Improve gene_constraint

README.md

@@ -7,7 +7,7 @@
 Check documentation of the [PyGAD](https://pygad.readthedocs.io/en/latest).
 
 [![PyPI Downloads](https://pepy.tech/badge/pygad)](https://pepy.tech/project/pygad) [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/pygad.svg?label=Conda%20Downloads)](
-https://anaconda.org/conda-forge/PyGAD) [![PyPI version](https://badge.fury.io/py/pygad.svg)](https://badge.fury.io/py/pygad) ![Docs](https://readthedocs.org/projects/pygad/badge) [![PyGAD PyTest / Python 3.11](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py311.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py311.yml) [![PyGAD PyTest / Python 3.10](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py310.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py310.yml) [![PyGAD PyTest / Python 3.9](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py39.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py39.yml) [![PyGAD PyTest / Python 3.8](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py38.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py38.yml) [![PyGAD PyTest / Python 3.7](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py37.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py37.yml) [![License](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![Translation](https://hosted.weblate.org/widgets/weblate/-/svg-badge.svg)](https://hosted.weblate.org/engage/weblate/) [![REUSE](https://api.reuse.software/badge/github.com/WeblateOrg/weblate)](https://api.reuse.software/info/github.com/WeblateOrg/weblate) [![Stack Overflow](https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg)](
+https://anaconda.org/conda-forge/PyGAD) [![PyPI version](https://badge.fury.io/py/pygad.svg)](https://badge.fury.io/py/pygad)![Docs](https://readthedocs.org/projects/pygad/badge)[![PyGAD PyTest / Python 3.13](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py313.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py313.yml) [![PyGAD PyTest / Python 3.12](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py312.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py312.yml) [![PyGAD PyTest / Python 3.11](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py311.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py311.yml) [![PyGAD PyTest / Python 3.10](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py310.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py310.yml) [![PyGAD PyTest / Python 3.9](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py39.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py39.yml) [![PyGAD PyTest / Python 3.8](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py38.yml/badge.svg)](https://github.com/ahmedfgad/GeneticAlgorithmPython/actions/workflows/main_py38.yml) [![License](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) [![Translation](https://hosted.weblate.org/widgets/weblate/-/svg-badge.svg)](https://hosted.weblate.org/engage/weblate/) [![REUSE](https://api.reuse.software/badge/github.com/WeblateOrg/weblate)](https://api.reuse.software/info/github.com/WeblateOrg/weblate) [![Stack Overflow](https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg)](
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 ![PYGAD-LOGO](https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png)
@@ -253,16 +253,6 @@ Get started with the genetic algorithm by reading the tutorial titled [**Introdu
 
 [![Introduction to Genetic Algorithm](https://user-images.githubusercontent.com/16560492/82078259-26252d00-96e1-11ea-9a02-52a99e1054b9.jpg)](https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad)
 
-## Tutorial: Build Neural Networks in Python
-
-Read about building neural networks in Python through the tutorial titled [**Artificial Neural Network Implementation using NumPy and Classification of the Fruits360 Image Dataset**](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad) available at these links:
-
-* [LinkedIn](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad)
-* [Towards Data Science](https://towardsdatascience.com/artificial-neural-network-implementation-using-numpy-and-classification-of-the-fruits360-image-3c56affa4491)
-* [KDnuggets](https://www.kdnuggets.com/2019/02/artificial-neural-network-implementation-using-numpy-and-image-classification.html)
-
-[![Building Neural Networks Python](https://user-images.githubusercontent.com/16560492/82078281-30472b80-96e1-11ea-8017-6a1f4383d602.jpg)](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad)
-
 ## Tutorial: Optimize Neural Networks with Genetic Algorithm
 
 Read about training neural networks using the genetic algorithm through the tutorial titled [**Artificial Neural Networks Optimization using Genetic Algorithm with Python**](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad) available at these links:
@@ -273,29 +263,6 @@ Read about training neural networks using the genetic algorithm through the tuto
 
 [![Training Neural Networks using Genetic Algorithm Python](https://user-images.githubusercontent.com/16560492/82078300-376e3980-96e1-11ea-821c-aa6b8ceb44d4.jpg)](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad)
 
-## Tutorial: Building CNN in Python
-
-To start with coding the genetic algorithm, you can check the tutorial titled [**Building Convolutional Neural Network using NumPy from Scratch**](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad) available at these links:
-
-- [LinkedIn](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)
-- [Towards Data Science](https://towardsdatascience.com/building-convolutional-neural-network-using-numpy-from-scratch-b30aac50e50a)
-- [KDnuggets](https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html)
-- [Chinese Translation](http://m.aliyun.com/yunqi/articles/585741)
-
-[This tutorial](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)) is prepared based on a previous version of the project but it still a good resource to start with coding CNNs.
-
-[![Building CNN in Python](https://user-images.githubusercontent.com/16560492/82431022-6c3a1200-9a8e-11ea-8f1b-b055196d76e3.png)](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)
-
-## Tutorial: Derivation of CNN from FCNN
-
-Get started with the genetic algorithm by reading the tutorial titled [**Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step**](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad) which is available at these links:
-
-* [LinkedIn](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad)
-* [Towards Data Science](https://towardsdatascience.com/derivation-of-convolutional-neural-network-from-fully-connected-network-step-by-step-b42ebafa5275)
-* [KDnuggets](https://www.kdnuggets.com/2018/04/derivation-convolutional-neural-network-fully-connected-step-by-step.html)
-
-[![Derivation of CNN from FCNN](https://user-images.githubusercontent.com/16560492/82431369-db176b00-9a8e-11ea-99bd-e845192873fc.png)](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad)
-
 ## Book: Practical Computer Vision Applications Using Deep Learning with CNNs
 
 You can also check my book cited as [**Ahmed Fawzy Gad 'Practical Computer Vision Applications Using Deep Learning with CNNs'. Dec. 2018, Apress, 978-1-4842-4167-7**](https://www.amazon.com/Practical-Computer-Vision-Applications-Learning/dp/1484241665) which discusses neural networks, convolutional neural networks, deep learning, genetic algorithm, and more.

examples/example_gene_constraint.py

@@ -0,0 +1,37 @@
+import pygad
+import numpy
+
+"""
+An example of using the gene_constraint parameter.
+
+"""
+
+function_inputs = [4,-2,3.5,5,-11,-4.7]
+desired_output = 44
+
+def fitness_func(ga_instance, solution, solution_idx):
+    output = numpy.sum(solution*function_inputs)
+    fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
+    return fitness
+
+ga_instance = pygad.GA(num_generations=100,
+                       num_parents_mating=5,
+                       sol_per_pop=10,
+                       num_genes=len(function_inputs),
+                       mutation_num_genes=6,
+                       fitness_func=fitness_func,
+                       allow_duplicate_genes=False,
+                       gene_space=range(100),
+                       gene_type=int,
+                       sample_size=100,
+                       random_seed=10,
+                       gene_constraint=[lambda x,v: [val for val in v if val>=98],
+                                        lambda x,v: [val for val in v if val>=98],
+                                        lambda x,v: [val for val in v if 80<val<90],
+                                        None,
+                                        lambda x,v: [val for val in v if 20<val<40],
+                                        None]
+                       )
+
+ga_instance.run()
+print(ga_instance.population)

pygad/helper/misc.py

@@ -128,6 +128,20 @@ def mutation_change_gene_dtype_and_round(self,
                                                           gene_value=gene_value)
         return gene_value_new
 
+    def validate_gene_constraint_callable_output(self,
+                                                 selected_values,
+                                                 values):
+        if type(selected_values) in [list, numpy.ndarray]:
+            selected_values_set = set(selected_values)
+            if selected_values_set.issubset(values):
+                pass
+            else:
+                return False
+        else:
+            return False
+
+        return True
+
     def filter_gene_values_by_constraint(self,
                                          values,
                                          solution,
@@ -144,30 +158,30 @@ def filter_gene_values_by_constraint(self,
         It returns None if no values satisfy the constraint. Otherwise, an array of values that satisfy the constraint is returned.
         """
 
-        # A list of the indices where the random values satisfy the constraint.
-        filtered_values_indices = []
+        if self.gene_constraint and self.gene_constraint[gene_idx]:
+            pass
+        else:
+            raise Exception(f"Either the gene at index {gene_idx} is not assigned a callable/function or the gene_constraint itself is not used.")
+
         # A temporary solution to avoid changing the original solution.
         solution_tmp = solution.copy()
-        # Loop through the random values to filter the ones satisfying the constraint.
-        for value_idx, value in enumerate(values):
-            solution_tmp[gene_idx] = value
-            # Check if the constraint is satisfied.
-            if self.gene_constraint[gene_idx](solution_tmp):
-                # The current value satisfies the constraint.
-                filtered_values_indices.append(value_idx)
+        filtered_values = self.gene_constraint[gene_idx](solution_tmp, values.copy())
+        result = self.validate_gene_constraint_callable_output(selected_values=filtered_values,
+                                                               values=values)
+        if result:
+            pass
+        else:
+            raise Exception("The output from the gene_constraint callable/function must be a list or NumPy array that is subset of the passed values (second argument).")
 
         # After going through all the values, check if any value satisfies the constraint.
-        if len(filtered_values_indices) > 0:
+        if len(filtered_values) > 0:
             # At least one value was found that meets the gene constraint.
             pass
         else:
             # No value found for the current gene that satisfies the constraint.
-            if not self.suppress_warnings:
-                warnings.warn(f"No value found for the gene at index {gene_idx} with value {solution[gene_idx]} that satisfies its gene constraint.")
+            if not self.suppress_warnings: warnings.warn(f"Failed to find a value that satisfies its gene constraint for the gene at index {gene_idx} with value {solution[gene_idx]} at generation {self.generations_completed}.")
             return None
 
-        filtered_values = values[filtered_values_indices]
-
         return filtered_values
 
     def get_gene_dtype(self, gene_index):

pygad/helper/unique.py

@@ -69,7 +69,7 @@ def solve_duplicate_genes_randomly(self,
 
                 if temp_val in new_solution:
                     num_unsolved_duplicates = num_unsolved_duplicates + 1
-                    if not self.suppress_warnings: warnings.warn(f"Failed to find a unique value for gene with index {duplicate_index} whose value is {solution[duplicate_index]}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.")
+                    if not self.suppress_warnings: warnings.warn(f"Failed to find a unique value for gene with index {duplicate_index} whose value is {solution[duplicate_index]} at generation {self.generations_completed}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.")
                 else:
                     # Unique gene value found.
                     new_solution[duplicate_index] = temp_val
@@ -320,7 +320,7 @@ def unique_genes_by_space(self,
 
             if temp_val in solution:
                 num_unsolved_duplicates = num_unsolved_duplicates + 1
-                if not self.suppress_warnings: warnings.warn(f"Failed to find a unique value for gene with index {duplicate_index} whose value is {solution[duplicate_index]}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.")
+                if not self.suppress_warnings: warnings.warn(f"Failed to find a unique value for gene with index {duplicate_index} whose value is {solution[duplicate_index]} at generation {self.generations_completed+1}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.")
             else:
                 solution[duplicate_index] = temp_val