def heat_map(file_path, X, headers, cmap=sns.color_palette("Blues")):
model = GraphLasso()
model.fit(X)
Cov = model.covariance_
std = np.diag(1. / np.sqrt(np.diag(Cov)))
Cor = std.dot(Cov).dot(std)
fig, ax = plt.subplots()
# the size of A4 paper
fig.set_size_inches(10, 8)
ax = sns.heatmap(Cor,
cmap=cmap,
square=True,
xticklabels=1,
yticklabels=1,
linewidths=.5)
ax.set_yticklabels(headers, rotation=0, fontsize=12)
ax.set_xticklabels(headers, rotation=90, fontsize=12)
plt.subplots_adjust(bottom=0.4, left=0.2)
sns.despine(left=True, bottom=True)
plt.tight_layout()
plt.savefig(file_path)
plt.show()
def save_network_graph_sequence(data, alpha_seq, labels, filename):
if len(alpha_seq) % 2 != 0:
print "make alpha an even number please."
return
n = len(alpha_seq)
labels = dict(zip(range(len(labels)), labels))
fig = plt.figure()
for i in range(n):
ax = fig.add_subplot(n / 2, 2, i + 1)
gl = GraphLasso(alpha=alpha_seq[i])
gl.fit(data)
D = nx.Graph(gl.precision_)
pos_labels = nx.circular_layout(D)
for k, item in pos_labels.iteritems():
pos_labels[k] = item + 0.1
nx.draw_circular(D,
scale=4,
node_size=150,
ax=ax,
with_labels=True,
labels=labels,
font_size=6)
#nx.draw_networkx_labels(D, pos_labels, ax=ax, labels= labels, font_size = 12)
ax.set_title(r"$\alpha$ = %.2e" % alpha_seq[i])
plt.savefig(filename)
def _fit(self, X):
self.estimator_ = GraphLasso(alpha=self.alpha,
assume_centered=self.assume_centered,
enet_tol=self.enet_tol,
max_iter=self.max_iter,
mode=self.mode,
tol=self.tol).fit(X)
_, self.labels_ = affinity_propagation(self.partial_corrcoef_,
**self._apcluster_params)
return self
def precisionCol(cleandata, k):
model = GraphLasso(mode = 'lars')
model.fit(cleandata)
pre_ = pd.DataFrame(model.get_precision())
pre_.index = cleandata.columns
pre_.columns = cleandata.columns
pre_.to_csv("precision.csv")
test = abs(pre_['Y'])
test.sort()
test = test[-k:]
coltest = (test.index).drop('Y')
return coltest
def create_skeleton_from_data(self, data, **kwargs):
"""
:param data: raw data df
:param kwargs: alpha hyper-parameter (
:return:
"""
alpha = kwargs.get('alpha', 0.01)
max_iter = kwargs.get('max_iter', 2000)
edge_model = GraphLasso(alpha=alpha, max_iter=max_iter)
edge_model.fit(data.as_matrix())
return edge_model.get_precision()
def get_other_precision(A):
# reference on sklearn's graph lasso: http://scikit-learn.org/stable/modules/generated/sklearn.covariance.GraphLasso.html
from sklearn.covariance import GraphLasso # our Algo code should replace this and input/output the same thing
graph_lasso = GraphLasso(
alpha=1e-5
) # alpha = regularization parameter: the higher alpha, the more regularization, the sparser the inverse covariance.
graph_lasso.fit(
A
) # A is the aggregated sentiment matrix, an arrray of (n_samples, n_features)
precision = graph_lasso.get_precision()
return precision
def myglasso(data, lam=0.5):
model = GraphLasso(alpha=lam)
# model=GraphLassoCV()
model.fit(data)
cov = model.covariance_
prec = model.precision_
# alpha=model.alpha_
n_samples, n_features = data.shape
part = np.zeros((n_features, n_features))
for i in range(n_features):
for j in range(n_features):
part[i, j] = -prec[i, j] / np.sqrt(prec[i, i] * prec[j, j])
return part, prec, cov
def predict(self, data, **kwargs):
"""
:param data: raw data df
:param kwargs: alpha hyper-parameter (
:return:
"""
alpha = kwargs.get('alpha', 0.01)
max_iter = kwargs.get('max_iter', 2000)
edge_model = GraphLasso(alpha=alpha, max_iter=max_iter)
edge_model.fit(data.values)
return nx.relabel_nodes(nx.DiGraph(edge_model.get_precision()),
{idx: i
for idx, i in enumerate(data.columns)})
def predict(self, data, alpha=0.01, max_iter=2000, **kwargs):
""" Predict the graph skeleton.
Args:
data (pandas.DataFrame): observational data
alpha (float): regularization parameter
max_iter (int): maximum number of iterations
Returns:
networkx.Graph: Graph skeleton
"""
edge_model = GraphLasso(alpha=alpha, max_iter=max_iter)
edge_model.fit(data.values)
return nx.relabel_nodes(nx.DiGraph(edge_model.get_precision()),
{idx: i for idx, i in enumerate(data.columns)})
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 __init__(self, n_components=2, n_iter=100, alpha = None):
self.n_components = n_components
self.n_iter = n_iter
self.min_covar = 1e-3
if alpha == None:
self.alpha = [10 for _ in range(self.n_components)]
else:
self.alpha = alpha
self.model = [GraphLasso(alpha=self.alpha[k], assume_centered=False, tol=1e-4) for k in range(self.n_components)]
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 (0., .1, .25):
covs = dict()
icovs = dict()
for method in ('cd', 'lars'):
cov_, icov_, costs = graph_lasso(emp_cov,
alpha=alpha,
mode=method,
return_costs=True)
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=3)
assert_array_almost_equal(icovs['cd'], icovs['lars'], decimal=3)
# Smoke test the estimator
model = GraphLasso(alpha=.25).fit(X)
model.score(X)
assert_array_almost_equal(model.covariance_, covs['cd'], decimal=3)
assert_array_almost_equal(model.covariance_, covs['lars'], decimal=3)
# 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_ = GraphLasso(assume_centered=assume_centered).fit(Z).precision_
precs.append(prec_)
assert_array_almost_equal(precs[0], precs[1])
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 (0., .1, .25):
covs = dict()
icovs = dict()
for method in ('cd', 'lars'):
cov_, icov_, costs = graph_lasso(emp_cov, alpha=alpha, mode=method,
return_costs=True)
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 = GraphLasso(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_ = GraphLasso(assume_centered=assume_centered).fit(Z).precision_
precs.append(prec_)
assert_array_almost_equal(precs[0], precs[1])
def computePartialCorrelations(coupling_data, reg_alpha):
# standardize
# coupling_data -= coupling_data.mean(axis=0)
# coupling_data /= coupling_data.std(axis=0)
# sparse inverse covariance matrix estimation
estimator = GraphLasso(alpha=reg_alpha, assume_centered=False, mode='cd', max_iter=500)
estimator.fit(coupling_data)
print("Sparse inverse covariance matrix was estiamted with {0} iterations.".format(estimator.n_iter_))
print("\t\t\t and by using the parameters: ", estimator.get_params())
prec = estimator.get_precision()
#diagonal of precision matrix
prec_diag = np.diag(prec)
# obtain partial correlations (proportional to prec matrix entries):
# rho_ij = - p_ij/ sqrt(p_ii * p_jj)
partial_correlations = -prec / np.sqrt(np.outer(prec_diag, prec_diag))
# d = 1 / np.sqrt(np.diag(prec))
# partial_correlations *= d
# partial_correlations *= d[:, np.newaxis]
# set lower half to zero
partial_correlations[np.tril_indices(400)] = 0
return estimator.get_precision(), partial_correlations
def fit(self, X):
self.mean_ = np.mean(X, axis=0)
if self.alpha_:
gl = GraphLasso(self.alpha_, max_iter=100000)
gl.fit(X)
self.precision_ = gl.precision_
elif self.method_ == 'cv':
gl = GraphLassoCV(verbose=self.verbose)
gl.fit(X)
self.alpha_ = gl.alpha_
self.precision_ = gl.precision_
elif self.method_ == 'bic':
min_score = np.inf
min_precision = None
alphas = np.arange(0.01, 0.5, 0.01)
for a in alphas:
if self.verbose:
print("[GMRF] Alpha = {}".format(a))
gl = GraphLasso(a, max_iter=100000)
try:
gl.fit(X)
self.precision_ = gl.precision_
score = self.bic(X, gamma=0.0)
self.bic_scores.append(score)
if score <= min_score:
min_score = score
self.alpha_ = a
min_precision = np.copy(self.precision_)
except:
self.bic_scores.append(None)
self.precision_ = min_precision
else:
raise NotImplementedError(self.method_ +
" is not a valid method, use 'cv' or 'bic'")
def save_network_graph_sequence( data, alpha_seq, labels, filename): if len(alpha_seq)%2 != 0: print "make alpha an even number please." return n = len(alpha_seq) labels = dict( zip( range( len(labels) ), labels) ) fig = plt.figure() for i in range(n): ax = fig.add_subplot(n/2,2,i+1) gl = GraphLasso( alpha = alpha_seq[i] ) gl.fit( data ) D = nx.Graph( gl.precision_ ) pos_labels = nx.circular_layout( D ) for k,item in pos_labels.iteritems(): pos_labels[k] = item + 0.1 nx.draw_circular( D, scale = 4, node_size = 150, ax = ax, with_labels = True, labels = labels, font_size = 6 ) #nx.draw_networkx_labels(D, pos_labels, ax=ax, labels= labels, font_size = 12) ax.set_title( r"$\alpha$ = %.2e"%alpha_seq[i]) plt.savefig( filename )
def _init_para(self, X, y):
'''
'''
p0, shape = BASE._init_para(self, X, y)
edges = []
if self.sparsity==0:
for i in itertools.combinations(range(y.shape[1]), 2):
e1 = min(i)
e2 = max(i)
edges.append([e1, e2])
else:
lasso = GraphLasso(alpha = 0.1)
lasso.fit(y)
graph = lasso.get_precision()!=0
for i in range(graph.shape[0]):
for j in range(i+1,graph.shape[1]):
if graph[i,j]==0:continue
edges.append([i, j])
self.edges = T.shared(np.array(edges).T).astype('int8')
if self.verbose:
print('(edges)',edges)
print('(y) shape:',y.shape,'labels:',np.unique(y))
print('(X) shape:',X.shape,'std:',np.std(X))
theta = []
for e1, e2 in edges:
if self.shared_copula:
theta.append(0.01)
else:
theta.append(np.ones((shape[1],shape[1]))*0.01)
p0['theta'] = tespo.parameter(theta, const=False)
return p0, shape
def __init__(self, n_components, n_iter=5, alpha=None):
self.n_components = n_components + 2
self.n_iter = n_iter
self.min_covar = 1e-3
self.tresh = 1e-3
self.lambd = 0.5
if alpha == None:
self.alpha = [10 for _ in range(self.n_components)]
else:
self.alpha = alpha
self.model = [
GraphLasso(alpha=self.alpha[k],
assume_centered=True,
tol=1e-4,
verbose=True) for k in range(self.n_components)
]
class SparseStructureLearning(BaseOutlierDetector):
"""Outlier detector using sparse structure learning.
Parameters
----------
alpha : float, default 0.01
Regularization parameter.
assume_centered : bool, default False
If True, data are not centered before computation.
contamination : float, default 0.1
Proportion of outliers in the data set. Used to define the threshold.
enet_tol : float, default 1e-04
Tolerance for the elastic net solver used to calculate the descent
direction. This parameter controls the accuracy of the search direction
for a given column update, not of the overall parameter estimate. Only
used for mode='cd'.
max_iter : integer, default 100
Maximum number of iterations.
mode : str, default 'cd'
Lasso solver to use: coordinate descent or LARS.
tol : float, default 1e-04
Tolerance to declare convergence.
apcluster_params : dict, default None
Additional parameters passed to
``sklearn.cluster.affinity_propagation``.
Attributes
----------
anomaly_score_ : array-like of shape (n_samples,)
Anomaly score for each training data.
threshold_ : float
Threshold.
covariance_ : array-like of shape (n_features, n_features)
Estimated covariance matrix.
graphical_model_ : networkx Graph
GGM.
isolates_ : array-like of shape (n_isolates,)
Indices of isolates.
labels_ : array-like of shape (n_features,)
Label of each feature.
location_ : array-like of shape (n_features,)
Estimated location.
n_iter_ : int
Number of iterations run.
partial_corrcoef_ : array-like of shape (n_features, n_features)
Partial correlation coefficient matrix.
precision_ : array-like of shape (n_features, n_features)
Estimated pseudo inverse matrix.
References
----------
.. [#ide09] Ide, T., Lozano, C., Abe, N., and Liu, Y.,
"Proximity-based anomaly detection using sparse structure learning,"
In Proceedings of SDM, pp. 97-108, 2009.
Examples
--------
>>> import numpy as np
>>> from kenchi.outlier_detection import SparseStructureLearning
>>> X = np.array([
... [0., 0.], [1., 1.], [2., 0.], [3., -1.], [4., 0.],
... [5., 1.], [6., 0.], [7., -1.], [8., 0.], [1000., 1.]
... ])
>>> det = SparseStructureLearning()
>>> det.fit_predict(X)
array([ 1, 1, 1, 1, 1, 1, 1, 1, 1, -1])
"""
@property
def _apcluster_params(self):
if self.apcluster_params is None:
return dict()
else:
return self.apcluster_params
@property
def covariance_(self):
return self.estimator_.covariance_
@property
def graphical_model_(self):
import networkx as nx
return nx.from_numpy_matrix(np.tril(self.partial_corrcoef_, k=-1))
@property
def isolates_(self):
import networkx as nx
return np.array(list(nx.isolates(self.graphical_model_)))
@property
def location_(self):
return self.estimator_.location_
@property
def n_iter_(self):
return self.estimator_.n_iter_
@property
def partial_corrcoef_(self):
n_features, _ = self.precision_.shape
diag = np.diag(self.precision_)[np.newaxis]
partial_corrcoef = -self.precision_ / np.sqrt(diag.T @ diag)
partial_corrcoef.flat[::n_features + 1] = 1.
return partial_corrcoef
@property
def precision_(self):
return self.estimator_.precision_
def __init__(self,
alpha=0.01,
assume_centered=False,
contamination=0.1,
enet_tol=1e-04,
max_iter=100,
mode='cd',
tol=1e-04,
apcluster_params=None):
super().__init__(contamination=contamination)
self.alpha = alpha
self.apcluster_params = apcluster_params
self.assume_centered = assume_centered
self.enet_tol = enet_tol
self.max_iter = max_iter
self.mode = mode
self.tol = tol
def _check_is_fitted(self):
super()._check_is_fitted()
check_is_fitted(self, [
'covariance_', 'labels_', 'location_', 'n_iter_',
'partial_corrcoef_', 'precision_'
])
def _fit(self, X):
self.estimator_ = GraphLasso(alpha=self.alpha,
assume_centered=self.assume_centered,
enet_tol=self.enet_tol,
max_iter=self.max_iter,
mode=self.mode,
tol=self.tol).fit(X)
_, self.labels_ = affinity_propagation(self.partial_corrcoef_,
**self._apcluster_params)
return self
def _anomaly_score(self, X):
return self.estimator_.mahalanobis(X)
def featurewise_anomaly_score(self, X):
"""Compute the feature-wise anomaly scores for each sample.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data.
Returns
-------
anomaly_score : array-like of shape (n_samples, n_features)
Feature-wise anomaly scores for each sample.
"""
self._check_is_fitted()
X = self._check_array(X, estimator=self)
return 0.5 * np.log(2. * np.pi / np.diag(
self.precision_)) + 0.5 / np.diag(self.precision_) * (
(X - self.location_) @ self.precision_)**2
def score(self, X, y=None):
"""Compute the mean log-likelihood of the given data.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Data.
y : ignored
Returns
-------
score : float
Mean log-likelihood of the given data.
"""
self._check_is_fitted()
X = self._check_array(X, estimator=self)
return self.estimator_.score(X)
def plot_graphical_model(self, **kwargs):
"""Plot the Gaussian Graphical Model (GGM).
Parameters
----------
ax : matplotlib Axes, default None
Target axes instance.
figsize : tuple, default None
Tuple denoting figure size of the plot.
filename : str, default None
If provided, save the current figure.
random_state : int, RandomState instance, default None
Seed of the pseudo random number generator.
title : string, default 'GGM (n_clusters, n_features, n_isolates)'
Axes title. To disable, pass None.
**kwargs : dict
Other keywords passed to ``nx.draw_networkx``.
Returns
-------
ax : matplotlib Axes
Axes on which the plot was drawn.
"""
self._check_is_fitted()
n_clusters = np.max(self.labels_) + 1
n_isolates, = self.isolates_.shape
title = (f'GGM ('
f'n_clusters={n_clusters}, '
f'n_features={self.n_features_}, '
f'n_isolates={n_isolates}'
f')')
kwargs['G'] = self.graphical_model_
kwargs.setdefault('node_color', self.labels_)
kwargs.setdefault('title', title)
return plot_graphical_model(**kwargs)
def plot_partial_corrcoef(self, **kwargs):
"""Plot the partial correlation coefficient matrix.
Parameters
----------
ax : matplotlib Axes, default None
Target axes instance.
cbar : bool, default True.
If Ture, to draw a colorbar.
figsize : tuple, default None
Tuple denoting figure size of the plot.
filename : str, default None
If provided, save the current figure.
title : string, default 'Partial correlation'
Axes title. To disable, pass None.
**kwargs : dict
Other keywords passed to ``ax.pcolormesh``.
Returns
-------
ax : matplotlib Axes
Axes on which the plot was drawn.
"""
self._check_is_fitted()
kwargs['partial_corrcoef'] = self.partial_corrcoef_
return plot_partial_corrcoef(**kwargs)
def get_conn_matrix(time_series, conn_model):
import warnings
warnings.simplefilter("ignore")
from nilearn.connectome import ConnectivityMeasure
from sklearn.covariance import GraphLassoCV
try:
from brainiak.fcma.util import compute_correlation
except ImportError:
pass
if conn_model == 'corr':
# credit: nilearn
print('\nComputing correlation matrix...\n')
conn_measure = ConnectivityMeasure(kind='correlation')
conn_matrix = conn_measure.fit_transform([time_series])[0]
elif conn_model == 'corr_fast':
# credit: brainiak
try:
print('\nComputing accelerated fcma correlation matrix...\n')
conn_matrix = compute_correlation(time_series, time_series)
except RuntimeError:
print(
'Cannot run accelerated correlation computation due to a missing dependency. You need brainiak installed!'
)
elif conn_model == 'partcorr':
# credit: nilearn
print('\nComputing partial correlation matrix...\n')
conn_measure = ConnectivityMeasure(kind='partial correlation')
conn_matrix = conn_measure.fit_transform([time_series])[0]
elif conn_model == 'tangent':
# credit: nilearn
print('\nComputing tangent matrix...\n')
conn_measure = ConnectivityMeasure(kind='tangent')
conn_matrix = conn_measure.fit_transform([time_series])[0]
elif conn_model == 'cov' or conn_model == 'sps':
##Fit estimator to matrix to get sparse matrix
estimator = GraphLassoCV()
try:
print('\nComputing covariance...\n')
estimator.fit(time_series)
except:
try:
print(
'Unstable Lasso estimation--Attempting to re-run by first applying shrinkage...'
)
from sklearn.covariance import GraphLasso, empirical_covariance, shrunk_covariance
emp_cov = empirical_covariance(time_series)
for i in np.arange(0.8, 0.99, 0.01):
shrunk_cov = shrunk_covariance(emp_cov, shrinkage=i)
alphaRange = 10.0**np.arange(-8, 0)
for alpha in alphaRange:
try:
estimator_shrunk = GraphLasso(alpha)
estimator_shrunk.fit(shrunk_cov)
print(
"Calculated graph-lasso covariance matrix for alpha=%s"
% alpha)
break
except FloatingPointError:
print("Failed at alpha=%s" % alpha)
if estimator_shrunk == None:
pass
else:
break
except:
raise ValueError(
'Unstable Lasso estimation! Shrinkage failed.')
if conn_model == 'sps':
try:
print(
'\nFetching precision matrix from covariance estimator...\n'
)
conn_matrix = -estimator.precision_
except:
print(
'\nFetching shrunk precision matrix from covariance estimator...\n'
)
conn_matrix = -estimator_shrunk.precision_
elif conn_model == 'cov':
try:
print(
'\nFetching covariance matrix from covariance estimator...\n'
)
conn_matrix = estimator.covariance_
except:
conn_matrix = estimator_shrunk.covariance_
elif conn_model == 'QuicGraphLasso':
from inverse_covariance import QuicGraphLasso
# Compute the sparse inverse covariance via QuicGraphLasso
# credit: skggm
model = QuicGraphLasso(init_method='cov',
lam=0.5,
mode='default',
verbose=1)
print('\nCalculating QuicGraphLasso precision matrix using skggm...\n')
model.fit(time_series)
conn_matrix = -model.precision_
elif conn_model == 'QuicGraphLassoCV':
from inverse_covariance import QuicGraphLassoCV
# Compute the sparse inverse covariance via QuicGraphLassoCV
# credit: skggm
model = QuicGraphLassoCV(init_method='cov', verbose=1)
print(
'\nCalculating QuicGraphLassoCV precision matrix using skggm...\n')
model.fit(time_series)
conn_matrix = -model.precision_
elif conn_model == 'QuicGraphLassoEBIC':
from inverse_covariance import QuicGraphLassoEBIC
# Compute the sparse inverse covariance via QuicGraphLassoEBIC
# credit: skggm
model = QuicGraphLassoEBIC(init_method='cov', verbose=1)
print(
'\nCalculating QuicGraphLassoEBIC precision matrix using skggm...\n'
)
model.fit(time_series)
conn_matrix = -model.precision_
elif conn_model == 'AdaptiveQuicGraphLasso':
from inverse_covariance import AdaptiveGraphLasso, QuicGraphLassoEBIC
# Compute the sparse inverse covariance via
# AdaptiveGraphLasso + QuicGraphLassoEBIC + method='binary'
# credit: skggm
model = AdaptiveGraphLasso(
estimator=QuicGraphLassoEBIC(init_method='cov', ),
method='binary',
)
print(
'\nCalculating AdaptiveQuicGraphLasso precision matrix using skggm...\n'
)
model.fit(time_series)
conn_matrix = -model.estimator_.precision_
return (conn_matrix)
def get_conn_matrix(time_series, conn_model, dir_path, node_size, smooth,
dens_thresh, network, ID, mask, min_span_tree, disp_filt,
parc, prune, atlas_select, uatlas_select, label_names,
coords, vox_array):
from nilearn.connectome import ConnectivityMeasure
from sklearn.covariance import GraphLassoCV
conn_matrix = None
if conn_model == 'corr':
# credit: nilearn
print('\nComputing correlation matrix...\n')
conn_measure = ConnectivityMeasure(kind='correlation')
conn_matrix = conn_measure.fit_transform([time_series])[0]
elif conn_model == 'partcorr':
# credit: nilearn
print('\nComputing partial correlation matrix...\n')
conn_measure = ConnectivityMeasure(kind='partial correlation')
conn_matrix = conn_measure.fit_transform([time_series])[0]
elif conn_model == 'cov' or conn_model == 'sps':
# Fit estimator to matrix to get sparse matrix
estimator_shrunk = None
estimator = GraphLassoCV()
try:
print('\nComputing covariance...\n')
estimator.fit(time_series)
except:
print(
'Unstable Lasso estimation--Attempting to re-run by first applying shrinkage...'
)
try:
from sklearn.covariance import GraphLasso, empirical_covariance, shrunk_covariance
emp_cov = empirical_covariance(time_series)
for i in np.arange(0.8, 0.99, 0.01):
shrunk_cov = shrunk_covariance(emp_cov, shrinkage=i)
alphaRange = 10.0**np.arange(-8, 0)
for alpha in alphaRange:
try:
estimator_shrunk = GraphLasso(alpha)
estimator_shrunk.fit(shrunk_cov)
print(
"Retrying covariance matrix estimate with alpha=%s"
% alpha)
if estimator_shrunk is None:
pass
else:
break
except:
print(
"Covariance estimation failed with shrinkage at alpha=%s"
% alpha)
continue
except ValueError:
print(
'Unstable Lasso estimation! Shrinkage failed. A different connectivity model may be needed.'
)
if estimator is None and estimator_shrunk is None:
raise RuntimeError('ERROR: Covariance estimation failed.')
if conn_model == 'sps':
if estimator_shrunk is None:
print(
'\nFetching precision matrix from covariance estimator...\n'
)
conn_matrix = -estimator.precision_
else:
print(
'\nFetching shrunk precision matrix from covariance estimator...\n'
)
conn_matrix = -estimator_shrunk.precision_
elif conn_model == 'cov':
if estimator_shrunk is None:
print(
'\nFetching covariance matrix from covariance estimator...\n'
)
conn_matrix = estimator.covariance_
else:
conn_matrix = estimator_shrunk.covariance_
elif conn_model == 'QuicGraphicalLasso':
try:
from inverse_covariance import QuicGraphicalLasso
except ImportError:
print('Cannot run QuicGraphLasso. Skggm not installed!')
# Compute the sparse inverse covariance via QuicGraphLasso
# credit: skggm
model = QuicGraphicalLasso(init_method='cov',
lam=0.5,
mode='default',
verbose=1)
print('\nCalculating QuicGraphLasso precision matrix using skggm...\n')
model.fit(time_series)
conn_matrix = -model.precision_
elif conn_model == 'QuicGraphLassoCV':
try:
from inverse_covariance import QuicGraphicalLassoCV
except ImportError:
print('Cannot run QuicGraphLassoCV. Skggm not installed!')
# Compute the sparse inverse covariance via QuicGraphLassoCV
# credit: skggm
model = QuicGraphicalLassoCV(init_method='cov', verbose=1)
print(
'\nCalculating QuicGraphLassoCV precision matrix using skggm...\n')
model.fit(time_series)
conn_matrix = -model.precision_
elif conn_model == 'QuicGraphicalLassoEBIC':
try:
from inverse_covariance import QuicGraphicalLassoEBIC
except ImportError:
print('Cannot run QuicGraphLassoEBIC. Skggm not installed!')
# Compute the sparse inverse covariance via QuicGraphLassoEBIC
# credit: skggm
model = QuicGraphicalLassoEBIC(init_method='cov', verbose=1)
print(
'\nCalculating QuicGraphLassoEBIC precision matrix using skggm...\n'
)
model.fit(time_series)
conn_matrix = -model.precision_
elif conn_model == 'AdaptiveQuicGraphLasso':
try:
from inverse_covariance import AdaptiveQuicGraphicalLasso, QuicGraphicalLassoEBIC
except ImportError:
print('Cannot run AdaptiveGraphLasso. Skggm not installed!')
# Compute the sparse inverse covariance via
# AdaptiveGraphLasso + QuicGraphLassoEBIC + method='binary'
# credit: skggm
model = AdaptiveQuicGraphicalLasso(
estimator=QuicGraphicalLassoEBIC(init_method='cov', ),
method='binary',
)
print(
'\nCalculating AdaptiveQuicGraphLasso precision matrix using skggm...\n'
)
model.fit(time_series)
conn_matrix = -model.estimator_.precision_
# Weight reuslting matrix by voxels in each label if using parcels as nodes
# if parc is True:
# norm_parcels = (vox_array - min(vox_array)) / (max(vox_array) - min(vox_array))
# conn_matrix_norm = normalize(conn_matrix)
# conn_matrix = norm_parcels * conn_matrix_norm
coords = np.array(coords)
label_names = np.array(label_names)
return conn_matrix, conn_model, dir_path, node_size, smooth, dens_thresh, network, ID, mask, min_span_tree, disp_filt, parc, prune, atlas_select, uatlas_select, label_names, coords
re_error_750_ppca = getReconstructionError(sample_750, re_750points_ppca.T)
drawReconstructionError(re_error_750_ppca)
plt.title('reconstruction error of 750 points of PPCA')
plt.show()
#reconstruct the 250 points
sample_250 = sample[750:1000]
re_250points_ppca = W_ppca.dot(sample_250.T)
re_250points_ppca = getPPCAInverseTransform(re_250points_ppca.T)
re_error_250_ppca = getReconstructionError(sample_250, re_250points_ppca.T)
drawReconstructionError(re_error_250_ppca)
plt.title('reconstruction error of 250 points of PPCA')
plt.show()
#==============================================================================
#problem 2.9
#==============================================================================
from sklearn.covariance import GraphLasso
gl = GraphLasso(0.01)
gl.fit(sample_750)
cov_gl = gl.covariance_
covarianceMatrix(cov_gl)
plt.title('convariance matrix of GL')
plt.show()
prec_gl = gl.get_precision()
covarianceMatrix(prec_gl)
plt.title('inverse convariance matrix of GL')
plt.show()
def get_conn_matrix(time_series, conn_model):
from nilearn.connectome import ConnectivityMeasure
from sklearn.covariance import GraphLassoCV
conn_matrix = None
if conn_model == 'corr':
# credit: nilearn
print('\nComputing correlation matrix...\n')
conn_measure = ConnectivityMeasure(kind='correlation')
conn_matrix = conn_measure.fit_transform([time_series])[0]
elif conn_model == 'partcorr':
# credit: nilearn
print('\nComputing partial correlation matrix...\n')
conn_measure = ConnectivityMeasure(kind='partial correlation')
conn_matrix = conn_measure.fit_transform([time_series])[0]
elif conn_model == 'cov' or conn_model == 'sps':
# Fit estimator to matrix to get sparse matrix
estimator_shrunk = None
estimator = GraphLassoCV()
try:
print('\nComputing covariance...\n')
estimator.fit(time_series)
except RuntimeWarning:
print('Unstable Lasso estimation--Attempting to re-run by first applying shrinkage...')
try:
from sklearn.covariance import GraphLasso, empirical_covariance, shrunk_covariance
emp_cov = empirical_covariance(time_series)
for i in np.arange(0.8, 0.99, 0.01):
shrunk_cov = shrunk_covariance(emp_cov, shrinkage=i)
alphaRange = 10.0 ** np.arange(-8, 0)
for alpha in alphaRange:
try:
estimator_shrunk = GraphLasso(alpha)
estimator_shrunk.fit(shrunk_cov)
print("Retrying covariance matrix estimate with alpha=%s" % alpha)
if estimator_shrunk is None:
pass
else:
break
except RuntimeWarning:
print("Covariance estimation failed with shrinkage at alpha=%s" % alpha)
continue
except ValueError:
print('Unstable Lasso estimation! Shrinkage failed. A different connectivity model may be needed.')
if estimator is None and estimator_shrunk is None:
raise RuntimeError('ERROR: Covariance estimation failed.')
if conn_model == 'sps':
if estimator_shrunk is None:
print('\nFetching precision matrix from covariance estimator...\n')
conn_matrix = -estimator.precision_
else:
print('\nFetching shrunk precision matrix from covariance estimator...\n')
conn_matrix = -estimator_shrunk.precision_
elif conn_model == 'cov':
if estimator_shrunk is None:
print('\nFetching covariance matrix from covariance estimator...\n')
conn_matrix = estimator.covariance_
else:
conn_matrix = estimator_shrunk.covariance_
elif conn_model == 'QuicGraphLasso':
from inverse_covariance import QuicGraphLasso
# Compute the sparse inverse covariance via QuicGraphLasso
# credit: skggm
model = QuicGraphLasso(
init_method='cov',
lam=0.5,
mode='default',
verbose=1)
print('\nCalculating QuicGraphLasso precision matrix using skggm...\n')
model.fit(time_series)
conn_matrix = -model.precision_
elif conn_model == 'QuicGraphLassoCV':
from inverse_covariance import QuicGraphLassoCV
# Compute the sparse inverse covariance via QuicGraphLassoCV
# credit: skggm
model = QuicGraphLassoCV(
init_method='cov',
verbose=1)
print('\nCalculating QuicGraphLassoCV precision matrix using skggm...\n')
model.fit(time_series)
conn_matrix = -model.precision_
elif conn_model == 'QuicGraphLassoEBIC':
from inverse_covariance import QuicGraphLassoEBIC
# Compute the sparse inverse covariance via QuicGraphLassoEBIC
# credit: skggm
model = QuicGraphLassoEBIC(
init_method='cov',
verbose=1)
print('\nCalculating QuicGraphLassoEBIC precision matrix using skggm...\n')
model.fit(time_series)
conn_matrix = -model.precision_
elif conn_model == 'AdaptiveQuicGraphLasso':
from inverse_covariance import AdaptiveGraphLasso, QuicGraphLassoEBIC
# Compute the sparse inverse covariance via
# AdaptiveGraphLasso + QuicGraphLassoEBIC + method='binary'
# credit: skggm
model = AdaptiveGraphLasso(
estimator=QuicGraphLassoEBIC(
init_method='cov',
),
method='binary',
)
print('\nCalculating AdaptiveQuicGraphLasso precision matrix using skggm...\n')
model.fit(time_series)
conn_matrix = -model.estimator_.precision_
return conn_matrix
sb_expression = pd.read_table(
"https://homes.cs.washington.edu/~suinlee/cse527/notes/yeast-comparison/sbay-expression.txt",
header=None)
conserved_gene = pd.read_table(
"https://homes.cs.washington.edu/~suinlee/cse527/notes/yeast-comparison/conserved-genes.txt",
header=None)
sc_experiment = pd.read_table(
"https://homes.cs.washington.edu/~suinlee/cse527/notes/yeast-comparison/scer-experiments.txt",
header=None)
sb_experiment = pd.read_table(
"https://homes.cs.washington.edu/~suinlee/cse527/notes/yeast-comparison/sbay-experiments.txt",
header=None)
#%%
#data normalization
sb_data = sb_expression.values.T
#sb_normdata = normalize(sb_data, axis=1)
means = np.mean(sb_data, axis=0)
stds = np.std(sb_data, axis=0)
sb_normdata = np.divide(np.subtract(sb_data, means), stds)
#%%
GL_sb = GraphLasso(alpha=1)
tic = time.time()
GL_sb.fit(sb_expression.values.T)
toc = time.time()
time1 = toc - tic
print(time1)
perc_sb = GL_sb.precision_
np.save('perc_sb.npy', perc_sb)
NCols = len(ProteinNames)
#Import the data and convert to a numpy array
X = open(os.path.join('data', 'sachsCtsHTF.txt'), 'r').read().split()
X = [float(x) for x in X]
X = np.array(X).reshape(-1, NCols)
X -= X.mean(axis=0).reshape(1, -1)
X /= np.sqrt(
1000
) #same as http://www-stat.stanford.edu/~tibs/ElemStatLearn/datasets/sachs.info
#Regularization parameters
Lambs = [36, 27, 7, 0]
for lam in Lambs:
GL = GraphLasso(lam)
GL.fit(X)
prec = GL.precision_
#Form graph
G = nx.Graph()
G.add_nodes_from(ProteinNames)
for i in range(NCols):
for j in range(i):
if prec[i, j] != 0:
G.add_edges_from([(ProteinNames[i], ProteinNames[j])])
ttl = 'lambda {}, nedges {}'.format(lam, len(G.edges))
print(ttl)
def estimatePrecisionFromSet(data, alpha):
model = GraphLasso(alpha=alpha)
model.fit(data)
return sp.csc_matrix(model.precision_)
def estimatePrecisionFromFile(file, dims, sparsity):
data = pd.read_csv(file)
model = GraphLasso()
model.fit(data)
return sp.csc_matrix(model.precision_)
# compute the empirical covariance matrix
C_emp = X.dot(X.T) / float(N)
print('Empirical Cov:')
print C_emp
# neighborhood selection
nhs = NeighborSelect(EDPP(),
ProximalGradientSolver(),
path_lb=0.2,
path_steps=5,
path_scale='log')
Cb = nhs.fit(np.ascontiguousarray(X))
print Cb
glasso = GraphLasso(alpha=0.005, tol=0.0001, max_iter=1000, verbose=False)
glasso.fit(X.T)
C = glasso.get_precision()
print glasso.error_norm(COV)
print('GraphLasso Cov:')
print C
# plot some example network
plt.figure()
plt.subplot(2, len(Cb), 1)
plt.title('Cov')
plt.pcolor(COV)
plt.subplot(2, len(Cb), 2)
plt.title('Emp. Cov')
plt.pcolor(C_emp)
'VLO', 'BAC', 'K', 'PFE', 'XRX', 'AIG', 'PEP', 'KO', 'PG', 'MCD', 'WMT',
'JPM', 'C', 'WFC', 'GE', 'T', 'VZ', 'IBM', 'MSFT', 'GOOG', 'AAPL', 'RIMM',
"^DJA", "CSCO", "YHOO", "ORCL", "SNDK", "DELL", "NVDA", "EBAY", "WIN",
"WFM", "WHR", "WU", "WAG", "VMC", "UTX", "UNP", "USB", "TSN", "TMO", "TXT",
"TXN", "TSO", "SYY", "SBUX", "SWK", "LUV", "CMCSA", "AMD", "S", "INTC",
"VXX", "^GSPC"
]
start_data = datetime.datetime(2010, 01, 03)
symbols, data = get_data(symbols, start_data)
close_data = np.concatenate(
[absolute_daily_returns(data[ts])[:, None] for ts in symbols], axis=1)
alpha = 0.47
print "alpha: ", alpha
gl = GraphLasso(alpha)
nclose_data = scale(close_data)
gl.fit(nclose_data)
#remove the SP500
cov_sp = gl.covariance_[:, :-1].T[:, :-1]
prec_sp = gl.precision_[:, :-1].T[:, :-1]
def community_cluster(cov_sp, symbols):
G = nx.Graph(cov_sp)
partition = community.best_partition(G)
for i in set(partition.values()):
print "Community: ", i
members = [
symbols[node] for node in partition.keys() if partition[node] == i
def grangercausalitytests(x,
mxlg,
autolag=None,
alpha=0.0001,
max_iter=1000,
addconst=True,
verbose=True):
"""four tests for granger non causality of 2 timeseries
all four tests give similar results
`params_ftest` and `ssr_ftest` are equivalent based on F test which is
identical to lmtest:grangertest in R
Parameters
----------
x : array, 2d
data for test whether the time series in the second column Granger
causes the time series in the first column
lags : list of integers
the Granger causality test results are calculated for all lags in the list
autolag: If 'aic' the lag which minimizes the information criterion is used
from the lags
verbose : bool
print results if true
Returns
-------
results : dictionary
all test results, dictionary keys are the number of lags. For each
lag the values are a tuple, with the first element a dictionary with
teststatistic, pvalues, degrees of freedom, the second element are
the OLS estimation results for the restricted model, the unrestricted
model and the restriction (contrast) matrix for the parameter f_test.
Notes
-----
TODO: convert to class and attach results properly
The Null hypothesis for grangercausalitytests is that the time series in
the second column, x2, does NOT Granger cause the time series in the first
column, x1. Grange causality means that past values of x2 have a
statistically significant effect on the current value of x1, taking past
values of x1 into account as regressors. We reject the null hypothesis
that x2 does not Granger cause x1 if the pvalues are below a desired size
of the test.
The null hypothesis for all four test is that the coefficients
corresponding to past values of the second time series are zero.
'params_ftest', 'ssr_ftest' are based on F distribution
'ssr_chi2test', 'lrtest' are based on chi-square distribution
References
----------
http://en.wikipedia.org/wiki/Granger_causality
Greene: Econometric Analysis
"""
from scipy import stats
from sklearn.covariance import GraphLasso
x = np.asarray(x)
if x.shape[0] <= 3 * mxlg + int(addconst):
raise ValueError(
"Insufficient observations. Maximum allowable "
"lag is {0}".format(int((x.shape[0] - int(addconst)) / 3) - 1))
result = {}
if verbose:
print('\nGranger Causality')
print('number of lags (no zero)', mxlg)
# create lagmat of both time series
dta = lagmat2ds(x, mxlg, trim='both', dropex=1)
#add constant
if addconst:
dtajoint = add_constant(dta[:, :], prepend=False)
else:
raise NotImplementedError('Not Implemented')
# Run Lasso on all variables
lassoreg = GraphLasso(alpha=alpha, max_iter=100)
lassoreg.fit(dtajoint)
result = lassoreg.covariance_
#non_zeros = [i for i, x in enumerate(result) if x != 0]
#non_zero_vars = set([(i+1)/(mxlg+1) for i in non_zeros])
return result
