Kaggle competition: SIIM-ISIC Melanoma Classification
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<load_from_csv>
import numpy as np
import pandas as pd
test_data = pd.read_csv('.. /input/test.csv')
 mnist_path = "mnist-original.mat"
mnist_alternative_url = "https://github.com/amplab/datascience-sp14/raw/master/lab7/mldata/mnist-original.mat"
response = urllib.request.urlopen(mnist_alternative_url)
 with open(mnist_path, "wb")as f:
content = response.read()
f.write(content)
 mnist_raw = loadmat(mnist_path)
 X = mnist_raw["data"].T
y = mnist_raw["label"][0].astype(np.long)
 X_test = test_data.values
X.shape, X_test.shape<compute_test_metric>
sub_image = pd.read_csv('/kaggle/input/melanoma-submissions2/submissionImage.csv')
 sub_tabular = pd.read_csv('/kaggle/input/melanoma-submissions2/submissionTabular.csv')
 sub_tabular78 = pd.read_csv('/kaggle/input/tabular-prediction/submission_tabular_78.csv')
 sub_multiple = pd.read_csv('/kaggle/input/melanoma-submissions2/submission_multiple_data_source.csv')
 sub_public_merge = pd.read_csv('/kaggle/input/submission-9/submission_935.csv')
 sub = pd.read_csv('/kaggle/input/siim-isic-melanoma-classification/sample_submission.csv')
 sub_score946 = pd.read_csv('.. /input/submission-9/submission_946.csv')
 sub_score940 = pd.read_csv('.. /input/submission-9/submission_940.csv')
 sub_mean = pd.read_csv('/kaggle/input/siim-isic-multiple-model-training-stacking-923/submission_mean.csv' )
def pairwise_distances(x, y):
x_norm =(x**2 ).sum(1 ).view(-1, 1)
 y_t = torch.transpose(y, 0, 1)
 y_norm =(y**2 ).sum(1 ).view(1, -1)
 dist = x_norm + y_norm - 2.0 * torch.mm(x, y_t)
 return torch.clamp(dist, 0.0, np.inf )<normalization>
sub.target = sub_score946.target *0.98 + sub_score940.target *0.0 + sub_tabular78.target *0.02
<concatenate><EOS> 
sub.to_csv('submission.csv', index = False )