sklearn.cross_validation.StratifiedShuffleSplit - error: "indices are out-of-bounds"

Question

我试图使用 Scikit-learn 的分层随机拆分拆分示例数据集。我遵循了 Scikit-learn 文档中显示的示例 here

import pandas as pd
import numpy as np
# UCI's wine dataset
wine = pd.read_csv("https://s3.amazonaws.com/demo-datasets/wine.csv")

# separate target variable from dataset
target = wine['quality']
data = wine.drop('quality',axis = 1)

# Stratified Split of train and test data
from sklearn.cross_validation import StratifiedShuffleSplit
sss = StratifiedShuffleSplit(target, n_iter=3, test_size=0.2)

for train_index, test_index in sss:
    xtrain, xtest = data[train_index], data[test_index]
    ytrain, ytest = target[train_index], target[test_index]

# Check target series for distribution of classes
ytrain.value_counts()
ytest.value_counts()

但是，在运行这个脚本上，我收到以下错误：

IndexError: indices are out-of-bounds

有人可以指出我在这里做错了什么吗？谢谢！

Answer 1

您运行进入 Pandas DataFrame 索引与 NumPy ndarray 索引的不同约定。数组 train_index 和 test_index 是行索引的集合。但是 data 是一个 Pandas DataFrame 对象，当您对该对象使用单个索引时，如 data[train_index]，Pandas 期望 train_index 以包含列标签而不是行索引。您可以使用 .values:

将数据帧转换为 NumPy 数组

data_array = data.values
for train_index, test_index in sss:
    xtrain, xtest = data_array[train_index], data_array[test_index]
    ytrain, ytest = target[train_index], target[test_index]

或使用 Pandas .iloc 访问器：

for train_index, test_index in sss:
    xtrain, xtest = data.iloc[train_index], data.iloc[test_index]
    ytrain, ytest = target[train_index], target[test_index]

我倾向于第二种方法，因为它给出 xtrain 和 xtest 类型 DataFrame 而不是 ndarray，因此保留了列标签。

sklearn.cross_validation.StratifiedShuffleSplit - error: "indices are out-of-bounds"

sklearn.cross_validation.StratifiedShuffleSplit - error: "indices are out-of-bounds"

python

pandas

scikit-learn