from sklearn.svm import SVC from sklearn.feature_selection import SelectKBest, f_classif from sklearn.cross_validation import StratifiedKFold from sklearn.cross_validation import cross_val_score from sklearn.cross_validation import LeaveOneLabelOut from sklearn.cross_validation import permutation_test_score from sklearn.pipeline import Pipeline from sklearn.dummy import DummyClassifier from nilearn.input_data import NiftiMasker import numpy as np import fmriUtils as fm n_folds = 10 f=fm.outTo() X,y = fm.loadData() y = fm.defineClass(y) XX = X yy = y #使用SVM分类和预测 svc = SVC(kernel='linear') # feature_selection = SelectKBest(f_classif, k=1000) anova_svc = Pipeline([('anova', feature_selection), ('svc', svc)]) cv = StratifiedKFold(yy, n_folds=n_folds) cv_scores = []
# -*- coding: utf-8 -*- """ 使用递归特征消除选取特征 """ from sklearn.svm import SVC from sklearn.cross_validation import StratifiedKFold from sklearn.feature_selection import RFECV import numpy as np import fmriUtils as fm n_folds = 5 f = fm.outTo() #输出重定向到文件 X, y = fm.loadData2() y = fm.defineClass(y) svc = SVC(kernel="linear") rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(y, n_folds), scoring='accuracy') rfecv.fit(X, y) print("Optimal number of features : %d" % rfecv.n_features_)
Created on Sun Jul 24 18:24:10 2016 @author: FF120 """ import numpy as np from time import time from sklearn.svm import SVC from sklearn.ensemble import ExtraTreesClassifier from sklearn.feature_selection import SelectFromModel from sklearn.cross_validation import StratifiedKFold import fmriUtils as fm n_folds = 10 f = fm.outTo() #输出重定向到文件 X,y = fm.loadData2() y = fm.defineClass(y) t0 = time() forest = ExtraTreesClassifier(n_estimators=1000, max_features=1000, n_jobs=2, random_state=0) forest.fit(X, y) model = SelectFromModel(forest, threshold='2*mean',prefit=True) XX = model.transform(X) yy = y cv = StratifiedKFold(yy,n_folds) cv_scores = []
from sklearn.svm import SVC from sklearn.feature_selection import SelectKBest, f_classif from sklearn.cross_validation import StratifiedKFold from sklearn.cross_validation import cross_val_score from sklearn.cross_validation import LeaveOneLabelOut from sklearn.cross_validation import permutation_test_score from sklearn.pipeline import Pipeline from sklearn.dummy import DummyClassifier from nilearn.input_data import NiftiMasker import numpy as np import fmriUtils as fm n_folds = 10 f = fm.outTo() X, y = fm.loadData() y = fm.defineClass(y) XX = X yy = y #使用SVM分类和预测 svc = SVC(kernel='linear') # feature_selection = SelectKBest(f_classif, k=1000) anova_svc = Pipeline([('anova', feature_selection), ('svc', svc)]) cv = StratifiedKFold(yy, n_folds=n_folds) cv_scores = []