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Start fixing forecast service 18

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This commit is contained in:
Urtzi Alfaro
2025-07-30 08:41:47 +02:00
parent c10a745695
commit 024290e4c0
2 changed files with 25 additions and 14 deletions
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8
services/forecasting/app/services/forecasting_service.py
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@@ -122,15 +122,17 @@ class ForecastingService:
# Metadata # Metadata
created_at=forecast.created_at, created_at=forecast.created_at,
processing_time_ms=forecast.processing_time_ms, processing_time_ms=int((datetime.now() - start_time).total_seconds() * 1000),
features_used=forecast.features_used features_used=forecast.features_used
) )
except Exception as e: except Exception as e:
processing_time = int((datetime.now() - start_time).total_seconds() * 1000)
logger.error("Error generating forecast", logger.error("Error generating forecast",
error=str(e), error=str(e),
product=request.product_name, product=request.product_name,
tenant_id=tenant_id) tenant_id=tenant_id,
processing_time=processing_time)
raise raise
async def _get_latest_model_with_fallback( async def _get_latest_model_with_fallback(
@@ -422,6 +424,8 @@ class ForecastingService:
) -> Forecast: ) -> Forecast:
"""Save forecast to database""" """Save forecast to database"""
start_time = datetime.now()
forecast = Forecast( forecast = Forecast(
tenant_id=tenant_id, tenant_id=tenant_id,
product_name=request.product_name, product_name=request.product_name,

29
services/training/app/ml/data_processor.py
View File

@@ -324,15 +324,18 @@ class BakeryDataProcessor:
if 'date' not in weather_clean.columns and 'ds' in weather_clean.columns: if 'date' not in weather_clean.columns and 'ds' in weather_clean.columns:
weather_clean = weather_clean.rename(columns={'ds': 'date'}) weather_clean = weather_clean.rename(columns={'ds': 'date'})
# ✅ FIX: Ensure timezone consistency # 🔧 CRITICAL FIX: Ensure both DataFrames have compatible datetime formats
weather_clean['date'] = pd.to_datetime(weather_clean['date']) weather_clean['date'] = pd.to_datetime(weather_clean['date'])
daily_sales['date'] = pd.to_datetime(daily_sales['date']) daily_sales['date'] = pd.to_datetime(daily_sales['date'])
# Remove timezone info from both to make them compatible # ✅ NEW FIX: Normalize both to timezone-naive datetime for merge compatibility
if weather_clean['date'].dt.tz is not None: if weather_clean['date'].dt.tz is not None:
weather_clean['date'] = weather_clean['date'].dt.tz_localize(None) # Convert timezone-aware to UTC then remove timezone info
weather_clean['date'] = weather_clean['date'].dt.tz_convert('UTC').dt.tz_localize(None)
if daily_sales['date'].dt.tz is not None: if daily_sales['date'].dt.tz is not None:
daily_sales['date'] = daily_sales['date'].dt.tz_localize(None) # Convert timezone-aware to UTC then remove timezone info
daily_sales['date'] = daily_sales['date'].dt.tz_convert('UTC').dt.tz_localize(None)
# Map weather columns to standard names # Map weather columns to standard names
weather_mapping = { weather_mapping = {
@@ -390,15 +393,19 @@ class BakeryDataProcessor:
if 'date' not in traffic_clean.columns and 'ds' in traffic_clean.columns: if 'date' not in traffic_clean.columns and 'ds' in traffic_clean.columns:
traffic_clean = traffic_clean.rename(columns={'ds': 'date'}) traffic_clean = traffic_clean.rename(columns={'ds': 'date'})
# 🔧 FIX: Ensure timezone awareness before merge # 🔧 CRITICAL FIX: Ensure both DataFrames have compatible datetime formats
traffic_clean['date'] = pd.to_datetime(traffic_clean['date']) traffic_clean['date'] = pd.to_datetime(traffic_clean['date'])
daily_sales['date'] = pd.to_datetime(daily_sales['date'])
# If timezone-naive, localize to UTC # ✅ NEW FIX: Normalize both to timezone-naive datetime for merge compatibility
if traffic_clean['date'].dt.tz is None: # This prevents the "datetime64[ns] and datetime64[ns, UTC]" merge error
traffic_clean['date'] = traffic_clean['date'].dt.tz_localize('UTC') if traffic_clean['date'].dt.tz is not None:
# If already timezone-aware but not UTC, convert to UTC # Convert timezone-aware to UTC then remove timezone info
elif str(traffic_clean['date'].dt.tz) != 'UTC': traffic_clean['date'] = traffic_clean['date'].dt.tz_convert('UTC').dt.tz_localize(None)
traffic_clean['date'] = traffic_clean['date'].dt.tz_convert('UTC')
if daily_sales['date'].dt.tz is not None:
# Convert timezone-aware to UTC then remove timezone info
daily_sales['date'] = daily_sales['date'].dt.tz_convert('UTC').dt.tz_localize(None)
# Map traffic columns to standard names # Map traffic columns to standard names
traffic_mapping = { traffic_mapping = {