Add Exception Handling

2019/Sentiment analysis/SVM_SA.py

@@ -10,62 +10,93 @@
 from sklearn.model_selection import StratifiedKFold
 from sklearn.externals import joblib
 
-data = np.load('data.npy')
+try:
+    data = np.load('data.npy')
+except FileNotFoundError:
+    print("Error: 'data.npy' file not found. Please check the file path.")
+    # Add further handling or exit the script as appropriate
 
 n_sar = 3554
 sar = 374
 # total  3928
-label_0 = np.repeat(0, n_sar) #not sarcasm
-label_1 = np.repeat(1, sar) #sarcasm
+label_0 = np.repeat(0, n_sar)  # not sarcasm
+label_1 = np.repeat(1, sar)  # sarcasm
 train_label = np.append(label_0, label_1)
 
-"""shuffle"""
-np.random.seed(10)
-shuffle_num = np.random.choice(range(1,n_sar+sar),n_sar+sar)
-data = data[shuffle_num,]
-data_label = train_label[shuffle_num,]
+try:
+    """shuffle"""
+    np.random.seed(10)
+    shuffle_num = np.random.choice(range(1, n_sar + sar), n_sar + sar)
+    data = data[shuffle_num,]
+    data_label = train_label[shuffle_num,]
+except IndexError:
+    print("Error: Index error occurred during data shuffling. Please check the array dimensions.")
+    # Add further handling or exit the script as appropriate
 
-"""use the last 3000 examples as validation data, you may use random split"""
-test = data[3000:]
-test_label = data_label[3000:]
-train = data[:3000]
-train_label = data_label[:3000]
+try:
+    """use the last 3000 examples as validation data, you may use random split"""
+    test = data[3000:]
+    test_label = data_label[3000:]
+    train = data[:3000]
+    train_label = data_label[:3000]
+except ValueError:
+    print("Error: Value error occurred during data splitting. Please check the array dimensions.")
+    # Add further handling or exit the script as appropriate
 
-import csv
-f = open('data.csv','w')
-writer = csv.writer(f, lineterminator="\n")
-writer.writerow(data)
-f.close()
-f = open('label.csv','w')
-writer = csv.writer(f, lineterminator="\n")
-writer.writerow(data_label)
-f.close()
+try:
+    import csv
+    f = open('data.csv', 'w')
+    writer = csv.writer(f, lineterminator="\n")
+    writer.writerow(data)
+    f.close()
+    f = open('label.csv', 'w')
+    writer = csv.writer(f, lineterminator="\n")
+    writer.writerow(data_label)
+    f.close()
+except Exception as e:
+    print(f"Error: An unexpected error occurred during file writing: {e}")
+    # Add further handling or exit the script as appropriate
 
-"""generate a SVM classifier"""
-classifier = svm.SVC()
+try:
+    """generate a SVM classifier"""
+    classifier = svm.SVC()
+except Exception as e:
+    print(f"Error: An unexpected error occurred during SVM classifier initialization: {e}")
+    # Add further handling or exit the script as appropriate
 
-"""train cross validation"""
-valid_score = []
-kfold = StratifiedKFold(n_splits=5, shuffle=False,  random_state=1)
-count = 0
-for train_index,valid_index in kfold.split(np.array([0]*3000),np.array([0]*3000)):
-    print('<<<<<COUNT>>>>> '+str(count))
-    classifier.fit(train[train_index],train_label[train_index])
-
-    predicted = classifier.predict(train[valid_index])
-    confus = metrics.confusion_matrix(train_label[valid_index], predicted)
-    acc = (confus[0][0]+confus[1][1])/sum(sum(confus))    
-    valid_score.extend([acc])
-    count = count+1
-print("valid: %.2f%% (+/- %.2f%%)" % (np.mean(valid_score), np.std(valid_score)))
-# train model, classifier.fit(資料:data numberxdata size, 分類目標:data numberxlabel size)
+try:
+    """train cross validation"""
+    valid_score = []
+    kfold = StratifiedKFold(n_splits=5, shuffle=False, random_state=1)
+    count = 0
+    for train_index, valid_index in kfold.split(np.array([0] * 3000), np.array([0] * 3000)):
+        print('<<<<<COUNT>>>>> ' + str(count))
+        classifier.fit(train[train_index], train_label[train_index])
 
-"""test model"""
-expected = test_label
-predicted = classifier.predict(test)
-confus = metrics.confusion_matrix(expected, predicted)
-acc = (confus[0][0]+confus[1][1])/sum(sum(confus))
-print('acc = '+str(acc))
+        predicted = classifier.predict(train[valid_index])
+        confus = metrics.confusion_matrix(train_label[valid_index], predicted)
+        acc = (confus[0][0] + confus[1][1]) / sum(sum(confus))
+        valid_score.extend([acc])
+        count = count + 1
+    print("valid: %.2f%% (+/- %.2f%%)" % (np.mean(valid_score), np.std(valid_score)))
+except Exception as e:
+    print(f"Error: An unexpected error occurred during model training: {e}")
+    # Add further handling or exit the script as appropriate
 
-"""save model"""
-joblib.dump(classifier, 'SVM_model.pkl')
+try:
+    """test model"""
+    expected = test_label
+    predicted = classifier.predict(test)
+    confus = metrics.confusion_matrix(expected, predicted)
+    acc = (confus[0][0] + confus[1][1]) / sum(sum(confus))
+    print('acc = ' + str(acc))
+except Exception as e:
+    print(f"Error: An unexpected error occurred during model testing: {e}")
+    # Add further handling or exit the script as appropriate
+
+try:
+    """save model"""
+    joblib.dump(classifier, 'SVM_model.pkl')
+except Exception as e:
+    print(f"Error: An unexpected error occurred during model saving: {e}")
+    # Add further handling or exit the script as appropriate