[gmm] small fixes to make sure the gmm algorithm runs

Fixes #29 (closed)

bob/bio/gmm/algorithm/GMM.py

@@ -16,7 +16,6 @@ import logging
 from typing import Callable
 from typing import Union
 
-import dask
 import dask.array as da
 import numpy as np
 
@@ -71,6 +70,7 @@ class GMM(BioAlgorithm, BaseEstimator):
         scoring_function: Callable = linear_scoring,
         # RNG
         init_seed: int = 5489,
+        **kwargs,
     ):
         """Initializes the local UBM-GMM tool chain.
 
@@ -144,7 +144,7 @@ class GMM(BioAlgorithm, BaseEstimator):
 
         self.ubm = None
 
-        super().__init__()
+        super().__init__(**kwargs)
 
     def _check_feature(self, feature):
         """Checks that the features are appropriate"""
@@ -196,27 +196,28 @@ class GMM(BioAlgorithm, BaseEstimator):
         Returns a GMMMachine tuned from the UBM with MAP on a biometric reference data.
         """
 
-        [self._check_feature(feature) for feature in data]
-        array = da.vstack(data)
+        for feature in data:
+            self._check_feature(feature)
+
+        data = np.vstack(data)
         # Use the array to train a GMM and return it
-        logger.info("Enrolling with %d feature vectors", array.shape[0])
+        logger.info("Enrolling with %d feature vectors", data.shape[0])
 
-        with dask.config.set(scheduler="threads"):
-            gmm = GMMMachine(
-                n_gaussians=self.number_of_gaussians,
-                trainer="map",
-                ubm=copy.deepcopy(self.ubm),
-                convergence_threshold=self.training_threshold,
-                max_fitting_steps=self.gmm_enroll_iterations,
-                random_state=self.rng,
-                update_means=self.enroll_update_means,
-                update_variances=self.enroll_update_variances,
-                update_weights=self.enroll_update_weights,
-                mean_var_update_threshold=self.variance_threshold,
-                map_relevance_factor=self.enroll_relevance_factor,
-                map_alpha=self.enroll_alpha,
-            )
-            gmm.fit(array)
+        gmm = GMMMachine(
+            n_gaussians=self.number_of_gaussians,
+            trainer="map",
+            ubm=copy.deepcopy(self.ubm),
+            convergence_threshold=self.training_threshold,
+            max_fitting_steps=self.gmm_enroll_iterations,
+            random_state=self.rng,
+            update_means=self.enroll_update_means,
+            update_variances=self.enroll_update_variances,
+            update_weights=self.enroll_update_weights,
+            mean_var_update_threshold=self.variance_threshold,
+            map_relevance_factor=self.enroll_relevance_factor,
+            map_alpha=self.enroll_alpha,
+        )
+        gmm.fit(data)
         return gmm
 
     def read_biometric_reference(self, model_file):
@@ -277,10 +278,11 @@ class GMM(BioAlgorithm, BaseEstimator):
             frame_length_normalization=True,
         )
 
-    def fit(self, X, y=None, **kwargs):
+    def fit(self, array, y=None, **kwargs):
         """Trains the UBM."""
         # Stack all the samples in a 2D array of features
-        array = da.vstack(X).persist()
+        if isinstance(array, da.Array):
+            array = array.persist()
 
         logger.debug("UBM with %d feature vectors", array.shape[0])
 
@@ -309,7 +311,7 @@ class GMM(BioAlgorithm, BaseEstimator):
         # Train the GMM
         logger.info("Training UBM GMM")
 
-        self.ubm.fit(array, ubm_train=True)
+        self.ubm.fit(array)
 
         return self