How to apply sklearn's EllipticEnvelope to find out top outliers in the given dataset?
Right way to do this is:

Divide data into normal and outliers.
Take large sample from normal data as normal_train for fitting the novelty detection model.
Create a test set with a sample from normal that is not used in training (say normal_test) and a sample from outlier (say outlier_test) in a way such that the distribution of the test data (normal_test + outlier_test) retains population distribution.
Predict on this test data to get usual metrics (accuracy, sensitivity, positive-predictive-value, etc.)

Wow. I have come a long way!
I am using sklearn's EllipticEnvelope to find outliers in dataset. But I am not sure about how to model my problem? Should I just use all the data (without dividing into training and test sets) and apply fit? Also how would I obtain the outlyingness of each datapoint? Should I use predict on the same dataset?