#!/usr/bin/env python
# -*- coding:utf-8 -*-
__author__ ='shouke'
importrandom
defget_class_instance_by_proportion(class_proportion_dict, amount):
"""
根据每种分类的样本数比例，及样本总数，为每每种分类构造样本数据
class_proportion_dict: 包含分类及其分类样本数占比的字典：{"分类(id)": 分类样本数比例}
amount: 所有分类的样本数量总和
返回一个列表：包含所有分类样本的list
"""
bucket =[]
proportion_sum =sum([weight forgroup_id, weight inclass_proportion_dict.items()])
residuals ={} # 存放每种分类的样本数计算差值
forclass_id, weight inclass_proportion_dict.items():
percent =weight /float(proportion_sum)
class_instance_num =int(round(amount *percent))
bucket.extend([class_id forx inrange(class_instance_num)])
residuals[class_id] =amount *percent -round(amount *percent)
iflen(bucket) < amount:
# 计算获取的分类样本总数小于给定的分类样本总数，则需要增加分类样本数,优先给样本数计算差值较小的分类增加样本数，每种分类样本数+1，直到满足数量为止
forclass_id in[l forl, r insorted(residuals.items(), key=lambdax: x[1], reverse=True)][: amount -len(bucket)]:
bucket.append(class_id)
eliflen(bucket) > amount:
# # 计算获取的分类样本总数大于给定的分类样本总数，则需要减少分类样本数,优先给样本数计算差值较大的分类减少样本数，每种分类样本数-1，直到满足数量为止
forclass_id in[l forl, r insorted(residuals.items(), key=lambdax: x[1])][: len(bucket) -amount]:
bucket.remove(class_id)
returnbucket
classA:
defto_string(self):
print('A class instance')
classB:
defto_string(self):
print('B class instance')
classC:
defto_string(self):
print('C class instance')
classD:
defto_string(self):
print('D class instance')
classes_map ={1: A, 2: B, 3:C, 4: D}
class_proportion_dict ={1: 3, 2: 5, 3:7, 4: 9} # {分类id: 样本数比例} ，即期望4个分类的样本数比例为为 3:5:7:9
class_instance_num =1000# 样本总数
result_list =get_class_instance_by_proportion(class_proportion_dict, class_instance_num)
forclass_id inclass_proportion_dict:
print('%s %s'%(classes_map[class_id], result_list.count(class_id)))
# 制造样本并随机获取样本
random.shuffle(result_list)
whileresult_list:
class_id =random.sample(result_list, 1)[0]
classes_map[class_id]().to_string()
result_list.remove(class_id)
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