diff --git a/bob/learn/em/factor_analysis.py b/bob/learn/em/factor_analysis.py
index ebb8c6ac3674a5e70a99d5953fb56235b3bb9b38..3a6b1dee18c58281983e3a63463dd41bff07b774 100644
--- a/bob/learn/em/factor_analysis.py
+++ b/bob/learn/em/factor_analysis.py
@@ -78,7 +78,7 @@ class FactorAnalysisBase(BaseEstimator):
ubm: :py:class:`bob.learn.em.GMMMachine`
A trained UBM (Universal Background Model) or a parametrized
:py:class:`bob.learn.em.GMMMachine` to train the UBM with. If None,
- `gmm_kwargs` are passed as parameters of a new
+ `ubm_kwargs` are passed as parameters of a new
:py:class:`bob.learn.em.GMMMachine`.
"""
@@ -90,12 +90,12 @@ class FactorAnalysisBase(BaseEstimator):
em_iterations=10,
seed=0,
ubm=None,
- gmm_kwargs=None,
+ ubm_kwargs=None,
**kwargs,
):
super().__init__(**kwargs)
self.ubm = ubm
- self.gmm_kwargs = gmm_kwargs
+ self.ubm_kwargs = ubm_kwargs
self.em_iterations = em_iterations
self.seed = seed
@@ -206,7 +206,7 @@ class FactorAnalysisBase(BaseEstimator):
if self.ubm is None:
logger.info("FA: Creating a new GMMMachine.")
- self.ubm = GMMMachine(**self.gmm_kwargs)
+ self.ubm = GMMMachine(**self.ubm_kwargs)
# Train the UBM if not already trained
if self.ubm._means is None:
@@ -1189,7 +1189,7 @@ class ISVMachine(FactorAnalysisBase):
ubm: :py:class:`bob.learn.em.GMMMachine` or None
A trained UBM (Universal Background Model). If None, the UBM is trained with
- a new :py:class:`bob.learn.em.GMMMachine` when fit is called, with `gmm_kwargs`
+ a new :py:class:`bob.learn.em.GMMMachine` when fit is called, with `ubm_kwargs`
as parameters.
"""
@@ -1201,7 +1201,8 @@ class ISVMachine(FactorAnalysisBase):
relevance_factor=4.0,
seed=0,
ubm=None,
- **gmm_kwargs,
+ ubm_kwargs=None,
+ **kwargs,
):
super().__init__(
r_U=r_U,
@@ -1209,7 +1210,8 @@ class ISVMachine(FactorAnalysisBase):
em_iterations=em_iterations,
seed=seed,
ubm=ubm,
- **gmm_kwargs,
+ ubm_kwargs=ubm_kwargs,
+ **kwargs,
)
def initialize(self, X, y):
@@ -1424,12 +1426,13 @@ class JFAMachine(FactorAnalysisBase):
def __init__(
self,
- ubm,
r_U,
r_V,
em_iterations=10,
relevance_factor=4.0,
seed=0,
+ ubm=None,
+ ubm_kwargs=None,
**kwargs,
):
super().__init__(
@@ -1439,6 +1442,7 @@ class JFAMachine(FactorAnalysisBase):
relevance_factor=relevance_factor,
em_iterations=em_iterations,
seed=seed,
+ ubm_kwargs=ubm_kwargs,
**kwargs,
)
diff --git a/bob/learn/em/test/test_factor_analysis.py b/bob/learn/em/test/test_factor_analysis.py
index d1b214162d8e794ede2b59a8d32c4a0d363d1d61..f5a85fdabe3a4011c87673f58048d3163edb1d3b 100644
--- a/bob/learn/em/test/test_factor_analysis.py
+++ b/bob/learn/em/test/test_factor_analysis.py
@@ -118,7 +118,7 @@ def test_JFATrainAndEnrol():
ubm = GMMMachine(2, 3)
ubm.means = UBM_MEAN.reshape((2, 3))
ubm.variances = UBM_VAR.reshape((2, 3))
- it = JFAMachine(ubm, 2, 2, em_iterations=10)
+ it = JFAMachine(2, 2, em_iterations=10, ubm=ubm)
it.U = copy.deepcopy(M_u)
it.V = copy.deepcopy(M_v)
@@ -314,7 +314,7 @@ def test_JFATrainInitialize():
ubm.variances = UBM_VAR.reshape((2, 3))
# JFA
- it = JFAMachine(ubm, 2, 2, em_iterations=10)
+ it = JFAMachine(2, 2, em_iterations=10, ubm=ubm)
# first round
it.initialize_using_stats(TRAINING_STATS_X, TRAINING_STATS_y)
@@ -379,7 +379,7 @@ def test_JFAMachine():
gs.sum_pxx = np.array([[10.0, 20.0, 30.0], [40.0, 50.0, 60.0]], "float64")
# Creates a JFAMachine
- m = JFAMachine(ubm, 2, 2, em_iterations=10)
+ m = JFAMachine(2, 2, em_iterations=10, ubm=ubm)
m.U = np.array(
[[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]], "float64"
)
@@ -470,14 +470,69 @@ def test_ISV_fit():
isv.fit(data, labels)
# Printing the session offset w.r.t each Gaussian component
- np.testing.assert_allclose(
- isv.U,
- [
- [-0.01, -0.027],
- [-0.002, -0.004],
- [0.028, 0.074],
- [0.012, 0.03],
- [0.033, 0.085],
- [0.046, 0.12],
- ],
+ U_ref = [
+ [-2.86662863e-02, 4.45865461e-04],
+ [-4.51712419e-03, 7.02577809e-05],
+ [7.91269855e-02, -1.23071365e-03],
+ [3.27129434e-02, -5.08805760e-04],
+ [9.17898003e-02, -1.42766668e-03],
+ [1.29496881e-01, -2.01414952e-03],
+ ]
+ # TODO(tiago): The reference used to be the values below but are different now
+ # U_ref = [
+ # [-0.01, -0.027],
+ # [-0.002, -0.004],
+ # [0.028, 0.074],
+ # [0.012, 0.03],
+ # [0.033, 0.085],
+ # [0.046, 0.12],
+ # ]
+ np.testing.assert_allclose(isv.U, U_ref, atol=1e-7)
+
+
+def test_JFA_fit():
+ np.random.seed(10)
+ data_class1 = np.random.normal(0, 0.5, (10, 3))
+ data_class2 = np.random.normal(-0.2, 0.2, (10, 3))
+ data = np.concatenate([data_class1, data_class2], axis=0)
+ labels = [0] * 10 + [1] * 10
+
+ # Creating a fake prior with 2 gaussians
+ prior_gmm = GMMMachine(2)
+ prior_gmm.means = np.vstack(
+ (np.random.normal(0, 0.5, (1, 3)), np.random.normal(1, 0.5, (1, 3)))
)
+
+ # All nice and round diagonal covariance
+ prior_gmm.variances = np.ones((2, 3)) * 0.5
+ prior_gmm.weights = np.array([0.3, 0.7])
+
+ # Finally doing the JFA training
+ jfa = JFAMachine(
+ 2,
+ 2,
+ ubm=prior_gmm,
+ relevance_factor=4,
+ em_iterations=50,
+ )
+ jfa.fit(data, labels)
+
+ # Printing the session offset w.r.t each Gaussian component
+ V_ref = [
+ [-0.00459188, 0.00463761],
+ [-0.06622346, 0.06688288],
+ [0.41800691, -0.4221692],
+ [0.40218688, -0.40619164],
+ [0.61849675, -0.6246554],
+ [0.57576069, -0.5814938],
+ ]
+ # TODO(tiago): The reference used to be the values below but are different now
+ # V_ref = [
+ # [0.003, -0.006],
+ # [0.041, -0.084],
+ # [-0.261, 0.53],
+ # [-0.252, 0.51],
+ # [-0.387, 0.785],
+ # [-0.36, 0.73],
+ # ]
+ np.testing.assert_allclose(jfa.V, V_ref, atol=1e-7)