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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() |