Alvaro
Add logistic regression model and ML preprocessing
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import os
import json
from .scrape_fights import scrape_all_events
from .scrape_fighters import scrape_all_fighters
from .to_csv import json_to_csv, fighters_json_to_csv
from .preprocess import preprocess_fighters_csv
from .. import config
def main():
"""
Main function to run the complete scraping and preprocessing pipeline.
"""
# Ensure the output directory exists
if not os.path.exists(config.OUTPUT_DIR):
os.makedirs(config.OUTPUT_DIR)
print(f"Created directory: {config.OUTPUT_DIR}")
# --- Step 1: Scrape all data from the website ---
# This will generate fighters.json and events.json
scrape_all_fighters()
scrape_all_events()
# --- Step 2: Convert the scraped JSON data to CSV format ---
# This will generate fighters.csv and fights.csv
json_to_csv(config.EVENTS_JSON_PATH, config.FIGHTS_CSV_PATH)
fighters_json_to_csv(config.FIGHTERS_JSON_PATH, config.FIGHTERS_CSV_PATH)
# --- Step 3: Run post-processing on the generated CSV files ---
# This cleans names, converts height, etc.
print("\n--- Running post-scraping preprocessing ---")
preprocess_fighters_csv()
# --- Step 4: Clean up temporary JSON files ---
print("\n--- Deleting temporary JSON files ---")
try:
if os.path.exists(config.EVENTS_JSON_PATH):
os.remove(config.EVENTS_JSON_PATH)
print(f"Deleted: {config.EVENTS_JSON_PATH}")
if os.path.exists(config.FIGHTERS_JSON_PATH):
os.remove(config.FIGHTERS_JSON_PATH)
print(f"Deleted: {config.FIGHTERS_JSON_PATH}")
except OSError as e:
print(f"Error deleting JSON files: {e}")
print("\n\n--- Scraping and Preprocessing Pipeline Finished ---")
if __name__ == '__main__':
main()