pandas sklearn.cross_validation.StratifiedShuffleSplit - 错误：“索引越界”
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sklearn.cross_validation.StratifiedShuffleSplit - error: "indices are out-of-bounds"
提问by Jason
I was trying to split the sample dataset using Scikit-learn's Stratified Shuffle Split. I followed the example shown on the Scikit-learn documentation here
我试图使用 Scikit-learn 的 Stratified Shuffle Split 拆分样本数据集。我按照此处Scikit-learn 文档中显示的示例进行操作
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()
However, upon running this script, I get the following error:
但是，在运行此脚本时，出现以下错误：
IndexError: indices are out-of-bounds
Could someone please point out what I am doing wrong here? Thanks!
有人可以指出我在这里做错了什么吗？谢谢！
回答by Mark Dickinson
You're running into the different conventions for Pandas DataFrameindexing versus NumPy ndarrayindexing. The arrays train_indexand test_indexare collections of row indices. But datais a Pandas DataFrameobject, and when you use a single index into that object, as in data[train_index], Pandas is expecting train_indexto contain columnlabels rather than row indices. You can either convert the dataframe to a NumPy array, using .values:
您DataFrame遇到了 Pandas索引与 NumPyndarray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 PandasDataFrame对象，当您在该对象中使用单个索引时，如 中所示data[train_index]，Pandas 期望train_index包含列标签而不是行索引。您可以使用以下方法将数据帧转换为 NumPy 数组.values：
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]
or use the Pandas .ilocaccessor:
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]
I tend to favour the second approach, since it gives xtrainand xtestof type DataFramerather than ndarray, and so keeps the column labels.
我倾向于支持第二种方法，因为它给出了xtrainandxtest的类型DataFrame而不是ndarray，因此保留了列标签。
