9.3.4. Voxel-Based Morphometry on Oasis dataset with Space-Net prior

Predicting age from gray-matter concentration maps from OASIS dataset. Note that age is a continuous variable, we use the regressor here, and not the classification object.

See also the documentation: SpaceNet: decoding with spatial structure for better maps.

9.3.4.1. Load the Oasis VBM dataset

import numpy as np
from nilearn import datasets
n_subjects = 200  # increase this number if you have more RAM on your box
dataset_files = datasets.fetch_oasis_vbm(n_subjects=n_subjects)
age = dataset_files.ext_vars['age'].astype(float)
age = np.array(age)
gm_imgs = np.array(dataset_files.gray_matter_maps)


# Split data into training set and test set
from sklearn.utils import check_random_state
from sklearn.model_selection import train_test_split
rng = check_random_state(42)
gm_imgs_train, gm_imgs_test, age_train, age_test = train_test_split(
    gm_imgs, age, train_size=.6, random_state=rng)

# Sort test data for better visualization (trend, etc.)
perm = np.argsort(age_test)[::-1]
age_test = age_test[perm]
gm_imgs_test = gm_imgs_test[perm]

Out:

/home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/numpy/lib/npyio.py:2405: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
  output = genfromtxt(fname, **kwargs)

9.3.4.2. Fit the SpaceNet and predict with it

To save time (because these are anat images with many voxels), we include only the 5-percent voxels most correlated with the age variable to fit. Also, we set memory_level=2 so that more of the intermediate computations are cached. We used a graph-net penalty here but more beautiful results can be obtained using the TV-l1 penalty, at the expense of longer runtimes. Also, you may pass and n_jobs=<some_high_value> to the SpaceNetRegressor class, to take advantage of a multi-core system.

from nilearn.decoding import SpaceNetRegressor
decoder = SpaceNetRegressor(memory="nilearn_cache", penalty="graph-net",
                            screening_percentile=5., memory_level=2)
decoder.fit(gm_imgs_train, age_train)  # fit
coef_img = decoder.coef_img_
y_pred = decoder.predict(gm_imgs_test).ravel()  # predict
mse = np.mean(np.abs(age_test - y_pred))
print('Mean square error (MSE) on the predicted age: %.2f' % mse)

Out:

[NiftiMasker.fit] Loading data from [/home/nicolas/nilearn_data/oasis1/OAS1_0003_MR1/mwrc1OAS1_0003_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0086_MR1/mwrc1OAS1_0086_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0052_MR1/mwrc1OAS1_0052_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0211_MR1/mwrc1OAS1_0211_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0216_MR1/mwrc1OAS1_0216_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0096_MR1/mwrc1OAS1_0096_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0182_MR1/mwrc1OAS1_0182_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0041_MR1/mwrc1OAS1_0041_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0214_MR1/mwrc1OAS1_0214_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0069_MR1/mwrc1OAS1_0069_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0025_MR1/mwrc1OAS1_0025_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0159_MR1/mwrc1OAS1_0159_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0113_MR1/mwrc1OAS1_0113_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0038_MR1/mwrc1OAS1_0038_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0013_MR1/mwrc1OAS1_0013_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0220_MR1/mwrc1OAS1_0220_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0180_MR1/mwrc1OAS1_0180_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0007_MR1/mwrc1OAS1_0007_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0031_MR1/mwrc1OAS1_0031_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0135_MR1/mwrc1OAS1_0135_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0005_MR1/mwrc1OAS1_0005_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0037_MR1/mwrc1OAS1_0037_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0160_MR1/mwrc1OAS1_0160_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0163_MR1/mwrc1OAS1_0163_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0123_MR1/mwrc1OAS1_0123_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0162_MR1/mwrc1OAS1_0162_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0012_MR1/mwrc1OAS1_0012_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0070_MR1/mwrc1OAS1_0070_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0126_MR1/mwrc1OAS1_0126_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0164_MR1/mwrc1OAS1_0164_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0188_MR1/mwrc1OAS1_0188_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0001_MR1/mwrc1OAS1_0001_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0226_MR1/mwrc1OAS1_0226_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0173_MR1/mwrc1OAS1_0173_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0078_MR1/mwrc1OAS1_0078_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0138_MR1/mwrc1OAS1_0138_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0072_MR1/mwrc1OAS1_0072_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0050_MR1/mwrc1OAS1_0050_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0184_MR1/mwrc1OAS1_0184_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0032_MR1/mwrc1OAS1_0032_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0045_MR1/mwrc1OAS1_0045_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0121_MR1/mwrc1OAS1_0121_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0176_MR1/mwrc1OAS1_0176_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0177_MR1/mwrc1OAS1_0177_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0029_MR1/mwrc1OAS1_0029_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0026_MR1/mwrc1OAS1_0026_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0208_MR1/mwrc1OAS1_0208_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0165_MR1/mwrc1OAS1_0165_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0091_MR1/mwrc1OAS1_0091_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0044_MR1/mwrc1OAS1_0044_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0190_MR1/mwrc1OAS1_0190_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0053_MR1/mwrc1OAS1_0053_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0106_MR1/mwrc1OAS1_0106_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0174_MR1/mwrc1OAS1_0174_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0049_MR1/mwrc1OAS1_0049_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0155_MR1/mwrc1OAS1_0155_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0004_MR1/mwrc1OAS1_0004_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0117_MR1/mwrc1OAS1_0117_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0060_MR1/mwrc1OAS1_0060_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0148_MR1/mwrc1OAS1_0148_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0204_MR1/mwrc1OAS1_0204_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0202_MR1/mwrc1OAS1_0202_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0209_MR1/mwrc1OAS1_0209_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0055_MR1/mwrc1OAS1_0055_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0090_MR1/mwrc1OAS1_0090_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0039_MR1/mwrc1OAS1_0039_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0009_MR1/mwrc1OAS1_0009_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0124_MR1/mwrc1OAS1_0124_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0103_MR1/mwrc1OAS1_0103_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0094_MR1/mwrc1OAS1_0094_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0200_MR1/mwrc1OAS1_0200_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0205_MR1/mwrc1OAS1_0205_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0101_MR1/mwrc1OAS1_0101_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0010_MR1/mwrc1OAS1_0010_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0015_MR1/mwrc1OAS1_0015_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0067_MR1/mwrc1OAS1_0067_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0193_MR1/mwrc1OAS1_0193_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0146_MR1/mwrc1OAS1_0146_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0019_MR1/mwrc1OAS1_0019_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0080_MR1/mwrc1OAS1_0080_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0199_MR1/mwrc1OAS1_0199_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0150_MR1/mwrc1OAS1_0150_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0189_MR1/mwrc1OAS1_0189_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0207_MR1/mwrc1OAS1_0207_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0071_MR1/mwrc1OAS1_0071_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0061_MR1/mwrc1OAS1_0061_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0120_MR1/mwrc1OAS1_0120_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0056_MR1/mwrc1OAS1_0056_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0223_MR1/mwrc1OAS1_0223_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0065_MR1/mwrc1OAS1_0065_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0054_MR1/mwrc1OAS1_0054_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0099_MR1/mwrc1OAS1_0099_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0023_MR1/mwrc1OAS1_0023_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0064_MR1/mwrc1OAS1_0064_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0181_MR1/mwrc1OAS1_0181_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0217_MR1/mwrc1OAS1_0217_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0144_MR1/mwrc1OAS1_0144_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0042_MR1/mwrc1OAS1_0042_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0178_MR1/mwrc1OAS1_0178_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0218_MR1/mwrc1OAS1_0218_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0002_MR1/mwrc1OAS1_0002_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0059_MR1/mwrc1OAS1_0059_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0167_MR1/mwrc1OAS1_0167_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0145_MR1/mwrc1OAS1_0145_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0169_MR1/mwrc1OAS1_0169_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0115_MR1/mwrc1OAS1_0115_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0111_MR1/mwrc1OAS1_0111_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0131_MR1/mwrc1OAS1_0131_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0098_MR1/mwrc1OAS1_0098_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0082_MR1/mwrc1OAS1_0082_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0136_MR1/mwrc1OAS1_0136_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0227_MR1/mwrc1OAS1_0227_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0022_MR1/mwrc1OAS1_0022_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0212_MR1/mwrc1OAS1_0212_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0079_MR1/mwrc1OAS1_0079_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0119_MR1/mwrc1OAS1_0119_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0016_MR1/mwrc1OAS1_0016_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0104_MR1/mwrc1OAS1_0104_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0203_MR1/mwrc1OAS1_0203_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/nicolas/nilearn_data/oasis1/OAS1_0114_MR1/mwrc1OAS1_0114_MR1_mpr_anon_fslswapdim_bet.nii.gz]
[NiftiMasker.fit] Computing the mask
[NiftiMasker.fit] Resampling mask
[NiftiMasker.transform_single_imgs] Loading data from Nifti1Image(
shape=(91, 109, 91, 120),
affine=array([[  -2.,    0.,    0.,   90.],
       [   0.,    2.,    0., -126.],
       [   0.,    0.,    2.,  -72.],
       [   0.,    0.,    0.,    1.]])
)
[NiftiMasker.transform_single_imgs] Extracting region signals
[NiftiMasker.transform_single_imgs] Cleaning extracted signals
/home/nicolas/GitRepos/nilearn-fork/nilearn/decoding/space_net.py:834: UserWarning: Brain mask is bigger than the volume of a standard human brain. This object is probably not tuned to be used on such data.
  self.screening_percentile_ = _adjust_screening_percentile(
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(<function _graph_net_squared_loss at 0x7ff0469b9ee0>, array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ...,  True],
        ...,
        [ True, ...,  True]],

       ...,

       [[ True, ...,  True],
        ...,
        [ True, ...,  True]]]),
None, [0.5], array([ 15, ..., 119]), array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]), {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 0), debias=False, verbose=1, screening_percentile=1.2652228933482088)
........./home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.18s to run.
If this happens often in your code, it can cause performance problems
(results will be correct in all cases).
The reason for this is probably some large input arguments for a wrapped
 function (e.g. large strings).
THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an
 example so that they can fix the problem.
  return [func(*args, **kwargs)
_____________________________________________________path_scores - 21.6s, 0.4min
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   23.9s remaining:    0.0s
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(<function _graph_net_squared_loss at 0x7ff0469b9ee0>, array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ...,  True],
        ...,
        [ True, ...,  True]],

       ...,

       [[ True, ...,  True],
        ...,
        [ True, ...,  True]]]),
None, [0.5], array([  0, ..., 119]), array([15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]), {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 1), debias=False, verbose=1, screening_percentile=1.2652228933482088)
........./home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.14s to run.
If this happens often in your code, it can cause performance problems
(results will be correct in all cases).
The reason for this is probably some large input arguments for a wrapped
 function (e.g. large strings).
THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an
 example so that they can fix the problem.
  return [func(*args, **kwargs)
_____________________________________________________path_scores - 22.9s, 0.4min
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(<function _graph_net_squared_loss at 0x7ff0469b9ee0>, array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ...,  True],
        ...,
        [ True, ...,  True]],

       ...,

       [[ True, ...,  True],
        ...,
        [ True, ...,  True]]]),
None, [0.5], array([  0, ..., 119]), array([30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]), {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 2), debias=False, verbose=1, screening_percentile=1.2652228933482088)
........./home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.13s to run.
If this happens often in your code, it can cause performance problems
(results will be correct in all cases).
The reason for this is probably some large input arguments for a wrapped
 function (e.g. large strings).
THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an
 example so that they can fix the problem.
  return [func(*args, **kwargs)
_____________________________________________________path_scores - 20.9s, 0.3min
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(<function _graph_net_squared_loss at 0x7ff0469b9ee0>, array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ...,  True],
        ...,
        [ True, ...,  True]],

       ...,

       [[ True, ...,  True],
        ...,
        [ True, ...,  True]]]),
None, [0.5], array([  0, ..., 119]), array([45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]), {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 3), debias=False, verbose=1, screening_percentile=1.2652228933482088)
........./home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.15s to run.
If this happens often in your code, it can cause performance problems
(results will be correct in all cases).
The reason for this is probably some large input arguments for a wrapped
 function (e.g. large strings).
THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an
 example so that they can fix the problem.
  return [func(*args, **kwargs)
_____________________________________________________path_scores - 20.8s, 0.3min
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(<function _graph_net_squared_loss at 0x7ff0469b9ee0>, array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ...,  True],
        ...,
        [ True, ...,  True]],

       ...,

       [[ True, ...,  True],
        ...,
        [ True, ...,  True]]]),
None, [0.5], array([  0, ..., 119]), array([60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74]), {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 4), debias=False, verbose=1, screening_percentile=1.2652228933482088)
........./home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.13s to run.
If this happens often in your code, it can cause performance problems
(results will be correct in all cases).
The reason for this is probably some large input arguments for a wrapped
 function (e.g. large strings).
THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an
 example so that they can fix the problem.
  return [func(*args, **kwargs)
_____________________________________________________path_scores - 21.6s, 0.4min
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(<function _graph_net_squared_loss at 0x7ff0469b9ee0>, array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ...,  True],
        ...,
        [ True, ...,  True]],

       ...,

       [[ True, ...,  True],
        ...,
        [ True, ...,  True]]]),
None, [0.5], array([  0, ..., 119]), array([75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89]), {'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 5), debias=False, verbose=1, screening_percentile=1.2652228933482088)
........./home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.24s to run.
If this happens often in your code, it can cause performance problems
(results will be correct in all cases).
The reason for this is probably some large input arguments for a wrapped
 function (e.g. large strings).
THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an
 example so that they can fix the problem.
  return [func(*args, **kwargs)
_____________________________________________________path_scores - 23.0s, 0.4min
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(<function _graph_net_squared_loss at 0x7ff0469b9ee0>, array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ...,  True],
        ...,
        [ True, ...,  True]],

       ...,

       [[ True, ...,  True],
        ...,
        [ True, ...,  True]]]),
None, [0.5], array([  0, ..., 119]), array([ 90,  91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102,
       103, 104]),
{'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 6), debias=False, verbose=1, screening_percentile=1.2652228933482088)
........./home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.26s to run.
If this happens often in your code, it can cause performance problems
(results will be correct in all cases).
The reason for this is probably some large input arguments for a wrapped
 function (e.g. large strings).
THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an
 example so that they can fix the problem.
  return [func(*args, **kwargs)
_____________________________________________________path_scores - 26.0s, 0.4min
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(<function _graph_net_squared_loss at 0x7ff0469b9ee0>, array([[0., ..., 0.],
       ...,
       [0., ..., 0.]]), array([73., ..., 62.]), array([[[ True, ...,  True],
        ...,
        [ True, ...,  True]],

       ...,

       [[ True, ...,  True],
        ...,
        [ True, ...,  True]]]),
None, [0.5], array([  0, ..., 104]), array([105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,
       118, 119]),
{'max_iter': 200, 'tol': 0.0001}, n_alphas=10, eps=0.001, is_classif=False, key=(0, 7), debias=False, verbose=1, screening_percentile=1.2652228933482088)
........./home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.13s to run.
If this happens often in your code, it can cause performance problems
(results will be correct in all cases).
The reason for this is probably some large input arguments for a wrapped
 function (e.g. large strings).
THIS IS A JOBLIB ISSUE. If you can, kindly provide the joblib's team with an
 example so that they can fix the problem.
  return [func(*args, **kwargs)
_____________________________________________________path_scores - 21.3s, 0.4min
[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:  3.3min finished
Time Elapsed: 207.776 seconds, 3 minutes.
[NiftiMasker.transform_single_imgs] Loading data from Nifti1Image(
shape=(91, 109, 91, 80),
affine=array([[  -2.,    0.,    0.,   90.],
       [   0.,    2.,    0., -126.],
       [   0.,    0.,    2.,  -72.],
       [   0.,    0.,    0.,    1.]])
)
[NiftiMasker.transform_single_imgs] Extracting region signals
[NiftiMasker.transform_single_imgs] Cleaning extracted signals
Mean square error (MSE) on the predicted age: 12.33

9.3.4.3. Visualize the decoding maps and quality of predictions

Visualize the resulting maps

from nilearn.plotting import plot_stat_map, show
# weights map
background_img = gm_imgs[0]
plot_stat_map(coef_img, background_img, title="graph-net weights",
              display_mode="z", cut_coords=1)

# Plot the prediction errors.
import matplotlib.pyplot as plt
plt.figure()
plt.suptitle("graph-net: Mean Absolute Error %.2f years" % mse)
linewidth = 3
ax1 = plt.subplot('211')
ax1.plot(age_test, label="True age", linewidth=linewidth)
ax1.plot(y_pred, '--', c="g", label="Predicted age", linewidth=linewidth)
ax1.set_ylabel("age")
plt.legend(loc="best")
ax2 = plt.subplot("212")
ax2.plot(age_test - y_pred, label="True age - predicted age",
         linewidth=linewidth)
ax2.set_xlabel("subject")
plt.legend(loc="best")

show()
  • plot oasis vbm space net
  • graph-net: Mean Absolute Error 12.33 years

Out:

/home/nicolas/GitRepos/nilearn-fork/examples/02_decoding/plot_oasis_vbm_space_net.py:74: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.
  ax1 = plt.subplot('211')
/home/nicolas/GitRepos/nilearn-fork/examples/02_decoding/plot_oasis_vbm_space_net.py:79: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.
  ax2 = plt.subplot("212")

Total running time of the script: ( 3 minutes 34.958 seconds)

Gallery generated by Sphinx-Gallery