Kaggle competition: SIIM-ISIC Melanoma Classification
<train_model><EOS> 
import pandas as pd
import numpy as np
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<compute_test_metric>
import pandas as pd
import numpy as np
<compute_test_metric><EOS> 
sub1 = pd.read_csv('.. /input/melanoma-dif-sub/pl_0.936.csv')
 sub2 = pd.read_csv('.. /input/melanoma-dif-sub/pl_0.940.csv')
 sub3 = pd.read_csv('.. /input/melanoma-dif-sub/sub_EfficientNetB2_384.csv')
 sub4 = pd.read_csv('.. /input/melanoma-dif-sub/sub_EfficientNetB3_384.csv')
 sub5 = pd.read_csv('.. /input/melanoma-dif-sub/sub_EfficientNetB3_384_v2.csv')
 def rank_data(sub):
sub['target'] = sub['target'].rank() / sub['target'].rank().max()
return sub
sub1 = rank_data(sub1)
 sub2 = rank_data(sub2)
 sub3 = rank_data(sub3)
 sub4 = rank_data(sub4)
 sub5 = rank_data(sub5)
 sub1.columns = ['image_name', 'target1']
sub2.columns = ['image_name', 'target2']
sub3.columns = ['image_name', 'target3']
sub4.columns = ['image_name', 'target4']
sub5.columns = ['image_name', 'target5']
f_sub = sub1.merge(sub2, on = 'image_name' ).merge(sub3, on = 'image_name' ).merge(sub4, on = 'image_name' ).merge(sub5, on = 'image_name')
 f_sub['target'] = f_sub['target1'] * 0.3 + f_sub['target2'] * 0.3 + f_sub['target3'] * 0.05 + f_sub['target4'] * 0.3 + f_sub['target5'] * 0.05
f_sub = f_sub[['image_name', 'target']]
f_sub.to_csv('blend_sub.csv', index = False )