python 分层抽样
2021-03-11 18:31
标签：ram pat 技术 xls list bsp 一个 数据 cat python 分层抽样 标签：ram pat 技术 xls list bsp 一个 数据 cat 原文地址：https://www.cnblogs.com/LXL616/p/14121674.htmlimport numpy as np
import pandas as pd
PATH_DES = ‘/Users/linxianli/Desktop/‘
df = pd.read_excel(PATH_DES + ‘工作簿1.xlsx‘)
df.head()
# 使用 sklearn 进行分层抽样
from sklearn.model_selection import train_test_split
# data[‘TYPE‘]是在data中的某一个属性列
X_train, X_test, y_train, y_test = train_test_split(df,df[‘TYPE‘], test_size=0.2, stratify=df[‘TYPE‘]) # test_size 测试集占比
print(X_train.shape)
print(X_test.shape)
‘‘‘
(885, 4)
(222, 4)
‘‘‘
# 普通方法进行分层抽样
test = pd.DataFrame() # 划分出的test集合
train = pd.DataFrame() # 剩余的train集合
tags = df[‘TYPE‘].unique().tolist() # 按照该标签进行等比例抽取
for tag in tags:
# 随机选取0.2的数据
data = df[(df[‘TYPE‘] == tag)]
sample = data.sample(int(0.2*len(data)))
sample_index = sample.index
# 剩余数据
all_index = data.index
residue_index = all_index.difference(sample_index) # 去除sample之后剩余的数据
residue = data.loc[residue_index] # 这里要使用.loc而非.iloc
# 保存
test = pd.concat([test, sample], ignore_index=True)
train = pd.concat([train, residue], ignore_index=True)
print(test.shape)
print(train.shape)
‘‘‘
(221, 4)
(886, 4)
‘‘‘