OneHotEncoder categorical_features 已弃用，如何转换特定列

Question

我需要将独立字段从字符串转换为算术符号。我正在使用 OneHotEncoder 进行转换。我的数据集有许多独立的列，其中一些列如下：

Country     |    Age       
--------------------------
Germany     |    23
Spain       |    25
Germany     |    24
Italy       |    30

我必须像

这样对国家/地区列进行编码

0     |    1     |     2     |       3
--------------------------------------
1     |    0     |     0     |      23
0     |    1     |     0     |      25
1     |    0     |     0     |      24 
0     |    0     |     1     |      30

我通过使用OneHotEncoder as

成功实现了愿望转换

#Encoding the categorical data
from sklearn.preprocessing import LabelEncoder

labelencoder_X = LabelEncoder()
X[:,0] = labelencoder_X.fit_transform(X[:,0])

#we are dummy encoding as the machine learning algorithms will be
#confused with the values like Spain > Germany > France
from sklearn.preprocessing import OneHotEncoder

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

现在我收到要使用的折旧消息 categories='auto'。如果我这样做，则正在对所有独立列（如国家/地区、年龄、薪水等）进行转换。

如何只对数据集第0列进行转换？

Answer 1

实际上有 2 个警告：

FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values. If you want the future behaviour and silence this warning, you can specify "categories='auto'". In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.

第二个：

The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.
"use the ColumnTransformer instead.", DeprecationWarning)

以后不要直接在OneHotEncoder中定义列，除非你想使用"categories='auto'"。第一条消息还告诉您直接使用 OneHotEncoder，而不是先使用 LabelEncoder。最后，第二条消息告诉你使用 ColumnTransformer，它就像一个用于列转换的管道。

这是您的案例的等效代码：

from sklearn.compose import ColumnTransformer 
ct = ColumnTransformer([("Name_Of_Your_Step", OneHotEncoder(),[0])], remainder="passthrough")) # The last arg ([0]) is the list of columns you want to transform in this step
ct.fit_transform(X)

另请参阅：ColumnTransformer documentation

对于上面的例子；

Encoding Categorical data (Basically Changing Text to Numerical data i.e, Country Name)

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
#Encode Country Column
labelencoder_X = LabelEncoder()
X[:,0] = labelencoder_X.fit_transform(X[:,0])
ct = ColumnTransformer([("Country", OneHotEncoder(), [0])], remainder = 'passthrough')
X = ct.fit_transform(X)

Answer 2

有一种方法可以使用 pandas 进行一次热编码。 Python:

import pandas as pd
ohe=pd.get_dummies(dataframe_name['column_name'])

为新形成的列命名，将其添加到您的数据框中。查看 pandas 文档 here。

Answer 3

transformer = ColumnTransformer(
    transformers=[
        ("Country",        # Just a name
         OneHotEncoder(), # The transformer class
         [0]            # The column(s) to be applied on.
         )
    ], remainder='passthrough'
)
X = transformer.fit_transform(X)

提醒将保留以前的数据，而将替换的第 [0] 列将被编码

Answer 4

我遇到了同样的问题，以下对我有用：

OneHotEncoder(categories='auto', sparse=False)

希望对您有所帮助

Answer 5

从 0.22 版本开始，您可以编写如下相同的代码：

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer([("Country", OneHotEncoder(), [0])], remainder = 'passthrough')
X = ct.fit_transform(X)

如您所见，您不再需要使用 LabelEncoder。

Answer 6

不用labelencoder直接用OneHotEncoder

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import make_column_transformer
A = make_column_transformer(
    (OneHotEncoder(categories='auto'), [0]), 
    remainder="passthrough")

x=A.fit_transform(x)

Answer 7

使用以下代码：-

from sklearn.preprocessing import OneHotEncoder

from sklearn.compose import ColumnTransformer

columnTransformer = ColumnTransformer([('encoder', OneHotEncoder(), [0])], remainder='passthrough')

X = np.array(columnTransformer.fit_transform(X), dtype = np.str)

print(X)

Answer 8

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
...
onehotencorder = ColumnTransformer(
   [('one_hot_encoder', OneHotEncoder(), [0])],
   remainder='passthrough'                     
)

X = onehotencorder.fit_transform(X)

Answer 9

# Data Preprocessing Template

# Importing the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

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

# Splitting the dataset into the Training set and Test set
#from sklearn.preprocessing import Imputer
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])

#encoding Categorical Data
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer

labelencoder_X = LabelEncoder()
X[:,0] = labelencoder_X.fit_transform(X[:,0])
onehotencoder = ColumnTransformer([("Country", OneHotEncoder(), [0])], remainder = "passthrough")
X = onehotencoder.fit_transform(X)


labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)

Answer 10

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
transformer = ColumnTransformer([('one_hot_encoder', OneHotEncoder(), [0])],remainder='passthrough')
x = py.array(transformer.fit_transform(x), dtype=py.float)


onehotencoder = oneHotEncoder(categorical_features=[0])

此代码应该可以解决错误。

Answer 11

从这里更新代码时：

one_hot_encoder = OneHotEncoder(categorical_features = [0, 1, 4, 5, 6])
X_train = one_hot_encoder.fit_transform(X_train).toarray()

为此：

ct = ColumnTransformer([('one_hot_encoder', OneHotEncoder(), [
                       0, 1, 4, 5, 6])], remainder='passthrough')
X_train = np.array(ct.fit_transform(X_train), dtype=np.float)

请注意，我必须添加 dtype=np.float 来修复错误消息 TypeError: can't convert np.ndarray of type numpy.object_.

我的专栏是 [0, 1, 4, 5, 6] 和 'one_hot_encoder' 是什么。

我的导入是：

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
import numpy as np

OneHotEncoder categorical_features 已弃用，如何转换特定列

OneHotEncoder categorical_features deprecated, how to transform specific column

python

machine-learning

categorical-data

one-hot-encoding