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,