本文是该系列读书笔记的第二章数据预处理部分
import pandas as pd
import numpy as np
import os
import tarfile
from six.moves import urllib
获取数据
download_root="https://raw.githubusercontent.com/ageron/handson-ml/master/"
house_path="datasets/housing"
housing_url=download_root+house_path+"/housing.tgz"
def fecthing_housing_data(housing_url=housing_url,house_path=house_path):
if not os.path.exists(house_path):
os.makedirs(house_path)
tgz_path=os.path.join(house_path,'housing.tgz')
urllib.request.urlretrieve(housing_url,tgz_path)
housing_tgz=tarfile.open(tgz_path)
housing_tgz.extractall(path=house_path)
housing_tgz.close()
def load_housing_data(house_path=house_path):
csv_path=os.path.join(house_path,"housing.csv")
return pd.read_csv(csv_path)
数据的初步分析，数据探索
# fecthing_housing_data() # 下载数据，解压出csv文件
housing=load_housing_data()
housing.head()
|
longitude |
latitude |
housing_median_age |
total_rooms |
total_bedrooms |
population |
households |
median_income |
median_house_value |
ocean_proximity |
| 0 |
-122.23 |
37.88 |
41.0 |
880.0 |
129.0 |
322.0 |
126.0 |
8.3252 |
452600.0 |
NEAR BAY |
| 1 |
-122.22 |
37.86 |
21.0 |
7099.0 |
1106.0 |
2401.0 |
1138.0 |
8.3014 |
358500.0 |
NEAR BAY |
| 2 |
-122.24 |
37.85 |
52.0 |
1467.0 |
190.0 |
496.0 |
177.0 |
7.2574 |
352100.0 |
NEAR BAY |
| 3 |
-122.25 |
37.85 |
52.0 |
1274.0 |
235.0 |
558.0 |
219.0 |
5.6431 |
341300.0 |
NEAR BAY |
| 4 |
-122.25 |
37.85 |
52.0 |
1627.0 |
280.0 |
565.0 |
259.0 |
3.8462 |
342200.0 |
NEAR BAY |
housing.info()
# total_bedrooms 存在缺失值，
# 前9列为float格式，经度，维度，房龄中位数，总的房间数，卧室数目，人口，家庭数，收入中位数，房屋价格的中位数，
# 最后一列为离海距离为object类型
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20640 entries, 0 to 20639
Data columns (total 10 columns):
longitude 20640 non-null float64
latitude 20640 non-null float64
housing_median_age 20640 non-null float64
total_rooms 20640 non-null float64
total_bedrooms 20433 non-null float64
population 20640 non-null float64
households 20640 non-null float64
median_income 20640 non-null float64
median_house_value 20640 non-null float64
ocean_proximity 20640 non-null object
dtypes: float64(9), object(1)
memory usage: 1.6+ MB
# 需要查看ocean_proximity都包含哪些,
housing['ocean_proximity'].value_counts()
<1H OCEAN 9136
INLAND 6551
NEAR OCEAN 2658
NEAR BAY 2290
ISLAND 5
Name: ocean_proximity, dtype: int64
# 对数值类型的特征进行初步的统计
housing.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 |
%matplotlib inline
import matplotlib.pyplot as plt
# 查看每个数值特征的分布，
housing.hist(bins=50,figsize=(20,15))
# plt.show()
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000179D4A20>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000019A2A128>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000019A557B8>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000019A7AE48>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000019AAB518>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000019AAB550>],
[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000019B03278>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000019B29908>,
<matplotlib.axes._subplots.AxesSubplot object at 0x0000000019B53F98>]],
dtype=object)
地理分布
housing.plot(kind="scatter", x="longitude", y="latitude")
<matplotlib.axes._subplots.AxesSubplot at 0x19bbfcc0>
housing.plot(kind="scatter", x="longitude", y="latitude",alpha=0.4)
# 标量，可选，默认值无，alpha混合值，介于0（透明）和1（不透明）之间
# 显示高密度区域的散点图，颜色越深，表示人口越密集，虽然我对加州的地理位置不是特别清楚
<matplotlib.axes._subplots.AxesSubplot at 0x1a705b70>
housing.plot(kind='scatter',x='longitude',y='latitude',alpha=0.4,
s=housing['population']/50,label='population',
c='median_house_value',cmap=plt.get_cmap("jet"),colorbar=True,
figsize=(9,6))
# import matplotlib
# plt.figure(figsize=(15,9))
# sc=plt.scatter(housing['longitude'],housing['latitude'],alpha=0.4,
# s=housing['population']/100,label='population',
# c=housing['median_house_value'],cmap=plt.get_cmap("jet"))
# plt.legend()
# matplotlib.rcParams["font.sans-serif"]=["SimHei"]
# matplotlib.rcParams['axes.unicode_minus'] = False
# matplotlib.rcParams['font.size'] =15
# plt.xlabel('经度')
# plt.ylabel('纬度')
# color_bar=plt.colorbar(sc)
# color_bar.set_label('meidan_house_value')
# plt.show()
#以上为使用plt的完整代码，将坐标轴的内容以及添加colorbar，设置中文坐标轴标题
<matplotlib.axes._subplots.AxesSubplot at 0x19ffb390>
# 房价与位置和人口密度联系密切，但是如何用数学的角度来描述几个变量之间的关联呢，可以使用标准相关系数standard correlation coefficient
# 常用的相关系数为皮尔逊相关系数
corr_matrix = housing.corr()
corr_matrix
|
longitude |
latitude |
housing_median_age |
total_rooms |
total_bedrooms |
population |
households |
median_income |
median_house_value |
| longitude |
1.000000 |
-0.924664 |
-0.108197 |
0.044568 |
0.069608 |
0.099773 |
0.055310 |
-0.015176 |
-0.045967 |
| latitude |
-0.924664 |
1.000000 |
0.011173 |
-0.036100 |
-0.066983 |
-0.108785 |
-0.071035 |
-0.079809 |
-0.144160 |
| housing_median_age |
-0.108197 |
0.011173 |
1.000000 |
-0.361262 |
-0.320451 |
-0.296244 |
-0.302916 |
-0.119034 |
0.105623 |
| total_rooms |
0.044568 |
-0.036100 |
-0.361262 |
1.000000 |
0.930380 |
0.857126 |
0.918484 |
0.198050 |
0.134153 |
| total_bedrooms |
0.069608 |
-0.066983 |
-0.320451 |
0.930380 |
1.000000 |
0.877747 |
0.979728 |
-0.007723 |
0.049686 |
| population |
0.099773 |
-0.108785 |
-0.296244 |
0.857126 |
0.877747 |
1.000000 |
0.907222 |
0.004834 |
-0.024650 |
| households |
0.055310 |
-0.071035 |
-0.302916 |
0.918484 |
0.979728 |
0.907222 |
1.000000 |
0.013033 |
0.065843 |
| median_income |
-0.015176 |
-0.079809 |
-0.119034 |
0.198050 |
-0.007723 |
0.004834 |
0.013033 |
1.000000 |
0.688075 |
| median_house_value |
-0.045967 |
-0.144160 |
0.105623 |
0.134153 |
0.049686 |
-0.024650 |
0.065843 |
0.688075 |
1.000000 |
数据特征的相关性
import seaborn as sns
plt.Figure(figsize=(25,20))
hm=sns.heatmap(corr_matrix,cbar=True,annot=True,square=True,fmt='.2f',annot_kws={'size':9}, cmap="YlGnBu")
plt.show()
corr_matrix['median_house_value'].sort_values(ascending=False)
"""
相关系数的范围是 -1 到 1。当接近 1 时，意味强正相关；
例如，当收入中位数增加时，房价中位数也会增加。
当相关系数接近 -1 时，意味强负相关；
纬度和房价中位数有轻微的负相关性（即，越往北，房价越可能降低）。
最后，相关系数接近 0，意味没有线性相关性。
"""
# 使用pandas中的scatter_matrix 可以从另外一种角度分析多个变量之间的相关性
from pandas.plotting import scatter_matrix
attributes=['median_house_value',"median_income","total_bedrooms","housing_median_age"]
scatter_matrix(housing[attributes],figsize=(12,9))
# sns.pairplot(housing[['median_house_value',"median_income",]],height=5)
# 使用seaborn中的pariplot可以实现同样的结果
housing.plot(kind="scatter",x='median_income',y='median_house_value',alpha=0.2)
<matplotlib.axes._subplots.AxesSubplot at 0x1e3df9e8>
创建新的特征
- 重点关注收入的中位数与房屋价值的中位数之间的关系，从上图以及相关系数都可以得到两者之间存在很明显的正相关
- 可以清洗的看到向上的趋势，并且数据点不是非常分散，
- 我们之前统计得到的最高房价位于5000000美元的水平线
- 从频率分布直方图hist可以看到housing_median_age ,meidan_house_value 具有长尾分布，可以尝试对其进行log或者开根号等转化
- 当然，不同项目的处理方法各不相同，但大体思路是相似的。
housing['rooms_per_household']=housing['total_rooms']/housing['households']
housing['bedrooms_per_room']= housing['total_bedrooms']/housing['total_rooms']
housing['population_per_household']=housing['population']/housing['households']
corr_matrix = housing.corr()
corr_matrix['median_house_value'].sort_values(ascending=False)
# """
# 新的特征房间中，卧室占比与房屋价值中位数有着更明显的负相关性，比例越低，房价越高；
# 每家的房间数也比街区的总房间数的更有信息，很明显，房屋越大，房价就越高
# """
median_house_value 1.000000
median_income 0.688075
rooms_per_household 0.151948
total_rooms 0.134153
housing_median_age 0.105623
households 0.065843
total_bedrooms 0.049686
population_per_household -0.023737
population -0.024650
longitude -0.045967
latitude -0.144160
bedrooms_per_room -0.255880
Name: median_house_value, dtype: float64
数据清洗， 创建处理流水线
- 缺失值处理
- 处理object文本数据类型
- 特征放缩
- 构建模型pepeline
- 以上几个步骤我们在之前的博客中基本上都已经用过，这里作为读书笔记不会再过多的详细解释
# total_bedrooms特征缺失值处理
"""
- 去掉含有缺失值的样本，dropna()
- 去掉含有缺失值的特征 dropna(axis=1)
- 进行填充（中位数，平均值，0，插值填充） fillna(housing['total_bedrooms'].median()) 较为方便的使用pandas中的方法
"""
from sklearn.preprocessing import Imputer
imputer=Imputer(strategy='mean')
housing_num=housing.drop('ocean_proximity',axis=1)
imputer.fit(housing_num)
Imputer(axis=0, copy=True, missing_values='NaN', strategy='mean', verbose=0)
housing_num_trans=pd.DataFrame(imputer.transform(housing_num),columns=housing_num.columns)
housing_num_trans.info()
# 缺失值补齐，总觉得如果是缺失值处理的话，可以直接用pandas中的fillna会节省一点时间，在原始的数据上直接处理掉，后面也就不用再去担心这个
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20640 entries, 0 to 20639
Data columns (total 12 columns):
longitude 20640 non-null float64
latitude 20640 non-null float64
housing_median_age 20640 non-null float64
total_rooms 20640 non-null float64
total_bedrooms 20640 non-null float64
population 20640 non-null float64
households 20640 non-null float64
median_income 20640 non-null float64
median_house_value 20640 non-null float64
rooms_per_household 20640 non-null float64
bedrooms_per_room 20640 non-null float64
population_per_household 20640 non-null float64
dtypes: float64(12)
memory usage: 1.9 MB
# 处理文本object类型数据
from sklearn.preprocessing import LabelEncoder
encoder= LabelEncoder()
house_cat=housing['ocean_proximity']
house_cat_encode=encoder.fit_transform(house_cat)
house_cat_encode
array([3, 3, 3, ..., 1, 1, 1], dtype=int64)
encoder.classes_
array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],
dtype=object)
- 在之前博客中也提到类似的操作，改操作可能会将两个临近的值
- 比两个疏远的值更为相似，因此一般情况下，对与类标才会使用LabelEncoder,对于特征不会使用该方式对特征转换
- 更为常用的操作是独热编码，给每个分类创建一个二元属性，比如当分类是INLAND，有则是1，没有则是0
- skleanrn中提供了编码器OneHotEncoder，类似与pandas中pd.get_dummies()
from sklearn.preprocessing import OneHotEncoder
# OneHotEncoder只能对数值型数据进行处理,只接受2D数组
encoder=OneHotEncoder()
housing_cat_1hot=encoder.fit_transform(house_cat_encode.reshape((-1,1)))
housing_cat_1hot
<20640x5 sparse matrix of type '<class 'numpy.float64'>'
with 20640 stored elements in Compressed Sparse Row format>
housing_cat_1hot.toarray()
array([[0., 0., 0., 1., 0.],
[0., 0., 0., 1., 0.],
[0., 0., 0., 1., 0.],
...,
[0., 1., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 1., 0., 0., 0.]])
# 使用LabelBinarizer 可以实现同样的效果
from sklearn.preprocessing import LabelBinarizer
encoder=LabelBinarizer()
housing_cat_1hot=encoder.fit_transform(house_cat)
housing_cat_1hot
array([[0, 0, 0, 1, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 1, 0],
...,
[0, 1, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 1, 0, 0, 0]])
# 直接在原始的数据上使用pandas.get_dummies()是最简单的方法
pd.get_dummies(housing[['ocean_proximity']]).head()
|
ocean_proximity_<1H OCEAN |
ocean_proximity_INLAND |
ocean_proximity_ISLAND |
ocean_proximity_NEAR BAY |
ocean_proximity_NEAR OCEAN |
| 0 |
0 |
0 |
0 |
1 |
0 |
| 1 |
0 |
0 |
0 |
1 |
0 |
| 2 |
0 |
0 |
0 |
1 |
0 |
| 3 |
0 |
0 |
0 |
1 |
0 |
| 4 |
0 |
0 |
0 |
1 |
0 |
# 特征放缩 我们常用到的MinMaxScaler和StandandScaler两种
# 一般会对不同范围内的特征进行放缩，有助于优化算法收敛的速度（尤其是针对梯度提升的优化算法）
# 归一化： 减去最小值，然后除以最大最小值的差
# 标准化： 减去平均值，然后除以方差，得到均值为0，方差为1的标准正态分布，受异常值影响比较小，决策树和随机森林不需要特征放缩
# 特征放缩一般针对训练数据集进行transform_fit，对测试集数据进行transform
# 从划分数据集→pipeline
from sklearn.model_selection import train_test_split
housing=load_housing_data()
# train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42) # 随机采样
from sklearn.model_selection import StratifiedShuffleSplit # 分层采样
split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
housing["income_cat"] = np.ceil(housing["median_income"] / 1.5)
housing["income_cat"].where(housing["income_cat"] < 5, 5.0, inplace=True)
for train_index, test_index in split.split(housing, housing["income_cat"]): # 按照收入中位数进行分层采样
strat_train_set = housing.loc[train_index]
strat_test_set = housing.loc[test_index]
housing = strat_train_set.copy() # 创建一个副本，以免损伤训练集，
housing.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 16512 entries, 17606 to 15775
Data columns (total 11 columns):
longitude 16512 non-null float64
latitude 16512 non-null float64
housing_median_age 16512 non-null float64
total_rooms 16512 non-null float64
total_bedrooms 16354 non-null float64
population 16512 non-null float64
households 16512 non-null float64
median_income 16512 non-null float64
median_house_value 16512 non-null float64
ocean_proximity 16512 non-null object
income_cat 16512 non-null float64
dtypes: float64(10), object(1)
memory usage: 1.5+ MB
#转化流水线
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
num_pipeline=Pipeline([('imputer',Imputer(strategy='median')),('std_scaler',StandardScaler())])
housing = strat_train_set.drop("median_house_value", axis=1)
housing_labels = strat_train_set["median_house_value"].copy()
housing_num=housing.drop('ocean_proximity',axis=1)
housing_num_tr = num_pipeline.fit_transform(housing_num)
housing_cat=housing['ocean_proximity']
housing_cat_tr= LabelBinarizer().fit_transform(housing_cat)
housing_train=np.c_[housing_num_tr,housing_cat_tr]
housing_train.shape
# 数字特征与categoriy 特征不能同时进行转化，需要进行FeatureUnion
# 你给它一列转换器（可以是所有的转换器），当调用它的transform()方法，每个转换器的transform()会被并行执行，
# 等待输出，然后将输出合并起来，并返回结果
# 当然也可以通过分批转化，然后通过np将转化好的数据集合并，本质上没有什么区别，只不过对于测试集仍然需要transform，然后再合并成转化好的测试集
(16512, 14)
import os
import sys
sys.path.append(os.getcwd())
from future_encoders import ColumnTransformer
from future_encoders import OneHotEncoder
num_attribs = list(housing_num)
cat_attribs = ["ocean_proximity"]
full_pipeline = ColumnTransformer([
("num", num_pipeline, num_attribs),
("cat", OneHotEncoder(), cat_attribs),
])
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared
array([[-1.15604281, 0.77194962, 0.74333089, ..., 0. ,
1. , 0. ],
[-1.17602483, 0.6596948 , -1.1653172 , ..., 0. ,
1. , 0. ],
[ 1.18684903, -1.34218285, 0.18664186, ..., 0. ,
1. , 1. ],
...,
[ 1.58648943, -0.72478134, -1.56295222, ..., 0. ,
1. , 0. ],
[ 0.78221312, -0.85106801, 0.18664186, ..., 0. ,
1. , 0. ],
[-1.43579109, 0.99645926, 1.85670895, ..., 0. ,
1. , 0. ]])
np.allclose(housing_prepared, housing_train)
True
后续内容已经放在github上，篇幅过大就只能把数据预处理的部分整理在这里，然后把后续的算法的实现部分整理在github中
