Delete learning_hub/model_trainer.py

learning_hub/model_trainer.py

@@ -1,74 +0,0 @@
-# learning_hub/model_trainer.py
-# (V32.0 - DORMANT: Model Retraining Logic)
-# ⚠️ This module is currently DISABLED by default.
-# It is kept for future manual triggering of model retraining.
-
-import os
-import asyncio
-import traceback
-import numpy as np
-import pandas as pd
-import xgboost as xgb
-from typing import List, Dict
-
-class ModelTrainer:
-    """
-    🏋️‍♂️ The Gym: Responsible for heavy lifting (Model Retraining).
-    Not active in the daily loop. Only triggered manually for regime updates.
-    """
-    def __init__(self, r2_service):
-        self.r2 = r2_service
-        self.local_model_path = "ml_models/layer2/Titan_XGB_V1.json"
-        print("💤 [ModelTrainer] Module loaded but dormant.")
-
-    async def train_model_manually(self, training_data: List[Dict]):
-        """
-        دالة للتدريب اليدوي عند الحاجة.
-        تتوقع قائمة من القواميس تحتوي على 'features' و 'label'.
-        """
-        print("🏋️‍♂️ [ModelTrainer] Manual training sequence started...")
-        try:
-            if not training_data:
-                print("❌ [Trainer] No data provided.")
-                return
-
-            # 1. Prepare Data
-            df = pd.DataFrame(training_data)
-            if 'features' not in df.columns or 'label' not in df.columns:
-                print("❌ [Trainer] Data missing features/label columns.")
-                return
-
-            X = np.array(df['features'].tolist())
-            y = np.array(df['label'].tolist())
-            dtrain = xgb.DMatrix(X, label=y)
-
-            # 2. Load Existing Model (Warm Start)
-            model = xgb.Booster()
-            if os.path.exists(self.local_model_path):
-                model.load_model(self.local_model_path)
-                print("   -> Loaded existing model for incremental training.")
-            
-            # 3. Train (Incremental)
-            params = {
-                'eta': 0.01,
-                'max_depth': 6,
-                'objective': 'binary:logistic',
-                'eval_metric': 'logloss'
-            }
-            
-            # تحديث النموذج بـ 50 جولة فقط
-            new_model = xgb.train(params, dtrain, num_boost_round=50, xgb_model=model)
-            
-            # 4. Save
-            new_model.save_model(self.local_model_path)
-            print(f"✅ [Trainer] Model updated and saved to {self.local_model_path}")
-            
-            # 5. Upload to R2 (Logic commented out until needed)
-            # await self.r2.upload_file_async(...)
-            
-            return "Training Success"
-
-        except Exception as e:
-            print(f"❌ [Trainer] Training failed: {e}")
-            traceback.print_exc()
-            return f"Error: {e}"