text: import pandas as pd
  from numpy._distributor_init import NUMPY_MKL
  import numpy as np
  from matplotlib import pyplot as plt
  from sklearn.metrics import confusion_matrix
  import itertools
  
  sub = pd.read_csv("submission_surf_svm.csv")
  y_pred = np.array(sub.pop('Label'))
  sub = pd.read_csv("submission_surf_knn.csv")
  y_test = np.array(sub.pop('TrueLabel'))
  class_names=['a','b','c','d','e','f','g','h','i','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y']
  
  def plot_confusion_matrix(cm, classes,
                            normalize=False,
                            title='Confusion matrix',
                            cmap=plt.cm.Blues):
      """
      This function prints and plots the confusion matrix.
      Normalization can be applied by setting `normalize=True`.
      """
      plt.imshow(cm, interpolation='nearest', cmap=cmap)
      plt.title(title)
      plt.colorbar()
      tick_marks = np.arange(len(classes))
      plt.xticks(tick_marks, classes, rotation=45)
      plt.yticks(tick_marks, classes)
  
      if normalize:
          cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
          print("Normalized confusion matrix")
      else:
          print('Confusion matrix, without normalization')
  
      print(cm)
      for i in cm:
          a=0
          for j in i:
              a=a+j
          print(a)
  
      thresh = cm.max() / 2.
      for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
          plt.text(j, i, cm[i, j],
                   horizontalalignment="center",
                   color="white" if cm[i, j] > thresh else "black")
  
      plt.tight_layout()
      plt.ylabel('True label')
      plt.xlabel('Predicted label')
  
  # Compute confusion matrix
  cnf_matrix = confusion_matrix(y_test, y_pred)
  np.set_printoptions(precision=2)
  
  # Plot non-normalized confusion matrix
  plt.figure()
  plot_confusion_matrix(cnf_matrix, classes=class_names,
                        title='Confusion matrix, without normalization')
  
  
  plt.show()
  