def _print_fancy_output(learner_result_dicts, output_file=sys.stdout):
    if not learner_result_dicts:
        raise ValueError('Result dictionary list is empty!')

    lrd = learner_result_dicts[0]
    print('Experiment Name: {}'.format(lrd['experiment_name']),
          file=output_file)
    print('SKLL Version: {}'.format(lrd['version']), file=output_file)
    print('Training Set: {}'.format(lrd['train_set_name']), file=output_file)
    print('Training Set Size: {}'.format(
        lrd['train_set_size']), file=output_file)
    print('Test Set: {}'.format(lrd['test_set_name']), file=output_file)
    print('Test Set Size: {}'.format(lrd['test_set_size']), file=output_file)
    print('Shuffle: {}'.format(lrd['shuffle']), file=output_file)
    print('Feature Set: {}'.format(lrd['featureset']), file=output_file)
    print('Learner: {}'.format(lrd['learner_name']), file=output_file)
    print('Task: {}'.format(lrd['task']), file=output_file)
    if lrd['task'] == 'cross_validate':
        print('Number of Folds: {}'.format(lrd['cv_folds']),
              file=output_file)
        print('Stratified Folds: {}'.format(lrd['stratified_folds']),
              file=output_file)
    print('Feature Scaling: {}'.format(lrd['feature_scaling']),
          file=output_file)
    print('Grid Search: {}'.format(lrd['grid_search']), file=output_file)
    print('Grid Search Folds: {}'.format(lrd['grid_search_folds']),
          file=output_file)
    print('Grid Objective Function: {}'.format(lrd['grid_objective']),
          file=output_file)
    print('Using Folds File: {}'.format(isinstance(lrd['cv_folds'], dict)),
          file=output_file)
    print('Scikit-learn Version: {}'.format(lrd['scikit_learn_version']),
          file=output_file)
    print('Start Timestamp: {}'.format(
        lrd['start_timestamp']), file=output_file)
    print('End Timestamp: {}'.format(lrd['end_timestamp']), file=output_file)
    print('Total Time: {}'.format(lrd['total_time']), file=output_file)
    print('\n', file=output_file)

    for lrd in learner_result_dicts:
        print('Fold: {}'.format(lrd['fold']), file=output_file)
        print('Model Parameters: {}'.format(lrd.get('model_params', '')),
              file=output_file)
        print('Grid Objective Score (Train) = {}'.format(lrd.get('grid_score',
                                                                 '')),
              file=output_file)
        if 'result_table' in lrd:
            print(lrd['result_table'], file=output_file)
            print('Accuracy = {}'.format(lrd['accuracy']),
                  file=output_file)
        if 'descriptive' in lrd:
            print('Descriptive statistics:', file=output_file)
            for desc_stat in ['min', 'max', 'avg', 'std']:
                actual = lrd['descriptive']['actual'][desc_stat]
                predicted = lrd['descriptive']['predicted'][desc_stat]
                print((' {} = {: .4f} (actual), {: .4f} '
                       '(predicted)').format(desc_stat.title(), actual,
                                             predicted),
                      file=output_file)
            print('Pearson = {: f}'.format(lrd['pearson']),
                  file=output_file)
        print('Objective Function Score (Test) = {}'.format(lrd['score']),
              file=output_file)
        print('', file=output_file)
Return the primary key value for ``instance``.
dataset/ETHPy150Open EducationalTestingService/skll/skll/experiments.py/_print_fancy_output--crossover with--dataset/ETHPy150Open lsbardel/python-stdnet/stdnet/odm/session.py/Manager.pkvalue