def doc_e_step(self, doc, ss, Elogsticks_1st, word_list, unique_words,
doc_word_ids, doc_word_counts, var_converge):
"""
e step for a single doc
"""
chunkids = [unique_words[id] for id in doc_word_ids]
Elogbeta_doc = self.m_Elogbeta[:, doc_word_ids]
## very similar to the hdp equations
v = np.zeros((2, self.m_K - 1))
v[0] = 1.0
v[1] = self.m_alpha
# back to the uniform
phi = np.ones((len(doc_word_ids), self.m_K)) * 1.0 / self.m_K
likelihood = 0.0
old_likelihood = -1e200
converge = 1.0
eps = 1e-100
iter = 0
max_iter = 100
# not yet support second level optimization yet, to be done in the future
while iter < max_iter and (converge < 0.0 or converge > var_converge):
### update variational parameters
# var_phi
if iter < 3:
var_phi = np.dot(phi.T, (Elogbeta_doc * doc_word_counts).T)
(log_var_phi,
log_norm) = matutils.ret_log_normalize_vec(var_phi)
var_phi = np.exp(log_var_phi)
else:
var_phi = np.dot(
phi.T, (Elogbeta_doc * doc_word_counts).T) + Elogsticks_1st
(log_var_phi,
log_norm) = matutils.ret_log_normalize_vec(var_phi)
var_phi = np.exp(log_var_phi)
# phi
if iter < 3:
phi = np.dot(var_phi, Elogbeta_doc).T
(log_phi, log_norm) = matutils.ret_log_normalize_vec(phi)
phi = np.exp(log_phi)
else:
phi = np.dot(var_phi, Elogbeta_doc).T + Elogsticks_2nd
(log_phi, log_norm) = matutils.ret_log_normalize_vec(phi)
phi = np.exp(log_phi)
# v
phi_all = phi * np.array(doc_word_counts)[:, np.newaxis]
v[0] = 1.0 + np.sum(phi_all[:, :self.m_K - 1], 0)
phi_cum = np.flipud(np.sum(phi_all[:, 1:], 0))
v[1] = self.m_alpha + np.flipud(np.cumsum(phi_cum))
Elogsticks_2nd = expect_log_sticks(v)
likelihood = 0.0
# compute likelihood
# var_phi part/ C in john's notation
likelihood += np.sum((Elogsticks_1st - log_var_phi) * var_phi)
# v part/ v in john's notation, john's beta is alpha here
log_alpha = np.log(self.m_alpha)
likelihood += (self.m_K - 1) * log_alpha
dig_sum = psi(np.sum(v, 0))
likelihood += np.sum(
(np.array([1.0, self.m_alpha])[:, np.newaxis] - v) *
(psi(v) - dig_sum))
likelihood -= np.sum(gammaln(np.sum(v, 0))) - np.sum(gammaln(v))
# Z part
likelihood += np.sum((Elogsticks_2nd - log_phi) * phi)
# X part, the data part
likelihood += np.sum(
phi.T * np.dot(var_phi, Elogbeta_doc * doc_word_counts))
converge = (likelihood - old_likelihood) / abs(old_likelihood)
old_likelihood = likelihood
if converge < -0.000001:
logger.warning('likelihood is decreasing!')
iter += 1
# update the suff_stat ss
# this time it only contains information from one doc
ss.m_var_sticks_ss += np.sum(var_phi, 0)
ss.m_var_beta_ss[:, chunkids] += np.dot(var_phi.T,
phi.T * doc_word_counts)
return likelihood
def doc_e_step(self, ss, Elogsticks_1st, unique_words, doc_word_ids, doc_word_counts, var_converge):
"""
e step for a single doc
"""
chunkids = [unique_words[id] for id in doc_word_ids]
Elogbeta_doc = self.m_Elogbeta[:, doc_word_ids]
# very similar to the hdp equations
v = np.zeros((2, self.m_K - 1))
v[0] = 1.0
v[1] = self.m_alpha
# back to the uniform
phi = np.ones((len(doc_word_ids), self.m_K)) * 1.0 / self.m_K
likelihood = 0.0
old_likelihood = -1e200
converge = 1.0
iter = 0
max_iter = 100
# not yet support second level optimization yet, to be done in the future
while iter < max_iter and (converge < 0.0 or converge > var_converge):
# update variational parameters
# var_phi
if iter < 3:
var_phi = np.dot(phi.T, (Elogbeta_doc * doc_word_counts).T)
(log_var_phi, log_norm) = matutils.ret_log_normalize_vec(var_phi)
var_phi = np.exp(log_var_phi)
else:
var_phi = np.dot(phi.T, (Elogbeta_doc * doc_word_counts).T) + Elogsticks_1st
(log_var_phi, log_norm) = matutils.ret_log_normalize_vec(var_phi)
var_phi = np.exp(log_var_phi)
# phi
if iter < 3:
phi = np.dot(var_phi, Elogbeta_doc).T
(log_phi, log_norm) = matutils.ret_log_normalize_vec(phi)
phi = np.exp(log_phi)
else:
phi = np.dot(var_phi, Elogbeta_doc).T + Elogsticks_2nd # noqa:F821
(log_phi, log_norm) = matutils.ret_log_normalize_vec(phi)
phi = np.exp(log_phi)
# v
phi_all = phi * np.array(doc_word_counts)[:, np.newaxis]
v[0] = 1.0 + np.sum(phi_all[:, :self.m_K - 1], 0)
phi_cum = np.flipud(np.sum(phi_all[:, 1:], 0))
v[1] = self.m_alpha + np.flipud(np.cumsum(phi_cum))
Elogsticks_2nd = expect_log_sticks(v)
likelihood = 0.0
# compute likelihood
# var_phi part/ C in john's notation
likelihood += np.sum((Elogsticks_1st - log_var_phi) * var_phi)
# v part/ v in john's notation, john's beta is alpha here
log_alpha = np.log(self.m_alpha)
likelihood += (self.m_K - 1) * log_alpha
dig_sum = psi(np.sum(v, 0))
likelihood += np.sum((np.array([1.0, self.m_alpha])[:, np.newaxis] - v) * (psi(v) - dig_sum))
likelihood -= np.sum(gammaln(np.sum(v, 0))) - np.sum(gammaln(v))
# Z part
likelihood += np.sum((Elogsticks_2nd - log_phi) * phi)
# X part, the data part
likelihood += np.sum(phi.T * np.dot(var_phi, Elogbeta_doc * doc_word_counts))
converge = (likelihood - old_likelihood) / abs(old_likelihood)
old_likelihood = likelihood
if converge < -0.000001:
logger.warning('likelihood is decreasing!')
iter += 1
# update the suff_stat ss
# this time it only contains information from one doc
ss.m_var_sticks_ss += np.sum(var_phi, 0)
ss.m_var_beta_ss[:, chunkids] += np.dot(var_phi.T, phi.T * doc_word_counts)
return likelihood
def doc_e_step(self, ss, Elogsticks_1st, unique_words, doc_word_ids, doc_word_counts, var_converge):
"""Performs E step for a single doc.
Parameters
----------
ss : :class:`~gensim.models.hdpmodel.SuffStats`
Stats for all document(s) in the chunk.
Elogsticks_1st : numpy.ndarray
Computed Elogsticks value by stick-breaking process.
unique_words : dict of (int, int)
Number of unique words in the chunk.
doc_word_ids : iterable of int
Word ids of for a single document.
doc_word_counts : iterable of int
Word counts of all words in a single document.
var_converge : float
Lower bound on the right side of convergence. Used when updating variational parameters for a single
document.
Returns
-------
float
Computed value of likelihood for a single document.
"""
chunkids = [unique_words[id] for id in doc_word_ids]
Elogbeta_doc = self.m_Elogbeta[:, doc_word_ids]
# very similar to the hdp equations
v = np.zeros((2, self.m_K - 1))
v[0] = 1.0
v[1] = self.m_alpha
# back to the uniform
phi = np.ones((len(doc_word_ids), self.m_K)) * 1.0 / self.m_K
likelihood = 0.0
old_likelihood = -1e200
converge = 1.0
iter = 0
max_iter = 100
# not yet support second level optimization yet, to be done in the future
while iter < max_iter and (converge < 0.0 or converge > var_converge):
# update variational parameters
# var_phi
if iter < 3:
var_phi = np.dot(phi.T, (Elogbeta_doc * doc_word_counts).T)
(log_var_phi, log_norm) = matutils.ret_log_normalize_vec(var_phi)
var_phi = np.exp(log_var_phi)
else:
var_phi = np.dot(phi.T, (Elogbeta_doc * doc_word_counts).T) + Elogsticks_1st
(log_var_phi, log_norm) = matutils.ret_log_normalize_vec(var_phi)
var_phi = np.exp(log_var_phi)
# phi
if iter < 3:
phi = np.dot(var_phi, Elogbeta_doc).T
(log_phi, log_norm) = matutils.ret_log_normalize_vec(phi)
phi = np.exp(log_phi)
else:
phi = np.dot(var_phi, Elogbeta_doc).T + Elogsticks_2nd # noqa:F821
(log_phi, log_norm) = matutils.ret_log_normalize_vec(phi)
phi = np.exp(log_phi)
# v
phi_all = phi * np.array(doc_word_counts)[:, np.newaxis]
v[0] = 1.0 + np.sum(phi_all[:, :self.m_K - 1], 0)
phi_cum = np.flipud(np.sum(phi_all[:, 1:], 0))
v[1] = self.m_alpha + np.flipud(np.cumsum(phi_cum))
Elogsticks_2nd = expect_log_sticks(v)
likelihood = 0.0
# compute likelihood
# var_phi part/ C in john's notation
likelihood += np.sum((Elogsticks_1st - log_var_phi) * var_phi)
# v part/ v in john's notation, john's beta is alpha here
log_alpha = np.log(self.m_alpha)
likelihood += (self.m_K - 1) * log_alpha
dig_sum = psi(np.sum(v, 0))
likelihood += np.sum((np.array([1.0, self.m_alpha])[:, np.newaxis] - v) * (psi(v) - dig_sum))
likelihood -= np.sum(gammaln(np.sum(v, 0))) - np.sum(gammaln(v))
# Z part
likelihood += np.sum((Elogsticks_2nd - log_phi) * phi)
# X part, the data part
likelihood += np.sum(phi.T * np.dot(var_phi, Elogbeta_doc * doc_word_counts))
converge = (likelihood - old_likelihood) / abs(old_likelihood)
old_likelihood = likelihood
if converge < -0.000001:
logger.warning('likelihood is decreasing!')
iter += 1
# update the suff_stat ss
# this time it only contains information from one doc
ss.m_var_sticks_ss += np.sum(var_phi, 0)
ss.m_var_beta_ss[:, chunkids] += np.dot(var_phi.T, phi.T * doc_word_counts)
return likelihood
def doc_e_step(self, ss, Elogsticks_1st, unique_words, doc_word_ids, doc_word_counts, var_converge):
"""Performs E step for a single doc.
Parameters
----------
ss : :class:`~gensim.models.hdpmodel.SuffStats`
Stats for all document(s) in the chunk.
Elogsticks_1st : numpy.ndarray
Computed Elogsticks value by stick-breaking process.
unique_words : dict of (int, int)
Number of unique words in the chunk.
doc_word_ids : iterable of int
Word ids of for a single document.
doc_word_counts : iterable of int
Word counts of all words in a single document.
var_converge : float
Lower bound on the right side of convergence. Used when updating variational parameters for a single
document.
Returns
-------
float
Computed value of likelihood for a single document.
"""
chunkids = [unique_words[id] for id in doc_word_ids]
Elogbeta_doc = self.m_Elogbeta[:, doc_word_ids]
# very similar to the hdp equations
v = np.zeros((2, self.m_K - 1))
v[0] = 1.0
v[1] = self.m_alpha
# back to the uniform
phi = np.ones((len(doc_word_ids), self.m_K)) * 1.0 / self.m_K
likelihood = 0.0
old_likelihood = -1e200
converge = 1.0
iter = 0
max_iter = 100
# not yet support second level optimization yet, to be done in the future
while iter < max_iter and (converge < 0.0 or converge > var_converge):
# update variational parameters
# var_phi
if iter < 3:
var_phi = np.dot(phi.T, (Elogbeta_doc * doc_word_counts).T)
(log_var_phi, log_norm) = matutils.ret_log_normalize_vec(var_phi)
var_phi = np.exp(log_var_phi)
else:
var_phi = np.dot(phi.T, (Elogbeta_doc * doc_word_counts).T) + Elogsticks_1st
(log_var_phi, log_norm) = matutils.ret_log_normalize_vec(var_phi)
var_phi = np.exp(log_var_phi)
# phi
if iter < 3:
phi = np.dot(var_phi, Elogbeta_doc).T
(log_phi, log_norm) = matutils.ret_log_normalize_vec(phi)
phi = np.exp(log_phi)
else:
phi = np.dot(var_phi, Elogbeta_doc).T + Elogsticks_2nd # noqa:F821
(log_phi, log_norm) = matutils.ret_log_normalize_vec(phi)
phi = np.exp(log_phi)
# v
phi_all = phi * np.array(doc_word_counts)[:, np.newaxis]
v[0] = 1.0 + np.sum(phi_all[:, :self.m_K - 1], 0)
phi_cum = np.flipud(np.sum(phi_all[:, 1:], 0))
v[1] = self.m_alpha + np.flipud(np.cumsum(phi_cum))
Elogsticks_2nd = expect_log_sticks(v)
likelihood = 0.0
# compute likelihood
# var_phi part/ C in john's notation
likelihood += np.sum((Elogsticks_1st - log_var_phi) * var_phi)
# v part/ v in john's notation, john's beta is alpha here
log_alpha = np.log(self.m_alpha)
likelihood += (self.m_K - 1) * log_alpha
dig_sum = psi(np.sum(v, 0))
likelihood += np.sum((np.array([1.0, self.m_alpha])[:, np.newaxis] - v) * (psi(v) - dig_sum))
likelihood -= np.sum(gammaln(np.sum(v, 0))) - np.sum(gammaln(v))
# Z part
likelihood += np.sum((Elogsticks_2nd - log_phi) * phi)
# X part, the data part
likelihood += np.sum(phi.T * np.dot(var_phi, Elogbeta_doc * doc_word_counts))
converge = (likelihood - old_likelihood) / abs(old_likelihood)
old_likelihood = likelihood
if converge < -0.000001:
logger.warning('likelihood is decreasing!')
iter += 1
# update the suff_stat ss
# this time it only contains information from one doc
ss.m_var_sticks_ss += np.sum(var_phi, 0)
ss.m_var_beta_ss[:, chunkids] += np.dot(var_phi.T, phi.T * doc_word_counts)
return likelihood
