Replace format() str method by f-string

Author: ahmedfgad

examples/KerasGA/XOR_classification.py

@@ -16,8 +16,8 @@ def fitness_func(ga_instanse, solution, sol_idx):
     return solution_fitness
 
 def on_generation(ga_instance):
-    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
-    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))
+    print(f"Generation = {ga_instance.generations_completed}")
+    print(f"Fitness    = {ga_instance.best_solution()[1]}")
 
 # Build the keras model using the functional API.
 input_layer  = tensorflow.keras.layers.Input(2)
@@ -62,23 +62,23 @@ def on_generation(ga_instance):
 
 # Returning the details of the best solution.
 solution, solution_fitness, solution_idx = ga_instance.best_solution()
-print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
-print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
+print(f"Fitness value of the best solution = {solution_fitness}")
+print(f"Index of the best solution : {solution_idx}")
 
 predictions = pygad.kerasga.predict(model=model,
                                     solution=solution,
                                     data=data_inputs)
-print("Predictions : \n", predictions)
+print(f"Predictions : \n{predictions}")
 
 # Calculate the binary crossentropy for the trained model.
 bce = tensorflow.keras.losses.BinaryCrossentropy()
-print("Binary Crossentropy : ", bce(data_outputs, predictions).numpy())
+print(f"Binary Crossentropy : {bce(data_outputs, predictions).numpy()}")
 
 # Calculate the classification accuracy for the trained model.
 ba = tensorflow.keras.metrics.BinaryAccuracy()
 ba.update_state(data_outputs, predictions)
 accuracy = ba.result().numpy()
-print("Accuracy : ", accuracy)
+print(f"Accuracy : {accuracy}")
 
 # model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
 

examples/KerasGA/cancer_dataset.py

@@ -17,8 +17,8 @@ def fitness_func(ga_instanse, solution, sol_idx):
     return solution_fitness
 
 def on_generation(ga_instance):
-    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
-    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution(ga_instance.last_generation_fitness)[1]))
+    print(f"Generation = {ga_instance.generations_completed}")
+    print(f"Fitness    = {ga_instance.best_solution()[1]}")
 
 # The dataset path.
 dataset_path = r'../data/Skin_Cancer_Dataset' 
@@ -71,8 +71,8 @@ def on_generation(ga_instance):
 
 # Returning the details of the best solution.
 solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness)
-print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
-print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
+print(f"Fitness value of the best solution = {solution_fitness}")
+print(f"Index of the best solution : {solution_idx}")
 
 predictions = pygad.kerasga.predict(model=model,
                                     solution=solution,
@@ -81,13 +81,13 @@ def on_generation(ga_instance):
 
 # Calculate the categorical crossentropy for the trained model.
 cce = tensorflow.keras.losses.CategoricalCrossentropy()
-print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy())
+print(f"Categorical Crossentropy : {cce(data_outputs, predictions).numpy()}")
 
 # Calculate the classification accuracy for the trained model.
 ca = tensorflow.keras.metrics.CategoricalAccuracy()
 ca.update_state(data_outputs, predictions)
 accuracy = ca.result().numpy()
-print("Accuracy : ", accuracy)
+print(f"Accuracy : {accuracy}")
 
 # model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
 

examples/KerasGA/cancer_dataset_generator.py

@@ -16,8 +16,8 @@ def fitness_func(ga_instanse, solution, sol_idx):
     return solution_fitness
 
 def on_generation(ga_instance):
-    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
-    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution(ga_instance.last_generation_fitness)[1]))
+    print(f"Generation = {ga_instance.generations_completed}")
+    print(f"Fitness    = {ga_instance.best_solution()[1]}")
 
 # The dataset path.
 dataset_path = r'../data/Skin_Cancer_Dataset' 
@@ -65,8 +65,8 @@ def on_generation(ga_instance):
 
 # Returning the details of the best solution.
 solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness)
-print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
-print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
+print(f"Fitness value of the best solution = {solution_fitness}")
+print(f"Index of the best solution : {solution_idx}")
 
 predictions = pygad.kerasga.predict(model=model,
                                     solution=solution,
@@ -75,13 +75,13 @@ def on_generation(ga_instance):
 
 # Calculate the categorical crossentropy for the trained model.
 cce = tensorflow.keras.losses.CategoricalCrossentropy()
-print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy())
+print(f"Categorical Crossentropy : {cce(data_outputs, predictions).numpy()}")
 
 # Calculate the classification accuracy for the trained model.
 ca = tensorflow.keras.metrics.CategoricalAccuracy()
 ca.update_state(data_outputs, predictions)
 accuracy = ca.result().numpy()
-print("Accuracy : ", accuracy)
+print(f"Accuracy : {accuracy}")
 
 # model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
 

examples/KerasGA/image_classification_CNN.py

@@ -16,8 +16,8 @@ def fitness_func(ga_instanse, solution, sol_idx):
     return solution_fitness
 
 def on_generation(ga_instance):
-    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
-    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))
+    print(f"Generation = {ga_instance.generations_completed}")
+    print(f"Fitness    = {ga_instance.best_solution()[1]}")
 
 # Build the keras model using the functional API.
 input_layer = tensorflow.keras.layers.Input(shape=(100, 100, 3))
@@ -66,8 +66,8 @@ def on_generation(ga_instance):
 
 # Returning the details of the best solution.
 solution, solution_fitness, solution_idx = ga_instance.best_solution()
-print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
-print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
+print(f"Fitness value of the best solution = {solution_fitness}")
+print(f"Index of the best solution : {solution_idx}")
 
 predictions = pygad.kerasga.predict(model=model,
                                     solution=solution,
@@ -76,13 +76,13 @@ def on_generation(ga_instance):
 
 # Calculate the categorical crossentropy for the trained model.
 cce = tensorflow.keras.losses.CategoricalCrossentropy()
-print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy())
+print(f"Categorical Crossentropy : {cce(data_outputs, predictions).numpy()}")
 
 # Calculate the classification accuracy for the trained model.
 ca = tensorflow.keras.metrics.CategoricalAccuracy()
 ca.update_state(data_outputs, predictions)
 accuracy = ca.result().numpy()
-print("Accuracy : ", accuracy)
+print(f"Accuracy : {accuracy}")
 
 # model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
 

examples/KerasGA/image_classification_Dense.py

@@ -16,8 +16,8 @@ def fitness_func(ga_instanse, solution, sol_idx):
     return solution_fitness
 
 def on_generation(ga_instance):
-    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
-    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))
+    print(f"Generation = {ga_instance.generations_completed}")
+    print(f"Fitness    = {ga_instance.best_solution()[1]}")
 
 # Build the keras model using the functional API.
 input_layer  = tensorflow.keras.layers.Input(360)
@@ -57,8 +57,8 @@ def on_generation(ga_instance):
 
 # Returning the details of the best solution.
 solution, solution_fitness, solution_idx = ga_instance.best_solution()
-print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
-print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
+print(f"Fitness value of the best solution = {solution_fitness}")
+print(f"Index of the best solution : {solution_idx}")
 
 # Fetch the parameters of the best solution.
 predictions = pygad.kerasga.predict(model=model,
@@ -68,13 +68,13 @@ def on_generation(ga_instance):
 
 # Calculate the categorical crossentropy for the trained model.
 cce = tensorflow.keras.losses.CategoricalCrossentropy()
-print("Categorical Crossentropy : ", cce(data_outputs, predictions).numpy())
+print(f"Categorical Crossentropy : {cce(data_outputs, predictions).numpy()}")
 
 # Calculate the classification accuracy for the trained model.
 ca = tensorflow.keras.metrics.CategoricalAccuracy()
 ca.update_state(data_outputs, predictions)
 accuracy = ca.result().numpy()
-print("Accuracy : ", accuracy)
+print(f"Accuracy : {accuracy}")
 
 # model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
 

examples/KerasGA/regression_example.py

@@ -17,8 +17,8 @@ def fitness_func(ga_instanse, solution, sol_idx):
     return solution_fitness
 
 def on_generation(ga_instance):
-    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
-    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))
+    print(f"Generation = {ga_instance.generations_completed}")
+    print(f"Fitness    = {ga_instance.best_solution()[1]}")
 
 # Create the Keras model.
 input_layer  = tensorflow.keras.layers.Input(3)
@@ -61,17 +61,17 @@ def on_generation(ga_instance):
 
 # Returning the details of the best solution.
 solution, solution_fitness, solution_idx = ga_instance.best_solution()
-print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
-print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
+print(f"Fitness value of the best solution = {solution_fitness}")
+print(f"Index of the best solution : {solution_idx}")
 
 predictions = pygad.kerasga.predict(model=model,
                                     solution=solution,
                                     data=data_inputs)
-print("Predictions : \n", predictions)
+print(f"Predictions : \n{predictions}")
 
 mae = tensorflow.keras.losses.MeanAbsoluteError()
 abs_error = mae(data_outputs, predictions).numpy()
-print("Absolute Error : ", abs_error)
+print(f"Absolute Error : {abs_error}")
 
 # model.compile(optimizer="Adam", loss="mse", metrics=["mae"])
 

examples/TorchGA/XOR_classification.py

@@ -14,8 +14,8 @@ def fitness_func(ga_instanse, solution, sol_idx):
     return solution_fitness
 
 def on_generation(ga_instance):
-    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
-    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))
+    print(f"Generation = {ga_instance.generations_completed}")
+    print(f"Fitness    = {ga_instance.best_solution()[1]}")
 
 # Create the PyTorch model.
 input_layer  = torch.nn.Linear(2, 4)
@@ -68,19 +68,19 @@ def on_generation(ga_instance):
 
 # Returning the details of the best solution.
 solution, solution_fitness, solution_idx = ga_instance.best_solution()
-print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
-print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
+print(f"Fitness value of the best solution = {solution_fitness}")
+print(f"Index of the best solution : {solution_idx}")
 
 predictions = pygad.torchga.predict(model=model, 
                                     solution=solution, 
                                     data=data_inputs)
-print("Predictions : \n", predictions.detach().numpy())
+print(f"Predictions : \n{predictions.detach().numpy()}")
 
 # Calculate the binary crossentropy for the trained model.
-print("Binary Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy())
+print(f"Binary Crossentropy : {loss_function(predictions, data_outputs).detach().numpy()}")
 
 # Calculate the classification accuracy of the trained model.
 a = torch.max(predictions, axis=1)
 b = torch.max(data_outputs, axis=1)
 accuracy = torch.true_divide(torch.sum(a.indices == b.indices), len(data_outputs))
-print("Accuracy : ", accuracy.detach().numpy())
+print(f"Accuracy : {accuracy.detach().numpy()}")

examples/TorchGA/image_classification_CNN.py

@@ -15,8 +15,8 @@ def fitness_func(ga_instanse, solution, sol_idx):
     return solution_fitness
 
 def on_generation(ga_instance):
-    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
-    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))
+    print(f"Generation = {ga_instance.generations_completed}")
+    print(f"Fitness    = {ga_instance.best_solution()[1]}")
 
 # Build the PyTorch model.
 input_layer = torch.nn.Conv2d(in_channels=3, out_channels=5, kernel_size=7)
@@ -78,17 +78,17 @@ def on_generation(ga_instance):
 
 # Returning the details of the best solution.
 solution, solution_fitness, solution_idx = ga_instance.best_solution()
-print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
-print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
+print(f"Fitness value of the best solution = {solution_fitness}")
+print(f"Index of the best solution : {solution_idx}")
 
 predictions = pygad.torchga.predict(model=model, 
                                     solution=solution, 
                                     data=data_inputs)
 # print("Predictions : \n", predictions)
 
 # Calculate the crossentropy for the trained model.
-print("Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy())
+print(f"Crossentropy : {loss_function(predictions, data_outputs).detach().numpy()}")
 
 # Calculate the classification accuracy for the trained model.
 accuracy = torch.true_divide(torch.sum(torch.max(predictions, axis=1).indices == data_outputs), len(data_outputs))
-print("Accuracy : ", accuracy.detach().numpy())
+print(f"Accuracy : {accuracy.detach().numpy()}")

examples/TorchGA/image_classification_Dense.py

@@ -15,8 +15,8 @@ def fitness_func(ga_instanse, solution, sol_idx):
     return solution_fitness
 
 def on_generation(ga_instance):
-    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
-    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))
+    print(f"Generation = {ga_instance.generations_completed}")
+    print(f"Fitness    = {ga_instance.best_solution()[1]}")
 
 # Build the PyTorch model using the functional API.
 input_layer = torch.nn.Linear(360, 50)
@@ -64,17 +64,17 @@ def on_generation(ga_instance):
 
 # Returning the details of the best solution.
 solution, solution_fitness, solution_idx = ga_instance.best_solution()
-print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
-print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
+print(f"Fitness value of the best solution = {solution_fitness}")
+print(f"Index of the best solution : {solution_idx}")
 
 predictions = pygad.torchga.predict(model=model, 
                                     solution=solution, 
                                     data=data_inputs)
 # print("Predictions : \n", predictions)
 
 # Calculate the crossentropy loss of the trained model.
-print("Crossentropy : ", loss_function(predictions, data_outputs).detach().numpy())
+print(f"Crossentropy : {loss_function(predictions, data_outputs).detach().numpy()}")
 
 # Calculate the classification accuracy for the trained model.
 accuracy = torch.true_divide(torch.sum(torch.max(predictions, axis=1).indices == data_outputs), len(data_outputs))
-print("Accuracy : ", accuracy.detach().numpy())
+print(f"Accuracy : {accuracy.detach().numpy()}")

examples/TorchGA/regression_example.py

@@ -15,8 +15,8 @@ def fitness_func(ga_instanse, solution, sol_idx):
     return solution_fitness
 
 def on_generation(ga_instance):
-    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
-    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))
+    print(f"Generation = {ga_instance.generations_completed}")
+    print(f"Fitness    = {ga_instance.best_solution()[1]}")
 
 # Create the PyTorch model.
 input_layer = torch.nn.Linear(3, 2)
@@ -64,13 +64,13 @@ def on_generation(ga_instance):
 
 # Returning the details of the best solution.
 solution, solution_fitness, solution_idx = ga_instance.best_solution()
-print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
-print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
+print(f"Fitness value of the best solution = {solution_fitness}")
+print(f"Index of the best solution : {solution_idx}")
 
 predictions = pygad.torchga.predict(model=model, 
                                     solution=solution, 
                                     data=data_inputs)
-print("Predictions : \n", predictions.detach().numpy())
+print("Predictions : \n{predictions.detach().numpy()}")
 
 abs_error = loss_function(predictions, data_outputs)
-print("Absolute Error : ", abs_error.detach().numpy())
+print("Absolute Error : {abs_error.detach().numpy()}")

examples/clustering/example_clustering_2.py

@@ -107,9 +107,9 @@ def fitness_func(ga_instance, solution, solution_idx):
 ga_instance.run()
 
 best_solution, best_solution_fitness, best_solution_idx = ga_instance.best_solution()
-print("Best solution is {bs}".format(bs=best_solution))
-print("Fitness of the best solution is {bsf}".format(bsf=best_solution_fitness))
-print("Best solution found after {gen} generations".format(gen=ga_instance.best_solution_generation))
+print(f"Best solution is {best_solution}")
+print(f"Fitness of the best solution is {best_solution_fitness}")
+print(f"Best solution found after {ga_instance.best_solution_generation} generations")
 
 cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist = cluster_data(best_solution, best_solution_idx)
 

examples/clustering/example_clustering_3.py

@@ -119,9 +119,9 @@ def fitness_func(ga_instance, solution, solution_idx):
 ga_instance.run()
 
 best_solution, best_solution_fitness, best_solution_idx = ga_instance.best_solution()
-print("Best solution is {bs}".format(bs=best_solution))
-print("Fitness of the best solution is {bsf}".format(bsf=best_solution_fitness))
-print("Best solution found after {gen} generations".format(gen=ga_instance.best_solution_generation))
+print(f"Best solution is {best_solution}")
+print(f"Fitness of the best solution is {best_solution_fitness}")
+print(f"Best solution found after {ga_instance.best_solution_generation} generations")
 
 cluster_centers, all_clusters_dists, cluster_indices, clusters, clusters_sum_dist = cluster_data(best_solution, best_solution_idx)
 

examples/cnn/example_image_classification.py

@@ -67,6 +67,6 @@
 num_wrong = numpy.where(predictions != train_outputs)[0]
 num_correct = train_outputs.size - num_wrong.size
 accuracy = 100 * (num_correct/train_outputs.size)
-print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct))
-print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size))
-print("Classification accuracy : {accuracy}.".format(accuracy=accuracy))
+print(f"Number of correct classifications : {num_correct}.")
+print(f"Number of wrong classifications : {num_wrong.size}.")
+print(f"Classification accuracy : {accuracy}.")