Python之sklearn：LabelEncoder函数简介(编码与编码还原)、使用方法、具体案例之详细攻略

LabelEncoder函数的简介(编码与编码还原)

class LabelEncoder Found at: sklearn.preprocessing._labelclass LabelEncoder(TransformerMixin, BaseEstimator):

"""Encode target labels with value between 0 and n_classes-1.

This transformer should be used to encode target values, *i.e.* `y`, and not the input `X`.

Read more in the :ref:`User Guide <preprocessing_targets>`.

""对目标标签进行编码，值在0到n_class -1之间。

这个转换器应该用于编码目标值，*即' y '，而不是输入' X '。

更多内容见:ref: ' User Guide '。

.. versionadded:: 0.12

Attributes

----------

classes_ : array of shape (n_class,)

Holds the label for each class.

Examples

--------

`LabelEncoder` can be used to normalize labels.

>>> from sklearn import preprocessing

>>> le = preprocessing.LabelEncoder()

>>> le.fit([1, 2, 2, 6])

LabelEncoder()

>>> le.classes_

array([1, 2, 6])

>>> le.transform([1, 1, 2, 6])

array([0, 0, 1, 2]...)

>>> le.inverse_transform([0, 0, 1, 2])

array([1, 1, 2, 6])

It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels.

>>> le = preprocessing.LabelEncoder()

>>> le.fit(["paris", "paris", "tokyo", "amsterdam"])

LabelEncoder()

>>> list(le.classes_)

['amsterdam', 'paris', 'tokyo']

>>> le.transform(["tokyo", "tokyo", "paris"])

array([2, 2, 1]...)

>>> list(le.inverse_transform([2, 2, 1]))

['tokyo', 'tokyo', 'paris']

See also

--------

sklearn.preprocessing.OrdinalEncoder : Encode categorical features using an ordinal encoding scheme.

sklearn.preprocessing.OneHotEncoder : Encode categorical features as a one-hot numeric array.

. .versionadded:: 0.12

属性

----------

classes_:形状数组(n_class，)

保存每个类的标签。

例子

--------

“LabelEncoder”可用于规范化标签。

>>> from sklearn import preprocessing

>>> le = preprocessing.LabelEncoder()

>>> le.fit([1, 2, 2, 6])

LabelEncoder()

>>> le.classes_

array([1, 2, 6])

>>> le.transform([1, 1, 2, 6])

array([0, 0, 1, 2]...)

>>> le.inverse_transform([0, 0, 1, 2])

array([1, 1, 2, 6])

它还可以用于将非数字标签(只要它们是可hashable和可比的)转换为数字标签。

>>> le = preprocessing.LabelEncoder()

>>> le.fit(["paris", "paris", "tokyo", "amsterdam"])

LabelEncoder()

>>> list(le.classes_)

['amsterdam', 'paris', 'tokyo']

>>> le.transform(["tokyo", "tokyo", "paris"])

array([2, 2, 1]...)

>>> list(le.inverse_transform([2, 2, 1]))

['tokyo', 'tokyo', 'paris']

另请参阅

--------

sklearn.preprocessing.OrdinalEncoder :序号编码器:使用序号编码方案编码分类特征。

sklearn.preprocessing.OneHotEncoder :  将分类特性编码为一个热的数字数组。

"""

def fit(self, y):

"""Fit label encoder

Parameters

----------

y : array-like of shape (n_samples,)

Target values.

Returns

-------

self : returns an instance of self.

"""

y = column_or_1d(y, warn=True)

self.classes_ = _encode(y)

return self

def fit_transform(self, y):

"""Fit label encoder and return encoded labels

Parameters

----------

y : array-like of shape [n_samples]

Target values.

Returns

-------

y : array-like of shape [n_samples]

"""

y = column_or_1d(y, warn=True)

self.classes_, y = _encode(y, encode=True)

return y

def transform(self, y):

"""Transform labels to normalized encoding.

Parameters

----------

y : array-like of shape [n_samples]

Target values.

Returns

-------

y : array-like of shape [n_samples]

"""

check_is_fitted(self)

y = column_or_1d(y, warn=True)

# transform of empty array is empty array

if _num_samples(y) == 0:

return np.array([])

_, y = _encode(y, uniques=self.classes_, encode=True)

return y

def inverse_transform(self, y):

"""Transform labels back to original encoding.

Parameters

----------

y : numpy array of shape [n_samples]

Target values.

Returns

-------

y : numpy array of shape [n_samples]

"""

check_is_fitted(self)

y = column_or_1d(y, warn=True)

# inverse transform of empty array is empty array

if _num_samples(y) == 0:

return np.array([])

diff = np.setdiff1d(y, np.arange(len(self.classes_)))

if len(diff):

raise ValueError(

"y contains previously unseen labels: %s" % str(diff))

y = np.asarray(y)

return self.classes_[y]

def _more_tags(self):

return {'X_types':['1dlabels']}

Methods

fit(y)

Fit label encoder

fit_transform(y)

Fit label encoder and return encoded labels

get_params([deep])

Get parameters for this estimator.

inverse_transform(y)

Transform labels back to original encoding.

set_params(**params)

Set the parameters of this estimator.

transform(y)

Transform labels to normalized encoding.

LabelEncoder函数的使用方法

import pandas as pdfrom sklearn.preprocessing import LabelEncoderfrom DataScienceNYY.DataAnalysis import dataframe_fillAnyNull,Dataframe2LabelEncoder#构造数据train_data_dict={'Name':['张三','李四','王五','赵六','张七','李八','王十','un'], 'Age':[22,23,24,25,22,22,22,None], 'District':['北京','上海','广东','深圳','山东','河南','浙江',' '], 'Job':['CEO','CTO','CFO','COO','CEO','CTO','CEO','']}test_data_dict={'Name':['张三','李四','王十一',None], 'Age':[22,23,22,'un'], 'District':['北京','上海','广东',''], 'Job':['CEO','CTO','UFO',' ']}train_data_df = pd.DataFrame(train_data_dict)test_data_df = pd.DataFrame(test_data_dict)print(train_data_df,'\n',test_data_df)#缺失数据填充for col in train_data_df.columns: train_data_df[col]=dataframe_fillAnyNull(train_data_df,col) test_data_df[col]=dataframe_fillAnyNull(test_data_df,col)print(train_data_df,'\n',test_data_df)#数据LabelEncoder化train_data,test_data=Dataframe2LabelEncoder(train_data_df,test_data_df)print(train_data,'\n',test_data) LabelEncoder函数的具体案例

1、基础案例

LabelEncoder can be used to normalize labels.>>>>>> from sklearn import preprocessing>>> le = preprocessing.LabelEncoder()>>> le.fit([1, 2, 2, 6])LabelEncoder()>>> le.classes_array([1, 2, 6])>>> le.transform([1, 1, 2, 6])array([0, 0, 1, 2]...)>>> le.inverse_transform([0, 0, 1, 2])array([1, 1, 2, 6])It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels.>>>>>> le = preprocessing.LabelEncoder()>>> le.fit(["paris", "paris", "tokyo", "amsterdam"])LabelEncoder()>>> list(le.classes_)['amsterdam', 'paris', 'tokyo']>>> le.transform(["tokyo", "tokyo", "paris"])array([2, 2, 1]...)>>> list(le.inverse_transform([2, 2, 1]))['tokyo', 'tokyo', 'paris'] 2、在数据缺失和test数据内存在新值(train数据未出现过)环境下的数据LabelEncoder化

参考文章：Python之sklearn：LabelEncoder函数的使用方法之使用LabelEncoder之前的必要操作

import numpy as npfrom sklearn.preprocessing import LabelEncoder#训练train数据LE= LabelEncoder()LE.fit(train_df[col])#test数据中的新值添加到LE.classes_test_df[col] =test_df[col].map(lambda s:'Unknown' if s not in LE.classes_ else s) LE.classes_ = np.append(LE.classes_, 'Unknown') #分别转化train、test数据train_df[col] = LE.transform(train_df[col]) test_df[col] = LE.transform(test_df[col])