Files
gogo2/debug_training_methods.py
Dobromir Popov 86373fd5a7 training
2025-07-27 19:45:16 +03:00

84 lines
3.3 KiB
Python

#!/usr/bin/env python3
"""
Debug Training Methods
This script checks what training methods are available on each model.
"""
import asyncio
from core.orchestrator import TradingOrchestrator
from core.data_provider import DataProvider
async def debug_training_methods():
"""Debug the available training methods on each model"""
print("=== Debugging Training Methods ===")
# Initialize orchestrator
print("1. Initializing orchestrator...")
data_provider = DataProvider()
orchestrator = TradingOrchestrator(data_provider=data_provider)
# Wait for initialization
await asyncio.sleep(2)
print("\n2. Checking available training methods on each model:")
for model_name, model_interface in orchestrator.model_registry.models.items():
print(f"\n--- {model_name} ---")
print(f"Interface type: {type(model_interface).__name__}")
# Get underlying model
underlying_model = getattr(model_interface, 'model', None)
if underlying_model:
print(f"Underlying model type: {type(underlying_model).__name__}")
else:
print("No underlying model found")
continue
# Check for training methods
training_methods = []
for method in ['train_on_outcome', 'add_experience', 'remember', 'replay', 'add_training_sample', 'train', 'train_with_reward', 'update_loss']:
if hasattr(underlying_model, method):
training_methods.append(method)
print(f"Available training methods: {training_methods}")
# Check for specific attributes
attributes = []
for attr in ['memory', 'batch_size', 'training_data']:
if hasattr(underlying_model, attr):
attr_value = getattr(underlying_model, attr)
if attr == 'memory' and hasattr(attr_value, '__len__'):
attributes.append(f"{attr}(len={len(attr_value)})")
elif attr == 'training_data' and hasattr(attr_value, '__len__'):
attributes.append(f"{attr}(len={len(attr_value)})")
else:
attributes.append(f"{attr}={attr_value}")
print(f"Relevant attributes: {attributes}")
# Check if it's an RL agent
if hasattr(underlying_model, 'act') and hasattr(underlying_model, 'remember'):
print("✅ Detected as RL Agent")
elif hasattr(underlying_model, 'predict') and hasattr(underlying_model, 'add_training_sample'):
print("✅ Detected as CNN Model")
else:
print("❓ Unknown model type")
print("\n3. Testing a simple training attempt:")
# Get a prediction first
predictions = await orchestrator._get_all_predictions('ETH/USDT')
print(f"Got {len(predictions)} predictions")
# Try to trigger training for each model
for model_name in orchestrator.model_registry.models.keys():
print(f"\nTesting training for {model_name}...")
try:
await orchestrator._trigger_immediate_training_for_model(model_name, 'ETH/USDT')
print(f"✅ Training attempt completed for {model_name}")
except Exception as e:
print(f"❌ Training failed for {model_name}: {e}")
if __name__ == "__main__":
asyncio.run(debug_training_methods())