def test_gpr_interpolation(kernel):
# Test the interpolating property for different kernels.
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_pred, y_cov = gpr.predict(X, return_cov=True)
assert_almost_equal(y_pred, y)
assert_almost_equal(np.diag(y_cov), 0.)
def get_globals():
X = np.array([
[0.00, 0.00],
[0.99, 0.99],
[0.00, 0.99],
[0.99, 0.00],
[0.50, 0.50],
[0.25, 0.50],
[0.50, 0.25],
[0.75, 0.50],
[0.50, 0.75],
])
def get_y(X):
return -(X[:, 0] - 0.3) ** 2 - 0.5 * (X[:, 1] - 0.6)**2 + 2
y = get_y(X)
mesh = np.dstack(
np.meshgrid(np.arange(0, 1, 0.01), np.arange(0, 1, 0.01))
).reshape(-1, 2)
GP = GaussianProcessRegressor(
kernel=Matern(),
n_restarts_optimizer=25,
)
GP.fit(X, y)
return {'x': X, 'y': y, 'gp': GP, 'mesh': mesh}
def test_y_normalization():
""" Test normalization of the target values in GP
Fitting non-normalizing GP on normalized y and fitting normalizing GP
on unnormalized y should yield identical results
"""
y_mean = y.mean(0)
y_norm = y - y_mean
for kernel in kernels:
# Fit non-normalizing GP on normalized y
gpr = GaussianProcessRegressor(kernel=kernel)
gpr.fit(X, y_norm)
# Fit normalizing GP on unnormalized y
gpr_norm = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
gpr_norm.fit(X, y)
# Compare predicted mean, std-devs and covariances
y_pred, y_pred_std = gpr.predict(X2, return_std=True)
y_pred = y_mean + y_pred
y_pred_norm, y_pred_std_norm = gpr_norm.predict(X2, return_std=True)
assert_almost_equal(y_pred, y_pred_norm)
assert_almost_equal(y_pred_std, y_pred_std_norm)
_, y_cov = gpr.predict(X2, return_cov=True)
_, y_cov_norm = gpr_norm.predict(X2, return_cov=True)
assert_almost_equal(y_cov, y_cov_norm)
def test_predict_cov_vs_std():
""" Test that predicted std.-dev. is consistent with cov's diagonal."""
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_mean, y_cov = gpr.predict(X2, return_cov=True)
y_mean, y_std = gpr.predict(X2, return_std=True)
assert_almost_equal(np.sqrt(np.diag(y_cov)), y_std)
def test_lml_improving():
""" Test that hyperparameter-tuning improves log-marginal likelihood. """
for kernel in kernels:
if kernel == fixed_kernel: continue
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
assert_greater(gpr.log_marginal_likelihood(gpr.kernel_.theta),
gpr.log_marginal_likelihood(kernel.theta))
def bo_(x_obs, y_obs):
kernel = kernels.Matern() + kernels.WhiteKernel()
gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=16)
gp.fit(x_obs, y_obs)
xs = list(repeat(np.atleast_2d(np.linspace(0, 10, 128)).T, 2))
x = cartesian_product(*xs)
a = a_EI(gp, x_obs=x_obs, y_obs=y_obs)
argmin_a_x = x[np.argmax(a(x))]
# heavy evaluation
print("f({})".format(argmin_a_x))
f_argmin_a_x = f2d(np.atleast_2d(argmin_a_x))
plot_2d(gp, x_obs, y_obs, argmin_a_x, a, xs)
plt.show()
bo_(
x_obs=np.vstack((x_obs, argmin_a_x)),
y_obs=np.hstack((y_obs, f_argmin_a_x)),
)
def test_gpr_interpolation():
"""Test the interpolating property for different kernels."""
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_pred, y_cov = gpr.predict(X, return_cov=True)
assert_true(np.allclose(y_pred, y))
assert_true(np.allclose(np.diag(y_cov), 0.))
def test_acquisition_api():
rng = np.random.RandomState(0)
X = rng.randn(10, 2)
y = rng.randn(10)
gpr = GaussianProcessRegressor()
gpr.fit(X, y)
for method in [gaussian_ei, gaussian_lcb, gaussian_pi]:
assert_array_equal(method(X, gpr).shape, 10)
assert_raises(ValueError, method, rng.rand(10), gpr)
def test_converged_to_local_maximum(kernel):
# Test that we are in local maximum after hyperparameter-optimization.
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
lml, lml_gradient = \
gpr.log_marginal_likelihood(gpr.kernel_.theta, True)
assert_true(np.all((np.abs(lml_gradient) < 1e-4) |
(gpr.kernel_.theta == gpr.kernel_.bounds[:, 0]) |
(gpr.kernel_.theta == gpr.kernel_.bounds[:, 1])))
def test_lml_gradient():
""" Compare analytic and numeric gradient of log marginal likelihood. """
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
lml, lml_gradient = gpr.log_marginal_likelihood(kernel.theta, True)
lml_gradient_approx = approx_fprime(
kernel.theta, lambda theta: gpr.log_marginal_likelihood(theta, False), 1e-10
)
assert_almost_equal(lml_gradient, lml_gradient_approx, 3)
def test_prior(kernel):
# Test that GP prior has mean 0 and identical variances.
gpr = GaussianProcessRegressor(kernel=kernel)
y_mean, y_cov = gpr.predict(X, return_cov=True)
assert_almost_equal(y_mean, 0, 5)
if len(gpr.kernel.theta) > 1:
# XXX: quite hacky, works only for current kernels
assert_almost_equal(np.diag(y_cov), np.exp(kernel.theta[0]), 5)
else:
assert_almost_equal(np.diag(y_cov), 1, 5)
def test_sample_statistics():
""" Test that statistics of samples drawn from GP are correct."""
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_mean, y_cov = gpr.predict(X2, return_cov=True)
samples = gpr.sample_y(X2, 300000)
# More digits accuracy would require many more samples
assert_almost_equal(y_mean, np.mean(samples, 1), 2)
assert_almost_equal(np.diag(y_cov) / np.diag(y_cov).max(), np.var(samples, 1) / np.diag(y_cov).max(), 1)
class SmoothFunctionCreator():
def __init__(self, seed=42):
self._gp = GaussianProcessRegressor()
x_train = np.array([0.0, 2.0, 6.0, 10.0])[:, np.newaxis]
source_train = np.array([0.0, 1.0, -1.0, 0.0])
self._gp.fit(x_train, source_train)
self._random_state = np.random.RandomState(seed)
def sample(self, n_samples):
x = np.linspace(0.0, 10.0, 100)[:, np.newaxis]
source = self._gp.sample_y(x, n_samples, random_state=self._random_state)
target = gaussian_filter1d(source, 1, order=1, axis=0)
target = np.tanh(10.0 * target)
return source, target
def fit_GP(x_train):
y_train = gaussian(x_train, mu, sig).ravel()
# Instanciate a Gaussian Process model
kernel = C(1.0, (1e-3, 1e3)) * RBF(1, (1e-2, 1e2))
gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)
# Fit to data using Maximum Likelihood Estimation of the parameters
gp.fit(x_train, y_train)
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred, sigma = gp.predict(x, return_std=True)
return y_train, y_pred, sigma
def test_no_fit_default_predict():
# Test that GPR predictions without fit does not break by default.
default_kernel = (C(1.0, constant_value_bounds="fixed") *
RBF(1.0, length_scale_bounds="fixed"))
gpr1 = GaussianProcessRegressor()
_, y_std1 = gpr1.predict(X, return_std=True)
_, y_cov1 = gpr1.predict(X, return_cov=True)
gpr2 = GaussianProcessRegressor(kernel=default_kernel)
_, y_std2 = gpr2.predict(X, return_std=True)
_, y_cov2 = gpr2.predict(X, return_cov=True)
assert_array_almost_equal(y_std1, y_std2)
assert_array_almost_equal(y_cov1, y_cov2)
def plot_gp(x_min, x_max, x, y, train_features, train_labels):
fig = plt.figure(figsize=(16, 10))
fig.suptitle('Gaussian Process and Utility Function After {} Steps'.format(len(train_features)), fontdict={'size':30})
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
axis = plt.subplot(gs[0])
acq = plt.subplot(gs[1])
gp = GaussianProcessRegressor(
kernel=Matern(nu=2.5),
n_restarts_optimizer=25, )
gp.fit(train_features, train_labels)
mu, sigma = gp.predict(x, return_std=True)
axis.plot(x, y, linewidth=3, label='Target')
axis.plot(train_features.flatten(), train_labels, 'D', markersize=8, label=u'Observations', color='r')
axis.plot(x, mu, '--', color='k', label='Prediction')
axis.fill(np.concatenate([x, x[::-1]]),
np.concatenate([mu - 1.9600 * sigma, (mu + 1.9600 * sigma)[::-1]]),
alpha=.6, fc='c', ec='None', label='95% confidence interval')
axis.set_xlim((x_min, x_max))
axis.set_ylim((None, None))
axis.set_ylabel('f(x)', fontdict={'size':20})
axis.set_xlabel('x', fontdict={'size':20})
bounds = np.asarray([[x_min, x_max]])
acquisition_fucntion_kappa = 5
mean, std = gp.predict(x, return_std=True)
acquisition_fucntion_values = mean + acquisition_fucntion_kappa * std
acq.plot(x, acquisition_fucntion_values, label='Utility Function', color='purple')
acq.plot(x[np.argmax(acquisition_fucntion_values)], np.max(acquisition_fucntion_values), '*', markersize=15,
label=u'Next Best Guess', markerfacecolor='gold', markeredgecolor='k', markeredgewidth=1)
acq.set_xlim((x_min, x_max))
acq.set_ylim((0, np.max(acquisition_fucntion_values) + 0.5))
acq.set_ylabel('Utility', fontdict={'size':20})
acq.set_xlabel('x', fontdict={'size':20})
axis.legend(loc=2, bbox_to_anchor=(1.01, 1), borderaxespad=0.)
acq.legend(loc=2, bbox_to_anchor=(1.01, 1), borderaxespad=0.)
def test_K_inv_reset(kernel):
y2 = f(X2).ravel()
# Test that self._K_inv is reset after a new fit
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
assert hasattr(gpr, '_K_inv')
assert gpr._K_inv is None
gpr.predict(X, return_std=True)
assert gpr._K_inv is not None
gpr.fit(X2, y2)
assert gpr._K_inv is None
gpr.predict(X2, return_std=True)
gpr2 = GaussianProcessRegressor(kernel=kernel).fit(X2, y2)
gpr2.predict(X2, return_std=True)
# the value of K_inv should be independent of the first fit
assert_array_equal(gpr._K_inv, gpr2._K_inv)
def __init__(self, name, title, Myy, trainHisto, dataHisto):
super(RooGPBkg, self).__init__(self, name, title, Myy)
self.name = name
self.title = title
self.Myy = Myy
self.trainHisto = trainHisto
self.dataHisto = dataHisto
self.kernel = C((2.98e4)**2, (1e-3, 1e15)) * RBF(60, (1,1e5 )) #squared exponential kernel
trainHisto.Scale(dataHisto.Integral()/trainHisto.Integral())
self.opt_kernel = self.setTrainMC(trainHisto)
#Need to think a bit more about how to set the range correctly here.
self.sigFunction = ROOT.TF1("dscb", DSCB, 105, 160, 2)
self.currentNSig = None
self.currentSBHist = None
GPh = GPHisto(dataHisto)
X = GPh.getXArr()
Y = GPh.getYArr()
dataErrs = GPh.getErrArr()
self.gp = GaussianProcessRegressor(kernel=self.opt_kernel, #previously optimized kernel
optimizer=None, # Dont reoptimize hyperparamters.
alpha=dataErrs**2)
self.gp.fit(X,Y)
y_pred, sigma = self.gp.predict(X, return_std=True)
#self.gpHisto = arrayToHisto("GPhisto", 105, 160, y_pred)
self.gpHisto = GPh.getHisto(y_pred, sigma, "GPhisto")
def fit_gpr_model(self):
'''
create and fit the gaussian process model.
the results in the model object are stored in a class variable
'''
# prepare input for fitting function
X = self.hist[['wness_bin_center', 'dw23_bin_center']].values
y = self.hist['entries'].values
# uncertainty of counts from a poisson distribution
dy = np.sqrt(y)
# "nugget" is used to inform fitting algorithm of input uncertainty
nugget = (dy / y) ** 2
inds = np.where(np.isnan(nugget))
nugget[inds] = 1.0
if self.flatten_wness:
y = self.do_flatten_wness()
# define kernel
if self.noise:
self.kernel = 1.0 * RBF([.1, .1]) + WhiteKernel(0.1)
else:
self.kernel = RBF([.1, .1])
# Instanciate a Gaussian Process model
self.gp = GaussianProcessRegressor(kernel=self.kernel,
alpha=nugget,
normalize_y=False,
n_restarts_optimizer=10)
# Fit to data using Maximum Likelihood Estimation of the parameters
self.gp.fit(X, y)
print self.gp.kernel_
def __init__(self, f, pbounds, random_state=None, verbose=1):
"""
:param f:
Function to be maximized.
:param pbounds:
Dictionary with parameters names as keys and a tuple with minimum
and maximum values.
:param verbose:
Whether or not to print progress.
"""
# Store the original dictionary
self.pbounds = pbounds
self.random_state = ensure_rng(random_state)
# Data structure containing the function to be optimized, the bounds of
# its domain, and a record of the evaluations we have done so far
self.space = TargetSpace(f, pbounds, random_state)
# Initialization flag
self.initialized = False
# Initialization lists --- stores starting points before process begins
self.init_points = []
self.x_init = []
self.y_init = []
# Counter of iterations
self.i = 0
# Internal GP regressor
self.gp = GaussianProcessRegressor(
kernel=Matern(nu=2.5),
n_restarts_optimizer=25,
random_state=self.random_state
)
# Utility Function placeholder
self.util = None
# PrintLog object
self.plog = PrintLog(self.space.keys)
# Output dictionary
self.res = {}
# Output dictionary
self.res['max'] = {'max_val': None,
'max_params': None}
self.res['all'] = {'values': [], 'params': []}
# non-public config for maximizing the aquisition function
# (used to speedup tests, but generally leave these as is)
self._acqkw = {'n_warmup': 100000, 'n_iter': 250}
# Verbose
self.verbose = verbose
def test_GP_brownian_motion(self):
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
# add data
t = np.linspace(0, 10, 100)
#
# Instanciate a Gaussian Process model
# kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
# Instanciate a Gaussian Process model
kernel = lambda x, y: 1. * min(x, y)
# kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)
# gp = GaussianProcessRegressor()
# Fit to data using Maximum Likelihood Estimation of the parameters
X = np.atleast_2d(t).T
gp.fit(X, y)
# gp = GaussianProcessRegressor()
# Fit to data using Maximum Likelihood Estimation of the parameters
# gp.fit(t, y)
# Make the prediction on the meshed x-axis (ask for MSE as well)
# y_star, err_y_star = gp.predict(t, return_std=True)
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred, sigma = gp.predict(t, return_std=True)
fig = plt.figure()
ax = fig.add_axes((0.1, 0.3, 0.8, 0.65))
ax.invert_yaxis()
ax.plot(t, y, color='blue', label='L bol', lw=2.5)
ax.errorbar(t, y, yerr=yerr, fmt='o', color='blue', label='%s obs.')
#
# ax.plot(t, y_star, color='red', ls='--', lw=1.5, label='GP')
ax.plot(t, y_pred, '-', color='gray')
# ax.fill_between(t, y_star - 2 * err_y_star, y_star + 2 * err_y_star, color='gray', alpha=0.3)
ax.fill(np.concatenate([t, t[::-1]]),
np.concatenate([y_pred - 1.9600 * sigma,
(y_pred + 1.9600 * sigma)[::-1]]),
alpha=.5, fc='b', ec='None', label='95% confidence interval')
plt.show()
def plot_gaussian(data, col):
'''
Plots the gaussian process regression with a characteristic length scale
of 10 years. Essentially this highlights the 'slow trend' in the data.
Parameters
----------
data: dataframe
pandas dataframe containing 'date', 'linMean' which is the average
runtime and 'linSD' which is the standard deviation.
col: string
the color in which the plot the data
'''
#extract the results from the dataframe
Year = np.array(data[u'date'].tolist())
Mean = np.array(data[u'linMean'].tolist())
SD = np.array(data[u'linSD'].tolist())
#initialize the gaussian process. Note that the process is calculated with a
#length scale of 10years to give the 'slow trend' in the results.
length_scale = 10.
kernel = 1.* RBF(length_scale)
gp = GaussianProcessRegressor(kernel=kernel, sigma_squared_n=(SD) ** 2, \
normalize_y=True)
#now fit the data and get the predicted mean and standard deviation
#Note: for reasons that are unclear, GaussianProcessRegressor won't take 1D
#arrays so the data are converted to 2D and then converted back for plotting
gp.fit(np.atleast_2d(Year).T, np.atleast_2d(Mean).T)
Year_array = np.atleast_2d(np.linspace(min(Year)-2, max(Year)+2, 100)).T
Mean_prediction, SD_prediction = gp.predict(Year_pred, return_std=True)
Year_array=Year_array.ravel()
Mean_prediction=Mean_prediction.ravel()
#plot the predicted best fit
plt.plot(Year_array, Mean_prediction, col, alpha=1)
#plot the 95% confidence interval
plt.fill_between(Year_array, (Mean_prediction - 1.9600 * SD_prediction), \
y2=(Mean_prediction + 1.9600 * SD_prediction), alpha=0.5, \
color=col)
plt.draw()
def fit(self, X, y):
""" Use X and y to train a Gaussian process. """
super(GP, self).fit(X, y)
# skip training the process if there aren't enough samples
if X.shape[0] < self.r_minimum:
return
self.gp = GaussianProcessRegressor(normalize_y=True)
self.gp.fit(X, y)
def fit(self, X, y):
def jitter(x, range):
y = np.copy(x)
scale_exp_min = np.abs(np.ceil(np.log10(range[0])))
scale_exp_max = np.abs(np.ceil(np.log10(range[1])))
scale_exp = (scale_exp_max + scale_exp_min) / 2.
r = np.random.rand(y.size) / (10**scale_exp)
y = y + r
return y
# Print msg. when going into gcp.fit
strMessage = "rows in X = %d, r_minimum = %d" % (X.shape[0], self.r_minimum)
logger.debug(strMessage)
# Use X and y to train a Gaussian Copula Process.
super(GCP, self).fit(X, y)
# skip training the process if there aren't enough samples
if X.shape[0] < self.r_minimum:
return
# -- Non-parametric model of 'y', estimated with kernel density
kernel_pdf = st.gaussian_kde(y)
kernel_cdf = make_cdf(kernel_pdf)
kernel_ppf = make_ppf(kernel_pdf)
y_kernel_model = {'pdf': kernel_pdf, 'cdf': kernel_cdf, 'ppf': kernel_ppf}
self.y_kernel_model = y_kernel_model
# - Transform y-->F-->vF-->norm.ppf-->v
vF = y_kernel_model['cdf'](y)
v = st.norm.ppf(vF)
# -- Non-parametric model of each feature in 'X', estimated with kernel density
X_kernel_model = []
for ki in range(X.shape[1]):
columnX = X[:, ki]
if self.tunables[ki][1].is_integer:
columnX = jitter(columnX, self.tunables[ki][1].range)
kernel_pdf = st.gaussian_kde(columnX)
kernel_cdf = make_cdf(kernel_pdf)
kernel_ppf = make_ppf(kernel_pdf)
kernel_model = {'pdf': kernel_pdf, 'cdf': kernel_cdf, 'ppf': kernel_ppf}
X_kernel_model.append(kernel_model)
self.X_kernel_model = X_kernel_model
# -- Transform X-->F-->uF-->norm.ppf-->U
U = np.empty_like(X)
for ki in range(X.shape[1]):
uF = X_kernel_model[ki]['cdf'](X[:, ki])
U[:, ki] = st.norm.ppf(uF)
# - Instantiate a GP and fit it with (U, v)
self.gcp = GaussianProcessRegressor(normalize_y=True)
self.gcp.fit(U, v)
def theta(self, weights):
self.weights = np.exp(np.asarray(weights, dtype=np.float))
# Parse weights into its components
self.theta_gp, self.theta_l, self.length_scales = \
self._parse_weights(self.weights)
# Train length-scale Gaussian Process
kernel = RBF(self.theta_l, length_scale_bounds="fixed")
self.gp_l = GaussianProcessRegressor(kernel=kernel)
self.gp_l.fit(self.X_, np.log10(self.length_scales))
def test_custom_optimizer():
""" Test that GPR can use externally defined optimizers. """
# Define a dummy optimizer that simply tests 50 random hyperparameters
def optimizer(obj_func, initial_theta, bounds):
rng = np.random.RandomState(0)
theta_opt, func_min = initial_theta, obj_func(initial_theta, eval_gradient=False)
for _ in range(50):
theta = np.atleast_1d(rng.uniform(np.maximum(-2, bounds[:, 0]), np.minimum(1, bounds[:, 1])))
f = obj_func(theta, eval_gradient=False)
if f < func_min:
theta_opt, func_min = theta, f
return theta_opt, func_min
for kernel in kernels:
if kernel == fixed_kernel:
continue
gpr = GaussianProcessRegressor(kernel=kernel, optimizer=optimizer)
gpr.fit(X, y)
# Checks that optimizer improved marginal likelihood
assert_greater(gpr.log_marginal_likelihood(gpr.kernel_.theta), gpr.log_marginal_likelihood(gpr.kernel.theta))
def _determine_contextparams(self, optimizer):
"""Select context and params jointly using ACES."""
# Choose the first samples uniform randomly
if len(optimizer.X_) < optimizer.initial_random_samples:
cx = np.random.uniform(self.cx_boundaries[:, 0],
self.cx_boundaries[:, 1])
return cx[:self.context_dims], cx[self.context_dims:]
# Prepare entropy search objective
self._init_es_ensemble()
# Generate data for function mapping
# query_context x query_parameters x eval_context -> entropy reduction
n_query_points = 500
n_data_dims = 2 * self.context_dims + self.dimension
X = np.empty((n_query_points, n_data_dims))
y = np.empty(n_query_points)
for i in range(n_query_points):
# Select query point and evaluation context randomly
query = np.random.uniform(self.cx_boundaries[:, 0],
self.cx_boundaries[:, 1])
ind = np.random.choice(self.n_context_samples)
# Store query point in X and value of entropy-search in y
X[i, :self.context_dims + self.dimension] = query
X[i, self.context_dims + self.dimension:] = \
self.context_samples[ind] - query[:self.context_dims]
y[i] = self.entropy_search_ensemble[ind](query)[0]
# Fit GP model to this data
kernel = C(1.0, (1e-10, 100.0)) \
* RBF(length_scale=(1.0,)*n_data_dims,
length_scale_bounds=[(0.01, 10.0),]*n_data_dims) \
+ WhiteKernel(1.0, (1e-10, 100.0))
self.es_surrogate = GaussianProcessRegressor(kernel=kernel)
self.es_surrogate.fit(X, y)
# Select query based on mean entropy reduction in surrogate model
# predictions
contexts = np.random.uniform(self.context_boundaries[:, 0],
self.context_boundaries[:, 1],
(250, self.context_dims))
def objective_function(cx):
X_query = np.empty((250, n_data_dims))
X_query[:, :self.context_dims + self.dimension] = cx
X_query[:, self.context_dims + self.dimension:] = \
contexts - cx[:self.context_dims]
es_pred, es_cov = \
self.es_surrogate.predict(X_query, return_cov=True)
return es_pred.mean() + self.kappa * np.sqrt(es_cov.mean())
cx = global_optimization(
objective_function, boundaries=self.cx_boundaries,
optimizer=self.optimizer, maxf=optimizer.maxf)
return cx[:self.context_dims], cx[self.context_dims:]
def test_random_starts():
# Test that an increasing number of random-starts of GP fitting only
# increases the log marginal likelihood of the chosen theta.
n_samples, n_features = 25, 2
rng = np.random.RandomState(0)
X = rng.randn(n_samples, n_features) * 2 - 1
y = np.sin(X).sum(axis=1) + np.sin(3 * X).sum(axis=1) \
+ rng.normal(scale=0.1, size=n_samples)
kernel = C(1.0, (1e-2, 1e2)) \
* RBF(length_scale=[1.0] * n_features,
length_scale_bounds=[(1e-4, 1e+2)] * n_features) \
+ WhiteKernel(noise_level=1e-5, noise_level_bounds=(1e-5, 1e1))
last_lml = -np.inf
for n_restarts_optimizer in range(5):
gp = GaussianProcessRegressor(
kernel=kernel, n_restarts_optimizer=n_restarts_optimizer,
random_state=0,).fit(X, y)
lml = gp.log_marginal_likelihood(gp.kernel_.theta)
assert_greater(lml, last_lml - np.finfo(np.float32).eps)
last_lml = lml
class GP(BaseTuner):
def __init__(self, tunables, gridding=0, r_minimum=2):
"""
Extra args:
r_minimum: the minimum number of past results this selector needs in
order to use gaussian process for prediction. If not enough
results are present during a fit(), subsequent calls to
propose() will revert to uniform selection.
"""
super(GP, self).__init__(tunables, gridding=gridding)
self.r_minimum = r_minimum
def fit(self, X, y):
""" Use X and y to train a Gaussian process. """
super(GP, self).fit(X, y)
# skip training the process if there aren't enough samples
if X.shape[0] < self.r_minimum:
return
self.gp = GaussianProcessRegressor(normalize_y=True)
self.gp.fit(X, y)
def predict(self, X):
if self.X.shape[0] < self.r_minimum:
# we probably don't have enough
logger.warn('GP: not enough data, falling back to uniform sampler')
return Uniform(self.tunables).predict(X)
y, stdev = self.gp.predict(X, return_std=True)
return np.array(list(zip(y, stdev)))
def _acquire(self, predictions):
"""
Predictions from the GP will be in the form (prediction, error).
The default acquisition function returns the index with the highest
predicted value, not factoring in error.
"""
return np.argmax(predictions[:, 0])
def bo_(x_obs, y_obs, n_iter):
if n_iter > 0:
kernel = kernels.Matern() + kernels.WhiteKernel()
gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=16)
gp.fit(x_obs, 1-y_obs)
a = a_EI(gp, x_obs=x_obs, y_obs=1-y_obs)
argmax_f_x_ = x[np.argmax(a(x))]
# heavy evaluation
f_argmax_f_x_ = cross_validation(argmax_f_x_)
y_ob = np.atleast_2d(mean_mean_validation_scores(f_argmax_f_x_)).T
return f_argmax_f_x_ + bo_(
x_obs=np.vstack((x_obs, argmax_f_x_)),
y_obs=np.vstack((y_obs, y_ob)),
n_iter=n_iter-1,
)
else:
return []
def do_expt(seed):
np.random.seed(seed)
perm = np.random.permutation(nseq)
perm = perm[:n_bo_init]
Xinit = Xall[perm]
yinit = yall[perm]
rnd_solver = RandomDiscreteOptimizer(Xall, n_iter=n_bo_iter + n_bo_init)
"""
# Embed sequence then pass to kernel.
# We use Matern kernel 1.5 since this only assumes first-orer differentiability.
kernel = ConstantKernel(1.0) * EmbedKernel(length_scale=1.0, nu=1.5,
embed_fn=lambda x: embedder.predict(x))
gpr = GaussianProcessRegressor(kernel=kernel, alpha=noise**2)
acq_fn = expected_improvement
n_seq = np.shape(Xall)[0]
acq_solver = EnumerativeDiscreteOptimizer(Xall, n_iter=n_seq)
bo_embed_solver_slow = BayesianOptimizer(
Xinit, yinit, gpr, acq_fn, acq_solver, n_iter=n_bo_iter)
"""
kernel = ConstantKernel(1.0) * Matern(length_scale=1.0, nu=1.5)
gpr = GaussianProcessRegressor(kernel=kernel, alpha=noise**2)
acq_fn = EI
bo_oracle_embed_solver = BayesianOptimizerEmbedEnum(Xall,
oracle_embed_fn,
Xinit,
yinit,
gpr,
acq_fn,
n_iter=n_bo_iter)
kernel = ConstantKernel(1.0) * Matern(length_scale=1.0, nu=1.5)
gpr = GaussianProcessRegressor(kernel=kernel, alpha=noise**2)
acq_fn = EI
bo_predictor_embed_solver = BayesianOptimizerEmbedEnum(Xall,
predictor_embed_fn,
Xinit,
yinit,
gpr,
acq_fn,
n_iter=n_bo_iter)
kernel = ConstantKernel(1.0) * Matern(length_scale=1.0, nu=1.5)
gpr = GaussianProcessRegressor(kernel=kernel, alpha=noise**2)
acq_fn = EI
bo_super_embed_solver = BayesianOptimizerEmbedEnum(Xall,
super_embed_fn,
Xinit,
yinit,
gpr,
acq_fn,
n_iter=n_bo_iter)
kernel = ConstantKernel(1.0) * Matern(length_scale=1.0, nu=1.5)
gpr = GaussianProcessRegressor(kernel=kernel, alpha=noise**2)
acq_fn = EI
bo_onehot_embed_solver = BayesianOptimizerEmbedEnum(Xall,
onehot_embed_fn,
Xinit,
yinit,
gpr,
acq_fn,
n_iter=n_bo_iter)
"""
# Pass integers to kernel.
kernel = ConstantKernel(1.0) * EmbedKernel(length_scale=1.0, nu=1.5,
embed_fn=lambda x: x)
gpr = GaussianProcessRegressor(kernel=kernel, alpha=noise**2)
acq_fn = expected_improvement
n_seq = 4**seq_len
acq_solver = EnumerativeStringOptimizer(seq_len, n_iter=n_seq)
bo_int_solver = BayesianOptimizer(Xinit, yinit, gpr, acq_fn, acq_solver, n_iter=n_bo_iter)
"""
methods = []
methods.append((bo_oracle_embed_solver, 'BO-oracle-embed-enum'))
methods.append((bo_predictor_embed_solver, 'BO-predictor_embed-enum'))
methods.append((bo_super_embed_solver, 'BO-super-embed-enum'))
methods.append((bo_onehot_embed_solver, 'BO-onehot-enum'))
#methods.append((bo_int_solver, 'BO-int-enum'))
methods.append((rnd_solver, 'RndSolver')) # Always do random last
ytrace = dict()
for solver, name in methods:
print("Running {}".format(name))
time_start = time()
solver.maximize(oracle)
print('time spent by {} = {:0.3f}\n'.format(name, time() - time_start))
ytrace[name] = np.maximum.accumulate(solver.val_history)
plt.figure()
styles = ['k-o', 'r:o', 'b--o', 'g-o', 'c:o', 'm--o', 'y-o']
for i, tuple in enumerate(methods):
style = styles[i]
name = tuple[1]
plt.plot(ytrace[name], style, label=name)
plt.axvline(n_bo_init)
plt.legend()
plt.title("seed = {}".format(seed))
plt.show()
class BayesianOptimizer(BaseOptimizer):
"""
optimize with bayesian optimizer
"""
def __init__(self, configFile, domain):
"""
intialize
"""
defValues = {}
defValues["opti.initial.model.training.size"] = (1000, None)
defValues["opti.acquisition.samp.size"] = (100, None)
defValues["opti.prob.acquisition.strategy"] = ("pi", None)
defValues["opti.acquisition.lcb.mult"] = (2.0, None)
super(BayesianOptimizer, self).__init__(configFile, defValues, domain)
self.model = GaussianProcessRegressor()
def run(self):
"""
run optimizer
"""
assert Candidare.fixedSz, "BayesianOptimizer works only for fixed size solution"
for sampler in self.compDataDistr:
assert sampler.isNumeric(
), "BayesianOptimizer works only for numerical data"
#inir=tial population and moel fit
trSize = self.config.getIntConfig(
"opti.initial.model.training.size")[0]
features, targets = self.createSamples(trSize)
self.model.fit(features, targets)
#iterate
acqSampSize = self.config.getIntConfig("opti.acquisition.samp.size")[0]
prAcqStrategy = self.config.getIntConfig(
"opti.prob.acquisition.strategy")[0]
acqLcbMult = self.config.getFloatConfig(
"opti.prob.acquisition.strategy")[0]
for i in range(self.numIter):
ofeature, otarget = optAcquire(features, targets, acqSampSize,
prAcqStrategy, acqLcbMult)
features = np.vstack((features, [ofeature]))
targets = np.vstack((targets, [otarget]))
self.model.fit(features, targets)
ix = np.argmax(targets)
def optAcquire(features, targets, acqSampSize, prAcqStrategy, acqLcbMult):
"""
run optimizer
"""
mu = self.model.predict(features)
best = min(mu)
sfeatures, stargets = self.createSamples(acqSampSize)
smu, sstd = self.model.predict(sfeatures, return_std=True)
if prAcqStrategy == "pi":
imp = best - smu
z = imp / (sstd + 1E-9)
scores = norm.cdf(z)
elif prAcqStrategy == "ei":
imp = best - smu
z = imp / (sstd + 1E-9)
scores = imp * norm.cdf(z) + sstd * norm.pdf(z)
elif prAcqStrategy == "lcb":
scores = smu - acqLcbMult * sstd
else:
raise ValueError(
"invalid acquisition strategy for next best candidate")
ix = np.argmax(scores)
sfeature = sfeatures[ix]
starget = stargets[ix]
return (sfeature, starget)
def createSamples(self, size):
"""
sample features and targets
"""
features = list()
targets = list()
for i in range(size):
cand = self.createCandidate()
features.append(cand.getSolnAsFloat())
targets.append(cand.cost)
features = np.asarray(features)
targets = np.asarray(targets).reshape(size, 1)
return (features, targets)
'weights': ['uniform', 'distance'],
'p': np.arange(1, 2, 0.25)
}
dt_params = {
'criterion': ['mse', 'friedman_mse', 'mae'],
'max_depth': np.arange(1, 50, 5)
}
models_list = [('LR', LinearRegression(), {}),
('Ridge', Ridge(), ridge_params),
('Lasso', Lasso(), lasso_params),
('ElasticNet', ElasticNet(), elasticnet_params),
('SGDRegressor', SGDRegressor(), sgdregressor_params),
('SVR', SVR(), svr_params),
('KNN', KNeighborsRegressor(), knn_params),
('GaussianProcess', GaussianProcessRegressor(), {}),
('DTree', DecisionTreeRegressor(), dt_params)]
rmsle_scores = []
r2_scores = []
model_names = []
best_estimators = []
for name, model, model_params in list(models_list):
print('-' * 100)
print('Fitting ', name)
model_names.append(name)
model_grid = GridSearchCV(estimator=model,
param_grid=model_params,
scoring='neg_root_mean_squared_error',
verbose=0,
# パラメータの取りうる範囲
x_grid = np.atleast_2d(np.linspace(0, 10, 1001)[:1000]).T
# 初期値として x=1, 9 の 2 点の探索をしておく.
X = np.atleast_2d([1., 9.]).T
y = blackbox_func(X).ravel()
# Gaussian Processs Upper Confidence Bound (GP-UCB)アルゴリズム
# --> 収束するまで繰り返す(収束条件などチューニングポイント)
n_iteration = 50
for i in range(n_iteration):
# 既に分かっている値でガウス過程フィッティング
# --> カーネル関数やパラメータはデフォルトにしています(チューニングポイント)
gp = GaussianProcessRegressor()
gp = KNeighborsRegressor(n_neighbors=2)
gp.fit(X, y)
# 事後分布が求まる
posterior_mean = gp.predict(x_grid)
# posterior_sig = dist_knn(X, x_grid)
posterior_sig = dist_knn(X, x_grid, min([i + 1, 5]))
# 目的関数を最大化する x を次のパラメータとして選択する
# --> βを大きくすると探索重視(初期は大きくし探索重視しイテレーションに同期して減衰させ活用を重視させるなど、チューニングポイント)
idx = acq_ucb(posterior_mean, posterior_sig, beta=100.0)
x_next = x_grid[idx]
plot(x_grid,
y,
def generate_gp(points,
data,
hp0,
kernel_type='squaredexponential',
fixed=False,
hyper_limits=None,
n_restarts_optimizer=9):
"""Gaussian Process for ndim dimensional parameter space.
Parameters
----------
points : array of shape (npoints, ndim).
Coordinates in paramete space of sampled data.
data : array of shape (npoints,).
Data at each of the sampled points.
hp0 : array of shape (ndim+2,)
Initial hyperparameter guess for optimizer.
Order is (sigma_f, ls_0, ls_1, ..., sigma_n).
kernel_type : 'squaredexponential', 'matern32', 'matern52'
limits : array of shape (ndim+2, 2)
Lower and upper bounds on the value of each hyperparameter.
n_restarts_optimizer : int
Number of random points in the hyperparameter space to restart optimization
routine for searching for the maximum log-likelihood.
Total number of optimizations will be n_restarts_optimizer+1.
Returns
-------
gp : GaussianProcessRegressor
"""
# ******* Generate kernel *******
# ConstantKernel = c multiplies *all* elements of kernel matrix by c
# If you want to specify sigma_f (where c=sigma_f^2) then use
# sigma_f^2 and bounds (sigma_flow^2, sigma_fhigh^2)
# WhiteKernel = c \delta_{ij} multiplies *diagonal* elements by c
# If you want to specify sigma_n (where c=sigma_n^2) then use
# sigma_n^2 and bounds (sigma_nlow^2, sigma_nhigh^2)
# radial part uses the length scales [l_0, l_1, ...] not [l_0^2, l_1^2, ...]
# Constant and noise term
if fixed == True:
const = ConstantKernel(hp0[0]**2)
noise = WhiteKernel(hp0[-1]**2)
elif fixed == False:
const = ConstantKernel(hp0[0]**2, hyper_limits[0]**2)
noise = WhiteKernel(hp0[-1]**2, hyper_limits[-1]**2)
else:
raise Exception, "'fixed' must be True or False."
# Radial term
if fixed == True:
if kernel_type == 'squaredexponential':
radial = RBF(hp0[1:-1])
elif kernel_type == 'matern32':
radial = Matern(hp0[1:-1], nu=1.5)
elif kernel_type == 'matern52':
radial = Matern(hp0[1:-1], nu=2.5)
else:
raise Exception, "Options for kernel_type are: 'squaredexponential', 'matern32', 'matern52'."
elif fixed == False:
if kernel_type == 'squaredexponential':
radial = RBF(hp0[1:-1], hyper_limits[1:-1])
elif kernel_type == 'matern32':
radial = Matern(hp0[1:-1], hyper_limits[1:-1], nu=1.5)
elif kernel_type == 'matern52':
radial = Matern(hp0[1:-1], hyper_limits[1:-1], nu=2.5)
else:
raise Exception, "Options for kernel_type are: 'squaredexponential', 'matern32', 'matern52'."
else:
raise Exception, "'fixed' must be True or False."
kernel = const * radial + noise
# ******* Initialize GaussianProcessRegressor and optimize hyperparameters if not fixed *******
if fixed == True:
gp = GaussianProcessRegressor(kernel=kernel, optimizer=None)
# Supply the points and data, but don't optimize the hyperparameters
gp.fit(points, data)
return gp
elif fixed == False:
gp = GaussianProcessRegressor(
kernel=kernel, n_restarts_optimizer=n_restarts_optimizer)
# Optimize the hyperparameters by maximizing the log-likelihood
gp.fit(points, data)
return gp
else:
raise Exception, "'fixed' must be True or False."
def HGPfunc(x, y, y_gn, plot, h1low, h1high, h2low, h2high, h1low_z, h1high_z,
h2low_z, h2high_z):
y = y.reshape(-1, 1)
y_gn = y_gn.reshape(-1, 1)
x = x.reshape(-1, 1)
if plot:
plt.plot(x, y, '+')
plt.xlabel("Pch (dBm)")
plt.ylabel("SNR (dB)")
plt.savefig('Adataset.png', dpi=200)
plt.show()
n = np.size(x)
scaler = StandardScaler().fit(y)
y = scaler.transform(y)
#scaler_gn = StandardScaler().fit(y_gn)
#y_gn = scaler_gn.transform(y_gn)
def sqexp(X, Y, k1, k2):
X = np.atleast_2d(X)
if Y is None:
dists = pdist(X / k2, metric='sqeuclidean')
K = np.exp(-.5 * dists)
# convert from upper-triangular matrix to square matrix
K = squareform(K)
np.fill_diagonal(K, 1)
# return gradient
K_gradient = (K * squareform(dists))[:, :, np.newaxis]
#K_gradient = (X[:, np.newaxis, :] - X[np.newaxis, :, :]) ** 2 \ # anisotropic case, see https://github.com/scikit-learn/scikit-learn/blob/95d4f0841d57e8b5f6b2a570312e9d832e69debc/sklearn/gaussian_process/kernels.py
# / (k2 ** 2)
#K_gradient *= K[..., np.newaxis]
return k1 * K, K_gradient
else:
dists = cdist(X / k2, Y / k2, metric='sqeuclidean')
K = np.exp(-.5 * dists)
return k1 * K
# heteroscedastic versions of functions
global Kyinvh
Kyinvh = 0.0
global Kfh
Kfh = 0.0
def lmlh(params, y, R, y_gn):
#print(params) # show progress of fit
[k1, k2] = params
global Kfh
Kfh = sqexp(x, None, k1, k2**0.5)[0]
#print(np.size(Kfh))
Ky = Kfh + R # calculate initial kernel with noise
global Kyinvh
Kyinvh = inv(Ky)
return -(-0.5 * mul(mul(T(y), Kyinvh), y) - 0.5 * np.log(
(det(Ky))) - 0.5 * n * np.log(2 * np.pi)) + -(
-0.5 * mul(mul(T(y_gn), Kyinvh), y_gn) - 0.5 * np.log(
(det(Ky))) - 0.5 * n * np.log(2 * np.pi)
) # marginal likelihood - (5.8)
def lmlgh(params, y, R, y_gn):
k1, k2 = params
al = mul(Kyinvh, y)
al_gn = mul(Kyinvh, y_gn)
dKdk1 = Kfh * (1 / k1)
dKdk2 = sqexp(x, None, k1, k2**0.5)[1].reshape(n, n)
lmlg1 = -(0.5 * np.trace(mul(mul(al, T(al)) - Kyinvh, dKdk1))) + -(
0.5 * np.trace(mul(mul(al_gn, T(al_gn)) - Kyinvh, dKdk1)))
lmlg2 = -(0.5 * np.trace(mul(mul(al, T(al)) - Kyinvh, dKdk2))) + -(
0.5 * np.trace(mul(mul(al_gn, T(al_gn)) - Kyinvh, dKdk2)))
return np.ndarray((2, ), buffer=np.array([lmlg1, lmlg2]), dtype=float)
def GPRfith(xs, k1, k2, R, Rs):
Ky = sqexp(x, None, k1, k2**0.5)[0] + R
Ks = sqexp(xs, x, k1, k2**0.5)
Kss = sqexp(xs, None, k1, k2)[0]
L = cholesky(Ky)
al = solve(T(L), solve(L, y))
fmst = mul(Ks, al)
varfmst = np.empty([n, 1])
for i in range(np.size(xs)):
v = solve(L, T(Ks[:, i]))
varfmst[i] = Kss[i, i] - mul(T(v), v) + Rs[i, i]
lmlopt = -0.5 * mul(T(y), al) - np.trace(
np.log(L)) - 0.5 * n * np.log(2 * np.pi)
#return fmst, varfmst[::-1], lmlopt
return fmst, varfmst, lmlopt
def hypopth(y, numrestarts, R, y_gn):
numh = 2 # number of hyperparameters in kernel function
k1s4 = np.empty([numrestarts, 1])
k2s4 = np.empty([numrestarts, 1])
for i in range(numrestarts):
#k1is4 = np.random.uniform(1e-2,1e3)
#k2is4 = np.random.uniform(1e-1,1e3)
k1is4 = np.random.uniform(h1low, h1high)
k2is4 = np.random.uniform(h2low, h2high)
kis4 = np.ndarray((numh, ),
buffer=np.array([k1is4, k2is4]),
dtype=float)
s4res = minimize(lmlh,
kis4,
args=(y, R, y_gn),
method='L-BFGS-B',
jac=lmlgh,
bounds=((h1low, h1high), (h2low, h2high)),
options={'maxiter': 1e2})
step4res = []
if s4res.success:
step4res.append(s4res.x)
print("successful k1:" + str(k1is4))
print("successful k2: " + str(k2is4))
else:
print("error " + str(k1is4))
print("error " + str(k2is4))
#raise ValueError(s4res.message)
#k1is4 = np.random.uniform(1e-2,1e3)
#k2is4 = np.random.uniform(2e-1,1e3)
k1is4 = np.random.uniform(h1low, h1high)
k2is4 = np.random.uniform(h2low, h2high)
print("error in hypopth() - reinitialising hyperparameters")
continue
k1s4[i] = step4res[0][0]
k2s4[i] = step4res[0][1]
lmltest = [
lmlh([k1s4[i], k2s4[i]], y, R, y_gn) for i in range(numrestarts)
]
#k1f = k1s4[np.argmin(lmltest)]
#k2f = k2s4[np.argmin(lmltest)]
k1f = k1s4[np.argmax(lmltest)]
k2f = k2s4[np.argmax(lmltest)]
#lml(params,y,sig)
return k1f, k2f
def hetloopSK(fmst, varfmst, numiters, numrestarts):
s = 200
#k1is3, k2is3, k1is4,k2is4 = np.random.uniform(1e-2,1e2,4)
MSE = np.empty([numiters, 1])
NLPD = np.empty([numiters, 1])
fmstf = np.empty([numiters, n])
varfmstf = np.empty([numiters, n])
lmloptf = np.empty([numiters, 1])
rf = np.empty([numiters, n])
i = 0
while i < numiters:
breakwhile = False
# Step 2: estimate empirical noise levels z
#k1is4,k2is4 = np.random.uniform(1e-2,1e2,2)
#k1is3, k1is4 = np.random.uniform(1e-2,1e2,2)
#k2is3, k2is4 = np.random.uniform(1e-1,1e2,2)
k1is3 = np.random.uniform(h1low_z, h1high_z, 1)
k2is3 = np.random.uniform(h2low_z, h2high_z, 1)
z = np.empty([n, 1])
for j in range(n):
#np.random.seed()
normdraw = normal(fmst[j], varfmst[j]**0.5, s).reshape(s, 1)
z[j] = np.log((1 / s) * 0.5 * sum((y[j] - normdraw)**2))
if math.isnan(z[j]): # True for NaN values
breakwhile = True
break
if breakwhile:
print("Nan value in z -- skipping iter " + str(i))
i = i + 1
continue
# Step 3: estimate GP2 on D' - (x,z)
kernel2 = C(k1is3,
(h1low_z, h1high_z)) * RBF(k2is3, (h2low_z, h2high_z))
gpr2 = GaussianProcessRegressor(kernel=kernel2,
n_restarts_optimizer=numrestarts,
normalize_y=False,
alpha=np.var(z))
gpr2.fit(x, z)
ystar2, sigma2 = gpr2.predict(x, return_std=True)
sigma2 = (sigma2**2 + 1)**0.5
# Step 4: train heteroscedastic GP3 using predictive mean of G2 to predict log noise levels r
r = exp(ystar2)
R = r * np.identity(n)
k1s4, k2s4 = hypopth(y, numrestarts, R,
y_gn) # needs to be modified
fmst4, varfmst4, lmlopt4 = GPRfith(x, k1s4, k2s4, R,
R) # needs to be modified
# test for convergence
MSE[i] = (1 / n) * sum(((y - fmst4)**2) / np.var(y))
#NLPD[i] = sum([(1/n)*(-np.log(norm.pdf(x[j], fmst4[j], varfmst4[j]**0.5))) for j in range(n) ])
nlpdarg = np.zeros([n, 1])
#nlpdtest = np.zeros([n,1])
for k in range(n):
nlpdarg[k] = -np.log10(
norm.pdf(x[k], fmst4[k], varfmst4[k]**0.5))
#nlpdtest[k] = norm.pdf(x[k], fmst4[k], varfmst4[k]**0.5)
#print("mean NLPD log arg " + str(nlpdtest) )
#test3[k] = -np.log(norm.pdf(x[k], fmst[k], varfmst[k]**0.5))
NLPD[i] = sum(nlpdarg) * (1 / n)
print("MSE = " + str(MSE[i]))
print("NLPD = " + str(NLPD[i]))
print("finished iteration " + str(i + 1))
fmstf[i, :] = fmst4.reshape(n)
varfmstf[i, :] = varfmst4.reshape(n)
lmloptf[i] = lmlopt4
fmst = fmst4
varfmst = varfmst4
rf[i, :] = r.reshape(n)
#k1is3 = k1s4
#k2is3 = k2s4
i = i + 1
return fmstf, varfmstf, lmloptf, MSE, rf, NLPD # , NLPD
numiters = 10
numrestarts = 20
#kernel1 = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-3, 1e3)) + W(1.0, (1e-5, 1e5))
#gpr1 = GaussianProcessRegressor(kernel=kernel1, n_restarts_optimizer = 0, normalize_y=True)
kernel1 = C(1.0, (h1low, h1high)) * RBF(1.0, (h2low, h2high))
gpr1 = GaussianProcessRegressor(kernel=kernel1,
n_restarts_optimizer=numrestarts,
normalize_y=False,
alpha=np.var(y))
gpr1.fit(x, y)
ystar1, sigma1 = gpr1.predict(x, return_std=True)
var1 = (sigma1**2 + np.var(y))
#sigma1 = np.reshape(sigma1,(np.size(sigma1), 1))
start_time = time.time()
fmstf, varfmstf, lmlopt, mse, _, NLPD = hetloopSK(ystar1, var1, numiters,
numrestarts)
duration = time.time() - start_time
ind = numiters - 1
#ind =
fmst4 = fmstf[ind]
varfmst4 = varfmstf[ind]
sigs4 = varfmst4**0.5
fmstps4 = fmst4 + sigs4
fmst4i = scaler.inverse_transform(fmst4)
fmstps4i = scaler.inverse_transform(fmstps4)
print("HGP fitting duration: " + str(duration))
return fmst4i, fmstps4i, lmlopt, mse, NLPD
import numpy as np
import pandas as pd
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import PowerTransformer
from tpot.builtins import ZeroCount
from tpot.export_utils import set_param_recursive
# NOTE: Make sure that the outcome column is labeled 'target' in the data file
tpot_data = pd.read_csv('PATH/TO/DATA/FILE',
sep='COLUMN_SEPARATOR',
dtype=np.float64)
features = tpot_data.drop('target', axis=1)
training_features, testing_features, training_target, testing_target = \
train_test_split(features, tpot_data['target'], random_state=123)
# Average CV score on the training set was: 0.9858937143431208
exported_pipeline = make_pipeline(
ZeroCount(), PowerTransformer(),
GaussianProcessRegressor(kernel=Matern(length_scale=2.9000000000000004,
nu=1.5),
n_restarts_optimizer=155,
normalize_y=True))
# Fix random state for all the steps in exported pipeline
set_param_recursive(exported_pipeline.steps, 'random_state', 123)
exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)
def test_y_multioutput():
# Test that GPR can deal with multi-dimensional target values
y_2d = np.vstack((y, y * 2)).T
# Test for fixed kernel that first dimension of 2d GP equals the output
# of 1d GP and that second dimension is twice as large
kernel = RBF(length_scale=1.0)
gpr = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=False)
gpr.fit(X, y)
gpr_2d = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=False)
gpr_2d.fit(X, y_2d)
y_pred_1d, y_std_1d = gpr.predict(X2, return_std=True)
y_pred_2d, y_std_2d = gpr_2d.predict(X2, return_std=True)
_, y_cov_1d = gpr.predict(X2, return_cov=True)
_, y_cov_2d = gpr_2d.predict(X2, return_cov=True)
assert_almost_equal(y_pred_1d, y_pred_2d[:, 0])
assert_almost_equal(y_pred_1d, y_pred_2d[:, 1] / 2)
# Standard deviation and covariance do not depend on output
for target in range(y_2d.shape[1]):
assert_almost_equal(y_std_1d, y_std_2d[..., target])
assert_almost_equal(y_cov_1d, y_cov_2d[..., target])
y_sample_1d = gpr.sample_y(X2, n_samples=10)
y_sample_2d = gpr_2d.sample_y(X2, n_samples=10)
assert y_sample_1d.shape == (5, 10)
assert y_sample_2d.shape == (5, 2, 10)
# Only the first target will be equal
assert_almost_equal(y_sample_1d, y_sample_2d[:, 0, :])
# Test hyperparameter optimization
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
gpr.fit(X, y)
gpr_2d = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
gpr_2d.fit(X, np.vstack((y, y)).T)
assert_almost_equal(gpr.kernel_.theta, gpr_2d.kernel_.theta, 4)
def test_warning_bounds():
kernel = RBF(length_scale_bounds=[1e-5, 1e-3])
gpr = GaussianProcessRegressor(kernel=kernel)
warning_message = (
"The optimal value found for dimension 0 of parameter "
"length_scale is close to the specified upper bound "
"0.001. Increasing the bound and calling fit again may "
"find a better value."
)
with pytest.warns(ConvergenceWarning, match=warning_message):
gpr.fit(X, y)
kernel_sum = WhiteKernel(noise_level_bounds=[1e-5, 1e-3]) + RBF(
length_scale_bounds=[1e3, 1e5]
)
gpr_sum = GaussianProcessRegressor(kernel=kernel_sum)
with pytest.warns(None) as record:
with warnings.catch_warnings():
# scipy 1.3.0 uses tostring which is deprecated in numpy
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
gpr_sum.fit(X, y)
assert len(record) == 2
assert (
record[0].message.args[0]
== "The optimal value found for "
"dimension 0 of parameter "
"k1__noise_level is close to the "
"specified upper bound 0.001. "
"Increasing the bound and calling "
"fit again may find a better value."
)
assert (
record[1].message.args[0]
== "The optimal value found for "
"dimension 0 of parameter "
"k2__length_scale is close to the "
"specified lower bound 1000.0. "
"Decreasing the bound and calling "
"fit again may find a better value."
)
X_tile = np.tile(X, 2)
kernel_dims = RBF(length_scale=[1.0, 2.0], length_scale_bounds=[1e1, 1e2])
gpr_dims = GaussianProcessRegressor(kernel=kernel_dims)
with pytest.warns(None) as record:
with warnings.catch_warnings():
# scipy 1.3.0 uses tostring which is deprecated in numpy
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
gpr_dims.fit(X_tile, y)
assert len(record) == 2
assert (
record[0].message.args[0]
== "The optimal value found for "
"dimension 0 of parameter "
"length_scale is close to the "
"specified lower bound 10.0. "
"Decreasing the bound and calling "
"fit again may find a better value."
)
assert (
record[1].message.args[0]
== "The optimal value found for "
"dimension 1 of parameter "
"length_scale is close to the "
"specified lower bound 10.0. "
"Decreasing the bound and calling "
"fit again may find a better value."
)
def test_gpr_fit_error(params, TypeError, err_msg):
"""Check that expected error are raised during fit."""
gpr = GaussianProcessRegressor(**params)
with pytest.raises(TypeError, match=err_msg):
gpr.fit(X, y)
def test_lml_precomputed(kernel):
# Test that lml of optimized kernel is stored correctly.
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
assert gpr.log_marginal_likelihood(gpr.kernel_.theta) == pytest.approx(
gpr.log_marginal_likelihood()
)
# ----------------------------------------------------------------------
# First the noiseless case
X = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T
# Observations
y = f(X).ravel()
# Mesh the input space for evaluations of the real function, the prediction and
# its MSE
x = np.atleast_2d(np.linspace(0, 10, 1000)).T
# Instantiate a Gaussian Process model
kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)
# Fit to data using Maximum Likelihood Estimation of the parameters
gp.fit(X, y)
# Make the prediction on the meshed x-axis (ask for MSE as well)
y_pred, sigma = gp.predict(x, return_std=True)
# Plot the function, the prediction and the 95% confidence interval based on
# the MSE
plt.figure()
plt.plot(x, f(x), 'r:', label=r'$f(x) = x\,\sin(x)$')
plt.plot(X, y, 'r.', markersize=10, label='Observations')
plt.plot(x, y_pred, 'b-', label='Prediction')
plt.fill(np.concatenate([x, x[::-1]]),
np.concatenate(
class BayesianOptimization(Observable):
"""
This class takes the function to optimize as well as the parameters bounds
in order to find which values for the parameters yield the maximum value
using bayesian optimization.
Parameters
----------
f: function
Function to be maximized.
pbounds: dict
Dictionary with parameters names as keys and a tuple with minimum
and maximum values.
random_state: int or numpy.random.RandomState, optional(default=None)
If the value is an integer, it is used as the seed for creating a
numpy.random.RandomState. Otherwise the random state provieded it is used.
When set to None, an unseeded random state is generated.
verbose: int, optional(default=2)
The level of verbosity.
bounds_transformer: DomainTransformer, optional(default=None)
If provided, the transformation is applied to the bounds.
Methods
-------
probe()
Evaluates the function on the given points.
Can be used to guide the optimizer.
maximize()
Tries to find the parameters that yield the maximum value for the
given function.
set_bounds()
Allows changing the lower and upper searching bounds
"""
def __init__(self, f, pbounds, random_state=None, verbose=2,
bounds_transformer=None):
self._random_state = ensure_rng(random_state)
# Data structure containing the function to be optimized, the bounds of
# its domain, and a record of the evaluations we have done so far
self._space = TargetSpace(f, pbounds, random_state)
self._queue = Queue()
# Internal GP regressor
self._gp = GaussianProcessRegressor(
kernel=Matern(nu=2.5),
alpha=1e-6,
normalize_y=True,
n_restarts_optimizer=5,
random_state=self._random_state,
)
self._verbose = verbose
self._bounds_transformer = bounds_transformer
if self._bounds_transformer:
if hasattr(self._bounds_transformer, "bounds")==False:
#raise TypeError('test')
try:
self._bounds_transformer.initialize(self._space)
except (AttributeError, TypeError):
raise TypeError('The transformer must be an instance of '
'DomainTransformer')
super(BayesianOptimization, self).__init__(events=DEFAULT_EVENTS)
@property
def space(self):
return self._space
@property
def max(self):
return self._space.max()
@property
def res(self):
return self._space.res()
def register(self, params, target):
"""Expect observation with known target"""
self._space.register(params, target)
self.dispatch(Events.OPTIMIZATION_STEP)
def probe(self, params, lazy=True):
"""
Evaluates the function on the given points. Useful to guide the optimizer.
Parameters
----------
params: dict or list
The parameters where the optimizer will evaluate the function.
lazy: bool, optional(default=True)
If True, the optimizer will evaluate the points when calling
maximize(). Otherwise it will evaluate it at the moment.
"""
if lazy:
self._queue.add(params)
else:
self._space.probe(params)
self.dispatch(Events.OPTIMIZATION_STEP)
def suggest(self, utility_function):
"""Most promising point to probe next"""
if len(self._space) == 0:
return self._space.array_to_params(self._space.random_sample())
# Sklearn's GP throws a large number of warnings at times, but
# we don't really need to see them here.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
self._gp.fit(self._space.params, self._space.target)
# Finding argmax of the acquisition function.
suggestion = acq_max(
ac=utility_function.utility,
gp=self._gp,
y_max=self._space.target.max(),
bounds=self._space.bounds,
random_state=self._random_state
)
return self._space.array_to_params(suggestion)
def _prime_queue(self, init_points):
"""Make sure there's something in the queue at the very beginning."""
if self._queue.empty and self._space.empty:
init_points = max(init_points, 1)
for _ in range(init_points):
self._queue.add(self._space.random_sample())
def _prime_subscriptions(self):
if not any([len(subs) for subs in self._events.values()]):
_logger = _get_default_logger(self._verbose)
self.subscribe(Events.OPTIMIZATION_START, _logger)
self.subscribe(Events.OPTIMIZATION_STEP, _logger)
self.subscribe(Events.OPTIMIZATION_END, _logger)
def maximize(self,
init_points=5,
n_iter=25,
acq='ucb',
kappa=2.576,
kappa_decay=1,
kappa_decay_delay=0,
xi=0.0,
**gp_params):
"""
Probes the target space to find the parameters that yield the maximum
value for the given function.
Parameters
----------
init_points : int, optional(default=5)
Number of iterations before the explorations starts the exploration
for the maximum.
n_iter: int, optional(default=25)
Number of iterations where the method attempts to find the maximum
value.
acq: {'ucb', 'ei', 'poi'}
The acquisition method used.
* 'ucb' stands for the Upper Confidence Bounds method
* 'ei' is the Expected Improvement method
* 'poi' is the Probability Of Improvement criterion.
kappa: float, optional(default=2.576)
Parameter to indicate how closed are the next parameters sampled.
Higher value = favors spaces that are least explored.
Lower value = favors spaces where the regression function is the
highest.
kappa_decay: float, optional(default=1)
`kappa` is multiplied by this factor every iteration.
kappa_decay_delay: int, optional(default=0)
Number of iterations that must have passed before applying the decay
to `kappa`.
xi: float, optional(default=0.0)
[unused]
"""
self._prime_subscriptions()
self.dispatch(Events.OPTIMIZATION_START)
self._prime_queue(init_points)
self.set_gp_params(**gp_params)
util = UtilityFunction(kind=acq,
kappa=kappa,
xi=xi,
kappa_decay=kappa_decay,
kappa_decay_delay=kappa_decay_delay)
iteration = 0
while not self._queue.empty or iteration < n_iter:
try:
x_probe = next(self._queue)
except StopIteration:
util.update_params()
x_probe = self.suggest(util)
iteration += 1
self.probe(x_probe, lazy=False)
if self._bounds_transformer:
self.set_bounds(
self._bounds_transformer.transform(self._space))
self.dispatch(Events.OPTIMIZATION_END)
def set_bounds(self, new_bounds):
"""
A method that allows changing the lower and upper searching bounds
Parameters
----------
new_bounds : dict
A dictionary with the parameter name and its new bounds
"""
self._space.set_bounds(new_bounds)
def set_gp_params(self, **params):
"""Set parameters to the internal Gaussian Process Regressor"""
self._gp.set_params(**params)
train_labels = feature_matrices['train_labels']
train_values = feature_matrices['train_values']
train_targets = feature_matrices['train_targets']
test_labels = feature_matrices['test_labels']
test_values = feature_matrices['test_values']
test_targets = feature_matrices['test_targets']
n_features = train_values.shape[1]
# Model Specific Information
# -----------------------
lbound = 1e-2
rbound = 1e1
n_restarts = 25
kernel = C(1.0, (lbound, rbound)) * Matern(n_features * [1], (lbound, rbound),
nu=2.5)
gp = GPR(kernel=kernel, n_restarts_optimizer=n_restarts)
gp.fit(train_values, train_targets)
test_model, sigma2_pred_test = gp.predict(test_values, return_std=True)
train_model, sigma2_pred_train = gp.predict(train_values, return_std=True)
# -----------------------
# Undo normalization in FP generation
test_model = np.multiply(test_model, std_target) + mean_target
sigma2_pred_test = np.multiply(sigma2_pred_test, std_target) # GP Specific
test_targets = np.multiply(test_targets, std_target) + mean_target
train_model = np.multiply(train_model, std_target) + mean_target
sigma2_pred_train = np.multiply(sigma2_pred_train, std_target) # GP Specific
train_targets = np.multiply(train_targets, std_target) + mean_target
ran = RANSACRegressor()
tsr = TheilSenRegressor(random_state=42)
br = BayesianRidge(n_iter=300, tol=0.001)
bgm = BayesianGaussianMixture()
knr = KNeighborsRegressor(n_neighbors=5)
rnr = RadiusNeighborsRegressor(radius=1.0)
pls = PLSRegression(n_components=1)
gnb = GaussianNB()
mnb = MultinomialNB()
svl = SVR(kernel='linear')
svr = SVR()
las = Lasso()
en = ElasticNet()
rr = Ridge()
kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
gpr = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)
estimators = {
'LR ': lr,
'DTR': dtr,
'RFR': rfr,
'OMP': omp,
'RAN': ran,
'BR ': br,
'BGM': bgm,
'KNR': knr,
'RNR': rnr,
'PLS': pls,
'SVL': svl,
'SVR': svr,
'LAS': las,
def test_y_normalization(kernel):
"""
Test normalization of the target values in GP
Fitting non-normalizing GP on normalized y and fitting normalizing GP
on unnormalized y should yield identical results. Note that, here,
'normalized y' refers to y that has been made zero mean and unit
variance.
"""
y_mean = np.mean(y)
y_std = np.std(y)
y_norm = (y - y_mean) / y_std
# Fit non-normalizing GP on normalized y
gpr = GaussianProcessRegressor(kernel=kernel)
gpr.fit(X, y_norm)
# Fit normalizing GP on unnormalized y
gpr_norm = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
gpr_norm.fit(X, y)
# Compare predicted mean, std-devs and covariances
y_pred, y_pred_std = gpr.predict(X2, return_std=True)
y_pred = y_pred * y_std + y_mean
y_pred_std = y_pred_std * y_std
y_pred_norm, y_pred_std_norm = gpr_norm.predict(X2, return_std=True)
assert_almost_equal(y_pred, y_pred_norm)
assert_almost_equal(y_pred_std, y_pred_std_norm)
_, y_cov = gpr.predict(X2, return_cov=True)
y_cov = y_cov * y_std**2
_, y_cov_norm = gpr_norm.predict(X2, return_cov=True)
assert_almost_equal(y_cov, y_cov_norm)
def hetloopSK(fmst, varfmst, numiters, numrestarts):
s = 200
#k1is3, k2is3, k1is4,k2is4 = np.random.uniform(1e-2,1e2,4)
MSE = np.empty([numiters, 1])
NLPD = np.empty([numiters, 1])
fmstf = np.empty([numiters, n])
varfmstf = np.empty([numiters, n])
lmloptf = np.empty([numiters, 1])
rf = np.empty([numiters, n])
i = 0
while i < numiters:
breakwhile = False
# Step 2: estimate empirical noise levels z
#k1is4,k2is4 = np.random.uniform(1e-2,1e2,2)
#k1is3, k1is4 = np.random.uniform(1e-2,1e2,2)
#k2is3, k2is4 = np.random.uniform(1e-1,1e2,2)
k1is3 = np.random.uniform(h1low_z, h1high_z, 1)
k2is3 = np.random.uniform(h2low_z, h2high_z, 1)
z = np.empty([n, 1])
for j in range(n):
#np.random.seed()
normdraw = normal(fmst[j], varfmst[j]**0.5, s).reshape(s, 1)
z[j] = np.log((1 / s) * 0.5 * sum((y[j] - normdraw)**2))
if math.isnan(z[j]): # True for NaN values
breakwhile = True
break
if breakwhile:
print("Nan value in z -- skipping iter " + str(i))
i = i + 1
continue
# Step 3: estimate GP2 on D' - (x,z)
kernel2 = C(k1is3,
(h1low_z, h1high_z)) * RBF(k2is3, (h2low_z, h2high_z))
gpr2 = GaussianProcessRegressor(kernel=kernel2,
n_restarts_optimizer=numrestarts,
normalize_y=False,
alpha=np.var(z))
gpr2.fit(x, z)
ystar2, sigma2 = gpr2.predict(x, return_std=True)
sigma2 = (sigma2**2 + 1)**0.5
# Step 4: train heteroscedastic GP3 using predictive mean of G2 to predict log noise levels r
r = exp(ystar2)
R = r * np.identity(n)
k1s4, k2s4 = hypopth(y, numrestarts, R,
y_gn) # needs to be modified
fmst4, varfmst4, lmlopt4 = GPRfith(x, k1s4, k2s4, R,
R) # needs to be modified
# test for convergence
MSE[i] = (1 / n) * sum(((y - fmst4)**2) / np.var(y))
#NLPD[i] = sum([(1/n)*(-np.log(norm.pdf(x[j], fmst4[j], varfmst4[j]**0.5))) for j in range(n) ])
nlpdarg = np.zeros([n, 1])
#nlpdtest = np.zeros([n,1])
for k in range(n):
nlpdarg[k] = -np.log10(
norm.pdf(x[k], fmst4[k], varfmst4[k]**0.5))
#nlpdtest[k] = norm.pdf(x[k], fmst4[k], varfmst4[k]**0.5)
#print("mean NLPD log arg " + str(nlpdtest) )
#test3[k] = -np.log(norm.pdf(x[k], fmst[k], varfmst[k]**0.5))
NLPD[i] = sum(nlpdarg) * (1 / n)
print("MSE = " + str(MSE[i]))
print("NLPD = " + str(NLPD[i]))
print("finished iteration " + str(i + 1))
fmstf[i, :] = fmst4.reshape(n)
varfmstf[i, :] = varfmst4.reshape(n)
lmloptf[i] = lmlopt4
fmst = fmst4
varfmst = varfmst4
rf[i, :] = r.reshape(n)
#k1is3 = k1s4
#k2is3 = k2s4
i = i + 1
return fmstf, varfmstf, lmloptf, MSE, rf, NLPD # , NLPD
def test_no_optimizer():
# Test that kernel parameters are unmodified when optimizer is None.
kernel = RBF(1.0)
gpr = GaussianProcessRegressor(kernel=kernel, optimizer=None).fit(X, y)
assert np.exp(gpr.kernel_.theta) == 1.0
def Regression(train_data, train_solution, test_data, test_solution, method):
## Fix Data Structure ##
train_data = train_data.values
train_solution = train_solution.values
test_data = test_data.values
test_solution = test_solution.values
## List of Method Options with Initialization ##
if method == 'lin_reg': # linear regression
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
elif method == 'ply_reg': # polynomial regression
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
poly_features = PolynomialFeatures(degree=2)
elif method == 'rdg_reg': # ridge regression
from sklearn.linear_model import Ridge
reg = Ridge()
elif method == 'lso_reg': # lasso regression
from sklearn.linear_model import Lasso
reg = Lasso(alpha=0.00001)
elif method == 'ela_net': # elastic net regression
from sklearn.linear_model import ElasticNet
reg = ElasticNet()
elif method == 'svr_lin': # SVM regression
from sklearn.svm import LinearSVR
reg = LinearSVR(epsilon=0.01, max_iter=10000)
elif method == 'svr_2nd': # SVR regression
from sklearn.svm import SVR
reg = SVR(kernel='poly', degree=2, epsilon=0.01) #C=100
elif method == 'svr_3rd': # SVR regression
from sklearn.svm import SVR
reg = SVR(kernel='poly', degree=3, epsilon=0.01) #C=100
elif method == 'dcn_tre': # decision tree
from sklearn.tree import DecisionTreeRegressor
reg = DecisionTreeRegressor()
elif method == 'rdm_for': # random forests
from sklearn.ensemble import RandomForestRegressor
reg = RandomForestRegressor(n_estimators=100, random_state=3)
elif method == 'ada_bst': # AdaBoost Regressor
from sklearn.ensemble import AdaBoostRegressor
reg = AdaBoostRegressor(n_estimators=100, random_state=3)
elif method == 'grd_bst': # Gradient Boosting Regressor
from sklearn.ensemble import GradientBoostingRegressor
reg = GradientBoostingRegressor(random_state=3)
elif method == 'gss_prc': # Gaussian Process Regressor
from sklearn.gaussian_process import GaussianProcessRegressor
reg = GaussianProcessRegressor(random_state=3)
elif method == 'knl_rdg': # Kernel Ridge Regression
from sklearn.kernel_ridge import KernelRidge
reg = KernelRidge()
elif method == 'nst_nbr_uni': # K Nearest Neighbors Regressor
from sklearn.neighbors import KNeighborsRegressor
reg = KNeighborsRegressor(weights='uniform')
elif method == 'nst_nbr_dst': # K Nearest Neighbors Regressor
from sklearn.neighbors import KNeighborsRegressor
reg = KNeighborsRegressor(weights='distance')
elif method == 'rad_nbr_uni': # Radius Neighbor Regressor
from sklearn.neighbors import RadiusNeighborsRegressor
reg = RadiusNeighborsRegressor(weights='uniform')
elif method == 'rad_nbr_dst': # Radius Neighbor Regressor
from sklearn.neighbors import RadiusNeighborsRegressor
reg = RadiusNeighborsRegressor(weights='distance')
elif method == 'mlp_reg':
from sklearn.neural_network import MLPRegressor
reg = MLPRegressor(random_state=3)
else:
print(
'Error: Regression method not recognized.\nPlease pick a valid method key (example: xxx_xxx).'
)
## Preprocessing and Setup ##
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data = scaler.fit_transform(train_data)
scaler = StandardScaler()
test_data = scaler.fit_transform(test_data)
solution = train_solution.reshape(-1, )
if method == 'ply_reg':
data = poly_features.fit_transform(data)
reg.fit(data, solution)
if len(test_data) < 5:
predictions = reg.predict(data)
elif len(test_data) > 5:
if method == 'ply_reg':
test_data = poly_features.transform(test_data)
test_solution = test_solution.reshape(-1, )
predictions_test = reg.predict(test_data)
solution = test_solution
predictions = predictions_test
else:
print('Error: test_set undetermined.')
Matrix_to_save = pd.DataFrame()
Matrix_to_save['Solution'] = solution
Matrix_to_save['Predictions'] = predictions
return Matrix_to_save
# Find the best optimum by starting from n_restart different random points.
for x0 in np.random.uniform(bounds[:, 0], bounds[:, 1], size=(25, dim)):
res = minimize(min_obj, x0=x0, bounds=bounds, method='L-BFGS-B')
if res.fun < 1:
min_val = res.fun[0]
min_x = res.x
X_next = min_x.reshape(-1, 1)
return X_next
X_init = np.array([[-0.9], [1.1]])
y_init = f(X_init)
kernel = ConstantKernel(1.0) + RBF(length_scale=2.0,
length_scale_bounds=(0, 10))
gpr = GaussianProcessRegressor(kernel=kernel,
random_state=42).fit(X_init, y_init)
forest = RandomForestRegressor(n_estimators=100,
random_state=42,
oob_score=True)
X_init = np.linspace(-2, 10, 10).reshape(-1, 1)
y_init = f(X_init)
for i in range(2):
forest.fit(X_init[i:10], y_init[i:10])
y_mean = forest.predict(X)
plt.plot(X_init, y_init, "ro", label="Initial samples")
plt.plot(X, y_mean, label="Surrogate model")
plt.plot(X, f(X), label="Objective")
kdt = KDTree(Otrain, leaf_size=100, metric='euclidean')
with open(path + 'kdt_P' + str(l_prior) + '.pkl', 'wb') as f:
pickle.dump(kdt, f)
else:
with open(path + 'kdt_P' + str(l_prior) + '.pkl', 'rb') as f:
kdt = pickle.load(f)
K = 10
kernel = RBF(length_scale=1.0, length_scale_bounds=(1e-1, 10.0))
i = 0
T = []
Err = []
import time
for e, o in zip(Etest, Otest):
# print i
# print o
st = time.time()
idx = kdt.query(o[:d].reshape(1,-1), k = K, return_distance=False)
O_nn = Otrain[idx,:].reshape(K, d)
E_nn = Etrain[idx].reshape(K, 1)
gpr = GaussianProcessRegressor(kernel=kernel).fit(O_nn, E_nn)
e_mean = gpr.predict(o.reshape(1, -1), return_std=False)[0][0]
T.append(time.time() - st)
Err.append(np.abs(e-e_mean))
# print e, e_mean, np.abs(e-e_mean), o[-1]
if i >=0:
print e, e_mean
i += 1
print "Time: " + str(np.mean(T))
print "Error: " + str(np.mean(Err))
if opts.analysis_type == "measured":
param_array = np.vstack((color,dmdt,dmdti)).T
elif opts.analysis_type == "inferred":
param_array = np.vstack((mej,vej,Xlan)).T
elif opts.analysis_type == "inferred_bulla":
param_array = np.vstack((mej,phi,theta)).T
param_array_postprocess = np.array(param_array)
param_mins, param_maxs = np.min(param_array_postprocess,axis=0),np.max(param_array_postprocess,axis=0)
for i in range(len(param_mins)):
param_array_postprocess[:,i] = (param_array_postprocess[:,i]-param_mins[i])/(param_maxs[i]-param_mins[i])
nsvds, nparams = param_array_postprocess.shape
kernel = 1.0 * RationalQuadratic(length_scale=1.0, alpha=0.1)
gp = GaussianProcessRegressor(kernel=kernel,n_restarts_optimizer=0,alpha=1.0)
gp.fit(param_array_postprocess, Mag)
M, sigma2_pred = gp.predict(np.atleast_2d(param_array_postprocess), return_std=True)
sigma_best = np.median(sigma2_pred)
sigma = sigma_best*np.ones(M.shape)
elif opts.fit_type == "linear":
if opts.analysis_type == "combined":
parameters = ["K","alpha","beta","gamma","delta","zeta","sigma"]
labels = [r'K', r'$\alpha$', r'$\beta$', r'$\gamma$', r"$\delta$",r"$\zeta$",r'$\sigma$']
n_params = len(parameters)
pymultinest.run(myloglike_combined, myprior_combined, n_params, importance_nested_sampling = False, resume = True, verbose = True, sampling_efficiency = 'parameter', n_live_points = n_live_points, outputfiles_basename='%s/2-'%plotDir, evidence_tolerance = evidence_tolerance, multimodal = False, max_iter = max_iter)
ConstantKernel() * Matern(nu=2.5) + WhiteKernel(),
ConstantKernel() * Matern(nu=2.5) + WhiteKernel() +
ConstantKernel() * DotProduct()
]
# オートスケーリング
autoscaled_y_train = (y_train - y_train.mean()) / y_train.std()
autoscaled_x_train = (x_train - x_train.mean()) / x_train.std()
# クロスバリデーションによるカーネル関数の最適化
cross_validation = KFold(n_splits=fold_number, random_state=9,
shuffle=True) # クロスバリデーションの分割の設定
r2cvs = [] # 空の list。カーネル関数ごとに、クロスバリデーション後の r2 を入れていきます
for index, kernel in enumerate(kernels):
print(index + 1, '/', len(kernels))
model = GaussianProcessRegressor(alpha=0, kernel=kernel)
estimated_y_in_cv = np.ndarray.flatten(
cross_val_predict(model,
autoscaled_x_train,
autoscaled_y_train,
cv=cross_validation))
estimated_y_in_cv = estimated_y_in_cv * y_train.std(
ddof=1) + y_train.mean()
r2cvs.append(r2_score(y_train, estimated_y_in_cv))
optimal_kernel_number = np.where(
r2cvs == np.max(r2cvs))[0][0] # クロスバリデーション後の r2 が最も大きいカーネル関数の番号
optimal_kernel = kernels[optimal_kernel_number] # クロスバリデーション後の r2 が最も大きいカーネル関数
print('クロスバリデーションで選択されたカーネル関数の番号 :', optimal_kernel_number)
print('クロスバリデーションで選択されたカーネル関数 :', optimal_kernel)
# モデル構築
class CVaR():
"""CVaR/VaR surrogate
p: function handle for distribution
p takes in an integer number of points and returns a 2D array
of vectors
beta: confidence level for CVaR/VaR
num_points_MC: number of points used in Monte Carlo
"""
def __init__(self,kernel,p,beta = 0.95, num_points_MC = 1000):
self.dim = 0; # input data dimension
self.X = None; # data points
self.fX = None; # function evals
self.GP = GPR(kernel=kernel) # gaussian process
self.p = p # pdf for U
self.beta = beta # confidence level for CVaR
self.num_points_MC = num_points_MC # number of points for monte carlo
# "fit" a GP to the data
def fit(self, X,fX):
# update data
self.X = X;
self.fX = fX;
self.dim = X.shape[1]
# fit sklearn GP
self.GP.fit(X,fX)
def predict(self, xx, std = False):
"""predict C(x) = min_alpha G_beta(x,alpha)
xx: 2D array of points
std: Bool
"""
if std == True:
print('')
print("ERROR: CVaR has no variance")
quit()
# storage
N = np.shape(xx)[0]
C = np.zeros(N)
# for each x in xx calculate C(x)
for i in range(N):
# f(x+U) with Monte Carlo on surrogate
U = self.p(self.num_points_MC)
S = self.GP.predict(xx[i]+U)
# sort S in ascending order
S.sort()
# compute the index of the minimizer
I = int(np.ceil(self.num_points_MC*self.beta))
# minimizer
VaR = S[I]
# CVaR
C[i] = S[I] + np.sum(S[I+1:]-S[I])/(1.-self.beta)/self.num_points_MC
return C
def update(self, xx,yy):
""" update gp with new points
"""
self.X = np.vstack((self.X,xx))
self.fX = np.concatenate((self.fX,[yy]))
self.fit(self.X,self.fX)
# In[ ]:
sub_knn.head(10)
# In[ ]:
# In[ ]:
# In[ ]:
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import DotProduct, ConstantKernel
gpr = GaussianProcessRegressor(random_state=5,
alpha=5e-9,
n_restarts_optimizer=0,
optimizer='fmin_l_bfgs_b',
copy_X_train=True)
param_grid = {
'normalize_y': [True, False],
'kernel': [DotProduct(), ConstantKernel(1.0, (1e-3, 1e3))]
}
grid_gpr = GridSearchCV(gpr,
param_grid,
cv=nr_cv,
verbose=1,
scoring=score_calc)
grid_gpr.fit(X_sc, y_sc)
class GP(Estimator):
"""Wrapper class for the Gaussian Proccess.
Uses the Sklearn implementation of the Gaussian Process.
"""
def __init__(
self,
kernel=C(constant_value=10, constant_value_bounds=(1, 1000)) *
RBF(length_scale=1, length_scale_bounds=(1e-3, 2)),
alpha=1e-7,
optimizer='fmin_l_bfgs_b',
n_restarts_optimizer=10,
normalize_y=True,
copy_X_train=True,
random_state=None,
std_min=0.,
kernel_once=False,
):
super().__init__()
self.std_min = std_min
self.kernel = kernel
self.gp = GaussianProcessRegressor(
kernel=kernel.kernel,
alpha=alpha,
optimizer=optimizer,
n_restarts_optimizer=n_restarts_optimizer,
normalize_y=normalize_y,
copy_X_train=copy_X_train,
random_state=random_state)
self.kernel_once = kernel_once
@ignore_warnings(category=ConvergenceWarning)
def get_mean(self, samples_x: np.ndarray, samples_y: np.ndarray,
test_x: np.ndarray) -> np.ndarray:
""" runs the gp estimator to fit the data and evaluate the mean estimate for the test data
:param samples_x: All input values of the samples
:param samples_y: All target values of the samples
:param test_x: test inputs to evaluate the means on
:return: mean predictions for the test x
"""
old_stdout = sys.stdout # backup current stdout
sys.stdout = open(os.devnull, "w")
self.gp.fit(samples_x, samples_y)
sys.stdout = old_stdout
return self.gp.predict(test_x)
@ignore_warnings(category=ConvergenceWarning)
def get_mean_and_cov(self, samples_x: np.ndarray, samples_y: np.ndarray,
test_x: np.ndarray) -> List[np.ndarray]:
""" runs the gp estimator to fit the data and evaluate the mean estimate and covariance for the test data
:param samples_x: All input values of the samples
:param samples_y: All target values of the samples
:param test_x: test inputs to evaluate the means on
:return: mean and covariance for the test x
"""
old_stdout = sys.stdout # backup current stdout
sys.stdout = open(os.devnull, "w")
self.gp.fit(samples_x, samples_y)
sys.stdout = old_stdout
mean, cov = self.gp.predict(test_x, return_cov=True)
return [mean, cov]
@ignore_warnings(category=ConvergenceWarning)
def fit(self, samples_x: np.ndarray, samples_y: np.ndarray) -> 'NoReturn':
"""fit the the given sample data.
:param samples_x: All input values of the samples
:param samples_y: All target values of the samples
"""
params = None
if not self.context.bo_step == 0 and self.kernel_once:
params = self.gp.kernel_.get_params()
params = self.gp.kernel.fix_params(params=params)
self.gp.kernel = self.gp.kernel.set_params(**params)
self.gp.kernel = self.gp.kernel_.set_params(**params)
old_stdout = sys.stdout # backup current stdout
sys.stdout = open(os.devnull, "w")
self.gp.fit(samples_x, samples_y)
sys.stdout = old_stdout
if self.context.inspector and self.context.inspector.store_estimators:
path = "{}/{}/{}".format(self.context.inspector.inspector_path,
self.__class__.__name__,
self.context.bo_step)
os.makedirs(path, exist_ok=True)
with open(path + "/gp.pickle", "wb") as f:
pickle.dump(self.gp, f)
@ignore_warnings(category=ConvergenceWarning)
def estimate(self,
samples_x: np.ndarray,
samples_y: np.ndarray,
test_x: np.ndarray,
inspect: bool = True) -> Tuple[np.array, np.array]:
"""train the underlying model with the given samples and then
get the estimation for mu and sigma for the test data
:param samples_x: input values of the samples
:param samples_y: target values of the samples
:param test_x: data to estimate, for which mu and sigma should be calculated
:param inspect: should the data be stored in the inspector
:return: mu and sigma values for the test data
"""
start_time = datetime.now()
old_stdout = sys.stdout # backup current stdout
sys.stdout = open(os.devnull, "w")
self.gp.fit(samples_x, samples_y)
time_elapsed = datetime.now() - start_time
mean, sigma = self.regress(test_x)
sys.stdout = old_stdout
if inspect:
self._inspect(mean, sigma, time_elapsed)
self._inspect_on_test_data()
return mean, sigma
@ignore_warnings(category=ConvergenceWarning)
def regress(self, test_x: np.ndarray) -> [np.ndarray, np.ndarray]:
"""only get the estimation for mu and sigma for the test data.
Assumes that the underlying model is already trained
:param test_x: data to estimate, for which mu and sigma should be calculated
:return: mu and sigma values for the test data
"""
mus, sigmas = self.gp.predict(test_x, return_std=True)
return mus, np.array([[x + self.std_min] for x in sigmas])
@ignore_warnings(category=ConvergenceWarning)
def _inspect(self, mu: np.ndarray, sigma: np.ndarray,
time_elapsed: int) -> NoReturn:
"""create a dictionary containing various interesting information for inspection
:param mu: estimated mu
:param sigma: estimated sigma
:param time_elapsed: time spent for the estimation
"""
if self.context.inspector and self.context.inspector.inspect_estimation:
inspection_data = {
"estimator": self.__class__.__name__,
"final_mu": mu,
"final_sigma": sigma,
"time_elapsed": time_elapsed,
"samples_x": np.copy(self.context.samples_x),
"samples_y": np.copy(self.context.samples_y),
}
self.context.inspector.add_estimation(inspection_data)
if self.context.inspector and self.context.inspector.store_estimators:
path = "{}/{}/{}".format(self.context.inspector.inspector_path,
self.__class__.__name__,
self.context.bo_step)
os.makedirs(path, exist_ok=True)
with open(path + "/gp.pickle", "wb") as f:
pickle.dump(self.gp, f)
@ignore_warnings(category=ConvergenceWarning)
def _inspect_on_test_data(self) -> NoReturn:
"""run the estimator on syntetic test data
:return:
"""
if self.context.inspector and self.context.inspector.estimate_test_data:
inspection_data = {
"estimator": self.__class__.__name__,
"final_mu": None,
"final_sigma": None,
"final_acq": None,
"samples_x": np.copy(self.context.samples_x),
"samples_y": np.copy(self.context.samples_y),
}
mus, sigmas = self.regress(self.context.inspector.test_x)
inspection_data["final_mu"] = mus
inspection_data["final_sigma"] = sigmas
inspection_data["final_acq"] = self.context.acq.evaluate(
mus, sigmas, np.max(self.context.samples_y), inspect=False)
self.context.inspector.add_estimation_test_data(inspection_data)
"""
@staticmethod
def get_inspector_mu_on_test_data(context, step):
return context.inspector.estimations_on_test_data[step]["final_mu"]
@staticmethod
def get_inspector_sigma_on_test_data(context, step):
return context.inspector.estimations_on_test_data[step]["final_sigma"]
@staticmethod
def get_inspector_samples_x_on_test_data(context, step):
print(context.inspector.estimations_on_test_data)
return context.inspector.estimations_on_test_data[step]["samples_x"]
@staticmethod
def get_inspector_samples_y_on_test_data(context, step):
return context.inspector.estimations_on_test_data[step]["samples_y"]
@staticmethod
def get_inspector_acq_on_test_data(context, step):
return context.inspector.estimations_on_test_data[step]["final_acq"]
"""
def load_model(self,
base_path: str,
step: int = None,
structured: bool = False) -> NoReturn:
""" Load saved model
:param base_path: basepath of the save model file
:param step: step number, needed for stuctured loading
:param structured: specify if file is loaded from the base_path or from base_path/GP/step/gp.pickle
:return:
"""
if not structured:
with open(base_path, "rb") as f:
self.gp = pickle.load(f)
else:
path = "{}/{}/{}".format(base_path, self.__class__.__name__, step)
with open(path + "/gp.pickle", "rb") as f:
self.gp = pickle.load(f)
@staticmethod
def read_from_config(config: 'ConfigObj') -> NoReturn:
"""read the config file and construct the GP instance accordingly
:param config: config object defining the object
:return:
"""
kernel = Kernel.read_from_config(config["Kernel"])
return GP(kernel=kernel,
alpha=config.as_float("alpha"),
optimizer=config["optimizer"],
n_restarts_optimizer=config.as_int("n_restarts_optimizer"),
normalize_y=config.as_bool("normalize_y"),
copy_X_train=config.as_bool("copy_X_train"),
random_state=config_list_int_or_none(config, "random_state"),
std_min=config.as_float("std_min"),
kernel_once=config.as_bool("kernel_once"))
x, y, test_size=number_of_test_samples, random_state=0)
#index = np.argsort(boston.target)
#y = boston.target[index]
#x = boston.data[index, :]
#x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=number_of_test_samples, shuffle=False)
# autoscaling
autoscaled_x_train = (x_train - x_train.mean(axis=0)) / x_train.std(axis=0,
ddof=1)
autoscaled_y_train = (y_train - y_train.mean()) / y_train.std(ddof=1)
autoscaled_x_test = (x_test - x_train.mean(axis=0)) / x_train.std(axis=0,
ddof=1)
# Gaussian process regression
model = GaussianProcessRegressor(ConstantKernel() * RBF() + WhiteKernel(),
alpha=0)
model.fit(autoscaled_x_train, autoscaled_y_train)
# AD
ad = ApplicabilityDomain(method_name=method_name,
rate_of_outliers=rate_of_outliers)
ad.fit(autoscaled_x_train)
# calculate y in training data
calculated_y_train = model.predict(autoscaled_x_train) * y_train.std(
ddof=1) + y_train.mean()
# yy-plot
plt.rcParams['font.size'] = 18 # 横軸や縦軸の名前の文字などのフォントのサイズ
plt.figure(figsize=figure.figaspect(1))
plt.scatter(y_train, calculated_y_train, c='blue')
y_max = np.max(np.array([np.array(y_train), calculated_y_train]))
#~ X = root2array('../no_truecc_cut_stride2_offset0.root',
#~ branches='recotrklenact',
#~ selection='mustopz<1275&&isnumucc==1',
#~ step=scaledown).reshape(-1,1)
#~ y = root2array('../no_truecc_cut_stride2_offset0.root',
#~ branches='trueemu',
#~ selection='mustopz<1275&&isnumucc==1',
#~ step=scaledown)
scaledown = 50
X = joblib.load(
'../../svm/muon/outlier_removed_data/muon_trklen_active_step{}neighbor50.pkl'
.format(scaledown))
y = joblib.load(
'../../svm/muon/outlier_removed_data/muon_truee_active_step{}neighbor50.pkl'
.format(scaledown))
# rescale the regressors
scaler = preprocessing.StandardScaler().fit(X)
# fit the model
gp = GaussianProcessRegressor(kernel=RBF(), n_restarts_optimizer=1)
Xnorm = scaler.transform(X)
gp.fit(Xnorm, y)
# get prediction
y_pred = gp.predict(Xnorm)
# plot
fig = plt.figure()
np.histogram2d(y, X)
def __init__(self,RiskKernel): self.dim = 0; # input data dimension self.X = None; # data points self.fX = None; # function evals self.RiskKernel = RiskKernel; self.GP = GPR(kernel=RiskKernel.GPkernel)
def show_landscape_gp(result,
plan_keys,
itr=-1,
fix_param=None,
log_scale=False):
"""
:param result: a OptimizerResult object
:param plan_keys: list,
should contain two names of parameters, e.g. ['Jz', 'Jx']
:param itr: int, default -1,
the landscape at which iteration to plot
:param fix_param: dict or None, default: None
specify the parameter value of parameter not in plan_keys, e.g. {'Jy': 1}
if None, it will be automatically set to optimal parameter values
:param log_scale: bool, default: False,
if True, the colorbar will be in logarithmic scale
:return: None
"""
assert isinstance(result, OptimizerResult)
bounds = [
result.parameter_space[plan_keys[0]],
result.parameter_space[plan_keys[1]]
]
xs = np.linspace(bounds[0][0], bounds[0][1], 100)
ys = np.linspace(bounds[1][0], bounds[1][1], 100)
def get_param(xi, yi):
param_names = list(result.parameter_space.keys())
p_point = np.zeros(len(result.parameter_space))
if len(plan_keys) == len(result.parameter_space):
return np.array([xi, yi])
elif fix_param:
i = 0
for param_name in param_names:
if param_name == plan_keys[0]:
p_point[i] = xi
elif param_name == plan_keys[1]:
p_point[i] = yi
else:
p_point[i] = fix_param[param_name]
i += 1
else:
i = 0
for param_name in param_names:
if param_name == plan_keys[0]:
p_point[i] = xi
elif param_name == plan_keys[1]:
p_point[i] = yi
else:
p_point[i] = result.BO_record[itr].max['params'][
param_name]
i += 1
return p_point
X = np.vstack([
np.array([
result.parameter_record[i][list(result.parameter_space.keys())[j]]
for j in range(len(result.parameter_space))
]) for i in range(len(result.loss_record))
])
Y = result.loss_record
GP = GaussianProcessRegressor(
kernel=Matern(nu=2.5, length_scale_bounds=(1e-05, 1000)),
alpha=1e-6,
optimizer='fmin_l_bfgs_b',
normalize_y=True,
n_restarts_optimizer=200,
)
# GP.fit(X, np.power(10, -Y))
GP.fit(X, Y)
predict_values = np.vstack(
[GP.predict(np.array([get_param(xi, yi) for xi in xs])) for yi in ys])
fig, ax = plt.subplots(figsize=[5, 5])
predict_values[np.where(predict_values < 0)] = 1e-3
# predict_values = -predict_values
if log_scale:
ctf = ax.contourf(xs,
ys,
predict_values,
cmap=plt.cm.gnuplot_r,
norm=colors.LogNorm(vmin=predict_values.min(),
vmax=predict_values.max()),
levels=np.power(
10,
np.linspace(np.log10(predict_values.min()),
np.log10(predict_values.max()),
100)))
else:
ctf = ax.contourf(xs,
ys,
predict_values,
cmap=plt.cm.gnuplot_r,
levels=100)
fig.colorbar(ctf)
ax.set_xlabel(plan_keys[0])
ax.set_ylabel(plan_keys[1])
return fig, ax
