def symbolicRegr(funcs):
gpRun = genetic.SymbolicRegressor(population_size=popSize,
generations=noGens,
tournament_size=20,
const_range=None,
function_set=funcs,
metric=nsae,
p_crossover=crossoverProb,
p_subtree_mutation=mutationProb,
p_hoist_mutation=mutationProb,
p_point_mutation=mutationProb,
verbose=0)
gpRun.fit(np.array(depVar).reshape(-1, 1), np.array(yVar))
print(
f'Expression: {gpRun._program}\nFitness: {gpRun._program.fitness_}\nLength: {gpRun._program.length_}'
)
return gpRun
def get_predictor(train_x, train_y, predictor_parameter_type=get_setting_cfg().get('GP', 'setting')):
parameter = get_gp_parameter(predictor_parameter_type)
mltd_predictor = gp.SymbolicRegressor(
function_set=cf.get_custom_function_list(), **parameter['predictor_parameter'])
return mltd_predictor.fit(train_x, train_y)
N = 20
# X_train = rng.uniform(xmin, xmax, N).reshape(N, 1)
# y_train = np.ravel(X_train)
def protexp(x):
return np.exp(-np.abs(x))
nexp = gf.make_function(protexp, 'negabsexp', 1)
f_s = ['add', 'sub', 'mul', 'div', 'inv', 'abs', nexp, 'log']
est_gp = gl.SymbolicRegressor(init_depth=(3, 6), population_size=4000,
tournament_size=20,
generations=30, stopping_criteria=0.01,
p_crossover=0.7, p_subtree_mutation=0.1,
p_hoist_mutation=0.05, p_point_mutation=0.1,
max_samples=0.9, verbose=1,
parsimony_coefficient=0.01, random_state=0,
function_set=f_s)
est_gp.fit(X_train, y_train)
y_gp = est_gp.predict(np.c_[X_traintemp.ravel()]).reshape(X_traintemp.shape)
print(est_gp.program)
plt.plot(X_traintemp, y_traintemp, '.')
plt.plot(X_traintemp, y_gp)
plt.title('SR on raw data')
plt.show()
with np.errstate(over='ignore'):
return np.where(np.abs(x) < 100, np.exp(-x), 0.)
pexp = gf.make_function(_protected_exponent, 'exp', 1)
pnexp = gf.make_function(_protected_negexponent, 'nexp', 1)
f_s = ['add', 'sub', 'mul', 'div', pexp, pnexp, 'neg']
est_gp = gl.SymbolicRegressor(init_depth=(2, 4),
population_size=3000,
tournament_size=20,
const_range=(-40, 40),
generations=20,
stopping_criteria=0.01,
p_crossover=0.7,
p_subtree_mutation=0.1,
warm_start=True,
p_hoist_mutation=0.05,
p_point_mutation=0.1,
max_samples=0.9,
verbose=1,
random_state=0,
function_set=f_s)
est_gp.fit(inTrain, outTrain)
y_gp = est_gp.predict(np.c_[domaingrid.ravel()]).reshape(domaingrid.shape)
print(est_gp.program)
plt.plot(inTrain, outTrain)
plt.plot(domaingrid, y_gp)
rng = check_random_state(0)
# Training samples
X_train = rng.uniform(-1, 1, 100).reshape(50, 2)
y_train = X_train[:, 0]**2 - X_train[:, 1]**2 + X_train[:, 1] - 1
# Testing samples
X_test = rng.uniform(-1, 1, 100).reshape(50, 2)
y_test = X_test[:, 0]**2 - X_test[:, 1]**2 + X_test[:, 1] - 1
est_gp = genetic.SymbolicRegressor(population_size=50,
generations=200,
stopping_criteria=0.01,
p_crossover=0.7,
p_subtree_mutation=0.1,
p_hoist_mutation=0.05,
p_point_mutation=0.1,
max_samples=0.9,
verbose=1,
parsimony_coefficient=0.01,
random_state=0)
est_gp.fit(X_train, y_train)
print(est_gp._program)
with open("../Data/programs_test", "w") as f:
for i in est_gp._programs[-1]:
f.write(i)
f.write("\n")
for i in est_gp._programs[-1]:
function_set = ['add', 'sub', 'mul', 'div', 'log', 'sin', 'cos', exp]
#create summed absolute error as metric
_sae = lambda y, t, w: np.sum(np.abs(y - t))
sae = fitness.make_fitness(_sae, False)
n_generations = 50
#Initialize genetic programm regressor
est_gp = genetic.SymbolicRegressor(population_size=1000,
generations=1,
stopping_criteria=0.01,
p_crossover=0.7,
p_subtree_mutation=0,
p_hoist_mutation=0,
p_point_mutation=0,
max_samples=0.9,
verbose=1,
parsimony_coefficient=0,
random_state=0,
metric=sae,
function_set=function_set)
est_gp.fit(x, y)
#Generate generations and save best fitness and size
fitness = []
size = []
for i in range(2, n_generations + 1):
est_gp.set_params(generations=i, warm_start=True)
est_gp.fit(x, y)
fitness.append(est_gp._program.raw_fitness_)