def test_program_init_depth():
"""'full' should create constant depth programs for single depth limit"""
params = {'function_set': ['add2', 'sub2', 'mul2', 'div2',
'sqrt1', 'log1', 'abs1', 'max2', 'min2'],
'arities': {1: ['sqrt1', 'log1', 'abs1'],
2: ['add2', 'sub2', 'mul2', 'div2', 'max2', 'min2']},
'init_depth': (6, 6),
'n_features': 10,
'const_range': (-1.0, 1.0),
'metric': 'mean absolute error',
'p_point_replace': 0.05,
'parsimony_coefficient': 0.1}
random_state = check_random_state(415)
programs = []
for i in range(20):
programs.append(_Program(init_method='full',
random_state=random_state, **params))
full_depth = np.bincount([gp.depth_ for gp in programs])
programs = []
for i in range(20):
programs.append(_Program(init_method='half and half',
random_state=random_state, **params))
hnh_depth = np.bincount([gp.depth_ for gp in programs])
programs = []
for i in range(20):
programs.append(_Program(init_method='grow',
random_state=random_state, **params))
grow_depth = np.bincount([gp.depth_ for gp in programs])
assert_true(full_depth[-1] == 20)
assert_false(hnh_depth[-1] == 20)
assert_false(grow_depth[-1] == 20)
def test_export_graphviz():
"""Check output of a simple program to Graphviz"""
params = {'function_set': ['add2', 'sub2', 'mul2', 'div2'],
'arities': {2: ['add2', 'sub2', 'mul2', 'div2']},
'init_depth': (2, 6),
'init_method': 'half and half',
'n_features': 10,
'const_range': (-1.0, 1.0),
'metric': 'mean absolute error',
'p_point_replace': 0.05,
'parsimony_coefficient': 0.1}
random_state = check_random_state(415)
# Test for a small program
test_gp = ['mul2', 'div2', 8, 1, 'sub2', 9, .5]
gp = _Program(random_state=random_state, program=test_gp, **params)
output = gp.export_graphviz()
tree = 'digraph program {\n' \
'node [style=filled]0 [label="mul", fillcolor="#136ed4"] ;\n' \
'1 [label="div", fillcolor="#136ed4"] ;\n' \
'2 [label="X8", fillcolor="#60a6f6"] ;\n' \
'3 [label="X1", fillcolor="#60a6f6"] ;\n' \
'1 -> 3 ;\n1 -> 2 ;\n' \
'4 [label="sub", fillcolor="#136ed4"] ;\n' \
'5 [label="X9", fillcolor="#60a6f6"] ;\n' \
'6 [label="0.500", fillcolor="#60a6f6"] ;\n' \
'4 -> 6 ;\n4 -> 5 ;\n0 -> 4 ;\n0 -> 1 ;\n}'
assert_true(output == tree)
# Test with fade_nodes
output = gp.export_graphviz(fade_nodes=[0, 1, 2, 3])
tree = 'digraph program {\n' \
'node [style=filled]0 [label="mul", fillcolor="#cecece"] ;\n' \
'1 [label="div", fillcolor="#cecece"] ;\n' \
'2 [label="X8", fillcolor="#cecece"] ;\n' \
'3 [label="X1", fillcolor="#cecece"] ;\n' \
'1 -> 3 ;\n1 -> 2 ;\n' \
'4 [label="sub", fillcolor="#136ed4"] ;\n' \
'5 [label="X9", fillcolor="#60a6f6"] ;\n' \
'6 [label="0.500", fillcolor="#60a6f6"] ;\n' \
'4 -> 6 ;\n4 -> 5 ;\n0 -> 4 ;\n0 -> 1 ;\n}'
assert_true(output == tree)
# Test a degenerative single-node program
test_gp = [1]
gp = _Program(random_state=random_state, program=test_gp, **params)
output = gp.export_graphviz()
tree = 'digraph program {\n' \
'node [style=filled]0 [label="X1", fillcolor="#60a6f6"] ;\n}'
assert_true(output == tree)
def test_genetic_operations():
"""Check all genetic operations are stable and don't change programs"""
params = {'function_set': ['add2', 'sub2', 'mul2', 'div2'],
'arities': {2: ['add2', 'sub2', 'mul2', 'div2']},
'init_depth': (2, 6),
'init_method': 'half and half',
'n_features': 10,
'const_range': (-1.0, 1.0),
'metric': 'mean absolute error',
'p_point_replace': 0.05,
'parsimony_coefficient': 0.1}
random_state = check_random_state(415)
# Test for a small program
test_gp = ['mul2', 'div2', 8, 1, 'sub2', 9, .5]
donor = ['add2', 0.1, 'sub2', 2, 7]
gp = _Program(random_state=random_state, program=test_gp, **params)
assert_equal(gp.reproduce(),
['mul2', 'div2', 8, 1, 'sub2', 9, 0.5])
assert_equal(gp.program, test_gp)
assert_equal(gp.crossover(donor, random_state)[0],
['sub2', 2, 7])
assert_equal(gp.program, test_gp)
assert_equal(gp.subtree_mutation(random_state)[0],
['mul2', 'div2', 8, 1, 'sub2', 'sub2', 3, 5, 'add2', 6, 3])
assert_equal(gp.program, test_gp)
assert_equal(gp.hoist_mutation(random_state)[0],
['div2', 8, 1])
assert_equal(gp.program, test_gp)
assert_equal(gp.point_mutation(random_state)[0],
['mul2', 'div2', 8, 1, 'sub2', 9, 0.5])
assert_equal(gp.program, test_gp)
def test_all_metrics():
"""Check all supported metrics work"""
params = {'function_set': ['add2', 'sub2', 'mul2', 'div2'],
'arities': {2: ['add2', 'sub2', 'mul2', 'div2']},
'init_depth': (2, 6),
'init_method': 'half and half',
'n_features': 10,
'const_range': (-1.0, 1.0),
'metric': 'mean absolute error',
'p_point_replace': 0.05,
'parsimony_coefficient': 0.1}
random_state = check_random_state(415)
# Test for a small program
test_gp = ['mul2', 'div2', 8, 1, 'sub2', 9, .5]
gp = _Program(random_state=random_state, program=test_gp, **params)
X = np.reshape(random_state.uniform(size=50), (5, 10))
y = random_state.uniform(size=5)
sample_weight = np.ones(5)
expected = [1.48719809776, 1.82389179833, 1.76013763179, 0.98663772258,
-0.2928200724, -0.5]
result = []
for m in ['mean absolute error', 'mse', 'rmse', 'rmsle',
'pearson', 'spearman']:
gp.metric = m
gp.raw_fitness_ = gp.raw_fitness(X, y, sample_weight)
result.append(gp.fitness())
assert_array_almost_equal(result, expected)
# And check a fake one
gp.metric = 'the larch'
assert_raises(ValueError, gp.raw_fitness, X, y, sample_weight)
def test_print_overloading():
"""Check that printing a program object results in 'pretty' output"""
params = {'function_set': ['add2', 'sub2', 'mul2', 'div2'],
'arities': {2: ['add2', 'sub2', 'mul2', 'div2']},
'init_depth': (2, 6),
'init_method': 'half and half',
'n_features': 10,
'const_range': (-1.0, 1.0),
'metric': 'mean absolute error',
'p_point_replace': 0.05,
'parsimony_coefficient': 0.1}
random_state = check_random_state(415)
test_gp = ['mul2', 'div2', 8, 1, 'sub2', 9, .5]
gp = _Program(random_state=random_state, program=test_gp, **params)
orig_stdout = sys.stdout
try:
out = StringIO()
sys.stdout = out
print(gp)
output = out.getvalue().strip()
finally:
sys.stdout = orig_stdout
lisp = "mul(div(X8, X1), sub(X9, 0.500))"
assert_true(output == lisp)
def test_validate_program():
"""Check that valid programs are accepted & invalid ones raise error"""
function_set = ['add2', 'sub2', 'mul2', 'div2',
'sqrt1', 'log1', 'abs1', 'max2', 'min2']
arities = {1: ['sqrt1', 'log1', 'abs1'],
2: ['add2', 'sub2', 'mul2', 'div2', 'max2', 'min2']}
init_depth = (2, 6)
init_method = 'half and half'
n_features = 10
const_range = (-1.0, 1.0)
metric = 'mean absolute error'
p_point_replace = 0.05
parsimony_coefficient = 0.1
random_state = check_random_state(415)
test_gp = ['sub2', 'abs1', 'sqrt1', 'log1', 'log1', 'sqrt1', 7, 'abs1',
'abs1', 'abs1', 'log1', 'sqrt1', 2]
# This one should be fine
_ = _Program(function_set, arities, init_depth, init_method, n_features,
const_range, metric, p_point_replace, parsimony_coefficient,
random_state, test_gp)
# Now try a couple that shouldn't be
assert_raises(ValueError, _Program, function_set, arities, init_depth,
init_method, n_features, const_range, metric,
p_point_replace, parsimony_coefficient, random_state,
test_gp[:-1])
assert_raises(ValueError, _Program, function_set, arities, init_depth,
init_method, n_features, const_range, metric,
p_point_replace, parsimony_coefficient, random_state,
test_gp + [1])
def test_program_init_method():
"""'full' should create longer and deeper programs than other methods"""
params = {'function_set': ['add2', 'sub2', 'mul2', 'div2',
'sqrt1', 'log1', 'abs1', 'max2', 'min2'],
'arities': {1: ['sqrt1', 'log1', 'abs1'],
2: ['add2', 'sub2', 'mul2', 'div2', 'max2', 'min2']},
'init_depth': (2, 6),
'n_features': 10,
'const_range': (-1.0, 1.0),
'metric': 'mean absolute error',
'p_point_replace': 0.05,
'parsimony_coefficient': 0.1}
random_state = check_random_state(415)
programs = []
for i in range(20):
programs.append(_Program(init_method='full',
random_state=random_state, **params))
full_length = np.mean([gp.length_ for gp in programs])
full_depth = np.mean([gp.depth_ for gp in programs])
programs = []
for i in range(20):
programs.append(_Program(init_method='half and half',
random_state=random_state, **params))
hnh_length = np.mean([gp.length_ for gp in programs])
hnh_depth = np.mean([gp.depth_ for gp in programs])
programs = []
for i in range(20):
programs.append(_Program(init_method='grow',
random_state=random_state, **params))
grow_length = np.mean([gp.length_ for gp in programs])
grow_depth = np.mean([gp.depth_ for gp in programs])
assert_greater(full_length, hnh_length)
assert_greater(hnh_length, grow_length)
assert_greater(full_depth, hnh_depth)
assert_greater(hnh_depth, grow_depth)
def test_execute():
"""Check executing the program works"""
params = {'function_set': ['add2', 'sub2', 'mul2', 'div2'],
'arities': {2: ['add2', 'sub2', 'mul2', 'div2']},
'init_depth': (2, 6),
'init_method': 'half and half',
'n_features': 10,
'const_range': (-1.0, 1.0),
'metric': 'mean absolute error',
'p_point_replace': 0.05,
'parsimony_coefficient': 0.1}
random_state = check_random_state(415)
# Test for a small program
test_gp = ['mul2', 'div2', 8, 1, 'sub2', 9, .5]
X = np.reshape(random_state.uniform(size=50), (5, 10))
gp = _Program(random_state=random_state, program=test_gp, **params)
result = gp.execute(X)
expected = [-0.19656208, 0.78197782, -1.70123845, -0.60175969, -0.01082618]
assert_array_almost_equal(result, expected)
def test_get_subtree():
"""Check that get subtree does the same thing for self and new programs"""
params = {'function_set': ['add2', 'sub2', 'mul2', 'div2'],
'arities': {2: ['add2', 'sub2', 'mul2', 'div2']},
'init_depth': (2, 6),
'init_method': 'half and half',
'n_features': 10,
'const_range': (-1.0, 1.0),
'metric': 'mean absolute error',
'p_point_replace': 0.05,
'parsimony_coefficient': 0.1}
random_state = check_random_state(415)
# Test for a small program
test_gp = ['mul2', 'div2', 8, 1, 'sub2', 9, .5]
gp = _Program(random_state=random_state, program=test_gp, **params)
self_test = gp.get_subtree(check_random_state(0))
external_test = gp.get_subtree(check_random_state(0), test_gp)
assert_equal(self_test, external_test)