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| import csv | |
| import os | |
| import sys | |
| from datetime import datetime | |
| from ..scrape.config import FIGHTS_CSV_PATH, FIGHTERS_CSV_PATH | |
| def load_fighters_data(): | |
| """Loads fighter data, including ELO scores, into a dictionary.""" | |
| if not os.path.exists(FIGHTERS_CSV_PATH): | |
| print(f"Error: Fighter data not found at '{FIGHTERS_CSV_PATH}'.") | |
| print("Please run the ELO analysis first ('python -m src.analysis.elo').") | |
| return None | |
| fighters = {} | |
| with open(FIGHTERS_CSV_PATH, 'r', encoding='utf-8') as f: | |
| reader = csv.DictReader(f) | |
| for row in reader: | |
| full_name = f"{row['first_name']} {row['last_name']}".strip() | |
| fighters[full_name] = {'elo': float(row.get('elo', 1500))} # Default ELO if missing | |
| return fighters | |
| def load_fights_data(): | |
| """Loads fight data and sorts it chronologically.""" | |
| if not os.path.exists(FIGHTS_CSV_PATH): | |
| print(f"Error: Fights data not found at '{FIGHTS_CSV_PATH}'.") | |
| return None | |
| with open(FIGHTS_CSV_PATH, 'r', encoding='utf-8') as f: | |
| fights = list(csv.DictReader(f)) | |
| # Sort fights chronologically to ensure a proper train/test split later | |
| fights.sort(key=lambda x: datetime.strptime(x['event_date'], '%B %d, %Y')) | |
| return fights | |
| def run_elo_baseline_model(fights, fighters): | |
| """ | |
| Runs a simple baseline prediction model where the fighter with the higher ELO is predicted to win. | |
| """ | |
| correct_predictions = 0 | |
| total_predictions = 0 | |
| for fight in fights: | |
| fighter1_name = fight['fighter_1'] | |
| fighter2_name = fight['fighter_2'] | |
| actual_winner = fight['winner'] | |
| # Skip fights that are draws or no contests | |
| if actual_winner in ["Draw", "NC", ""]: | |
| continue | |
| fighter1 = fighters.get(fighter1_name) | |
| fighter2 = fighters.get(fighter2_name) | |
| if not fighter1 or not fighter2: | |
| continue # Skip if fighter data is missing | |
| elo1 = fighter1.get('elo', 1500) | |
| elo2 = fighter2.get('elo', 1500) | |
| # Predict winner based on higher ELO | |
| predicted_winner = fighter1_name if elo1 > elo2 else fighter2_name | |
| if predicted_winner == actual_winner: | |
| correct_predictions += 1 | |
| total_predictions += 1 | |
| accuracy = (correct_predictions / total_predictions) * 100 if total_predictions > 0 else 0 | |
| return accuracy, total_predictions | |
| def main(): | |
| """ | |
| Main function to run the prediction pipeline. | |
| """ | |
| print("--- Starting ML Prediction Pipeline ---") | |
| # Load data | |
| fighters_data = load_fighters_data() | |
| fights_data = load_fights_data() | |
| if not fighters_data or not fights_data: | |
| print("Aborting pipeline due to missing data.") | |
| return | |
| # Run baseline model | |
| print("\nRunning Baseline Model (Predicting winner by highest ELO)...") | |
| accuracy, total_fights = run_elo_baseline_model(fights_data, fighters_data) | |
| print("\n--- Baseline Model Evaluation ---") | |
| print(f"Total Fights Evaluated: {total_fights}") | |
| print(f"Model Accuracy: {accuracy:.2f}%") | |
| print("---------------------------------") | |
| if __name__ == '__main__': | |
| main() |