partial update of alad

CHANGES.txt

@@ -169,3 +169,4 @@ v<1.0.4>, <07/29/2022> -- Add LUNAR (#413).
 v<1.0.4>, <07/29/2022> -- Add LUNAR (#415).
 v<1.0.5>, <07/29/2022> -- Import optimization.
 v<1.0.5>, <08/27/2022> -- Code optimization.
+v<1.0.5>, <09/14/2022> -- Add ALAD.

docs/pyod.models.rst

@@ -11,6 +11,15 @@ pyod.models.abod module
     :show-inheritance:
     :inherited-members:
 
+pyod.models.alad module
+-----------------------
+
+.. automodule:: pyod.models.alad
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:
+
 pyod.models.anogan module
 -------------------------
 

docs/zreferences.bib

@@ -458,4 +458,13 @@ @inproceedings{you2017provable
   booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
   pages={3395--3404},
   year={2017}
+}
+
+@inproceedings{zenati2018adversarially,
+  title={Adversarially learned anomaly detection},
+  author={Zenati, Houssam and Romain, Manon and Foo, Chuan-Sheng and Lecouat, Bruno and Chandrasekhar, Vijay},
+  booktitle={2018 IEEE International conference on data mining (ICDM)},
+  pages={727--736},
+  year={2018},
+  organization={IEEE}
 }

examples/alad_example.py

@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
-"""Example of using Adversarially Learned Anomaly Detection(ALAD) for outlier detection
+"""Example of using Adversarially Learned Anomaly Detection (ALAD) for outlier
+detection
 """
 from __future__ import division
 from __future__ import print_function
@@ -33,22 +34,22 @@
 
     # train ALAD detector
     clf_name = 'ALAD'
-    clf  = ALAD( epochs = 100,   latent_dim = 2,     
-                 learning_rate_disc = 0.0001,  
-                 learning_rate_gen = 0.0001,  
-                 dropout_rate = 0.2, 
-                 add_recon_loss = False, 
-                 lambda_recon_loss= 0.05, 
-                 add_disc_zz_loss = True,
-                 dec_layers=[ 75, 100 ], 
-                 enc_layers=[ 100, 75 ], 
-                 disc_xx_layers= [ 100, 75 ], 
-                 disc_zz_layers= [ 25, 25 ], 
-                 disc_xz_layers= [ 100, 75 ], 
-                 spectral_normalization = False, 
-                 activation_hidden_disc = 'tanh', activation_hidden_gen = 'tanh' , 
-                 preprocessing=True, batch_size = 200, contamination = contamination)
-    
+    clf = ALAD(epochs=100, latent_dim=2,
+               learning_rate_disc=0.0001,
+               learning_rate_gen=0.0001,
+               dropout_rate=0.2,
+               add_recon_loss=False,
+               lambda_recon_loss=0.05,
+               add_disc_zz_loss=True,
+               dec_layers=[75, 100],
+               enc_layers=[100, 75],
+               disc_xx_layers=[100, 75],
+               disc_zz_layers=[25, 25],
+               disc_xz_layers=[100, 75],
+               spectral_normalization=False,
+               activation_hidden_disc='tanh', activation_hidden_gen='tanh',
+               preprocessing=True, batch_size=200, contamination=contamination)
+
     clf.fit(X_train)
 
     # get the prediction labels and outlier scores of the training data