class ClassicalActiveSubspaceGP(ConfidenceBoundModel):
"""
Base class for GP optimization.
Handles common functionality.
"""
def set_new_kernel(self, d, W=None, variance=None, lengthscale=None):
self.kernel = TripathyMaternKernel(real_dim=self.domain.d,
active_dim=d,
W=W,
variance=variance,
lengthscale=lengthscale)
def set_new_gp(self, noise_var=None):
self.gp = GPRegression(
input_dim=self.domain.d,
kernel=self.kernel,
noise_var=noise_var
if noise_var else 2., # TODO: replace with config value!
calculate_gradients=True # TODO: replace with config value!
)
def set_new_gp_and_kernel(self, d, W, variance, lengthscale, noise_var):
self.set_new_kernel(d, W, variance, lengthscale)
self.set_new_gp(noise_var)
# # from .t_kernel import TripathyMaternKernel
# TripathyMaternKernel.__module__ = "tripathy.src.t_kernel"
def __init__(self, domain):
super(ClassicalActiveSubspaceGP, self).__init__(domain)
self.optimizer = TripathyOptimizer()
# TODO: d is chosen to be an arbitrary value rn!
# self.set_new_kernel(2, None, None)
# self.set_new_gp(None)
self.set_new_gp_and_kernel(2, None, None, None, None)
# calling of the kernel
# self.gp = self._get_gp() # TODO: does this actually create a new gp?
# number of data points
self.t = 0
self.kernel = self.kernel.copy()
self._woodbury_chol = np.asfortranarray(
self.gp.posterior._woodbury_chol
) # we create a copy of the matrix in fortranarray, such that we can directly pass it to lapack dtrtrs without doing another copy
self._woodbury_vector = self.gp.posterior._woodbury_vector.copy()
self._X = self.gp.X.copy()
self._Y = np.empty(shape=(0, 1))
self._beta = 2
self._bias = self.config.bias
@property
def beta(self):
return self._beta
@property
def scale(self):
if self.gp.kern.name == 'sum':
return sum([part.variance for part in self.gp.kern.parts])
else:
return np.sqrt(self.gp.kern.variance)
@property
def bias(self):
return self._bias
def _get_gp(self):
return GPRegression(
self.domain.d,
self.kernel,
noise_var=self.config.noise_var,
calculate_gradients=self.config.calculate_gradients)
def add_data(self, x, y):
"""
Add a new function observation to the GPs.
Parameters
----------
x: 2d-array
y: 2d-array
"""
self.i = 1 if not ("i" in dir(self)) else self.i + 1
print("Add data ", self.i)
x = np.atleast_2d(x)
y = np.atleast_2d(y)
self.set_data(x, y, append=True)
# TODO: check if this is called anyhow!
def optimize(self):
# if self.config.optimize_bias:
# self._optimize_bias()
# if self.config.optimize_var:
# self._optimize_var()
# self.optimizer.find_active_subspace(self.X, self.Y)
self._update_beta()
def _update_cache(self):
# if not self.config.calculate_gradients:
self._woodbury_chol = np.asfortranarray(
self.gp.posterior._woodbury_chol)
self._woodbury_vector = self.gp.posterior._woodbury_vector.copy()
self._X = self.gp.X.copy()
self._update_beta()
def _optimize_bias(self):
self._bias = minimize(self._bias_loss, self._bias,
method='L-BFGS-B')['x'].copy()
self._set_bias(self._bias)
logger.info(f"Updated bias to {self._bias}")
def _bias_loss(self, c):
# calculate mean and norm for new bias via a new woodbury_vector
new_woodbury_vector, _ = dpotrs(self._woodbury_chol,
self._Y - c,
lower=1)
K = self.gp.kern.K(self.gp.X)
mean = np.dot(K, new_woodbury_vector)
norm = new_woodbury_vector.T.dot(mean)
# loss is least_squares_error + norm
return np.asscalar(np.sum(np.square(mean + c - self._Y)) + norm)
def _set_bias(self, c):
self._bias = c
self.gp.set_Y(self._Y - c)
self._woodbury_vector = self.gp.posterior._woodbury_vector.copy()
def _update_beta(self):
logdet = self._get_logdet()
logdet_priornoise = self._get_logdet_prior_noise()
self._beta = np.sqrt(2 * np.log(1 / self.delta) +
(logdet - logdet_priornoise)) + self._norm()
def _optimize_var(self):
# fix all parameters
for p in self.gp.parameters:
p.fix()
if self.gp.kern.name == 'sum':
for part in self.gp.kern.parts:
part.variance.unfix()
else:
self.gp.kern.variance.unfix()
self.gp.optimize()
if self.gp.kern.name == 'sum':
values = []
for part in self.gp.kern.parts:
values.append(np.asscalar(part.variance.values))
else:
values = np.asscalar(self.gp.kern.variance.values)
logger.info(f"Updated prior variance to {values}")
# unfix all parameters
for p in self.gp.parameters:
p.unfix()
def _get_logdet(self):
return 2. * np.sum(np.log(np.diag(self.gp.posterior._woodbury_chol)))
def _get_logdet_prior_noise(self):
return self.t * np.log(self.gp.likelihood.variance.values)
def mean_var(self, x):
"""Recompute the confidence intervals form the GP.
Parameters
----------
context: ndarray
Array that contains the context used to compute the sets
"""
x = np.atleast_2d(x)
if self.config.calculate_gradients:
mean, var = self.gp.predict_noiseless(x)
else:
mean, var = self._raw_predict(x)
return mean + self._bias, var
def mean_var_grad(self, x):
return self.gp.predictive_gradients(x)
def var(self, x):
return self.mean_var(x)[1]
# TODO: is this a bug?
def predictive_var(self, X, X_cond, S_X, var_Xcond=None):
X = np.atleast_2d(X)
X_cond = np.atleast_2d(X_cond)
var_X, KXX = self._raw_predict_covar(X, X_cond)
if var_Xcond is None:
var_Xcond = self.var(X_cond)
return var_Xcond - KXX * KXX / (S_X * S_X + var_X)
def mean(self, x):
return self.mean_var(x)[0]
def set_data(self, X, Y, append=True):
if append:
X = np.concatenate((self.gp.X, X))
Y = np.concatenate((self.gp.Y, Y))
# Do our optimization now
if self.i % 3 == 0:
import time
start_time = time.time()
W_hat, sn, l, s, d = self.optimizer.find_active_subspace(X, Y)
print("--- %s seconds ---" % (time.time() - start_time))
# Overwrite GP and kernel values
# TODO: W_hat not used ----
self.set_new_gp_and_kernel(d=d,
W=W_hat,
variance=s,
lengthscale=l,
noise_var=sn)
self.gp.set_XY(X, Y)
self.t = X.shape[0]
self._update_cache()
# TODO: merge all the following code with the current function!
# print("Looking for optimal subspace!")
# W_hat, sn, l, s, d = self.optimizer.find_active_subspace(X=X, Y=Y)
#
# print("Found optimal subspace")
#
# # Set the newly found hyperparameters everywhere
# # Not found by pycharm bcs the kernel is an abstract object as of now
# # self.kernel.update_params(W=W_hat, s=s, l=l)
# # self.gp.kern.update_params(W=W_hat, s=s, l=l)
#
# # Create a new GP (bcs this is spaghetti code!)
# self.set_new_kernel_and_gp(
# d=d,
# variance=s,
# lengthscale=l,
# noise_var=sn
# )
def sample(self, X=None):
class GPSampler:
def __init__(self, X, Y, kernel, var):
self.X = X
self.Y = Y
self.N = var * np.ones(shape=Y.shape)
self.kernel = kernel
self.m = GPy.models.GPHeteroscedasticRegression(
self.X, self.Y, self.kernel)
self.m['.*het_Gauss.variance'] = self.N
def __call__(self, X):
X = np.atleast_2d(X)
sample = np.empty(shape=(X.shape[0], 1))
# iteratively generate sample values for all x in x_test
for i, x in enumerate(X):
sample[i] = self.m.posterior_samples_f(x.reshape((1, -1)),
size=1)
# add observation as without noise
self.X = np.vstack((self.X, x))
self.Y = np.vstack((self.Y, sample[i]))
self.N = np.vstack((self.N, 0))
# recalculate model
self.m = GPy.models.GPHeteroscedasticRegression(
self.X, self.Y, self.kernel)
self.m[
'.*het_Gauss.variance'] = self.N # Set the noise parameters to the error in Y
return sample
return GPSampler(self.gp.X.copy(), self.gp.Y.copy(), self.kernel,
self.gp.likelihood.variance)
def _raw_predict(self, Xnew):
Kx = self.kernel.K(self._X, Xnew)
mu = np.dot(Kx.T, self._woodbury_vector)
if len(mu.shape) == 1:
mu = mu.reshape(-1, 1)
Kxx = self.kernel.Kdiag(Xnew)
tmp = lapack.dtrtrs(self._woodbury_chol,
Kx,
lower=1,
trans=0,
unitdiag=0)[0]
var = (Kxx - np.square(tmp).sum(0))[:, None]
return mu, var
def _raw_predict_covar(self, Xnew, Xcond):
Kx = self.kernel.K(self._X, np.vstack((Xnew, Xcond)))
tmp = lapack.dtrtrs(self._woodbury_chol,
Kx,
lower=1,
trans=0,
unitdiag=0)[0]
n = Xnew.shape[0]
tmp1 = tmp[:, :n]
tmp2 = tmp[:, n:]
Kxx = self.kernel.K(Xnew, Xcond)
var = Kxx - (tmp1.T).dot(tmp2)
Kxx_new = self.kernel.Kdiag(Xnew)
var_Xnew = (Kxx_new - np.square(tmp1).sum(0))[:, None]
return var_Xnew, var
def _norm(self):
norm = self._woodbury_vector.T.dot(self.gp.kern.K(self.gp.X)).dot(
self._woodbury_vector)
return np.asscalar(np.sqrt(norm))
def __getstate__(self):
self_dict = self.__dict__.copy()
del self_dict[
'gp'] # remove the gp from state dict to allow pickling. calculations are done via the cache woodbury/cholesky
return self_dict
class TripathyGP(ConfidenceBoundModel):
"""
Base class for GP optimization.
Handles common functionality.
"""
def set_new_kernel(self, d, W=None, variance=None, lengthscale=None):
self.kernel = TripathyMaternKernel(
real_dim=self.domain.d,
active_dim=d,
W=W,
variance=variance,
lengthscale=lengthscale
)
def set_new_gp(self, noise_var=None):
self.gp = GPRegression(
input_dim=self.domain.d,
kernel=self.kernel,
noise_var=noise_var if noise_var else 2., # self.config.noise_var,
calculate_gradients=self.config.calculate_gradients
)
def set_new_gp_and_kernel(self, d, W, variance, lengthscale, noise_var):
self.set_new_kernel(d, W, variance, lengthscale)
self.set_new_gp(noise_var)
def __init__(self, domain, calculate_always=False):
super(TripathyGP, self).__init__(domain)
self.optimizer = TripathyOptimizer()
# TODO: d is chosen to be an arbitrary value rn!
self.set_new_gp_and_kernel(2, None, None, None, None)
# number of data points
self.t = 0
self.kernel = self.kernel.copy()
self._woodbury_chol = np.asfortranarray(self.gp.posterior._woodbury_chol) # we create a copy of the matrix in fortranarray, such that we can directly pass it to lapack dtrtrs without doing another copy
self._woodbury_vector = self.gp.posterior._woodbury_vector.copy()
self._X = self.gp.X.copy()
self._Y = np.empty(shape=(0,1))
self._beta = 2
self._bias = self.config.bias
self.calculate_always = calculate_always
@property
def beta(self):
return self._beta
@property
def scale(self):
if self.gp.kern.name == 'sum':
return sum([part.variance for part in self.gp.kern.parts])
else:
return np.sqrt(self.gp.kern.variance)
@property
def bias(self):
return self._bias
def _get_gp(self):
return GPRegression(self.domain.d, self.kernel, noise_var=self.config.noise_var, calculate_gradients=self.config.calculate_gradients)
def add_data(self, x, y):
"""
Add a new function observation to the GPs.
Parameters
----------
x: 2d-array
y: 2d-array
"""
self.i = 1 if not ("i" in dir(self)) else self.i + 1
# print("Add data ", self.i)
x = np.atleast_2d(x)
y = np.atleast_2d(y)
self.set_data(x, y, append=True)
# TODO: check if this is called anyhow!
def optimize(self):
self._update_beta()
def _update_cache(self):
# if not self.config.calculate_gradients:
self._woodbury_chol = np.asfortranarray(self.gp.posterior._woodbury_chol)
self._woodbury_vector = self.gp.posterior._woodbury_vector.copy()
self._X = self.gp.X.copy()
self._update_beta()
def _optimize_bias(self):
self._bias = minimize(self._bias_loss, self._bias, method='L-BFGS-B')['x'].copy()
self._set_bias(self._bias)
logger.info(f"Updated bias to {self._bias}")
def _bias_loss(self, c):
# calculate mean and norm for new bias via a new woodbury_vector
new_woodbury_vector,_= dpotrs(self._woodbury_chol, self._Y - c, lower=1)
K = self.gp.kern.K(self.gp.X)
mean = np.dot(K, new_woodbury_vector)
norm = new_woodbury_vector.T.dot(mean)
# loss is least_squares_error + norm
return np.asscalar(np.sum(np.square(mean + c - self._Y)) + norm)
def _set_bias(self, c):
self._bias = c
self.gp.set_Y(self._Y - c)
self._woodbury_vector = self.gp.posterior._woodbury_vector.copy()
def _update_beta(self):
logdet = self._get_logdet()
logdet_priornoise = self._get_logdet_prior_noise()
self._beta = np.sqrt(2 * np.log(1/self.delta) + (logdet - logdet_priornoise)) + self._norm()
def _optimize_var(self):
# fix all parameters
for p in self.gp.parameters:
p.fix()
if self.gp.kern.name == 'sum':
for part in self.gp.kern.parts:
part.variance.unfix()
else:
self.gp.kern.variance.unfix()
self.gp.optimize()
if self.gp.kern.name == 'sum':
values = []
for part in self.gp.kern.parts:
values.append(np.asscalar(part.variance.values))
else:
values = np.asscalar(self.gp.kern.variance.values)
logger.info(f"Updated prior variance to {values}")
# unfix all parameters
for p in self.gp.parameters:
p.unfix()
def _get_logdet(self):
return 2.*np.sum(np.log(np.diag(self.gp.posterior._woodbury_chol)))
def _get_logdet_prior_noise(self):
return self.t * np.log(self.gp.likelihood.variance.values)
def mean_var(self, x):
"""Recompute the confidence intervals form the GP.
Parameters
----------
context: ndarray
Array that contains the context used to compute the sets
"""
x = np.atleast_2d(x)
if self.config.calculate_gradients or True:
mean,var = self.gp.predict_noiseless(x)
else:
mean,var = self._raw_predict(x)
return mean + self._bias, var
def mean_var_grad(self, x):
return self.gp.predictive_gradients(x)
def var(self, x):
return self.mean_var(x)[1]
def mean(self, x):
return self.mean_var(x)[0]
def set_data(self, X, Y, append=True):
if append:
X = np.concatenate((self.gp.X, X))
Y = np.concatenate((self.gp.Y, Y))
# Do our optimization now
if self.i % 100 == 99 or self.calculate_always:
import time
start_time = time.time()
print("Adding data: ", self.i)
# TODO: UNCOMMENT THE FOLLOWING LINE AGAIN!
# This is just to check if tripathy conforms with the other version
# W_hat, sn, l, s, d = self.optimizer.find_active_subspace(X, Y)
d = 2
W_hat = np.asarray([
[-0.31894555, 0.78400512, 0.38970008, 0.06119476, 0.35776912],
[-0.27150973, 0.066002, 0.42761931, -0.32079484, -0.79759551]
]).T
s = 1.
l = 1.5
sn = 2. # 0.01 #self.config.noise_var
print("--- %s seconds ---" % (time.time() - start_time))
# Overwrite GP and kernel values
self.set_new_gp_and_kernel(d=d, W=W_hat, variance=s, lengthscale=l, noise_var=sn)
# TODO: Should the following not come before the optimization?
self.gp.set_XY(X, Y)
self.t = X.shape[0]
self._update_cache()
def sample(self, X=None):
class GPSampler:
def __init__(self, X, Y, kernel, var):
self.X = X
self.Y = Y
self.N = var * np.ones(shape=Y.shape)
self.kernel = kernel
self.m = GPy.models.GPHeteroscedasticRegression(self.X, self.Y, self.kernel)
self.m['.*het_Gauss.variance'] = self.N
def __call__(self, X):
X = np.atleast_2d(X)
sample = np.empty(shape=(X.shape[0], 1))
# iteratively generate sample values for all x in x_test
for i, x in enumerate(X):
sample[i] = self.m.posterior_samples_f(x.reshape((1, -1)), size=1)
# add observation as without noise
self.X = np.vstack((self.X, x))
self.Y = np.vstack((self.Y, sample[i]))
self.N = np.vstack((self.N, 0))
# recalculate model
self.m = GPy.models.GPHeteroscedasticRegression(self.X, self.Y, self.kernel)
self.m['.*het_Gauss.variance'] = self.N # Set the noise parameters to the error in Y
return sample
return GPSampler(self.gp.X.copy(), self.gp.Y.copy(), self.kernel, self.gp.likelihood.variance)
def _raw_predict(self, Xnew):
Kx = self.kernel.K(self._X, Xnew)
mu = np.dot(Kx.T, self._woodbury_vector)
if len(mu.shape)==1:
mu = mu.reshape(-1,1)
Kxx = self.kernel.Kdiag(Xnew)
tmp = lapack.dtrtrs(self._woodbury_chol, Kx, lower=1, trans=0, unitdiag=0)[0]
var = (Kxx - np.square(tmp).sum(0))[:,None]
return mu, var
def _raw_predict_covar(self, Xnew, Xcond):
Kx = self.kernel.K(self._X, np.vstack((Xnew,Xcond)))
tmp = lapack.dtrtrs(self._woodbury_chol, Kx, lower=1, trans=0, unitdiag=0)[0]
n = Xnew.shape[0]
tmp1 = tmp[:,:n]
tmp2 = tmp[:,n:]
Kxx = self.kernel.K(Xnew, Xcond)
var = Kxx - (tmp1.T).dot(tmp2)
Kxx_new = self.kernel.Kdiag(Xnew)
var_Xnew = (Kxx_new - np.square(tmp1).sum(0))[:,None]
return var_Xnew, var
def _norm(self):
norm = self._woodbury_vector.T.dot(self.gp.kern.K(self.gp.X)).dot(self._woodbury_vector)
return np.asscalar(np.sqrt(norm))
def __getstate__(self):
self_dict = self.__dict__.copy()
del self_dict['gp'] # remove the gp from state dict to allow pickling. calculations are done via the cache woodbury/cholesky
return self_dict
class BoringGP(ConfidenceBoundModel):
"""
Base class for GP optimization.
Handles common functionality.
"""
def create_kernels(self,
active_dimensions,
passive_dimensions,
first=False,
k_variance=None,
k_lengthscales=None):
# Use tripathy kernel here instead, because it includes W in it's stuff
active_kernel = Matern32(
input_dim=active_dimensions,
variance=1. if k_variance is None else k_variance,
lengthscale=1.5
if k_lengthscales is None else k_lengthscales, # 0.5,
ARD=True,
active_dims=np.arange(active_dimensions),
name="active_subspace_kernel")
self.kernel = active_kernel
if first: # TODO: need to change this back!
# Now adding the additional kernels:
for i in range(passive_dimensions):
cur_kernel = RBF(
input_dim=1,
variance=2.,
lengthscale=0.5, # 0.5,
ARD=False,
active_dims=[active_dimensions + i],
name="passive_subspace_kernel_dim_" + str(i))
self.kernel += cur_kernel
print("Got kernel: ")
print(self.kernel)
def create_gp(self):
self.gp = GPRegression(input_dim=self.domain.d,
kernel=self.kernel,
noise_var=0.01,
calculate_gradients=False)
# Let the GP take over datapoints from the datasaver!
X = self.datasaver_gp.X
Y = self.datasaver_gp.Y
# Apply the Q transform if it was spawned already!
if self.Q is not None:
X = np.dot(X, self.Q)
if self.Q is not None:
assert X.shape[1] >= 2, ("Somehow, Q was not projected!", X.shape,
2) # TODO: change this back to ==!
self.gp.set_XY(X, Y)
self._update_cache()
def create_gp_and_kernels(self,
active_dimensions,
passive_dimensions,
first=False,
k_variance=None,
k_lengthscales=None):
self.create_kernels(active_dimensions,
passive_dimensions,
first=first,
k_variance=k_variance,
k_lengthscales=k_lengthscales)
self.create_gp()
# From here on, it's the usual functions
def __init__(self, domain, always_calculate=False):
super(BoringGP, self).__init__(domain)
# passive projection matrix still needs to be created first!
# print("WARNING: CONFIG MODE IS: ", config.DEV)
self.burn_in_samples = 101 # 101 # 102
self.recalculate_projection_every = 101
self.active_projection_matrix = None
self.passive_projection_matrix = None
self.Q = None
# some other parameters that are cached
self.t = 0
# Setting the datasaver (infrastructure which allows us to save the data to be projected again and again)
placeholder_kernel = RBF(input_dim=self.domain.d)
self.datasaver_gp = GPRegression(input_dim=self.domain.d,
kernel=placeholder_kernel,
noise_var=0.01,
calculate_gradients=False)
# Create a new kernel and create a new GP
self.create_gp_and_kernels(self.domain.d, 0,
first=True) # self.domain.d - 2
# Some post-processing
self.kernel = self.kernel.copy()
self._woodbury_chol = np.asfortranarray(
self.gp.posterior._woodbury_chol
) # we create a copy of the matrix in fortranarray, such that we can directly pass it to lapack dtrtrs without doing another copy
self._woodbury_vector = self.gp.posterior._woodbury_vector.copy()
self._X = self.gp.X.copy()
self._Y = np.empty(shape=(0, 1))
self._bias = self.config.bias
self.always_calculate = always_calculate
@property
def beta(self):
return np.sqrt(np.log(self.datasaver_gp.X.shape[0]))
@property
def scale(self):
if self.gp.kern.name == 'sum':
return sum([part.variance for part in self.gp.kern.parts])
else:
return np.sqrt(self.gp.kern.variance)
@property
def bias(self):
return self._bias
def _get_gp(self):
return self.gp # GPRegression(self.domain.d, self.kernel, noise_var=self.config.noise_var, calculate_gradients=self.config.calculate_gradients)
def add_data(self, x, y):
"""
Add a new function observation to the GPs.
Parameters
----------
x: 2d-array
y: 2d-array
"""
self.i = 1 if not ("i" in dir(self)) else self.i + 1
# print("Add data ", self.i)
x = np.atleast_2d(x)
y = np.atleast_2d(y)
self.set_data(x, y, append=True)
# TODO: check if this is called anyhow!
def optimize(self):
self._update_beta()
def _update_cache(self):
# if not self.config.calculate_gradients:
self._woodbury_chol = np.asfortranarray(
self.gp.posterior._woodbury_chol)
self._woodbury_vector = self.gp.posterior._woodbury_vector.copy()
self._X = self.gp.X.copy()
self._update_beta()
def _optimize_bias(self):
self._bias = minimize(self._bias_loss, self._bias,
method='L-BFGS-B')['x'].copy()
self._set_bias(self._bias)
logger.info(f"Updated bias to {self._bias}")
def _bias_loss(self, c):
# calculate mean and norm for new bias via a new woodbury_vector
new_woodbury_vector, _ = dpotrs(self._woodbury_chol,
self._Y - c,
lower=1)
K = self.gp.kern.K(self.gp.X)
mean = np.dot(K, new_woodbury_vector)
norm = new_woodbury_vector.T.dot(mean)
# loss is least_squares_error + norm
return np.asscalar(np.sum(np.square(mean + c - self._Y)) + norm)
def _set_bias(self, c):
self._bias = c
self.gp.set_Y(self._Y - c)
self._woodbury_vector = self.gp.posterior._woodbury_vector.copy()
def _update_beta(self):
logdet = self._get_logdet()
logdet_priornoise = self._get_logdet_prior_noise()
self._beta = np.sqrt(2 * np.log(1 / self.delta) +
(logdet - logdet_priornoise)) + self._norm()
def _optimize_var(self):
# fix all parameters
for p in self.gp.parameters:
p.fix()
if self.gp.kern.name == 'sum':
for part in self.gp.kern.parts:
part.variance.unfix()
else:
self.gp.kern.variance.unfix()
self.gp.optimize()
if self.gp.kern.name == 'sum':
values = []
for part in self.gp.kern.parts:
values.append(np.asscalar(part.variance.values))
else:
values = np.asscalar(self.gp.kern.variance.values)
logger.info(f"Updated prior variance to {values}")
# unfix all parameters
for p in self.gp.parameters:
p.unfix()
def _get_logdet(self):
return 2. * np.sum(np.log(np.diag(self.gp.posterior._woodbury_chol)))
def _get_logdet_prior_noise(self):
return self.t * np.log(self.gp.likelihood.variance.values)
def mean_var(self, x):
"""Recompute the confidence intervals form the GP.
Parameters
----------
context: ndarray
Array that contains the context used to compute the sets
"""
x = np.atleast_2d(x)
assert not np.isnan(x).all(), ("X is nan at some point!", x)
if self.config.calculate_gradients or True:
# In the other case, projection is done in a subfunction
if self.Q is not None:
x = np.dot(x, self.Q)
mean, var = self.gp.predict_noiseless(x)
else:
mean, var = self._raw_predict(x)
return mean + self._bias, var
def mean_var_grad(self, x):
# TODO: should this be here aswell?
# TODO: check, that this is not actually used for new AND saved gp's!
if self.Q is not None:
x = np.dot(x, self.Q)
return self.gp.predictive_gradients(x)
def var(self, x):
return self.mean_var(x)[1]
def mean(self, x):
return self.mean_var(x)[0]
def set_data(self, X, Y, append=True):
# First of all, save everything in the saver GP
if append:
X = np.concatenate((self.datasaver_gp.X, X), axis=0)
Y = np.concatenate((self.datasaver_gp.Y, Y),
axis=0) # Should be axis=0
self.datasaver_gp.set_XY(X, Y)
# Now, save everything in the other GP but with a projected X value
#
# TODO: This is pretty wrong!
X = self.datasaver_gp.X
Y = self.datasaver_gp.Y
# Do our optimization now
if self.burn_in_samples == self.i or self.always_calculate: # (self.i >= self.burn_in_samples and self.i % self.recalculate_projection_every == 1) or
import time
start_time = time.time()
# print("Adding data: ", self.i)
optimizer = TripathyOptimizer()
# TODO: the following part is commented out, so we can test, if the function works well if we give it the real matrix!
self.active_projection_matrix, sn, l, s, d = optimizer.find_active_subspace(
X, Y)
#
# print("BORING sampled the following active matrix: ")
# print(self.active_projection_matrix)
#
passive_dimensions = max(self.domain.d - d, 0)
passive_dimensions = min(passive_dimensions, 2)
# passive_dimensions = 1 # TODO: take out this part!
# # passive_dimensions = 0
#
# Generate A^{bot} if there's more dimensions
if passive_dimensions > 0:
self.passive_projection_matrix = generate_orthogonal_matrix_to_A(
self.active_projection_matrix, passive_dimensions)
else:
self.passive_projection_matrix = None
#
# print("BORING sampled the following passive matrix: ")
# print(self.passive_projection_matrix)
# d = 2
# self.active_projection_matrix = np.asarray([
# [-0.31894555, 0.78400512, 0.38970008, 0.06119476, 0.35776912],
# [-0.27150973, 0.066002, 0.42761931, -0.32079484, -0.79759551]
# ]).T
# s = 1.
# l = 1.5
# passive_dimensions = 0
# Create Q by concatenateing the active and passive projections
if passive_dimensions > 0:
self.Q = np.concatenate((self.active_projection_matrix,
self.passive_projection_matrix),
axis=1)
else:
self.Q = self.active_projection_matrix
assert not np.isnan(
self.Q).all(), ("The projection matrix contains nan's!",
self.Q)
# print("BORING sampled the following matrix: ")
# print(self.Q)
assert d == self.active_projection_matrix.shape[1]
self.create_gp_and_kernels(active_dimensions=d,
passive_dimensions=passive_dimensions,
first=True,
k_variance=s,
k_lengthscales=l)
# print("Projection matrix is: ", self.Q.shape)
# print("Dimensions found are: ", d)
# print("Active projection matrix is ", self.active_projection_matrix.shape)
# print("How many datapoints do we have in the kernel?", self.gp.X.shape)
# print("How many datapoints do we have in the kernel?", self.datasaver_gp.X.shape)
print("--- %s seconds ---" % (time.time() - start_time))
# if self.i > self.burn_in_samples:
# assert self.Q is not None, "After the burning in, self.Q is still None!"
# Add the points to the newly shapes GP!
if (self.i < self.burn_in_samples
or self.Q is None) and (not self.always_calculate):
# print("Still using the old method!")
self.gp.set_XY(X, Y)
else:
# print("We use the dot product thingy from now on!")
Z = np.dot(X, self.Q)
# print("Old shape: ", X.shape)
# print("New shape: ", Z.shape)
self.gp.set_XY(Z, Y)
# print("Added data: ", self.i)
# print("Datasave has shape: ", self.datasaver_gp.X.shape)
# print("Another shape: ", self.gp.X.shape)
self.t = X.shape[0]
self._update_cache()
def _raw_predict(self, Xnew):
m, n = Xnew.shape
# Need to project Xnew here?
# if self.Q is not None:
# Xnew = np.dot(Xnew, self.Q)
# assert Xnew.shape[1] == self.Q.reshape(self.Q.shape[0], -1).shape[1], ("Shapes are wrong: ", Xnew.shape, self.Q.shape)
# else:
# assert Xnew.shape[1] == self.domain.d, ("Shapes are wrong when we have no Q!", Xnew.shape, self.domain.d)
if not hasattr(self.kernel, 'parts'): # TODO: take this out?
mu, var = self._raw_predict_given_kernel(Xnew, self.kernel)
# print("Using the cool values! ")
else:
mu = np.zeros((Xnew.shape[0], 1))
var = np.zeros((Xnew.shape[0], 1))
for kernel in self.kernel.parts:
cur_mu, cur_var = self._raw_predict_given_kernel(Xnew, kernel)
assert not np.isnan(cur_mu).all(), (
"nan encountered for mean!", cur_mu)
assert not np.isnan(cur_var).all(), (
"nan encountered for var!", cur_var)
mu += cur_mu
var += cur_var
assert not np.isnan(mu).all(), ("nan encountered for mean!", mu)
assert not np.isnan(var).all(), ("nan encountered for mean!", var)
assert mu.shape == (m, 1), ("Shape of mean is different! ", mu.shape,
(m, 1))
assert var.shape == (m, 1), ("Shape of variance is different! ",
var.shape, (m, 1))
return mu, var
def _raw_predict_given_kernel(self, Xnew, kernel):
Kx = kernel.K(self._X, Xnew)
mu = np.dot(Kx.T, self._woodbury_vector)
if len(mu.shape) == 1:
mu = mu.reshape(-1, 1)
Kxx = kernel.Kdiag(Xnew)
tmp = lapack.dtrtrs(self._woodbury_chol,
Kx,
lower=1,
trans=0,
unitdiag=0)[0]
var = (Kxx - np.square(tmp).sum(0))[:, None]
return mu, var
def _norm(self):
norm = self._woodbury_vector.T.dot(self.gp.kern.K(self.gp.X)).dot(
self._woodbury_vector)
return np.asscalar(np.sqrt(norm))
def __getstate__(self):
self_dict = self.__dict__.copy()
del self_dict[
'gp'] # remove the gp from state dict to allow pickling. calculations are done via the cache woodbury/cholesky
return self_dict