从0.15版开始，TfidfVectorizer可以通过属性访问由a获知的特征的全局项加权，该属性idf_将返回一个长度等于特征维的数组。按此权重对要素进行排序，以获得权重最高的要素：
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
lectures = ["this is some food", "this is some drink"]
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(lectures)
indices = np.argsort(vectorizer.idf_)[::-1]
features = vectorizer.get_feature_names()
top_n = 2
top_features = [features[i] for i in indices[:top_n]]
print top_features
输出：
[u'food', u'drink']
使用ngram获取主要功能的第二个问题可以使用相同的想法来完成，还有一些额外的步骤将功能分为不同的组：
from sklearn.feature_extraction.text import TfidfVectorizer
from collections import defaultdict
lectures = ["this is some food", "this is some drink"]
vectorizer = TfidfVectorizer(ngram_range=(1,2))
X = vectorizer.fit_transform(lectures)
features_by_gram = defaultdict(list)
for f, w in zip(vectorizer.get_feature_names(), vectorizer.idf_):
features_by_gram[len(f.split(' '))].append((f, w))
top_n = 2
for gram, features in features_by_gram.iteritems():
top_features = sorted(features, key=lambda x: x[1], reverse=True)[:top_n]
top_features = [f[0] for f in top_features]
print '{}-gram top:'.format(gram), top_features
输出：
1-gram top: [u'drink', u'food']
2-gram top: [u'some drink', u'some food']
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