顺序编码器--OrdinalEncoder类

OrdinalEncoder

sklearn.preprocessing.OrdinalEncoder(*, categories='auto', dtype=<class 'numpy.float64'>, handle_unknown='error', unknown_value=None, encoded_missing_value=nan)
1

将分类特征转化为整数数组
编码器的输入应该是以整数或字符串为元素的类数组，表示由分类的(离散的)特征所获得的值，这些特征被转换为序列整数，这将导致每个特征产生一个整数列

The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The features are converted to ordinal integers. This results in a single column of integers (0 to n_categories - 1) per feature

参数

categories

‘auto’ or a list of array-like, default=’auto’

dtype

number type, default np.float64
期望的输出数据类型

handle_unknown

{‘error’, ‘use_encoded_value’}, default=’error’
当被设置为error时，当transform过程中遇到未知分类特征时将会抛出一个错误

unknown_value

int or np.nan, default=None
当参数handle_unknown被设置为use_encoded_value时，该参数是必须的

encoded_missing_value

int or np.nan, default=np.nan

缺失类别的编码值。如果设置为np.Nan，那么参数dtype必须是浮点型

属性

categories_

list of arrays
在拟合过程中确定每个特征的类别

The categories of each feature determined during fit (in order of the features in X and corresponding with the output of transform). This does not include categories that weren’t seen during fit.

n_features_in_

int
拟合过程中的特征数量

feature_names_in_

ndarray of shape (n_features_in_,)

拟合过程中的特征名称

Names of features seen during fit. Defined only when X has feature names that are all strings.

方法

fit(X[, y])

拟合数据

Fit the OrdinalEncoder to X.

fit_transform(X[, y])

拟合数据并进行转换

Fit to data, then transform it.

get_feature_names_out([input_features])

返回输出特征名称

Get output feature names for transformation.

get_params([deep])

返回模型参数

Get parameters for this estimator.

inverse_transform(X)

还原数据

Convert the data back to the original representation.

set_params(**params)

设置模型参数

Set the parameters of this estimator.

transform(X)

转换数据为序列代码

Transform X to ordinal codes.

使用示例

from sklearn.preprocessing import OrdinalEncoder
encoder = OrdinalEncoder()
x = [['Male', 1], ['Female', 3], ['Female', 2]]
x_transform=encoder.fit_transform(x)
x_transform
>>> array([[1., 0.],
       [0., 2.],
       [0., 1.]])
encoder.inverse_transform(x_transform)
>>>array([['Male', 1],
       ['Female', 3],
       ['Female', 2]], dtype=object)
1
2
3
4
5
6
7
8
9
10
11
12