Kaggle competition: SIIM-ISIC Melanoma Classification
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<train_on_grid>
def MinMaxBestBaseStacking(input_folder, best_base, output_path):
sub_base = pd.read_csv(best_base)
 all_files = os.listdir(input_folder)
 outs = [pd.read_csv(os.path.join(input_folder, f), index_col=0)for f in all_files]
concat_sub = pd.concat(outs, axis=1)
 cols = list(map(lambda x: "target" + str(x), range(len(concat_sub.columns))))
concat_sub.columns = cols
concat_sub.reset_index(inplace=True)
 concat_sub['is_iceberg_max'] = concat_sub.iloc[:, 1:6].max(axis=1)
 concat_sub['is_iceberg_min'] = concat_sub.iloc[:, 1:6].min(axis=1)
 concat_sub['is_iceberg_mean'] = concat_sub.iloc[:, 1:6].mean(axis=1)
 concat_sub['is_iceberg_median'] = concat_sub.iloc[:, 1:6].median(axis=1)
 cutoff_lo = 0.73
cutoff_hi = 0.33
concat_sub['is_iceberg_base'] = sub_base['target']
concat_sub['target'] = np.where(np.all(concat_sub.iloc[:, 1:6] > cutoff_lo, axis=1),
concat_sub['is_iceberg_max'],
np.where(np.all(concat_sub.iloc[:, 1:6] < cutoff_hi, axis=1),
concat_sub['is_iceberg_min'],
concat_sub['is_iceberg_base']))
concat_sub[['image_name', 'target']].to_csv(output_path,
index=False, float_format='%.12f')
<save_to_csv><EOS> 
MinMaxBestBaseStacking('.. /input/csv001/', '.. /input/base001/datasets_766461_1344911_submission_rank-then-blend.csv', 'submission.csv')