code: def execute():
      env = Environment(
          train_dataset=get_toy_classification_data(),
          results_path="HyperparameterHunterAssets",
          # Both `holdout_dataset`, and `train_dataset` can be any of the following: pandas.DataFrame, filepath, or None
          # If a filepath is provided, it will be passed to :meth:`pandas.read_csv`.
          # In addition to the above types, `holdout_dataset` can also be provided as a callable (see above :func:`get_holdout_set`)
          holdout_dataset=get_holdout_set,
          test_dataset=get_toy_classification_data(),
          # By default, `holdout_dataset` will be scored with the provided metrics, just like OOF predictions
          # However, you can provide the additional `metrics_params` kwarg to specify which metrics are calculated for each dataset
          # See the documentation in :class:`environment.Environment` and :class:`metrics.ScoringMixIn` for more information
          metrics=["roc_auc_score"],
          cv_type=StratifiedKFold,
          cv_params=dict(n_splits=5, shuffle=True, random_state=32),
      )
  
      experiment = CVExperiment(
          model_initializer=XGBClassifier,
          model_init_params=dict(subsample=0.5),
      )
  
imports: from hyperparameter_hunter import Environment, CVExperiment
  from hyperparameter_hunter.utils.learning_utils import get_toy_classification_data
  from sklearn.model_selection import StratifiedKFold
  from xgboost import XGBClassifier
label_desc: Use image node_id 2 for calling a global function with example usage: execute() without return types