sklearn.cross_validation.StratifiedShuffleSplit - 錯誤：「索引超出範圍」

Question

我試圖使用Scikit-learn的分層隨機拆分拆分示例數據集。我也跟着上所示的例子Scikit學習文檔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() 

然而，一旦運行該腳本，我得到以下錯誤：

IndexError: indices are out-of-bounds 

可能有人請指出我在做什麼錯在這裏？謝謝！

Answer 1

您正在運行Pandas DataFrame索引與NumPy ndarray索引的不同約定。陣列train_index和是行索引的集合。但是data是Pandas DataFrame對象，並且當您使用單個索引導入該對象時，如在data[train_index]中，Pandas期望train_index包含列列標籤而非行索引。您可以將數據幀轉換爲NumPy的陣列，使用.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] 

或使用熊貓.iloc訪問：

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] 

我傾向於第二種方法，因爲它提供了xtrain和xtest鍵入DataFrame而不是ndarray，並保留列標籤。