SFS finalize_fit() support for numpy >= 2.0 (#1107)

* finalize_fit() support for numpy >= 2.0

* updated changelog

* flake8: removed blank line

* flake8: removed trailing whitespaces

* fixed changelog indents

* fix for isort / black formatter

* added change/feature description

docs/sources/CHANGELOG.md

@@ -23,9 +23,11 @@ Files updated:
   - ['mlxtend.frequent_patterns.fpcommon']
   - ['mlxtend.frequent_patterns.fpgrowth'](https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/fpgrowth/)
   - ['mlxtend.frequent_patterns.fpmax'](https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/fpmax/)
+  - [`mlxtend.feature_selection.SequentialFeatureSelector`](https://github.com/rasbt/mlxtend/blob/master/mlxtend/feature_selection/sequential_feature_selector.py)
+    - Updated negative infinity constant to be compatible with old and new (>=2.0) `numpy` versions
   - [`mlxtend.frequent_patterns.association_rules`](https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/association_rules/)
-- [`mlxtend.frequent_patterns.association_rules`](https://rasbt.github.io/mlxtend/user_guide/frequent_patterns/association_rules/)Implemented three new metrics: Jaccard, Certainty, and Kulczynski. ([#1096](https://github.com/rasbt/mlxtend/issues/1096))
-- Integrated scikit-learn's `set_output` method into `TransactionEncoder` ([#1087](https://github.com/rasbt/mlxtend/issues/1087) via[it176131](https://github.com/it176131))
+    - Implemented three new metrics: Jaccard, Certainty, and Kulczynski. ([#1096](https://github.com/rasbt/mlxtend/issues/1096))
+  - Integrated scikit-learn's `set_output` method into `TransactionEncoder` ([#1087](https://github.com/rasbt/mlxtend/issues/1087) via [it176131](https://github.com/it176131))
 
 ##### Changes
 

mlxtend/feature_selection/sequential_feature_selector.py

@@ -651,7 +651,11 @@ def fit(self, X, y, groups=None, **fit_params):
         return self
 
     def finalize_fit(self):
-        max_score = np.NINF
+        if np.__version__ < "2.0":
+            ninf = np.NINF
+        else:
+            ninf = -np.inf
+        max_score = ninf
         for k in self.subsets_:
             if (
                 k >= self.min_k
@@ -662,7 +666,7 @@ def finalize_fit(self):
                 best_subset = k
 
         k_score = max_score
-        if k_score == np.NINF:
+        if k_score == ninf:
             # i.e. all keys of self.subsets_ are not in interval `[self.min_k, self.max_k]`
             # this happens if KeyboardInterrupt happens
             keys = list(self.subsets_.keys())