Accept gene values in gene_constraint callables

Author: Ahmed Gad

README.md

@@ -7,7 +7,7 @@
 Check documentation of the [PyGAD](https://pygad.readthedocs.io/en/latest).
 
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.DS_Store renamed to docs/.DS_Store

@@ -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)

examples/example_gene_constraint.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/misc.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
 

pygad/helper/unique.py

@@ -545,13 +545,13 @@ def __init__(self,
                             if item is None:
                                 pass
                             elif item and callable(item):
-                                if item.__code__.co_argcount == 1:
-                                    # Every callable is valid if it receives a single argument.
-                                    # This argument represents the solution.
+                                if item.__code__.co_argcount == 2:
+                                    # Every callable is valid if it receives 2 arguments.
+                                    # The 2 arguments: 1) solution 2) A list or numpy.ndarray of values to check if they meet the constraint.
                                     pass
                                 else:
                                     self.valid_parameters = False
-                                    raise ValueError(f"Every callable inside the gene_constraint parameter must accept a single argument representing the solution/chromosome. But the callable at index {constraint_idx} named '{item.__code__.co_name}' accepts {item.__code__.co_argcount} argument(s).")
+                                    raise ValueError(f"Every callable inside the gene_constraint parameter must accept 2 arguments representing 1) The solution/chromosome where the gene exists 2) A list of NumPy array of values to check if they meet the constraint. But the callable at index {constraint_idx} named '{item.__code__.co_name}' accepts {item.__code__.co_argcount} argument(s).")
                             else:
                                 self.valid_parameters = False
                                 raise TypeError(f"The expected type of an element in the 'gene_constraint' parameter is None or a callable (e.g. function). But {item} at index {constraint_idx} of type {type(item)} found.")
@@ -1424,7 +1424,18 @@ def initialize_population(self,
                 for gene_idx in range(self.num_genes):
                     # Check that a constraint is available for the gene and that the current value does not satisfy that constraint
                     if self.gene_constraint[gene_idx]:
-                        if not self.gene_constraint[gene_idx](solution):
+                        # Remember that the second argument to the gene constraint callable is a list/numpy.ndarray of the values to check if they meet the gene constraint.
+                        values = [solution[gene_idx]]
+                        filtered_values = self.gene_constraint[gene_idx](solution, values)
+                        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).")
+
+                        # Check if the gene value satisfies the gene constraint.
+                        if len(filtered_values) == 1 and filtered_values[0] == solution[gene_idx]:
                             range_min, range_max = self.get_initial_population_range(gene_index=gene_idx)
                             # While initializing the population, we follow a mutation by replacement approach. So, the original gene value is not needed.
                             values_filtered = self.get_valid_gene_constraint_values(range_min=range_min,