def test_correlation_nans():
row1 = 5
col = 10
row2 = 6
mat1 = prng.rand(row1, col).astype(np.float32)
mat2 = prng.rand(row2, col).astype(np.float32)
mat1[0, 0] = np.nan
corr = compute_correlation(mat1, mat2, return_nans=False)
assert np.all(corr == 0, axis=1)[0]
assert np.sum(corr == 0) == row2
corr = compute_correlation(mat1, mat2, return_nans=True)
assert np.all(np.isnan(corr), axis=1)[0]
assert np.sum(np.isnan(corr)) == row2
def test_correlation_computation():
row1 = 5
col = 10
row2 = 6
mat1 = prng.rand(row1, col).astype(np.float32)
mat2 = prng.rand(row2, col).astype(np.float32)
corr = compute_correlation(mat1, mat1)
expected_corr = np.corrcoef(mat1)
assert np.allclose(corr, expected_corr, atol=1e-5), \
'high performance correlation computation does not provide correct correlation results within the same set'
corr = compute_correlation(mat1, mat2)
mat = np.concatenate((mat1, mat2), axis=0)
expected_corr = np.corrcoef(mat)[0:row1, row1:]
assert np.allclose(corr, expected_corr, atol=1e-5), \
'high performance correlation computation does not provide correct correlation results between two sets'
def test_correlation_computation():
row1 = 5
col = 10
row2 = 6
mat1 = prng.rand(row1, col).astype(np.float32)
mat2 = prng.rand(row2, col).astype(np.float32)
corr = compute_correlation(mat1, mat1)
expected_corr = np.corrcoef(mat1)
assert np.allclose(corr, expected_corr, atol=1e-5), \
'high performance correlation computation does not provide correct correlation results within the same set'
corr = compute_correlation(mat1, mat2)
mat = np.concatenate((mat1, mat2), axis=0)
expected_corr = np.corrcoef(mat)[0:row1, row1:]
assert np.allclose(corr, expected_corr, atol=1e-5), \
'high performance correlation computation does not provide correct correlation results between two sets'
def time_segment_matching_accuracy(data, win_size=6):
nsubjs = len(data)
(ndim, nsample) = data[0].shape
accu = np.zeros(shape=nsubjs)
nseg = nsample - win_size
# mysseg prediction prediction
trn_data = np.zeros((ndim*win_size, nseg),order='f')
# the trn data also include the tst data, but will be subtracted when
# calculating A
for m in range(nsubjs):
for w in range(win_size):
trn_data[w*ndim:(w+1)*ndim,:] += data[m][:,w:(w+nseg)]
for tst_subj in range(nsubjs):
tst_data = np.zeros((ndim*win_size, nseg),order='f')
for w in range(win_size):
tst_data[w*ndim:(w+1)*ndim,:] = data[tst_subj][:,w:(w+nseg)]
A = np.nan_to_num(stats.zscore((trn_data - tst_data),axis=0, ddof=1))
B = np.nan_to_num(stats.zscore(tst_data,axis=0, ddof=1))
# compute correlation matrix
corr_mtx = compute_correlation(B.T,A.T)
for i in range(nseg):
for j in range(nseg):
if abs(i-j)<win_size and i != j :
corr_mtx[i,j] = -np.inf
max_idx = np.argmax(corr_mtx, axis=1)
accu[tst_subj] = sum(max_idx == range(nseg)) / float(nseg)
return accu
def get_conn_matrix(time_series, conn_model, NETWORK, ID, dir_path, thr):
if conn_model == 'corr':
conn_measure = ConnectivityMeasure(kind='correlation')
conn_matrix = conn_measure.fit_transform([time_series])[0]
est_path = dir_path + '/' + ID + '_est_corr' + '_' + str(thr) + '.txt'
elif conn_model == 'corr_fast':
try:
conn_matrix = compute_correlation(time_series,time_series)
est_path = dir_path + '/' + ID + '_est_corr_fast' + '_' + str(thr) + '.txt'
except RuntimeError:
print('Cannot run accelerated correlation computation due to a missing dependency. You need brainiak installed!')
elif conn_model == 'partcorr':
conn_measure = ConnectivityMeasure(kind='partial correlation')
conn_matrix = conn_measure.fit_transform([time_series])[0]
est_path = dir_path + '/' + ID + '_est_part_corr' + '_' + str(thr) + '.txt'
elif conn_model == 'cov' or conn_model == 'sps':
##Fit estimator to matrix to get sparse matrix
estimator = GraphLassoCV()
try:
print("Fitting Lasso estimator...")
est = estimator.fit(time_series)
except RuntimeError:
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)
#est=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
print('Unstable Lasso estimation. Try again!')
sys.exit()
if NETWORK != None:
est_path = dir_path + '/' + ID + '_' + NETWORK + '_est%s'%('_sps_inv' if conn_model=='sps' else 'cov') + '_' + str(thr) + '.txt'
else:
est_path = dir_path + '/' + ID + '_est%s'%('_sps_inv' if conn_model=='sps' else 'cov') + '_' + str(thr) + '.txt'
if conn_model == 'sps':
try:
conn_matrix = -estimator.precision_
except:
conn_matrix = -estimator_shrunk.precision_
elif conn_model == 'cov':
try:
conn_matrix = estimator.covariance_
except:
conn_matrix = estimator_shrunk.covariance_
np.savetxt(est_path, conn_matrix, delimiter='\t')
return(conn_matrix, est_path)
def isfc(D, collapse_subj=True):
"""Intersubject functional correlation
Computes the correlation between the timecoure of each voxel in each
subject with the average of all other subjects' timecourses in *all*
voxels. By default the result is averaged across subjects, unless
collapse_subj is set to False.
Uses the high performance compute_correlation routine from fcma.util
Parameters
----------
D : voxel by time by subject ndarray
fMRI data for which to compute ISFC
collapse_subj : bool, default:True
Whether to average across subjects before returning result
Returns
-------
ISFC : voxel by voxel ndarray
(or voxel by voxel by subject ndarray, if collapse_subj=False)
pearson correlation between all pairs of voxels, across subjects
"""
n_vox = D.shape[0]
n_subj = D.shape[2]
ISFC = np.zeros((n_vox, n_vox, n_subj))
# Loop across choice of leave-one-out subject
for loo_subj in range(D.shape[2]):
group = np.mean(D[:, :, np.arange(n_subj) != loo_subj], axis=2)
subj = D[:, :, loo_subj]
ISFC[:, :, loo_subj] = compute_correlation(group, subj)
# Symmetrize matrix
ISFC[:, :,
loo_subj] = (ISFC[:, :, loo_subj] + ISFC[:, :, loo_subj].T) / 2
if collapse_subj:
ISFC = np.mean(ISFC, axis=2)
return ISFC
def isfc(D, collapse_subj=True):
"""Intersubject functional correlation
Computes the correlation between the timecoure of each voxel in each
subject with the average of all other subjects' timecourses in *all*
voxels. By default the result is averaged across subjects, unless
collapse_subj is set to False.
Uses the high performance compute_correlation routine from fcma.util
Parameters
----------
D : voxel by time by subject ndarray
fMRI data for which to compute ISFC
collapse_subj : bool, default:True
Whether to average across subjects before returning result
Returns
-------
ISFC : voxel by voxel ndarray
(or voxel by voxel by subject ndarray, if collapse_subj=False)
pearson correlation between all pairs of voxels, across subjects
"""
n_vox = D.shape[0]
n_subj = D.shape[2]
ISFC = np.zeros((n_vox, n_vox, n_subj))
# Loop across choice of leave-one-out subject
for loo_subj in range(D.shape[2]):
group = np.mean(D[:, :, np.arange(n_subj) != loo_subj], axis=2)
subj = D[:, :, loo_subj]
ISFC[:, :, loo_subj] = compute_correlation(group, subj)
# Symmetrize matrix
ISFC[:, :, loo_subj] = (ISFC[:, :, loo_subj] +
ISFC[:, :, loo_subj].T) / 2
if collapse_subj:
ISFC = np.mean(ISFC, axis=2)
return ISFC
sys.exit(1)
data_dir = sys.argv[1]
extension = sys.argv[2]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
images = dataset.load_images_from_dir(data_dir, extension)
mask = dataset.load_boolean_mask(mask_file)
conditions = dataset.load_labels(epoch_file)
(raw_data, ) = image.multimask_images(images, (mask, ))
epoch_info = generate_epochs_info(conditions)
for idx, epoch in enumerate(epoch_info):
label = epoch[0]
sid = epoch[1]
start = epoch[2]
end = epoch[3]
mat = raw_data[sid][:, start:end]
mat = np.ascontiguousarray(mat, dtype=np.float32)
logger.info(
'start to compute correlation for subject %d epoch %d with label %d'
% (sid, idx, label))
corr = compute_correlation(mat, mat)
mdict = {}
mdict['corr'] = corr
filename = str(label) + '_' + str(sid) + '_' + str(idx)
logger.info('start to write the correlation matrix to disk as %s' %
filename)
scipy.io.savemat(filename, mdict)
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)
extension = sys.argv[2]
mask_file = sys.argv[3]
epoch_file = sys.argv[4]
images = dataset.load_images_from_dir(data_dir, extension)
mask = dataset.load_boolean_mask(mask_file)
conditions = dataset.load_labels(epoch_file)
(raw_data,) = image.multimask_images(images, (mask,))
epoch_info = generate_epochs_info(conditions)
for idx, epoch in enumerate(epoch_info):
label = epoch[0]
sid = epoch[1]
start = epoch[2]
end = epoch[3]
mat = raw_data[sid][:, start:end]
mat = np.ascontiguousarray(mat, dtype=np.float32)
logger.info(
'start to compute correlation for subject %d epoch %d with label %d' %
(sid, idx, label)
)
corr = compute_correlation(mat, mat)
mdict = {}
mdict['corr'] = corr
filename = str(label) + '_' + str(sid) + '_' + str(idx)
logger.info(
'start to write the correlation matrix to disk as %s' %
filename
)
scipy.io.savemat(filename, mdict)
def isfc(D, collapse_subj=True, return_p=False,
num_perm=1000, two_sided=False, random_state=0):
"""Intersubject functional correlation
Computes the correlation between the timecoure of each voxel in each
subject with the average of all other subjects' timecourses in *all*
voxels. By default the result is averaged across subjects, unless
collapse_subj is set to False. A null distribution can optionally be
computed using phase randomization, to compute a p value for each voxel-to-
voxel correlation.
Uses the high performance compute_correlation routine from fcma.util
Parameters
----------
D : voxel by time by subject ndarray
fMRI data for which to compute ISFC
collapse_subj : bool, default:True
Whether to average across subjects before returning result
return_p : bool, default:False
Whether to use phase randomization to compute a p value for each voxel
num_perm : int, default:1000
Number of null samples to use for computing p values
two_sided : bool, default:False
Whether the p value should be one-sided (testing only for being
above the null) or two-sided (testing for both significantly positive
and significantly negative values)
random_state : RandomState or an int seed (0 by default)
A random number generator instance to define the state of the
random permutations generator.
Returns
-------
ISFC : voxel by voxel ndarray
(or voxel by voxel by subject ndarray, if collapse_subj=False)
pearson correlation between all pairs of voxels, across subjects
p : ndarray the same shape as ISC (if return_p = True)
p values for each ISC value under the null distribution
"""
n_vox = D.shape[0]
n_subj = D.shape[2]
if return_p:
n_perm = num_perm
else:
n_perm = 0
ISFC = np.zeros((n_vox, n_vox, n_subj, n_perm + 1))
for p in range(n_perm + 1):
# Loop across choice of leave-one-out subject
for loo_subj in range(D.shape[2]):
group = np.mean(D[:, :, np.arange(n_subj) != loo_subj], axis=2)
subj = D[:, :, loo_subj]
ISFC[:, :, loo_subj, p] = compute_correlation(group, subj)
# Symmetrize matrix
ISFC[:, :, loo_subj, p] = (ISFC[:, :, loo_subj, p] +
ISFC[:, :, loo_subj, p].T) / 2
# Randomize phases of D to create next null dataset
D = phase_randomize(D, random_state)
if collapse_subj:
ISFC = np.mean(ISFC, axis=2)
if return_p:
p = p_from_null(ISFC, two_sided)
return ISFC[..., 0], p
else:
return ISFC[..., 0]
def isfc(D, collapse_subj=True, return_p=False, num_perm=1000,
two_sided=False, random_state=0, float_type=np.float64):
"""Intersubject functional correlation
Computes the correlation between the timecoure of each voxel in each
subject with the average of all other subjects' timecourses in *all*
voxels. By default the result is averaged across subjects, unless
collapse_subj is set to False. A null distribution can optionally be
computed using phase randomization, to compute a p value for each voxel-to-
voxel correlation.
Uses the high performance compute_correlation routine from fcma.util
Parameters
----------
D : voxel by time by subject ndarray
fMRI data for which to compute ISFC
collapse_subj : bool, default:True
Whether to average across subjects before returning result
return_p : bool, default:False
Whether to use phase randomization to compute a p value for each voxel
num_perm : int, default:1000
Number of null samples to use for computing p values
two_sided : bool, default:False
Whether the p value should be one-sided (testing only for being
above the null) or two-sided (testing for both significantly positive
and significantly negative values)
random_state : RandomState or an int seed (0 by default)
A random number generator instance to define the state of the
random permutations generator.
float_type : either float16, float32, or float64
Depends on the required precision
and available memory in the system.
All the arrays generated during the execution will be cast
to specified float type in order to save memory.
Returns
-------
ISFC : voxel by voxel ndarray
(or voxel by voxel by subject ndarray, if collapse_subj=False)
pearson correlation between all pairs of voxels, across subjects
p : ndarray the same shape as ISC (if return_p = True)
p values for each ISC value under the null distribution
"""
n_vox = D.shape[0]
n_subj = D.shape[2]
n_perm = num_perm*int(return_p)
max_null = -np.ones(n_perm, dtype=float_type)
min_null = np.ones(n_perm, dtype=float_type)
ISFC = np.zeros((n_vox, n_vox, n_subj), dtype=float_type)
for loo_subj in range(D.shape[2]):
group = np.mean(D[:, :, np.arange(n_subj) != loo_subj], axis=2)
subj = D[:, :, loo_subj]
tmp_ISFC = compute_correlation(group, subj).astype(float_type)
# Symmetrize matrix
tmp_ISFC = (tmp_ISFC+tmp_ISFC.T)/2
ISFC[:, :, loo_subj] = tmp_ISFC
if collapse_subj:
ISFC = np.mean(ISFC, axis=2)
for p in range(n_perm):
# Randomize phases of D to create next null dataset
D = phase_randomize(D, random_state)
# Loop across choice of leave-one-out subject
ISFC_null = np.zeros((n_vox, n_vox), dtype=float_type)
for loo_subj in range(D.shape[2]):
group = np.mean(D[:, :, np.arange(n_subj) != loo_subj], axis=2)
subj = D[:, :, loo_subj]
tmp_ISFC = compute_correlation(group, subj).astype(float_type)
# Symmetrize matrix
tmp_ISFC = (tmp_ISFC+tmp_ISFC.T)/2
if not collapse_subj:
max_null[p] = max(np.max(tmp_ISFC), max_null[p])
min_null[p] = min(np.min(tmp_ISFC), min_null[p])
ISFC_null = ISFC_null + tmp_ISFC/n_subj
if collapse_subj:
max_null[p] = np.max(ISFC_null)
min_null[p] = np.min(ISFC_null)
if return_p:
p = p_from_null(ISFC, two_sided,
max_null_input=max_null,
min_null_input=min_null)
return ISFC, p
else:
return ISFC
def isfc(D,
collapse_subj=True,
return_p=False,
num_perm=1000,
two_sided=False,
random_state=0,
float_type=np.float64):
"""Intersubject functional correlation
Computes the correlation between the timecoure of each voxel in each
subject with the average of all other subjects' timecourses in *all*
voxels. By default the result is averaged across subjects, unless
collapse_subj is set to False. A null distribution can optionally be
computed using phase randomization, to compute a p value for each voxel-to-
voxel correlation.
Uses the high performance compute_correlation routine from fcma.util
Parameters
----------
D : voxel by time by subject ndarray
fMRI data for which to compute ISFC
collapse_subj : bool, default:True
Whether to average across subjects before returning result
return_p : bool, default:False
Whether to use phase randomization to compute a p value for each voxel
num_perm : int, default:1000
Number of null samples to use for computing p values
two_sided : bool, default:False
Whether the p value should be one-sided (testing only for being
above the null) or two-sided (testing for both significantly positive
and significantly negative values)
random_state : RandomState or an int seed (0 by default)
A random number generator instance to define the state of the
random permutations generator.
float_type : either float16, float32, or float64
Depends on the required precision
and available memory in the system.
All the arrays generated during the execution will be cast
to specified float type in order to save memory.
Returns
-------
ISFC : voxel by voxel ndarray
(or voxel by voxel by subject ndarray, if collapse_subj=False)
pearson correlation between all pairs of voxels, across subjects
p : ndarray the same shape as ISC (if return_p = True)
p values for each ISC value under the null distribution
"""
n_vox = D.shape[0]
n_subj = D.shape[2]
n_perm = num_perm * int(return_p)
max_null = -np.ones(n_perm, dtype=float_type)
min_null = np.ones(n_perm, dtype=float_type)
ISFC = np.zeros((n_vox, n_vox, n_subj), dtype=float_type)
for loo_subj in range(D.shape[2]):
group = np.mean(D[:, :, np.arange(n_subj) != loo_subj], axis=2)
subj = D[:, :, loo_subj]
tmp_ISFC = compute_correlation(group, subj).astype(float_type)
# Symmetrize matrix
tmp_ISFC = (tmp_ISFC + tmp_ISFC.T) / 2
ISFC[:, :, loo_subj] = tmp_ISFC
if collapse_subj:
ISFC = np.mean(ISFC, axis=2)
for p in range(n_perm):
# Randomize phases of D to create next null dataset
D = phase_randomize(D, random_state)
# Loop across choice of leave-one-out subject
ISFC_null = np.zeros((n_vox, n_vox), dtype=float_type)
for loo_subj in range(D.shape[2]):
group = np.mean(D[:, :, np.arange(n_subj) != loo_subj], axis=2)
subj = D[:, :, loo_subj]
tmp_ISFC = compute_correlation(group, subj).astype(float_type)
# Symmetrize matrix
tmp_ISFC = (tmp_ISFC + tmp_ISFC.T) / 2
if not collapse_subj:
max_null[p] = max(np.max(tmp_ISFC), max_null[p])
min_null[p] = min(np.min(tmp_ISFC), min_null[p])
ISFC_null = ISFC_null + tmp_ISFC / n_subj
if collapse_subj:
max_null[p] = np.max(ISFC_null)
min_null[p] = np.min(ISFC_null)
if return_p:
p = p_from_null(ISFC,
two_sided,
max_null_input=max_null,
min_null_input=min_null)
return ISFC, p
else:
return ISFC
