def fit_cmd(argv=sys.argv[1:]):  # pragma: no cover
    """\
Fit a model and save to database.

Will use 'dataset_loader_train', 'model', and 'model_perister' from
the configuration file, to load a dataset to train a model with, and
persist it.

Usage:
  pld-fit [options]

Options:
  -n --no-save              Don't persist the fitted model to disk.

  --no-activate             Don't activate the fitted model.

  --save-if-better-than=<k> Persist only if test score better than given
                            value.

  -e --evaluate             Evaluate fitted model on train and test set and
                            print out results.

  -h --help                 Show this screen.
"""
    arguments = docopt(fit_cmd.__doc__, argv=argv)
    no_save = arguments['--no-save']
    no_activate = arguments['--no-activate']
    save_if_better_than = arguments['--save-if-better-than']
    evaluate = arguments['--evaluate'] or bool(save_if_better_than)
    if save_if_better_than is not None:
        save_if_better_than = float(save_if_better_than)
    initialize_config(__mode__='fit')
    fit(
        persist=not no_save,
        activate=not no_activate,
        evaluate=evaluate,
        persist_if_better_than=save_if_better_than,
        )
\
Fit a model and save to database.

Will use 'dataset_loader_train', 'model', and 'model_perister' from
the configuration file, to load a dataset to train a model with, and
persist it.

Usage:
  pld-fit [options]

Options:
  -n --no-save              Don't persist the fitted model to disk.

  --no-activate             Don't activate the fitted model.

  --save-if-better-than=<k> Persist only if test score better than given
                            value.

  -e --evaluate             Evaluate fitted model on train and test set and
                            print out results.

  -h --help                 Show this screen.