前言
使用的相关数据链接如下：
点我跳转
数据预处理
无量纲化
1
2
|
#归一化 收到0~1之间
#正则化不是归一化
|
1
2
3
4
5
6
7
8
|
from sklearn.preprocessing import MinMaxScaler
data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
#不太熟悉numpy的小伙伴，能够判断data的结构吗？
#如果换成表是什么样子？
import pandas as pd
pd.DataFrame(data)
|
|
0 |
1 |
| 0 |
-1.0 |
2 |
| 1 |
-0.5 |
6 |
| 2 |
0.0 |
10 |
| 3 |
1.0 |
18 |
1
2
3
4
5
6
7
8
|
#实现归一化
#当X中的特征数量非常多的时候，fit会报错并表示，数据量太大了我计算不了
#此时使用partial_fit作为训练接口
#scaler = scaler.partial_fit(data)
scaler = MinMaxScaler() #实例化
scaler = scaler.fit(data) #fit，在这里本质是生成min(x)和max(x)
result = scaler.transform(data) #通过接口导出结果,归一化
result
|
array([[0. , 0. ],
[0.25, 0.25],
[0.5 , 0.5 ],
[1. , 1. ]])
1
2
|
result_ = scaler.fit_transform(data) #训练和导出结果一步达成
result
|
array([[0. , 0. ],
[0.25, 0.25],
[0.5 , 0.5 ],
[1. , 1. ]])
1
|
scaler.inverse_transform(result) #将归一化后的结果逆转
|
array([[-1. , 2. ],
[-0.5, 6. ],
[ 0. , 10. ],
[ 1. , 18. ]])
1
2
3
4
5
|
#使用MinMaxScaler的参数feature_range实现将数据归一化到[0,1]以外的范围中
data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
scaler = MinMaxScaler(feature_range=[5,10]) #依然实例化
result = scaler.fit_transform(data) #fit_transform一步导出结果
result
|
array([[ 5. , 5. ],
[ 6.25, 6.25],
[ 7.5 , 7.5 ],
[10. , 10. ]])
1
2
3
|
import numpy as np
X = np.array([[-1, 2], [-0.5, 6], [0, 10], [1, 18]])
X
|
array([[-1. , 2. ],
[-0.5, 6. ],
[ 0. , 10. ],
[ 1. , 18. ]])
1
2
3
|
#归一化
X_nor = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_nor
|
array([[0. , 0. ],
[0.25, 0.25],
[0.5 , 0.5 ],
[1. , 1. ]])
1
2
3
|
#逆转归一化
X_returned = X_nor * (X.max(axis=0) - X.min(axis=0)) + X.min(axis=0)
X_returned
|
array([[-1. , 2. ],
[-0.5, 6. ],
[ 0. , 10. ],
[ 1. , 18. ]])
1
2
|
from sklearn.preprocessing import StandardScaler
data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
|
1
2
|
scaler = StandardScaler() #实例化
scaler.fit(data) #fit，本质是生成均值和方差
|
StandardScaler()
1
|
scaler.mean_ #查看均值的属性mean_
|
array([-0.125, 9. ])
1
|
scaler.var_ #查看方差的属性var_
|
array([ 0.546875, 35. ])
1
2
|
x_std = scaler.transform(data) #通过接口导出结果
x_std
|
array([[-1.18321596, -1.18321596],
[-0.50709255, -0.50709255],
[ 0.16903085, 0.16903085],
[ 1.52127766, 1.52127766]])
1
|
x_std.mean() #导出的结果是一个数组，用mean()查看均值
|
0.0
1
|
x_std.std() #用std()查看方差
|
1.0
1
|
scaler.fit_transform(data) #使用fit_transform(data)一步达成结果
|
array([[-1.18321596, -1.18321596],
[-0.50709255, -0.50709255],
[ 0.16903085, 0.16903085],
[ 1.52127766, 1.52127766]])
1
|
scaler.inverse_transform(x_std) #使用inverse_transform逆转标准化
|
array([[-1. , 2. ],
[-0.5, 6. ],
[ 0. , 10. ],
[ 1. , 18. ]])
处理缺失值
1
2
3
4
5
6
|
import pandas as pd
data = pd.read_csv(r"Narrativedata.csv"
,index_col=0
)#index_col=0将第0列作为索引，不写则认为第0列为特征
data.head()
|
|
Age |
Sex |
Embarked |
Survived |
| 0 |
22.0 |
male |
S |
No |
| 1 |
38.0 |
female |
C |
Yes |
| 2 |
26.0 |
female |
S |
Yes |
| 3 |
35.0 |
female |
S |
Yes |
| 4 |
35.0 |
male |
S |
No |
<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 0 to 890
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Age 714 non-null float64
1 Sex 891 non-null object
2 Embarked 889 non-null object
3 Survived 891 non-null object
dtypes: float64(1), object(3)
memory usage: 34.8+ KB
1
2
|
Age = data.loc[:,"Age"].values.reshape(-1,1) #sklearn当中特征矩阵必须是二维
Age[:20]
|
array([[22.],
[38.],
[26.],
[35.],
[35.],
[nan],
[54.],
[ 2.],
[27.],
[14.],
[ 4.],
[58.],
[20.],
[39.],
[14.],
[55.],
[ 2.],
[nan],
[31.],
[nan]])
1
2
3
4
|
from sklearn.impute import SimpleImputer
imp_mean = SimpleImputer() #实例化，默认均值填补
imp_median = SimpleImputer(strategy="median") #用中位数填补
imp_0 = SimpleImputer(strategy="constant",fill_value=0) #用0填补
|
1
2
3
|
imp_mean = imp_mean.fit_transform(Age) #fit_transform一步完成调取结果
imp_median = imp_median.fit_transform(Age)
imp_0 = imp_0.fit_transform(Age)
|
array([[22. ],
[38. ],
[26. ],
[35. ],
[35. ],
[29.69911765],
[54. ],
[ 2. ],
[27. ],
[14. ],
[ 4. ],
[58. ],
[20. ],
[39. ],
[14. ],
[55. ],
[ 2. ],
[29.69911765],
[31. ],
[29.69911765]])
array([[22.],
[38.],
[26.],
[35.],
[35.],
[28.],
[54.],
[ 2.],
[27.],
[14.],
[ 4.],
[58.],
[20.],
[39.],
[14.],
[55.],
[ 2.],
[28.],
[31.],
[28.]])
array([[22.],
[38.],
[26.],
[35.],
[35.],
[ 0.],
[54.],
[ 2.],
[27.],
[14.],
[ 4.],
[58.],
[20.],
[39.],
[14.],
[55.],
[ 2.],
[ 0.],
[31.],
[ 0.]])
1
2
3
4
5
|
#在这里我们使用中位数填补Age
data.loc[:,"Age"] = imp_median
data.info()
|
<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 0 to 890
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Age 891 non-null float64
1 Sex 891 non-null object
2 Embarked 889 non-null object
3 Survived 891 non-null object
dtypes: float64(1), object(3)
memory usage: 34.8+ KB
1
2
3
4
5
6
|
#使用众数填补Embarked
Embarked = data.loc[:,"Embarked"].values.reshape(-1,1)
imp_mode = SimpleImputer(strategy = "most_frequent")#可以填文字，不一定是数值
data.loc[:,"Embarked"] = imp_mode.fit_transform(Embarked)
data.info()
|
<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 0 to 890
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Age 891 non-null float64
1 Sex 891 non-null object
2 Embarked 891 non-null object
3 Survived 891 non-null object
dtypes: float64(1), object(3)
memory usage: 34.8+ KB
1
2
3
4
5
6
|
import pandas as pd
data_ = pd.read_csv("Narrativedata.csv"
,index_col=0
)#index_col=0将第0列作为索引，不写则认为第0列为特征
data_.head()
|
|
Age |
Sex |
Embarked |
Survived |
| 0 |
22.0 |
male |
S |
No |
| 1 |
38.0 |
female |
C |
Yes |
| 2 |
26.0 |
female |
S |
Yes |
| 3 |
35.0 |
female |
S |
Yes |
| 4 |
35.0 |
male |
S |
No |
1
2
|
data_.loc[:,"Age"] = data_.loc[:,"Age"].fillna(data_.loc[:,"Age"].median())
#.fillna 在DataFrame里面直接进行填补
|
1
2
3
4
|
data_.dropna(axis=0,inplace=True)
#.dropna(axis=0)删除所有有缺失值的行，.dropna(axis=1)删除所有有缺失值的列
#参数inplace，为True表示在原数据集上进行修改，为False表示生成一个复制对象，不修改原数据，默认False
# _data_ = data_.drop(axis=0,inplace=False)
|
编码与哑变量(字符转数值)
1
2
3
4
5
6
7
|
from sklearn.preprocessing import LabelEncoder
y = data_.iloc[:,-1] #要输入的是标签，不是特征矩阵，所以允许一维
le = LabelEncoder() #实例化
le = le.fit(y) #导入数据
label = le.transform(y) #transform接口调取结果
|
1
|
le.classes_ #属性.classes_查看标签中究竟有多少类别
|
array(['No', 'Unknown', 'Yes'], dtype=object)
array([0, 2, 2, 2, 0, 0, 0, 0, 2, 2, 1, 2, 0, 0, 0, 1, 0, 2, 0, 2, 1, 2,
2, 2, 0, 1, 0, 0, 2, 0, 0, 2, 2, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 1,
2, 0, 0, 2, 0, 0, 0, 0, 2, 2, 0, 2, 2, 0, 2, 0, 0, 0, 0, 0, 2, 2,
0, 2, 0, 0, 0, 0, 0, 2, 1, 0, 1, 2, 2, 0, 2, 2, 0, 2, 2, 0, 0, 2,
0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 2, 2, 0, 1, 0, 0,
2, 0, 0, 2, 0, 0, 0, 1, 1, 2, 0, 0, 0, 2, 0, 0, 1, 0, 1, 1, 0, 0,
0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 2, 0, 0, 0, 1, 0, 2, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 1, 2, 0, 2, 2, 0, 0, 2, 0, 2, 1, 2, 2, 0, 0, 1,
0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 2, 1, 2, 1, 0, 0, 2, 2, 0, 2, 0, 2,
0, 0, 0, 2, 0, 2, 0, 0, 0, 2, 1, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0,
0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 1, 0, 2, 2, 2, 2, 2, 0, 2, 0, 1, 0,
0, 1, 2, 2, 1, 0, 2, 2, 0, 2, 2, 0, 0, 1, 1, 0, 0, 0, 2, 0, 0, 2,
0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 0,
2, 2, 2, 0, 0, 0, 2, 2, 1, 2, 2, 0, 1, 2, 2, 0, 2, 0, 1, 2, 2, 2,
0, 1, 0, 2, 0, 0, 2, 1, 0, 2, 2, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0,
0, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 2, 1, 2, 0, 0, 0, 0,
2, 2, 0, 0, 0, 2, 2, 0, 2, 0, 0, 0, 2, 0, 2, 2, 2, 0, 2, 2, 0, 0,
0, 0, 2, 2, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 2, 0, 2, 0, 1, 2, 0,
0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 0, 2, 0, 0, 2,
0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 1, 2, 0, 2, 2, 0, 2, 1, 0,
0, 0, 0, 0, 0, 0, 1, 1, 0, 2, 2, 0, 0, 1, 0, 0, 2, 0, 0, 0, 2, 2,
1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 0, 2, 1, 2,
2, 1, 2, 2, 0, 2, 2, 1, 0, 2, 0, 2, 0, 2, 0, 0, 2, 0, 0, 2, 0, 0,
0, 2, 0, 1, 2, 0, 2, 0, 2, 0, 2, 2, 0, 1, 2, 0, 0, 2, 2, 1, 2, 2,
0, 0, 2, 2, 0, 2, 0, 2, 2, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 2, 2, 1,
2, 0, 0, 2, 2, 0, 2, 2, 2, 0, 0, 0, 2, 0, 2, 1, 0, 0, 2, 0, 0, 0,
0, 2, 0, 0, 2, 2, 0, 0, 0, 2, 0, 1, 2, 2, 1, 0, 0, 2, 0, 0, 1, 0,
0, 2, 0, 0, 2, 2, 0, 0, 0, 1, 2, 1, 0, 1, 0, 2, 0, 0, 2, 0, 0, 0,
0, 0, 2, 0, 2, 2, 2, 1, 2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 2, 0, 0, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0, 2, 0, 2, 0, 2, 0,
0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 1, 1, 1, 1, 2, 0, 0, 2, 2, 0, 0, 0,
0, 1, 2, 2, 2, 2, 0, 1, 0, 1, 1, 2, 1, 0, 0, 2, 0, 0, 0, 2, 0, 2,
2, 1, 1, 2, 0, 1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2, 0,
0, 2, 2, 1, 0, 2, 2, 0, 0, 0, 2, 0, 0, 2, 2, 0, 2, 0, 0, 0, 0, 0,
1, 0, 0, 1, 1, 0, 2, 0, 2, 2, 2, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0,
0, 0, 0, 2, 2, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 2, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 2, 0, 0,
2, 2, 0, 0, 2, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 2, 0, 2, 2, 2, 2, 0,
0, 0, 2, 0, 1, 1, 1, 0, 0, 2, 0, 1, 0, 0, 2, 2, 0, 0, 0, 2, 2, 0,
0, 1, 0, 0, 0, 2, 0, 1, 0])
1
|
#le.fit_transform(y) #也可以直接fit_transform一步到位
|
1
|
#le.inverse_transform(label) #使用inverse_transform可以逆转
|
1
2
|
#data.iloc[:,-1] = label #让标签等于我们运行出来的结果
data.head()
|
|
Age |
Sex |
Embarked |
Survived |
| 0 |
22.0 |
male |
S |
0 |
| 1 |
38.0 |
female |
C |
2 |
| 2 |
26.0 |
female |
S |
2 |
| 3 |
35.0 |
female |
S |
2 |
| 4 |
35.0 |
male |
S |
0 |
1
2
3
|
#如果不需要展示的话会这么写：
from sklearn.preprocessing import LabelEncoder
data.iloc[:,-1] = LabelEncoder().fit_transform(data.iloc[:,-1])#标签列=转码实例化.训练加调用(原始标签)
|
OrdinalEncoder将分类特征转换为分类数值
1
2
3
4
5
|
from sklearn.preprocessing import OrdinalEncoder
#接口categories_对应LabelEncoder的接口classes_，一模一样的功能
data_ = data.copy()
data_.head()
|
|
Age |
Sex |
Embarked |
Survived |
| 0 |
22.0 |
male |
S |
0 |
| 1 |
38.0 |
female |
C |
2 |
| 2 |
26.0 |
female |
S |
2 |
| 3 |
35.0 |
female |
S |
2 |
| 4 |
35.0 |
male |
S |
0 |
1
|
OrdinalEncoder().fit(data_.iloc[:,1:-1]).categories_
|
[array(['female', 'male'], dtype=object), array(['C', 'Q', 'S'], dtype=object)]
1
|
data_.iloc[:,1:-1] = OrdinalEncoder().fit_transform(data_.iloc[:,1:-1])
|
|
Age |
Sex |
Embarked |
Survived |
| 0 |
22.0 |
1.0 |
2.0 |
0 |
| 1 |
38.0 |
0.0 |
0.0 |
2 |
| 2 |
26.0 |
0.0 |
2.0 |
2 |
| 3 |
35.0 |
0.0 |
2.0 |
2 |
| 4 |
35.0 |
1.0 |
2.0 |
0 |
OrdinalEncoder 相当于 LabelEncoder 支持多列版，在列多时候有性能优势，处理特征时优先使用OrdinalEncoder.
|
Age |
Sex |
Embarked |
Survived |
| 0 |
22.0 |
male |
S |
0 |
| 1 |
38.0 |
female |
C |
2 |
| 2 |
26.0 |
female |
S |
2 |
| 3 |
35.0 |
female |
S |
2 |
| 4 |
35.0 |
male |
S |
0 |
1
2
3
4
5
6
|
from sklearn.preprocessing import OneHotEncoder
X = data.iloc[:,1:-1]#取sex和embarked两列
enc = OneHotEncoder(categories='auto').fit(X)
result = enc.transform(X).toarray()#训练后结果转换为数组
result
|
array([[0., 1., 0., 0., 1.],
[1., 0., 1., 0., 0.],
[1., 0., 0., 0., 1.],
...,
[1., 0., 0., 0., 1.],
[0., 1., 1., 0., 0.],
[0., 1., 0., 1., 0.]])
1
2
|
#依然可以直接一步到位，但为了给大家展示模型属性，所以还是写成了三步
OneHotEncoder(categories='auto').fit_transform(X).toarray()
|
array([[0., 1., 0., 0., 1.],
[1., 0., 1., 0., 0.],
[1., 0., 0., 0., 1.],
...,
[1., 0., 0., 0., 1.],
[0., 1., 1., 0., 0.],
[0., 1., 0., 1., 0.]])
1
2
|
#依然可以还原
pd.DataFrame(enc.inverse_transform(result))
|
|
0 |
1 |
| 0 |
male |
S |
| 1 |
female |
C |
| 2 |
female |
S |
| 3 |
female |
S |
| 4 |
male |
S |
| ... |
... |
... |
| 886 |
male |
S |
| 887 |
female |
S |
| 888 |
female |
S |
| 889 |
male |
C |
| 890 |
male |
Q |
891 rows × 2 columns
1
|
enc.get_feature_names()#返回每一个经过哑变量后生成稀疏矩阵列的名字
|
array(['x0_female', 'x0_male', 'x1_C', 'x1_Q', 'x1_S'], dtype=object)
array([[0., 1., 0., 0., 1.],
[1., 0., 1., 0., 0.],
[1., 0., 0., 0., 1.],
...,
[1., 0., 0., 0., 1.],
[0., 1., 1., 0., 0.],
[0., 1., 0., 1., 0.]])
(891, 5)
1
2
|
#axis=1,表示跨行进行合并，也就是将两表左右相连，如果是axis=0，就是将量表上下相连
newdata = pd.concat([data,pd.DataFrame(result)],axis=1)
|
|
Age |
Sex |
Embarked |
Survived |
0 |
1 |
2 |
3 |
4 |
| 0 |
22.0 |
male |
S |
0 |
0.0 |
1.0 |
0.0 |
0.0 |
1.0 |
| 1 |
38.0 |
female |
C |
2 |
1.0 |
0.0 |
1.0 |
0.0 |
0.0 |
| 2 |
26.0 |
female |
S |
2 |
1.0 |
0.0 |
0.0 |
0.0 |
1.0 |
| 3 |
35.0 |
female |
S |
2 |
1.0 |
0.0 |
0.0 |
0.0 |
1.0 |
| 4 |
35.0 |
male |
S |
0 |
0.0 |
1.0 |
0.0 |
0.0 |
1.0 |
1
|
newdata.drop(["Sex","Embarked"],axis=1,inplace=True)#删除列
|
1
2
|
newdata.columns = ["Age","Survived","Female","Male","Embarked_C","Embarked_Q","Embarked_S"]
newdata.head()
|
|
Age |
Survived |
Female |
Male |
Embarked_C |
Embarked_Q |
Embarked_S |
| 0 |
22.0 |
0 |
0.0 |
1.0 |
0.0 |
0.0 |
1.0 |
| 1 |
38.0 |
2 |
1.0 |
0.0 |
1.0 |
0.0 |
0.0 |
| 2 |
26.0 |
2 |
1.0 |
0.0 |
0.0 |
0.0 |
1.0 |
| 3 |
35.0 |
2 |
1.0 |
0.0 |
0.0 |
0.0 |
1.0 |
| 4 |
35.0 |
0 |
0.0 |
1.0 |
0.0 |
0.0 |
1.0 |
