如何将数据集拆分为训练集和验证集，保持类之间的比例? [英] how to split a dataset into training and validation set keeping ratio between classes?

问题描述

我有一个多类分类问题，我的数据集有偏差，我有一个特定类的 100 个实例，并且说一些不同类的 10 个，所以我想在类之间拆分我的数据集保持比率，如果我有 100 个实例一个特定的类，我希望 30% 的记录进入训练集中，我希望有 30 个实例代表我的 100 个记录类和 3 个实例代表我的 10 个记录类等等.

解决方案

可以使用 sklearn 的 StratifiedKFold，来自在线文档:

<块引用>

分层 K 折交叉验证迭代器

提供训练/测试在训练测试集中拆分数据的索引.

这个交叉验证对象是 KFold 的一种变体，它返回分层折叠.褶皱是通过保留每个类的样本百分比来制作.

<预><代码>>>>从 sklearn 导入 cross_validation>>>X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])>>>y = np.array([0, 0, 1, 1])>>>skf = cross_validation.StratifiedKFold(y, n_folds=2)>>>长度(skf)2>>>打印(skf)sklearn.cross_validation.StratifiedKFold(labels=[0 0 1 1], n_folds=2,shuffle=False，random_state=None)>>>对于 skf 中的 train_index、test_index:...打印(火车:"，train_index，测试:"，test_index)... X_train, X_test = X[train_index], X[test_index]... y_train, y_test = y[train_index], y[test_index]训练:[1 3] 测试:[0 2]训练:[0 2] 测试:[1 3]

这将保留您的班级比率，以便拆分保留班级比率，这将适用于 Pandas dfs.

根据@Ali_m 的建议，您可以使用 StratifiedShuffledSplit 接受分流比参数:

sss = StratifiedShuffleSplit(y, 3, test_size=0.7, random_state=0)

会产生 70% 的分割.

I have a multi class classification problem and my dataset is skewed, I have 100 instances of a particular class and say 10 of some different class, so I want to split my dataset keeping ratio between classes, if I have 100 instances of a particular class and I want 30% of records to go in the training set I want to have there 30 instances of my 100 record represented class and 3 instances of my 10 record represented class and so on.

解决方案

You can use sklearn's StratifiedKFold, from the online docs:

Stratified K-Folds cross validation iterator

Provides train/test indices to split data in train test sets.

This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class.

>>> from sklearn import cross_validation
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> skf = cross_validation.StratifiedKFold(y, n_folds=2)
>>> len(skf)
2
>>> print(skf)  
sklearn.cross_validation.StratifiedKFold(labels=[0 0 1 1], n_folds=2,
                                         shuffle=False, random_state=None)
>>> for train_index, test_index in skf:
...    print("TRAIN:", train_index, "TEST:", test_index)
...    X_train, X_test = X[train_index], X[test_index]
...    y_train, y_test = y[train_index], y[test_index]
TRAIN: [1 3] TEST: [0 2]
TRAIN: [0 2] TEST: [1 3]

This will preserve your class ratios so that the splits retain the class ratios, this will work fine with pandas dfs.

As suggested by @Ali_m you could use StratifiedShuffledSplit which accepts a split ratio param:

sss = StratifiedShuffleSplit(y, 3, test_size=0.7, random_state=0)

would produce a 70% split.

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