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

一个处女座的程序猿 2020-11-13 05:48:52
Python 函数 SKlearn 简介 labelencoder


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

 

 

目录

LabelEncoder函数的简介(编码与编码还原)

Methods

LabelEncoder函数的使用方法

LabelEncoder函数的具体案例

1、基础案例

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


 

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 pd
from sklearn.preprocessing import LabelEncoder
from 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 np
from 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])

 

 

 

 

 

 

 

 

 

 

 

 

 

版权声明
本文为[一个处女座的程序猿]所创,转载请带上原文链接,感谢
https://yunyaniu.blog.csdn.net/article/details/109408387

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