def sample_functions(self, X_test, n_funcs=1):
"""
Samples F function values from the current posterior at the N
specified test point.
Parameters
----------
X_test: np.ndarray (N, D)
Input test points
n_funcs: int
Number of function values that are drawn at each test point.
Returns
----------
np.array(F, N)
The F function values drawn at the N test points.
"""
if self.normalize_input:
X_test_norm, _, _ = zero_mean_unit_var_normalization(
X_test, self.x_mean, self.x_std)
else:
X_test_norm = X_test
f = np.zeros([n_funcs, X_test_norm.shape[0]])
for i in range(n_funcs):
lasagne.layers.set_all_param_values(self.net, self.samples[i])
out = self.single_predict(X_test_norm)[:, 0]
if self.normalize_output:
f[i, :] = zero_mean_unit_var_unnormalization(
out, self.y_mean, self.y_std)
else:
f[i, :] = out
return f
def predict(self, X_test, return_individual_predictions=False, *args, **kwargs):
"""
Returns the predictive mean and variance of the objective function at
the given test points.
Parameters
----------
X_test: np.ndarray (N, D)
Input test points
return_individual_predictions: bool
If set to true than the individual predictions of all samples are returned.
Returns
----------
np.array(N,)
predictive mean
np.array(N,)
predictive variance
"""
if not self.is_trained:
logging.error("Model is not trained!")
return
# Normalize input
if self.normalize_input:
X_, _, _ = zero_mean_unit_var_normalization(X_test, self.x_mean, self.x_std)
else:
X_ = X_test
f_out = []
theta_noise = []
for sample in self.samples:
lasagne.layers.set_all_param_values(self.net, sample)
out = self.single_predict(X_)
f_out.append(out[:, 0])
theta_noise.append(np.exp(out[:, 1]))
f_out = np.asarray(f_out)
theta_noise = np.asarray(theta_noise)
if return_individual_predictions:
if self.normalize_output:
f_out = zero_mean_unit_var_unnormalization(f_out, self.y_mean, self.y_std)
theta_noise *= self.y_std**2
return f_out, theta_noise
m = np.mean(f_out, axis=0)
# Total variance
# v = np.mean(f_out ** 2 + theta_noise, axis=0) - m ** 2
v = np.mean((f_out - m) ** 2, axis=0)
if self.normalize_output:
m = zero_mean_unit_var_unnormalization(m, self.y_mean, self.y_std)
v *= self.y_std ** 2
return m, v
def train(self, X, y, do_optimize=True):
"""
Computes the Cholesky decomposition of the covariance of X and
estimates the GP hyperparameters by optimizing the marginal
loglikelihood. The prior mean of the GP is set to the empirical
mean of X.
Parameters
----------
X: np.ndarray (N, D)
Input data points. The dimensionality of X is (N, D),
with N as the number of points and D is the number of features.
y: np.ndarray (N,)
The corresponding target values.
do_optimize: boolean
If set to true the hyperparameters are optimized otherwise
the default hyperparameters of the kernel are used.
"""
if self.normalize_input:
# Normalize input to be in [0, 1]
self.X, self.lower, self.upper = normalization.zero_one_normalization(
X, self.lower, self.upper)
else:
self.X = X
if self.normalize_output:
# Normalize output to have zero mean and unit standard deviation
self.y, self.y_mean, self.y_std = normalization.zero_mean_unit_var_normalization(
y)
if self.y_std == 0:
raise ValueError(
"Cannot normalize output. All targets have the same value")
else:
self.y = y
# Use the empirical mean of the data as mean for the GP
self.mean = np.mean(self.y, axis=0)
self.gp = george.GP(self.kernel, mean=self.mean)
if do_optimize:
self.hypers = self.optimize()
self.gp.kernel[:] = self.hypers[:-1]
self.noise = np.exp(self.hypers[-1]) # sigma^2
else:
self.hypers = self.gp.kernel[:]
self.hypers = np.append(self.hypers, np.log(self.noise))
logger.debug("GP Hyperparameters: " + str(self.hypers))
try:
self.gp.compute(self.X, yerr=np.sqrt(self.noise))
except np.linalg.LinAlgError:
self.noise *= 10
self.gp.compute(self.X, yerr=np.sqrt(self.noise))
self.is_trained = True
def predict(self, X_test):
r"""
Returns the predictive mean and variance of the objective function at
the given test points.
Parameters
----------
X_test: np.ndarray (N, D)
N input test points
Returns
----------
np.array(N,)
predictive mean
np.array(N,)
predictive variance
"""
# Normalize inputs
if self.normalize_input:
X_, _, _ = zero_mean_unit_var_normalization(
X_test, self.X_mean, self.X_std)
else:
X_ = X_test
# Get features from the net
layers = lasagne.layers.get_all_layers(self.network)
theta = lasagne.layers.get_output(layers[:-1], X_)[-1].eval()
# Marginalise predictions over hyperparameters of the BLR
mu = np.zeros([len(self.models), X_test.shape[0]])
var = np.zeros([len(self.models), X_test.shape[0]])
for i, m in enumerate(self.models):
mu[i], var[i] = m.predict(theta)
# See the algorithm runtime prediction paper by Hutter et al
# for the derivation of the total variance
m = np.mean(mu, axis=0)
v = np.mean(mu**2 + var, axis=0) - m**2
# Clip negative variances and set them to the smallest
# positive float value
if v.shape[0] == 1:
v = np.clip(v, np.finfo(v.dtype).eps, np.inf)
else:
v = np.clip(v, np.finfo(v.dtype).eps, np.inf)
v[np.where((v < np.finfo(v.dtype).eps)
& (v > -np.finfo(v.dtype).eps))] = 0
if self.normalize_output:
m = zero_mean_unit_var_unnormalization(m, self.y_mean, self.y_std)
v *= self.y_std**2
return m, v
def test_zero_mean_unit_var_normalization(self):
X = np.random.rand(100, 3)
X_norm, m, s = normalization.zero_mean_unit_var_normalization(X)
np.testing.assert_almost_equal(np.mean(X_norm, axis=0), np.zeros(X_norm.shape[1]), decimal=1)
np.testing.assert_almost_equal(np.var(X_norm, axis=0), np.ones(X_norm.shape[1]), decimal=1)
assert X_norm.shape == X.shape
assert m.shape[0] == X.shape[1]
assert s.shape[0] == X.shape[1]
def train(self, X, y, do_optimize=True):
"""
Computes the Cholesky decomposition of the covariance of X and
estimates the GP hyperparameters by optimizing the marginal
loglikelihood. The prior mean of the GP is set to the empirical
mean of X.
Parameters
----------
X: np.ndarray (N, D)
Input data points. The dimensionality of X is (N, D),
with N as the number of points and D is the number of features.
y: np.ndarray (N,)
The corresponding target values.
do_optimize: boolean
If set to true the hyperparameters are optimized otherwise
the default hyperparameters of the kernel are used.
"""
if self.normalize_input:
# Normalize input to be in [0, 1]
self.X, self.lower, self.upper = normalization.zero_one_normalization(X, self.lower, self.upper)
else:
self.X = X
if self.normalize_output:
# Normalize output to have zero mean and unit standard deviation
self.y, self.y_mean, self.y_std = normalization.zero_mean_unit_var_normalization(y)
if self.y_std == 0:
raise ValueError("Cannot normalize output. All targets have the same value")
else:
self.y = y
# Use the empirical mean of the data as mean for the GP
self.mean = np.mean(self.y, axis=0)
self.gp = george.GP(self.kernel, mean=self.mean)
if do_optimize:
self.hypers = self.optimize()
self.gp.kernel[:] = self.hypers[:-1]
self.noise = np.exp(self.hypers[-1]) # sigma^2
else:
self.hypers = self.gp.kernel[:]
self.hypers = np.append(self.hypers, np.log(self.noise))
logger.debug("GP Hyperparameters: " + str(self.hypers))
try:
self.gp.compute(self.X, yerr=np.sqrt(self.noise))
except np.linalg.LinAlgError:
self.noise *= 10
self.gp.compute(self.X, yerr=np.sqrt(self.noise))
self.is_trained = True
def predict(self, X_test):
r"""
Returns the predictive mean and variance of the objective function at
the given test points.
Parameters
----------
X_test: np.ndarray (N, D)
N input test points
Returns
----------
np.array(N,)
predictive mean
np.array(N,)
predictive variance
"""
# Normalize inputs
if self.normalize_input:
X_, _, _ = zero_mean_unit_var_normalization(X_test, self.X_mean, self.X_std)
else:
X_ = X_test
# Get features from the net
layers = lasagne.layers.get_all_layers(self.network)
theta = lasagne.layers.get_output(layers[:-1], X_)[-1].eval()
# Marginalise predictions over hyperparameters of the BLR
mu = np.zeros([len(self.models), X_test.shape[0]])
var = np.zeros([len(self.models), X_test.shape[0]])
for i, m in enumerate(self.models):
mu[i], var[i] = m.predict(theta)
# See the algorithm runtime prediction paper by Hutter et al
# for the derivation of the total variance
m = np.mean(mu, axis=0)
v = np.mean(mu ** 2 + var, axis=0) - m ** 2
# Clip negative variances and set them to the smallest
# positive float value
if v.shape[0] == 1:
v = np.clip(v, np.finfo(v.dtype).eps, np.inf)
else:
v = np.clip(v, np.finfo(v.dtype).eps, np.inf)
v[np.where((v < np.finfo(v.dtype).eps) & (v > -np.finfo(v.dtype).eps))] = 0
if self.normalize_output:
m = zero_mean_unit_var_unnormalization(m, self.y_mean, self.y_std)
v *= self.y_std ** 2
return m, v
def train(self, X, y, do_optimize=True, **kwargs):
"""
Performs MCMC sampling to sample hyperparameter configurations from the
likelihood and trains for each sample a GP on X and y
Parameters
----------
X: np.ndarray (N, D)
Input data points. The dimensionality of X is (N, D),
with N as the number of points and D is the number of features.
y: np.ndarray (N,)
The corresponding target values.
do_optimize: boolean
If set to true we perform MCMC sampling otherwise we just use the
hyperparameter specified in the kernel.
"""
if self.normalize_input:
# Normalize input to be in [0, 1]
self.X, self.lower, self.upper = normalization.zero_one_normalization(X, self.lower, self.upper)
else:
self.X = X
if self.normalize_output:
# Normalize output to have zero mean and unit standard deviation
self.y, self.y_mean, self.y_std = normalization.zero_mean_unit_var_normalization(y)
if self.y_std == 0:
raise ValueError("Cannot normalize output. All targets have the same value")
else:
self.y = y
# Use the mean of the data as mean for the GP
self.mean = np.mean(self.y, axis=0)
self.gp = george.GP(self.kernel, mean=self.mean)
if do_optimize:
# We have one walker for each hyperparameter configuration
sampler = emcee.EnsembleSampler(self.n_hypers,
len(self.kernel.pars) + 1,
self.loglikelihood)
# Do a burn-in in the first iteration
if not self.burned:
# Initialize the walkers by sampling from the prior
if self.prior is None:
self.p0 = np.random.rand(self.n_hypers, len(self.kernel.pars) + 1)
else:
self.p0 = self.prior.sample_from_prior(self.n_hypers)
# Run MCMC sampling
self.p0, _, _ = sampler.run_mcmc(self.p0,
self.burnin_steps,
rstate0=self.rng)
self.burned = True
# Start sampling
pos, _, _ = sampler.run_mcmc(self.p0,
self.chain_length,
rstate0=self.rng)
# Save the current position, it will be the start point in
# the next iteration
self.p0 = pos
# Take the last samples from each walker
self.hypers = sampler.chain[:, -1]
else:
self.hypers = self.gp.kernel[:].tolist()
self.hypers.append(self.noise)
self.hypers = [self.hypers]
self.models = []
for sample in self.hypers:
# Instantiate a GP for each hyperparameter configuration
kernel = deepcopy(self.kernel)
kernel.pars = np.exp(sample[:-1])
noise = np.exp(sample[-1])
model = GaussianProcess(kernel,
normalize_output=self.normalize_output,
normalize_input=self.normalize_input,
noise=noise,
lower=self.lower,
upper=self.upper,
rng=self.rng)
model.train(X, y, do_optimize=False)
self.models.append(model)
self.is_trained = True
def train(self, X, y, *args, **kwargs):
"""
Trains the model on the provided data.
Parameters
----------
X: np.ndarray (N, D)
Input data points. The dimensionality of X is (N, D),
with N as the number of points and D is the number of features.
y: np.ndarray (N,)
The corresponding target values.
"""
# Clear old samples
start_time = time.time()
self.net = self.get_net(n_inputs=X.shape[1])
nll, mse = self.negativ_log_likelihood(self.net, self.Xt, self.Yt,
X.shape[0], self.weight_prior,
self.variance_prior)
params = lasagne.layers.get_all_params(self.net, trainable=True)
seed = self.rng.randint(1, 100000)
srng = theano.sandbox.rng_mrg.MRG_RandomStreams(seed)
if self.sampling_method == "sghmc":
self.sampler = SGHMCSampler(rng=srng,
precondition=self.precondition,
ignore_burn_in=False)
elif self.sampling_method == "sgld":
self.sampler = SGLDSampler(rng=srng,
precondition=self.precondition)
else:
logging.error("Sampling Strategy % does not exist!" %
self.sampling_method)
self.compute_err = theano.function([self.Xt, self.Yt], [mse, nll])
self.single_predict = theano.function([self.Xt],
lasagne.layers.get_output(
self.net, self.Xt))
self.samples.clear()
if self.normalize_input:
self.X, self.x_mean, self.x_std = zero_mean_unit_var_normalization(
X)
else:
self.X = X
if self.normalize_output:
self.y, self.y_mean, self.y_std = zero_mean_unit_var_normalization(
y)
else:
self.y = y
self.sampler.prepare_updates(nll,
params,
self.l_rate,
mdecay=self.mdecay,
inputs=[self.Xt, self.Yt],
scale_grad=X.shape[0])
logging.info("Starting sampling")
# Check if we have enough data points to form a minibatch
# otherwise set the batchsize equal to the number of input points
if self.X.shape[0] < self.bsize:
self.bsize = self.X.shape[0]
logging.error("Not enough datapoint to form a minibatch. "
"Set the batchsize to {}".format(self.bsize))
i = 0
while i < self.n_iters and len(self.samples) < self.n_nets:
if self.X.shape[0] == self.bsize:
start = 0
else:
start = np.random.randint(0, self.X.shape[0] - self.bsize)
xmb = floatX(self.X[start:start + self.bsize])
ymb = floatX(self.y[start:start + self.bsize, None])
if i < self.burn_in:
_, nll_value = self.sampler.step_burn_in(xmb, ymb)
else:
_, nll_value = self.sampler.step(xmb, ymb)
if i % 1000 == 0:
total_err, total_nll = self.compute_err(
floatX(self.X),
floatX(self.y).reshape(-1, 1))
t = time.time() - start_time
logging.info("Iter {} : NLL = {} MSE = {} "
"Collected samples= {} Time = {}".format(
i, total_nll, total_err, len(self.samples),
t))
if i % 100 == 0 and i >= self.burn_in:
self.samples.append(
lasagne.layers.get_all_param_values(self.net))
i += 1
self.is_trained = True
def train(self, X, y, do_optimize=True):
"""
Trains the model on the provided data.
Parameters
----------
X: np.ndarray (N, D)
Input data points. The dimensionality of X is (N, D),
with N as the number of points and D is the number of features.
y: np.ndarray (N,)
The corresponding target values.
do_optimize: boolean
If set to true the hyperparameters are optimized otherwise
the default hyperparameters are used.
"""
start_time = time.time()
# Normalize inputs
if self.normalize_input:
self.X, self.X_mean, self.X_std = zero_mean_unit_var_normalization(X)
else:
self.X = X
# Normalize ouputs
if self.normalize_output:
self.y, self.y_mean, self.y_std = zero_mean_unit_var_normalization(y)
else:
self.y = y
self.y = self.y[:, None]
# Check if we have enough points to create a minibatch otherwise use all data points
if self.X.shape[0] <= self.batch_size:
batch_size = self.X.shape[0]
else:
batch_size = self.batch_size
# Create the neural network
features = X.shape[1]
self.network = self._build_net(self.input_var, features)
prediction = lasagne.layers.get_output(self.network)
# Define loss function for training
loss = T.mean(T.square(prediction - self.target_var)) / 0.001
loss = loss.mean()
params = lasagne.layers.get_all_params(self.network, trainable=True)
self.learning_rate = theano.shared(np.array(self.init_learning_rate,
dtype=theano.config.floatX))
updates = lasagne.updates.adam(loss, params, learning_rate=self.learning_rate)
logging.debug("... compiling theano functions")
self.train_fn = theano.function([self.input_var, self.target_var], loss,
updates=updates,
allow_input_downcast=True)
# Start training
lc = np.zeros([self.num_epochs])
for epoch in range(self.num_epochs):
epoch_start_time = time.time()
train_err = 0
train_batches = 0
for batch in self.iterate_minibatches(self.X, self.y,
batch_size, shuffle=True):
inputs, targets = batch
train_err += self.train_fn(inputs, targets)
train_batches += 1
lc[epoch] = train_err / train_batches
logging.debug("Epoch {} of {}".format(epoch + 1, self.num_epochs))
curtime = time.time()
epoch_time = curtime - epoch_start_time
total_time = curtime - start_time
logging.debug("Epoch time {:.3f}s, total time {:.3f}s".format(epoch_time, total_time))
logging.debug("Training loss:\t\t{:.5g}".format(train_err / train_batches))
# Adapt the learning rate
if epoch % self.adapt_epoch == 0:
self.learning_rate.set_value(
np.float32(self.init_learning_rate * 0.1))
# Design matrix
layers = lasagne.layers.get_all_layers(self.network)
self.Theta = lasagne.layers.get_output(layers[:-1], self.X)[-1].eval()
if do_optimize:
if self.do_mcmc:
self.sampler = emcee.EnsembleSampler(self.n_hypers, 2,
self.marginal_log_likelihood)
# Do a burn-in in the first iteration
if not self.burned:
# Initialize the walkers by sampling from the prior
self.p0 = self.prior.sample_from_prior(self.n_hypers)
# Run MCMC sampling
self.p0, _, _ = self.sampler.run_mcmc(self.p0,
self.burnin_steps,
rstate0=self.rng)
self.burned = True
# Start sampling
pos, _, _ = self.sampler.run_mcmc(self.p0,
self.chain_length,
rstate0=self.rng)
# Save the current position, it will be the startpoint in
# the next iteration
self.p0 = pos
# Take the last samples from each walker
self.hypers = np.exp(self.sampler.chain[:, -1])
else:
# Optimize hyperparameters of the Bayesian linear regression
res = optimize.fmin(self.nll, np.random.rand(2))
self.hypers = [[np.exp(res[0]), np.exp(res[1])]]
else:
self.hypers = [[self.alpha, self.beta]]
logging.info("Hypers: %s" % self.hypers)
self.models = []
for sample in self.hypers:
# Instantiate a model for each hyperparameter configuration
model = BayesianLinearRegression(alpha=sample[0],
beta=sample[1],
basis_func=None)
model.train(self.Theta, self.y[:, 0], do_optimize=False)
self.models.append(model)
def f(x):
return np.sinc(x * 10 - 5).sum(axis=1)[:, None]
rng = np.random.RandomState(42)
X = init_random_uniform(np.zeros(1), np.ones(1), 20, rng)
y = f(X)[:, 0]
model = DNGO()
model.train(X, y)
predictions = lasagne.layers.get_output(model.network,
zero_mean_unit_var_normalization(X, model.X_mean, model.X_std)[0],
deterministic=True).eval()
predictions = zero_mean_unit_var_unnormalization(predictions, model.y_mean, model.y_std)
X_test = np.linspace(0, 1, 100)[:, None]
X_test_norm = zero_mean_unit_var_normalization(X_test, model.X_mean, model.X_std)[0]
# Get features from the net
layers = lasagne.layers.get_all_layers(model.network)
basis_funcs = lasagne.layers.get_output(layers[:-1], X_test_norm)[-1].eval()
fvals = f(X_test)[:, 0]
m, v = model.predict(X_test)
def train(self, X, y, do_optimize=True, **kwargs):
X_norm, _, _ = normalization.zero_one_normalization(X[:, :-1], self.lower, self.upper)
s_ = self.basis_func(X[:, -1])[:, None]
self.X = np.concatenate((X_norm, s_), axis=1)
if self.normalize_output:
# Normalize output to have zero mean and unit standard deviation
self.y, self.y_mean, self.y_std = normalization.zero_mean_unit_var_normalization(y)
else:
self.y = y
# Use the mean of the data as mean for the GP
mean = np.mean(self.y, axis=0)
self.gp = george.GP(self.kernel, mean=mean)
if do_optimize:
# We have one walker for each hyperparameter configuration
sampler = emcee.EnsembleSampler(self.n_hypers,
len(self.kernel) + 1,
self.loglikelihood)
# Do a burn-in in the first iteration
if not self.burned:
# Initialize the walkers by sampling from the prior
if self.prior is None:
self.p0 = np.random.rand(self.n_hypers, len(self.kernel.pars) + 1)
else:
self.p0 = self.prior.sample_from_prior(self.n_hypers)
# Run MCMC sampling
self.p0, _, _ = sampler.run_mcmc(self.p0,
self.burnin_steps,
rstate0=self.rng)
self.burned = True
# Start sampling
pos, _, _ = sampler.run_mcmc(self.p0,
self.chain_length,
rstate0=self.rng)
# Save the current position, it will be the start point in
# the next iteration
self.p0 = pos
# Take the last samples from each walker
self.hypers = sampler.chain[:, -1]
else:
if self.hypers is None:
self.hypers = self.gp.kernel[:].tolist()
self.hypers.append(self.noise)
self.hypers = [self.hypers]
self.models = []
for sample in self.hypers:
# Instantiate a GP for each hyperparameter configuration
kernel = deepcopy(self.kernel)
#kernel.set_parameter_vector(sample[:-1])
kernel.pars = np.exp(sample[:-1])
noise = np.exp(sample[-1])
model = FabolasGP(kernel,
basis_function=self.basis_func,
normalize_output=self.normalize_output,
noise=noise,
lower=self.lower,
upper=self.upper,
rng=self.rng)
model.train(X, y, do_optimize=False)
self.models.append(model)
self.is_trained = True
def train(self, X, y, do_optimize=True, **kwargs):
X_norm, _, _ = normalization.zero_one_normalization(X[:, :-1], self.lower, self.upper)
s_ = self.basis_func(X[:, -1])[:, None]
self.X = np.concatenate((X_norm, s_), axis=1)
if self.normalize_output:
# Normalize output to have zero mean and unit standard deviation
self.y, self.y_mean, self.y_std = normalization.zero_mean_unit_var_normalization(y)
else:
self.y = y
# Use the mean of the data as mean for the GP
mean = np.mean(self.y, axis=0)
self.gp = george.GP(self.kernel, mean=mean)
if do_optimize:
# We have one walker for each hyperparameter configuration
sampler = emcee.EnsembleSampler(self.n_hypers,
len(self.kernel.pars) + 1,
self.loglikelihood)
# Do a burn-in in the first iteration
if not self.burned:
# Initialize the walkers by sampling from the prior
if self.prior is None:
self.p0 = np.random.rand(self.n_hypers, len(self.kernel.pars) + 1)
else:
self.p0 = self.prior.sample_from_prior(self.n_hypers)
# Run MCMC sampling
self.p0, _, _ = sampler.run_mcmc(self.p0,
self.burnin_steps,
rstate0=self.rng)
self.burned = True
# Start sampling
pos, _, _ = sampler.run_mcmc(self.p0,
self.chain_length,
rstate0=self.rng)
# Save the current position, it will be the start point in
# the next iteration
self.p0 = pos
# Take the last samples from each walker
self.hypers = sampler.chain[:, -1]
else:
if self.hypers is None:
self.hypers = self.gp.kernel[:].tolist()
self.hypers.append(self.noise)
self.hypers = [self.hypers]
self.models = []
for sample in self.hypers:
# Instantiate a GP for each hyperparameter configuration
kernel = deepcopy(self.kernel)
kernel.pars = np.exp(sample[:-1])
noise = np.exp(sample[-1])
model = FabolasGP(kernel,
basis_function=self.basis_func,
normalize_output=self.normalize_output,
noise=noise,
lower=self.lower,
upper=self.upper,
rng=self.rng)
model.train(X, y, do_optimize=False)
self.models.append(model)
self.is_trained = True
def train(self, X, y, do_optimize=True):
"""
Trains the model on the provided data.
Parameters
----------
X: np.ndarray (N, D)
Input data points. The dimensionality of X is (N, D),
with N as the number of points and D is the number of features.
y: np.ndarray (N,)
The corresponding target values.
do_optimize: boolean
If set to true the hyperparameters are optimized otherwise
the default hyperparameters are used.
"""
start_time = time.time()
# Normalize inputs
if self.normalize_input:
self.X, self.X_mean, self.X_std = zero_mean_unit_var_normalization(
X)
else:
self.X = X
# Normalize ouputs
if self.normalize_output:
self.y, self.y_mean, self.y_std = zero_mean_unit_var_normalization(
y)
else:
self.y = y
self.y = self.y[:, None]
# Check if we have enough points to create a minibatch otherwise use all data points
if self.X.shape[0] <= self.batch_size:
batch_size = self.X.shape[0]
else:
batch_size = self.batch_size
# Create the neural network
features = X.shape[1]
self.network = self._build_net(self.input_var, features)
prediction = lasagne.layers.get_output(self.network)
# Define loss function for training
loss = T.mean(T.square(prediction - self.target_var)) / 0.001
loss = loss.mean()
params = lasagne.layers.get_all_params(self.network, trainable=True)
self.learning_rate = theano.shared(
np.array(self.init_learning_rate, dtype=theano.config.floatX))
updates = lasagne.updates.adam(loss,
params,
learning_rate=self.learning_rate)
logging.debug("... compiling theano functions")
self.train_fn = theano.function([self.input_var, self.target_var],
loss,
updates=updates,
allow_input_downcast=True)
# Start training
lc = np.zeros([self.num_epochs])
for epoch in range(self.num_epochs):
epoch_start_time = time.time()
train_err = 0
train_batches = 0
for batch in self.iterate_minibatches(self.X,
self.y,
batch_size,
shuffle=True):
inputs, targets = batch
train_err += self.train_fn(inputs, targets)
train_batches += 1
lc[epoch] = train_err / train_batches
logging.debug("Epoch {} of {}".format(epoch + 1, self.num_epochs))
curtime = time.time()
epoch_time = curtime - epoch_start_time
total_time = curtime - start_time
logging.debug("Epoch time {:.3f}s, total time {:.3f}s".format(
epoch_time, total_time))
logging.debug("Training loss:\t\t{:.5g}".format(train_err /
train_batches))
# Adapt the learning rate
if epoch % self.adapt_epoch == 0:
self.learning_rate.set_value(
np.float32(self.init_learning_rate * 0.1))
# Design matrix
layers = lasagne.layers.get_all_layers(self.network)
self.Theta = lasagne.layers.get_output(layers[:-1], self.X)[-1].eval()
if do_optimize:
if self.do_mcmc:
self.sampler = emcee.EnsembleSampler(
self.n_hypers, 2, self.marginal_log_likelihood)
# Do a burn-in in the first iteration
if not self.burned:
# Initialize the walkers by sampling from the prior
self.p0 = self.prior.sample_from_prior(self.n_hypers)
# Run MCMC sampling
self.p0, _, _ = self.sampler.run_mcmc(self.p0,
self.burnin_steps,
rstate0=self.rng)
self.burned = True
# Start sampling
pos, _, _ = self.sampler.run_mcmc(self.p0,
self.chain_length,
rstate0=self.rng)
# Save the current position, it will be the startpoint in
# the next iteration
self.p0 = pos
# Take the last samples from each walker
self.hypers = np.exp(self.sampler.chain[:, -1])
else:
# Optimize hyperparameters of the Bayesian linear regression
res = optimize.fmin(self.nll, np.random.rand(2))
self.hypers = [[np.exp(res[0]), np.exp(res[1])]]
else:
self.hypers = [[self.alpha, self.beta]]
logging.info("Hypers: %s" % self.hypers)
self.models = []
for sample in self.hypers:
# Instantiate a model for each hyperparameter configuration
model = BayesianLinearRegression(alpha=sample[0],
beta=sample[1],
basis_func=None)
model.train(self.Theta, self.y[:, 0], do_optimize=False)
self.models.append(model)
def train(self, X, y, do_optimize=True, **kwargs):
"""
Performs MCMC sampling to sample hyperparameter configurations from the
likelihood and trains for each sample a GP on X and y
Parameters
----------
X: np.ndarray (N, D)
Input data points. The dimensionality of X is (N, D),
with N as the number of points and D is the number of features.
y: np.ndarray (N,)
The corresponding target values.
do_optimize: boolean
If set to true we perform MCMC sampling otherwise we just use the
hyperparameter specified in the kernel.
"""
if self.normalize_input:
# Normalize input to be in [0, 1]
self.X, self.lower, self.upper = normalization.zero_one_normalization(X, self.lower, self.upper)
else:
self.X = X
if self.normalize_output:
# Normalize output to have zero mean and unit standard deviation
self.y, self.y_mean, self.y_std = normalization.zero_mean_unit_var_normalization(y)
if self.y_std == 0:
raise ValueError("Cannot normalize output. All targets have the same value")
else:
self.y = y
# Use the mean of the data as mean for the GP
self.mean = np.mean(self.y, axis=0)
self.gp = george.GP(self.kernel, mean=self.mean)
if do_optimize:
# We have one walker for each hyperparameter configuration
sampler = emcee.EnsembleSampler(self.n_hypers,
len(self.kernel.pars) + 1,
self.loglikelihood)
sampler.random_state = self.rng.get_state()
# Do a burn-in in the first iteration
if not self.burned:
# Initialize the walkers by sampling from the prior
if self.prior is None:
self.p0 = self.rng.rand(self.n_hypers, len(self.kernel.pars) + 1)
else:
self.p0 = self.prior.sample_from_prior(self.n_hypers)
# Run MCMC sampling
self.p0, _, _ = sampler.run_mcmc(self.p0,
self.burnin_steps,
rstate0=self.rng)
self.burned = True
# Start sampling
pos, _, _ = sampler.run_mcmc(self.p0,
self.chain_length,
rstate0=self.rng)
# Save the current position, it will be the start point in
# the next iteration
self.p0 = pos
# Take the last samples from each walker
self.hypers = sampler.chain[:, -1]
else:
self.hypers = self.gp.kernel[:].tolist()
self.hypers.append(self.noise)
self.hypers = [self.hypers]
self.models = []
for sample in self.hypers:
# Instantiate a GP for each hyperparameter configuration
kernel = deepcopy(self.kernel)
kernel.pars = np.exp(sample[:-1])
noise = np.exp(sample[-1])
model = GaussianProcess(kernel,
normalize_output=self.normalize_output,
normalize_input=self.normalize_input,
noise=noise,
lower=self.lower,
upper=self.upper,
rng=self.rng)
model.train(X, y, do_optimize=False)
self.models.append(model)
self.is_trained = True
