code: import pandas as pd
  import mlflow.sklearn
  from sqlalchemy import create_engine
  from config import DATABASE_URI
  
  from predictions.common_predictor import CommonPredictor
  from config import ROOT_DIR
  
  pd.set_option("display.width", 1000)
  pd.set_option("display.max_columns", 50)
  
  
  class LoLPredictor(CommonPredictor):
      def __init__(self, debug: bool = False):
          super().__init__(debug=debug)
          self.training_columns = [
              "avg",
              "gold_per_minute",
              "gold_differential_per_minute",
              "gold_differential_at_15",
              "cs_per_minute",
              "cs_differential_at_15",
              "tower_differential_at_15",
              "tower_ratio",
              "first_tower",
              "damage_per_minute",
              "kills_per_game",
              "deaths_per_game",
              "kda",
              "dragon_game",
              "dragons_15",
              "nashors_game",
              "wards_per_minute",
              "c_avg",
              "c_gold_per_minute",
              "c_gold_differential_per_minute",
              "c_gold_differential_at_15",
              "c_cs_differential_at_15",
              "c_tower_differential_at_15",
              "c_tower_ratio",
              "c_first_tower",
              "c_damage_per_minute",
              "c_kills_per_game",
              "c_deaths_per_game",
              "c_kda",
              "c_dragon_game",
              "c_dragons_15",
              "c_nashors_game",
          ]
          self.y_col_name = "main_team_won"
  
  
  if __name__ == "__main__":
  
      print()
  
      mlflow.set_tracking_uri(f"file:///{ROOT_DIR}/mlruns")
      mlflow.set_experiment("hazard_lol")
      lol = LoLPredictor()
      con = create_engine(DATABASE_URI + "lol", echo=False)
      df_all = pd.read_sql("SELECT * FROM averaged_predictions", con=con)
      lol.main_train(df_all, run_name="save run", n_runs=30)
      print()
      # todo musi byt v current run
      # mlflow.sklearn.save_model(lol.model, path=f"{ROOT_DIR}/models/ttt", conda_env=f"{ROOT_DIR}/environment.yaml")
      # mlflow.sklearn.log_model(lol.model, artifact_path=f"{ROOT_DIR}/ttt", conda_env=f"{ROOT_DIR}/environment.yaml")
  
apis: pandas.read_sql pandas.set_option