关于OrdinalEncoder 、OneHotEncoder
2019-04-29 本文已影响0人
SeekerLinJunYu
OrdinalEncoder / OneHotEncoder /get_dummies 三个方法都能够将离散的类别特征转换成由数字代表的类别特征.但是三者的特征又不尽相同
不扩展特征个数
OrdinalEncoder (与LabelEncoder用法 效果都是一致的,这里就不再单独说明LabelEncoder)
- scikit-learn中提供的方法;可以将每一个类别的特征转换成一个新的整数(0到类别数n-1之间),即并非0或1
- 传入的对象必须要求是2D的数据结构
- 并不会增添特征的维度,只是对该特征类别值进行一个映射,这与One_Hot Encoder有明显的转换上的不同
- 但是这种方法并不是对所有的scikit-learn估计器都适用
In: MSSubClass_data = train_df.MSSubClass.astype(str) #在所有操作前,将特征转换成字符串是必须的操作
In: label_encoder = preprocessing.LabelEncoder()
In: MSSubClass_data_encoded = label_encoder.fit_transform(MSSubClass_data)
扩展特征个数
OneHotEncoder
- Scikit-Learn OneHotEncoder
- OneHotEncoder是一种能够被scikit-learn的估计器使用的类别特征转换函数
- 原理是将有n个类别的值转换成n个二分特征属性,属性值取0或者1
- 因此,One-Hot Encoder是会根据特征取值的类别改变数据特征数目的
- 因为扩展了特征的个数,并返回二值类别数值,势必会造成稀疏矩阵.参数spare可以用来设置是否返回稀疏矩阵
- 传入的对象必须要求是2D的数据结构
- 需要注意的是,如果用来fit的数据并不包含所有的潜在类别,那么在传参时需要传入ignore,即忽略没有拟合的类别,否则会报错
enc = preprocessing.OneHotEncoder(handle_unknown='ignore'). 如果要求识别出所有类别必须指明:
>>> genders = ['female', 'male']
>>> locations = ['from Africa', 'from Asia', 'from Europe', 'from US']
>>> browsers = ['uses Chrome', 'uses Firefox', 'uses IE', 'uses Safari']
>>> enc = preprocessing.OneHotEncoder(categories=[genders, locations, browsers])
>>> # Note that for there are missing categorical values for the 2nd and 3rd
>>> # feature
>>> X = [['male', 'from US', 'uses Safari'], ['female', 'from Europe', 'uses Firefox']]
>>> enc.fit(X)
OneHotEncoder(categorical_features=None,
categories=[...],
dtype=<... 'numpy.float64'>, handle_unknown='error',
n_values=None, sparse=True)
>>> enc.transform([['female', 'from Asia', 'uses Chrome']]).toarray()
array([[1., 0., 0., 1., 0., 0., 1., 0., 0., 0.]])
- 对array([[1., 0., 0., 1., 0., 0., 1., 0., 0., 0.]])的解读:
- 前两位1,0是对genders的分类编码.即扩展出的两个特征(因为gender下一共就两种类别)gender_female特征下的值为1, gender_male特征下的值为0
- 中间四位0,1,0,0是对locations的分类编码.即locations_from_Africa = 0,locations_from_Asia= 1,
locations_from_Europe= 0, locations_from_US= 0, - 最后四位1,0,0,0是对browsers的分类编码.原理与上述一致,不再赘述.
最基本的用法:
In: enc = preprocessing.OneHotEncoder()
In: result = enc.fit_transform(MSSubClass_data.values.reshape(-1,1))
get_dummies
- get_dummies 是pandas中提供的方法
- 原理与OneHotEncoder基本一致
最基本的用法:
In: all_df.MSSubClass = pd.get_dummies(all_df['MSSubClass'],prefix='MSSubClass')
转换前.png
转换后.png
关于怎么使用Encoder方法改变原数据集?
在写这篇文章的时候,其实最困扰我的问题是如何利用Encoder接口实现对原数据集的有针对性的更改.后来在Scikit-Learn官网上找到一段代码,也算是能解答这个问题.下面贴出来:
from __future__ import print_function
import pandas as pd
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split,GridSearchCV
np.random.seed(0)
# Read data from Titanic dataset.
titanic_url = ('https://raw.githubusercontent.com/amueller/'
'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')
data = pd.read_csv(titanic_url)
# We will train our classifier with the following features:
# Numeric Features:
# - age: float.
# - fare: float.
# Categorical Features:
# - embarked: categories encoded as strings {'C', 'S', 'Q'}.
# - sex: categories encoded as strings {'female', 'male'}.
# - pclass: ordinal integers {1, 2, 3}.
# We create the preprocessing pipelines for both numeric and categorical data.
numeric_features = ['age', 'fare']
numeric_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant',fill_value = 'missing')),
('onehot',OneHotEncoder(handle_unknown = 'ignore'))]
categorical_features = ['embarked', 'sex', 'pclass']
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('onehot', OneHotEncoder(handle_unknown='ignore'))])
preprocessor = ColumnTransformer( # 这一步实现对数值型数据和类别数据的分别更改
transformers=[
('num', numeric_transformer, numeric_features),
('cat', categorical_transformer, categorical_features)])
# Append classifier to preprocessing pipeline.
# Now we have a full prediction pipeline.
clf = Pipeline(steps=[('preprocessor', preprocessor),
('classifier', LogisticRegression(solver='lbfgs'))])
X = data.drop('survived', axis=1)
y = data['survived']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
clf.fit(X_train, y_train)
print("model score: %.3f" % clf.score(X_test, y_test))</pre>
