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

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:: 0.12Attributes----------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 encoderParameters----------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 selfdef fit_transform(self, y):"""Fit label encoder and return encoded labelsParameters----------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 ydef 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 arrayif _num_samples(y) == 0:return np.array([])_, y = _encode(y, uniques=self.classes_, encode=True)return ydef 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 arrayif _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']}Methodsfit(y)Fit label encoderfit_transform(y)Fit label encoder and return encoded labelsget_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])

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