diff --git a/bob/learn/em/factor_analysis.py b/bob/learn/em/factor_analysis.py
index 8a76d29d084e68abc9d3cc1dbe4f9bcb5eaecbb0..18cdaa0b669fe0179afb8aac80eb9ec1703a2efd 100644
--- a/bob/learn/em/factor_analysis.py
+++ b/bob/learn/em/factor_analysis.py
@@ -89,9 +89,12 @@ class FactorAnalysisBase(BaseEstimator):
em_iterations=10,
seed=0,
ubm=None,
- **gmm_kwargs,
+ gmm_kwargs=None,
+ **kwargs,
):
+ super().__init__(**kwargs)
self.ubm = ubm
+ self.gmm_kwargs = gmm_kwargs
self.em_iterations = em_iterations
self.seed = seed
@@ -1146,7 +1149,7 @@ class FactorAnalysisBase(BaseEstimator):
"""
- return self.score(model, self.ubm.acc_statistics(data))
+ return self.score(model, self.ubm.transform(data))
class ISVMachine(FactorAnalysisBase):
@@ -1338,7 +1341,7 @@ class ISVMachine(FactorAnalysisBase):
z
"""
- return self.enroll([self.ubm.acc_statistics(X)], iterations)
+ return self.enroll([self.ubm.transform(X)], iterations)
def score(self, latent_z, data):
"""
@@ -1764,7 +1767,7 @@ class JFAMachine(FactorAnalysisBase):
z
"""
- return self.enroll([self.ubm.acc_statistics(X)], iterations)
+ return self.enroll([self.ubm.transform(X)], iterations)
def fit(self, X, y):
"""
diff --git a/bob/learn/em/test/test_jfa.py b/bob/learn/em/test/test_jfa.py
index de4f76115a8611bce13955d719c082a35eb4cc28..8e20b4a4f965cbb31f59caa01c36e876c76752f0 100644
--- a/bob/learn/em/test/test_jfa.py
+++ b/bob/learn/em/test/test_jfa.py
@@ -16,35 +16,26 @@ def test_JFAMachine():
eps = 1e-10
# Creates a UBM
- weights = np.array([0.4, 0.6], "float64")
- means = np.array([[1, 6, 2], [4, 3, 2]], "float64")
- variances = np.array([[1, 2, 1], [2, 1, 2]], "float64")
ubm = GMMMachine(2, 3)
- ubm.weights = weights
- ubm.means = means
- ubm.variances = variances
+ ubm.weights = np.array([0.4, 0.6], "float64")
+ ubm.means = np.array([[1, 6, 2], [4, 3, 2]], "float64")
+ ubm.variances = np.array([[1, 2, 1], [2, 1, 2]], "float64")
# Defines GMMStats
gs = GMMStats(2, 3)
- log_likelihood = -3.0
- T = 1
- n = np.array([0.4, 0.6], "float64")
- sumpx = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], "float64")
- sumpxx = np.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], "float64")
- gs.log_likelihood = log_likelihood
- gs.t = T
- gs.n = n
- gs.sum_px = sumpx
- gs.sum_pxx = sumpxx
+ gs.log_likelihood = -3.0
+ gs.t = 1
+ gs.n = np.array([0.4, 0.6], "float64")
+ gs.sum_px = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], "float64")
+ gs.sum_pxx = np.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], "float64")
# Creates a JFAMachine
- U = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], "float64")
- V = np.array([[6, 5], [4, 3], [2, 1], [1, 2], [3, 4], [5, 6]], "float64")
- d = np.array([0, 1, 0, 1, 0, 1], "float64")
m = JFAMachine(ubm, 2, 2, em_iterations=10)
- m.U = U
- m.V = V
- m.D = d
+ m.U = np.array(
+ [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], "float64"
+ )
+ m.V = np.array([[6, 5], [4, 3], [2, 1], [1, 2], [3, 4], [5, 6]], "float64")
+ m.D = np.array([0, 1, 0, 1, 0, 1], "float64")
# Preparing the model
y = np.array([1, 2], "float64")
@@ -53,15 +44,14 @@ def test_JFAMachine():
score_ref = -2.111577181208289
score = m.score(model, gs)
- assert abs(score_ref - score) < eps
+ np.testing.assert_allclose(score, score_ref, atol=eps)
# Scoring with numpy array
np.random.seed(0)
X = np.random.normal(loc=0.0, scale=1.0, size=(50, 3))
score_ref = 2.028009315286946
score = m.score_with_array(model, X)
-
- assert abs(score_ref - score) < eps
+ np.testing.assert_allclose(score, score_ref, atol=eps)
def test_ISVMachine():
@@ -69,48 +59,36 @@ def test_ISVMachine():
eps = 1e-10
# Creates a UBM
- weights = np.array([0.4, 0.6], "float64")
- means = np.array([[1, 6, 2], [4, 3, 2]], "float64")
- variances = np.array([[1, 2, 1], [2, 1, 2]], "float64")
ubm = GMMMachine(2, 3)
- ubm.weights = weights
- ubm.means = means
- ubm.variances = variances
+ ubm.weights = np.array([0.4, 0.6], "float64")
+ ubm.means = np.array([[1, 6, 2], [4, 3, 2]], "float64")
+ ubm.variances = np.array([[1, 2, 1], [2, 1, 2]], "float64")
# Creates a ISVMachine
- U = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], "float64")
- # V = numpy.array([[0], [0], [0], [0], [0], [0]], 'float64')
- d = np.array([0, 1, 0, 1, 0, 1], "float64")
isv_machine = ISVMachine(ubm, r_U=2, em_iterations=10)
- isv_machine.U = U
- # base.v = V
- isv_machine.D = d
+ isv_machine.U = np.array(
+ [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], "float64"
+ )
+ # base.v = numpy.array([[0], [0], [0], [0], [0], [0]], 'float64')
+ isv_machine.D = np.array([0, 1, 0, 1, 0, 1], "float64")
# Defines GMMStats
gs = GMMStats(2, 3)
- log_likelihood = -3.0
- T = 1
- n = np.array([0.4, 0.6], "float64")
- sumpx = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], "float64")
- sumpxx = np.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], "float64")
- gs.log_likelihood = log_likelihood
- gs.t = T
- gs.n = n
- gs.sum_px = sumpx
- gs.sum_pxx = sumpxx
+ gs.log_likelihood = -3.0
+ gs.t = 1
+ gs.n = np.array([0.4, 0.6], "float64")
+ gs.sum_px = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], "float64")
+ gs.sum_pxx = np.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], "float64")
# Enrolled model
latent_z = np.array([3, 4, 1, 2, 0, 1], "float64")
score = isv_machine.score(latent_z, gs)
score_ref = -3.280498193082100
-
- assert abs(score_ref - score) < eps
+ np.testing.assert_allclose(score, score_ref, atol=eps)
# Scoring with numpy array
np.random.seed(0)
X = np.random.normal(loc=0.0, scale=1.0, size=(50, 3))
score_ref = -1.2343813195374242
-
score = isv_machine.score_with_array(latent_z, X)
-
- assert abs(score_ref - score) < eps
+ np.testing.assert_allclose(score, score_ref, atol=eps)
diff --git a/bob/learn/em/test/test_jfa_trainer.py b/bob/learn/em/test/test_jfa_trainer.py
index f2b08d1d9ba055fb918298c783059b3e5c43c72f..cc3d6ad1b57c22fe847f3f26e2674831f49b5c0b 100644
--- a/bob/learn/em/test/test_jfa_trainer.py
+++ b/bob/learn/em/test/test_jfa_trainer.py
@@ -163,9 +163,9 @@ def test_JFATrainAndEnrol():
)
eps = 1e-10
- assert np.allclose(it.V, v_ref, eps)
- assert np.allclose(it.U, u_ref, eps)
- assert np.allclose(it.D, d_ref, eps)
+ np.testing.assert_allclose(it.V, v_ref, rtol=eps, atol=1e-8)
+ np.testing.assert_allclose(it.U, u_ref, rtol=eps, atol=1e-8)
+ np.testing.assert_allclose(it.D, d_ref, rtol=eps, atol=1e-8)
# Calls the enroll function
@@ -209,8 +209,8 @@ def test_JFATrainAndEnrol():
"float64",
)
- assert np.allclose(latent_y, y_ref, eps)
- assert np.allclose(latent_z, z_ref, eps)
+ np.testing.assert_allclose(latent_y, y_ref, rtol=eps, atol=1e-8)
+ np.testing.assert_allclose(latent_z, z_ref, rtol=eps, atol=1e-8)
def test_JFATrainAndEnrolWithNumpy():
@@ -262,9 +262,9 @@ def test_JFATrainAndEnrolWithNumpy():
)
eps = 1e-10
- assert np.allclose(it.V, v_ref, eps)
- assert np.allclose(it.U, u_ref, eps)
- assert np.allclose(it.D, d_ref, eps)
+ np.testing.assert_allclose(it.V, v_ref, rtol=eps, atol=1e-8)
+ np.testing.assert_allclose(it.U, u_ref, rtol=eps, atol=1e-8)
+ np.testing.assert_allclose(it.D, d_ref, rtol=eps, atol=1e-8)
"""
Calls the enroll function with arrays as input
@@ -275,20 +275,18 @@ def test_JFATrainAndEnrolWithNumpy():
latent_y_ref = np.array([-0.13922039, 0.10686916])
latent_z_ref = np.array(
[
- [
- -1.37073043e-17,
- 1.15641870e-12,
- -8.29922598e-10,
- -4.17108194e-16,
- -2.27107305e-09,
- 2.94293314e-18,
- ]
+ -1.37073043e-17,
+ 1.15641870e-12,
+ -8.29922598e-10,
+ -4.17108194e-16,
+ -2.27107305e-09,
+ 2.94293314e-18,
]
)
latent_y, latent_z = it.enroll_with_array(X)
- assert np.allclose(latent_z, latent_z_ref, eps)
- assert np.allclose(latent_y, latent_y_ref, eps)
+ np.testing.assert_allclose(latent_z, latent_z_ref, rtol=eps, atol=1e-8)
+ np.testing.assert_allclose(latent_y, latent_y_ref, rtol=eps, atol=1e-8)
def test_ISVTrainAndEnrol():
@@ -319,12 +317,14 @@ def test_ISVTrainAndEnrol():
)
z_ref = np.array(
[
- -0.079315777443826,
- 0.092702428248543,
- -0.342488761656616,
- -0.059922635809136,
- 0.133539981073604,
- 0.213118695516570,
+ [
+ -0.079315777443826,
+ 0.092702428248543,
+ -0.342488761656616,
+ -0.059922635809136,
+ 0.133539981073604,
+ 0.213118695516570,
+ ]
],
"float64",
)
@@ -346,8 +346,8 @@ def test_ISVTrainAndEnrol():
it.U = copy.deepcopy(M_u)
it = it.fit(TRAINING_STATS_X, TRAINING_STATS_y)
- assert np.allclose(it.D, d_ref, eps)
- assert np.allclose(it.U, u_ref, eps)
+ np.testing.assert_allclose(it.D, d_ref, rtol=eps, atol=1e-8)
+ np.testing.assert_allclose(it.U, u_ref, rtol=eps, atol=1e-8)
"""
Calls the enroll function
@@ -379,7 +379,7 @@ def test_ISVTrainAndEnrol():
gse = [gse1, gse2]
latent_z = it.enroll(gse, 5)
- assert np.allclose(latent_z, z_ref, eps)
+ np.testing.assert_allclose(latent_z, z_ref, rtol=eps, atol=1e-8)
def test_ISVTrainAndEnrolWithNumpy():
@@ -426,8 +426,8 @@ def test_ISVTrainAndEnrolWithNumpy():
it.U = copy.deepcopy(M_u)
it = it.fit(TRAINING_STATS_X, TRAINING_STATS_y)
- assert np.allclose(it.D, d_ref, eps)
- assert np.allclose(it.U, u_ref, eps)
+ np.testing.assert_allclose(it.D, d_ref, rtol=eps, atol=1e-8)
+ np.testing.assert_allclose(it.U, u_ref, rtol=eps, atol=1e-8)
"""
Calls the enroll function with arrays as input
@@ -449,7 +449,7 @@ def test_ISVTrainAndEnrolWithNumpy():
)
latent_z = it.enroll_with_array(X)
- assert np.allclose(latent_z, latent_z_ref, eps)
+ np.testing.assert_allclose(latent_z, latent_z_ref, rtol=eps, atol=1e-8)
def test_JFATrainInitialize():
@@ -477,9 +477,9 @@ def test_JFATrainInitialize():
v2 = it.V
d2 = it.D
- assert np.allclose(u1, u2, eps)
- assert np.allclose(v1, v2, eps)
- assert np.allclose(d1, d2, eps)
+ np.testing.assert_allclose(u1, u2, rtol=eps, atol=1e-8)
+ np.testing.assert_allclose(v1, v2, rtol=eps, atol=1e-8)
+ np.testing.assert_allclose(d1, d2, rtol=eps, atol=1e-8)
def test_ISVTrainInitialize():
@@ -505,5 +505,5 @@ def test_ISVTrainInitialize():
u2 = it.U
d2 = it.D
- assert np.allclose(u1, u2, eps)
- assert np.allclose(d1, d2, eps)
+ np.testing.assert_allclose(u1, u2, rtol=eps, atol=1e-8)
+ np.testing.assert_allclose(d1, d2, rtol=eps, atol=1e-8)