Shows the case where nilearn.decoding.SearchLight gives an error if the cv parameter passed is a generator not an iterable. (line# 75 onwards)

iterables_for_SearchLight.py

"""
From 
nilearn/examples/02_decoding/plot_haxby_searchlight.py
Erring code section is after Line # 75.
"""
"""
Searchlight analysis of face vs house recognition
==================================================

Searchlight analysis requires fitting a classifier a large amount of
times. As a result, it is an intrinsically slow method. In order to speed
up computing, in this example, Searchlight is run only on one slice on
the fMRI (see the generated figures).

"""

#########################################################################
# Load Haxby dataset
# -------------------
import pandas as pd
from nilearn import datasets
from nilearn.image import new_img_like, load_img

# We fetch 2nd subject from haxby datasets (which is default)
haxby_dataset = datasets.fetch_haxby()

# print basic information on the dataset
print('Anatomical nifti image (3D) is located at: %s' % haxby_dataset.mask)
print('Functional nifti image (4D) is located at: %s' % haxby_dataset.func[0])

fmri_filename = haxby_dataset.func[0]
labels = pd.read_csv(haxby_dataset.session_target[0], sep=" ")
y = labels['labels']
session = labels['chunks']

#########################################################################
# Restrict to faces and houses
# ------------------------------
from nilearn.image import index_img
condition_mask = y.isin(['face', 'house'])

fmri_img = index_img(fmri_filename, condition_mask)
y, session = y[condition_mask], session[condition_mask]

#########################################################################
# Prepare masks
# --------------
# - mask_img is the original mask
# - process_mask_img is a subset of mask_img, it contains the voxels that
#   should be processed (we only keep the slice z = 26 and the back of the
#   brain to speed up computation)
import numpy as np

mask_img = load_img(haxby_dataset.mask)

# .astype() makes a copy.
process_mask = mask_img.get_data().astype(np.int)
picked_slice = 29
process_mask[..., (picked_slice + 1):] = 0
process_mask[..., :picked_slice] = 0
process_mask[:, 30:] = 0
process_mask_img = new_img_like(mask_img, process_mask)

#########################################################################
# Searchlight computation
# -------------------------

# Make processing parallel
# /!\ As each thread will print its progress, n_jobs > 1 could mess up the
#     information output.
n_jobs = 1

# 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

'''

------------- COMMON SECTION ENDS -------------------------------

sklearn.cross_validation.KFold section begins

'''
from sklearn.cross_validation import KFold as KFold_cv
cross_validation_cv = KFold_cv(y.size, n_folds=4)  # This is an iterable.

import nilearn.decoding
# The radius is the one of the Searchlight sphere that will scan the volume
searchlight = nilearn.decoding.SearchLight(
    mask_img,
    process_mask_img=process_mask_img,
    radius=5.6, n_jobs=n_jobs,
    verbose=1, cv=cross_validation_cv)  # THIS WORKS.
searchlight.fit(fmri_img, y)  # No Error.

'''

sklearn.model_selection.KFold section begins

'''

from sklearn.model_selection import KFold as KFold_ms
model_selection_cv = KFold_ms(n_splits=4)  # model_selection_cv.split() gives a generator.

import nilearn.decoding
# The radius is the one of the Searchlight sphere that will scan the volume
searchlight = nilearn.decoding.SearchLight(
    mask_img,
    process_mask_img=process_mask_img,
    radius=5.6, n_jobs=n_jobs,
    verbose=1,
        #     THIS FAILS. It worked in other replacements I made so far, but not with SearchLight.
        cv=model_selection_cv.split(X=y),   
        )
searchlight.fit(fmri_img, y)  # The Error Occurs in the fit() function.

searchlight = nilearn.decoding.SearchLight(
    mask_img,
    process_mask_img=process_mask_img,
    radius=5.6, n_jobs=n_jobs,
    verbose=1,
        #     THIS WORKS. It is inconsitent, and new users might not understand why this is being done.
        cv=list(model_selection_cv.split(X=y)),   
        )
searchlight.fit(fmri_img, y)  # No Error.