Merge pull request #50 from msamsami/make-distribution-non-kw-arg

fix: make `distributions` param positional-or-keyword in `GeneralNB`

README.md

@@ -7,7 +7,7 @@
 
 <div align="center">
 
-![Lastest Release](https://img.shields.io/badge/release-v0.7.0-green)
+![Lastest Release](https://img.shields.io/badge/release-v0.8.0-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%20%7C%203.13-blue)<br>
 ![GitHub Workflow Status (build)](https://github.com/msamsami/wnb/actions/workflows/build.yml/badge.svg)
@@ -57,12 +57,12 @@ from wnb import GeneralNB, Distribution as D
 
 2. Initialize a classifier with likelihood distributions specified
 ```python
-gnb = GeneralNB(distributions=[D.NORMAL, D.CATEGORICAL, D.EXPONENTIAL, D.EXPONENTIAL])
+clf = GeneralNB([D.NORMAL, D.CATEGORICAL, D.EXPONENTIAL, D.EXPONENTIAL])
 ```
 or
 ```python
 # Columns not explicitly specified will default to Gaussian (normal) distribution
-gnb = GeneralNB(
+clf = GeneralNB(
     distributions=[
         (D.CATEGORICAL, [1]),
         (D.EXPONENTIAL, ["col3", "col4"]),
@@ -72,12 +72,12 @@ gnb = GeneralNB(
 
 3. Fit the classifier to a training set (with four features)
 ```python
-gnb.fit(X_train, y_train)
+clf.fit(X_train, y_train)
 ```
 
 4. Predict on test data
 ```python
-gnb.predict(X_test)
+clf.predict(X_test)
 ```
 
 ### Weighted naive Bayes
@@ -91,17 +91,17 @@ from wnb import GaussianWNB
 
 2. Initialize a classifier
 ```python
-gwnb = GaussianWNB(max_iter=25, step_size=1e-2, penalty="l2")
+clf = GaussianWNB(max_iter=25, step_size=1e-2, penalty="l2")
 ```
 
 3. Fit the classifier to a training set
 ```python
-gwnb.fit(X_train, y_train)
+clf.fit(X_train, y_train)
 ```
 
 4. Predict on test data
 ```python
-gwnb.predict(X_test)
+clf.predict(X_test)
 ```
 
 ## Compatibility with Scikit-learn 🤝

examples/gnb_digits.py

@@ -18,12 +18,12 @@
 # Split the data into training and test sets
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
 
-# Train and score sklearn GaussianNB classifier
+# Train and score sklearn's GaussianNB classifier
 gnb = GaussianNB()
 gnb.fit(X_train, y_train)
 print("sklearn | GaussianNB >> score >>", gnb.score(X_test, y_test))
 
-# Train and score wnb GeneralNB classifier with Poisson likelihoods
-gnb = GeneralNB(distributions=[D.POISSON] * X.shape[1])
+# Train and score wnb's GeneralNB classifier with Poisson likelihoods
+gnb = GeneralNB([D.POISSON] * X.shape[1])
 gnb.fit(X_train, y_train)
 print("wnb | GeneralNB >> score >>", gnb.score(X_test, y_test))

examples/gnb_wine.py

@@ -13,12 +13,12 @@
 # Split the data into training and test sets
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
 
-# Train and score sklearn GaussianNB classifier
+# Train and score sklearn's GaussianNB classifier
 gnb = GaussianNB()
 gnb.fit(X_train, y_train)
 print("sklearn | GaussianNB >> score >>", gnb.score(X_test, y_test))
 
-# Train and score wnb GeneralNB classifier with Log-normal likelihoods
+# Train and score wnb's GeneralNB classifier with Log-normal likelihoods
 gnb = GeneralNB(distributions=[(D.LOGNORMAL, range(X.shape[1]))])
 gnb.fit(X_train, y_train)
 print("wnb | GeneralNB >> score >>", gnb.score(X_test, y_test))

examples/gwnb_breast_cancer.py

@@ -12,12 +12,12 @@
 # Split the data into training and test sets
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)
 
-# Train and score sklearn GaussianNB classifier
+# Train and score sklearn's GaussianNB classifier
 gnb = GaussianNB()
 gnb.fit(X_train, y_train)
 print("sklearn | GaussianNB >> score >>", gnb.score(X_test, y_test))
 
-# Train and score wnb GaussianWNB classifier
+# Train and score wnb's GaussianWNB classifier
 gwnb = GaussianWNB(max_iter=20, step_size=0.01)
 gwnb.fit(X_train, y_train)
 print("wnb | GaussianWNB >> score >>", gwnb.score(X_test, y_test))