def test_custom_filter(self):
def kernel(x):
return x / (1. + x)
f = filters.Filter(self._G, kernels=kernel)
self.assertEqual(f.Nf, 1)
self.assertIs(f._kernels[0], kernel)
self._test_methods(f, tight=False)
def compute_spectrogram(G, atom=None, M=100, **kwargs):
r"""
Compute the norm of the Tig for all nodes with a kernel shifted along the
spectral axis.
Parameters
----------
G : Graph
Graph on which to compute the spectrogram.
atom : func
Kernel to use in the spectrogram (default = exp(-M*(x/lmax)²)).
M : int (optional)
Number of samples on the spectral scale. (default = 100)
kwargs: dict
Additional parameters to be passed to the
:func:`pygsp.filters.Filter.filter` method.
"""
if not atom:
def atom(x):
return np.exp(-M * (x / G.lmax)**2)
scale = np.linspace(0, G.lmax, M)
spectr = np.empty((G.N, M))
for shift_idx in range(M):
shift_filter = filters.Filter(G, lambda x: atom(x - scale[shift_idx]))
tig = compute_norm_tig(shift_filter, **kwargs).squeeze()**2
spectr[:, shift_idx] = tig
G.spectr = spectr
return spectr
def test_frame_bounds(self):
# Not a frame, it as a null-space.
g = filters.Rectangular(self._G)
A, B = g.estimate_frame_bounds()
self.assertEqual(A, 0)
self.assertEqual(B, 1)
# Identity is tight.
g = filters.Filter(self._G, lambda x: np.full_like(x, 2))
A, B = g.estimate_frame_bounds()
self.assertEqual(A, 4)
self.assertEqual(B, 4)
def interpolate(G,
f_subsampled,
keep_inds,
order=100,
reg_eps=0.005,
**kwargs):
r"""Interpolate a graph signal.
Parameters
----------
G : Graph
f_subsampled : ndarray
A graph signal on the graph G.
keep_inds : ndarray
List of indices on which the signal is sampled.
order : int
Degree of the Chebyshev approximation (default = 100).
reg_eps : float
The regularized graph Laplacian is $\bar{L}=L+\epsilon I$.
A smaller epsilon may lead to better regularization,
but will also require a higher order Chebyshev approximation.
Returns
-------
f_interpolated : ndarray
Interpolated graph signal on the full vertex set of G.
References
----------
See :cite:`pesenson2009variational`
"""
L_reg = G.L + reg_eps * sparse.eye(G.N)
K_reg = getattr(G.mr, 'K_reg', kron_reduction(L_reg, keep_inds))
green_kernel = getattr(G.mr, 'green_kernel',
filters.Filter(G, lambda x: 1. / (reg_eps + x)))
alpha = K_reg.dot(f_subsampled)
try:
Nv = np.shape(f_subsampled)[1]
f_interpolated = np.zeros((G.N, Nv))
except IndexError:
f_interpolated = np.zeros((G.N))
f_interpolated[keep_inds] = alpha
return _analysis(green_kernel, f_interpolated, order=order, **kwargs)
def generate_test_vectors(G,
num_vectors=10,
method="Gauss-Seidel",
iterations=5,
lambda_cut=0.1):
L = G.L
N = G.N
X = np.random.randn(N, num_vectors) / np.sqrt(N)
if method == "GS" or method == "Gauss-Seidel":
L_upper = sp.sparse.triu(L, 1, format="csc")
L_lower_diag = sp.sparse.triu(L, 0, format="csc").T
for j in range(num_vectors):
x = X[:, j]
for t in range(iterations):
x = -sp.sparse.linalg.spsolve_triangular(
L_lower_diag, L_upper @ x)
X[:, j] = x
return X
if method == "JC" or method == "Jacobi":
deg = G.dw.astype(np.float)
D = sp.sparse.diags(deg, 0)
deginv = deg**(-1)
deginv[deginv == np.Inf] = 0
Dinv = sp.sparse.diags(deginv, 0)
M = Dinv.dot(D - L)
for j in range(num_vectors):
x = X[:, j]
for t in range(iterations):
x = 0.5 * x + 0.5 * M.dot(x)
X[:, j] = x
return X
elif method == "Chebychev":
from pygsp import filters
f = filters.Filter(
G, lambda x: ((x <= lambda_cut) * 1).astype(np.float32))
return f.filter(X, method="chebyshev", order=50)
def multiresolution(G, levels, sparsify=True):
sparsify_eps = min(10. / np.sqrt(G.N), 0.3)
reg_eps = 0.005
G.estimate_lmax()
Gs = [G]
Gs[0].mr = {'idx': np.arange(G.N), 'orig_idx': np.arange(G.N)}
for i in range(levels):
if hasattr(Gs[i], '_U'):
V = Gs[i].U[:, -1]
else:
V = linalg.eigs(Gs[i].L, 1)[1][:, 0]
V *= np.sign(V[0])
ind = np.nonzero(V >= 0)[0]
Gs.append(kronReduction(Gs[i], ind))
if sparsify and Gs[i + 1].N > 2:
Gs[i + 1] = sparsifyGraph(
Gs[i + 1], min(max(sparsify_eps, 2. / np.sqrt(Gs[i + 1].N)),
1.))
Gs[i + 1].estimate_lmax()
Gs[i + 1].mr = {
'idx': ind,
'orig_idx': Gs[i].mr['orig_idx'][ind],
'level': i
}
L_reg = Gs[i].L + reg_eps * sparse.eye(Gs[i].N)
Gs[i].mr['K_reg'] = kronReduction(L_reg, ind)
Gs[i].mr['green_kernel'] = filters.Filter(Gs[i], lambda x: 1. /
(reg_eps + x))
return Gs
def test_regression_tikhonov_3(self, tau=3.5):
"""Solve a relaxed regression problem."""
G = graphs.Sensor(100)
G.estimate_lmax()
# Create a smooth signal.
filt = filters.Filter(G, lambda x: 1 / (1 + 10 * x))
rs = np.random.RandomState(1)
signal = filt.analyze(rs.normal(size=(G.n_vertices, 6)))
# Make the input signal.
mask = rs.uniform(0, 1, G.n_vertices) > 0.5
measures = signal.copy()
measures[~mask] = 18
measures_bak = measures.copy()
L = G.L.toarray()
recovery = np.matmul(np.linalg.inv(np.diag(1 * mask) + tau * L),
(mask * measures.T).T)
# Solve the problem.
recovery0 = learning.regression_tikhonov(G, measures, mask, tau=tau)
np.testing.assert_allclose(measures_bak, measures)
recovery1 = np.zeros_like(recovery0)
for i in range(recovery0.shape[1]):
recovery1[:, i] = learning.regression_tikhonov(
G, measures[:, i], mask, tau)
np.testing.assert_allclose(measures_bak, measures)
G = graphs.Graph(G.W.toarray())
recovery2 = learning.regression_tikhonov(G, measures, mask, tau)
recovery3 = np.zeros_like(recovery0)
for i in range(recovery0.shape[1]):
recovery3[:, i] = learning.regression_tikhonov(
G, measures[:, i], mask, tau)
np.testing.assert_allclose(recovery0, recovery, atol=1e-5)
np.testing.assert_allclose(recovery1, recovery, atol=1e-5)
np.testing.assert_allclose(recovery2, recovery, atol=1e-5)
np.testing.assert_allclose(recovery3, recovery, atol=1e-5)
np.testing.assert_allclose(measures_bak, measures)
def test_regression_tikhonov_2(self):
"""Solve a regression problem with a constraint."""
G = graphs.Sensor(100)
G.estimate_lmax()
# Create a smooth signal.
filt = filters.Filter(G, lambda x: 1 / (1 + 10 * x))
rs = np.random.RandomState(1)
signal = filt.analyze(rs.normal(size=(G.n_vertices, 5)))
# Make the input signal.
mask = rs.uniform(0, 1, [G.n_vertices]) > 0.5
measures = signal.copy()
measures[~mask] = np.nan
measures_bak = measures.copy()
# Solve the problem.
recovery0 = learning.regression_tikhonov(G, measures, mask, tau=0)
np.testing.assert_allclose(measures_bak, measures)
recovery1 = np.zeros_like(recovery0)
for i in range(recovery0.shape[1]):
recovery1[:, i] = learning.regression_tikhonov(G,
measures[:, i],
mask,
tau=0)
np.testing.assert_allclose(measures_bak, measures)
G = graphs.Graph(G.W.toarray())
recovery2 = learning.regression_tikhonov(G, measures, mask, tau=0)
recovery3 = np.zeros_like(recovery0)
for i in range(recovery0.shape[1]):
recovery3[:, i] = learning.regression_tikhonov(G,
measures[:, i],
mask,
tau=0)
np.testing.assert_allclose(recovery1, recovery0)
np.testing.assert_allclose(recovery2, recovery0)
np.testing.assert_allclose(recovery3, recovery0)
np.testing.assert_allclose(measures_bak, measures)
def _pyramid_single_interpolation(G, ca, pe, keep_inds, h_filter, **kwargs):
r"""Synthesize a single level of the graph pyramid transform.
Parameters
----------
G : Graph
Graph structure on which the signal resides.
ca : ndarray
Coarse approximation of the signal on a reduced graph.
pe : ndarray
Prediction error that was made when forming the current coarse approximation.
keep_inds : ndarray
The indices of the vertices to keep when downsampling the graph and signal.
h_filter : lambda expression
The filter in use at this level.
use_landweber : bool
To use the Landweber iteration approximation in the least squares synthesis.
Default is False.
reg_eps : float
Interpolation parameter. Default is 0.005.
landweber_its : int
Number of iterations in the Landweber approximation for least squares synthesis.
Default is 50.
landweber_tau : float
Parameter for the Landweber iteration. Default is 1.
Returns
-------
finer_approx :
Coarse approximation of the signal on a higher resolution graph.
"""
nb_ind = keep_inds.shape
N = G.N
reg_eps = float(kwargs.pop('reg_eps', 0.005))
use_landweber = bool(kwargs.pop('use_landweber', False))
landweber_its = int(kwargs.pop('landweber_its', 50))
landweber_tau = float(kwargs.pop('landweber_tau', 1.))
# index matrix (nb_ind x N) of keep_inds, S_i,j = 1 iff keep_inds[i] = j
S = sparse.csr_matrix(([1] * nb_ind, (range(nb_ind), keep_inds)), shape=(nb_ind, N))
if use_landweber:
x = np.zeros(N)
z = np.concatenate((ca, pe), axis=0)
green_kernel = filters.Filter(G, lambda x: 1./(x+reg_eps))
PhiVlt = _analysis(green_kernel, S.T, **kwargs).T
filt = filters.Filter(G, h_filter, **kwargs)
for iteration in range(landweber_its):
h_filtered_sig = _analysis(filt, x, **kwargs)
x_bar = h_filtered_sig[keep_inds]
y_bar = x - interpolate(G, x_bar, keep_inds, **kwargs)
z_delt = np.concatenate((x_bar, y_bar), axis=0)
z_delt = z - z_delt
alpha_new = PhiVlt * z_delt[nb_ind:]
x_up = sparse.csr_matrix((z_delt, (range(nb_ind), [1] * nb_ind)), shape=(N, 1))
reg_L = G.L + reg_esp * sparse.eye(N)
elim_inds = np.setdiff1d(np.arange(N, dtype=int), keep_inds)
L_red = reg_L[np.ix_(keep_inds, keep_inds)]
L_in_out = reg_L[np.ix_(keep_inds, elim_inds)]
L_out_in = reg_L[np.ix_(elim_inds, keep_inds)]
L_comp = reg_L[np.ix_(elim_inds, elim_inds)]
next_term = L_red * alpha_new - L_in_out * linalg.spsolve(L_comp, L_out_in * alpha_new)
next_up = sparse.csr_matrix((next_term, (keep_inds, [1] * nb_ind)), shape=(N, 1))
x += landweber_tau * _analysis(filt, x_up - next_up, **kwargs) + z_delt[nb_ind:]
finer_approx = x
else:
# When the graph is small enough, we can do a full eigendecomposition
# and compute the full analysis operator T_a
H = G.U * sparse.diags(h_filter(G.e), 0) * G.U.T
Phi = G.U * sparse.diags(1./(reg_eps + G.e), 0) * G.U.T
Ta = np.concatenate((S * H, sparse.eye(G.N) - Phi[:, keep_inds] * linalg.spsolve(Phi[np.ix_(keep_inds, keep_inds)], S*H)), axis=0)
finer_approx = linalg.spsolve(Ta.T * Ta, Ta.T * np.concatenate((ca, pe), axis=0))
def pyramid_analysis(Gs, f, **kwargs):
r"""Compute the graph pyramid transform coefficients.
Parameters
----------
Gs : list of graphs
A multiresolution sequence of graph structures.
f : ndarray
Graph signal to analyze.
h_filters : list
A list of filter that will be used for the analysis and sythesis operator.
If only one filter is given, it will be used for all levels.
Default is h(x) = 1 / (2x+1)
Returns
-------
ca : ndarray
Coarse approximation at each level
pe : ndarray
Prediction error at each level
h_filters : list
Graph spectral filters applied
References
----------
See :cite:`shuman2013framework` and :cite:`pesenson2009variational`.
"""
if np.shape(f)[0] != Gs[0].N:
raise ValueError("PYRAMID ANALYSIS: The signal to analyze should have the same dimension as the first graph.")
levels = len(Gs) - 1
# check if the type of filters is right.
h_filters = kwargs.pop('h_filters', lambda x: 1. / (2*x+1))
if not isinstance(h_filters, list):
if hasattr(h_filters, '__call__'):
logger.warning('Converting filters into a list.')
h_filters = [h_filters]
else:
logger.error('Filters must be a list of functions.')
if len(h_filters) == 1:
h_filters = h_filters * levels
elif len(h_filters) != levels:
message = 'The number of filters must be one or equal to {}.'.format(levels)
raise ValueError(message)
ca = [f]
pe = []
for i in range(levels):
# Low pass the signal
s_low = _analysis(filters.Filter(Gs[i], h_filters[i]), ca[i], **kwargs)
# Keep only the coefficient on the selected nodes
ca.append(s_low[Gs[i+1].mr['idx']])
# Compute prediction
s_pred = interpolate(Gs[i], ca[i+1], Gs[i+1].mr['idx'], **kwargs)
# Compute errors
pe.append(ca[i] - s_pred)
return ca, pe
def graph_multiresolution(G, levels, sparsify=True, sparsify_eps=None,
downsampling_method='largest_eigenvector',
reduction_method='kron', compute_full_eigen=False,
reg_eps=0.005):
r"""Compute a pyramid of graphs (by Kron reduction).
'graph_multiresolution(G,levels)' computes a multiresolution of
graph by repeatedly downsampling and performing graph reduction. The
default downsampling method is the largest eigenvector method based on
the polarity of the components of the eigenvector associated with the
largest graph Laplacian eigenvalue. The default graph reduction method
is Kron reduction followed by a graph sparsification step.
*param* is a structure of optional parameters.
Parameters
----------
G : Graph structure
The graph to reduce.
levels : int
Number of level of decomposition
lambd : float
Stability parameter. It adds self loop to the graph to give the
algorithm some stability (default = 0.025). [UNUSED?!]
sparsify : bool
To perform a spectral sparsification step immediately after
the graph reduction (default is True).
sparsify_eps : float
Parameter epsilon used in the spectral sparsification
(default is min(10/sqrt(G.N),.3)).
downsampling_method: string
The graph downsampling method (default is 'largest_eigenvector').
reduction_method : string
The graph reduction method (default is 'kron')
compute_full_eigen : bool
To also compute the graph Laplacian eigenvalues and eigenvectors
for every graph in the multiresolution sequence (default is False).
reg_eps : float
The regularized graph Laplacian is :math:`\bar{L}=L+\epsilon I`.
A smaller epsilon may lead to better regularization, but will also
require a higher order Chebyshev approximation. (default is 0.005)
Returns
-------
Gs : list
A list of graph layers.
Examples
--------
>>> from pygsp import reduction
>>> levels = 5
>>> G = graphs.Sensor(N=512)
>>> G.compute_fourier_basis()
>>> Gs = reduction.graph_multiresolution(G, levels, sparsify=False)
>>> for idx in range(levels):
... Gs[idx].plotting['plot_name'] = 'Reduction level: {}'.format(idx)
... Gs[idx].plot()
"""
if sparsify_eps is None:
sparsify_eps = min(10. / np.sqrt(G.N), 0.3)
if compute_full_eigen:
G.compute_fourier_basis()
else:
G.estimate_lmax()
Gs = [G]
Gs[0].mr = {'idx': np.arange(G.N), 'orig_idx': np.arange(G.N)}
for i in range(levels):
if downsampling_method == 'largest_eigenvector':
if hasattr(Gs[i], '_U'):
V = Gs[i].U[:, -1]
else:
V = linalg.eigs(Gs[i].L, 1)[1][:, 0]
V *= np.sign(V[0])
ind = np.nonzero(V >= 0)[0]
else:
raise NotImplementedError('Unknown graph downsampling method.')
if reduction_method == 'kron':
Gs.append(kron_reduction(Gs[i], ind))
else:
raise NotImplementedError('Unknown graph reduction method.')
if sparsify and Gs[i+1].N > 2:
Gs[i+1] = graph_sparsify(Gs[i+1], min(max(sparsify_eps, 2. / np.sqrt(Gs[i+1].N)), 1.))
# TODO : Make in place modifications instead!
if compute_full_eigen:
Gs[i+1].compute_fourier_basis()
else:
Gs[i+1].estimate_lmax()
Gs[i+1].mr = {'idx': ind, 'orig_idx': Gs[i].mr['orig_idx'][ind], 'level': i}
L_reg = Gs[i].L + reg_eps * sparse.eye(Gs[i].N)
Gs[i].mr['K_reg'] = kron_reduction(L_reg, ind)
Gs[i].mr['green_kernel'] = filters.Filter(Gs[i], lambda x: 1./(reg_eps + x))
return Gs
def plot_coarsening(Gall,
Call,
size=3,
edge_width=0.8,
node_size=20,
alpha=0.55,
title='',
smooth=0):
"""
Plot a (hierarchical) coarsening
Parameters
----------
G_all : list of pygsp Graphs
Call : list of np.arrays
Returns
-------
fig : matplotlib figure
"""
# colors signify the size of a coarsened subgraph ('k' is 1, 'g' is 2, 'b' is 3, and so on)
colors = ['k', 'g', 'b', 'r', 'y']
n_levels = len(Gall) - 1
if n_levels == 0: return None
fig = plt.figure(figsize=(n_levels * size * 3, size * 2))
for level in range(n_levels):
G = Gall[level]
edges = np.array(G.get_edge_list()[0:2])
Gc = Gall[level + 1]
# Lc = C.dot(G.L.dot(C.T))
# Wc = sp.sparse.diags(Lc.diagonal(), 0) - Lc;
# Wc = (Wc + Wc.T) / 2
# Gc = gsp.graphs.Graph(Wc, coords=(C.power(2)).dot(G.coords))
edges_c = np.array(Gc.get_edge_list()[0:2])
C = Call[level]
C = C.toarray()
if level > 0 and smooth > 0:
f = filters.Filter(G, lambda x: np.exp(-smooth * x))
G.estimate_lmax()
G.set_coordinates(f.filter(G.coords))
if G.coords.shape[1] == 2:
ax = fig.add_subplot(1, n_levels + 1, level + 1)
ax.axis('off')
ax.set_title(f'{title} | level = {level}, N = {G.N}')
[x, y] = G.coords.T
for eIdx in range(0, edges.shape[1]):
ax.plot(x[edges[:, eIdx]],
y[edges[:, eIdx]],
color='k',
alpha=alpha,
lineWidth=edge_width)
for i in range(Gc.N):
subgraph = np.arange(G.N)[C[i, :] > 0]
ax.scatter(x[subgraph],
y[subgraph],
c=colors[np.clip(len(subgraph) - 1, 0, 4)],
s=node_size * len(subgraph),
alpha=alpha)
elif G.coords.shape[1] == 3:
ax = fig.add_subplot(1, n_levels + 1, level + 1, projection='3d')
ax.axis('off')
[x, y, z] = G.coords.T
for eIdx in range(0, edges.shape[1]):
ax.plot(x[edges[:, eIdx]],
y[edges[:, eIdx]],
zs=z[edges[:, eIdx]],
color='k',
alpha=alpha,
lineWidth=edge_width)
for i in range(Gc.N):
subgraph = np.arange(G.N)[C[i, :] > 0]
ax.scatter(x[subgraph],
y[subgraph],
z[subgraph],
c=colors[np.clip(len(subgraph) - 1, 0, 4)],
s=node_size * len(subgraph),
alpha=alpha)
# the final graph
Gc = Gall[-1]
edges_c = np.array(Gc.get_edge_list()[0:2])
if smooth > 0:
f = filters.Filter(Gc, lambda x: np.exp(-smooth * x))
Gc.estimate_lmax()
Gc.set_coordinates(f.filter(Gc.coords))
if G.coords.shape[1] == 2:
ax = fig.add_subplot(1, n_levels + 1, n_levels + 1)
ax.axis('off')
[x, y] = Gc.coords.T
ax.scatter(x, y, c='k', s=node_size, alpha=alpha)
for eIdx in range(0, edges_c.shape[1]):
ax.plot(x[edges_c[:, eIdx]],
y[edges_c[:, eIdx]],
color='k',
alpha=alpha,
lineWidth=edge_width)
elif G.coords.shape[1] == 3:
ax = fig.add_subplot(1, n_levels + 1, n_levels + 1, projection='3d')
ax.axis('off')
[x, y, z] = Gc.coords.T
ax.scatter(x, y, z, c='k', s=node_size, alpha=alpha)
for eIdx in range(0, edges_c.shape[1]):
ax.plot(x[edges_c[:, eIdx]],
y[edges_c[:, eIdx]],
z[edges_c[:, eIdx]],
color='k',
alpha=alpha,
lineWidth=edge_width)
ax.set_title(f'{title} | level = {n_levels}, n = {Gc.N}')
fig.tight_layout()
return fig
def estimate_PSD(data,G,U,lamb,method="perraudin",plot=True):
#### This functions estimates the PSD of the graph signal with two different methods.
## The maximum-likelihood estimator (likelihood)
## and the method described in "Stationary signal processing on graphs" (Perraudin 2017)
### Input.
## data= matrix of size Txp where T is the time horizon, p the number of covariance
## G= graph over the which the signal is defined
## U= eigenvectors of the GSO
## lamb= eigenvalues of the GSO
## method = which algorithm to use in order to estimate the GFT
## plot = whether or not plot the PSD estimator
### Output
## PSD= Power Spectral Density
p=data.shape[1]
N=data.shape[0]
if method=="likelihood":
PSD=np.diag(np.cov(U.transpose().dot(data.transpose())))
if plot:
M=300 #### Number of filters
m=np.arange(0,M)
l_max=np.max(lamb)
tau=((M+1)*l_max)/M**2
plt.plot(m*tau,PSD)
plt.show()
return(PSD)
if method=="perraudin":
###### Parameters initialization
M=100 #### Number of filters
degree=15
m=np.arange(0,M)
l_max=np.max(lamb)
noise=np.random.normal(size=(p,10))
norm_filters=np.zeros(M)
norm_localized_filters=np.zeros(M)
PSD=np.zeros(M)
tau=((M+1)*l_max)/M**2
##### Applying filters to noise
for i in m:
G_filter=filters.Filter(G, lambda x: gaussian_filter(x,i,M,l_max))
filter_noise=G_filter.filter(noise)
norm_filters[i]=np.sum(np.apply_along_axis(lambda x: np.mean(x**2),1,filter_noise))
localized_filter=G_filter.filter(data.transpose())
norm_localized_filters[i]=np.sum(np.apply_along_axis(lambda x: np.mean(x**2),1,localized_filter))
PSD[i]=norm_localized_filters[i]/norm_filters[i]
PSD=PSD
coeff=np.polyfit(m*tau,PSD,deg=degree)
if plot:
plt.plot(m*tau,PSD)
plt.show()
p = np.poly1d(coeff)
PSD=p(lamb)
index_PSD=np.where(PSD<=0)[0]
PSD[index_PSD]=np.min(PSD[PSD>0])
return(PSD)
def test_default_filters(G, fu):
g = filters.Filter(G)
g1 = filters.Filter(G, filters=fu)
def generate_filter(self):
#### This function defines the Filter that will be applied to withe noise.
self.H = filters.Filter(self.G, lambda x: self.spectral_profile(x))
self.PSD = (self.spectral_profile(self.G.e))**2