Python 之 sklearn 交叉验证数据拆分

分手后的思念是犯贱 2022-08-03 03:08 185阅读 0赞

本文K折验证拟采用的是 Python 中 sklearn 包中的 [StratifiedKFold][] 方法。

**方法思想详见：****http://scikit-learn.org/stable/modules/cross\_validation.html**

[StratifiedKFold][] is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set.

【译】

[StratifiedKFold][] 是一种将数据集中每一类样本的数据成分，按均等方式拆分的方法。

其它划分方法详见：**http://scikit-learn.org/stable/modules/cross\_validation.html**

**闲言少叙，直接上代码。**

**【屌丝源码】**

import numpy
    import h5py
    import sklearn
    from sklearn import cluster,cross_validation
    from sklearn.cluster import AgglomerativeClustering
    from sklearn.cross_validation import StratifiedKFold
    
    ## 生成一个随机矩阵并保存
    #arr = numpy.random.random([200,400])
    #labvec = []
    #for i in numpy.arange(0,200):
    #    j = i%10
    #    arr[i,j*20:j*20+20] = arr[i,j*20:j*20+20]+10
    #    labvec.append(j)
    #arr = arr.T
    #file = h5py.File('arr.mat','w')    
    #file.create_dataset('arr', data = arr)
    #file.close()
    #file = h5py.File('labvec.mat','w')    
    #file.create_dataset('labvec', data = labvec)
    #file.close()
    # 读方式打开文件
    myfile=h5py.File('arr.mat','r')
    arr = myfile['arr'][:]
    myfile.close()
    arr = arr.T
    myfile=h5py.File('labvec.mat','r')
    labvec = myfile['labvec'][:]
    myfile.close()
    skf = StratifiedKFold(labvec, 4)
    train_set = []
    test_set = []
    for train, test in skf:
        train_set.append(train)
        test_set.append(test)

**详见：** **http://scikit-learn.org/stable/modules/cross\_validation.html**

[StratifiedKFold]: http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.StratifiedKFold.html#sklearn.cross_validation.StratifiedKFold