def _maximization(self, data, posterior_probabilities):
"""Update function for the means and variances.
Parameters
----------
data : array-like, shape=[n_samples, n_features,]
Training data, where n_samples is the number of samples and
n_features is the number of features.
posterior_probabilities : array-like, shape=[n_samples, n_gaussians,]
Probability of a given sample to belong to a component
of the GMM, computed for all components.
"""
self.update_posterior_probabilities(posterior_probabilities)
if (gs.mean(self.mixture_coefficients) != gs.mean(
self.mixture_coefficients)):
logging.warning('UPDATE : mixture coefficients '
'contain elements that are not numbers')
self.update_means(data, posterior_probabilities)
if self.means.mean() != self.means.mean():
logging.warning('UPDATE : means contain' 'not a number elements')
self.update_variances(data, posterior_probabilities)
if self.variances.mean() != self.variances.mean():
logging.warning('UPDATE : variances contain'
'not a number elements')
def setup_method(self):
gs.random.seed(1234)
self.n_samples = 20
# Set up for hypersphere
self.dim_sphere = 4
self.shape_sphere = (self.dim_sphere + 1, )
self.sphere = Hypersphere(dim=self.dim_sphere)
X = gs.random.rand(self.n_samples)
self.X_sphere = X - gs.mean(X)
self.intercept_sphere_true = self.sphere.random_point()
self.coef_sphere_true = self.sphere.projection(
gs.random.rand(self.dim_sphere + 1))
self.y_sphere = self.sphere.metric.exp(
self.X_sphere[:, None] * self.coef_sphere_true,
base_point=self.intercept_sphere_true,
)
self.param_sphere_true = gs.vstack(
[self.intercept_sphere_true, self.coef_sphere_true])
self.param_sphere_guess = gs.vstack([
self.y_sphere[0],
self.sphere.to_tangent(gs.random.normal(size=self.shape_sphere),
self.y_sphere[0]),
])
# Set up for special euclidean
self.se2 = SpecialEuclidean(n=2)
self.metric_se2 = self.se2.left_canonical_metric
self.metric_se2.default_point_type = "matrix"
self.shape_se2 = (3, 3)
X = gs.random.rand(self.n_samples)
self.X_se2 = X - gs.mean(X)
self.intercept_se2_true = self.se2.random_point()
self.coef_se2_true = self.se2.to_tangent(
5.0 * gs.random.rand(*self.shape_se2), self.intercept_se2_true)
self.y_se2 = self.metric_se2.exp(
self.X_se2[:, None, None] * self.coef_se2_true[None],
self.intercept_se2_true,
)
self.param_se2_true = gs.vstack([
gs.flatten(self.intercept_se2_true),
gs.flatten(self.coef_se2_true),
])
self.param_se2_guess = gs.vstack([
gs.flatten(self.y_se2[0]),
gs.flatten(
self.se2.to_tangent(gs.random.normal(size=self.shape_se2),
self.y_se2[0])),
])
def _expectation(self, data):
"""Update the posterior probabilities.
Parameters
----------
data : array-like, shape=[n_samples, n_features]
Training data, where n_samples is the number of samples and
n_features is the number of features.
"""
probability_distribution_function = gmm_pdf(
data,
self.means,
self.variances,
norm_func=find_normalization_factor,
metric=self.metric,
variances_range=self.variances_range,
norm_func_var=self.normalization_factor_var,
)
if gs.isnan(probability_distribution_function.mean()):
logging.warning("EXPECTATION : Probability distribution function"
"contain elements that are not numbers")
num_normalized_pdf = gs.einsum("j,...j->...j",
self.mixture_coefficients,
probability_distribution_function)
valid_pdf_condition = gs.amin(gs.sum(num_normalized_pdf, -1))
if valid_pdf_condition <= PDF_TOL:
num_normalized_pdf[gs.sum(num_normalized_pdf, -1) <= PDF_TOL] = 1
sum_pdf = gs.sum(num_normalized_pdf, -1)
posterior_probabilities = gs.einsum("...i,...->...i",
num_normalized_pdf, 1 / sum_pdf)
if gs.any(gs.mean(posterior_probabilities)) is None:
logging.warning("EXPECTATION : posterior probabilities "
"contain elements that are not numbers.")
if (1 - SUM_CHECK_PDF >= gs.mean(gs.sum(posterior_probabilities, 1)) >=
1 + SUM_CHECK_PDF):
logging.warning("EXPECTATION : posterior probabilities "
"do not sum to 1.")
if gs.any(gs.sum(posterior_probabilities, 0) < PDF_TOL):
logging.warning("EXPECTATION : Gaussian got no elements "
"(precision error) reinitialize")
posterior_probabilities[posterior_probabilities == 0] = PDF_TOL
return posterior_probabilities
def _circle_mean(points):
"""Determine the mean on a circle.
Data are expected in radians in the range [-pi, pi). The mean is returned
in the same range. If the mean is unique, this algorithm is guaranteed to
find it. It is not vulnerable to local minima of the Frechet function. If
the mean is not unique, the algorithm only returns one of the means. Which
mean is returned depends on numerical rounding errors.
Reference
---------
..[HH15] Hotz, T. and S. F. Huckemann (2015), "Intrinsic means on the circle:
Uniqueness, locus and asymptotics", Annals of the Institute of
Statistical Mathematics 67 (1), 177–193.
https://arxiv.org/abs/1108.2141
"""
if points.ndim > 1:
points_ = Hypersphere.extrinsic_to_angle(points)
else:
points_ = gs.copy(points)
sample_size = points_.shape[0]
mean0 = gs.mean(points_)
var0 = gs.sum((points_ - mean0) ** 2)
sorted_points = gs.sort(points_)
means = _circle_variances(mean0, var0, sample_size, sorted_points)
return means[gs.argmin(means[:, 1]), 0]
def setup_method(self):
gs.random.seed(1234)
self.n_samples = 20
# Set up for hypersphere
self.dim_sphere = 2
self.shape_sphere = (self.dim_sphere + 1, )
self.sphere = Hypersphere(dim=self.dim_sphere)
self.intercept_sphere_true = gs.array([0.0, -1.0, 0.0])
self.coef_sphere_true = gs.array([1.0, 0.0, 0.5])
# set up the prior
self.prior = lambda x: self.sphere.metric.exp(
x * self.coef_sphere_true,
base_point=self.intercept_sphere_true,
)
self.kernel = ConstantKernel(1.0, (1e-3, 1e3)) * RBF(10.0, (1e-2, 1e2))
# generate data
X = gs.linspace(0.0, 1.5 * gs.pi, self.n_samples)
self.X_sphere = gs.reshape((X - gs.mean(X)), (-1, 1))
# generate the geodesic
y = self.prior(self.X_sphere)
# Then add orthogonal sinusoidal oscillations
o = (1.0 / 20.0) * gs.array([-0.5, 0.0, 1.0])
o = self.sphere.to_tangent(o, base_point=y)
s = self.X_sphere * gs.sin(5.0 * gs.pi * self.X_sphere)
self.y_sphere = self.sphere.metric.exp(s * o, base_point=y)
def fit(self, X, y, weights=None, compute_training_score=False):
"""Estimate the parameters of the geodesic regression.
Estimate the intercept and the coefficient defining the
geodesic regression model.
Parameters
----------
X : {array-like, sparse matrix}, shape=[...,}]
Training input samples.
y : array-like, shape=[..., {dim, [n,n]}]
Training target values.
weights : array-like, shape=[...,]
Weights associated to the points.
Optional, default: None.
compute_training_score : bool
Whether to compute R^2.
Optional, default: False.
Returns
-------
self : object
Returns self.
"""
times = gs.copy(X)
if self.center_X:
self.mean_ = gs.mean(X)
times -= self.mean_
if self.method == "extrinsic":
return self._fit_extrinsic(times, y, weights,
compute_training_score)
if self.method == "riemannian":
return self._fit_riemannian(times, y, weights,
compute_training_score)
def _maximization(self,
data,
posterior_probabilities,
lr_means,
conv_factor_mean,
max_iter=DEFAULT_MAX_ITER):
"""Update function for the means and variances.
Parameters
----------
data : array-like, shape=[n_samples, n_features,]
Training data, where n_samples is the number of samples and
n_features is the number of features.
posterior_probabilities : array-like, shape=[n_samples, n_gaussians,]
Probability of a given sample to belong to a component
of the GMM, computed for all components.
lr_means : float
Learning rate for computing the means.
conv_factor_mean : float
Convergence factor for means.
max_iter : int
Optional, default: 100.
Maximum number of iterations for computing
the means.
"""
self.update_posterior_probabilities(posterior_probabilities)
if(gs.mean(self.mixture_coefficients)
!= gs.mean(self.mixture_coefficients)):
logging.warning('UPDATE : mixture coefficients '
'contain elements that are not numbers')
self.update_means(data,
posterior_probabilities,
lr_means=lr_means,
tau_means=conv_factor_mean,
max_iter=max_iter)
if self.means.mean() != self.means.mean():
logging.warning('UPDATE : means contain'
'not a number elements')
self.update_variances(data, posterior_probabilities)
if self.variances.mean() != self.variances.mean():
logging.warning('UPDATE : variances contain'
'not a number elements')
def _expectation(self, data):
"""Update the posterior probabilities.
Parameters
----------
data : array-like, shape=[n_samples, n_features]
Training data, where n_samples is the number of samples and
n_features is the number of features.
"""
probability_distribution_function = \
PoincareBall.gmm_pdf(
data, self.means, self.variances,
norm_func=self.riemannian_metric.find_normalization_factor,
metric=self.riemannian_metric,
variances_range=self.variances_range,
norm_func_var=self.normalization_factor_var)
if gs.isnan(probability_distribution_function.mean()):
logging.warning('EXPECTATION : Probability distribution function'
'contain elements that are not numbers')
num_normalized_pdf = gs.einsum('j,...j->...j',
self.mixture_coefficients,
probability_distribution_function)
valid_pdf_condition = gs.amin(gs.sum(num_normalized_pdf, -1))
if valid_pdf_condition <= PDF_TOL:
num_normalized_pdf[gs.sum(num_normalized_pdf, -1) <= PDF_TOL] = 1
sum_pdf = gs.sum(num_normalized_pdf, -1)
posterior_probabilities =\
gs.einsum('...i,...->...i', num_normalized_pdf, 1 / sum_pdf)
if gs.any(gs.mean(posterior_probabilities)) is None:
logging.warning('EXPECTATION : posterior probabilities '
'contain elements that are not numbers.')
if 1 - SUM_CHECK_PDF >= gs.mean(gs.sum(
posterior_probabilities, 1)) >= 1 + SUM_CHECK_PDF:
logging.warning('EXPECTATION : posterior probabilities '
'do not sum to 1.')
return posterior_probabilities
def empirical_frechet_var_bubble(n_samples, theta, dim, n_expectation=1000):
"""Variance of the empirical Fréchet mean for a bubble distribution.
Draw n_sampless from a bubble distribution, computes its empirical
Fréchet mean and the square distance to the asymptotic mean. This
is repeated n_expectation times to compute an approximation of its
expectation (i.e. its variance) by sampling.
The bubble distribution is an isotropic distributions on a Riemannian
hyper sub-sphere of radius 0 < theta < Pi around the north pole of the
sphere of dimension dim.
Parameters
----------
n_samples : int
Number of samples to draw.
theta: float
Radius of the bubble distribution.
dim : int
Dimension of the sphere (embedded in R^{dim+1}).
n_expectation: int, optional (defaults to 1000)
Number of computations for approximating the expectation.
Returns
-------
tuple (variance, std-dev on the computed variance)
"""
if dim <= 1:
raise ValueError(
'Dim > 1 needed to draw a uniform sample on sub-sphere.')
var = []
sphere = Hypersphere(dim=dim)
bubble = Hypersphere(dim=dim - 1)
north_pole = gs.zeros(dim + 1)
north_pole[dim] = 1.0
for _ in range(n_expectation):
# Sample n points from the uniform distribution on a sub-sphere
# of radius theta (i.e cos(theta) in ambient space)
# TODO (nina): Add this code as a method of hypersphere
data = gs.zeros((n_samples, dim + 1), dtype=gs.float64)
directions = bubble.random_uniform(n_samples)
directions = gs.to_ndarray(directions, to_ndim=2)
for i in range(n_samples):
for j in range(dim):
data[i, j] = gs.sin(theta) * directions[i, j]
data[i, dim] = gs.cos(theta)
# TODO (nina): Use FrechetMean here
current_mean = _adaptive_gradient_descent(data,
metric=sphere.metric,
max_iter=32,
init_point=north_pole)
var.append(sphere.metric.squared_dist(north_pole, current_mean))
return gs.mean(var), 2 * gs.std(var) / gs.sqrt(n_expectation)
def update_posterior_probabilities(self, posterior_probabilities):
"""Posterior probabilities update function.
Parameters
----------
posterior_probabilities : array-like, shape=[n_samples, n_gaussians,]
Probability of a given sample to belong to a component
of the GMM, computed for all components.
"""
self.mixture_coefficients = gs.mean(posterior_probabilities, 0)
def empirical_frechet_var_bubble(n_samples, theta, dim, n_expectation=1000):
"""Variance of the empirical Fréchet mean for a bubble distribution.
Draw n_sampless from a bubble distribution, computes its empirical
Fréchet mean and the square distance to the asymptotic mean. This
is repeated n_expectation times to compute an approximation of its
expectation (i.e. its variance) by sampling.
The bubble distribution is an isotropic distributions on a Riemannian
hyper sub-sphere of radius 0 < theta < Pi around the north pole of the
sphere of dimension dim.
Parameters
----------
n_samples : int
Number of samples to draw.
theta: float
Radius of the bubble distribution.
dim : int
Dimension of the sphere (embedded in R^{dim+1}).
n_expectation: int, optional (defaults to 1000)
Number of computations for approximating the expectation.
Returns
-------
tuple (variance, std-dev on the computed variance)
"""
if dim <= 1:
raise ValueError(
"Dim > 1 needed to draw a uniform sample on sub-sphere.")
var = []
sphere = Hypersphere(dim=dim)
bubble = Hypersphere(dim=dim - 1)
north_pole = gs.zeros(dim + 1)
north_pole[dim] = 1.0
for _ in range(n_expectation):
# Sample n points from the uniform distribution on a sub-sphere
# of radius theta (i.e cos(theta) in ambient space)
# TODO (nina): Add this code as a method of hypersphere
last_col = gs.cos(theta) * gs.ones(n_samples)
last_col = last_col[:, None] if (n_samples > 1) else last_col
directions = bubble.random_uniform(n_samples)
rest_col = gs.sin(theta) * directions
data = gs.concatenate([rest_col, last_col], axis=-1)
estimator = FrechetMean(sphere.metric,
max_iter=32,
method="adaptive",
init_point=north_pole)
estimator.fit(data)
current_mean = estimator.estimate_
var.append(sphere.metric.squared_dist(north_pole, current_mean))
return gs.mean(var), 2 * gs.std(var) / gs.sqrt(n_expectation)
def fit(self, X, max_iter=100):
"""Predict for each data point the closest center in terms of
riemannian_metric distance
Parameters
----------
X : array-like, shape=[n_samples, n_features]
Training data, where n_samples is the number of samples
and n_features is the number of features.
max_iter : Maximum number of iterations
Returns
-------
self : object
Return centroids array
"""
n_samples = X.shape[0]
belongs = gs.zeros(n_samples)
self.centroids = [
gs.expand_dims(X[randint(0, n_samples - 1)], 0)
for i in range(self.n_clusters)
]
self.centroids = gs.concatenate(self.centroids)
index = 0
while index < max_iter:
index += 1
dists = [
gs.to_ndarray(
self.riemannian_metric.dist(self.centroids[i], X), 2, 1)
for i in range(self.n_clusters)
]
dists = gs.hstack(dists)
belongs = gs.argmin(dists, 1)
old_centroids = gs.copy(self.centroids)
for i in range(self.n_clusters):
fold = gs.squeeze(X[belongs == i])
if len(fold) > 0:
self.centroids[i] = self.riemannian_metric.mean(fold)
else:
self.centroids[i] = X[randint(0, n_samples - 1)]
centroids_distances = self.riemannian_metric.dist(
old_centroids, self.centroids)
if gs.mean(centroids_distances) < self.tol:
if self.verbose > 0:
print("Convergence Reached after ", index, " iterations")
return gs.copy(self.centroids)
return gs.copy(self.centroids)
def test_spd_frechet_mean(self):
"""Test if the frechet mean of the samples is close to mean"""
mean = gs.eye(self.n)
cov = gs.eye(self.spd_cov_n)
data = LogNormal(self.spd, mean, cov).sample(5000)
_fm = gs.mean(self.spd.logm(data), axis=0)
fm = self.spd.expm(_fm)
expected = mean
result = fm
self.assertAllClose(result, expected, atol=5 * 1e-2)
def test_euclidean_frechet_mean(self):
"""Test if the frechet mean of the samples is close to mean"""
mean = gs.zeros(self.n)
cov = gs.eye(self.n)
data = LogNormal(self.euclidean, mean, cov).sample(5000)
log_data = gs.log(data)
fm = gs.mean(log_data, axis=0)
expected = mean
result = fm
self.assertAllClose(result, expected, atol=5 * 1e-2)
def test_linear_mean(self):
euclidean = Euclidean(3)
point = euclidean.random_point(self.n_samples)
estimator = ExponentialBarycenter(euclidean)
estimator.fit(point)
result = estimator.estimate_
expected = gs.mean(point, axis=0)
self.assertAllClose(result, expected)
def test_sample(self):
"""
Test that the sample method samples variates from beta distributions
with the specified parameters, using the law of large numbers
"""
n_samples = self.n_samples
tol = (n_samples * 10)**(-0.5)
point = self.beta.random_uniform(n_samples)
samples = self.beta.sample(point, n_samples * 10)
result = gs.mean(samples, axis=1)
expected = point[:, 0] / gs.sum(point, axis=1)
self.assertAllClose(result, expected, rtol=tol, atol=tol)
def main():
r"""Compute a geodesic regression on Grassmann manifold (2, 3).
The generative model of the data is:
:math:`Z = Exp_{\beta_0}(\beta_1.X)` and :math:`Y = Exp_Z(\epsilon)`
where:
- :math:`Exp` denotes the Riemannian exponential,
- :math:`\beta_0` is called the intercept,
- :math:`\beta_1` is called the coefficient,
- :math:`\epsilon \sim N(0, 1)` is a standard Gaussian noise,
- :math:`X` is called the input, :math:`Y` is called the y.
"""
# Generate data
n_samples = 10
data = gs.random.rand(n_samples)
data -= gs.mean(data)
intercept = SPACE.random_uniform()
beta = SPACE.to_tangent(GeneralLinear(3).random_point(), intercept)
target = METRIC.exp(tangent_vec=gs.einsum("...,jk->...jk", data, beta),
base_point=intercept)
# Fit geodesic regression
gr = GeodesicRegression(
SPACE,
metric=METRIC,
center_X=False,
method="riemannian",
max_iter=50,
init_step_size=0.1,
verbose=True,
)
gr.fit(data, target, compute_training_score=True)
intercept_hat, beta_hat = gr.intercept_, gr.coef_
# Measure Mean Squared Error
mse_intercept = METRIC.squared_dist(intercept_hat, intercept)
mse_beta = METRIC.squared_norm(
METRIC.parallel_transport(beta_hat, METRIC.log(
intercept_hat, intercept), intercept_hat) - beta,
intercept,
)
# Measure goodness of fit
r2_hat = gr.training_score_
print(f"MSE on the intercept: {mse_intercept:.2e}")
print(f"MSE on the initial velocity beta: {mse_beta:.2e}")
print(f"Determination coefficient: R^2={r2_hat:.2f}")
def center(point):
"""Center landmarks around 0.
Parameters
----------
point : array-like, shape=[..., k_landmarks, m_ambient]
Point in Matrices space.
Returns
-------
centered : array-like, shape=[..., k_landmarks, m_ambient]
Point with centered landmarks.
"""
mean = gs.mean(point, axis=-2)
return point - mean[..., None, :]
def empirical_frechet_var_bubble(n_samples, theta, dim, n_expectation=1000):
"""Variance of the empirical Fréchet mean for a bubble distribution.
Draw n_sampless from a bubble distribution, computes its empirical
Fréchet mean and the square distance to the asymptotic mean. This
is repeated n_expectation times to compute an approximation of its
expectation (i.e. its variance) by sampling.
The bubble distribution is an isotropic distributions on a Riemannian
hyper sub-sphere of radius 0 < theta < Pi around the north pole of the
sphere of dimension dim.
Parameters
----------
n_samples: number of samples to draw
theta: radius of the bubble distribution
dim: dimension of the sphere (embedded in R^{dim+1})
n_expectation: number of computations for approximating the expectation
Returns
-------
tuple (variance, std-dev on the computed variance)
"""
assert dim > 1, "Dim > 1 needed to draw a uniform sample on sub-sphere"
var = []
sphere = Hypersphere(dimension=dim)
bubble = Hypersphere(dimension=dim - 1)
north_pole = gs.zeros(dim + 1)
north_pole[dim] = 1.0
for k in range(n_expectation):
# Sample n points from the uniform distribution on a sub-sphere
# of radius theta (i.e cos(theta) in ambient space)
# TODO(nina): Add this code as a method of hypersphere
data = gs.zeros((n_samples, dim + 1), dtype=gs.float64)
directions = bubble.random_uniform(n_samples)
for i in range(n_samples):
for j in range(dim):
data[i, j] = gs.sin(theta) * directions[i, j]
data[i, dim] = gs.cos(theta)
current_mean = sphere.metric.adaptive_gradientdescent_mean(
data, n_max_iterations=64, init_points=[north_pole])
var.append(sphere.metric.squared_dist(north_pole, current_mean))
return gs.mean(var), 2 * gs.std(var) / gs.sqrt(n_expectation)
def is_centered(point, atol=gs.atol):
"""Check that landmarks are centered around 0.
Parameters
----------
point : array-like, shape=[..., k_landmarks, m_ambient]
Point in Matrices space.
atol : float
Tolerance at which to evaluate mean == 0.
Optional, default: backend atol.
Returns
-------
is_centered : array-like, shape=[...,]
Boolean evaluating if point is centered.
"""
mean = gs.mean(point, axis=-2)
return gs.all(gs.isclose(mean, 0.0, atol=atol), axis=-1)
def to_tangent(self, vector, base_point=None):
"""Project a vector to the tangent space.
Project a vector in Euclidean space on the tangent space of
the simplex at a base point.
Parameters
----------
vector : array-like, shape=[..., dim + 1]
Vector in Euclidean space.
base_point : array-like, shape=[..., dim + 1]
Point on the simplex defining the tangent space,
where the vector will be projected.
Returns
-------
vector : array-like, shape=[..., dim + 1]
Tangent vector in the tangent space of the simplex
at the base point.
"""
geomstats.errors.check_belongs(vector, self.embedding_space)
component_mean = gs.mean(vector, axis=-1)
return gs.transpose(gs.transpose(vector) - component_mean)
def main():
r"""Compute and visualize a geodesic regression on the sphere.
The generative model of the data is:
:math:`Z = Exp_{\beta_0}(\beta_1.X)` and :math:`Y = Exp_Z(\epsilon)`
where:
- :math:`Exp` denotes the Riemannian exponential,
- :math:`\beta_0` is called the intercept,
- :math:`\beta_1` is called the coefficient,
- :math:`\epsilon \sim N(0, 1)` is a standard Gaussian noise,
- :math:`X` is the input, :math:`Y` is the target.
"""
# Generate noise-free data
n_samples = 50
X = gs.random.rand(n_samples)
X -= gs.mean(X)
intercept = SPACE.random_uniform()
coef = SPACE.to_tangent(5.0 * gs.random.rand(EMBEDDING_DIM),
base_point=intercept)
y = METRIC.exp(X[:, None] * coef, base_point=intercept)
# Generate normal noise
normal_noise = gs.random.normal(size=(n_samples, EMBEDDING_DIM))
noise = SPACE.to_tangent(normal_noise, base_point=y) / gs.pi / 2
rss = gs.sum(METRIC.squared_norm(noise, base_point=y)) / n_samples
# Add noise
y = METRIC.exp(noise, y)
# True noise level and R2
estimator = FrechetMean(METRIC)
estimator.fit(y)
variance_ = variance(y, estimator.estimate_, metric=METRIC)
r2 = 1 - rss / variance_
# Fit geodesic regression
gr = GeodesicRegression(SPACE,
center_X=False,
method="extrinsic",
verbose=True)
gr.fit(X, y, compute_training_score=True)
intercept_hat, coef_hat = gr.intercept_, gr.coef_
# Measure Mean Squared Error
mse_intercept = METRIC.squared_dist(intercept_hat, intercept)
tangent_vec_to_transport = coef_hat
tangent_vec_of_transport = METRIC.log(intercept, base_point=intercept_hat)
transported_coef_hat = METRIC.parallel_transport(
tangent_vec=tangent_vec_to_transport,
base_point=intercept_hat,
direction=tangent_vec_of_transport,
)
mse_coef = METRIC.squared_norm(transported_coef_hat - coef,
base_point=intercept)
# Measure goodness of fit
r2_hat = gr.training_score_
print(f"MSE on the intercept: {mse_intercept:.2e}")
print(f"MSE on the coef, i.e. initial velocity: {mse_coef:.2e}")
print(f"Determination coefficient: R^2={r2_hat:.2f}")
print(f"True R^2: {r2:.2f}")
# Plot
fitted_data = gr.predict(X)
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, projection="3d")
sphere_visu = visualization.Sphere(n_meridians=30)
ax = sphere_visu.set_ax(ax=ax)
path = METRIC.geodesic(initial_point=intercept_hat,
initial_tangent_vec=coef_hat)
regressed_geodesic = path(
gs.linspace(0.0, 1.0, 100) * gs.pi * 2 / METRIC.norm(coef))
regressed_geodesic = gs.to_numpy(gs.autodiff.detach(regressed_geodesic))
size = 10
marker = "o"
sphere_visu.draw_points(ax,
gs.array([intercept_hat]),
marker=marker,
c="r",
s=size)
sphere_visu.draw_points(ax, y, marker=marker, c="b", s=size)
sphere_visu.draw_points(ax, fitted_data, marker=marker, c="g", s=size)
ax.plot(
regressed_geodesic[:, 0],
regressed_geodesic[:, 1],
regressed_geodesic[:, 2],
c="gray",
)
sphere_visu.draw(ax, linewidth=1)
ax.grid(False)
plt.axis("off")
plt.show()
def _fit(self, X, base_point=None, point_type='vector'):
"""Fit the model by computing full SVD on X"""
if point_type == 'matrix':
raise NotImplementedError(
'This is currently only implemented for vectors.')
if base_point is None:
base_point = self.metric.mean(X)
tangent_vecs = self.metric.log(X, base_point=base_point)
# Convert to sklearn format
X = tangent_vecs
X = check_array(X,
dtype=[gs.float64, gs.float32],
ensure_2d=True,
copy=self.copy)
# Handle n_components==None
if self.n_components is None:
n_components = min(X.shape)
else:
n_components = self.n_components
n_samples, n_features = X.shape
if n_components == 'mle':
if n_samples < n_features:
raise ValueError("n_components='mle' is only supported "
"if n_samples >= n_features")
elif not 0 <= n_components <= min(n_samples, n_features):
raise ValueError("n_components=%r must be between 0 and "
"min(n_samples, n_features)=%r with "
"svd_solver='full'" %
(n_components, min(n_samples, n_features)))
elif n_components >= 1:
if not isinstance(n_components, (numbers.Integral, gs.integer)):
raise ValueError("n_components=%r must be of type int "
"when greater than or equal to 1, "
"was of type=%r" %
(n_components, type(n_components)))
# Center data
self.mean_ = gs.mean(X, axis=0)
X -= self.mean_
U, S, V = linalg.svd(X, full_matrices=False)
# flip eigenvectors' sign to enforce deterministic output
U, V = svd_flip(U, V)
components_ = V
# Get variance explained by singular values
explained_variance_ = (S**2) / (n_samples - 1)
total_var = explained_variance_.sum()
explained_variance_ratio_ = explained_variance_ / total_var
singular_values_ = S.copy() # Store the singular values.
# Postprocess the number of components required
if n_components == 'mle':
n_components = \
_infer_dimension_(explained_variance_, n_samples, n_features)
elif 0 < n_components < 1.0:
# number of components for which the cumulated explained
# variance percentage is superior to the desired threshold
ratio_cumsum = stable_cumsum(explained_variance_ratio_)
n_components = gs.searchsorted(ratio_cumsum, n_components) + 1
# Compute noise covariance using Probabilistic PCA model
# The sigma2 maximum likelihood (cf. eq. 12.46)
if n_components < min(n_features, n_samples):
self.noise_variance_ = explained_variance_[n_components:].mean()
else:
self.noise_variance_ = 0.
self.n_samples_, self.n_features_ = n_samples, n_features
self.components_ = components_[:n_components]
self.n_components_ = n_components
self.explained_variance_ = explained_variance_[:n_components]
self.explained_variance_ratio_ = \
explained_variance_ratio_[:n_components]
self.singular_values_ = singular_values_[:n_components]
return U, S, V
def fit(self,
data,
max_iter=DEFAULT_MAX_ITER,
lr_mean=DEFAULT_LR,
conv_factor_mean=DEFAULT_CONV_FACTOR):
"""Fit a Gaussian mixture model (GMM) given the data.
Alternates between Expectation and Maximization steps
for some number of iterations.
Parameters
----------
data : array-like, shape=[n_samples, n_features]
Training data, where n_samples is the number of samples
and n_features is the number of features.
max_iter : int
Optional, default: 100.
Maximum number of iterations.
lr_mean : float
Optional, default: 5e-2.
Learning rate for the mean.
conv_factor_mean : float
Optional, default: 5e-3.
Convergence factor for the mean.
Returns
-------
self : object
Return the components of the computed
Gaussian mixture model: means, variances and mixture_coefficients.
"""
self._dimension = data.shape[-1]
self.means = (gs.random.rand(
self.n_gaussians,
self._dimension) - 0.5) / self._dimension
self.variances = gs.random.rand(self.n_gaussians) / 10 + 0.8
self.mixture_coefficients = \
gs.ones(self.n_gaussians) / self.n_gaussians
posterior_probabilities = gs.ones((data.shape[0],
self.means.shape[0]))
self.variances_range,\
self.normalization_factor_var, \
self.phi_inv_var =\
self.riemannian_metric.normalization_factor_init(
gs.arange(
ZETA_LOWER_BOUND, ZETA_UPPER_BOUND, ZETA_STEP))
for epoch in range(max_iter):
old_posterior_probabilities = posterior_probabilities
posterior_probabilities = self._expectation(data)
condition = gs.mean(gs.abs(old_posterior_probabilities
- posterior_probabilities))
if(condition < EM_CONV_RATE and epoch > MINIMUM_EPOCHS):
logging.info('EM converged in %s iterations', epoch)
return self.means, self.variances, self.mixture_coefficients
self._maximization(data,
posterior_probabilities,
lr_means=lr_mean,
conv_factor_mean=conv_factor_mean)
logging.info('WARNING: EM did not converge \n'
'Please increase MINIMUM_EPOCHS.')
return self.means, self.variances, self.mixture_coefficients
def setup_method(self):
gs.random.seed(1234)
self.n_samples = 20
# Set up for euclidean
self.dim_eucl = 3
self.shape_eucl = (self.dim_eucl, )
self.eucl = Euclidean(dim=self.dim_eucl)
X = gs.random.rand(self.n_samples)
self.X_eucl = X - gs.mean(X)
self.intercept_eucl_true = self.eucl.random_point()
self.coef_eucl_true = self.eucl.random_point()
self.y_eucl = (self.intercept_eucl_true +
self.X_eucl[:, None] * self.coef_eucl_true)
self.param_eucl_true = gs.vstack(
[self.intercept_eucl_true, self.coef_eucl_true])
self.param_eucl_guess = gs.vstack([
self.y_eucl[0],
self.y_eucl[0] + gs.random.normal(size=self.shape_eucl)
])
# Set up for hypersphere
self.dim_sphere = 4
self.shape_sphere = (self.dim_sphere + 1, )
self.sphere = Hypersphere(dim=self.dim_sphere)
X = gs.random.rand(self.n_samples)
self.X_sphere = X - gs.mean(X)
self.intercept_sphere_true = self.sphere.random_point()
self.coef_sphere_true = self.sphere.projection(
gs.random.rand(self.dim_sphere + 1))
self.y_sphere = self.sphere.metric.exp(
self.X_sphere[:, None] * self.coef_sphere_true,
base_point=self.intercept_sphere_true,
)
self.param_sphere_true = gs.vstack(
[self.intercept_sphere_true, self.coef_sphere_true])
self.param_sphere_guess = gs.vstack([
self.y_sphere[0],
self.sphere.to_tangent(gs.random.normal(size=self.shape_sphere),
self.y_sphere[0]),
])
# Set up for special euclidean
self.se2 = SpecialEuclidean(n=2)
self.metric_se2 = self.se2.left_canonical_metric
self.metric_se2.default_point_type = "matrix"
self.shape_se2 = (3, 3)
X = gs.random.rand(self.n_samples)
self.X_se2 = X - gs.mean(X)
self.intercept_se2_true = self.se2.random_point()
self.coef_se2_true = self.se2.to_tangent(
5.0 * gs.random.rand(*self.shape_se2), self.intercept_se2_true)
self.y_se2 = self.metric_se2.exp(
self.X_se2[:, None, None] * self.coef_se2_true[None],
self.intercept_se2_true,
)
self.param_se2_true = gs.vstack([
gs.flatten(self.intercept_se2_true),
gs.flatten(self.coef_se2_true),
])
self.param_se2_guess = gs.vstack([
gs.flatten(self.y_se2[0]),
gs.flatten(
self.se2.to_tangent(gs.random.normal(size=self.shape_se2),
self.y_se2[0])),
])
# Set up for discrete curves
n_sampling_points = 8
self.curves_2d = DiscreteCurves(R2)
self.metric_curves_2d = self.curves_2d.srv_metric
self.metric_curves_2d.default_point_type = "matrix"
self.shape_curves_2d = (n_sampling_points, 2)
X = gs.random.rand(self.n_samples)
self.X_curves_2d = X - gs.mean(X)
self.intercept_curves_2d_true = self.curves_2d.random_point(
n_sampling_points=n_sampling_points)
self.coef_curves_2d_true = self.curves_2d.to_tangent(
5.0 * gs.random.rand(*self.shape_curves_2d),
self.intercept_curves_2d_true)
# Added because of GitHub issue #1575
intercept_curves_2d_true_repeated = gs.tile(
gs.expand_dims(self.intercept_curves_2d_true, axis=0),
(self.n_samples, 1, 1),
)
self.y_curves_2d = self.metric_curves_2d.exp(
self.X_curves_2d[:, None, None] * self.coef_curves_2d_true[None],
intercept_curves_2d_true_repeated,
)
self.param_curves_2d_true = gs.vstack([
gs.flatten(self.intercept_curves_2d_true),
gs.flatten(self.coef_curves_2d_true),
])
self.param_curves_2d_guess = gs.vstack([
gs.flatten(self.y_curves_2d[0]),
gs.flatten(
self.curves_2d.to_tangent(
gs.random.normal(size=self.shape_curves_2d),
self.y_curves_2d[0])),
])
def _ball_gradient_descent(points,
metric,
weights=None,
max_iter=32,
lr=1e-3,
tau=5e-3):
"""Perform ball gradient descent."""
points = gs.to_ndarray(points, to_ndim=2)
if len(points) == 1:
return points[0]
if weights is None:
iteration = 0
convergence = math.inf
barycenter = gs.mean(points, axis=0, keepdims=True)
while convergence > tau and max_iter > iteration:
iteration += 1
grad_tangent = 2 * metric.log(points, barycenter)
cc_barycenter = metric.exp(lr * grad_tangent.sum(0, keepdims=True),
barycenter)
convergence = metric.dist(cc_barycenter, barycenter).max().item()
barycenter = cc_barycenter
else:
weights = gs.expand_dims(weights, -1)
weights = gs.repeat(weights, points.shape[-1], axis=2)
barycenter = (points * weights).sum(0, keepdims=True) / weights.sum(0)
barycenter_gs = gs.squeeze(barycenter)
points_gs = gs.squeeze(points)
points_flattened = gs.reshape(points_gs, (-1, points_gs.shape[-1]))
convergence = math.inf
iteration = 0
while convergence > tau and max_iter > iteration:
iteration += 1
barycenter_flattened = gs.repeat(barycenter,
len(points_gs),
axis=0)
barycenter_flattened = gs.reshape(
barycenter_flattened, (-1, barycenter_flattened.shape[-1]))
grad_tangent = 2 * metric.log(points_flattened,
barycenter_flattened)
grad_tangent = gs.reshape(grad_tangent, points.shape)
grad_tangent = grad_tangent * weights
lr_grad_tangent = lr * grad_tangent.sum(0, keepdims=True)
lr_grad_tangent_s = lr_grad_tangent.squeeze()
cc_barycenter = metric.exp(barycenter_gs, lr_grad_tangent_s)
convergence = metric.dist(cc_barycenter,
barycenter_gs).max().item()
barycenter_gs = cc_barycenter
barycenter = gs.expand_dims(cc_barycenter, 0)
barycenter = gs.squeeze(barycenter)
if iteration == max_iter:
logging.warning('Maximum number of iterations {} reached. The '
'mean may be inaccurate'.format(max_iter))
return barycenter
def fit(self, X, max_iter=100):
"""Provide clusters centroids and data labels.
Alternate between computing the mean of each cluster
and labelling data according to the new positions of the centroids.
Parameters
----------
X : array-like, shape=[n_samples, n_features]
Training data, where n_samples is the number of samples and
n_features is the number of features.
max_iter : int
Maximum number of iterations
Returns
-------
self : array-like, shape=[n_clusters,]
centroids array
"""
n_samples = X.shape[0]
belongs = gs.zeros(n_samples)
self.centroids = [
gs.expand_dims(X[randint(0, n_samples - 1)], 0)
for i in range(self.n_clusters)
]
self.centroids = gs.concatenate(self.centroids)
index = 0
while index < max_iter:
index += 1
dists = [
gs.to_ndarray(
self.riemannian_metric.dist(self.centroids[i], X), 2, 1)
for i in range(self.n_clusters)
]
dists = gs.hstack(dists)
belongs = gs.argmin(dists, 1)
old_centroids = gs.copy(self.centroids)
for i in range(self.n_clusters):
fold = gs.squeeze(X[belongs == i])
if len(fold) > 0:
mean = FrechetMean(metric=self.riemannian_metric,
method=self.mean_method,
max_iter=150)
mean.fit(fold)
self.centroids[i] = mean.estimate_
else:
self.centroids[i] = X[randint(0, n_samples - 1)]
centroids_distances = self.riemannian_metric.dist(
old_centroids, self.centroids)
if gs.mean(centroids_distances) < self.tol:
if self.verbose > 0:
logging.info('Convergence reached after {} '
'iterations'.format(index))
return gs.copy(self.centroids)
if index == max_iter:
logging.warning('K-means maximum number of iterations {} reached. '
'The mean may be inaccurate'.format(max_iter))
return gs.copy(self.centroids)
def fit(self, X):
"""Provide clusters centroids and data labels.
Alternate between computing the mean of each cluster
and labelling data according to the new positions of the centroids.
Parameters
----------
X : array-like, shape=[..., n_features]
Training data, where n_samples is the number of samples and
n_features is the number of features.
max_iter : int
Maximum number of iterations.
Optional, default: 100.
Returns
-------
self : array-like, shape=[n_clusters,]
Centroids.
"""
n_samples = X.shape[0]
if self.verbose > 0:
logging.info("Initializing...")
if self.init == "kmeans++":
centroids = [gs.expand_dims(X[randint(0, n_samples - 1)], 0)]
for i in range(self.n_clusters - 1):
dists = [
gs.to_ndarray(self.metric.dist(centroids[j], X), 2, 1)
for j in range(i + 1)
]
dists = gs.hstack(dists)
dists_to_closest_centroid = gs.amin(dists, 1)
indices = gs.arange(n_samples)
weights = dists_to_closest_centroid / gs.sum(
dists_to_closest_centroid)
index = rv_discrete(values=(indices, weights)).rvs()
centroids.append(gs.expand_dims(X[index], 0))
else:
centroids = [
gs.expand_dims(X[randint(0, n_samples - 1)], 0)
for i in range(self.n_clusters)
]
self.centroids = gs.concatenate(centroids, axis=0)
self.init_centroids = gs.concatenate(centroids, axis=0)
dists = [
gs.to_ndarray(self.metric.dist(self.centroids[i], X), 2, 1)
for i in range(self.n_clusters)
]
dists = gs.hstack(dists)
self.labels = gs.argmin(dists, 1)
index = 0
while index < self.max_iter:
index += 1
if self.verbose > 0:
logging.info(f"Iteration {index}...")
old_centroids = gs.copy(self.centroids)
for i in range(self.n_clusters):
fold = gs.squeeze(X[self.labels == i])
if len(fold) > 0:
mean = FrechetMean(
metric=self.metric,
max_iter=self.max_iter_mean,
point_type=self.point_type,
method=self.mean_method,
init_step_size=self.init_step_size,
)
mean.fit(fold)
self.centroids[i] = mean.estimate_
else:
self.centroids[i] = X[randint(0, n_samples - 1)]
dists = [
gs.to_ndarray(self.metric.dist(self.centroids[i], X), 2, 1)
for i in range(self.n_clusters)
]
dists = gs.hstack(dists)
self.labels = gs.argmin(dists, 1)
dists_to_closest_centroid = gs.amin(dists, 1)
self.inertia = gs.sum(dists_to_closest_centroid**2)
centroids_distances = self.metric.dist(old_centroids,
self.centroids)
if self.verbose > 0:
logging.info(
f"Convergence criterion at the end of iteration {index} "
f"is {gs.mean(centroids_distances)}.")
if gs.mean(centroids_distances) < self.tol:
if self.verbose > 0:
logging.info(
f"Convergence reached after {index} iterations.")
if self.n_clusters == 1:
self.centroids = gs.squeeze(self.centroids, axis=0)
return gs.copy(self.centroids)
if index == self.max_iter:
logging.warning(
f"K-means maximum number of iterations {self.max_iter} reached. "
"The mean may be inaccurate.")
if self.n_clusters == 1:
self.centroids = gs.squeeze(self.centroids, axis=0)
return gs.copy(self.centroids)
def fit(self, data):
"""Fit a Gaussian mixture model (GMM) given the data.
Alternates between Expectation and Maximization steps
for some number of iterations.
Parameters
----------
data : array-like, shape=[n_samples, n_features]
Training data, where n_samples is the number of samples
and n_features is the number of features.
Returns
-------
self : object
Return the components of the computed
Gaussian mixture model: means, variances and mixture_coefficients.
"""
self._dimension = data.shape[-1]
if self.initialisation_method == 'kmeans':
kmeans = RiemannianKMeans(metric=self.metric,
n_clusters=self.n_gaussians,
init='random',
mean_method='batch',
lr=self.lr_mean)
centroids = kmeans.fit(X=data)
labels = kmeans.predict(X=data)
self.means = centroids
self.variances = gs.zeros(self.n_gaussians)
labeled_data = gs.vstack([labels, gs.transpose(data)])
labeled_data = gs.transpose(labeled_data)
for label, centroid in enumerate(centroids):
label_mask = gs.where(labeled_data[:, 0] == label)
grouped_by_label = labeled_data[label_mask][:, 1:]
v = variance(grouped_by_label, centroid, self.metric)
if grouped_by_label.shape[0] == 1:
v += MIN_VAR_INIT
self.variances[label] = v
else:
self.means = (gs.random.rand(self.n_gaussians, self._dimension) -
0.5) / self._dimension
self.variances = gs.random.rand(self.n_gaussians) / 10 + 0.8
self.mixture_coefficients = \
gs.ones(self.n_gaussians) / self.n_gaussians
posterior_probabilities = gs.ones((data.shape[0], self.means.shape[0]))
self.variances_range,\
self.normalization_factor_var, \
self.phi_inv_var =\
self.normalization_factor_init(
gs.arange(
ZETA_LOWER_BOUND, ZETA_UPPER_BOUND, ZETA_STEP))
for epoch in range(self.max_iter):
old_posterior_probabilities = posterior_probabilities
posterior_probabilities = self._expectation(data)
condition = gs.mean(
gs.abs(old_posterior_probabilities - posterior_probabilities))
if condition < EM_CONV_RATE and epoch > MINIMUM_EPOCHS:
logging.info('EM converged in %s iterations', epoch)
return self.means, self.variances, self.mixture_coefficients
self._maximization(data, posterior_probabilities)
logging.info('WARNING: EM did not converge \n'
'Please increase MINIMUM_EPOCHS.')
return self.means, self.variances, self.mixture_coefficients
def _fit(self, X, base_point=None):
"""Fit the model by computing full SVD on X.
Parameters
----------
X : array-like, shape=[..., n_features]
Training data, where n_samples is the number of samples
and n_features is the number of features.
y : Ignored (Compliance with scikit-learn interface)
base_point : array-like, shape=[..., n_features]
Point at which to perform the tangent PCA.
Optional, default to Frechet mean if None.
Returns
-------
U, S, V : array-like
Matrices of the SVD decomposition
"""
if base_point is None:
mean = FrechetMean(metric=self.metric, point_type=self.point_type)
mean.fit(X)
base_point = mean.estimate_
tangent_vecs = self.metric.log(X, base_point=base_point)
if self.point_type == 'matrix':
if Matrices.is_symmetric(tangent_vecs).all():
X = SymmetricMatrices.to_vector(tangent_vecs)
else:
X = gs.reshape(tangent_vecs, (len(X), -1))
else:
X = tangent_vecs
if self.n_components is None:
n_components = min(X.shape)
else:
n_components = self.n_components
n_samples, n_features = X.shape
if n_components == 'mle':
if n_samples < n_features:
raise ValueError("n_components='mle' is only supported "
"if n_samples >= n_features")
elif not 0 <= n_components <= min(n_samples, n_features):
raise ValueError("n_components=%r must be between 0 and "
"min(n_samples, n_features)=%r with "
"svd_solver='full'" %
(n_components, min(n_samples, n_features)))
elif n_components >= 1:
if not isinstance(n_components, numbers.Integral):
raise ValueError("n_components=%r must be of type int "
"when greater than or equal to 1, "
"was of type=%r" %
(n_components, type(n_components)))
# Center data - the mean should be 0 if base_point is the Frechet mean
self.mean_ = gs.mean(X, axis=0)
X -= self.mean_
U, S, V = gs.linalg.svd(X, full_matrices=False)
# flip eigenvectors' sign to enforce deterministic output
U, V = svd_flip(U, V)
components_ = V
# Get variance explained by singular values
explained_variance_ = (S**2) / (n_samples - 1)
total_var = explained_variance_.sum()
explained_variance_ratio_ = explained_variance_ / total_var
singular_values_ = gs.copy(S) # Store the singular values.
# Postprocess the number of components required
if n_components == 'mle':
n_components = \
_infer_dimension_(explained_variance_, n_samples, n_features)
elif 0 < n_components < 1.0:
# number of components for which the cumulated explained
# variance percentage is superior to the desired threshold
ratio_cumsum = stable_cumsum(explained_variance_ratio_)
n_components = gs.searchsorted(ratio_cumsum, n_components) + 1
# Compute noise covariance using Probabilistic PCA model
# The sigma2 maximum likelihood (cf. eq. 12.46)
if n_components < min(n_features, n_samples):
self.noise_variance_ = explained_variance_[n_components:].mean()
else:
self.noise_variance_ = 0.
self.base_point_fit = base_point
self.n_samples_, self.n_features_ = n_samples, n_features
self.components_ = components_[:n_components]
self.n_components_ = int(n_components)
self.explained_variance_ = explained_variance_[:n_components]
self.explained_variance_ratio_ = \
explained_variance_ratio_[:n_components]
self.singular_values_ = singular_values_[:n_components]
return U, S, V