import pygad

import numpy

import os

import matplotlib

# Use Agg backend for headless testing (no GUI needed)

matplotlib.use('Agg')

import matplotlib.pyplot as plt

# Global constants for testing

num_generations = 5

num_parents_mating = 4

sol_per_pop = 10

num_genes = 3

random_seed = 42

def fitness_func(ga_instance, solution, solution_idx):

return numpy.sum(solution**2)

def fitness_func_multi(ga_instance, solution, solution_idx):

return [numpy.sum(solution**2), numpy.sum(solution)]

def test_plot_fitness_parameters():

"""Test all parameters of plot_fitness()."""

ga_instance = pygad.GA(num_generations=num_generations,

num_parents_mating=num_parents_mating,

fitness_func=fitness_func,

sol_per_pop=sol_per_pop,

num_genes=num_genes,

random_seed=random_seed,

suppress_warnings=True

)

ga_instance.run()

# Test different plot types

for p_type in ["plot", "scatter", "bar"]:

fig = ga_instance.plot_fitness(plot_type=p_type,

title=f"Title {p_type}",

xlabel="X", ylabel="Y",

linewidth=2, font_size=12, color="blue")

assert isinstance(fig, matplotlib.figure.Figure)

plt.close(fig)

# Test multi-objective specific parameters

ga_multi = pygad.GA(num_generations=2,

num_parents_mating=2,

fitness_func=fitness_func_multi,

sol_per_pop=5,

num_genes=3,

parent_selection_type="nsga2",

suppress_warnings=True)

ga_multi.run()

fig = ga_multi.plot_fitness(linewidth=[2, 4],

color=["blue", "green"],

label=["Obj A", "Obj B"])

assert isinstance(fig, matplotlib.figure.Figure)

plt.close(fig)

print("test_plot_fitness_parameters passed.")

def test_plot_new_solution_rate_parameters():

"""Test all parameters of plot_new_solution_rate() and its validation."""

ga_instance = pygad.GA(num_generations=num_generations,

num_parents_mating=num_parents_mating,

fitness_func=fitness_func,

sol_per_pop=sol_per_pop,

num_genes=num_genes,

random_seed=random_seed,

save_solutions=True,

suppress_warnings=True

)

ga_instance.run()

# Test different plot types and parameters

for p_type in ["plot", "scatter", "bar"]:

fig = ga_instance.plot_new_solution_rate(title=f"Rate {p_type}",

plot_type=p_type,

linewidth=2, color="purple")

assert isinstance(fig, matplotlib.figure.Figure)

plt.close(fig)

# Validation: Test error when save_solutions=False

ga_instance_no_save = pygad.GA(num_generations=1,

num_parents_mating=1,

fitness_func=fitness_func,

sol_per_pop=5,

num_genes=2,

save_solutions=False,

suppress_warnings=True)

ga_instance_no_save.run()

try:

ga_instance_no_save.plot_new_solution_rate()

except RuntimeError:

print("plot_new_solution_rate validation caught.")

print("test_plot_new_solution_rate_parameters passed.")

def test_plot_genes_parameters():

"""Test all parameters of plot_genes()."""

ga_instance = pygad.GA(num_generations=num_generations,

num_parents_mating=num_parents_mating,

fitness_func=fitness_func,

sol_per_pop=sol_per_pop,

num_genes=num_genes,

random_seed=random_seed,

save_solutions=True,

save_best_solutions=True,

suppress_warnings=True

)

ga_instance.run()

# Test different graph types and parameters

for g_type in ["plot", "boxplot", "histogram"]:

fig = ga_instance.plot_genes(graph_type=g_type, fill_color="yellow", color="black")

assert isinstance(fig, matplotlib.figure.Figure)

plt.close(fig)

# Test solutions="best"

fig = ga_instance.plot_genes(solutions="best")

assert isinstance(fig, matplotlib.figure.Figure)

plt.close(fig)

print("test_plot_genes_parameters passed.")

def test_plot_pareto_front_curve_parameters():

"""Test all parameters of plot_pareto_front_curve() and its validation."""

ga_instance = pygad.GA(num_generations=num_generations,

num_parents_mating=num_parents_mating,

fitness_func=fitness_func_multi,

sol_per_pop=sol_per_pop,

num_genes=num_genes,

random_seed=random_seed,

parent_selection_type="nsga2",

suppress_warnings=True

)

ga_instance.run()

fig = ga_instance.plot_pareto_front_curve(title="Pareto",

linewidth=4,

label="Frontier",

color="red",

color_fitness="black",

grid=False,

alpha=0.5,

marker="x")

assert isinstance(fig, matplotlib.figure.Figure)

plt.close(fig)

# Validation: Test error for single-objective

ga_instance_single = pygad.GA(num_generations=1,

num_parents_mating=1,

fitness_func=fitness_func,

sol_per_pop=5,

num_genes=2,

suppress_warnings=True)

ga_instance_single.run()

try:

ga_instance_single.plot_pareto_front_curve()

except RuntimeError:

print("plot_pareto_front_curve validation (multi-objective required) caught.")

print("test_plot_pareto_front_curve_parameters passed.")

def test_visualize_save_dir():

"""Test save_dir parameter for all methods."""

ga_instance = pygad.GA(num_generations=2,

num_parents_mating=2,

fitness_func=fitness_func,

sol_per_pop=5,

num_genes=2,

save_solutions=True,

suppress_warnings=True

)

ga_instance.run()

methods = [

(ga_instance.plot_fitness, {}),

(ga_instance.plot_new_solution_rate, {}),

(ga_instance.plot_genes, {"graph_type": "plot"})

]

for method, kwargs in methods:

filename = f"test_{method.__name__}.png"

if os.path.exists(filename): os.remove(filename)

method(save_dir=filename, **kwargs)

assert os.path.exists(filename)

os.remove(filename)

print("test_visualize_save_dir passed.")

if __name__ == "__main__":

test_plot_fitness_parameters()

test_plot_new_solution_rate_parameters()

test_plot_genes_parameters()

test_plot_pareto_front_curve_parameters()

test_visualize_save_dir()

print("\nAll visualization tests passed!")