Make some ruff fixes (#8154)

* Make some ruff fixes

* Undo manual fix

* Undo manual fix

* Updates from ruff=0.0.251

machine_learning/gradient_descent.py

@@ -55,6 +55,7 @@ def output(example_no, data_set):
         return train_data[example_no][1]
     elif data_set == "test":
         return test_data[example_no][1]
+    return None
 
 
 def calculate_hypothesis_value(example_no, data_set):
@@ -68,6 +69,7 @@ def calculate_hypothesis_value(example_no, data_set):
         return _hypothesis_value(train_data[example_no][0])
     elif data_set == "test":
         return _hypothesis_value(test_data[example_no][0])
+    return None
 
 
 def summation_of_cost_derivative(index, end=m):

machine_learning/k_means_clust.py

@@ -229,7 +229,7 @@ def report_generator(
     """
     # Fill missing values with given rules
     if fill_missing_report:
-        df.fillna(value=fill_missing_report, inplace=True)
+        df = df.fillna(value=fill_missing_report)
     df["dummy"] = 1
     numeric_cols = df.select_dtypes(np.number).columns
     report = (
@@ -338,7 +338,7 @@ def report_generator(
     )
     report.columns.name = ""
     report = report.reset_index()
-    report.drop(columns=["index"], inplace=True)
+    report = report.drop(columns=["index"])
     return report
 
 

machine_learning/sequential_minimum_optimization.py

@@ -129,7 +129,7 @@ class SmoSVM:
             #     error
             self._unbound = [i for i in self._all_samples if self._is_unbound(i)]
             for s in self.unbound:
-                if s == i1 or s == i2:
+                if s in (i1, i2):
                     continue
                 self._error[s] += (
                     y1 * (a1_new - a1) * k(i1, s)
@@ -225,7 +225,7 @@ class SmoSVM:
     def _choose_alphas(self):
         locis = yield from self._choose_a1()
         if not locis:
-            return
+            return None
         return locis
 
     def _choose_a1(self):
@@ -423,9 +423,8 @@ class Kernel:
         return np.exp(-1 * (self.gamma * np.linalg.norm(v1 - v2) ** 2))
 
     def _check(self):
-        if self._kernel == self._rbf:
-            if self.gamma < 0:
-                raise ValueError("gamma value must greater than 0")
+        if self._kernel == self._rbf and self.gamma < 0:
+            raise ValueError("gamma value must greater than 0")
 
     def _get_kernel(self, kernel_name):
         maps = {"linear": self._linear, "poly": self._polynomial, "rbf": self._rbf}