MAINT: Updated f-string method (#6230)

* MAINT: Used f-string method

Updated the code with f-string methods wherever required for a better and cleaner understanding of the code.

* Updated files with f-string method

* Update rsa_key_generator.py

* Update rsa_key_generator.py

* Update elgamal_key_generator.py

* Update lru_cache.py

I don't think this change is efficient but it might tackle the error as the error was due to using long character lines.

* Update lru_cache.py

* Update lru_cache.py

Co-authored-by: cyai <seriesscar@gmail.com>
Co-authored-by: Christian Clauss <cclauss@me.com>

machine_learning/gradient_boosting_regressor.py

@@ -47,9 +47,9 @@ def main():
     y_pred = model.predict(X_test)
 
     # The mean squared error
-    print("Mean squared error: %.2f" % mean_squared_error(y_test, y_pred))
+    print(f"Mean squared error: {mean_squared_error(y_test, y_pred):.2f}")
     # Explained variance score: 1 is perfect prediction
-    print("Test Variance score: %.2f" % r2_score(y_test, y_pred))
+    print(f"Test Variance score: {r2_score(y_test, y_pred):.2f}")
 
     # So let's run the model against the test data
     fig, ax = plt.subplots()

machine_learning/k_means_clust.py

@@ -164,9 +164,7 @@ def kmeans(
             num_changed = np.sum(prev_cluster_assignment != cluster_assignment)
             if verbose:
                 print(
-                    "    {:5d} elements changed their cluster assignment.".format(
-                        num_changed
-                    )
+                    f"    {num_changed:5d} elements changed their cluster assignment."
                 )
 
         # Record heterogeneity convergence metric

machine_learning/linear_regression.py

@@ -99,7 +99,7 @@ def main():
     len_result = theta.shape[1]
     print("Resultant Feature vector : ")
     for i in range(0, len_result):
-        print("%.5f" % (theta[0, i]))
+        print(f"{theta[0, i]:.5f}")
 
 
 if __name__ == "__main__":