pandas 如何将数据集拆分为训练集和验证集，保持类之间的比例？
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how to split a dataset into training and validation set keeping ratio between classes?
提问by HymanNova
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.
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，所以如果我有 100 个特定类的实例，我想在类之间拆分我的数据集保持比率并且我希望 30% 的记录进入训练集中我希望有我的 100 个记录代表类的 30 个实例和我的 10 个记录代表类的 3 个实例等等。
采纳答案by EdChum
You can use sklearn's StratifiedKFold, from the online docs:
您可以使用 sklearn's StratifiedKFold，来自在线文档：
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.
分层 K 折交叉验证迭代器
提供训练/测试索引以拆分训练测试集中的数据。
这个交叉验证对象是 KFold 的变体，它返回分层折叠。通过保留每个类别的样本百分比来进行折叠。
>>> 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.
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。
As suggested by @Ali_m you could use StratifiedShuffledSplitwhich accepts a split ratio param:
正如@Ali_m 所建议的，您可以使用StratifiedShuffledSplit它接受分流比参数：
sss = StratifiedShuffleSplit(y, 3, test_size=0.7, random_state=0)
sss = StratifiedShuffleSplit(y, 3, test_size=0.7, random_state=0)
would produce a 70% split.
将产生 70% 的分割。
回答by Th? Sinh
As simple as :
就像这样简单：
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,
stratify=y,
test_size=0.25)
