Merge pull request #55 from vc1492a/54-add-regression-tests-for-refac…

…tor-validation

54 add regression tests for refactor validation

.gitignore

@@ -6,12 +6,168 @@ nasaValve
 rel_research
 PyNomaly/loop_dev.py
 /PyNomaly.egg-info/
-.pytest_cache
-build
-htmlcov/
-*.egg
 *.pyc
-.coverage
 *.coverage.*
 .coveragerc
-venv/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
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+# Django stuff:
+*.log
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+docs/_build/
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+profile_default/
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+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
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+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+#Pipfile.lock
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+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
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+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
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+/site
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+.mypy_cache/
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+.pytype/
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+cython_debug/
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+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+

PyNomaly/loop.py

@@ -11,7 +11,7 @@
     pass
 
 __author__ = 'Valentino Constantinou'
-__version__ = '0.3.4'
+__version__ = '0.3.3'
 __license__ = 'Apache License, Version 2.0'
 
 

changelog.md

@@ -4,15 +4,6 @@ All notable changes to PyNomaly will be documented in this Changelog.
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) 
 and adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
 
-## 0.3.4
-### Changed 
-- Unit tests from using the `sklearn.utils.testing` submodule 
-to standard Python assertions, as the submodule will be changed 
-to private functions after scikit-learn version 0.24. 
-- Logging statements or warnings when testing with numba disabled or 
-enabled (respectively) to reflect the effect of numba just-in-time 
-compilation on code coverage statistics. 
-
 ## 0.3.3
 ### Changed
 - The implementation of the progress bar to support use when the number of 
@@ -226,4 +217,4 @@ in computing the neighborhood distance for each observation.
 ### Added
 - readme.md file documenting methodology, package dependencies, use cases, 
 how to contribute, and acknowledgements.
-- Initial open release of PyNomaly codebase on Github. 
+- Initial open release of PyNomaly codebase on Github. 

readme.md

@@ -5,10 +5,10 @@ LoOP is a local density based outlier detection method by Kriegel, Kröger, Schu
 scores in the range of [0,1] that are directly interpretable as the probability of a sample being an outlier.
 
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
-[![PyPi](https://img.shields.io/badge/pypi-0.3.4-blue.svg)](https://pypi.python.org/pypi/PyNomaly/0.3.4)
+[![PyPi](https://img.shields.io/badge/pypi-0.3.3-blue.svg)](https://pypi.python.org/pypi/PyNomaly/0.3.3)
 ![](https://img.shields.io/pypi/dm/PyNomaly.svg?logoColor=blue)
-[![Build Status](https://travis-ci.org/vc1492a/PyNomaly.svg?branch=master)](https://travis-ci.org/vc1492a/PyNomaly)
-[![Coverage Status](https://coveralls.io/repos/github/vc1492a/PyNomaly/badge.svg?branch=master)](https://coveralls.io/github/vc1492a/PyNomaly?branch=master)
+[![Build Status](https://travis-ci.org/vc1492a/PyNomaly.svg?branch=main)](https://travis-ci.org/vc1492a/PyNomaly)
+[![Coverage Status](https://coveralls.io/repos/github/vc1492a/PyNomaly/badge.svg?branch=main)](https://coveralls.io/github/vc1492a/PyNomaly?branch=main)
 [![JOSS](http://joss.theoj.org/papers/f4d2cfe680768526da7c1f6a2c103266/status.svg)](http://joss.theoj.org/papers/f4d2cfe680768526da7c1f6a2c103266)
 
 The outlier score of each sample is called the Local Outlier Probability.
@@ -234,13 +234,13 @@ plt.close()
 Your results should look like the following:
 
 **LoOP Scores without Clustering**
-![LoOP Scores without Clustering](https://github.com/vc1492a/PyNomaly/blob/master/images/scores.png)
+![LoOP Scores without Clustering](https://github.com/vc1492a/PyNomaly/blob/main/images/scores.png)
 
 **LoOP Scores with Clustering**
-![LoOP Scores with Clustering](https://github.com/vc1492a/PyNomaly/blob/master/images/scores_clust.png)
+![LoOP Scores with Clustering](https://github.com/vc1492a/PyNomaly/blob/main/images/scores_clust.png)
 
 **DBSCAN Cluster Assignments**
-![DBSCAN Cluster Assignments](https://github.com/vc1492a/PyNomaly/blob/master/images/cluster_assignments.png)
+![DBSCAN Cluster Assignments](https://github.com/vc1492a/PyNomaly/blob/main/images/cluster_assignments.png)
 
 
 Note the differences between using LocalOutlierProbability with and without clustering. In the example without clustering, samples are
@@ -312,7 +312,7 @@ scores = m.local_outlier_probabilities
 The below visualization shows the results by a few known distance metrics:
 
 **LoOP Scores by Distance Metric**
-![DBSCAN Cluster Assignments](https://github.com/vc1492a/PyNomaly/blob/master/images/scores_by_distance_metric.png)
+![DBSCAN Cluster Assignments](https://github.com/vc1492a/PyNomaly/blob/main/images/scores_by_distance_metric.png)
 
 ## Streaming Data
 
@@ -383,7 +383,7 @@ plt.close()
 ```
 
 **LoOP Scores using Stream Approach with n=10**
-![LoOP Scores using Stream Approach with n=10](https://github.com/vc1492a/PyNomaly/blob/master/images/scores_stream.png)
+![LoOP Scores using Stream Approach with n=10](https://github.com/vc1492a/PyNomaly/blob/main/images/scores_stream.png)
 
 ### Notes
 When calculating the LoOP score of incoming data, the original fitted scores are not updated.
@@ -401,10 +401,10 @@ any changes to a branch which corresponds to an open issue. Hot fixes
 and bug fixes can be represented by branches with the prefix `fix/` versus 
 `feature/` for new capabilities or code improvements. Pull requests will 
 then be made from these branches into the repository's `dev` branch 
-prior to being pulled into `master`. Pull requests which are works in 
+prior to being pulled into `main`. Pull requests which are works in 
 progress or ready for merging should be indicated by their respective 
 prefixes ([WIP] and [MRG]). Pull requests with the [MRG] prefix will be 
-reviewed prior to being pulled into the `master` branch. 
+reviewed prior to being pulled into the `main` branch. 
 
 ### Tests
 When contributing, please ensure to run unit tests and add additional tests as 

setup.py

@@ -3,14 +3,14 @@
 setup(
     name='PyNomaly',
     packages=['PyNomaly'],
-    version='0.3.4',
+    version='0.3.3',
     description='A Python 3 implementation of LoOP: Local Outlier '
                 'Probabilities, a local density based outlier detection '
                 'method providing an outlier score in the range of [0,1].',
     author='Valentino Constantinou',
     author_email='[email protected]',
     url='https://github.com/vc1492a/PyNomaly',
-    download_url='https://github.com/vc1492a/PyNomaly/archive/0.3.4.tar.gz',
+    download_url='https://github.com/vc1492a/PyNomaly/archive/0.3.3.tar.gz',
     keywords=['outlier', 'anomaly', 'detection', 'machine', 'learning',
               'probability'],
     classifiers=[],