Made EM and JFA training less verbose

bob/learn/em/train.py

@@ -58,7 +58,7 @@ def train(trainer, machine, data, max_iterations=50, convergence_threshold=None,
         average_output = trainer.compute_likelihood(machine)
 
     for i in range(max_iterations):
-        logger.info("Iteration = %d/%d", i, max_iterations)
+        logger.debug("Iteration = %d/%d", i+1, max_iterations)
         average_output_previous = average_output
         trainer.m_step(machine, data)
         trainer.e_step(machine, data)
@@ -67,15 +67,16 @@ def train(trainer, machine, data, max_iterations=50, convergence_threshold=None,
             average_output = trainer.compute_likelihood(machine)
 
             if type(machine) is bob.learn.em.KMeansMachine:
-                logger.info("average euclidean distance = %f", average_output)
+                logger.debug("average euclidean distance = %f", average_output)
             else:
-                logger.info("log likelihood = %f", average_output)
+                logger.debug("log likelihood = %f", average_output)
 
             convergence_value = abs((average_output_previous - average_output) / average_output_previous)
-            logger.info("convergence value = %f", convergence_value)
+            logger.debug("convergence value = %f", convergence_value)
 
             # Terminates if converged (and likelihood computation is set)
             if convergence_threshold != None and convergence_value <= convergence_threshold:
+                logger.info("EM training converged after %d iterations with convergence value %f", convergence_value)
                 break
     if hasattr(trainer, "finalize"):
         trainer.finalize(machine, data)
@@ -109,7 +110,7 @@ def train_jfa(trainer, jfa_base, data, max_iterations=10, initialize=True, rng=N
     # V Subspace
     logger.info("V subspace estimation...")
     for i in range(max_iterations):
-        logger.info("Iteration = %d/%d", i, max_iterations)
+        logger.debug("Iteration = %d/%d", i+1, max_iterations)
         trainer.e_step_v(jfa_base, data)
         trainer.m_step_v(jfa_base, data)
     trainer.finalize_v(jfa_base, data)
@@ -117,7 +118,7 @@ def train_jfa(trainer, jfa_base, data, max_iterations=10, initialize=True, rng=N
     # U subspace
     logger.info("U subspace estimation...")
     for i in range(max_iterations):
-        logger.info("Iteration = %d/%d", i, max_iterations)
+        logger.debug("Iteration = %d/%d", i+1, max_iterations)
         trainer.e_step_u(jfa_base, data)
         trainer.m_step_u(jfa_base, data)
     trainer.finalize_u(jfa_base, data)
@@ -125,7 +126,7 @@ def train_jfa(trainer, jfa_base, data, max_iterations=10, initialize=True, rng=N
     # D subspace
     logger.info("D subspace estimation...")
     for i in range(max_iterations):
-        logger.info("Iteration = %d/%d", i, max_iterations)
+        logger.debug("Iteration = %d/%d", i+1, max_iterations)
         trainer.e_step_d(jfa_base, data)
         trainer.m_step_d(jfa_base, data)
     trainer.finalize_d(jfa_base, data)