Add POI feature and imporve the overall backend implementation
This commit is contained in:
Urtzi Alfaro
204
services/training/app/ml/poi_feature_integrator.py
Normal file
204
services/training/app/ml/poi_feature_integrator.py
Normal file
@@ -0,0 +1,204 @@
|
||||
"""
|
||||
POI Feature Integrator
|
||||
|
||||
Integrates POI features into ML training pipeline.
|
||||
Fetches POI context from External service and merges features into training data.
|
||||
"""
|
||||
|
||||
import httpx
|
||||
from typing import Dict, Any, Optional, List
|
||||
import structlog
|
||||
import pandas as pd
|
||||
|
||||
logger = structlog.get_logger()
|
||||
|
||||
|
||||
class POIFeatureIntegrator:
|
||||
"""
|
||||
POI feature integration for ML training.
|
||||
|
||||
Fetches POI context from External service and adds features
|
||||
to training dataframes for location-based demand forecasting.
|
||||
"""
|
||||
|
||||
def __init__(self, external_service_url: str = "http://external-service:8000"):
|
||||
"""
|
||||
Initialize POI feature integrator.
|
||||
|
||||
Args:
|
||||
external_service_url: Base URL for external service
|
||||
"""
|
||||
self.external_service_url = external_service_url.rstrip("/")
|
||||
self.poi_context_endpoint = f"{self.external_service_url}/poi-context"
|
||||
|
||||
async def fetch_poi_features(
|
||||
self,
|
||||
tenant_id: str,
|
||||
latitude: float,
|
||||
longitude: float,
|
||||
force_refresh: bool = False
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
Fetch POI features for tenant location.
|
||||
|
||||
First checks if POI context exists, if not, triggers detection.
|
||||
|
||||
Args:
|
||||
tenant_id: Tenant UUID
|
||||
latitude: Bakery latitude
|
||||
longitude: Bakery longitude
|
||||
force_refresh: Force re-detection
|
||||
|
||||
Returns:
|
||||
Dictionary with POI features or None if detection fails
|
||||
"""
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=60.0) as client:
|
||||
# Try to get existing POI context first
|
||||
if not force_refresh:
|
||||
try:
|
||||
response = await client.get(
|
||||
f"{self.poi_context_endpoint}/{tenant_id}"
|
||||
)
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
poi_context = data.get("poi_context", {})
|
||||
|
||||
# Check if stale
|
||||
if not data.get("is_stale", False):
|
||||
logger.info(
|
||||
"Using existing POI context",
|
||||
tenant_id=tenant_id
|
||||
)
|
||||
return poi_context.get("ml_features", {})
|
||||
else:
|
||||
logger.info(
|
||||
"POI context is stale, refreshing",
|
||||
tenant_id=tenant_id
|
||||
)
|
||||
force_refresh = True
|
||||
except httpx.HTTPStatusError as e:
|
||||
if e.response.status_code != 404:
|
||||
raise
|
||||
logger.info(
|
||||
"No existing POI context, will detect",
|
||||
tenant_id=tenant_id
|
||||
)
|
||||
|
||||
# Detect or refresh POIs
|
||||
logger.info(
|
||||
"Detecting POIs for tenant",
|
||||
tenant_id=tenant_id,
|
||||
location=(latitude, longitude)
|
||||
)
|
||||
|
||||
response = await client.post(
|
||||
f"{self.poi_context_endpoint}/{tenant_id}/detect",
|
||||
params={
|
||||
"latitude": latitude,
|
||||
"longitude": longitude,
|
||||
"force_refresh": force_refresh
|
||||
}
|
||||
)
|
||||
response.raise_for_status()
|
||||
|
||||
result = response.json()
|
||||
poi_context = result.get("poi_context", {})
|
||||
ml_features = poi_context.get("ml_features", {})
|
||||
|
||||
logger.info(
|
||||
"POI detection completed",
|
||||
tenant_id=tenant_id,
|
||||
total_pois=poi_context.get("total_pois_detected", 0),
|
||||
feature_count=len(ml_features)
|
||||
)
|
||||
|
||||
return ml_features
|
||||
|
||||
except httpx.HTTPError as e:
|
||||
logger.error(
|
||||
"Failed to fetch POI features",
|
||||
tenant_id=tenant_id,
|
||||
error=str(e),
|
||||
exc_info=True
|
||||
)
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"Unexpected error fetching POI features",
|
||||
tenant_id=tenant_id,
|
||||
error=str(e),
|
||||
exc_info=True
|
||||
)
|
||||
return None
|
||||
|
||||
def add_poi_features_to_dataframe(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
poi_features: Dict[str, Any]
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Add POI features to training dataframe.
|
||||
|
||||
POI features are static (don't vary by date), so they're
|
||||
broadcast to all rows in the dataframe.
|
||||
|
||||
Args:
|
||||
df: Training dataframe
|
||||
poi_features: Dictionary of POI ML features
|
||||
|
||||
Returns:
|
||||
Dataframe with POI features added as columns
|
||||
"""
|
||||
if not poi_features:
|
||||
logger.warning("No POI features to add")
|
||||
return df
|
||||
|
||||
logger.info(
|
||||
"Adding POI features to dataframe",
|
||||
feature_count=len(poi_features),
|
||||
dataframe_rows=len(df)
|
||||
)
|
||||
|
||||
# Add each POI feature as a column with constant value
|
||||
for feature_name, feature_value in poi_features.items():
|
||||
df[feature_name] = feature_value
|
||||
|
||||
logger.info(
|
||||
"POI features added successfully",
|
||||
new_columns=list(poi_features.keys())
|
||||
)
|
||||
|
||||
return df
|
||||
|
||||
def get_poi_feature_names(self, poi_features: Dict[str, Any]) -> List[str]:
|
||||
"""
|
||||
Get list of POI feature names for model registration.
|
||||
|
||||
Args:
|
||||
poi_features: Dictionary of POI ML features
|
||||
|
||||
Returns:
|
||||
List of feature names
|
||||
"""
|
||||
return list(poi_features.keys()) if poi_features else []
|
||||
|
||||
async def check_poi_service_health(self) -> bool:
|
||||
"""
|
||||
Check if POI service is accessible.
|
||||
|
||||
Returns:
|
||||
True if service is healthy, False otherwise
|
||||
"""
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=5.0) as client:
|
||||
response = await client.get(
|
||||
f"{self.poi_context_endpoint}/health"
|
||||
)
|
||||
return response.status_code == 200
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"POI service health check failed",
|
||||
error=str(e)
|
||||
)
|
||||
return False
|
||||
Reference in New Issue
Block a user