Add class_weight parameter to Naive Bayes classifiers - #32769

sklearn/naive_bayes.py:468:89: E501 Line too long (95 > 88)
    |
466 |                     "For 'balanced' weights, use compute_class_weight('balanced', "
467 |                     "classes=np.unique(y), y=y). For repeated calls to partial_fit, "
468 |                     "y should be the complete target vector to compute accurate class weights."
    |                                                                                         ^^^^^^^ E501
469 |                 )
470 |             # Convert to numpy for compute_sample_weight
    |

sklearn/naive_bayes.py:608:89: E501 Line too long (105 > 88)
    |
606 |     }
607 |
608 |     def __init__(self, alpha=1.0, fit_prior=True, class_prior=None, force_alpha=True, class_weight=None):
    |                                                                                         ^^^^^^^^^^^^^^^^^ E501
609 |         self.alpha = alpha
610 |         self.fit_prior = fit_prior
    |

sklearn/naive_bayes.py:771:89: E501 Line too long (95 > 88)
    |
769 |                     "For 'balanced' weights, use compute_class_weight('balanced', "
770 |                     "classes=np.unique(y), y=y). For repeated calls to partial_fit, "
771 |                     "y should be the complete target vector to compute accurate class weights."
    |                                                                                         ^^^^^^^ E501
772 |                 )
773 |             class_sample_weight = compute_sample_weight(self.class_weight, y)
    |

sklearn/naive_bayes.py:978:89: E501 Line too long (97 > 88)
    |
977 |     def __init__(
978 |         self, *, alpha=1.0, force_alpha=True, fit_prior=True, class_prior=None, class_weight=None
    |                                                                                         ^^^^^^^^^ E501
979 |     ):
980 |         super().__init__(
    |

Found 4 errors.

--- sklearn/naive_bayes.py
+++ sklearn/naive_bayes.py
@@ -471,7 +471,7 @@
             y_np = _convert_to_numpy(y, xp=xp_y)
             class_sample_weight = compute_sample_weight(self.class_weight, y_np)
             # Convert back if needed
-            if xp.__name__ != 'numpy':
+            if xp.__name__ != "numpy":
                 class_sample_weight = xp.asarray(class_sample_weight, device=device_)
             if sample_weight is not None:
                 sample_weight = sample_weight * class_sample_weight
@@ -605,7 +605,14 @@
         "class_weight": [None, dict, StrOptions({"balanced"})],
     }
 
-    def __init__(self, alpha=1.0, fit_prior=True, class_prior=None, force_alpha=True, class_weight=None):
+    def __init__(
+        self,
+        alpha=1.0,
+        fit_prior=True,
+        class_prior=None,
+        force_alpha=True,
+        class_weight=None,
+    ):
         self.alpha = alpha
         self.fit_prior = fit_prior
         self.class_prior = class_prior
@@ -975,7 +982,13 @@
     """
 
     def __init__(
-        self, *, alpha=1.0, force_alpha=True, fit_prior=True, class_prior=None, class_weight=None
+        self,
+        *,
+        alpha=1.0,
+        force_alpha=True,
+        fit_prior=True,
+        class_prior=None,
+        class_weight=None,
     ):
         super().__init__(
             alpha=alpha,

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