def test_fetch_oasis_vbm():
local_url = "file://" + datadir
ids = np.asarray(['OAS1_%4d' % i for i in range(457)])
ids = ids.view(dtype=[('ID', 'S9')])
file_mock.add_csv('oasis_cross-sectional.csv', ids)
# Disabled: cannot be tested without actually fetching covariates CSV file
dataset = datasets.fetch_oasis_vbm(data_dir=tmpdir, url=local_url,
verbose=0)
assert_equal(len(dataset.gray_matter_maps), 403)
assert_equal(len(dataset.white_matter_maps), 403)
assert_true(isinstance(dataset.gray_matter_maps[0], _basestring))
assert_true(isinstance(dataset.white_matter_maps[0], _basestring))
assert_true(isinstance(dataset.ext_vars, np.recarray))
assert_true(isinstance(dataset.data_usage_agreement, _basestring))
assert_equal(len(url_request.urls), 3)
dataset = datasets.fetch_oasis_vbm(data_dir=tmpdir, url=local_url,
dartel_version=False, verbose=0)
assert_equal(len(dataset.gray_matter_maps), 415)
assert_equal(len(dataset.white_matter_maps), 415)
assert_true(isinstance(dataset.gray_matter_maps[0], _basestring))
assert_true(isinstance(dataset.white_matter_maps[0], _basestring))
assert_true(isinstance(dataset.ext_vars, np.recarray))
assert_true(isinstance(dataset.data_usage_agreement, _basestring))
assert_equal(len(url_request.urls), 4)
Note that more power would be obtained from using a larger sample of subjects.
"""
# Authors: Bertrand Thirion, <*****@*****.**>, July 2018
# Elvis Dhomatob, <*****@*****.**>, Apr. 2014
# Virgile Fritsch, <*****@*****.**>, Apr 2014
# Gael Varoquaux, Apr 2014
n_subjects = 100 # more subjects requires more memory
############################################################################
# Load Oasis dataset
# ------------------
from nilearn import datasets
oasis_dataset = datasets.fetch_oasis_vbm(n_subjects=n_subjects)
gray_matter_map_filenames = oasis_dataset.gray_matter_maps
age = oasis_dataset.ext_vars['age'].astype(float)
###############################################################################
# Sex is encoded as 'M' or 'F'. make it a binary variable
sex = oasis_dataset.ext_vars['mf'] == b'F'
###############################################################################
# Print basic information on the dataset
print('First gray-matter anatomy image (3D) is located at: %s' %
oasis_dataset.gray_matter_maps[0]) # 3D data
print('First white-matter anatomy image (3D) is located at: %s' %
oasis_dataset.white_matter_maps[0]) # 3D data
###############################################################################
____
"""
# Authors: Elvis Dhomatob, <*****@*****.**>, Apr. 2014
# Virgile Fritsch, <*****@*****.**>, Apr 2014
# Gael Varoquaux, Apr 2014
import numpy as np
import matplotlib.pyplot as plt
import nibabel
from nilearn import datasets
from nilearn.input_data import NiftiMasker
n_subjects = 100 # more subjects requires more memory
### Load Oasis dataset ########################################################
dataset_files = datasets.fetch_oasis_vbm(n_subjects=n_subjects)
age = dataset_files.ext_vars['age'].astype(float)
### Preprocess data ###########################################################
nifti_masker = NiftiMasker(
standardize=False,
smoothing_fwhm=2,
memory='nilearn_cache') # cache options
# remove features with too low between-subject variance
gm_maps_masked = nifti_masker.fit_transform(dataset_files.gray_matter_maps)
gm_maps_masked[:, gm_maps_masked.var(0) < 0.01] = 0.
# final masking
new_images = nifti_masker.inverse_transform(gm_maps_masked)
gm_maps_masked = nifti_masker.fit_transform(new_images)
n_samples, n_features = gm_maps_masked.shape
print n_samples, "subjects, ", n_features, "features"
import warnings
import numpy as np
from nilearn.datasets import fetch_oasis_vbm
from sklearn.model_selection import ShuffleSplit
from photonai.base import Hyperpipe, PipelineElement, OutputSettings
from photonai_neuro import NeuroBranch
warnings.filterwarnings("ignore", category=DeprecationWarning)
# GET DATA FROM OASIS
n_subjects = 50
dataset_files = fetch_oasis_vbm(n_subjects=n_subjects)
age = dataset_files.ext_vars['age'].astype(float)
y = np.array(age)
X = np.array(dataset_files.gray_matter_maps)
# DEFINE OUTPUT SETTINGS
settings = OutputSettings(project_folder='./tmp/')
# DESIGN YOUR PIPELINE
pipe = Hyperpipe('GrayMatter',
optimizer='grid_search',
metrics=['mean_absolute_error'],
best_config_metric='mean_absolute_error',
outer_cv=ShuffleSplit(n_splits=1, test_size=0.2),
inner_cv=ShuffleSplit(n_splits=1, test_size=0.2),
verbosity=2,
cache_folder="./cache",
eval_final_performance=False,
output_settings=settings)
"""
# Authors: Elvis Dhomatob, <*****@*****.**>, Apr. 2014
# Virgile Fritsch, <*****@*****.**>, Apr 2014
# Gael Varoquaux, Apr 2014
import numpy as np
import matplotlib.pyplot as plt
from nilearn import datasets
from nilearn.input_data import NiftiMasker
n_subjects = 100 # more subjects requires more memory
############################################################################
# Load Oasis dataset
# -------------------
oasis_dataset = datasets.fetch_oasis_vbm(n_subjects=n_subjects)
gray_matter_map_filenames = oasis_dataset.gray_matter_maps
age = oasis_dataset.ext_vars['age'].astype(float)
# print basic information on the dataset
print('First gray-matter anatomy image (3D) is located at: %s' %
oasis_dataset.gray_matter_maps[0]) # 3D data
print('First white-matter anatomy image (3D) is located at: %s' %
oasis_dataset.white_matter_maps[0]) # 3D data
#############################################################################
# Preprocess data
# ----------------
nifti_masker = NiftiMasker(
standardize=False,
smoothing_fwhm=2,
# Virgile Fritsch, <*****@*****.**>, Apr 2014
# Gael Varoquaux, Apr 2014
# Andres Hoyos-Idrobo, Apr 2017
import numpy as np
import matplotlib.pyplot as plt
from nilearn import datasets
from nilearn.maskers import NiftiMasker
from nilearn.image import get_data
n_subjects = 100 # more subjects requires more memory
############################################################################
# Load Oasis dataset
# -------------------
oasis_dataset = datasets.fetch_oasis_vbm(
n_subjects=n_subjects, legacy_format=False
)
gray_matter_map_filenames = oasis_dataset.gray_matter_maps
age = oasis_dataset.ext_vars['age'].values
# Split data into training set and test set
from sklearn.model_selection import train_test_split
gm_imgs_train, gm_imgs_test, age_train, age_test = train_test_split(
gray_matter_map_filenames, age, train_size=.6, random_state=0)
# print basic information on the dataset
print('First gray-matter anatomy image (3D) is located at: %s' %
oasis_dataset.gray_matter_maps[0]) # 3D data
print('First white-matter anatomy image (3D) is located at: %s' %
oasis_dataset.white_matter_maps[0]) # 3D data
