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Refactor code to remove duplicate sections
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@ahmedfgad
ahmedfgad committed Dec 8, 2024
1 parent f492bb3 commit c6949e1
Showing 1 changed file with 140 additions and 72 deletions.
212 changes: 140 additions & 72 deletions pygad/helper/unique.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ def solve_duplicate_genes_randomly(self,
max_val (int): The maximum value of the range to sample a number randomly.
mutation_by_replacement (bool): Indicates if mutation is performed by replacement.
gene_type (type): The data type of the gene (e.g., int, float).
num_trials (int): The maximum number of attempts to resolve duplicates by changing the gene values.
num_trials (int): The maximum number of attempts to resolve duplicates by changing the gene values. Only works for floating-point gene types.
Returns:
tuple:
Expand All @@ -42,53 +42,48 @@ def solve_duplicate_genes_randomly(self,
num_unsolved_duplicates = 0
if len(not_unique_indices) > 0:
for duplicate_index in not_unique_indices:
for trial_index in range(num_trials):
if self.gene_type_single == True:
dtype = gene_type
else:
dtype = gene_type[duplicate_index]

if dtype[0] in pygad.GA.supported_int_types:
temp_val = self.unique_int_gene_from_range(solution=new_solution,
gene_index=duplicate_index,
min_val=min_val,
max_val=max_val,
mutation_by_replacement=mutation_by_replacement,
gene_type=gene_type)
else:
temp_val = numpy.random.uniform(low=min_val,
high=max_val,
size=1)[0]
if mutation_by_replacement:
if self.gene_type_single == True:
dtype = gene_type
else:
dtype = gene_type[duplicate_index]

if dtype[0] in pygad.GA.supported_int_types:
temp_val = self.unique_int_gene_from_range(solution=new_solution,
gene_index=duplicate_index,
min_val=min_val,
max_val=max_val,
mutation_by_replacement=mutation_by_replacement,
gene_type=gene_type)
else:
temp_val = self.unique_float_gene_from_range(solution=new_solution,
gene_index=duplicate_index,
min_val=min_val,
max_val=max_val,
mutation_by_replacement=mutation_by_replacement,
gene_type=gene_type,
num_trials=num_trials)
"""
temp_val = numpy.random.uniform(low=min_val,
high=max_val,
size=1)[0]
if mutation_by_replacement:
pass
else:
else:
temp_val = new_solution[duplicate_index] + temp_val
"""

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.")
else:
# Unique gene value found.
new_solution[duplicate_index] = temp_val

# Update the list of duplicate indices after each iteration.
_, unique_gene_indices = numpy.unique(new_solution, return_index=True)
not_unique_indices = set(range(len(solution))) - set(unique_gene_indices)
# self.logger.info("not_unique_indices INSIDE", not_unique_indices)

# Similar to the round_genes() method in the pygad module,
# Create a round_gene() method to round a single gene.
if not dtype[1] is None:
temp_val = numpy.round(dtype[0](temp_val),
dtype[1])
else:
temp_val = dtype[0](temp_val)

if temp_val in new_solution and trial_index == (num_trials - 1):
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.")
elif temp_val in new_solution:
# Keep trying in the other remaining trials.
continue
else:
# Unique gene value found.
new_solution[duplicate_index] = temp_val
break

# TODO Move this code outside the loops.
# Update the list of duplicate indices after each iteration.
_, unique_gene_indices = numpy.unique(new_solution, return_index=True)
not_unique_indices = set(range(len(solution))) - set(unique_gene_indices)
# self.logger.info("not_unique_indices INSIDE", not_unique_indices)

return new_solution, not_unique_indices, num_unsolved_duplicates

def solve_duplicate_genes_by_space(self,
Expand Down Expand Up @@ -167,14 +162,14 @@ def unique_int_gene_from_range(self,
Args:
solution (list): A solution containing genes, potentially with duplicate values.
gene_index (int): The index of the gene for which to find a unique value.
min_val (int): The minimum value of the range to sample a number randomly.
max_val (int): The maximum value of the range to sample a number randomly.
min_val (int): The minimum value of the range to sample an integer randomly.
max_val (int): The maximum value of the range to sample an integer randomly.
mutation_by_replacement (bool): Indicates if mutation is performed by replacement.
gene_type (type): The data type of the gene (e.g., int, float).
gene_type (type): The data type of the gene (e.g., int, int8, uint16, etc).
step (int, optional): The step size for generating candidate values. Defaults to 1.
Returns:
int: The new value of the gene. If no unique value can be found, the original gene value is returned.
int: The new integer value of the gene. If no unique value can be found, the original gene value is returned.
"""

# The gene_type is of the form [type, precision]
Expand All @@ -194,22 +189,86 @@ def unique_int_gene_from_range(self,
else:
all_gene_values = all_gene_values + solution[gene_index]

# After adding solution[gene_index] to the list, we have to change the data type again.
# TODO: The gene data type is converted twine. One above and one here.
all_gene_values = numpy.asarray(all_gene_values,
dtype)
# After adding solution[gene_index] to the list, we have to change the data type again.
all_gene_values = numpy.asarray(all_gene_values,
dtype)

values_to_select_from = list(set(list(all_gene_values)) - set(solution))

if len(values_to_select_from) == 0:
# If there are no values, then keep the current gene value.
if not self.suppress_warnings: warnings.warn("You set 'allow_duplicate_genes=False' but there is no enough values to prevent duplicates.")
selected_value = solution[gene_index]
else:
selected_value = random.choice(values_to_select_from)

selected_value = dtype[0](selected_value)

return selected_value

def unique_float_gene_from_range(self,
solution,
gene_index,
min_val,
max_val,
mutation_by_replacement,
gene_type,
num_trials=10):

"""
Finds a unique floating-point value for a specific gene in a solution.
Args:
solution (list): A solution containing genes, potentially with duplicate values.
gene_index (int): The index of the gene for which to find a unique value.
min_val (int): The minimum value of the range to sample a floating-point number randomly.
max_val (int): The maximum value of the range to sample a floating-point number randomly.
mutation_by_replacement (bool): Indicates if mutation is performed by replacement.
gene_type (type): The data type of the gene (e.g., float, float16, float32, etc).
num_trials (int): The maximum number of attempts to resolve duplicates by changing the gene values.
Returns:
int: The new floating-point value of the gene. If no unique value can be found, the original gene value is returned.
"""

# The gene_type is of the form [type, precision]
dtype = gene_type

for trial_index in range(num_trials):
temp_val = numpy.random.uniform(low=min_val,
high=max_val,
size=1)[0]

# If mutation is by replacement, do not add the current gene value into the list.
# This is to avoid replacing the value by itself again. We are doing nothing in this case.
if mutation_by_replacement:
pass
else:
temp_val = temp_val + solution[gene_index]

if not dtype[1] is None:
# Precision is available and we have to round the number.
# Convert the data type and round the number.
temp_val = numpy.round(dtype[0](temp_val),
dtype[1])
else:
# There is no precision and rounding the number is not needed. The type is [type, None]
# Just convert the data type.
temp_val = dtype[0](temp_val)

if temp_val in solution and trial_index == (num_trials - 1):
# If there are no values, then keep the current gene value.
if not self.suppress_warnings: warnings.warn("You set 'allow_duplicate_genes=False' but cannot find a value to prevent duplicates.")
selected_value = solution[gene_index]
elif temp_val in solution:
# Keep trying in the other remaining trials.
continue
else:
# Unique gene value found.
selected_value = temp_val
break

return selected_value

def unique_genes_by_space(self,
new_solution,
gene_type,
Expand All @@ -225,7 +284,7 @@ def unique_genes_by_space(self,
new_solution (list): A solution containing genes with duplicate values.
gene_type (type): The data type of the gene (e.g., int, float).
not_unique_indices (list): The indices of genes with duplicate values.
num_trials (int): The maximum number of attempts to resolve duplicates for each gene.
num_trials (int): The maximum number of attempts to resolve duplicates for each gene. Only works for floating-point numbers.
Returns:
tuple:
Expand All @@ -236,22 +295,18 @@ def unique_genes_by_space(self,

num_unsolved_duplicates = 0
for duplicate_index in not_unique_indices:
for trial_index in range(num_trials):
temp_val = self.unique_gene_by_space(solution=new_solution,
gene_idx=duplicate_index,
gene_type=gene_type,
build_initial_pop=build_initial_pop)

if temp_val in new_solution and trial_index == (num_trials - 1):
# self.logger.info("temp_val, duplicate_index", temp_val, duplicate_index, 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 {new_solution[duplicate_index]}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.")
elif temp_val in new_solution:
continue
else:
new_solution[duplicate_index] = temp_val
# self.logger.info("SOLVED", duplicate_index)
break
temp_val = self.unique_gene_by_space(solution=new_solution,
gene_idx=duplicate_index,
gene_type=gene_type,
build_initial_pop=build_initial_pop,
num_trials=num_trials)

if temp_val in new_solution:
# self.logger.info("temp_val, duplicate_index", temp_val, duplicate_index, 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 {new_solution[duplicate_index]}. Consider adding more values in the gene space or use a wider range for initial population or random mutation.")
else:
new_solution[duplicate_index] = temp_val

# Update the list of duplicate indices after each iteration.
_, unique_gene_indices = numpy.unique(new_solution, return_index=True)
Expand All @@ -264,7 +319,8 @@ def unique_gene_by_space(self,
solution,
gene_idx,
gene_type,
build_initial_pop=False):
build_initial_pop=False,
num_trials=10):

"""
Returns a unique value for a specific gene based on its value space to resolve duplicates.
Expand All @@ -273,6 +329,7 @@ def unique_gene_by_space(self,
solution (list): A solution containing genes with duplicate values.
gene_idx (int): The index of the gene that has a duplicate value.
gene_type (type): The data type of the gene (e.g., int, float).
num_trials (int): The maximum number of attempts to resolve duplicates for each gene. Only works for floating-point numbers.
Returns:
Any: A unique value for the gene, if one exists; otherwise, the original gene value. """
Expand Down Expand Up @@ -320,9 +377,20 @@ def unique_gene_by_space(self,
low = self.random_mutation_min_val
high = self.random_mutation_max_val

"""
value_from_space = numpy.random.uniform(low=low,
high=high,
size=1)[0]
"""

value_from_space = self.unique_float_gene_from_range(solution=solution,
gene_index=gene_idx,
min_val=low,
max_val=high,
mutation_by_replacement=True,
gene_type=dtype,
num_trials=num_trials)


elif type(curr_gene_space) is dict:
if self.gene_type_single == True:
Expand Down

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