如何将 BinaryRelevance.predict 结果转换为标签名称？

如何解决如何将 BinaryRelevance.predict 结果转换为标签名称？

我创建了一个使用 skmultilearn 尝试进行多标签文本分类的小例子：

import skmultilearn
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
from scipy.sparse import csr_matrix
from pandas.core.common import flatten
from sklearn.naive_bayes import MultinomialNB
from skmultilearn.problem_transform import BinaryRelevance

TRAIN_DATA = [
     ['How to connect to MySQL using PHP ?',['development','database']],['What are the best VPN clients these days?',['networks']],['What is the equivalent of the boolean type in Oracle?',['database']],['How to remove unwanted entity from Hibernate session?',['development']],['How to implement TCP connection pooling in java?','networks']],['How can I connect to PostgreSQL database remotely from another network?',['database',['What is the python function to remove accents in a string?',['How to remove indexes in SQL Server?',['How to configure firewall with DMZ?',['networks']]
] 
data_frame = pd.DataFrame(TRAIN_DATA,columns=['text','labels'])
corpus = data_frame['text']
unique_labels = set(flatten(data_frame['labels']))
for u in unique_labels:
    data_frame[u] = 0
    data_frame[u] = pd.to_numeric(data_frame[u])
for i,row in data_frame.iterrows():
    for u in unique_labels:
        if u in row.labels:
            data_frame.at[i,u] = 1
tfidf = TfidfVectorizer()
Xfeatures = tfidf.fit_transform(corpus).toarray()
y = data_frame[unique_labels]
binary_rel_clf = BinaryRelevance(MultinomialNB())
binary_rel_clf.fit(Xfeatures,y)
predict_text = ['SQL Server and PHP?']
X_predict = tfidf.transform(predict_text)
br_prediction = binary_rel_clf.predict(X_predict)
print(br_prediction)

然而，结果是这样的：

(0,1)  1.

有没有办法将此结果转换为标签名称，例如 ['development','database']？

解决方法

BinaryRelevance 估计器的返回类型是 scipy csc_matrix。您可以执行以下操作：

首先，将 csc_matrix 转换为 bool 类型的密集 numpy 数组：

br_prediction = br_prediction.toarray().astype(bool)

然后，使用转换后的预测作为 y 可能标签名称的掩码：

predictions = [y.columns.values[prediction].tolist() for prediction in br_prediction]

这会将每个预测映射到相应的标签。例如：

print(y.columns.values)
# output: ['development' 'database' 'networks']

print(br_prediction)
# output: (0,1)    1

br_prediction = br_prediction.toarray().astype(bool)
print(br_prediction)
# output: [[False True False]]

predictions = [y.columns.values[prediction].tolist() for prediction in br_prediction]
print(predictions)
# output: [['database']]