Update Fonctions.py, Results/results_all.txt

Deleted Results/result_1.png, Results/result_2.png

Fonctions.py

@@ -478,7 +478,7 @@ def testMLP(data,epoch,learningrate):
   ytestpred = logistic_regression_model.forward(X_test)
   yhat = torch.round(ytestpred)
   train_acc = torch.sum(yhat == y_test)
-  final_train_acc = train_acc//yhat.shape[0]
+  final_train_acc = train_acc/yhat.shape[0]
   P_score = precision_score(y_test.detach().numpy(),yhat.detach().numpy())
   R_score = recall_score(y_test.detach().numpy(),yhat.detach().numpy())
   fpr, tpr, thresholds = roc_curve(y_test.detach().numpy(),ytestpred.detach().numpy())

Results/results_all.txt

+test_Gaussian_NB (__main__.mytest) ... The test of gaussian naive bayes model for chronic_kidney_disease
+The accuracy of the Gaussian Naive Bayes classifier is:  0.6625 
+ The precision of the Gaussian Naive Bayes classifier is:  0.6582278481012658 
+ The recall of the Gaussian Naive Bayes classifier is:  1.0 
+ The AUC of the Gaussian Naive Bayes classifier is:  0.8921703296703297 
+
+Figure(640x480)
+The test of gaussian naive bayes model for banknote_authentication_dataset
+The accuracy of the Gaussian Naive Bayes classifier is:  0.8763636363636363 
+ The precision of the Gaussian Naive Bayes classifier is:  0.9428571428571428 
+ The recall of the Gaussian Naive Bayes classifier is:  0.7795275590551181 
+ The AUC of the Gaussian Naive Bayes classifier is:  0.9564801021493935 
+
+Figure(640x480)
+ok
+test_MLP (__main__.mytest) ... The test of mlp model for chronic_kidney_disease
+The accuracy of the MLP model is:  tensor(0.9900) 
+ The precision of the MLP model is:  1.0 
+ The recall figure of the MLP model is:  0.9846153846153847 
+ The AUC of the MLP model is:  1.0 
+
+Figure(640x480)
+The test of mlp model for banknote_authentication_dataset
+The accuracy of the MLP model is:  tensor(0.9329) 
+ The precision of the MLP model is:  0.9448275862068966 
+ The recall figure of the MLP model is:  0.9013157894736842 
+ The AUC of the MLP model is:  0.9892187930559384 
+
+Figure(640x480)
+ok
+test_SVM (__main__.mytest) ... The test of SVM model for chronic_kidney_disease
+The accuracy of the SVM classifier is:  0.99 
+ The precision of the SVM classifier is:  1.0 
+ The recall of the SVM classifier is:  0.9846153846153847 
+ The AUC of the SVM classifier is:  1.0 
+
+Figure(640x480)
+The test of SVM model for banknote_authentication_dataset
+The accuracy of the SVM classifier is:  0.9912536443148688 
+ The precision of the SVM classifier is:  0.9806451612903225 
+ The recall of the SVM classifier is:  1.0 
+ The AUC of the SVM classifier is:  1.0 
+
+Figure(640x480)
+ok
+test_decision_tree (__main__.mytest) ... The test of decision tree model for chronic_kidney_disease
+The accuracy of the decision tree model is:  0.9933333333333334 
+ The precision of the decision tree model is:  1.0 
+ The recall figure of the decision tree model is:  1.0 
+ The AUC of the decision tree model is:  1.0 
+
+Figure(640x480)
+The test of decision tree model for banknote_authentication_dataset
+The accuracy of the decision tree model is:  0.983485627260613 
+ The precision of the decision tree model is:  1.0 
+ The recall figure of the decision tree model is:  0.9868421052631579 
+ The AUC of the decision tree model is:  0.993421052631579 
+
+Figure(640x480)
+ok
+test_logistic (__main__.mytest) ... The test of logistic regression model for chronic_kidney_disease
+Figure(640x480)
+The accuracy of the logistic regression model is:  0.95 
+ The precision of the logistic regression model is:  1.0 
+ The recall figure of the logistic regression model is:  0.9230769230769231 
+ The AUC of the logistic regression model is:  0.9876923076923078 
+
+Figure(640x480)
+The test of logistic regression model for banknote_authentication_dataset
+Figure(640x480)
+The accuracy of the logistic regression model is:  0.8775510204081632 
+ The precision of the logistic regression model is:  0.8985507246376812 
+ The recall figure of the logistic regression model is:  0.8157894736842105 
+ The AUC of the logistic regression model is:  0.9578051804904932