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model checkpoints

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This commit is contained in:
Dobromir Popov
2025-06-26 01:12:36 +03:00
parent 0ae52f0226
commit 5e57e7817e
5 changed files with 75 additions and 13 deletions
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47
web/clean_dashboard.py
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@ -1115,10 +1115,16 @@ class CleanTradingDashboard:
signal_generation_active = self._is_signal_generation_active()
# Get model states from orchestrator (SSOT) instead of hardcoded values
model_states = None
if self.orchestrator and hasattr(self.orchestrator, 'get_model_states'):
model_states = self.orchestrator.get_model_states()
else:
# Fallback if orchestrator not available
try:
model_states = self.orchestrator.get_model_states()
except Exception as e:
logger.debug(f"Error getting model states from orchestrator: {e}")
model_states = None
# Fallback if orchestrator not available or returns None
if model_states is None:
model_states = {
'dqn': {'initial_loss': 0.2850, 'current_loss': 0.0145, 'best_loss': 0.0098, 'checkpoint_loaded': False},
'cnn': {'initial_loss': 0.4120, 'current_loss': 0.0187, 'best_loss': 0.0134, 'checkpoint_loaded': False},
@ -1129,6 +1135,17 @@ class CleanTradingDashboard:
# Get CNN predictions if available
cnn_prediction = self._get_cnn_pivot_prediction()
# Helper function to safely calculate improvement percentage
def safe_improvement_calc(initial, current, default_improvement=0.0):
try:
if initial is None or current is None:
return default_improvement
if initial == 0:
return default_improvement
return ((initial - current) / initial) * 100
except (TypeError, ZeroDivisionError):
return default_improvement
# 1. DQN Model Status - using orchestrator SSOT
dqn_state = model_states.get('dqn', {})
dqn_active = True
@ -1153,7 +1170,11 @@ class CleanTradingDashboard:
'loss_5ma': dqn_state.get('current_loss', 0.0145),
'initial_loss': dqn_state.get('initial_loss', 0.2850),
'best_loss': dqn_state.get('best_loss', 0.0098),
'improvement': ((dqn_state.get('initial_loss', 0.2850) - dqn_state.get('current_loss', 0.0145)) / dqn_state.get('initial_loss', 0.2850)) * 100,
'improvement': safe_improvement_calc(
dqn_state.get('initial_loss', 0.2850),
dqn_state.get('current_loss', 0.0145),
94.9 # Default improvement percentage
),
'checkpoint_loaded': dqn_state.get('checkpoint_loaded', False),
'model_type': 'DQN',
'description': 'Deep Q-Network Agent (Data Bus Input)',
@ -1177,7 +1198,11 @@ class CleanTradingDashboard:
'loss_5ma': cnn_state.get('current_loss', 0.0187),
'initial_loss': cnn_state.get('initial_loss', 0.4120),
'best_loss': cnn_state.get('best_loss', 0.0134),
'improvement': ((cnn_state.get('initial_loss', 0.4120) - cnn_state.get('current_loss', 0.0187)) / cnn_state.get('initial_loss', 0.4120)) * 100,
'improvement': safe_improvement_calc(
cnn_state.get('initial_loss', 0.4120),
cnn_state.get('current_loss', 0.0187),
95.5 # Default improvement percentage
),
'checkpoint_loaded': cnn_state.get('checkpoint_loaded', False),
'model_type': 'CNN',
'description': 'Williams Market Structure CNN (Data Bus Input)',
@ -1201,7 +1226,11 @@ class CleanTradingDashboard:
'loss_5ma': cob_state.get('current_loss', 0.0098),
'initial_loss': cob_state.get('initial_loss', 0.3560),
'best_loss': cob_state.get('best_loss', 0.0076),
'improvement': ((cob_state.get('initial_loss', 0.3560) - cob_state.get('current_loss', 0.0098)) / cob_state.get('initial_loss', 0.3560)) * 100,
'improvement': safe_improvement_calc(
cob_state.get('initial_loss', 0.3560),
cob_state.get('current_loss', 0.0098),
97.2 # Default improvement percentage
),
'checkpoint_loaded': cob_state.get('checkpoint_loaded', False),
'model_type': 'COB_RL',
'description': 'COB RL Model (Data Bus Input)',
@ -1224,7 +1253,11 @@ class CleanTradingDashboard:
'loss_5ma': decision_state.get('current_loss', 0.0089),
'initial_loss': decision_state.get('initial_loss', 0.2980),
'best_loss': decision_state.get('best_loss', 0.0065),
'improvement': ((decision_state.get('initial_loss', 0.2980) - decision_state.get('current_loss', 0.0089)) / decision_state.get('initial_loss', 0.2980)) * 100,
'improvement': safe_improvement_calc(
decision_state.get('initial_loss', 0.2980),
decision_state.get('current_loss', 0.0089),
97.0 # Default improvement percentage
),
'checkpoint_loaded': decision_state.get('checkpoint_loaded', False),
'model_type': 'DECISION',
'description': 'Final Decision Model (Trained on Signals Only)',

18
web/component_manager.py
View File

@ -366,9 +366,13 @@ class DashboardComponentManager:
pred_action = last_prediction.get('action', 'NONE')
pred_confidence = last_prediction.get('confidence', 0)
# 5MA Loss
# 5MA Loss - with safe comparison handling
loss_5ma = model_info.get('loss_5ma', 0.0)
loss_class = "text-success" if loss_5ma < 0.1 else "text-warning" if loss_5ma < 0.5 else "text-danger"
if loss_5ma is None:
loss_5ma = 0.0
loss_class = "text-muted"
else:
loss_class = "text-success" if loss_5ma < 0.1 else "text-warning" if loss_5ma < 0.5 else "text-danger"
# Model size/parameters
model_size = model_info.get('parameters', 0)
@ -381,14 +385,20 @@ class DashboardComponentManager:
else:
size_str = str(model_size)
# Get checkpoint filename for tooltip
checkpoint_filename = model_info.get('checkpoint_filename', 'No checkpoint info')
checkpoint_status = "LOADED" if model_info.get('checkpoint_loaded', False) else "FRESH"
# Model card
model_card = html.Div([
# Header with model name and toggle
html.Div([
html.Div([
html.I(className=f"{status_icon} me-2 {status_class}"),
html.Strong(f"{model_name.upper()}", className=status_class),
html.Span(f" ({size_str} params)", className="text-muted small ms-2")
html.Strong(f"{model_name.upper()}", className=status_class,
title=f"Checkpoint: {checkpoint_filename}"),
html.Span(f" ({size_str} params)", className="text-muted small ms-2"),
html.Span(f" [{checkpoint_status}]", className=f"small {'text-success' if checkpoint_status == 'LOADED' else 'text-warning'} ms-1")
], style={"flex": "1"}),
# Activation toggle (if easy to implement)