Computer Vision News - March 2019

23 Focus on Computer Vision News Python open source toolbox for Outlier Detection # plot outliers and contour subplot = plt . subplot ( 2 , 2 , i + 1 ) subplot . contourf ( xx , yy , ZZ , levels = np . linspace ( ZZ . min (), threshold , 15 )) subplot . contour ( xx , yy , ZZ , levels =[ threshold ], linewidths = 2 , colors = 'red' ) # fill orange contour lines where range of anomaly score is from threshold to maximum anomaly score subplot . contourf ( xx , yy , ZZ , levels =[ threshold , ZZ . max ()], colors = 'blue' ) # scatter plot of inliers with white dots subplot . scatter ( X_train [:- n_outliers , 0 ], X_train [:- n_outliers , 1 ], c = 'white' , s = 12 , edgecolor = 'g' ) # scatter plot of outliers with black dots subplot . scatter ( X_train [- n_outliers :, 0 ], X_train [- n_outliers :, 1 ], c = 'black' , s = 12 , edgecolor = 'g' ) subplot . axis ( 'tight' ) subplot . set_title ( clf_name ) subplot . set_xlim ((- 15 , 15 )) subplot . set_ylim ((- 15 , 15 )) plt . show () Where the output is: We clearly see that ABOD, Isolation Forest and KNN found the main groups (red contour) and left the outlier out, while the BAG failed to find the outliers.