Merge pull request ahmedfgad#221 from ahmedfgad/github-actions

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Author: ahmedfgad

docs/source/gann.rst

@@ -535,7 +535,7 @@ value (i.e. accuracy) of 100 is reached after around 180 generations.
 
    ga_instance.plot_fitness()
 
-.. figure:: https://user-images.githubusercontent.com/16560492/82078638-c11e0700-96e1-11ea-8aa9-c36761c5e9c7.png
+.. image:: https://user-images.githubusercontent.com/16560492/82078638-c11e0700-96e1-11ea-8aa9-c36761c5e9c7.png
    :alt: 
 
 By running the code again, a different initial population is created and
@@ -930,7 +930,7 @@ The number of wrong classifications is only 1 and the accuracy is
 
 The next figure shows how fitness value evolves by generation.
 
-.. figure:: https://user-images.githubusercontent.com/16560492/82152993-21898180-9865-11ea-8387-b995f88b83f7.png
+.. image:: https://user-images.githubusercontent.com/16560492/82152993-21898180-9865-11ea-8387-b995f88b83f7.png
    :alt: 
 
 Regression Example 1
@@ -998,10 +998,10 @@ for regression.
        GANN_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_solution()[1]))
-       print("Change     = {change}".format(change=ga_instance.best_solution()[1] - last_fitness))
+       print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1]))
+       print("Change     = {change}".format(change=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1] - last_fitness))
 
-       last_fitness = ga_instance.best_solution()[1].copy()
+       last_fitness = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1].copy()
 
    # Holds the fitness value of the previous generation.
    last_fitness = 0
@@ -1011,8 +1011,8 @@ for regression.
                               [8, 15, 20, 13]])
 
    # Preparing the NumPy array of the outputs.
-   data_outputs = numpy.array([0.1, 
-                               1.5])
+   data_outputs = numpy.array([[0.1, 0.2],
+                               [1.8, 1.5]])
 
    # The length of the input vector for each sample (i.e. number of neurons in the input layer).
    num_inputs = data_inputs.shape[1]
@@ -1022,7 +1022,7 @@ for regression.
    GANN_instance = pygad.gann.GANN(num_solutions=num_solutions,
                                    num_neurons_input=num_inputs,
                                    num_neurons_hidden_layers=[2],
-                                   num_neurons_output=1,
+                                   num_neurons_output=2,
                                    hidden_activations=["relu"],
                                    output_activation="None")
 
@@ -1071,7 +1071,7 @@ for regression.
    ga_instance.plot_fitness()
 
    # Returning the details of the best solution.
-   solution, solution_fitness, solution_idx = ga_instance.best_solution()
+   solution, solution_fitness, solution_idx = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)
    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))
@@ -1092,7 +1092,7 @@ for regression.
 The next figure shows how the fitness value changes for the generations
 used.
 
-.. figure:: https://user-images.githubusercontent.com/16560492/92948154-3cf24b00-f459-11ea-94ea-952b66ab2145.png
+.. image:: https://user-images.githubusercontent.com/16560492/92948154-3cf24b00-f459-11ea-94ea-952b66ab2145.png
    :alt: 
 
 Regression Example 2 - Fish Weight Prediction
@@ -1164,15 +1164,15 @@ Here is the complete code.
        GANN_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_solution()[1]))
-       print("Change     = {change}".format(change=ga_instance.best_solution()[1] - last_fitness))
+       print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1]))
+       print("Change     = {change}".format(change=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1] - last_fitness))
 
-       last_fitness = ga_instance.best_solution()[1].copy()
+       last_fitness = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1].copy()
 
    # Holds the fitness value of the previous generation.
    last_fitness = 0
 
-   data = numpy.array(pandas.read_csv("Fish.csv"))
+   data = numpy.array(pandas.read_csv("../data/Fish.csv"))
 
    # Preparing the NumPy array of the inputs.
    data_inputs = numpy.asarray(data[:, 2:], dtype=numpy.float32)
@@ -1237,7 +1237,7 @@ Here is the complete code.
    ga_instance.plot_fitness()
 
    # Returning the details of the best solution.
-   solution, solution_fitness, solution_idx = ga_instance.best_solution()
+   solution, solution_fitness, solution_idx = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)
    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))
@@ -1258,5 +1258,5 @@ Here is the complete code.
 The next figure shows how the fitness value changes for the 500
 generations used.
 
-.. figure:: https://user-images.githubusercontent.com/16560492/92948486-bbe78380-f459-11ea-9e31-0d4c7269d606.png
+.. image:: https://user-images.githubusercontent.com/16560492/92948486-bbe78380-f459-11ea-9e31-0d4c7269d606.png
    :alt: 

docs/source/pygad.rst

@@ -4138,6 +4138,149 @@ and also saved in the text file.
    2023-04-03 19:04:27 INFO: Generation = 10
    2023-04-03 19:04:27 INFO: Fitness    = 0.000389832593101348
 
+Solve Non-Deterministic Problems
+================================
+
+PyGAD can be used to solve both deterministic and non-deterministic
+problems. Deterministic are those that return the same fitness for the
+same solution. For non-deterministic problems, a different fitness value
+would be returned for the same solution.
+
+By default, PyGAD settings are set to solve deterministic problems.
+PyGAD can save the explored solutions and their fitness to reuse in the
+future. These instances attributes can save the solutions:
+
+1. ``solutions``: Exists if ``save_solutions=True``.
+
+2. ``best_solutions``: Exists if ``save_best_solutions=True``.
+
+3. ``last_generation_elitism``: Exists if ``keep_elitism`` > 0.
+
+4. ``last_generation_parents``: Exists if ``keep_parents`` > 0 or
+   ``keep_parents=-1``.
+
+To configure PyGAD for non-deterministic problems, we have to disable
+saving the previous solutions. This is by setting these parameters:
+
+1. ``keep_elisitm=0``
+
+2. ``keep_parents=0``
+
+3. ``keep_solutions=False``
+
+4. ``keep_best_solutions=False``
+
+.. code:: python
+
+   import pygad
+   ...
+   ga_instance = pygad.GA(...,
+                          keep_elitism=0,
+                          keep_parents=0,
+                          save_solutions=False,
+                          save_best_solutions=False,
+                          ...)
+
+This way PyGAD will not save any explored solution and thus the fitness
+function have to be called for each individual solution.
+
+Reuse the Fitness instead of Calling the Fitness Function
+=========================================================
+
+It may happen that a previously explored solution in generation X is
+explored again in another generation Y (where Y > X). For some problems,
+calling the fitness function takes much time.
+
+For deterministic problems, it is better to not call the fitness
+function for an already explored solutions. Instead, reuse the fitness
+of the old solution. PyGAD supports some options to help you save time
+calling the fitness function for a previously explored solution.
+
+The parameters explored in this section can be set in the constructor of
+the ``pygad.GA`` class.
+
+The ``cal_pop_fitness()`` method of the ``pygad.GA`` class checks these
+parameters to see if there is a possibility of reusing the fitness
+instead of calling the fitness function.
+
+.. _1-savesolutions:
+
+1. ``save_solutions``
+---------------------
+
+It defaults to ``False``. If set to ``True``, then the population of
+each generation is saved into the ``solutions`` attribute of the
+``pygad.GA`` instance. In other words, every single solution is saved in
+the ``solutions`` attribute.
+
+.. _2-savebestsolutions:
+
+2. ``save_best_solutions``
+--------------------------
+
+It defaults to ``False``. If ``True``, then it only saves the best
+solution in every generation.
+
+.. _3-keepelitism:
+
+3. ``keep_elitism``
+-------------------
+
+It accepts an integer and defaults to 1. If set to a positive integer,
+then it keeps the elitism of one generation available in the next
+generation.
+
+.. _4-keepparents:
+
+4. ``keep_parents``
+-------------------
+
+It accepts an integer and defaults to -1. It set to ``-1`` or a positive
+integer, then it keeps the parents of one generation available in the
+next generation.
+
+Why the Fitness Function is not Called for Solution at Index 0?
+===============================================================
+
+PyGAD has a parameter called ``keep_elitism`` which defaults to 1. This
+parameter defines the number of best solutions in generation **X** to
+keep in the next generation **X+1**. The best solutions are just copied
+from generation **X** to generation **X+1** without making any change.
+
+.. code:: python
+
+   ga_instance = pygad.GA(...,
+                          keep_elitism=1,
+                          ...)
+
+The best solutions are copied at the beginning of the population. If
+``keep_elitism=1``, this means the best solution in generation X is kept
+in the next generation X+1 at index 0 of the population. If
+``keep_elitism=2``, this means the 2 best solutions in generation X are
+kept in the next generation X+1 at indices 0 and 1 of the population of
+generation 1.
+
+Because the fitness of these best solutions are already calculated in
+generation X, then their fitness values will not be recalculated at
+generation X+1 (i.e. the fitness function will not be called for these
+solutions again). Instead, their fitness values are just reused. This is
+why you see that no solution with index 0 is passed to the fitness
+function.
+
+To force calling the fitness function for each solution in every
+generation, consider setting ``keep_elitism`` and ``keep_parents`` to 0.
+Moreover, keep the 2 parameters ``save_solutions`` and
+``save_best_solutions`` to their default value ``False``.
+
+.. code:: python
+
+   ga_instance = pygad.GA(...,
+                          keep_elitism=0,
+                          keep_parents=0,
+                          save_solutions=False,
+                          save_best_solutions=False,
+                          ...)
+
 Batch Fitness Calculation
 =========================
 

pygad/__init__.py

@@ -1,3 +1,3 @@
 from .pygad import * # Relative import.
 
-__version__ = "3.1.0"
+__version__ = "3.1.1"

pygad/pygad.py

@@ -345,7 +345,11 @@ def __init__(self,
             elif len(gene_type) == 2 and gene_type[0] in GA.supported_float_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None):
                 self.gene_type = gene_type
                 self.gene_type_single = True
-            # A single data type of int with precision.
+            # A single data type of integer with precision None ([int, None]).
+            elif len(gene_type) == 2 and gene_type[0] in GA.supported_int_types and gene_type[1] is None:
+                self.gene_type = gene_type
+                self.gene_type_single = True
+            # Raise an exception for a single data type of int with integer precision.
             elif len(gene_type) == 2 and gene_type[0] in GA.supported_int_types and (type(gene_type[1]) in GA.supported_int_types or gene_type[1] is None):
                 self.gene_type_single = False
                 raise ValueError(f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.")
@@ -362,10 +366,8 @@ def __init__(self,
                     self.valid_parameters = False
                     raise ValueError(f"When the parameter 'gene_type' is nested, then it can be either [float, int<precision>] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.")
                 for gene_type_idx, gene_type_val in enumerate(gene_type):
-                    if gene_type_val in GA.supported_float_types:
-                        # If the gene type is float and no precision is passed, set it to None.
-                        gene_type[gene_type_idx] = [gene_type_val, None]
-                    elif gene_type_val in GA.supported_int_types:
+                    if gene_type_val in GA.supported_int_float_types:
+                        # If the gene type is float and no precision is passed or an integer, set its precision to None.
                         gene_type[gene_type_idx] = [gene_type_val, None]
                     elif type(gene_type_val) in [list, tuple, numpy.ndarray]:
                         # A float type is expected in a list/tuple/numpy.ndarray of length 2.
@@ -376,6 +378,12 @@ def __init__(self,
                                 else:
                                     self.valid_parameters = False
                                     raise TypeError(f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.")
+                            elif gene_type_val[0] in GA.supported_int_types:
+                                if gene_type_val[1] is None:
+                                    pass
+                                else:
+                                    self.valid_parameters = False
+                                    raise TypeError(f"In the 'gene_type' parameter, either do not set a precision for integer data types or set it to None. But the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.")
                             else:
                                 self.valid_parameters = False
                                 raise TypeError(
@@ -1638,11 +1646,14 @@ def cal_pop_fitness(self):
                     # The functions numpy.any()/numpy.all()/numpy.where()/numpy.equal() are very slow.
                     # So, list membership operator 'in' is used to check if the solution exists in the 'self.solutions' list.
                     # Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'.
-                    # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(self.solutions == numpy.array(sol), axis=1))):
-                    # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(numpy.equal(self.solutions, numpy.array(sol)), axis=1))):
+                    # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(self.solutions == numpy.array(sol), axis=1)))
+                    # if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(numpy.equal(self.solutions, numpy.array(sol)), axis=1)))
                     if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions):
                         solution_idx = self.solutions.index(list(sol))
                         fitness = self.solutions_fitness[solution_idx]
+                    elif (self.save_best_solutions) and (len(self.best_solutions) > 0) and (list(sol) in self.best_solutions):
+                        solution_idx = self.best_solutions.index(list(sol))
+                        fitness = self.best_solutions_fitness[solution_idx]
                     elif (self.keep_elitism > 0) and (self.last_generation_elitism is not None) and (len(self.last_generation_elitism) > 0) and (list(sol) in last_generation_elitism_as_list):
                         # Return the index of the elitism from the elitism array 'self.last_generation_elitism'.
                         # This is not its index within the population. It is just its index in the 'self.last_generation_elitism' array.
@@ -1851,7 +1862,7 @@ def run(self):
 
             # Appending the best solution in the initial population to the best_solutions list.
             if self.save_best_solutions:
-                self.best_solutions.append(best_solution)
+                self.best_solutions.append(list(best_solution))
 
             for generation in range(generation_first_idx, generation_last_idx):
                 if not (self.on_fitness is None):
@@ -2077,7 +2088,7 @@ def run(self):
 
                 # Appending the best solution in the current generation to the best_solutions list.
                 if self.save_best_solutions:
-                    self.best_solutions.append(best_solution)
+                    self.best_solutions.append(list(best_solution))
 
                 # If the on_generation attribute is not None, then cal the callback function after the generation.
                 if not (self.on_generation is None):

pyproject.toml

@@ -9,7 +9,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pygad"
-version = "3.1.0"
+version = "3.1.1"
 description = "PyGAD: A Python Library for Building the Genetic Algorithm and Training Machine Learning Algoithms (Keras & PyTorch)."
 readme = {file = "README.md", content-type = "text/markdown"}
 requires-python = ">=3"

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(  
     name="pygad",  
-    version="3.1.0",  
+    version="3.1.1",  
     author="Ahmed Fawzy Gad",
     install_requires=["numpy", "matplotlib", "cloudpickle",],
     author_email="ahmed.f.gad@gmail.com",