predict.py

import os
import joblib
import pandas as pd
from data_loader import DataLoader
from feature_engineer import FeatureEngineer
from rule_based_model import RuleBasedModel

def get_predictions(model_dir='artifacts'):
    loader = DataLoader()
    rankings = loader.get_rankings()
    form = loader.get_team_form()
    teams = loader.get_teams()
    matches = loader.get_matches()
    try:
        manual_features = loader.get_manual_features()
    except Exception as e:
        print(f"WARNING: Could not load manual features: {e}")
        manual_features = None
    odds_summary = loader.get_latest_odds_summary()
    weather_summary = loader.get_latest_weather_summary()
    loader.close()
    
    engineer = FeatureEngineer(rankings, form, teams, manual_features)
    
    # Select upcoming matches
    upcoming = matches[matches['status'].isin(['scheduled', 'active'])].copy()
    if len(upcoming) == 0:
        return {"predictions": [], "skipped": []}
        
    X_pred, _ = engineer.build_dataset(upcoming)
    
    # Load model
    model_path = os.path.join(model_dir, 'rule_based_baseline.pkl')
    if not os.path.exists(model_path):
        raise FileNotFoundError(f"Model not found at {model_path}. Run train.py first.")
        
    clf = joblib.load(model_path)
    
    # Predict probabilities
    probs = clf.predict_proba(X_pred)
    
    X_pred['prob_away_win'] = probs[:, 0]
    X_pred['prob_draw'] = probs[:, 1]
    X_pred['prob_home_win'] = probs[:, 2]
    
    # Merge back to upcoming by match_id
    upcoming = upcoming.merge(X_pred[['match_id', 'prob_away_win', 'prob_draw', 'prob_home_win']], left_on='id', right_on='match_id', how='left')
    
    results = []
    skipped = []
    
    for _, row in upcoming.iterrows():
        if pd.isna(row['prob_home_win']):
            # Determine reason
            reason = "unknown"
            if pd.isna(row['home_team_id']) or pd.isna(row['away_team_id']):
                reason = "missing_team_placeholder"
            else:
                reason = "missing_features"
                
            skipped.append({
                "match_id": row['id'],
                "home_team_id": row['home_team_id'] if not pd.isna(row['home_team_id']) else None,
                "away_team_id": row['away_team_id'] if not pd.isna(row['away_team_id']) else None,
                "reason": reason
            })
            continue
            
        # Check if manual features were applied
        applied = False
        if manual_features is not None and row['id'] in manual_features.index:
            applied = True

        odds_rows = []
        if odds_summary is not None and not odds_summary.empty:
            match_odds = odds_summary[odds_summary['match_id'] == row['id']]
            for _, odds in match_odds.iterrows():
                odds_rows.append({
                    "bookmaker_key": odds.get("bookmaker_key"),
                    "bookmaker_title": odds.get("bookmaker_title"),
                    "market_key": odds.get("market_key"),
                    "market_title": odds.get("market_title"),
                    "home_odds": float(odds["home_odds"]) if not pd.isna(odds.get("home_odds")) else None,
                    "draw_odds": float(odds["draw_odds"]) if not pd.isna(odds.get("draw_odds")) else None,
                    "away_odds": float(odds["away_odds"]) if not pd.isna(odds.get("away_odds")) else None,
                    "last_update": str(odds.get("last_update")) if not pd.isna(odds.get("last_update")) else None,
                })

        weather = None
        if weather_summary is not None and not weather_summary.empty and row['id'] in weather_summary.index:
            w = weather_summary.loc[row['id']]
            weather = {
                "forecast_time": str(w.get("forecast_time")) if not pd.isna(w.get("forecast_time")) else None,
                "temperature_c": float(w["temperature_c"]) if not pd.isna(w.get("temperature_c")) else None,
                "apparent_temperature_c": float(w["apparent_temperature_c"]) if not pd.isna(w.get("apparent_temperature_c")) else None,
                "humidity_pct": float(w["humidity_pct"]) if not pd.isna(w.get("humidity_pct")) else None,
                "precipitation_probability_pct": float(w["precipitation_probability_pct"]) if not pd.isna(w.get("precipitation_probability_pct")) else None,
                "precipitation_mm": float(w["precipitation_mm"]) if not pd.isna(w.get("precipitation_mm")) else None,
                "wind_speed_kmh": float(w["wind_speed_kmh"]) if not pd.isna(w.get("wind_speed_kmh")) else None,
                "wind_gusts_kmh": float(w["wind_gusts_kmh"]) if not pd.isna(w.get("wind_gusts_kmh")) else None,
                "weather_code": int(w["weather_code"]) if not pd.isna(w.get("weather_code")) else None,
            }

        results.append({
            "match_id": row['id'],
            "home_team_id": row['home_team_id'],
            "away_team_id": row['away_team_id'],
            "prob_home_win": float(row['prob_home_win']),
            "prob_draw": float(row['prob_draw']),
            "prob_away_win": float(row['prob_away_win']),
            "manual_features_applied": applied,
            "odds": odds_rows,
            "weather": weather
        })
        
    return {
        "predictions": results,
        "skipped": skipped
    }

def run_predictions():
    data = get_predictions()
    print("\nUpcoming Match Predictions:")
    for p in data['predictions']:
        print(f"Match {p['match_id']} | {p['home_team_id']} vs {p['away_team_id']} "
              f"| 1: {p['prob_home_win']:.2f} X: {p['prob_draw']:.2f} 2: {p['prob_away_win']:.2f}")

if __name__ == "__main__":
    run_predictions()