Merge pull request #14 from msamsami/alpha-smoothing

New feature: smoothing parameter

README.md

@@ -1,14 +1,14 @@
 # WNB: General and weighted naive Bayes classifiers
 
-![](https://img.shields.io/badge/version-v0.1.14-green)
+![](https://img.shields.io/badge/version-v0.1.15-green)
 ![](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue)
 ![](https://github.com/msamsami/weighted-naive-bayes/actions/workflows/python-publish.yml/badge.svg)
 [![](https://img.shields.io/pypi/v/wnb)](https://pypi.org/project/wnb/)
 ![](https://img.shields.io/pypi/dm/wnb)
 
 
 <p>
-<img src="https://raw.githubusercontent.com/msamsami/weighted-naive-bayes/main/logo.png" alt="wnb logo" width="275" />
+<img src="https://raw.githubusercontent.com/msamsami/weighted-naive-bayes/main/docs/logo.png" alt="wnb logo" width="275" />
 <br>
 </p>
 

setup.py

@@ -4,29 +4,31 @@
 
 
 setup(
-    name='wnb',
-    version='0.1.14',
-    description='Python library for the implementations of general and weighted naive Bayes (WNB) classifiers.',
-    keywords=['python', 'bayes', 'naivebayes', 'classifier', 'probabilistic'],
-    author='Mehdi Samsami',
-    author_email='[email protected]',
-    url='https://github.com/msamsami/weighted-naive-bayes',
-    long_description=codecs.open(path.join(path.abspath(path.dirname(__file__)), 'README.md'), encoding='utf-8').read(),
-    long_description_content_type='text/markdown',
-    packages=['wnb'],
+    name="wnb",
+    version="0.1.15",
+    description="Python library for the implementations of general and weighted naive Bayes (WNB) classifiers.",
+    keywords=["python", "bayes", "naivebayes", "classifier", "probabilistic"],
+    author="Mehdi Samsami",
+    author_email="[email protected]",
+    url="https://github.com/msamsami/weighted-naive-bayes",
+    long_description=codecs.open(
+        path.join(path.abspath(path.dirname(__file__)), "README.md"), encoding="utf-8"
+    ).read(),
+    long_description_content_type="text/markdown",
+    packages=["wnb"],
     classifiers=[
-        'Intended Audience :: Science/Research',
-        'Intended Audience :: Developers',
-        'Topic :: Scientific/Engineering',
-        'Topic :: Scientific/Engineering :: Artificial Intelligence',
-        'Topic :: Scientific/Engineering :: Information Analysis',
-        'Topic :: Software Development :: Libraries :: Python Modules',
-        'Programming Language :: Python :: 3.7',
-        'Programming Language :: Python :: 3.8',
-        'Programming Language :: Python :: 3.9',
-        'License :: OSI Approved :: MIT License',
+        "Intended Audience :: Science/Research",
+        "Intended Audience :: Developers",
+        "Topic :: Scientific/Engineering",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "Topic :: Scientific/Engineering :: Information Analysis",
+        "Topic :: Software Development :: Libraries :: Python Modules",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "License :: OSI Approved :: MIT License",
     ],
     python_requires=">=3.7",
-    install_requires=['pandas==1.4.1', 'scikit-learn>=1.0.2'],
-    extras_require={},
+    install_requires=["pandas==1.4.1", "scikit-learn>=1.0.2"],
+    extras_require={"dev": "pytest==7.3.1"},
 )

wnb/__init__.py

@@ -1,14 +1,15 @@
-__version__ = "0.1.14"
+__version__ = "0.1.15"
 __author__ = "Mehdi Samsami"
 
 __all__ = [
-    'GeneralNB',
-    'GaussianWNB',
-    'Distribution',
-    'ContinuousDistMixin',
-    'DiscreteDistMixin'
+    "GeneralNB",
+    "GaussianWNB",
+    "Distribution",
+    "ContinuousDistMixin",
+    "DiscreteDistMixin",
 ]
 
+
 from ._base import ContinuousDistMixin, DiscreteDistMixin
 from ._enums import Distribution
 from .gnb import GeneralNB

wnb/_base.py

@@ -1,6 +1,6 @@
 from abc import ABCMeta
-import inspect
 from functools import wraps
+import inspect
 from numbers import Number
 from typing import List, Tuple, Union
 import warnings
@@ -9,10 +9,7 @@
 
 from ._enums import Distribution
 
-__all__ = [
-    'ContinuousDistMixin',
-    'DiscreteDistMixin'
-]
+__all__ = ["ContinuousDistMixin", "DiscreteDistMixin"]
 
 
 def vectorize(otypes=None, excluded=None, signature=None):
@@ -21,7 +18,9 @@ def vectorize(otypes=None, excluded=None, signature=None):
     """
 
     def decorator(func):
-        vectorized = np.vectorize(func, otypes=otypes, excluded=excluded, signature=signature)
+        vectorized = np.vectorize(
+            func, otypes=otypes, excluded=excluded, signature=signature
+        )
 
         @wraps(func)
         def wrapper(*args):
@@ -41,8 +40,11 @@ class DistMixin(metaclass=ABCMeta):
     _support: Union[List[float], Tuple[float, float]] = None
 
     @classmethod
-    def from_data(cls, data):
-        """Creates an instance of the class from given data. Distribution parameters will be estimated from the data.
+    def from_data(cls, data, **kwargs):
+        """Creates an instance of the class from given data. Distribution parameters will be estimated from data.
+
+        Args:
+            data: Input data from which distribution parameters will be estimated.
 
         Returns:
             self: An instance of the class.
@@ -93,28 +95,39 @@ def get_params(self) -> dict:
     def support(self) -> Union[List[float], Tuple[float, float]]:
         """Returns the support of the probability distribution.
 
-        If support is a list, it represents a limited number of discrete values. If it is a tuple, it indicates a limited or unlimited range of continuous values.
+        If support is a list, it represents a limited number of discrete values.
+        If it is a tuple, it indicates a limited or unlimited range of continuous values.
 
         """
         return self._support
 
     def _check_support(self, x):
-        if (isinstance(self.support, list) and x not in self.support) or \
-           (isinstance(self.support, tuple) and (x < self.support[0] or x > self.support[1])):
-            warnings.warn("Value doesn't lie within the support of the distribution", RuntimeWarning)
+        if (isinstance(self.support, list) and x not in self.support) or (
+            isinstance(self.support, tuple)
+            and (x < self.support[0] or x > self.support[1])
+        ):
+            warnings.warn(
+                "Value doesn't lie within the support of the distribution",
+                RuntimeWarning,
+            )
         else:
             pass
 
     def __repr__(self) -> str:
-        return "".join([
-            "<",
-            self.__class__.__name__,
-            "(",
-            ", ".join(
-                [f"{k}={v:.4f}" if isinstance(v, Number) else f"{k}={v}" for k, v in self.get_params().items()]
-            ),
-            ")>"
-        ])
+        return "".join(
+            [
+                "<",
+                self.__class__.__name__,
+                "(",
+                ", ".join(
+                    [
+                        f"{k}={v:.4f}" if isinstance(v, Number) else f"{k}={v}"
+                        for k, v in self.get_params().items()
+                    ]
+                ),
+                ")>",
+            ]
+        )
 
 
 class ContinuousDistMixin(DistMixin, metaclass=ABCMeta):

wnb/_enums.py

@@ -1,15 +1,14 @@
 from enum import Enum
 
 
-__all__ = [
-    "Distribution"
-]
+__all__ = ["Distribution"]
 
 
 class Distribution(str, Enum):
     """
     Names of probability distributions.
     """
+
     NORMAL = "Normal"
     LOGNORMAL = "Lognormal"
     EXPONENTIAL = "Exponential"