code: # import comet_ml in the top of your file
  from comet_ml import Experiment
      
  # Add the following code anywhere in your machine learning file
  experiment = Experiment(api_key="QCxbRVX2qhQj1t0ajIZl2nk2c",
                          project_name="ml-tools", workspace="hirune924",
                          auto_param_logging=False)
  
  from omegaconf import DictConfig, OmegaConf
  import hydra
  from hydra import utils
  import sys
  import os
  sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))
  import numpy as np
  import pandas as pd
  
  from sklearn.model_selection import StratifiedKFold
  
  from speeder.model.trainer import Trainer
  from speeder.feature.feature_manager import load_features, load_feature   
  from speeder.utils import flatten_dict_cfg
  @hydra.main(config_path="../config/modeling_lgbm.yaml", strict=False)
  def main(cfg: DictConfig) -> None:
      print(cfg.pretty())
      experiment_name = '/'.join(os.getcwd().split('/')[-2:])
      experiment.set_name(experiment_name)
      experiment.log_parameters(flatten_dict_cfg(cfg, sep='/'))
      # Define CV
      cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=2020)
  
      # Load Features
      feature_list = cfg['feature']
      feature_names = [f + '_train.ftr' for f in feature_list]
      train_df = load_features(feature_names, dir=utils.to_absolute_path('features'), ignore_columns = None)
      feature_names = [f + '_test.ftr' for f in feature_list]
      test_df = load_features(feature_names, dir=utils.to_absolute_path('features'), ignore_columns = None)
  
      target_df = load_feature('_train.ftr', dir=utils.to_absolute_path('features'), ignore_columns = None)[['Survived']]
      sub_df = load_feature('_test.ftr', dir=utils.to_absolute_path('features'), ignore_columns = None)[['PassengerId']]
  
      #print(train_df.head())
      #print(test_df.head())
      #print(target_df.head())
  
      trainer = Trainer(configs=cfg, X_train=train_df, y_train=target_df, X_test=test_df, cv=cv, experiment=experiment)
      trainer.run_train_cv()
      trainer.run_predict_cv()
      trainer.submission(sub_df)
  
  if __name__ == "__main__":
      main()
  
apis: sklearn.model_selection.StratifiedKFold