ADD: some logs

Author: JoeZJH

example/example.py

@@ -51,6 +51,6 @@
 
 # load from disk
 llda_model_new = llda.LldaModel()
-llda_model_new.load_model_from_dir(save_model_dir)
+llda_model_new.load_model_from_dir(save_model_dir, load_derivative_properties=True)
 print "llda_model_new", llda_model_new
 print "llda_model", llda_model

model/labeled_lda.py

@@ -258,6 +258,11 @@ def _gibbs_sample_training(self):
 
  p_vector = beta_vector * theta_vector
  # print p_vector
+ """
+ for some special document m (only have one word) p_vector may be zero here, sum(p_vector) will be zero too
+ 1.0 * p_vector / sum(p_vector) will be [...nan...]
+ so we should avoid inputting the special document 
+ """
  p_vector = 1.0 * p_vector / sum(p_vector)
  # print p_vector
  sample_z = LldaModel._multinomial_sample(p_vector)
@@ -513,6 +518,8 @@ def theta_m(self, m):
  @property
  def beta(self):
  """
+ This name "beta" comes from
+ "Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora, Daniel Ramage..."
  topic-term distribution
  beta[k, t] is the probability of term t(word) to be generated from topic k
  :return: a matrix, shape is K * T
@@ -542,25 +549,27 @@ def theta(self):
  def log_perplexity(self):
  """
  log perplexity of LDA topic model
+ Reference: Parameter estimation for text analysis, Gregor Heinrich.
  :return: a float value
  """
  beta = self.beta
  # theta = self.theta
  log_likelihood = 0
- word_count = 0
+ # word_count = 0
  for m, theta_m in enumerate(self.theta):
  for t in self.W[m]:
  likelihood_t = np.inner(beta[:, t], theta_m)
  # print likelihood_t
  log_likelihood += -np.log(likelihood_t)
- word_count += 1
- assert word_count == self.WN, "word_count: %s\tself.WN: %s" % (word_count, self.WN)
+ # word_count += 1
+ # assert word_count == self.WN, "word_count: %s\tself.WN: %s" % (word_count, self.WN)
  return 1.0 * log_likelihood / self.WN
 
  @property
  def perplexity(self):
  """
  perplexity of LDA topic model
+ Reference: Parameter estimation for text analysis, Gregor Heinrich.
  :return: a float value, perplexity = exp{log_perplexity}
  """
  return np.exp(self.log_perplexity)