def sample(self, n, seed=3):
with util.NumpySeedContext(seed=seed):
X = np.log(self.nonhom_linear(size=n))
if len(X.shape) == 1:
# This can happen if d=1
X = X[:, np.newaxis]
return Data(X)
def gaussbern_rbm_probs(stds_perturb_B, dx=50, dh=10, n=sample_size):
"""
Get a sequence of Gaussian-Bernoulli RBM problems.
We follow the parameter settings as described in section 6 of Liu et al.,
2016.
- stds_perturb_B: a list of Gaussian noise standard deviations for perturbing B.
- dx: observed dimension
- dh: latent dimension
"""
probs = []
for i, std in enumerate(stds_perturb_B):
with util.NumpySeedContext(seed=i + 1000):
B = np.random.randint(0, 2, (dx, dh)) * 2 - 1.0
b = np.random.randn(dx)
c = np.random.randn(dh)
p = density.GaussBernRBM(B, b, c)
if std <= 1e-8:
B_perturb = B
else:
B_perturb = B + np.random.randn(dx, dh) * std
gb_rbm = data.DSGaussBernRBM(B_perturb, b, c, burnin=2000)
probs.append((std, p, gb_rbm))
return probs
def sample(self, n, seed=29):
pmix = self.pmix
means = self.means
variances = self.variances
k, d = self.means.shape
sam_list = []
with util.NumpySeedContext(seed=seed):
# counts for each mixture component
counts = np.random.multinomial(n, pmix, size=1)
# counts is a 2d array
counts = counts[0]
# For each component, draw from its corresponding mixture component.
for i, nc in enumerate(counts):
# construct the component
# https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.multivariate_normal.html
cov = variances[i]
mnorm = stats.multivariate_normal(means[i], cov)
# Sample from ith component
sam_i = mnorm.rvs(size=nc)
sam_list.append(sam_i)
sample = np.vstack(sam_list)
assert sample.shape[0] == n
np.random.shuffle(sample)
return Data(sample)
def sample(self, n, seed=2):
with util.NumpySeedContext(seed=seed):
d = len(self.mean)
mean = self.mean
variance = self.variance
X = np.random.randn(n, d) * np.sqrt(variance) + mean
return Data(X)
def sample(self, n, seed=872):
"""
Rejection sampling.
"""
d = len(self.freqs)
sigma2 = self.sigma2
freqs = self.freqs
with util.NumpySeedContext(seed=seed):
# rejection sampling
sam = np.zeros((n, d))
# sample block_size*d at a time.
block_size = 500
from_ind = 0
while from_ind < n:
# The proposal q is N(0, sigma2*I)
X = np.random.randn(block_size, d) * np.sqrt(sigma2)
q_un = np.exp(-np.sum(X**2, 1) / (2.0 * sigma2))
# unnormalized density p
p_un = q_un * (1 + np.prod(np.cos(X * freqs), 1))
c = 2.0
I = stats.uniform.rvs(size=block_size) < p_un / (c * q_un)
# accept
accepted_count = np.sum(I)
to_take = min(n - from_ind, accepted_count)
end_ind = from_ind + to_take
AX = X[I, :]
X_take = AX[:to_take, :]
sam[from_ind:end_ind, :] = X_take
from_ind = end_ind
return Data(sam)
def sample(self, n, seed=3):
with util.NumpySeedContext(seed=seed):
X = np.log(1 / self.inh2d(lamb_bar=n) - 1)
if len(X.shape) == 1:
# This can happen if d=1
X = X[:, np.newaxis]
return Data(X)
def sample(self, n, seed=3):
with util.NumpySeedContext(seed=seed):
X = stats.gamma.rvs(self.alpha, size=n, scale=1.0 / self.beta)
if len(X.shape) == 1:
# This can happen if d=1
X = X[:, np.newaxis]
return Data(X)
def simulate_null_dist(eigs, J, n_simulate=2000, seed=7):
"""
Simulate the null distribution using the spectrums of the covariance
matrix of the U-statistic. The simulated statistic is n*FSSD^2 where
FSSD is an unbiased estimator.
- eigs: a numpy array of estimated eigenvalues of the covariance
matrix. eigs is of length d*J, where d is the input dimension, and
- J: the number of test locations.
Return a numpy array of simulated statistics.
"""
d = len(eigs) / J
assert d > 0
# draw at most d x J x block_size values at a time
block_size = max(20, int(1000.0 / (d * J)))
fssds = np.zeros(n_simulate)
from_ind = 0
with util.NumpySeedContext(seed=seed):
while from_ind < n_simulate:
to_draw = min(block_size, n_simulate - from_ind)
# draw chi^2 random variables.
chi2 = np.random.randn(d * J, to_draw)**2
# an array of length to_draw
sim_fssds = eigs.dot(chi2 - 1.0)
# store
end_ind = from_ind + to_draw
fssds[from_ind:end_ind] = sim_fssds
from_ind = end_ind
return fssds
def sample(self, n, seed=3):
with util.NumpySeedContext(seed=seed):
X_gmm, llh = self.gmm_sample(N=n)
X = X_gmm
if len(X.shape) == 1:
# This can happen if d=1
X = X[:, np.newaxis]
return Data(X)
def sample(self, n, seed=3):
with util.NumpySeedContext(seed=seed):
mvn = stats.multivariate_normal(self.mean, self.cov)
X = mvn.rvs(size=n)
if len(X.shape) == 1:
# This can happen if d=1
X = X[:, np.newaxis]
return Data(X)
def test_grad_log(self):
n = 8
with util.NumpySeedContext(seed=17):
for d in [4, 1]:
variance = 1.2
mean = np.random.randn(d) + 1
X = np.random.rand(n, d) - 2
isonorm = density.IsotropicNormal(mean, variance)
grad_log = isonorm.grad_log(X)
my_grad_log = -(X-mean)/variance
# check correctness
np.testing.assert_almost_equal(grad_log, my_grad_log)
def test_log_den(self):
n = 7
with util.NumpySeedContext(seed=16):
for d in [3, 1]:
variance = 1.1
mean = np.random.randn(d)
X = np.random.rand(n, d) + 1
isonorm = density.IsotropicNormal(mean, variance)
log_dens = isonorm.log_den(X)
my_log_dens = -np.sum((X-mean)**2, 1)/(2.0*variance)
# check correctness
np.testing.assert_almost_equal(log_dens, my_log_dens)
def test_multivariate_normal_density(self):
for i in range(4):
with util.NumpySeedContext(seed=i + 8):
d = i + 2
cov = stats.wishart(df=10 + d, scale=np.eye(d)).rvs(size=1)
mean = np.random.randn(d)
X = np.random.randn(11, d)
den_estimate = density.GaussianMixture.multivariate_normal_density(
mean, cov, X)
mnorm = stats.multivariate_normal(mean=mean, cov=cov)
den_truth = mnorm.pdf(X)
np.testing.assert_almost_equal(den_estimate, den_truth)
def test_basic(self):
"""
Nothing special. Just test basic things.
"""
# sample
n = 10
d = 3
with util.NumpySeedContext(seed=29):
X = np.random.randn(n, d) * 3
k = kernel.KGauss(sigma2=1)
K = k.eval(X, X)
self.assertEqual(K.shape, (n, n))
self.assertTrue(np.all(K >= 0 - 1e-6))
self.assertTrue(np.all(K <= 1 + 1e-6), 'K not bounded by 1')
def test_pair_gradX_Y(self):
# sample
n = 11
d = 3
with util.NumpySeedContext(seed=20):
X = np.random.randn(n, d) * 4
Y = np.random.randn(n, d) * 2
k = kernel.KGauss(sigma2=2.1)
# n x d
pair_grad = k.pair_gradX_Y(X, Y)
loop_grad = np.zeros((n, d))
for i in range(n):
for j in range(d):
loop_grad[i, j] = k.gradX_Y(X[[i], :], Y[[i], :], j)
testing.assert_almost_equal(pair_grad, loop_grad)
def test_gradX_y(self):
n = 10
with util.NumpySeedContext(seed=10):
for d in [1, 3]:
y = np.random.randn(d) * 2
X = np.random.rand(n, d) * 3
sigma2 = 1.3
k = kernel.KGauss(sigma2=sigma2)
# n x d
G = k.gradX_y(X, y)
# check correctness
K = k.eval(X, y[np.newaxis, :])
myG = -K / sigma2 * (X - y)
self.assertEqual(G.shape, myG.shape)
testing.assert_almost_equal(G, myG)
def med_heuristic(models, ref, subsample=1000, seed=100):
# subsample first
n = ref.shape[0]
assert subsample > 0
sub_models = []
with util.NumpySeedContext(seed=seed):
ind = np.random.choice(n, min(subsample, n), replace=False)
for i in range(len(models)):
sub_models.append(models[i][ind, :])
sub_ref = ref[ind, :]
med_mz = np.zeros(len(sub_models))
for i, model in enumerate(sub_models):
sq_pdist_mz = util.dist_matrix(model, sub_ref)**2
med_mz[i] = np.median(sq_pdist_mz)**0.5
sigma2 = 0.5 * np.mean(med_mz)**2
return sigma2
def perform_test(self,
dat,
return_simulated_stats=False,
return_ustat_gram=False):
"""
dat: a instance of Data
"""
with util.ContextTimer() as t:
alpha = self.alpha
n_simulate = self.n_simulate
X = dat.data()
n = X.shape[0]
_, H = self.compute_stat(dat, return_ustat_gram=True)
test_stat = n * np.mean(H)
# bootrapping
sim_stats = np.zeros(n_simulate)
with util.NumpySeedContext(seed=self.seed):
for i in range(n_simulate):
W = self.bootstrapper(n)
# n * [ (1/n^2) * \sum_i \sum_j h(x_i, x_j) w_i w_j ]
boot_stat = W.dot(H.dot(old_div(W, float(n))))
# This is a bootstrap version of n*V_n
sim_stats[i] = boot_stat
# approximate p-value with the permutations
pvalue = np.mean(sim_stats > test_stat)
results = {
'alpha': self.alpha,
'pvalue': pvalue,
'test_stat': test_stat,
'h0_rejected': pvalue < alpha,
'n_simulate': n_simulate,
'time_secs': t.secs,
"H_mu": H.mean(),
"H_sigma": H.std()
}
if return_simulated_stats:
results['sim_stats'] = sim_stats
if return_ustat_gram:
results['H'] = H
return results
def training_model(model, data, SAVE_DIR):
held_out, train_set = data[:HELD_OUT], data[HELD_OUT:]
x = tf.placeholder(tf.float32, [None, DIM], name="subsample")
loss = model.loss(x)
ploss = model.ploss(x, BETA)
opt = tf.train.AdamOptimizer(LEARNING_RATE).minimize(ploss)
saver = tf.train.Saver(tf.trainable_variables(model.name))
minLoss = 1e10
noChange = 0
with tf.Session() as sess:
print(" Training ")
sess.run([
tf.global_variables_initializer(),
tf.local_variables_initializer()
])
tf.get_default_graph().finalize()
for i in range(N_EPOCHS):
with util.NumpySeedContext(i):
train_set = np.random.permutation(train_set)
for j in range(int(TRAIN_SIZE / BATCH_SIZE)):
subsample = train_set[j * BATCH_SIZE:(j + 1) * BATCH_SIZE, :]
_, = sess.run([opt], feed_dict={"subsample:0": subsample})
## Early stopping
val, pval = sess.run([loss, ploss],
feed_dict={"subsample:0": held_out})
print(val, pval)
minLoss = np.min([minLoss, val])
if np.allclose(minLoss, val):
print("{0} loss at epoch: {1}".format(minLoss, i))
noChange = noChange + 1
if not os.path.isdir(SAVE_DIR + "/{0}".format(i)):
os.mkdir(SAVE_DIR + "/{0}".format(i))
saver.save(sess,
SAVE_DIR + "/{0}/model".format(i),
write_meta_graph=False)
else:
noChange = 0
if noChange > GIVE_UP:
break
print(" END and SAVE {0}".format(i))
if not os.path.isdir(SAVE_DIR + "/end"):
os.mkdir(SAVE_DIR + "/end")
saver.save(sess, SAVE_DIR + "/end/model", write_meta_graph=False)
def test_gradXY_sum(self):
n = 11
with util.NumpySeedContext(seed=12):
for d in [3, 1]:
X = np.random.randn(n, d)
sigma2 = 1.4
k = kernel.KGauss(sigma2=sigma2)
# n x n
myG = np.zeros((n, n))
K = k.eval(X, X)
for i in range(n):
for j in range(n):
diffi2 = np.sum((X[i, :] - X[j, :])**2)
#myG[i, j] = -diffi2*K[i, j]/(sigma2**2)+ d*K[i, j]/sigma2
myG[i, j] = K[i, j] / sigma2 * (d - diffi2 / sigma2)
# check correctness
G = k.gradXY_sum(X, X)
self.assertEqual(G.shape, myG.shape)
testing.assert_almost_equal(G, myG)
def sample(self, n, seed=29):
pmix = self.pmix
means = self.means
variances = self.variances
k, d = self.means.shape
sam_list = []
with util.NumpySeedContext(seed=seed):
# counts for each mixture component
counts = np.random.multinomial(n, pmix, size=1)
# counts is a 2d array
counts = counts[0]
# For each component, draw from its corresponding mixture component.
for i, nc in enumerate(counts):
# Sample from ith component
sam_i = np.random.randn(nc, d)*np.sqrt(variances[i]) + means[i]
sam_list.append(sam_i)
sample = np.vstack(sam_list)
assert sample.shape[0] == n
np.random.shuffle(sample)
return Data(sample)
def gaussbern_rbm_tuple(var, dx=50, dh=10, n=sample_size):
"""
Get a tuple of Gaussian-Bernoulli RBM problems.
We follow the parameter settings as described in section 6 of Liu et al.,
2016.
- var: Gaussian noise variance for perturbing B.
- dx: observed dimension
- dh: latent dimension
Return p, a DataSource
"""
with util.NumpySeedContext(seed=1000):
B = np.random.randint(0, 2, (dx, dh)) * 2 - 1.0
b = np.random.randn(dx)
c = np.random.randn(dh)
p = density.GaussBernRBM(B, b, c)
B_perturb = B + np.random.randn(dx, dh) * np.sqrt(var)
gb_rbm = data.DSGaussBernRBM(B_perturb, b, c, burnin=50)
return p, gb_rbm
def gbrbm_perturb(var_perturb_B, dx=50, dh=10):
"""
Get a Gaussian-Bernoulli RBM problem where the first entry of the B matrix
(the matrix linking the latent and the observation) is perturbed.
- var_perturb_B: Gaussian noise variance for perturbing B.
- dx: observed dimension
- dh: latent dimension
Return p (density), data source
"""
with util.NumpySeedContext(seed=10):
B = np.random.randint(0, 2, (dx, dh))*2 - 1.0
b = np.random.randn(dx)
c = np.random.randn(dh)
p = density.GaussBernRBM(B, b, c)
B_perturb = np.copy(B)
if var_perturb_B > 1e-7:
B_perturb[0, 0] = B_perturb[0, 0] + \
np.random.randn(1)*np.sqrt(var_perturb_B)
ds = data.DSGaussBernRBM(B_perturb, b, c, burnin=2000)
return p, ds
def sample(self, n, seed=3, return_latent=False):
"""
Sample by blocked Gibbs sampling
"""
B = self.B
b = self.b
c = self.c
dh = len(c)
dx = len(b)
# Initialize the state of the Markov chain
with util.NumpySeedContext(seed=seed):
X = np.random.randn(n, dx)
H = np.random.randint(1, 2, (n, dh)) * 2 - 1.0
# burn-in
for t in range(self.burnin):
X, H = self._blocked_gibbs_next(X, H)
# sampling
X, H = self._blocked_gibbs_next(X, H)
if return_latent:
return Data(X), H
else:
return Data(X)
def sample(self, n, seed=4):
with util.NumpySeedContext(seed=seed):
X = np.random.laplace(loc=self.loc,
scale=self.scale,
size=(n, self.d))
return Data(X)
def sample(self, n, seed=5):
with util.NumpySeedContext(seed=seed):
X = stats.t.rvs(df=self.df, size=n)
X = X[:, np.newaxis]
return Data(X)
