Add dimensional explainability to LOF detector

examples/lof_interpretability.py

@@ -0,0 +1,72 @@
+# -*- coding: utf-8 -*-
+"""Example of using LOF for outlier detection with interpretability
+Sample wise interpretation is provided here.
+"""
+# Author: [Your Name/Contribution]
+# License: BSD 2 clause
+
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+
+# temporary solution for relative imports in case pyod is not installed
+# if pyod is installed, no need to use the following line
+sys.path.append(
+    os.path.abspath(os.path.join(os.path.dirname("__file__"), '..')))
+
+from scipy.io import loadmat
+from sklearn.model_selection import train_test_split
+
+from pyod.models.lof import LOF
+from pyod.utils.utility import standardizer
+
+if __name__ == "__main__":
+    # Define data file and read X and y
+    mat_file = 'cardio.mat'
+
+    mat = loadmat(os.path.join('data', mat_file))
+    X = mat['X']
+    y = mat['y'].ravel()
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4,
+                                                        random_state=1)
+
+    # standardizing data for processing
+    X_train_norm, X_test_norm = standardizer(X_train, X_test)
+
+    # train LOF detector
+    clf_name = 'LOF'
+    clf = LOF(n_neighbors=10, novelty=False)
+    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
+
+    print('Training data has %d samples' % X_train.shape[0])
+    print('Training data has %d features' % X_train.shape[1])
+
+    # Explain the first sample
+    print('\nExplaining sample 0:')
+    print('True label:', 'Outlier' if y_train[0] == 1 else 'Inlier')
+    print('Predicted label:', 'Outlier' if y_train_pred[0] == 1 else 'Inlier')
+    print('Outlier score: %.4f' % y_train_scores[0])
+    print('\nGenerating dimensional outlier explanation...')
+
+    clf.explain_outlier(0)
+
+    # The horizontal bar chart shows per-feature LOF scores (1D approximation).
+    # Each bar represents the LOF score computed on that dimension independently.
+    # Features with bars exceeding the cutoff lines (dashed/dash-dot vertical lines)
+    # exhibit unusual local density patterns and are primary contributors to
+    # the sample being flagged as an outlier.
+
+    # Example with custom cutoffs
+    print('\n' + '='*60)
+    print('Example with custom cutoff bands (80th and 95th percentiles):')
+    print('='*60)
+
+    clf.explain_outlier(0, cutoffs=[0.80, 0.95])
+