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,
standardize="zscore_sample",
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 data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12ed750>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:118: 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
0x7f0fc12ed750>
[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
[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 2.8s 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
Fold 1 | Best SVM parameters: C=1000, penalty=l1, dual=False with score: 0.9173553719008264
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: 0.7987012987012987
Fold 4 | Best SVM parameters: C=100, penalty=l1, dual=False with score: 0.8073593073593074
Fold 5 | Best SVM parameters: C=1000, penalty=l2, dual=True with score: 0.735930735930736
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12ed750>
[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,
standardize="zscore_sample",
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 data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12ef7c0>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:158: 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
0x7f0fc12ef7c0>
[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
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.1s 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: 2.000
Mean CV score: 0.8307
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12ef1c0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Validation score: 0.6111
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12ec850>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:158: 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
0x7f0fc12ec850>
[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
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.5s 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: 4.000
Mean CV score: 0.8493
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12ec520>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Validation score: 0.3889
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12eee30>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:158: 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
0x7f0fc12eee30>
[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
[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: 0.8456
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12ee530>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Validation score: 0.6667
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12ed4b0>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:158: 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
0x7f0fc12ed4b0>
[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
[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: 0.8593
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12eed40>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Validation score: 0.5556
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12ecc10>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:158: 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
0x7f0fc12ecc10>
[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
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 5.9s 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
Sreening Percentile: 32.000
Mean CV score: 0.8844
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12ee680>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Validation score: 0.3333
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12ec3d0>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:158: 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
0x7f0fc12ec3d0>
[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
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 10.8s 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
Sreening Percentile: 64.000
Mean CV score: 0.8467
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12edc00>
[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,
standardize="zscore_sample",
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 data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc12ee440>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc12ee440>
[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
[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
0x7f0fe7d54190>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d8eb00>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d8eb00>
[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
/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
0x7f0fe7db1a50>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d8dcc0>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d8dcc0>
[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
/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: 2.5s 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
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d8dc90>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d25f00>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d25f00>
[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
/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
0x7f0fe7be4a00>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d27580>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d27580>
[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
/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: 4.5s 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
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5a760b0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d26020>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d26020>
[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
/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: 7.6s 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
0x7f0fe64666e0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d27580>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d27580>
[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
[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
0x7f0fc5d89690>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d24c70>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d24c70>
[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
[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
0x7f0fc5a75d20>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d27fd0>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d27fd0>
[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
[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
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d8eb00>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d24700>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d24700>
[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
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 2.5s 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
[Decoder.predict] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5a772b0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d26620>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d26620>
[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
[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
0x7f0fc5d8dc90>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d27a60>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d27a60>
[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
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 7.9s 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
0x7f0fc5a741f0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d24d60>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d24d60>
[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
[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
0x7f0fc5d8d360>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d27d30>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d27d30>
[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
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.9s 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
0x7f0fc5a77df0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d26020>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d26020>
[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
[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
0x7f0fc5d8dcc0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d25f60>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d25f60>
[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
[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
0x7f0fc5a75d20>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d27700>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d27700>
[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
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 4.2s 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
0x7f0fc5d8eb00>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
[Decoder.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f0fc5d27c40>
[Decoder.fit] Loading mask from
'/home/runner/nilearn_data/haxby2001/mask.nii.gz'
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:202: 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
0x7f0fc5d27c40>
[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
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 6.7s 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
0x7f0fc5a772b0>
[Decoder.predict] Smoothing images
[Decoder.predict] Extracting region signals
[Decoder.predict] Cleaning extracted signals
Nested CV score: 0.6713
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()
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_haxby_grid_search.py:229: UserWarning: You are using the 'agg' matplotlib backend that is non-interactive.
No figure will be plotted when calling matplotlib.pyplot.show() or nilearn.plotting.show().
You can fix this by installing a different backend: for example via
pip install PyQt6
show()
Total running time of the script: (2 minutes 25.343 seconds)
Estimated memory usage: 1002 MB
