[precommit] fixes

bob/learn/em/__init__.py

 import bob.extension
 
+from .factor_analysis import ISVMachine, JFAMachine
 from .gmm import GMMMachine, GMMStats
 from .k_means import KMeansMachine
 from .linear_scoring import linear_scoring  # noqa: F401
 from .wccn import WCCN
 from .whitening import Whitening
-from .factor_analysis import ISVMachine, JFAMachine
 
 
 def get_config():

bob/learn/em/factor_analysis.py

@@ -6,7 +6,9 @@ import logging
 
 import numpy as np
 
+from h5py import File as HDF5File
 from sklearn.base import BaseEstimator
+
 from . import linear_scoring
 
 logger = logging.getLogger(__name__)

bob/learn/em/test/test_jfa.py

@@ -7,8 +7,8 @@
 # Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
 
 import numpy as np
+
 from bob.learn.em import GMMMachine, GMMStats, ISVMachine, JFAMachine
-import copy
 
 
 def test_JFAMachine():
@@ -97,4 +97,3 @@ def test_ISVMachine():
     score_ref = -3.280498193082100
 
     assert abs(score_ref - score) < eps
-    pass

bob/learn/em/test/test_jfa_trainer.py

@@ -6,9 +6,11 @@
 #
 # Copyright (C) 2011-2014 Idiap Research Institute, Martigny, Switzerland
 
+import copy
+
 import numpy as np
+
 from bob.learn.em import GMMMachine, GMMStats, ISVMachine, JFAMachine
-import copy
 
 # Define Training set and initial values for tests
 F1 = np.array(
@@ -120,12 +122,11 @@ def test_JFATrainAndEnrol():
     ubm.means = UBM_MEAN.reshape((2, 3))
     ubm.variances = UBM_VAR.reshape((2, 3))
     it = JFAMachine(ubm, 2, 2, em_iterations=10)
-    # n_acc, f_acc = it.initialize(TRAINING_STATS_X, TRAINING_STATS_y)
+
     it.U = copy.deepcopy(M_u)
     it.V = copy.deepcopy(M_v)
     it.D = copy.deepcopy(M_d)
     it.fit(TRAINING_STATS_X, TRAINING_STATS_y)
-    # bob.learn.em.train_jfa(t, mb, TRAINING_STATS, initialize=False)
 
     v_ref = np.array(
         [
@@ -211,28 +212,6 @@ def test_JFATrainAndEnrol():
     assert np.allclose(latent_y, y_ref, eps)
     assert np.allclose(latent_z, z_ref, eps)
 
-    # Testing exceptions
-    """
-    nose.tools.assert_raises(RuntimeError, t.initialize, mb, [1, 2, 2])
-    nose.tools.assert_raises(RuntimeError, t.initialize, mb, [[1, 2, 2]])
-    nose.tools.assert_raises(RuntimeError, t.e_step_u, mb, [1, 2, 2])
-    nose.tools.assert_raises(RuntimeError, t.e_step_u, mb, [[1, 2, 2]])
-    nose.tools.assert_raises(RuntimeError, t.m_step_u, mb, [1, 2, 2])
-    nose.tools.assert_raises(RuntimeError, t.m_step_u, mb, [[1, 2, 2]])
-
-    nose.tools.assert_raises(RuntimeError, t.e_step_v, mb, [1, 2, 2])
-    nose.tools.assert_raises(RuntimeError, t.e_step_v, mb, [[1, 2, 2]])
-    nose.tools.assert_raises(RuntimeError, t.m_step_v, mb, [1, 2, 2])
-    nose.tools.assert_raises(RuntimeError, t.m_step_v, mb, [[1, 2, 2]])
-
-    nose.tools.assert_raises(RuntimeError, t.e_step_d, mb, [1, 2, 2])
-    nose.tools.assert_raises(RuntimeError, t.e_step_d, mb, [[1, 2, 2]])
-    nose.tools.assert_raises(RuntimeError, t.m_step_d, mb, [1, 2, 2])
-    nose.tools.assert_raises(RuntimeError, t.m_step_d, mb, [[1, 2, 2]])
-
-    nose.tools.assert_raises(RuntimeError, t.enroll, m, [[1, 2, 2]], 5)
-    """
-
 
 def test_ISVTrainAndEnrol():
     # Train and enroll an 'ISVMachine'
@@ -325,13 +304,6 @@ def test_ISVTrainAndEnrol():
     latent_z = it.enroll(gse, 5)
     assert np.allclose(latent_z, z_ref, eps)
 
-    # Testing exceptions
-    # nose.tools.assert_raises(RuntimeError, t.initialize, mb, [1, 2, 2])
-    # nose.tools.assert_raises(RuntimeError, t.initialize, mb, [[1, 2, 2]])
-    # nose.tools.assert_raises(RuntimeError, t.e_step, mb, [1, 2, 2])
-    # nose.tools.assert_raises(RuntimeError, t.e_step, mb, [[1, 2, 2]])
-    # nose.tools.assert_raises(RuntimeError, t.enroll, m, [[1, 2, 2]], 5)
-
 
 def test_JFATrainInitialize():
     # Check that the initialization is consistent and using the rng (cf. issue #118)

doc/plot/plot_ISV.py

+import matplotlib.pyplot as plt
+import numpy as np
+
 from sklearn.datasets import load_iris
 
 import bob.learn.em
-import matplotlib.pyplot as plt
-import numpy as np
 
 np.random.seed(2)  # FIXING A SEED
 

doc/plot/plot_JFA.py

+import matplotlib.pyplot as plt
+import numpy as np
+
 from sklearn.datasets import load_iris
 
 import bob.learn.em
-import matplotlib.pyplot as plt
-import numpy as np
 
 np.random.seed(2)  # FIXING A SEED