728 lines
34 KiB
Python
728 lines
34 KiB
Python
# services/training/app/ml/prophet_manager.py
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"""
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Simplified Prophet Manager with Built-in Hyperparameter Optimization
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Direct replacement for existing BakeryProphetManager - optimization always enabled.
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"""
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from typing import Dict, List, Any, Optional, Tuple
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import pandas as pd
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import numpy as np
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from prophet import Prophet
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import logging
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from datetime import datetime, timedelta
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import uuid
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import os
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import joblib
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from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
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from sklearn.model_selection import TimeSeriesSplit
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import json
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from pathlib import Path
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import math
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import warnings
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warnings.filterwarnings('ignore')
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import text
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from app.models.training import TrainedModel
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from shared.database.base import create_database_manager
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from app.repositories import ModelRepository
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# Simple optimization import
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import optuna
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optuna.logging.set_verbosity(optuna.logging.WARNING)
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from app.core.config import settings
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logger = logging.getLogger(__name__)
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class BakeryProphetManager:
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"""
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Simplified Prophet Manager with built-in hyperparameter optimization.
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Drop-in replacement for the existing manager - optimization runs automatically.
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"""
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def __init__(self, database_manager=None):
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self.models = {} # In-memory model storage
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self.model_metadata = {} # Store model metadata
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self.database_manager = database_manager or create_database_manager(settings.DATABASE_URL, "training-service")
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self.db_session = None # Will be set when session is available
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# Ensure model storage directory exists
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os.makedirs(settings.MODEL_STORAGE_PATH, exist_ok=True)
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async def train_bakery_model(self,
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tenant_id: str,
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product_name: str,
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df: pd.DataFrame,
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job_id: str) -> Dict[str, Any]:
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"""
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Train a Prophet model with automatic hyperparameter optimization.
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Same interface as before - optimization happens automatically.
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"""
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try:
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logger.info(f"Training optimized bakery model for {product_name}")
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# Validate input data
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await self._validate_training_data(df, product_name)
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# Prepare data for Prophet
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prophet_data = await self._prepare_prophet_data(df)
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# Get regressor columns
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regressor_columns = self._extract_regressor_columns(prophet_data)
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# Automatically optimize hyperparameters (this is the new part)
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logger.info(f"Optimizing hyperparameters for {product_name}...")
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best_params = await self._optimize_hyperparameters(prophet_data, product_name, regressor_columns)
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# Create optimized Prophet model
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model = self._create_optimized_prophet_model(best_params, regressor_columns)
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# Add regressors to model
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for regressor in regressor_columns:
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if regressor in prophet_data.columns:
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model.add_regressor(regressor)
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# Fit the model
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model.fit(prophet_data)
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# Calculate enhanced training metrics first
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training_metrics = await self._calculate_training_metrics(model, prophet_data, best_params)
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# Store model and metrics - Generate proper UUID for model_id
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model_id = str(uuid.uuid4())
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model_path = await self._store_model(
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tenant_id, product_name, model, model_id, prophet_data, regressor_columns, best_params, training_metrics
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)
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# Return same format as before, but with optimization info
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model_info = {
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"model_id": model_id,
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"model_path": model_path,
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"type": "prophet_optimized", # Changed from "prophet"
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"training_samples": len(prophet_data),
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"features": regressor_columns,
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"hyperparameters": best_params, # Now contains optimized params
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"training_metrics": training_metrics,
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"trained_at": datetime.now().isoformat(),
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"data_period": {
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"start_date": prophet_data['ds'].min().isoformat(),
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"end_date": prophet_data['ds'].max().isoformat(),
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"total_days": len(prophet_data)
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}
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}
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logger.info(f"Optimized model trained successfully for {product_name}. "
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f"MAPE: {training_metrics.get('optimized_mape', 'N/A')}%")
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return model_info
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except Exception as e:
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logger.error(f"Failed to train optimized bakery model for {product_name}: {str(e)}")
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raise
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async def _optimize_hyperparameters(self,
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df: pd.DataFrame,
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product_name: str,
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regressor_columns: List[str]) -> Dict[str, Any]:
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"""
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Automatically optimize Prophet hyperparameters using Bayesian optimization.
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Simplified - no configuration needed.
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"""
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# Determine product category automatically
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product_category = self._classify_product(product_name, df)
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# Set optimization parameters based on category
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n_trials = {
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'high_volume': 30, # Reduced from 75 for speed
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'medium_volume': 25, # Reduced from 50
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'low_volume': 20, # Reduced from 30
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'intermittent': 15 # Reduced from 25
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}.get(product_category, 25)
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logger.info(f"Product {product_name} classified as {product_category}, using {n_trials} trials")
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# Check data quality and adjust strategy
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total_sales = df['y'].sum()
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zero_ratio = (df['y'] == 0).sum() / len(df)
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mean_sales = df['y'].mean()
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non_zero_days = len(df[df['y'] > 0])
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logger.info(f"Data analysis for {product_name}: total_sales={total_sales:.1f}, "
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f"zero_ratio={zero_ratio:.2f}, mean_sales={mean_sales:.2f}, non_zero_days={non_zero_days}")
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# Adjust strategy based on data characteristics
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if zero_ratio > 0.8 or non_zero_days < 30:
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logger.warning(f"Very sparse data for {product_name}, using minimal optimization")
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return {
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'changepoint_prior_scale': 0.001,
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'seasonality_prior_scale': 0.01,
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'holidays_prior_scale': 0.01,
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'changepoint_range': 0.8,
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'seasonality_mode': 'additive',
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'daily_seasonality': False,
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'weekly_seasonality': True,
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'yearly_seasonality': False,
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'uncertainty_samples': 100 # ✅ FIX: Minimal uncertainty sampling for very sparse data
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}
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elif zero_ratio > 0.6:
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logger.info(f"Moderate sparsity for {product_name}, using conservative optimization")
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return {
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'changepoint_prior_scale': 0.01,
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'seasonality_prior_scale': 0.1,
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'holidays_prior_scale': 0.1,
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'changepoint_range': 0.8,
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'seasonality_mode': 'additive',
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'daily_seasonality': False,
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'weekly_seasonality': True,
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'yearly_seasonality': len(df) > 365, # Only if we have enough data
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'uncertainty_samples': 200 # ✅ FIX: Conservative uncertainty sampling for moderately sparse data
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}
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# Use unique seed for each product to avoid identical results
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product_seed = hash(product_name) % 10000
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def objective(trial):
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try:
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# Sample hyperparameters with product-specific ranges
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if product_category == 'high_volume':
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# More conservative for high volume (less overfitting)
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changepoint_scale_range = (0.001, 0.1)
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seasonality_scale_range = (1.0, 10.0)
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elif product_category == 'intermittent':
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# Very conservative for intermittent
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changepoint_scale_range = (0.001, 0.05)
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seasonality_scale_range = (0.01, 1.0)
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else:
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# Default ranges
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changepoint_scale_range = (0.001, 0.5)
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seasonality_scale_range = (0.01, 10.0)
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# ✅ FIX: Determine appropriate uncertainty samples range based on product category
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if product_category == 'high_volume':
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uncertainty_range = (300, 800) # More samples for stable high-volume products
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elif product_category == 'medium_volume':
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uncertainty_range = (200, 500) # Moderate samples for medium volume
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elif product_category == 'low_volume':
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uncertainty_range = (150, 300) # Fewer samples for low volume
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else: # intermittent
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uncertainty_range = (100, 200) # Minimal samples for intermittent demand
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params = {
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'changepoint_prior_scale': trial.suggest_float(
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'changepoint_prior_scale',
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changepoint_scale_range[0],
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changepoint_scale_range[1],
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log=True
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),
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'seasonality_prior_scale': trial.suggest_float(
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'seasonality_prior_scale',
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seasonality_scale_range[0],
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seasonality_scale_range[1],
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log=True
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),
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'holidays_prior_scale': trial.suggest_float('holidays_prior_scale', 0.01, 10.0, log=True),
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'changepoint_range': trial.suggest_float('changepoint_range', 0.8, 0.95),
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'seasonality_mode': 'additive' if product_category == 'high_volume' else trial.suggest_categorical('seasonality_mode', ['additive', 'multiplicative']),
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'daily_seasonality': trial.suggest_categorical('daily_seasonality', [True, False]),
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'weekly_seasonality': True, # Always keep weekly
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'yearly_seasonality': trial.suggest_categorical('yearly_seasonality', [True, False]),
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'uncertainty_samples': trial.suggest_int('uncertainty_samples', uncertainty_range[0], uncertainty_range[1]) # ✅ FIX: Adaptive uncertainty sampling
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}
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# Simple 2-fold cross-validation for speed
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tscv = TimeSeriesSplit(n_splits=2)
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cv_scores = []
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for train_idx, val_idx in tscv.split(df):
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train_data = df.iloc[train_idx].copy()
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val_data = df.iloc[val_idx].copy()
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if len(val_data) < 7: # Need at least a week
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continue
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try:
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# Create and train model with adaptive uncertainty sampling
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uncertainty_samples = params.get('uncertainty_samples', 200) # ✅ FIX: Use adaptive uncertainty samples
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model = Prophet(**{k: v for k, v in params.items() if k != 'uncertainty_samples'},
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interval_width=0.8, uncertainty_samples=uncertainty_samples)
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for regressor in regressor_columns:
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if regressor in train_data.columns:
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model.add_regressor(regressor)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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model.fit(train_data)
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# Predict on validation set
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future_df = model.make_future_dataframe(periods=0)
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for regressor in regressor_columns:
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if regressor in df.columns:
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future_df[regressor] = df[regressor].values[:len(future_df)]
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forecast = model.predict(future_df)
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val_predictions = forecast['yhat'].iloc[train_idx[-1]+1:train_idx[-1]+1+len(val_data)]
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val_actual = val_data['y'].values
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# Calculate MAPE with improved handling for low values
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if len(val_predictions) > 0 and len(val_actual) > 0:
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# Use MAE for very low sales values to avoid MAPE issues
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if val_actual.mean() < 1:
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mae = np.mean(np.abs(val_actual - val_predictions.values))
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# Convert MAE to percentage-like metric
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mape_like = (mae / max(val_actual.mean(), 0.1)) * 100
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else:
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non_zero_mask = val_actual > 0.1 # Use threshold instead of zero
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if np.sum(non_zero_mask) > 0:
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mape = np.mean(np.abs((val_actual[non_zero_mask] - val_predictions.values[non_zero_mask]) / val_actual[non_zero_mask])) * 100
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mape_like = min(mape, 200) # Cap at 200%
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else:
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mape_like = 100
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if not np.isnan(mape_like) and not np.isinf(mape_like):
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cv_scores.append(mape_like)
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except Exception as fold_error:
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logger.debug(f"Fold failed for {product_name} trial {trial.number}: {str(fold_error)}")
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continue
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return np.mean(cv_scores) if len(cv_scores) > 0 else 100.0
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except Exception as trial_error:
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logger.debug(f"Trial {trial.number} failed for {product_name}: {str(trial_error)}")
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return 100.0
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# Run optimization with product-specific seed
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study = optuna.create_study(
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direction='minimize',
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sampler=optuna.samplers.TPESampler(seed=product_seed) # Unique seed per product
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)
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study.optimize(objective, n_trials=n_trials, timeout=600, show_progress_bar=False)
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# Return best parameters
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best_params = study.best_params
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best_score = study.best_value
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logger.info(f"Optimization completed for {product_name}. Best score: {best_score:.2f}%. "
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f"Parameters: {best_params}")
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# ✅ FIX: Log uncertainty sampling configuration for debugging confidence intervals
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uncertainty_samples = best_params.get('uncertainty_samples', 500)
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logger.info(f"Prophet model will use {uncertainty_samples} uncertainty samples for {product_name} "
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f"(category: {product_category}, zero_ratio: {zero_ratio:.2f})")
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return best_params
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def _classify_product(self, product_name: str, sales_data: pd.DataFrame) -> str:
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"""Automatically classify product for optimization strategy - improved for bakery data"""
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product_lower = product_name.lower()
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# Calculate sales statistics
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total_sales = sales_data['y'].sum()
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mean_sales = sales_data['y'].mean()
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zero_ratio = (sales_data['y'] == 0).sum() / len(sales_data)
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non_zero_days = len(sales_data[sales_data['y'] > 0])
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logger.info(f"Product classification for {product_name}: total_sales={total_sales:.1f}, "
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f"mean_sales={mean_sales:.2f}, zero_ratio={zero_ratio:.2f}, non_zero_days={non_zero_days}")
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# Improved classification logic for bakery products
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# Consider both volume and consistency
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# Check for truly intermittent demand (high zero ratio)
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if zero_ratio > 0.8 or non_zero_days < 30:
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return 'intermittent'
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# High volume products (consistent daily sales)
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if any(pattern in product_lower for pattern in ['cafe', 'pan', 'bread', 'coffee']):
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# Even if absolute volume is low, these are core products
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return 'high_volume' if zero_ratio < 0.3 else 'medium_volume'
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# Volume-based classification for other products
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if mean_sales >= 10 and zero_ratio < 0.4:
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return 'high_volume'
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elif mean_sales >= 5 and zero_ratio < 0.6:
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return 'medium_volume'
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elif mean_sales >= 2 and zero_ratio < 0.7:
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return 'low_volume'
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else:
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return 'intermittent'
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def _create_optimized_prophet_model(self, optimized_params: Dict[str, Any], regressor_columns: List[str]) -> Prophet:
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"""Create Prophet model with optimized parameters and adaptive uncertainty sampling"""
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holidays = self._get_spanish_holidays()
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# Determine uncertainty samples based on data characteristics
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uncertainty_samples = optimized_params.get('uncertainty_samples', 500)
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model = Prophet(
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holidays=holidays if not holidays.empty else None,
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daily_seasonality=optimized_params.get('daily_seasonality', True),
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weekly_seasonality=optimized_params.get('weekly_seasonality', True),
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yearly_seasonality=optimized_params.get('yearly_seasonality', True),
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seasonality_mode=optimized_params.get('seasonality_mode', 'additive'),
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changepoint_prior_scale=optimized_params.get('changepoint_prior_scale', 0.05),
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seasonality_prior_scale=optimized_params.get('seasonality_prior_scale', 10.0),
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holidays_prior_scale=optimized_params.get('holidays_prior_scale', 10.0),
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changepoint_range=optimized_params.get('changepoint_range', 0.8),
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interval_width=0.8,
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mcmc_samples=0,
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uncertainty_samples=uncertainty_samples
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)
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return model
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# All the existing methods remain the same, just with enhanced metrics
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async def _calculate_training_metrics(self,
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model: Prophet,
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training_data: pd.DataFrame,
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optimized_params: Dict[str, Any] = None) -> Dict[str, float]:
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"""Calculate training metrics with optimization info and improved MAPE handling"""
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try:
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# Generate in-sample predictions
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forecast = model.predict(training_data[['ds'] + [col for col in training_data.columns if col not in ['ds', 'y']]])
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# Calculate metrics
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y_true = training_data['y'].values
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y_pred = forecast['yhat'].values
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# Basic metrics
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mae = mean_absolute_error(y_true, y_pred)
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mse = mean_squared_error(y_true, y_pred)
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rmse = np.sqrt(mse)
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# Improved MAPE calculation for bakery data
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mean_actual = y_true.mean()
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median_actual = np.median(y_true[y_true > 0]) if np.any(y_true > 0) else 1.0
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# Use different strategies based on sales volume
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if mean_actual < 2.0:
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# For very low volume products, use normalized MAE
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normalized_mae = mae / max(median_actual, 1.0)
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mape = min(normalized_mae * 100, 200) # Cap at 200%
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logger.info(f"Using normalized MAE for low-volume product (mean={mean_actual:.2f})")
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elif mean_actual < 5.0:
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# For low-medium volume, use modified MAPE with higher threshold
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threshold = 1.0
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valid_mask = y_true >= threshold
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if np.sum(valid_mask) == 0:
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mape = 150.0 # High but not extreme
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else:
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mape_values = np.abs((y_true[valid_mask] - y_pred[valid_mask]) / y_true[valid_mask])
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mape = np.median(mape_values) * 100 # Use median instead of mean to reduce outlier impact
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mape = min(mape, 150) # Cap at reasonable level
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else:
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# Standard MAPE for higher volume products
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threshold = 0.5
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valid_mask = y_true > threshold
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if np.sum(valid_mask) == 0:
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mape = 100.0
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else:
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mape_values = np.abs((y_true[valid_mask] - y_pred[valid_mask]) / y_true[valid_mask])
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mape = np.mean(mape_values) * 100
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# Cap MAPE at reasonable maximum
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if math.isinf(mape) or math.isnan(mape) or mape > 200:
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mape = min(200.0, (mae / max(mean_actual, 1.0)) * 100)
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# R-squared
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ss_res = np.sum((y_true - y_pred) ** 2)
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ss_tot = np.sum((y_true - np.mean(y_true)) ** 2)
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r2 = 1 - (ss_res / ss_tot) if ss_tot != 0 else 0.0
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# Calculate realistic improvement estimate based on actual product performance
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# Use more granular categories and realistic baselines
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total_sales = training_data['y'].sum()
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zero_ratio = (training_data['y'] == 0).sum() / len(training_data)
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mean_sales = training_data['y'].mean()
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non_zero_days = len(training_data[training_data['y'] > 0])
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# More nuanced categorization
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if zero_ratio > 0.8 or non_zero_days < 30:
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category = 'very_sparse'
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baseline_mape = 80.0
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elif zero_ratio > 0.6:
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category = 'sparse'
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baseline_mape = 60.0
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elif mean_sales >= 10 and zero_ratio < 0.3:
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category = 'high_volume'
|
|
baseline_mape = 25.0
|
|
elif mean_sales >= 5 and zero_ratio < 0.5:
|
|
category = 'medium_volume'
|
|
baseline_mape = 35.0
|
|
else:
|
|
category = 'low_volume'
|
|
baseline_mape = 45.0
|
|
|
|
# Calculate improvement - be more conservative
|
|
if mape < baseline_mape * 0.8: # Only claim improvement if significant
|
|
improvement_pct = (baseline_mape - mape) / baseline_mape * 100
|
|
else:
|
|
improvement_pct = 0 # No meaningful improvement
|
|
|
|
# Quality score based on data characteristics
|
|
quality_score = max(0.1, min(1.0, (1 - zero_ratio) * (non_zero_days / len(training_data))))
|
|
|
|
# Enhanced metrics with optimization info
|
|
metrics = {
|
|
"mae": round(mae, 2),
|
|
"mse": round(mse, 2),
|
|
"rmse": round(rmse, 2),
|
|
"mape": round(mape, 2),
|
|
"r2": round(r2, 3),
|
|
"optimized": True,
|
|
"optimized_mape": round(mape, 2),
|
|
"baseline_mape_estimate": round(baseline_mape, 2),
|
|
"improvement_estimated": round(improvement_pct, 1),
|
|
"product_category": category,
|
|
"data_quality_score": round(quality_score, 2),
|
|
"mean_sales_volume": round(mean_sales, 2),
|
|
"sales_consistency": round(non_zero_days / len(training_data), 2),
|
|
"total_demand": round(total_sales, 1)
|
|
}
|
|
|
|
logger.info(f"Training metrics calculated: MAPE={mape:.1f}%, "
|
|
f"Category={category}, Improvement={improvement_pct:.1f}%")
|
|
|
|
return metrics
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error calculating training metrics: {str(e)}")
|
|
return {
|
|
"mae": 0.0, "mse": 0.0, "rmse": 0.0, "mape": 100.0, "r2": 0.0,
|
|
"optimized": False, "improvement_estimated": 0.0
|
|
}
|
|
|
|
async def _store_model(self,
|
|
tenant_id: str,
|
|
product_name: str,
|
|
model: Prophet,
|
|
model_id: str,
|
|
training_data: pd.DataFrame,
|
|
regressor_columns: List[str],
|
|
optimized_params: Dict[str, Any] = None,
|
|
training_metrics: Dict[str, Any] = None) -> str:
|
|
"""Store model with database integration"""
|
|
|
|
# Create model directory
|
|
model_dir = Path(settings.MODEL_STORAGE_PATH) / tenant_id
|
|
model_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
# Store model file
|
|
model_path = model_dir / f"{model_id}.pkl"
|
|
joblib.dump(model, model_path)
|
|
|
|
# Enhanced metadata
|
|
metadata = {
|
|
"model_id": model_id,
|
|
"tenant_id": tenant_id,
|
|
"product_name": product_name,
|
|
"regressor_columns": regressor_columns,
|
|
"training_samples": len(training_data),
|
|
"data_period": {
|
|
"start_date": training_data['ds'].min().isoformat(),
|
|
"end_date": training_data['ds'].max().isoformat()
|
|
},
|
|
"optimized": True,
|
|
"optimized_parameters": optimized_params or {},
|
|
"created_at": datetime.now().isoformat(),
|
|
"model_type": "prophet_optimized",
|
|
"file_path": str(model_path)
|
|
}
|
|
|
|
metadata_path = model_path.with_suffix('.json')
|
|
with open(metadata_path, 'w') as f:
|
|
json.dump(metadata, f, indent=2, default=str)
|
|
|
|
# Store in memory
|
|
model_key = f"{tenant_id}:{product_name}"
|
|
self.models[model_key] = model
|
|
self.model_metadata[model_key] = metadata
|
|
|
|
# 🆕 NEW: Store in database using new session
|
|
try:
|
|
async with self.database_manager.get_session() as db_session:
|
|
# Deactivate previous models for this product
|
|
await self._deactivate_previous_models_with_session(db_session, tenant_id, product_name)
|
|
|
|
# Create new database record
|
|
db_model = TrainedModel(
|
|
id=model_id,
|
|
tenant_id=tenant_id,
|
|
product_name=product_name,
|
|
model_type="prophet_optimized",
|
|
job_id=model_id.split('_')[0], # Extract job_id from model_id
|
|
model_path=str(model_path),
|
|
metadata_path=str(metadata_path),
|
|
hyperparameters=optimized_params or {},
|
|
features_used=regressor_columns,
|
|
is_active=True,
|
|
is_production=True, # New models are production-ready
|
|
training_start_date=training_data['ds'].min().to_pydatetime().replace(tzinfo=None) if training_data['ds'].min().tz is None else training_data['ds'].min().to_pydatetime(),
|
|
training_end_date=training_data['ds'].max().to_pydatetime().replace(tzinfo=None) if training_data['ds'].max().tz is None else training_data['ds'].max().to_pydatetime(),
|
|
training_samples=len(training_data)
|
|
)
|
|
|
|
# Add training metrics if available
|
|
if training_metrics:
|
|
db_model.mape = training_metrics.get('mape')
|
|
db_model.mae = training_metrics.get('mae')
|
|
db_model.rmse = training_metrics.get('rmse')
|
|
db_model.r2_score = training_metrics.get('r2')
|
|
db_model.data_quality_score = training_metrics.get('data_quality_score')
|
|
|
|
db_session.add(db_model)
|
|
await db_session.commit()
|
|
|
|
logger.info(f"Model {model_id} stored in database successfully")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Failed to store model in database: {str(e)}")
|
|
# Continue execution - file storage succeeded
|
|
|
|
logger.info(f"Optimized model stored at: {model_path}")
|
|
return str(model_path)
|
|
|
|
async def _deactivate_previous_models_with_session(self, db_session, tenant_id: str, product_name: str):
|
|
"""Deactivate previous models for the same product using provided session"""
|
|
try:
|
|
# ✅ FIX: Wrap SQL string with text() for SQLAlchemy 2.0
|
|
query = text("""
|
|
UPDATE trained_models
|
|
SET is_active = false, is_production = false
|
|
WHERE tenant_id = :tenant_id AND product_name = :product_name
|
|
""")
|
|
|
|
await db_session.execute(query, {
|
|
"tenant_id": tenant_id,
|
|
"product_name": product_name
|
|
})
|
|
|
|
# Note: Don't commit here, let the calling method handle the transaction
|
|
logger.info(f"Successfully deactivated previous models for {product_name}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"Failed to deactivate previous models: {str(e)}")
|
|
raise
|
|
|
|
# Keep all existing methods unchanged
|
|
async def generate_forecast(self,
|
|
model_path: str,
|
|
future_dates: pd.DataFrame,
|
|
regressor_columns: List[str]) -> pd.DataFrame:
|
|
"""Generate forecast using stored model (unchanged)"""
|
|
try:
|
|
model = joblib.load(model_path)
|
|
|
|
for regressor in regressor_columns:
|
|
if regressor not in future_dates.columns:
|
|
logger.warning(f"Missing regressor {regressor}, filling with median")
|
|
future_dates[regressor] = 0
|
|
|
|
forecast = model.predict(future_dates)
|
|
return forecast
|
|
|
|
except Exception as e:
|
|
logger.error(f"Failed to generate forecast: {str(e)}")
|
|
raise
|
|
|
|
async def _validate_training_data(self, df: pd.DataFrame, product_name: str):
|
|
"""Validate training data quality (unchanged)"""
|
|
if df.empty:
|
|
raise ValueError(f"No training data available for {product_name}")
|
|
|
|
if len(df) < settings.MIN_TRAINING_DATA_DAYS:
|
|
raise ValueError(
|
|
f"Insufficient training data for {product_name}: "
|
|
f"{len(df)} days, minimum required: {settings.MIN_TRAINING_DATA_DAYS}"
|
|
)
|
|
|
|
required_columns = ['ds', 'y']
|
|
missing_columns = [col for col in required_columns if col not in df.columns]
|
|
if missing_columns:
|
|
raise ValueError(f"Missing required columns: {missing_columns}")
|
|
|
|
if df['ds'].isna().any():
|
|
raise ValueError("Invalid dates found in training data")
|
|
|
|
if df['y'].isna().all():
|
|
raise ValueError("No valid target values found")
|
|
|
|
async def _prepare_prophet_data(self, df: pd.DataFrame) -> pd.DataFrame:
|
|
"""Prepare data for Prophet training with timezone handling"""
|
|
prophet_data = df.copy()
|
|
|
|
if 'ds' not in prophet_data.columns:
|
|
raise ValueError("Missing 'ds' column in training data")
|
|
if 'y' not in prophet_data.columns:
|
|
raise ValueError("Missing 'y' column in training data")
|
|
|
|
# Convert to datetime and remove timezone information
|
|
prophet_data['ds'] = pd.to_datetime(prophet_data['ds'])
|
|
|
|
# Remove timezone if present (Prophet doesn't support timezones)
|
|
if prophet_data['ds'].dt.tz is not None:
|
|
logger.info("Removing timezone information from 'ds' column for Prophet compatibility")
|
|
prophet_data['ds'] = prophet_data['ds'].dt.tz_localize(None)
|
|
|
|
# Sort by date and clean data
|
|
prophet_data = prophet_data.sort_values('ds').reset_index(drop=True)
|
|
prophet_data['y'] = pd.to_numeric(prophet_data['y'], errors='coerce')
|
|
prophet_data = prophet_data.dropna(subset=['y'])
|
|
|
|
# Additional data cleaning for Prophet
|
|
# Remove any duplicate dates (keep last occurrence)
|
|
prophet_data = prophet_data.drop_duplicates(subset=['ds'], keep='last')
|
|
|
|
# Ensure y values are non-negative (Prophet works better with non-negative values)
|
|
prophet_data['y'] = prophet_data['y'].clip(lower=0)
|
|
|
|
logger.info(f"Prepared Prophet data: {len(prophet_data)} rows, date range: {prophet_data['ds'].min()} to {prophet_data['ds'].max()}")
|
|
|
|
return prophet_data
|
|
|
|
def _extract_regressor_columns(self, df: pd.DataFrame) -> List[str]:
|
|
"""Extract regressor columns (unchanged)"""
|
|
excluded_columns = ['ds', 'y']
|
|
regressor_columns = []
|
|
|
|
for col in df.columns:
|
|
if col not in excluded_columns and df[col].dtype in ['int64', 'float64']:
|
|
regressor_columns.append(col)
|
|
|
|
logger.info(f"Identified regressor columns: {regressor_columns}")
|
|
return regressor_columns
|
|
|
|
def _get_spanish_holidays(self) -> pd.DataFrame:
|
|
"""Get Spanish holidays (unchanged)"""
|
|
try:
|
|
holidays_list = []
|
|
years = range(2020, 2030)
|
|
|
|
for year in years:
|
|
holidays_list.extend([
|
|
{'holiday': 'new_year', 'ds': f'{year}-01-01'},
|
|
{'holiday': 'epiphany', 'ds': f'{year}-01-06'},
|
|
{'holiday': 'labor_day', 'ds': f'{year}-05-01'},
|
|
{'holiday': 'assumption', 'ds': f'{year}-08-15'},
|
|
{'holiday': 'national_day', 'ds': f'{year}-10-12'},
|
|
{'holiday': 'all_saints', 'ds': f'{year}-11-01'},
|
|
{'holiday': 'constitution_day', 'ds': f'{year}-12-06'},
|
|
{'holiday': 'immaculate_conception', 'ds': f'{year}-12-08'},
|
|
{'holiday': 'christmas', 'ds': f'{year}-12-25'}
|
|
])
|
|
|
|
if holidays_list:
|
|
holidays_df = pd.DataFrame(holidays_list)
|
|
holidays_df['ds'] = pd.to_datetime(holidays_df['ds'])
|
|
return holidays_df
|
|
else:
|
|
return pd.DataFrame()
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Could not load Spanish holidays: {str(e)}")
|
|
return pd.DataFrame() |