Thresholding

Full example: threshold_example.py

1. Import models

   from pyod.models.knn import KNN              # kNN detector
   from pyod.models.thresholds import FILTER    # Filter thresholder
   from pyod.utils.data import generate_data    # synthetic data generator
   

2. Generate sample data with pyod.utils.data.generate_data():

   contamination = 0.1  # percentage of outliers
   n_train = 200  # number of training points
   n_test = 100  # number of testing points
   
   X_train, X_test, y_train, y_test = generate_data(
       n_train=n_train, n_test=n_test, contamination=contamination)
   

3. Initialize a pyod.models.knn.KNN detector, fit the model, and make the prediction.

   # train kNN detector and apply FILTER thresholding
   clf_name = 'KNN'
   clf = KNN(contamination=FILTER())
   clf.fit(X_train)
   
   # get the prediction labels and outlier scores of the training data
   y_train_pred = clf.labels_  # binary labels (0: inliers, 1: outliers)
   y_train_scores = clf.decision_scores_  # raw outlier scores