Merge pull request #20 from selimfirat/develop

Release v0.3.2

Author: selimfirat

.coveragerc

@@ -1,2 +1,4 @@
 [run]
 omit = pysad/models/kitnet_model/*
+       setup.py
+       _build_readme.py

.gitignore

@@ -7,6 +7,8 @@ docs/_build
 # Byte-compiled / optimized / DLL files
 *.py[cod]
 *$py.class
+paper
+.vscode
 
 # C extensions
 *.so

CHANGES.txt

@@ -1,4 +1,5 @@
 v<0.1.0>, <08/15/2020> -- Initial release.
 v<0.2.0> <06/10/2023> -- Upgraded some libraries like numpy,pandas and small refactorings.
 v<0.3.0>, <05/23/2025> -- Updated dependencies to latest versions, improved documentation, added quick start example in README. Added Inqmad model. Special thanks to @TechyNilesh @Joaggi @onixlas.
-v<0.3.1>, <06/04/2025> -- Enhanced README with comprehensive community engagement section, educational content, third-party integrations, and developer community highlights. Fixed sublist formatting in reStructuredText. Improved error handling for Inqmad model imports.
+v<0.3.1>, <06/04/2025> -- Enhanced README with comprehensive community engagement section, educational content, third-party integrations, and developer community highlights. Fixed sublist formatting in reStructuredText. Improved error handling for Inqmad model imports.
+v<0.3.2>, <06/14/2025> -- Updated PyOD version and added Python 3.13 support. Fixed Flaticon link in documentation. Enhanced error handling for optional JAX dependencies. Updated rrcf version to 0.4.4 and improved quantization in RelativeEntropy model. Improved test coverage and configuration. Added deprecation warning tests and enhanced model methods array handling. Special thanks to @onixlas for multiple contributions.

README.rst

@@ -95,65 +95,6 @@ Free and Open Source Software (FOSS)
 
 `PySAD` is distributed under `BSD License 2.0 <https://github.com/selimfirat/pysad/blob/master/LICENSE>`_ and favors FOSS principles.
 
-Community Engagement
-=====================
-
-PySAD has built a strong and active community with significant adoption across academia and industry:
-
-Academic Recognition
-^^^^^^^^^^^^^^^^^^^^
-
-* **Cited in academic literature** with growing adoption in streaming data research with more than 50 citations to the arXiv version (excluding GitHub link-only citations). See `Google Scholar <https://scholar.google.com/citations?view_op=view_citation&hl=tr&user=R6Hwp20AAAAJ&citation_for_view=R6Hwp20AAAAJ:2osOgNQ5qMEC>`_ for detailed citation metrics.
-
-GitHub Community
-^^^^^^^^^^^^^^^^^
-
-* **260+ GitHub Stars** demonstrating widespread community interest and adoption among developers and researchers in the machine learning community.
-
-Active Usage
-^^^^^^^^^^^^
-
-* **Strong PyPI download statistics** according to `pypistats.org <https://pypistats.org/packages/pysad>`_ with 2K+ downloads in the May 2025 and consistent weekly usage.
-
-Educational Content
-^^^^^^^^^^^^^^^^^^^
-
-Featured in educational content across multiple platforms:
-
-* **Medium Articles:**
-
-  * `Real-time Anomaly Detection with Python <https://medium.com/data-science/real-time-anomaly-detection-with-python-36e3455e84e2>`_
-  * `Real-time Anomaly Detection for Quality Control <https://medium.com/data-science/real-time-anomaly-detection-for-quality-control-e6af28a3350d>`_
-  * `The Challenges of AI in an Industrial Environment <https://medium.com/@anthonycvn/the-challenges-of-ai-in-an-industrial-environment-6e118a8daa67>`_
-
-* **Resource Collections:**
-
-  * `Anomaly Detection Resources <https://andrewm4894.com/2021/01/03/anomaly-detection-resources/>`_ comprehensive guide
-  * `Anomaly Detection Resources by Yue Zhao <https://github.com/yzhao062/anomaly-detection-resources>`_ curated collection of papers, algorithms, and datasets
-
-Third-party Integrations
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-PySAD has been adopted and integrated into major machine learning frameworks:
-
-* **TurboML Integration:** `PySAD example documentation <https://docs.turboml.com/wyo_models/pysad_example/>`_ showing adoption in machine learning workflow platforms.
-
-* **Apache Beam Integration:** PySAD modules adapted into Apache Beam's ML package with `zscore <https://beam.apache.org/releases/pydoc/2.64.0/apache_beam.ml.anomaly.detectors.zscore.html>`_ and `robust_zscore <https://beam.apache.org/releases/pydoc/2.64.0/apache_beam.ml.anomaly.detectors.robust_zscore.html>`_ anomaly detectors.
-
-* **River ML Integration:** The prominent online machine learning library has adapted PySAD algorithms, including the `StandardAbsoluteDeviation detector <https://riverml.xyz/0.20.0/api/anomaly/StandardAbsoluteDeviation/?query=pysad>`_ with explicit PySAD references.
-
-Developer Community
-^^^^^^^^^^^^^^^^^^^
-
-* **Widespread GitHub usage** with 50+ files using ``import pysad`` and 200+ files using ``from pysad`` across various repositories: `import usage <https://github.com/search?q=%22import+pysad%22&type=code>`_, `from usage <https://github.com/search?q=%22from+pysad%22&type=code>`_.
-
-* **External projects** demonstrating practical applications across diverse domains:
-
-  * `Online Isolation Forest implementation <https://github.com/ineveLoppiliF/Online-Isolation-Forest>`_
-  * `Anomaly Detection Final Project <https://github.com/berfinkavsut/anomaly-detection-final-project>`_
-  * `Natural Gas Wells Anomaly Detection <https://github.com/charles-cao/Anomaly-detection-in-natural-gas-wells>`_
-  * `EuroPython 2024 Conference Material <https://github.com/ciortanmadalina/europython2024>`_
-
 Installation
 ============
 
@@ -183,7 +124,7 @@ Alternatively, you can install the library directly using the source code in Git
 * numpy==2.0.2
 * scikit-learn==1.5.2
 * scipy==1.13.1
-* pyod==1.1.0
+* pyod==2.0.5
 * combo==0.1.3
 
 **Optional Dependencies:**
@@ -247,6 +188,19 @@ Quick Links
 
 
 
+Contributors
+============
+
+.. raw:: html
+
+   <div align="center">
+     <a href="https://github.com/selimfirat/pysad/graphs/contributors">
+       <img src="https://contrib.rocks/image?repo=selimfirat/pysad" alt="Contributors" />
+     </a>
+   </div>
+
+We thank all our contributors for their valuable input and efforts to make PySAD better!
+
 Versioning
 ==========
 

README_template.rst

@@ -37,6 +37,19 @@ Here's a simple example showing how to use PySAD for anomaly detection on stream
 
 .. include:: docs/quick_links.rst
 
+Contributors
+============
+
+.. raw:: html
+
+   <div align="center">
+     <a href="https://github.com/selimfirat/pysad/graphs/contributors">
+       <img src="https://contrib.rocks/image?repo=selimfirat/pysad" alt="Contributors" />
+     </a>
+   </div>
+
+We thank all our contributors for their valuable input and efforts to make PySAD better!
+
 .. include:: docs/versioning.rst
 
 .. include:: docs/license.rst

docs/installation.rst

@@ -27,7 +27,7 @@ Alternatively, you can install the library directly using the source code in Git
 * numpy==2.0.2
 * scikit-learn==1.5.2
 * scipy==1.13.1
-* pyod==1.1.0
+* pyod==2.0.5
 * combo==0.1.3
 
 **Optional Dependencies:**

docs/references.rst

@@ -4,4 +4,4 @@ References
 .. bibliography:: refs.bib
    :labelprefix: B
 
-The infinity icon made by `Freepik <http://www.freepik.com/>`_ from `Flaticon <www.flaticon.com>`_.
+The infinity icon made by `Freepik <https://www.freepik.com/>`_ from `Flaticon <https://www.flaticon.com/>`_.

pyproject.toml

@@ -4,12 +4,12 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pysad"
-version = "0.3.1"
+version = "0.3.2"
 dependencies = [
   "numpy",
   "scikit-learn",
   "scipy",
-  "pyod==1.1.0",
+  "pyod==2.0.5",
   "combo==0.1.3",
 ]
 requires-python = ">=3.10"
@@ -40,10 +40,11 @@ classifiers = [
         "Programming Language :: Python :: 3.10",
         "Programming Language :: Python :: 3.11",
         "Programming Language :: Python :: 3.12",
+        "Programming Language :: Python :: 3.13"
     ]
 
 [project.optional-dependencies]
-rrcf = ["rrcf==0.4.3"]
+rrcf = ["rrcf==0.4.4"]
 slop = ["PyNomaly==0.3.3"]
 xStream = ["mmh3==2.5.1"]
 pandas = ["pandas"]

pysad/core/base_model.py

@@ -70,7 +70,8 @@ def score(self, X):
         """
         y_pred = np.empty(X.shape[0], dtype=np.float64)
         for i, (xi, _) in enumerate(_iterate(X)):
-            y_pred[i] = self.score_partial(xi)
+            score = self.score_partial(xi)
+            y_pred[i] = np.asarray(score).item() if np.asarray(score).ndim > 0 else score
 
         return y_pred
 

pysad/core/base_postprocessor.py

@@ -53,7 +53,8 @@ def transform(self, scores):
         """
         processed_scores = np.empty(scores.shape[0], dtype=np.float64)
         for i, (score, _) in enumerate(_iterate(scores)):
-            processed_scores[i] = self.transform_partial(score)
+            result = self.transform_partial(score)
+            processed_scores[i] = np.asarray(result).item() if np.asarray(result).ndim > 0 else result
 
         return processed_scores
 
@@ -82,6 +83,7 @@ def fit_transform(self, scores):
         """
         processed_scores = np.empty(scores.shape[0], dtype=np.float64)
         for i, (score, _) in enumerate(_iterate(scores)):
-            processed_scores[i] = self.fit_transform_partial(score)
+            result = self.fit_transform_partial(score)
+            processed_scores[i] = np.asarray(result).item() if np.asarray(result).ndim > 0 else result
 
         return processed_scores