我正在嘗試使用Scikit學習來學習文本的多標簽分類，我正在嘗試調整Scikit附帶的一個初始示例教程，用于使用wikipedia文章作為培訓數據對語言進行分類。我試圖在下面實現這一點，但代碼仍然為每個返回一個標簽，我希望最后一個預測返回fr，en

有誰能建議正確的方法來啟用多標簽分類。在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]))

返回-

“這是語言檢測測試”的語言是“en”

“Ceci est un test de detection de la langue.”的語言是“fr”

“死在考驗中，嗯，我是在考驗。”是“德”

“你好，朋友”的語言。這是一個語言檢測測試

總結

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