我正在尝试使用 Scikit-learn 了解文本的多标签分类，我正在尝试将 scikit 附带的初始示例教程之一改编为使用维基百科文章作为训练数据的语言分类。我正在尝试在下面实现这一点，但代码仍然为每个我希望最后一个预测返回 fr, en 的地方返回一个标签
任何人都可以就启用多标签分类的正确方法提出建议。
import sys
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import LabelBinarizer
from sklearn.svm import LinearSVC
from sklearn.pipeline import Pipeline
from sklearn.datasets import load_files
from sklearn.cross_validation import train_test_split
from sklearn import metrics
from sklearn.multiclass import OneVsRestClassifier
#change model_selection to cross_validation
# The training data folder must be passed as first argument - This uses the example wiki language data files
languages_data_folder = sys.argv[1]
dataset = load_files(languages_data_folder)
# Split the dataset in training and test set:
docs_train, docs_test, y_train, y_test = train_test_split(
dataset.data, dataset.target, test_size=0.5)
#pipeline
clf = Pipeline([
('vectorizer', CountVectorizer(ngram_range=(1,2))),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(LinearSVC())),
])
target_names=dataset.target_names
# TASK: Fit the pipeline on the training set
clf.fit(docs_train, y_train)
# TASK: Predict the outcome on the testing set in a variable named y_predicted
y_predicted = clf.predict(docs_test)
print target_names
# Predict the result on some short new sentences:
sentences = [
u'This is a language detection test.',
u'Ceci est un test de d\xe9tection de la langue.',
u'Dies ist ein Test, um die Sprache zu erkennen.',
u'Bonjour Mon ami. This is a language detection test.',
]
predicted = clf.predict(sentences)
for s, p in zip(sentences, predicted):
print(u'The language of "%s" is "%s"' % (s, target_names[p]))
回报 -
“这是一个语言检测测试”的语言。是“恩”
“Ceci est un test de détection de la langue”的语言。是“fr”
“Dies ist ein Test, um die Sprache zu erkennen”的语言。是“德”
“Bonjour Mon ami。这是一个语言检测测试”的语言。是“恩”