Python outTo示例

示例#1

文件： classify_use_selectKBest.py 项目： FF120/python

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 = []

示例#2

# -*- 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_)

示例#3

文件： classify_use_tree.py 项目： FF120/python

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 = []

示例#4

文件： classify_use_selectKBest.py 项目： FF120/python

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 = []