Merge pull request #530 from yzhao062/development

v1.1.1

.github/workflows/testing-cron.yml

@@ -28,7 +28,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install -r requirements_ci.txt
+        pip install -r docs/requirements.txt
         pip install pytest
         pip install coverage
         pip install coveralls

.readthedocs.yaml

@@ -0,0 +1,22 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the version of Python and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.11"
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+  configuration: docs/conf.py
+
+# We recommend specifying your dependencies to enable reproducible builds:
+# https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
+python:
+  install:
+    - requirements: docs/requirements.txt

CHANGES.txt

@@ -178,4 +178,5 @@ v<1.0.8>, <03/08/2023> -- Add QMCD detector (#452).
 v<1.0.8>, <03/08/2023> -- Optimized ECDF and drop Statsmodels dependency (#467).
 v<1.0.9>, <03/19/2023> -- Hot fix for errors in ECOD and COPOD due to the issue of scipy.
 v<1.1.0>, <06/19/2023> -- Further integration of PyThresh.
-v<1.1.1>, <07/03/2023> -- Bump up sklearn requirement and some hot fixes.
+v<1.1.1>, <07/03/2023> -- Bump up sklearn requirement and some hot fixes.
+v<1.1.1>, <10/24/2023> -- Add deep isolation forest (#506)

README.rst

@@ -58,7 +58,7 @@ Python Outlier Detection (PyOD)
 
 -----
 
-**News**: We just released a 45-page, the most comprehensive `anomaly detection benchmark paper <https://www.andrew.cmu.edu/user/yuezhao2/papers/22-neurips-adbench.pdf>`_.
+**News**: We have a 45-page, the most comprehensive `anomaly detection benchmark paper <https://www.andrew.cmu.edu/user/yuezhao2/papers/22-neurips-adbench.pdf>`_.
 The fully `open-sourced ADBench <https://github.com/Minqi824/ADBench>`_ compares 30 anomaly detection algorithms on 57 benchmark datasets.
 
 **For time-series outlier detection**, please use `TODS <https://github.com/datamllab/tods>`_.
@@ -70,7 +70,7 @@ multivariate data. This exciting yet challenging field is commonly referred as
 or `Anomaly Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_.
 
 PyOD includes more than 40 detection algorithms, from classical LOF (SIGMOD 2000) to
-the latest ECOD (TKDE 2022). Since 2017, PyOD has been successfully used in numerous academic researches and
+the latest ECOD and DIF (TKDE 2022 and 2023). Since 2017, PyOD has been successfully used in numerous academic researches and
 commercial products with more than `10 million downloads <https://pepy.tech/project/pyod>`_.
 It is also well acknowledged by the machine learning community with various dedicated posts/tutorials, including
 `Analytics Vidhya <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>`_,
@@ -199,9 +199,10 @@ Alternatively, you could clone and run setup.py file:
 * numpy>=1.19
 * numba>=0.51
 * scipy>=1.5.1
-* scikit_learn>=0.20.0
+* scikit_learn>=0.22.0
 * six
 
+
 **Optional Dependencies (see details below)**\ :
 
 * combo (optional, required for models/combination.py and FeatureBagging)
@@ -392,6 +393,7 @@ Proximity-Based      SOD                 Subspace Outlier Detection
 Proximity-Based      ROD                 Rotation-based Outlier Detection                                                                        2020   [#Almardeny2020A]_
 Outlier Ensembles    IForest             Isolation Forest                                                                                        2008   [#Liu2008Isolation]_
 Outlier Ensembles    INNE                Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles                                      2018   [#Bandaragoda2018Isolation]_
+Outlier Ensembles    DIF                 Deep Isolation Forest for Anomaly Detection                                                             2023   [#Xu2023Deep]_
 Outlier Ensembles    FB                  Feature Bagging                                                                                         2005   [#Lazarevic2005Feature]_
 Outlier Ensembles    LSCP                LSCP: Locally Selective Combination of Parallel Outlier Ensembles                                       2019   [#Zhao2019LSCP]_
 Outlier Ensembles    XGBOD               Extreme Boosting Based Outlier Detection **(Supervised)**                                               2018   [#Zhao2018XGBOD]_
@@ -684,6 +686,8 @@ Reference
 
 .. [#Wang2020adVAE] Wang, X., Du, Y., Lin, S., Cui, P., Shen, Y. and Yang, Y., 2019. adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection. *Knowledge-Based Systems*.
 
+.. [#Xu2023Deep] Xu, H., Pang, G., Wang, Y., Wang, Y., 2023. Deep isolation forest for anomaly detection. *IEEE Transactions on Knowledge and Data Engineering*.
+
 .. [#You2017Provable] You, C., Robinson, D.P. and Vidal, R., 2017. Provable self-representation based outlier detection in a union of subspaces. In Proceedings of the IEEE conference on computer vision and pattern recognition.
 
 .. [#Zenati2018Adversarially] Zenati, H., Romain, M., Foo, C.S., Lecouat, B. and Chandrasekhar, V., 2018, November. Adversarially learned anomaly detection. In 2018 IEEE International conference on data mining (ICDM) (pp. 727-736). IEEE.

docs/index.rst

@@ -76,7 +76,7 @@ multivariate data. This exciting yet challenging field is commonly referred as
 or `Anomaly Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_.
 
 PyOD includes more than 40 detection algorithms, from classical LOF (SIGMOD 2000) to
-the latest ECOD (TKDE 2022). Since 2017, PyOD :cite:`a-zhao2019pyod` has been successfully used in numerous
+the latest ECOD and DIF (TKDE 2022 and 2023). Since 2017, PyOD :cite:`a-zhao2019pyod` has been successfully used in numerous
 academic researches and commercial products with more than `10 million downloads <https://pepy.tech/project/pyod>`_.
 It is also well acknowledged by the machine learning community with various dedicated posts/tutorials, including
 `Analytics Vidhya <https://www.analyticsvidhya.com/blog/2019/02/outlier-detection-python-pyod/>`_,
@@ -209,6 +209,7 @@ Proximity-Based      SOD               Subspace Outlier Detection
 Proximity-Based      ROD               Rotation-based Outlier Detection                                                                        2020   :class:`pyod.models.rod.ROD`                         :cite:`a-almardeny2020novel`
 Outlier Ensembles    IForest           Isolation Forest                                                                                        2008   :class:`pyod.models.iforest.IForest`                 :cite:`a-liu2008isolation,a-liu2012isolation`
 Outlier Ensembles    INNE              Isolation-based Anomaly Detection Using Nearest-Neighbor Ensembles                                      2018   :class:`pyod.models.inne.INNE`                       :cite:`a-bandaragoda2018isolation`
+Outlier Ensembles    DIF               Deep Isolation Forest for Anomaly Detection                                                             2023   :class:`pyod.models.dif.DIF`                         :cite:`a-Xu2023Deep`
 Outlier Ensembles    FB                Feature Bagging                                                                                         2005   :class:`pyod.models.feature_bagging.FeatureBagging`  :cite:`a-lazarevic2005feature`
 Outlier Ensembles    LSCP              LSCP: Locally Selective Combination of Parallel Outlier Ensembles                                       2019   :class:`pyod.models.lscp.LSCP`                       :cite:`a-zhao2019lscp`
 Outlier Ensembles    XGBOD             Extreme Boosting Based Outlier Detection **(Supervised)**                                               2018   :class:`pyod.models.xgbod.XGBOD`                     :cite:`a-zhao2018xgbod`

docs/pyod.models.rst

@@ -105,6 +105,17 @@ pyod.models.deep\_svdd module
     :show-inheritance:
     :inherited-members:
 
+pyod.models.dif module
+-----------------------------
+
+.. automodule:: pyod.models.dif
+    :members:
+    :exclude-members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:
+
+
 pyod.models.ecod module
 ------------------------
 

docs/requirements.txt

@@ -6,17 +6,18 @@ keras
 matplotlib
 nose
 numpy>=1.19
-numba==0.53 # need to lift this later see github for issue
+numba>=0.51
 pyclustering
 pytest
 pythresh>=0.3.1
 ruptures
 scipy>=1.5.1
-scikit_learn>=0.20.0
+scikit_learn>=0.22.0
 scikit-lego
 six
 sphinx-rtd-theme
 sphinxcontrib-bibtex
+statsmodels
 suod
 tensorflow
 torch

docs/zreferences.bib

@@ -489,4 +489,15 @@ @article{fang2001wrap
   pages={608--624},
   year={2001},
   publisher={Elsevier}
+}
+
+@article{xu2023dif,
+  author={Xu, Hongzuo and Pang, Guansong and Wang, Yijie and Wang, Yongjun},
+  journal={IEEE Transactions on Knowledge and Data Engineering},
+  title={Deep Isolation Forest for Anomaly Detection},
+  year={2023},
+  volume={},
+  number={},
+  pages={1-14},
+  doi={10.1109/TKDE.2023.3270293}
 }

examples/dif_example.py

@@ -0,0 +1,53 @@
+# -*- coding: utf-8 -*-
+"""Example of using Deep Isolation Forest for
+outlier detection"""
+# Author: Hongzuo Xu <[email protected]>
+# 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 pyod.models.dif import DIF
+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 = 1000  # number of training points
+	n_test = 200  # number of testing points
+	n_features = 30  # 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)
+
+	# train deep isolation forest detector
+	clf_name = 'DIF'
+	clf = DIF()
+	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)