def test_graphical_lasso_iris():
# Hard-coded solution from R glasso package for alpha=1.0
# (need to set penalize.diagonal to FALSE)
cov_R = np.array(
[
[0.68112222, 0.0000000, 0.265820, 0.02464314],
[0.00000000, 0.1887129, 0.000000, 0.00000000],
[0.26582000, 0.0000000, 3.095503, 0.28697200],
[0.02464314, 0.0000000, 0.286972, 0.57713289],
]
)
icov_R = np.array(
[
[1.5190747, 0.000000, -0.1304475, 0.0000000],
[0.0000000, 5.299055, 0.0000000, 0.0000000],
[-0.1304475, 0.000000, 0.3498624, -0.1683946],
[0.0000000, 0.000000, -0.1683946, 1.8164353],
]
)
X = datasets.load_iris().data
emp_cov = empirical_covariance(X)
for method in ("cd", "lars"):
cov, icov = graphical_lasso(emp_cov, alpha=1.0, return_costs=False, mode=method)
assert_array_almost_equal(cov, cov_R)
assert_array_almost_equal(icov, icov_R)
def test_graphical_lasso_iris_singular():
# Small subset of rows to test the rank-deficient case
# Need to choose samples such that none of the variances are zero
indices = np.arange(10, 13)
# Hard-coded solution from R glasso package for alpha=0.01
cov_R = np.array([
[0.08, 0.056666662595, 0.00229729713223, 0.00153153142149],
[0.056666662595, 0.082222222222, 0.00333333333333, 0.00222222222222],
[0.002297297132, 0.003333333333, 0.00666666666667, 0.00009009009009],
[0.001531531421, 0.002222222222, 0.00009009009009, 0.00222222222222]
])
icov_R = np.array([
[24.42244057, -16.831679593, 0.0, 0.0],
[-16.83168201, 24.351841681, -6.206896552, -12.5],
[0.0, -6.206896171, 153.103448276, 0.0],
[0.0, -12.499999143, 0.0, 462.5]
])
X = datasets.load_iris().data[indices, :]
emp_cov = empirical_covariance(X)
for method in ('cd', 'lars'):
cov, icov = graphical_lasso(emp_cov, alpha=0.01, return_costs=False,
mode=method)
assert_array_almost_equal(cov, cov_R, decimal=5)
assert_array_almost_equal(icov, icov_R, decimal=5)
def graphical_lasso_wrap(emp_cov, alpha, max_iter):
try:
_, precision = graphical_lasso(emp_cov[0], alpha=alpha, max_iter=max_iter)
return precision
except FloatingPointError:
return graphical_lasso_wrap(emp_cov, alpha=alpha * 1.1, max_iter=max_iter)
def test_graphical_lasso_iris_singular():
# Small subset of rows to test the rank-deficient case
# Need to choose samples such that none of the variances are zero
indices = np.arange(10, 13)
# Hard-coded solution from R glasso package for alpha=0.01
cov_R = np.array([
[0.08, 0.056666662595, 0.00229729713223, 0.00153153142149],
[0.056666662595, 0.082222222222, 0.00333333333333, 0.00222222222222],
[0.002297297132, 0.003333333333, 0.00666666666667, 0.00009009009009],
[0.001531531421, 0.002222222222, 0.00009009009009, 0.00222222222222],
])
icov_R = np.array([
[24.42244057, -16.831679593, 0.0, 0.0],
[-16.83168201, 24.351841681, -6.206896552, -12.5],
[0.0, -6.206896171, 153.103448276, 0.0],
[0.0, -12.499999143, 0.0, 462.5],
])
X = datasets.load_iris().data[indices, :]
emp_cov = empirical_covariance(X)
for method in ("cd", "lars"):
cov, icov = graphical_lasso(emp_cov,
alpha=0.01,
return_costs=False,
mode=method)
assert_array_almost_equal(cov, cov_R, decimal=5)
assert_array_almost_equal(icov, icov_R, decimal=5)
def fit_qda(self, data, lbls, idx):
self.di_moments = dict(
zip(self.uy, [{
'mu': np.repeat(0, self.p).astype(float),
'Sigma': np.zeros([self.p, self.p]),
'iSigma': np.zeros([self.p, self.p])
} for z in self.uy]))
for yy in self.uy:
self.di_moments[yy]['n'] = self.ny[yy]
for ii in idx[yy]:
x_ii = np.c_[self.enc.cenc.transform(data.iloc[ii,
self.enc.cidx]),
self.enc.nenc.transform(data.iloc[ii,
self.enc.nidx])]
self.di_moments[yy]['mu'] += x_ii.sum(axis=0)
self.di_moments[yy]['Sigma'] += x_ii.T.dot(x_ii)
# Adjust raw numbers
self.di_moments[yy]['mu'] = self.di_moments[yy]['mu'].reshape(
[self.p, 1]) / self.ny[yy]
self.di_moments[yy]['Sigma'] = (
self.di_moments[yy]['Sigma'] - self.ny[yy] *
self.di_moments[yy]['mu'].dot(self.di_moments[yy]['mu'].T)) / (
self.ny[yy] - 1)
#self.di_moments[yy]['ldet'] = np.log(np.linalg.det(self.di_moments[yy]['Sigma']))
#self.di_moments[yy]['iSigma'] = np.linalg.pinv(self.di_moments[yy]['Sigma'])
self.di_moments[yy]['iSigma'] = graphical_lasso(
emp_cov=self.di_moments[yy]['Sigma'], alpha=0.001)
def test_graph_lasso_2D():
# Hard-coded solution from Python skggm package
# obtained by calling `quic(emp_cov, lam=.1, tol=1e-8)`
cov_skggm = np.array([[3.09550269, 1.186972], [1.186972, 0.57713289]])
icov_skggm = np.array([[1.52836773, -3.14334831], [-3.14334831, 8.19753385]])
X = datasets.load_iris().data[:, 2:]
emp_cov = empirical_covariance(X)
for method in ("cd", "lars"):
cov, icov = graphical_lasso(emp_cov, alpha=0.1, return_costs=False, mode=method)
assert_array_almost_equal(cov, cov_skggm)
assert_array_almost_equal(icov, icov_skggm)
def fit(self, X, W=None):
'''
X: data matrix, (n x d)
each row corresponds to a single instance
Must be shifted to zero already.
W: connectivity graph, (n x n)
+1 for positive pairs, -1 for negative.
'''
print('SDML.fit ...', numpy.shape(X))
self.mean_ = numpy.mean(X, axis=0)
X = numpy.matrix(X - self.mean_)
# set up prior M
#print 'X', X.shape
if self.use_cov:
M = np.cov(X.T)
else:
M = np.identity(X.shape[1])
if W is None:
W = np.ones((X.shape[1], X.shape[1]))
#print 'W', W.shape
L = laplacian(W, normed=False)
#print 'L', L.shape
inner = X.dot(L.T)
loss_matrix = inner.T.dot(X)
#print 'loss', loss_matrix.shape
#print 'pinv', pinvh(M).shape
P = pinvh(M) + self.balance_param * loss_matrix
#print 'P', P.shape
emp_cov = pinvh(P)
# hack: ensure positive semidefinite
emp_cov = emp_cov.T.dot(emp_cov)
M, _ = graphical_lasso(emp_cov,
self.sparsity_param,
verbose=self.verbose)
self.M = M
C = numpy.linalg.cholesky(self.M)
self.dewhiten_ = C
self.whiten_ = numpy.linalg.inv(C)
# U: rotation matrix, S: scaling matrix
#U, S, _ = scipy.linalg.svd(M)
#s = np.sqrt(S.clip(self.EPS))
#s_inv = np.diag(1./s)
#s = np.diag(s)
#self.whiten_ = np.dot(np.dot(U, s_inv), U.T)
#self.dewhiten_ = np.dot(np.dot(U, s), U.T)
#print 'M:', M
print('SDML.fit done')
def test_graph_lasso_2D():
# Hard-coded solution from Python skggm package
# obtained by calling `quic(emp_cov, lam=.1, tol=1e-8)`
cov_skggm = np.array([[3.09550269, 1.186972],
[1.186972, 0.57713289]])
icov_skggm = np.array([[1.52836773, -3.14334831],
[-3.14334831, 8.19753385]])
X = datasets.load_iris().data[:, 2:]
emp_cov = empirical_covariance(X)
for method in ('cd', 'lars'):
cov, icov = graphical_lasso(emp_cov, alpha=.1, return_costs=False,
mode=method)
assert_array_almost_equal(cov, cov_skggm)
assert_array_almost_equal(icov, icov_skggm)
def update_omega(self, X, Y, W, b, N, K, sample_weight=None):
"""
Update conditional covariance matrix among responeses.
If assume sparse, then solve by graphical lasso (implemented by scikit_learn).
Note that this option sometimes encounter warning related to PSD from
the graphical lasso implementation.
Parameters
----------
X: numpy array
N x D, features
Y: numpy array
N x K, responses
W: numpy array
D x K, coefficients of tasks
b: numpy array
K x 1, intercepts of tasks
N: integer
sample size
K: integer
dimension of tasks/responses
sample_weight: numpy array
N x 1, weight of each sample
Returns
-------
omega: numpy array
K x K, conditional covariance among responeses
omega_i: numpy array
K x K, inverse of omega
"""
_dif = Y - X @ W - b
H = np.diag(sample_weight)
if self.sparse_omega:
omega, omega_i = graphical_lasso((_dif.T @ _dif) / N, self.lam2)
else:
omega = (_dif.T @ H @ _dif + self.lam2 * np.identity(K)) / np.sum(H)
omega_i = np.linalg.inv(omega)
return omega, omega_i
def test_graphical_lasso(random_state=0):
# Sample area_data from a sparse multivariate normal
dim = 20
n_samples = 100
random_state = check_random_state(random_state)
prec = make_sparse_spd_matrix(dim, alpha=.95, random_state=random_state)
cov = linalg.inv(prec)
X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
emp_cov = empirical_covariance(X)
for alpha in (0., .1, .25):
covs = dict()
icovs = dict()
for method in ('cd', 'lars'):
cov_, icov_, costs = graphical_lasso(emp_cov,
return_costs=True,
alpha=alpha,
mode=method)
covs[method] = cov_
icovs[method] = icov_
costs, dual_gap = np.array(costs).T
# Check that the costs always decrease (doesn't hold if alpha == 0)
if not alpha == 0:
assert_array_less(np.diff(costs), 0)
# Check that the 2 approaches give similar results
assert_array_almost_equal(covs['cd'], covs['lars'], decimal=4)
assert_array_almost_equal(icovs['cd'], icovs['lars'], decimal=4)
# Smoke test the estimator
model = GraphicalLasso(alpha=.25).fit(X)
model.score(X)
assert_array_almost_equal(model.covariance_, covs['cd'], decimal=4)
assert_array_almost_equal(model.covariance_, covs['lars'], decimal=4)
# For a centered matrix, assume_centered could be chosen True or False
# Check that this returns indeed the same result for centered area_data
Z = X - X.mean(0)
precs = list()
for assume_centered in (False, True):
prec_ = GraphicalLasso(
assume_centered=assume_centered).fit(Z).precision_
precs.append(prec_)
assert_array_almost_equal(precs[0], precs[1])
def _graphical_lasso(self,
expected_value_resid_square,
alpha=None,
normalize_param=None,
Sigma_init=None):
"""
Given Gamma, we estimate Omega, the graphical lasso solution for the precision matrix
:param expected_value_resid_square:
:param Sigma_init:
:param normalize_param: number of rows in Y matrix. This is not self.data.n when we use CV.
:return:
"""
if not normalize_param:
normalize_param = self.data.n
if not alpha:
alpha = self.alpha
expected_value_resid_square *= (1 / normalize_param)
expected_value_resid_square = np.array(expected_value_resid_square)
# TODO: for now we do not do CV since the func that does this requres (Y-Z\Gamma),
# and not (Y-Z\Gamma)^T(Y-Z\Gamma). In theory, we can do SVD of the latter to return to (Y-Z\Gamma),
# but this is weird, because this is an expectation... If we think about it, in the CV func we will get
# (Y-Z\Gamma)^T(Y-Z\Gamma) again as the covariance, so it's probably OK.
if Sigma_init is not None:
Sigma_init = np.matrix(Sigma_init)
mode = 'cd'
if self.data.n < self.data.p:
# We preffer the LARS solver for very sparse underlying graphs
# TODO: move this so we want need to check this every iteration
mode = 'lars'
Sigma, Omega = graphical_lasso(expected_value_resid_square,
alpha=alpha,
cov_init=Sigma_init,
max_iter=self.glasso_max_iter,
mode=mode)
return Sigma, Omega
def test_graphical_lasso(random_state=0):
# Sample data from a sparse multivariate normal
dim = 20
n_samples = 100
random_state = check_random_state(random_state)
prec = make_sparse_spd_matrix(dim, alpha=.95,
random_state=random_state)
cov = linalg.inv(prec)
X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples)
emp_cov = empirical_covariance(X)
for alpha in (0., .1, .25):
covs = dict()
icovs = dict()
for method in ('cd', 'lars'):
cov_, icov_, costs = graphical_lasso(emp_cov, return_costs=True,
alpha=alpha, mode=method)
covs[method] = cov_
icovs[method] = icov_
costs, dual_gap = np.array(costs).T
# Check that the costs always decrease (doesn't hold if alpha == 0)
if not alpha == 0:
assert_array_less(np.diff(costs), 0)
# Check that the 2 approaches give similar results
assert_array_almost_equal(covs['cd'], covs['lars'], decimal=4)
assert_array_almost_equal(icovs['cd'], icovs['lars'], decimal=4)
# Smoke test the estimator
model = GraphicalLasso(alpha=.25).fit(X)
model.score(X)
assert_array_almost_equal(model.covariance_, covs['cd'], decimal=4)
assert_array_almost_equal(model.covariance_, covs['lars'], decimal=4)
# For a centered matrix, assume_centered could be chosen True or False
# Check that this returns indeed the same result for centered data
Z = X - X.mean(0)
precs = list()
for assume_centered in (False, True):
prec_ = GraphicalLasso(
assume_centered=assume_centered).fit(Z).precision_
precs.append(prec_)
assert_array_almost_equal(precs[0], precs[1])
def test_graphical_lasso_iris():
# Hard-coded solution from R glasso package for alpha=1.0
# (need to set penalize.diagonal to FALSE)
cov_R = np.array([
[0.68112222, 0.0000000, 0.265820, 0.02464314],
[0.00000000, 0.1887129, 0.000000, 0.00000000],
[0.26582000, 0.0000000, 3.095503, 0.28697200],
[0.02464314, 0.0000000, 0.286972, 0.57713289]
])
icov_R = np.array([
[1.5190747, 0.000000, -0.1304475, 0.0000000],
[0.0000000, 5.299055, 0.0000000, 0.0000000],
[-0.1304475, 0.000000, 0.3498624, -0.1683946],
[0.0000000, 0.000000, -0.1683946, 1.8164353]
])
X = datasets.load_iris().data
emp_cov = empirical_covariance(X)
for method in ('cd', 'lars'):
cov, icov = graphical_lasso(emp_cov, alpha=1.0, return_costs=False,
mode=method)
assert_array_almost_equal(cov, cov_R)
assert_array_almost_equal(icov, icov_R)
def glasso_R(data, alphas, mode='cd'):
"""
Estimates the graph with graphical lasso based on its implementation in R.
Parameters
----------
data: numpy ndarray
The input data for to reconstruct/estimate a graph on. Features as columns and observations as rows.
alphas: float
Non-negative regularization parameter of the graphical lasso algorithm.
Returns
-------
adjacency matrix : the estimated adjacency matrix.
"""
scaler = StandardScaler()
data = scaler.fit_transform(data)
_ , n_samples = data.shape
cov_emp = np.dot(data.T, data) / n_samples
covariance, precision_matrix = graphical_lasso(emp_cov=cov_emp, alpha=alphas, mode=mode)
adjacency_matrix = precision_matrix.astype(bool).astype(int)
adjacency_matrix[np.diag_indices_from(adjacency_matrix)] = 0
return adjacency_matrix
def test_graphical_lasso_iris():
# Hard-coded solution from R glasso package for alpha=1.0
# The iris datasets in R and scikit-learn do not match in a few places,
# these values are for the scikit-learn version.
cov_R = np.array([
[0.68112222, 0.0, 0.2651911, 0.02467558],
[0.00, 0.1867507, 0.0, 0.00],
[0.26519111, 0.0, 3.0924249, 0.28774489],
[0.02467558, 0.0, 0.2877449, 0.57853156]
])
icov_R = np.array([
[1.5188780, 0.0, -0.1302515, 0.0],
[0.0, 5.354733, 0.0, 0.0],
[-0.1302515, 0.0, 0.3502322, -0.1686399],
[0.0, 0.0, -0.1686399, 1.8123908]
])
X = datasets.load_iris().data
emp_cov = empirical_covariance(X)
for method in ('cd', 'lars'):
cov, icov = graphical_lasso(emp_cov, alpha=1.0, return_costs=False,
mode=method)
assert_array_almost_equal(cov, cov_R)
assert_array_almost_equal(icov, icov_R)
def ta_sparse_covariance(df: Typing.PatchedPandas,
convert_to='returns',
covariance='ewma',
cov_arg=0.97,
rho=0.1,
inverse=False,
**kwargs):
from sklearn.covariance import graphical_lasso
if covariance in ['ewma', 'weighted']:
cov_func = ta_ewma_covariance
elif covariance in ['rolling', 'moving']:
cov_func = ta_moving_covariance
elif covariance in ['garch', 'mgarch']:
cov_func = ta_mgarch_covariance
else:
raise ValueError("unknown covariance expected one of [ewma, moving]")
return \
cov_func(df, cov_arg, convert_to) \
.groupby(level=0) \
.apply(lambda x: x if x.isnull().values.any() else \
_pd.DataFrame(graphical_lasso(x.values, rho, **kwargs)[int(inverse)], index=x.index, columns=x.columns))
def _fit(self, pairs, y):
if not HAS_SKGGM:
if self.verbose:
print("SDML will use scikit-learn's graphical lasso solver.")
else:
if self.verbose:
print("SDML will use skggm's graphical lasso solver.")
pairs, y = self._prepare_inputs(pairs, y, type_of_inputs='tuples')
# set up (the inverse of) the prior M
if self.use_cov:
X = np.vstack(
{tuple(row)
for row in pairs.reshape(-1, pairs.shape[2])})
prior_inv = np.atleast_2d(np.cov(X, rowvar=False))
else:
prior_inv = np.identity(pairs.shape[2])
diff = pairs[:, 0] - pairs[:, 1]
loss_matrix = (diff.T * y).dot(diff)
emp_cov = prior_inv + self.balance_param * loss_matrix
# our initialization will be the matrix with emp_cov's eigenvalues,
# with a constant added so that they are all positive (plus an epsilon
# to ensure definiteness). This is empirical.
w, V = np.linalg.eigh(emp_cov)
min_eigval = np.min(w)
if min_eigval < 0.:
warnings.warn(
"Warning, the input matrix of graphical lasso is not "
"positive semi-definite (PSD). The algorithm may diverge, "
"and lead to degenerate solutions. "
"To prevent that, try to decrease the balance parameter "
"`balance_param` and/or to set use_cov=False.",
ConvergenceWarning)
w -= min_eigval # we translate the eigenvalues to make them all positive
w += 1e-10 # we add a small offset to avoid definiteness problems
sigma0 = (V * w).dot(V.T)
try:
if HAS_SKGGM:
theta0 = pinvh(sigma0)
M, _, _, _, _, _ = quic(emp_cov,
lam=self.sparsity_param,
msg=self.verbose,
Theta0=theta0,
Sigma0=sigma0)
else:
_, M = graphical_lasso(emp_cov,
alpha=self.sparsity_param,
verbose=self.verbose,
cov_init=sigma0)
raised_error = None
w_mahalanobis, _ = np.linalg.eigh(M)
not_spd = any(w_mahalanobis < 0.)
not_finite = not np.isfinite(M).all()
except Exception as e:
raised_error = e
not_spd = False # not_spd not applicable here so we set to False
not_finite = False # not_finite not applicable here so we set to False
if raised_error is not None or not_spd or not_finite:
msg = ("There was a problem in SDML when using {}'s graphical "
"lasso solver."
).format("skggm" if HAS_SKGGM else "scikit-learn")
if not HAS_SKGGM:
skggm_advice = (
" skggm's graphical lasso can sometimes converge "
"on non SPD cases where scikit-learn's graphical "
"lasso fails to converge. Try to install skggm and "
"rerun the algorithm (see the README.md for the "
"right version of skggm).")
msg += skggm_advice
if raised_error is not None:
msg += " The following error message was thrown: {}.".format(
raised_error)
raise RuntimeError(msg)
self.transformer_ = transformer_from_metric(np.atleast_2d(M))
return self
def _fit(self, pairs, y):
if self.use_cov != 'deprecated':
warnings.warn(
'"use_cov" parameter is not used.'
' It has been deprecated in version 0.5.0 and will be'
'removed in 0.6.0. Use "prior" instead.', DeprecationWarning)
if not HAS_SKGGM:
if self.verbose:
print("SDML will use scikit-learn's graphical lasso solver.")
else:
if self.verbose:
print("SDML will use skggm's graphical lasso solver.")
pairs, y = self._prepare_inputs(pairs, y, type_of_inputs='tuples')
# set up (the inverse of) the prior M
# if the prior is the default (None), we raise a warning
if self.prior is None:
# TODO:
# replace prior=None by prior='identity' in v0.6.0 and remove the
# warning
msg = (
"Warning, no prior was set (`prior=None`). As of version 0.5.0, "
"the default prior will now be set to "
"'identity', instead of 'covariance'. If you still want to use "
"the inverse of the covariance matrix as a prior, "
"set prior='covariance'. This warning will disappear in "
"v0.6.0, and `prior` parameter's default value will be set to "
"'identity'.")
warnings.warn(msg, ChangedBehaviorWarning)
prior = 'identity'
else:
prior = self.prior
_, prior_inv = _initialize_metric_mahalanobis(
pairs,
prior,
return_inverse=True,
strict_pd=True,
matrix_name='prior',
random_state=self.random_state)
diff = pairs[:, 0] - pairs[:, 1]
loss_matrix = (diff.T * y).dot(diff)
emp_cov = prior_inv + self.balance_param * loss_matrix
# our initialization will be the matrix with emp_cov's eigenvalues,
# with a constant added so that they are all positive (plus an epsilon
# to ensure definiteness). This is empirical.
w, V = np.linalg.eigh(emp_cov)
min_eigval = np.min(w)
if min_eigval < 0.:
warnings.warn(
"Warning, the input matrix of graphical lasso is not "
"positive semi-definite (PSD). The algorithm may diverge, "
"and lead to degenerate solutions. "
"To prevent that, try to decrease the balance parameter "
"`balance_param` and/or to set prior='identity'.",
ConvergenceWarning)
w -= min_eigval # we translate the eigenvalues to make them all positive
w += 1e-10 # we add a small offset to avoid definiteness problems
sigma0 = (V * w).dot(V.T)
try:
if HAS_SKGGM:
theta0 = pinvh(sigma0)
M, _, _, _, _, _ = quic(emp_cov,
lam=self.sparsity_param,
msg=self.verbose,
Theta0=theta0,
Sigma0=sigma0)
else:
_, M = graphical_lasso(emp_cov,
alpha=self.sparsity_param,
verbose=self.verbose,
cov_init=sigma0)
raised_error = None
w_mahalanobis, _ = np.linalg.eigh(M)
not_spd = any(w_mahalanobis < 0.)
not_finite = not np.isfinite(M).all()
except Exception as e:
raised_error = e
not_spd = False # not_spd not applicable here so we set to False
not_finite = False # not_finite not applicable here so we set to False
if raised_error is not None or not_spd or not_finite:
msg = ("There was a problem in SDML when using {}'s graphical "
"lasso solver."
).format("skggm" if HAS_SKGGM else "scikit-learn")
if not HAS_SKGGM:
skggm_advice = (
" skggm's graphical lasso can sometimes converge "
"on non SPD cases where scikit-learn's graphical "
"lasso fails to converge. Try to install skggm and "
"rerun the algorithm (see the README.md for the "
"right version of skggm).")
msg += skggm_advice
if raised_error is not None:
msg += " The following error message was thrown: {}.".format(
raised_error)
raise RuntimeError(msg)
self.components_ = components_from_metric(np.atleast_2d(M))
return self
def fit_sklearn(self):
return graphical_lasso(emp_cov=self.emp_cov, alpha=self.alpha)
def fit(self, reltol=1e-5, max_itr=1000, verbose=True):
nr_itr = 0
has_not_converged = True
Theta0 = self.Theta_init.copy()
mu0 = self.mu_init
tau = np.zeros(self.N)
tol = np.inf
while has_not_converged and nr_itr < max_itr:
print(f'{nr_itr} / {max_itr}, {tol}')
if verbose:
print(f' {nr_itr} / {max_itr}')
if self.mu_zero:
for t in range(self.N):
tau[t] = (self.nu + self.N) / (self.nu + np.dot(
self.x[t, :], Theta0).dot(self.x[t, :]))
# S_hat = np.zeros((self.p, self.p))
# for i in range(self.N):
# S_hat += np.outer(self.x[i,:], self.x[i,:]) * tau[i]
S_hat = np.array([
np.outer(self.x[i, :], self.x[i, :]) * tau[i]
for i in range(self.N)
]).sum(0)
# print(S_hat[:5,:5])
_, Theta_t = graphical_lasso(S_hat, self.alpha)
tol = np.linalg.norm(Theta_t - Theta0, 'fro') / np.linalg.norm(
Theta0, 'fro')
has_not_converged = (np.linalg.norm(Theta_t - Theta0, 'fro') /
np.linalg.norm(Theta0, 'fro') > reltol)
Theta0 = Theta_t.copy()
else:
for t in range(self.N):
tau[t] = (self.nu + self.N) / (self.nu + np.dot(
self.x[t, :] - mu0, Theta0).dot(self.x[t, :] - mu0))
sum_tau = np.sum(tau)
mu_hat = np.array(
[tau[i] * self.x[i, :]
for i in range(self.p)]).sum(0) / sum_tau
S_hat = np.array([
np.outer(self.x[i, :] - mu_hat, self.x[i, :] - mu_hat) *
tau[i] for i in range(self.N)
]).sum(0)
_, Theta_t = graphical_lasso(S_hat, self.alpha)
tol = np.linalg.norm(Theta_t - Theta0, 'fro') / np.linalg.norm(
Theta0, 'fro')
has_not_converged = (tol > reltol)
Theta0 = Theta_t.copy()
mu0 = mu_hat.copy()
nr_itr += 1
return ((self.nu - 2.0) / self.nu) * Theta_t
def markov_network(sigma2, k, lambda_vec, tol=10**-14, opt=False):
""" For details, see here.
Parameters
----------
sigma2 : array, shape(n_, n_)
k : scalar
lambda_vec : array, shape(l_,)
tol : scalar
opt : bool
Returns
----------
sigma2_bar : array, shape(n_, n_, l_)
c2_bar : array, shape(n_, n_, l_)
phi2_bar : array, shape(n_, n_, l_)
lambda_bar : scalar
conv : scalar
l_bar : scalar
"""
lambda_vec = np.sort(lambda_vec)
l_ = len(lambda_vec)
c2_bar = np.zeros(sigma2.shape + (l_, ))
phi2_bar = np.zeros(sigma2.shape + (l_, ))
z = np.zeros(l_)
# Compute correlation
c2, sigma_vec = cov_2_corr(sigma2)
for l in range(l_):
lam = lambda_vec[l]
# perform glasso shrinkage
_, invs2_tilde, *_ = graphical_lasso(c2, lam)
# correlation extraction
c2_tilde = np.linalg.solve(invs2_tilde, np.eye(invs2_tilde.shape[0]))
c2_bar[:, :, l] = cov_2_corr(c2_tilde)[0] # estimated corr.
# inv. corr.
phi2_bar[:, :, l] = np.linalg.solve(c2_bar[:, :, l],
np.eye(c2_bar[:, :, l].shape[0]))
tmp = abs(phi2_bar[:, :, l])
z[l] = np.sum(tmp < tol)
# selection
index = list(np.where(z >= k)[0])
if len(index) == 0:
index.append(l)
conv = 0 # target of k null entries not reached
else:
conv = 1 # target of k null entries reached
l_bar = index[0]
lambda_bar = lambda_vec[l_bar]
# output
if not opt:
c2_bar = c2_bar[:, :, l_bar] # shrunk correlation
phi2_bar = phi2_bar[:, :, l_bar] # shrunk inverse correlation
l_bar = None
# shrunk covariance
sigma2_bar = np.diag(sigma_vec) @ c2_bar @ np.diag(sigma_vec)
else:
sigma2_bar = np.zeros(sigma2.shape + (l_, ))
for l in range(l_):
sigma2_bar[:, :, l] = np.diag(sigma_vec) @ c2_bar[:, :, l] @ \
np.diag(sigma_vec)
return sigma2_bar, c2_bar, phi2_bar, lambda_bar, conv, l_bar
def fit(self, X, y):
"""Fit the QDA to the training data"""
methods = [
None, 'nonpara', "fr", "kl", "mean", "wass", "reg", "freg",
"sparse", "kl_new"
]
rules = ["qda", "da", "fda"]
if self.method not in methods:
raise ValueError("method must be in {}; got (method={})".format(
methods, self.method))
if self.rule not in rules:
raise ValueError("rule must be in {}; got (rule={})".format(
rules, self.rule))
X, y = check_X_y(X, y)
self.labels_, self.n_samples_ = np.unique(y, return_counts=True)
self.n_class_ = self.labels_.size
n_samples, self.n_features_ = X.shape
self.rho_ = np.array([self.rho]).ravel()
if self.rho == -1:
chi_quantile = chi2.ppf(
0.5,
self.n_features_ * (self.n_features_ + 3) / 2)
self.rho_ = chi_quantile * np.ones(self.n_class_) / self.n_samples_
else:
if self.rho_.size == 1:
self.rho_ = self.rho_[0] * np.ones(self.n_class_)
if self.adaptive:
self.rho_ *= np.sqrt(self.n_features_)
# PRINT!!!!
#print(self.n_features_, chi_quantile,self.n_samples_,self.rho_)
if self.priors is None:
self.priors_ = self.n_samples_ / n_samples
else:
self.priors_ = self.priors
self.mean_ = []
self.covariance_ = []
self.cov_sqrt_ = []
self.prec_ = []
self.prec_sqrt_ = []
self.logdet_ = []
self.rotations_ = []
self.scalings_ = []
for n_c, label in enumerate(self.labels_):
mask = (y == label)
X_c = X[mask, :]
X_c_mean = np.mean(X_c, 0)
X_c_bar = X_c - X_c_mean
U, s, Vt = np.linalg.svd(X_c_bar, full_matrices=False)
s2 = (s**2) / (len(X_c_bar) - 1)
self.mean_.append(X_c_mean)
if self.method == 'reg':
s2 += self.rho_[n_c]
inv_s2 = 1 / s2
elif self.method in [
'fr', 'kl', 'mean', 'freg', 'kl_new', 'nonpara'
]:
sc = StandardScaler()
X_c_ = sc.fit_transform(X_c)
cov_c = ledoit_wolf(X_c_)[0]
cov_c = sc.scale_[:, np.newaxis] * cov_c * sc.scale_[
np.newaxis, :]
s2, V = np.linalg.eigh(cov_c)
s2 = np.abs(s2)
inv_s2 = 1 / s2
Vt = V.T
elif self.method == 'sparse':
try:
cov_c = GraphicalLasso(alpha=self.rho_[n_c]).fit(X_c_bar)
cov_c = cov_c.covariance__
except:
tol = self.tol * 1e6
cov_c = graphical_lasso(
np.dot(((1 - tol) * s2 + tol) * Vt.T, Vt),
self.rho_[n_c])[0]
s2, V = np.linalg.eigh(cov_c)
s2 = np.abs(s2)
inv_s2 = 1 / s2
Vt = V.T
elif self.method == 'wass':
f = lambda gamma: gamma * (self.rho_[n_c] ** 2 - 0.5 * np.sum(s2)) - self.n_features_ + \
0.5 * (np.sum(np.sqrt((gamma ** 2) * (s2 ** 2) + 4 * s2 * gamma)))
lb = 0
gamma_0 = 0
ub = np.sum(np.sqrt(1 / (s2 + self.tol))) / self.rho_[n_c]
f_ub = f(ub)
for bsect in range(100):
gamma_0 = 0.5 * (ub + lb)
f_gamma_0 = f(gamma_0)
if f_ub * f_gamma_0 > 0:
ub = gamma_0
f_ub = f_gamma_0
else:
lb = gamma_0
if abs(ub - lb) < self.tol:
break
inv_s2 = gamma_0 * (1 - 2 / (1 + np.sqrt(1 + 4 /
(gamma_0 *
(s2 + self.tol)))))
s2 = 1 / (inv_s2 + self.tol)
else:
s2 += self.tol
inv_s2 = 1 / s2
self.covariance_.append(np.dot(s2 * Vt.T, Vt))
self.cov_sqrt_.append(np.dot(np.sqrt(s2) * Vt.T, Vt))
self.prec_.append(np.dot(inv_s2 * Vt.T, Vt))
self.prec_sqrt_.append(np.dot(np.sqrt(inv_s2) * Vt.T, Vt))
self.logdet_.append(np.log(s2).sum())
#print(self.logdet_)
self.rotations_.append(Vt)
self.scalings_.append(s2)
return self
def _fit(self, pairs, y):
if not HAS_SKGGM:
if self.verbose:
print("SDML will use scikit-learn's graphical lasso solver.")
else:
if self.verbose:
print("SDML will use skggm's graphical lasso solver.")
pairs, y = self._prepare_inputs(pairs, y,
type_of_inputs='tuples')
# set up (the inverse of) the prior M
if self.use_cov:
X = np.vstack({tuple(row) for row in pairs.reshape(-1, pairs.shape[2])})
prior_inv = np.atleast_2d(np.cov(X, rowvar=False))
else:
prior_inv = np.identity(pairs.shape[2])
diff = pairs[:, 0] - pairs[:, 1]
loss_matrix = (diff.T * y).dot(diff)
emp_cov = prior_inv + self.balance_param * loss_matrix
# our initialization will be the matrix with emp_cov's eigenvalues,
# with a constant added so that they are all positive (plus an epsilon
# to ensure definiteness). This is empirical.
w, V = np.linalg.eigh(emp_cov)
min_eigval = np.min(w)
if min_eigval < 0.:
warnings.warn("Warning, the input matrix of graphical lasso is not "
"positive semi-definite (PSD). The algorithm may diverge, "
"and lead to degenerate solutions. "
"To prevent that, try to decrease the balance parameter "
"`balance_param` and/or to set use_cov=False.",
ConvergenceWarning)
w -= min_eigval # we translate the eigenvalues to make them all positive
w += 1e-10 # we add a small offset to avoid definiteness problems
sigma0 = (V * w).dot(V.T)
try:
if HAS_SKGGM:
theta0 = pinvh(sigma0)
M, _, _, _, _, _ = quic(emp_cov, lam=self.sparsity_param,
msg=self.verbose,
Theta0=theta0, Sigma0=sigma0)
else:
_, M = graphical_lasso(emp_cov, alpha=self.sparsity_param,
verbose=self.verbose,
cov_init=sigma0)
raised_error = None
w_mahalanobis, _ = np.linalg.eigh(M)
not_spd = any(w_mahalanobis < 0.)
not_finite = not np.isfinite(M).all()
except Exception as e:
raised_error = e
not_spd = False # not_spd not applicable here so we set to False
not_finite = False # not_finite not applicable here so we set to False
if raised_error is not None or not_spd or not_finite:
msg = ("There was a problem in SDML when using {}'s graphical "
"lasso solver.").format("skggm" if HAS_SKGGM else "scikit-learn")
if not HAS_SKGGM:
skggm_advice = (" skggm's graphical lasso can sometimes converge "
"on non SPD cases where scikit-learn's graphical "
"lasso fails to converge. Try to install skggm and "
"rerun the algorithm (see the README.md for the "
"right version of skggm).")
msg += skggm_advice
if raised_error is not None:
msg += " The following error message was thrown: {}.".format(
raised_error)
raise RuntimeError(msg)
self.transformer_ = transformer_from_metric(np.atleast_2d(M))
return self
def solve_glasso2(cov, lambda_):
return graphical_lasso(cov, lambda_)[1]
# (a)(ii) Observations
# All covariances are positive, with smallest value 3.63e-05
# Many pairwise precisions are near 0, but not exactly 0
# Suggests that the underlying undirected graphical model is not sparse
# (b) Graphical Lasso
# As alpha increases in size, the covariance elements shrink towards 0
# Sparsity of graph also shrinks
from sklearn.covariance import graphical_lasso
penalized_covs, penalized_precs = {}, {}
for alpha in [1e-5, 1e-4, 1e-3]:
penalized_cov, penalized_prec = graphical_lasso(
emp_cov=sample_cov,
alpha=alpha,
max_iter=1000)
penalized_covs[alpha] = penalized_cov
penalized_precs[alpha] = penalized_prec
print(f'Number of Non-Zero Edges (alpha = {alpha}): {np.sum(penalized_prec != 0.)}', )
fig, axes = plt.subplots(nrows=1, ncols=2)
axes[0].set_xlabel('Pairwise Covariances')
axes[1].set_xlabel('Pairwise Precisions')
for alpha, penalized_cov in penalized_covs.items():
axes[0].hist(penalized_cov.flatten(),
label=r'$\alpha = $' + str(alpha),
bins=50)
axes[1].hist(penalized_precs[alpha].flatten(),
label=r'$\alpha = $' + str(alpha),
bins=50)
def get_alpha_max(X, observation, sigma_min, pb_name, alpha_Sigma_inv=None):
"""Compute alpha_max specific to pb_name.
Parameters:
----------
X: np.array, shape (n_channels, n_sources)
observation: np.array, shape (n_channels, n_times) or
(n_epochs, n_channels, n_times)
sigma_min: float, >0
pb_name: string, "SGCL" "CLaR" "MTL" "MTLME"
Output:
-------
float
alpha_max of the optimization problem.
"""
n_channels, n_times = observation.shape[-2], observation.shape[-1]
if observation.ndim == 3:
Y = observation.mean(axis=0)
else:
Y = observation
if pb_name == "MTL":
n_channels, n_times = Y.shape
alpha_max = l_2_inf(X.T @ Y) / (n_times * n_channels)
elif pb_name == "MTLME":
observations = observation.transpose((1, 0, 2))
observations = observations.reshape(observations.shape[0], -1)
alpha_max = get_alpha_max(X, observations, sigma_min, "MTL")
elif pb_name == "SGCL":
assert observation.ndim == 2
_, S_max_inv = clp_sqrt(Y @ Y.T / n_times, sigma_min)
alpha_max = l_2_inf(X.T @ S_max_inv @ Y)
alpha_max /= (n_channels * n_times)
elif pb_name == "CLAR" or pb_name == "NNCVX":
n_epochs = observation.shape[0]
cov_Yl = 0
for l in range(n_epochs):
cov_Yl += observation[l, :, :] @ observation[l, :, :].T
cov_Yl /= (n_epochs * n_times)
_, S_max_inv = clp_sqrt(cov_Yl, sigma_min)
alpha_max = l_2_inf(X.T @ S_max_inv @ Y)
alpha_max /= (n_channels * n_times)
elif pb_name == "mrce":
assert observation.ndim == 3
assert alpha_Sigma_inv is not None
emp_cov = get_emp_cov(observation)
Sigma, Sigma_inv = graphical_lasso(emp_cov,
alpha_Sigma_inv,
max_iter=10**6)
alpha_max = l_2_inf(X.T @ Sigma_inv @ Y) / (n_channels * n_times)
elif pb_name == "glasso":
assert observation.ndim == 2
assert alpha_Sigma_inv is not None
emp_cov = observation @ observation.T / n_times
Sigma, Sigma_inv = graphical_lasso(emp_cov, alpha_Sigma_inv)
alpha_max = l_2_inf(X.T @ Sigma_inv @ Y) / (n_channels * n_times)
elif pb_name == "mrce":
assert observation.ndim == 2
assert alpha_Sigma_inv is not None
emp_cov = observation @ observation.T / n_times
Sigma, Sigma_inv = graphical_lasso(emp_cov,
alpha_Sigma_inv,
max_iter=10**6)
alpha_max = np.abs(X.T @ Sigma_inv @ Y).max() / (n_channels * n_times)
else:
raise NotImplementedError("No solver '{}' in sgcl".format(pb_name))
return alpha_max
def fit_lfm_roblasso(x,
z,
p=None,
nu=1e9,
lambda_beta=0.,
lambda_phi=0.,
tol=1e-3,
fit_intercept=True,
maxiter=500,
print_iter=False,
rescale=False):
"""For details, see here.
Parameters
----------
x : array, shape(t_, n_)
z : array, shape(t_, k_)
p : array, optional, shape(t_)
nu : scalar, optional
lambda_beta : scalar, optional
lambda_phil : scalar, optional
tol : float, optional
fit_intercept: bool, optional
maxiter : scalar, optional
print_iter : bool, optional
rescale : bool, optional
Returns
-------
alpha_RMLFP : array, shape(n_,)
beta_RMLFP : array, shape(n_,k_)
sig2_RMLFP : array, shape(n_,n_)
"""
if len(x.shape) == 1:
x = x.reshape(-1, 1)
if len(z.shape) == 1:
z = z.reshape(-1, 1)
t_, n_ = x.shape
if p is None:
p = np.ones(t_) / t_
# rescale the variables
if rescale is True:
_, sigma2_x = meancov_sp(x, p)
sigma_x = np.sqrt(np.diag(sigma2_x))
x = x / sigma_x
_, sigma2_z = meancov_sp(z, p)
sigma_z = np.sqrt(np.diag(sigma2_z))
z = z / sigma_z
# Step 0: Set initial values using method of moments
alpha, beta, sigma2, u = fit_lfm_lasso(x,
z,
p,
lambda_beta,
fit_intercept=fit_intercept)
mu_u = np.zeros(n_)
for i in range(maxiter):
# Step 1: Update the weights
if nu >= 1e3 and np.linalg.det(sigma2) < 1e-13:
w = np.ones(t_)
else:
w = (nu + n_) / (nu + mahalanobis_dist(u, mu_u, sigma2)**2)
q = w * p
q = q / np.sum(q)
# Step 2: Update location and dispersion parameters
alpha_old, beta_old = alpha, beta
alpha, beta, sigma2, u = fit_lfm_lasso(x,
z,
q,
lambda_beta,
fit_intercept=fit_intercept)
sigma2, _ = graphical_lasso((w @ p) * sigma2, lambda_phi)
# Step 3: Check convergence
errors = [
np.linalg.norm(alpha - alpha_old, ord=np.inf) /
max(np.linalg.norm(alpha_old, ord=np.inf), 1e-20),
np.linalg.norm(beta - beta_old, ord=np.inf) /
max(np.linalg.norm(beta_old, ord=np.inf), 1e-20)
]
# print the loglikelihood and the error
if print_iter is True:
print('Iter: %i; Loglikelihood: %.5f; Errors: %.5f' %
(i, p @ mvt_logpdf(u, mu_u, sigma2, nu) -
lambda_beta * np.linalg.norm(beta, ord=1), max(errors)))
if max(errors) <= tol:
break
if rescale is True:
alpha = alpha * sigma_x
beta = ((beta / sigma_z).T * sigma_x).T
sigma2 = (sigma2.T * sigma_x).T * sigma_x
return alpha, beta, sigma2
def fit_CV(self):
""""
test many alphas, pick the one with best EBIC
"""
ebic_vals = [] #np.ones(len(alphas)) * np.inf
prec_list = [] # np.zeros((len(alphas), self.p, self.p))
cov_list = [] # np.zeros((len(alphas), self.p, self.p))
alpha_list = []
alpha = 0.5
max_itr = 1000
best_not_found = True
nr_iterations = 0
best_ebic = np.inf
time_since_last_min = 0
not_all_sparse = True
while (best_not_found) and (nr_iterations <
max_itr) and (not_all_sparse):
# print(f'{nr_iterations} {alpha}')
alpha_list.append(alpha)
try:
out_cov, out_prec = graphical_lasso(
emp_cov=self.emp_cov.copy(), alpha=alpha)
except FloatingPointError:
ebic_vals.append(np.inf)
prec_list.append(np.inf)
cov_list.append(np.inf)
alpha += 0.01
nr_iterations += 1
continue
except ValueError:
ebic_vals.append(np.inf)
prec_list.append(np.inf)
cov_list.append(np.inf)
alpha += 0.01
nr_iterations += 1
continue
ebic_t = EBIC(self.N, self.emp_cov, out_prec, beta=self.beta)
print(
f'{nr_iterations} {alpha} {ebic_t} {self.N * gaussian_likelihood(self.emp_cov, out_prec)}'
)
ebic_vals.append(ebic_t)
time_since_last_min += 1
if ebic_t < best_ebic:
best_ebic = ebic_t
time_since_last_min = 0
best_alpha = alpha
prec_list.append(out_prec)
cov_list.append(out_cov)
if time_since_last_min > 50:
best_not_found = False
alpha += 0.01
nr_iterations += 1
not_all_sparse = (np.count_nonzero(np.triu(out_prec, 1)) != 0)
best_idx = np.argmin(ebic_vals)
if np.isscalar(best_idx):
best_prec = prec_list[best_idx]
best_alpha = alpha_list[best_idx]
else:
best_prec = prec_list[best_idx[0]]
best_alpha = alpha_list[best_idx[0]]
return best_prec, prec_list, cov_list, ebic_vals, best_alpha, alpha_list
def fit(
self,
TS,
alpha=0.01,
max_iter=100,
tol=0.0001,
threshold_type='degree',
**kwargs
):
"""Performs a graphical lasso.
For details see [1, 2].
The results dictionary also stores the covariance matrix as
`'weights_matrix'`, the precision matrix as `'precision_matrix'`,
and the thresholded version of the covariance matrix as
`'thresholded_matrix'`.
This implementation uses `scikit-learn`'s implementation of the
graphical lasso; for convenience two control parameters `tol` and
`max_iter` are available to interface with their method.
Parameters
----------
TS (np.ndarray)
Array consisting of :math:`L` observations from :math:`N`
sensors.
alpha (float, default=0.01)
Coefficient of penalization, higher values means more
sparseness
max_iter (int, default=100)
Maximum number of iterations.
tol (float, default=0.0001)
Stop the algorithm when the duality gap is below a certain
threshold.
threshold_type (str)
Which thresholding function to use on the matrix of
weights. See `netrd.utilities.threshold.py` for
documentation. Pass additional arguments to the thresholder
using ``**kwargs``.
Returns
-------
G (nx.Graph)
A reconstructed graph with :math:`N` nodes.
References
----------
.. [1] J. Friedman, T. Hastie, R. Tibshirani, "Sparse inverse
covariance estimation with the graphical lasso",
Biostatistics 9, pp. 432–441 (2008).
.. [2] https://github.com/CamDavidsonPilon/Graphical-Lasso-in-Finance
"""
emp_cov = np.cov(TS)
cov, prec = graphical_lasso(emp_cov, alpha, max_iter=max_iter, tol=tol)
self.results['weights_matrix'] = cov
self.results['precision_matrix'] = prec
# threshold the network
self.results['thresholded_matrix'] = threshold(
self.results['weights_matrix'], threshold_type, **kwargs
)
# construct the network
G = create_graph(self.results['thresholded_matrix'])
self.results['graph'] = G
return G
def run_samples_lasso(N, B, alpha, theta1, theta2, s1, s2):
import myKernels.RandomWalk as rw
test_info = pd.DataFrame()
k = theta1.shape[0]
for sample in tqdm.tqdm(range(N)):
Gs1 = []
Gs2 = []
error_1 = []
error_2 = []
n = 50
for i in range(50):
x1 = np.random.multivariate_normal(mean=np.zeros(k),
cov=theta1,
size=100)
A1 = np.corrcoef(x1.T)
if alpha == 0:
np.fill_diagonal(A1, 0)
A1[np.abs(A1) < 1e-5] = 0
else:
gl = graphical_lasso(A1, alpha=alpha, max_iter=1000)
A1 = gl[0]
A1[np.abs(A1) < 1e-5] = 0
np.fill_diagonal(A1, 0)
Gs1.append(nx.from_numpy_matrix(A1))
error_1.append(
np.sum(
np.logical_xor(
np.abs(np.triu(A1, 1)) > 0,
np.abs(np.triu(theta1, 1)) > 0)))
x2 = np.random.multivariate_normal(mean=np.zeros(k),
cov=theta2,
size=100)
A2 = np.corrcoef(x2.T)
if alpha == 0:
np.fill_diagonal(A2, 0)
A2[np.abs(A2) < 1e-5] = 0
else:
gl = graphical_lasso(A2, alpha=alpha, max_iter=1000)
A2 = gl[0]
A2[np.abs(A2) < 1e-5] = 0
np.fill_diagonal(A2, 0)
Gs2.append(nx.from_numpy_matrix(A2))
error_2.append(
np.sum(
np.logical_xor(
np.abs(np.triu(A2, 1)) > 0,
np.abs(np.triu(theta2, 1)) > 0)))
Gs = Gs1 + Gs2
try:
#rw_kernel = rw.RandomWalk(Gs, c = 0.0001, normalize=0)
#K = rw_kernel.fit_ARKU_plus(r = 6, normalize_adj=False, edge_attr= None, verbose=False)
graph_list = gk.graph_from_networkx(Gs)
kernel = [{"name": "SP", "with_labels": 0}]
init_kernel = gk.GraphKernel(kernel=kernel, normalize=0)
K = init_kernel.fit_transform(graph_list)
except:
continue
MMD_functions = [mg.MMD_b, mg.MMD_u]
kernel_hypothesis = mg.BoostrapMethods(MMD_functions)
function_arguments = [dict(n=n, m=n), dict(n=n, m=n)]
kernel_hypothesis.Bootstrap(K, function_arguments, B=B)
#print(f'p_value {kernel_hypothesis.p_values}')
#print(f"MMD_u {kernel_hypothesis.sample_test_statistic['MMD_u']}")
test_info = pd.concat(
(test_info,
pd.DataFrame(
{
'p_val': kernel_hypothesis.p_values['MMD_u'],
'sample': sample,
'mean_error_1': np.mean(error_1),
'mean_error_2': np.mean(error_2),
'alpha': alpha,
's1': s1,
's2': s2
},
index=[0])),
ignore_index=True)
return test_info