code: #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  """
  data6 = Breast cancer
  
  """
  from chip_clas_new import chip_clas_new
  import statistics
  from functions import remove_noise
  from sklearn.model_selection import train_test_split, KFold
  from sklearn.preprocessing import MinMaxScaler
  import numpy as np
  import pandas as pd
  
  data_name = "Breast cancer"
  print(data_name)
  
  url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data'
  data1 = pd.read_csv(url, sep=',', header=None, skiprows=1)
  
  data = data1.iloc[:,1:].copy() # the first is the id
  
  # converting object data into category dtype
  data.iloc[:,5] = data.iloc[:,5].astype('category') 
  # encoding labels
  data.iloc[:,5] = data.iloc[:,5].cat.codes
  
  X = data.iloc[:,:-1]
  min_max_scaler = MinMaxScaler(feature_range=(-1, 1)) # Normalizing data between -1 and 1
  X = pd.DataFrame(min_max_scaler.fit_transform(X))
  
  y = data.iloc[:,-1].copy() #  Class: (2 for benign, 4 for malignant cancer)
  y[y == 2] = 1
  y[y == 4] = -1
  
  # Filtering data:
  X_new, y_new = remove_noise(X, y)
  
  X_train, X_test, y_train, y_test = train_test_split(X_new, y_new, test_size=0.2, random_state=42)
  
  f = open("results_window_size.txt", "a+")
  f.write("\n\nDatabase: %s \n" % data_name)
  f.write("Size before filter: %d \n" % X.shape[0])
  f.write("Dimension: %d \n" % X.shape[1])
  
  f.write("Size after filter: %d \n" % X_new.shape[0])
  f.write("Train Size: %d \n" % X_train.shape[0])
  
  window_size = [50, 30, 20, 10, 5, 1]
  
  for split in window_size:
  
      y_hat, y_test, result, runtime, final_split_size, arestas_suporte_size  = chip_clas_new(X_train, X_test, y_train, y_test, method = "parallel", split_size = split)
  
  
      f.write("\nSplit: %d \n" % split)
      f.write("AUC: %f \n" % result)
      f.write("Runtime: %d \n" % runtime)
      f.write("Final_split_size: %d \n" % final_split_size)
      f.write("arestas_suporte_size: %d \n" % arestas_suporte_size)
          
  f.write("#######################################################################")        
  f.close()
apis: pandas.read_csv sklearn.preprocessing.MinMaxScaler sklearn.model_selection.train_test_split