Add random forest classifier

competition/random_forest.py

@@ -0,0 +1,50 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sn
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, confusion_matrix
+from sklearn.model_selection import train_test_split
+
+
+def ip_to_bin(x):
+    parts = x.split('.')
+    return (int(parts[0]) << 24) + (int(parts[1]) << 16) + (int(parts[2]) << 8) + int(parts[3])
+
+
+df = pd.read_csv('training4tuplabeled.csv',
+                 converters={
+                     'sourceIPAddress': lambda x: ip_to_bin(x),
+                     'destinationIPAddress': lambda x: ip_to_bin(x)
+                 })
+df.drop(['flowStartMilliseconds'], 1, inplace=True)
+X = np.array(df.drop(columns=['sublabel']))
+y = np.array(df['sublabel'])
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True)
+
+clf = RandomForestClassifier(n_estimators=50, n_jobs=-1, criterion='gini', random_state=0, class_weight="balanced")
+clf.fit(X_train, y_train)
+
+accuracy = clf.score(X_test, y_test)
+print('Accuracy: ', accuracy)
+y_pred_train = clf.predict(X_train)
+y_pred_test = clf.predict(X_test)
+print("\n *************** TRAINING ****************")
+cm_train = confusion_matrix(y_train, y_pred_train)
+plt.figure(figsize=(10, 7))
+sn.heatmap(cm_train, annot=True)
+plt.xlabel('Truth')
+plt.ylabel('Predicted')
+plt.show()
+print(classification_report(y_train, y_pred_train))
+print("\n ************** VALIDATION ***************")
+cm_test = confusion_matrix(y_test, y_pred_test)
+plt.figure(figsize=(10, 7))
+sn.heatmap(cm_test, annot=True)
+plt.xlabel('Truth')
+plt.ylabel('Predicted')
+plt.show()
+print(classification_report(y_test, y_pred_test))
+
+example_measure = np.array([ip_to_bin('2.1.1.1'), ip_to_bin('2.1.1.2'), 0, 0, 1])