Clean GMM test, add write_model and multiple_probe

bob/bio/gmm/bioalgorithm/GMM.py

@@ -268,7 +268,11 @@ class GMM(BioAlgorithm, BaseEstimator):
     #                Feature comparison                  #
     def read_model(self, model_file):
         """Reads the model, which is a GMM machine"""
-        return GMMMachine.from_hdf5(bob.io.base.HDF5File(model_file))
+        return GMMMachine.from_hdf5(bob.io.base.HDF5File(model_file), ubm=self.ubm)
+
+    def write_model(self, model, model_file):
+        """Write the features (GMM_Stats)"""
+        return model.save(model_file)
 
     def score(self, biometric_reference: GMMMachine, data: GMMStats):
         """Computes the score for the given model and the given probe.
@@ -283,13 +287,13 @@ class GMM(BioAlgorithm, BaseEstimator):
             The probe data to compare to the model.
         """
 
-        assert isinstance(biometric_reference, GMMMachine)  # TODO is it a list?
+        assert isinstance(biometric_reference, GMMMachine)
         assert isinstance(data, GMMStats)
         return self.scoring_function(
             models_means=[biometric_reference],
             ubm=self.ubm,
             test_stats=data,
-            frame_length_normalisation=True,
+            frame_length_normalization=True,
         )[0, 0]
 
     def score_multiple_biometric_references(
@@ -307,26 +311,27 @@ class GMM(BioAlgorithm, BaseEstimator):
             The probe data to compare to the models.
         """
 
-        assert isinstance(biometric_references, GMMMachine)  # TODO is it a list?
+        assert isinstance(biometric_references, GMMMachine)
         assert isinstance(data, GMMStats)
         return self.scoring_function(
             models_means=biometric_references,
             ubm=self.ubm,
             test_stats=data,
-            frame_length_normalisation=True,
+            frame_length_normalization=True,
         )
 
-    # def score_for_multiple_probes(self, model, probes):
-    #     """This function computes the score between the given model and several given probe files."""
-    #     assert isinstance(model, GMMMachine)
-    #     for probe in probes:
-    #         assert isinstance(probe, GMMStats)
-    #     #    logger.warn("Please verify that this function is correct")
-    #     return self.probe_fusion_function(
-    #         self.scoring_function(
-    #             model.means, self.ubm, probes, [], frame_length_normalisation=True
-    #         )
-    #     )
+    def score_for_multiple_probes(self, model, probes):
+        """This function computes the score between the given model and several given probe files."""
+        assert isinstance(model, GMMMachine)
+        for probe in probes:
+            assert isinstance(probe, GMMStats)
+        #    logger.warn("Please verify that this function is correct")
+        return self.scoring_function(
+            models_means=model.means,
+            ubm=self.ubm,
+            test_stats=probes,
+            frame_length_normalization=True,
+        ).mean()
 
     def fit(self, X, y=None, **kwargs):
         """Trains the UBM."""

bob/bio/gmm/test/test_algorithms.py

@@ -24,7 +24,6 @@ import sys
 
 import numpy
 import pkg_resources
-import pytest
 
 import bob.bio.gmm
 import bob.io.base
@@ -35,7 +34,7 @@ from bob.bio.base.test import utils
 
 logger = logging.getLogger(__name__)
 
-regenerate_refs = True
+regenerate_refs = False
 
 seed_value = 5489
 
@@ -73,11 +72,8 @@ def _compare_complex(
             assert numpy.allclose(d, r, atol=1e-5)
 
 
-@pytest.mark.isolated_gmm
 def test_gmm():
-    temp_file = (
-        "./temptest/test_file"  # TODO bob.io.base.test_utils.temporary_filename()
-    )
+    temp_file = bob.io.base.test_utils.temporary_filename()
     gmm1 = bob.bio.base.load_resource(
         "gmm", "bioalgorithm", preferred_package="bob.bio.gmm"
     )
@@ -85,11 +81,9 @@ def test_gmm():
     assert isinstance(
         gmm1, bob.bio.base.pipelines.vanilla_biometrics.abstract_classes.BioAlgorithm
     )
-    # assert gmm1.performs_projection
-    # assert gmm1.requires_projector_training
-    # assert not gmm1.use_projected_features_for_enrollment
-    # assert not gmm1.split_training_features_by_client
-    # assert not gmm1.requires_enroller_training
+
+    # Fix the number of gaussians for tests
+    gmm1.number_of_gaussians = 2
 
     # create smaller GMM object
     gmm2 = bob.bio.gmm.bioalgorithm.GMM(
@@ -149,7 +143,7 @@ def test_gmm():
     probe = gmm1.read_feature(
         pkg_resources.resource_filename("bob.bio.gmm.test", "data/gmm_projected.hdf5")
     )
-    reference_score = -0.01676570
+    reference_score = -0.01992773
     assert (
         abs(gmm1.score(model, probe) - reference_score) < 1e-5
     ), "The scores differ: %3.8f, %3.8f" % (gmm1.score(model, probe), reference_score)