Improve training code
This commit is contained in:
Urtzi Alfaro
@@ -1,77 +1,76 @@
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# services/training/app/ml/trainer.py
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"""
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ML Trainer for Training Service
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Orchestrates the complete training process
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ML Trainer - Main ML pipeline coordinator
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Receives prepared data and orchestrates the complete ML training process
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"""
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from typing import Dict, List, Any, Optional, Tuple
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from typing import Dict, List, Any, Optional
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from datetime import datetime
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import logging
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import asyncio
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import uuid
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from pathlib import Path
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from app.ml.prophet_manager import BakeryProphetManager
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from app.ml.data_processor import BakeryDataProcessor
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from app.ml.prophet_manager import BakeryProphetManager
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from app.services.training_orchestrator import TrainingDataSet
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from app.core.config import settings
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from sqlalchemy.ext.asyncio import AsyncSession
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logger = logging.getLogger(__name__)
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class BakeryMLTrainer:
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"""
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Main ML trainer that orchestrates the complete training process.
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Replaces the old Celery-based training system with clean async implementation.
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Main ML trainer that orchestrates the complete ML training pipeline.
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Receives prepared TrainingDataSet and coordinates data processing and model training.
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"""
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def __init__(self):
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self.prophet_manager = BakeryProphetManager()
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def __init__(self, db_session: AsyncSession = None):
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self.data_processor = BakeryDataProcessor()
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self.prophet_manager = BakeryProphetManager(db_session=db_session)
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async def train_tenant_models(self,
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tenant_id: str,
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sales_data: List[Dict],
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weather_data: List[Dict] = None,
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traffic_data: List[Dict] = None,
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job_id: str = None) -> Dict[str, Any]:
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training_dataset: TrainingDataSet,
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job_id: Optional[str] = None) -> Dict[str, Any]:
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"""
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Train models for all products of a tenant.
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Train models for all products using prepared training dataset.
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Args:
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tenant_id: Tenant identifier
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sales_data: Historical sales data
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weather_data: Weather data (optional)
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traffic_data: Traffic data (optional)
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training_dataset: Prepared training dataset with aligned dates
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job_id: Training job identifier
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Returns:
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Dictionary with training results for each product
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"""
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if not job_id:
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job_id = f"training_{tenant_id}_{uuid.uuid4().hex[:8]}"
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job_id = f"ml_training_{tenant_id}_{uuid.uuid4().hex[:8]}"
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logger.info(f"Starting training job {job_id} for tenant {tenant_id}")
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logger.info(f"Starting ML training pipeline {job_id} for tenant {tenant_id}")
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try:
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# Convert input data to DataFrames
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sales_df = pd.DataFrame(sales_data) if sales_data else pd.DataFrame()
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weather_df = pd.DataFrame(weather_data) if weather_data else pd.DataFrame()
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traffic_df = pd.DataFrame(traffic_data) if traffic_data else pd.DataFrame()
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# Convert sales data to DataFrame
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sales_df = pd.DataFrame(training_dataset.sales_data)
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weather_df = pd.DataFrame(training_dataset.weather_data)
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traffic_df = pd.DataFrame(training_dataset.traffic_data)
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# Validate input data
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await self._validate_input_data(sales_df, tenant_id)
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# Get unique products
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# Get unique products from the sales data
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products = sales_df['product_name'].unique().tolist()
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logger.info(f"Training models for {len(products)} products: {products}")
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# Process data for each product
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logger.info("Processing data for all products...")
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processed_data = await self._process_all_products(
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sales_df, weather_df, traffic_df, products
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)
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# Train models for each product
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# Train models for each processed product
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logger.info("Training models for all products...")
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training_results = await self._train_all_models(
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tenant_id, processed_data, job_id
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)
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@@ -85,50 +84,56 @@ class BakeryMLTrainer:
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"status": "completed",
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"products_trained": len([r for r in training_results.values() if r.get('status') == 'success']),
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"products_failed": len([r for r in training_results.values() if r.get('status') == 'error']),
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"products_skipped": len([r for r in training_results.values() if r.get('status') == 'skipped']),
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"total_products": len(products),
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"training_results": training_results,
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"summary": summary,
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"data_info": {
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"date_range": {
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"start": training_dataset.date_range.start.isoformat(),
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"end": training_dataset.date_range.end.isoformat(),
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"duration_days": (training_dataset.date_range.end - training_dataset.date_range.start).days
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},
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"data_sources": [source.value for source in training_dataset.date_range.available_sources],
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"constraints_applied": training_dataset.date_range.constraints
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},
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"completed_at": datetime.now().isoformat()
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}
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logger.info(f"Training job {job_id} completed successfully")
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logger.info(f"ML training pipeline {job_id} completed successfully")
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return result
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except Exception as e:
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logger.error(f"Training job {job_id} failed: {str(e)}")
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logger.error(f"ML training pipeline {job_id} failed: {str(e)}")
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raise
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async def train_single_product(self,
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tenant_id: str,
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product_name: str,
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sales_data: List[Dict],
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weather_data: List[Dict] = None,
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traffic_data: List[Dict] = None,
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job_id: str = None) -> Dict[str, Any]:
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async def train_single_product_model(self,
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tenant_id: str,
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product_name: str,
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training_dataset: TrainingDataSet,
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job_id: Optional[str] = None) -> Dict[str, Any]:
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"""
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Train model for a single product.
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Train model for a single product using prepared training dataset.
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Args:
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tenant_id: Tenant identifier
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product_name: Product name
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sales_data: Historical sales data
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weather_data: Weather data (optional)
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traffic_data: Traffic data (optional)
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training_dataset: Prepared training dataset
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job_id: Training job identifier
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Returns:
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Training result for the product
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"""
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if not job_id:
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job_id = f"training_{tenant_id}_{product_name}_{uuid.uuid4().hex[:8]}"
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job_id = f"single_ml_{tenant_id}_{product_name}_{uuid.uuid4().hex[:8]}"
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logger.info(f"Starting single product training {job_id} for {product_name}")
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logger.info(f"Starting single product ML training {job_id} for {product_name}")
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try:
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# Convert input data to DataFrames
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sales_df = pd.DataFrame(sales_data) if sales_data else pd.DataFrame()
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weather_df = pd.DataFrame(weather_data) if weather_data else pd.DataFrame()
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traffic_df = pd.DataFrame(traffic_data) if traffic_data else pd.DataFrame()
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# Convert training data to DataFrames
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sales_df = pd.DataFrame(training_dataset.sales_data)
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weather_df = pd.DataFrame(training_dataset.weather_data)
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traffic_df = pd.DataFrame(training_dataset.traffic_data)
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# Filter sales data for the specific product
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product_sales = sales_df[sales_df['product_name'] == product_name].copy()
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@@ -137,7 +142,7 @@ class BakeryMLTrainer:
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if product_sales.empty:
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raise ValueError(f"No sales data found for product: {product_name}")
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# Prepare training data
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# Process data for this specific product
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processed_data = await self.data_processor.prepare_training_data(
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sales_data=product_sales,
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weather_data=weather_df,
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@@ -160,29 +165,38 @@ class BakeryMLTrainer:
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"status": "success",
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"model_info": model_info,
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"data_points": len(processed_data),
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"data_info": {
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"date_range": {
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"start": training_dataset.date_range.start.isoformat(),
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"end": training_dataset.date_range.end.isoformat(),
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"duration_days": (training_dataset.date_range.end - training_dataset.date_range.start).days
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},
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"data_sources": [source.value for source in training_dataset.date_range.available_sources],
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"constraints_applied": training_dataset.date_range.constraints
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},
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"completed_at": datetime.now().isoformat()
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}
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logger.info(f"Single product training {job_id} completed successfully")
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logger.info(f"Single product ML training {job_id} completed successfully")
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return result
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except Exception as e:
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logger.error(f"Single product training {job_id} failed: {str(e)}")
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logger.error(f"Single product ML training {job_id} failed: {str(e)}")
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raise
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async def evaluate_model_performance(self,
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tenant_id: str,
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product_name: str,
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model_path: str,
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test_data: List[Dict]) -> Dict[str, Any]:
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test_dataset: TrainingDataSet) -> Dict[str, Any]:
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"""
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Evaluate model performance on test data.
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Evaluate model performance using test dataset.
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Args:
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tenant_id: Tenant identifier
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product_name: Product name
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model_path: Path to the trained model
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test_data: Test data for evaluation
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test_dataset: Test dataset for evaluation
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Returns:
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Performance metrics
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@@ -190,46 +204,75 @@ class BakeryMLTrainer:
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try:
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logger.info(f"Evaluating model performance for {product_name}")
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# Convert test data to DataFrame
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test_df = pd.DataFrame(test_data)
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# Convert test data to DataFrames
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test_sales_df = pd.DataFrame(test_dataset.sales_data)
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test_weather_df = pd.DataFrame(test_dataset.weather_data)
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test_traffic_df = pd.DataFrame(test_dataset.traffic_data)
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# Prepare test data
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test_prepared = await self.data_processor.prepare_prediction_features(
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future_dates=test_df['ds'],
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weather_forecast=test_df if 'temperature' in test_df.columns else pd.DataFrame(),
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traffic_forecast=test_df if 'traffic_volume' in test_df.columns else pd.DataFrame()
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# Filter for specific product
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product_test_sales = test_sales_df[test_sales_df['product_name'] == product_name].copy()
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if product_test_sales.empty:
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raise ValueError(f"No test data found for product: {product_name}")
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# Process test data
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processed_test_data = await self.data_processor.prepare_training_data(
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sales_data=product_test_sales,
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weather_data=test_weather_df,
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traffic_data=test_traffic_df,
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product_name=product_name
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)
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# Get regressor columns
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regressor_columns = [col for col in test_prepared.columns if col not in ['ds', 'y']]
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# Create future dataframe for prediction
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future_dates = processed_test_data[['ds']].copy()
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# Add regressor columns
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regressor_columns = [col for col in processed_test_data.columns if col not in ['ds', 'y']]
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for col in regressor_columns:
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future_dates[col] = processed_test_data[col]
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# Generate predictions
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forecast = await self.prophet_manager.generate_forecast(
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model_path=model_path,
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future_dates=test_prepared,
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future_dates=future_dates,
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regressor_columns=regressor_columns
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)
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# Calculate performance metrics if we have actual values
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metrics = {}
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if 'y' in test_df.columns:
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from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
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y_true = test_df['y'].values
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y_pred = forecast['yhat'].values
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metrics = {
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"mae": float(mean_absolute_error(y_true, y_pred)),
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"rmse": float(np.sqrt(mean_squared_error(y_true, y_pred))),
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"mape": float(np.mean(np.abs((y_true - y_pred) / y_true)) * 100),
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"r2_score": float(r2_score(y_true, y_pred))
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}
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# Calculate performance metrics
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from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
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y_true = processed_test_data['y'].values
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y_pred = forecast['yhat'].values
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# Ensure arrays are the same length
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min_len = min(len(y_true), len(y_pred))
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y_true = y_true[:min_len]
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y_pred = y_pred[:min_len]
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metrics = {
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"mae": float(mean_absolute_error(y_true, y_pred)),
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"rmse": float(np.sqrt(mean_squared_error(y_true, y_pred))),
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"r2_score": float(r2_score(y_true, y_pred))
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}
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# Calculate MAPE safely
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non_zero_mask = y_true > 0.1
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if np.sum(non_zero_mask) > 0:
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mape = np.mean(np.abs((y_true[non_zero_mask] - y_pred[non_zero_mask]) / y_true[non_zero_mask])) * 100
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metrics["mape"] = float(min(mape, 200)) # Cap at 200%
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else:
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metrics["mape"] = 100.0
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result = {
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"tenant_id": tenant_id,
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"product_name": product_name,
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"evaluation_metrics": metrics,
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"forecast_samples": len(forecast),
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"test_samples": len(processed_test_data),
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"prediction_samples": len(forecast),
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"test_period": {
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"start": test_dataset.date_range.start.isoformat(),
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"end": test_dataset.date_range.end.isoformat()
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},
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"evaluated_at": datetime.now().isoformat()
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}
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@@ -244,6 +287,7 @@ class BakeryMLTrainer:
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if sales_df.empty:
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raise ValueError(f"No sales data provided for tenant {tenant_id}")
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# Handle quantity column mapping
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if 'quantity_sold' in sales_df.columns and 'quantity' not in sales_df.columns:
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sales_df['quantity'] = sales_df['quantity_sold']
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logger.info("Mapped 'quantity_sold' to 'quantity' column")
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@@ -261,14 +305,17 @@ class BakeryMLTrainer:
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# Check for valid quantities
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if not sales_df['quantity'].dtype in ['int64', 'float64']:
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raise ValueError("Quantity column must be numeric")
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try:
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sales_df['quantity'] = pd.to_numeric(sales_df['quantity'], errors='coerce')
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except Exception:
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raise ValueError("Quantity column must be numeric")
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async def _process_all_products(self,
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sales_df: pd.DataFrame,
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weather_df: pd.DataFrame,
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traffic_df: pd.DataFrame,
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products: List[str]) -> Dict[str, pd.DataFrame]:
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"""Process data for all products"""
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"""Process data for all products using the data processor"""
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processed_data = {}
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for product_name in products:
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@@ -278,7 +325,11 @@ class BakeryMLTrainer:
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# Filter sales data for this product
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product_sales = sales_df[sales_df['product_name'] == product_name].copy()
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# Process the product data
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if product_sales.empty:
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logger.warning(f"No sales data found for product: {product_name}")
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continue
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# Use data processor to prepare training data
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processed_product_data = await self.data_processor.prepare_training_data(
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sales_data=product_sales,
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weather_data=weather_df,
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@@ -300,7 +351,7 @@ class BakeryMLTrainer:
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tenant_id: str,
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processed_data: Dict[str, pd.DataFrame],
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job_id: str) -> Dict[str, Any]:
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"""Train models for all processed products"""
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"""Train models for all processed products using Prophet manager"""
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training_results = {}
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for product_name, product_data in processed_data.items():
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@@ -313,11 +364,13 @@ class BakeryMLTrainer:
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'status': 'skipped',
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'reason': 'insufficient_data',
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'data_points': len(product_data),
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'min_required': settings.MIN_TRAINING_DATA_DAYS
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'min_required': settings.MIN_TRAINING_DATA_DAYS,
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'message': f'Need at least {settings.MIN_TRAINING_DATA_DAYS} data points, got {len(product_data)}'
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}
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logger.warning(f"Skipping {product_name}: insufficient data ({len(product_data)} < {settings.MIN_TRAINING_DATA_DAYS})")
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continue
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# Train the model
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# Train the model using Prophet manager
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model_info = await self.prophet_manager.train_bakery_model(
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tenant_id=tenant_id,
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product_name=product_name,
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@@ -339,7 +392,8 @@ class BakeryMLTrainer:
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training_results[product_name] = {
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'status': 'error',
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'error_message': str(e),
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'data_points': len(product_data) if product_data is not None else 0
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'data_points': len(product_data) if product_data is not None else 0,
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'failed_at': datetime.now().isoformat()
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}
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return training_results
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@@ -360,17 +414,27 @@ class BakeryMLTrainer:
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if metrics_list and all(metrics_list):
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avg_metrics = {
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'avg_mae': np.mean([m.get('mae', 0) for m in metrics_list]),
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'avg_rmse': np.mean([m.get('rmse', 0) for m in metrics_list]),
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'avg_mape': np.mean([m.get('mape', 0) for m in metrics_list]),
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'avg_r2': np.mean([m.get('r2_score', 0) for m in metrics_list])
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'avg_mae': round(np.mean([m.get('mae', 0) for m in metrics_list]), 2),
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'avg_rmse': round(np.mean([m.get('rmse', 0) for m in metrics_list]), 2),
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'avg_mape': round(np.mean([m.get('mape', 0) for m in metrics_list]), 2),
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'avg_r2': round(np.mean([m.get('r2', 0) for m in metrics_list]), 3),
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'avg_improvement': round(np.mean([m.get('improvement_estimated', 0) for m in metrics_list]), 1)
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}
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# Calculate data quality insights
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data_points_list = [r.get('data_points', 0) for r in training_results.values()]
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return {
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'total_products': total_products,
|
||||
'successful_products': successful_products,
|
||||
'failed_products': failed_products,
|
||||
'skipped_products': skipped_products,
|
||||
'success_rate': round(successful_products / total_products * 100, 2) if total_products > 0 else 0,
|
||||
'average_metrics': avg_metrics
|
||||
'average_metrics': avg_metrics,
|
||||
'data_summary': {
|
||||
'total_data_points': sum(data_points_list),
|
||||
'avg_data_points_per_product': round(np.mean(data_points_list), 1) if data_points_list else 0,
|
||||
'min_data_points': min(data_points_list) if data_points_list else 0,
|
||||
'max_data_points': max(data_points_list) if data_points_list else 0
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user