diff --git a/bob/learn/em/factor_analysis.py b/bob/learn/em/factor_analysis.py
index ea66294e90fb9a93f35c86a75a35643eba2385f8..779068ba07708df1afdcf51e6c867d3b181fecbf 100644
--- a/bob/learn/em/factor_analysis.py
+++ b/bob/learn/em/factor_analysis.py
@@ -112,8 +112,8 @@ class FactorAnalysisBase(BaseEstimator):
relevance_factor: float
Factor analysis relevance factor
- seed: int
- Seed for the random number generator
+ random_state: int
+ random_state for the random number generator
ubm: :py:class:`bob.learn.em.GMMMachine`
A trained UBM (Universal Background Model) or a parametrized
@@ -128,7 +128,7 @@ class FactorAnalysisBase(BaseEstimator):
r_V=None,
relevance_factor=4.0,
em_iterations=10,
- seed=0,
+ random_state=0,
ubm=None,
ubm_kwargs=None,
**kwargs,
@@ -137,7 +137,7 @@ class FactorAnalysisBase(BaseEstimator):
self.ubm = ubm
self.ubm_kwargs = ubm_kwargs
self.em_iterations = em_iterations
- self.seed = seed
+ self.random_state = random_state
# axis 1 dimensions of U and V
self.r_U = r_U
@@ -231,7 +231,8 @@ class FactorAnalysisBase(BaseEstimator):
if self.ubm is None:
logger.info("FA: Creating a new GMMMachine and training it.")
- self.ubm = GMMMachine(**self.ubm_kwargs)
+ gmm_class = self.ubm_kwargs.pop("gmm_class", GMMMachine)
+ self.ubm: GMMMachine = gmm_class(**self.ubm_kwargs)
self.ubm.fit(X)
if self.ubm._means is None:
@@ -280,8 +281,8 @@ class FactorAnalysisBase(BaseEstimator):
D: (n_gaussians*feature_dimension) represents the client offset vector
"""
- if self.seed is not None:
- np.random.seed(self.seed)
+ if self.random_state is not None:
+ np.random.seed(self.random_state)
U_shape = (self.supervector_dimension, self.r_U)
@@ -1258,7 +1259,7 @@ class FactorAnalysisBase(BaseEstimator):
"""
- return self.score_using_stats(model, self.ubm.transform(data))
+ return self.score_using_stats(model, self.ubm.acc_stats(data))
def fit(self, X, y):
@@ -1275,12 +1276,18 @@ class FactorAnalysisBase(BaseEstimator):
class_indices = y == class_id
X_new.append(X[class_indices])
y_new.append(y[class_indices])
+ __import__("ipdb").set_trace()
X, y = X_new, y_new
stats = [
dask.delayed(self.ubm.stats_per_sample)(xx).persist()
for xx in X
]
+ # try:
+ # client = dask.distributed.Client.current()
+ # stats = client.scatter(stats)
+ # except ValueError:
+ # pass
else:
stats = self.ubm.stats_per_sample(X)
@@ -1309,8 +1316,8 @@ class ISVMachine(FactorAnalysisBase):
relevance_factor: float
Factor analysis relevance factor
- seed: int
- Seed for the random number generator
+ random_state: int
+ random_state for the random number generator
ubm: :py:class:`bob.learn.em.GMMMachine` or None
A trained UBM (Universal Background Model). If None, the UBM is trained with
@@ -1324,7 +1331,7 @@ class ISVMachine(FactorAnalysisBase):
r_U,
em_iterations=10,
relevance_factor=4.0,
- seed=0,
+ random_state=0,
ubm=None,
ubm_kwargs=None,
**kwargs,
@@ -1333,7 +1340,7 @@ class ISVMachine(FactorAnalysisBase):
r_U=r_U,
relevance_factor=relevance_factor,
em_iterations=em_iterations,
- seed=seed,
+ random_state=random_state,
ubm=ubm,
ubm_kwargs=ubm_kwargs,
**kwargs,
@@ -1447,7 +1454,7 @@ class ISVMachine(FactorAnalysisBase):
return self
def transform(self, X):
- ubm_projected_X = self.ubm.transform(X)
+ ubm_projected_X = self.ubm.acc_stats(X)
return self.estimate_ux(ubm_projected_X)
def enroll_using_stats(self, X, iterations=1):
@@ -1519,7 +1526,7 @@ class ISVMachine(FactorAnalysisBase):
z
"""
- return self.enroll_using_stats([self.ubm.transform(X)], iterations)
+ return self.enroll_using_stats([self.ubm.acc_stats(X)], iterations)
def score_using_stats(self, latent_z, data):
"""
@@ -1584,8 +1591,8 @@ class JFAMachine(FactorAnalysisBase):
relevance_factor: float
Factor analysis relevance factor
- seed: int
- Seed for the random number generator
+ random_state: int
+ random_state for the random number generator
"""
@@ -1595,7 +1602,7 @@ class JFAMachine(FactorAnalysisBase):
r_V,
em_iterations=10,
relevance_factor=4.0,
- seed=0,
+ random_state=0,
ubm=None,
ubm_kwargs=None,
**kwargs,
@@ -1606,7 +1613,7 @@ class JFAMachine(FactorAnalysisBase):
r_V=r_V,
relevance_factor=relevance_factor,
em_iterations=em_iterations,
- seed=seed,
+ random_state=random_state,
ubm_kwargs=ubm_kwargs,
**kwargs,
)
@@ -2059,7 +2066,7 @@ class JFAMachine(FactorAnalysisBase):
z
"""
- return self.enroll_using_stats([self.ubm.transform(X)], iterations)
+ return self.enroll_using_stats([self.ubm.acc_stats(X)], iterations)
def fit_using_stats(self, X, y):
"""
diff --git a/bob/learn/em/gmm.py b/bob/learn/em/gmm.py
index 25159f17aaa95f68f1795c6306f9ed57a9181b71..988ef3a5b512bb6b4075e6886816f96f22b03edb 100644
--- a/bob/learn/em/gmm.py
+++ b/bob/learn/em/gmm.py
@@ -850,10 +850,15 @@ class GMMMachine(BaseEstimator):
)
return self
- def transform(self, X):
+ def acc_stats(self, X):
"""Returns the statistics for `X`."""
+ # we need this because sometimes the transform function gets overridden
return e_step(data=X, machine=self)
+ def transform(self, X):
+ """Returns the statistics for `X`."""
+ return self.acc_stats(X)
+
def stats_per_sample(self, X):
return [e_step(data=xx, machine=self) for xx in X]
diff --git a/bob/learn/em/test/test_factor_analysis.py b/bob/learn/em/test/test_factor_analysis.py
index d2399b26bfaa4f0711c67bea7c34340a8921b5a7..596a6a9fe2b75fa52b062e6286d4589e5ccff36d 100644
--- a/bob/learn/em/test/test_factor_analysis.py
+++ b/bob/learn/em/test/test_factor_analysis.py
@@ -557,7 +557,7 @@ def test_ISV_JFA_fit():
relevance_factor=4,
em_iterations=50,
ubm_kwargs=ubm_kwargs,
- seed=10,
+ random_state=10,
)
if machine_type == "isv":