1. Learn
    2. /
  2. Courses
    3. /
  3. Designing Machine Learning Workflows in Python

Exercise

Bringing it all together

In addition to the distance-based learning anomaly detection pipeline you created in the last exercise, you want to also support a feature-based learning one with one-class SVM. You decide to extract two features: first, the length of the string, and, second, a numerical encoding of the first letter of the string, obtained using the function LabelEncoder() described in Chapter 1. To ensure a fair comparison, you will input the outlier scores into an AUC calculation. The following have been imported: LabelEncoder(), roc_auc_score() as auc() and OneClassSVM. The data is available as a pandas data frame called proteins with two columns, label and seq, and two classes, IMMUNE SYSTEM and VIRUS. A fitted LoF detector is available as lof_detector.

Instructions

100 XP
  • For a string s, len(s) returns its length. Apply that to the seq column to obtain a new column len.
  • For a string s, list(s) returns a list of its characters. Use this to extract the first letter of each sequence, and encode it using LabelEncoder().
  • LoF scores are in the negative_outlier_factor_ attribute. Compute their AUC.
  • Fit a 1-class SVM to a data frame with only len and first as columns. Extract the scores and assess both the LoF scores and the SVM scores using AUC.