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"url": "https://blog.heaplinker.com/How-to-make-a-custom-confusion-matrix-on-matplotlib-with-heatmap-and-annotation-2021070218326405004/",
"title": "How to make a custom confusion matrix on matplotlib with heatmap and annotation",
"body": "2021/07/02 - A confusion matrix is also known as an error matrix or matching matrix1. Each row and each column of this matrix represents the actual class and the predicted class respectively. Here in this post, I have discussed how we can build a custom confusion matrix for multi-class classification with matplotlib in python. Here is the post outline: Characteristics of the custom confusion matrix Generate random data Configure matplotlib Generate confusion_matrix Some necessary calculation Make annotation Make customized confusion matrix Make DataFrame Show heatmap EvaluationCharacteristics of the custom confusion matrix: This matrix will contain six different classes and every cell contains two different data one is the total number of being truly or falsely predicted and another one is the percentage(prediction rate per class). 1 to 6th cell of the seventh row contains precision 1 to 6th cell of the seventh column contains recall, and support Remaining one contains the accuracy Generated matrix will show the heatmap depending on the total numbers of true_positive, true_negative, false_positive, and false_negative or percentagesGenerate random data: Import some necessary modules: import numpy as npimport pandas as pdimport matplotlib. pyplot as pltimport matplotlib. pylab as pylabimport seaborn as snsfrom sklearn. metrics import confusion_matrixFor the visualization purpose we have to generate some random data like this: np. random. seed(46) # for consistent result# generate random true data for 6 classesy_true = np. random. randint(0, 6, 1000) # generate random predicted data for 6 classesy_pred = np. random. randint(0, 6, 1000) # labeling the classes as followslabels = [ 'Potato__Early_blight', 'Potato__Late_blight', 'Potato__healthy', 'Tomato__Early_blight', 'Tomato__Late_blight', 'Tomato__healthy']print(y_true[:10])print(y_pred[:10])Output first 10 digits: [2 2 2 1 0 1 0 4 4 1][4 3 4 1 4 4 3 5 2 1]Configure matplotlib: Set the necessary design parameters for the matplotlib: params = { 'figure. figsize': (10, 10), 'axes. titleweight': 'bold', 'axes. labelsize': '16', 'axes. titlesize':'20', 'axes. labelweight':'bold', 'xtick. labelsize':'14', 'ytick. labelsize':'14', 'font. size': '14'}pylab. rcParams. update(params)Generate confusion_matrix: Now generate the confusion matrix by sklearn. metrics. confusion_matrix module: c_m = confusion_matrix(y_true, y_pred, labels=list(range(0, len(labels))))print(c_m)Output: [[37 38 28 21 24 24] [22 19 22 30 23 31] [20 38 30 19 32 34] [19 30 39 29 32 24] [25 17 27 26 28 29] [21 34 27 31 35 35]]Some necessary calculation: For multi-class classification let’s calculate the sum of true_positive(TP) and false_positive(FP) of every class: axis = 1 because of column-wise calculation, for example:37 + 38 + 28 + 21 + 24 + 24 = 172To preserve the previous dimension of the array, keepdims = True c_m_sum = np. sum(c_m, axis=1, keepdims=True)print(c_m_sum)Output: [[172] [147] [173] [173] [152] [183]]Calculate the element-wise probability for every cell along with that we have to calculate row-wise, column-wise and diagonal (total number of true_positive) sum for every row and column of the matrix. prediction_rate = c_m / c_m_sum. astype(float) * 100row_wise_sum = c_m. sum(axis=0)col_wise_sum = c_m. sum(axis=1)total_true_pos = c_m. diagonal()print(prediction_rate, row_wise_sum, col_wise_sum, total_true_pos, sep='\n')Output: [[21. 51162791 22. 09302326 16. 27906977 12. 20930233 13. 95348837 13. 95348837] [14. 96598639 12. 92517007 14. 96598639 20. 40816327 15. 6462585 21. 08843537] [11. 56069364 21. 96531792 17. 34104046 10. 98265896 18. 49710983 19. 65317919] [10. 98265896 17. 34104046 22. 5433526 16. 76300578 18. 49710983 13. 87283237] [16. 44736842 11. 18421053 17. 76315789 17. 10526316 18. 42105263 19. 07894737] [11. 47540984 18. 57923497 14. 75409836 16. 93989071 19. 12568306 19. 12568306]][144 176 173 156 174 177][172 147 173 173 152 183][37 19 30 29 28 35]Calculate the precision, recall and accuracy percentages according to these formulas: accuracy = total_true_pos. sum() / len(y_true) * 100precision = total_true_pos / col_wise_sum * 100recall = total_true_pos / row_wise_sum * 100print(accuracy, precision, recall, sep='\n')Output: 17. 8[21. 51162791 12. 92517007 17. 34104046 16. 76300578 18. 42105263 19. 12568306][25. 69444444 10. 79545455 17. 34104046 18. 58974359 16. 09195402 19. 7740113 ]Make annotation: Take an empty numpy. ndarray like the shape of the confusion matrix: annotation = np. empty_like(c_m). astype(str)rows, cols = c_m. shapeprint(rows, cols)Output: 6 6Fill the empty annotation matrix: for i in range(rows): for j in range(cols): count = c_m[i, j] pre = prediction_rate[i, j] annotation[i, j] = '%d\n%. 1f%%' % (count, pre)print(annotation)Output: [['37\n21. 5%' '38\n22. 1%' '28\n16. 3%' '21\n12. 2%' '24\n14. 0%' '24\n14. 0%'] ['22\n15. 0%' '19\n12. 9%' '22\n15. 0%' '30\n20. 4%' '23\n15. 6%' '31\n21. 1%'] ['20\n11. 6%' '38\n22. 0%' '30\n17. 3%' '19\n11. 0%' '32\n18. 5%' '34\n19. 7%'] ['19\n11. 0%' '30\n17. 3%' '39\n22. 5%' '29\n16. 8%' '32\n18. 5%' '24\n13. 9%'] ['25\n16. 4%' '17\n11. 2%' '27\n17. 8%' '26\n17. 1%' '28\n18. 4%' '29\n19. 1%'] ['21\n11. 5%' '34\n18. 6%' '27\n14. 8%' '31\n16. 9%' '35\n19. 1%' '35\n19. 1%']]To add the accuracy and the total number of true_positive to the matrix, we need to append those to precision and col_wise_sum array respectively. col_wise_sum = np. append(col_wise_sum, total_true_pos. sum())precision = np. append(precision, accuracy)print(col_wise_sum, precision, sep='\n')Output: [172 147 173 173 152 183 178][21. 51162791 12. 92517007 17. 34104046 16. 76300578 18. 42105263 19. 12568306 17. 8]Make tuple of (total_num, percentage) to make annotation for both recall and precision. recall = np. array([[i, j] for i,j in zip(row_wise_sum, recall)])precision = np. array([[i, j] for i,j in zip(col_wise_sum, precision)])print(recall, precision, sep='\n')Output: [[144. 25. 69444444] [176. 10. 79545455] [173. 17. 34104046] [156. 18. 58974359] [174. 16. 09195402] [177. 19. 7740113 ]][[172. 21. 51162791] [147. 12. 92517007] [173. 17. 34104046] [173. 16. 76300578] [152. 18. 42105263] [183. 19. 12568306] [178. 17. 8 ]]Add newly created annotation (from recall) to the previous annotation. annotation = np. concatenate((annotation, [list(map(lambda data: '%d\n%. 1f%%' % (data[0], data[1]), recall))]), axis=0)print(annotation)Output: [['37\n21. 5%' '38\n22. 1%' '28\n16. 3%' '21\n12. 2%' '24\n14. 0%' '24\n14. 0%'] ['22\n15. 0%' '19\n12. 9%' '22\n15. 0%' '30\n20. 4%' '23\n15. 6%' '31\n21. 1%'] ['20\n11. 6%' '38\n22. 0%' '30\n17. 3%' '19\n11. 0%' '32\n18. 5%' '34\n19. 7%'] ['19\n11. 0%' '30\n17. 3%' '39\n22. 5%' '29\n16. 8%' '32\n18. 5%' '24\n13. 9%'] ['25\n16. 4%' '17\n11. 2%' '27\n17. 8%' '26\n17. 1%' '28\n18. 4%' '29\n19. 1%'] ['21\n11. 5%' '34\n18. 6%' '27\n14. 8%' '31\n16. 9%' '35\n19. 1%' '35\n19. 1%'] ['144\n25. 7%' '176\n10. 8%' '173\n17. 3%' '156\n18. 6%' '174\n16. 1%' '177\n19. 8%']]Add newly created annotation (from precision) to the previous annotation. annotation = np. concatenate((annotation, np. array([list(map(lambda data: '%d\n%. 1f%%' % (data[0], data[1]), precision))]). T), axis=1)print(annotation)Output: [['37\n21. 5%' '38\n22. 1%' '28\n16. 3%' '21\n12. 2%' '24\n14. 0%' '24\n14. 0%' '172\n21. 5%'] ['22\n15. 0%' '19\n12. 9%' '22\n15. 0%' '30\n20. 4%' '23\n15. 6%' '31\n21. 1%' '147\n12. 9%'] ['20\n11. 6%' '38\n22. 0%' '30\n17. 3%' '19\n11. 0%' '32\n18. 5%' '34\n19. 7%' '173\n17. 3%'] ['19\n11. 0%' '30\n17. 3%' '39\n22. 5%' '29\n16. 8%' '32\n18. 5%' '24\n13. 9%' '173\n16. 8%'] ['25\n16. 4%' '17\n11. 2%' '27\n17. 8%' '26\n17. 1%' '28\n18. 4%' '29\n19. 1%' '152\n18. 4%'] ['21\n11. 5%' '34\n18. 6%' '27\n14. 8%' '31\n16. 9%' '35\n19. 1%' '35\n19. 1%' '183\n19. 1%'] ['144\n25. 7%' '176\n10. 8%' '173\n17. 3%' '156\n18. 6%' '174\n16. 1%' '177\n19. 8%' '178\n17. 8%']]Previously I have added extra row and column so that we have to add another label name as precision_recall_accuracy labels. append('precision_recall_accuracy')print(labels)Output: ['Potato__Early_blight', 'Potato__Late_blight', 'Potato__healthy', 'Tomato__Early_blight', 'Tomato__Late_blight', 'Tomato__healthy', 'precision_recall_accuracy']Make customized confusion matrix: Change the structure of the previously created confusion matrix to add precision and recall to the row and column respectively. So that here I have created a new c_m_1 matrix with the total numbers of recall and precision from c_m. c_m_1 = np. concatenate((c_m, [recall[:, 0]]), axis=0)c_m_1 = np. concatenate((c_m_1, np. array([precision[:, 0]]). T), axis=1)print(c_m)print(c_m_1)Output: [[37 38 28 21 24 24] [22 19 22 30 23 31] [20 38 30 19 32 34] [19 30 39 29 32 24] [25 17 27 26 28 29] [21 34 27 31 35 35]][[ 37. 38. 28. 21. 24. 24. 172. ] [ 22. 19. 22. 30. 23. 31. 147. ] [ 20. 38. 30. 19. 32. 34. 173. ] [ 19. 30. 39. 29. 32. 24. 173. ] [ 25. 17. 27. 26. 28. 29. 152. ] [ 21. 34. 27. 31. 35. 35. 183. ] [144. 176. 173. 156. 174. 177. 178. ]]Create another matrix named c_m_2 which will contain the prediction rate and precision-recall percentage from prediction_rate matrix. c_m_2 = np. concatenate((prediction_rate, [recall[:, 1]]), axis=0)c_m_2 = np. concatenate((c_m_2, np. array([precision[:, 1]]). T), axis=1)print(prediction_rate)print(c_m_2)Output: [[21. 51162791 22. 09302326 16. 27906977 12. 20930233 13. 95348837 13. 95348837] [14. 96598639 12. 92517007 14. 96598639 20. 40816327 15. 6462585 21. 08843537] [11. 56069364 21. 96531792 17. 34104046 10. 98265896 18. 49710983 19. 65317919] [10. 98265896 17. 34104046 22. 5433526 16. 76300578 18. 49710983 13. 87283237] [16. 44736842 11. 18421053 17. 76315789 17. 10526316 18. 42105263 19. 07894737] [11. 47540984 18. 57923497 14. 75409836 16. 93989071 19. 12568306 19. 12568306]][[21. 51162791 22. 09302326 16. 27906977 12. 20930233 13. 95348837 13. 95348837 21. 51162791] [14. 96598639 12. 92517007 14. 96598639 20. 40816327 15. 6462585 21. 08843537 12. 92517007] [11. 56069364 21. 96531792 17. 34104046 10. 98265896 18. 49710983 19. 65317919 17. 34104046] [10. 98265896 17. 34104046 22. 5433526 16. 76300578 18. 49710983 13. 87283237 16. 76300578] [16. 44736842 11. 18421053 17. 76315789 17. 10526316 18. 42105263 19. 07894737 18. 42105263] [11. 47540984 18. 57923497 14. 75409836 16. 93989071 19. 12568306 19. 12568306 19. 12568306] [25. 69444444 10. 79545455 17. 34104046 18. 58974359 16. 09195402 19. 7740113 17. 8 ]]Make DataFrame: We need to create DataFrame for both c_m_1 and c_m_2 and set their index or column name as follows: c_m_1 = pd. DataFrame(c_m_1, index=labels, columns=labels)c_m_1. index. name = 'Output Class'c_m_1. columns. name = 'Target Class'c_m_2 = pd. DataFrame(c_m_2, index=labels, columns=labels)c_m_2. index. name = 'Output Class'c_m_2. columns. name = 'Target Class'c_m_1. info()Output: <class 'pandas. core. frame. DataFrame'>Index: 7 entries, Potato__Early_blight to precision_recall_accuracyData columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Potato__Early_blight 7 non-null float64 1 Potato__Late_blight 7 non-null float64 2 Potato__healthy 7 non-null float64 3 Tomato__Early_blight 7 non-null float64 4 Tomato__Late_blight 7 non-null float64 5 Tomato__healthy 7 non-null float64 6 precision_recall_accuracy 7 non-null float64dtypes: float64(7)memory usage: 448. 0+ bytesShow heatmap: Let’s show a heatmap depending on the number of classes. color intensities varies yellow(low) to green(high) heatmap_1 = sns. heatmap(c_m_1, annot=annotation, fmt='', cbar=True, cmap='YlGn')heatmap_1. set_xticklabels(heatmap_1. get_xticklabels(), rotation=45, horizontalalignment='right')plt. title('Confusion Matrix(number of classes)')plt. show()Output:Showing a heatmap depending on percentages: heatmap_2 = sns. heatmap(c_m_2, annot=annotation, fmt='', cbar=True, cmap='YlGn')heatmap_2. set_xticklabels(heatmap_2. get_xticklabels(), rotation=45, horizontalalignment='right')plt. title('Confusion Matrix(percentages)')plt. show()Output: Let’s eliminate the color map for better view: heatmap = sns. heatmap(c_m_2, annot=annotation, fmt='', cbar=False, cmap='YlGn')heatmap. set_xticklabels(heatmap. get_xticklabels(), rotation=45, horizontalalignment='right')plt. title('Confusion Matrix')plt. show()Output: Evaluation: Now we can evaluate our accuracy, precision, recall and support scores with sklearn. metrics module as follows: from sklearn. metrics import classification_reportprint(classification_report(y_true, y_pred, labels=list(range(len(labels[:-1]))), target_names=labels[:-1]))Output: precision recall f1-score supportPotato__Early_blight 0. 26 0. 22 0. 23 172 Potato__Late_blight 0. 11 0. 13 0. 12 147 Potato__healthy 0. 17 0. 17 0. 17 173Tomato__Early_blight 0. 19 0. 17 0. 18 173 Tomato__Late_blight 0. 16 0. 18 0. 17 152 Tomato__healthy 0. 20 0. 19 0. 19 183 accuracy 0. 18 1000 macro avg 0. 18 0. 18 0. 18 1000 weighted avg 0. 18 0. 18 0. 18 1000Accuracy, Precision, Recall and Support scores are matched with ours. This is the full code:A github repository can be found on this link. "
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