diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 1515754e0bef52e755e793e05c3400ecf532c4c7..1daa2300fc671ed82d211795f4abe5f019b8d7fe 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -2,12 +2,12 @@
# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/timothycrosley/isort
- rev: 5.9.3
+ rev: 5.10.1
hooks:
- id: isort
args: [--settings-path, "pyproject.toml"]
- repo: https://github.com/psf/black
- rev: 21.7b0
+ rev: 22.3.0
hooks:
- id: black
- repo: https://gitlab.com/pycqa/flake8
@@ -15,7 +15,7 @@ repos:
hooks:
- id: flake8
- repo: https://github.com/pre-commit/pre-commit-hooks
- rev: v4.0.1
+ rev: v4.1.0
hooks:
- id: check-ast
- id: check-case-conflict
diff --git a/bob/learn/em/gmm.py b/bob/learn/em/gmm.py
index 38908cc7feee4ab3fa9d6f28e853534cd0c59357..2d5b0b9340e69dda2f9f585ebc9c16c83d006d64 100644
--- a/bob/learn/em/gmm.py
+++ b/bob/learn/em/gmm.py
@@ -51,12 +51,14 @@ def log_weighted_likelihood(data, machine):
The weighted log likelihood of each sample of each Gaussian.
"""
# Compute the likelihood for each data point on each Gaussian
- n_gaussians, n_samples = len(machine.means), len(data)
- z = np.empty(shape=(n_gaussians, n_samples), like=data)
+ n_gaussians = len(machine.means)
+ z = []
for i in range(n_gaussians):
- z[i] = np.sum(
+ temp = np.sum(
(data - machine.means[i]) ** 2 / machine.variances[i], axis=-1
)
+ z.append(temp)
+ z = np.vstack(z)
ll = -0.5 * (machine.g_norms[:, None] + z)
log_weighted_likelihoods = machine.log_weights[:, None] + ll
return log_weighted_likelihoods
@@ -717,17 +719,15 @@ class GMMMachine(BaseEstimator):
)
kmeans_machine = kmeans_machine.fit(data)
+ # Set the GMM machine's gaussians with the results of k-means
+ self.means = copy.deepcopy(kmeans_machine.centroids_)
logger.debug(
"Estimating the variance and weights of each gaussian from kmeans."
)
(
- variances,
- weights,
+ self.variances,
+ self.weights,
) = kmeans_machine.get_variances_and_weights_for_each_cluster(data)
-
- # Set the GMM machine's gaussians with the results of k-means
- self.means = copy.deepcopy(kmeans_machine.centroids_)
- self.variances, self.weights = dask.compute(variances, weights)
logger.debug("Done.")
def log_weighted_likelihood(
@@ -962,13 +962,10 @@ def map_gmm_m_step(
# n_threshold[statistics.n > mean_var_update_threshold] = statistics.n[
# statistics.n > mean_var_update_threshold
# ]
- new_means = (
- np.multiply(
- alpha[:, None],
- (statistics.sum_px / n_threshold[:, None]),
- )
- + np.multiply((1 - alpha[:, None]), machine.ubm.means)
- )
+ new_means = np.multiply(
+ alpha[:, None],
+ (statistics.sum_px / n_threshold[:, None]),
+ ) + np.multiply((1 - alpha[:, None]), machine.ubm.means)
machine.means = np.where(
statistics.n[:, None] < mean_var_update_threshold,
machine.ubm.means,
diff --git a/bob/learn/em/kmeans.py b/bob/learn/em/kmeans.py
index b5b6094988109f32fecd9084e7baa9ee5883ab8d..8a17af323e3574d9e17bd991f26a6756f290cc3b 100644
--- a/bob/learn/em/kmeans.py
+++ b/bob/learn/em/kmeans.py
@@ -37,13 +37,27 @@ def get_centroids_distance(x: np.ndarray, means: np.ndarray) -> np.ndarray:
"""
x = np.atleast_2d(x)
if isinstance(x, da.Array):
- return np.sum((means[:, None] - x[None, :]) ** 2, axis=-1)
+ distances = []
+ for i in range(means.shape[0]):
+ distances.append(np.sum((means[i] - x) ** 2, axis=-1))
+ return da.vstack(distances)
else:
return scipy.spatial.distance.cdist(means, x, metric="sqeuclidean")
def get_closest_centroid_index(centroids_dist: np.ndarray) -> np.ndarray:
- """Returns the index of the closest cluster mean to x."""
+ """Returns the index of the closest cluster mean to x.
+
+ Parameters
+ ----------
+ centroids_dist: ndarray of shape (n_clusters, n_samples)
+ The squared Euclidian distance (or distances) to each cluster mean.
+
+ Returns
+ -------
+ closest_centroid_indices: ndarray of shape (n_samples,)
+ The index of the closest cluster mean to x.
+ """
return np.argmin(centroids_dist, axis=0)
@@ -120,6 +134,43 @@ def m_step(stats, n_samples):
return means, average_min_distance
+def accumulate_indices_means_vars(data, means):
+ """Accumulates statistics needed to compute weights and variances of the clusters."""
+ n_clusters, n_features = len(means), data.shape[1]
+ dist = get_centroids_distance(data, means)
+ closest_centroid_indices = get_closest_centroid_index(dist)
+ # the means_sum and variances_sum must be initialized with zero here since
+ # they get accumulated in the next function
+ means_sum = np.zeros((n_clusters, n_features), like=data)
+ variances_sum = np.zeros((n_clusters, n_features), like=data)
+ for i in range(n_clusters):
+ means_sum[i] = np.sum(data[closest_centroid_indices == i], axis=0)
+ for i in range(n_clusters):
+ variances_sum[i] = np.sum(
+ data[closest_centroid_indices == i] ** 2, axis=0
+ )
+ return closest_centroid_indices, means_sum, variances_sum
+
+
+def reduce_indices_means_vars(stats):
+ """Computes weights and variances of the clusters given the statistics."""
+ closest_centroid_indices = [s[0] for s in stats]
+ means_sum = [s[1] for s in stats]
+ variances_sum = [s[2] for s in stats]
+
+ closest_centroid_indices = np.concatenate(closest_centroid_indices, axis=0)
+ means_sum = np.sum(means_sum, axis=0)
+ variances_sum = np.sum(variances_sum, axis=0)
+
+ n_clusters = len(means_sum)
+ weights_count = np.bincount(closest_centroid_indices, minlength=n_clusters)
+ weights = weights_count / weights_count.sum()
+ means = means_sum / weights_count[:, None]
+ variances = (variances_sum / weights_count[:, None]) - (means**2)
+
+ return variances, weights
+
+
def check_and_persist_dask_input(data):
# check if input is a dask array. If so, persist and rebalance data
input_is_dask = False
@@ -250,28 +301,24 @@ class KMeansMachine(BaseEstimator):
weights: ndarray of shape (n_clusters, )
Weight (proportion of quantity of data point) of each cluster.
"""
- _, data = check_and_persist_dask_input(data)
- n_clusters, n_features = self.n_clusters, data.shape[1]
- dist = get_centroids_distance(data, self.centroids_)
- closest_centroid_indices = get_closest_centroid_index(dist)
- weights_count = np.bincount(
- closest_centroid_indices, minlength=n_clusters
- )
- weights = weights_count / weights_count.sum()
-
- # Accumulate
- means_sum = np.zeros((n_clusters, n_features), like=data)
- variances_sum = np.zeros((n_clusters, n_features), like=data)
- for i in range(n_clusters):
- means_sum[i] = np.sum(data[closest_centroid_indices == i], axis=0)
- for i in range(n_clusters):
- variances_sum[i] = np.sum(
- data[closest_centroid_indices == i] ** 2, axis=0
- )
+ input_is_dask, data = check_and_persist_dask_input(data)
+ data = array_to_delayed_list(data, input_is_dask)
- # Reduce
- means = means_sum / weights_count[:, None]
- variances = (variances_sum / weights_count[:, None]) - (means ** 2)
+ if input_is_dask:
+ stats = [
+ dask.delayed(accumulate_indices_means_vars)(
+ xx, means=self.centroids_
+ )
+ for xx in data
+ ]
+ variances, weights = dask.compute(
+ dask.delayed(reduce_indices_means_vars)(stats)
+ )[0]
+ else:
+ # Accumulate
+ stats = accumulate_indices_means_vars(data, self.centroids_)
+ # Reduce
+ variances, weights = reduce_indices_means_vars([stats])
return variances, weights
@@ -336,7 +383,9 @@ class KMeansMachine(BaseEstimator):
convergence_value = abs(
(distance_previous - distance) / distance_previous
)
- logger.debug(f"Convergence value = {convergence_value}")
+ logger.debug(
+ f"Convergence value = {convergence_value} and threshold is {self.convergence_threshold}"
+ )
# Terminates if converged (and threshold is set)
if (
diff --git a/doc/conf.py b/doc/conf.py
index dd19c99be128f833ccb7c093637a6547e7e14c94..59964034e8fac4f87cb9144f31c4ff36fc5ce89b 100755
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -73,10 +73,10 @@ source_suffix = ".rst"
master_doc = "index"
# General information about the project.
-project = u"bob.learn.em"
+project = "bob.learn.em"
import time
-copyright = u"%s, Idiap Research Institute" % time.strftime("%Y")
+copyright = "%s, Idiap Research Institute" % time.strftime("%Y")
# Grab the setup entry
distribution = pkg_resources.require(project)[0]
@@ -126,8 +126,8 @@ pygments_style = "sphinx"
# Some variables which are useful for generated material
project_variable = project.replace(".", "_")
-short_description = u"Core utilities required on all Bob modules"
-owner = [u"Idiap Research Institute"]
+short_description = "Core utilities required on all Bob modules"
+owner = ["Idiap Research Institute"]
# -- Options for HTML output ---------------------------------------------------
@@ -209,7 +209,7 @@ html_favicon = "img/favicon.ico"
# html_file_suffix = None
# Output file base name for HTML help builder.
-htmlhelp_basename = project_variable + u"_doc"
+htmlhelp_basename = project_variable + "_doc"
# -- Post configuration --------------------------------------------------------