### Define the cross-validation scheme used for validation.
# Here we use a KFold cross-validation on the session, which corresponds to
# splitting the samples in 4 folds and make 4 runs using each fold as a test
# set once and the others as learning sets
from sklearn.cross_validation import KFold
cv = KFold(y.size, k=4)
### Fit #######################################################################
from nisl import searchlight
# The radius is the one of the Searchlight sphere that will scan the volume
searchlight = searchlight.SearchLight(mask, process_mask, radius=1.5,
n_jobs=n_jobs, score_func=score_func, verbose=1, cv=cv)
searchlight.fit(X, y)
### Visualization #############################################################
import pylab as pl
pl.figure(1)
# searchlight.scores_ contains per voxel cross validation scores
s_scores = np.ma.array(searchlight.scores_, mask=np.logical_not(process_mask))
pl.imshow(np.rot90(mean_img[..., 37]), interpolation='nearest',
cmap=pl.cm.gray)
pl.imshow(np.rot90(s_scores[..., 37]), interpolation='nearest',
cmap=pl.cm.hot, vmax=1)
pl.axis('off')
pl.title('Searchlight')
pl.show()
### Show the F_score
### Define the cross-validation scheme used for validation.
# Here we use a KFold cross-validation on the session, which corresponds to
# splitting the samples in 4 folds and make 4 runs using each fold as a test
# set once and the others as learning sets
from sklearn.cross_validation import KFold
cv = KFold(y.size, k=4)
### Fit #######################################################################
from nisl import searchlight
# The radius is the one of the Searchlight sphere that will scan the volume
searchlight = searchlight.SearchLight(mask, process_mask, radius=1.5,
n_jobs=n_jobs, score_func=score_func, verbose=1, cv=cv)
searchlight.fit(X_preprocessed, y)
### Visualization #############################################################
import pylab as pl
pl.figure(1)
# searchlight.scores_ contains per voxel cross validation scores
s_scores = np.ma.array(searchlight.scores_, mask=np.logical_not(process_mask))
pl.imshow(np.rot90(mean_img[..., 37]), interpolation='nearest',
cmap=pl.cm.gray)
pl.imshow(np.rot90(s_scores[..., 37]), interpolation='nearest',
cmap=pl.cm.hot, vmax=1)
pl.axis('off')
pl.title('Searchlight')
pl.show()
### Show the F_score
cv = KFold(y.size, k=4)
### Fit #######################################################################
from nisl import searchlight
# The radius is the one of the Searchlight sphere that will scan the volume
searchlight = searchlight.SearchLight(mask,
process_mask,
radius=1.5,
n_jobs=n_jobs,
score_func=score_func,
verbose=1,
cv=cv)
searchlight.fit(X_detrended, y)
### Visualization #############################################################
import pylab as pl
pl.figure(1)
# searchlight.scores_ contains per voxel cross validation scores
s_scores = np.ma.array(searchlight.scores_, mask=np.logical_not(process_mask))
pl.imshow(np.rot90(mean_img[..., 37]),
interpolation='nearest',
cmap=pl.cm.gray)
pl.imshow(np.rot90(s_scores[..., 37]),
interpolation='nearest',
cmap=pl.cm.hot,
vmax=1)
pl.axis('off')
pl.title('Searchlight')
### Define the cross-validation scheme used for validation.
# Here we use a KFold cross-validation on the session, which corresponds to
# splitting the samples in 4 folds and make 4 runs using each fold as a test
# set once and the others as learning sets
from sklearn.cross_validation import KFold
cv = KFold(y.size, k=4)
### Fit #######################################################################
from nisl import searchlight
# The radius is the one of the Searchlight sphere that will scan the volume
searchlight = searchlight.SearchLight(mask, process_mask, radius=1.5,
n_jobs=n_jobs, score_func=score_func, verbose=1, cv=cv)
searchlight.fit(X_detrended, y)
### Visualization #############################################################
import pylab as pl
pl.figure(1)
# searchlight.scores_ contains per voxel cross validation scores
s_scores = np.ma.array(searchlight.scores_, mask=np.logical_not(process_mask))
pl.imshow(np.rot90(mean_img[..., 37]), interpolation='nearest',
cmap=pl.cm.gray)
pl.imshow(np.rot90(s_scores[..., 37]), interpolation='nearest',
cmap=pl.cm.hot, vmax=1)
pl.axis('off')
pl.title('Searchlight')
pl.show()
### Show the F_score
# Here we use a KFold cross-validation on the session, which corresponds to
# splitting the samples in 4 folds and make 4 runs using each fold as a test
# set once and the others as learning sets
from sklearn.cross_validation import KFold
cv = KFold(y.size, k=4)
### Fit #######################################################################
from nisl import searchlight
# The radius is the one of the Searchlight sphere that will scan the volume
searchlight = searchlight.SearchLight(mask=mask, process_mask=process_mask,
radius=1.5, n_jobs=n_jobs,
score_func=score_func, verbose=1, cv=cv)
searchlight.fit(X_masked, y)
### Visualization #############################################################
import pylab as pl
pl.figure(1)
# searchlight.scores_ contains per voxel cross validation scores
s_scores = np.ma.array(searchlight.scores_, mask=np.logical_not(process_mask))
pl.imshow(np.rot90(mean_img[..., 37]), interpolation='nearest',
cmap=pl.cm.gray)
pl.imshow(np.rot90(s_scores[..., 37]), interpolation='nearest',
cmap=pl.cm.hot, vmax=1)
pl.axis('off')
pl.title('Searchlight')
pl.show()
### Show the F_score
cv = KFold(y.size, k=4)
### Fit #######################################################################
from nisl import searchlight
# The radius is the one of the Searchlight sphere that will scan the volume
searchlight = searchlight.SearchLight(mask=mask,
process_mask=process_mask,
radius=1.5,
n_jobs=n_jobs,
score_func=score_func,
verbose=1,
cv=cv)
searchlight.fit(X_masked, y)
### Visualization #############################################################
import pylab as pl
pl.figure(1)
# searchlight.scores_ contains per voxel cross validation scores
s_scores = np.ma.array(searchlight.scores_, mask=np.logical_not(process_mask))
pl.imshow(np.rot90(mean_img[..., 37]),
interpolation='nearest',
cmap=pl.cm.gray)
pl.imshow(np.rot90(s_scores[..., 37]),
interpolation='nearest',
cmap=pl.cm.hot,
vmax=1)
pl.axis('off')
pl.title('Searchlight')
