# In the example below, two analyses are produced, one using an RBF kernel and a second one
# using a linear kernel.
# 2. Parameters defined as a nested list [[Param1, Param2, .. ParamN]] are iterated over in a
# nested leave-one-out cross-validation procedure using GridSearchCV, to find the optimal
# parameter in each fold.
# In the example below, a gridsearch is performed for the parameter C on the values 0.1 and 1,
# both for the analysis using an RBF kernel and for the analysis using a linear kernel.
clf_args = dict(kernel=['rbf', 'linear'], C=[[0.1, 1]])
# Here we use a variance-based feature selection and start two analyses with two different
# thresholds. Please note that Decereb expands all permutations of classifier parameters and feature
# selection parameters, i.e. in this case the code produces 2x2=4 analyses.
fs = 'variance'
fs_args = dict(threshold=[0.01, 0.1])
analysis = SimpleChain(data=X, clf=clf, clf_args=clf_args, fs=fs, fs_args=fs_args, labels=y)
# n_jobs_links: distribute different analyses across n_jobs_links processors
# n_jobs_folds: distribute cross-validation folds of each analysis across n_jobs_folds processors
# verbose: increase in order to view more detailed results (e.g. fold-wise results)
# output_path: directory to store the result
# skip_ioerror: set True if the analysis chain should continue despite an I/O error when saving the,
# previous result
# skip_runerror: set True if the analysis chain should continue despite any runtime error in the
# previous analysis
# detailed_save: save more detailed results (currently undocumented)
result = analysis.run(n_jobs_links=1, n_jobs_folds=1, verbose=1, output_path='/tmp/decereb/',
skip_ioerror=False, skip_runerror=False, detailed_save=True)
print('Finished example!')
# Decereb results are stored in a simple SQL-based database (https://dataset.readthedocs.org/),
import os import numpy as np import nibabel from decereb.chain import SimpleChain, Data from decereb.estimators.searchlight import SearchLight from nilearn import datasets from nilearn.image import index_img from nilearn._utils import check_niimg_4d from sklearn.cross_validation import KFold haxby_dataset = datasets.fetch_haxby_simple() conditions = np.recfromtxt(haxby_dataset.conditions_target)['f0'] condition_mask = np.logical_or(conditions == beta'face', conditions == beta'house') labels = conditions[condition_mask] fmri_img = nibabel.load(haxby_dataset.func) fmri_img = index_img(fmri_img, condition_mask) fmri_img = [img for img in check_niimg_4d(fmri_img, return_iterator=True)] mask_img = nibabel.load(haxby_dataset.mask) cv = KFold(len(labels), n_folds=4) clf_args = dict(mask_img=mask_img, process_mask_img=mask_img, cv=cv, radius=5.6) data = Data(data=fmri_img, labels=labels) chain = SimpleChain(clf=SearchLight, clf_args=clf_args, data=data) root = os.path.dirname(haxby_dataset['session_target']) output_path = os.path.join(root, 'searchlight.nii') chain.run(n_jobs_folds=1, verbose=3, output_path=output_path)
# 2. Parameters defined as a nested list [[Param1, Param2, .. ParamN]] are iterated over in a
# nested leave-one-out cross-validation procedure using GridSearchCV, to find the optimal
# parameter in each fold.
# In the example below, a gridsearch is performed for the parameter alpha on the values 0.1 and 1,
# both for the analysis using an RBF kernel and for the analysis using a linear kernel.
clf_args = dict(kernel=['rbf', 'linear'], C=[[0.1, 1]])
# Here we use a variance-based feature selection and start two analyses with two different
# thresholds. Please note that Decereb expands all permutations of classifier parameters and feature
# selection parameters, i.e. in this case the code produces 2x2=4 analyses.
fs = 'variance'
fs_args = dict(threshold=[0.01, 0.1])
analysis = SimpleChain(data=X,
clf=clf,
clf_args=clf_args,
fs=fs,
fs_args=fs_args,
labels=y)
# n_jobs_links: distribute different analyses across n_jobs_links processors
# n_jobs_folds: distribute cross-validation folds of each analysis across n_jobs_folds processors
# verbose: increase in order to view more detailed results (e.g. fold-wise results)
# output_path: directory to store the result
# skip_ioerror: set True if the analysis chain should continue despite an I/O error when saving the,
# previous result
# skip_runerror: set True if the analysis chain should continue despite any runtime error in the
# previous analysis
# detailed_save: save more detailed results (currently undocumented)
result = analysis.run(n_jobs_links=1,
n_jobs_folds=1,
verbose=1,