Open nininininini opened 4 years ago
谢谢提醒,可以帮忙提交一下 pull request 一起来完善吗?
any response? same problem here
Hi there, I reckon this is the only way:
=> BOOK: NLP with PyTorch
from sklearn.feature_extraction.text import CountVectorizer import seaborn as sns
corpus = ['Time flies flies like an arrow.', 'Fruit flies like an banana.'] print(one_hotvectorizer.vocabulary) => {'time': 6, 'flies': 3, 'like': 5, 'an': 0, 'arrow': 1, 'fruit': 4, 'banana': 2}
print(sorted(one_hotvectorizer.vocabulary)) => ['an', 'arrow', 'banana', 'flies', 'fruit', 'like', 'time'] vocab = sorted(one_hotvectorizer.vocabulary)
one_hot_vectorizer = CountVectorizer(binary=True) one_hot = one_hot_vectorizer.fit_transform(corpus).toarray()
sns.heatmap(one_hot, annot=True, cbar=False, xticklabels= vocab, yticklabels= ['Sentence_1','Sentence_2']);
BTW, you can also do things like this to pick up the words you want
vocab_2 = sorted(one_hotvectorizer.vocabulary)[1:5] sns.heatmap(one_hot[:,1:5], annot=True, cbar=False, xticklabels= vocab_2, yticklabels= ['Sentence_1','Sentence_2']);
Does there some code issue in the chapter1? such as Example 1-1. The following code in the example 1-1, xticklabels = vocab, I can't find the vocab was stated in the previous text. and if run this code, can't show any image. It likes the code loss import the matplotlib and should add the code plt.show().
from sklearn.feature_extraction.text import CountVectorizer import seaborn as sns corpus = ['Time flies flies like an arrow.', 'Fruit flies like a banana.'] one_hot_vectorizer = CountVectorizer(binary=True) one_hot = one_hot_vectorizer.fit_transform(corpus).toarray() sns.heatmap(one_hot, annot=True, cbar=False, xticklabels=vocab, yticklabels=['Sentence 2'])