def test_trigonometric():
"""Check that using trig functions work and that results differ"""
est1 = SymbolicRegressor(random_state=0)
est1.fit(boston.data[:400, :], boston.target[:400])
est1 = mean_absolute_error(est1.predict(boston.data[400:, :]),
boston.target[400:])
est2 = SymbolicRegressor(trigonometric=True, random_state=0)
est2.fit(boston.data[:400, :], boston.target[:400])
est2 = mean_absolute_error(est2.predict(boston.data[400:, :]),
boston.target[400:])
assert_true(abs(est1 - est2) > 0.01)
def test_subsample():
"""Check that subsample work and that results differ"""
est1 = SymbolicRegressor(max_samples=1.0, random_state=0)
est1.fit(boston.data[:400, :], boston.target[:400])
est1 = mean_absolute_error(est1.predict(boston.data[400:, :]),
boston.target[400:])
est2 = SymbolicRegressor(max_samples=0.7, random_state=0)
est2.fit(boston.data[:400, :], boston.target[:400])
est2 = mean_absolute_error(est2.predict(boston.data[400:, :]),
boston.target[400:])
assert_true(abs(est1 - est2) > 0.01)
def test_parsimony_coefficient():
"""Check that parsimony coefficients work and that results differ"""
est1 = SymbolicRegressor(parsimony_coefficient=0.001, random_state=0)
est1.fit(boston.data[:400, :], boston.target[:400])
est1 = mean_absolute_error(est1.predict(boston.data[400:, :]),
boston.target[400:])
est2 = SymbolicRegressor(parsimony_coefficient=0.1, random_state=0)
est2.fit(boston.data[:400, :], boston.target[:400])
est2 = mean_absolute_error(est2.predict(boston.data[400:, :]),
boston.target[400:])
est3 = SymbolicRegressor(parsimony_coefficient='auto', random_state=0)
est3.fit(boston.data[:400, :], boston.target[:400])
est3 = mean_absolute_error(est3.predict(boston.data[400:, :]),
boston.target[400:])
assert_true(abs(est1 - est2) > 0.01)
assert_true(abs(est1 - est3) > 0.01)
assert_true(abs(est2 - est3) > 0.01)
def main():
escalation = {
Position.GOALKEEPER: 1,
Position.DEFENDER: 2,
Position.SIDE: 2,
Position.MIDFIELD: 4,
Position.ATTACKER: 2,
Position.COACH: 1
}
print("Getting auth")
auth = get_auth()
print("Getting teams")
teams = get_teams()
print("Getting athletes")
athletes = get_athletes(teams)
print("Getting scores")
scores = [athlete.get_row(auth) for athlete in athletes]
max_length = 0
for score in scores:
if len(score) > max_length:
max_length = len(score)
fixed_score = []
for score in scores:
fixed_score.append([0.0] * (max_length - len(score)) + score)
generations = 2000
print("Training using " + str(generations) + " generations. It can take a long time to end")
est_gp = SymbolicRegressor(
population_size=5000,
generations=generations,
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,
const_range=(-50., 50.),
function_set=(
'add', 'sub', 'mul', 'div', 'sqrt', 'log', 'abs', 'neg', 'inv', 'max', 'min', 'sin', 'cos', 'tan'))
est_gp.fit([x[:-1] for x in fixed_score], [x[-1] for x in fixed_score])
predictions = est_gp.predict([x[:-1] for x in fixed_score])
print("Getting results")
results = [[athlete, prediction] for athlete, prediction in zip(athletes, predictions)]
results.sort(key=lambda x: -x[1])
print("\"Scale\",\"Name\",\"Team\",\"Position\",\"Status\",\"Price\",\"Prediction\"")
for result in results:
athlete = result[0]
prediction = result[1]
scale = athlete.status == Status.Probable and escalation[athlete.position] > 0
if scale:
escalation[athlete.position] = escalation[athlete.position] - 1
print("\"" +
("*" if scale else " ") + "\",\"" +
athlete.nick + "\",\"" +
athlete.club.name + "\",\"" +
str(athlete.position.name) + "\",\"" +
str(athlete.status.name) + "\"," +
str(athlete.price) + "," +
str(prediction))
print("Done")
p_hoist_mutation=0.0001,
p_point_mutation=0.0001,
max_samples=1.0,
verbose=1,
function_set=('add', 'sub', 'mul', 'div', gp_tanh, 'sqrt', 'log', 'abs',
'neg', 'inv', 'max', 'min', 'tan', 'cos', 'sin'),
#function_set = (gp_tanh, 'add', 'sub', 'mul', 'div'),
metric='mean absolute error',
warm_start=True,
n_jobs=1,
parsimony_coefficient=0.0001,
random_state=111)
if (os.path.exists(f'est_gp.pickle')):
pickle_in = open(f'est_gp.pickle', 'rb')
est_gp = pickle.load(pickle_in)
print("Model Loaded")
est_gp.generations = est_gp.generations + 9
est_gp.fit(X_tr, y_tr)
with open(f'est_gp.pickle', 'wb') as f:
pickle.dump(est_gp, f)
print('Model Saved')
print("gpLearn Program:", est_gp._program)
y_gp = est_gp.predict(X_tr)
gpLearn_MAE = mean_absolute_error(y_tr, y_gp)
print("gpLearn MAE:", gpLearn_MAE)
print(y_tr, y_gp)
verbose=1,
parsimony_coefficient=0.01,
random_state=0)
_ = [i for i in range(x_data.shape[0])]
est_gp.fit(x_data, _)
ts = int(time.time())
graph = pydotplus.graphviz.graph_from_dot_data(
est_gp._program.export_graphviz())
graph.write_png("outputs/gp-{suffix}.png".format(suffix=ts))
res = cheating_func(x_arr_pointer, CONFIG_N_DIM, x_len)
y_truth = np.array([float(res[i]) for i in range(n_data)])
y_pred = np.array(est_gp.predict(x_data))
n_data_plot = 200
indicies_plot = sorted(np.random.choice(n_data, n_data_plot, replace=False))
canvas = gp_plot.GPCanvas()
canvas.draw_line_chart_2d(range(0, n_data_plot),
y_truth[indicies_plot],
color="blue",
label="y_truth",
line_style="solid")
canvas.draw_line_chart_2d(range(0, n_data_plot),
y_pred[indicies_plot],
color="red",
label="y_pred")
def main():
escalation = {
Position.GOALKEEPER: 1,
Position.DEFENDER: 2,
Position.SIDE: 2,
Position.MIDFIELD: 4,
Position.ATTACKER: 2,
Position.COACH: 1
}
print("Getting auth")
auth = get_auth()
print("Getting teams")
teams = get_teams()
print("Getting athletes")
athletes = get_athletes(teams)
print("Getting scores")
scores = [athlete.get_row(auth) for athlete in athletes]
max_length = 0
for score in scores:
if len(score) > max_length:
max_length = len(score)
fixed_score = []
for score in scores:
fixed_score.append([0.0] * (max_length - len(score)) + score)
generations = 2000
print("Training using " + str(generations) +
" generations. It can take a long time to end")
est_gp = SymbolicRegressor(
population_size=5000,
generations=generations,
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,
const_range=(-50., 50.),
function_set=('add', 'sub', 'mul', 'div', 'sqrt', 'log', 'abs', 'neg',
'inv', 'max', 'min', 'sin', 'cos', 'tan'))
est_gp.fit([x[:-1] for x in fixed_score], [x[-1] for x in fixed_score])
predictions = est_gp.predict([x[:-1] for x in fixed_score])
print("Getting results")
results = [[athlete, prediction]
for athlete, prediction in zip(athletes, predictions)]
results.sort(key=lambda x: -x[1])
print(
"\"Scale\",\"Name\",\"Team\",\"Position\",\"Status\",\"Price\",\"Prediction\""
)
for result in results:
athlete = result[0]
prediction = result[1]
scale = athlete.status == Status.Probable and escalation[
athlete.position] > 0
if scale:
escalation[athlete.position] = escalation[athlete.position] - 1
print("\"" + ("*" if scale else " ") + "\",\"" + athlete.nick +
"\",\"" + athlete.club.name + "\",\"" +
str(athlete.position.name) + "\",\"" + str(athlete.status.name) +
"\"," + str(athlete.price) + "," + str(prediction))
print("Done")
max_samples=0.9, verbose=1,
parsimony_coefficient=0.01, random_state=0,
function_set=('add', 'sub', 'mul', 'div', 'sqrt', 'log'
, 'abs', 'neg', 'inv', 'max', 'min', 'sin', 'cos', 'tan'))
est_gp.fit(trainSet, z_train)
print(est_gp._program)
score_gp = est_gp.score(testSet, z_test)
#score_gp = est_gp.mean_absolute_error(testSet, z_test)
print(score_gp)
#19 generations required
#min(add(add(log(add(inv(div(sin(X0), mul(X0, 0.952))),neg(abs(X0)))), neg(inv(div(sin(X1), mul(X0, 0.952))))), min(add(log(add(min(mul(-0.020, X1), cos(X1)), neg(add(log(add(min(inv(div(sin(X1), mul(X0, 0.952))), cos(X1)), div(sin(X1), add(log(add(min(mul(-0.020, X1), cos(X1)), neg(add(log(cos(X1)), neg(0.952))))), add(tan(tan(sin(X1))), neg(inv(div(sin(X1), neg(div(X1, 0.794)))))))))), neg(0.952))))), add(tan(tan(sin(X1))), neg(inv(div(sin(X1), neg(div(X1, 0.794))))))), div(neg(cos(tan(X1))), inv(mul(add(X1, X1), log(X1)))))), div(neg(cos(inv(mul(add(X1, X1), log(X1))))), inv(mul(add(X1, X1), log(X1)))))
z_gp = est_gp.predict(np.c_[x.ravel(), y.ravel()]).reshape(x.shape)
#print(z_gp)
ax = plt.figure().gca(projection = '3d')
ax.set_xlim(-10,10)
ax.set_ylim(-10, 10)
#surf = ax.plot_trisurf(x_test, y_test, z_gp, color='green')
surf = ax.plot_surface(x, y, z_gp, rstride=1, cstride=1, color='green', alpha=0.5)
points = ax.scatter(x_train, y_train, z_train)
score = ax.text(-.7, 1, .2, "$R^2 =\/ %.6f$" % score_gp, 'x', fontsize=14)
title = "TestId: 2; Symbolic Regressor"
plt.title(title)
plt.show()
ax = plt.figure().gca(projection='3d')
ax.set_xlim(-10, 10)
