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loss /performance display

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This commit is contained in:
Dobromir Popov
2025-07-02 03:29:38 +03:00
parent 29e4076638
commit 11718bf92f
3 changed files with 128 additions and 48 deletions
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50
NN/models/saved/checkpoint_metadata.json
View File

@ -271,15 +271,15 @@
],
"decision": [
{
"checkpoint_id": "decision_20250702_020007",
"checkpoint_id": "decision_20250702_031158",
"model_name": "decision",
"model_type": "decision_fusion",
"file_path": "NN\\models\\saved\\decision\\decision_20250702_020007.pt",
"created_at": "2025-07-02T02:00:07.439094",
"file_path": "NN\\models\\saved\\decision\\decision_20250702_031158.pt",
"created_at": "2025-07-02T03:11:58.134610",
"file_size_mb": 0.06720924377441406,
"performance_score": 9.999997759969705,
"performance_score": 101.79191339107027,
"accuracy": null,
"loss": 2.240030294586859e-06,
"loss": 8.087262915050057e-05,
"val_accuracy": null,
"val_loss": null,
"reward": null,
@ -291,15 +291,15 @@
"wandb_artifact_name": null
},
{
"checkpoint_id": "decision_20250702_020007",
"checkpoint_id": "decision_20250702_031158",
"model_name": "decision",
"model_type": "decision_fusion",
"file_path": "NN\\models\\saved\\decision\\decision_20250702_020007.pt",
"created_at": "2025-07-02T02:00:07.707012",
"file_path": "NN\\models\\saved\\decision\\decision_20250702_031158.pt",
"created_at": "2025-07-02T03:11:58.418736",
"file_size_mb": 0.06720924377441406,
"performance_score": 9.999997758801166,
"performance_score": 101.78996565336683,
"accuracy": null,
"loss": 2.2411988334327916e-06,
"loss": 0.00010035353615320573,
"val_accuracy": null,
"val_loss": null,
"reward": null,
@ -311,15 +311,15 @@
"wandb_artifact_name": null
},
{
"checkpoint_id": "decision_20250702_020007",
"checkpoint_id": "decision_20250702_031157",
"model_name": "decision",
"model_type": "decision_fusion",
"file_path": "NN\\models\\saved\\decision\\decision_20250702_020007.pt",
"created_at": "2025-07-02T02:00:07.570949",
"file_path": "NN\\models\\saved\\decision\\decision_20250702_031157.pt",
"created_at": "2025-07-02T03:11:57.126366",
"file_size_mb": 0.06720924377441406,
"performance_score": 9.999997757764104,
"performance_score": 101.78856752244535,
"accuracy": null,
"loss": 2.2422358958193754e-06,
"loss": 0.00011433784719530295,
"val_accuracy": null,
"val_loss": null,
"reward": null,
@ -331,15 +331,15 @@
"wandb_artifact_name": null
},
{
"checkpoint_id": "decision_20250702_020007",
"checkpoint_id": "decision_20250702_031157",
"model_name": "decision",
"model_type": "decision_fusion",
"file_path": "NN\\models\\saved\\decision\\decision_20250702_020007.pt",
"created_at": "2025-07-02T02:00:07.867047",
"file_path": "NN\\models\\saved\\decision\\decision_20250702_031157.pt",
"created_at": "2025-07-02T03:11:57.884663",
"file_size_mb": 0.06720924377441406,
"performance_score": 9.999997757753505,
"performance_score": 101.78849664377086,
"accuracy": null,
"loss": 2.2422464945511442e-06,
"loss": 0.00011504679653424116,
"val_accuracy": null,
"val_loss": null,
"reward": null,
@ -351,15 +351,15 @@
"wandb_artifact_name": null
},
{
"checkpoint_id": "decision_20250702_020007",
"checkpoint_id": "decision_20250702_031156",
"model_name": "decision",
"model_type": "decision_fusion",
"file_path": "NN\\models\\saved\\decision\\decision_20250702_020007.pt",
"created_at": "2025-07-02T02:00:07.302999",
"file_path": "NN\\models\\saved\\decision\\decision_20250702_031156.pt",
"created_at": "2025-07-02T03:11:56.934135",
"file_size_mb": 0.06720924377441406,
"performance_score": 9.999997754320662,
"performance_score": 101.7830878040414,
"accuracy": null,
"loss": 2.245679338091438e-06,
"loss": 0.00016915056666120008,
"val_accuracy": null,
"val_loss": null,
"reward": null,

75
utils/checkpoint_manager.py
View File

@ -12,6 +12,7 @@ from typing import Dict, List, Optional, Tuple, Any
from dataclasses import dataclass, asdict
from collections import defaultdict
import torch
import random
try:
import wandb
@ -150,36 +151,80 @@ class CheckpointManager:
return None
def _calculate_performance_score(self, metrics: Dict[str, float]) -> float:
"""Calculate performance score with improved sensitivity for training models"""
score = 0.0
if 'accuracy' in metrics:
score += metrics['accuracy'] * 100
if 'val_accuracy' in metrics:
score += metrics['val_accuracy'] * 100
# Prioritize loss reduction for active training models
if 'loss' in metrics:
score += max(0, 10 - metrics['loss'])
if 'val_loss' in metrics:
score += max(0, 10 - metrics['val_loss'])
if 'reward' in metrics:
score += metrics['reward']
if 'pnl' in metrics:
score += metrics['pnl']
# Invert loss so lower loss = higher score, with better scaling
loss_value = metrics['loss']
if loss_value > 0:
score += max(0, 100 / (1 + loss_value)) # More sensitive to loss changes
else:
score += 100 # Perfect loss
# Add other metrics with appropriate weights
if 'accuracy' in metrics:
score += metrics['accuracy'] * 50 # Reduced weight to balance with loss
if 'val_accuracy' in metrics:
score += metrics['val_accuracy'] * 50
if 'val_loss' in metrics:
val_loss = metrics['val_loss']
if val_loss > 0:
score += max(0, 50 / (1 + val_loss))
if 'reward' in metrics:
score += metrics['reward'] * 10
if 'pnl' in metrics:
score += metrics['pnl'] * 5
if 'training_samples' in metrics:
# Bonus for processing more training samples
score += min(10, metrics['training_samples'] / 10)
# Ensure minimum score for any training activity
if score == 0.0 and metrics:
# Use the first available metric with better scaling
first_metric = next(iter(metrics.values()))
score = first_metric if first_metric > 0 else 0.1
if first_metric > 0:
score = max(0.1, min(10, first_metric))
else:
score = 0.1
return max(score, 0.1)
def _should_save_checkpoint(self, model_name: str, performance_score: float) -> bool:
"""Improved checkpoint saving logic with more frequent saves during training"""
if model_name not in self.checkpoints or not self.checkpoints[model_name]:
return True
return True # Always save first checkpoint
# Allow more checkpoints during active training
if len(self.checkpoints[model_name]) < self.max_checkpoints:
return True
worst_score = min(cp.performance_score for cp in self.checkpoints[model_name])
return performance_score > worst_score
# Get current best and worst scores
scores = [cp.performance_score for cp in self.checkpoints[model_name]]
best_score = max(scores)
worst_score = min(scores)
# Save if better than worst (more frequent saves)
if performance_score > worst_score:
return True
# For high-performing models (score > 100), be more sensitive to small improvements
if best_score > 100:
# Save if within 0.1% of best score (very sensitive for converged models)
if performance_score >= best_score * 0.999:
return True
else:
# Also save if we're within 10% of best score (capture near-optimal models)
if performance_score >= best_score * 0.9:
return True
# Save more frequently during active training (every 5th attempt instead of 10th)
if random.random() < 0.2: # 20% chance to save anyway
logger.info(f"Saving checkpoint for {model_name} - periodic save during active training")
return True
return False
def _save_model_file(self, model, file_path: Path, model_type: str) -> bool:
try:

51
web/clean_dashboard.py
View File

@ -3981,19 +3981,45 @@ class CleanTradingDashboard:
async def _on_trading_decision(self, decision):
"""Handle trading decision from orchestrator."""
try:
action = getattr(decision, 'action', decision.get('action'))
# Handle both object and dict formats
if hasattr(decision, 'action'):
action = getattr(decision, 'action', 'HOLD')
symbol = getattr(decision, 'symbol', 'ETH/USDT')
confidence = getattr(decision, 'confidence', 0.0)
else:
action = decision.get('action', 'HOLD')
symbol = decision.get('symbol', 'ETH/USDT')
confidence = decision.get('confidence', 0.0)
if action == 'HOLD':
return
symbol = getattr(decision, 'symbol', decision.get('symbol', 'ETH/USDT'))
if 'ETH' not in symbol.upper():
return
dashboard_decision = asdict(decision) if not isinstance(decision, dict) else decision.copy()
dashboard_decision['timestamp'] = datetime.now()
dashboard_decision['executed'] = False
# Convert to dict format for dashboard storage
if hasattr(decision, '__dict__'):
dashboard_decision = {
'action': action,
'symbol': symbol,
'confidence': confidence,
'timestamp': datetime.now(),
'executed': False
}
# Add any other attributes from the decision object
for attr in ['price', 'quantity', 'reasoning', 'model_source']:
if hasattr(decision, attr):
dashboard_decision[attr] = getattr(decision, attr)
else:
dashboard_decision = decision.copy()
dashboard_decision['timestamp'] = datetime.now()
dashboard_decision['executed'] = False
self.recent_decisions.append(dashboard_decision)
if len(self.recent_decisions) > 200:
self.recent_decisions.pop(0)
logger.info(f"[ORCHESTRATOR SIGNAL] Received: {action} for {symbol}")
logger.info(f"[ORCHESTRATOR SIGNAL] Received: {action} for {symbol} (confidence: {confidence:.3f})")
except Exception as e:
logger.error(f"Error handling trading decision: {e}")
@ -4523,6 +4549,10 @@ class CleanTradingDashboard:
network.train()
action_logits, predicted_confidence = network(features_tensor)
# Ensure predicted_confidence has a batch dimension if it doesn't already
if predicted_confidence.dim() == 0:
predicted_confidence = predicted_confidence.unsqueeze(0)
# Calculate losses
action_loss = nn.CrossEntropyLoss()(action_logits, action_target_tensor)
confidence_loss = nn.MSELoss()(predicted_confidence, confidence_target_tensor)
@ -4559,7 +4589,10 @@ class CleanTradingDashboard:
performance_metrics = {
'loss': avg_loss,
'training_samples': training_samples,
'model_parameters': sum(p.numel() for p in network.parameters())
'model_parameters': sum(p.numel() for p in network.parameters()),
'loss_improvement': 1.0 / (1.0 + avg_loss), # Higher is better
'training_iterations': loss_count,
'average_confidence': confidence_target if 'confidence_target' in locals() else 0.5
}
metadata = save_checkpoint(
@ -4577,7 +4610,9 @@ class CleanTradingDashboard:
logger.error(f"Error saving decision fusion checkpoint: {e}")
if training_samples > 0:
logger.info(f"DECISION TRAINING: Processed {training_samples} decision fusion samples")
avg_loss_info = f", avg_loss={total_loss/loss_count:.6f}" if loss_count > 0 else ""
performance_score = 100 / (1 + (total_loss/loss_count)) if loss_count > 0 else 0.1
logger.info(f"DECISION TRAINING: Processed {training_samples} decision fusion samples{avg_loss_info}, perf_score={performance_score:.4f}")
except Exception as e:
logger.error(f"Error in real decision fusion training: {e}")