def main():
# Apply our decomposition estimator with reduction
n_components = 70
n_jobs = 1
raw = True
init = True
mask, func_filenames = get_hcp_data(raw=raw)
reduction_list = [1, 2, 4, 8, 12]
alpha_list = [1e-2, 1e-3, 1e-4]
Parallel(n_jobs=n_jobs, verbose=10)(delayed(run)(idx, reduction, alpha,
mask, raw, n_components,
init, func_filenames) for
idx, (reduction, alpha)
in enumerate(
itertools.product(reduction_list, alpha_list)))
def main():
# Apply our decomposition estimator with reduction
n_components = 70
n_jobs = 1
raw = True
init = True
mask, func_filenames = get_hcp_data(raw=raw)
reduction_list = [1, 2, 4, 8, 12]
alpha_list = [1e-2, 1e-3, 1e-4]
Parallel(n_jobs=n_jobs, verbose=10)(
delayed(run)(idx, reduction, alpha, mask, raw, n_components, init,
func_filenames)
for idx, (
reduction,
alpha) in enumerate(itertools.product(reduction_list, alpha_list)))
def main(output_dir, n_jobs):
dir_list = [join(output_dir, f) for f in os.listdir(output_dir) if
os.path.isdir(join(output_dir, f))]
mask, func_filenames = get_hcp_data(raw=True)
masker = NiftiMasker(mask_img=mask, smoothing_fwhm=None,
standardize=False)
masker.fit()
test_data = func_filenames[(-n_test_records * 2)::2]
n_samples, n_voxels = np.load(test_data[-1], mmap_mode='r').shape
X = np.empty((n_test_records * n_samples, n_voxels))
for i, this_data in enumerate(test_data):
X[i * n_samples:(i + 1) * n_samples] = np.load(this_data,
mmap_mode='r')
Parallel(n_jobs=n_jobs, verbose=1, temp_folder='/dev/shm')(
delayed(analyse_dir)(dir_name, X, masker) for dir_name in dir_list)
def get_init_objective(output_dir):
mask, func_filenames = get_hcp_data(data_dir=data_dir, raw=True)
masker = NiftiMasker(mask_img=mask, smoothing_fwhm=None, standardize=False)
masker.fit()
rsn70 = fetch_atlas_smith_2009().rsn70
components = masker.transform(rsn70)
print(components.shape)
enet_scale(components.T, inplace=True)
print(np.sum(np.abs(components), axis=1))
test_data = func_filenames[(-n_test_records * 2)::2]
n_samples, n_voxels = np.load(test_data[-1], mmap_mode='r').shape
X = np.empty((n_test_records * n_samples, n_voxels))
for i, this_data in enumerate(test_data):
X[i * n_samples:(i + 1) * n_samples] = np.load(this_data,
mmap_mode='r')
exp_var = {}
for alpha in [1e-2, 1e-3, 1e-4]:
exp_var[alpha] = objective_function(X, components, alpha)
json.dump(open(join(output_dir, 'init_objective.json'), 'r'))
def get_init_objective(output_dir):
mask, func_filenames = get_hcp_data(raw=True)
masker = NiftiMasker(mask_img=mask, smoothing_fwhm=None,
standardize=False)
masker.fit()
rsn70 = fetch_atlas_smith_2009().rsn70
components = masker.transform(rsn70)
print(components.shape)
enet_scale(components.T, inplace=True)
print(np.sum(np.abs(components), axis=1))
test_data = func_filenames[(-n_test_records * 2)::2]
n_samples, n_voxels = np.load(test_data[-1], mmap_mode='r').shape
X = np.empty((n_test_records * n_samples, n_voxels))
for i, this_data in enumerate(test_data):
X[i * n_samples:(i + 1) * n_samples] = np.load(this_data,
mmap_mode='r')
exp_var = {}
for alpha in [1e-2, 1e-3, 1e-4]:
exp_var[alpha] = objective_function(X, components, alpha)
json.dump(open(join(output_dir, 'init_objective.json'), 'r'))
# Author: Arthur Mensch
# License: BSD
# Adapted from nilearn example
import time
from os.path import expanduser
import numpy as np
from nilearn.datasets import fetch_atlas_smith_2009
from modl._utils.system.mkl import num_threads
from modl.datasets.hcp import get_hcp_data
from modl.spca_fmri import SpcaFmri
mask, func_filenames = get_hcp_data(data_dir='/storage/data')
func_filenames = func_filenames[:2]
# Apply our decomposition estimator with reduction
n_components = 70
n_jobs = 20
raw = True
init = True
dict_fact = SpcaFmri(mask=mask,
smoothing_fwhm=3,
shelve=not raw,
n_components=n_components,
dict_init=fetch_atlas_smith_2009().rsn70 if init else None,
reduction=12,
alpha=0.001,