def plot_gp_pred(sigma, **fillargs):
# pdb.set_trace()
nugget = (sigma ** 2 / (0.1 + d.astype('float') ** 2))
gp = GaussianProcess(corr='squared_exponential', nugget=nugget)
gp.fit(np.atleast_2d(range(n)).T, np.atleast_2d(d).T)
x = np.atleast_2d(np.linspace(0, n - 1)).T
y_pred, MSE = gp.predict(x, eval_MSE=True)
pylab.plot(x, y_pred)
pylab.fill_between(x.T[0], y_pred + MSE, y_pred - MSE, **fillargs)
print("Sklearn RT")
t0 = time.time()
rt_sklearn = DecisionTreeRegressor(max_depth=7,
max_features="sqrt",
random_state=2016).fit(
X_train, y_train)
y_pred = rt_sklearn.predict(X_test)
print("Time taken: %0.3f" % (time.time() - t0))
score = mean_absolute_error(y_test, y_pred)
print("Error: %0.3f" % score)
print("")
print("Skearn GP")
gp = GaussianProcess(regr="constant",
corr='absolute_exponential',
beta0=None,
storage_mode='full',
verbose=False,
theta0=0.1,
thetaL=None,
thetaU=None,
optimizer='fmin_cobyla',
random_start=1,
normalize=True,
nugget=0.05,
random_state=2016).fit(X_train, y_train)
y_pred = gp.predict(X_test)
print("Time taken: %0.3f" % (time.time() - t0))
score = mean_absolute_error(y_test, y_pred)
print("Error: %0.3f" % score)
print("")
