major core refactor in gaussian wnb classifier

tests/test_gwnb.py

@@ -1,6 +1,7 @@
 import re
 
 import numpy as np
+import pandas as pd
 import pytest
 from sklearn.base import is_classifier
 from sklearn.utils._testing import assert_array_almost_equal, assert_array_equal
@@ -194,3 +195,25 @@ def test_gwnb_no_cost_hist():
     clf = GaussianWNB(max_iter=10)
     clf.fit(X, y)
     assert clf.cost_hist_ is None
+
+
+def test_gwnb_attrs():
+    """
+    Test whether the attributes are properly set.
+    """
+    clf = GaussianWNB().fit(X, y)
+    assert np.array_equal(clf.class_count_, np.array([3, 3]))
+    assert np.array_equal(clf.class_prior_, np.array([0.5, 0.5]))
+    assert np.array_equal(clf.classes_, np.array([1, 2]))
+    assert clf.n_classes_ == 2
+    assert clf.n_features_in_ == 2
+    assert not hasattr(clf, "feature_names_in_")
+    assert np.array_equal(clf.error_weights_, np.array([[0, 1], [-1, 0]]))
+    assert clf.theta_.shape == (2, 2)
+    assert clf.std_.shape == (2, 2)
+    assert clf.var_.shape == (2, 2)
+    assert clf.coef_.shape == (2,)
+
+    feature_names = [f"x{i}" for i in range(X.shape[1])]
+    clf = GaussianWNB().fit(pd.DataFrame(X, columns=feature_names), y)
+    assert np.array_equal(clf.feature_names_in_, np.array(feature_names))

wnb/gnb.py

@@ -145,14 +145,10 @@ def _check_X_y(self, X, y) -> tuple[np.ndarray, np.ndarray]:
         return X, y
 
     def _init_parameters(self) -> None:
-        self.class_prior_: np.ndarray
-
         # Set priors if not specified
         if self.priors is None:
-            self.class_prior_ = (
-                self.class_count_ / self.class_count_.sum()
-            )  # Calculate empirical prior probabilities
-
+            # Calculate empirical prior probabilities
+            self.class_prior_ = self.class_count_ / self.class_count_.sum()
         else:
             # Check that the provided priors match the number of classes
             if len(self.priors) != self.n_classes_:
@@ -213,7 +209,7 @@ def fit(self, X: MatrixLike, y: ArrayLike) -> Self:
         X, y = self._check_X_y(X, y)
 
         self.classes_, y_, self.class_count_ = np.unique(y, return_counts=True, return_inverse=True)
-        self.n_classes_: int = len(self.classes_)
+        self.n_classes_ = len(self.classes_)
 
         self._init_parameters()