.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/02_decoding/plot_oasis_vbm_space_net.py" .. LINE NUMBERS ARE GIVEN BELOW. .. 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`. .. GENERATED FROM PYTHON SOURCE LINES 14-16 Load the Oasis VBM dataset --------------------------- .. GENERATED FROM PYTHON SOURCE LINES 16-40 .. 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, legacy_format=False ) 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] .. GENERATED FROM PYTHON SOURCE LINES 41-51 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. .. GENERATED FROM PYTHON SOURCE LINES 51-61 .. 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/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, 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/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:836: 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. [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/nicolas/GitRepos/joblib-fork/joblib/parallel.py:262: UserWarning: Persisting input arguments took 1.10s 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. _____________________________________________________path_scores - 19.4s, 0.3min [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 21.5s 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/nicolas/GitRepos/joblib-fork/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. _____________________________________________________path_scores - 20.2s, 0.3min ________________________________________________________________________________ [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/nicolas/GitRepos/joblib-fork/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. _____________________________________________________path_scores - 20.8s, 0.3min ________________________________________________________________________________ [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/nicolas/GitRepos/joblib-fork/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. _____________________________________________________path_scores - 20.7s, 0.3min ________________________________________________________________________________ [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/nicolas/GitRepos/joblib-fork/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. _____________________________________________________path_scores - 20.8s, 0.3min ________________________________________________________________________________ [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/nicolas/GitRepos/joblib-fork/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. _____________________________________________________path_scores - 20.7s, 0.3min ________________________________________________________________________________ [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/nicolas/GitRepos/joblib-fork/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. _____________________________________________________path_scores - 20.8s, 0.3min ________________________________________________________________________________ [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/nicolas/GitRepos/joblib-fork/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. _____________________________________________________path_scores - 20.8s, 0.3min [Parallel(n_jobs=1)]: Done 8 out of 8 | elapsed: 3.0min finished Time Elapsed: 193.021 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 .. GENERATED FROM PYTHON SOURCE LINES 62-65 Visualize the decoding maps and quality of predictions ------------------------------------------------------- Visualize the resulting maps .. GENERATED FROM PYTHON SOURCE LINES 65-88 .. 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-sg:: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_space_net_001.png :alt: plot oasis vbm space net :srcset: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_space_net_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_space_net_002.png :alt: graph-net: Mean Absolute Error 12.33 years :srcset: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_space_net_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 3 minutes 20.987 seconds) **Estimated memory usage:** 2075 MB .. _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:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/main?filepath=examples/auto_examples/02_decoding/plot_oasis_vbm_space_net.ipynb :alt: Launch binder :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 `Gallery generated by Sphinx-Gallery `_