强化学习实验绘图(使用seaborn)

强化学习实验中的绘图技巧-使用seaborn绘制paper中的图片

seaborn可以说是matplotlib的升级版,使用seaborn绘制折线图时参数数据可以传递ndarray或者pandas。

1.从一个演示示例开始

1.1 极简示例
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns # 导入模块
sns.set() # 设置美化参数,一般默认就好

rewards = np.array([0, 0.1,0,0.2,0.4,0.5,0.6,0.9,0.9,0.9])
plt.plot(rewards)
plt.show()

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可以看一下如果把sns.set()注释掉的效果

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1.2 使用sns.lineplot

加上x,y轴的label和标题

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns;
sns.set() # 因为sns.set()一般不用改,可以在导入模块时顺便设置好

rewards = np.array([0, 0.1,0,0.2,0.4,0.5,0.6,0.9,0.9,0.9])
sns.lineplot(x=range(len(rewards)),y=rewards)
# sns.relplot(x=range(len(rewards)),y=rewards,kind="line") # 与上面一行等价
plt.xlabel("episode")
plt.ylabel("reward")
plt.title("data")
plt.show()

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1.3 绘制rewards聚合图

当我们对同一实验作出多次得到一组rewards时,如下:

import numpy as np

rewards1 = np.array([0, 0.1,0,0.2,0.4,0.5,0.6,0.9,0.9,0.9])
rewards2 = np.array([0, 0,0.1,0.4,0.5,0.5,0.55,0.8,0.9,1])
rewards3 = np.vstack((rewards1,rewards2)) # 合并成二维数组
rewards4 = np.concatenate((rewards1,rewards2)) # 合并成一维数组
print(np.shape(rewards3))
print(rewards3)
print(np.shape(rewards4))
print(rewards4)

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我们希望绘制出聚合图,但是sns.lineplot无法输入一维以上的数据,我们可以将它们全部转为一维,虽然有些难看:

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns; 
sns.set() # 因为sns.set()一般不用改,可以在导入模块时顺便设置好

rewards1 = np.array([0, 0.1,0,0.2,0.4,0.5,0.6,0.9,0.9,0.9])
rewards2 = np.array([0, 0,0.1,0.4,0.5,0.5,0.55,0.8,0.9,1])
rewards=np.concatenate((rewards1,rewards2)) # 合并数组
episode1=range(len(rewards1))
episode2=range(len(rewards2))
episode=np.concatenate((episode1,episode2))
sns.lineplot(x=episode,y=rewards)
plt.xlabel("episode")
plt.ylabel("reward")
plt.show()

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1.4 使用pandas传参

上面都是用ndarray传参,用pandas传参,就需要先把array转成DataFrame形式,如下:

import numpy as np
import pandas as pd
rewards1 = np.array([0, 0.1,0,0.2,0.4,0.5,0.6,0.9,0.9,0.9])
rewards2 = np.array([0, 0,0.1,0.4,0.5,0.5,0.55,0.8,0.9,1])
rewards=np.vstack((rewards1,rewards2)) # 合并数组
df = pd.DataFrame(rewards).melt(var_name='episode',value_name='reward') # 推荐这种转换方法
print(df)

上述转化方法,这样无论rewards多少维都不影响最终的绘图方式,其中melt方法将所有维合并成一列,var_name='episode',value_name='reward'则更改对应的列名,转化结果如下:

完整的绘图程序:

import seaborn as sns
sns.set()
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

rewards1 = np.array([0, 0.1,0,0.2,0.4,0.5,0.6,0.9,0.9,0.9])
rewards2 = np.array([0, 0,0.1,0.4,0.5,0.5,0.55,0.8,0.9,1])
rewards=np.vstack((rewards1,rewards2)) # 合并为二维数组
df = pd.DataFrame(rewards).melt(var_name='episode',value_name='reward')

sns.lineplot(x="episode", y="reward", data=df)
plt.show()

这里的x,y不再传入数组,而是传入DataFrame中对应的列名,类似于python字典中的键,结果如下:

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1.5 一个稍微复杂的示例
import seaborn as sns 
sns.set()
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

def get_data():
    '''获取数据
    '''
    basecond = np.array([[18, 20, 19, 18, 13, 4, 1],[20, 17, 12, 9, 3, 0, 0],[20, 20, 20, 12, 5, 3, 0]])
    cond1 = np.array([[18, 19, 18, 19, 20, 15, 14],[19, 20, 18, 16, 20, 15, 9],[19, 20, 20, 20, 17, 10, 0]])
    cond2 = np.array([[20, 20, 20, 20, 19, 17, 4],[20, 20, 20, 20, 20, 19, 7],[19, 20, 20, 19, 19, 15, 2]])
    cond3 = np.array([[20, 20, 20, 20, 19, 17, 12],[18, 20, 19, 18, 13, 4, 1], [20, 19, 18, 17, 13, 2, 0]])
    return basecond, cond1, cond2, cond3

data = get_data()
label = ['algo1', 'algo2', 'algo3', 'algo4']
df=[]
for i in range(len(data)):
    df.append(pd.DataFrame(data[i]).melt(var_name='episode',value_name='loss'))
    df[i]['algo']= label[i]
df=pd.concat(df) # 合并
print(df)
sns.lineplot(x="episode", y="loss", hue="algo", style="algo",data=df)
plt.title("some loss")
plt.show()

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2.读取csv文件并绘图

kaggle上一个酒店房间预定的数据,数据和本篇文章的代码都可以从这个链接获取:https://www.jianguoyun.com/p/Ddc6RhEQnNm0CRjc2aAE。

2.1 简单示例

读取数据

import pandas as pd
df=pd.read_csv('hotel_bookings.csv')
print(df.head())

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我们这里主要看两个数据,一个是arrival_date_month,一个是stays_in_week_nights,分别表示客人到来的月份和住的时间。使用seaborn的lineplot的时候,调用API的方式有点不一样,这里xy是直接指定我们数据的索引,x这里就是df['arrival_date_month']这个数据,最后通过data参数来指定我们要传入的数据。

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns # 导入模块
sns.set() # 设置美化参数,一般默认就好
df=pd.read_csv('hotel_bookings.csv')
sns.lineplot(x="arrival_date_month",y="stays_in_week_nights",data=df)
plt.show()

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2.2 复杂示例

下面来看一个更加复杂的例子。我们希望将几个月内的住宿情况可视化,但我们也希望将入住年份考虑在内。这时候画图需要将月份、年份和入住情况三个数据都表示在图上。

import pandas as pd
df=pd.read_csv('hotel_bookings.csv')
df=df[['arrival_date_year','arrival_date_month','stays_in_week_nights']]
print(df)

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使用pivot_table,也就是透视图(excel中)来表示数据,pivot_table的作用就是将我们设定的index作为索引,然后去匹配我们设定的列,我们设定的value值也就是中间部分要显示的内容。

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns # 导入模块
sns.set() # 设置美化参数,一般默认就好

df=pd.read_csv('hotel_bookings.csv')
df=df[['arrival_date_year','arrival_date_month','stays_in_week_nights']]
# order=df['arrival_date_month']

df_wide=df.pivot_table(index='arrival_date_month',columns='arrival_date_year',values='stays_in_week_nights')
print(df_wide)
sns.lineplot(data=df_wide)
plt.show()

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我们也可以按照在原始的csv文件中,arrival_date_month的顺序来画图,也就是上面我们设定的order=df['arrival_date_month']的作用。

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns # 导入模块
sns.set() # 设置美化参数,一般默认就好

df=pd.read_csv('hotel_bookings.csv')
df=df[['arrival_date_year','arrival_date_month','stays_in_week_nights']]
order=df['arrival_date_month']

df_wide=df.pivot_table(index='arrival_date_month',columns='arrival_date_year',values='stays_in_week_nights')

df_wide=df_wide.reindex(order,axis=0)
print(df_wide)
sns.lineplot(data=df_wide)
plt.show()

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更为简洁的方式

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns # 导入模块
sns.set() # 设置美化参数,一般默认就好
df=pd.read_csv('hotel_bookings.csv')
sns.lineplot(x="arrival_date_month",y="stays_in_week_nights",hue="arrival_date_year",data=df)
plt.show()

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参考资料:

https://zhuanlan.zhihu.com/p/147847062

https://www.guyuehome.com/36179

本图文内容来源于网友网络收集整理提供,作为学习参考使用,版权属于原作者。
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