diff --git a/bob/learn/em/factor_analysis.py b/bob/learn/em/factor_analysis.py
index 1f2e8c38e68178ad3613a167b5125c656d101bca..cb5680af7674382d7b7718c339f90e29445c948c 100644
--- a/bob/learn/em/factor_analysis.py
+++ b/bob/learn/em/factor_analysis.py
@@ -4,12 +4,14 @@
import logging
+import dask
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.utils.multiclass import unique_labels
from .gmm import GMMMachine
+from .kmeans import check_and_persist_dask_input
from .linear_scoring import linear_scoring
logger = logging.getLogger(__name__)
@@ -204,12 +206,8 @@ class FactorAnalysisBase(BaseEstimator):
"""
if self.ubm is None:
- logger.info("FA: Creating a new GMMMachine.")
+ logger.info("FA: Creating a new GMMMachine and training it.")
self.ubm = GMMMachine(**self.ubm_kwargs)
-
- # Train the UBM if not already trained
- if self.ubm._means is None:
- logger.info(f"FA: Training the UBM with {self.ubm}.")
self.ubm.fit(X) # GMMMachine.fit takes non-labeled data
# Initializing the state matrix
@@ -1153,8 +1151,14 @@ class FactorAnalysisBase(BaseEstimator):
return self.score_using_stats(model, self.ubm.transform(data))
def fit(self, X, y):
+ input_is_dask, X = check_and_persist_dask_input(X)
self.initialize(X)
- stats = [self.ubm.transform(xx) for xx in X]
+ if input_is_dask:
+ stats = [dask.delayed(self.ubm.transform)(xx) for xx in X]
+ stats = dask.compute(*stats)
+ else:
+ stats = [self.ubm.transform(xx) for xx in X]
+ del X # we don't need to persist X anymore
return self.fit_using_stats(stats, y)
@@ -1269,7 +1273,7 @@ class ISVMachine(FactorAnalysisBase):
# TODO: Point of MAP-REDUCE
n_acc, f_acc = self.initialize_using_stats(X, y)
for i in range(self.em_iterations):
- logger.info("U Training: Iteration %d", i)
+ logger.info("U Training: Iteration %d", i + 1)
# TODO: Point of MAP-REDUCE
acc_U_A1, acc_U_A2 = self.e_step(X, y, n_acc, f_acc)
self.m_step(acc_U_A1, acc_U_A2)
@@ -1309,7 +1313,7 @@ class ISVMachine(FactorAnalysisBase):
latent_x, _, latent_z = self.initialize_XYZ(y)
latent_y = None
for i in range(iterations):
- logger.info("Enrollment: Iteration %d", i)
+ logger.info("Enrollment: Iteration %d", i + 1)
latent_x = self.update_x(X, y, UProd, latent_x, latent_y, latent_z)
latent_z = self.update_z(
X, y, latent_x, latent_y, latent_z, n_acc, f_acc
@@ -1738,7 +1742,7 @@ class JFAMachine(FactorAnalysisBase):
latent_x, latent_y, latent_z = self.initialize_XYZ(y)
for i in range(iterations):
- logger.info("Enrollment: Iteration %d", i)
+ logger.info("Enrollment: Iteration %d", i + 1)
latent_y = self.update_y(
X, y, VProd, latent_x, latent_y, latent_z, n_acc, f_acc
)
@@ -1803,7 +1807,7 @@ class JFAMachine(FactorAnalysisBase):
# Updating V
for i in range(self.em_iterations):
- logger.info("V Training: Iteration %d", i)
+ logger.info("V Training: Iteration %d", i + 1)
# TODO: Point of MAP-REDUCE
acc_V_A1, acc_V_A2 = self.e_step_v(X, y, n_acc, f_acc)
self.m_step_v(acc_V_A1, acc_V_A2)
@@ -1811,7 +1815,7 @@ class JFAMachine(FactorAnalysisBase):
# Updating U
for i in range(self.em_iterations):
- logger.info("U Training: Iteration %d", i)
+ logger.info("U Training: Iteration %d", i + 1)
# TODO: Point of MAP-REDUCE
acc_U_A1, acc_U_A2 = self.e_step_u(X, y, latent_y)
self.m_step_u(acc_U_A1, acc_U_A2)
@@ -1820,7 +1824,7 @@ class JFAMachine(FactorAnalysisBase):
# Updating D
for i in range(self.em_iterations):
- logger.info("D Training: Iteration %d", i)
+ logger.info("D Training: Iteration %d", i + 1)
# TODO: Point of MAP-REDUCE
acc_D_A1, acc_D_A2 = self.e_step_d(
X, y, latent_x, latent_y, n_acc, f_acc
diff --git a/bob/learn/em/gmm.py b/bob/learn/em/gmm.py
index 831d3fc64a0aa0ebbc44cc2c1ab59a3b757ffb3d..ce8581a1380da416936452b21dd6c3cfc9175f27 100644
--- a/bob/learn/em/gmm.py
+++ b/bob/learn/em/gmm.py
@@ -855,24 +855,7 @@ class GMMMachine(BaseEstimator):
def transform(self, X, **kwargs):
"""Returns the statistics for `X`."""
- input_is_dask, X = check_and_persist_dask_input(X)
-
- if input_is_dask:
- stats = [
- dask.delayed(e_step)(
- data=xx,
- machine=self,
- )
- for xx in X
- ]
- stats = functools.reduce(operator.iadd, stats)
- stats = stats.compute()
- else:
- stats = e_step(
- data=X,
- machine=self,
- )
- return stats
+ return e_step(data=X, machine=self)
def _more_tags(self):
return {
diff --git a/bob/learn/em/test/test_factor_analysis.py b/bob/learn/em/test/test_factor_analysis.py
index ca1bd2a167684cbf8786cc0a10845770157a2118..3b9ca21dc871a81477b80644777f94b0a327f8af 100644
--- a/bob/learn/em/test/test_factor_analysis.py
+++ b/bob/learn/em/test/test_factor_analysis.py
@@ -9,6 +9,9 @@ import numpy as np
from bob.learn.em import GMMMachine, GMMStats, ISVMachine, JFAMachine
+from .test_gmm import multiprocess_dask_client
+from .test_kmeans import to_dask_array, to_numpy
+
# Define Training set and initial values for tests
F1 = np.array(
[
@@ -443,37 +446,34 @@ def test_ISVMachine():
np.testing.assert_allclose(score, score_ref, atol=eps)
+def _create_ubm_prior(means):
+ # Creating a fake prior with 2 gaussians
+ prior_gmm = GMMMachine(2)
+ prior_gmm.means = means.copy()
+ # All nice and round diagonal covariance
+ prior_gmm.variances = np.ones((2, 3)) * 0.5
+ prior_gmm.weights = np.array([0.3, 0.7])
+ return prior_gmm
+
+
def test_ISV_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(
+ 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])
for prior, machine_type, ref in [
(
None,
"isv",
- [
- [0.02619036, 0.07607595],
- [-0.02570657, -0.07451667],
- [-0.0430513, -0.12514552],
- [-0.09729266, -0.28582205],
- [-0.01035388, -0.03041718],
- [0.0733034, 0.21534741],
- ],
+ 0.0,
),
(
- prior_gmm,
+ True,
"isv",
[
[-0.02361267, 0.0157274],
@@ -497,16 +497,16 @@ def test_ISV_JFA_fit():
None,
"jfa",
[
- [-1.72285693e-01, 1.47171193e-01],
- [-1.08402014e-01, 9.25999920e-02],
- [1.55349449e-02, -1.32703786e-02],
- [2.13389657e-04, -1.82283334e-04],
- [1.84127661e-05, -1.57286929e-05],
- [-1.90492196e-04, 1.62723691e-04],
+ [-0.04687046, -0.06302095],
+ [-0.04380423, -0.05889816],
+ [-0.02083793, -0.0280182],
+ [-0.04728452, -0.06357768],
+ [-0.04371283, -0.05877527],
+ [-0.0203464, -0.0273573],
],
),
(
- prior_gmm,
+ True,
"jfa",
[
[6.54547662e-03, 1.98699266e-04],
@@ -526,35 +526,40 @@ def test_ISV_JFA_fit():
),
]:
ref = np.asarray(ref)
- ubm_kwargs = dict(n_gaussians=2) if prior is None else None
# Doing the training
- if machine_type == "isv":
- machine = ISVMachine(
- 2,
- ubm=prior,
+ for transform in (to_numpy, to_dask_array):
+ data, labels = transform(data, labels)
+
+ if prior is None:
+ ubm = None
+ ubm_kwargs = dict(n_gaussians=2, ubm=_create_ubm_prior(means))
+ else:
+ ubm = _create_ubm_prior(means)
+ ubm_kwargs = None
+
+ machine_kwargs = dict(
+ ubm=ubm,
relevance_factor=4,
em_iterations=50,
ubm_kwargs=ubm_kwargs,
seed=10,
)
- test_attr = "U"
- else:
- machine = JFAMachine(
- 2,
- 2,
- ubm=prior,
- relevance_factor=4,
- em_iterations=50,
- ubm_kwargs=ubm_kwargs,
- seed=10,
+
+ if machine_type == "isv":
+ machine = ISVMachine(2, **machine_kwargs)
+ test_attr = "U"
+ else:
+ machine = JFAMachine(2, 2, **machine_kwargs)
+ test_attr = "V"
+
+ with multiprocess_dask_client():
+ machine.fit(data, labels)
+
+ arr = getattr(machine, test_attr)
+ np.testing.assert_allclose(
+ arr,
+ ref,
+ atol=1e-7,
+ err_msg=f"Test failed with prior={prior} and machine_type={machine_type} and transform={transform}",
)
- test_attr = "V"
- machine.fit(data, labels)
- arr = getattr(machine, test_attr)
- np.testing.assert_allclose(
- arr,
- ref,
- atol=1e-7,
- err_msg=f"Test failed with prior={prior} and machine_type={machine_type}",
- )
diff --git a/doc/guide.rst b/doc/guide.rst
index a94857f099b9235e5e946b2510ae13c9141f13f5..537d6be46d7bf8e290ce543a2096b8a1f94153c0 100644
--- a/doc/guide.rst
+++ b/doc/guide.rst
@@ -273,7 +273,7 @@ prior GMM.
>>> # Training a GMM with 2 Gaussians of dimension 3
>>> prior_gmm = bob.learn.em.GMMMachine(2).fit(data)
>>> # Creating the container
- >>> gmm_stats = prior_gmm.acc_statistics(data)
+ >>> gmm_stats = prior_gmm.transform(data)
>>> # Printing the responsibilities
>>> print(gmm_stats.n/gmm_stats.t)
[0.6 0.4]
@@ -335,7 +335,7 @@ The snippet bellow shows how to:
>>> # The input the the ISV Training is the statistics of the GMM
>>> # Here we are creating a GMMStats for each datapoints, which is NOT usual,
>>> # but it is done for testing purposes
- >>> gmm_stats = [ubm.acc_statistics(x[np.newaxis]) for x in X]
+ >>> gmm_stats = [ubm.transform(x[np.newaxis]) for x in X]
>>> # Finally doing the ISV training with U subspace with dimension of 2
>>> isv_machine = bob.learn.em.ISVMachine(ubm, r_U=2).fit(gmm_stats, y)
@@ -410,7 +410,7 @@ such session variability model.
>>> # The input the the JFA Training is the statistics of the GMM
>>> # Here we are creating a GMMStats for each datapoints, which is NOT usual,
>>> # but it is done for testing purposes
- >>> gmm_stats = [ubm.acc_statistics(x[np.newaxis]) for x in X]
+ >>> gmm_stats = [ubm.transform(x[np.newaxis]) for x in X]
>>> # Finally doing the JFA training with U and V subspaces with dimension of 2
>>> jfa_machine = bob.learn.em.JFAMachine(ubm, r_U=2, r_V=2).fit(gmm_stats, y)
diff --git a/doc/plot/plot_ISV.py b/doc/plot/plot_ISV.py
index cc7b5593057db9d6802e2b47598eab32c9749fc2..f3a9c39617ddfbce6970078fc45dae99e66b750a 100644
--- a/doc/plot/plot_ISV.py
+++ b/doc/plot/plot_ISV.py
@@ -8,37 +8,6 @@ import bob.learn.em
np.random.seed(2) # FIXING A SEED
-def isv_train(features, ubm):
- """
- Train U matrix
-
- **Parameters**
- features: List of :py:class:`bob.learn.em.GMMStats` organized by class
-
- n_gaussians: UBM (:py:class:`bob.learn.em.GMMMachine`)
-
- """
-
- stats = []
- for user in features:
- user_stats = []
- for f in user:
- s = bob.learn.em.GMMStats(ubm.shape[0], ubm.shape[1])
- ubm.acc_statistics(f, s)
- user_stats.append(s)
- stats.append(user_stats)
-
- relevance_factor = 4
- subspace_dimension_of_u = 1
-
- isvbase = bob.learn.em.ISVBase(ubm, subspace_dimension_of_u)
- trainer = bob.learn.em.ISVTrainer(relevance_factor)
- # trainer.rng = bob.core.random.mt19937(int(self.init_seed))
- bob.learn.em.train(trainer, isvbase, stats, max_iterations=50)
-
- return isvbase
-
-
# GENERATING DATA
iris_data = load_iris()
X = np.column_stack((iris_data.data[:, 0], iris_data.data[:, 3]))
@@ -72,19 +41,12 @@ ubm.variances = np.array(
ubm.weights = np.array([0.36, 0.36, 0.28])
-gmm_stats = [ubm.acc_statistics(x[np.newaxis]) for x in X]
-isv_machine = bob.learn.em.ISVMachine(ubm, r_U, em_iterations=50)
+isv_machine = bob.learn.em.ISVMachine(r_U, em_iterations=50, ubm=ubm)
isv_machine.U = np.array(
[[-0.150035, -0.44441, -1.67812, 2.47621, -0.52885, 0.659141]]
).T
-isv_machine = isv_machine.fit(gmm_stats, y)
-
-# gmm_stats = [ubm.acc_statistics(x) for x in [setosa, versicolor, virginica]]
-# isv_machine = bob.learn.em.ISVMachine(ubm, r_U).fit(gmm_stats, [0, 1, 2])
-
-
-# isvbase = isv_train([setosa, versicolor, virginica], ubm)
+isv_machine = isv_machine.fit(X, y)
# Variability direction
u0 = isv_machine.U[0:2, 0] / np.linalg.norm(isv_machine.U[0:2, 0])
diff --git a/doc/plot/plot_JFA.py b/doc/plot/plot_JFA.py
index 5c19474a49322178bd6f9ffb8bc3f1788f684355..6cfa4bc1051ce917448db37b9d44d7721a41385e 100644
--- a/doc/plot/plot_JFA.py
+++ b/doc/plot/plot_JFA.py
@@ -24,7 +24,7 @@ def isv_train(features, ubm):
user_stats = []
for f in user:
s = bob.learn.em.GMMStats(ubm.shape[0], ubm.shape[1])
- ubm.acc_statistics(f, s)
+ ubm.transform(f, s)
user_stats.append(s)
stats.append(user_stats)
@@ -75,7 +75,7 @@ ubm.variances = np.array(
ubm.weights = np.array([0.36, 0.36, 0.28])
# .fit(X)
-gmm_stats = [ubm.acc_statistics(x[np.newaxis]) for x in X]
+gmm_stats = [ubm.transform(x[np.newaxis]) for x in X]
jfa_machine = bob.learn.em.JFAMachine(ubm, r_U, r_V, em_iterations=50)
# Initializing with old bob initialization
@@ -94,7 +94,7 @@ jfa_machine = jfa_machine.fit(gmm_stats, y)
# .fit(gmm_stats, y)
-# gmm_stats = [ubm.acc_statistics(x) for x in [setosa, versicolor, virginica]]
+# gmm_stats = [ubm.transform(x) for x in [setosa, versicolor, virginica]]
# jfa_machine = bob.learn.em.JFAMachine(ubm, r_U, r_V).fit(gmm_stats, [0, 1, 2])