Easy way to extract common measures such as accuracy, precision, recall from 3x3 confusion matrix wi-CodePudding

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Hi there, do anyone knows how to speed up this process. Just a easy way to extract common measures such as accuracy, precision, recall, f-score from 3x3 confusion matrix only with numpy or pandas? Any suggestions? I am attaching you a image with the my outcome.

cm =confusion_matrix.to_numpy()
acuracy_0 = np.round_(cm[0][0]/cm[3][3], 2)
acuracy_1 = np.round_(cm[1][1]/cm[3][3], 2)
acuracy_2 = np.round_(cm[2][2]/cm[3][3], 2)
acuracy = (acuracy_0, acuracy_1, acuracy_2)
col1 = np.array(acuracy)

precision_0 = np.round_(cm[0][0]/cm[3][0], 2)
precision_1 = np.round_(cm[1][1]/cm[3][1], 2)
precision_2 = np.round_(cm[2][2]/cm[3][2], 2)
precision = (precision_0, precision_1, precision_2)
col2 = np.array(precision)

recall_0 = np.round_(cm[0][0]/cm[0][3], 2)
recall_1 = np.round_(cm[1][1]/cm[1][3], 2)
recall_2 = np.round_(cm[2][2]/cm[2][3], 2)
recall = (recall_0, recall_1, recall_2)
col3 = np.array(recall)

f_score_0 = np.round_((2*precision_0*recall_0)/(precision_0   recall_0), 2)
f_score_1 = np.round_((2*precision_1*recall_1)/(precision_1   recall_1), 2)
f_score_2 = np.round_((2*precision_2*recall_2)/(precision_2   recall_2), 2)
f_score = (f_score_0, f_score_1, f_score_2)
col4 = np.array(f_score)

d = {'Acuracy': col1, 'Precision':col2, 'Recall':col3, 'F1_score':col4}
measurs = pd.DataFrame(data=d)
measurs.index.name = 'Class_Label'
measurs

CodePudding user response：

Since you are working with pandas dataframe, you can do:

# df is your confusion_matrix
# these are your classes
classes = df.columns[:-1]

# the true positives
true_positives = np.diagonal(df.loc[classes, classes])

accuracy = true_positives / df.loc['All','All']
precisions = true_positives / df.loc['All', classes]
recalls = true_positives / df.loc[classes, 'All']
fscores = 2 * precisions * recalls / (precisions   recalls)

out = pd.DataFrame({'accuracy': accuracy, 
                    'precisions': precisions, 
                    'recall': recalls,
                    'fscore': fscores
                   }, index=classes           # in case your classes are different from 0,1,2,...
                   ).round(2)                 # round all at once

Output:

   accuracy  precisions  recall  fscore
0      0.17        0.38    0.67    0.48
1      0.14        0.38    0.31    0.34
2      0.08        0.43    0.27    0.33

CodePudding user response：

For what it's worth, code like

accuracy_0 = cm[0][0]/cm[3][3]
accuracy_1 = cm[1][1]/cm[3][3]
accuracy_2 = cm[2][2]/cm[3][3]
accuracy = (accuracy_0, accuracy_1, accuracy_2)

can be replaced with the more concise

accuracy = cm.diagonal()[:-1]/cm[-1,-1]

so you might rewrite your code as

cm = confusion_matrix.to_numpy()
diag = cm.diagonal()[:-1]

accuracy  = diag / cm[ -1, -1]
precision = diag / cm[  3,:-1]
recall    = diag / cm[:-1,  3]
f_score   = 2 * precision * recall / (precision   recall)

out = pd.DataFrame({'Accuracy': accuracy, 
                    'Precision': precision, 
                    'Recall': recall,
                    'F-score': f_score}).round(2)