Question

我正在使用sklearn进行多分类任务。我需要将alldata拆分为train_set和test_set。我想从每个班级中随机抽取相同的样本编号。实际上，我正在使用这个功能

X_train, X_test, y_train, y_test = cross_validation.train_test_split(Data, Target, test_size=0.3, random_state=0)

但它提供了不平衡的数据集！任何建议。

Answer 1

您可以使用StratifiedShuffleSplit创建与原始类别相同百分比的数据集：

import numpy as np
from sklearn.cross_validation import StratifiedShuffleSplit
X = np.array([[1, 3], [3, 7], [2, 4], [4, 8]])
y = np.array([0, 1, 0, 1])
stratSplit = StratifiedShuffleSplit(y, 1, test_size=0.5,random_state=42)
StratifiedShuffleSplit(y, n_iter=1, test_size=0.5)
for train_idx,test_idx in stratSplit:
    X_train=X[train_idx]
    y_train=y[train_idx]
print(X_train)
print(y_train)
//[[3 7]
// [2 4]]
//[1 0]

Answer 2

虽然克里斯蒂安的建议是正确的，但技术上train_test_split应该使用stratify参数给你分层结果。

所以你可以这样做：

X_train, X_test, y_train, y_test = cross_validation.train_test_split(Data, Target, test_size=0.3, random_state=0, stratify=Target)

这里的诀窍是从0.17中的版本 sklearn开始。

有关参数stratify的文档：

分层：类似数组或无（默认为无）如果不是None，则数据以分层方式分割，使用此作为标签数组。版本0.17中的新功能：分层拆分

Answer 3

如果课程不平衡但你想要平衡分裂，那么分层就无济于事了。似乎没有一种方法可以在sklearn中进行平衡采样，但是使用基本的numpy很容易，例如像这样的函数可能对你有帮助：

def split_balanced(data, target, test_size=0.2):

    classes = np.unique(target)
    # can give test_size as fraction of input data size of number of samples
    if test_size<1:
        n_test = np.round(len(target)*test_size)
    else:
        n_test = test_size
    n_train = max(0,len(target)-n_test)
    n_train_per_class = max(1,int(np.floor(n_train/len(classes))))
    n_test_per_class = max(1,int(np.floor(n_test/len(classes))))

    ixs = []
    for cl in classes:
        if (n_train_per_class+n_test_per_class) > np.sum(target==cl):
            # if data has too few samples for this class, do upsampling
            # split the data to training and testing before sampling so data points won't be
            #  shared among training and test data
            splitix = int(np.ceil(n_train_per_class/(n_train_per_class+n_test_per_class)*np.sum(target==cl)))
            ixs.append(np.r_[np.random.choice(np.nonzero(target==cl)[0][:splitix], n_train_per_class),
                np.random.choice(np.nonzero(target==cl)[0][splitix:], n_test_per_class)])
        else:
            ixs.append(np.random.choice(np.nonzero(target==cl)[0], n_train_per_class+n_test_per_class,
                replace=False))

    # take same num of samples from all classes
    ix_train = np.concatenate([x[:n_train_per_class] for x in ixs])
    ix_test = np.concatenate([x[n_train_per_class:(n_train_per_class+n_test_per_class)] for x in ixs])

    X_train = data[ix_train,:]
    X_test = data[ix_test,:]
    y_train = target[ix_train]
    y_test = target[ix_test]

    return X_train, X_test, y_train, y_test

请注意，如果您使用此项并为每个类采样的点数多于输入数据中的点数，则会对这些点进行上采样（带替换的样本）。结果，一些数据点将出现多次，这可能会对精度度量等产生影响。如果某个类只有一个数据点，则会出现错误。您可以使用np.unique(target, return_counts=True)

轻松检查每个班级的积分数

Answer 4

这是我用来获取训练/测试数据索引的实现

def get_safe_balanced_split(target, trainSize=0.8, getTestIndexes=True, shuffle=False, seed=None):
    classes, counts = np.unique(target, return_counts=True)
    nPerClass = float(len(target))*float(trainSize)/float(len(classes))
    if nPerClass > np.min(counts):
        print("Insufficient data to produce a balanced training data split.")
        print("Classes found %s"%classes)
        print("Classes count %s"%counts)
        ts = float(trainSize*np.min(counts)*len(classes)) / float(len(target))
        print("trainSize is reset from %s to %s"%(trainSize, ts))
        trainSize = ts
        nPerClass = float(len(target))*float(trainSize)/float(len(classes))
    # get number of classes
    nPerClass = int(nPerClass)
    print("Data splitting on %i classes and returning %i per class"%(len(classes),nPerClass ))
    # get indexes
    trainIndexes = []
    for c in classes:
        if seed is not None:
            np.random.seed(seed)
        cIdxs = np.where(target==c)[0]
        cIdxs = np.random.choice(cIdxs, nPerClass, replace=False)
        trainIndexes.extend(cIdxs)
    # get test indexes
    testIndexes = None
    if getTestIndexes:
        testIndexes = list(set(range(len(target))) - set(trainIndexes))
    # shuffle
    if shuffle:
        trainIndexes = random.shuffle(trainIndexes)
        if testIndexes is not None:
            testIndexes = random.shuffle(testIndexes)
    # return indexes
    return trainIndexes, testIndexes

如何在sklearn上平衡训练集和测试集上的数据

4 个答案: