def define_val_model(self, N, P, K):
# Define new graph
self.z_test = Gamma(2. * tf.ones([N, K]), 1. * tf.ones([N, K]))
self.l_test = TransformedDistribution(
distribution=Normal(self.mean_llib * tf.ones([N, 1]),
np.sqrt(self.std_llib) * tf.ones([N, 1])),
bijector=tf.contrib.distributions.bijectors.Exp())
rho_test = tf.matmul(self.z_test, self.W0)
rho_test = rho_test / tf.reshape(tf.reduce_sum(rho_test, axis=1),
(-1, 1)) # NxP
self.lam_test = Gamma(self.r, self.r / (rho_test * self.l_test))
if self.zero_inflation:
logit_pi_test = tf.matmul(self.z_test, self.W1)
pi_test = tf.minimum(
tf.maximum(tf.nn.sigmoid(logit_pi_test), 1e-7), 1. - 1e-7)
cat_test = Categorical(
probs=tf.stack([pi_test, 1. - pi_test], axis=2))
components_test = [
Poisson(rate=1e-30 * tf.ones([N, P])),
Poisson(rate=self.lam_test)
]
self.likelihood_test = Mixture(cat=cat_test,
components=components_test)
else:
self.likelihood_test = Poisson(rate=self.lam_test)
def __init__(self, datastore=None, USE_FEEDBACK=USE_FEEDBACK):
"""Set the variables and load model data."""
self.datastore = datastore
self.USE_FEEDBACK = convert_string2bool_env(USE_FEEDBACK)
self.package_id_dict = OrderedDict()
self.id_package_dict = OrderedDict()
self.beta = None
self.theta = None
self.alpha = None
self.manifest_id_dict = OrderedDict()
self.feedback_id_dict = OrderedDict()
self.manifests = 0
self.packages = 0
self.epsilon = Gamma(tf.constant(
a_c), tf.constant(a_c) / tf.constant(b_c)).\
prob(tf.constant(K, dtype=tf.float32)).eval(session=tf.Session())
self.theta_dummy = Poisson(
np.array([
self.epsilon * Gamma(tf.constant(a), self.epsilon).prob(
tf.constant(K,
dtype=tf.float32)).eval(session=tf.Session())
] * K,
dtype=float))
if isinstance(datastore, S3DataStore): # pragma: no-cover
self.load_s3()
else:
self.load_local()
self.manifests = self.theta.shape[0]
self.packages = self.beta.shape[0]
self.dummy_result = self.theta_dummy.prob(
self.beta).eval(session=tf.Session())
def test_map_default(self):
with self.test_session() as sess:
x = Gamma(2.0, 0.5)
inference = ed.MAP([x])
inference.initialize(auto_transform=True, n_iter=500)
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
info_dict = inference.update()
# Check approximation on constrained space has same mode as
# target distribution.
qx = inference.latent_vars[x]
stats = sess.run([x.mode(), qx])
self.assertAllClose(stats[0], stats[1], rtol=1e-5, atol=1e-5)
def test_map_custom(self):
with self.test_session() as sess:
x = Gamma(2.0, 0.5)
qx = PointMass(tf.nn.softplus(tf.Variable(0.5)))
inference = ed.MAP({x: qx})
inference.initialize(auto_transform=True, n_iter=500)
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
info_dict = inference.update()
# Check approximation on constrained space has same mode as
# target distribution.
stats = sess.run([x.mode(), qx])
self.assertAllClose(stats[0], stats[1], rtol=1e-5, atol=1e-5)
def test_map_custom(self):
with self.test_session() as sess:
x = Gamma(2.0, 0.5)
qx = PointMass(tf.nn.softplus(tf.Variable(0.5)))
inference = ed.MAP({x: qx})
inference.initialize(auto_transform=True, n_iter=500)
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
info_dict = inference.update()
# Check approximation on constrained space has same mode as
# target distribution.
stats = sess.run([x.mode(), qx])
self.assertAllClose(stats[0], stats[1], rtol=1e-5, atol=1e-5)
def test_map_default(self):
with self.test_session() as sess:
x = Gamma(2.0, 0.5)
inference = ed.MAP([x])
inference.initialize(auto_transform=True, n_iter=500)
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
info_dict = inference.update()
# Check approximation on constrained space has same mode as
# target distribution.
qx = inference.latent_vars[x]
stats = sess.run([x.mode(), qx])
self.assertAllClose(stats[0], stats[1], rtol=1e-5, atol=1e-5)
def test_nonnegative(self):
with self.test_session():
x = Gamma(1.0, 1.0)
y = ed.transform(x)
self.assertIsInstance(y, TransformedDistribution)
sample = y.sample(10, seed=1).eval()
self.assertSamplePosNeg(sample)
def define_stochastic_model(self, P, K):
M = self.minibatch_size
self.W0 = Gamma(0.1 * tf.ones([K, P]), 0.3 * tf.ones([K, P]))
if self.zero_inflation:
self.W1 = Normal(tf.zeros([K, P]), tf.ones([K, P]))
self.z = Gamma(2. * tf.ones([M, K]), 1. * tf.ones([M, K]))
self.r = Gamma(2. * tf.ones([
P,
]), 1. * tf.ones([
P,
]))
self.l = TransformedDistribution(
distribution=Normal(self.mean_llib * tf.ones([M, 1]),
self.std_llib * tf.ones([M, 1])),
bijector=tf.contrib.distributions.bijectors.Exp())
self.rho = tf.matmul(self.z, self.W0)
self.rho = self.rho / tf.reshape(tf.reduce_sum(self.rho, axis=1),
(-1, 1)) # NxP
self.lam = Gamma(self.r, self.r / (self.rho * self.l))
if self.zero_inflation:
self.logit_pi = tf.matmul(self.z, self.W1)
self.pi = tf.minimum(
tf.maximum(tf.nn.sigmoid(self.logit_pi), 1e-7), 1. - 1e-7)
self.cat = Categorical(
probs=tf.stack([self.pi, 1. - self.pi], axis=2))
self.components = [
Poisson(rate=1e-30 * tf.ones([M, P])),
Poisson(rate=self.lam)
]
self.likelihood = Mixture(cat=self.cat, components=self.components)
else:
self.likelihood = Poisson(rate=self.lam)
def gamma_q(shape):
# Parameterize Gamma q's via shape and scale, with softplus unconstraints.
min_shape = 1e-3
min_scale = 1e-5
shape_init = 0.5 + 0.1 * tf.random_normal(shape)
scale_init = 0.1 * tf.random_normal(shape)
rv = Gamma(
tf.maximum(tf.nn.softplus(tf.Variable(shape_init)), min_shape),
tf.maximum(1.0 / tf.nn.softplus(tf.Variable(scale_init)),
1.0 / min_scale))
return rv
def __init__(self, datastore=None,
scoring_region=HPF_SCORING_REGION):
"""Set the variables and load model data."""
self.datastore = datastore
self.scoring_region = scoring_region
self.package_id_dict = None
self.id_package_dict = None
self.rating_matrix = None
self.beta = None
self.manifest_id_dict = None
self.manifests = 0
self.packages = 0
self.sess = tf.Session()
self.epsilon = Gamma(tf.constant(
a_c), tf.constant(a_c) / tf.constant(b_c)).eval(session=self.sess)
self.theta = np.array([self.epsilon * Gamma(tf.constant(
a), self.epsilon).eval(session=self.sess)] * K)
self.loadS3()
self.dummy_result = Poisson(
np.dot(self.theta, np.transpose(self.beta))).eval(session=self.sess)
self.normalize_result()
def check(dist):
q_s = Gamma(a_s, b_s)
q_v = Gamma(a_v, b_v)
sess = tf.InteractiveSession()
init = tf.global_variables_initializer()
init.run()
no_sample = 100
s_sample = q_s.sample(no_sample).eval()
v_sample = q_v.sample(no_sample).eval()
n = np.zeros([C_u, C_i])
result = np.zeros([C_u, C_i])
n_expected = np.zeros([C_u, C_i])
for i in range(0, no_sample):
n = np.add(n, np.matmul(s_sample[i], np.transpose(v_sample[i])))
n_expected = n / no_sample
#mean of poisson is rate param. So this is fine.
#sample response
#distribution specific
result = dist_mean(dist, n_expected)
return mae(result)
def recommend_new_user(self, input_user_stack, k=config.K):
"""Implement the 'fold-in' logic.
Calculates user factors for a new user and adds the user to the user matrix to make
prediction.
"""
# initializing parameters
_logger.info(
"Could not find a match, calculating factors to recommend.")
k_shp = a_c + k * a
# TODO: t_shp, Why is it not required here?
nY = len(input_user_stack)
Y = np.ones(shape=(nY, ))
seed = np.random.seed(int(time.time()))
theta = Gamma(a, 1 / b_c).sample(sample_shape=(k, ),
seed=seed).eval(session=tf.Session())
k_rte = b_c + np.sum(theta)
gamma_rte = \
Gamma(a_c, b_c / a_c).sample(
sample_shape=(1,), seed=seed).eval(session=tf.Session()) + self.beta.sum(axis=0)
gamma_shp = \
gamma_rte * theta * \
Uniform(low=.85, high=1.15).sample(
sample_shape=(k,), seed=seed).eval(session=tf.Session())
np.nan_to_num(gamma_shp, copy=False)
np.nan_to_num(gamma_rte, copy=False)
phi = np.empty((nY, k), dtype='float32')
add_k_rte = a_c / b_c
theta_prev = theta.copy()
# running the loop
for iter_num in range(iter_score):
for i in range(nY):
iid = input_user_stack[i]
sumphi = 10e-6
maxval = -10**1
phi_st = i
for j in range(k):
phi[phi_st,
j] = psi(gamma_shp[j]) - log(gamma_rte[j]) + psi(
self.lam_shp[iid, j]) - log(self.lam_rte[iid, j])
if phi[phi_st, j] > maxval:
maxval = phi[phi_st, j]
for j in range(k):
phi[phi_st, j] = exp(phi[phi_st, j] - maxval)
sumphi += phi[phi_st, j]
for j in range(k):
phi[phi_st, j] *= Y[i] / sumphi
gamma_rte = (k_shp / k_rte +
self.beta.sum(axis=0, keepdims=True)).reshape(-1)
gamma_shp = a + phi.sum(axis=0)
theta = gamma_shp / gamma_rte
k_rte = add_k_rte + theta.sum()
# checking for early stop
if np.linalg.norm(theta - theta_prev) < stop_thr:
break
theta_prev = theta.copy()
rec = np.dot(theta, self.beta.T)
return self.filter_recommendation(rec, input_user_stack)
def gamma_q(shape, name=None):
# Parameterize Gamma q's via shape and scale, with softplus unconstraints.
with tf.variable_scope(name, default_name="gamma_q"):
min_shape = 1e-3
min_scale = 1e-5
shape_var = tf.get_variable(
"shape", shape,
initializer=tf.random_normal_initializer(mean=0.5, stddev=0.1))
scale_var = tf.get_variable(
"scale", shape,
initializer=tf.random_normal_initializer(stddev=0.1))
rv = Gamma(tf.maximum(tf.nn.softplus(shape_var), min_shape),
tf.maximum(1.0 / tf.nn.softplus(scale_var), 1.0 / min_scale))
return rv
def main(_):
ed.set_seed(42)
# Prior on scalar hyperparameter to Dirichlet.
alpha = Gamma(1.0, 1.0)
# Prior on size of Dirichlet.
n = 1 + tf.cast(Exponential(0.5), tf.int32)
# Build a vector of ones whose size is n; multiply it by alpha.
p = Dirichlet(tf.ones([n]) * alpha)
sess = ed.get_session()
print(sess.run(p))
# [ 0.01012419 0.02939712 0.05036638 0.51287931 0.31020424 0.0485355
# 0.0384932 ]
print(sess.run(p))
def define_model(self, N, P, K, batch_idx=None):
self.W0 = Gamma(.1 * tf.ones([K + self.n_batches, P]),
.3 * tf.ones([K + self.n_batches, P]))
if self.zero_inflation:
self.W1 = Normal(tf.zeros([K + self.n_batches, P]),
tf.ones([K + self.n_batches, P]))
self.z = Gamma(2. * tf.ones([N, K]), 1. * tf.ones([N, K]))
disp_size = 1
if self.gene_dispersion:
disp_size = P
self.r = Gamma(2. * tf.ones([
disp_size,
]), 1. * tf.ones([
disp_size,
]))
self.l = TransformedDistribution(
distribution=Normal(self.mean_llib * tf.ones([N, 1]),
np.sqrt(self.std_llib) * tf.ones([N, 1])),
bijector=tf.contrib.distributions.bijectors.Exp())
if batch_idx is not None and self.n_batches > 0:
self.rho = tf.matmul(
tf.concat([
self.z,
tf.cast(tf.one_hot(batch_idx[:, 0], self.n_batches),
tf.float32)
],
axis=1), self.W0)
else:
self.rho = tf.matmul(self.z, self.W0)
if self.scalings:
self.rho = self.rho / tf.reshape(tf.reduce_sum(self.rho, axis=1),
(-1, 1)) # NxP
self.lam = Gamma(self.r, self.r / (self.rho * self.l))
else:
self.lam = Gamma(self.r, self.r / self.rho)
if self.zero_inflation:
if batch_idx is not None and self.n_batches > 0:
self.logit_pi = tf.matmul(
tf.concat([
self.z,
tf.cast(tf.one_hot(batch_idx[:, 0], self.n_batches),
tf.float32)
],
axis=1), self.W1)
else:
self.logit_pi = tf.matmul(self.z, self.W1)
self.pi = tf.minimum(
tf.maximum(tf.nn.sigmoid(self.logit_pi), 1e-7), 1. - 1e-7)
self.cat = Categorical(
probs=tf.stack([self.pi, 1. - self.pi], axis=2))
self.components = [
Poisson(rate=1e-30 * tf.ones([N, P])),
Poisson(rate=self.lam)
]
self.likelihood = Mixture(cat=self.cat, components=self.components)
else:
self.likelihood = Poisson(rate=self.lam)
"qhinge_loc",
shape=X_hinge_grid.shape,
initializer=tf.random_normal_initializer(
mean=X_hinge_grid * tf.ones(shape=X_hinge_grid.shape, dtype=tfdt),
stddev=0.001 * tf.ones(shape=X_hinge_grid.shape, dtype=tfdt))),
scale=softplus(
tf.get_variable(
"qhinge_scale",
shape=X_hinge_grid.shape,
initializer=tf.random_normal_initializer(
mean=2.0, stddev=0.1))))
gamma_shape = 1.05
gamma_rate = 12.0
gamma = Gamma(gamma_shape, gamma_rate, sample_shape=[1, M - 1])
qgamma = lognormal_q([1, M - 1])
# Model definition
X_plh = tf.placeholder(tfdt, shape=[N, D])
PHI = rbf_kernel(X=X_plh, Y=hinge_grid, gamma=gamma, tfdt=tfdt)
w = Normal(loc=tf.zeros((M, 1), dtype=tfdt),
scale=900 * tf.ones((M, 1), dtype=tfdt))
y = Bernoulli(logits=tf.matmul(PHI, w))
# Variational distributions
qw_loc = tf.get_variable("qw_loc", [M, 1], dtype=tfdt)
qw_scale = softplus(120 * tf.get_variable("qw_scale", [M, 1], dtype=tfdt))
qw = Normal(loc=qw_loc, scale=qw_scale)
# Inference settings
def evaluate_loglikelihood(self, X, batch_idx=None):
"""
This is the ELBO, which is a lower bound on the marginal log-likelihood.
We perform some local optimization on the new data points to obtain the ELBO of the new data.
"""
N = X.shape[0]
P = X.shape[1]
K = self.n_components
# Define new graph conditioned on the posterior global factors
z_test = Gamma(2. * tf.ones([N, K]), 1. * tf.ones([N, K]))
l_test = TransformedDistribution(
distribution=Normal(self.mean_llib * tf.ones([N, 1]),
np.sqrt(self.std_llib) * tf.ones([N, 1])),
bijector=tf.contrib.distributions.bijectors.Exp())
if batch_idx is not None and self.n_batches > 0:
rho_test = tf.matmul(
tf.concat([
z_test,
tf.cast(tf.one_hot(batch_idx[:, 0], self.n_batches),
tf.float32)
],
axis=1), self.W0)
else:
rho_test = tf.matmul(z_test, self.W0)
rho_test = rho_test / tf.reshape(tf.reduce_sum(rho_test, axis=1),
(-1, 1)) # NxP
lam_test = Gamma(self.r, self.r / (rho_test * l_test))
if self.zero_inflation:
if batch_idx is not None and self.n_batches > 0:
logit_pi_test = tf.matmul(
tf.concat([
z_test,
tf.cast(tf.one_hot(batch_idx[:, 0], self.n_batches),
tf.float32)
],
axis=1), self.W1)
else:
logit_pi_test = tf.matmul(z_test, self.W1)
pi_test = tf.minimum(
tf.maximum(tf.nn.sigmoid(logit_pi_test), 1e-7), 1. - 1e-7)
cat_test = Categorical(
probs=tf.stack([pi_test, 1. - pi_test], axis=2))
components_test = [
Poisson(rate=1e-30 * tf.ones([N, P])),
Poisson(rate=lam_test)
]
likelihood_test = Mixture(cat=cat_test, components=components_test)
else:
likelihood_test = Poisson(rate=lam_test)
qz_test = TransformedDistribution(
distribution=Normal(
tf.Variable(tf.ones(z_test.shape)),
tf.nn.softplus(tf.Variable(1. * tf.ones(z_test.shape)))),
bijector=tf.contrib.distributions.bijectors.Exp())
qlam_test = TransformedDistribution(
distribution=Normal(
tf.Variable(tf.ones(lam_test.shape)),
tf.nn.softplus(tf.Variable(0.01 * tf.ones(lam_test.shape)))),
bijector=tf.contrib.distributions.bijectors.Exp())
ql_test = TransformedDistribution(
distribution=Normal(
tf.Variable(self.mean_llib * tf.ones(l_test.shape)),
tf.nn.softplus(
tf.Variable(
np.sqrt(self.std_llib) * tf.ones(l_test.shape)))),
bijector=tf.contrib.distributions.bijectors.Exp())
if self.zero_inflation:
inference_local = ed.ReparameterizationKLqp(
{
z_test: qz_test,
lam_test: qlam_test,
l_test: ql_test
},
data={
likelihood_test: tf.cast(X, tf.float32),
self.W0: self.est_qW0,
self.W1: self.est_qW1,
self.r: self.est_qr
})
else:
inference_local = ed.ReparameterizationKLqp(
{
z_test: qz_test,
lam_test: qlam_test,
l_test: ql_test
},
data={
likelihood_test: tf.cast(X, tf.float32),
self.W0: self.est_qW0,
self.r: self.est_qr
})
inference_local.run(n_iter=self.test_iterations,
n_samples=self.n_mc_samples)
return -self.sess.run(inference_local.loss,
feed_dict={likelihood_test: X.astype('float32')
}) / N
def _test(alpha, beta, n): x = Gamma(alpha=alpha, beta=beta) val_est = get_dims(x.sample(n)) val_true = n + get_dims(alpha) assert val_est == val_true
We build a random variable whose size depends on a sample from another random variable. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import edward as ed import tensorflow as tf from edward.models import Exponential, Dirichlet, Gamma ed.set_seed(42) # Prior on scalar hyperparameter to Dirichlet. alpha = Gamma(alpha=1.0, beta=1.0) # Prior on size of Dirichlet. n = 1 + tf.cast(Exponential(lam=0.5), tf.int32) # Build a vector of ones whose size is n; multiply it by alpha. p = Dirichlet(alpha=tf.ones([n]) * alpha) sess = ed.get_session() print(sess.run(p.value())) # [ 0.01012419 0.02939712 0.05036638 0.51287931 0.31020424 0.0485355 # 0.0384932 ] print(sess.run(p.value())) # [ 0.12836078 0.23335715 0.63828212]
word_idx = np.logical_and(
np.sum(x_train != 0, 1) >= 2,
np.sum(x_train, 1) >= 10)
words = [word for word, idx in zip(words, word_idx) if idx]
x_train = x_train[word_idx, :]
x_train = x_train.T
N = x_train.shape[0] # number of documents
D = x_train.shape[1] # vocabulary size
K = [100, 30, 15] # number of components per layer
q = 'lognormal' # choice of q; 'lognormal' or 'gamma'
shape = 0.1 # gamma shape parameter
lr = 1e-4 # learning rate step-size
# MODEL
W2 = Gamma(0.1, 0.3, sample_shape=[K[2], K[1]])
W1 = Gamma(0.1, 0.3, sample_shape=[K[1], K[0]])
W0 = Gamma(0.1, 0.3, sample_shape=[K[0], D])
z3 = Gamma(0.1, 0.1, sample_shape=[N, K[2]])
z2 = Gamma(shape, shape / tf.matmul(z3, W2))
z1 = Gamma(shape, shape / tf.matmul(z2, W1))
x = Poisson(tf.matmul(z1, W0))
# INFERENCE
def pointmass_q(shape):
min_mean = 1e-3
mean_init = tf.random_normal(shape)
rv = PointMass(tf.maximum(tf.nn.softplus(tf.Variable(mean_init)),
min_mean))
# ips_weights = 1. / poisson.pmf(cau.todense(), reconstr_cau_train)
ips_weights = 1. / 0.25**np.array(4 - train_data.todense())
# ips_weights = ips_weights / np.sum(ips_weights) * np.sum(cau.todense())
# ips different end
tf.reset_default_graph()
sess = tf.InteractiveSession()
idx_ph = tf.placeholder(tf.int32, M)
cau_ph = tf.placeholder(tf.float32, [M, N])
sd_ph = tf.placeholder(tf.float32, [M, N])
U = Gamma(0.3 * tf.ones([M, K]), 0.3 * tf.ones([M, K]))
V = Gamma(0.3 * tf.ones([N, K]), 0.3 * tf.ones([N, K]))
x = Poisson(tf.matmul(U, V, transpose_b=True))
qU_variables = [tf.Variable(tf.random_uniform([D, K])), \
tf.Variable(tf.random_uniform([D, K]))]
qU = PointMass(
params=tf.nn.softplus(tf.gather(qU_variables[0], idx_ph)))
qV_variables = [tf.Variable(tf.random_uniform([N, K])), \
tf.Variable(tf.random_uniform([N, K]))]
qV = PointMass(params=tf.nn.softplus(qV_variables[0]))
# In[21]:
K = 175
train_data = np.array(x_train, dtype=int)
N = train_data.shape[0]
D = train_data.shape[1]
tf.reset_default_graph()
sess = tf.InteractiveSession()
idx_ph = tf.placeholder(tf.int32, M)
x_ph = tf.placeholder(tf.float32, [M, D])
U = Gamma(0.1, 0.5, sample_shape=[M, K])
V = Gamma(0.1, 0.3, sample_shape=[D, K])
x = Poisson(tf.matmul(U, V, transpose_b=True))
min_scale = 1e-5
qV_variables = [
tf.Variable(tf.random_uniform([D, K])),
tf.Variable(tf.random_uniform([D, K]))
]
qV = TransformedDistribution(
distribution=Normal(qV_variables[0],\
tf.maximum(tf.nn.softplus(qV_variables[1]), \
min_scale)),
bijector=tf.contrib.distributions.bijectors.Exp())
# ips_weights = 1. / poisson.pmf(cau.todense(), reconstr_cau)
ips_weights = 1. / 0.25**np.array(4 - train_data.todense())
# ips_weights = ips_weights / np.sum(ips_weights) * np.sum(cau.todense())
# ips different end
tf.reset_default_graph()
sess = tf.InteractiveSession()
idx_ph = tf.placeholder(tf.int32, M)
cau_ph = tf.placeholder(tf.float32, [M, N])
sd_ph = tf.placeholder(tf.float32, [M, N])
U = Gamma(0.3 * tf.ones([M, K]), 0.3 * tf.ones([M, K]))
V = Gamma(0.3 * tf.ones([N, K]), 0.3 * tf.ones([N, K]))
x = Poisson(tf.matmul(U, V, transpose_b=True))
qU_variables = [tf.Variable(tf.random_uniform([D, K])), \
tf.Variable(tf.random_uniform([D, K]))]
qU = PointMass(
params=tf.nn.softplus(tf.gather(qU_variables[0], idx_ph)))
qV_variables = [tf.Variable(tf.random_uniform([N, K])), \
tf.Variable(tf.random_uniform([N, K]))]
qV = PointMass(params=tf.nn.softplus(qV_variables[0]))
plt.plot(xn, 'go')
plt.title('Simulated dataset')
plt.show()
print('mu=7')
print('sigma=1')
# Priors definition
m = tf.constant([0.])
beta = tf.constant([0.0001])
a = tf.constant([0.001])
b = tf.constant([0.001])
# Posterior inference
# Probabilistic model
mu = Normal(loc=m, scale=beta)
sigma = Gamma(a, b)
x = Normal(loc=tf.tile(mu, [N]), scale=tf.tile(sigma, [N]))
# Variational model
qmu = Normal(loc=tf.Variable(tf.random_normal([1])),
scale=tf.nn.softplus(tf.Variable(tf.random_normal([1]))))
qsigma = Gamma(tf.nn.softplus(tf.Variable(tf.random_normal([1]))),
tf.nn.softplus(tf.Variable(tf.random_normal([1]))))
# Inference
inference = ed.KLqp({mu: qmu, sigma: qsigma}, data={x: xn})
inference.run(n_iter=1500, n_samples=30)
sess = ed.get_session()
print('Inferred mu={}'.format(sess.run(qmu.mean())))
def _test(alpha, beta, n):
x = Gamma(alpha=alpha, beta=beta)
val_est = get_dims(x.sample(n))
val_true = n + get_dims(alpha)
assert val_est == val_true
We build a random variable whose size depends on a sample from another random variable. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import edward as ed import tensorflow as tf from edward.models import Exponential, Dirichlet, Gamma ed.set_seed(42) # Prior on scalar hyperparameter to Dirichlet. alpha = Gamma(1.0, 1.0) # Prior on size of Dirichlet. n = 1 + tf.cast(Exponential(0.5), tf.int32) # Build a vector of ones whose size is n; multiply it by alpha. p = Dirichlet(tf.ones([n]) * alpha) sess = ed.get_session() print(sess.run(p)) # [ 0.01012419 0.02939712 0.05036638 0.51287931 0.31020424 0.0485355 # 0.0384932 ] print(sess.run(p)) # [ 0.12836078 0.23335715 0.63828212]
from edward.models import Dirichlet, Categorical, Gamma
# debugging
import sys
sys.path.append("/home/victor/Documents/community_detection/Variational")
from sbm import SBM
# SBM parameters
n_vert = 100
n_comm = 3
# fake a dataset
# sort the ground truth community identities to make it easy to parse them
z_gt = tf.Variable(tf.nn.top_k(Categorical(p=tf.ones([n_vert, n_comm])/n_comm).sample(),k=n_vert).values)
eta_gt = tf.Variable(Gamma(tf.ones([n_comm, n_comm]), tf.ones([n_comm, n_comm])).sample())
g=SBM(zs = z_gt, eta = eta_gt, n_comm = n_comm)
data = SBM(zs = z_gt, eta = eta_gt, n_comm = n_comm).sample()
with tf.Session() as sess:
init = tf.global_variables_initializer()
init.run()
dataset = data.eval()
z_gt = z_gt.eval()
eta_gt = eta_gt.eval()
# Model
# higher level parameters
# alpha = tf.Variable(3.0,dtype=tf.float32)
# lam = tf.Variable(1,dtype=tf.float32)
def _build_model_and_approximations(self):
""" Form likelihood and approximating distributions of Ouija
"""
N = self.N
G = self.G
Q = self.Q
ds = tf.contrib.distributions
k = Normal(loc = tf.zeros([G,Q]), scale = 50 * tf.ones([G,Q]), name = "k")
t0 = Normal(loc = 0.5 * tf.ones(G), scale = 1 * tf.ones(G))
mu0 = Gamma(concentration = 2 * tf.ones(G), rate = tf.ones(G))
z = Normal(loc = 0.5 * tf.ones([N,Q]), scale = tf.ones([N,Q]))
phi = Gamma(concentration = 2 * tf.ones(1), rate = tf.ones(1))
pbeta = Normal(loc = tf.zeros(2), scale = tf.ones(2))
cell_mat = tf.stack([tf.reshape(z, [-1]), -tf.ones(N)], 1)
gene_mat = tf.stack([tf.reshape(k, [-1]), tf.reshape(k, [-1]) * tf.reshape(t0, [-1])], 1)
factor_mult = tf.matmul(cell_mat, gene_mat, transpose_b = True)
mu = mu0 * tf.nn.sigmoid(factor_mult)
prob_dropout = pbeta[0] + pbeta[1] * mu
Y = DropoutNormal(p_dropout = prob_dropout, loc = mu, scale = tf.sqrt(1 + phi * mu))
Y._p_dropout = prob_dropout
self.qk = Normal(loc = tf.Variable(tf.zeros([G, Q])),
scale = tf.nn.softplus(tf.Variable(tf.zeros([G, Q]))))
self.qz = ed.models.TransformedDistribution(
distribution = ed.models.NormalWithSoftplusScale(loc = tf.Variable(tf.zeros([N,Q])),
scale = tf.Variable(tf.ones([N,Q]))),
bijector = LogitShiftBijector(a = tf.zeros([N,Q]), b = tf.ones([N,Q])),
name = "qz"
)
self.qmu0 = ed.models.TransformedDistribution(
distribution = ed.models.NormalWithSoftplusScale(loc = tf.Variable(tf.zeros(G)),
scale = tf.Variable(tf.ones(G))),
bijector = ds.bijectors.Exp(),
name = "qmu0"
)
self.qphi = ed.models.TransformedDistribution(
distribution = ed.models.NormalWithSoftplusScale(loc = tf.Variable(tf.zeros(1)),
scale = tf.Variable(tf.ones(1))),
bijector = ds.bijectors.Exp(),
name = "qphi"
)
self.qt0 = ed.models.TransformedDistribution(
distribution = ed.models.NormalWithSoftplusScale(loc = tf.Variable(tf.zeros(G)),
scale = tf.Variable(tf.ones(G))),
bijector = LogitShiftBijector(a = tf.zeros(G), b = tf.ones(G)),
name = "qt0"
)
self.qbeta = Normal(loc = tf.Variable(tf.zeros(2)),
scale = tf.nn.softplus(tf.Variable(tf.ones(2))))
approx_dict = {
k: self.qk,
z: self.qz,
mu0: self.qmu0,
phi: self.qphi,
t0: self.qt0,
pbeta: self.qbeta
}
return approx_dict, Y
def main(_):
ed.set_seed(42)
# DATA
x_train, metadata = nips(FLAGS.data_dir)
documents = metadata['columns']
words = metadata['rows']
# Subset to documents in 2011 and words appearing in at least two
# documents and have a total word count of at least 10.
doc_idx = [
i for i, document in enumerate(documents)
if document.startswith('2011')
]
documents = [documents[doc] for doc in doc_idx]
x_train = x_train[:, doc_idx]
word_idx = np.logical_and(
np.sum(x_train != 0, 1) >= 2,
np.sum(x_train, 1) >= 10)
words = [word for word, idx in zip(words, word_idx) if idx]
x_train = x_train[word_idx, :]
x_train = x_train.T
N = x_train.shape[0] # number of documents
D = x_train.shape[1] # vocabulary size
# MODEL
W2 = Gamma(0.1, 0.3, sample_shape=[FLAGS.K[2], FLAGS.K[1]])
W1 = Gamma(0.1, 0.3, sample_shape=[FLAGS.K[1], FLAGS.K[0]])
W0 = Gamma(0.1, 0.3, sample_shape=[FLAGS.K[0], D])
z3 = Gamma(0.1, 0.1, sample_shape=[N, FLAGS.K[2]])
z2 = Gamma(FLAGS.shape, FLAGS.shape / tf.matmul(z3, W2))
z1 = Gamma(FLAGS.shape, FLAGS.shape / tf.matmul(z2, W1))
x = Poisson(tf.matmul(z1, W0))
# INFERENCE
qW2 = pointmass_q(W2.shape)
qW1 = pointmass_q(W1.shape)
qW0 = pointmass_q(W0.shape)
if FLAGS.q == 'gamma':
qz3 = gamma_q(z3.shape)
qz2 = gamma_q(z2.shape)
qz1 = gamma_q(z1.shape)
else:
qz3 = lognormal_q(z3.shape)
qz2 = lognormal_q(z2.shape)
qz1 = lognormal_q(z1.shape)
# We apply variational EM with E-step over local variables
# and M-step to point estimate the global weight matrices.
inference_e = ed.KLqp({
z1: qz1,
z2: qz2,
z3: qz3
},
data={
x: x_train,
W0: qW0,
W1: qW1,
W2: qW2
})
inference_m = ed.MAP({
W0: qW0,
W1: qW1,
W2: qW2
},
data={
x: x_train,
z1: qz1,
z2: qz2,
z3: qz3
})
optimizer_e = tf.train.RMSPropOptimizer(FLAGS.lr)
optimizer_m = tf.train.RMSPropOptimizer(FLAGS.lr)
kwargs = {
'optimizer': optimizer_e,
'n_print': 100,
'logdir': FLAGS.logdir,
'log_timestamp': False
}
if FLAGS.q == 'gamma':
kwargs['n_samples'] = 30
inference_e.initialize(**kwargs)
inference_m.initialize(optimizer=optimizer_m)
sess = ed.get_session()
tf.global_variables_initializer().run()
n_epoch = 20
n_iter_per_epoch = 10000
for epoch in range(n_epoch):
print("Epoch {}".format(epoch))
nll = 0.0
pbar = Progbar(n_iter_per_epoch)
for t in range(1, n_iter_per_epoch + 1):
pbar.update(t)
info_dict_e = inference_e.update()
info_dict_m = inference_m.update()
nll += info_dict_e['loss']
# Compute perplexity averaged over a number of training iterations.
# The model's negative log-likelihood of data is upper bounded by
# the variational objective.
nll /= n_iter_per_epoch
perplexity = np.exp(nll / np.sum(x_train))
print("Negative log-likelihood <= {:0.3f}".format(nll))
print("Perplexity <= {:0.3f}".format(perplexity))
# Print top 10 words for first 10 topics.
qW0_vals = sess.run(qW0)
for k in range(10):
top_words_idx = qW0_vals[k, :].argsort()[-10:][::-1]
top_words = " ".join([words[i] for i in top_words_idx])
print("Topic {}: {}".format(k, top_words))
def multivariate_bayesian_linear_mixed_model_and_factorization():
###########
# Load/Simulate in data
############
num_genes = 5000
num_individuals = 100
cells_per_individual = 100
K = 3
num_samples = num_individuals*cells_per_individual
print('start loading')
Y_train, Z_train, true_random_effects, true_gene_random_effects_sdevs, true_gene_residual_sdevs, U_true, V_true, gene_mean = generate_multivariate_bayesian_linear_mixed_model_and_factorization_data(num_genes, num_individuals, cells_per_individual, K)
print('data loaded')
###############################
## MODEL
# Y ~ 1 + (1|individual)
###############################
# Set up placeholders for the data inputs.
ind_ph = tf.placeholder(tf.int32, [num_samples, 1])
# Set up fixed effects.
mu = tf.get_variable("mu", [num_genes])
sigma_ind = tf.sqrt(tf.exp(tf.get_variable("sigma_ind", [num_genes])))
sigma_resid = tf.sqrt(tf.exp(tf.get_variable("sigma_resid", [num_genes])))
# Set up random effects
eta_ind = Normal(loc=tf.zeros([num_individuals, num_genes]), scale= tf.matmul(tf.ones([num_individuals,num_genes]),tf.matrix_diag(sigma_ind)))
# Set up factors
#U = Normal(loc=0.0, scale=1, sample_shape=[num_samples, K])
#V = Normal(loc=0.0, scale=1.0, sample_shape=[K, num_genes])
#U = tf.exp(tf.get_variable("U", [num_samples, K]))
# higher values of sparsity parameter result in a more sparse solution
sparsity_parameter = 1.0
U = Gamma(concentration=1.0, rate=sparsity_parameter, sample_shape=[num_samples,K])
V = tf.get_variable("V", [K, num_genes])
yhat = (tf.matmul(U, V) + tf.gather_nd(eta_ind, ind_ph) + tf.matmul(tf.ones([num_samples, num_genes]), tf.matrix_diag(mu)))
y = Normal(loc=yhat, scale=tf.matmul(tf.ones([num_samples, num_genes]), tf.matrix_diag(sigma_resid)))
###############################
## Inference set up
###############################
q_ind_s = Normal(
loc=tf.get_variable("q_ind_s/loc", [num_individuals, num_genes]),
scale=tf.nn.softplus(tf.get_variable("q_ind_s/scale", [num_individuals, num_genes])))
qU = lognormal_q(U.shape)
#qU = Normal(loc=tf.get_variable("qU/loc", [num_samples, K]),
# scale=tf.nn.softplus(
# tf.get_variable("qU/scale", [num_samples, K])))
#qV = Normal(loc=tf.get_variable("qV/loc", [K, num_genes]),
# scale=tf.nn.softplus(
# tf.get_variable("qV/scale", [K, num_genes])))
latent_vars = {
U: qU,
eta_ind: q_ind_s}
data = {
y: Y_train,
ind_ph: Z_train}
inference = ed.KLqp(latent_vars, data)
tf.global_variables_initializer().run()
inference.run(n_iter=500)
# qU.distribution.scale.eval()
pdb.set_trace()
print("K0", dim, "K", K)
D = train_data.shape[0]
N = train_data.shape[1]
weights = train_data * alpha
cau = exp_to_imp(train_data)
tf.reset_default_graph()
sess = tf.InteractiveSession()
idx_ph = tf.placeholder(tf.int32, M)
cau_ph = tf.placeholder(tf.float32, [M, N])
sd_ph = tf.placeholder(tf.float32, [M, N])
reconstr_cau_ph = tf.placeholder(tf.float32, [M, N])
U = Gamma(0.3 * tf.ones([M, K]), 0.3 * tf.ones([M, K]))
V = Gamma(0.3 * tf.ones([N, K]), 0.3 * tf.ones([N, K]))
gamma = Gamma(tf.ones([M, 1]), tf.ones([M, 1]))
beta0 = Gamma(0.3 * tf.ones([1, 1]), 0.3 * tf.ones([1, 1]))
x = Poisson(tf.add(tf.matmul(U, V, transpose_b=True),\
tf.multiply(tf.matmul(gamma, tf.ones([1, N])), \
reconstr_cau_ph)) + beta0)
qU_variables = [tf.Variable(tf.random_uniform([D, K])), \
tf.Variable(tf.random_uniform([D, K]))]
qU = PointMass(
params=tf.nn.softplus(tf.gather(qU_variables[0], idx_ph)))
print("K0", dim, "K", K)
D = train_data.shape[0]
N = train_data.shape[1]
weights = train_data * alpha
cau = exp_to_imp(train_data)
tf.reset_default_graph()
sess = tf.InteractiveSession()
idx_ph = tf.placeholder(tf.int32, M)
cau_ph = tf.placeholder(tf.float32, [M, N])
sd_ph = tf.placeholder(tf.float32, [M, N])
reconstr_cau_ph = tf.placeholder(tf.float32, [M, N])
U = Gamma(0.3 * tf.ones([M, K]), 0.3 * tf.ones([M, K]))
V = Gamma(0.3 * tf.ones([N, K]), 0.3 * tf.ones([N, K]))
gamma = Gamma(tf.ones([M, 1]), tf.ones([M, 1]))
beta0 = Gamma(0.3 * tf.ones([1, 1]), 0.3 * tf.ones([1, 1]))
x = Poisson(tf.add(tf.matmul(U, V, transpose_b=True),\
tf.multiply(tf.matmul(gamma, tf.ones([1, N])), \
reconstr_cau_ph)) + beta0)
qU_variables = [tf.Variable(tf.random_uniform([D, K])), \
tf.Variable(tf.random_uniform([D, K]))]
qU = PointMass(
params=tf.nn.softplus(tf.gather(qU_variables[0], idx_ph)))
