• sklearn
• numpy
• cvxopt
• pandas
• Run on command:

  pip install -r requirements.txt

Download datasets : Link

• K-Means clustering algorithm:

  python test.py --state train --path_data <path to dataset> --n_clus <number of cluters> --alpha <alpha> --option Kmean --threshold <not default> --max_loop<max training loop>

• Approximate q-quantiles algorithm:

  python test.py --state train --path_data <path to dataset> --n_clus <number of cluters> --alpha <alpha> --option Quantile --threshold <not default> --max_loop<max training loop>

• Evaluate accuracy, f1 score, precision score, recall socre with number of clusters (level-quantiles) and time training:

  python test.py --state eval --path_data <path to dataset> --alpha <alpha> --min_iters <start iter> --max_iters <end iter>

• Beside, we also provide custom procedure for evaluating accuracy, f1 score, precision score, recall socre with alpha in file model.py. That is

  def weight_eval_alpha() 

• Data in together group K-Mean (level-quantiles) is a element. You'll see this Warning, to disable that Warning you can add -W ignore before function.py
  

• Framingham Heart Disease datasets
  
• German Credit datasets

• Framingham Heart Disease datasets
  
• German Credit datasets

If you don't select threshold, output model is accuracy of original logistic regression which is provided by sklearn library

python test.py --state train --path_data <path to dataset> --n_clus <number of cluters> --alpha <alpha> --option Kmean(or Quantile)