class_docstr: Fit and predict one or more estimators on one or more datasets.
class_docstr_tok: ["Fit","and","predict","one","or","more","estimators","on","one","or","more","datasets","."]
docstr: Print progress.
docstr_tok: ["Print","progress","."]
code: def _print_progress(
      self,
      dataset_name,
      strategy_name,
      cv_fold,
      train_or_test,
      fit_or_predict,
      verbose,
  ):
      """Print progress."""
      if verbose:
          fit_or_predict = fit_or_predict.capitalize()
          if train_or_test == "train" and fit_or_predict == "predict":
              on_train = " (training set)"
          else:
              on_train = ""
  
          n_splits = self.cv.get_n_splits() - 1  # zero indexing
  
          log.warn(
              f"strategy: {self._strategy_counter}/{self.n_strategies} - "
              f"{strategy_name} "
              f"on CV-fold: {cv_fold}/{n_splits} "
              f"of dataset: {self._dataset_counter}/{self.n_datasets} - "
              f"{dataset_name}{on_train}"
          )
  
file_path: sktime/sktime/benchmarking/orchestration.py
imports: import logging
  import pandas
  from sklearn.base import clone
  from sktime.benchmarking.tasks import TSCTask, TSRTask
label_desc: Use image node_id 9 for calling the Orchestrator obj's underlying member method code with example usage: obj._print_progress(dataset_name, strategy_name, cv_fold, train_or_test, fit_or_predict, verbose) without return types