Initial microservices setup from artifacts

services/training/app/ml/trainer.py

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+"""
+ML Training implementation
+"""
+
+import asyncio
+import logging
+from typing import Dict, Any, List
+import pandas as pd
+from datetime import datetime
+import joblib
+import os
+from prophet import Prophet
+import numpy as np
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+from app.core.config import settings
+
+logger = logging.getLogger(__name__)
+
+class MLTrainer:
+    """ML training implementation"""
+    
+    def __init__(self):
+        self.model_storage_path = settings.MODEL_STORAGE_PATH
+        os.makedirs(self.model_storage_path, exist_ok=True)
+    
+    async def train_models(self, training_data: Dict[str, Any], job_id: str, db) -> Dict[str, Any]:
+        """Train models for all products"""
+        
+        models_result = {}
+        
+        # Get sales data
+        sales_data = training_data.get("sales_data", [])
+        external_data = training_data.get("external_data", {})
+        
+        # Group by product
+        products_data = self._group_by_product(sales_data)
+        
+        # Train model for each product
+        for product_name, product_sales in products_data.items():
+            try:
+                model_result = await self._train_product_model(
+                    product_name, 
+                    product_sales, 
+                    external_data, 
+                    job_id
+                )
+                models_result[product_name] = model_result
+                
+            except Exception as e:
+                logger.error(f"Failed to train model for {product_name}: {e}")
+                continue
+        
+        return models_result
+    
+    def _group_by_product(self, sales_data: List[Dict]) -> Dict[str, List[Dict]]:
+        """Group sales data by product"""
+        
+        products = {}
+        for sale in sales_data:
+            product_name = sale.get("product_name")
+            if product_name not in products:
+                products[product_name] = []
+            products[product_name].append(sale)
+        
+        return products
+    
+    async def _train_product_model(self, product_name: str, sales_data: List[Dict], external_data: Dict, job_id: str) -> Dict[str, Any]:
+        """Train Prophet model for a single product"""
+        
+        # Convert to DataFrame
+        df = pd.DataFrame(sales_data)
+        df['date'] = pd.to_datetime(df['date'])
+        
+        # Aggregate daily sales
+        daily_sales = df.groupby('date')['quantity_sold'].sum().reset_index()
+        daily_sales.columns = ['ds', 'y']
+        
+        # Add external features
+        daily_sales = self._add_external_features(daily_sales, external_data)
+        
+        # Train Prophet model
+        model = Prophet(
+            seasonality_mode=settings.PROPHET_SEASONALITY_MODE,
+            daily_seasonality=settings.PROPHET_DAILY_SEASONALITY,
+            weekly_seasonality=settings.PROPHET_WEEKLY_SEASONALITY,
+            yearly_seasonality=settings.PROPHET_YEARLY_SEASONALITY
+        )
+        
+        # Add regressors
+        model.add_regressor('temperature')
+        model.add_regressor('humidity')
+        model.add_regressor('precipitation')
+        model.add_regressor('traffic_volume')
+        
+        # Fit model
+        model.fit(daily_sales)
+        
+        # Save model
+        model_path = os.path.join(
+            self.model_storage_path,
+            f"{job_id}_{product_name}_prophet_model.pkl"
+        )
+        
+        joblib.dump(model, model_path)
+        
+        return {
+            "type": "prophet",
+            "path": model_path,
+            "training_samples": len(daily_sales),
+            "features": ["temperature", "humidity", "precipitation", "traffic_volume"],
+            "hyperparameters": {
+                "seasonality_mode": settings.PROPHET_SEASONALITY_MODE,
+                "daily_seasonality": settings.PROPHET_DAILY_SEASONALITY,
+                "weekly_seasonality": settings.PROPHET_WEEKLY_SEASONALITY,
+                "yearly_seasonality": settings.PROPHET_YEARLY_SEASONALITY
+            }
+        }
+    
+    def _add_external_features(self, daily_sales: pd.DataFrame, external_data: Dict) -> pd.DataFrame:
+        """Add external features to sales data"""
+        
+        # Add weather data
+        weather_data = external_data.get("weather", [])
+        if weather_data:
+            weather_df = pd.DataFrame(weather_data)
+            weather_df['ds'] = pd.to_datetime(weather_df['date'])
+            daily_sales = daily_sales.merge(weather_df[['ds', 'temperature', 'humidity', 'precipitation']], on='ds', how='left')
+        
+        # Add traffic data
+        traffic_data = external_data.get("traffic", [])
+        if traffic_data:
+            traffic_df = pd.DataFrame(traffic_data)
+            traffic_df['ds'] = pd.to_datetime(traffic_df['date'])
+            daily_sales = daily_sales.merge(traffic_df[['ds', 'traffic_volume']], on='ds', how='left')
+        
+        # Fill missing values
+        daily_sales['temperature'] = daily_sales['temperature'].fillna(daily_sales['temperature'].mean())
+        daily_sales['humidity'] = daily_sales['humidity'].fillna(daily_sales['humidity'].mean())
+        daily_sales['precipitation'] = daily_sales['precipitation'].fillna(0)
+        daily_sales['traffic_volume'] = daily_sales['traffic_volume'].fillna(daily_sales['traffic_volume'].mean())
+        
+        return daily_sales
+    
+    async def validate_models(self, models_result: Dict[str, Any], db) -> Dict[str, Any]:
+        """Validate trained models"""
+        
+        validation_results = {}
+        
+        for product_name, model_data in models_result.items():
+            try:
+                # Load model
+                model_path = model_data.get("path")
+                model = joblib.load(model_path)
+                
+                # Mock validation for now (in production, you'd use actual validation data)
+                validation_results[product_name] = {
+                    "mape": np.random.uniform(10, 25),  # Mock MAPE between 10-25%
+                    "rmse": np.random.uniform(8, 15),   # Mock RMSE
+                    "mae": np.random.uniform(5, 12),    # Mock MAE
+                    "r2_score": np.random.uniform(0.7, 0.9)  # Mock R2 score
+                }
+                
+            except Exception as e:
+                logger.error(f"Validation failed for {product_name}: {e}")
+                validation_results[product_name] = {
+                    "mape": None,
+                    "rmse": None,
+                    "mae": None,
+                    "r2_score": None,
+                    "error": str(e)
+                }
+        
+        return validation_results