sklearn.preprocessing LabelBinarizer 使用

sklearn.preprocessing LabelBinarizer 使用






將 muti-class labels 轉換成 binary labels

fit 後可得到3個參數
classes_  列出分類
y_type_  物件型態
sparse_input_ 不確定

傳入一維字串

['a' , 'b' , 'c']
=>
[[1,0,0]
,[0,1,0]
,[0,0,1]]


傳入二維陣列
二維無法傳入字串
也須使用 np.array
np.array([[0,0,0,0,0]])
將等效於
 [0,1,2,3,4]

code

import numpy as np
import pandas as pd

from sklearn.preprocessing import LabelBinarizer


data = ['a', 'h', 'io', 'po']

lb = LabelBinarizer(sparse_output=False)
lb.fit(data)

print("LabelBinarizer.classes_ \n" , lb.classes_)
print("LabelBinarizer.y_type_ \n" , lb.y_type_)
print("LabelBinarizer.sparse_input_ \n" , lb.sparse_input_)



print("LabelBinarizer : \n" , lb.fit_transform(data))


muldata = np.array([[0,0,0,0,0]])

lb = LabelBinarizer(sparse_output=False)
lb.fit(muldata)

print("LabelBinarizer.classes_ \n" , lb.classes_)
print("LabelBinarizer.y_type_ \n" , lb.y_type_)
print("LabelBinarizer.sparse_input_ \n" , lb.sparse_input_)

print("LabelBinarizer : \n" , lb.transform([0, 1, 2, 1,3,4]))
 

out

LabelBinarizer.classes_
 ['a' 'h' 'io' 'po']
LabelBinarizer.y_type_
 multiclass
LabelBinarizer.sparse_input_
 False
LabelBinarizer :
 [[1 0 0 0]
 [0 1 0 0]
 [0 0 1 0]
 [0 0 0 1]]
LabelBinarizer.classes_
 [0 1 2 3 4]
LabelBinarizer.y_type_
 multilabel-indicator
LabelBinarizer.sparse_input_
 False
LabelBinarizer :
 [[1 0 0 0 0]
 [0 1 0 0 0]
 [0 0 1 0 0]
 [0 1 0 0 0]
 [0 0 0 1 0]
 [0 0 0 0 1]]

source code