《机器学习实战》学习记录-ch2

本文主要是介绍《机器学习实战》学习记录-ch2，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

PS: 个人笔记，建议不看
原书资料：https://github.com/ageron/handson-ml2

2.1数据获取

import pandas as pd
data = pd.read_csv(r"C:\Users\cyan\Desktop\AI\ML\handson-ml2\datasets\housing\housing.csv")

data.head()
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20640 entries, 0 to 20639
Data columns (total 10 columns):#   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  0   longitude           20640 non-null  float641   latitude            20640 non-null  float642   housing_median_age  20640 non-null  float643   total_rooms         20640 non-null  float644   total_bedrooms      20433 non-null  float645   population          20640 non-null  float646   households          20640 non-null  float647   median_income       20640 non-null  float648   median_house_value  20640 non-null  float649   ocean_proximity     20640 non-null  object 
dtypes: float64(9), object(1)
memory usage: 1.6+ MB

data.columns

Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms','total_bedrooms', 'population', 'households', 'median_income','median_house_value', 'ocean_proximity'],dtype='object')

data['ocean_proximity'].value_counts().plot()

在这里插入图片描述

data.describe()

	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value
count	20640.000000	20640.000000	20640.000000	20640.000000	20433.000000	20640.000000	20640.000000	20640.000000	20640.000000
mean	-119.569704	35.631861	28.639486	2635.763081	537.870553	1425.476744	499.539680	3.870671	206855.816909
std	2.003532	2.135952	12.585558	2181.615252	421.385070	1132.462122	382.329753	1.899822	115395.615874
min	-124.350000	32.540000	1.000000	2.000000	1.000000	3.000000	1.000000	0.499900	14999.000000
25%	-121.800000	33.930000	18.000000	1447.750000	296.000000	787.000000	280.000000	2.563400	119600.000000
50%	-118.490000	34.260000	29.000000	2127.000000	435.000000	1166.000000	409.000000	3.534800	179700.000000
75%	-118.010000	37.710000	37.000000	3148.000000	647.000000	1725.000000	605.000000	4.743250	264725.000000
max	-114.310000	41.950000	52.000000	39320.000000	6445.000000	35682.000000	6082.000000	15.000100	500001.000000

import matplotlib.pyplot as plt

%matplotlib inline # 这是IPython的内置绘图命令，PyCharm用不了，可以省略plt.show()
#data.hist(bins=100,figsize=(20,15),column = 'longitude') # 选一列
# 绘制直方图
data.hist(bins=50,figsize=(20,15)) # bins 代表柱子的数目，高度为覆盖宽度内取值数目之和# plt.show()

在这里插入图片描述

# 划分数据集与测试集
import numpy as np
# 自定义划分函数
def split_train_test(data, test_ratio):shuffled_indices = np.random.permutation(len(data)) # 将 0 ~ len(data) 随机打乱test_set_size = int(len(data) * test_ratio)test_indices = shuffled_indices[:test_set_size]train_indices = shuffled_indices[test_set_size:]return data.iloc[train_indices], data.iloc[test_indices]

train_data,test_data = my_split_train_test(data,.2)
len(train_data),len(test_data)

(16512, 4128)

from sklearn.model_selection import train_test_split
# 利用 sklean的包 切分数据集，random_state 类似 np.random.seed(42), 保证了每次运行切分出的测试集相同
train_set, test_set = train_test_split(data, test_size=0.2, random_state=42)
len(train_set),len(test_set)

(16512, 4128)

# 但是仅仅随机抽取作为测试集是不合理的，要保证测试集的数据分布跟样本一致
# 创建收入类别属性，为了服从房价中位数的分布对数据进行划分
data["income_cat"] = pd.cut(data["median_income"],bins=[0., 1.5, 3.0, 4.5, 6., np.inf],labels=[1, 2, 3, 4, 5])

# 分层抽样
from sklearn.model_selection import StratifiedShuffleSplit
split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42) # 
for train_index, test_index in split.split(data, data["income_cat"]):strat_train_set = data.loc[train_index]strat_test_set = data.loc[test_index]

# 查看测试集数据分布比例
strat_test_set["income_cat"].value_counts() / len(strat_test_set),data["income_cat"].value_counts() / len(data)

(3    0.3505332    0.3187984    0.1763575    0.1143411    0.039971Name: income_cat, dtype: float64,3    0.3505812    0.3188474    0.1763085    0.1144381    0.039826Name: income_cat, dtype: float64)

# 删除添加的 income_cat 属性
strat_test_set.drop("income_cat",axis=1,inplace=True)
strat_train_set.drop("income_cat",axis=1,inplace=True)
# 或者如此删除，可能效率更高，或者更美观吧
for set_ in (strat_train_set, strat_test_set):set_.drop("income_cat", axis=1, inplace=True)