def test_gp_prune_nodes():
new_gp = gp.GP()
n_nodes = new_gp._prune_nodes(10)
assert n_nodes == 10
new_gp.prunning_ratio = 1
n_nodes = new_gp._prune_nodes(10)
assert n_nodes == 2
def test_gp_update():
new_gp = gp.GP()
tree_space = tree.TreeSpace(n_agents=10,
n_terminals=2,
n_variables=1,
min_depth=1,
max_depth=2,
functions=['SUM', 'SUB', 'MUL', 'DIV'],
lower_bound=[0],
upper_bound=[10])
new_gp.update(tree_space)
def test_gp_cross():
new_gp = gp.GP()
tree_space = tree.TreeSpace(n_agents=10,
n_terminals=2,
n_variables=1,
min_depth=1,
max_depth=5,
functions=['SUM'],
lower_bound=[0],
upper_bound=[10])
new_gp._cross(tree_space.trees[0], tree_space.trees[1], 1, 1)
def test_gp_reproduction():
new_gp = gp.GP()
tree_space = tree.TreeSpace(n_agents=10,
n_terminals=2,
n_variables=1,
min_depth=1,
max_depth=5,
functions=['SUM'],
lower_bound=[0],
upper_bound=[10])
new_gp._reproduction(tree_space)
def test_gp_params_setter():
new_gp = gp.GP()
try:
new_gp.p_reproduction = "a"
except:
new_gp.p_reproduction = 0.75
try:
new_gp.p_reproduction = -1
except:
new_gp.p_reproduction = 0.75
assert new_gp.p_reproduction == 0.75
try:
new_gp.p_mutation = "b"
except:
new_gp.p_mutation = 0.5
try:
new_gp.p_mutation = -1
except:
new_gp.p_mutation = 0.5
assert new_gp.p_mutation == 0.5
try:
new_gp.p_crossover = "c"
except:
new_gp.p_crossover = 0.25
try:
new_gp.p_crossover = -1
except:
new_gp.p_crossover = 0.25
assert new_gp.p_crossover == 0.25
try:
new_gp.prunning_ratio = "d"
except:
new_gp.prunning_ratio = 0.25
try:
new_gp.prunning_ratio = -1
except:
new_gp.prunning_ratio = 0.25
assert new_gp.prunning_ratio == 0.25
def test_gp_params_setter():
new_gp = gp.GP()
try:
new_gp.p_reproduction = 'a'
except:
new_gp.p_reproduction = 0.75
try:
new_gp.p_reproduction = -1
except:
new_gp.p_reproduction = 0.75
assert new_gp.p_reproduction == 0.75
try:
new_gp.p_mutation = 'b'
except:
new_gp.p_mutation = 0.5
try:
new_gp.p_mutation = -1
except:
new_gp.p_mutation = 0.5
assert new_gp.p_mutation == 0.5
try:
new_gp.p_crossover = 'c'
except:
new_gp.p_crossover = 0.25
try:
new_gp.p_crossover = -1
except:
new_gp.p_crossover = 0.25
assert new_gp.p_crossover == 0.25
try:
new_gp.prunning_ratio = 'd'
except:
new_gp.prunning_ratio = 0.25
try:
new_gp.prunning_ratio = -1
except:
new_gp.prunning_ratio = 0.25
assert new_gp.prunning_ratio == 0.25
def test_gp_update():
new_gp = gp.GP()
tree_space = tree.TreeSpace(
n_agents=10,
n_terminals=2,
n_variables=1,
min_depth=1,
max_depth=2,
functions=["SUM", "SUB", "MUL", "DIV"],
lower_bound=[0],
upper_bound=[10],
)
new_gp.update(tree_space)
def test_gp_crossover():
new_gp = gp.GP()
tree_space = tree.TreeSpace(
n_agents=10,
n_terminals=2,
n_variables=1,
min_depth=1,
max_depth=5,
functions=["SUM"],
lower_bound=[0],
upper_bound=[10],
)
new_gp._crossover(tree_space)
def test_gp_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_gp = gp.GP()
tree_space = tree.TreeSpace(n_trees=10,
n_terminals=2,
n_variables=1,
n_iterations=500,
min_depth=1,
max_depth=2,
functions=['SUM', 'SUB', 'MUL', 'DIV'],
lower_bound=[0],
upper_bound=[10])
history = new_gp.run(tree_space, new_function, pre_evaluation=hook)
print(tree_space.best_tree)
print(tree_space.best_tree.post_order)
tree_space = tree.TreeSpace(
n_trees=10,
n_terminals=2,
n_variables=1,
n_iterations=500,
min_depth=2,
max_depth=3,
functions=['EXP', 'LOG', 'SQRT', 'ABS', 'COS', 'SIN'],
lower_bound=[0],
upper_bound=[10])
history = new_gp.run(tree_space, new_function, pre_evaluation=hook)
print(tree_space.best_tree)
print(tree_space.best_tree.post_order)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
best_fitness = history.best_agent[-1][1]
assert best_fitness <= constants.TEST_EPSILON, 'The algorithm gp failed to converge.'
def test_gp_params():
params = {
'p_reproduction': 1.0,
'p_mutation': 0.5,
'p_crossover': 0.5,
'prunning_ratio': 0.5
}
new_gp = gp.GP(params=params)
assert new_gp.p_reproduction == 1.0
assert new_gp.p_mutation == 0.5
assert new_gp.p_crossover == 0.5
assert new_gp.prunning_ratio == 0.5
def test_gp_params():
params = {
"p_reproduction": 1.0,
"p_mutation": 0.5,
"p_crossover": 0.5,
"prunning_ratio": 0.5,
}
new_gp = gp.GP(params=params)
assert new_gp.p_reproduction == 1.0
assert new_gp.p_mutation == 0.5
assert new_gp.p_crossover == 0.5
assert new_gp.prunning_ratio == 0.5
def test_gp_evaluate():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
tree_space = tree.TreeSpace(n_agents=1000,
n_terminals=2,
n_variables=1,
min_depth=1,
max_depth=5,
functions=['SUM'],
lower_bound=[0],
upper_bound=[10])
new_gp = gp.GP()
new_gp.evaluate(tree_space, new_function)
assert tree_space.best_agent.fit < sys.float_info.max
def test_gp_reproduction():
new_gp = gp.GP()
def test_gp_mutate():
new_gp = gp.GP()
def test_gp_crossover():
new_gp = gp.GP()
def test_gp_cross():
new_gp = gp.GP()
def test_gp_mutation():
new_gp = gp.GP()
def test_gp_build():
new_gp = gp.GP()
assert new_gp.built == True
