From 6a2dedc7866c6e99c6ee689bf789353834ada690 Mon Sep 17 00:00:00 2001 From: Pierre-Louis Barbarant Date: Wed, 16 Oct 2024 18:36:09 +0200 Subject: [PATCH 1/7] Fix grammar --- content/svm_decoding.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/svm_decoding.md b/content/svm_decoding.md index 4e24dc1..4574b32 100644 --- a/content/svm_decoding.md +++ b/content/svm_decoding.md @@ -25,7 +25,7 @@ A SVM aims at finding an optimal hyperplane to separate two classes in high-dime ``` We are going to train a support vector machine (SVM) classifier for brain decoding on the Haxby dataset. SVM is often successful in high dimensional spaces, and it is a popular technique in neuroimaging. -In the SVM algorithm, we plot each data item as a point in N-dimensional space that N depends on the number of features that distinctly classify the data points (e.g. when the number of features is 3 the hyperplane becomes a two-dimensional plane.). The objective here is finding a hyperplane (decision boundaries that help classify the data points) with the maximum margin (i.e the maximum distance between data points of both classes). Data points falling on either side of the hyperplane can be attributed to different classes. +In the SVM algorithm, we plot each data item as a point in N-dimensional space where N depends on the number of features that distinctly classify the data points (e.g. when the number of features is 3 the hyperplane becomes a two-dimensional plane.). The objective here is finding a hyperplane (decision boundaries that help classify the data points) with the maximum margin (i.e the maximum distance between data points of both classes). Data points falling on either side of the hyperplane can be attributed to different classes. The scikit-learn [documentation](https://scikit-learn.org/stable/modules/svm.html) contains a detailed description of different variants of SVM, as well as example of applications with simple datasets. From ea03cd6b788f51bf1281484cd2f9e99f7e159fd2 Mon Sep 17 00:00:00 2001 From: Pierre-Louis Barbarant Date: Wed, 16 Oct 2024 18:36:23 +0200 Subject: [PATCH 2/7] Fix typo in frem --- content/svm_decoding.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/svm_decoding.md b/content/svm_decoding.md index 4574b32..891b29a 100644 --- a/content/svm_decoding.md +++ b/content/svm_decoding.md @@ -180,7 +180,7 @@ Note that the resulting accuracy is in general slightly higher: ```{code-cell} ipython3 print('F1 scoreswith FREM') for category in categories: - print(category, '\t\t {:.2f}'.format(np.mean(decoder.cv_scores_[category]))) + print(category, '\t\t {:.2f}'.format(np.mean(frem.cv_scores_[category]))) ``` ## Exercises From e25c4a285d104968995005cbb547a4b1b40d2f17 Mon Sep 17 00:00:00 2001 From: Pierre-Louis Barbarant Date: Wed, 16 Oct 2024 18:36:49 +0200 Subject: [PATCH 3/7] Fix typo --- content/svm_decoding.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/content/svm_decoding.md b/content/svm_decoding.md index 891b29a..88a8354 100644 --- a/content/svm_decoding.md +++ b/content/svm_decoding.md @@ -163,7 +163,8 @@ plotting.view_img( Note: the Decoder implements a one-vs-all strategy. Note that this is a better choice in general than one-vs-one. ## Getting more meaningful weight maps with Frem -It is often tempting to interpret regions with high weights as 'important' for the prediction task. However, there is no statistical guarantee on these maps. Moreover, they iften do not even exhibit very clear structure. To improve that, a regularization can be brought by using the so-called Fast Regularized Ensembles of models (FREM), that rely on simple averaging and clustering tools to provide smoother maps, yet with minimal computational overhead. + +It is often tempting to interpret regions with high weights as 'important' for the prediction task. However, there is no statistical guarantee on these maps. Moreover, they often do not even exhibit very clear structure. To improve that, a regularization can be brought by using the so-called Fast Regularized Ensembles of models (FREM), that rely on simple averaging and clustering tools to provide smoother maps, yet with minimal computational overhead. ```{code-cell} ipython3 from nilearn.decoding import FREMClassifier From af14493f5772e153ae3a4379166dec6c05f0ad64 Mon Sep 17 00:00:00 2001 From: Pierre-Louis Barbarant Date: Wed, 16 Oct 2024 18:43:02 +0200 Subject: [PATCH 4/7] Refactor import statement for NiftiMasker in svm_decoding.md --- content/svm_decoding.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/svm_decoding.md b/content/svm_decoding.md index 88a8354..7f534e9 100644 --- a/content/svm_decoding.md +++ b/content/svm_decoding.md @@ -45,7 +45,7 @@ haxby_dataset = datasets.fetch_haxby(subjects=[sub_no], fetch_stimuli=True, data func_file = haxby_dataset.func[0] # mask the data -from nilearn.input_data import NiftiMasker +from nilearn.maskers import NiftiMasker mask_filename = haxby_dataset.mask_vt[0] masker = NiftiMasker(mask_img=mask_filename, standardize=True, detrend=True) X = masker.fit_transform(func_file) From 864705d226154f67cd4d75b32c3474da61d0093e Mon Sep 17 00:00:00 2001 From: Pierre-Louis Barbarant Date: Wed, 16 Oct 2024 19:18:40 +0200 Subject: [PATCH 5/7] Improve formatting of F1 scores output --- content/svm_decoding.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/svm_decoding.md b/content/svm_decoding.md index 7f534e9..5cfd6a9 100644 --- a/content/svm_decoding.md +++ b/content/svm_decoding.md @@ -35,7 +35,7 @@ We are going to download the dataset from Haxby and colleagues (2001) {cite:p}`H ```{code-cell} ipython3 import os import warnings -warnings.filterwarnings(action='once') +warnings.filterwarnings(action='ignore') from nilearn import datasets # We are fetching the data for subject 4 @@ -153,7 +153,7 @@ We can now look at the results: F1 score and coefficient image: ```{code-cell} ipython3 print('F1 scores') for category in categories: - print(category, '\t\t {:.2f}'.format(np.mean(decoder.cv_scores_[category]))) + print(f"{category.ljust(15)} {np.mean(decoder.cv_scores_[category]):.2f}") plotting.view_img( decoder.coef_img_['face'], bg_img=haxby_dataset.anat[0], title="SVM weights for face", dim=-1, resampling_interpolation='nearest' @@ -179,9 +179,9 @@ plotting.view_img( Note that the resulting accuracy is in general slightly higher: ```{code-cell} ipython3 -print('F1 scoreswith FREM') +print('F1 scores with FREM') for category in categories: - print(category, '\t\t {:.2f}'.format(np.mean(frem.cv_scores_[category]))) + print(f"{category.ljust(15)} {np.mean(frem.cv_scores_[category]):.2f}") ``` ## Exercises From b2b560b79fe2f2d582690fd77df8d3672a7b0262 Mon Sep 17 00:00:00 2001 From: Pierre-Louis Barbarant Date: Thu, 17 Oct 2024 18:49:25 +0200 Subject: [PATCH 6/7] Add regularizations figure --- content/svm_decoding/regularizations.png | Bin 0 -> 281736 bytes 1 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00:00:00 2001 From: Pierre-Louis Barbarant Date: Thu, 17 Oct 2024 18:49:45 +0200 Subject: [PATCH 7/7] Add material to svm decoding --- content/svm_decoding.md | 78 ++++++++++++++++++++++++++++++++++++++++- 1 file changed, 77 insertions(+), 1 deletion(-) diff --git a/content/svm_decoding.md b/content/svm_decoding.md index 5cfd6a9..fdb230f 100644 --- a/content/svm_decoding.md +++ b/content/svm_decoding.md @@ -137,7 +137,6 @@ We can use the high-level `Decoder` object from Nilearn. See [Decoder object](ht from nilearn.decoding import Decoder # Specify the classifier to the decoder object. # With the decoder we can input the masker directly. -# We are using the svc_l1 here because it is intra subject. # # cv=5 means that we use 5-fold cross-validation # @@ -162,6 +161,32 @@ plotting.view_img( Note: the Decoder implements a one-vs-all strategy. Note that this is a better choice in general than one-vs-one. + +## Generating sharper weight maps with L1 regularization +Nilearn offers different flavours of SVCs. While the default uses L2 regularization under the hood, +we can can obtain sharper weight maps by encouraging sparsity with L1 regularization. + +```{figure} svm_decoding/regularizations.png +--- +width: 750px +name: regularizations-fig +--- +L1 penalty promotes sparsity of the estimated coefficients, while L2 penalty promotes weight sharing among all +components. One can combine both L1 and L2 regularization to obtain the [ElasticNet](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html) penalty. +``` + +```{code-cell} ipython3 +# Let's swap the estimator with the svc_l1 +l1_decoder = Decoder(estimator='svc_l1', cv=5, mask=mask_filename, scoring='f1') +l1_decoder.fit(func_file, y) +plotting.view_img( + l1_decoder.coef_img_['face'], bg_img=haxby_dataset.anat[0], + title="L1-SVM weights for face", dim=-1, resampling_interpolation='nearest' +) +``` + +We can observe that far fewer components are selected and the information is much more localized. + ## Getting more meaningful weight maps with Frem It is often tempting to interpret regions with high weights as 'important' for the prediction task. However, there is no statistical guarantee on these maps. Moreover, they often do not even exhibit very clear structure. To improve that, a regularization can be brought by using the so-called Fast Regularized Ensembles of models (FREM), that rely on simple averaging and clustering tools to provide smoother maps, yet with minimal computational overhead. @@ -184,6 +209,57 @@ for category in categories: print(f"{category.ljust(15)} {np.mean(frem.cv_scores_[category]):.2f}") ``` + +## ⚡️ (Experimental) Running a surfacic analysis + +```{code-cell} ipython3 +from nilearn.surface import vol_to_surf +from nilearn.experimental.surface._datasets import load_fsaverage +from nilearn.experimental.surface._surface_image import SurfaceImage + +# We first load the fsaverage mesh +mesh = load_fsaverage("fsaverage4")["pial"] + +# We then project the data on each hemisphere +data_lh = vol_to_surf(func_file, mesh["left_hemisphere"]).T +data_rh = vol_to_surf(func_file, mesh["right_hemisphere"]).T + +# Then we build the SurfaceImage object +surf_img = SurfaceImage( + mesh=mesh, + data={ + "left_hemisphere": data_lh, + "right_hemisphere": data_rh, + }, +) +print(f"Image shape: {surf_img.shape}") +``` + +```{code-cell} ipython3 +# The following is just disabling a couple of checks performed by the decoder +# that would force us to use a `NiftiMasker`. +from nilearn._utils import param_validation +def monkeypatch_masker_checks(): + def adjust_screening_percentile(screening_percentile, *args, **kwargs): + return screening_percentile + + param_validation.adjust_screening_percentile = adjust_screening_percentile +monkeypatch_masker_checks() + +from nilearn.experimental.surface import SurfaceMasker + +decoder = Decoder(mask=SurfaceMasker(), cv=3, screening_percentile=1) +decoder.fit(surf_img, y) +``` + +```{code-cell} ipython3 +plotting.view_surf( + decoder.coef_img_["face"].mesh["right_hemisphere"], + decoder.coef_img_["face"].data["right_hemisphere"], + cmap="coolwarm", +) +``` + ## Exercises * What is the most difficult category to decode? Why? * The model seemed to overfit. Can you find a parameter value for `C` in `SVC` such that the model does not overfit as much?