How to use the rsmtool.test_utils.do_run_prediction function in rsmtool

def test_run_experiment_predict_no_output_dir():
    '''
    rsmpredict experiment where experiment_dir
    does not containt output directory
    '''
    source = 'lr-predict-no-output-dir'
    config_file = join(rsmtool_test_dir,
                       'data',
                       'experiments',
                       source,
                       'rsmpredict.json')
    do_run_prediction(source, config_file)

def test_run_experiment_predict_no_output_dir():

    # rsmpredict experiment where experiment_dir
    # does not containt output directory
    source = 'lr-predict-no-output-dir'
    config_file = join(test_dir,
                       'data',
                       'experiments',
                       source,
                       'rsmpredict.json')
    do_run_prediction(source, config_file)

def test_run_experiment_lr_predict_with_candidate():

    # basic experiment using rsmpredict with candidate column

    source = 'lr-predict-with-candidate'
    config_file = join(test_dir,
                       'data',
                       'experiments',
                       source,
                       'rsmpredict.json')
    do_run_prediction(source, config_file)

    output_dir = join('test_outputs', source, 'output')
    expected_output_dir = join(test_dir, 'data', 'experiments', source, 'output')

    for csv_file in ['predictions.csv', 'preprocessed_features.csv']:
        output_file = join(output_dir, csv_file)
        expected_output_file = join(expected_output_dir, csv_file)

        yield check_csv_output, output_file, expected_output_file

def test_run_experiment_predict_expected_scores_builtin_model():
    '''
    rsmpredict experiment for expected scores but with
    a built-in model which is not supporte
    '''
    source = 'lr-predict-expected-scores-builtin-model'
    config_file = join(rsmtool_test_dir,
                       'data',
                       'experiments',
                       source,
                       'rsmpredict.json')
    do_run_prediction(source, config_file)

def test_run_experiment_lr_predict_with_score():

    # rsmpredict experiment with human score

    source = 'lr-predict-with-score'
    config_file = join(test_dir,
                       'data',
                       'experiments',
                       source,
                       'rsmpredict.json')
    do_run_prediction(source, config_file)

    output_dir = join('test_outputs', source, 'output')
    expected_output_dir = join(test_dir, 'data', 'experiments', source, 'output')

    for csv_file in ['predictions.csv', 'preprocessed_features.csv']:
        output_file = join(output_dir, csv_file)
        expected_output_file = join(expected_output_dir, csv_file)

        yield check_csv_output, output_file, expected_output_file

def test_run_experiment_lr_predict_missing_postprocessing_file():

    # rsmpredict experiment with missing post-processing file
    source = 'lr-predict-missing-postprocessing-file'
    config_file = join(test_dir,
                       'data',
                       'experiments',
                       source,
                       'rsmpredict.json')
    do_run_prediction(source, config_file)

def test_run_experiment_predict_no_experiment_id():

    # rsmpredict experiment ehere the experiment_dir
    # does not contain the experiment with the stated id
    source = 'lr-predict-no-experiment-id'
    config_file = join(test_dir,
                       'data',
                       'experiments',
                       source,
                       'rsmpredict.json')
    do_run_prediction(source, config_file)

def test_run_experiment_lr_predict_tsv_input_files():

    # rsmpredict experiment with input file in .tsv format

    source = 'lr-predict-tsv-input-files'
    config_file = join(test_dir,
                       'data',
                       'experiments',
                       source,
                       'rsmpredict.json')
    do_run_prediction(source, config_file)

    output_dir = join('test_outputs', source, 'output')
    expected_output_dir = join(test_dir, 'data', 'experiments', source, 'output')

    for csv_file in ['predictions.csv', 'preprocessed_features.csv']:
        output_file = join(output_dir, csv_file)
        expected_output_file = join(expected_output_dir, csv_file)

        yield check_csv_output, output_file, expected_output_file

def test_run_experiment_lr_predict_missing_values():

    # basic experiment using rsmpredict when the supplied feature file
    # contains reponses with non-numeric feature values

    source = 'lr-predict-missing-values'
    config_file = join(test_dir,
                       'data',
                       'experiments',
                       source,
                       'rsmpredict.json')
    do_run_prediction(source, config_file)

    output_dir = join('test_outputs', source, 'output')
    expected_output_dir = join(test_dir, 'data', 'experiments', source, 'output')

    for csv_file in ['predictions.csv', 'predictions_excluded_responses.csv',
                     'preprocessed_features.csv']:
        output_file = join(output_dir, csv_file)
        expected_output_file = join(expected_output_dir, csv_file)

        yield check_csv_output, output_file, expected_output_file

def test_run_experiment_lr_predict_no_numeric_feature_values():
    '''
    rsmpredict experiment with missing post-processing file
    '''
    source = 'lr-predict-no-numeric-feature-values'
    config_file = join(rsmtool_test_dir,
                       'data',
                       'experiments',
                       source,
                       'rsmpredict.json')
    do_run_prediction(source, config_file)