diff --git a/bob/learn/em/gmm.py b/bob/learn/em/gmm.py
index ddf985321c3bffbe298218e23512d962a5d2d98a..38908cc7feee4ab3fa9d6f28e853534cd0c59357 100644
--- a/bob/learn/em/gmm.py
+++ b/bob/learn/em/gmm.py
@@ -35,21 +35,15 @@ def logaddexp_reduce(array, axis=0, keepdims=False):
)
-def log_weighted_likelihood(data, means, variances, g_norms, log_weights):
+def log_weighted_likelihood(data, machine):
"""Returns the weighted log likelihood for each Gaussian for a set of data.
Parameters
----------
data
Data to compute the log likelihood on.
- means
- Means of the Gaussians.
- variances
- Variances of the Gaussians.
- g_norms
- Normalization factors of the Gaussians.
- log_weights
- Log weights of the Gaussians.
+ machine
+ The GMM machine.
Returns
-------
@@ -57,12 +51,14 @@ def log_weighted_likelihood(data, means, variances, g_norms, log_weights):
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(means), len(data)
+ n_gaussians, n_samples = len(machine.means), len(data)
z = np.empty(shape=(n_gaussians, n_samples), like=data)
for i in range(n_gaussians):
- z[i] = np.sum((data - means[i]) ** 2 / variances[i], axis=-1)
- ll = -0.5 * (g_norms[:, None] + z)
- log_weighted_likelihoods = log_weights[:, None] + ll
+ z[i] = np.sum(
+ (data - machine.means[i]) ** 2 / machine.variances[i], axis=-1
+ )
+ ll = -0.5 * (machine.g_norms[:, None] + z)
+ log_weighted_likelihoods = machine.log_weights[:, None] + ll
return log_weighted_likelihoods
@@ -82,13 +78,15 @@ def reduce_loglikelihood(log_weighted_likelihoods):
return log_likelihood
-def log_likelihood(data, means, variances, g_norms, log_weights):
+def log_likelihood(data, machine):
"""Returns the current log likelihood for a set of data in this Machine.
Parameters
----------
data
Data to compute the log likelihood on.
+ machine
+ The GMM machine.
Returns
-------
@@ -100,29 +98,26 @@ def log_likelihood(data, means, variances, g_norms, log_weights):
# All likelihoods [array of shape (n_gaussians, n_samples)]
log_weighted_likelihoods = log_weighted_likelihood(
data=data,
- means=means,
- variances=variances,
- g_norms=g_norms,
- log_weights=log_weights,
+ machine=machine,
)
# Likelihoods of each sample on this machine. [array of shape (n_samples,)]
ll_reduced = reduce_loglikelihood(log_weighted_likelihoods)
return ll_reduced
-def e_step(data, weights, means, variances, g_norms, log_weights):
+def e_step(data, machine):
"""Expectation step of the e-m algorithm."""
# Ensure data is a series of samples (2D array)
data = np.atleast_2d(data)
- n_gaussians = len(weights)
+ n_gaussians = len(machine.weights)
# Allow the absence of previous statistics
statistics = GMMStats(n_gaussians, data.shape[-1])
# Log weighted Gaussian likelihoods [array of shape (n_gaussians,n_samples)]
log_weighted_likelihoods = log_weighted_likelihood(
- data, means, variances, g_norms, log_weights
+ data=data, machine=machine
)
# Log likelihood [array of shape (n_samples,)]
@@ -153,29 +148,22 @@ def e_step(data, weights, means, variances, g_norms, log_weights):
def m_step(
- machine,
statistics,
- update_means,
- update_variances,
- update_weights,
- mean_var_update_threshold,
- map_relevance_factor,
- map_alpha,
- trainer,
+ machine,
):
"""Maximization step of the e-m algorithm."""
- m_step_func = map_gmm_m_step if trainer == "map" else ml_gmm_m_step
+ m_step_func = map_gmm_m_step if machine.trainer == "map" else ml_gmm_m_step
statistics = functools.reduce(operator.iadd, statistics)
m_step_func(
machine=machine,
statistics=statistics,
- update_means=update_means,
- update_variances=update_variances,
- update_weights=update_weights,
- mean_var_update_threshold=mean_var_update_threshold,
- reynolds_adaptation=map_relevance_factor is not None,
- alpha=map_alpha,
- relevance_factor=map_relevance_factor,
+ update_means=machine.update_means,
+ update_variances=machine.update_variances,
+ update_weights=machine.update_weights,
+ mean_var_update_threshold=machine.mean_var_update_threshold,
+ reynolds_adaptation=machine.map_relevance_factor is not None,
+ alpha=machine.map_alpha,
+ relevance_factor=machine.map_relevance_factor,
)
average_output = float(statistics.log_likelihood / statistics.t)
return machine, average_output
@@ -684,12 +672,10 @@ class GMMMachine(BaseEstimator):
def __eq__(self, other):
return (
- np.array_equal(self.means, other.means)
- and np.array_equal(self.variances, other.variances)
- and np.array_equal(
- self.variance_thresholds, other.variance_thresholds
- )
- and np.array_equal(self.weights, other.weights)
+ np.allclose(self.means, other.means)
+ and np.allclose(self.variances, other.variances)
+ and np.allclose(self.variance_thresholds, other.variance_thresholds)
+ and np.allclose(self.weights, other.weights)
)
def is_similar_to(self, other, rtol=1e-5, atol=1e-8):
@@ -762,10 +748,7 @@ class GMMMachine(BaseEstimator):
"""
return log_weighted_likelihood(
data=data,
- means=self.means,
- variances=self.variances,
- g_norms=self.g_norms,
- log_weights=self.log_weights,
+ machine=self,
)
def log_likelihood(
@@ -786,10 +769,7 @@ class GMMMachine(BaseEstimator):
"""
return log_likelihood(
data=data,
- means=self.means,
- variances=self.variances,
- g_norms=self.g_norms,
- log_weights=self.log_weights,
+ machine=self,
)
def fit(self, X, y=None):
@@ -808,17 +788,6 @@ class GMMMachine(BaseEstimator):
)
self.variances = np.ones_like(self.means)
- m_step_func = functools.partial(
- m_step,
- update_means=self.update_means,
- update_variances=self.update_variances,
- update_weights=self.update_weights,
- mean_var_update_threshold=self.mean_var_update_threshold,
- map_relevance_factor=self.map_relevance_factor,
- map_alpha=self.map_alpha,
- trainer=self.trainer,
- )
-
X = array_to_delayed_list(X, input_is_dask)
average_output = 0
@@ -842,16 +811,12 @@ class GMMMachine(BaseEstimator):
stats = [
dask.delayed(e_step)(
data=xx,
- weights=self.weights,
- means=self.means,
- variances=self.variances,
- g_norms=self.g_norms,
- log_weights=self.log_weights,
+ machine=self,
)
for xx in X
]
new_machine, average_output = dask.compute(
- dask.delayed(m_step_func)(self, stats)
+ dask.delayed(m_step)(stats, self)
)[0]
for attr in ["weights", "means", "variances"]:
setattr(self, attr, getattr(new_machine, attr))
@@ -859,14 +824,10 @@ class GMMMachine(BaseEstimator):
stats = [
e_step(
data=X,
- weights=self.weights,
- means=self.means,
- variances=self.variances,
- g_norms=self.g_norms,
- log_weights=self.log_weights,
+ machine=self,
)
]
- _, average_output = m_step_func(self, stats)
+ _, average_output = m_step(stats, self)
logger.debug(f"log likelihood = {average_output}")
if step > 1:
@@ -897,12 +858,8 @@ class GMMMachine(BaseEstimator):
def transform(self, X, **kwargs):
"""Returns the statistics for `X`."""
return e_step(
- X,
- self.weights,
- self.means,
- self.variances,
- self.g_norms,
- self.log_weights,
+ data=X,
+ machine=self,
)
def _more_tags(self):
diff --git a/bob/learn/em/test/test_gmm.py b/bob/learn/em/test/test_gmm.py
index 65743ce3b95ff9bfa6a5712800a023942316c22f..a8962637b7ff113f6df29abd83f4f82cb98c6c72 100644
--- a/bob/learn/em/test/test_gmm.py
+++ b/bob/learn/em/test/test_gmm.py
@@ -14,7 +14,6 @@ import tempfile
from copy import deepcopy
-import dask.array as da
import numpy as np
from dask.distributed import Client
@@ -44,6 +43,33 @@ def multiprocess_dask_client():
client.close()
+def loadGMM():
+ gmm = GMMMachine(n_gaussians=2)
+
+ gmm.weights = load_array(
+ resource_filename("bob.learn.em", "data/gmm.init_weights.hdf5")
+ )
+ gmm.means = load_array(
+ resource_filename("bob.learn.em", "data/gmm.init_means.hdf5")
+ )
+ gmm.variances = load_array(
+ resource_filename("bob.learn.em", "data/gmm.init_variances.hdf5")
+ )
+
+ return gmm
+
+
+def assert_gmm_equal(gmm1, gmm2):
+ """Asserts that two GMMs are equal"""
+ np.testing.assert_almost_equal(gmm1.weights, gmm2.weights)
+ np.testing.assert_almost_equal(gmm1.means, gmm2.means)
+ np.testing.assert_almost_equal(gmm1.variances, gmm2.variances)
+ np.testing.assert_almost_equal(
+ gmm1.variance_thresholds, gmm2.variance_thresholds
+ )
+ assert gmm1 == gmm2
+
+
def test_GMMStats():
# Test a GMMStats
# Initializes a GMMStats
@@ -197,20 +223,16 @@ def test_GMMMachine():
gmm6.variances = variances
gmm6.variance_thresholds = varianceThresholds2
- assert gmm == gmm2
+ assert_gmm_equal(gmm, gmm2)
assert (gmm != gmm2) is False
assert gmm.is_similar_to(gmm2)
assert gmm != gmm3
- assert (gmm == gmm3) is False
assert gmm.is_similar_to(gmm3) is False
assert gmm != gmm4
- assert (gmm == gmm4) is False
assert gmm.is_similar_to(gmm4) is False
assert gmm != gmm5
- assert (gmm == gmm5) is False
assert gmm.is_similar_to(gmm5) is False
assert gmm != gmm6
- assert (gmm == gmm6) is False
assert gmm.is_similar_to(gmm6) is False
# Saving and loading
@@ -225,7 +247,7 @@ def test_GMMMachine():
assert type(gmm1.update_weights) is np.bool_
assert type(gmm1.trainer) is str
assert gmm1.ubm is None
- assert gmm == gmm1
+ assert_gmm_equal(gmm, gmm1)
# Using load
gmm1 = GMMMachine(n_gaussians=gmm.n_gaussians)
gmm1.load(HDF5File(filename, "r"))
@@ -235,13 +257,13 @@ def test_GMMMachine():
assert type(gmm1.update_weights) is np.bool_
assert type(gmm1.trainer) is str
assert gmm1.ubm is None
- assert gmm == gmm1
+ assert_gmm_equal(gmm, gmm1)
with tempfile.NamedTemporaryFile(suffix=".hdf5") as f:
filename = f.name
gmm.save(filename)
gmm1 = GMMMachine.from_hdf5(filename)
- assert gmm == gmm1
+ assert_gmm_equal(gmm, gmm1)
# Weights
n_gaussians = 5
@@ -286,11 +308,7 @@ def test_GMMMachine_stats():
stats = gmm_module.e_step(
arrayset,
- gmm.weights,
- gmm.means,
- gmm.variances,
- gmm.g_norms,
- gmm.log_weights,
+ gmm,
)
stats_ref = GMMStats(n_gaussians=2, n_features=2)
@@ -365,11 +383,7 @@ def test_GMMStats_operations():
# Populate the GMMStats
stats = gmm_module.e_step(
data,
- machine.weights,
- machine.means,
- machine.variances,
- machine.g_norms,
- machine.log_weights,
+ machine,
)
# Check shapes
@@ -473,13 +487,15 @@ def test_gmm_kmeans_plusplus_init():
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]])
- expected_variances = np.array(
- [[1 / 16, 1 / 16], [1 / 16, 1 / 16], [1 / 16, 1 / 16]]
- )
- np.testing.assert_almost_equal(machine.means, expected_means, decimal=3)
- np.testing.assert_almost_equal(machine.variances, expected_variances)
+ for transform in (to_numpy, to_dask_array):
+ data = transform(data)
+ machine = machine.fit(data)
+ expected_means = np.array([[2.25, 2.25], [-1.25, 0.25], [1.25, 1.25]])
+ expected_variances = np.array(
+ [[1 / 16, 1 / 16], [1 / 16, 1 / 16], [1 / 16, 1 / 16]]
+ )
+ np.testing.assert_almost_equal(machine.means, expected_means, decimal=3)
+ np.testing.assert_almost_equal(machine.variances, expected_variances)
def test_gmm_kmeans_parallel_init():
@@ -517,16 +533,18 @@ def test_likelihood():
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,
- ]
- )
- np.testing.assert_almost_equal(log_likelihood, expected_ll)
+ for transform in (to_numpy, to_dask_array):
+ data = transform(data)
+ log_likelihood = machine.log_likelihood(data)
+ expected_ll = np.array(
+ [
+ -3.6519900964986527,
+ -3.83151883210222,
+ -3.83151883210222,
+ -5.344374066745753,
+ ]
+ )
+ np.testing.assert_almost_equal(log_likelihood, expected_ll)
def test_likelihood_variance():
@@ -541,16 +559,18 @@ def test_likelihood_variance():
[1, 1, 1],
]
)
- log_likelihood = machine.log_likelihood(data)
- expected_ll = np.array(
- [
- -2.202846959440514,
- -3.8699524542323793,
- -4.229029034375473,
- -6.940892214952679,
- ]
- )
- np.testing.assert_almost_equal(log_likelihood, expected_ll)
+ for transform in (to_numpy, to_dask_array):
+ data = transform(data)
+ log_likelihood = machine.log_likelihood(data)
+ expected_ll = np.array(
+ [
+ -2.202846959440514,
+ -3.8699524542323793,
+ -4.229029034375473,
+ -6.940892214952679,
+ ]
+ )
+ np.testing.assert_almost_equal(log_likelihood, expected_ll)
def test_likelihood_weight():
@@ -560,16 +580,18 @@ def test_likelihood_weight():
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(
- [
- -4.206596356117164,
- -3.492325679996329,
- -3.634745457950943,
- -6.49485678536014,
- ]
- )
- np.testing.assert_almost_equal(log_likelihood, expected_ll)
+ for transform in (to_numpy, to_dask_array):
+ data = transform(data)
+ log_likelihood = machine.log_likelihood(data)
+ expected_ll = np.array(
+ [
+ -4.206596356117164,
+ -3.492325679996329,
+ -3.634745457950943,
+ -6.49485678536014,
+ ]
+ )
+ np.testing.assert_almost_equal(log_likelihood, expected_ll)
def test_GMMMachine_object():
@@ -618,22 +640,11 @@ def test_ml_em():
stats = gmm_module.e_step(
data,
- machine.weights,
- machine.means,
- machine.variances,
- machine.g_norms,
- machine.log_weights,
+ machine,
)
gmm_module.m_step(
- machine,
[stats],
- machine.update_means,
- machine.update_variances,
- machine.update_weights,
- machine.mean_var_update_threshold,
- machine.map_relevance_factor,
- machine.map_alpha,
- machine.trainer,
+ machine,
)
expected_means = np.array([[1.5, 1.5, 2.0], [7.0, 8.0, 8.0]])
@@ -674,22 +685,11 @@ def test_map_em():
stats = gmm_module.e_step(
post_data,
- machine.weights,
- machine.means,
- machine.variances,
- machine.g_norms,
- machine.log_weights,
+ machine,
)
gmm_module.m_step(
- machine,
[stats],
- machine.update_means,
- machine.update_variances,
- machine.update_weights,
- machine.mean_var_update_threshold,
- machine.map_relevance_factor,
- machine.map_alpha,
- machine.trainer,
+ machine,
)
expected_means = np.array(
@@ -718,27 +718,31 @@ def test_ml_transformer():
machine.means = np.array([[2, 2, 2], [8, 8, 8]])
machine.variances = np.ones_like(machine.means)
- machine = machine.fit(data)
-
- expected_means = np.array([[1.5, 1.5, 2.0], [7.0, 8.0, 8.0]])
- np.testing.assert_almost_equal(machine.means, expected_means)
- expected_weights = np.array([2 / 5, 3 / 5])
- np.testing.assert_almost_equal(machine.weights, expected_weights)
- eps = np.finfo(float).eps
- expected_variances = np.array([[1 / 4, 1 / 4, eps], [eps, 2 / 3, 2 / 3]])
- np.testing.assert_almost_equal(machine.variances, expected_variances)
+ for transform in (to_numpy, to_dask_array):
+ data = transform(data)
+ machine = machine.fit(data)
+
+ expected_means = np.array([[1.5, 1.5, 2.0], [7.0, 8.0, 8.0]])
+ np.testing.assert_almost_equal(machine.means, expected_means)
+ expected_weights = np.array([2 / 5, 3 / 5])
+ np.testing.assert_almost_equal(machine.weights, expected_weights)
+ eps = np.finfo(float).eps
+ expected_variances = np.array(
+ [[1 / 4, 1 / 4, eps], [eps, 2 / 3, 2 / 3]]
+ )
+ np.testing.assert_almost_equal(machine.variances, expected_variances)
- stats = machine.transform(test_data)
+ stats = machine.transform(test_data)
- expected_stats = GMMStats(n_gaussians, n_features)
- expected_stats.init_fields(
- log_likelihood=-6755399441055685.0,
- t=test_data.shape[0],
- n=np.array([2, 2], dtype=float),
- sum_px=np.array([[2, 2, 3], [16, 17, 17]], dtype=float),
- sum_pxx=np.array([[2, 2, 5], [128, 145, 145]], dtype=float),
- )
- assert stats.is_similar_to(expected_stats)
+ expected_stats = GMMStats(n_gaussians, n_features)
+ expected_stats.init_fields(
+ log_likelihood=-6755399441055685.0,
+ t=test_data.shape[0],
+ n=np.array([2, 2], dtype=float),
+ sum_px=np.array([[2, 2, 3], [16, 17, 17]], dtype=float),
+ sum_pxx=np.array([[2, 2, 5], [128, 145, 145]], dtype=float),
+ )
+ assert stats.is_similar_to(expected_stats)
def test_map_transformer():
@@ -762,79 +766,69 @@ def test_map_transformer():
update_weights=True,
)
- machine = machine.fit(post_data)
+ for transform in (to_numpy, to_dask_array):
+ post_data = transform(post_data)
+ machine = machine.fit(post_data)
- expected_means = np.array(
- [[1.83333333, 1.83333333, 2.0], [7.57142857, 8, 8]]
- )
- np.testing.assert_almost_equal(machine.means, expected_means)
- eps = np.finfo(float).eps
- expected_vars = np.array([[eps, eps, eps], [eps, eps, eps]])
- np.testing.assert_almost_equal(machine.variances, expected_vars)
- expected_weights = np.array([0.46226415, 0.53773585])
- np.testing.assert_almost_equal(machine.weights, expected_weights)
-
- stats = machine.transform(test_data)
-
- expected_stats = GMMStats(n_gaussians, n_features)
- expected_stats.init_fields(
- log_likelihood=-1.3837590691807108e16,
- t=test_data.shape[0],
- n=np.array([2, 2], dtype=float),
- sum_px=np.array([[2, 2, 3], [16, 17, 17]], dtype=float),
- sum_pxx=np.array([[2, 2, 5], [128, 145, 145]], dtype=float),
- )
- assert stats.is_similar_to(expected_stats)
+ expected_means = np.array(
+ [[1.83333333, 1.83333333, 2.0], [7.57142857, 8, 8]]
+ )
+ np.testing.assert_almost_equal(machine.means, expected_means)
+ eps = np.finfo(float).eps
+ expected_vars = np.array([[eps, eps, eps], [eps, eps, eps]])
+ np.testing.assert_almost_equal(machine.variances, expected_vars)
+ expected_weights = np.array([0.46226415, 0.53773585])
+ np.testing.assert_almost_equal(machine.weights, expected_weights)
+
+ stats = machine.transform(test_data)
+
+ expected_stats = GMMStats(n_gaussians, n_features)
+ expected_stats.init_fields(
+ log_likelihood=-1.3837590691807108e16,
+ t=test_data.shape[0],
+ n=np.array([2, 2], dtype=float),
+ sum_px=np.array([[2, 2, 3], [16, 17, 17]], dtype=float),
+ sum_pxx=np.array([[2, 2, 5], [128, 145, 145]], dtype=float),
+ )
+ assert stats.is_similar_to(expected_stats)
# Tests from `test_em.py`
-def loadGMM():
- gmm = GMMMachine(n_gaussians=2)
-
- gmm.weights = load_array(
- resource_filename("bob.learn.em", "data/gmm.init_weights.hdf5")
- )
- gmm.means = load_array(
- resource_filename("bob.learn.em", "data/gmm.init_means.hdf5")
- )
- gmm.variances = load_array(
- resource_filename("bob.learn.em", "data/gmm.init_variances.hdf5")
- )
-
- return gmm
-
-
def test_gmm_ML_1():
"""Trains a GMMMachine with ML_GMMTrainer"""
ar = load_array(
resource_filename("bob.learn.em", "data/faithful.torch3_f64.hdf5")
)
- gmm = loadGMM()
+ gmm_ref = GMMMachine.from_hdf5(
+ HDF5File(resource_filename("bob.learn.em", "data/gmm_ML.hdf5"), "r")
+ )
- # test rng handling
- gmm.convergence_threshold = 0.001
- gmm.update_means = True
- gmm.update_variances = True
- gmm.update_weights = True
- gmm.random_state = np.random.RandomState(seed=12345)
- gmm = gmm.fit(ar)
+ for transform in (to_numpy, to_dask_array):
+ ar = transform(ar)
- gmm = loadGMM()
- gmm.convergence_threshold = 0.001
- gmm.update_means = True
- gmm.update_variances = True
- gmm.update_weights = True
- gmm = gmm.fit(ar)
+ gmm = loadGMM()
- # Generate reference
- # gmm.save(HDF5File(resource_filename("bob.learn.em", "data/gmm_ML.hdf5"), "w"))
+ # test rng handling
+ gmm.convergence_threshold = 0.001
+ gmm.update_means = True
+ gmm.update_variances = True
+ gmm.update_weights = True
+ gmm.random_state = np.random.RandomState(seed=12345)
+ gmm = gmm.fit(ar)
- gmm_ref = GMMMachine.from_hdf5(
- HDF5File(resource_filename("bob.learn.em", "data/gmm_ML.hdf5"), "r")
- )
- assert gmm == gmm_ref
+ gmm = loadGMM()
+ gmm.convergence_threshold = 0.001
+ gmm.update_means = True
+ gmm.update_variances = True
+ gmm.update_weights = True
+ # Generate reference
+ # gmm.save(HDF5File(resource_filename("bob.learn.em", "data/gmm_ML.hdf5"), "w"))
+
+ gmm = gmm.fit(ar)
+
+ assert_gmm_equal(gmm, gmm_ref)
def test_gmm_ML_2():
@@ -843,33 +837,6 @@ def test_gmm_ML_2():
resource_filename("bob.learn.em", "data/dataNormalized.hdf5")
)
- # Initialize GMMMachine
- gmm = GMMMachine(n_gaussians=5)
- gmm.means = load_array(
- resource_filename("bob.learn.em", "data/meansAfterKMeans.hdf5")
- ).astype("float64")
- gmm.variances = load_array(
- resource_filename("bob.learn.em", "data/variancesAfterKMeans.hdf5")
- ).astype("float64")
- gmm.weights = np.exp(
- load_array(
- resource_filename("bob.learn.em", "data/weightsAfterKMeans.hdf5")
- ).astype("float64")
- )
-
- threshold = 0.001
- gmm.variance_thresholds = threshold
-
- # Initialize ML Trainer
- gmm.mean_var_update_threshold = 0.001
- gmm.max_fitting_steps = 25
- gmm.convergence_threshold = 0.000001
- gmm.update_means = True
- gmm.update_variances = True
- gmm.update_weights = True
-
- # Run ML
- gmm = gmm.fit(ar)
# Test results
# Load torch3vision reference
meansML_ref = load_array(
@@ -882,10 +849,42 @@ def test_gmm_ML_2():
resource_filename("bob.learn.em", "data/weightsAfterML.hdf5")
)
- # Compare to current results
- np.testing.assert_allclose(gmm.means, meansML_ref, atol=3e-3)
- np.testing.assert_allclose(gmm.variances, variancesML_ref, atol=3e-3)
- np.testing.assert_allclose(gmm.weights, weightsML_ref, atol=1e-4)
+ for transform in (to_numpy, to_dask_array):
+ ar = transform(ar)
+ # Initialize GMMMachine
+ gmm = GMMMachine(n_gaussians=5)
+ gmm.means = load_array(
+ resource_filename("bob.learn.em", "data/meansAfterKMeans.hdf5")
+ ).astype("float64")
+ gmm.variances = load_array(
+ resource_filename("bob.learn.em", "data/variancesAfterKMeans.hdf5")
+ ).astype("float64")
+ gmm.weights = np.exp(
+ load_array(
+ resource_filename(
+ "bob.learn.em", "data/weightsAfterKMeans.hdf5"
+ )
+ ).astype("float64")
+ )
+
+ threshold = 0.001
+ gmm.variance_thresholds = threshold
+
+ # Initialize ML Trainer
+ gmm.mean_var_update_threshold = 0.001
+ gmm.max_fitting_steps = 25
+ gmm.convergence_threshold = 0.000001
+ gmm.update_means = True
+ gmm.update_variances = True
+ gmm.update_weights = True
+
+ # Run ML
+ gmm = gmm.fit(ar)
+
+ # Compare to current results
+ np.testing.assert_allclose(gmm.means, meansML_ref, atol=3e-3)
+ np.testing.assert_allclose(gmm.variances, variancesML_ref, atol=3e-3)
+ np.testing.assert_allclose(gmm.weights, weightsML_ref, atol=1e-4)
def test_gmm_MAP_1():
@@ -920,8 +919,6 @@ def test_gmm_MAP_1():
gmm.update_variances = False
gmm.update_weights = False
- gmm = gmm.fit(ar)
-
# Generate reference
# gmm.save(HDF5File(resource_filename("bob.learn.em", "data/gmm_MAP.hdf5"), "w"))
@@ -929,9 +926,15 @@ def test_gmm_MAP_1():
HDF5File(resource_filename("bob.learn.em", "data/gmm_MAP.hdf5"), "r")
)
- np.testing.assert_almost_equal(gmm.means, gmm_ref.means, decimal=3)
- np.testing.assert_almost_equal(gmm.variances, gmm_ref.variances, decimal=3)
- np.testing.assert_almost_equal(gmm.weights, gmm_ref.weights, decimal=3)
+ for transform in (to_numpy, to_dask_array):
+ ar = transform(ar)
+ gmm = gmm.fit(ar)
+
+ np.testing.assert_almost_equal(gmm.means, gmm_ref.means, decimal=3)
+ np.testing.assert_almost_equal(
+ gmm.variances, gmm_ref.variances, decimal=3
+ )
+ np.testing.assert_almost_equal(gmm.weights, gmm_ref.weights, decimal=3)
def test_gmm_MAP_2():
@@ -964,14 +967,16 @@ def test_gmm_MAP_2():
gmm_adapted.variances = variances
gmm_adapted.weights = weights
- gmm_adapted = gmm_adapted.fit(data)
-
new_means = load_array(
resource_filename("bob.learn.em", "data/new_adapted_mean.hdf5")
)
- # Compare to matlab reference
- np.testing.assert_allclose(new_means.T, gmm_adapted.means, rtol=1e-4)
+ for transform in (to_numpy, to_dask_array):
+ data = transform(data)
+ gmm_adapted = gmm_adapted.fit(data)
+
+ # Compare to matlab reference
+ np.testing.assert_allclose(new_means.T, gmm_adapted.means, rtol=1e-4)
def test_gmm_MAP_3():
@@ -1011,9 +1016,6 @@ def test_gmm_MAP_3():
)
gmm.variance_thresholds = threshold
- # Train
- gmm = gmm.fit(ar)
-
# Test results
# Load torch3vision reference
meansMAP_ref = load_array(
@@ -1026,13 +1028,18 @@ def test_gmm_MAP_3():
resource_filename("bob.learn.em", "data/weightsAfterMAP.hdf5")
)
- # Compare to current results
- # Gaps are quite large. This might be explained by the fact that there is no
- # adaptation of a given Gaussian in torch3 when the corresponding responsibilities
- # are below the responsibilities threshold
- np.testing.assert_allclose(gmm.means, meansMAP_ref, atol=2e-1)
- np.testing.assert_allclose(gmm.variances, variancesMAP_ref, atol=1e-4)
- np.testing.assert_allclose(gmm.weights, weightsMAP_ref, atol=1e-4)
+ for transform in (to_numpy, to_dask_array):
+ ar = transform(ar)
+ # Train
+ gmm = gmm.fit(ar)
+
+ # Compare to current results
+ # Gaps are quite large. This might be explained by the fact that there is no
+ # adaptation of a given Gaussian in torch3 when the corresponding responsibilities
+ # are below the responsibilities threshold
+ np.testing.assert_allclose(gmm.means, meansMAP_ref, atol=2e-1)
+ np.testing.assert_allclose(gmm.variances, variancesMAP_ref, atol=1e-4)
+ np.testing.assert_allclose(gmm.weights, weightsMAP_ref, atol=1e-4)
def test_gmm_test():
@@ -1058,126 +1065,10 @@ def test_gmm_test():
# Test against the model
score_mean_ref = -1.50379e06
- score = gmm.log_likelihood(ar).sum()
- score /= len(ar)
-
- # Compare current results to torch3vision
- assert abs(score - score_mean_ref) / score_mean_ref < 1e-4
-
-
-def test_gmm_ML_dask():
- # Trains a GMMMachine with dask array data; compares to a reference
-
- ar = da.array(
- load_array(
- resource_filename("bob.learn.em", "data/dataNormalized.hdf5")
- )
- )
-
- # Initialize GMMMachine
- gmm = GMMMachine(n_gaussians=5)
- gmm.means = load_array(
- resource_filename("bob.learn.em", "data/meansAfterKMeans.hdf5")
- ).astype("float64")
- gmm.variances = load_array(
- resource_filename("bob.learn.em", "data/variancesAfterKMeans.hdf5")
- ).astype("float64")
- gmm.weights = np.exp(
- load_array(
- resource_filename("bob.learn.em", "data/weightsAfterKMeans.hdf5")
- ).astype("float64")
- )
-
- threshold = 0.001
- gmm.variance_thresholds = threshold
-
- # Initialize ML Trainer
- gmm.mean_var_update_threshold = 0.001
- gmm.max_fitting_steps = 25
- gmm.convergence_threshold = 0.00001
- gmm.update_means = True
- gmm.update_variances = True
- gmm.update_weights = True
-
- # Run ML
- gmm.fit(ar)
-
- # Test results
- # Load torch3vision reference
- meansML_ref = load_array(
- resource_filename("bob.learn.em", "data/meansAfterML.hdf5")
- )
- variancesML_ref = load_array(
- resource_filename("bob.learn.em", "data/variancesAfterML.hdf5")
- )
- weightsML_ref = load_array(
- resource_filename("bob.learn.em", "data/weightsAfterML.hdf5")
- )
-
- # Compare to current results
- np.testing.assert_allclose(gmm.means, meansML_ref, atol=3e-3)
- np.testing.assert_allclose(gmm.variances, variancesML_ref, atol=3e-3)
- np.testing.assert_allclose(gmm.weights, weightsML_ref, atol=1e-4)
-
-
-def test_gmm_MAP_dask():
- # Test a GMMMachine for MAP with a dask array as data.
- ar = da.array(
- load_array(resource_filename("bob.learn.em", "data/dataforMAP.hdf5"))
- )
-
- # Initialize GMMMachine
- n_gaussians = 5
- prior_gmm = GMMMachine(n_gaussians)
- prior_gmm.means = load_array(
- resource_filename("bob.learn.em", "data/meansAfterML.hdf5")
- )
- prior_gmm.variances = load_array(
- resource_filename("bob.learn.em", "data/variancesAfterML.hdf5")
- )
- prior_gmm.weights = load_array(
- resource_filename("bob.learn.em", "data/weightsAfterML.hdf5")
- )
-
- threshold = 0.001
- prior_gmm.variance_thresholds = threshold
-
- # Initialize MAP Trainer
- prior = 0.001
- accuracy = 0.00001
- gmm = GMMMachine(
- n_gaussians,
- trainer="map",
- ubm=prior_gmm,
- convergence_threshold=prior,
- max_fitting_steps=1,
- update_means=True,
- update_variances=False,
- update_weights=False,
- mean_var_update_threshold=accuracy,
- map_relevance_factor=None,
- )
- gmm.variance_thresholds = threshold
-
- # Train
- gmm = gmm.fit(ar)
-
- # Test results
- # Load torch3vision reference
- meansMAP_ref = load_array(
- resource_filename("bob.learn.em", "data/meansAfterMAP.hdf5")
- )
- variancesMAP_ref = load_array(
- resource_filename("bob.learn.em", "data/variancesAfterMAP.hdf5")
- )
- weightsMAP_ref = load_array(
- resource_filename("bob.learn.em", "data/weightsAfterMAP.hdf5")
- )
+ for transform in (to_numpy, to_dask_array):
+ ar = transform(ar)
+ score = gmm.log_likelihood(ar).sum()
+ score /= len(ar)
- # Compare to current results
- # Gaps are quite large. This might be explained by the fact that there is no
- # adaptation of a given Gaussian in torch3 when the corresponding responsibilities
- # are below the responsibilities threshold
- np.testing.assert_allclose(gmm.means, meansMAP_ref, atol=2e-1)
- np.testing.assert_allclose(gmm.variances, variancesMAP_ref, atol=1e-4)
- np.testing.assert_allclose(gmm.weights, weightsMAP_ref, atol=1e-4)
+ # Compare current results to torch3vision
+ assert abs(score - score_mean_ref) / score_mean_ref < 1e-4
diff --git a/bob/learn/em/test/test_kmeans.py b/bob/learn/em/test/test_kmeans.py
index 8edc205ab9ecf2f6f15a8c9e4213f57cb219f804..613a562bf864b4d66b31ae5a410fc6ad6298e164 100644
--- a/bob/learn/em/test/test_kmeans.py
+++ b/bob/learn/em/test/test_kmeans.py
@@ -33,7 +33,7 @@ def to_dask_array(*args):
for x in args:
x = np.asarray(x)
chunks = list(x.shape)
- chunks[0] //= 2
+ chunks[0] = int(np.ceil(chunks[0] / 2))
result.append(da.from_array(x, chunks=chunks))
if len(result) == 1:
return result[0]