def compare_agraph_explicit(X, Y):
"""does the comparison"""
# make solution manipulator
sol_manip = agm(X.shape[1], 16, nloads=2)
sol_manip.add_node_type(AGNodes.Add)
sol_manip.add_node_type(AGNodes.Multiply)
# make predictor manipulator
pred_manip = fpm(32, Y.shape[0])
# create training data
Y = Y.reshape([-1, 1])
training_data = ExplicitTrainingData(X, Y)
# create fitness metric
explicit_regressor = 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)
assert islmngr.run_islands(MAX_STEPS,
EPSILON,
step_increment=N_STEPS,
make_plots=False)
def compare_agraph_potential(X, Y):
"""does the comparison using agraph"""
# make solution manipulator
sol_manip = agm(1, 16, nloads=2)
sol_manip.add_node_type(AGNodes.Add)
sol_manip.add_node_type(AGNodes.Multiply)
# sol_manip.add_node_type(AGNodes.Subtract)
# sol_manip.add_node_type(AGNodes.Divide)
# sol_manip.add_node_type(AGNodes.Exp)
# sol_manip.add_node_type(AGNodes.Sin)
# sol_manip.add_node_type(AGNodes.Cos)
# make predictor manipulator
pred_manip = fpm(32, X.shape[0])
# create training data
training_data = PairwiseAtomicTrainingData(potential_energy=Y,
configurations=X)
# create fitness metric
atomic_regressor = PairwiseAtomicPotential()
# make and run island manager
islmngr = SerialIslandManager(N_ISLANDS,
solution_training_data=training_data,
solution_manipulator=sol_manip,
predictor_manipulator=pred_manip,
fitness_metric=atomic_regressor)
success = islmngr.run_islands(MAX_STEPS,
EPSILON,
step_increment=N_STEPS,
make_plots=False)
assert success
def compare_ag_implicit(X, Y, operator, params):
"""does const symbolic regression and tests convergence"""
# convert to single array
X = np.hstack((X, Y.reshape([-1, 1])))
# make solution manipulator
sol_manip = agm(X.shape[1], 16, nloads=2)
sol_manip.add_node_type(AGNodes.Add)
sol_manip.add_node_type(AGNodes.Subtract)
sol_manip.add_node_type(AGNodes.Multiply)
sol_manip.add_node_type(AGNodes.Divide)
sol_manip.add_node_type(AGNodes.Exp)
sol_manip.add_node_type(AGNodes.Log)
sol_manip.add_node_type(AGNodes.Sin)
sol_manip.add_node_type(AGNodes.Cos)
sol_manip.add_node_type(AGNodes.Abs)
sol_manip.add_node_type(AGNodes.Pow)
# make true equation
equ = sol_manip.generate()
equ.command_list[0] = (AGNodes.LoadData, (0, ))
equ.command_list[1] = (AGNodes.LoadData, (1, ))
equ.command_list[2] = (AGNodes.LoadData, (2, ))
equ.command_list[3] = (operator, params)
equ.command_list[-1] = (AGNodes.Subtract, (3, 2))
# make predictor manipulator
pred_manip = fpm(32, X.shape[0])
# make training data
training_data = ImplicitTrainingData(X)
# make fitness metric
implicit_regressor = ImplicitRegression()
# make and run island manager
islmngr = SerialIslandManager(N_ISLANDS,
solution_training_data=training_data,
solution_manipulator=sol_manip,
predictor_manipulator=pred_manip,
fitness_metric=implicit_regressor)
epsilon = 1.05 * islmngr.isles[0].solution_fitness_true(equ) + 1.0e-10
assert islmngr.run_islands(MAX_STEPS,
epsilon,
step_increment=N_STEPS,
make_plots=False)
def compare_ag_explicit(X, Y, operator, params):
"""does the comparison"""
# make solution manipulator
sol_manip = agm(X.shape[1], 16, nloads=2)
sol_manip.add_node_type(AGNodes.Add)
sol_manip.add_node_type(AGNodes.Subtract)
sol_manip.add_node_type(AGNodes.Multiply)
sol_manip.add_node_type(AGNodes.Divide)
sol_manip.add_node_type(AGNodes.Exp)
sol_manip.add_node_type(AGNodes.Log)
sol_manip.add_node_type(AGNodes.Sin)
sol_manip.add_node_type(AGNodes.Cos)
sol_manip.add_node_type(AGNodes.Abs)
sol_manip.add_node_type(AGNodes.Pow)
# make true equation
equ = sol_manip.generate()
equ.command_list[0] = (AGNodes.LoadData, (0, ))
equ.command_list[1] = (AGNodes.LoadData, (1, ))
equ.command_list[-1] = (operator, params)
# make predictor manipulator
pred_manip = fpm(32, Y.shape[0])
# make training data
Y = Y.reshape([-1, 1])
training_data = ExplicitTrainingData(X, Y)
# make fitness_metric
explicit_regressor = 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
assert islmngr.run_islands(MAX_STEPS,
epsilon,
step_increment=N_STEPS,
make_plots=False)
# ag.command_list[-1] = (AGNodes.Subtract, (5, 4))
#
# print(ag)
# print(ag.latexstring())
# # for x, y in zip(X[fp.indices, :], Y[fp.indices, :]):
# # print(x, y, ag.evaluate_deriv(x), ag.evaluate_deriv(x)*y)
#
# print(fp.fit_func(ag, X, Y, False))
ac = AGraphCpp.AGraphCppManipulator(2, 16, nloads=2)
ac.add_node_type(2)
ac.add_node_type(3)
gc = ac.generate()
gc_list, _ = ac.dump(gc)
a = agm(2, 16, nloads=2, constant_optimization=True)
a.add_node_type(AGNodes.Add)
a.add_node_type(AGNodes.Subtract)
for i, (node, params) in enumerate(gc_list):
if node is 1:
if params[0] is -1:
gc_list[i] = (node, (None, ))
g = a.load(gc_list)
#print(g)
#print(gc)
x = np.linspace(1, 100, 100)
x = x.reshape((-1, 2))
y = x[:, 0] + 50
def test_const_opt_agraph_implicit():
"""test optimization code"""
print("-----test_const_opt_agraph_implicit-----")
# get independent vars
x_true = snake_walk()
# make solutions
consts = np.random.rand(6) * 90 + 10
print("CONSTANTS: ", consts[1:])
y = consts[0] + consts[1] * x_true[:, 0] + consts[2] * x_true[:, 1] + \
consts[3] * x_true[:, 0] * x_true[:, 0] + \
consts[4] * x_true[:, 1] * x_true[:, 1] + \
consts[5] * x_true[:, 0] * x_true[:, 1]
x_true = np.hstack((x_true, y.reshape((-1, 1))))
# create manipulator to set things up
sol_manip = agm(x_true.shape[1], 23, nloads=2)
sol_manip.add_node_type(AGNodes.Add)
sol_manip.add_node_type(AGNodes.Subtract)
sol_manip.add_node_type(AGNodes.Multiply)
# create gene with proper functional form
sol = sol_manip.generate()
sol.command_list[0] = (AGNodes.LoadConst, (None, ))
sol.command_list[1] = (AGNodes.LoadConst, (None, ))
sol.command_list[2] = (AGNodes.LoadConst, (None, ))
sol.command_list[3] = (AGNodes.LoadConst, (None, ))
sol.command_list[4] = (AGNodes.LoadConst, (None, ))
sol.command_list[5] = (AGNodes.LoadConst, (None, ))
sol.command_list[6] = (AGNodes.LoadData, (0, ))
sol.command_list[7] = (AGNodes.LoadData, (1, ))
sol.command_list[8] = (AGNodes.Multiply, (6, 6))
sol.command_list[9] = (AGNodes.Multiply, (7, 7))
sol.command_list[10] = (AGNodes.Multiply, (6, 7))
sol.command_list[11] = (AGNodes.Multiply, (1, 6))
sol.command_list[12] = (AGNodes.Multiply, (2, 7))
sol.command_list[13] = (AGNodes.Multiply, (3, 8))
sol.command_list[14] = (AGNodes.Multiply, (4, 9))
sol.command_list[15] = (AGNodes.Multiply, (5, 10))
sol.command_list[16] = (AGNodes.Add, (0, 11))
sol.command_list[17] = (AGNodes.Add, (16, 12))
sol.command_list[18] = (AGNodes.Add, (17, 13))
sol.command_list[19] = (AGNodes.Add, (18, 14))
sol.command_list[20] = (AGNodes.Add, (19, 15))
sol.command_list[21] = (AGNodes.LoadData, (2, ))
sol.command_list[22] = (AGNodes.Subtract, (20, 21))
# print(sol.latexstring())
sol.set_constants(consts)
# create training data
training_data = ImplicitTrainingData(x_true)
# create fitness metric
implicit_regressor = ImplicitRegression(required_params=3)
# fit the constants
t0 = time.time()
implicit_regressor.evaluate_fitness(sol, training_data)
t1 = time.time()
print("fit time: ", t1 - t0, "seconds")
# make sure constants are close
c_fit = sol.constants
print(
"average accuracy: ",
np.mean(
np.abs(np.array(c_fit[1:]) - np.array(consts[1:])) /
np.array(consts[1:])))
print("FITTED: ", c_fit[1:])
for tru, fit in zip(consts[1:], c_fit[1:]):
print(np.abs(tru - fit) / tru)
assert np.abs(tru - fit) / tru < 0.1
def test_const_opt_agraph_explicit():
"""test optimization code"""
print("-----test_const_opt_agraph_explicit-----")
# get independent vars
x_true = snake_walk()
# make solutions
consts = np.random.rand(6) * 90 + 10
print("CONSTANTS: ", consts)
y = consts[0] + consts[1] * x_true[:, 0] + consts[2] * x_true[:, 1] + \
consts[3] * x_true[:, 0] * x_true[:, 0] + \
consts[4] * x_true[:, 1] * x_true[:, 1] + \
consts[5] * x_true[:, 0] * x_true[:, 1]
# create manipulator to set things up
sol_manip = agm(x_true.shape[1], 21, nloads=2)
sol_manip.add_node_type(AGNodes.Add)
sol_manip.add_node_type(AGNodes.Multiply)
# create gene with proper functional form
sol = sol_manip.generate()
sol.command_list[0] = (AGNodes.LoadConst, (None, ))
sol.command_list[1] = (AGNodes.LoadConst, (None, ))
sol.command_list[2] = (AGNodes.LoadConst, (None, ))
sol.command_list[3] = (AGNodes.LoadConst, (None, ))
sol.command_list[4] = (AGNodes.LoadConst, (None, ))
sol.command_list[5] = (AGNodes.LoadConst, (None, ))
sol.command_list[6] = (AGNodes.LoadData, (0, ))
sol.command_list[7] = (AGNodes.LoadData, (1, ))
sol.command_list[8] = (AGNodes.Multiply, (6, 6))
sol.command_list[9] = (AGNodes.Multiply, (7, 7))
sol.command_list[10] = (AGNodes.Multiply, (6, 7))
sol.command_list[11] = (AGNodes.Multiply, (1, 6))
sol.command_list[12] = (AGNodes.Multiply, (2, 7))
sol.command_list[13] = (AGNodes.Multiply, (3, 8))
sol.command_list[14] = (AGNodes.Multiply, (4, 9))
sol.command_list[15] = (AGNodes.Multiply, (5, 10))
sol.command_list[16] = (AGNodes.Add, (0, 11))
sol.command_list[17] = (AGNodes.Add, (16, 12))
sol.command_list[18] = (AGNodes.Add, (17, 13))
sol.command_list[19] = (AGNodes.Add, (18, 14))
sol.command_list[20] = (AGNodes.Add, (19, 15))
# print(sol.latexstring())
sol.set_constants(consts)
# create training data
y = y.reshape([-1, 1])
training_data = ExplicitTrainingData(x_true, y)
# create fitness metric
explicit_regressor = StandardRegression()
# fit the constants
explicit_regressor.evaluate_fitness(sol, training_data)
# make sure constants are close
c_fit = sol.constants
print("FITTED: ", c_fit)
print("REL DIFF: ", (consts - c_fit) / consts)
for tru, fit in zip(consts, c_fit):
assert np.abs(tru - fit) < 1e-8