improving the logging info... and correcting division per zero error

bob/learn/em/train.py

@@ -10,6 +10,7 @@ import logging
 logger = logging.getLogger('bob.learn.em')
 
 def train(trainer, machine, data, max_iterations = 50, convergence_threshold=None, initialize=True, rng=None):
+
   """
   Trains a machine given a trainer and the proper data
 
@@ -40,26 +41,24 @@ def train(trainer, machine, data, max_iterations = 50, convergence_threshold=Non
   average_output          = 0
   average_output_previous = 0
 
-  if convergence_threshold!=None and hasattr(trainer,"compute_likelihood"):
+  if hasattr(trainer,"compute_likelihood"):
     average_output          = trainer.compute_likelihood(machine)
 
   for i in range(max_iterations):
-    
+    logger.info("Iteration = %d/%d", i, max_iterations)
     average_output_previous = average_output
     trainer.m_step(machine, data)
     trainer.e_step(machine, data)
-
-    if convergence_threshold!=None and hasattr(trainer,"compute_likelihood"):
+    
+    if hasattr(trainer,"compute_likelihood"):
       average_output = trainer.compute_likelihood(machine)
-    else:
-      logger.info("Iteration = %d ", i)
-    #Terminates if converged (and likelihood computation is set)
-    if convergence_threshold!=None: 
+      logger.info("log likelihood = %f", average_output)
       convergence_value = abs((average_output_previous - average_output)/average_output_previous)
-      logger.info("Iteration = %d \t convergence value = %f ", i, convergence_value)
-      if convergence_value <= convergence_threshold:
+      logger.info("convergence value = %f",convergence_value)
+    
+      #Terminates if converged (and likelihood computation is set)
+      if convergence_threshold!=None and convergence_value <= convergence_threshold:
         break
-
   if hasattr(trainer,"finalize"):
     trainer.finalize(machine, data)
 
@@ -90,19 +89,25 @@ def train_jfa(trainer, jfa_base, data, max_iterations=10, initialize=True, rng=N
       trainer.initialize(jfa_base, data)
 
   #V Subspace
+  logger.info("V subspace estimation...")
   for i in range(max_iterations):
+    logger.info("Iteration = %d/%d", i, max_iterations)
     trainer.e_step_v(jfa_base, data)
     trainer.m_step_v(jfa_base, data)
   trainer.finalize_v(jfa_base, data)
 
   #U subspace
+  logger.info("U subspace estimation...")  
   for i in range(max_iterations):
+    logger.info("Iteration = %d/%d", i, max_iterations)
     trainer.e_step_u(jfa_base, data)
     trainer.m_step_u(jfa_base, data)
   trainer.finalize_u(jfa_base, data)
 
-  # d subspace
+  # D subspace
+  logger.info("D subspace estimation...")  
   for i in range(max_iterations):
+    logger.info("Iteration = %d/%d", i, max_iterations)
     trainer.e_step_d(jfa_base, data)
     trainer.m_step_d(jfa_base, data)
   trainer.finalize_d(jfa_base, data)