Merge pull request #230 from ahmedfgad/github-actions

PyGAD 3.2.0

Author: ahmedfgad

NSGA-II/non_dominant_sorting.py

@@ -1,6 +1,6 @@
 # PyGAD:  Genetic Algorithm in Python
 
-[PyGAD](https://pypi.org/project/pygad) is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine learning algorithms. It supports Keras and PyTorch.
+[PyGAD](https://pypi.org/project/pygad) is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine learning algorithms. It supports Keras and PyTorch. PyGAD supports optimizing both single-objective and multi-objective problems.
 
 Check documentation of the [PyGAD](https://pygad.readthedocs.io/en/latest).
 
@@ -146,7 +146,7 @@ on_stop()
 
 # Example
 
-Check the [PyGAD's documentation](https://pygad.readthedocs.io/en/latest/pygad.html) for information about the implementation of this example.
+Check the [PyGAD's documentation](https://pygad.readthedocs.io/en/latest/pygad.html) for information about the implementation of this example. It solves a single-objective problem.
 
 ```python
 import pygad
@@ -183,9 +183,9 @@ num_genes = len(function_inputs)
 last_fitness = 0
 def callback_generation(ga_instance):
     global last_fitness
-    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
-    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))
-    print("Change     = {change}".format(change=ga_instance.best_solution()[1] - last_fitness))
+    print(f"Generation = {ga_instance.generations_completed}")
+    print(f"Fitness    = {ga_instance.best_solution()[1]}")
+    print(f"Change     = {ga_instance.best_solution()[1] - last_fitness}")
     last_fitness = ga_instance.best_solution()[1]
 
 # Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor.
@@ -204,15 +204,15 @@ ga_instance.plot_fitness()
 
 # Returning the details of the best solution.
 solution, solution_fitness, solution_idx = ga_instance.best_solution()
-print("Parameters of the best solution : {solution}".format(solution=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"Parameters of the best solution : {solution}")
+print(f"Fitness value of the best solution = {solution_fitness}")
+print(f"Index of the best solution : {solution_idx}")
 
 prediction = numpy.sum(numpy.array(function_inputs)*solution)
-print("Predicted output based on the best solution : {prediction}".format(prediction=prediction))
+print(f"Predicted output based on the best solution : {prediction}")
 
 if ga_instance.best_solution_generation != -1:
-    print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation))
+    print(g"Best fitness value reached after {ga_instance.best_solution_generation} generations.")
 
 # Saving the GA instance.
 filename = 'genetic' # The filename to which the instance is saved. The name is without extension.

README.md

@@ -644,9 +644,9 @@ addition to the classification accuracy.
    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}.")
 
 It is very important to note that it is not expected that the
 classification accuracy is high because no training algorithm is used.
@@ -743,6 +743,6 @@ files before running this code.
    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}.")

docs/source/cnn.rst

@@ -22,7 +22,7 @@
 author = 'Ahmed Fawzy Gad'
 
 # The full version, including alpha/beta/rc tags
-release = '3.1.0'
+release = '3.2.0'
 
 master_doc = 'index'
 

docs/source/conf.py

@@ -405,7 +405,7 @@ solutions within the population.
        population_matrices = gacnn.population_as_matrices(population_networks=GACNN_instance.population_networks, population_vectors=ga_instance.population)
        GACNN_instance.update_population_trained_weights(population_trained_weights=population_matrices)
 
-       print("Generation = {generation}".format(generation=ga_instance.generations_completed))
+       print(f"Generation = {ga_instance.generations_completed}")
 
 After preparing the fitness and callback function, next is to create an
 instance of the ``pygad.GA`` class.
@@ -462,7 +462,7 @@ be called to show how the fitness values evolve by generation.
 
    ga_instance.plot_fitness()
 
-.. figure:: https://user-images.githubusercontent.com/16560492/83429675-ab744580-a434-11ea-8f21-9d3804b50d15.png
+.. image:: https://user-images.githubusercontent.com/16560492/83429675-ab744580-a434-11ea-8f21-9d3804b50d15.png
    :alt: 
 
 Information about the Best Solution
@@ -483,9 +483,9 @@ Here is how such information is returned.
 .. code:: python
 
    solution, solution_fitness, solution_idx = ga_instance.best_solution()
-   print("Parameters of the best solution : {solution}".format(solution=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"Parameters of the best solution : {solution}")
+   print(f"Fitness value of the best solution = {solution_fitness}")
+   print(f"Index of the best solution : {solution_idx}")
 
 .. code:: 
 
@@ -504,7 +504,7 @@ the labels correctly.
 .. code:: python
 
    predictions = pygad.cnn.predict(last_layer=GANN_instance.population_networks[solution_idx], data_inputs=data_inputs)
-   print("Predictions of the trained network : {predictions}".format(predictions=predictions))
+   print(f"Predictions of the trained network : {predictions}")
 
 Calculating Some Statistics
 ---------------------------
@@ -518,9 +518,9 @@ addition to the classification accuracy.
    num_wrong = numpy.where(predictions != data_outputs)[0]
    num_correct = data_outputs.size - num_wrong.size
    accuracy = 100 * (num_correct/data_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}.")
 
 .. code:: 
 
@@ -575,8 +575,8 @@ complete code is listed below.
 
        GACNN_instance.update_population_trained_weights(population_trained_weights=population_matrices)
 
-       print("Generation = {generation}".format(generation=ga_instance.generations_completed))
-       print("Fitness    = {fitness}".format(fitness=ga_instance.best_solutions_fitness))
+       print(f"Generation = {ga_instance.generations_completed}")
+       print(f"Fitness    = {ga_instance.best_solutions_fitness}")
 
    data_inputs = numpy.load("dataset_inputs.npy")
    data_outputs = numpy.load("dataset_outputs.npy")
@@ -642,21 +642,21 @@ complete code is listed below.
 
    # Returning the details of the best solution.
    solution, solution_fitness, solution_idx = ga_instance.best_solution()
-   print("Parameters of the best solution : {solution}".format(solution=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"Parameters of the best solution : {solution}")
+   print(f"Fitness value of the best solution = {solution_fitness}")
+   print(f"Index of the best solution : {solution_idx}")
 
    if ga_instance.best_solution_generation != -1:
-       print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation))
+       print(f"Best fitness value reached after {ga_instance.best_solution_generation} generations.")
 
    # Predicting the outputs of the data using the best solution.
    predictions = GACNN_instance.population_networks[solution_idx].predict(data_inputs=data_inputs)
-   print("Predictions of the trained network : {predictions}".format(predictions=predictions))
+   print(f"Predictions of the trained network : {predictions}")
 
    # Calculating some statistics
    num_wrong = numpy.where(predictions != data_outputs)[0]
    num_correct = data_outputs.size - num_wrong.size
    accuracy = 100 * (num_correct/data_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}.")