def find_reference_point_using_direct(tasks, module, input_space, grid_size = 20000):
def create_fun_neg(task):
def fun(params, gradient = False):
if len(params.shape) > 1 and params.shape[ 1 ] > 1:
params = params.flatten()
params = input_space.from_unit(np.array([ params ])).flatten()
return -1.0 * module.main(0, paramify_no_types(input_space.paramify(params)))[ task ]
return fun
funs_neg = [ create_fun_neg(task) for task in tasks ]
reference_point = np.zeros(len(funs_neg))
for i in range(len(funs_neg)):
def f(x, user_data):
if x.ndim == 1:
x = x[None,:]
value = funs_neg[ i ](x)
return value, 0
l = np.zeros(input_space.num_dims) * 1.0
u = np.ones(input_space.num_dims) * 1.0
x, y_opt, ierror = solve(f, l, u, maxf = 85000)
reference_point[ i ] = -1.0 * y_opt + np.abs(-1.0 * y_opt * 0.01)
return reference_point
def optimize(self, x0, f=None, df=None, f_df=None):
"""
:param x0: initial point for a local optimizer.
:param f: function to optimize.
:param df: gradient of the function to optimize.
:param f_df: returns both the function to optimize and its gradient.
"""
# Based on the documentation of DIRECT, it does not seem we can pass through an initial point x0
try:
from DIRECT import solve
def DIRECT_f_wrapper(f):
def g(x, user_data):
return f(np.array([x])), 0
return g
lB = np.asarray(self.bounds)[:, 0]
uB = np.asarray(self.bounds)[:, 1]
x, _, _ = solve(DIRECT_f_wrapper(f), lB, uB, maxT=self.maxiter)
return np.atleast_2d(x), f(np.atleast_2d(x))
except ImportError:
print(
"Cannot find DIRECT library, please install it to use this option."
)
def optimize(self, x0=None, f=None, df=None, f_df=None):
"""
:param x0: initial point for a local optimizer.
:param f: function to optimize.
:param df: gradient of the function to optimize.
:param f_df: returns both the function to optimize and its gradient.
"""
try:
from DIRECT import solve
import numpy as np
def DIRECT_f_wrapper(f):
def g(x, user_data):
return f(np.array([x])), 0
return g
lB = np.asarray(self.space.get_bounds())[:, 0]
uB = np.asarray(self.space.get_bounds())[:, 1]
x, _, _ = solve(DIRECT_f_wrapper(f), lB, uB, maxT=self.maxiter)
return np.atleast_2d(x), f(np.atleast_2d(x))
except:
print(
"Cannot find DIRECT library, please install it to use this option."
)
def acq_max_direct(ac, gp, y_max, bounds):
"""
A function to find the maximum of the acquisition function using
the 'DIRECT' library.
Input Parameters
----------
ac: The acquisition function object that return its point-wise value.
gp: A gaussian process fitted to the relevant data.
y_max: The current maximum known value of the target function.
bounds: The variables bounds to limit the search of the acq max.
Returns
-------
x_max, The arg max of the acquisition function.
"""
try:
from DIRECT import solve
except:
print("Cannot find DIRECT library")
def DIRECT_f_wrapper(ac):
def g(x, user_data):
return ac(np.array([x]), gp, y_max), 0
return g
lB = np.asarray(bounds)[:, 0]
uB = np.asarray(bounds)[:, 1]
#x,_,_ = solve(DIRECT_f_wrapper(f),lB,uB, maxT=750, maxf=2000,volper=0.005) # this can be used to speed up DIRECT (losses precission)
x, _, _ = solve(DIRECT_f_wrapper(ac), lB, uB)
return np.reshape(x, len(bounds))
def wrapper_DIRECT(f, bounds):
'''
Wrapper for DIRECT optimization method. It works partitioning iteratively the domain
of the function. Only requieres f and the box constrains to work
:param f: function to optimize, acquisition.
:param bounds: tuple determining the limits of the optimizer.
'''
try:
from DIRECT import solve
import numpy as np
def DIRECT_f_wrapper(f):
def g(x, user_data):
return f(np.array([x])), 0
return g
lB = np.asarray(bounds)[:, 0]
uB = np.asarray(bounds)[:, 1]
x, _, _ = solve(DIRECT_f_wrapper(f),
lB,
uB,
maxT=750,
maxf=2000,
volper=0.005)
return reshape(x, len(bounds))
except:
print(
"Cannot find DIRECT library, please install it to use this option."
)
def optimizeGPDIRECT(self):
"""
Optimize the marginal posterior of the GP to obtain the covariance and model parameters
"""
# Do a sensible initialization
lower = np.zeros(self.nTotalPars)
upper = np.zeros(self.nTotalPars)
lower[0:self.nTotalParCovariance] = [
np.log(1.0 / 3.0**2), np.log(1e-5**2)
]
upper[0:self.nTotalParCovariance] = [
np.log(1.0 / 0.5**2), np.log(1e-2**2)
]
lower[self.nTotalParCovariance:] = [
0, 0.0, 0.0, -5.0, 0.0, 0.0, 0.03, 0.0
]
upper[self.nTotalParCovariance:] = [
2000.0, 180.0, 180.0, 5.0, 2.0, 4.0, 0.3, 10.0
]
x, fmin, ierror = solve(self.funcWrapperDIRECT,
lower,
upper,
volper=1e-15,
algmethod=1)
self.optimalPars = x
self.marginalLikelihood(self.optimalPars)
return
def optimize_acq_f(self, n_iter=20, method='random'):
self.logger.debug('optimizer for acq:{}'.format(method))
self.logger.debug('n_iter={}'.format(n_iter))
self.logger.debug('optninit={}'.format(self.config))
if self.acq_name == 'ES' or self.acq_name == 'MES':
self.acquisition.update(
) #especially for information-theory based acq
def obj_LBFGS(x):
return -self.acq_f(x)
def obj_DIRECT(x, u):
return -self.acq_f(x), 0
x_tries = np.random.uniform(self.bounds[0, :],
self.bounds[1, :],
size=(self.config['optninit'],
self.bounds.shape[1]))
if method == 'random':
x_seeds = np.random.uniform(self.bounds[0, :],
self.bounds[1, :],
size=(n_iter, self.bounds.shape[1]))
ys = -obj_LBFGS(x_seeds)
x_max = x_tries[ys.argmax()].reshape((1, -1))
max_acq = ys.max()
for x_try in x_seeds:
res = minimize(obj_LBFGS,
x_try.reshape(1, -1),
bounds=self.reformat_bounds(self.bounds),
method='L-BFGS-B')
if not res.success:
continue
if max_acq is None or -res.fun[0]:
x_max = res.x
max_acq = -res.fun[0]
elif method == 'direct':
x, _, _ = solve(obj_DIRECT,
self.bounds[0, :],
self.bounds[1, :],
maxf=1000,
logfilename=logfile)
x = minimize(obj_LBFGS,
x,
bounds=self.reformat_bounds(self.bounds),
method='L-BFGS-B').x
x_max = x
else:
raise NotImplementedError
x_max = x_max.reshape((1, -1))
self.logger.debug(
'end optimizing acq_f, with x_max={}, self.acq_f(x_max)={}'.format(
x_max, self.acq_f(x_max)))
return np.clip(x_max, self.bounds[0, :], self.bounds[1, :]).reshape(
(1, -1))
def maximize(self, model_predict: callable, lower_bound: np.ndarray,
upper_bound: np.ndarray):
def acquisition_curve(x: float, dummy):
# DIRECT.solve() calls this function with x and a dummy value
_, uncertainty = model_predict(x[None])
return -uncertainty[:, None]
xopt, fopt, _ = solve(acquisition_curve,
lower_bound,
upper_bound,
maxT=self.maxT,
algmethod=self.algmethod)
return xopt, fopt
def optimizeDirectGP(self):
"""
Optimize the hyperparameters of the GP using the DIRECT optimization method
Returns
-------
x: array of float
Value of the optimal hyperpameters
"""
l = [0.05, np.log(np.min(self.variance) / self.nLines)]
u = [4.0, np.log(10.0*np.max(self.variance) / self.nLines)]
x, fmin, ierror = solve(self._objDirect, l, u, algmethod=1, maxf=300)
print 'Optimal lambdaGP={0}, sigmaGP={1} - loglambdaGP={2}, logsigmaGP={3} - logL={4}'.format(np.exp(x[0]), np.exp(x[1]), x[0], x[1], fmin)
return x[0], x[1]
def predict_optimal(self, context):
context = np.array([context])
def DIRECT_obj(x, user_data):
return -self.acq_f(np.concatenate((x, context)), alpha=0), 0
x, fmin, ierror = solve(DIRECT_obj, self.lower_bounds,
self.upper_bounds)
def LBFGS_obj(x):
return -self.acq_f(np.concatenate((x, context)), alpha=0)
bounds = [(self.lower_bounds[i], self.upper_bounds[i]) \
for i in range(self.n_inputs)]
res = minimize(LBFGS_obj, x, method='L-BFGS-B', bounds=bounds)
return res.x
def predict_optimal(self, context):
"""
Given a context, predict the optimizer
:param context:
:return: the optimizer
"""
def obj_DIRECT(x, _):
return -self.acq_f(np.concatenate((x, context)), alpha=0), 0
def obj_LBFGS(x):
return -self.acq_f(np.concatenate((x, context)), alpha=0)
context = np.array([context])
x, _, _ = solve(obj_DIRECT, self.bounds_lower, self.bounds_upper)
res = minimize(obj_LBFGS, x, method='L-BFGS-B', bounds=self.bounds)
return res.x
def optimize_acq_f(self, context):
def obj_sw_DIRECT(x, user_data):
return -self.acq_f(x), 0
def obj_sw_LBFGS(x_sw):
return -self.acq_f(x_sw)
x, _, _ = solve(obj_sw_DIRECT,
self.bounds_lower,
self.bounds_upper,
maxf=500)
return np.array(
minimize(obj_sw_LBFGS,
x,
method='L-BFGS-B',
bounds=self.reformat_bounds(self.bounds)).x)
def optimize_acq_f(self):
def obj_sw_DIRECT(x, user_data):
return -self.acq_f(x), 0
def obj_sw_LBFGS(x_sw):
return -self.acq_f(x_sw)
x, _, _ = solve(obj_sw_DIRECT,
self.bounds_lower,
self.bounds_upper,
maxf=500)
x = minimize(obj_sw_LBFGS,
x,
method='L-BFGS-B',
bounds=self.reformat_bounds(self.bounds)).x
return np.array(x).reshape((1, self.num_inputs))
def optimizeGPDIRECT(self): """ Optimize the marginal posterior of the GP to obtain the covariance and model parameters """ # Do a sensible initialization lower = np.zeros(self.nTotalPars) upper = np.zeros(self.nTotalPars) lower[0:self.nTotalParCovariance] = [np.log(1.0/3.0**2),np.log(1e-5**2)] upper[0:self.nTotalParCovariance] = [np.log(1.0/0.5**2),np.log(1e-1**2)] lower[self.nTotalParCovariance:] = [0.0, 0.0, 0.0, 0.0] upper[self.nTotalParCovariance:] = [0.5, 0.2, 1.0, 2*np.pi] x, fmin, ierror = solve(self.funcWrapperDIRECT, lower, upper, volper=1e-13, algmethod=1) self.optimalPars = x self.marginalLikelihood(self.optimalPars) return
def optimize(self, f, boundaries, max_iter=6000):
def DIRECT_wrapper(f):
def g(x, user_data):
return -f(np.array([x])), 0
return g
lb = boundaries[:, 0]
ub = boundaries[:, 1]
#maxf= 80000
x, val, _ = solve(DIRECT_wrapper(f),
lb,
ub,
maxT=max_iter,
algmethod=0)
logger.info("DIRECT:", x, val)
return x
def optimize(self, n_iterations):
'''
Iteratively optimizes the objective cost function and updates GP model.
n_iterations: Number of optimization iterations to run
'''
for _ in range(n_iterations):
try:
self.iterations += 1
print('Iteration ' + str(self.iterations))
context = self.context_space[self.iterations % \
len(self.context_space)]
# DIRECT
def DIRECT_obj(x, user_data):
return self.acq_f(np.concatenate([x, context], axis=0)), 0
x, fmin, ierror = solve(DIRECT_obj, \
self.lower_bounds[:self.n_parameters], \
self.upper_bounds[:self.n_parameters], maxf=500)
# L-BFGS-B
def LBFGS_obj(x):
return self.acq_f(np.concatenate([x, context], axis=0))
bounds = [(self.lower_bounds[i], self.upper_bounds[i]) \
for i in range(self.n_parameters)]
res = minimize(LBFGS_obj, x, method='L-BFGS-B', bounds=bounds)
# Evalaute new x with true objective function and re-train GP
self.parameters = np.concatenate((self.parameters, \
np.reshape(res.x, (1,self.n_parameters))))
self.contexts = np.concatenate((self.contexts, \
np.reshape(context, (1,self.n_contexts))))
self.X = np.concatenate((self.parameters,self.contexts),axis=1)
# Evaluating obj_f
obj, info = self.obj_f(np.concatenate([res.x, context]))
self.Y = np.concatenate((self.Y, [obj]))
self.info = np.concatenate((self.info, [info]))
self.train_GP(self.X, self.Y)
self.model.optimize()
except Exception:
print('Exception encountered during optimization')
traceback.print_exc()
def optimize_acq_f(self, x_cn):
def obj_sw_DIRECT(x_uc, user_data):
x_uc = np.array(x_uc).reshape((1, self.n_uc))
return -self.acq_f(np.concatenate((x_uc, x_cn), axis=1)), 0
def obj_sw_LBFGS(x_uc):
x_uc = np.array(x_uc).reshape((1, self.n_uc))
return -self.acq_f(np.concatenate((x_uc, x_cn), axis=1))
x, _, _ = solve(obj_sw_DIRECT,
self.bounds_lower,
self.bounds_upper,
maxf=500)
x = minimize(obj_sw_LBFGS,
x,
method='L-BFGS-B',
bounds=self.reformat_bounds(self.bounds)).x
return np.array(x).reshape((1, self.n_uc))
def predict_optimal(self, context):
'''
Returns the predicted optimal parameters for a given context
context: A scalar value representing a specified context.
This value doesn't have to be in the given context_space
'''
def DIRECT_obj(x, user_data):
return self.acq_f(np.concatenate((x, context)), use_mean=True), 0
x, fmin, ierror = solve(DIRECT_obj, self.lower_bounds, \
self.upper_bounds, maxf=1000)
def LBFGS_obj(x):
return self.acq_f(np.concatenate((x, context)), use_mean=True)
bounds = [(self.lower_bounds[i], self.upper_bounds[i]) \
for i in range(self.n_parameters)]
res = minimize(LBFGS_obj, x, method='L-BFGS-B', bounds=bounds)
return obj_f(np.concatenate([res.x, context]))[0]
def optimize(self, f, x0=None, df=None, f_df=None):
from DIRECT import solve
def f_direct(x, userdata):
x = np.atleast_2d(x)
return f(x) * self.cost(x), 0
lower_bounds = [b[0] for b in self.bounds]
upper_bounds = [b[1] for b in self.bounds]
x, fmin, _ = solve(f_direct,
lower_bounds,
upper_bounds,
algmethod=1,
maxT=self.iters,
maxf=self.funcs)
return np.atleast_2d(x), np.atleast_1d(fmin)
def find_next_sample(self):
""" Find lcoation of next sample """
# Optimization range:
if self.prior_type == "normal":
mean = self.prior_parameters['mean']
cov = self.prior_parameters['cov']
# TODO: Check if picking diag is OK
lower_const = mean - 6.0 * np.sqrt(cov.diagonal())
upper_const = mean + 6.0 * np.sqrt(cov.diagonal())
# Wrap the optimization objective to use it within solve:
def mod_opt_obj(X, self):
return (self.opt_objective(X))
# Optimize: search for new sample
'''
# For 1 dimensionl input use grid search
if (self.dim == 1):
# Use grid:
GRID_STEP = self.opt_parameters["grid_step"]
# Generate grid:
X_grid = np.arange(lower_const[0], upper_const[0], GRID_STEP)
X_grid = to_column(X_grid)
# Calculate objective:
objective = np.apply_along_axis(self.opt_objective, 1, X_grid, False)
objective = objective.tolist()
# Pick X that maximizes the objective:
max_ind = objective.index(min(objective)) # min since -cost
Xstar = np.array([X_grid[max_ind]])
else:'''
# Use DIRECT:
kwargs = self.opt_parameters
Xstar, _, _ = solve(mod_opt_obj,
lower_const,
upper_const,
user_data=self,
**kwargs)
# Assign result:
self.Xstar = to_row(Xstar)
print("Predicted new sample (Xstar): " + str(Xstar))
def find_next_sample(self):
""" Find lcoation of next sample """
# Optimization range:
if self.prior_type == "normal":
mean = self.prior_parameters['mean']
cov = self.prior_parameters['cov']
# TODO: Check if picking diag is OK
lower_const = mean - 6.0*np.sqrt(cov.diagonal())
upper_const = mean + 6.0*np.sqrt(cov.diagonal())
# Wrap the optimization objective to use it within solve:
def mod_opt_obj(X, self):
return(self.opt_objective(X))
# Optimize: search for new sample
'''
# For 1 dimensionl input use grid search
if (self.dim == 1):
# Use grid:
GRID_STEP = self.opt_parameters["grid_step"]
# Generate grid:
X_grid = np.arange(lower_const[0], upper_const[0], GRID_STEP)
X_grid = to_column(X_grid)
# Calculate objective:
objective = np.apply_along_axis(self.opt_objective, 1, X_grid, False)
objective = objective.tolist()
# Pick X that maximizes the objective:
max_ind = objective.index(min(objective)) # min since -cost
Xstar = np.array([X_grid[max_ind]])
else:'''
# Use DIRECT:
kwargs = self.opt_parameters
Xstar, _, _ = solve(mod_opt_obj,
lower_const,
upper_const,
user_data=self,
**kwargs)
# Assign result:
self.Xstar = to_row(Xstar)
print("Predicted new sample (Xstar): " + str(Xstar))
def optimize(self, x0=None, f=None, df=None, f_df=None):
"""
:param x0: initial point for a local optimizer.
:param f: function to optimize.
:param df: gradient of the function to optimize.
:param f_df: returns both the function to optimize and its gradient.
"""
try:
from DIRECT import solve
import numpy as np
def DIRECT_f_wrapper(f):
def g(x, user_data):
return f(np.array([x])), 0
return g
lB = np.asarray(self.space.get_bounds())[:,0]
uB = np.asarray(self.space.get_bounds())[:,1]
x,_,_ = solve(DIRECT_f_wrapper(f),lB,uB, maxT=self.maxiter)
return np.atleast_2d(x), f(np.atleast_2d(x))
except:
print("Cannot find DIRECT library, please install it to use this option.")
def wrapper_DIRECT(f,bounds):
'''
Wrapper for DIRECT optimization method. It works partitioning iteratively the domain
of the function. Only requieres f and the box constrains to work
:param f: function to optimize, acquisition.
:param bounds: tuple determining the limits of the optimizer.
'''
try:
from DIRECT import solve
import numpy as np
def DIRECT_f_wrapper(f):
def g(x, user_data):
return f(np.array([x])), 0
return g
lB = np.asarray(bounds)[:,0]
uB = np.asarray(bounds)[:,1]
x,_,_ = solve(DIRECT_f_wrapper(f),lB,uB, maxT=750, maxf=2000,volper=0.005)
return reshape(x,len(bounds))
except:
print("Cannot find DIRECT library, please install it to use this option.")
def minimize(func, bounds, maxiter=15000, n_restarts_optimizer=10):
"""
# Arguments
func: if `approx_grad`=0, `func` returns function values and gradients. Otherwise, it returns only function values. `
Note: the backend minimizer evaluates `func` at each single input;thus `func` does not have to be able to perform sample-wise evaluation.
When `func` cannot be evaluated at a batch of samples, simply set `n_warmup` = 0.
"""
def real_func(x, user_data):
return func(x), 0
xmins = []
fmins = []
for i in range(n_restarts_optimizer):
xmin, fmin, _ = solve(real_func, l=bounds[:,0], u=bounds[:,1], eps=1e-4, maxf=2000, \
maxT=6000 if maxiter is None else maxiter,
algmethod=0, fglobal=-1e100, fglper=0.01, volper=-1.0,
sigmaper=-1.0, logfilename='DIRresults.txt', user_data=None)
xmins.append(xmin)
fmins.append(fmin)
ind = np.argmin(fmins)
return xmins[ind], fmins[ind]
def direct(self, boundaries, max_iter=3000):
try:
from DIRECT import solve
except ImportError as exc:
raise ImportError("To use the DIRECT optimization install the Python DIRECT wrapper\n", exc)
def wrapper(f):
def g(x, user_data):
return -f(np.array([x]), user_data), 0
return g
if self.get_info("minimization"):
best = np.min(self.get_Y())
else:
best = np.max(self.get_Y())
lb = boundaries[:, 0]
ub = boundaries[:, 1]
# maxf= 80000
#max_iter=6000,1000
x, val, _ = solve(wrapper(self._acquistion.call), lb, ub, maxT=max_iter, user_data=best, algmethod=1)
logger.info("DIRECT:", x, val)
return np.atleast_2d(x)
def optimize(self, n_iterations):
for _ in range(n_iterations):
try:
self.iterations += 1
print('Iteration ' + str(self.iterations))
context = [self.context_space[self.context_index]]
self.context_index = (self.context_index + 1) % \
len(self.context_space)
# DIRECT
def DIRECT_obj(x, user_data):
return -self.acq_f(np.concatenate((x, context))), 0
x, fmin, ierror = solve(DIRECT_obj, self.lower_bounds, \
self.upper_bounds, maxf=500)
# L-BFGS-B
def LBFGS_obj(x):
return -self.acq_f(np.concatenate((x, context)))
bounds = [(self.lower_bounds[i], self.upper_bounds[i]) \
for i in range(self.n_inputs)]
res = minimize(LBFGS_obj, x, method='L-BFGS-B', bounds=bounds)
self.parameters = np.concatenate((self.parameters, \
np.reshape(res.x, (1,self.n_inputs))))
self.contexts = np.concatenate((self.contexts, \
np.reshape(context, (1,1))))
self.X = np.concatenate((self.parameters, self.contexts),
axis=1)
self.Y = np.concatenate((self.Y, \
[self.obj_f(np.concatenate((res.x, context)))]))
self.train_GP(self.X, self.Y)
self.model.optimize()
except Exception:
print('Exception encountered during optimization')
traceback.print_exc()
thPart = th[0:kk + 1, :]
pSampPart = pSamp[0:kk + 1]
m = GPy.models.GPRegression(thPart, pSampPart, kernel)
if ((np.remainder(kk, 10) == 0) & (kk > par.preIter)):
m.optimize()
m.optimize_restarts(num_restarts=10)
# Find the maximum expected value
Mup, ys2, up95, lo95 = m.predict(np.array(thPart))
mumax[kk] = np.max(Mup)
# Compute the next point in which to sample the posterior
x, fmin, ierror = solve(EIeval,
l,
u,
user_data=(m, mumax[kk], par.epsilonEI),
maxf=1000,
maxT=1000)
# Set the new point and save the estimate of the EI
th[kk + 1, :] = x
EI[kk + 1] = fmin
print kk
# Estimate the argument that maximising the expected information matrix
muhat, llmax, ierror = solve(MUeval, l, u, user_data=m)
# Evaluate the surrogate function over a grid
xx = arange(0.00, 1.01, 0.01)
yy = arange(0.00, 1.01, 0.01)
def gpo(self, sm, sys, thSys):
#=====================================================================
# Initalisation
#=====================================================================
# Set initial settings
sm.calcGradientFlag = False
sm.calcHessianFlag = False
self.nPars = thSys.nParInference
self.filePrefix = thSys.filePrefix
runNextIter = True
self.iter = 0
# Check algorithm settings and set to default if needed
setSettings(self, "gpo")
# Make a grid to evaluate the EI on
l = np.array(self.lowerBounds[0:thSys.nParInference], dtype=np.float64)
u = np.array(self.upperBounds[0:thSys.nParInference], dtype=np.float64)
# Allocate vectors
AQ = np.zeros((self.maxIter + 1, 1))
mumax = np.zeros((self.maxIter + 1))
thp = np.zeros((self.maxIter + 1, self.nPars))
obp = np.zeros((self.maxIter + 1, 1))
thhat = np.zeros((self.maxIter, self.nPars))
thhatHessian = np.zeros((self.maxIter, self.nPars, self.nPars))
obmax = np.zeros((self.maxIter + 1, 1))
hyperParams = np.zeros((self.maxIter, 3 + self.nPars))
xhatf = np.zeros((self.maxIter + 1, sys.T))
#=====================================================================
# Pre-run using random sampling to estimate hyperparameters
#=====================================================================
# Pre allocate vectors
thPre = np.zeros((self.preIter, self.nPars))
obPre = np.zeros((self.preIter, 1))
#=====================================================================
# Main loop
#=====================================================================
# Pre-compute hypercube points if required
if (self.preSamplingMethod == "latinHyperCube"):
lhd = lhs(self.nPars, samples=self.preIter)
for kk in range(0, self.preIter):
# Sampling parameters using uniform sampling or Latin hypercubes
if (self.preSamplingMethod == "latinHyperCube"):
# Sample parameters using a Latin hypercube over the parameter
# bounds
thPre[kk, :] = l + (u - l) * lhd[kk, :]
elif (self.preSamplingMethod == "sobol"):
# Sample parameters using a Sobol sequence over the parameter
# bounds
thPre[kk, :] = l + (u - l) * i4_sobol(self.nPars, 100 + kk)[0]
else:
# Draw parameters uniform over the parameter bounds
thPre[kk, :] = l + (u - l) * np.random.random(self.nPars)
# Evaluate the objective function in the parameters
thSys.storeParameters(thPre[kk, :], sys)
obPre[kk], tmp1 = self.evaluateObjectiveFunction(sm, sys, thSys)
# Transform and save the parameters
thSys.transform()
thPre[kk, :] = thSys.returnParameters()[0:thSys.nParInference]
# Write out progress if requested
if (self.verbose):
print("gpo: Pre-iteration: " + str(kk) + " of " +
str(self.preIter) + " completed, sampled " +
str(np.round(thPre[kk, :], 3)) + " with " +
str(np.round(obPre[kk], 2)) + ".")
#=====================================================================
# Fit the GP regression
#=====================================================================
# Remove nan values for the objective function
idxNotNaN = ~np.isnan(obPre)
thPre = thPre[(idxNotNaN).any(axis=1)]
obPre = obPre[(idxNotNaN).any(axis=1)]
# Specify the kernel ( Matern52 with ARD plus bias kernel to compensate
# for non-zero mean )
kernel = GPy.kern.Matern52(
input_dim=self.nPars,
ARD=True) + GPy.kern.Bias(input_dim=self.nPars)
# Normalize the objective function evaluations
ynorm = (obPre - np.mean(obPre)) / np.sqrt(np.var(obPre))
# Create the model object
m = GPy.models.GPRegression(thPre, ynorm, kernel, normalizer=False)
#=====================================================================
# Update hyperparameters
#=====================================================================
# Set constraints on hyperparameters
m.Gaussian_noise.variance.constrain_bounded(0.01, 10.0)
m.kern.Mat52.lengthscale.constrain_bounded(0.01, 10.0)
m.kern.Mat52.variance.constrain_bounded(0.01, 25.0)
# Run empirical Bayes to estimate the hyperparameters
m.optimize('bfgs', max_iters=200)
m.optimize_restarts(num_restarts=10, robust=True)
self.GaussianNoiseVariance = np.array(m.Gaussian_noise.variance,
copy=True)
#=====================================================================
# Write to output
#=====================================================================
self.thPre = thPre
self.obPre = obPre
self.m = m
#=====================================================================
# Main loop
#=====================================================================
# Save the initial parameters
thSys.storeParameters(self.initPar, sys)
thp[self.iter, :] = thSys.returnParameters()
thSys.transform()
while (runNextIter):
# Store the parameter
thSys.storeParameters(thp[self.iter, :], sys)
thSys.transform()
#------------------------------------------------------------------
# Evalute the objective function
#------------------------------------------------------------------
obp[self.iter], xhatf[
self.iter, :] = self.evaluateObjectiveFunction(sm, sys, thSys)
# Collect the sampled data (if the objective is finite)
idxNotNaN = ~np.isnan(obp[range(self.iter), :])
x = np.vstack((thPre, thp[(idxNotNaN).any(axis=1)]))
y = np.vstack((obPre, obp[(idxNotNaN).any(axis=1)]))
#------------------------------------------------------------------
# Fit the GP to the sampled data
#------------------------------------------------------------------
ynorm = (y - np.mean(y)) / np.sqrt(np.var(y))
self.ynormMean = np.mean(y)
self.ynormVar = np.var(y)
m = GPy.models.GPRegression(x, ynorm, kernel, normalizer=False)
#------------------------------------------------------------------
# Re-estimate the hyperparameters
#------------------------------------------------------------------
if (np.remainder(self.iter + 1,
self.EstimateHyperparametersInterval) == 0):
# Set constraints on hyperparameters
m.Gaussian_noise.variance.constrain_bounded(0.01, 10.0)
m.kern.Mat52.lengthscale.constrain_bounded(0.01, 10.0)
m.kern.Mat52.variance.constrain_bounded(0.01, 25.0)
# Run empirical Bayes to estimate the hyperparameters
m.optimize('bfgs', max_iters=200)
m.optimize_restarts(num_restarts=10, robust=True)
# Save the current noise variance
self.GaussianNoiseVariance = np.array(
m.Gaussian_noise.variance, copy=True)
else:
# Overload current noise estimate (sets to 1.0 every time we
# add data otherwise)
m.Gaussian_noise.variance = self.GaussianNoiseVariance
# Save all the hyperparameters
hyperParams[self.iter, 0] = np.array(m.Gaussian_noise.variance,
copy=True)
hyperParams[self.iter, 1] = np.array(m.kern.bias.variance,
copy=True)
hyperParams[self.iter, 2] = np.array(m.kern.Mat52.variance,
copy=True)
hyperParams[self.iter, range(3, 3 + self.nPars)] = np.array(
m.kern.Mat52.lengthscale, copy=True)
#------------------------------------------------------------------
# Find the maximum expected value of the GP over the sampled parameters
#------------------------------------------------------------------
Mup, ys2 = m.predict(x)
mumax[self.iter] = np.max(Mup)
#------------------------------------------------------------------
# Compute the next point in which to sample the posterior
#------------------------------------------------------------------
# Optimize the AQ function
aqThMax, aqMax, ierror = solve(self.AQfunction,
l,
u,
user_data=(m, mumax[self.iter],
self.epsilon),
maxf=1000,
maxT=1000)
# Jitter the parameter estimates
if (self.jitterParameters == True):
flag = 0.0
while (flag == 0.0):
z = np.random.multivariate_normal(
np.zeros(self.nPars), self.jitteringCovariance[
range(self.nPars), :][:, range(self.nPars)])
flag = self.checkProposedParameters(aqThMax + z)
thSys.storeParameters(aqThMax + z, sys)
aqThMax += z
# Set the new point and save the estimate of the AQ
thp[self.iter + 1, :] = aqThMax
AQ[self.iter + 1] = -aqMax
# Update counter
self.iter += 1
#------------------------------------------------------------------
# Check exit conditions
#------------------------------------------------------------------
# AQ function criteria
if (AQ[self.iter] < self.tolLevel):
print("GPO: reaches tolLevel, so exiting...")
runNextIter = False
# Max iteration criteria
if (self.iter == self.maxIter):
print("GPO: reaches maxIter, so exiting...")
runNextIter = False
#------------------------------------------------------------------
# Estimate the current parameters by maximizing the GP
#------------------------------------------------------------------
if ((self.EstimateThHatEveryIteration == True) |
(runNextIter == False)):
thhatCurrent, obmaxCurrent, ierror = solve(self.MUeval,
l,
u,
user_data=m,
algmethod=1,
maxf=1000,
maxT=1000)
thhat[self.iter - 1, :] = thhatCurrent
obmax[self.iter - 1, :] = obmaxCurrent
print((thhatCurrent, obmaxCurrent))
if (self.EstimateHessianEveryIteration == True):
self.estimateHessian(thhatCurrent)
thhatHessian[self.iter - 1, :, :] = self.invHessianEstimate
#------------------------------------------------------------------
# Print output to console
#------------------------------------------------------------------
if (self.verbose):
if (self.EstimateThHatEveryIteration == True):
parm = ["%.4f" % v for v in thhat[self.iter - 1, :]]
print(
"##############################################################################################"
)
print("Iteration: " + str(self.iter) +
" with current parameters: " + str(parm) +
" and AQ: " + str(np.round(AQ[self.iter], 2)))
print(
"##############################################################################################"
)
else:
parm = ["%.4f" % v for v in thp[self.iter - 1, :]]
print(
"##############################################################################################"
)
print("Iteration: " + str(self.iter) +
" sampled objective function at parameters: " +
str(parm) + " with value: " +
str(np.round(obp[self.iter - 1], 2)))
print(
"##############################################################################################"
)
#=====================================================================
# Generate output
#=====================================================================
tmp = range(self.iter - 1)
self.ob = obmax[tmp]
self.th = thhat[tmp, :]
self.thhat = thhat[self.iter - 1, :]
self.thHessian = thhatHessian
self.thhatHessian = thhatHessian[self.iter - 1, :, :]
self.aq = AQ[range(self.iter)]
self.obp = obp[tmp]
self.thp = thp[range(self.iter), :]
self.m = m
self.x = x
self.y = y
self.xhatf = xhatf
self.ynorm = ynorm
self.hp = hyperParams
def minimize(func,
bounds,
approx_grad=0,
maxiter=15000,
n_warmup=100000,
method='lbfgs',
n_restarts_optimizer=10,
initializer=None,
x_init=None,
random_state=None):
"""
# Arguments
func: if `approx_grad`=0, `func` returns function values and gradients. Otherwise, it returns only function values. `
Note: the backend minimizer evaluates `func` at each single input;thus `func` does not have to be able to perform sample-wise evaluation.
When `func` cannot be evaluated at a batch of samples, simply set `n_warmup` = 0.
"""
if initializer is None:
initializer = random_sampler
if method == 'lbfgs':
if n_warmup > 0:
X0 = latin_hypercube_sampler(n_warmup, bounds.shape[0], bounds,
random_state)
if approx_grad:
ys = func(X0)
else:
ys, _ = func(X0)
xmin = X0[ys.argmin()]
fmin = ys.min()
else:
xmin = None
fmin = np.inf
# Actual optimize
X0 = latin_hypercube_sampler(n_restarts_optimizer, bounds.shape[0],
bounds, random_state)
if n_warmup > 0:
X0 = np.vstack((xmin.reshape(1, -1), X0))
if x_init is not None:
X0 = np.vstack((x_init.reshape(1, -1), X0))
for j, x0 in enumerate(X0):
if maxiter is not None:
_xmin, _fmin, d = scipy.optimize.fmin_l_bfgs_b(
func,
x0.reshape(1, -1),
approx_grad=approx_grad,
bounds=bounds,
maxiter=maxiter)
else:
_xmin, _fmin, d = scipy.optimize.fmin_l_bfgs_b(
func,
x0.reshape(1, -1),
approx_grad=approx_grad,
bounds=bounds)
if _fmin < fmin:
fmin = _fmin
xmin = _xmin
return xmin, fmin
elif method == 'DIRECT': # `func` returns function values only.
def real_func(x, user_data):
return func(x), 0
xmin, fmin, _ = solve(real_func, l=bounds[:,0], u=bounds[:,1], eps=1e-4, maxf=2000, \
maxT=6000 if maxiter is None else maxiter,
algmethod=0, fglobal=-1e100, fglper=0.01, volper=-1.0,
sigmaper=-1.0, logfilename='DIRresults.txt', user_data=None)
return xmin, fmin
else:
raise NotImplementedError('Not recognized %s' % (method))
x1 = x[0]
x2 = x[1]
f = (4 - 2.1 * (x1 * x1) +
(x1 * x1 * x1 * x1) / 3.0) * (x1 * x1) + x1 * x2 + (-4 + 4 *
(x2 * x2)) * (x2 *
x2)
return f, 0
if __name__ == '__main__':
l = [-3, -2]
u = [3, 2]
x, fmin, ierror = solve(obj, l, u)
print 'Optimal point:', x
print 'Optimal value:', fmin
print 'Exit status:', ierror
#
# Plot the results.
#
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y = np.mgrid[x[0] - 1:x[0] + 1:50j, x[1] - 1:x[1] + 1:50j]
Z = np.zeros_like(X)
for i in range(X.size):
from DIRECT import solve
import numpy as np
trans = np.array([-1, 2, -4, 3])
def func(x, user_data):
x -= trans
return np.dot(x, x), 0
if __name__ == '__main__':
l = np.array([-10, -10, -10, -10], dtype=np.float64)
u = np.array([10, 10, 10, 10], dtype=np.float64)
x, fmin, ierror = solve(func, l, u)
print 'Optimal point:', x
print 'Optimal value:', fmin
print 'Exit status:', ierror
thP = (th[kk, :], sys.par[1], sys.par[2], sys.par[3])
thSys.storeParameters(thP, sys, par)
smc.faPF(data, thSys, par)
pSamp[kk] = smc.ll
# Fit the GP
thPart = th[0 : kk + 1, :]
pSampPart = pSamp[0 : kk + 1]
m = GPy.models.GPRegression(thPart, pSampPart, kernel)
# Find the maximum expected value
Mup, ys2, up95, lo95 = m.predict(np.array(thPart))
mumax[kk] = np.max(Mup)
# Compute the next point in which to sample the posterior
x, fmin, ierror = solve(EIeval, l, u, user_data=(m, mumax[kk], par.epsilonEI), maxf=1000, maxT=1000)
# Set the new point and save the estimate of the EI
th[kk + 1, :] = x
EI[kk + 1] = fmin
tmp2, tmp3, ierror = solve(MUeval, l, u, user_data=m, algmethod=1)
thhat[kk, :] = tmp2
llmax[kk, :] = tmp3
user_data = (m, mumax[kk], par.epsilonEI)
Mup, ys2, up95, lo95 = m.predict(np.array(grid.reshape((len(grid), 1))))
for ii in range(0, len(grid)):
gridA[ii, kk], tmp = EIeval(np.array(grid[ii]), user_data)
def optimizeDIRECT(self): x, fmin, ierror = solve(self.logLike, self.lower, self.upper, volper=1e-10, algmethod=1)
j = np.arange(1, 6)
tmp1 = np.dot(j, np.cos((j+1)*x[0] + j))
tmp2 = np.dot(j, np.cos((j+1)*x[1] + j))
return tmp1 * tmp2, 0
if __name__ == '__main__':
l = [-10, -10]
u = [10, 10]
x, fmin, ierror = solve(
obj,
l,
u
)
print 'Optimal point:', x
print 'Optimal value:', fmin
print 'Exit status:', ierror
#
# Plot the results.
#
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y = np.mgrid[x[0]-2:x[0]+2:50j, x[1]-2:x[1]+2:50j]
Z = np.zeros_like(X)
# Fit the GP
thPart = th[0:kk+1,:];
pSampPart = pSamp[0:kk+1];
m = GPy.models.GPRegression(thPart,pSampPart,kernel)
if (( np.remainder(kk,10) == 0) & ( kk > par.preIter) ) :
m.optimize()
m.optimize_restarts(num_restarts = 10)
# Find the maximum expected value
Mup, ys2, up95, lo95 = m.predict( np.array(thPart) )
mumax[kk] = np.max(Mup);
# Compute the next point in which to sample the posterior
x, fmin, ierror = solve(EIeval,l,u,user_data=(m,mumax[kk],par.epsilonEI),maxf=1000,maxT=1000);
# Set the new point and save the estimate of the EI
th[kk+1,:] = x;
EI[kk+1] = fmin;
print kk;
# Estimate the argument that maximising the expected information matrix
muhat, llmax, ierror = solve(MUeval,l,u,user_data=m);
# Evaluate the surrogate function over a grid
xx = arange(0.00,1.01,0.01)
yy = arange(0.00,1.01,0.01)
mout = np.zeros((len(xx)*len(yy)))
kk=0;
def integrate(self, m, P, fun, *args):
''' Input:
m - column vector
P - matrix
Output:
x - column vector
Variables:
X, Y - matrix of row vectors
z - column vector
m, x, mu - row vector
'''
# Initial sample and fitted GP:
m = to_row(m)
X = m
Y = np.apply_along_axis(fun, 1, X, *args)
gp = self.gp_fit(X,Y)
# Optimization constraints:
N_SIGMA = 3
P_diag = np.sqrt(np.diag(P))
lower_const = (m - N_SIGMA*P_diag)[0].tolist()
upper_const = (m + N_SIGMA*P_diag)[0].tolist()
# Perform sampling
for i in range(0, self.N_SAMPLES):
# Set extra params to pass to optimizer
user_data = {"gp":gp, "m":m, "P":P, "grid_search": False}
if (X.shape[1] == 1):
''' GRID SEARCH: when we are in 1 dimension '''
user_data['grid_search'] = True
X_grid = np.linspace(lower_const[0], upper_const[0], self.opt_par["GRID_SIZE"])
X_grid = to_column(X_grid)
objective = self.optimization_objective(X_grid, user_data)
max_ind = objective.index(max(objective))
x_star = np.array([X_grid[max_ind]])
else:
''' OPTIMIZATION: for higher dimensions '''
x_star, _, _ = solve(self.optimization_objective, lower_const, upper_const,
user_data=user_data, algmethod = 1,
maxT = self.opt_par["MAX_T"],
maxf = self.opt_par["MAX_F"])
x_star = to_row(x_star)
X = np.vstack((X, x_star))
Y = np.apply_along_axis(fun, 1, X, *args)
gp = self.gp_fit(X, Y)
# Reoptimize GP:
# TODO: Remove unique rows:
if (len(unique_rows(X)) != len(X)):
print("Removing duplicated rows")
X = unique_rows(X)
Y = np.apply_along_axis(fun, 1, X, *args)
gp = self.gp_fit(X, Y)
# Compute integral
# Fitted GP parameters
w_0 = gp.rbf.variance.tolist()[0]
w_d = np.power(gp.rbf.lengthscale.tolist(), 2)
# Prior parameters
A = np.diag(w_d)
I = np.eye(self.X_DIM)
# Compute weigths
z = [self.compute_z(x, A, m, P, I, w_0) for x in X]
z = to_column(np.array(z))
K = gp.kern.K(X); K = (K.T + K)/2.0
W = (mo.mrdivide(z.T, K).squeeze()).tolist()
# Compute mean, covariance and cross-cov
mu_ = (z.T).dot( mo.mldivide(K, Y) )
mu_ = to_row(mu_)
# Initiale Sigma and CC
Sigma_ = CC_ = None
# TODO: Sigma computation as closed form
# Seems to cause problems!
# Sigma_ = w_0/np.sqrt(np.linalg.det( 2*mo.mldivide(A,P) + I) ) - (z.T).dot(mo.mldivide(K,z))
# Compute cov matrix and cross cov matrix
for i in range(0,len(W)):
# TODO: Sigma computation as sumation:
# Seems to work better for multidimensional problems and doesn't
# cause problems (might be slower though):
YY_i = ( to_column(Y[i]-mu_) ).dot( to_row(Y[i]-mu_) )
Sigma_ = W[i] * YY_i if i == 0 else Sigma_ + W[i] * YY_i
XY_i = ( to_column(X[i]-m) ).dot( to_row(Y[i]-mu_) )
CC_ = W[i] * XY_i if i == 0 else CC_ + W[i] * XY_i
mu_ = to_column(mu_)
Sigma_ = symetrize_cov(Sigma_)
# Return results
return(mu_, Sigma_, CC_)
def gpo(self,sm,sys,thSys):
#=====================================================================
# Initalisation
#=====================================================================
# Set initial settings
sm.calcGradientFlag = False;
sm.calcHessianFlag = False;
self.nPars = thSys.nParInference;
self.filePrefix = thSys.filePrefix;
runNextIter = True;
self.iter = 0;
# Check algorithm settings and set to default if needed
setSettings(self,"gpo");
# Make a grid to evaluate the EI on
l = np.array(self.lowerBounds[0:thSys.nParInference], dtype=np.float64)
u = np.array(self.upperBounds[0:thSys.nParInference], dtype=np.float64)
# Allocate vectors
AQ = np.zeros((self.maxIter+1,1))
mumax = np.zeros((self.maxIter+1))
thp = np.zeros((self.maxIter+1,self.nPars))
obp = np.zeros((self.maxIter+1,1))
thhat = np.zeros((self.maxIter,self.nPars))
obmax = np.zeros((self.maxIter+1,1))
#=====================================================================
# Specify the GP regression model
#=====================================================================
# Load the GP regression model
m = pyGPs.GPR()
# Specify the GP prior
priorMean = pyGPs.mean.Zero();
#priorKernel = pyGPs.cov.Matern(d=5) + pyGPs.cov.Const();
priorKernel = pyGPs.cov.RBFard(D=self.nPars) + pyGPs.cov.Const();
m.setPrior(mean=priorMean, kernel=priorKernel)
# Setup the optimization routine
m.setOptimizer('Minimize', num_restarts=10)
#=====================================================================
# Pre-run using random sampling to estimate hyperparameters
#=====================================================================
# Pre allocate vectors
thPre = np.zeros((self.preIter,self.nPars));
obPre = np.zeros((self.preIter,1));
#=====================================================================
# Main loop
#=====================================================================
for kk in range(0,self.preIter):
# Sample parameters
#if (self.optType == "MAPparameterEstimation"):
# Sample prior
#thPre[kk,:] = thSys.samplePrior()
#else:
# Draw parameters uniform over the parameter bounds
thPre[kk,:] = l + (u-l) * np.random.random( self.nPars )
# Evaluate the objective function in the parameters
thSys.storeParameters(thPre[kk,:],sys);
obPre[kk] = self.evaluateObjectiveFunction( sm, sys, thSys );
# Transform and save the parameters
thSys.transform();
thPre[kk,:] = thSys.returnParameters()[0:thSys.nParInference];
# Write out progress if requested
if (self.verbose):
print("gpo: Pre-iteration: " + str(kk) + " of " + str(self.preIter) + " completed, sampled " + str(thPre[kk,:]) + " with " + str(obPre[kk]) + ".")
#=====================================================================
# Fit the GP regression
#=====================================================================
# Remove nan values for the objective function
idxNotNaN = ~np.isnan( obPre );
thPre = thPre[(idxNotNaN).any(axis=1)];
obPre = obPre[(idxNotNaN).any(axis=1)];
# Normalize the objective function evaluations
ynorm = ( obPre - np.mean(obPre) ) / np.sqrt( np.var(obPre) )
# Optimize the Hyperparameters
m.optimize(thPre, ynorm)
#yp = m.predict(np.arange(0.01,1.00,0.01))
#m.plot()
#=====================================================================
# Write to output
#=====================================================================
self.thPre = thPre;
self.obPre = obPre;
self.m = m;
#=====================================================================
# Main loop
#=====================================================================
# Save the initial parameters
thSys.storeParameters(self.initPar,sys);
thp[self.iter,:] = thSys.returnParameters();
thSys.transform();
while ( runNextIter ):
# Store the parameter
thSys.storeParameters(thp[self.iter,:],sys);
thSys.transform();
#------------------------------------------------------------------
# Evalute the objective function
#------------------------------------------------------------------
obp[self.iter] = self.evaluateObjectiveFunction( sm, sys, thSys );
# Collect the sampled data (if the objective is finite)
if np.isfinite(obp[self.iter]):
x = np.vstack( (thPre,thp[range(self.iter),:]) );
y = np.vstack( (obPre,obp[range(self.iter),:]) );
# Normalize the objective function evaluations
ynorm = ( y - np.mean(y) ) / np.sqrt( np.var(y) )
#------------------------------------------------------------------
# Fit the GP to the sampled data
#------------------------------------------------------------------
# Optimize the hyperparameters if needed
#if ( np.remainder(self.iter+1,self.EstimateHyperparametersInterval) == 0):
# m.optimize(x, ynorm)
#else:
# m.setData(x, ynorm)
m.optimize(x, ynorm)
# Extract the posterior values of the hyperparameters
post = m.posterior;
#------------------------------------------------------------------
# Find the maximum expected value of the GP over the sampled parameters
#------------------------------------------------------------------
Mup, ys2, fmu, fs2, lp = m.predict_with_posterior(post, np.array( np.vstack( (thPre,thp[range(self.iter),:]) ) ) )
mumax[self.iter] = np.max( Mup );
#------------------------------------------------------------------
# Compute the next point in which to sample the posterior
#------------------------------------------------------------------
# Optimize the AQ function
aqThMax, aqMax, ierror = solve(self.AQfunction,l,u,user_data=(m,mumax[self.iter],self.epsilon,post),maxf=1000,maxT=1000);
# Jitter the parameter estimates
if ( self.jitterParameters == True ):
aqThMax += np.random.multivariate_normal( np.zeros(self.nPars), self.jitteringCovariance[range(self.nPars),:][:,range(self.nPars)] );
# Set the new point and save the estimate of the AQ
thp[self.iter+1,:] = aqThMax;
AQ[self.iter+1] = -aqMax;
# Update counter
self.iter += 1;
#------------------------------------------------------------------
# Check exit conditions
#------------------------------------------------------------------
# AQ function criteria
if ( AQ[self.iter] < self.tolLevel ):
print("GPO: reaches tolLevel, so exiting...")
runNextIter = False;
# Max iteration criteria
if ( self.iter == self.maxIter ):
print("GPO: reaches maxIter, so exiting...")
runNextIter = False;
#------------------------------------------------------------------
# Estimate the current parameters by maximizing the GP
#------------------------------------------------------------------
if ( ( self.EstimateThHatEveryIteration == True ) | runNextIter == False ):
thhatCurrent, obmaxCurrent, ierror = solve(self.MUeval,l,u,user_data=(m,post),algmethod=1);
thhat[self.iter-1,:] = thhatCurrent;
obmax[self.iter-1,:] = obmaxCurrent;
#------------------------------------------------------------------
# Print output to console
#------------------------------------------------------------------
if ( self.verbose ):
if ( self.EstimateThHatEveryIteration == True ):
parm = ["%.4f" % v for v in thhat[self.iter-1,:]];
print("##############################################################################################")
print("Iteration: " + str(self.iter) + " with current parameters: " + str(parm) + " and AQ: " + str(AQ[self.iter]) )
print("##############################################################################################")
else:
parm = ["%.4f" % v for v in thp[self.iter-1,:]];
print("##############################################################################################")
print("Iteration: " + str(self.iter) + " sampled objective function at parameters: " + str(parm) + " with value: " + str(obp[self.iter-1]) )
print("##############################################################################################")
#=====================================================================
# Generate output
#=====================================================================
tmp = range(self.iter-1);
self.ob = obmax[tmp];
self.th = thhat[tmp,:];
self.thhat = thhat[self.iter-1,:]
self.aq = AQ[range(self.iter)];
self.obp = obp[tmp];
self.thp = thp[range(self.iter),:];
self.m = m;
self.x = x;
self.y = y;
def optimize_acq_f(self, n_iter=20, method='random'):
self.logger.debug("optimizer for acq: {}".format(method))
self.logger.debug("n_iter={}".format(n_iter))
self.logger.debug("optninit={}".format(self.config))
self.acquisition.update() #especially for information-theory based acq
def obj_LBFGS(x):
#self.logger.debug('input to obj_LBFGS={}'.format(x))
return -self.acq_f(x)
def obj_DIRECT(x, u):
return -self.acq_f(x), 0
x_tries = np.random.uniform(self.bounds[0, :],
self.bounds[1, :],
size=(self.config['optninit'],
self.bounds.shape[1]))
if method == 'random':
x_seeds = np.random.uniform(self.bounds[0, :],
self.bounds[1, :],
size=(n_iter, self.bounds.shape[1]))
ys = -obj_LBFGS(x_tries)
x_max = x_tries[ys.argmax()].reshape((1, -1))
max_acq = ys.max()
self.logger.debug('max_acq from random={}'.format(max_acq))
for x_try in x_seeds:
# Find the minimum of minus the acquisition function
res = minimize(obj_LBFGS,
x_try.reshape(1, -1),
bounds=self.reformat_bounds(self.bounds),
method="L-BFGS-B")
if not res.success:
self.logger.debug(
'minimize is not successful and going to try another random init(x_seeds) for minimize'
)
continue
# Store it if better than previous minimum(maximum).
if max_acq is None or -res.fun[0] > max_acq:
x_max = res.x
max_acq = -res.fun[0]
self.logger.debug(
'use a result from minimize whose max_acq={}'.format(
max_acq))
elif method == 'direct':
x, _, _ = solve(obj_DIRECT,
self.bounds[0, :],
self.bounds[1, :],
maxf=1000,
logfilename=logfile)
x = minimize(obj_LBFGS,
x,
bounds=self.reformat_bounds(self.bounds),
method='L-BFGS-B').x
x_max = x
else:
raise NotImplementedError
x_max = x_max.reshape((1, -1))
x_max[:, -self.zdim:] = self._roundfidelity(x_max[:, -self.zdim:])
self.logger.debug(
'end optimizing acq_f, with x_max={}, self.acq_f(x_max)={}'.format(
x_max, self.acq_f(x_max)))
return np.clip(x_max, self.bounds[0, :], self.bounds[1, :]).reshape(
(1, -1))
from DIRECT import solve
import numpy as np
trans = np.array([-1, 2, -4, 3])
def func(x, user_data):
x -= trans
return np.dot(x, x), 0
if __name__ == '__main__':
l = np.array([-10, -10, -10, -10], dtype=np.float64)
u = np.array([10, 10, 10, 10], dtype=np.float64)
x, fmin, ierror = solve(
func,
l,
u
)
print 'Optimal point:', x
print 'Optimal value:', fmin
print 'Exit status:', ierror
def optimize_posterior_mean(self, n_iter=20, method='random'):
self.logger.debug("optimizer for posterior mean: {}".format(method))
self.logger.debug("n_iter={}".format(n_iter))
def obj_LBFGS(x):
x = np.reshape(x, (-1, self.xdim))
mean, _ = self.gps.predict(x)
if self.acq_name == 'ES' or self.acq_name == 'MES':
mean = -mean
return -mean
def obj_DIRECT(x, u):
x = np.reshape(x, (-1, self.xdim))
mean, _ = self.gps.predict(x)
if self.acq_name == 'ES' or self.acq_name == 'MES':
mean = -mean
return -mean, 0
x_tries = np.random.uniform(self.bounds[0, :],
self.bounds[1, :],
size=(self.config['optninit'],
self.bounds.shape[1]))
if method == 'random':
x_seeds = np.random.uniform(self.bounds[0, :],
self.bounds[1, :],
size=(n_iter, self.bounds.shape[1]))
ys = -obj_LBFGS(x_tries)
x_max = x_tries[ys.argmax()].reshape((1, -1))
max_acq = ys.max()
self.logger.debug('max_acq from random={}'.format(max_acq))
for x_try in x_seeds:
res = minimize(obj_LBFGS,
x_try.reshape(1, -1),
bounds=self.reformat_bounds(self.bounds),
method='L-BFGS-B')
if not res.success:
self.logger.debug(
'minimize is not successful and going to try another random init(x_seeds) for minimize'
)
continue
if max_acq is None or -res.fun[0] > max_acq:
x_max = res.x
max_acq = -res.fun[0]
self.logger.debug(
'use a result from minimize whose max_acq={}'.format(
max_acq))
elif method == 'direct':
x, _, _ = solve(obj_DIRECT,
self.bounds[0, :],
self.bounds[1, :],
maxf=1000,
logfilename=logfile)
x = minimize(obj_LBFGS,
x,
bounds=self.reformat_bounds(self.bounds),
method='L-BFGS-B').x
x_max = x
else:
raise NotImplementedError
x_max = x_max.reshape((1, -1))
self.logger.debug(
'end optimizing posterior mean, with x_max={}, posteror_mean(x_max)={}'
.format(x_max, -obj_LBFGS(x_max)))
return np.clip(x_max, self.bounds[0, :], self.bounds[1, :]).reshape(
(1, -1))
gp = user_data['gp']
mu = user_data['mu'].squeeze()
Sigma = user_data['Sigma']
X = to_row(X)
_, gp_cov = gp.predict(X)
pi_x = pi(X, mu, Sigma)
cost = (pi_x**2 * gp_cov).squeeze()
return( -cost , 0 )
# Find new sample
user_data = {"gp":gp, "mu":x0, "Sigma":p0}
x_star, _, _ = solve(optimization_objective, lower_const, upper_const,
user_data=user_data, algmethod = 1,
maxT = 3000, maxf = 10000)
x_star = np.reshape(x_star, (1, X_DIM))
y_star, _ = gp.predict(x_star)
X = np.vstack((X, x_star))
Y = np.vstack((Y, y_star))
#gp = GPy.models.GPRegression(X,Y,kernel)
#gp.optimize_restarts(num_restarts=16, verbose=False, parallel=False)
# Compute integral
cov_Q = np.eye(Y_DIM)
N = len(X)
# Fitted GP parameters
w_0 = gp.rbf.variance.tolist()[0]
def gpo(self, sm, sys, thSys):
#=====================================================================
# Initalisation
#=====================================================================
# Set initial settings
sm.calcGradientFlag = False
sm.calcHessianFlag = False
self.nPars = thSys.nParInference
self.filePrefix = thSys.filePrefix
runNextIter = True
self.iter = 0
# Check algorithm settings and set to default if needed
setSettings(self, "gpo")
# Make a grid to evaluate the EI on
l = np.array(self.lowerBounds[0:thSys.nParInference], dtype=np.float64)
u = np.array(self.upperBounds[0:thSys.nParInference], dtype=np.float64)
# Allocate vectors
AQ = np.zeros((self.maxIter + 1, 1))
mumax = np.zeros((self.maxIter + 1))
thp = np.zeros((self.maxIter + 1, self.nPars))
obp = np.zeros((self.maxIter + 1, 1))
thhat = np.zeros((self.maxIter, self.nPars))
thhatHessian = np.zeros((self.maxIter, self.nPars, self.nPars))
obmax = np.zeros((self.maxIter + 1, 1))
hyperParams = np.zeros((self.maxIter, 3 + self.nPars))
xhatf = np.zeros((self.maxIter + 1, sys.T))
#=====================================================================
# Pre-run using random sampling to estimate hyperparameters
#=====================================================================
# Pre allocate vectors
thPre = np.zeros((self.preIter, self.nPars))
obPre = np.zeros((self.preIter, 1))
#=====================================================================
# Main loop
#=====================================================================
# Pre-compute hypercube points if required
if (self.preSamplingMethod == "latinHyperCube"):
lhd = lhs(self.nPars, samples=self.preIter)
for kk in range(0, self.preIter):
# Sampling parameters using uniform sampling or Latin hypercubes
if (self.preSamplingMethod == "latinHyperCube"):
# Sample parameters using a Latin hypercube over the parameter
# bounds
thPre[kk, :] = l + (u - l) * lhd[kk, :]
elif (self.preSamplingMethod == "sobol"):
# Sample parameters using a Sobol sequence over the parameter
# bounds
thPre[kk, :] = l + (u - l) * i4_sobol(self.nPars, 100 + kk)[0]
else:
# Draw parameters uniform over the parameter bounds
thPre[kk, :] = l + (u - l) * np.random.random(self.nPars)
# Evaluate the objective function in the parameters
thSys.storeParameters(thPre[kk, :], sys)
obPre[kk], tmp1 = self.evaluateObjectiveFunction(sm, sys, thSys)
# Transform and save the parameters
thSys.transform()
thPre[kk, :] = thSys.returnParameters()[0:thSys.nParInference]
# Write out progress if requested
if (self.verbose):
print("gpo: Pre-iteration: " + str(kk) + " of " + str(self.preIter) + " completed, sampled " +
str(np.round(thPre[kk, :], 3)) + " with " + str(np.round(obPre[kk], 2)) + ".")
#=====================================================================
# Fit the GP regression
#=====================================================================
# Remove nan values for the objective function
idxNotNaN = ~np.isnan(obPre)
thPre = thPre[(idxNotNaN).any(axis=1)]
obPre = obPre[(idxNotNaN).any(axis=1)]
# Specify the kernel ( Matern52 with ARD plus bias kernel to compensate
# for non-zero mean )
kernel = GPy.kern.Matern52(
input_dim=self.nPars, ARD=True) + GPy.kern.Bias(input_dim=self.nPars)
# Normalize the objective function evaluations
ynorm = (obPre - np.mean(obPre)) / np.sqrt(np.var(obPre))
# Create the model object
m = GPy.models.GPRegression(thPre, ynorm, kernel, normalizer=False)
#=====================================================================
# Update hyperparameters
#=====================================================================
# Set constraints on hyperparameters
m.Gaussian_noise.variance.constrain_bounded(0.01, 10.0)
m.kern.Mat52.lengthscale.constrain_bounded(0.01, 10.0)
m.kern.Mat52.variance.constrain_bounded(0.01, 25.0)
# Run empirical Bayes to estimate the hyperparameters
m.optimize('bfgs', max_iters=200)
m.optimize_restarts(num_restarts=10, robust=True)
self.GaussianNoiseVariance = np.array(
m.Gaussian_noise.variance, copy=True)
#=====================================================================
# Write to output
#=====================================================================
self.thPre = thPre
self.obPre = obPre
self.m = m
#=====================================================================
# Main loop
#=====================================================================
# Save the initial parameters
thSys.storeParameters(self.initPar, sys)
thp[self.iter, :] = thSys.returnParameters()
thSys.transform()
while (runNextIter):
# Store the parameter
thSys.storeParameters(thp[self.iter, :], sys)
thSys.transform()
#------------------------------------------------------------------
# Evalute the objective function
#------------------------------------------------------------------
obp[self.iter], xhatf[self.iter,
:] = self.evaluateObjectiveFunction(sm, sys, thSys)
# Collect the sampled data (if the objective is finite)
idxNotNaN = ~np.isnan(obp[range(self.iter), :])
x = np.vstack((thPre, thp[(idxNotNaN).any(axis=1)]))
y = np.vstack((obPre, obp[(idxNotNaN).any(axis=1)]))
#------------------------------------------------------------------
# Fit the GP to the sampled data
#------------------------------------------------------------------
ynorm = (y - np.mean(y)) / np.sqrt(np.var(y))
self.ynormMean = np.mean(y)
self.ynormVar = np.var(y)
m = GPy.models.GPRegression(x, ynorm, kernel, normalizer=False)
#------------------------------------------------------------------
# Re-estimate the hyperparameters
#------------------------------------------------------------------
if (np.remainder(self.iter + 1, self.EstimateHyperparametersInterval) == 0):
# Set constraints on hyperparameters
m.Gaussian_noise.variance.constrain_bounded(0.01, 10.0)
m.kern.Mat52.lengthscale.constrain_bounded(0.01, 10.0)
m.kern.Mat52.variance.constrain_bounded(0.01, 25.0)
# Run empirical Bayes to estimate the hyperparameters
m.optimize('bfgs', max_iters=200)
m.optimize_restarts(num_restarts=10, robust=True)
# Save the current noise variance
self.GaussianNoiseVariance = np.array(
m.Gaussian_noise.variance, copy=True)
else:
# Overload current noise estimate (sets to 1.0 every time we
# add data otherwise)
m.Gaussian_noise.variance = self.GaussianNoiseVariance
# Save all the hyperparameters
hyperParams[self.iter, 0] = np.array(
m.Gaussian_noise.variance, copy=True)
hyperParams[self.iter, 1] = np.array(
m.kern.bias.variance, copy=True)
hyperParams[self.iter, 2] = np.array(
m.kern.Mat52.variance, copy=True)
hyperParams[self.iter, range(
3, 3 + self.nPars)] = np.array(m.kern.Mat52.lengthscale, copy=True)
#------------------------------------------------------------------
# Find the maximum expected value of the GP over the sampled parameters
#------------------------------------------------------------------
Mup, ys2 = m.predict(x)
mumax[self.iter] = np.max(Mup)
#------------------------------------------------------------------
# Compute the next point in which to sample the posterior
#------------------------------------------------------------------
# Optimize the AQ function
aqThMax, aqMax, ierror = solve(self.AQfunction, l, u, user_data=(
m, mumax[self.iter], self.epsilon), maxf=1000, maxT=1000)
# Jitter the parameter estimates
if (self.jitterParameters == True):
flag = 0.0
while (flag == 0.0):
z = np.random.multivariate_normal(np.zeros(self.nPars), self.jitteringCovariance[
range(self.nPars), :][:, range(self.nPars)])
flag = self.checkProposedParameters(aqThMax + z)
thSys.storeParameters(aqThMax + z, sys)
aqThMax += z
# Set the new point and save the estimate of the AQ
thp[self.iter + 1, :] = aqThMax
AQ[self.iter + 1] = -aqMax
# Update counter
self.iter += 1
#------------------------------------------------------------------
# Check exit conditions
#------------------------------------------------------------------
# AQ function criteria
if (AQ[self.iter] < self.tolLevel):
print("GPO: reaches tolLevel, so exiting...")
runNextIter = False
# Max iteration criteria
if (self.iter == self.maxIter):
print("GPO: reaches maxIter, so exiting...")
runNextIter = False
#------------------------------------------------------------------
# Estimate the current parameters by maximizing the GP
#------------------------------------------------------------------
if ((self.EstimateThHatEveryIteration == True) | (runNextIter == False)):
thhatCurrent, obmaxCurrent, ierror = solve(
self.MUeval, l, u, user_data=m, algmethod=1, maxf=1000, maxT=1000)
thhat[self.iter - 1, :] = thhatCurrent
obmax[self.iter - 1, :] = obmaxCurrent
print((thhatCurrent, obmaxCurrent))
if (self.EstimateHessianEveryIteration == True):
self.estimateHessian(thhatCurrent)
thhatHessian[self.iter - 1, :, :] = self.invHessianEstimate
#------------------------------------------------------------------
# Print output to console
#------------------------------------------------------------------
if (self.verbose):
if (self.EstimateThHatEveryIteration == True):
parm = ["%.4f" % v for v in thhat[self.iter - 1, :]]
print(
"##############################################################################################")
print("Iteration: " + str(self.iter) + " with current parameters: " +
str(parm) + " and AQ: " + str(np.round(AQ[self.iter], 2)))
print(
"##############################################################################################")
else:
parm = ["%.4f" % v for v in thp[self.iter - 1, :]]
print(
"##############################################################################################")
print("Iteration: " + str(self.iter) + " sampled objective function at parameters: " +
str(parm) + " with value: " + str(np.round(obp[self.iter - 1], 2)))
print(
"##############################################################################################")
#=====================================================================
# Generate output
#=====================================================================
tmp = range(self.iter - 1)
self.ob = obmax[tmp]
self.th = thhat[tmp, :]
self.thhat = thhat[self.iter - 1, :]
self.thHessian = thhatHessian
self.thhatHessian = thhatHessian[self.iter - 1, :, :]
self.aq = AQ[range(self.iter)]
self.obp = obp[tmp]
self.thp = thp[range(self.iter), :]
self.m = m
self.x = x
self.y = y
self.xhatf = xhatf
self.ynorm = ynorm
self.hp = hyperParams
def integrate(self, m, P, fun, **kwargs):
''' Input:
m - column vector
P - matrix
Output:
x - column vector
Variables:
X, Y - matrix of row vectors
z - column vector
m, x, mu - row vector
'''
# Initial sample and fitted GP:
m = to_row(m)
X = m
Y = np.apply_along_axis(fun, 1, X, **kwargs)
gp = self.gp_fit(X,Y)
# Optimization constraints:
N_SIGMA = 3
P_diag = np.sqrt(np.diag(P))
lower_const = (m - N_SIGMA*P_diag)[0].tolist()
upper_const = (m + N_SIGMA*P_diag)[0].tolist()
# Perform sampling
for i in range(0, self.N_SAMPLES):
# Set extra params to pass to optimizer
user_data = {"gp":gp, "m":m, "P":P}
x_star, _, _ = solve(self.optimization_objective, lower_const, upper_const,
user_data=user_data, algmethod = 1,
maxT = self.opt_par["MAX_T"],
maxf = self.opt_par["MAX_F"])
x_star = to_row(x_star)
X = np.vstack((X, x_star))
Y = np.apply_along_axis(fun, 1, X, **kwargs)
gp = self.gp_fit(X, Y)
# Reoptimize GP:
# TODO: Remove unique rows:
X = unique_rows(X)
Y = np.apply_along_axis(fun, 1, X, **kwargs)
gp = self.gp_fit(X, Y)
# Compute integral
# Fitted GP parameters
w_0 = gp.rbf.variance.tolist()[0]
w_d = gp.rbf.lengthscale.tolist()
# Prior parameters
A = np.diag(w_d)
I = np.eye(self.X_DIM)
# Compute weigths
z = [self.compute_z(a, A, m, P, I, w_0) for a in X]
z = to_column(np.array(z))
K = gp.kern.K(X); K = (K.T + K)/2.0
W = (mo.mrdivide(z.T, K).squeeze()).tolist()
# Compute mean, covariance and cross-cov
mu_ = (z.T).dot( mo.mldivide(K, Y) )
mu_ = to_row(mu_)
Sigma_ = CC_ = None
for i in range(0,len(z)):
YY_i = ( to_column(Y[i]-mu_) ).dot( to_row(Y[i]-mu_) )
Sigma_ = W[i] * YY_i if i == 0 else Sigma_ + W[i] * YY_i
XY_i = ( to_column(X[i]-m) ).dot( to_row(Y[i]-mu_) )
CC_ = W[i] * XY_i if i == 0 else CC_ + W[i] * XY_i
mu_ = to_column(mu_)
Sigma_ = symetrize_cov(Sigma_)
# Return results
return(mu_, Sigma_, CC_)
