Untitled

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import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import KFold
from sklearn.metrics import roc_auc_score
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def main(iworkmode):

  iscenario = 0  ## =0 step size enumration Question 3,
                 ## =1 iteration number enumeration, Question 4
  
  # load the data
  X, y = load_breast_cancer(return_X_y=True)  ## X input, y output

  print(X.shape, y.shape)
  mdata,ndim = X.shape 

  ## to convert the {0,1} output into {-1,+1}
  y = 2*y - 1    

  ## hyperparameters of the learning problem

  if iscenario == 0:  ## Question 3, step size enumeration
    ## list of eta, the stepsize or learning rate is enumerated
    neta = 10   ## number of different step size
    eta0 = 0.1  ## first setp size
    leta = [ eta0*(i+1) for i in range(neta)]  ## list of step sizes

    ## number of iteration  
    iteration =50

  elif iscenario == 1: ## Question 4, iteration number enumeration
    ## list of iteration numbers
    niteration = 10  ## number of different iteration
    iteration0 = 10  ## first iteration number
    literation = [ iteration0*(i+1) for i in range(niteration)]

    ## step size
    eta = 0.1

  
  nfold = 5         ## number of folds 

  np.random.seed(12345) 

  ## split the data into 5-folds
  cselection = KFold(n_splits=nfold, random_state=None, shuffle=False)

  ## normalization 
  ## scaling the rows by maximum absolute value, L infinite norm of columns  
  X /= np.outer(np.ones(mdata),np.max(np.abs(X),0))

    
  ## run the cross-validation


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if __name__ == "__main__":
  if len(sys.argv)==1:
    iworkmode=0
  elif len(sys.argv)>=2:
    iworkmode=eval(sys.argv[1])
  main(iworkmode)