Fix the scoring functions and enroll parameters

bob/bio/gmm/algorithm/GMM.py

@@ -23,7 +23,7 @@ import numpy as np
 from h5py import File as HDF5File
 from sklearn.base import BaseEstimator
 
-from bob.bio.base.pipelines.vanilla_biometrics.abstract_classes import BioAlgorithm
+from bob.bio.base.pipelines.vanilla_biometrics import BioAlgorithm
 from bob.learn.em import GMMMachine
 from bob.learn.em import GMMStats
 from bob.learn.em import KMeansMachine
@@ -213,8 +213,8 @@ class GMM(BioAlgorithm, BaseEstimator):
                 update_variances=self.enroll_update_variances,
                 update_weights=self.enroll_update_weights,
                 mean_var_update_threshold=self.variance_threshold,
-                relevance_factor=self.enroll_relevance_factor,
-                alpha=self.enroll_alpha,
+                map_relevance_factor=self.enroll_relevance_factor,
+                map_alpha=self.enroll_alpha,
             )
             gmm.fit(array)
         return gmm
@@ -244,7 +244,6 @@ class GMM(BioAlgorithm, BaseEstimator):
             The probe data to compare to the model.
         """
 
-        logger.debug(f"scoring {biometric_reference}, {probe}")
         if not isinstance(probe, GMMStats):
             # Projection is done here instead of in transform (or it would be applied to enrollment data too...)
             probe = self.project(probe)
@@ -253,7 +252,7 @@ class GMM(BioAlgorithm, BaseEstimator):
             ubm=self.ubm,
             test_stats=probe,
             frame_length_normalization=True,
-        )[0, 0]
+        )[0]
 
     def score_multiple_biometric_references(
         self, biometric_references: "list[GMMMachine]", probe: GMMStats
@@ -270,32 +269,13 @@ class GMM(BioAlgorithm, BaseEstimator):
             The probe data to compare to the models.
         """
 
-        logger.debug(f"scoring {biometric_references}, {probe}")
-        assert isinstance(biometric_references[0], GMMMachine), type(
-            biometric_references[0]
-        )
         stats = self.project(probe) if not isinstance(probe, GMMStats) else probe
         return self.scoring_function(
             models_means=biometric_references,
             ubm=self.ubm,
             test_stats=stats,
             frame_length_normalization=True,
-        ).reshape((-1,))
-
-    def score_for_multiple_probes(self, biometric_reference, probes):
-        """This function computes the score between the given model and several given probe files."""
-        logger.debug(f"scoring {biometric_reference}, {probes}")
-        assert isinstance(biometric_reference, GMMMachine)
-        stats = [
-            self.project(probe) if not isinstance(probe, GMMStats) else probe
-            for probe in probes
-        ]
-        return self.scoring_function(
-            models_means=biometric_reference.means,
-            ubm=self.ubm,
-            test_stats=stats,
-            frame_length_normalization=True,
-        ).reshape((-1,))
+        )
 
     def fit(self, X, y=None, **kwargs):
         """Trains the UBM."""

bob/bio/gmm/test/test_gmm.py

@@ -160,16 +160,10 @@ def test_score():
         gmm1.score(biometric_reference, probe), reference_score, decimal=5
     )
 
-    multi_probes = gmm1.score_for_multiple_probes(
-        biometric_reference, [probe, probe, probe]
-    )
-    assert multi_probes.shape == (3,), multi_probes.shape
-    numpy.testing.assert_almost_equal(multi_probes, reference_score, decimal=5)
-
     multi_refs = gmm1.score_multiple_biometric_references(
         [biometric_reference, biometric_reference, biometric_reference], probe
     )
-    assert multi_refs.shape == (3,), multi_refs.shape
+    assert multi_refs.shape == (3, 1), multi_refs.shape
     numpy.testing.assert_almost_equal(multi_refs, reference_score, decimal=5)
 
     # With not projected data