LabelBinarizer进行单分类和多分类one-hot编码

LabelBinarizer进行单分类和多分类one-hot编码

此种场景适用的字符串， 之间没有天然内在顺序

5.1 Encoding Nominal Categorical Feature¶
feature
# 加载库   使用LabelBinarizer  进行one-hot编码
import numpy as np
from sklearn.preprocessing import LabelBinarizer, MultiLabelBinarizer

feature = np.array([
    ["Texas"],
    ["California"],
    ["Texas"],
    ["Delaware"],
    ["Texas"]
])

array([[0, 0, 1],
       [1, 0, 0],
       [0, 0, 1],
       [0, 1, 0],
       [0, 0, 1]])
feature
array([['Texas'],
       ['California'],
       ['Texas'],
       ['Delaware'],
       ['Texas']], dtype='<U10')
# feature
# create one-hot encoder   创建 one-hot编码
one_hot = LabelBinarizer()

# one-hot encode feature 进行编码
one_hot.fit_transform(feature)
array([[0, 0, 1],
       [1, 0, 0],
       [0, 0, 1],
       [0, 1, 0],
       [0, 0, 1]])
  查看特征分类
# view feature classes  查看特征分类
one_hot.classes_
array(['California', 'Delaware', 'Texas'], dtype='<U10')
逆转换
# reverse one-hot encoding  逆转换
one_hot.inverse_transform(one_hot.transform(feature))
array(['Texas', 'California', 'Texas', 'Delaware', 'Texas'], dtype='<U10')
变量
import pandas as pd

# 加载库   创建虚拟变量
pd.get_dummies(feature[:, 0])
California  Delaware  Texas
0  0  0  1
1  1  0  0
2  0  0  1
3  0  1  0
4  0  0  1
# create multiclass feature  处理多个分类特征
multiclass_feature = [
    ("Texas", "Florida"),
    ("California", "Alabama"),
    ("Texas", "Florida"),
    ("Delaware", "Florida"),
    ("Texas", "Alabama")
]


multiclass_feature

# create multiclass one-hot encoder
one_hot_multiclass = MultiLabelBinarizer()

# one-hot encode multiclass feature
one_hot_multiclass.fit_transform(multiclass_feature)
array([[0, 0, 0, 1, 1],
       [1, 1, 0, 0, 0],
       [0, 0, 0, 1, 1],
       [0, 0, 1, 1, 0],
       [1, 0, 0, 0, 1]])
# view classes  查看分类
one_hot_multiclass.classes_
array(['Alabama', 'California', 'Delaware', 'Florida', 'Texas'],
      dtype=object)