|
||||
# Create README.md
|
||||
readme_content = _generate_study_readme(study_name, config, template_name)
|
||||
readme_path = study_path / "README.md"
|
||||
with open(readme_path, 'w') as f:
|
||||
f.write(readme_content)
|
||||
|
||||
print(f"Created study '{study_name}' from template '{template_name}'")
|
||||
print(f" Location: {study_path}")
|
||||
print(f" Config: {config_path}")
|
||||
print(f"\nNext steps:")
|
||||
print(f" 1. Add your NX model files to: {model_dir}")
|
||||
print(f" 2. Update design variable bounds in optimization_config.json")
|
||||
print(f" 3. Run: python {run_script_path} --trials 50")
|
||||
|
||||
return study_path
|
||||
|
||||
|
||||
def _deep_update(base: Dict, updates: Dict) -> Dict:
|
||||
"""Recursively update a dictionary."""
|
||||
for key, value in updates.items():
|
||||
if key in base and isinstance(base[key], dict) and isinstance(value, dict):
|
||||
_deep_update(base[key], value)
|
||||
else:
|
||||
base[key] = value
|
||||
return base
|
||||
|
||||
|
||||
def _generate_run_script(study_name: str, config: Dict[str, Any]) -> str:
|
||||
"""Generate the run_optimization.py script for a study."""
|
||||
return f'''"""
|
||||
Optimization Runner for {study_name}
|
||||
|
||||
Auto-generated from template: {config.get('created_from_template', 'unknown')}
|
||||
Created: {config.get('created_at', 'unknown')}
|
||||
|
||||
Usage:
|
||||
python run_optimization.py --trials 50
|
||||
python run_optimization.py --trials 25 --resume
|
||||
python run_optimization.py --trials 100 --enable-nn
|
||||
"""
|
||||
|
||||
import sys
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
# Add project root to path
|
||||
project_root = Path(__file__).parent.parent.parent
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
from optimization_engine.study_runner import run_study
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="{config.get('description', study_name)}")
|
||||
parser.add_argument('--trials', type=int, default=30, help='Number of trials to run')
|
||||
parser.add_argument('--resume', action='store_true', help='Resume existing study')
|
||||
parser.add_argument('--enable-nn', action='store_true', help='Enable neural network acceleration')
|
||||
parser.add_argument('--validate-only', action='store_true', help='Only validate setup, do not run')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
study_dir = Path(__file__).parent
|
||||
config_path = study_dir / "1_setup" / "optimization_config.json"
|
||||
|
||||
if args.validate_only:
|
||||
from optimization_engine.validators import validate_study
|
||||
result = validate_study("{study_name}")
|
||||
print(result)
|
||||
return
|
||||
|
||||
run_study(
|
||||
config_path=config_path,
|
||||
n_trials=args.trials,
|
||||
resume=args.resume,
|
||||
enable_neural=args.enable_nn
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
'''
|
||||
|
||||
|
||||
def _generate_study_readme(study_name: str, config: Dict[str, Any], template_name: str) -> str:
|
||||
"""Generate a README.md for the study."""
|
||||
objectives = config.get("objectives", [])
|
||||
design_vars = config.get("design_variables", [])
|
||||
constraints = config.get("constraints", [])
|
||||
|
||||
obj_list = "\n".join([f"- **{o.get('name', 'unnamed')}**: {o.get('goal', 'minimize')} - {o.get('description', '')}" for o in objectives])
|
||||
dv_list = "\n".join([f"- **{d.get('parameter', 'unnamed')}**: [{d.get('bounds', [0, 1])[0]}, {d.get('bounds', [0, 1])[1]}] - {d.get('description', '')}" for d in design_vars])
|
||||
const_list = "\n".join([f"- **{c.get('name', 'unnamed')}**: {c.get('type', 'less_than')} {c.get('threshold', 0)} - {c.get('description', '')}" for c in constraints])
|
||||
|
||||
return f'''# {study_name}
|
||||
|
||||
{config.get('description', 'Optimization study')}
|
||||
|
||||
**Template**: {template_name}
|
||||
**Created**: {config.get('created_at', 'unknown')}
|
||||
|
||||
## Engineering Context
|
||||
|
||||
{config.get('engineering_context', 'No context provided')}
|
||||
|
||||
## Objectives
|
||||
|
||||
{obj_list if obj_list else 'None defined'}
|
||||
|
||||
## Design Variables
|
||||
|
||||
{dv_list if dv_list else 'None defined'}
|
||||
|
||||
## Constraints
|
||||
|
||||
{const_list if const_list else 'None defined'}
|
||||
|
||||
## Setup Instructions
|
||||
|
||||
1. **Add NX Model Files**
|
||||
|
||||
Copy your NX part (.prt), simulation (.sim), and FEM (.fem) files to:
|
||||
```
|
||||
1_setup/model/
|
||||
```
|
||||
|
||||
2. **Configure Design Variables**
|
||||
|
||||
Edit `1_setup/optimization_config.json`:
|
||||
- Ensure `design_variables[].parameter` matches your NX expression names
|
||||
- Adjust bounds to your design space
|
||||
|
||||
3. **Validate Setup**
|
||||
|
||||
```bash
|
||||
python run_optimization.py --validate-only
|
||||
```
|
||||
|
||||
## Running the Optimization
|
||||
|
||||
### Basic Run
|
||||
```bash
|
||||
python run_optimization.py --trials 50
|
||||
```
|
||||
|
||||
### Resume Interrupted Run
|
||||
```bash
|
||||
python run_optimization.py --trials 25 --resume
|
||||
```
|
||||
|
||||
### With Neural Network Acceleration
|
||||
```bash
|
||||
python run_optimization.py --trials 100 --enable-nn
|
||||
```
|
||||
|
||||
## Results
|
||||
|
||||
After optimization, results are saved in `2_results/`:
|
||||
- `study.db` - Optuna database with all trials
|
||||
- `history.json` - Trial history
|
||||
- `optimization_summary.json` - Summary with best parameters
|
||||
|
||||
## Visualization
|
||||
|
||||
View results with Optuna Dashboard:
|
||||
```bash
|
||||
optuna-dashboard sqlite:///2_results/study.db
|
||||
```
|
||||
|
||||
Or generate a report:
|
||||
```bash
|
||||
python -m optimization_engine.generate_report {study_name}
|
||||
```
|
||||
'''
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description="Atomizer Template Loader")
|
||||
subparsers = parser.add_subparsers(dest="command", help="Commands")
|
||||
|
||||
# List templates
|
||||
list_parser = subparsers.add_parser("list", help="List available templates")
|
||||
|
||||
# Create study
|
||||
create_parser = subparsers.add_parser("create", help="Create study from template")
|
||||
create_parser.add_argument("--template", "-t", required=True, help="Template name")
|
||||
create_parser.add_argument("--name", "-n", required=True, help="Study name")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.command == "list":
|
||||
templates = list_templates()
|
||||
if not templates:
|
||||
print("No templates found in templates/")
|
||||
else:
|
||||
print("Available templates:")
|
||||
print("-" * 60)
|
||||
for t in templates:
|
||||
print(f" {t['name']}")
|
||||
print(f" {t['description']}")
|
||||
print(f" Category: {t['category']} | Analysis: {t['analysis_type']}")
|
||||
print(f" Design vars: {t['design_variables']} | Objectives: {t['objectives']}")
|
||||
print()
|
||||
|
||||
elif args.command == "create":
|
||||
try:
|
||||
study_path = create_study_from_template(
|
||||
template_name=args.template,
|
||||
study_name=args.name
|
||||
)
|
||||
except (FileNotFoundError, FileExistsError) as e:
|
||||
print(f"Error: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
else:
|
||||
parser.print_help()
|
||||
Reference in New Issue
Block a user