models cleanup
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Dobromir Popov
285
cleanup_and_setup_models.py
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285
cleanup_and_setup_models.py
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#!/usr/bin/env python3
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"""
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Model Cleanup and Training Setup Script
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This script:
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1. Backs up current models
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2. Cleans old/conflicting models
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3. Sets up proper training progression system
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4. Initializes fresh model training
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"""
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import os
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import shutil
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import json
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import logging
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from datetime import datetime
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from pathlib import Path
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import torch
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# Setup logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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class ModelCleanupManager:
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"""Manager for cleaning up and organizing model files"""
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def __init__(self):
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self.root_dir = Path(".")
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self.models_dir = self.root_dir / "models"
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self.backup_dir = self.root_dir / "model_backups" / f"backup_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
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self.training_progress_file = self.models_dir / "training_progress.json"
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# Create backup directory
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self.backup_dir.mkdir(parents=True, exist_ok=True)
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logger.info(f"Created backup directory: {self.backup_dir}")
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def backup_existing_models(self):
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"""Backup all existing models before cleanup"""
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logger.info("🔄 Backing up existing models...")
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model_files = [
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# CNN models
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"models/cnn_final_20250331_001817.pt.pt",
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"models/cnn_best.pt.pt",
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"models/cnn_BTC_USDT_*.pt",
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"models/cnn_BTC_USD_*.pt",
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# RL models
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"models/trading_agent_*.pt",
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"models/trading_agent_*.backup",
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# Other models
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"models/saved/cnn_model_best.pt"
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]
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# Backup model files
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backup_count = 0
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for pattern in model_files:
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for file_path in self.root_dir.glob(pattern):
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if file_path.is_file():
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backup_path = self.backup_dir / file_path.relative_to(self.root_dir)
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backup_path.parent.mkdir(parents=True, exist_ok=True)
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shutil.copy2(file_path, backup_path)
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backup_count += 1
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logger.info(f" 📁 Backed up: {file_path}")
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logger.info(f"✅ Backed up {backup_count} model files to {self.backup_dir}")
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def clean_old_models(self):
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"""Remove old/conflicting model files"""
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logger.info("🧹 Cleaning old model files...")
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files_to_remove = [
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# Old CNN models with architecture conflicts
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"models/cnn_final_20250331_001817.pt.pt",
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"models/cnn_best.pt.pt",
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"models/cnn_BTC_USDT_20250329_021800.pt",
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"models/cnn_BTC_USDT_20250329_021448.pt",
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"models/cnn_BTC_USD_20250329_020711.pt",
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"models/cnn_BTC_USD_20250329_020430.pt",
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"models/cnn_BTC_USD_20250329_015217.pt",
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# Old RL models
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"models/trading_agent_final.pt",
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"models/trading_agent_best_pnl.pt",
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"models/trading_agent_best_reward.pt",
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"models/trading_agent_final.pt.backup",
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"models/trading_agent_best_net_pnl.pt",
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"models/trading_agent_best_net_pnl.pt.backup",
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"models/trading_agent_best_pnl.pt.backup",
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"models/trading_agent_best_reward.pt.backup",
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"models/trading_agent_live_trained.pt",
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# Checkpoint files
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"models/trading_agent_checkpoint_1650.pt.minimal",
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"models/trading_agent_checkpoint_1650.pt.params.json",
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"models/trading_agent_best_net_pnl.pt.policy.jit",
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"models/trading_agent_best_net_pnl.pt.params.json",
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"models/trading_agent_best_pnl.pt.params.json"
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]
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removed_count = 0
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for file_path in files_to_remove:
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path = Path(file_path)
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if path.exists():
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path.unlink()
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removed_count += 1
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logger.info(f" 🗑️ Removed: {path}")
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logger.info(f"✅ Removed {removed_count} old model files")
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def setup_training_progression(self):
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"""Set up training progression tracking system"""
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logger.info("📊 Setting up training progression system...")
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# Create training progress structure
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training_progress = {
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"created": datetime.now().isoformat(),
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"version": "1.0",
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"models": {
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"cnn": {
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"current_version": 1,
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"best_model": None,
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"training_history": [],
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"architecture": {
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"input_channels": 5,
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"window_size": 20,
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"output_classes": 3
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}
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},
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"rl": {
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"current_version": 1,
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"best_model": None,
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"training_history": [],
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"architecture": {
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"state_size": 100,
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"action_space": 3,
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"hidden_size": 256
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}
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},
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"williams_cnn": {
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"current_version": 1,
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"best_model": None,
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"training_history": [],
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"architecture": {
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"input_shape": [900, 50],
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"output_size": 10,
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"enabled": False # Disabled until TensorFlow available
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}
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}
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},
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"training_stats": {
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"total_sessions": 0,
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"best_accuracy": 0.0,
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"best_pnl": 0.0,
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"last_training": None
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}
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}
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# Save training progress
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with open(self.training_progress_file, 'w') as f:
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json.dump(training_progress, f, indent=2)
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logger.info(f"✅ Created training progress file: {self.training_progress_file}")
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def create_model_directories(self):
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"""Create clean model directory structure"""
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logger.info("📁 Creating clean model directory structure...")
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directories = [
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"models/cnn/current",
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"models/cnn/training",
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"models/cnn/best",
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"models/rl/current",
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"models/rl/training",
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"models/rl/best",
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"models/williams_cnn/current",
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"models/williams_cnn/training",
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"models/williams_cnn/best",
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"models/checkpoints",
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"models/training_logs"
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]
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for directory in directories:
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Path(directory).mkdir(parents=True, exist_ok=True)
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logger.info(f" 📂 Created: {directory}")
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logger.info("✅ Model directory structure created")
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def initialize_fresh_models(self):
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"""Initialize fresh model files for training"""
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logger.info("🆕 Initializing fresh models...")
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# Keep only the essential saved model
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essential_models = ["models/saved/cnn_model_best.pt"]
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for model_path in essential_models:
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if Path(model_path).exists():
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logger.info(f" ✅ Keeping essential model: {model_path}")
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else:
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logger.warning(f" ⚠️ Essential model not found: {model_path}")
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logger.info("✅ Fresh model initialization complete")
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def update_model_registry(self):
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"""Update model registry to use new structure"""
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logger.info("⚙️ Updating model registry configuration...")
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registry_config = {
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"model_paths": {
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"cnn_current": "models/cnn/current/",
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"cnn_best": "models/cnn/best/",
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"rl_current": "models/rl/current/",
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"rl_best": "models/rl/best/",
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"williams_current": "models/williams_cnn/current/",
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"williams_best": "models/williams_cnn/best/"
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},
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"auto_load_best": True,
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"memory_limit_gb": 8.0,
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"training_enabled": True
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}
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config_path = Path("models/registry_config.json")
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with open(config_path, 'w') as f:
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json.dump(registry_config, f, indent=2)
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logger.info(f"✅ Model registry config saved: {config_path}")
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def run_cleanup(self):
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"""Execute complete cleanup and setup process"""
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logger.info("🚀 Starting model cleanup and setup process...")
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logger.info("=" * 60)
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try:
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# Step 1: Backup existing models
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self.backup_existing_models()
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# Step 2: Clean old conflicting models
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self.clean_old_models()
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# Step 3: Setup training progression system
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self.setup_training_progression()
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# Step 4: Create clean directory structure
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self.create_model_directories()
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# Step 5: Initialize fresh models
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self.initialize_fresh_models()
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# Step 6: Update model registry
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self.update_model_registry()
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logger.info("=" * 60)
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logger.info("✅ Model cleanup and setup completed successfully!")
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logger.info(f"📁 Backup created at: {self.backup_dir}")
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logger.info("🔄 Ready for fresh training with enhanced RL!")
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except Exception as e:
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logger.error(f"❌ Error during cleanup: {e}")
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import traceback
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logger.error(traceback.format_exc())
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raise
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def main():
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"""Main execution function"""
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print("🧹 MODEL CLEANUP AND TRAINING SETUP")
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print("=" * 50)
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print("This script will:")
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print("1. Backup existing models")
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print("2. Remove old/conflicting models")
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print("3. Set up training progression tracking")
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print("4. Create clean directory structure")
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print("5. Initialize fresh training environment")
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print("=" * 50)
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response = input("Continue? (y/N): ").strip().lower()
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if response != 'y':
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print("❌ Cleanup cancelled")
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return
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cleanup_manager = ModelCleanupManager()
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cleanup_manager.run_cleanup()
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if __name__ == "__main__":
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main()
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