improve variance estimation speed in kmeans, convert data to proper arrays

bob/learn/em/gmm.py

@@ -672,6 +672,9 @@ class GMMMachine(BaseEstimator):
             )
             kmeans_machine = kmeans_machine.fit(data)
 
+            logger.debug(
+                "Estimating the variance and weights of each gaussian from kmeans."
+            )
             (
                 variances,
                 weights,
@@ -680,6 +683,7 @@ class GMMMachine(BaseEstimator):
             # Set the GMM machine's gaussians with the results of k-means
             self.means = copy.deepcopy(kmeans_machine.centroids_)
             self.variances, self.weights = dask.compute(variances, weights)
+            logger.debug("Done.")
 
     def log_weighted_likelihood(
         self,
@@ -833,7 +837,7 @@ class GMMMachine(BaseEstimator):
     def fit(self, X, y=None):
         """Trains the GMM on data until convergence or maximum step is reached."""
 
-        input_is_dask = check_and_persist_dask_input(X)
+        input_is_dask, X = check_and_persist_dask_input(X)
 
         if self._means is None:
             self.initialize_gaussians(X)
@@ -912,7 +916,9 @@ class GMMMachine(BaseEstimator):
                     (average_output_previous - average_output)
                     / average_output_previous
                 )
-                logger.debug(f"convergence val = {convergence_value}")
+                logger.debug(
+                    f"convergence val = {convergence_value} and threshold = {self.convergence_threshold}"
+                )
 
                 # Terminates if converged (and likelihood computation is set)
                 if (