如何在 scikit-learn 中的 tfidf 之后查看术语文档矩阵的前 n 个条目 [英] How to see top n entries of term-document matrix after tfidf in scikit-learn

问题描述

我是 scikit-learn 的新手，我使用 TfidfVectorizer 在一组文档中查找术语的 tfidf 值.我使用以下代码获得相同的结果.

I am new to scikit-learn, and I was using TfidfVectorizer to find the tfidf values of terms in a set of documents. I used the following code to obtain the same.

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，但是如何根据 tfidf 分数找到前 n 个条目.除此之外，是否有任何方法可以帮助我根据每 ngram 的 tfidf 分数找到前 n 个条目，即 unigram、bigram、trigram 等中的前 n 个条目?

Now If I print X, I am able to see all the entries in matrix, but how can I find top n entries based on tfidf score. In addition to that is there any method that will help me to find top n entries based on tfidf score per ngram i.e. top entries among unigram,bigram,trigram and so on?

推荐答案

从 0.15 版本开始，全局术语权重由 TfidfVectorizer 可以通过属性idf_访问code>，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最高权重的特征:

Since version 0.15, the global term weighting of the features learnt by a TfidfVectorizer can be accessed through the attribute idf_, which will return an array of length equal to the feature dimension. Sort the features by this weighting to get the top weighted features:

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 获取顶级特征的第二个问题可以使用相同的想法来完成，但有一些额外的步骤将特征分成不同的组:

The second problem of getting the top features by ngram can be done using the same idea, with some extra steps of splitting the features into different groups:

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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