mo_gaal_example.py

# -*- coding: utf-8 -*-
"""Example of using Multiple-Objective Generative Adversarial Active
Learning (MO_GAAL) for outlier detection
"""
# Author: Winston Li <jk_zhengli@hotmail.com>
# License: BSD 2 clause

from __future__ import division
from __future__ import print_function

import os
import sys
import torch

# 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 pyod.models.mo_gaal import MO_GAAL
from pyod.utils.data import generate_data
from pyod.utils.data import evaluate_print

if __name__ == "__main__":
    contamination = 0.1  # percentage of outliers
    n_train = 30000  # number of training points
    n_test = 3000  # number of testing points
    n_features = 300  # number of features

    # Generate sample data
    X_train, X_test, y_train, y_test = \
        generate_data(n_train=n_train,
                      n_test=n_test,
                      n_features=n_features,
                      contamination=contamination,
                      random_state=42)

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    X_train = torch.tensor(X_train, dtype=torch.float32).to(device).cpu().numpy()
    X_test = torch.tensor(X_test, dtype=torch.float32).to(device).cpu().numpy()

    # train MO_GAAL detector
    clf_name = 'MO_GAAL'
    clf = MO_GAAL(k=3, stop_epochs=2, contamination=contamination)
    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

    # get the prediction on the test data
    y_test_pred = clf.predict(X_test)  # outlier labels (0 or 1)
    y_test_scores = clf.decision_function(X_test)  # outlier scores

    # evaluate and print the results
    print("\nOn Training Data:")
    evaluate_print(clf_name, y_train, y_train_scores)
    print("\nOn Test Data:")
    evaluate_print(clf_name, y_test, y_test_scores)