我如何使用MultiLabelBinarizer

Question

時，你得到的類名後面我有一個CSV文件看起來像這樣：

target,data 
AAA,some text document 
AAA;BBB,more text 
AAC,more text 

下面是代碼：

from sklearn.multiclass import OneVsRestClassifier 
from sklearn.preprocessing import MultiLabelBinarizer 
from sklearn.feature_extraction.text import HashingVectorizer 
from sklearn.naive_bayes import BernoulliNB 
import pandas as pd 

pdf = pd.read_csv("Train.csv", sep=',') 
pdfT = pd.read_csv("Test.csv", sep=',') 

X1 = pdf['data'] 
Y1 = [[t for t in tar.split(';')] for tar in pdf['target']] 
X2 = pdfT['data'] 
Y2 = [[t for t in tar.split(';')] for tar in pdfT['target']] 

# Vectorizer data 
hv = HashingVectorizer(stop_words='english', non_negative=True) 
X1 = hv.transform(X1) 
X2 = hv.transform(X2) 

mlb = MultiLabelBinarizer() 
mlb.fit(Y1+Y2) 
Y1 = mlb.transform(Y1) 
# mlb.classes_ looks like ['AAA','AAC','BBB',...] len(mlb.classes_)==1363 

# Y1 looks like [[0,0,0,....0,0,0], ... ] now 

# fit 
clsf = OneVsRestClassifier(BernoulliNB(alpha=.001)) 
clsf.fit(X1,Y1) 

# predict_proba 
proba = clsf.predict_proba(X2) 

# want to get class names back 
classnames = mlb.inverse_transform(clsf.classes_) # booom, shit happens 

for i in range(len(proba)): 
    # get classnames,probability dict 
    preDict = dict(zip(classnames, proba[i])) 
    # sort dict by probability value, print actual and top 5 predict results 
    print(Y2[i], dict(sorted(preDict.items(),key=lambda d:d[1],reverse=True)[0:5])) 

問題是clsf.fit後（ X1，Y1） clsf.classes_是一個int數組[0,1,2,3，... 1362]

爲什麼它不像Y1？我如何從clsf.classes_獲取類名？ mlb.classes_ == clsf.classes_或不是，具有相同的順序？

Answer 1

當你適應OneVsRestClassifier與多個標籤一LabelBinarizer的fit通話過程中被調用，這將在multilabels轉化爲每個類獨特的標籤。

您可以訪問clsf對象的label_binarizer_屬性，該屬性的類別屬性將包含調用clsf的類中的類定義。