Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[MAINT] deal with new warnings related to sklearn 1.3 #3801

Merged
merged 10 commits into from
Jul 5, 2023
Merged

[MAINT] deal with new warnings related to sklearn 1.3 #3801

Show file tree
Hide file tree
Changes from 3 commits
Commits
Show all changes
10 commits
Select commit Hold shift + click to select a range
cd56d4d
set dual defaults for LinearSVC
Remi-Gau Jul 4, 2023
9b30d4d
silence more warnings
Remi-Gau Jul 4, 2023
e9db0d8
[full doc] request full build
Remi-Gau Jul 4, 2023
5787a6e
use default
Remi-Gau Jul 4, 2023
060e0f8
deal with warnings in searchlight
Remi-Gau Jul 4, 2023
cd3de2f
silence more warnings
Remi-Gau Jul 4, 2023
2c4b5e9
[full doc] request full build
Remi-Gau Jul 4, 2023
252821a
fix tet
Remi-Gau Jul 4, 2023
cd76cfc
change arg name
Remi-Gau Jul 5, 2023
c074e46
rm args to be deprecated
Remi-Gau Jul 5, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
8 changes: 3 additions & 5 deletions examples/02_decoding/plot_miyawaki_encoding.py
View file
Open in desktop
Remi-Gau marked this conversation as resolved.
Show resolved Hide resolved
Original file line number Diff line number Diff line change
Expand Up @@ -143,11 +143,9 @@
for train, test in cv.split(X=stimuli):
# we train the Ridge estimator on the training set
# and predict the fMRI activity for the test set
predictions = (
Ridge(alpha=100.0)
.fit(stimuli.reshape(-1, 100)[train], fmri_data[train])
.predict(stimuli.reshape(-1, 100)[test])
)
predictions = estimator.fit(
Copy link
Collaborator Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

estimator is defined above but was not used in this file

stimuli.reshape(-1, 100)[train], fmri_data[train]
).predict(stimuli.reshape(-1, 100)[test])
# we compute how much variance our encoding model explains in each voxel
scores.append(
r2_score(fmri_data[test], predictions, multioutput="raw_values")
Expand Down
2 changes: 1 addition & 1 deletion examples/02_decoding/plot_miyawaki_reconstruction.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -160,7 +160,7 @@ def flatten(list_of_2d_array):
[
("selection", SelectKBest(f_classif, k=500)),
("scl", StandardScaler()),
("clf", OMP(normalize=False, n_nonzero_coefs=10)),
("clf", OMP(normalize="deprecated", n_nonzero_coefs=10)),
Remi-Gau marked this conversation as resolved.
Show resolved Hide resolved
]
)
clf.fit(X_train, y_train[:, i])
Expand Down
4 changes: 2 additions & 2 deletions nilearn/decoding/decoder.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -52,8 +52,8 @@

SUPPORTED_ESTIMATORS = dict(
svc_l1=LinearSVC(penalty="l1", dual=False, max_iter=10000),
svc_l2=LinearSVC(penalty="l2", max_iter=10000),
svc=LinearSVC(penalty="l2", max_iter=10000),
svc_l2=LinearSVC(penalty="l2", dual=True, max_iter=10000),
Remi-Gau marked this conversation as resolved.
Show resolved Hide resolved
svc=LinearSVC(penalty="l2", dual=True, max_iter=10000),
logistic_l1=LogisticRegressionCV(penalty="l1", solver="liblinear"),
logistic_l2=LogisticRegressionCV(penalty="l2", solver="liblinear"),
logistic=LogisticRegressionCV(penalty="l2", solver="liblinear"),
Expand Down