Start fixing forecast service API 14
This commit is contained in:
Urtzi Alfaro
@@ -17,6 +17,7 @@ import pandas as pd
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import httpx
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from pathlib import Path
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import os
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import joblib
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from app.core.config import settings
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from shared.monitoring.metrics import MetricsCollector
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@@ -89,7 +90,11 @@ class PredictionService:
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raise
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async def _load_model(self, model_id: str, model_path: str):
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"""Load model from shared volume using API metadata"""
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"""Load model from shared volume using joblib"""
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if not await self._validate_model_file(model_path):
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logger.error(f"Model file not valid: {model_path}")
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return None
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# Check cache first
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if model_id in self.model_cache:
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@@ -98,11 +103,10 @@ class PredictionService:
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return cached_model
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try:
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# Load model directly from shared volume (fast!)
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if os.path.exists(model_path):
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with open(model_path, 'rb') as f:
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model = pickle.load(f)
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# ✅ FIX: Use joblib.load instead of pickle.load
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model = joblib.load(model_path)
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# Cache the model
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self.model_cache[model_id] = (model, datetime.now())
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logger.info(f"Model loaded from shared volume: {model_path}")
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@@ -114,6 +118,34 @@ class PredictionService:
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except Exception as e:
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logger.error(f"Error loading model: {e}")
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return None
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async def _validate_model_file(self, model_path: str) -> bool:
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"""Validate model file before loading"""
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try:
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if not os.path.exists(model_path):
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logger.error(f"Model file not found: {model_path}")
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return False
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# Check file size (should be > 1KB for a trained model)
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file_size = os.path.getsize(model_path)
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if file_size < 1024:
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logger.warning(f"Model file too small ({file_size} bytes): {model_path}")
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return False
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# Try to peek at file header to detect format
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with open(model_path, 'rb') as f:
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header = f.read(8)
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# Check for joblib signature
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if header.startswith(b']\x93PICKLE') or header.startswith(b'\x80\x03'):
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return True
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else:
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logger.warning(f"Unrecognized file format: {model_path}")
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return False
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except Exception as e:
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logger.error(f"Model validation error: {e}")
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return False
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def _prepare_prophet_features(self, features: Dict[str, Any]) -> pd.DataFrame:
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"""Convert features to Prophet-compatible DataFrame"""
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@@ -136,18 +168,67 @@ class PredictionService:
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else:
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df[feature] = 0.0
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# Add categorical features
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df['day_of_week'] = int(features.get('day_of_week', 0))
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df['is_weekend'] = int(features.get('is_weekend', False))
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# Extract date information for temporal features
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forecast_date = pd.to_datetime(features['date'])
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day_of_week = forecast_date.weekday() # 0=Monday, 6=Sunday
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# Add temporal features (MUST match training service exactly!)
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df['day_of_week'] = int(day_of_week)
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df['day_of_month'] = int(forecast_date.day)
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df['month'] = int(forecast_date.month)
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df['quarter'] = int(forecast_date.quarter)
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df['week_of_year'] = int(forecast_date.isocalendar().week)
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# Bakery-specific temporal features (match training exactly!)
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df['is_weekend'] = int(day_of_week >= 5) # Saturday=5, Sunday=6
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df['is_monday'] = int(day_of_week == 0) # ✅ FIX: Add missing is_monday
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df['is_tuesday'] = int(day_of_week == 1)
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df['is_wednesday'] = int(day_of_week == 2)
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df['is_thursday'] = int(day_of_week == 3)
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df['is_friday'] = int(day_of_week == 4)
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df['is_saturday'] = int(day_of_week == 5)
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df['is_sunday'] = int(day_of_week == 6)
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# Month-based features
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df['is_january'] = int(forecast_date.month == 1)
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df['is_february'] = int(forecast_date.month == 2)
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df['is_march'] = int(forecast_date.month == 3)
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df['is_april'] = int(forecast_date.month == 4)
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df['is_may'] = int(forecast_date.month == 5)
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df['is_june'] = int(forecast_date.month == 6)
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df['is_july'] = int(forecast_date.month == 7)
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df['is_august'] = int(forecast_date.month == 8)
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df['is_september'] = int(forecast_date.month == 9)
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df['is_october'] = int(forecast_date.month == 10)
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df['is_november'] = int(forecast_date.month == 11)
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df['is_december'] = int(forecast_date.month == 12)
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# Season-based features
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season = ((forecast_date.month % 12) + 3) // 3 # 1=spring, 2=summer, 3=autumn, 4=winter
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df['is_spring'] = int(season == 1)
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df['is_summer'] = int(season == 2)
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df['is_autumn'] = int(season == 3)
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df['is_winter'] = int(season == 4)
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# Business context features
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df['is_holiday'] = int(features.get('is_holiday', False))
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# Business type encoding
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business_type = features.get('business_type', 'individual')
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df['is_central_workshop'] = int(business_type == 'central_workshop')
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df['is_individual_bakery'] = int(business_type == 'individual')
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# Special day features (these might be in training data)
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df['is_month_start'] = int(forecast_date.day <= 3)
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df['is_month_end'] = int(forecast_date.day >= 28)
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df['is_quarter_start'] = int(forecast_date.month in [1, 4, 7, 10] and forecast_date.day <= 7)
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df['is_quarter_end'] = int(forecast_date.month in [3, 6, 9, 12] and forecast_date.day >= 25)
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logger.debug("Prepared Prophet features",
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features_count=len(df.columns),
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date=features['date'])
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date=features['date'],
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day_of_week=day_of_week,
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is_monday=df['is_monday'].iloc[0])
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return df
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