from base.mv import best_subsets
from base.classifiers import OnevsallContinuous
from sklearn.metrics import roc_auc_score
o_clf = OnevsallContinuous.load('classifier.pkl')
best_subsets(o_clf, roc_auc_score, outfile='results/nine_regions.csv')
import cPickle
from sklearn.decomposition import RandomizedPCA
dataset = Dataset.load('../data/datasets/abs_60topics_filt_jul.pkl')
roi_mask = '../masks/mpfc_nfp.nii.gz'
global_mask = "../masks/MNI152_T1_2mm_brain.nii.gz"
n_regions = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# print resolution
clf_file = "../data/clfs/all_vox_Ridge_mpfc.pkl"
print "trying to load"
try:
clf = OnevsallContinuous.load(clf_file)
except:
print "Loading failed"
clf = OnevsallContinuous(dataset, None, classifier=Ridge())
clf.classify(scoring=r2_score, processes=8)
try:
clf.save(clf_file)
except:
pass
reduc = RandomizedPCA(n_components=100)
print "Setting up clustering"
clstr = cluster.Clusterer(dataset,
'coactivation',
global_mask=global_mask,
import cPickle
from sklearn.decomposition import RandomizedPCA
dataset = Dataset.load('../data/datasets/abs_60topics_filt_jul.pkl')
roi_mask = '../masks/mpfc_nfp.nii.gz'
global_mask = "../masks/MNI152_T1_2mm_brain.nii.gz"
n_regions = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# print resolution
clf_file = "../data/clfs/all_vox_Ridge_mpfc.pkl"
print "trying to load"
try:
clf = OnevsallContinuous.load(clf_file)
except:
print "Loading failed"
clf = OnevsallContinuous(dataset, None, classifier = Ridge())
clf.classify(scoring=r2_score, processes=8)
try:
clf.save(clf_file)
except:
pass
reduc = RandomizedPCA(n_components=100)
print "Setting up clustering"
clstr = cluster.Clusterer(dataset, 'coactivation', global_mask=global_mask,
output_dir='../results/cluster/coact_PCA_mpfc/',
roi_mask=roi_mask, min_studies_per_voxel=25, voxel_parcellation=reduc,
