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196 lines (172 loc) · 7.77 KB
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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!")