text: import dbispipeline.result_handlers as result_handlers
  from dbispipeline.evaluators import GridEvaluator
  
  from sklearn.linear_model import Ridge
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import MinMaxScaler
  
  import common
  
  from dataloaders import MsdBbLoader
  
  import evaluations
  
  dataloader = MsdBbLoader(
      hits_file_path='/storage/nas3/datasets/music/billboard/msd_bb_matches.csv',
      non_hits_file_path=
      '/storage/nas3/datasets/music/billboard/msd_bb_non_matches.csv',
      features_path='/storage/nas3/datasets/music/billboard',
      non_hits_per_hit=1,
      features=[
          *common.all_filtered_list(),
      ],
      label='peak',
      nan_value=150,
      random_state=42,
  )
  
  pipeline = Pipeline([
      ('scale', MinMaxScaler()),
      ('linreg', Ridge(normalize=False)),
  ])
  
  evaluator = GridEvaluator(
      parameters={},
      grid_parameters=evaluations.grid_parameters(),
  )
  
  result_handlers = [
      result_handlers.print_gridsearch_results,
  ]
  