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session-2-anomalies/1_Classification_LinearRegression.py

+
+# load libraries 
+import numpy as np
+from sklearn import linear_model
+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]
+
+# ----------------------------------------
+# classification-based anomaly detection 
+# tutorial slides, page 49 - 59
+# use linear regression model for detection 
+# ----------------------------------------
+
+# step 1. choose a classification model (linear regression)
+model = linear_model.LinearRegression()
+
+# step 2. train the model using examples 
+model.fit(sample_train, label_train)
+
+# step 3. apply model to predict whether an example is normal or anomaly 
+label_pred = model.predict(sample_test) 
+# we can treat above output as anomalous score, and get AUC score from it 
+auc_score = roc_auc_score(label_test, label_pred)
+# because this is a regression model, we need to threshold its output to get detection error and f1-score
+threshold = 0.4
+label_pred[label_pred <= threshold] = 0
+label_pred[label_pred > threshold] = 1
+err = 1 - accuracy_score(label_test,label_pred) 
+f1score = f1_score(label_test,label_pred)
+
+# step 4. print results 
+print('\nClassification-based Approach (Linear Regression Model)')
+print('Detection Error = %.4f' % err)
+print('F1 Score = %.4f' % f1score)
+print('AUC Score = %.4f' % auc_score)
+
+
+# -----------
+# Assignment 
+# -----------
+# play with different threshold values (line 37), what do you observe?
+
+
+