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NLP.py

+# Natural Language Processing for Predict Network Anomaly
+
+# Importing the libraries
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import joblib
+
+# Importing the dataset
+dataset = pd.read_csv('dataset.tsv', delimiter = '\t', quoting = 3)
+
+# Cleaning the texts
+import re
+import nltk
+nltk.download('stopwords')
+from nltk.corpus import stopwords
+from nltk.stem.porter import PorterStemmer
+corpus = []
+for i in range(0, 5550):
+    log = re.sub('[^a-zA-Z0-9]', ' ', dataset['Info'][i])
+    log = log.lower()
+    log = log.split()
+    ps = PorterStemmer()
+    log = [ps.stem(word) for word in log if not word in set(stopwords.words('english'))]
+    log = ' '.join(log)
+    corpus.append(log)
+
+# Creating the Bag of Words model
+from sklearn.feature_extraction.text import CountVectorizer
+cv = CountVectorizer(max_features = 3000)
+X = cv.fit_transform(corpus).toarray()
+y = dataset.iloc[:, 1].values
+
+# Splitting the dataset into the Training set and Test set
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)
+
+# Training the Naive Bayes model on the Training set
+from sklearn.naive_bayes import GaussianNB
+classifier = GaussianNB()
+classifier.fit(X_train, y_train)
+
+# Predicting the Test set results
+y_pred = classifier.predict(X_test)
+
+# Making the Confusion Matrix
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)
+print("Confusion Matrix")
+print(cm)
+
+#Accuracy
+from sklearn.metrics import accuracy_score
+# y_test is the actual values, y_pred is the predicted values
+accuracy = accuracy_score(y_test, y_pred)
+print('Accuracy: %.3f' % accuracy)
+
+#Precision Score
+from sklearn.metrics import precision_score
+# y_test is the actual values, y_pred is the predicted values
+precision = precision_score(y_test, y_pred, average='binary')
+print('Precision: %.3f' % precision)
+
+#Recall Score
+from sklearn.metrics import recall_score
+# y_test is the actual values, y_pred is the predicted values
+recall = recall_score(y_test, y_pred)
+print('Recall: %.3f' % recall)
+
+#F1 Score
+from sklearn.metrics import f1_score
+# y_test is the actual values, y_pred is the predicted values
+f1 = f1_score(y_test, y_pred, average='binary')
+print('F1 Score: %.3f' % f1)
+
+print("Predicted Outcomes of the Model")
+print(y_pred)
+
+print("Actual Outcomes of the Predicted Results")
+print(y_test)
+
+# Visualizing the confusion matrix as a heatmap
+sns.heatmap(cm, annot=True, cmap='Blues')
+plt.title('Confusion Matrix')
+plt.xlabel('Predicted Label')
+plt.ylabel('True Label')
+plt.show()
+
+# Calculating the number of true positives, true negatives, false positives, and false negatives
+tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()
+
+# Creating a bar chart to show the comparison between the predicted and true outcomes
+fig, ax = plt.subplots()
+ax.bar(['True Negatives', 'False Positives', 'False Negatives', 'True Positives'], [tn, fp, fn, tp])
+ax.set_xlabel('Outcome')
+ax.set_ylabel('Count')
+ax.set_title('Comparison of Predicted and True Outcomes')
+plt.show()
+########################################
+# Dumping the trained model to a file
+joblib.dump(classifier, 'naive_bayes_model.joblib')
+
+# Dumping the CountVectorizer
+joblib.dump(cv, 'count_vectorizer.joblib')
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