def test_evaluate_explicit():
print("-----test_evaluate_explicit-----")
n_lin = int(math.pow(500, 1.0 / 3)) + 1
x_1 = np.linspace(0, 5, n_lin)
x_2 = np.linspace(0, 5, n_lin)
x_3 = np.linspace(0, 5, n_lin)
x = np.array(np.meshgrid(x_1, x_2, x_3)).T.reshape(-1, 3)
x = x[np.random.choice(x.shape[0], 500, replace=False), :]
# make solution
y_t = (x[:, 0] * x[:, 0] + 3.5 * x[:, 1])
y = y_t.reshape(-1, 1)
py_training_data = ExplicitTrainingData(x, y)
py_explicit_regressor = StandardRegression()
c_explicit_regressor = bingocpp.StandardRegression()
c_training_data = bingocpp.ExplicitTrainingData(x, y)
py_manip = AGraphCpp.AGraphCppManipulator(3, 64, nloads=2)
py_manip.add_node_type(2)
py_manip.add_node_type(3)
py_manip.add_node_type(4)
c_manip = bingocpp.AcyclicGraphManipulator(3, 64, nloads=2)
c_manip.add_node_type(2)
c_manip.add_node_type(3)
c_manip.add_node_type(4)
py_1 = py_manip.generate()
c_1 = c_manip.generate()
c_1.stack = np.copy(py_1.command_array)
c_manip.simplify_stack(c_1)
py_fit = py_explicit_regressor.evaluate_fitness(py_1, py_training_data)
c_fit = c_explicit_regressor.evaluate_fitness(c_1, c_training_data)
assert py_fit == pytest.approx(c_fit)
def test_getting_subset_of_training_data(python):
data_input = np.arange(5).reshape((-1, 1))
training_data = ExplicitTrainingData(data_input, data_input) \
if python \
else bingocpp.ExplicitTrainingData(data_input, data_input)
subset_training_data = training_data[[0, 2, 3]]
expected_subset = np.array([[0], [2], [3]])
np.testing.assert_array_equal(subset_training_data.x, expected_subset)
np.testing.assert_array_equal(subset_training_data.y, expected_subset)
def test_explicit_get_item():
print("-----test_explicit_get_item-----")
n_lin = int(math.pow(500, 1.0 / 3)) + 1
x_1 = np.linspace(0, 5, n_lin)
x_2 = np.linspace(0, 5, n_lin)
x_3 = np.linspace(0, 5, n_lin)
x = np.array(np.meshgrid(x_1, x_2, x_3)).T.reshape(-1, 3)
x = x[np.random.choice(x.shape[0], 500, replace=False), :]
# make solution
y_t = (x[:, 0] * x[:, 0] + 3.5 * x[:, 1])
y = y_t.reshape(-1, 1)
py_training_data = ExplicitTrainingData(x, y)
c_training_data = bingocpp.ExplicitTrainingData(x, y)
items = [5, 10, 15, 18, 64, 92, 129, 186, 201, 215, 293, 355, 389]
py_result = py_training_data.__getitem__(items)
c_result = c_training_data.__getitem__(items)
assert py_result.x.all() == c_result.x.all()
assert py_result.y.all() == c_result.y.all()
def explicit_data_nan(request, dummy_sum_equation, dummy_sum_equation_cpp):
x, y = init_x_and_y()
x[0, 0] = np.nan
if request.param == "python":
return (SampleTrainingData(x, y), dummy_sum_equation)
return (bingocpp.ExplicitTrainingData(x, y), dummy_sum_equation_cpp)
def dummy_training_data_cpp():
if bingocpp is None:
return None
x, y = init_x_and_y()
return bingocpp.ExplicitTrainingData(x, y)
def compare_cpp_agcpp_explicit(X, Y, operator, params):
"""does the comparison"""
Y = Y.reshape([-1, 1])
# make solution manipulator
sol_manip = bingocpp.AcyclicGraphManipulator(X.shape[1], 16, nloads=2)
sol_manip.add_node_type(2)
sol_manip.add_node_type(3)
sol_manip.add_node_type(4)
sol_manip.add_node_type(5)
sol_manip.add_node_type(6)
sol_manip.add_node_type(7)
sol_manip.add_node_type(8)
sol_manip.add_node_type(9)
sol_manip.add_node_type(10)
sol_manip.add_node_type(11)
sol_manip.add_node_type(12)
# make true equation
equ = sol_manip.generate()
stack = np.copy(equ.stack)
stack[0] = (0, 0, 0)
stack[1] = (0, 1, 1)
stack[-1] = (operator, params[0], params[1])
equ.stack = np.copy(stack)
# make predictor manipulator
pred_manip = fpm(32, X.shape[0])
# make training data
training_data = bingocpp.ExplicitTrainingData(X, Y)
# make fitness_metric
explicit_regressor = bingocpp.StandardRegression()
# make and run island manager
islmngr = SerialIslandManager(N_ISLANDS,
solution_training_data=training_data,
solution_manipulator=sol_manip,
predictor_manipulator=pred_manip,
fitness_metric=explicit_regressor)
epsilon = 1.05 * islmngr.isles[0].solution_fitness_true(equ) + 1.0e-10
print("epsilon: ", epsilon)
converged = islmngr.run_islands(MAX_STEPS,
epsilon,
step_increment=N_STEPS,
make_plots=False)
if not converged:
# try to run again if it fails
islmngr = SerialIslandManager(N_ISLANDS,
solution_training_data=training_data,
solution_manipulator=sol_manip,
predictor_manipulator=pred_manip,
fitness_metric=explicit_regressor)
epsilon = 1.05 * islmngr.isles[0].solution_fitness_true(equ) + 1.0e-10
print("epsilon: ", epsilon)
converged = islmngr.run_islands(MAX_STEPS,
epsilon,
step_increment=N_STEPS,
make_plots=False)
assert converged
def explicit_regression_cpp():
training_data = cppBackend.ExplicitTrainingData(TEST_X_PARTIALS, TEST_Y_ZEROS)
return cppBackend.ExplicitRegression(training_data)