def runTest(self):
from dcgpy import symbolic_regression, generate_koza_quintic, kernel_set_double, es4cgp
import pygmo as pg
import pickle
X, Y = generate_koza_quintic()
# Interface for the UDPs
udp = symbolic_regression(points=X,
labels=Y,
rows=1,
cols=20,
levels_back=21,
arity=2,
kernels=kernel_set_double(
["sum", "diff", "mul", "pdiv"])(),
n_eph=2,
multi_objective=False,
parallel_batches=0)
prob = pg.problem(udp)
pop = pg.population(prob, 10)
# Interface for the UDAs
uda = es4cgp(gen=20, mut_n=3, ftol=1e-3, learn_constants=True, seed=34)
algo = pg.algorithm(uda)
algo.set_verbosity(0)
# Testing some evolutions
pop = algo.evolve(pop)
# In parallel
archi = pg.archipelago(prob=prob, algo=algo, n=16, pop_size=4)
archi.evolve()
archi.wait_check()
# Pickling.
self.assertTrue(repr(algo) == repr(pickle.loads(pickle.dumps(algo))))
def run(dataset_train, dataset_test, cols, gen):
ss = dcgpy.kernel_set_double([
"sum", "diff", "mul", "pdiv", "sin", "cos", "tanh", "log", "exp",
"psqrt"
])
Xtrain, ytrain = dataset_train[:, :-1], dataset_train[:, -1]
Xtest, ytest = dataset_test[:, :-1], dataset_test[:, -1]
udp = dcgpy.symbolic_regression(points=Xtrain,
labels=ytrain[:, np.newaxis],
kernels=ss(),
rows=1,
cols=cols,
levels_back=21,
arity=2,
n_eph=3,
multi_objective=False,
parallel_batches=0)
uda = dcgpy.es4cgp(gen=gen) #, mut_n = 1)
algo = pg.algorithm(uda)
pop = pg.population(udp, 4)
pop = algo.evolve(pop)
return RMSE(udp.predict(Xtrain, pop.champion_x),
ytrain), RMSE(udp.predict(Xtest, pop.champion_x), ytest)
def main():
# Some necessary imports.
import dcgpy
import pygmo as pg
# Sympy is nice to have for basic symbolic manipulation.
from sympy import init_printing
from sympy.parsing.sympy_parser import parse_expr
init_printing()
# Fundamental for plotting.
from matplotlib import pyplot as plt
# We load our data from some available ones shipped with dcgpy.
# In this particular case we use the problem chwirut2 from
# (https://www.itl.nist.gov/div898/strd/nls/data/chwirut2.shtml)
X, Y = dcgpy.generate_chwirut2()
# And we plot them as to visualize the problem.
_ = plt.plot(X, Y, '.')
_ = plt.title('54 measurments')
_ = plt.xlabel('metal distance')
_ = plt.ylabel('ultrasonic response')
# We define our kernel set, that is the mathematical operators we will
# want our final model to possibly contain. What to choose in here is left
# to the competence and knowledge of the user. A list of kernels shipped with dcgpy
# can be found on the online docs. The user can also define its own kernels (see the corresponding tutorial).
ss = dcgpy.kernel_set_double(["sum", "diff", "mul", "pdiv"])
# We instantiate the symbolic regression optimization problem (note: many important options are here not
# specified and thus set to their default values)
udp = dcgpy.symbolic_regression(points=X, labels=Y, kernels=ss())
print(udp)
# We instantiate here the evolutionary strategy we want to use to search for models.
uda = dcgpy.es4cgp(gen=10000, max_mut=2)
prob = pg.problem(udp)
algo = pg.algorithm(uda)
# Note that the screen output will happen on the terminal, not on your Jupyter notebook.
# It can be recovered afterwards from the log.
algo.set_verbosity(10)
pop = pg.population(prob, 4)
pop = algo.evolve(pop)
# Lets have a look to the symbolic representation of our model (using sympy)
parse_expr(udp.prettier(pop.champion_x))
# And lets see what our model actually predicts on the inputs
Y_pred = udp.predict(X, pop.champion_x)
# Lets comapre to the data
_ = plt.plot(X, Y_pred, 'r.')
_ = plt.plot(X, Y, '.', alpha=0.2)
_ = plt.title('54 measurments')
_ = plt.xlabel('metal distance')
_ = plt.ylabel('ultrasonic response')
# Here we get the log of the latest call to the evolve
log = algo.extract(dcgpy.es4cgp).get_log()
gen = [it[0] for it in log]
loss = [it[2] for it in log]
# And here we plot, for example, the generations against the best loss
_ = plt.semilogy(gen, loss)
_ = plt.title('last call to evolve')
_ = plt.xlabel('metal distance')
_ = plt.ylabel('ultrasonic response')
_ = plt.ylabel('ultrasonic response')
# We define our kernel set, that is the mathematical operators we will
# want our final model to possibly contain. What to choose in here is left
# to the competence and knowledge of the user. A list of kernels shipped with dcgpy
# can be found on the online docs. The user can also define its own kernels (see the corresponding tutorial).
ss = dcgpy.kernel_set_double(["sum", "diff", "mul", "pdiv"])
# We instantiate the symbolic regression optimization problem (note: many important options are here not
# specified and thus set to their default values)
udp = dcgpy.symbolic_regression(points = X, labels = Y, kernels=ss())
print(udp)
# We instantiate here the evolutionary strategy we want to use to search for models.
uda = dcgpy.es4cgp(gen = 10000, mut_n = 2)
prob = pg.problem(udp)
algo = pg.algorithm(uda)
# Note that the screen output will happen on the terminal, not on your Jupyter notebook.
# It can be recovered afterwards from the log.
algo.set_verbosity(10)
pop = pg.population(prob, 4)
pop = algo.evolve(pop)
# Lets have a look to the symbolic representation of our model (using sympy)
parse_expr(udp.prettier(pop.champion_x))
# And lets see what our model actually predicts on the inputs
Y_pred = udp.predict(X, pop.champion_x)