def testTestFns():
"""
Test test likelihood and optimization functions from likelihood.py.
"""
# Check 2D Rosenbrock function, compare to the known global minimum
test = lh.rosenbrockLnlike([1, 1])
errMsg = "2D Rosenbrock function is incorrect"
truth = 0
assert np.allclose(test, truth), errMsg
# Check 5D Rosenbrock function, compare to the known global minimum
test = lh.rosenbrockLnlike([1, 1, 1, 1, 1])
errMsg = "5D Rosenbrock function is incorrect"
truth = 0
assert np.allclose(test, truth), errMsg
# Check sphere function, compare to the known global minimum
test = lh.sphereLnlike([0, 0])
errMsg = "Sphere function is incorrect"
truth = 0
assert np.allclose(test, truth), errMsg
# Check 1D BayesOpt test function, compare to the known global maximum
test = lh.testBOFn(-0.359)
errMsg = "1D test BayesOpt function is incorrect"
truth = 0.5003589
assert np.allclose(test, truth), errMsg
def testInitGPNoAmp():
"""
Test default GP initialization.
Parameters
----------
Returns
-------
"""
# For reproducibility
m0 = 50
seed = 57
np.random.seed(seed)
# Randomly sample initial conditions from the prior
theta = np.array(lh.rosenbrockSample(m0))
# Evaluate forward model log likelihood + lnprior for each theta
y = np.zeros(len(theta))
for ii in range(len(theta)):
y[ii] = lh.rosenbrockLnlike(theta[ii]) + lh.rosenbrockLnprior(
theta[ii])
# Set up a gp with a ExpSquaredKernel
gp = gpUtils.defaultGP(theta, y, fitAmp=False)
errMsg = "ERROR: Default initialization with incorrect parameters!"
true = [-31.02658091, -1.0552327, -1.16092752]
assert np.allclose(true, gp.get_parameter_vector()), errMsg
def testUtilsGPNoAmp():
"""
Test the utility functions! This probes the gp_utils.setup_gp function
(which is rather straight-forward) and makes sure the utility functions
produce the right result (which is also straight-forward). Based on the
Wang+2017 Rosenbrock function example.
Parameters
----------
Returns
-------
"""
# For reproducibility
m0 = 20
seed = 57
np.random.seed(seed)
# Randomly sample initial conditions from the prior
theta = np.array(lh.rosenbrockSample(m0))
# Evaluate forward model log likelihood + lnprior for each theta
y = np.zeros(len(theta))
for ii in range(len(theta)):
y[ii] = lh.rosenbrockLnlike(theta[ii]) + lh.rosenbrockLnprior(
theta[ii])
# Set up a gp
gp = gpUtils.defaultGP(theta, y, fitAmp=False)
# Compute the AGP utility function at some point
thetaTest = np.array([-2.3573, 4.673])
testUtil = ut.AGPUtility(thetaTest, y, gp, lh.rosenbrockLnprior)
errMsg = "ERROR: AGP util fn bug. Did you change gp_utils.setup_gp?"
assert np.allclose(testUtil, 37.41585067, rtol=1.0e-4), errMsg
# Now do the same using the BAPE utility function
testUtil = ut.BAPEUtility(thetaTest, y, gp, lh.rosenbrockLnprior)
errMsg = "ERROR: BAPE util fn bug. Did you change gp_utils.setup_gp?"
assert np.allclose(testUtil, 76.15271103, rtol=1.0e-4), errMsg
# Now do the same using the Jones+1998 utility function
testUtil = ut.JonesUtility(thetaTest, y, gp, lh.rosenbrockLnprior)
errMsg = "ERROR: Jones util fn bug. Did you change gp_utils.setup_gp?"
assert np.allclose(testUtil, 0, rtol=1.0e-4), errMsg
def testFindNoAmp():
"""
Test the findNextPoint function.
"""
# Define algorithm parameters
m0 = 50 # Initial size of training set
bounds = ((-5, 5), (-5, 5)) # Prior bounds
algorithm = "bape"
# For reproducibility
seed = 57
np.random.seed(seed)
# Randomly sample initial conditions from the prior
# Note: adding corner cases because approxposterior loves corners
theta = np.array(list(lh.rosenbrockSample(m0)) + [[-5, 5], [5, 5]])
# Evaluate forward model log likelihood + lnprior for each theta
y = np.zeros(len(theta))
for ii in range(len(theta)):
y[ii] = lh.rosenbrockLnlike(theta[ii]) + lh.rosenbrockLnprior(
theta[ii])
# Set up a gp
gp = gpUtils.defaultGP(theta, y, fitAmp=False)
# Initialize object using the Wang & Li (2017) Rosenbrock function example
# using default ExpSquaredKernel GP
ap = approx.ApproxPosterior(theta=theta,
y=y,
gp=gp,
lnprior=lh.rosenbrockLnprior,
lnlike=lh.rosenbrockLnlike,
priorSample=lh.rosenbrockSample,
bounds=bounds,
algorithm=algorithm)
# Find new point!
thetaT = ap.findNextPoint(computeLnLike=False, bounds=bounds, seed=seed)
err_msg = "findNextPoint selected incorrect thetaT."
assert (np.allclose(thetaT, [0.79813416, 0.85542199],
rtol=1.0e-3)), err_msg
def testGPOptNoAmp():
"""
Test optimizing the GP hyperparameters.
Parameters
----------
Returns
-------
"""
# For reproducibility
m0 = 50
seed = 57
np.random.seed(seed)
# Randomly sample initial conditions from the prior
theta = np.array(lh.rosenbrockSample(m0))
# Evaluate forward model log likelihood + lnprior for each theta
y = np.zeros(len(theta))
for ii in range(len(theta)):
y[ii] = lh.rosenbrockLnlike(theta[ii]) + lh.rosenbrockLnprior(
theta[ii])
# Set up a gp
gp = gpu.defaultGP(theta, y, fitAmp=False)
# Optimize gp using default opt parameters
gp = gpu.optimizeGP(gp, theta, y, seed=seed, nGPRestarts=5)
# Extract GP hyperparameters, compare to truth
# Ignore mean fit - just focus on scale lengths and amplitude
hypeTest = gp.get_parameter_vector()[1:]
print(hypeTest)
errMsg = "ERROR: GP hyperparameters are not close to the true value!"
hypeTrue = [-1.54256578, 3.24723589]
assert np.allclose(hypeTest, hypeTrue, rtol=1.0e-2), errMsg
# Define algorithm parameters
m0 = 50 # Initial size of training set
m = 20 # Num points to find each iteration
nmax = 2 # Maximum number of iterations
bounds = [(-5, 5), (-5, 5)] # Prior bounds
algorithm = "bape" # Use the Kandasamy et al. (2017) formalism
np.random.seed(57)
samplerKwargs = {"nwalkers": 20} # emcee.EnsembleSampler
mcmcKwargs = {"iterations": int(2.0e4)} # emcee.EnsembleSampler.run_mcmc
# Evaluate model lnlikelihood + lnprior for each theta sampled from prior
theta = lh.rosenbrockSample(m0)
y = np.zeros(len(theta))
for ii in range(len(theta)):
y[ii] = lh.rosenbrockLnlike(theta[ii]) + lh.rosenbrockLnprior(theta[ii])
# Use default GP with squared exponential kernel
gp = gpUtils.defaultGP(theta, y, white_noise=-12)
# Initialize object using the Wang & Li (2018) Rosenbrock function example
ap = approx.ApproxPosterior(theta=theta,
y=y,
gp=gp,
lnprior=lh.rosenbrockLnprior,
lnlike=lh.rosenbrockLnlike,
priorSample=lh.rosenbrockSample,
bounds=bounds,
algorithm=algorithm)
# Run!