def _cov(X, shrinkage=None):
"""Estimate covariance matrix (using optional shrinkage).
Parameters
----------
X : array-like, shape (n_samples, n_features)
Input data.
shrinkage : string or float, optional
Shrinkage parameter, possible values:
- None or 'empirical': no shrinkage (default).
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage parameter.
Returns
-------
s : array, shape (n_features, n_features)
Estimated covariance matrix.
"""
shrinkage = "empirical" if shrinkage is None else shrinkage
if isinstance(shrinkage, str):
if shrinkage == 'auto':
sc = StandardScaler() # standardize features
X = sc.fit_transform(X)
s = ledoit_wolf(X)[0]
# rescale
s = sc.scale_[:, np.newaxis] * s * sc.scale_[np.newaxis, :]
elif shrinkage == 'empirical':
s = empirical_covariance(X)
else:
raise ValueError('unknown shrinkage parameter')
elif isinstance(shrinkage, float) or isinstance(shrinkage, int):
if shrinkage < 0 or shrinkage > 1:
raise ValueError('shrinkage parameter must be between 0 and 1')
s = shrunk_covariance(empirical_covariance(X), shrinkage)
else:
raise TypeError('shrinkage must be of string or int type')
return s
def test_covariance():
"""Tests Covariance module on a simple dataset.
"""
# test covariance fit from data
cov = EmpiricalCovariance()
cov.fit(X)
assert_array_almost_equal(empirical_covariance(X), cov.covariance_, 4)
assert_almost_equal(cov.error_norm(empirical_covariance(X)), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X), norm='spectral'), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X), norm='frobenius'), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X), scaling=False), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X), squared=False), 0)
# Mahalanobis distances computation test
mahal_dist = cov.mahalanobis(X)
assert(np.amax(mahal_dist) < 250)
assert(np.amin(mahal_dist) > 50)
# test with n_features = 1
X_1d = X[:, 0].reshape((-1, 1))
cov = EmpiricalCovariance()
cov.fit(X_1d)
assert_array_almost_equal(empirical_covariance(X_1d), cov.covariance_, 4)
assert_almost_equal(cov.error_norm(empirical_covariance(X_1d)), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X_1d), norm='spectral'), 0)
# test integer type
X_integer = np.asarray([[0, 1], [1, 0]])
result = np.asarray([[0.25, -0.25], [-0.25, 0.25]])
assert_array_almost_equal(empirical_covariance(X_integer), result)
def test_covariance():
"""Tests Covariance module on a simple dataset.
"""
# test covariance fit from data
cov = EmpiricalCovariance()
cov.fit(X)
assert_array_almost_equal(empirical_covariance(X), cov.covariance_, 4)
assert_almost_equal(cov.error_norm(empirical_covariance(X)), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X), norm='spectral'), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X), norm='frobenius'), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X), scaling=False), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X), squared=False), 0)
# Mahalanobis distances computation test
mahal_dist = cov.mahalanobis(X)
assert(np.amax(mahal_dist) < 250)
assert(np.amin(mahal_dist) > 50)
# test with n_features = 1
X_1d = X[:, 0].reshape((-1, 1))
cov = EmpiricalCovariance()
cov.fit(X_1d)
assert_array_almost_equal(empirical_covariance(X_1d), cov.covariance_, 4)
assert_almost_equal(cov.error_norm(empirical_covariance(X_1d)), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X_1d), norm='spectral'), 0)
# test integer type
X_integer = np.asarray([[0, 1], [1, 0]])
result = np.asarray([[0.25, -0.25], [-0.25, 0.25]])
assert_array_almost_equal(empirical_covariance(X_integer), result)
def test_shrunk_covariance():
"""Tests ShrunkCovariance module on a simple dataset.
"""
# compare shrunk covariance obtained from data and from MLE estimate
cov = ShrunkCovariance(shrinkage=0.5)
cov.fit(X)
assert_array_almost_equal(
shrunk_covariance(empirical_covariance(X), shrinkage=0.5),
cov.covariance_, 4)
# same test with shrinkage not provided
cov = ShrunkCovariance()
cov.fit(X)
assert_array_almost_equal(
shrunk_covariance(empirical_covariance(X)), cov.covariance_, 4)
# same test with shrinkage = 0 (<==> empirical_covariance)
cov = ShrunkCovariance(shrinkage=0.)
cov.fit(X)
assert_array_almost_equal(empirical_covariance(X), cov.covariance_, 4)
# test with n_features = 1
X_1d = X[:, 0].reshape((-1, 1))
cov = ShrunkCovariance(shrinkage=0.3)
cov.fit(X_1d)
assert_array_almost_equal(empirical_covariance(X_1d), cov.covariance_, 4)
# test shrinkage coeff on a simple data set (without saving precision)
cov = ShrunkCovariance(shrinkage=0.5, store_precision=False)
cov.fit(X)
assert(cov.precision_ is None)
def _nonrobust_covariance(self, data, assume_centered=False):
"""Non-robust estimation of the covariance to be used within MCD.
Parameters
----------
data: array_like, shape (n_samples, n_features)
Data for which to compute the non-robust covariance matrix.
assume_centered: Boolean
Whether or not the observations should be considered as centered.
Returns
-------
nonrobust_covariance: array_like, shape (n_features, n_features)
The non-robust covariance of the data.
"""
try:
cov, prec = graph_lasso(
empirical_covariance(data, assume_centered=assume_centered),
self.shrinkage)
except:
print " > Exception!"
emp_cov = empirical_covariance(
data, assume_centered=assume_centered)
emp_cov.flat[::data.shape[1] + 1] += 1e-06
cov, prec = graph_lasso(emp_cov, self.shrinkage)
return cov
def test_shrunk_covariance():
# Tests ShrunkCovariance module on a simple dataset.
# compare shrunk covariance obtained from data and from MLE estimate
cov = ShrunkCovariance(shrinkage=0.5)
cov.fit(X)
assert_array_almost_equal(
shrunk_covariance(empirical_covariance(X), shrinkage=0.5),
cov.covariance_, 4)
# same test with shrinkage not provided
cov = ShrunkCovariance()
cov.fit(X)
assert_array_almost_equal(shrunk_covariance(empirical_covariance(X)),
cov.covariance_, 4)
# same test with shrinkage = 0 (<==> empirical_covariance)
cov = ShrunkCovariance(shrinkage=0.)
cov.fit(X)
assert_array_almost_equal(empirical_covariance(X), cov.covariance_, 4)
# test with n_features = 1
X_1d = X[:, 0].reshape((-1, 1))
cov = ShrunkCovariance(shrinkage=0.3)
cov.fit(X_1d)
assert_array_almost_equal(empirical_covariance(X_1d), cov.covariance_, 4)
# test shrinkage coeff on a simple data set (without saving precision)
cov = ShrunkCovariance(shrinkage=0.5, store_precision=False)
cov.fit(X)
assert (cov.precision_ is None)
def _nonrobust_covariance(self, data, assume_centered=False):
"""Non-robust estimation of the covariance to be used within MCD.
Parameters
----------
data: array_like, shape (n_samples, n_features)
Data for which to compute the non-robust covariance matrix.
assume_centered: Boolean
Whether or not the observations should be considered as centered.
Returns
-------
nonrobust_covariance: array_like, shape (n_features, n_features)
The non-robust covariance of the data.
"""
try:
cov, prec = graph_lasso(
empirical_covariance(data, assume_centered=assume_centered),
self.shrinkage)
except:
print " > Exception!"
emp_cov = empirical_covariance(data,
assume_centered=assume_centered)
emp_cov.flat[::data.shape[1] + 1] += 1e-06
cov, prec = graph_lasso(emp_cov, self.shrinkage)
return cov
def flgl_path(X_train, links=None, etas=[0.1], mus=[0.1],
X_test=None, tol=1e-3, max_iter=200,
update_rho=False, verbose=0, score='ebic',
random_state=None):
score_func = {'likelihood': log_likelihood,
'bic': BIC,
'ebic': partial(EBIC, n=X_test.shape[0]),
'ebicm': partial(EBIC_m, n=X_test.shape[0])}
try:
score_func = score_func[score]
except KeyError:
warnings.warn("The score type passed is not available, using log likelihood.")
score_func = log_likelihood
emp_cov = empirical_covariance(X_train)
covariance_ = emp_cov.copy()
covariances_ = list()
precisions_ = list()
hiddens_ = list()
scores_ = list()
if X_test is not None:
test_emp_cov = empirical_covariance(X_test)
for eta in etas:
for mu in mus:
try:
# Capture the errors, and move on
cov_, prec_, hid_,_ = two_layers_fixed_links_GL(
emp_cov, links, mu, eta, max_iter=max_iter,
random_state=random_state, return_n_iter=False)
covariances_.append(cov_)
precisions_.append(prec_)
hiddens_.append(hid_)
if X_test is not None:
this_score = score_func(test_emp_cov, prec_)
except FloatingPointError:
this_score = -np.inf
covariances_.append(np.nan)
precisions_.append(np.nan)
if X_test is not None:
if not np.isfinite(this_score):
this_score = -np.inf
scores_.append(this_score)
if verbose:
if X_test is not None:
print('[graphical_lasso_path] eta: %.2e, mu: %.2e, score: %.2e'
% (eta, mu, this_score))
else:
print('[graphical_lasso_path] eta: %.2e, mu: %.2e' % (eta, mu))
if X_test is not None:
return covariances_, precisions_, hiddens_, scores_
return covariances_, precisions_, hiddens_
def test_covariance():
"""Tests Covariance module on a simple dataset.
"""
# test covariance fit from data
cov = EmpiricalCovariance()
cov.fit(X)
emp_cov = empirical_covariance(X)
assert_array_almost_equal(emp_cov, cov.covariance_, 4)
assert_almost_equal(cov.error_norm(emp_cov), 0)
assert_almost_equal(
cov.error_norm(emp_cov, norm='spectral'), 0)
assert_almost_equal(
cov.error_norm(emp_cov, norm='frobenius'), 0)
assert_almost_equal(
cov.error_norm(emp_cov, scaling=False), 0)
assert_almost_equal(
cov.error_norm(emp_cov, squared=False), 0)
assert_raises(NotImplementedError,
cov.error_norm, emp_cov, norm='foo')
# Mahalanobis distances computation test
mahal_dist = cov.mahalanobis(X)
print(np.amin(mahal_dist), np.amax(mahal_dist))
assert(np.amin(mahal_dist) > 0)
# test with n_features = 1
X_1d = X[:, 0].reshape((-1, 1))
cov = EmpiricalCovariance()
cov.fit(X_1d)
assert_array_almost_equal(empirical_covariance(X_1d), cov.covariance_, 4)
assert_almost_equal(cov.error_norm(empirical_covariance(X_1d)), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X_1d), norm='spectral'), 0)
# test with one sample
# FIXME I don't know what this test does
X_1sample = np.arange(5)
cov = EmpiricalCovariance()
assert_warns(UserWarning, cov.fit, X_1sample)
assert_array_almost_equal(cov.covariance_,
np.zeros(shape=(5, 5), dtype=np.float64))
# test integer type
X_integer = np.asarray([[0, 1], [1, 0]])
result = np.asarray([[0.25, -0.25], [-0.25, 0.25]])
assert_array_almost_equal(empirical_covariance(X_integer), result)
# test centered case
cov = EmpiricalCovariance(assume_centered=True)
cov.fit(X)
assert_array_equal(cov.location_, np.zeros(X.shape[1]))
def test_covariance():
"""Tests Covariance module on a simple dataset.
"""
# test covariance fit from data
cov = EmpiricalCovariance()
cov.fit(X)
emp_cov = empirical_covariance(X)
assert_array_almost_equal(emp_cov, cov.covariance_, 4)
assert_almost_equal(cov.error_norm(emp_cov), 0)
assert_almost_equal(
cov.error_norm(emp_cov, norm='spectral'), 0)
assert_almost_equal(
cov.error_norm(emp_cov, norm='frobenius'), 0)
assert_almost_equal(
cov.error_norm(emp_cov, scaling=False), 0)
assert_almost_equal(
cov.error_norm(emp_cov, squared=False), 0)
assert_raises(NotImplementedError,
cov.error_norm, emp_cov, norm='foo')
# Mahalanobis distances computation test
mahal_dist = cov.mahalanobis(X)
print np.amin(mahal_dist), np.amax(mahal_dist)
assert(np.amin(mahal_dist) > 0)
# test with n_features = 1
X_1d = X[:, 0].reshape((-1, 1))
cov = EmpiricalCovariance()
cov.fit(X_1d)
assert_array_almost_equal(empirical_covariance(X_1d), cov.covariance_, 4)
assert_almost_equal(cov.error_norm(empirical_covariance(X_1d)), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X_1d), norm='spectral'), 0)
# test with one sample
X_1sample = np.arange(5)
cov = EmpiricalCovariance()
with warnings.catch_warnings(record=True):
cov.fit(X_1sample)
# test integer type
X_integer = np.asarray([[0, 1], [1, 0]])
result = np.asarray([[0.25, -0.25], [-0.25, 0.25]])
assert_array_almost_equal(empirical_covariance(X_integer), result)
# test centered case
cov = EmpiricalCovariance(assume_centered=True)
cov.fit(X)
assert_equal(cov.location_, np.zeros(X.shape[1]))
def test_covariance():
# Tests Covariance module on a simple dataset.
# test covariance fit from data
cov = EmpiricalCovariance()
cov.fit(X)
emp_cov = empirical_covariance(X)
assert_array_almost_equal(emp_cov, cov.covariance_, 4)
assert_almost_equal(cov.error_norm(emp_cov), 0)
assert_almost_equal(cov.error_norm(emp_cov, norm='spectral'), 0)
assert_almost_equal(cov.error_norm(emp_cov, norm='frobenius'), 0)
assert_almost_equal(cov.error_norm(emp_cov, scaling=False), 0)
assert_almost_equal(cov.error_norm(emp_cov, squared=False), 0)
with pytest.raises(NotImplementedError):
cov.error_norm(emp_cov, norm='foo')
# Mahalanobis distances computation test
mahal_dist = cov.mahalanobis(X)
assert np.amin(mahal_dist) > 0
# test with n_features = 1
X_1d = X[:, 0].reshape((-1, 1))
cov = EmpiricalCovariance()
cov.fit(X_1d)
assert_array_almost_equal(empirical_covariance(X_1d), cov.covariance_, 4)
assert_almost_equal(cov.error_norm(empirical_covariance(X_1d)), 0)
assert_almost_equal(
cov.error_norm(empirical_covariance(X_1d), norm='spectral'), 0)
# test with one sample
# Create X with 1 sample and 5 features
X_1sample = np.arange(5).reshape(1, 5)
cov = EmpiricalCovariance()
warn_msg = (
"Only one sample available. You may want to reshape your data array")
with pytest.warns(UserWarning, match=warn_msg):
cov.fit(X_1sample)
assert_array_almost_equal(cov.covariance_,
np.zeros(shape=(5, 5), dtype=np.float64))
# test integer type
X_integer = np.asarray([[0, 1], [1, 0]])
result = np.asarray([[0.25, -0.25], [-0.25, 0.25]])
assert_array_almost_equal(empirical_covariance(X_integer), result)
# test centered case
cov = EmpiricalCovariance(assume_centered=True)
cov.fit(X)
assert_array_equal(cov.location_, np.zeros(X.shape[1]))
def LinRegErr(A,B,iteractions=50): #retorna os erros do slope e do intercept n=len(A) meanA=np.mean(A) meanB=np.mean(B) Iteractions=iteractions cont=0 Slps=[] Ints=[] Covs=[] while cont <Iteractions: Sample= mvn.rvs(mean=[meanA,meanB], cov=np.cov(A,B),size=n) # criando amostras de tamanho 'n' extraída da distribuição estimada da população popt, pcov =curve_fit(linf,Sample[:,0],Sample[:,1]) #ajustando a reta Slope=popt[0] Intercept=popt[1] Sample_cov=empirical_covariance(Sample)[0,1] Slps=np.append(Slps,Slope) Ints=np.append(Ints, Intercept) Covs=np.append(Covs,Sample_cov) print(cont) cont+=1 return(np.std(Slps),np.std(Ints),np.mean(Sample_cov))
def get_cov(data):
dat = data.training_data_all_ways + data.testing_data_all_ways
num_ways = len(data.get_list_of_ways())
m = {}
i = 0
for way in data.get_list_of_ways():
m[way] = i
i += 1
mat = np.zeros((num_ways,num_ways))
for elem in dat:
ways = elem[1]
for way in ways:
mat[m[way],m[way]] = mat[m[way],m[way]] + 1
for w1 in ways:
for w2 in ways:
if w1 == w2: continue
mat[m[w1],m[w2]] = mat[m[w1],m[w2]] + 1
print mat
emp_cov = empirical_covariance(mat)
print emp_cov
corr = np.zeros((num_ways,num_ways))
for i in range(num_ways):
for j in range(num_ways):
corr[i,j] = emp_cov[i,j]/(math.sqrt(emp_cov[i,i])*math.sqrt(emp_cov[j,j]))
print corr
sns.heatmap(corr,vmin = -1, vmax = 1,square=True,xticklabels=m.keys(),yticklabels=m.keys())
sns.plt.title("Covariance of WAYS frequencies")
sns.plt.show()
def launch_mcd_on_dataset(n_samples, n_features, n_outliers, tol_loc, tol_cov,
tol_support):
rand_gen = np.random.RandomState(0)
data = rand_gen.randn(n_samples, n_features)
# add some outliers
outliers_index = rand_gen.permutation(n_samples)[:n_outliers]
outliers_offset = 10. * \
(rand_gen.randint(2, size=(n_outliers, n_features)) - 0.5)
data[outliers_index] += outliers_offset
inliers_mask = np.ones(n_samples).astype(bool)
inliers_mask[outliers_index] = False
pure_data = data[inliers_mask]
# compute MCD by fitting an object
mcd_fit = MinCovDet(random_state=rand_gen).fit(data)
T = mcd_fit.location_
S = mcd_fit.covariance_
H = mcd_fit.support_
# compare with the estimates learnt from the inliers
error_location = np.mean((pure_data.mean(0) - T) ** 2)
assert (error_location < tol_loc)
error_cov = np.mean((empirical_covariance(pure_data) - S) ** 2)
assert (error_cov < tol_cov)
assert (np.sum(H) >= tol_support)
assert_array_almost_equal(mcd_fit.mahalanobis(data), mcd_fit.dist_)
def fit(self, X, y=None):
"""Fit the GraphLasso model to X.
Parameters
----------
X : ndarray, shape (n_time, n_samples, n_features), or
(n_samples, n_features, n_time)
Data from which to compute the covariance estimate.
If shape is (n_samples, n_features, n_time), then set
`bypass_transpose = False`.
y : (ignored)
"""
if not self.bypass_transpose:
X = X.transpose(2, 0, 1) # put time as first dimension
# Covariance does not make sense for a single feature
# X = check_array(X, allow_nd=True, estimator=self)
# if X.ndim != 3:
# raise ValueError("Found array with dim %d. %s expected <= 2."
# % (X.ndim, self.__class__.__name__))
X = np.array([check_array(x, ensure_min_features=2,
ensure_min_samples=2, estimator=self) for x in X])
if self.assume_centered:
self.location_ = np.zeros((X.shape[0], 1, X.shape[2]))
else:
self.location_ = X.mean(1).reshape(X.shape[0], 1, X.shape[2])
self.emp_cov = np.array([empirical_covariance(
x, assume_centered=self.assume_centered) for x in X])
self.precision_, self.latent_, self.covariance_, self.n_iter_ = \
latent_time_graph_lasso(
self.emp_cov, alpha=self.alpha, tau=self.tau, rho=self.rho,
beta=self.beta, eta=self.eta, mode=self.mode,
tol=self.tol, rtol=self.rtol, psi=self.psi, phi=self.phi,
max_iter=self.max_iter, verbose=self.verbose,
return_n_iter=True, return_history=False)
return self
def test_oas():
"""Tests OAS module on a simple dataset.
"""
# test shrinkage coeff on a simple data set
oa = OAS()
oa.fit(X, assume_centered=True)
assert_almost_equal(oa.shrinkage_, 0.018740, 4)
assert_almost_equal(oa.score(X, assume_centered=True), -5.03605, 4)
# compare shrunk covariance obtained from data and from MLE estimate
oa_cov_from_mle, oa_shinkrage_from_mle = oas(X, assume_centered=True)
assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
# compare estimates given by OAS and ShrunkCovariance
scov = ShrunkCovariance(shrinkage=oa.shrinkage_)
scov.fit(X, assume_centered=True)
assert_array_almost_equal(scov.covariance_, oa.covariance_, 4)
# test with n_features = 1
oa = OAS()
oa.fit(X_1d, assume_centered=True)
oa_cov_from_mle, oa_shinkrage_from_mle = oas(X_1d, assume_centered=True)
assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
assert_array_almost_equal((X_1d ** 2).sum() / n_samples, oa.covariance_, 4)
# test shrinkage coeff on a simple data set (without saving precision)
oa = OAS(store_precision=False)
oa.fit(X, assume_centered=True)
assert_almost_equal(oa.score(X, assume_centered=True), -5.03605, 4)
assert(oa.precision_ is None)
### Same tests without assuming centered data
# test shrinkage coeff on a simple data set
oa = OAS()
oa.fit(X)
assert_almost_equal(oa.shrinkage_, 0.020236, 4)
assert_almost_equal(oa.score(X), 2.079025, 4)
# compare shrunk covariance obtained from data and from MLE estimate
oa_cov_from_mle, oa_shinkrage_from_mle = oas(X)
assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
# compare estimates given by OAS and ShrunkCovariance
scov = ShrunkCovariance(shrinkage=oa.shrinkage_)
scov.fit(X)
assert_array_almost_equal(scov.covariance_, oa.covariance_, 4)
# test with n_features = 1
oa = OAS()
oa.fit(X_1d)
oa_cov_from_mle, oa_shinkrage_from_mle = oas(X_1d)
assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
assert_array_almost_equal(empirical_covariance(X_1d), oa.covariance_, 4)
# test shrinkage coeff on a simple data set (without saving precision)
oa = OAS(store_precision=False)
oa.fit(X)
assert_almost_equal(oa.score(X), 2.079025, 4)
assert(oa.precision_ is None)
def construct_motion_gaussian_models(char_dict):
# fit a 2D gaussian model for each key
# plot the mean and variance ellipse on the keyboard layout
# img = mpimg.imread('keyboard_screen_shot.jpg')
# imgplot = plt.imshow(img)
# convert data to numpy
char_list = [
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n',
'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', ' '
]
# create a gaussian model for every character
model_dict = {}
scaler = MinMaxScaler()
for char in char_list:
if char in char_dict.keys():
# print(char)
# print(np.array(char_dict[char]).T.shape)
dim = np.array(char_dict[char]).shape
X = np.array(char_dict[char])
scaler.fit(X)
# X_t = scaler.transform(X)
X_t = X
dim = X_t.shape
mu = np.mean(X_t, axis=0)
# print(np.array(char_dict[char].shape))
if dim[0] > 1:
# cannot use np.cov when there is only one data point
# cov = np.cov(np.array(char_dict[char]).T)
cov = empirical_covariance(X_t)
# print(cov)
else:
cov = np.zeros((dim[1], dim[1]))
# save the model parameter in a dictionary
model_dict[char] = {}
model_dict[char]['mean'] = mu
# model_dict[char]['cov'] = np.diag(np.diag(cov))
model_dict[char]['cov'] = cov
sigma_det = np.linalg.det(cov)
# by definition sigma_det should not be zero!!!
# as the covariance matrix is semi-positive definite
if sigma_det <= 0:
print(char + " : " + str(sigma_det))
print(X_t.shape)
print(cov)
# print(cov.shape)
# print("Generated plot for " + char)
# plt.scatter(x, y)
# plt.savefig(posture + '_training_data_pattern/' + posture + '_2D_Gauss.png', dpi=200)
return model_dict
def fit(self, X, y):
"""Fit the TimeGraphicalLasso model to X.
Parameters
----------
X : ndarray, shape = (n_samples * n_times, n_dimensions)
Data matrix.
y : ndarray, shape = (n_times,)
Indicate the temporal belonging of each sample.
"""
# Covariance does not make sense for a single feature
X, y = check_X_y(X, y, accept_sparse=False, dtype=np.float64, order="C", ensure_min_features=2, estimator=self)
n_dimensions = X.shape[1]
self.classes_, n_samples = np.unique(y, return_counts=True)
n_times = self.classes_.size
# n_samples = np.array([x.shape[0] for x in X])
if self.assume_centered:
self.location_ = np.zeros((n_times, n_dimensions))
else:
self.location_ = np.array([X[y == cl].mean(0) for cl in self.classes_])
emp_cov = np.array(
[empirical_covariance(X[y == cl], assume_centered=self.assume_centered) for cl in self.classes_]
)
return self._fit(emp_cov, n_samples)
def score(self, X_test, y=None):
"""Computes the log-likelihood of a Gaussian data set with
`self.covariance_` as an estimator of its covariance matrix.
Parameters
----------
X_test : lenght-2 list of array-like of shape (n_samples1, n_features1)
and (n_samples2, n_features2)
Test data of which we compute the likelihood, where n_samples is
the number of samples and n_features is the number of features.
X_test is assumed to be drawn from the same distribution than
the data used in fit (including centering). The number of features
must correspond.
y : not used, present for API consistence purpose.
Returns
-------
res : float
The likelihood of the data set with `self.covariance1_` and
`self.covariance1_`as an estimator of its covariance matrix.
"""
if self.covariance_ is None :
sys.error("The estimator is not fit on training data.")
sys.exit(0)
check_data_dimensions(X_test, layers=2)
# compute empirical covariance of the test set
test_cov = empirical_covariance(x - self.location_, assumed_centered=True)
res = log_likelihood(test_cov, self.get_precision())
return res
def fit(self, X, y=None):
"""Fit the GraphLasso model to X.
Parameters
----------
X : array-like shape (n_samples, n_features)
Data from which to compute the covariance estimate.
y : (ignored)
"""
self.random_state = check_random_state(self.random_state)
# check_data_dimensions(X, layers=2)
X = check_array(X, ensure_min_features=2,
ensure_min_samples=2, estimator=self)
self.X_train = X
if self.assume_centered:
self.location_ = np.zeros((X.shape[0], X.shape[1]))
else:
self.location_ = X.mean(0)
emp_cov = empirical_covariance(
X, assume_centered=self.assume_centered)
self.precision_, self.hidden_, \
self.observed_, self.emp_cov, \
self.n_iter_ = two_layers_fixed_links_GL(
emp_cov, self.L, eta=self.eta, mu=self.mu,
rho=self.rho, tol=self.tol, rtol=self.rtol,
max_iter=self.max_iter, verbose=self.verbose,
return_n_iter=True, return_history=False,
compute_objective=self.compute_objective)
return self
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 test_graph_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 (.1, .01):
covs = dict()
for method in ('cd', 'lars'):
cov_, _, costs = graph_lasso(emp_cov, alpha=.1, return_costs=True)
covs[method] = cov_
costs, dual_gap = np.array(costs).T
# Check that the costs always decrease
assert_array_less(np.diff(costs), 0)
# Check that the 2 approaches give similar results
assert_array_almost_equal(covs['cd'], covs['lars'])
# Smoke test the estimator
model = GraphLasso(alpha=.1).fit(X)
assert_array_almost_equal(model.covariance_, covs['cd'])
def test_graph_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 (.1, .01):
covs = dict()
for method in ('cd', 'lars'):
cov_, _, costs = graph_lasso(emp_cov, alpha=.1, return_costs=True)
covs[method] = cov_
costs, dual_gap = np.array(costs).T
# Check that the costs always decrease
assert_array_less(np.diff(costs), 0)
# Check that the 2 approaches give similar results
assert_array_almost_equal(covs['cd'], covs['lars'])
# Smoke test the estimator
model = GraphLasso(alpha=.1).fit(X)
assert_array_almost_equal(model.covariance_, covs['cd'])
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 get_moment(samples, name):
if name == 'covariance':
return empirical_covariance(samples)[0, 1]
elif name == 'var':
return np.square(np.std(samples, axis=0))
else:
raise ValueError("unknown name: {}".format(name))
def pca_it(self, spec, recipes, process_recipe):
MB_SIZE = 10
process_func = ProcessFunc(process_recipe, spec)
output_director = MinibatchOutputDirector2(
MB_SIZE,
x_shape=(spec['target_channels'], spec['target_h'],
spec['target_w']),
y_shape=(self.Y_SHAPE, ))
iterator = create_standard_iterator(process_func,
recipes,
output_director,
pool_size=6,
buffer_size=40,
chunk_size=MB_SIZE * 3)
print 'computing eigenvalues ...'
X = np.concatenate(
[batch['mb_x'][0, ...].reshape((3, -1)).T for batch in iterator])
n = X.shape[0]
limit = 125829120
if n > limit:
X = X[np.random.randint(n, size=limit), :]
print X.shape
cov = empirical_covariance(X)
print cov
evs, U = eigh(cov)
print evs
print U
return evs, U
def fit(self, X, y=None):
"""Fits the GraphLasso model to X.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Data from which to compute the covariance estimate
y : (ignored)
"""
# Covariance does not make sense for a single feature
X = check_array(X, ensure_min_features=2, ensure_min_samples=2,
estimator=self)
if self.assume_centered:
self.location_ = np.zeros(X.shape[1])
else:
self.location_ = X.mean(0)
emp_cov = empirical_covariance(X, assume_centered=self.assume_centered)
self.precision_, self.covariance_, self.n_iter_ = graph_lasso(
emp_cov, alpha=self.alpha, tol=self.tol, rtol=self.rtol,
max_iter=self.max_iter, over_relax=self.over_relax, rho=self.rho,
verbose=self.verbose, return_n_iter=True, return_history=False,
mode=self.mode, update_rho_options=self.update_rho_options,
compute_objective=self.compute_objective)
return self
def friedman_results(data_grid, K, K_obs, ells, alpha):
from rpy2.robjects.packages import importr
glasso = importr('glasso').glasso
tic = time.time()
iters = []
precisions = []
for d in data_grid.transpose(2, 0, 1):
emp_cov = empirical_covariance(d)
out = glasso(emp_cov, alpha)
iters.append(int(out[-1][0]))
precisions.append(np.array(out[1]))
tac = time.time()
iterations = np.max(iters)
precisions = np.array(precisions)
F1score = utils.structure_error(K, precisions)['f1']
MSE_observed = None
MSE_precision = utils.error_norm(K, precisions, upper_triangular=True)
MSE_latent = None
mean_rank_error = None
res = dict(n_dim_obs=K.shape[1],
time=tac - tic,
iterations=iterations,
F1score=F1score,
MSE_precision=MSE_precision,
MSE_observed=MSE_observed,
MSE_latent=MSE_latent,
mean_rank_error=mean_rank_error,
likelihood=likelihood_score(data_grid.transpose(2, 0, 1),
precisions),
note=None,
estimator=None)
return res
def chandresekeran_results(data_grid, K, K_obs, ells, tau, alpha, **kwargs):
emp_cov = np.array([
empirical_covariance(x, assume_centered=True)
for x in data_grid.transpose(2, 0, 1)
]).transpose(1, 2, 0)
rho = 1. / np.sqrt(data_grid.shape[0])
result = lvglasso(emp_cov, alpha, tau, rho)
ma_output = Bunch(**result)
R = np.array(ma_output.R).T
S = np.array(ma_output.S).T
L = np.array(ma_output.L).T
ss = utils.structure_error(K, S)
MSE_observed = utils.error_norm(K_obs, R)
MSE_precision = utils.error_norm(K, S, upper_triangular=True)
MSE_latent = utils.error_norm(ells, L)
mean_rank_error = utils.error_rank(ells, L)
res = dict(n_dim_obs=K.shape[1],
time=ma_output.elapsed_time,
iterations=np.max(ma_output.iter),
MSE_precision=MSE_precision,
MSE_observed=MSE_observed,
MSE_latent=MSE_latent,
mean_rank_error=mean_rank_error,
note=None,
estimator=ma_output,
likelihood=likelihood_score(data_grid.transpose(2, 0, 1), R),
latent=L)
res = dict(res, **ss)
return res
def GWishartFit(X, G, GWprior, mode='covsel'):
"""Fit G-Wishart distribution."""
n_samples, n_dim = X.shape
d0 = GWprior.d0
S0 = GWprior.S0
# check prior size violations
if G.shape[0] != n_dim or G.shape[1] != n_dim:
raise ValueError('G must be p-by-p, with p dimensions X')
if S0.shape[0] != n_dim or S0.shape[1] != n_dim:
raise ValueError('GWprior.S0 must be p-by-p, with p dimensions X')
# compute posterior scatter matrix
dn = n_samples + d0
# X'*X - but I dont assume X to be centered
emp_cov = empirical_covariance(X)
S = n_samples * emp_cov
C = (S + S0) / (dn - 2)
if mode == 'covsel':
precision = precision_selection(G, n_dim, C)
else:
# use graph_lasso
# convert G to alpha
alpha = np.zeros_like(G, dtype=float)
alpha[~(G + G.T)] = np.inf
precision = graphical_lasso(emp_cov=C, alpha=alpha)[0]
return precision, S
def launch_mcd_on_dataset(n_samples, n_features, n_outliers, tol_loc, tol_cov,
tol_support):
rand_gen = np.random.RandomState(0)
data = rand_gen.randn(n_samples, n_features)
# add some outliers
outliers_index = rand_gen.permutation(n_samples)[:n_outliers]
outliers_offset = 10. * \
(rand_gen.randint(2, size=(n_outliers, n_features)) - 0.5)
data[outliers_index] += outliers_offset
inliers_mask = np.ones(n_samples).astype(bool)
inliers_mask[outliers_index] = False
pure_data = data[inliers_mask]
# compute MCD by fitting an object
mcd_fit = MinCovDet(random_state=rand_gen).fit(data)
T = mcd_fit.location_
S = mcd_fit.covariance_
H = mcd_fit.support_
# compare with the estimates learnt from the inliers
error_location = np.mean((pure_data.mean(0) - T) ** 2)
assert(error_location < tol_loc)
error_cov = np.mean((empirical_covariance(pure_data) - S) ** 2)
assert(error_cov < tol_cov)
assert(np.sum(H) >= tol_support)
assert_array_almost_equal(mcd_fit.mahalanobis(data), mcd_fit.dist_)
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 save(X, Y, **kwargs):
"""
Parameters
----------
X : array-like, shape = [N, D]
Training data, where N is the number of samples and
D is the number of features.
Y : array-like, shape = [N]
Response variable, where n_samples is the number of samples
Argument dictionary should contain:
kwargs = {
'd' : intrinsic dimension (int)
'n_levelsets' : number of slices to use (int)
'split_by' : 'dyadic' (dyadic decomposition) or 'stateq' (statistically equivalent blocks) (default: 'dyadic')
'return_mat' : Boolean whether key SIR matrix should be returned (defaults
to False).
}
Returns
-----------
proj_vecs : array-like, shape = [n_features, d]
Orthonormal system spanning the sufficient dimension subspace, where
d refers to the intrinsic dimension.
M : SAVE matrix, only if return_mat option is True
}
"""
# Extract arguments from dictionary
d = kwargs['d']
n_levelsets = kwargs['n_levelsets']
split_by = kwargs.get('split_by', 'dyadic')
return_mat = kwargs.get('return_mat', False)
N, D = X.shape
# Standardize X
Z, cov_all_sqrtinv = whiten_data(X)
# Create partition
labels = split(Y, n_levelsets, split_by)
M = np.zeros((D, D)) # Container for key matrix in SIR
# Compute SAVE matrix
empirical_probabilities = np.zeros(n_levelsets)
for i in range(n_levelsets):
empirical_probabilities[i] = float(len(
np.where(labels == i)[0])) / float(N)
if empirical_probabilities[i] == 0:
continue
cov_sub = empirical_covariance(
Z[labels == i, :]) # Covariance of all samples
M += empirical_probabilities[i] * (np.eye(D) -
cov_sub).dot(np.eye(D) - cov_sub)
U, S, V = np.linalg.svd(M)
# Apply inverse transformation
vecs = cov_all_sqrtinv.dot(U[:, :d])
proj_vecs, dummy = np.linalg.qr(vecs)
if return_mat:
return proj_vecs, M
else:
return proj_vecs
def kfold_cv(X, K=10, isotonic=True):
"""K-fold cross-validated eigenvalues for LW nonlinear shrinkage"""
S = empirical_covariance(X)
lam, U = np.linalg.eigh(S)
d = _nls_cv(X, S, K)
if isotonic:
d = isotonic_regression(d, increasing=True)
return U @ np.diag(d) @ U.T
def fit(self, X, alpha):
self.alpha = alpha
emp_cov = empirical_covariance(X)
self.covariance_, self.precision_ = graph_lasso(emp_cov,
alpha=self.alpha,
tol=self.tol,
max_iter=self.max_iter)
return self.covariance_, self.precision_
def empirical_covariances(subjects, assume_centered=False, standardize=False):
"""Compute empirical covariances for several signals.
Parameters
----------
subjects : list of numpy.ndarray, shape for each (n_samples, n_features)
input subjects. Each subject is a 2D array, whose columns contain
signals. Sample number can vary from subject to subject, but all
subjects must have the same number of features (i.e. of columns).
assume_centered : bool, optional
if True, assume that all input signals are centered. This slightly
decreases computation time by avoiding useless computation.
Default=False.
standardize : bool, optional
if True, set every signal variance to one before computing their
covariance matrix (i.e. compute a correlation matrix).
Default=False.
Returns
-------
emp_covs : numpy.ndarray, shape : (feature number, feature number, subject number)
empirical covariances.
n_samples : numpy.ndarray, shape: (subject number,)
number of samples for each subject. dtype is np.float64.
"""
if not hasattr(subjects, "__iter__"):
raise ValueError("'subjects' input argument must be an iterable. "
"You provided {0}".format(subjects.__class__))
n_subjects = [s.shape[1] for s in subjects]
if len(set(n_subjects)) > 1:
raise ValueError("All subjects must have the same number of "
"features.\nYou provided: {0}".format(
str(n_subjects)))
n_subjects = len(subjects)
n_features = subjects[0].shape[1]
# Enable to change dtype here because depending on user, conversion from
# single precision to double will be required or not.
emp_covs = np.empty((n_features, n_features, n_subjects), order="F")
for k, s in enumerate(subjects):
if standardize:
s = s / s.std(axis=0) # copy on purpose
M = empirical_covariance(s, assume_centered=assume_centered)
# Force matrix symmetry, for numerical stability
# of _group_sparse_covariance
emp_covs[..., k] = M + M.T
emp_covs /= 2
n_samples = np.asarray([s.shape[0] for s in subjects], dtype=np.float64)
return emp_covs, n_samples
def likelihood_score(X, precision_):
# compute empirical covariance of the test set
location_ = X.mean(1).reshape(X.shape[0], 1, X.shape[2])
test_cov = np.array(
[empirical_covariance(x, assume_centered=True) for x in X - location_])
res = sum(log_likelihood(S, K) for S, K in zip(test_cov, precision_))
return res
def covariances():
subject_to_means, subject_to_values = load_data(TRAINING_DATA_FILENAME, True)
subject_to_covariance = {}
full_matrix = None
for key in subject_to_values.keys():
if full_matrix is None:
full_matrix = subject_to_values[key]
subject_to_covariance[key] = empirical_covariance(subject_to_values[key])
print subject_to_means[key]
print subject_to_covariance[key]
else:
full_matrix = np.append(full_matrix, subject_to_values[key], axis = 0)
subject_to_covariance[key] = empirical_covariance(subject_to_values[key])
full_mean = full_matrix.mean(axis=0)
full_covariance = empirical_covariance(full_matrix)
print full_mean
print full_covariance
return subject_to_covariance, full_covariance, full_mean
def objective_function(self, data, location, covariance):
"""Objective function minimized at each step of the MCD algorithm.
"""
precision = pinvh(covariance)
det = fast_logdet(precision)
trace = np.trace(
np.dot(empirical_covariance(data - location, assume_centered=True),
precision))
pen = self.shrinkage * np.trace(precision)
return -det + trace + pen
def test_covariance():
"""Tests Covariance module on a simple dataset.
"""
# test covariance fit from data
cov = EmpiricalCovariance()
cov.fit(X)
emp_cov = empirical_covariance(X)
assert_array_almost_equal(emp_cov, cov.covariance_, 4)
assert_almost_equal(cov.error_norm(emp_cov), 0)
assert_almost_equal(cov.error_norm(emp_cov, norm="spectral"), 0)
assert_almost_equal(cov.error_norm(emp_cov, norm="frobenius"), 0)
assert_almost_equal(cov.error_norm(emp_cov, scaling=False), 0)
assert_almost_equal(cov.error_norm(emp_cov, squared=False), 0)
assert_raises(NotImplementedError, cov.error_norm, emp_cov, norm="foo")
# Mahalanobis distances computation test
mahal_dist = cov.mahalanobis(X)
print(np.amin(mahal_dist), np.amax(mahal_dist))
assert np.amin(mahal_dist) > 0
# test with n_features = 1
X_1d = X[:, 0].reshape((-1, 1))
cov = EmpiricalCovariance()
cov.fit(X_1d)
assert_array_almost_equal(empirical_covariance(X_1d), cov.covariance_, 4)
assert_almost_equal(cov.error_norm(empirical_covariance(X_1d)), 0)
assert_almost_equal(cov.error_norm(empirical_covariance(X_1d), norm="spectral"), 0)
# test with one sample
X_1sample = np.arange(5)
cov = EmpiricalCovariance()
with warnings.catch_warnings(record=True):
cov.fit(X_1sample)
# test integer type
X_integer = np.asarray([[0, 1], [1, 0]])
result = np.asarray([[0.25, -0.25], [-0.25, 0.25]])
assert_array_almost_equal(empirical_covariance(X_integer), result)
# test centered case
cov = EmpiricalCovariance(assume_centered=True)
cov.fit(X)
assert_array_equal(cov.location_, np.zeros(X.shape[1]))
def objective_function(self, data, location, covariance):
"""Objective function minimized at each step of the MCD algorithm.
"""
precision = pinvh(covariance)
det = fast_logdet(precision)
trace = np.trace(
np.dot(empirical_covariance(data - location, assume_centered=True),
precision))
pen = self.shrinkage * np.trace(precision)
return -det + trace + pen
def test_empirical_covariance(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.covariance.empirical_covariance()
expected = covariance.empirical_covariance(iris.data)
self.assertIsInstance(result, pdml.ModelFrame)
tm.assert_index_equal(result.index, df.data.columns)
tm.assert_index_equal(result.columns, df.data.columns)
self.assert_numpy_array_almost_equal(result.values, expected)
def test_empirical_covariance(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.covariance.empirical_covariance()
expected = covariance.empirical_covariance(iris.data)
self.assertTrue(isinstance(result, pdml.ModelFrame))
self.assert_index_equal(result.index, df.data.columns)
self.assert_index_equal(result.columns, df.data.columns)
self.assert_numpy_array_almost_equal(result.values, expected)
def empirical_covariances(subjects, assume_centered=False, standardize=False):
"""Compute empirical covariances for several signals.
Parameters
----------
subjects : list of numpy.ndarray, shape for each (n_samples, n_features)
input subjects. Each subject is a 2D array, whose columns contain
signals. Sample number can vary from subject to subject, but all
subjects must have the same number of features (i.e. of columns).
assume_centered : bool, optional
if True, assume that all input signals are centered. This slightly
decreases computation time by avoiding useless computation.
standardize : bool, optional
if True, set every signal variance to one before computing their
covariance matrix (i.e. compute a correlation matrix).
Returns
-------
emp_covs : numpy.ndarray, shape : (feature number, feature number, subject number)
empirical covariances.
n_samples : numpy.ndarray, shape: (subject number,)
number of samples for each subject. dtype is np.float.
"""
if not hasattr(subjects, "__iter__"):
raise ValueError("'subjects' input argument must be an iterable. "
"You provided {0}".format(subjects.__class__))
n_subjects = [s.shape[1] for s in subjects]
if len(set(n_subjects)) > 1:
raise ValueError("All subjects must have the same number of "
"features.\nYou provided: {0}".format(str(n_subjects))
)
n_subjects = len(subjects)
n_features = subjects[0].shape[1]
# Enable to change dtype here because depending on user, conversion from
# single precision to double will be required or not.
emp_covs = np.empty((n_features, n_features, n_subjects), order="F")
for k, s in enumerate(subjects):
if standardize:
s = s / s.std(axis=0) # copy on purpose
M = empirical_covariance(s, assume_centered=assume_centered)
# Force matrix symmetry, for numerical stability
# of _group_sparse_covariance
emp_covs[..., k] = M + M.T
emp_covs /= 2
n_samples = np.asarray([s.shape[0] for s in subjects], dtype=np.float)
return emp_covs, n_samples
def feat_select(f):
cp = load(read)
(X, y, t) = cp.export_data(f)
data = numpy.c_[X, y]
cov = empirical_covariance(data, False)
print cov
for i in range(cov.shape[0] - 1):
print cov[i, -1]
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 train(self, use_entropy=False):
""" Train the classifier for all the models that it knows. """
if len(self.dict_categories) < 2:
print "At least two categories are needed for training..."
print "Training is skipped."
return
(X, Y, W) = self._get_example_matrix(use_entropy)
if (hasattr(self.classifier, 'metric') and
self.classifier.metric == 'mahalanobis'):
# The mahalanobis distance needs the covariance of the data
cov = covariance.empirical_covariance(X)
self.classifier.metric_kwds['V'] = cov
print "Training with {} categories and {} views.".format(
len(self.dict_categories), len(Y))
print self.classifier.fit(X, Y)
def plot_all(X): tsne = manifold.TSNE(n_components=2, init='pca', random_state=0) #---------------------------------------------------------------------- # Pre-processing print "t-SNE Scaling" X_scaled = preprocessing.scale(X) #zero mean, unit variance X_tsne_scaled = tsne.fit_transform(X_scaled) #normalize the data (scaling individual samples to have unit norm) print "t-SNE L2 Norm" X_normalized = preprocessing.normalize(X, norm='l2') X_tsne_norm = tsne.fit_transform(X_normalized) #whiten the data print "t-SNE Whitening" # the mean computed by the scaler is for the feature dimension. # We want the normalization to be in feature dimention. # Zero mean for each sample assumes stationarity which is not necessarily true for CNN features. # X: NxD where N is number of examples and D is number of features. # scaler = preprocessing.StandardScaler(with_std=False).fit(X) scaler = preprocessing.StandardScaler().fit(X) #this scales each feature to have std-dev 1 X_centered = scaler.transform(X) # U, s, Vh = linalg.svd(X_centered) shapeX = X_centered.shape IPython.embed() # this is DxD matrix where D is the feature dimension # still to figure out: It seems computation is not a problem but carrying around a 50kx50k matrix is memory killer! sig = (1/shapeX[0]) * np.dot(X_centered.T, X_centered) sig2= covariance.empirical_covariance(X_centered, assume_centered=True) #estimated -- this is better. sig3, shrinkage= covariance.oas(X_centered, assume_centered=True) #estimated U, s, Vh = linalg.svd(sig, full_matrices=False) eps = 1e-2 # this affects how many low- freq eigevalues are eliminated invS = np.diag (np.reciprocal(np.sqrt(s+eps))) #PCA_whiten X_pca = np.dot(invS, np.dot(U.T, X_centered)) X_tsne_pca = tsne.fit_transform(X_pca) #whiten the data (ZCA) X_zca = np.dot(U, X_pca) X_tsne_zca = tsne.fit_transform(X_zca) return X_tsne_scaled, X_tsne_norm, X_tsne_pca, X_tsne_zca
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 _naive_ledoit_wolf_shrinkage(X):
# A simple implementation of the formulas from Ledoit & Wolf
# The computation below achieves the following computations of the
# "O. Ledoit and M. Wolf, A Well-Conditioned Estimator for
# Large-Dimensional Covariance Matrices"
# beta and delta are given in the beginning of section 3.2
n_samples, n_features = X.shape
emp_cov = empirical_covariance(X, assume_centered=False)
mu = np.trace(emp_cov) / n_features
delta_ = emp_cov.copy()
delta_.flat[::n_features + 1] -= mu
delta = (delta_ ** 2).sum() / n_features
X2 = X ** 2
beta_ = 1. / (n_features * n_samples) \
* np.sum(np.dot(X2.T, X2) / n_samples - emp_cov ** 2)
beta = min(beta_, delta)
shrinkage = beta / delta
return shrinkage
def lasso_gsc_comparison():
"""Check that graph lasso and group-sparse covariance give the same
output for a single task."""
from sklearn.covariance import graph_lasso, empirical_covariance
parameters = {'n_tasks': 1, 'n_var': 20, 'density': 0.15,
'rho': .2, 'tol': 1e-4, 'max_iter': 50}
_, _, gt = create_signals(parameters, output_dir=output_dir)
signals = gt["signals"]
_, gsc_precision = utils.timeit(group_sparse_covariance)(
signals, parameters['rho'], max_iter=parameters['max_iter'],
tol=parameters['tol'], verbose=1, debug=False)
emp_cov = empirical_covariance(signals[0])
_, gl_precision = utils.timeit(graph_lasso)(
emp_cov, parameters['rho'], tol=parameters['tol'],
max_iter=parameters['max_iter'])
np.testing.assert_almost_equal(gl_precision, gsc_precision[..., 0],
decimal=4)
def prepareProblem(filePath, shrinkage=False, subset=False, subsetSize=0):
# Import data from .csv
df = pd.read_csv(filePath, sep=';')
df.index = df.date
df = df.drop('date', axis=1)
# Subset, if called via subset == True
if subset == True:
df = df.tail(subsetSize)
# Estimate covariance using Empirical/MLE
# Expected input is returns, hence set: assume_centered = True
mleFitted = empirical_covariance(X=df, assume_centered=True)
sigma = mleFitted
if shrinkage == True:
# Estimate covariance using LedoitWolf, first create instance of object
lw = LedoitWolf(assume_centered=True)
lwFitted = lw.fit(X=df).covariance_
sigma = lwFitted
return sigma
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_graph_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 = graph_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 _nonrobust_covariance(self, data, assume_centered=False):
"""Non-robust estimation of the covariance to be used within MCD.
Parameters
----------
data: array_like, shape (n_samples, n_features)
Data for which to compute the non-robust covariance matrix.
assume_centered: Boolean
Whether or not the observations should be considered as centered.
Returns
-------
nonrobust_covariance: array_like, shape (n_features, n_features)
The non-robust covariance of the data.
"""
if self.cov_computation_method is None:
cov = empirical_covariance(data, assume_centered=assume_centered)
cov.flat[::data.shape[1] + 1] += self.shrinkage
elif self.cov_computation_method == "diag":
cov = np.diag(np.var(data, 0)) / self.shrinkage
else:
raise NotImplemented
return cov
def set_optimal_shrinkage_amount(self, X, verbose=False):
"""
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.
Returns
-------
optimal_shrinkage: The optimal amount of shrinkage, chosen with a
10-fold cross-validation. (or a Leave-One Out cross-validation
if n_samples < 10).
"""
n_samples, n_features = X.shape
std_shrinkage = np.trace(empirical_covariance(X)) / \
float(n_samples * n_features)
# use L2 here? (was done during research work, changed for consistency)
rmcd = RMCDl1(shrinkage=std_shrinkage).fit(X)
cov = GraphLassoCV().fit(X[rmcd.raw_support_])
self.shrinkage = cov.alpha_
return cov.cv_alphas_, cov.cv_scores
def fit(self,X,Y):
self.predictions=[]
n=len(Y)
self.MSEs=[]
self.weights=[]
for reg in self.regList:
self.predictions.append(cross_val_predict(reg,X,Y,cv=self.cv))
MSE=sum([(p-a)**2. for (p,a) in zip(self.predictions[-1],Y)])/n
self.MSEs.append(MSE)
reg.fit(X,Y)
if self.weighting=='uniform':
self.weights=[1./len(self.regList)]*len(self.regList)
elif self.weighting=='score':
tot=sum([1./s for s in self.MSEs])
self.weights=[1./(s*tot) for s in self.MSEs]
elif self.weighting=='varMin':
self.covariance=empirical_covariance(np.array(self.predictions).T)
self.weights=smallestVarianceWeights(self.covariance,self.MSEs,self.biasWeighting)
elif self.weighting=='linearReg':
self.stacker.fit(np.array(self.predictions).T,Y)
self.weights=self.stacker.coef_
print self.weights
def launch_mcd_on_dataset(n_samples, n_features, n_outliers):
rand_gen = np.random.RandomState(0)
data = rand_gen.randn(n_samples, n_features)
# add some outliers
outliers_index = rand_gen.permutation(n_samples)[:n_outliers]
outliers_offset = 10. * \
(rand_gen.randint(2, size=(n_outliers, n_features)) - 0.5)
data[outliers_index] += outliers_offset
inliers_mask = np.ones(n_samples).astype(bool)
inliers_mask[outliers_index] = False
pure_data = data[inliers_mask]
# compute MCD by fitting an object
mcd_fit = MCD().fit(data)
T = mcd_fit.location_
S = mcd_fit.covariance_
# compare with the estimates learnt from the inliers
error_location = np.mean((pure_data.mean(0) - T) ** 2)
print error_location
assert(error_location < 1.)
error_cov = np.mean((empirical_covariance(pure_data) - S) ** 2)
print error_cov
assert(error_cov < 1.)
def test_oas():
"""Tests OAS module on a simple dataset.
"""
# test shrinkage coeff on a simple data set
X_centered = X - X.mean(axis=0)
oa = OAS(assume_centered=True)
oa.fit(X_centered)
shrinkage_ = oa.shrinkage_
score_ = oa.score(X_centered)
# compare shrunk covariance obtained from data and from MLE estimate
oa_cov_from_mle, oa_shinkrage_from_mle = oas(X_centered,
assume_centered=True)
assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
# compare estimates given by OAS and ShrunkCovariance
scov = ShrunkCovariance(shrinkage=oa.shrinkage_, assume_centered=True)
scov.fit(X_centered)
assert_array_almost_equal(scov.covariance_, oa.covariance_, 4)
# test with n_features = 1
X_1d = X[:, 0].reshape((-1, 1))
oa = OAS(assume_centered=True)
oa.fit(X_1d)
oa_cov_from_mle, oa_shinkrage_from_mle = oas(X_1d, assume_centered=True)
assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
assert_array_almost_equal((X_1d ** 2).sum() / n_samples, oa.covariance_, 4)
# test shrinkage coeff on a simple data set (without saving precision)
oa = OAS(store_precision=False, assume_centered=True)
oa.fit(X_centered)
assert_almost_equal(oa.score(X_centered), score_, 4)
assert(oa.precision_ is None)
### Same tests without assuming centered data
# test shrinkage coeff on a simple data set
oa = OAS()
oa.fit(X)
assert_almost_equal(oa.shrinkage_, shrinkage_, 4)
assert_almost_equal(oa.score(X), score_, 4)
# compare shrunk covariance obtained from data and from MLE estimate
oa_cov_from_mle, oa_shinkrage_from_mle = oas(X)
assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
# compare estimates given by OAS and ShrunkCovariance
scov = ShrunkCovariance(shrinkage=oa.shrinkage_)
scov.fit(X)
assert_array_almost_equal(scov.covariance_, oa.covariance_, 4)
# test with n_features = 1
X_1d = X[:, 0].reshape((-1, 1))
oa = OAS()
oa.fit(X_1d)
oa_cov_from_mle, oa_shinkrage_from_mle = oas(X_1d)
assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4)
assert_almost_equal(oa_shinkrage_from_mle, oa.shrinkage_)
assert_array_almost_equal(empirical_covariance(X_1d), oa.covariance_, 4)
# test with one sample
X_1sample = np.arange(5)
oa = OAS()
with warnings.catch_warnings(record=True):
oa.fit(X_1sample)
# test shrinkage coeff on a simple data set (without saving precision)
oa = OAS(store_precision=False)
oa.fit(X)
assert_almost_equal(oa.score(X), score_, 4)
assert(oa.precision_ is None)
def test_ledoit_wolf():
"""Tests LedoitWolf module on a simple dataset.
"""
# test shrinkage coeff on a simple data set
lw = LedoitWolf()
lw.fit(X, assume_centered=True)
assert_almost_equal(lw.shrinkage_, 0.00192, 4)
assert_almost_equal(lw.score(X, assume_centered=True), -2.89795, 4)
# compare shrunk covariance obtained from data and from MLE estimate
lw_cov_from_mle, lw_shinkrage_from_mle = ledoit_wolf(X,
assume_centered=True)
assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4)
assert_almost_equal(lw_shinkrage_from_mle, lw.shrinkage_)
# compare estimates given by LW and ShrunkCovariance
scov = ShrunkCovariance(shrinkage=lw.shrinkage_)
scov.fit(X, assume_centered=True)
assert_array_almost_equal(scov.covariance_, lw.covariance_, 4)
# test with n_features = 1
X_1d = X[:, 0].reshape((-1, 1))
lw = LedoitWolf()
lw.fit(X_1d, assume_centered=True)
lw_cov_from_mle, lw_shinkrage_from_mle = ledoit_wolf(X_1d,
assume_centered=True)
assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4)
assert_almost_equal(lw_shinkrage_from_mle, lw.shrinkage_)
assert_array_almost_equal((X_1d ** 2).sum() / n_samples, lw.covariance_, 4)
# test shrinkage coeff on a simple data set (without saving precision)
lw = LedoitWolf(store_precision=False)
lw.fit(X, assume_centered=True)
assert_almost_equal(lw.score(X, assume_centered=True), -2.89795, 4)
assert(lw.precision_ is None)
# Same tests without assuming centered data
# test shrinkage coeff on a simple data set
lw = LedoitWolf()
lw.fit(X)
assert_almost_equal(lw.shrinkage_, 0.007582, 4)
assert_almost_equal(lw.score(X), 2.243483, 4)
# compare shrunk covariance obtained from data and from MLE estimate
lw_cov_from_mle, lw_shinkrage_from_mle = ledoit_wolf(X)
assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4)
assert_almost_equal(lw_shinkrage_from_mle, lw.shrinkage_)
# compare estimates given by LW and ShrunkCovariance
scov = ShrunkCovariance(shrinkage=lw.shrinkage_)
scov.fit(X)
assert_array_almost_equal(scov.covariance_, lw.covariance_, 4)
# test with n_features = 1
X_1d = X[:, 0].reshape((-1, 1))
lw = LedoitWolf()
lw.fit(X_1d)
lw_cov_from_mle, lw_shinkrage_from_mle = ledoit_wolf(X_1d)
assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4)
assert_almost_equal(lw_shinkrage_from_mle, lw.shrinkage_)
assert_array_almost_equal(empirical_covariance(X_1d), lw.covariance_, 4)
# test shrinkage coeff on a simple data set (without saving precision)
lw = LedoitWolf(store_precision=False)
lw.fit(X)
assert_almost_equal(lw.score(X), 2.2434839, 4)
assert(lw.precision_ is None)