├── README.md
├── Confusion_Matrix_Result.py
├── Confusion_Matrix_Plot.py
└── Confusion_Matrix.py


/README.md:
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1 | # Confusion_Matrix
2 | Confusion Matrix function.
3 | 


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/Confusion_Matrix_Result.py:
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 1 | from sklearn import metrics
 2 | from sklearn.metrics import classification_report
 3 | from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, confusion_matrix
 4 | 
 5 | def CM_result(y_dev, y_pred,pos=1,report_digits=4):
 6 |     accuracy = accuracy_score(y_dev, y_pred)
 7 |     precision = precision_score(y_dev, y_pred,pos_label=pos)
 8 |     recall = recall_score(y_dev, y_pred,pos_label=pos)
 9 |     f1 = f1_score(y_dev, y_pred,pos_label=pos)
10 |     CM_matrix = confusion_matrix(y_dev, y_pred)
11 |     report = classification_report(y_dev, y_pred,digits=report_digits)
12 |     
13 |     print('Accuracy = ' + str(accuracy) +'\nPrecision = ' + str(precision) +'\nRecall = ' + str(recall))
14 |     return accuracy, precision, recall, f1, report, CM_matrix
15 | 


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/Confusion_Matrix_Plot.py:
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 1 | import matplotlib.pyplot as plt
 2 | import itertools
 3 | 
 4 | def CM_plot(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
 5 |     """
 6 |     This function prints and plots the confusion matrix.
 7 |     Normalization can be applied by setting `normalize=True`.
 8 |     """
 9 |     if normalize:
10 |         cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
11 |         print("Normalized confusion matrix")
12 |     else:
13 |         print('Confusion matrix, without normalization')
14 | 
15 |     print(cm)
16 | 
17 |     plt.imshow(cm, interpolation='nearest', cmap=cmap)
18 |     plt.title(title,fontsize = 50)
19 |     #plt.colorbar()
20 |     tick_marks = np.arange(len(classes))
21 |     plt.xticks(tick_marks, classes, rotation=45,fontsize = 40)
22 |     plt.yticks(tick_marks, classes,fontsize = 40)
23 | 
24 |     fmt = '.2f' if normalize else 'd'
25 |     thresh = cm.max() / 2.
26 |     for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
27 |         plt.text(j, i, format(cm[i, j], fmt),
28 |                  horizontalalignment="center",fontsize = 60,
29 |                  color="white" if cm[i, j] > thresh else "black")
30 | 
31 |     plt.tight_layout()
32 |     plt.ylabel('True label',fontsize = 40)
33 |     plt.xlabel('Predicted label',fontsize = 40)


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/Confusion_Matrix.py:
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 1 | import matplotlib.pyplot as plt
 2 | import itertools
 3 | from sklearn import metrics
 4 | from sklearn.metrics import classification_report
 5 | from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, confusion_matrix
 6 | 
 7 | def CM_plot(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
 8 |     """
 9 |     This function prints and plots the confusion matrix.
10 |     Normalization can be applied by setting `normalize=True`.
11 |     """
12 |     if normalize:
13 |         cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
14 |         print("Normalized confusion matrix")
15 |     else:
16 |         print('Confusion matrix, without normalization')
17 | 
18 |     print(cm)
19 | 
20 |     plt.imshow(cm, interpolation='nearest', cmap=cmap)
21 |     plt.title(title,fontsize = 50)
22 |     #plt.colorbar()
23 |     tick_marks = np.arange(len(classes))
24 |     plt.xticks(tick_marks, classes, rotation=45,fontsize = 40)
25 |     plt.yticks(tick_marks, classes,fontsize = 40)
26 | 
27 |     fmt = '.2f' if normalize else 'd'
28 |     thresh = cm.max() / 2.
29 |     for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
30 |         plt.text(j, i, format(cm[i, j], fmt),
31 |                  horizontalalignment="center",fontsize = 60,
32 |                  color="white" if cm[i, j] > thresh else "black")
33 | 
34 |     plt.tight_layout()
35 |     plt.ylabel('True label',fontsize = 40)
36 |     plt.xlabel('Predicted label',fontsize = 40)
37 | 
38 | def CM_result(y_dev, y_pred,pos=1,report_digits=4):
39 |     accuracy = accuracy_score(y_dev, y_pred)
40 |     precision = precision_score(y_dev, y_pred,pos_label=pos)
41 |     recall = recall_score(y_dev, y_pred,pos_label=pos)
42 |     f1 = f1_score(y_dev, y_pred,pos_label=pos)
43 |     CM_matrix = confusion_matrix(y_dev, y_pred)
44 |     report = classification_report(y_dev, y_pred,digits=report_digits)
45 |     
46 |     print('Accuracy = ' + str(accuracy) +'\nPrecision = ' + str(precision) +'\nRecall = ' + str(recall))
47 |     return accuracy, precision, recall, f1, report, CM_matrix


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