Edit gene_constraint callables

Author: Ahmed Gad

.DS_Store

@@ -25,11 +25,11 @@ def fitness_func(ga_instance, solution, solution_idx):
                        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],
+                       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],
+                                        lambda x,v: [val for val in v if 20<val<40],
                                         None]
                        )
 

examples/example_gene_constraint.py

@@ -1434,8 +1434,15 @@ def initialize_population(self,
                         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]:
+                        if len(filtered_values) ==1 and filtered_values[0] != solution[gene_idx]:
+                            # Error by the user's defined gene constraint callable.
+                            raise Exception(f"It is expected to receive a list/numpy.ndarray from the gene_constraint callable with a single value equal to {values[0]}, but the value {filtered_values[0]} found.")
+
+                        # Check if the gene value does not satisfy the gene constraint.
+                        # Note that we already passed a list of a single value.
+                        # It is expected to receive a list of either a single value or an empty list.
+                        if len(filtered_values) < 1:
+                            # Search for a value that satisfies the gene constraint.
                             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,
@@ -1450,6 +1457,12 @@ def initialize_population(self,
                                     warnings.warn(f"No value satisfied the constraint for the gene at index {gene_idx} with value {solution[gene_idx]} while creating the initial population.")
                             else:
                                 self.population[sol_idx, gene_idx] = random.choice(values_filtered)
+                        elif len(filtered_values) == 1:
+                            # The value already satisfied the gene constraint.
+                            pass
+                        else:
+                            # Error by the user's defined gene constraint callable.
+                            raise Exception(f"It is expected to receive a list/numpy.ndarray from the gene_constraint callable that is either empty or has a single value equal, but received a list/numpy.ndarray of length {len(filtered_values)}.")
 
         # 4) Solve duplicate genes.
         if allow_duplicate_genes == False:

pygad/.DS_Store

@@ -69,7 +69,11 @@ def fitness_func_no_batch_multi(ga, solution, idx):
 
 #### Single-Objective
 def test_initial_population_int_by_replacement():
-    gene_constraint=[lambda x: x[0]>=8,lambda x: x[1]>=8,lambda x: 5>=x[2]>=1,lambda x: 5>x[3]>3,lambda x: x[4]<2]
+    gene_constraint=[lambda x,v: [val for val in v if val>=8],
+                     lambda x,v: [val for val in v if val>=8],
+                     lambda x,v: [val for val in v if 5>=val>=1],
+                     lambda x,v: [val for val in v if 5>val>3],
+                     lambda x,v: [val for val in v if val<2]]
     ga_instance = population_gene_constraint(gene_constraint=gene_constraint,
                                              init_range_low=0,
                                              init_range_high=10,
@@ -79,17 +83,21 @@ def test_initial_population_int_by_replacement():
                                              gene_type=int,
                                              mutation_by_replacement=True)
     initial_population = ga_instance.initial_population
-    # print(initial_population)
 
     assert numpy.all(initial_population[:, 0] >= 8), "Not all values in column 0 are >= 8"
     assert numpy.all(initial_population[:, 1] >= 8), "Not all values in column 1 are >= 8"
     assert numpy.all(initial_population[:, 2] >= 1), "Not all values in column 2 are >= 1"
     assert numpy.all((initial_population[:, 2] >= 1) & (initial_population[:, 2] <= 5)), "Not all values in column 2 between 1 and 5 (inclusive)"
     assert numpy.all(initial_population[:, 3] == 4), "Not all values in column 3 between 3 and 5 (exclusive)"
     assert numpy.all(initial_population[:, 4] < 2), "Not all values in column 4 < 2"
+    print("All constraints are met")
 
 def test_initial_population_int_by_replacement_no_duplicates():
-    gene_constraint=[lambda x: x[0]>=5,lambda x: x[1]>=5,lambda x: x[2]>=5,lambda x: x[3]>=5,lambda x: x[4]>=5]
+    gene_constraint=[lambda x,v: [val for val in v if val>=5],
+                     lambda x,v: [val for val in v if val>=5],
+                     lambda x,v: [val for val in v if val>=5],
+                     lambda x,v: [val for val in v if val>=5],
+                     lambda x,v: [val for val in v if val>=5]]
     ga_instance = population_gene_constraint(gene_constraint=gene_constraint,
                                              init_range_low=1,
                                              init_range_high=10,
@@ -117,9 +125,15 @@ def test_initial_population_int_by_replacement_no_duplicates():
     assert numpy.all(initial_population[:, 2] >= 5), "Not all values in column 2 >= 5"
     assert numpy.all(initial_population[:, 3] >= 5), "Not all values in column 3 >= 5"
     assert numpy.all(initial_population[:, 4] >= 5), "Not all values in column 4 >= 5"
+    print("All constraints are met")
 
 def test_initial_population_int_by_replacement_no_duplicates2():
-    gene_constraint=[lambda x: x[0]>=98,lambda x: x[1]>=98,lambda x: 20<x[2]<40,lambda x: x[3]<40,lambda x: x[4]<50,lambda x: x[5]<100]
+    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 20<val<40],
+                     lambda x,v: [val for val in v if val<40],
+                     lambda x,v: [val for val in v if val<50],
+                     lambda x,v: [val for val in v if val<100]]
     ga_instance = population_gene_constraint(gene_constraint=gene_constraint,
                                              random_mutation_min_val=1,
                                              random_mutation_max_val=100,
@@ -149,9 +163,14 @@ def test_initial_population_int_by_replacement_no_duplicates2():
     assert numpy.all(initial_population[:, 3] < 40), "Not all values in column 3 < 40"
     assert numpy.all(initial_population[:, 4] < 50), "Not all values in column 4 < 50"
     assert numpy.all(initial_population[:, 5] < 100), "Not all values in column 4 < 100"
+    print("All constraints are met")
 
 def test_initial_population_float_by_replacement_no_duplicates():
-    gene_constraint=[lambda x: x[0]>=5,lambda x: x[1]>=5,lambda x: x[2]>=5,lambda x: x[3]>=5,lambda x: x[4]>=5]
+    gene_constraint=[lambda x,v: [val for val in v if val>=5],
+                     lambda x,v: [val for val in v if val>=5],
+                     lambda x,v: [val for val in v if val>=5],
+                     lambda x,v: [val for val in v if val>=5],
+                     lambda x,v: [val for val in v if val>=5]]
     ga_instance = population_gene_constraint(gene_constraint=gene_constraint,
                                              init_range_low=1,
                                              init_range_high=10,
@@ -178,9 +197,48 @@ def test_initial_population_float_by_replacement_no_duplicates():
     assert numpy.all(initial_population[:, 2] >= 5), "Not all values in column 2 >= 5"
     assert numpy.all(initial_population[:, 3] >= 5), "Not all values in column 3 >= 5"
     assert numpy.all(initial_population[:, 4] >= 5), "Not all values in column 4 >= 5"
+    print("All constraints are met")
 
 def test_initial_population_float_by_replacement_no_duplicates2():
-    gene_constraint=[lambda x: x[0]>=1,lambda x: x[1]>=1,lambda x: x[2]>=1,lambda x: x[3]>=1,lambda x: x[4]>=1]
+    gene_constraint=[lambda x,v: [val for val in v if val>=1],
+                     lambda x,v: [val for val in v if val>=1],
+                     lambda x,v: [val for val in v if val>=1],
+                     lambda x,v: [val for val in v if val>=1],
+                     lambda x,v: [val for val in v if val>=1]]
+    ga_instance = population_gene_constraint(gene_constraint=gene_constraint,
+                                             init_range_low=1,
+                                             init_range_high=2,
+                                             gene_type=[float, 1],
+                                             num_genes=5,
+                                             crossover_type=None,
+                                             mutation_by_replacement=False,
+                                             allow_duplicate_genes=False)
+
+    num_duplicates = 0
+    for idx, solution in enumerate(ga_instance.solutions):
+        num = len(solution) - len(set(solution))
+        if num != 0:
+            print(solution, idx)
+        num_duplicates += num
+
+    assert num_duplicates == 0
+
+    initial_population = ga_instance.initial_population
+    # print(initial_population)
+
+    assert numpy.all(initial_population[:, 0] >= 1), "Not all values in column 0 >= 1"
+    assert numpy.all(initial_population[:, 1] >= 1), "Not all values in column 1 >= 1"
+    assert numpy.all(initial_population[:, 2] >= 1), "Not all values in column 2 >= 1"
+    assert numpy.all(initial_population[:, 3] >= 1), "Not all values in column 3 >= 1"
+    assert numpy.all(initial_population[:, 4] >= 1), "Not all values in column 4 >= 1"
+    print("All constraints are met")
+
+def test_initial_population_float_by_replacement_no_duplicates_None_constraints():
+    gene_constraint=[lambda x,v: [val for val in v if val>=1],
+                     None,
+                     lambda x,v: [val for val in v if val>=1],
+                     None,
+                     lambda x,v: [val for val in v if val>=1]]
     ga_instance = population_gene_constraint(gene_constraint=gene_constraint,
                                              init_range_low=1,
                                              init_range_high=2,
@@ -207,6 +265,7 @@ def test_initial_population_float_by_replacement_no_duplicates2():
     assert numpy.all(initial_population[:, 2] >= 1), "Not all values in column 2 >= 1"
     assert numpy.all(initial_population[:, 3] >= 1), "Not all values in column 3 >= 1"
     assert numpy.all(initial_population[:, 4] >= 1), "Not all values in column 4 >= 1"
+    print("All constraints are met")
 
 if __name__ == "__main__":
     #### Single-objective
@@ -221,3 +280,5 @@ def test_initial_population_float_by_replacement_no_duplicates2():
     print()
     test_initial_population_float_by_replacement_no_duplicates2()
     print()
+    test_initial_population_float_by_replacement_no_duplicates_None_constraints()
+    print()