.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_02_decoding_plot_oasis_vbm_space_net.py:
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: :ref:`space_net`.
Load the Oasis VBM dataset
---------------------------
.. code-block:: default
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]
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
/usr/lib/python3/dist-packages/numpy/lib/npyio.py:2358: VisibleDeprecationWarning: Reading unicode strings without specifying the encoding argument is deprecated. Set the encoding, use None for the system default.
output = genfromtxt(fname, **kwargs)
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= to the
SpaceNetRegressor class, to take advantage of a multi-core system.
.. code-block:: default
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)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[NiftiMasker.fit] Loading data from [/home/varoquau/nilearn_data/oasis1/OAS1_0003_MR1/mwrc1OAS1_0003_MR1_mpr_anon_fslswapdim_bet.nii.gz, /home/varoquau/nilearn_data/oasis1/OAS1_0086_MR1/mwrc1OAS1_0086_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/varoquau/dev/nilearn/nilearn/decoding/space_net.py:835: 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(, 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/varoquau/dev/joblib/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.40s 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 - 33.3s, 0.6min
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 36.0s remaining: 0.0s
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(, 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/varoquau/dev/joblib/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.37s 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 - 31.5s, 0.5min
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(, 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/varoquau/dev/joblib/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.39s 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 - 32.5s, 0.5min
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(, 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/varoquau/dev/joblib/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.37s 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 - 32.9s, 0.5min
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(, 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/varoquau/dev/joblib/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.43s 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 - 32.6s, 0.5min
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(, 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/varoquau/dev/joblib/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.37s 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 - 32.2s, 0.5min
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(, 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/varoquau/dev/joblib/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.37s 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 - 31.6s, 0.5min
________________________________________________________________________________
[Memory] Calling nilearn.decoding.space_net.path_scores...
path_scores(, 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/varoquau/dev/joblib/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.37s 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 - 31.9s, 0.5min
[Parallel(n_jobs=1)]: Done 8 out of 8 | elapsed: 4.7min finished
Time Elapsed: 304.144 seconds, 5 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
Visualize the decoding maps and quality of predictions
-------------------------------------------------------
Visualize the resulting maps
.. code-block:: default
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()
.. rst-class:: sphx-glr-horizontal
*
.. image:: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_space_net_001.png
:alt: plot oasis vbm space net
:class: sphx-glr-multi-img
*
.. image:: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_space_net_002.png
:alt: graph-net: Mean Absolute Error 12.33 years
:class: sphx-glr-multi-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
/home/varoquau/dev/nilearn/nilearn/plotting/displays.py:1608: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
ax = fh.add_axes([fraction * index * (x1 - x0) + x0, y0,
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 5 minutes 12.326 seconds)
.. _sphx_glr_download_auto_examples_02_decoding_plot_oasis_vbm_space_net.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: https://mybinder.org/badge_logo.svg
:target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/master?filepath=examples/auto_examples/02_decoding/plot_oasis_vbm_space_net.ipynb
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_oasis_vbm_space_net.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_oasis_vbm_space_net.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
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