def test_expand(available_kernels, kernel_to_tree, tree_to_str):
for kernel_name, kernel in available_kernels.items():
# Without defering this build time this takes is unbearable.
with gpflow.defer_build():
res = [
tree_to_str(kernel_to_tree(k))
for k in expand_kernel(kernel(1))
]
assert res == [
f'{kernel_name}',
'constant',
'linear',
'periodic',
'rbf',
'white',
f'{kernel_name} + constant',
f'{kernel_name} * constant',
f'{kernel_name} * (constant + constant)',
f'{kernel_name} + linear',
f'{kernel_name} * linear',
f'{kernel_name} * (linear + constant)',
f'{kernel_name} + periodic',
f'{kernel_name} * periodic',
f'{kernel_name} * (periodic + constant)',
f'{kernel_name} + rbf',
f'{kernel_name} * rbf',
f'{kernel_name} * (rbf + constant)',
f'{kernel_name} + white',
f'{kernel_name} * white',
f'{kernel_name} * (white + constant)',
]
with gpflow.defer_build():
res_strs = [
tree_to_str(kernel_to_tree(k))
for k in expand_kernel(kernel(1),
base_kernels_to_exclude=['constant'])
]
res_start_should_be = [f'{kernel_name}'
] if kernel_name != 'constant' else []
res_complex_should_be = [
f'{kernel_name} + linear',
f'{kernel_name} * linear',
f'{kernel_name} * (linear + constant)',
f'{kernel_name} + periodic',
f'{kernel_name} * periodic',
f'{kernel_name} * (periodic + constant)',
f'{kernel_name} + rbf',
f'{kernel_name} * rbf',
f'{kernel_name} * (rbf + constant)',
f'{kernel_name} + white',
f'{kernel_name} * white',
f'{kernel_name} * (white + constant)',
] if kernel_name != 'constant' else []
assert res_strs == res_start_should_be + [
'linear', 'periodic', 'rbf', 'white'
] + res_complex_should_be
def ST_Model(X, y, use_priors=False, e_s=0):
with gpflow.defer_build():
like = gpflow.likelihoods.Poisson(binsize=e_s)
kern_s_effect = gpflow.kernels.RBF(2,
active_dims=[1, 2],
name='space_effect')
kern_t_effect = gpflow.kernels.RBF(1,
active_dims=[0],
name='time_effect')
full_kern = kern_t_effect + kern_s_effect
m = gpflow.models.VGP(X,
y,
full_kern,
likelihood=like,
mean_function=None)
t_prior = gpflow.priors.StudentT(mean=0, scale=1, deg_free=4)
if use_priors:
m.kern.rbf_1.variance.prior = t_prior
m.kern.rbf_2.variance.prior = t_prior
m.kern.rbf_1.lengthscales.prior = t_prior
m.kern.rbf_2.lengthscales.prior = t_prior
return m
def evalMCMC(X, Y): # type: (Any, {shape}) -> Tuple[DataFrame, GPR]
with gpflow.defer_build():
k = gpflow.kernels.Matern52(1, lengthscales=0.3)
meanf = gpflow.mean_functions.Zero()
m = gpflow.models.GPR(X, Y, k, meanf)
m.clear()
m.kern.lengthscales.prior = gpflow.priors.Beta(1., 3.)
m.kern.variance.prior = gpflow.priors.Beta(1., 3.)
m.likelihood.variance.prior = gpflow.priors.Beta(1., 3.)
# m.mean_function.A.prior = gpflow.priors.Gaussian(0., 10.)
# m.mean_function.b.prior = gpflow.priors.Gaussian(0., 10.)
m.compile()
print(m.as_pandas_table())
sampler = gpflow.train.HMC()
traces = sampler.sample(m,
num_samples=2000,
burn=1000,
epsilon=0.05,
lmin=1,
lmax=3,
logprobs=False)
return traces, m
def test_construction(self):
with self.test_context():
gpflow.ParamList([])
gpflow.ParamList([gpflow.Param(1)])
gpflow.ParamList([1.0, np.array([1, 2]), gpflow.Param(1.0)])
with self.assertRaises(ValueError):
gpflow.ParamList([gpflow.Param(1), 'stringsnotallowed'])
with self.assertRaises(ValueError):
# tuples not valid in constuctor:
gpflow.ParamList((gpflow.Param(1),))
with self.assertRaises(ValueError):
# param objects not valid in constructor (must be in list)
gpflow.ParamList(gpflow.Param(1))
with gpflow.defer_build():
p = gpflow.ParamList([0.0])
p[0] = gpflow.Param(1.0)
with self.assertRaises(ValueError):
p[0] = 1.0
with self.assertRaises(ValueError):
p[0] = "test"
p = gpflow.ParamList([])
p.append(gpflow.Param(1.0))
p.append(gpflow.Param(2.0))
p.append(2.0)
self.assertEqual(len(p), 3)
with self.assertRaises(ValueError):
p.append("test")
def __init__(self, X, y, hyperparameters=None, optimize_hyperparameters=False,
learning_rate=0.001, maxiter=5000, **kwargs):
# Store model kwargs
self._model_kwargs = {
'hyperparameters': hyperparameters,
'optimize_hyperparameters': optimize_hyperparameters,
'learning_rate': learning_rate,
'maxiter': maxiter,
}
# Store kernel kwargs
kernel_kwargs = self._get_kernel_kwargs(X_dim=X.shape[1], **kwargs)
if hyperparameters is not None:
self._assign_kernel_hyperparams(hyperparameters, kernel_kwargs)
self._kernel_kwargs = copy.deepcopy(kernel_kwargs)
# Build the kernels and the model
with gpflow.defer_build():
k = self._build_kernel(kernel_kwargs, optimize_hyperparameters=optimize_hyperparameters, **kwargs)
m = GPRC(X, y, kern=k)
if hyperparameters is not None and self._LIKELIHOOD_HP_KEY in hyperparameters:
m.likelihood.variance = hyperparameters[self._LIKELIHOOD_HP_KEY]
m.compile()
# If enabled, optimize the hyperparameters
if optimize_hyperparameters:
opt = gpflow.train.AdamOptimizer(learning_rate)
opt.minimize(m, maxiter=maxiter)
self.model = m
def train_arrival_time_gp(X, Y, number, kernel="rbf_linear"):
"""GP which maps (%, v) -> arrival_time."""
with gpflow.defer_build():
if kernel is None or kernel == "white":
kern = gpflow.kernels.White(input_dim=1)
elif kernel == "matern32":
kern = gpflow.kernels.Matern32(input_dim=1)
elif kernel == "linear":
kern = gpflow.kernels.Linear(input_dim=1)
elif kernel == "rbf":
kern = gpflow.kernels.RBF(input_dim=1)
elif kernel == "rbf_linear":
kern = gpflow.kernels.RBF(
input_dim=1, lengthscales=0.1) + gpflow.kernels.Linear(
input_dim=1, variance=500)
else:
raise Exception("Kernel {} unknown!".format(kernel))
m = gpflow.models.GPR(X, Y, kern=kern)
m.likelihood.variance = 1 #10
m.compile()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m)
logger.info("Arrival Time Pred GP #{} trained.".format(number))
return m
def smooth_direction_time(weights,X,Y,freqs, M=None,minibatch=None,iterations=1000, n_kern = 1):
N, num_latent = Y.shape
M = M or N
Z = np.random.choice(X,size=M,replace=False)
# Z = kmeans2(X, M, minit='points')[0]
with gp.defer_build():
kern = [gp.kernels.Matern32(2,active_dims=[1,2])*gp.kernels.Periodic(1,active_dims=[0])*gp.kernels.Matern32(1,active_dims = [0],variance=0.1**2) for _ in range(n_kern)]
for k in kern:
k.children['matern32_1'].variance.set_trainable(False)
k.children['periodic'].variance.prior = gp.priors.Gaussian(0.,0.1)
k.children['matern32_2'].variance.set_trainable(False)
likelihood = make_likelihood(freqs)
mean = gp.mean_functions.Constant()
#X, Y, Z, kernels, likelihood,
model = WeightedDGP(weights,X, Y, Z, kern, likelihood,
mean_function = mean, minibatch_size=minibatch,
num_outputs=num_latent-1)
for layer in model.layers[:-1]:
layer.q_sqrt = layer.q_sqrt.value * 1e-5
model.likelihood.variance = np.pi/30.
model.compile()
opt = gp.train.AdamOptimizer(5e-3)
opt.minimize(model, maxiter=iterations)
ystar,varstar = batch_predict_y(model,X,batch_size=100,S=200)
l = batch_predict_density(model,X,Y,batch_size=100,S=200)
return l.mean(), ystar, np.sqrt(varstar)
def test_construction(self):
with self.test_context():
gpflow.ParamList([])
gpflow.ParamList([gpflow.Param(1)])
gpflow.ParamList([1.0, np.array([1, 2]), gpflow.Param(1.0)])
with self.assertRaises(ValueError):
gpflow.ParamList([gpflow.Param(1), 'stringsnotallowed'])
with self.assertRaises(ValueError):
# tuples not valid in constuctor:
gpflow.ParamList((gpflow.Param(1), ))
with self.assertRaises(ValueError):
# param objects not valid in constructor (must be in list)
gpflow.ParamList(gpflow.Param(1))
with gpflow.defer_build():
p = gpflow.ParamList([0.0])
p[0] = gpflow.Param(1.0)
with self.assertRaises(ValueError):
p[0] = 1.0
with self.assertRaises(ValueError):
p[0] = "test"
p = gpflow.ParamList([])
p.append(gpflow.Param(1.0))
p.append(gpflow.Param(2.0))
p.append(2.0)
self.assertEqual(len(p), 3)
with self.assertRaises(ValueError):
p.append("test")
def test_monitor(session_tf):
np.random.seed(0)
X = np.random.rand(10000, 1) * 10
Y = np.sin(X) + np.random.randn(*X.shape)
with gpflow.defer_build():
m = gpflow.models.SVGP(X, Y, gpflow.kernels.RBF(1), gpflow.likelihoods.Gaussian(),
Z=np.linspace(0, 10, 5)[:, None],
minibatch_size=100, name="SVGP")
m.likelihood.variance = 0.01
m.compile()
global_step = tf.Variable(0, trainable=False, name="global_step")
session_tf.run(global_step.initializer)
adam = gpflow.train.AdamOptimizer(0.01).make_optimize_action(m, global_step=global_step)
# create a filewriter for summaries
fw = tf.summary.FileWriter('./model_tensorboard', m.graph)
print_lml = mon.PrintTimings(itertools.count(), mon.Trigger.ITER, single_line=True, global_step=global_step)
sleep = mon.SleepAction(itertools.count(), mon.Trigger.ITER, 0.0)
saver = mon.StoreSession(itertools.count(step=3), mon.Trigger.ITER, session_tf,
hist_path="./monitor-saves/checkpoint", global_step=global_step)
tensorboard = mon.ModelTensorBoard(itertools.count(step=3), mon.Trigger.ITER, m, fw, global_step=global_step)
lml_tensorboard = mon.LmlTensorBoard(itertools.count(step=5), mon.Trigger.ITER, m, fw, global_step=global_step)
callback = mon.CallbackAction(mon.seq_exp_lin(2.0, np.inf, 1e-3), mon.Trigger.TOTAL_TIME, lambda x, b: x, m)
actions = [adam, print_lml, tensorboard, lml_tensorboard, saver, sleep, callback]
gpflow.actions.Loop(actions, stop=11)()
def run(self, save_path, init_design_size=1, n_iter=24, plot=False, likelihood_uncert=1.,design = None):
self.save_path = save_path
self.iter = 0
self.X = [] + self.init_X
self.Y = [] + self.init_Y
domain = np.sum(self.domain)
# if self.first_tries is not None:
# Y = [self.objective(x).flatten() for x in self.first_tries]
# self.Y = self.Y + Y
# self.X = self.X + list(self.first_tries)
if init_design_size > 0:
if design == 'latin':
design = LatinHyperCube(init_design_size,domain)
if design == 'fac':
design = FactorialDesign(2, domain)
X = list(design.generate())
Y = [self.objective(x).flatten() for x in X]
self.Y = self.Y + Y
self.X = self.X + X
self.X, self.Y = np.stack(self.X,axis=0), np.stack(self.Y,axis=0)
u = np.concatenate([self.X,self.Y],axis=1)
u, idx = np.unique(u,return_index=True,axis=0)
self.X = list(self.X[idx,:])
self.Y = list(self.Y[idx,:])
self.burnin = len(self.X)
logging.warning("Beginnig search")
# logging.warning("Initial solutions\n{}".format(list(zip(self.X, self.Y))))
with gp.defer_build():
kern = gp.kernels.Matern52(domain.size, ARD=True)# + gp.kernels.White(domain.size)
m = gp.models.GPR(np.stack(self.X,axis=0), np.stack(self.Y,axis=0), kern)
lik_var = log_normal_solve(likelihood_uncert**2, 0.5*likelihood_uncert**2)
m.likelihood.variance = likelihood_uncert**2#np.exp(lik_var[0])
m.likelihood.variance.prior = gp.priors.LogNormal(lik_var[0], lik_var[1]**2)
m.likelihood.variance.trainable = False
m.compile()
self.ei = MomentGeneratingFunctionImprovement(m, self.t)
opt = optim.StagedOptimizer([optim.MCOptimizer(domain, 5000), optim.SciPyOptimizer(domain)])
optimizer = BayesianOptimizer(domain, self.ei, optimizer=opt, hyper_draws=1)
#with optimizer.silent():
result = optimizer.optimize(self.objective, n_iter=n_iter)
logging.warning(result)
if plot:
self.plot_results()
result = self.get_kwargs(result.x)
self.print_top_k(5)
return result
def _build_model(self, Y_var, freqs, X, Y, kern_params=None, Z=None, q_mu = None, q_sqrt = None, M=None, P=None, L=None, W=None, num_data=None, jitter=1e-6, tec_scale=None, W_trainable=False, use_mc=False, **kwargs):
"""
Build the model from the data.
X,Y: tensors the X and Y of data
Returns:
gpflow.models.Model
"""
settings.numerics.jitter = jitter
with gp.defer_build():
# Define the likelihood
likelihood = ComplexHarmonicPhaseOnlyGaussianEncodedHetero(tec_scale=tec_scale)
# likelihood.variance = 0.3**2#(5.*np.pi/180.)**2
# likelihood_var = log_normal_solve((5.*np.pi/180.)**2, 0.5*(5.*np.pi/180.)**2)
# likelihood.variance.prior = LogNormal(likelihood_var[0],likelihood_var[1]**2)
# likelihood.variance.transform = gp.transforms.positiveRescale(np.exp(likelihood_var[0]))
likelihood.variance.trainable = False
q_mu = q_mu/tec_scale #M, L
q_sqrt = q_sqrt/tec_scale# L, M, M
kern = mk.SeparateMixedMok([self._build_kernel(None, None, None, #kern_params[l].w, kern_params[l].mu, kern_params[l].v,
kern_var = np.var(q_mu[:,l]), **kwargs.get("priors",{})) for l in range(L)], W)
kern.W.trainable = W_trainable
kern.W.prior = gp.priors.Gaussian(W, 0.01**2)
feature = mf.MixedKernelSeparateMof([InducingPoints(Z) for _ in range(L)])
mean = Zero()
model = HeteroscedasticPhaseOnlySVGP(Y_var, freqs, X, Y, kern, likelihood,
feat = feature,
mean_function=mean,
minibatch_size=None,
num_latent = P,
num_data = num_data,
whiten = False,
q_mu = None,
q_sqrt = None,
q_diag = True)
for feat in feature.feat_list:
feat.Z.trainable = True #True
model.q_mu.trainable = True
model.q_mu.prior = gp.priors.Gaussian(0., 0.05**2)
model.q_sqrt.trainable = True
# model.q_sqrt.prior = gp.priors.Gaussian(0., (0.005/tec_scale)**2)
model.compile()
tf.summary.image('W',kern.W.constrained_tensor[None,:,:,None])
tf.summary.image('q_mu',model.q_mu.constrained_tensor[None,:,:,None])
# tf.summary.image('q_sqrt',model.q_sqrt.constrained_tensor[:,:,:,None])
return model
def prepare(self, autobuild=True):
rng = np.random.RandomState()
with gpflow.defer_build():
m = Foo()
m.X = gpflow.DataHolder(rng.randn(2, 2))
m.Y = gpflow.DataHolder(rng.randn(2, 2))
m.Z = gpflow.DataHolder(rng.randn(2, 2))
if autobuild:
m.compile()
return m, rng
def prepare(self, autobuild=True):
rng = np.random.RandomState()
with gpflow.defer_build():
m = Foo()
m.X = gpflow.DataHolder(rng.randn(2, 2))
m.Y = gpflow.DataHolder(rng.randn(2, 2))
m.Z = gpflow.DataHolder(rng.randn(2, 2))
if autobuild:
m.compile()
return m, rng
def test_expand_combinations_simple_combs(tree_to_str, kernel_to_tree, k1, k2,
k3, k4):
with gpflow.defer_build():
res_strs = [
tree_to_str(kernel_to_tree(k))
for k in _expand_combinations((k1 * k2 + k3) * k4)
]
assert res_strs == [
f'{k1.name.lower()} * {k2.name.lower()} + {k3.name.lower()}',
f'{k4.name.lower()}'
]
def _make_model(self, X, Y):
_, num_input_dimensions = X.shape
with gpflow.defer_build():
kernel = gpflow.kernels.RBF(input_dim=num_input_dimensions,
ARD=True)
model = gpflow.models.GPR(X, Y, kern=kernel)
if self.min_noise_variance is not None:
model.likelihood.variance.transform = gpflow.transforms.Log1pe(
lower=self.min_noise_variance)
model.build()
return model
def train_inv_f1_gp(X_train, Y_train, logger):
"""GP which maps Tau -> lng or lat."""
with gpflow.defer_build():
m = gpflow.models.GPR(X_train, Y_train, kern=gpflow.kernels.RBF(1))
m.compile()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m)
logger.info("Model optimized")
logger.info(m)
logger.info("Inverse f1 GP trained.")
return m
def __init__(self, N, country, plot=False):
df = pd.read_csv('data/previous-case-counts-%s.csv' % country)
df['WHO report date'] = pd.to_datetime(df['WHO report date'],
format="%d/%m/%Y")
df['delta_time_days'] = (df['WHO report date'] -
df['WHO report date'].min()).dt.days
df = df.sort_values('delta_time_days')
print(df)
self.df = df
self.N = N
self.country = country
self.plot = plot
# Differential case counts
self.delta_cases = df['Total Cases'].values[1:] - df[
'Total Cases'].values[:-1]
# Differential death counts
self.delta_deaths = df['Total Deaths'].values[1:] - df[
'Total Deaths'].values[:-1]
# GP fit
with gpflow.defer_build():
k = gpflow.kernels.Matern52(1)
self.mc = gpflow.models.GPR(
df['delta_time_days'].values[:, np.newaxis].astype('float'),
df['Total Cases'].values[:, np.newaxis].astype('float'),
kern=k)
self.mc.likelihood.variance.trainable = False
self.mc.likelihood.variance = 300**2
self.mc.compile()
gpflow.train.ScipyOptimizer().minimize(self.mc)
print(self.mc.as_pandas_table())
with gpflow.defer_build():
k = gpflow.kernels.Matern52(1)
self.md = gpflow.models.GPR(
df['delta_time_days'].values[:, np.newaxis].astype('float'),
df['Total Deaths'].values[:, np.newaxis].astype('float'),
kern=k)
self.md.likelihood.variance.trainable = False
self.md.likelihood.variance = 300**2
self.md.compile()
gpflow.train.ScipyOptimizer().minimize(self.md)
print(self.md.as_pandas_table())
def test_gpflow():
import gpflow
import numpy as np
with gpflow.defer_build():
X = np.random.rand(20, 1)
Y = np.sin(
12 * X) + 0.66 * np.cos(25 * X) + np.random.randn(20, 1) * 0.01
m = gpflow.models.GPR(X,
Y,
kern=gpflow.kernels.Matern32(1) +
gpflow.kernels.Linear(1))
print(m)
def train_GP(X_tau, Y, session, number, gp_name):
"""GP which maps tau -> lng or lat."""
with gpflow.defer_build():
m = gpflow.models.GPR(X_tau, Y, kern=gpflow.kernels.RBF(1))
m.likelihood.variance = 1e-03
m.likelihood.variance.trainable = False
m.compile()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m)
logger.info("{} GP #{} trained.".format(gp_name, number))
session.save(BASE_DIR + "GP/model_{}_{}".format(number, gp_name), m)
return m
def SafeMatern32_Model(X,
y,
use_priors=False,
e_s=0,
period=12,
partial=False):
with gpflow.defer_build():
like = gpflow.likelihoods.Poisson(binsize=e_s)
kern_s_effect = gpflow.kernels.SafeMatern32(input_dim=2,
active_dims=[1, 2],
name='space_effect')
kern_t_effect = gpflow.kernels.RBF(1,
active_dims=[0],
name='time_effect')
## Will have to write custom kernel to match Flaxman 2014
kern_p_effect = gpflow.kernels.Periodic(1,
active_dims=[0],
name='periodic_effect')
kern_st_effect = gpflow.kernels.Product([kern_s_effect, kern_t_effect])
full_kern = kern_t_effect + kern_s_effect + kern_p_effect + kern_st_effect
if partial:
m = PartialVGP(X,
y,
full_kern,
likelihood=like,
mean_function=None)
else:
m = gpflow.models.VGP(X,
y,
full_kern,
likelihood=like,
mean_function=None)
m.kern.periodic.period = period
m.kern.periodic.period.trainable = True
t_prior = gpflow.priors.StudentT(mean=0, scale=5, deg_free=4)
if use_priors:
m.kern.safematern32.variance.prior = t_prior
m.kern.periodic.variance.prior = t_prior
m.kern.rbf.variance.prior = t_prior
m.kern.safematern32.lengthscales.prior = t_prior
m.kern.rbf.lengthscales.prior = t_prior
m.kern.periodic.lengthscales.prior = t_prior
return m
def __init__(self,
data,
n_latent_dims=1,
n_inducing_points=10,
kernel={
'name': 'RBF',
'ls': 1.0,
'var': 1.0
},
mData=None,
latent_prior_mean=None,
latent_prior_var=1.,
latent_mean=None,
latent_var=0.1,
inducing_inputs=None,
dtype='float64'):
self.Y = None
self.Q = n_latent_dims
self.M = n_inducing_points
self.kern = None
self.mData = mData
self.X_prior_mean = None
self.X_prior_var = None
self.X_mean = None
self.X_var = None
self.Z = None
self.set_Y(data)
self.N, self.D = self.Y.shape
self.set_kern(kernel)
self.set_X_prior_mean(latent_prior_mean)
self.set_X_prior_var(latent_prior_var)
self.set_X_mean(latent_mean)
self.set_X_var(latent_var)
self.set_inducing_inputs(inducing_inputs)
self.fitting_time = 0
with gpflow.defer_build():
self.m = gpflow.models.BayesianGPLVM(
Y=self.Y,
kern=self.kern,
X_prior_mean=self.X_prior_mean,
X_prior_var=self.X_prior_var,
X_mean=self.X_mean.copy(),
X_var=self.X_var.copy(),
Z=self.Z.copy(),
M=self.M)
self.m.likelihood.variance = 0.01
def sparse_mcmc_example():
#%matplotlib inline
plt.style.use('ggplot')
np.random.seed(1)
# Make a one dimensional classification problem.
X = np.random.rand(100, 1)
K = np.exp(-0.5 * np.square(X - X.T) / 0.01) + np.eye(100) * 1e-6
f = np.dot(np.linalg.cholesky(K), np.random.randn(100, 3))
Y = np.array(np.argmax(f, 1).reshape(-1, 1), dtype=float)
plt.figure(figsize=(12, 6))
plt.plot(X, f, '.')
with gpflow.defer_build():
model = gpflow.models.SGPMC(
X,
Y,
kern=gpflow.kernels.Matern32(1, lengthscales=0.1) +
gpflow.kernels.White(1, variance=0.01),
likelihood=gpflow.likelihoods.MultiClass(3),
Z=X[::5].copy(),
num_latent=3)
model.kern.kernels[0].variance.prior = gpflow.priors.Gamma(1.0, 1.0)
model.kern.kernels[0].lengthscales.prior = gpflow.priors.Gamma(
2.0, 2.0)
model.kern.kernels[1].variance.trainables = False
model.compile()
print(model.as_pandas_table())
opt = gpflow.train.ScipyOptimizer()
opt.minimize(model, maxiter=10)
print(model.kern.as_pandas_table())
hmc = gpflow.train.HMC()
samples = hmc.sample(model,
num_samples=500,
epsilon=0.04,
lmax=15,
logprobs=False) # pands.DataFrame.
#print('Columns =', samples.columns.values)
plot_from_samples(model, samples)
print(samples.head())
_ = plt.hist(np.vstack(
samples['SGPMC/kern/kernels/0/lengthscales']).flatten(),
50,
density=True)
plt.xlabel('lengthscale')
def restore():
with gpflow.defer_build():
m = init()
tf.local_variables_initializer()
tf.global_variables_initializer()
tf_graph = m.enquire_graph()
tf_session = m.enquire_session()
m.compile(tf_session)
saver = tf.train.Saver()
save_path = saver.restore(tf_session, save_dir + "/model.ckpt")
print("Model loaded from path: %s" % save_path)
return m
def test_train(iterations):
with gpflow.defer_build():
m = init()
tf.local_variables_initializer()
tf.global_variables_initializer()
tf_session = m.enquire_session()
m.compile(tf_session)
op_adam = AdamOptimizer(0.01).make_optimize_tensor(m)
bool_2400 = False
bool_2300 = False
bool_2200 = False
for it in range(iterations):
tf_session.run(op_adam)
if it % 100 == 0:
likelihood = tf_session.run(m.likelihood_tensor)
print('{}, ELBO={:.4f}'.format(it, likelihood))
if likelihood >= -2400:
if bool_2400 == False:
saver = tf.train.Saver()
save_path = saver.save(
tf_session, save_dir + '/model' + '2800' + '.ckpt')
bool_2400 = True
print("Model saved in path: %s" % save_path)
if likelihood >= -2300:
if bool_2300 == False:
saver = tf.train.Saver()
save_path = saver.save(
tf_session, save_dir + '/model' + '2600' + '.ckpt')
bool_2300 = True
print("Model saved in path: %s" % save_path)
if likelihood >= -2200 and bool_2200 == False:
saver = tf.train.Saver()
save_path = saver.save(
tf_session, save_dir + '/model' + '2500' + '.ckpt')
bool_2200 = True
print("Model saved in path: %s" % save_path)
saver = tf.train.Saver()
save_path = saver.save(tf_session, save_dir + "/model.ckpt")
print("Model saved in path: %s" % save_path)
m.anchor(tf_session)
return m
def main():
np.random.seed(1234)
X, y = generate_dataset(200)
float_type = gpflow.settings.float_type
prior = Gamma(float_type(0.01), float_type(0.01))
with gpflow.defer_build():
kern = gpflow.kernels.RBF(1)
likelihood = gpflow.likelihoods.Gaussian()
priors = {kern.variance: prior, kern.lengthscales: prior}
Z = np.linspace(min(X), max(X), 10)[:, np.newaxis]
model = FVGP(X, y, kern, likelihood, Z=Z, priors=priors)
result_path = Path('advi_model_params.npz')
if result_path.exists():
print('loading parameters values from {}'.format(result_path))
params = dict(np.load(str(result_path)))
model.assign(params)
else:
optimizer = gpflow.training.AdamOptimizer(0.01)
optimizer.minimize(model, maxiter=5000)
np.savez(str(result_path), **model.read_values())
sess = model.enquire_session()
var_samples = [kern.variance.read_value(sess) for _ in range(2000)]
len_samples = [kern.lengthscales.read_value(sess) for _ in range(2000)]
xs = np.linspace(X[0, 0], X[-1, 0], 500)[:, np.newaxis]
f_samples = [model.predict_f_samples(xs, 1) for _ in range(200)]
f_samples = np.vstack(f_samples)[..., 0]
# TODO fix model printing?
# print(model)
fig, axes = plt.subplots(1, 3, figsize=(16, 6))
axes[0].grid()
axes[0].plot(X, y, 'o')
axes[0].plot(xs, f_samples.T, 'k', alpha=0.1)
axes[0].plot(xs, np.mean(f_samples, 0), lw=2)
axes[1].set_title('kernel variance posterior')
axes[1].grid()
sb.distplot(var_samples, ax=axes[1])
axes[2].set_title('kernel lengthscale posterior')
axes[2].grid()
sb.distplot(len_samples, ax=axes[2])
fig.tight_layout()
plt.show()
def _build_model(self, Y_var, X, Y, Z=None, q_mu = None, q_sqrt = None, M=None, P=None, L=None, W=None, num_data=None, jitter=1e-6, tec_scale=None, W_diag=False, **kwargs):
"""
Build the model from the data.
X,Y: tensors the X and Y of data
Returns:
gpflow.models.Model
"""
settings.numerics.jitter = jitter
with gp.defer_build():
# Define the likelihood
likelihood = GaussianTecHetero(tec_scale=tec_scale)
q_mu = q_mu/tec_scale #M, L
q_sqrt = q_sqrt/tec_scale# L, M, M
kern = mk.SeparateMixedMok([self._build_kernel(kern_var = np.var(q_mu[:,l]), **kwargs.get("priors",{})) for l in range(L)], W)
if W_diag:
# kern.W.transform = Reshape(W.shape,(P,L,L))(gp.transforms.DiagMatrix(L)(gp.transforms.positive))
kern.W.trainable = False
else:
kern.W.transform = Reshape(W.shape,(P//L,L,L))(MatrixSquare()(gp.transforms.LowerTriangular(L,P//L)))
kern.W.trainable = True
feature = mf.MixedKernelSeparateMof([InducingPoints(Z) for _ in range(L)])
mean = Zero()
model = HeteroscedasticTecSVGP(Y_var, X, Y, kern, likelihood,
feat = feature,
mean_function=mean,
minibatch_size=None,
num_latent = P,
num_data = num_data,
whiten = False,
q_mu = q_mu,
q_sqrt = q_sqrt)
for feat in feature.feat_list:
feat.Z.trainable = True
model.q_mu.trainable = True
model.q_sqrt.trainable = True
# model.q_sqrt.prior = gp.priors.Gaussian(q_sqrt, 0.005**2)
model.compile()
tf.summary.image('W',kern.W.constrained_tensor[None,:,:,None])
tf.summary.image('q_mu',model.q_mu.constrained_tensor[None,:,:,None])
tf.summary.image('q_sqrt',model.q_sqrt.constrained_tensor[:,:,:,None])
return model
def init_gsm(x, y, M, Q, max_freq=1.0, max_len=1.0, ell=1.0, n_inits=10,
minibatch_size=256, noise_var=10.0, ARD=False, likelihood=None):
print('Initializing GSM...')
best_loglik = -np.inf
best_m = None
N, input_dim = x.shape
for k in range(n_inits):
print('init:', k)
try:
#gpflow.reset_default_graph_and_session()
with gpflow.defer_build():
Z = random_Z(x, N, M)
p = _gsm_rand_params(M, Q, input_dim, max_freq=max_freq, max_len=max_len, ell=ell, ARD=ARD)
if likelihood is not None:
likhood = likelihood
else:
likhood = gpflow.likelihoods.Gaussian(noise_var)
likhood.variance.prior = gpflow.priors.LogNormal(mu=0, var=1)
spectral = SpectralSVGP(X=x, Y=y, Z=Z, ARD=ARD, likelihood=likhood,
max_freq=max_freq,
minibatch_size=minibatch_size,
variances=p['variances'],
frequencies=p['frequencies'],
lengthscales=p['lengthscales'],
Kvar=p['Kvar'], Kfreq=p['Kfreq'],
Klen=p['Klen'])
spectral.feature.Z.prior = gpflow.priors.Gaussian(0, 1)
spectral.compile()
loglik = spectral.compute_log_likelihood()
print('loglik:', loglik)
if loglik > best_loglik:
best_loglik = loglik
best_m = spectral
#best_dir = tempfile.TemporaryDirectory()
#gpflow.saver.Saver().save(best_dir.name + 'model.gpflow', best_m)
del spectral
gc.collect()
except tf.errors.InvalidArgumentError: # cholesky may fail sometimes
pass
print('Best initialization: %f' % best_loglik)
print(best_m)
#gpflow.reset_default_graph_and_session()
#best_m = gpflow.saver.Saver().load(best_dir.name + 'model.gpflow')
#best_m.compile()
#print(best_m)
return best_m
def test_read_values(self):
def check_values(values, expected_dict, unexpected_dicts):
self.assertTrue(values == expected_dict)
for unexpected_dict in unexpected_dicts:
self.assertFalse(values == unexpected_dict)
expected_dict = {'p/a': 10., 'p/b': 11., 'p/c/d': 12.}
unexpected_dicts = [{
'p': 10.,
'p/b': 11.,
'p/c/d': 12.
}, {
'p/a': 11.,
'p/b': 11.,
'p/c/d': 12.
}, {
'p/a': 11.
}]
with self.test_context() as session:
session_new = tf.Session(graph=session.graph)
self.assertNotEqual(session_new, session)
with session_new.as_default():
with gpflow.defer_build():
p = self.create_layout()
values = p.read_values()
check_values(values, expected_dict, unexpected_dicts)
p.compile()
values = p.read_values()
check_values(values, expected_dict, unexpected_dicts)
with self.assertRaises(tf.errors.FailedPreconditionError):
p.read_values(session=session)
with self.test_context() as session_fail:
self.assertFalse(session == session_fail)
with self.assertRaises(tf.errors.FailedPreconditionError):
p.read_values(session=session_fail)
with self.test_context() as session_intialize:
p.initialize(session=session_intialize)
values = p.read_values(session=session_intialize)
check_values(values, expected_dict, unexpected_dicts)
values = p.read_values(session=session_new)
check_values(values, expected_dict, unexpected_dicts)
session_new.close()
def test_keep_custom_parameters(self):
for t in [gpflow.transforms.Log1pe, gpflow.transforms.Identity,
gpflow.transforms.Exp, gpflow.transforms.Logistic]:
with self.test_context():
with gpflow.defer_build():
m = self.get_model()
m.q_mu.prior = gpflow.priors.Gamma(3., 1./3.)
m.q_sqrt.transform = t()
m.compile()
m.X = np.random.rand(11, 1)
m.Y = np.random.rand(11, 1)
m.clear()
m.compile()
self.assertIsInstance(m.q_mu.prior, gpflow.priors.Gamma)
self.assertIsInstance(m.q_sqrt.transform, t)
self.assertTupleEqual(m.q_mu.shape, (11, 1))
self.assertTupleEqual(m.q_sqrt.shape, (1, 11, 11))
def evalMLE(X, Y): # type: (Any, {shape}) -> Tuple[None, GPR]
with gpflow.defer_build():
k = gpflow.kernels.Matern52(1, lengthscales=0.3)
meanf = gpflow.mean_functions.Zero()
m = gpflow.models.GPR(X, Y, k, meanf)
m.clear()
m.kern.lengthscales.prior = gpflow.priors.Beta(1., 3.)
m.kern.variance.prior = gpflow.priors.Beta(1., 3.)
m.likelihood.variance.prior = gpflow.priors.Beta(1., 3.)
m.compile()
print(m.as_pandas_table())
# gpflow.train.scipy_optimizer.ScipyOptimizer().minimize(m)
gpflow.train.AdamOptimizer(learning_rate=.05, beta1=.5,
beta2=.99).minimize(m)
return None, m
def test_append(self):
with self.test_context():
p1 = gpflow.Param(1.2)
p4 = gpflow.Param(np.array([3.4, 5.6], settings.float_type))
with gpflow.defer_build():
p2 = gpflow.Param(1.2)
param_list = gpflow.ParamList([p1])
param_list.append(p2)
p3 = gpflow.Param(1.2)
param_list.append(p3)
param_list.compile()
with self.assertRaises(gpflow.GPflowError):
param_list.append(p4)
self.assertTrue(p1 in param_list.params)
self.assertTrue(p2 in param_list.params)
self.assertTrue(p3 in param_list.params)
self.assertFalse(p4 in param_list.params)
with self.assertRaises(ValueError):
param_list.append('foo')
def test_append(self):
with self.test_context():
p1 = gpflow.Param(1.2)
p4 = gpflow.Param(np.array([3.4, 5.6], settings.float_type))
with gpflow.defer_build():
p2 = gpflow.Param(1.2)
param_list = gpflow.ParamList([p1])
param_list.append(p2)
p3 = gpflow.Param(1.2)
param_list.append(p3)
param_list.compile()
with self.assertRaises(gpflow.GPflowError):
param_list.append(p4)
self.assertTrue(p1 in param_list.params)
self.assertTrue(p2 in param_list.params)
self.assertTrue(p3 in param_list.params)
self.assertFalse(p4 in param_list.params)
with self.assertRaises(ValueError):
param_list.append('foo')
def deep_structure():
a = gp.Param(1)
b = gp.Param(2)
c_a = gp.Param(3)
c_b = gp.Param(4)
with gp.defer_build():
p = gp.Parameterized()
p.c = gp.Parameterized()
p.c.c = gp.Parameterized()
p.c.c.a = gp.Param(3)
p.c.c.b = gp.Param(4)
p.a = a
p.b = b
p.c.a = c_a
p.c.b = c_b
p.compile()
return p
def train_f1_gp(X_train, Y_train, logger):
"""GP which maps lng, lat -> Tau.
X_train should be standardised and should not contain any stops."""
with gpflow.defer_build():
logger.info(X_train.shape)
logger.info(X_train)
logger.info(Y_train)
m = gpflow.models.GPR(X_train, Y_train, kern=gpflow.kernels.Matern32(2))
logger.info(m)
m.compile()
opt = gpflow.train.ScipyOptimizer()
opt.minimize(m)
logger.info("Model optimized")
logger.info(m)
logger.info("f1 GP trained.")
return m
def test_read_values(self):
def check_values(values, expected_dict, unexpected_dicts):
self.assertTrue(values == expected_dict)
for unexpected_dict in unexpected_dicts:
self.assertFalse(values == unexpected_dict)
expected_dict = {'p/a': 10., 'p/b': 11., 'p/c/d': 12.}
unexpected_dicts = [
{'p': 10., 'p/b': 11., 'p/c/d': 12.},
{'p/a': 11., 'p/b': 11., 'p/c/d': 12.},
{'p/a': 11.}
]
with self.test_context() as session:
session_new = tf.Session(graph=session.graph)
self.assertNotEqual(session_new, session)
with session_new.as_default():
with gpflow.defer_build():
p = self.create_layout()
values = p.read_values()
check_values(values, expected_dict, unexpected_dicts)
p.compile()
values = p.read_values()
check_values(values, expected_dict, unexpected_dicts)
with self.assertRaises(tf.errors.FailedPreconditionError):
p.read_values(session=session)
with self.test_context() as session_fail:
self.assertFalse(session == session_fail)
with self.assertRaises(tf.errors.FailedPreconditionError):
p.read_values(session=session_fail)
with self.test_context() as session_intialize:
p.initialize(session=session_intialize)
values = p.read_values(session=session_intialize)
check_values(values, expected_dict, unexpected_dicts)
values = p.read_values(session=session_new)
check_values(values, expected_dict, unexpected_dicts)
session_new.close()
def prepare(self):
with gpflow.defer_build():
return gpflow.models.GPR(
np.ones((1, 1)),
np.ones((1, 1)),
kern=gpflow.kernels.Matern52(1))
def prepare(self):
with gpflow.defer_build():
X = np.random.rand(100, 1)
Y = np.sin(X) + np.random.randn(*X.shape) * 0.01
k = gpflow.kernels.RBF(1)
return gpflow.models.GPR(X, Y, k)
def setUp(self):
with self.test_context(), gpflow.defer_build():
self.m = gpflow.params.Parameterized(name='m')
self.m.p = gpflow.params.Parameterized()
self.m.b = gpflow.params.Parameterized()