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_dump(): print("-----test_dump-----") constants = np.array([1, 5, 6, 7, 8, 32, 54, 68]) 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() py_1.constants = np.copy(constants) orig_stack = np.copy(py_1.command_array) orig_const = np.copy(py_1.constants) orig_age = py_1.genetic_age c_1 = c_manip.generate() c_1.stack = np.copy(orig_stack) c_1.constants = np.copy(orig_const) c_manip.simplify_stack(c_1) py_stack, py_con, py_age = py_manip.dump(py_1) c_pair, c_age = c_manip.dump(c_1) c_stack = c_pair[0] c_con = c_pair[1] assert py_stack.all() == c_stack.all() assert py_con.all() == c_con.all() assert py_age == c_age
def test_count_constants(): print("-----test_count_constants-----") 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() py_1.command_array[0] = (0, 0, 0) py_1.command_array[1] = (1, 0, 0) py_1.command_array[1] = (1, 1, 1) py_1.command_array[-1] = (2, 2, 1) c_1.stack = np.copy(py_1.command_array) c_manip.simplify_stack(c_1) constants = np.array([1, 5, 6, 7, 8, 32, 54, 68]) py_1.set_constants(constants) c_1.set_constants(constants) py_con = py_1.count_constants() c_con = c_1.count_constants() assert py_con == c_con
def test_evaluate_fitness_vector_implicit(): print("-----test_evaluate_fitness_vector_implicit-----") x_t = snake_walk() y = (x_t[:, 0] + x_t[:, 1]) x = np.hstack((x_t, y.reshape([-1, 1]))) py_training_data = ImplicitTrainingData(x) py_implicit_regressor = ImplicitRegression() c_training_data = bingocpp.ImplicitTrainingData(x) c_implicit_regressor = bingocpp.ImplicitRegression() 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) temp = np.array([[1, 0, 0], [0, 0, 0], [0, 1, 1], [3, 2, 2], [4, 2, 3], [4, 4, 0], [3, 2, 0], [3, 0, 0], [3, 4, 2], [3, 1, 4], [2, 6, 0], [3, 8, 7], [2, 3, 11], [3, 7, 7], [3, 2, 7], [2, 9, 5], [4, 4, 14], [3, 9, 1], [3, 4, 5], [0, 1, 1], [4, 8, 4], [1, -1, -1], [3, 20, 5], [2, 9, 17], [1, -1, -1], [4, 23, 4], [4, 25, 19], [2, 26, 14], [4, 22, 10], [0, 0, 0], [2, 0, 17], [2, 29, 16], [4, 23, 14], [4, 3, 22], [0, 2, 2], [2, 31, 27], [2, 35, 28], [2, 25, 29], [2, 36, 28], [3, 8, 29], [3, 4, 24], [2, 14, 9], [4, 25, 9], [0, 2, 2], [3, 26, 10], [3, 12, 6], [0, 1, 1], [4, 42, 26], [3, 41, 6], [3, 13, 1], [3, 42, 36], [3, 15, 34], [2, 14, 23], [2, 13, 12], [0, 2, 2], [2, 28, 45], [3, 1, 12], [3, 15, 30], [3, 34, 38], [3, 43, 50], [2, 31, 9], [2, 54, 46], [1, -1, -1], [4, 60, 29]]) py_1 = py_manip.generate() c_1 = c_manip.generate() py_1.command_array = np.copy(temp) c_1.stack = np.copy(temp) c_manip.simplify_stack(c_1) assert py_training_data.x.all() == pytest.approx(c_training_data.x.all()) assert py_training_data.dx_dt.all() == pytest.approx( c_training_data.dx_dt.all()) py_fit = py_implicit_regressor.evaluate_fitness(py_1, py_training_data) py_fit = py_implicit_regressor.evaluate_fitness_vector( py_1, py_training_data) c_fit = c_implicit_regressor.evaluate_fitness(c_1, c_training_data) c_fit = c_implicit_regressor.evaluate_fitness_vector(c_1, c_training_data) assert py_fit.all() == pytest.approx(c_fit.all())
def test_agcpp_evaluate_deriv(): print("-----test_agcpp_evaluate_deriv-----") 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), :] 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() py_1.command_array[0] = (0, 0, 0) py_1.command_array[1] = (0, 1, 1) py_1.command_array[2] = (1, 0, 0) py_1.command_array[3] = (1, 1, 1) py_1.command_array[4] = (2, 3, 1) py_1.command_array[-1] = (2, 4, 2) c_1.stack = np.copy(py_1.command_array) c_manip.simplify_stack(c_1) constants = np.array([1, 5, 6, 7, 8, 32, 54, 68]) py_1.set_constants(constants) c_1.set_constants(constants) py_fit = py_1.evaluate_deriv(x) c_fit = c_1.evaluate_deriv(x) py_fit_const = py_1.evaluate_with_const_deriv(x) c_fit_const = c_1.evaluate_with_const_deriv(x) assert py_fit[0].all() == pytest.approx(c_fit[0].all()) assert py_fit[1].all() == pytest.approx(c_fit[1].all()) assert py_fit_const[0].all() == pytest.approx(c_fit_const[0].all()) assert py_fit_const[1].all() == pytest.approx(c_fit_const[1].all())
def test_complexity(): print("-----test_complexity-----") 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_complexity = py_1.complexity() c_complexity = c_1.complexity() assert py_complexity == c_complexity
def test_load(): print("-----test_load-----") constants = np.array([1, 5, 6, 7, 8, 32, 54, 68]) 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() orig_stack = np.copy(py_1.command_array) py_1 = py_manip.load([orig_stack, constants, 0]) c_1 = c_manip.load([[orig_stack, constants], 0]) assert py_1.command_array.all() == c_1.stack.all() assert py_1.constants.all() == c_1.constants.all() assert py_1.genetic_age == c_1.genetic_age
def test_needs_optimization(): print("-----test_needs_optimization-----") 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() py_1.command_array[0] = (0, 0, 0) py_1.command_array[1] = (1, -1, -1) py_1.command_array[-1] = (2, 1, 0) c_1.stack = np.copy(py_1.command_array) c_manip.simplify_stack(c_1) py_opt = py_1.needs_optimization() c_opt = c_1.needs_optimization() assert py_opt == c_opt
def test_distance(): print("-----test_distance-----") 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() py_2 = py_manip.generate() c_1 = c_manip.generate() c_2 = c_manip.generate() c_1.stack = np.copy(py_1.command_array) c_2.stack = np.copy(py_2.command_array) c_manip.simplify_stack(c_1) c_manip.simplify_stack(c_2) py_dist = py_manip.distance(py_1, py_2) c_dist = c_manip.distance(c_1, c_2) assert py_dist == c_dist
def compare_cpp_agcpp_implicit(X, Y, operator, params): """does the comparison""" X = np.hstack((X, Y.reshape([-1, 1]))) Y = None # 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[2] = (0, 2, 2) stack[3] = (operator, params[0], params[1]) stack[-1] = (3, 3, 2) equ.stack = np.copy(stack) sol_manip.simplify_stack(equ) print(stack) print("equstack\n", equ.stack) # make predictor manipulator pred_manip = fpm(32, X.shape[0]) # make training data training_data = bingocpp.ImplicitTrainingData(X) # make fitness_metric explicit_regressor = bingocpp.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=explicit_regressor) epsilon = 1.05 * islmngr.isles[0].solution_fitness_true(equ) + 1.0e-10 print("EPSILON IS - ", epsilon, equ.latexstring()) 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 IS - ", epsilon, equ.latexstring()) converged = islmngr.run_islands(MAX_STEPS, epsilon, step_increment=N_STEPS, make_plots=False)
def main(max_steps, epsilon, data_size): """main function which runs regression""" comm = MPI.COMM_WORLD rank = comm.Get_rank() # load data on rank 0 if rank == 0: # make data # n_lin = int(math.pow(data_size, 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], data_size, replace=False), :] # make solution # y = (x[:,0]*x[:,0]+3.5*x[:,1]) # x_true = x # y_true = y x = snake_walk() y = (x[:, 0] + x[:, 1]) x_true = np.hstack((x, y.reshape([-1, 1]))) y_true = None else: x_true = None y_true = None # then broadcast to all ranks x_true = MPI.COMM_WORLD.bcast(x_true, root=0) y_true = MPI.COMM_WORLD.bcast(y_true, root=0) # make solution manipulator # sol_manip = agm(x_true.shape[1], 64, 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.Sqrt) # make solution manipulator # y_true = y_true.reshape(-1, 1) # sol_manip2 = AGraphCpp.AGraphCppManipulator(x_true.shape[1], 64, nloads=2) sol_manip2 = bingocpp.AcyclicGraphManipulator(x_true.shape[1], 64, nloads=2) # sol_manip2 = bingocpp.AcyclicGraphManipulator(x_true.shape[1], 64, nloads=2, opt_rate=0) # 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) # sin # sol_manip.add_node_type(7) # cos # sol_manip.add_node_type(8) # exp # sol_manip.add_node_type(9) # log # # sol_manip.add_node_type(10) # pow # sol_manip.add_node_type(11) # abs # sol_manip.add_node_type(12) # sqrt sol_manip2.add_node_type(2) # + sol_manip2.add_node_type(3) # - sol_manip2.add_node_type(4) # * sol_manip2.add_node_type(5) # / sol_manip2.add_node_type(6) # sin sol_manip2.add_node_type(7) # cos sol_manip2.add_node_type(8) # exp sol_manip2.add_node_type(9) # log # sol_manip2.add_node_type(10) # pow sol_manip2.add_node_type(11) # abs sol_manip2.add_node_type(12) # sqrt # make predictor manipulator pred_manip = fpm(128, data_size) # make training data # training_data = ImplicitTrainingData(x_true) training_data = bingocpp.ImplicitTrainingData(x_true) # training_data = ExplicitTrainingData(x_true, y_true) # training_data2 = bingocpp.ExplicitTrainingData(x_true, y_true) # make fitness metric # implicit_regressor = ImplicitRegression() implicit_regressor = bingocpp.ImplicitRegression() # explicit_regressor = StandardRegression(const_deriv=True) # explicit_regressor2 = bingocpp.StandardRegression() # make and run island manager islmngr = ParallelIslandManager(#restart_file='test.p', solution_training_data=training_data, solution_manipulator=sol_manip2, predictor_manipulator=pred_manip, solution_pop_size=64, fitness_metric=implicit_regressor) # fitness_metric=explicit_regressor) # islmngr2 = ParallelIslandManager(#restart_file='test.p', # solution_training_data=training_data, # solution_manipulator=sol_manip, # predictor_manipulator=pred_manip, # solution_pop_size=64, # fitness_metric=explicit_regressor) # islmngr = ParallelIslandManager(#restart_file='test.p', # data_x=x_true, data_y=y_true, # solution_manipulator=sol_manip, # predictor_manipulator=pred_manip, # solution_pop_size=64, # fitness_metric=StandardRegression) # islmngr2 = ParallelIslandManager(#restart_file='test.p', # data_x=x_true, data_y=y_true, # solution_manipulator=sol_manip, # predictor_manipulator=pred_manip, # solution_pop_size=64, # fitness_metric=StandardRegression) non_one = time.time() islmngr.run_islands(max_steps, epsilon, min_steps=500, step_increment=500, when_update=50) non_two = time.time() non_time = non_two - non_one timesN.append(non_time) agesN.append(islmngr.age)