机器学习之置信区间上界算法
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# Importing the libraries 导入库
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# 使图像能够调整
%matplotlib notebook
#中文字体显示
plt.rc('font', family='SimHei', size=8)
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dataset = pd.read_csv('Ads_CTR_Optimisation.csv') # 数据表示虚拟环境，模拟我将投放哪些广告
dataset
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In [27]:
import random
N = 10000 # 1000个用户
d = 10 # 10个广告
ads_selected = [] # 广告选择
total_reward = 0
for n in range(0, N): # 每个用户循环
ad = random.randrange(d) # 随机选择广告
ads_selected.append(ad) # 将选择的广告加入list中
reward = dataset.values[n, ad] # 取出数据集中n行ad列查看是否命中，命中值为1，未命中值为0（1即为奖励）
total_reward = total_reward + reward # 每轮奖励累计相加
print(total_reward)
# 画图
plt.hist(ads_selected)
plt.title(u'广告选择直方图')
plt.xlabel(u'广告')
plt.ylabel(u'每个广告的点击数')
plt.show()
In [55]:
import math
N = 10000 # 1000个用户
d = 10 # 10个广告
ads_selected = [] # 广告选择
numbers_of_selections = [0] * d # 多项选择
sums_of_rewards = [0] * d # 奖励总和
total_reward = 0
for n in range(0, N): # 第n个用户
ad = 0 # 广告初始化
max_upper_bound = 0 # 最大上界初始化
for i in range(0, d): # 第i个广告
if (numbers_of_selections[i] > 0):
average_reward = float(sums_of_rewards[i]) / float(numbers_of_selections[i]) # 平均奖励，这里如果python2记得用float
delta_i = math.sqrt(3/2 * math.log(n + 1) / numbers_of_selections[i]) # 置信区间
upper_bound = average_reward + delta_i # 置信区间上界
# print(average_reward)
# print(delta_i )
else:
upper_bound = 10000
if upper_bound > max_upper_bound:
max_upper_bound = upper_bound
ad = i
# print(ad)
ads_selected.append(ad)
numbers_of_selections[ad] = numbers_of_selections[ad] + 1
reward = dataset.values[n, ad]
sums_of_rewards[ad] = sums_of_rewards[ad] + reward
total_reward = total_reward + reward
# print(ads_selected)
print(total_reward)
# print(numbers_of_selections)
# print(sums_of_rewards)
In [56]:
# 画图
plt.hist(ads_selected)
plt.title(u'广告选择直方图')
plt.xlabel(u'广告')
plt.ylabel(u'每个广告的点击数')
plt.show()