如何在scikit-learn中查看词条-文档矩阵的前n个条目

Question

我是scikit-learn的新手，我使用TfidfVectorizer在一组文档中查找术语的tfidf值。我使用下面的代码来获得相同的结果。

vectorizer = TfidfVectorizer(stop_words=u'english',ngram_range=(1,5),lowercase=True)
X = vectorizer.fit_transform(lectures)

现在如果我打印X，我可以看到矩阵中的所有条目，但是我如何根据tfidf分数找到前n个条目呢?除此之外，有没有什么方法可以帮助我根据每个ngram的tfidf分数找到前n个条目，也就是说，在unigram,bigram,trigram等等中找到前n个条目?

Answer 1

从0.15版本开始，TfidfVectorizer学习到的特征的全局项权重可以通过属性idf_来访问，它将返回一个长度等于特征维度的数组。按此权重对特征进行排序，以获得权重最高的特征:

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']

通过ngram获得顶级特征的第二个问题可以使用相同的思想，通过一些额外的步骤将特征分成不同的组:

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']