Merge fc76a49 into c2839e0

examples/anogan_example.py → examples/rgraph_example.py

@@ -1,9 +1,7 @@
 # -*- coding: utf-8 -*-
-"""Example for Anomaly Detection with Generative Adversarial Networks  (AnoGAN)
- Paper: https://arxiv.org/pdf/1703.05921.pdf
- Note, that this is another implementation of AnoGAN as the one from https://github.com/fuchami/ANOGAN
+"""Example for R-graph
 """
-# Author: Michiel Bongaerts (but not author of the AnoGAN method)
+# Author: Michiel Bongaerts (but not author of the R-graph method)
 # License: BSD 2 clause
 
 
@@ -18,39 +16,42 @@
 sys.path.append(
     os.path.abspath(os.path.join(os.path.dirname("__file__"), '..')))
 
-from pyod.models.anogan import AnoGAN
+from pyod.models.rgraph import RGraph
 from pyod.utils.data import generate_data
 from pyod.utils.data import evaluate_print
-from pyod.utils.example import visualize
+
 
 if __name__ == "__main__":
+
     contamination = 0.1  # percentage of outliers
-    n_train = 200  # number of training points
+    n_train = 100  # number of training points
     n_test = 100  # number of testing points
 
     # Generate sample data
     X_train, X_test, y_train, y_test = generate_data(
         n_train=n_train,
         n_test=n_test,
-        n_features=2,
+        n_features=70,
         contamination=contamination,
         behaviour="new",
         random_state=42,
     )
 
-    # train AutoEncoder detector
-    clf_name = 'AnoGAN'
-    clf = AnoGAN(G_layers=[10, 20], D_layers=[20, 2],
-                 preprocessing=True, index_D_layer_for_recon_error=1,
-                 epochs=200, contamination=contamination, verbose=1)
+
+    # train R-graph detector
+    clf_name = 'R-graph'
+    clf = RGraph(n_nonzero = 100, transition_steps = 20 , gamma = 50, blocksize_test_data = 20,
+                 tau = 1, preprocessing=True, active_support = False, gamma_nz = False,
+                 algorithm= 'lasso_lars', maxiter= 100, verbose =1 )
 
     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
+    # # 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
 
@@ -60,8 +61,5 @@
     print("\nOn Test Data:")
     evaluate_print(clf_name, y_test, y_test_scores)
 
-    # visualize the results
-    visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
-              y_test_pred, show_figure=True, save_figure=False)
 
-    clf.plot_learning_curves(window_smoothening=30)
+