From ded8a6e9bdca810f34eeedcd44071461c9e50ef7 Mon Sep 17 00:00:00 2001
From: Yannick DAYER <yannick.dayer@idiap.ch>
Date: Tue, 30 Nov 2021 14:29:06 +0100
Subject: [PATCH] Adapt GMMMachine to new KMeansMachine.
---
bob/learn/em/data/gmm_ML.hdf5 | Bin 12920 -> 12920 bytes
bob/learn/em/mixture/gmm.py | 34 +++++++++++++++++-----------------
bob/learn/em/test/test_gmm.py | 28 ++++++++++++++++++++--------
3 files changed, 37 insertions(+), 25 deletions(-)
diff --git a/bob/learn/em/data/gmm_ML.hdf5 b/bob/learn/em/data/gmm_ML.hdf5
index 0326c186f11ad38387b3a4c4b1e3cc7a66d32b80..20362881b1661a826a8773d1658a8df559099d46 100644
GIT binary patch
delta 186
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delta 194
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diff --git a/bob/learn/em/mixture/gmm.py b/bob/learn/em/mixture/gmm.py
index d17c7ae..65c8137 100644
--- a/bob/learn/em/mixture/gmm.py
+++ b/bob/learn/em/mixture/gmm.py
@@ -13,7 +13,6 @@ import numpy as np
from sklearn.base import BaseEstimator
from bob.learn.em.cluster import KMeansMachine
-from bob.learn.em.cluster import KMeansTrainer
from h5py import File as HDF5File
@@ -274,7 +273,7 @@ class GMMMachine(BaseEstimator):
max_fitting_steps: Union[int, None] = 200,
random_state: Union[int, np.random.RandomState] = 0,
weights: "Union[np.ndarray[('n_gaussians',), float], None]" = None,
- k_means_trainer: Union[KMeansTrainer, None] = None,
+ k_means_trainer: Union[KMeansMachine, None] = None,
update_means: bool = True,
update_variances: bool = False,
update_weights: bool = False,
@@ -317,7 +316,7 @@ class GMMMachine(BaseEstimator):
Ratio for MAP adaptation. Used when `trainer == "map"` and
`relevance_factor is None`)
relevance_factor:
- Factor for the computation of alpha with Reyolds adaptation. (Used when
+ Factor for the computation of alpha with Reynolds adaptation. (Used when
`trainer == "map"`)
variance_thresholds:
The variance flooring thresholds, i.e. the minimum allowed value of variance in each dimension.
@@ -395,14 +394,14 @@ class GMMMachine(BaseEstimator):
def variances(self, variances: "np.ndarray[('n_gaussians', 'n_features'), float]"):
self._variances = np.maximum(self.variance_thresholds, variances)
# Recompute g_norm for each gaussian [array of shape (n_gaussians,)]
- n_log_2pi = self.variances.shape[-1] * np.log(2 * np.pi)
+ n_log_2pi = self._variances.shape[-1] * np.log(2 * np.pi)
self._g_norms = np.array(n_log_2pi + np.log(self._variances).sum(axis=-1))
@property
def variance_thresholds(self):
"""Threshold below which variances are clamped to prevent precision losses."""
if self._variance_thresholds is None:
- raise ValueError("GMMMachine variance thresholds were never set.")
+ return EPSILON
return self._variance_thresholds
@variance_thresholds.setter
@@ -411,7 +410,8 @@ class GMMMachine(BaseEstimator):
threshold: "Union[float, np.ndarray[('n_gaussians', 'n_features'), float]]",
):
self._variance_thresholds = threshold
- self.variances = np.maximum(threshold, self.variances)
+ if self._variances is not None:
+ self.variances = np.maximum(threshold, self._variances)
@property
def g_norms(self):
@@ -542,12 +542,11 @@ class GMMMachine(BaseEstimator):
if data is None:
raise ValueError("Data is required when training with k-means.")
logger.info("Initializing GMM with k-means.")
- kmeans_trainer = self.k_means_trainer or KMeansTrainer(
+ kmeans_machine = self.k_means_trainer or KMeansMachine(
+ self.n_gaussians,
random_state=self.random_state,
)
- kmeans_machine = KMeansMachine(self.n_gaussians).fit(
- data, trainer=kmeans_trainer
- )
+ kmeans_machine = kmeans_machine.fit(data)
(
variances,
@@ -706,6 +705,10 @@ class GMMMachine(BaseEstimator):
else:
logger.debug("GMM means already set. Initialization was not run!")
+ if self._variances is None:
+ logger.warning("Variances were not defined before fit. Using variance=1")
+ self.variances = np.ones_like(self.means)
+
average_output = 0
logger.info("Training GMM...")
step = 0
@@ -713,11 +716,7 @@ class GMMMachine(BaseEstimator):
step += 1
logger.info(
f"Iteration {step:3d}"
- + (
- f"/{self.max_fitting_steps:3d}"
- if self.max_fitting_steps is not None
- else ""
- )
+ + (f"/{self.max_fitting_steps:3d}" if self.max_fitting_steps else "")
)
average_output_previous = average_output
@@ -750,8 +749,9 @@ class GMMMachine(BaseEstimator):
and convergence_value <= self.convergence_threshold
):
logger.info("Reached convergence threshold. Training stopped.")
- return self
- logger.info("Reached maximum step. Training stopped without convergence.")
+ break
+ else:
+ logger.info("Reached maximum step. Training stopped without convergence.")
self.compute()
return self
diff --git a/bob/learn/em/test/test_gmm.py b/bob/learn/em/test/test_gmm.py
index 43a9449..c11d5e1 100644
--- a/bob/learn/em/test/test_gmm.py
+++ b/bob/learn/em/test/test_gmm.py
@@ -22,7 +22,7 @@ from bob.io.base import load as load_array
from bob.learn.em.mixture import GMMMachine
from bob.learn.em.mixture import GMMStats
-from bob.learn.em.cluster import KMeansTrainer
+from bob.learn.em.cluster import KMeansMachine
def test_GMMStats():
# Test a GMMStats
@@ -133,11 +133,10 @@ def test_GMMMachine_1():
# Checks particular varianceThresholds-related methods
varianceThresholds1D = np.array([0.3, 1, 0.5], "float64")
gmm.variance_thresholds = varianceThresholds1D
- np.testing.assert_equal(gmm.variance_thresholds[0,:], varianceThresholds1D)
- np.testing.assert_equal(gmm.variance_thresholds[1,:], varianceThresholds1D)
+ np.testing.assert_equal(gmm.variance_thresholds, varianceThresholds1D)
gmm.variance_thresholds = 0.005
- np.testing.assert_equal(gmm.variance_thresholds, np.full((2,3), 0.005))
+ np.testing.assert_equal(gmm.variance_thresholds, 0.005)
gmm.means = newMeans
gmm.variances = newVariances
@@ -251,6 +250,7 @@ def test_GMMStats_2():
machine = GMMMachine(n_gaussians)
machine.means = np.array([[0, 0, 0], [8, 8, 8]])
+ machine.variances = np.ones_like(machine.means)
# Populate the GMMStats
stats = machine.acc_statistics(data)
@@ -314,6 +314,7 @@ def test_machine_parameters():
n_features = 2
machine = GMMMachine(n_gaussians)
machine.means = np.repeat([[0], [1], [-1]], n_features, 1)
+ machine.variances = np.ones_like(machine.means)
np.testing.assert_equal(machine.means, np.repeat([[0], [1], [-1]], n_features, 1))
np.testing.assert_equal(machine.variances, np.ones((n_gaussians, n_features)))
@@ -331,7 +332,10 @@ def test_machine_parameters():
def test_kmeans_plusplus_init():
n_gaussians = 3
- machine = GMMMachine(n_gaussians, k_means_trainer=KMeansTrainer("k-means++"))
+ machine = GMMMachine(
+ n_gaussians,
+ k_means_trainer=KMeansMachine(n_clusters=n_gaussians, init_method="k-means++"),
+ )
data = np.array([[1.5, 1], [1, 1.5], [-1, 0.5], [-1.5, 0], [2, 2], [2.5, 2.5]])
machine = machine.fit(data)
expected_means = np.array([[2.25, 2.25], [-1.25, 0.25], [1.25, 1.25]])
@@ -342,7 +346,10 @@ def test_kmeans_plusplus_init():
def test_kmeans_parallel_init():
n_gaussians = 3
- machine = GMMMachine(n_gaussians, k_means_trainer=KMeansTrainer("k-means||"))
+ machine = GMMMachine(
+ n_gaussians,
+ k_means_trainer=KMeansMachine(n_clusters=n_gaussians, init_method="k-means||"),
+ )
data = np.array([[1.5, 1], [1, 1.5], [-1, 0.5], [-1.5, 0], [2, 2], [2.5, 2.5]])
machine = machine.fit(data)
expected_means = np.array([[1.25, 1.25], [-1.25, 0.25], [2.25, 2.25]])
@@ -356,6 +363,7 @@ def test_likelihood():
n_gaussians = 3
machine = GMMMachine(n_gaussians)
machine.means = np.repeat([[0], [1], [-1]], 3, 1)
+ machine.variances = np.ones_like(machine.means)
log_likelihood = machine.log_likelihood(data)
expected_ll = np.array(
[-3.6519900964986527, -3.83151883210222, -3.83151883210222, -5.344374066745753]
@@ -390,6 +398,7 @@ def test_likelihood_weight():
n_gaussians = 3
machine = GMMMachine(n_gaussians)
machine.means = np.repeat([[0], [1], [-1]], 3, 1)
+ machine.variances = np.ones_like(machine.means)
machine.weights = [0.6, 0.1, 0.3]
log_likelihood = machine.log_likelihood(data)
expected_ll = np.array(
@@ -429,7 +438,7 @@ def test_ml_em():
machine = GMMMachine(n_gaussians, update_means=True, update_variances=True, update_weights=True)
machine.means = np.repeat([[2], [8]], n_features, 1)
- machine.initialize_gaussians(None)
+ machine.variances = np.ones_like(machine.means)
stats = machine.e_step( data)
machine.m_step(stats)
@@ -447,6 +456,7 @@ def test_map_em():
n_gaussians = 2
prior_machine = GMMMachine(n_gaussians)
prior_machine.means = np.array([[2, 2, 2], [8, 8, 8]])
+ prior_machine.variances = np.ones_like(prior_machine.means)
prior_machine.weights = np.array([0.5, 0.5])
machine = GMMMachine(n_gaussians, trainer="map", ubm=prior_machine, update_means=True, update_variances=True, update_weights=True)
@@ -483,6 +493,7 @@ def test_ml_transformer():
machine = GMMMachine(n_gaussians, update_means=True, update_variances=True, update_weights=True)
machine.means = np.array([[2, 2, 2], [8, 8, 8]])
+ machine.variances = np.ones_like(machine.means)
machine = machine.fit(data)
@@ -514,6 +525,7 @@ def test_map_transformer():
n_features = 3
prior_machine = GMMMachine(n_gaussians)
prior_machine.means = np.array([[2, 2, 2], [8, 8, 8]])
+ prior_machine.variances = np.ones_like(prior_machine.means)
prior_machine.weights = np.array([0.5, 0.5])
machine = GMMMachine(n_gaussians, trainer="map", ubm=prior_machine, update_means=True, update_variances=True, update_weights=True)
@@ -586,7 +598,7 @@ def test_gmm_ML_1():
def test_gmm_ML_2():
- """Trains a GMMMachine with ML_GMMTrainer; compares to an old reference"""
+ """Trains a GMMMachine with ML_GMMTrainer; compares to a reference"""
ar = load_array(resource_filename("bob.learn.em", "data/dataNormalized.hdf5"))
# Initialize GMMMachine
--
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