【机器学习100天】Day-1数据预处理-代码调整

 随着python版本的更迭，机器学习100天中部分原有的代码无法与较新版本的python相容，为了让大家更方便的运行代码，了解其中原理，笔者将其中需要改正的部分列举出来，供大家参考。

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1.ImportError: cannot import name 'Imputer' from 'sklearn.preprocessing'

 原代码：

from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
imputer = imputer.fit(X[ : , 1:3])
X[ : , 1:3] = imputer.transform(X[ : , 1:3])

 错误原因：

        在0.22版本的sklearn中，imputer不在preprocessing里了，而是在sklearn.impute里。SimpleImputer进行缺失值处理，详细用法见缺失值处理

修改后代码：

from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values = np.nan, strategy = "mean")
imputer = imputer.fit(X[:,1:3])
X[:, 1:3] = imputer.transform(X[:,1:3])

2.TypeError: __init__() got an unexpected keyword argument 'categorical_features'

  

原代码：

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])

onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()

labelencoder_Y = LabelEncoder()
Y =  labelencoder_Y.fit_transform(Y)

错误原因：

        sklearn 版本在0.22.1往后,OneHotEncoder没有参数categorical_features，可以引入ColumnTransformer（）,将OneHotEncoder这个转换器放在里边。

修改后代码：

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
labelencoder_X = LabelEncoder()
X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])

onehotencoder = ColumnTransformer([('encoder', OneHotEncoder(), [0])], remainder = 'passthrough')
X = onehotencoder.fit_transform(X)

labelencoder_Y = LabelEncoder()
Y =  labelencoder_Y.fit_transform(Y)

3.ModuleNotFoundError: No module named 'sklearn.cross_validation'

原代码：

from sklearn.cross_validation import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)

错误原因：

        在sklearn 0.18及以上的版本中，出现了sklearn.cross_validation无法导入的情况，原因是新版本中此包被废弃，只需将 cross_validation 改为 model_selection 即可。

修改后代码：

from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)

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附完整代码：

import numpy as np
import pandas as pd

dataset = pd.read_csv('Data.csv')
X = dataset.iloc[:,:-1].values
Y = dataset.iloc[:, 3].values

from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values = np.nan, strategy = "mean")
imputer = imputer.fit(X[:,1:3])
X[:, 1:3] = imputer.transform(X[:,1:3])

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
labelencoder_X = LabelEncoder()
X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])

onehotencoder = ColumnTransformer([('encoder', OneHotEncoder(), [0])], remainder = 'passthrough')
X = onehotencoder.fit_transform(X)


labelencoder_Y = LabelEncoder()
Y =  labelencoder_Y.fit_transform(Y)

from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)

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参考文章：

https://blog.csdn.net/qq_44635691/article/details/104374481

https://blog.csdn.net/yunfenglw/article/details/105835732

https://blog.csdn.net/qq_43965708/article/details/115625768​​​​​​​