Kaggle competition: SIIM-ISIC Melanoma Classification
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<predict_on_test>
import numpy as np
import pandas as pd
test_pred = lr_clf.predict(test_features )<save_to_csv>
data1 = pd.read_csv('.. /input/siimmy-submissions/submission(3 ).csv')
 data2 = pd.read_csv('.. /input/stacking-ensemble-on-my-submissions/submission_mean.csv')
 data3 = pd.read_csv('.. /input/stacking-ensemble-on-my-submissions/submission_median.csv')
 data4 = pd.read_csv('.. /input/eda-modelling-of-the-external-data-inc-ensemble/external_meta_ensembled.csv')
 data5 = pd.read_csv('.. /input/new-basline-np-log2-ensemble-top-10/submission.csv')
 data6 = pd.read_csv('.. /input/minmax-highest-public-lb-9619/submission.csv')
 submission = data1.copy()
submission['target'] = test_pred
submission.to_csv('submission.csv', index=False )<set_options>
ncol = data1.shape[1]
data2['target'] = data4.iloc[:, 1:ncol].mean(axis=1)
 data3['target'] = data4.iloc[:, 1:ncol].median(axis=1)
%matplotlib inline
seed = 4098653265
seed_all(seed, det_cudnn=True)
 def mnist_learner(data: ImageDataBunch, model_name: Optional[str] = None)-> Learner:
cbs =(( partial(SaveModelCallback, monitor='accuracy', name=model_name),)
 if model_name else None)
 lrn = cnn_learner(data, models.resnet152, metrics=accuracy,
opt_func=partial(optim.SGD, momentum=0.9),
callback_fns=cbs)
 seed = 224391980
seed_all(seed, det_cudnn=True)
 return lrn<load_from_csv>
submission['target'] = 1/6 * data1['target'] + 1/6 * data2['target'] + 1/6 * data3['target'] + 1/6 *data4['target']+ 1/6 *data5['target']+ 1/6 *data6['target']
data = ImageDataBunch.from_csv('data', test='test', num_workers=0)
 data.test_ds.x.items = np.array(sorted(data.test_ds.x.items, key=attrgetter('stem')))
 learner = mnist_learner(data, 'best-freezed')
 learner.lr_find()
learner.recorder.plot(suggestion=True )<train_model>
submission.to_csv('submission.csv', index=False, float_format='%.6f' )
<load_from_csv><EOS> 
s1= pd.read_csv('.. /input/siimmy-submissions/submission(6 ).csv')
 s2= pd.read_csv('.. /input/siimmy-submissions/submission(7 ).csv')
 s3= pd.read_csv('.. /input/siimmy-submissions/b3submission.csv')
 submission= s1.copy()
submission['target'] = 1/3 * s1['target'] + 1/3 * s2['target'] + 1/3 * s3['target']
submission.to_csv('b3submission.csv', index=False, float_format='%.6f' )