[Confusion Matrix] How to calculate confusion matrix, precision and recall list from scratch

I directly give an example which is with 10 categories, such as CIFAR-10 and MNIST. It explains how to calculate the confusion matrix, precision and recall list from scratch in Python. My data is generated at random. You should replace by yours. Here it goes:

import numpy
import json

CATEGORY = 10
SAMPLES = 1000
label_list = [i for i in range(CATEGORY)]

pred_list = numpy.random.randint(0, CATEGORY-1, size=SAMPLES)
y_batch_list = numpy.random.randint(0, CATEGORY-1, size=SAMPLES)
print(pred_list, y_batch_list)

class confusion_matrix:
  def __init__(self, pred_list, y_batch_list, label_list):
    if len(pred_list) != len(y_batch_list):
      raise Exception('Prediction length is different from Label list!')
    self.pred_list = pred_list
    self.y_batch_list = y_batch_list
    self.matrix_size = len(label_list)
    
    # this matrix are 2 dimensions(y_batch, pred)
    self.confusion_matrix = [[ x*0 for x in range(self.matrix_size)] for y in range(self.matrix_size)]
    self.precision_list = [x*0 for x in range(self.matrix_size)]
    self.recall_list = [x*0 for x in range(self.matrix_size)]

  def calculate_confusion_matrix(self):
    for i in range(len(self.pred_list)):
      # dimension => [y_batch, pred]
      self.confusion_matrix[self.y_batch_list[i]][self.pred_list[i]] += 1

  def calculate_recall_precision_list(self):
    # calculate recall
    for i in range(self.matrix_size):
      tmp_value = 0
      for j in range(self.matrix_size):
        tmp_value += self.confusion_matrix[i][j]
        if tmp_value is not 0:
          self.recall_list[i] = float(self.confusion_matrix[i][i]) / tmp_value

    # calculate precision
    for j in range(self.matrix_size):
      tmp_value = 0
      for i in range(self.matrix_size):
        tmp_value += self.confusion_matrix[i][j]
        if tmp_value is not 0:
          self.precision_list[j] = float(self.confusion_matrix[j][j]) / tmp_value


  def gen_json_data(self):
    data = {'confusion_matrix': self.confusion_matrix,
            'precision_list': self.precision_list,
            'recall_list': self.recall_list
           }
    return data

ret = confusion_matrix(pred_list.tolist(), y_batch_list.tolist(), label_list)
ret.calculate_confusion_matrix()
ret.calculate_recall_precision_list()

Result:

print(ret.gen_json_data())
{'precision_list': [0.0625, 0.14912280701754385, 0.02654867256637168, 0.1452991452991453, 0.07377049180327869, 0.10526315789473684, 0.11320754716981132, 0.13, 0.13725490196078433, 0], 
 'confusion_matrix': [[7, 14, 10, 15, 17, 19, 17, 18, 14, 0], [10, 17, 14, 9, 5, 11, 9, 12, 12, 0], [11, 11, 3, 19, 16, 13, 4, 11, 7, 0], [13, 18, 16, 17, 13, 12, 11, 11, 12, 0], [15, 12, 15, 14, 9, 13, 17, 9, 11, 0], [19, 8, 11, 11, 17, 12, 13, 10, 8, 0], [9, 9, 10, 11, 14, 11, 12, 7, 15, 0], [20, 14, 13, 10, 18, 10, 11, 13, 9, 0], [8, 11, 21, 11, 13, 13, 12, 9, 14, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 
 'recall_list': [0.05343511450381679, 0.1717171717171717, 0.031578947368421054, 0.13821138211382114, 0.0782608695652174, 0.11009174311926606, 0.12244897959183673, 0.11016949152542373, 0.125, 0]
}