643 lines
31 KiB
Python
643 lines
31 KiB
Python
"""
|
|
Enhanced ML Trainer with Repository Pattern
|
|
Main ML pipeline coordinator using repository pattern for data access and dependency injection
|
|
"""
|
|
|
|
from typing import Dict, List, Any, Optional
|
|
import pandas as pd
|
|
import numpy as np
|
|
from datetime import datetime
|
|
import structlog
|
|
import uuid
|
|
import time
|
|
|
|
from app.ml.data_processor import EnhancedBakeryDataProcessor
|
|
from app.ml.prophet_manager import BakeryProphetManager
|
|
from app.services.training_orchestrator import TrainingDataSet
|
|
from app.core.config import settings
|
|
|
|
from shared.database.base import create_database_manager
|
|
from shared.database.transactions import transactional
|
|
from shared.database.unit_of_work import UnitOfWork
|
|
from shared.database.exceptions import DatabaseError
|
|
|
|
from app.repositories import (
|
|
ModelRepository,
|
|
TrainingLogRepository,
|
|
PerformanceRepository,
|
|
ArtifactRepository
|
|
)
|
|
|
|
from app.services.messaging import TrainingStatusPublisher
|
|
|
|
logger = structlog.get_logger()
|
|
|
|
class EnhancedBakeryMLTrainer:
|
|
"""
|
|
Enhanced ML trainer using repository pattern for data access and comprehensive tracking.
|
|
Orchestrates the complete ML training pipeline with proper database abstraction.
|
|
"""
|
|
|
|
def __init__(self, database_manager=None):
|
|
self.database_manager = database_manager or create_database_manager(settings.DATABASE_URL, "training-service")
|
|
self.enhanced_data_processor = EnhancedBakeryDataProcessor(self.database_manager)
|
|
self.prophet_manager = BakeryProphetManager(database_manager=self.database_manager)
|
|
|
|
async def _get_repositories(self, session):
|
|
"""Initialize repositories with session"""
|
|
return {
|
|
'model': ModelRepository(session),
|
|
'training_log': TrainingLogRepository(session),
|
|
'performance': PerformanceRepository(session),
|
|
'artifact': ArtifactRepository(session)
|
|
}
|
|
|
|
async def train_tenant_models(self,
|
|
tenant_id: str,
|
|
training_dataset: TrainingDataSet,
|
|
job_id: Optional[str] = None,
|
|
session=None) -> Dict[str, Any]:
|
|
"""
|
|
Train models for all products using repository pattern with enhanced tracking.
|
|
|
|
Args:
|
|
tenant_id: Tenant identifier
|
|
training_dataset: Prepared training dataset with aligned dates
|
|
job_id: Training job identifier
|
|
|
|
Returns:
|
|
Dictionary with training results for each product
|
|
"""
|
|
if not job_id:
|
|
job_id = f"enhanced_ml_{tenant_id}_{uuid.uuid4().hex[:8]}"
|
|
|
|
logger.info("Starting enhanced ML training pipeline",
|
|
job_id=job_id,
|
|
tenant_id=tenant_id)
|
|
|
|
self.status_publisher = TrainingStatusPublisher(job_id, tenant_id)
|
|
|
|
try:
|
|
# Get database session and repositories
|
|
async with self.database_manager.get_session() as db_session:
|
|
repos = await self._get_repositories(db_session)
|
|
|
|
# Convert sales data to DataFrame
|
|
sales_df = pd.DataFrame(training_dataset.sales_data)
|
|
weather_df = pd.DataFrame(training_dataset.weather_data)
|
|
traffic_df = pd.DataFrame(training_dataset.traffic_data)
|
|
|
|
# Validate input data
|
|
await self._validate_input_data(sales_df, tenant_id)
|
|
|
|
# Get unique products from the sales data
|
|
products = sales_df['inventory_product_id'].unique().tolist()
|
|
logger.info("Training enhanced models",
|
|
products_count=len(products),
|
|
products=products)
|
|
|
|
self.status_publisher.products_total = len(products)
|
|
|
|
# Create initial training log entry
|
|
await repos['training_log'].update_log_progress(
|
|
job_id, 5, "data_processing", "running"
|
|
)
|
|
|
|
# Process data for each product using enhanced processor
|
|
logger.info("Processing data using enhanced processor")
|
|
processed_data = await self._process_all_products_enhanced(
|
|
sales_df, weather_df, traffic_df, products, tenant_id, job_id
|
|
)
|
|
|
|
await self.status_publisher.progress_update(
|
|
progress=20,
|
|
step="feature_engineering",
|
|
step_details="Enhanced processing with repository tracking"
|
|
)
|
|
|
|
# Train models for each processed product
|
|
logger.info("Training models with repository integration")
|
|
training_results = await self._train_all_models_enhanced(
|
|
tenant_id, processed_data, job_id, repos
|
|
)
|
|
|
|
# Calculate overall training summary with enhanced metrics
|
|
summary = await self._calculate_enhanced_training_summary(
|
|
training_results, repos, tenant_id
|
|
)
|
|
|
|
await self.status_publisher.progress_update(
|
|
progress=90,
|
|
step="model_validation",
|
|
step_details="Enhanced validation with repository tracking"
|
|
)
|
|
|
|
# Create comprehensive result with repository data
|
|
result = {
|
|
"job_id": job_id,
|
|
"tenant_id": tenant_id,
|
|
"status": "completed",
|
|
"products_trained": len([r for r in training_results.values() if r.get('status') == 'success']),
|
|
"products_failed": len([r for r in training_results.values() if r.get('status') == 'error']),
|
|
"products_skipped": len([r for r in training_results.values() if r.get('status') == 'skipped']),
|
|
"total_products": len(products),
|
|
"training_results": training_results,
|
|
"enhanced_summary": summary,
|
|
"models_trained": summary.get('models_created', {}),
|
|
"data_info": {
|
|
"date_range": {
|
|
"start": training_dataset.date_range.start.isoformat(),
|
|
"end": training_dataset.date_range.end.isoformat(),
|
|
"duration_days": (training_dataset.date_range.end - training_dataset.date_range.start).days
|
|
},
|
|
"data_sources": [source.value for source in training_dataset.date_range.available_sources],
|
|
"constraints_applied": training_dataset.date_range.constraints
|
|
},
|
|
"repository_metadata": {
|
|
"total_records_created": summary.get('total_db_records', 0),
|
|
"performance_metrics_stored": summary.get('performance_metrics_created', 0),
|
|
"artifacts_created": summary.get('artifacts_created', 0)
|
|
},
|
|
"completed_at": datetime.now().isoformat()
|
|
}
|
|
|
|
logger.info("Enhanced ML training pipeline completed successfully",
|
|
job_id=job_id,
|
|
models_created=len([r for r in training_results.values() if r.get('status') == 'success']))
|
|
|
|
return result
|
|
|
|
except Exception as e:
|
|
logger.error("Enhanced ML training pipeline failed",
|
|
job_id=job_id,
|
|
error=str(e))
|
|
raise
|
|
|
|
async def _process_all_products_enhanced(self,
|
|
sales_df: pd.DataFrame,
|
|
weather_df: pd.DataFrame,
|
|
traffic_df: pd.DataFrame,
|
|
products: List[str],
|
|
tenant_id: str,
|
|
job_id: str) -> Dict[str, pd.DataFrame]:
|
|
"""Process data for all products using enhanced processor with repository tracking"""
|
|
processed_data = {}
|
|
|
|
for inventory_product_id in products:
|
|
try:
|
|
logger.info("Processing data for product using enhanced processor",
|
|
inventory_product_id=inventory_product_id)
|
|
|
|
# Filter sales data for this product
|
|
product_sales = sales_df[sales_df['inventory_product_id'] == inventory_product_id].copy()
|
|
|
|
if product_sales.empty:
|
|
logger.warning("No sales data found for product",
|
|
inventory_product_id=inventory_product_id)
|
|
continue
|
|
|
|
# Use enhanced data processor with repository tracking
|
|
processed_product_data = await self.enhanced_data_processor.prepare_training_data(
|
|
sales_data=product_sales,
|
|
weather_data=weather_df,
|
|
traffic_data=traffic_df,
|
|
inventory_product_id=inventory_product_id,
|
|
tenant_id=tenant_id,
|
|
job_id=job_id
|
|
)
|
|
|
|
processed_data[inventory_product_id] = processed_product_data
|
|
logger.info("Enhanced processing completed",
|
|
inventory_product_id=inventory_product_id,
|
|
data_points=len(processed_product_data))
|
|
|
|
except Exception as e:
|
|
logger.error("Failed to process data using enhanced processor",
|
|
inventory_product_id=inventory_product_id,
|
|
error=str(e))
|
|
continue
|
|
|
|
return processed_data
|
|
|
|
async def _train_all_models_enhanced(self,
|
|
tenant_id: str,
|
|
processed_data: Dict[str, pd.DataFrame],
|
|
job_id: str,
|
|
repos: Dict) -> Dict[str, Any]:
|
|
"""Train models with enhanced repository integration"""
|
|
training_results = {}
|
|
i = 0
|
|
total_products = len(processed_data)
|
|
base_progress = 45
|
|
max_progress = 85
|
|
|
|
for inventory_product_id, product_data in processed_data.items():
|
|
product_start_time = time.time()
|
|
try:
|
|
logger.info("Training enhanced model",
|
|
inventory_product_id=inventory_product_id)
|
|
|
|
# Check if we have enough data
|
|
if len(product_data) < settings.MIN_TRAINING_DATA_DAYS:
|
|
training_results[inventory_product_id] = {
|
|
'status': 'skipped',
|
|
'reason': 'insufficient_data',
|
|
'data_points': len(product_data),
|
|
'min_required': settings.MIN_TRAINING_DATA_DAYS,
|
|
'message': f'Need at least {settings.MIN_TRAINING_DATA_DAYS} data points, got {len(product_data)}'
|
|
}
|
|
logger.warning("Skipping product due to insufficient data",
|
|
inventory_product_id=inventory_product_id,
|
|
data_points=len(product_data),
|
|
min_required=settings.MIN_TRAINING_DATA_DAYS)
|
|
continue
|
|
|
|
# Train the model using Prophet manager
|
|
model_info = await self.prophet_manager.train_bakery_model(
|
|
tenant_id=tenant_id,
|
|
inventory_product_id=inventory_product_id,
|
|
df=product_data,
|
|
job_id=job_id
|
|
)
|
|
|
|
# Store model record using repository
|
|
model_record = await self._create_model_record(
|
|
repos, tenant_id, inventory_product_id, model_info, job_id, product_data
|
|
)
|
|
|
|
# Create performance metrics record
|
|
if model_info.get('training_metrics'):
|
|
await self._create_performance_metrics(
|
|
repos, model_record.id if model_record else None,
|
|
tenant_id, inventory_product_id, model_info['training_metrics']
|
|
)
|
|
|
|
training_results[inventory_product_id] = {
|
|
'status': 'success',
|
|
'model_info': model_info,
|
|
'model_record_id': model_record.id if model_record else None,
|
|
'data_points': len(product_data),
|
|
'training_time_seconds': time.time() - product_start_time,
|
|
'trained_at': datetime.now().isoformat()
|
|
}
|
|
|
|
logger.info("Successfully trained enhanced model",
|
|
inventory_product_id=inventory_product_id,
|
|
model_record_id=model_record.id if model_record else None)
|
|
|
|
completed_products = i + 1
|
|
i += 1
|
|
progress = base_progress + int((completed_products / total_products) * (max_progress - base_progress))
|
|
|
|
if self.status_publisher:
|
|
self.status_publisher.products_completed = completed_products
|
|
|
|
await self.status_publisher.progress_update(
|
|
progress=progress,
|
|
step="model_training",
|
|
current_product=inventory_product_id,
|
|
step_details=f"Enhanced training completed for {inventory_product_id}"
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.error("Failed to train enhanced model",
|
|
inventory_product_id=inventory_product_id,
|
|
error=str(e))
|
|
training_results[inventory_product_id] = {
|
|
'status': 'error',
|
|
'error_message': str(e),
|
|
'data_points': len(product_data) if product_data is not None else 0,
|
|
'training_time_seconds': time.time() - product_start_time,
|
|
'failed_at': datetime.now().isoformat()
|
|
}
|
|
|
|
completed_products = i + 1
|
|
i += 1
|
|
progress = base_progress + int((completed_products / total_products) * (max_progress - base_progress))
|
|
|
|
if self.status_publisher:
|
|
self.status_publisher.products_completed = completed_products
|
|
await self.status_publisher.progress_update(
|
|
progress=progress,
|
|
step="model_training",
|
|
current_product=inventory_product_id,
|
|
step_details=f"Enhanced training failed for {inventory_product_id}: {str(e)}"
|
|
)
|
|
|
|
return training_results
|
|
|
|
async def _create_model_record(self,
|
|
repos: Dict,
|
|
tenant_id: str,
|
|
inventory_product_id: str,
|
|
model_info: Dict,
|
|
job_id: str,
|
|
processed_data: pd.DataFrame):
|
|
"""Create model record using repository"""
|
|
try:
|
|
model_data = {
|
|
"tenant_id": tenant_id,
|
|
"inventory_product_id": inventory_product_id,
|
|
"job_id": job_id,
|
|
"model_type": "enhanced_prophet",
|
|
"model_path": model_info.get("model_path"),
|
|
"metadata_path": model_info.get("metadata_path"),
|
|
"mape": model_info.get("training_metrics", {}).get("mape"),
|
|
"mae": model_info.get("training_metrics", {}).get("mae"),
|
|
"rmse": model_info.get("training_metrics", {}).get("rmse"),
|
|
"r2_score": model_info.get("training_metrics", {}).get("r2"),
|
|
"training_samples": len(processed_data),
|
|
"hyperparameters": model_info.get("hyperparameters"),
|
|
"features_used": list(processed_data.columns),
|
|
"normalization_params": self.enhanced_data_processor.get_scalers(), # Include scalers for prediction consistency
|
|
"is_active": True,
|
|
"is_production": True,
|
|
"data_quality_score": model_info.get("data_quality_score", 100.0)
|
|
}
|
|
|
|
model_record = await repos['model'].create_model(model_data)
|
|
logger.info("Created enhanced model record",
|
|
inventory_product_id=inventory_product_id,
|
|
model_id=model_record.id)
|
|
|
|
# Create artifacts for model files
|
|
if model_info.get("model_path"):
|
|
await repos['artifact'].create_artifact({
|
|
"model_id": str(model_record.id),
|
|
"tenant_id": tenant_id,
|
|
"artifact_type": "enhanced_model_file",
|
|
"file_path": model_info["model_path"],
|
|
"storage_location": "local"
|
|
})
|
|
|
|
return model_record
|
|
|
|
except Exception as e:
|
|
logger.error("Failed to create enhanced model record",
|
|
inventory_product_id=inventory_product_id,
|
|
error=str(e))
|
|
return None
|
|
|
|
async def _create_performance_metrics(self,
|
|
repos: Dict,
|
|
model_id: str,
|
|
tenant_id: str,
|
|
inventory_product_id: str,
|
|
metrics: Dict):
|
|
"""Create performance metrics record using repository"""
|
|
try:
|
|
metric_data = {
|
|
"model_id": str(model_id),
|
|
"tenant_id": tenant_id,
|
|
"inventory_product_id": inventory_product_id,
|
|
"mae": metrics.get("mae"),
|
|
"mse": metrics.get("mse"),
|
|
"rmse": metrics.get("rmse"),
|
|
"mape": metrics.get("mape"),
|
|
"r2_score": metrics.get("r2"),
|
|
"accuracy_percentage": 100 - metrics.get("mape", 0) if metrics.get("mape") else None,
|
|
"evaluation_samples": metrics.get("data_points", 0)
|
|
}
|
|
|
|
await repos['performance'].create_performance_metric(metric_data)
|
|
logger.info("Created enhanced performance metrics",
|
|
inventory_product_id=inventory_product_id,
|
|
model_id=model_id)
|
|
|
|
except Exception as e:
|
|
logger.error("Failed to create enhanced performance metrics",
|
|
inventory_product_id=inventory_product_id,
|
|
error=str(e))
|
|
|
|
async def _calculate_enhanced_training_summary(self,
|
|
training_results: Dict[str, Any],
|
|
repos: Dict,
|
|
tenant_id: str) -> Dict[str, Any]:
|
|
"""Calculate enhanced summary statistics with repository data"""
|
|
total_products = len(training_results)
|
|
successful_products = len([r for r in training_results.values() if r.get('status') == 'success'])
|
|
failed_products = len([r for r in training_results.values() if r.get('status') == 'error'])
|
|
skipped_products = len([r for r in training_results.values() if r.get('status') == 'skipped'])
|
|
|
|
# Calculate average training metrics for successful models
|
|
successful_results = [r for r in training_results.values() if r.get('status') == 'success']
|
|
|
|
avg_metrics = {}
|
|
if successful_results:
|
|
metrics_list = [r['model_info'].get('training_metrics', {}) for r in successful_results]
|
|
|
|
if metrics_list and all(metrics_list):
|
|
avg_metrics = {
|
|
'avg_mae': round(np.mean([m.get('mae', 0) for m in metrics_list]), 2),
|
|
'avg_rmse': round(np.mean([m.get('rmse', 0) for m in metrics_list]), 2),
|
|
'avg_mape': round(np.mean([m.get('mape', 0) for m in metrics_list]), 2),
|
|
'avg_r2': round(np.mean([m.get('r2', 0) for m in metrics_list]), 3),
|
|
'avg_training_time': round(np.mean([r.get('training_time_seconds', 0) for r in successful_results]), 2)
|
|
}
|
|
|
|
# Calculate data quality insights
|
|
data_points_list = [r.get('data_points', 0) for r in training_results.values()]
|
|
|
|
# Get database statistics
|
|
try:
|
|
# Get tenant model count from repository
|
|
tenant_models = await repos['model'].get_models_by_tenant(tenant_id)
|
|
models_created = [r.get('model_record_id') for r in successful_results if r.get('model_record_id')]
|
|
|
|
db_stats = {
|
|
'total_tenant_models': len(tenant_models),
|
|
'models_created_this_job': len(models_created),
|
|
'total_db_records': len(models_created),
|
|
'performance_metrics_created': len(models_created), # One per model
|
|
'artifacts_created': len([r for r in successful_results if r.get('model_info', {}).get('model_path')])
|
|
}
|
|
except Exception as e:
|
|
logger.warning("Failed to get database statistics", error=str(e))
|
|
db_stats = {
|
|
'total_tenant_models': 0,
|
|
'models_created_this_job': 0,
|
|
'total_db_records': 0,
|
|
'performance_metrics_created': 0,
|
|
'artifacts_created': 0
|
|
}
|
|
|
|
# Build models_created with proper model result structure
|
|
models_created = {}
|
|
for product, result in training_results.items():
|
|
if result.get('status') == 'success' and result.get('model_info'):
|
|
model_info = result['model_info']
|
|
models_created[product] = {
|
|
'status': 'completed',
|
|
'model_path': model_info.get('model_path'),
|
|
'metadata_path': model_info.get('metadata_path'),
|
|
'metrics': model_info.get('training_metrics', {}),
|
|
'hyperparameters': model_info.get('hyperparameters', {}),
|
|
'features_used': model_info.get('features_used', []),
|
|
'data_points': result.get('data_points', 0),
|
|
'data_quality_score': model_info.get('data_quality_score', 100.0),
|
|
'model_record_id': result.get('model_record_id')
|
|
}
|
|
|
|
enhanced_summary = {
|
|
'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,
|
|
'enhanced_average_metrics': avg_metrics,
|
|
'enhanced_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
|
|
},
|
|
'database_statistics': db_stats,
|
|
'models_created': models_created
|
|
}
|
|
|
|
# Add database statistics to the summary
|
|
enhanced_summary.update(db_stats)
|
|
|
|
return enhanced_summary
|
|
|
|
async def _validate_input_data(self, sales_df: pd.DataFrame, tenant_id: str):
|
|
"""Validate input sales data with enhanced error reporting"""
|
|
if sales_df.empty:
|
|
raise ValueError(f"No sales data provided for tenant {tenant_id}")
|
|
|
|
# Handle quantity column mapping
|
|
if 'quantity_sold' in sales_df.columns and 'quantity' not in sales_df.columns:
|
|
sales_df['quantity'] = sales_df['quantity_sold']
|
|
logger.info("Mapped quantity column",
|
|
from_column='quantity_sold',
|
|
to_column='quantity')
|
|
|
|
required_columns = ['date', 'product_name', 'quantity']
|
|
missing_columns = [col for col in required_columns if col not in sales_df.columns]
|
|
if missing_columns:
|
|
raise ValueError(f"Missing required columns: {missing_columns}")
|
|
|
|
# Check for valid dates
|
|
try:
|
|
sales_df['date'] = pd.to_datetime(sales_df['date'])
|
|
except Exception:
|
|
raise ValueError("Invalid date format in sales data")
|
|
|
|
# Check for valid quantities
|
|
if not sales_df['quantity'].dtype in ['int64', 'float64']:
|
|
try:
|
|
sales_df['quantity'] = pd.to_numeric(sales_df['quantity'], errors='coerce')
|
|
except Exception:
|
|
raise ValueError("Quantity column must be numeric")
|
|
|
|
async def evaluate_model_performance_enhanced(self,
|
|
tenant_id: str,
|
|
inventory_product_id: str,
|
|
model_path: str,
|
|
test_dataset: TrainingDataSet) -> Dict[str, Any]:
|
|
"""
|
|
Enhanced model evaluation with repository integration.
|
|
"""
|
|
try:
|
|
logger.info("Enhanced model evaluation starting",
|
|
tenant_id=tenant_id,
|
|
product_name=product_name)
|
|
|
|
# Get database session and repositories
|
|
async with self.database_manager.get_session() as db_session:
|
|
repos = await self._get_repositories(db_session)
|
|
|
|
# Convert test data to DataFrames
|
|
test_sales_df = pd.DataFrame(test_dataset.sales_data)
|
|
test_weather_df = pd.DataFrame(test_dataset.weather_data)
|
|
test_traffic_df = pd.DataFrame(test_dataset.traffic_data)
|
|
|
|
# Filter for specific product
|
|
product_test_sales = test_sales_df[test_sales_df['inventory_product_id'] == inventory_product_id].copy()
|
|
|
|
if product_test_sales.empty:
|
|
raise ValueError(f"No test data found for product: {inventory_product_id}")
|
|
|
|
# Process test data using enhanced processor
|
|
processed_test_data = await self.enhanced_data_processor.prepare_training_data(
|
|
sales_data=product_test_sales,
|
|
weather_data=test_weather_df,
|
|
traffic_data=test_traffic_df,
|
|
inventory_product_id=inventory_product_id,
|
|
tenant_id=tenant_id
|
|
)
|
|
|
|
# Create future dataframe for prediction
|
|
future_dates = processed_test_data[['ds']].copy()
|
|
|
|
# Add regressor columns
|
|
regressor_columns = [col for col in processed_test_data.columns if col not in ['ds', 'y']]
|
|
for col in regressor_columns:
|
|
future_dates[col] = processed_test_data[col]
|
|
|
|
# Generate predictions
|
|
forecast = await self.prophet_manager.generate_forecast(
|
|
model_path=model_path,
|
|
future_dates=future_dates,
|
|
regressor_columns=regressor_columns
|
|
)
|
|
|
|
# Calculate performance metrics
|
|
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
|
|
|
|
y_true = processed_test_data['y'].values
|
|
y_pred = forecast['yhat'].values
|
|
|
|
# Ensure arrays are the same length
|
|
min_len = min(len(y_true), len(y_pred))
|
|
y_true = y_true[:min_len]
|
|
y_pred = y_pred[:min_len]
|
|
|
|
metrics = {
|
|
"mae": float(mean_absolute_error(y_true, y_pred)),
|
|
"rmse": float(np.sqrt(mean_squared_error(y_true, y_pred))),
|
|
"r2_score": float(r2_score(y_true, y_pred))
|
|
}
|
|
|
|
# Calculate MAPE safely
|
|
non_zero_mask = y_true > 0.1
|
|
if np.sum(non_zero_mask) > 0:
|
|
mape = np.mean(np.abs((y_true[non_zero_mask] - y_pred[non_zero_mask]) / y_true[non_zero_mask])) * 100
|
|
metrics["mape"] = float(min(mape, 200)) # Cap at 200%
|
|
else:
|
|
metrics["mape"] = 100.0
|
|
|
|
# Store evaluation metrics in repository
|
|
model_records = await repos['model'].get_models_by_product(tenant_id, inventory_product_id)
|
|
if model_records:
|
|
latest_model = max(model_records, key=lambda x: x.created_at)
|
|
await self._create_performance_metrics(
|
|
repos, latest_model.id, tenant_id, inventory_product_id, metrics
|
|
)
|
|
|
|
result = {
|
|
"tenant_id": tenant_id,
|
|
"inventory_product_id": inventory_product_id,
|
|
"enhanced_evaluation_metrics": metrics,
|
|
"test_samples": len(processed_test_data),
|
|
"prediction_samples": len(forecast),
|
|
"test_period": {
|
|
"start": test_dataset.date_range.start.isoformat(),
|
|
"end": test_dataset.date_range.end.isoformat()
|
|
},
|
|
"evaluated_at": datetime.now().isoformat(),
|
|
"repository_integration": {
|
|
"metrics_stored": True,
|
|
"model_record_found": len(model_records) > 0 if model_records else False
|
|
}
|
|
}
|
|
|
|
return result
|
|
|
|
except Exception as e:
|
|
logger.error("Enhanced model evaluation failed", error=str(e))
|
|
raise
|
|
|
|
|
|
# Legacy compatibility alias
|
|
BakeryMLTrainer = EnhancedBakeryMLTrainer |