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 main(max_steps, epsilon, data_size, n_islands):
"""main regression function"""
# --------------------------------------------
# MAKE DATA (uncomment one of the below lines)
# --------------------------------------------
# data for standard regression
# (use with ExplicitTrainingData and StandardRegression)
# x_true, y_true = make_1d_data(data_size, 1)
x_true, y_true = make_1d_data(data_size, 2)
# x_true, y_true = make_1d_data(data_size, 3)
# x_true, y_true = make_1d_data(data_size, 4)
# x_true, y_true = make_norm_data(data_size)
# data for implicit regression
# (use with ImplicitTrainingData and ImplicitRegression)
# x_true = make_circle_data(data_size)
# ------------------------------------------------
# MAKE TRAINING DATA (uncomment one of the below)
# ------------------------------------------------
training_data = ExplicitTrainingData(x_true, y_true)
# training_data = ImplicitTrainingData(x_true)
# ------------------------------------------------------------
# MAKE SOLUTION MANIPULATOR (uncomment one of the below blocks)
# ------------------------------------------------------------
# using AGraph.py for the solution manipulator
# sol_manip = agm(x_true.shape[1], 32, 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)
# using AGraphCpp.py for the solution manipulator
sol_manip = AGraphCpp.AGraphCppManipulator(x_true.shape[1], 32, 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(12) # sqrt
# ----------------------------------
# MAKE FITNESS PREDICTOR MANIPULATOR
# ----------------------------------
pred_manip = fpm(128, data_size)
# ------------------------------------------------
# MAKE FITNESS METRIC (uncomment one of the below)
# ------------------------------------------------
regressor = StandardRegression()
# regressor = ImplicitRegression()
# --------------------------------------
# MAKE SERIAL ISLAND MANAGER THEN RUN IT
# --------------------------------------
islmngr = SerialIslandManager(n_islands,
solution_training_data=training_data,
solution_manipulator=sol_manip,
predictor_manipulator=pred_manip,
solution_pop_size=64,
fitness_metric=regressor,
solution_age_fitness=True
)
islmngr.run_islands(max_steps, epsilon, step_increment=100)
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 and send it to all ranks
x_true = None
y_true = None
if rank == 0:
# --------------------------------------------
# MAKE DATA (uncomment one of the below lines)
# --------------------------------------------
# standard regression
# (use with ExplicitTrainingData and StandardRegression)
# x_true, y_true = make_1d_data(data_size, 1)
x_true, y_true = make_1d_data(data_size, 2)
# x_true, y_true = make_1d_data(data_size, 3)
# x_true, y_true = make_1d_data(data_size, 4)
# x_true, y_true = make_norm_data(data_size)
# implicit regression
# (use with ImplicitTrainingData and ImplicitRegression)
# x_true = make_circle_data(data_size)
# 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 TRAINING DATA (uncomment one of the below)
# ------------------------------------------------
training_data = ExplicitTrainingData(x_true, y_true)
# training_data = ImplicitTrainingData(x_true)
# ------------------------------------------------------------
# MAKE SOLUTION MANIPULATOR (uncomment one of the below blocks)
# ------------------------------------------------------------
# using AGraph.py for the solution manipulator
# sol_manip = agm(x_true.shape[1], 32, 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)
# using AGraphCpp.py for the solution manipulator
sol_manip = AGraphCpp.AGraphCppManipulator(x_true.shape[1], 32, 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(12) # sqrt
# ----------------------------------
# MAKE FITNESS PREDICTOR MANIPULATOR
# ----------------------------------
pred_manip = fpm(128, data_size)
# ------------------------------------------------
# MAKE FITNESS METRIC (uncomment one of the below)
# ------------------------------------------------
regressor = StandardRegression(const_deriv=True)
# regressor = ImplicitRegression()
# --------------------------------------
# MAKE SERIAL ISLAND MANAGER THEN RUN IT
# --------------------------------------
islmngr = ParallelIslandManager(solution_training_data=training_data,
solution_manipulator=sol_manip,
predictor_manipulator=pred_manip,
solution_pop_size=64,
fitness_metric=regressor,
solution_age_fitness=True)
islmngr.run_islands(max_steps,
epsilon,
min_steps=1000,
step_increment=1000)
def main(max_steps, epsilon, data_size, parallel):
# STEP 1
# Create your x and y data, on parallel, broadcast it to all other ranks
##################################################
##################### SINGLE #####################
##################################################
if not parallel:
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
y_true = y_true.reshape(-1, 1)
##################################################
##################################################
##################################################
##################################################
#################### PARALLEL ####################
##################################################
if parallel:
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
if rank == 0:
#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), :]
x_true = np.array([i for i in range(25, 425, 25)])
y_true = np.array([
5.38, 2.91, 2.07, 1.71, 1.46, 1.35, 1.29, 1.24, 1.2, 1.19,
1.22, 1.23, 1.23, 1.23, 1.26, 1.26
])
#y = (x[:,0]*x[:,0]+3.5*x[:,1])
#x_true = x
#y_true = y
y_true = y_true.reshape(-1, 1)
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)
##################################################
##################################################
##################################################
# STEP 2
# Create solution manipulators. The solution manipulator is what creates
# the representations of the functions as the acyclic graph
####### SOLUTION MANIPULATOR #######
# nvars - number of independent variables
# ag_size - length of the command stack
# nloads - number of load operation which are required at the
# start of stack - Default 1
# float_lim - (0, max) of floats which are generated - Default 10.0
# terminal_prob: probability that a new node will be a terminal -Default .1
sol_manip = AGraphCpp.AGraphCppManipulator(1, 64, nloads=2)
####### OPERATIONS #######
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
####### PREDICTION MANIPULATOR #######
pred_manip = fpm(16, data_size)
# STEP 3
# Create the training data from your x and y data,
# and create the fitness metric.
# For this example, we are using explicit (standard)
####### TRAINING DATA #######
training_data = ExplicitTrainingData(x_true, y_true)
####### FITNESS METRIC #######
explicit_regressor = StandardRegression()
# STEP 4
# Create the island manager, this will run the steps on the population, and
# determine when to stop running
####### ISLAND MANAGER #######
islmngr = 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)
####### RUN ISLAND MANAGER #######
##################################################
##################### SINGLE #####################
##################################################
# max_steps - Max amount to go if no convergence happens
# epsilon - error which defines convergence
# min_steps - minimum number of steps required - Default 0
# step_increment - number of steps between convergence
# checks / migration - Default 1000
# make_plots - bool whether or not to produce plots - Default True
# checkpoint_file - base file name for checkpoint files
if not parallel:
islmngr.run_islands(max_steps,
epsilon,
min_steps=500,
step_increment=500)
##################################################
##################################################
##################################################
##################################################
#################### PARALLEL ####################
##################################################
# when_update - how often rank 0 gets updated on ages - Default 10
# non_block - bool to determine to run nonblocking - Default True
if parallel:
islmngr.run_islands(max_steps,
epsilon,
min_steps=500,
step_increment=500,
when_update=50)