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Start fixing forecast service 16

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This commit is contained in:
Urtzi Alfaro
2025-07-30 08:13:32 +02:00
parent 1d05e125a5
commit e5c3375d53
4 changed files with 101 additions and 36 deletions
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1
services/forecasting/app/main.py
View File

@@ -46,6 +46,7 @@ async def lifespan(app: FastAPI):
# Register custom metrics
metrics_collector.register_counter("forecasts_generated_total", "Total forecasts generated")
metrics_collector.register_counter("predictions_served_total", "Total predictions served")
metrics_collector.register_counter("prediction_errors_total", "Total prediction errors")
metrics_collector.register_histogram("forecast_processing_time_seconds", "Time to process forecast request")
metrics_collector.register_gauge("active_models_count", "Number of active models")

107
services/forecasting/app/services/forecasting_service.py
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@@ -89,16 +89,39 @@ class ForecastingService:
prediction=adjusted_prediction['prediction'])
return ForecastResponse(
id=forecast.id,
forecast_date=forecast.forecast_date,
id=str(forecast.id),
tenant_id=str(forecast.tenant_id),
product_name=forecast.product_name,
predicted_quantity=forecast.predicted_quantity,
location=forecast.location,
forecast_date=forecast.forecast_date,
# Predictions
predicted_demand=forecast.predicted_demand,
confidence_lower=forecast.confidence_lower,
confidence_upper=forecast.confidence_upper,
confidence_level=forecast.confidence_level,
lower_bound=forecast.lower_bound,
upper_bound=forecast.upper_bound,
model_id=forecast.model_id,
# Model info
model_id=str(forecast.model_id),
model_version=forecast.model_version,
algorithm=forecast.algorithm,
# Context
business_type=forecast.business_type,
is_holiday=forecast.is_holiday,
is_weekend=forecast.is_weekend,
day_of_week=forecast.day_of_week,
# External factors
weather_temperature=forecast.weather_temperature,
weather_precipitation=forecast.weather_precipitation,
weather_description=forecast.weather_description,
traffic_volume=forecast.traffic_volume,
# Metadata
created_at=forecast.created_at,
external_factors=forecast.external_factors
processing_time_ms=forecast.processing_time_ms,
features_used=forecast.features_used
)
except Exception as e:
@@ -171,7 +194,7 @@ class ForecastingService:
await self._add_weather_features_with_fallbacks(features, tenant_id)
# Add traffic data with fallbacks
# await self._add_traffic_features_with_fallbacks(features, tenant_id)
await self._add_traffic_features_with_fallbacks(features, tenant_id)
return features
@@ -248,23 +271,23 @@ class ForecastingService:
) -> None:
"""Add traffic features with fallbacks"""
try:
traffic_data = await self.data_client.get_traffic_data(
tenant_id=tenant_id,
latitude=40.4168,
longitude=-3.7038
)
if traffic_data:
features.update({
"traffic_volume": traffic_data.get("traffic_volume", 100),
"pedestrian_count": traffic_data.get("pedestrian_count", 50),
})
logger.info("Traffic data acquired successfully", tenant_id=tenant_id)
return
# try:
# traffic_data = await self.data_client.get_traffic_data(
# tenant_id=tenant_id,
# latitude=40.4168,
# longitude=-3.7038
# )
#
# if traffic_data:
# features.update({
# "traffic_volume": traffic_data.get("traffic_volume", 100),
# "pedestrian_count": traffic_data.get("pedestrian_count", 50),
# })
# logger.info("Traffic data acquired successfully", tenant_id=tenant_id)
# return
except Exception as e:
logger.warning("Traffic data acquisition failed", error=str(e))
# except Exception as e:
# logger.warning("Traffic data acquisition failed", error=str(e))
# Fallback: Use typical values based on day of week
day_of_week = features["day_of_week"]
@@ -273,6 +296,7 @@ class ForecastingService:
features.update({
"traffic_volume": int(100 * weekend_factor),
"pedestrian_count": int(50 * weekend_factor),
"congestion_level": 1
})
logger.warning("Using default traffic values", tenant_id=tenant_id)
@@ -398,15 +422,36 @@ class ForecastingService:
forecast = Forecast(
tenant_id=tenant_id,
forecast_date=request.forecast_date,
product_name=request.product_name,
predicted_quantity=prediction["prediction"],
location=request.location,
forecast_date=request.forecast_date,
# Predictions
predicted_demand=prediction['prediction'],
confidence_lower=prediction['lower_bound'],
confidence_upper=prediction['upper_bound'],
confidence_level=request.confidence_level,
lower_bound=prediction["lower_bound"],
upper_bound=prediction["upper_bound"],
model_id=model_data["model_id"],
external_factors=features,
created_at=datetime.utcnow()
# Model info
model_id=model_data['model_id'],
model_version=model_data.get('version', '1.0'),
algorithm=model_data.get('algorithm', 'prophet'),
# Context
business_type=features.get('business_type', 'individual'),
is_holiday=features.get('is_holiday', False),
is_weekend=features.get('is_weekend', False),
day_of_week=features.get('day_of_week', 0),
# External factors
weather_temperature=features.get('temperature'),
weather_precipitation=features.get('precipitation'),
weather_description=features.get('weather_description'),
traffic_volume=features.get('traffic_volume'),
# Metadata
processing_time_ms=int((datetime.now() - start_time).total_seconds() * 1000),
features_used=features
)
db.add(forecast)

9
services/forecasting/app/services/prediction_service.py
View File

@@ -220,7 +220,7 @@ class PredictionService:
# Add numeric features with safe conversion
numeric_features = [
'temperature', 'precipitation', 'humidity', 'wind_speed',
'traffic_volume', 'pedestrian_count', 'pressure' # ✅ FIX: Added pressure
'traffic_volume', 'pedestrian_count', 'pressure'
]
for feature in numeric_features:
@@ -313,15 +313,13 @@ class PredictionService:
df['is_heavy_rain'] = int(precip > 10.0)
df['rain_intensity'] = int(self._get_rain_intensity(precip))
# Traffic-based features (if available)
# Traffic-based features
if 'traffic_volume' in df.columns and df['traffic_volume'].iloc[0] > 0:
traffic = df['traffic_volume'].iloc[0]
# Simple categorization since we don't have historical data for quantiles
df['high_traffic'] = int(traffic > 150) # Assumption based on typical values
df['low_traffic'] = int(traffic < 50)
df['traffic_normalized'] = float((traffic - 100) / 50) # Simple normalization
# ✅ FIX: Add additional traffic features that might be in training
df['congestion_level'] = int(min(5, max(1, traffic // 50)))
df['traffic_squared'] = traffic ** 2
df['traffic_log'] = float(np.log1p(traffic)) # log(1+traffic) to handle zeros
else:
@@ -330,6 +328,7 @@ class PredictionService:
df['traffic_normalized'] = 0.0
df['traffic_squared'] = 0.0
df['traffic_log'] = 0.0
df['congestion_level'] = 1
# Interaction features (common in training)
if 'is_weekend' in df.columns and 'temperature' in df.columns:

20
services/training/app/ml/data_processor.py
View File

@@ -27,6 +27,21 @@ class BakeryDataProcessor:
self.imputers = {} # Store imputers for missing value handling
self.date_alignment_service = DateAlignmentService()
def _ensure_timezone_aware(self, df: pd.DataFrame, date_column: str = 'date') -> pd.DataFrame:
"""Ensure date column is timezone-aware to prevent conversion errors"""
if date_column in df.columns:
# Convert to datetime if not already
df[date_column] = pd.to_datetime(df[date_column])
# If timezone-naive, localize to UTC
if df[date_column].dt.tz is None:
df[date_column] = df[date_column].dt.tz_localize('UTC')
# If already timezone-aware but not UTC, convert to UTC
elif str(df[date_column].dt.tz) != 'UTC':
df[date_column] = df[date_column].dt.tz_convert('UTC')
return df
async def prepare_training_data(self,
sales_data: pd.DataFrame,
weather_data: pd.DataFrame,
@@ -50,6 +65,11 @@ class BakeryDataProcessor:
# Step 1: Convert and validate sales data
sales_clean = await self._process_sales_data(sales_data, product_name)
# FIX: Ensure timezone awareness before any operations
sales_clean = self._ensure_timezone_aware(sales_clean)
weather_data = self._ensure_timezone_aware(weather_data) if not weather_data.empty else weather_data
traffic_data = self._ensure_timezone_aware(traffic_data) if not traffic_data.empty else traffic_data
# Step 2: Apply date alignment if we have date constraints
sales_clean = await self._apply_date_alignment(sales_clean, weather_data, traffic_data)