在scikit learn中从LDA获取主题-词分布

Question

在scikit learn中从LDA获取主题-词分布

pythonscikit-learnlda

16

我想知道在scikit learn的LDA实现中是否有一种方法可以返回主题-词分布，就像genism的show_topics()方法一样。我查看了文档但没有找到任何信息。

- Niro

1个回答

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- blacksite · Accepted Answer

请看sklearn.decomposition.LatentDirichletAllocation.components_:

components_ : 数组, [主题数, 特征数]

主题单词分布。 components_[i, j] 表示主题 i 中的单词 j。

这是一个最简单的例子:

import numpy as np
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

data = ['blah blah foo bar', 'foo foo foo foo bar', 'bar bar bar bar foo',
        'foo bar bar bar baz foo', 'foo foo foo bar baz', 'blah banana', 
        'cookies candy', 'more text please', 'hey there are more words here',
        'bananas', 'i am a real boy', 'boy', 'girl']

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(data)

vocab = vectorizer.get_feature_names()

n_top_words = 5
k = 2

model = LatentDirichletAllocation(n_topics=k, random_state=100)

id_topic = model.fit_transform(X)

topic_words = {}

for topic, comp in enumerate(model.components_):
    # for the n-dimensional array "arr":
    # argsort() returns a ranked n-dimensional array of arr, call it "ranked_array"
    # which contains the indices that would sort arr in a descending fashion
    # for the ith element in ranked_array, ranked_array[i] represents the index of the
    # element in arr that should be at the ith index in ranked_array
    # ex. arr = [3,7,1,0,3,6]
    # np.argsort(arr) -> [3, 2, 0, 4, 5, 1]
    # word_idx contains the indices in "topic" of the top num_top_words most relevant
    # to a given topic ... it is sorted ascending to begin with and then reversed (desc. now)    
    word_idx = np.argsort(comp)[::-1][:n_top_words]

    # store the words most relevant to the topic
    topic_words[topic] = [vocab[i] for i in word_idx]

查看结果：

for topic, words in topic_words.items():
    print('Topic: %d' % topic)
    print('  %s' % ', '.join(words))

Topic: 0
  more, blah, here, hey, words
Topic: 1
  foo, bar, blah, baz, boy

显然，您应该使用更大的文本体来尝试此代码，但这是获取给定主题的最具信息性的单词的一种方法。