Kaggle competition: Predicting Molecular Properties
def sigmoid(x):
return 1 /(1+np.exp(-x))
-np.log(( 1-sigmoid(0.1)) *(sigmoid(0.7)) *(sigmoid(0.8)))/3<choose_model_class>
sub1 = pd.read_csv('.. /input/lgb-public-kernels-plus-more-features/sub_lgb_model_individual.csv')
 sub2 = pd.read_csv('.. /input/staking-and-stealing-like-a-molecule/submission.csv')
 sample = pd.read_csv('.. /input/champs-scalar-coupling/sample_submission.csv' )
<train_model><EOS> 
sample['scalar_coupling_constant'] =(0.6*sub2['scalar_coupling_constant'] + 0.4*sub1['scalar_coupling_constant'])
 sample.to_csv('stackers_blend.csv', index=False )