Upload New File

session-2-anomalies/5_Neighborhood.py

+
+# load libraries 
+import numpy as np
+from sklearn.neighbors import NearestNeighbors
+from sklearn.metrics import accuracy_score,f1_score,roc_auc_score
+
+# load data set 
+data = np.loadtxt('crimerate_binary.csv', delimiter=',')
+[n,p] = data.shape
+
+# split data into a training set and a testing set
+size_train = int(0.75*n) # we use first 75% data for training, the rest for testing
+sample_train = data[0:size_train,0:-1]
+label_train = data[0:size_train,-1]
+sample_test = data[size_train:,0:-1]
+label_test = data[size_train:,-1]
+
+# ------------------------------------
+# Neighborhood-based anomaly detection 
+# tutorial slides, page 85 - 93
+# ------------------------------------
+
+# step 1. choose number of neighbors 
+num_neighbor = 30
+
+# step 2. construct a neighborhood using ONLY NORMAL examples 
+nbrs = NearestNeighbors(n_neighbors=num_neighbor, algorithm='ball_tree').fit(sample_train[label_train==0,:])
+# the following code trains the model using both normal and abnormal examples
+# nbrs = NearestNeighbors(n_neighbors=num_neighbor, algorithm='ball_tree').fit(sample_train)
+
+# step 3. compute distance from each testing example to its num_neighbor neighbors 
+distances, indices = nbrs.kneighbors(sample_test) 
+# treat average distance as anomalous score 
+adscore = np.sum(distances,axis=1)/num_neighbor
+# get AUC score 
+auc_score = roc_auc_score(label_test, adscore)
+# to get detection error and f1-score, we need to threshold anomalous score 
+# the range of adscore is [0.9, 3.3]
+threshold = 2
+adscore[adscore <= threshold] = 0
+adscore[adscore > threshold] = 1
+# evaluate detection error and f1-score 
+# now evaluate error and f1-score 
+err = 1 - accuracy_score(label_test,adscore) 
+f1score = f1_score(label_test,adscore) 
+
+# step 4. print results 
+print('\nNeighborhood-based Approach')
+print('Detection Error = %.4f' % err)
+print('F1 Score = %.4f' % f1score)
+print('AUC Score = %.4f' % auc_score)
+
+
+# -----------
+# Assignment 
+# -----------
+# 1. construct neighborhood using both normal and abnormal examples (replace line 27 with line 29), what do you observe? 
+# 2. play with different size of neighborhood (line 24), what do you observe? 
+# 3. play with different thresholds (line 39), what do you observe? 
+