Merge pull request #35 from msamsami/fix-maintain-precommit

maint:pre-commit configs, script names, end of files, black version, README

.github/workflows/python-publish.yml

@@ -25,6 +25,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install build
+        pip install -r requirements.txt
         pip install -r requirements_dev.txt
 
     - name: Run tests

.github/workflows/run-tests.yml

@@ -24,6 +24,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install build
+        pip install -r requirements.txt
         pip install -r requirements_dev.txt
 
     - name: Run tests

.gitignore

@@ -135,4 +135,4 @@ dmypy.json
 /test
 test.ipynb
 dummy.py
-.DS_Store
+.DS_Store

.pre-commit-config.yaml

@@ -12,3 +12,8 @@ repos:
     rev: 5.13.2
     hooks:
       - id: isort
+
+  - repo: https://github.com/psf/black
+    rev: 24.8.0
+    hooks:
+      - id: black

LICENSE

@@ -25,4 +25,4 @@ PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

MANIFEST.in

@@ -1,4 +1,4 @@
 include README.md
 include LICENSE
 recursive-include wnb *.py
-recursive-include tests *.py
+recursive-include tests *.py

README.md

@@ -1,16 +1,16 @@
 <div align="center">
-<img src="https://raw.githubusercontent.com/msamsami/weighted-naive-bayes/main/docs/logo.png" alt="wnb logo" width="275" />
+<img src="https://raw.githubusercontent.com/msamsami/wnb/main/docs/logo.png" alt="wnb logo" width="275" />
 </div>
 
 <div align="center"> <b>General and weighted naive Bayes classifiers</b> </div>
 <div align="center">Scikit-learn-compatible</div> <br>
 
 <div align="center">
 
-![Lastest Release](https://img.shields.io/badge/release-v0.2.6-green)
+![Lastest Release](https://img.shields.io/badge/release-v0.2.7-green)
 [![PyPI Version](https://img.shields.io/pypi/v/wnb)](https://pypi.org/project/wnb/)
 ![Python Versions](https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue)<br>
-![GitHub Workflow Status (build)](https://github.com/msamsami/weighted-naive-bayes/actions/workflows/python-publish.yml/badge.svg)
+![GitHub Workflow Status (build)](https://github.com/msamsami/wnb/actions/workflows/python-publish.yml/badge.svg)
 ![PyPI License](https://img.shields.io/pypi/l/wnb)
 [![PyPi Downloads](https://static.pepy.tech/badge/wnb)](https://pepy.tech/project/wnb)
 
@@ -26,14 +26,35 @@ The issue with the well-known implementations of the naive Bayes algorithm (such
 Although naive Bayes has many advantages such as simplicity and interpretability, its conditional independence assumption rarely holds true in real-world applications. In order to alleviate its conditional independence assumption, many attribute weighting naive Bayes (WNB) approaches have been proposed. Most of the proposed methods involve computationally demanding optimization problems that do not allow for controlling the model's bias due to class imbalance. Minimum Log-likelihood Difference WNB (MLD-WNB) is a novel weighting approach that optimizes the weights according to the Bayes optimal decision rule and includes hyperparameters for controlling the model's bias. **WNB** library provides an efficient implementation of gaussian MLD-WNB.
 
 ## Installation
-The easiest way to install the **wnb** library is by using `pip`:
-```
+This library is shipped as an all-in-one module implementation with minimalistic dependencies and requirements. Furthermore, it fully **adheres to Scikit-learn API** ❤️.
+
+### Prerequisites
+Ensure that Python 3.8 or higher is installed on your machine before installing **WNB**.
+
+### PyPi
+```bash
 pip install wnb
 ```
-This library is shipped as an all-in-one module implementation with minimalistic dependencies and requirements. Furthermore, it fully **adheres to Scikit-learn API** ❤️.
+
+### Poetry
+```bash
+poetry add wnb
+```
+
+### GitHub
+```bash
+# Clone the repository
+git clone https://github.com/msamsami/wnb.git
+
+# Navigate into the project directory
+cd wnb
+
+# Install the package
+pip install .
+```
 
 ## Getting started ⚡️
-Here, we show how you can use the library to train general and weighted naive Bayes classifiers. 
+Here, we show how you can use the library to train general and weighted naive Bayes classifiers.
 
 ### General naive Bayes
 
@@ -114,13 +135,14 @@ These benchmarks highlight the potential of WNB classifiers to provide better pe
 The benchmark scripts used to obtain these results can be found under _tests/benchmarks/_ directory.
 
 ## Tests
-To run the tests, make sure to clone the repository and install the development requirements:
-```
+To run the tests, make sure to clone the repository and install the development requirements in addition to base requirements:
+```bash
+pip install -r requirements.txt
 pip install -r requirements_dev.txt
 ```
 
 Then, run pytest:
-```
+```bash
 pytest
 ```
 
@@ -129,7 +151,7 @@ You can support the project in the following ways:
 
 ⭐ Star WNB on GitHub (click the star button in the top right corner)
 
-💡 Provide your feedback or propose ideas in the [Issues section](https://github.com/msamsami/weighted-naive-bayes/issues)
+💡 Provide your feedback or propose ideas in the [Issues section](https://github.com/msamsami/wnb/issues)
 
 📰 Post about WNB on LinkedIn or other platforms
 
@@ -144,6 +166,6 @@ If you utilize this repository, please consider citing it with:
   year = {2023},
   publisher = {GitHub},
   journal = {GitHub repository},
-  howpublished = {\url{https://github.com/msamsami/weighted-naive-bayes}},
+  howpublished = {\url{https://github.com/msamsami/wnb}},
 }
-```
+```

pyproject.toml

@@ -41,10 +41,14 @@ dependencies = [
     "typing-extensions>=4.8.0",
 ]
 
+[project.urls]
+Homepage = "https://github.com/msamsami/wnb"
+Source = "https://github.com/msamsami/wnb"
+
 [project.optional-dependencies]
 dev = [
     "pytest>=7.0.0",
-    "black>=24.4.2",
+    "black==24.8.0",
     "tqdm>=4.65.0",
     "pre-commit>=3.7.1",
     "isort==5.13.2",

requirements.txt

@@ -2,4 +2,4 @@ pandas>=1.4.1
 numpy<2.0.0
 scipy>=1.8.0
 scikit-learn>=1.0.2
-typing-extensions>=4.8.0
+typing-extensions>=4.8.0

requirements_dev.txt

@@ -1,10 +1,5 @@
-pandas>=1.4.1
-numpy<2.0.0
-scipy>=1.8.0
-scikit-learn>=1.0.2
-typing-extensions>=4.8.0
 pytest>=7.0.0
-black>=24.4.2
+black==24.8.0
 tqdm>=4.65.0
 pre-commit>=3.7.1
-isort==5.13.2
+isort==5.13.2

setup.cfg

@@ -3,14 +3,14 @@ name = wnb
 version = attr: wnb.__version__
 author = Mehdi Samsami
 author_email = [email protected]
-url = https://github.com/msamsami/weighted-naive-bayes
+url = https://github.com/msamsami/wnb
 description = Python library for the implementations of general and weighted naive Bayes (WNB) classifiers.
 long_description = file: README.md
 long_description_content_type = text/markdown
 license = BSD
 license_file = LICENSE
 keywords = python, bayes, naive bayes, classifier, probabilistic
-classifiers = 
+classifiers =
     Intended Audience :: Science/Research
     Intended Audience :: Developers
     Topic :: Scientific/Engineering
@@ -38,7 +38,7 @@ install_requires =
 [options.extras_require]
 dev =
     pytest>=7.0.0
-    black>=24.4.2
+    black==24.8.0
     tqdm>=4.65.0
     pre-commit>=3.7.1
     isort==5.13.2
@@ -47,5 +47,5 @@ dev =
 test = pytest
 
 [tool:pytest]
-filterwarnings = 
+filterwarnings =
     ignore

setup.py

@@ -13,7 +13,7 @@
     author="Mehdi Samsami",
     author_email="[email protected]",
     license="BSD License",
-    url="https://github.com/msamsami/weighted-naive-bayes",
+    url="https://github.com/msamsami/wnb",
     long_description=codecs.open(
         path.join(path.abspath(path.dirname(__file__)), "README.md"), encoding="utf-8"
     ).read(),
@@ -45,7 +45,7 @@
     extras_require={
         "dev": [
             "pytest>=7.0.0",
-            "black>=24.4.2",
+            "black==24.8.0",
             "tqdm>=4.65.0",
             "pre-commit>=3.7.1",
             "isort==5.13.2",

wnb/__init__.py

@@ -2,11 +2,11 @@
 Python library for the implementations of general and weighted naive Bayes (WNB) classifiers.
 """
 
-__version__ = "0.2.6"
+__version__ = "0.2.7"
 __author__ = "Mehdi Samsami"
 
 
-from ._base import ContinuousDistMixin, DiscreteDistMixin
+from .base import ContinuousDistMixin, DiscreteDistMixin
 from .enums import Distribution
 from .gnb import GeneralNB
 from .gwnb import GaussianWNB

wnb/_typing.py

@@ -5,7 +5,7 @@
 import pandas as pd
 from scipy.sparse import spmatrix
 
-from ._base import ContinuousDistMixin, DiscreteDistMixin
+from .base import ContinuousDistMixin, DiscreteDistMixin
 from .enums import Distribution
 
 __all__ = ["MatrixLike", "ArrayLike", "Int", "Float", "DistibutionLike"]

wnb/dist.py

@@ -4,7 +4,7 @@
 from scipy.special import beta, gamma
 from scipy.stats import chi2
 
-from ._base import ContinuousDistMixin, DiscreteDistMixin
+from .base import ContinuousDistMixin, DiscreteDistMixin
 from .enums import Distribution as D
 
 __all__ = [

wnb/gnb.py

@@ -14,15 +14,13 @@
 from sklearn.utils.validation import check_is_fitted
 from typing_extensions import Self
 
-from ._base import DistMixin
 from ._typing import ArrayLike, DistibutionLike, Float, MatrixLike
+from .base import DistMixin
 from .dist import NonNumericDistributions
 from .enums import Distribution
 from .utils import get_dist_class, is_dist_supported
 
-__all__ = [
-    "GeneralNB",
-]
+__all__ = ["GeneralNB"]
 
 
 class GeneralNB(ClassifierMixin, BaseEstimator, metaclass=ABCMeta):

wnb/gwnb.py

@@ -18,9 +18,7 @@
 
 from ._typing import ArrayLike, Float, Int, MatrixLike
 
-__all__ = [
-    "GaussianWNB",
-]
+__all__ = ["GaussianWNB"]
 
 
 class GaussianWNB(ClassifierMixin, BaseEstimator, metaclass=ABCMeta):

wnb/utils.py

@@ -1,8 +1,8 @@
 import contextlib
 from typing import Optional
 
-from ._base import DistMixin
 from ._typing import DistibutionLike
+from .base import DistMixin
 from .dist import AllDistributions
 from .enums import Distribution
 
@@ -11,8 +11,7 @@
 
 def is_dist_supported(dist: DistibutionLike) -> bool:
     with contextlib.suppress(TypeError):
-        issubclass(dist, DistMixin)
-        return True
+        return issubclass(dist, DistMixin)
 
     if (
         isinstance(dist, Distribution)
@@ -26,8 +25,8 @@ def is_dist_supported(dist: DistibutionLike) -> bool:
 
 def get_dist_class(name_or_type: DistibutionLike) -> Optional[DistMixin]:
     with contextlib.suppress(TypeError):
-        issubclass(name_or_type, DistMixin)
-        return name_or_type
+        if issubclass(name_or_type, DistMixin):
+            return name_or_type
 
     if isinstance(name_or_type, Distribution) or name_or_type in Distribution.__members__.values():
         return AllDistributions[name_or_type]