Improve training code 3

2025-07-28 21:30:49 +02:00
parent 7cd595df81
commit c788c7e406
2 changed files with 129 additions and 17 deletions
--- a/services/training/app/schemas/training.py
+++ b/services/training/app/schemas/training.py
@@ -55,16 +55,58 @@ class SingleProductTrainingRequest(BaseModel):
    weekly_seasonality: bool = Field(True, description="Enable weekly seasonality")
    yearly_seasonality: bool = Field(True, description="Enable yearly seasonality")

+class DateRangeInfo(BaseModel):
+    """Schema for date range information"""
+    start: str = Field(..., description="Start date in ISO format")
+    end: str = Field(..., description="End date in ISO format")
+
+class DataSummary(BaseModel):
+    """Schema for training data summary"""
+    sales_records: int = Field(..., description="Number of sales records used")
+    weather_records: int = Field(..., description="Number of weather records used")
+    traffic_records: int = Field(..., description="Number of traffic records used")
+    date_range: DateRangeInfo = Field(..., description="Date range of training data")
+    data_sources_used: List[str] = Field(..., description="List of data sources used")
+    constraints_applied: Dict[str, str] = Field(default_factory=dict, description="Constraints applied during data collection")
+
+class ProductTrainingResult(BaseModel):
+    """Schema for individual product training results"""
+    product_name: str = Field(..., description="Product name")
+    status: str = Field(..., description="Training status for this product")
+    model_id: Optional[str] = Field(None, description="Trained model identifier")
+    data_points: int = Field(..., description="Number of data points used for training")
+    metrics: Optional[Dict[str, float]] = Field(None, description="Training metrics (MAE, MAPE, etc.)")
+    training_time_seconds: Optional[float] = Field(None, description="Time taken to train this model")
+    error_message: Optional[str] = Field(None, description="Error message if training failed")
+
+class TrainingResults(BaseModel):
+    """Schema for overall training results"""
+    total_products: int = Field(..., description="Total number of products")
+    successful_trainings: int = Field(..., description="Number of successfully trained models")
+    failed_trainings: int = Field(..., description="Number of failed trainings")
+    products: List[ProductTrainingResult] = Field(..., description="Results for each product")
+    overall_training_time_seconds: float = Field(..., description="Total training time")
+
 class TrainingJobResponse(BaseModel):
-    """Response schema for training job creation"""
+    """Enhanced response schema for training job with detailed results"""
    job_id: str = Field(..., description="Unique training job identifier")
-    status: TrainingStatus = Field(..., description="Current job status")
-    message: str = Field(..., description="Status message")
    tenant_id: str = Field(..., description="Tenant identifier")
+    status: TrainingStatus = Field(..., description="Overall job status")
+    
+    # Required fields for basic response (backwards compatibility)
+    message: str = Field(..., description="Status message")
    created_at: datetime = Field(..., description="Job creation timestamp")
    estimated_duration_minutes: int = Field(..., description="Estimated completion time in minutes")
    
-    # ✅ FIX: Add custom validator to convert UUID to string
+    # New detailed fields (optional for backwards compatibility)
+    training_results: Optional[TrainingResults] = Field(None, description="Detailed training results")
+    data_summary: Optional[DataSummary] = Field(None, description="Summary of training data used")
+    completed_at: Optional[str] = Field(None, description="Job completion timestamp in ISO format")
+    
+    # Additional optional fields
+    error_details: Optional[Dict[str, Any]] = Field(None, description="Detailed error information if failed")
+    processing_metadata: Optional[Dict[str, Any]] = Field(None, description="Additional processing metadata")
+    
    @validator('tenant_id', 'job_id', pre=True)
    def convert_uuid_to_string(cls, v):
        """Convert UUID objects to strings for JSON serialization"""
--- a/services/training/app/services/training_service.py
+++ b/services/training/app/services/training_service.py
@@ -81,27 +81,55 @@ class TrainingService:
            )
            
            # Step 3: Compile final results
-            logger.info(f"Training job {job_id} completed successfully")
-            return {
+            final_result = {
                "job_id": job_id,
-                "status": "completed",  # or "running" if async
-                "message": "Training job completed successfully",
                "tenant_id": tenant_id,
-                "created_at": datetime.now(),
-                "estimated_duration_minutes": 5  # reasonable estimate
+                "status": "completed",
+                "training_results": training_results,
+                "data_summary": {
+                    "sales_records": len(training_dataset.sales_data),
+                    "weather_records": len(training_dataset.weather_data),
+                    "traffic_records": len(training_dataset.traffic_data),
+                    "date_range": {
+                        "start": training_dataset.date_range.start.isoformat(),
+                        "end": training_dataset.date_range.end.isoformat()
+                    },
+                    "data_sources_used": [source.value for source in training_dataset.date_range.available_sources],
+                    "constraints_applied": training_dataset.date_range.constraints
+                },
+                "completed_at": datetime.now().isoformat()
            }
            
+            logger.info(f"Training job {job_id} completed successfully")
+            return TrainingService.create_detailed_training_response(final_result)
+            
        except Exception as e:
            logger.error(f"Training job {job_id} failed: {str(e)}")
-            # Return error response that still matches schema
-            return {
+             # Return error response in same detailed format
+            final_result = {
                "job_id": job_id,
-                "status": "failed",
-                "message": f"Training job failed: {str(e)}",
                "tenant_id": tenant_id,
-                "created_at": datetime.now(),
-                "estimated_duration_minutes": 0
+                "status": "failed",
+                "training_results": {
+                    "total_products": 0,
+                    "successful_trainings": 0,
+                    "failed_trainings": 0,
+                    "models_trained": {},
+                    "total_training_time": 0
+                },
+                "data_summary": {
+                    "sales_records": 0,
+                    "weather_records": 0,
+                    "traffic_records": 0,
+                    "date_range": {"start": "", "end": ""},
+                    "data_sources_used": [],
+                    "constraints_applied": {}
+                },
+                "completed_at": datetime.now().isoformat(),
+                "error_message": str(e)
            }
+            
+            return TrainingService.create_detailed_training_response(final_result)
    
    async def start_single_product_training(
        self,
@@ -290,4 +318,46 @@ class TrainingService:
                "reasons": [f"Error analyzing data: {str(e)}"],
                "recommended_products": [],
                "optimal_config": {}
-            }
+            }
+
+    def create_detailed_training_response(final_result: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Convert your final_result structure to match the TrainingJobResponse schema
+        """
+        # Extract training results and convert to schema format
+        training_results_data = final_result.get("training_results", {})
+        
+        # Convert product results to schema format
+        products = []
+        if "models_trained" in training_results_data:
+            for product_name, result in training_results_data["models_trained"].items():
+                products.append({
+                    "product_name": product_name,
+                    "status": result.get("status", "completed"),
+                    "model_id": result.get("model_id"),
+                    "data_points": result.get("data_points", 0),
+                    "metrics": result.get("metrics"),
+                    "training_time_seconds": result.get("training_time_seconds"),
+                    "error_message": result.get("error_message")
+                })
+        
+        # Build the response matching your structure
+        response_data = {
+            "job_id": final_result["job_id"],
+            "tenant_id": final_result["tenant_id"],
+            "status": final_result["status"],
+            "message": f"Training {final_result['status']} successfully",
+            "created_at": datetime.now(),
+            "estimated_duration_minutes": 0,  # Already completed
+            "training_results": {
+                "total_products": len(products),
+                "successful_trainings": len([p for p in products if p["status"] == "completed"]),
+                "failed_trainings": len([p for p in products if p["status"] == "failed"]),
+                "products": products,
+                "overall_training_time_seconds": training_results_data.get("total_training_time", 0)
+            },
+            "data_summary": final_result.get("data_summary", {}),
+            "completed_at": final_result.get("completed_at")
+        }
+        
+        return response_data