diff --git a/bob/learn/em/test/test_kmeans_trainer.py b/bob/learn/em/test/test_kmeans_trainer.py
index 3bc499ea8194e7f29150826776e1601d1ddb1f82..978ce7234461725d7d96c006eb4ba66c299dd104 100644
--- a/bob/learn/em/test/test_kmeans_trainer.py
+++ b/bob/learn/em/test/test_kmeans_trainer.py
@@ -15,159 +15,179 @@ from bob.io.base.test_utils import datafile
from bob.learn.em import KMeansMachine, KMeansTrainer
+
def equals(x, y, epsilon):
- return (abs(x - y) < epsilon).all()
+ return (abs(x - y) < epsilon).all()
+
def kmeans_plus_plus(machine, data, seed):
- """Python implementation of K-Means++ (initialization)"""
- n_data = data.shape[0]
- rng = bob.core.random.mt19937(seed)
- u = bob.core.random.uniform('int32', 0, n_data-1)
- index = u(rng)
- machine.set_mean(0, data[index,:])
- weights = numpy.zeros(shape=(n_data,), dtype=numpy.float64)
-
- for m in range(1,machine.dim_c):
- for s in range(n_data):
- s_cur = data[s,:]
- w_cur = machine.get_distance_from_mean(s_cur, 0)
- for i in range(m):
- w_cur = min(machine.get_distance_from_mean(s_cur, i), w_cur)
- weights[s] = w_cur
- weights *= weights
- weights /= numpy.sum(weights)
- d = bob.core.random.discrete('int32', weights)
- index = d(rng)
- machine.set_mean(m, data[index,:])
+ """Python implementation of K-Means++ (initialization)"""
+ n_data = data.shape[0]
+ rng = bob.core.random.mt19937(seed)
+ u = bob.core.random.uniform('int32', 0, n_data - 1)
+ index = u(rng)
+ machine.set_mean(0, data[index, :])
+ weights = numpy.zeros(shape=(n_data,), dtype=numpy.float64)
+
+ for m in range(1, machine.dim_c):
+ for s in range(n_data):
+ s_cur = data[s, :]
+ w_cur = machine.get_distance_from_mean(s_cur, 0)
+ for i in range(m):
+ w_cur = min(machine.get_distance_from_mean(s_cur, i), w_cur)
+ weights[s] = w_cur
+ weights *= weights
+ weights /= numpy.sum(weights)
+ d = bob.core.random.discrete('int32', weights)
+ index = d(rng)
+ machine.set_mean(m, data[index, :])
def NormalizeStdArray(path):
- array = bob.io.base.load(path).astype('float64')
- std = array.std(axis=0)
- return (array/std, std)
+ array = bob.io.base.load(path).astype('float64')
+ std = array.std(axis=0)
+ return (array / std, std)
+
def multiplyVectorsByFactors(matrix, vector):
- for i in range(0, matrix.shape[0]):
- for j in range(0, matrix.shape[1]):
- matrix[i, j] *= vector[j]
+ for i in range(0, matrix.shape[0]):
+ for j in range(0, matrix.shape[1]):
+ matrix[i, j] *= vector[j]
+
def flipRows(array):
- if len(array.shape) == 2:
- return numpy.array([numpy.array(array[1, :]), numpy.array(array[0, :])], 'float64')
- elif len(array.shape) == 1:
- return numpy.array([array[1], array[0]], 'float64')
- else:
- raise Exception('Input type not supportd by flipRows')
+ if len(array.shape) == 2:
+ return numpy.array([numpy.array(array[1, :]), numpy.array(array[0, :])], 'float64')
+ elif len(array.shape) == 1:
+ return numpy.array([array[1], array[0]], 'float64')
+ else:
+ raise Exception('Input type not supportd by flipRows')
+
if hasattr(KMeansTrainer, 'KMEANS_PLUS_PLUS'):
- def test_kmeans_plus_plus():
+ def test_kmeans_plus_plus():
+ # Tests the K-Means++ initialization
+ dim_c = 5
+ dim_d = 7
+ n_samples = 150
+ data = numpy.random.randn(n_samples, dim_d)
+ seed = 0
+
+ # C++ implementation
+ machine = KMeansMachine(dim_c, dim_d)
+ trainer = KMeansTrainer()
+ trainer.rng = bob.core.random.mt19937(seed)
+ trainer.initialization_method = 'KMEANS_PLUS_PLUS'
+ trainer.initialize(machine, data)
+
+ # Python implementation
+ py_machine = KMeansMachine(dim_c, dim_d)
+ kmeans_plus_plus(py_machine, data, seed)
+ assert equals(machine.means, py_machine.means, 1e-8)
- # Tests the K-Means++ initialization
- dim_c = 5
- dim_d = 7
- n_samples = 150
- data = numpy.random.randn(n_samples,dim_d)
- seed = 0
- # C++ implementation
+def test_kmeans_noduplicate():
+ # Data/dimensions
+ dim_c = 2
+ dim_d = 3
+ seed = 0
+ data = numpy.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [4, 5, 6.]])
+ # Defines machine and trainer
machine = KMeansMachine(dim_c, dim_d)
trainer = KMeansTrainer()
- trainer.rng = bob.core.random.mt19937(seed)
- trainer.initialization_method = 'KMEANS_PLUS_PLUS'
- trainer.initialize(machine, data)
-
- # Python implementation
- py_machine = KMeansMachine(dim_c, dim_d)
- kmeans_plus_plus(py_machine, data, seed)
- assert equals(machine.means, py_machine.means, 1e-8)
-
-def test_kmeans_noduplicate():
- # Data/dimensions
- dim_c = 2
- dim_d = 3
- seed = 0
- data = numpy.array([[1,2,3],[1,2,3],[1,2,3],[4,5,6.]])
- # Defines machine and trainer
- machine = KMeansMachine(dim_c, dim_d)
- trainer = KMeansTrainer()
- rng = bob.core.random.mt19937(seed)
- trainer.initialization_method = 'RANDOM_NO_DUPLICATE'
- trainer.initialize(machine, data, rng)
- # Makes sure that the two initial mean vectors selected are different
- assert equals(machine.get_mean(0), machine.get_mean(1), 1e-8) == False
+ rng = bob.core.random.mt19937(seed)
+ trainer.initialization_method = 'RANDOM_NO_DUPLICATE'
+ trainer.initialize(machine, data, rng)
+ # Makes sure that the two initial mean vectors selected are different
+ assert equals(machine.get_mean(0), machine.get_mean(1), 1e-8) == False
def test_kmeans_a():
+ # Trains a KMeansMachine
+ # This files contains draws from two 1D Gaussian distributions:
+ # * 100 samples from N(-10,1)
+ # * 100 samples from N(10,1)
+ data = bob.io.base.load(datafile("samplesFrom2G_f64.hdf5", __name__, path="../data/"))
- # Trains a KMeansMachine
- # This files contains draws from two 1D Gaussian distributions:
- # * 100 samples from N(-10,1)
- # * 100 samples from N(10,1)
- data = bob.io.base.load(datafile("samplesFrom2G_f64.hdf5", __name__, path="../data/"))
-
- machine = KMeansMachine(2, 1)
-
- trainer = KMeansTrainer()
- #trainer.train(machine, data)
- bob.learn.em.train(trainer,machine,data)
+ machine = KMeansMachine(2, 1)
- [variances, weights] = machine.get_variances_and_weights_for_each_cluster(data)
- variances_b = numpy.ndarray(shape=(2,1), dtype=numpy.float64)
- weights_b = numpy.ndarray(shape=(2,), dtype=numpy.float64)
- machine.__get_variances_and_weights_for_each_cluster_init__(variances_b, weights_b)
- machine.__get_variances_and_weights_for_each_cluster_acc__(data, variances_b, weights_b)
- machine.__get_variances_and_weights_for_each_cluster_fin__(variances_b, weights_b)
- m1 = machine.get_mean(0)
- m2 = machine.get_mean(1)
+ trainer = KMeansTrainer()
+ # trainer.train(machine, data)
+ bob.learn.em.train(trainer, machine, data)
+
+ [variances, weights] = machine.get_variances_and_weights_for_each_cluster(data)
+ variances_b = numpy.ndarray(shape=(2, 1), dtype=numpy.float64)
+ weights_b = numpy.ndarray(shape=(2,), dtype=numpy.float64)
+ machine.__get_variances_and_weights_for_each_cluster_init__(variances_b, weights_b)
+ machine.__get_variances_and_weights_for_each_cluster_acc__(data, variances_b, weights_b)
+ machine.__get_variances_and_weights_for_each_cluster_fin__(variances_b, weights_b)
+ m1 = machine.get_mean(0)
+ m2 = machine.get_mean(1)
+
+ ## Check means [-10,10] / variances [1,1] / weights [0.5,0.5]
+ if (m1 < m2):
+ means = numpy.array(([m1[0], m2[0]]), 'float64')
+ else:
+ means = numpy.array(([m2[0], m1[0]]), 'float64')
+ assert equals(means, numpy.array([-10., 10.]), 2e-1)
+ assert equals(variances, numpy.array([1., 1.]), 2e-1)
+ assert equals(weights, numpy.array([0.5, 0.5]), 1e-3)
+
+ assert equals(variances, variances_b, 1e-8)
+ assert equals(weights, weights_b, 1e-8)
- ## Check means [-10,10] / variances [1,1] / weights [0.5,0.5]
- if(m1<m2): means=numpy.array(([m1[0],m2[0]]), 'float64')
- else: means=numpy.array(([m2[0],m1[0]]), 'float64')
- assert equals(means, numpy.array([-10.,10.]), 2e-1)
- assert equals(variances, numpy.array([1.,1.]), 2e-1)
- assert equals(weights, numpy.array([0.5,0.5]), 1e-3)
- assert equals(variances, variances_b, 1e-8)
- assert equals(weights, weights_b, 1e-8)
+def test_kmeans_b():
+ # Trains a KMeansMachine
+ (arStd, std) = NormalizeStdArray(datafile("faithful.torch3.hdf5", __name__, path="../data/"))
+ machine = KMeansMachine(2, 2)
+ trainer = KMeansTrainer()
+ # trainer.seed = 1337
+ bob.learn.em.train(trainer, machine, arStd, convergence_threshold=0.001)
-def test_kmeans_b():
+ [variances, weights] = machine.get_variances_and_weights_for_each_cluster(arStd)
- # Trains a KMeansMachine
- (arStd,std) = NormalizeStdArray(datafile("faithful.torch3.hdf5", __name__, path="../data/"))
+ means = numpy.array(machine.means)
+ variances = numpy.array(variances)
- machine = KMeansMachine(2, 2)
+ multiplyVectorsByFactors(means, std)
+ multiplyVectorsByFactors(variances, std ** 2)
- trainer = KMeansTrainer()
- #trainer.seed = 1337
- bob.learn.em.train(trainer,machine, arStd, convergence_threshold=0.001)
+ gmmWeights = bob.io.base.load(datafile('gmm.init_weights.hdf5', __name__, path="../data/"))
+ gmmMeans = bob.io.base.load(datafile('gmm.init_means.hdf5', __name__, path="../data/"))
+ gmmVariances = bob.io.base.load(datafile('gmm.init_variances.hdf5', __name__, path="../data/"))
- [variances, weights] = machine.get_variances_and_weights_for_each_cluster(arStd)
+ if (means[0, 0] < means[1, 0]):
+ means = flipRows(means)
+ variances = flipRows(variances)
+ weights = flipRows(weights)
- means = numpy.array(machine.means)
- variances = numpy.array(variances)
+ assert equals(means, gmmMeans, 1e-3)
+ assert equals(weights, gmmWeights, 1e-3)
+ assert equals(variances, gmmVariances, 1e-3)
- multiplyVectorsByFactors(means, std)
- multiplyVectorsByFactors(variances, std ** 2)
+ # Check that there is no duplicate means during initialization
+ machine = KMeansMachine(2, 1)
+ trainer = KMeansTrainer()
+ trainer.initialization_method = 'RANDOM_NO_DUPLICATE'
+ data = numpy.array([[1.], [1.], [1.], [1.], [1.], [1.], [2.], [3.]])
+ bob.learn.em.train(trainer, machine, data)
+ assert (numpy.isnan(machine.means).any()) == False
- gmmWeights = bob.io.base.load(datafile('gmm.init_weights.hdf5', __name__, path="../data/"))
- gmmMeans = bob.io.base.load(datafile('gmm.init_means.hdf5', __name__, path="../data/"))
- gmmVariances = bob.io.base.load(datafile('gmm.init_variances.hdf5', __name__, path="../data/"))
- if (means[0, 0] < means[1, 0]):
- means = flipRows(means)
- variances = flipRows(variances)
- weights = flipRows(weights)
+def test_trainer_execption():
+ from nose.tools import assert_raises
- assert equals(means, gmmMeans, 1e-3)
- assert equals(weights, gmmWeights, 1e-3)
- assert equals(variances, gmmVariances, 1e-3)
+ # Testing Inf
+ machine = KMeansMachine(2, 2)
+ data = numpy.array([[1.0, 2.0], [2, 3.], [1, 1.], [2, 5.], [numpy.inf, 1.0]])
+ trainer = KMeansTrainer()
+ assert_raises(ValueError, bob.learn.em.train, trainer, machine, data, 0.001)
- # Check that there is no duplicate means during initialization
- machine = KMeansMachine(2, 1)
- trainer = KMeansTrainer()
- trainer.initialization_method = 'RANDOM_NO_DUPLICATE'
- data = numpy.array([[1.], [1.], [1.], [1.], [1.], [1.], [2.], [3.]])
- bob.learn.em.train(trainer, machine, data)
- assert (numpy.isnan(machine.means).any()) == False
+ # Testing Nan
+ machine = KMeansMachine(2, 2)
+ data = numpy.array([[1.0, 2.0], [2, 3.], [1, numpy.nan], [2, 5.], [2.0, 1.0]])
+ trainer = KMeansTrainer()
+ assert_raises(ValueError, bob.learn.em.train, trainer, machine, data, 0.001)
diff --git a/bob/learn/em/train.py b/bob/learn/em/train.py
index 81a41cbe2e85ec90200238bcc5c9879690bc2131..a4263e7830a7e7afeb5e441bf02b056b6725745e 100644
--- a/bob/learn/em/train.py
+++ b/bob/learn/em/train.py
@@ -7,112 +7,123 @@
import numpy
import bob.learn.em
import logging
+
logger = logging.getLogger('bob.learn.em')
-def train(trainer, machine, data, max_iterations = 50, convergence_threshold=None, initialize=True, rng=None):
-
- """
- Trains a machine given a trainer and the proper data
-
- **Parameters**:
- trainer : one of :py:class:`KMeansTrainer`, :py:class:`MAP_GMMTrainer`, :py:class:`ML_GMMTrainer`, :py:class:`ISVTrainer`, :py:class:`IVectorTrainer`, :py:class:`PLDATrainer`, :py:class:`EMPCATrainer`
- A trainer mechanism
- machine : one of :py:class:`KMeansMachine`, :py:class:`GMMMachine`, :py:class:`ISVBase`, :py:class:`IVectorMachine`, :py:class:`PLDAMachine`, :py:class:`bob.learn.linear.Machine`
- A container machine
- data : array_like <float, 2D>
- The data to be trained
- max_iterations : int
- The maximum number of iterations to train a machine
- convergence_threshold : float
- The convergence threshold to train a machine. If None, the training procedure will stop with the iterations criteria
- initialize : bool
- If True, runs the initialization procedure
- rng : :py:class:`bob.core.random.mt19937`
- The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loop
- """
- #Initialization
- if initialize:
- if rng is not None:
- trainer.initialize(machine, data, rng)
- else:
- trainer.initialize(machine, data)
-
- trainer.e_step(machine, data)
- average_output = 0
- average_output_previous = 0
-
- if hasattr(trainer,"compute_likelihood"):
- average_output = trainer.compute_likelihood(machine)
-
- for i in range(max_iterations):
- logger.info("Iteration = %d/%d", i, max_iterations)
- average_output_previous = average_output
- trainer.m_step(machine, data)
+
+def train(trainer, machine, data, max_iterations=50, convergence_threshold=None, initialize=True, rng=None,
+ check_inputs=True):
+ """
+ Trains a machine given a trainer and the proper data
+
+ **Parameters**:
+ trainer : one of :py:class:`KMeansTrainer`, :py:class:`MAP_GMMTrainer`, :py:class:`ML_GMMTrainer`, :py:class:`ISVTrainer`, :py:class:`IVectorTrainer`, :py:class:`PLDATrainer`, :py:class:`EMPCATrainer`
+ A trainer mechanism
+ machine : one of :py:class:`KMeansMachine`, :py:class:`GMMMachine`, :py:class:`ISVBase`, :py:class:`IVectorMachine`, :py:class:`PLDAMachine`, :py:class:`bob.learn.linear.Machine`
+ A container machine
+ data : array_like <float, 2D>
+ The data to be trained
+ max_iterations : int
+ The maximum number of iterations to train a machine
+ convergence_threshold : float
+ The convergence threshold to train a machine. If None, the training procedure will stop with the iterations criteria
+ initialize : bool
+ If True, runs the initialization procedure
+ rng : :py:class:`bob.core.random.mt19937`
+ The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loop
+ check_inputs: Shallow checks in the inputs. Check for inf and NaN
+ """
+
+ if check_inputs:
+ if numpy.isinf(numpy.sum(data)):
+ raise ValueError("Please, check your inputs; numpy.inf detected in `data` ")
+
+ if numpy.isnan(numpy.sum(data)):
+ raise ValueError("Please, check your inputs; numpy.nan detected in `data` ")
+
+ # Initialization
+ if initialize:
+ if rng is not None:
+ trainer.initialize(machine, data, rng)
+ else:
+ trainer.initialize(machine, data)
+
trainer.e_step(machine, data)
-
- if hasattr(trainer,"compute_likelihood"):
- average_output = trainer.compute_likelihood(machine)
-
- if type(machine) is bob.learn.em.KMeansMachine:
- logger.info("average euclidean distance = %f", average_output)
- else:
- logger.info("log likelihood = %f", average_output)
-
- convergence_value = abs((average_output_previous - average_output)/average_output_previous)
- logger.info("convergence value = %f",convergence_value)
-
- #Terminates if converged (and likelihood computation is set)
- if convergence_threshold!=None and convergence_value <= convergence_threshold:
- break
- if hasattr(trainer,"finalize"):
- trainer.finalize(machine, data)
+ average_output = 0
+ average_output_previous = 0
+
+ if hasattr(trainer, "compute_likelihood"):
+ average_output = trainer.compute_likelihood(machine)
+
+ for i in range(max_iterations):
+ logger.info("Iteration = %d/%d", i, max_iterations)
+ average_output_previous = average_output
+ trainer.m_step(machine, data)
+ trainer.e_step(machine, data)
+
+ if hasattr(trainer, "compute_likelihood"):
+ average_output = trainer.compute_likelihood(machine)
+
+ if type(machine) is bob.learn.em.KMeansMachine:
+ logger.info("average euclidean distance = %f", average_output)
+ else:
+ logger.info("log likelihood = %f", average_output)
+
+ convergence_value = abs((average_output_previous - average_output) / average_output_previous)
+ logger.info("convergence value = %f", convergence_value)
+
+ # Terminates if converged (and likelihood computation is set)
+ if convergence_threshold != None and convergence_value <= convergence_threshold:
+ break
+ if hasattr(trainer, "finalize"):
+ trainer.finalize(machine, data)
def train_jfa(trainer, jfa_base, data, max_iterations=10, initialize=True, rng=None):
- """
- Trains a :py:class:`bob.learn.em.JFABase` given a :py:class:`bob.learn.em.JFATrainer` and the proper data
-
- **Parameters**:
- trainer : :py:class:`bob.learn.em.JFATrainer`
- A JFA trainer mechanism
- jfa_base : :py:class:`bob.learn.em.JFABase`
- A container machine
- data : [[:py:class:`bob.learn.em.GMMStats`]]
- The data to be trained
- max_iterations : int
- The maximum number of iterations to train a machine
- initialize : bool
- If True, runs the initialization procedure
- rng : :py:class:`bob.core.random.mt19937`
- The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loops
- """
-
- if initialize:
- if rng is not None:
- trainer.initialize(jfa_base, data, rng)
- else:
- trainer.initialize(jfa_base, data)
-
- #V Subspace
- logger.info("V subspace estimation...")
- for i in range(max_iterations):
- logger.info("Iteration = %d/%d", i, max_iterations)
- trainer.e_step_v(jfa_base, data)
- trainer.m_step_v(jfa_base, data)
- trainer.finalize_v(jfa_base, data)
-
- #U subspace
- logger.info("U subspace estimation...")
- for i in range(max_iterations):
- logger.info("Iteration = %d/%d", i, max_iterations)
- trainer.e_step_u(jfa_base, data)
- trainer.m_step_u(jfa_base, data)
- trainer.finalize_u(jfa_base, data)
-
- # D subspace
- logger.info("D subspace estimation...")
- for i in range(max_iterations):
- logger.info("Iteration = %d/%d", i, max_iterations)
- trainer.e_step_d(jfa_base, data)
- trainer.m_step_d(jfa_base, data)
- trainer.finalize_d(jfa_base, data)
+ """
+ Trains a :py:class:`bob.learn.em.JFABase` given a :py:class:`bob.learn.em.JFATrainer` and the proper data
+
+ **Parameters**:
+ trainer : :py:class:`bob.learn.em.JFATrainer`
+ A JFA trainer mechanism
+ jfa_base : :py:class:`bob.learn.em.JFABase`
+ A container machine
+ data : [[:py:class:`bob.learn.em.GMMStats`]]
+ The data to be trained
+ max_iterations : int
+ The maximum number of iterations to train a machine
+ initialize : bool
+ If True, runs the initialization procedure
+ rng : :py:class:`bob.core.random.mt19937`
+ The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loops
+ """
+
+ if initialize:
+ if rng is not None:
+ trainer.initialize(jfa_base, data, rng)
+ else:
+ trainer.initialize(jfa_base, data)
+
+ # V Subspace
+ logger.info("V subspace estimation...")
+ for i in range(max_iterations):
+ logger.info("Iteration = %d/%d", i, max_iterations)
+ trainer.e_step_v(jfa_base, data)
+ trainer.m_step_v(jfa_base, data)
+ trainer.finalize_v(jfa_base, data)
+
+ # U subspace
+ logger.info("U subspace estimation...")
+ for i in range(max_iterations):
+ logger.info("Iteration = %d/%d", i, max_iterations)
+ trainer.e_step_u(jfa_base, data)
+ trainer.m_step_u(jfa_base, data)
+ trainer.finalize_u(jfa_base, data)
+
+ # D subspace
+ logger.info("D subspace estimation...")
+ for i in range(max_iterations):
+ logger.info("Iteration = %d/%d", i, max_iterations)
+ trainer.e_step_d(jfa_base, data)
+ trainer.m_step_d(jfa_base, data)
+ trainer.finalize_d(jfa_base, data)