Note
Go to the end to download the full example code or to run this example in your browser via Binder.
Setting a parameter by cross-validation¶
Here we set the number of features selected in an Anova-SVC approach to maximize the cross-validation score.
After separating 2 runs for validation, we vary that parameter and measure the cross-validation score. We also measure the prediction score on the left-out validation data. As we can see, the two scores vary by a significant amount: this is due to sampling noise in cross validation, and choosing the parameter k to maximize the cross-validation score, might not maximize the score on left-out data.
Thus using data to maximize a cross-validation score computed on that same data is likely to be too optimistic and lead to an overfit.
The proper approach is known as a “nested cross-validation”. It consists in doing cross-validation loops to set the model parameters inside the cross-validation loop used to judge the prediction performance: the parameters are set separately on each fold, never using the data used to measure performance.
For decoding tasks, in nilearn, this can be done using the
Decoder object, which will automatically select
the best parameters of an estimator from a grid of parameter values.
One difficulty is that the Decoder object is a composite estimator: a pipeline of feature selection followed by Support Vector Machine. Tuning the SVM’s parameters is already done automatically inside the Decoder, but performing cross-validation for the feature selection must be done manually.
Load the Haxby dataset¶
from nilearn import datasets
from nilearn.plotting import show
# by default 2nd subject data will be fetched on which we run our analysis
haxby_dataset = datasets.fetch_haxby()
fmri_img = haxby_dataset.func[0]
mask_img = haxby_dataset.mask
# print basic information on the dataset
print(f"Mask nifti image (3D) is located at: {haxby_dataset.mask}")
print(f"Functional nifti image (4D) are located at: {haxby_dataset.func[0]}")
# Load the behavioral data
import pandas as pd
labels = pd.read_csv(haxby_dataset.session_target[0], sep=" ")
y = labels["labels"]
# Keep only data corresponding to shoes or bottles
from nilearn.image import index_img
condition_mask = y.isin(["shoe", "bottle"])
fmri_niimgs = index_img(fmri_img, condition_mask)
y = y[condition_mask]
run = labels["chunks"][condition_mask]
[fetch_haxby] Dataset found in /home/runner/nilearn_data/haxby2001
Mask nifti image (3D) is located at: /home/runner/nilearn_data/haxby2001/mask.nii.gz
Functional nifti image (4D) are located at: /home/runner/nilearn_data/haxby2001/subj2/bold.nii.gz
ANOVA pipeline with Decoder object¶
Nilearn Decoder object aims to provide smooth user experience by acting as a pipeline of several tasks: preprocessing with NiftiMasker, reducing dimension by selecting only relevant features with ANOVA – a classical univariate feature selection based on F-test, and then decoding with different types of estimators (in this example is Support Vector Machine with a linear kernel) on nested cross-validation.
from nilearn.decoding import Decoder
# We provide a grid of hyperparameter values to the Decoder's internal
# cross-validation. If no param_grid is provided, the Decoder will use a
# default grid with sensible values for the chosen estimator
param_grid = [
{
"penalty": ["l2"],
"dual": [True],
"C": [100, 1000],
},
{
"penalty": ["l1"],
"dual": [False],
"C": [100, 1000],
},
]
# Here screening_percentile is set to 2 percent, meaning around 800
# features will be selected with ANOVA.
decoder = Decoder(
estimator="svc",
cv=5,
mask=mask_img,
smoothing_fwhm=4,
screening_percentile=2,
param_grid=param_grid,
verbose=1,
)
Fit the Decoder and predict the responses¶
As a complete pipeline by itself, decoder will perform cross-validation for the estimator, in this case Support Vector Machine. We can output the best parameters selected for each cross-validation fold. See https://scikit-learn.org/stable/modules/cross_validation.html for an excellent explanation of how cross-validation works.
# Fit the Decoder
decoder.fit(fmri_niimgs, y)
# Print the best parameters for each fold
for i, (best_c, best_penalty, best_dual, cv_score) in enumerate(
zip(
decoder.cv_params_["shoe"]["C"],
decoder.cv_params_["shoe"]["penalty"],
decoder.cv_params_["shoe"]["dual"],
decoder.cv_scores_["shoe"],
strict=False,
)
):
print(
f"Fold {i + 1} | Best SVM parameters: C={best_c}"
f", penalty={best_penalty}, dual={best_dual} with score: {cv_score}"
)
# Output the prediction with Decoder
y_pred = decoder.predict(fmri_niimgs)
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc31e350>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:117: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(fmri_niimgs, y)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc31e350>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 2
[Decoder.fit] Corrected screening-percentile: 1.9171
[Decoder.fit] The decoding model will be trained on 399 features.
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 2.9s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
Fold 1 | Best SVM parameters: C=1000, penalty=l1, dual=False with score: 0.9483471074380164
Fold 2 | Best SVM parameters: C=1000, penalty=l2, dual=True with score: 0.9177489177489176
Fold 3 | Best SVM parameters: C=100, penalty=l1, dual=False with score: 0.7489177489177489
Fold 4 | Best SVM parameters: C=100, penalty=l1, dual=False with score: 0.7792207792207793
Fold 5 | Best SVM parameters: C=1000, penalty=l1, dual=False with score: 0.7597402597402597
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc31e350>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Compute prediction scores with different values of screening percentile¶
import numpy as np
screening_percentile_range = [2, 4, 8, 16, 32, 64]
cv_scores = []
val_scores = []
for sp in screening_percentile_range:
decoder = Decoder(
estimator="svc",
mask=mask_img,
smoothing_fwhm=4,
cv=3,
screening_percentile=sp,
param_grid=param_grid,
verbose=1,
)
decoder.fit(index_img(fmri_niimgs, run < 10), y[run < 10])
cv_scores.append(np.mean(decoder.cv_scores_["bottle"]))
print(f"Sreening Percentile: {sp:.3f}")
print(f"Mean CV score: {cv_scores[-1]:.4f}")
y_pred = decoder.predict(index_img(fmri_niimgs, run == 10))
val_scores.append(np.mean(y_pred == y[run == 10]))
print(f"Validation score: {val_scores[-1]:.4f}")
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d92f10490>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:156: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, run < 10), y[run < 10])
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d92f10490>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 2
[Decoder.fit] Corrected screening-percentile: 1.9171
[Decoder.fit] The decoding model will be trained on 399 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.0s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
Sreening Percentile: 2.000
Mean CV score: 0.8300
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d92f139a0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Validation score: 0.4444
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc56f0>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:156: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, run < 10), y[run < 10])
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc56f0>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 4
[Decoder.fit] Corrected screening-percentile: 3.83419
[Decoder.fit] The decoding model will be trained on 1197 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.7s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
Sreening Percentile: 4.000
Mean CV score: 0.8493
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc4be0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Validation score: 0.3889
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc7160>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:156: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, run < 10), y[run < 10])
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc7160>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 8
[Decoder.fit] Corrected screening-percentile: 7.66838
[Decoder.fit] The decoding model will be trained on 2793 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 2.4s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
Sreening Percentile: 8.000
Mean CV score: 0.8700
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc6320>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Validation score: 0.3889
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d98203310>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:156: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, run < 10), y[run < 10])
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d98203310>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 16
[Decoder.fit] Corrected screening-percentile: 15.3368
[Decoder.fit] The decoding model will be trained on 5986 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 3.5s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
Sreening Percentile: 16.000
Mean CV score: 0.8637
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d98201690>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Validation score: 0.7222
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dba81f0d0>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:156: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, run < 10), y[run < 10])
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dba81f0d0>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 32
[Decoder.fit] Corrected screening-percentile: 30.6735
[Decoder.fit] The decoding model will be trained on 11973 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 6.0s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
Sreening Percentile: 32.000
Mean CV score: 0.8733
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d982017e0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Validation score: 0.4444
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc75b0>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:156: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, run < 10), y[run < 10])
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc75b0>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 64
[Decoder.fit] Corrected screening-percentile: 61.3471
[Decoder.fit] The decoding model will be trained on 24346 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 10.1s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
Sreening Percentile: 64.000
Mean CV score: 0.8722
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d985dcac0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Validation score: 0.3333
Nested cross-validation¶
We are going to tune the parameter ‘screening_percentile’ in the pipeline.
import warnings
from sklearn.exceptions import ConvergenceWarning
from sklearn.model_selection import KFold
cv = KFold(n_splits=3)
nested_cv_scores = []
for train, test in cv.split(run):
y_train = np.array(y)[train]
y_test = np.array(y)[test]
val_scores = []
for sp in screening_percentile_range:
with warnings.catch_warnings():
# silence warnings about Liblinear not converging
# increase the number of iterations.
warnings.filterwarnings(
action="ignore", category=ConvergenceWarning
)
decoder = Decoder(
estimator="svc",
mask=mask_img,
smoothing_fwhm=4,
cv=3,
screening_percentile=sp,
param_grid=param_grid,
verbose=1,
)
decoder.fit(index_img(fmri_niimgs, train), y_train)
y_pred = decoder.predict(index_img(fmri_niimgs, test))
val_scores.append(np.mean(y_pred == y_test))
nested_cv_scores.append(np.max(val_scores))
print(f"Nested CV score: {np.mean(nested_cv_scores):.4f}")
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dba81f070>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dba81f070>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 2
[Decoder.fit] Corrected screening-percentile: 1.9171
[Decoder.fit] The decoding model will be trained on 399 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.8s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc31cb50>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d97f20730>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d97f20730>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 4
[Decoder.fit] Corrected screening-percentile: 3.83419
[Decoder.fit] The decoding model will be trained on 1197 features.
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.3s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc93c8b0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbbbe3520>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbbbe3520>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 8
[Decoder.fit] Corrected screening-percentile: 7.66838
[Decoder.fit] The decoding model will be trained on 2793 features.
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 2.3s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbbbe0f10>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbbbe3f70>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbbbe3f70>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 16
[Decoder.fit] Corrected screening-percentile: 15.3368
[Decoder.fit] The decoding model will be trained on 5986 features.
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 3.0s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbbbe2470>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc93c8b0>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc93c8b0>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 32
[Decoder.fit] Corrected screening-percentile: 30.6735
[Decoder.fit] The decoding model will be trained on 11973 features.
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 4.6s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbbbe3a60>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d97f22680>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d97f22680>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 64
[Decoder.fit] Corrected screening-percentile: 61.3471
[Decoder.fit] The decoding model will be trained on 24346 features.
/home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.10/site-packages/sklearn/svm/_base.py:1250: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
warnings.warn(
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 6.9s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d92f10580>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc737250>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc737250>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 2
[Decoder.fit] Corrected screening-percentile: 1.9171
[Decoder.fit] The decoding model will be trained on 399 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.6s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc735000>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc2410>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc2410>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 4
[Decoder.fit] Corrected screening-percentile: 3.83419
[Decoder.fit] The decoding model will be trained on 1197 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.0s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc1ae0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc34c0>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc34c0>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 8
[Decoder.fit] Corrected screening-percentile: 7.66838
[Decoder.fit] The decoding model will be trained on 2793 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.6s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc18d0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc733250>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc733250>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 16
[Decoder.fit] Corrected screening-percentile: 15.3368
[Decoder.fit] The decoding model will be trained on 5986 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 2.6s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc732f80>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc732020>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc732020>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 32
[Decoder.fit] Corrected screening-percentile: 30.6735
[Decoder.fit] The decoding model will be trained on 11973 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 4.0s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc733700>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc1b70>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc1b70>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 64
[Decoder.fit] Corrected screening-percentile: 61.3471
[Decoder.fit] The decoding model will be trained on 24346 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 8.1s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc6e60>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc1db0>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc1db0>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 2
[Decoder.fit] Corrected screening-percentile: 1.9171
[Decoder.fit] The decoding model will be trained on 399 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.7s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc0430>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc735cf0>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc735cf0>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 4
[Decoder.fit] Corrected screening-percentile: 3.83419
[Decoder.fit] The decoding model will be trained on 1197 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.0s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc1c00>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc735840>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc735840>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 8
[Decoder.fit] Corrected screening-percentile: 7.66838
[Decoder.fit] The decoding model will be trained on 2793 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.4s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d38cc2d10>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbbbe0f10>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbbbe0f10>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 16
[Decoder.fit] Corrected screening-percentile: 15.3368
[Decoder.fit] The decoding model will be trained on 5986 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 2.2s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d9801a470>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d96908b20>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d96908b20>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 32
[Decoder.fit] Corrected screening-percentile: 30.6735
[Decoder.fit] The decoding model will be trained on 11973 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 3.6s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbc778a00>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbbbe3e20>
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:199: UserWarning: [NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
decoder.fit(index_img(fmri_niimgs, train), y_train)
[Decoder.fit] Resampling mask
[Decoder.fit] Finished fit
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5dbbbe3e20>
[Decoder.fit] Smoothing images
[Decoder.fit] Extracting region signals
[Decoder.fit] Cleaning extracted signals
[Decoder.fit] Mask volume = 1.96442e+06mm^3 = 1964.42cm^3
[Decoder.fit] Standard brain volume = 1.88299e+06mm^3
[Decoder.fit] Original screening-percentile: 64
[Decoder.fit] Corrected screening-percentile: 61.3471
[Decoder.fit] The decoding model will be trained on 24346 features.
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 7.1s finished
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.fit] Computing image from signals
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f5d98019ff0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Nested CV score: 0.6667
Plot the prediction scores using matplotlib¶
from matplotlib import pyplot as plt
plt.figure(figsize=(6, 4))
plt.plot(cv_scores, label="Cross validation scores")
plt.plot(val_scores, label="Left-out validation data scores")
plt.xticks(
np.arange(len(screening_percentile_range)), screening_percentile_range
)
plt.axis("tight")
plt.xlabel("ANOVA screening percentile")
plt.axhline(
np.mean(nested_cv_scores), label="Nested cross-validation", color="r"
)
plt.legend(loc="best", frameon=False)
show()
Total running time of the script: (1 minutes 57.357 seconds)
Estimated memory usage: 1763 MB
