hmchzb19<em></em 阅读(62) 评论(0)
最近在看一个Sequence Learning 的视频,然后研究了别人的代码,因为我的"typo" 导致代码有点”歪“,但是揭露了一个有趣的现象.

1. 这个代码是一个category_predictor, 需要用到下面的这个库. -> sklearn
在kali linux上安装则需要执行这个命令:

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  1. pip3 install sklearn

2. 代码如下:

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  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. # Author: hezhb
  4. # Created Time: Mon 26 Feb 2018 04:27:53 PM CST

  5. from sklearn.datasets import fetch_20newsgroups
  6. from sklearn.naive_bayes import MultinomialNB
  7. from sklearn.feature_extraction.text import TfidfTransformer
  8. from sklearn.feature_extraction.text import CountVectorizer

  9. #Define the category map
  10. category_map = {
  11.     "talk.politics.misc" : "Politics",
  12.     "rec.autos" : "Autos",
  13.     "rec.sport.hockey": "Hockey",
  14.     "sci.electronics": "Electronics",
  15.     "sci.med" : "Medicine" }

  16. #get the training dataset
  17. training_data = fetch_20newsgroups(subset="train",
  18.     categories=category_map.keys(), shuffle=True, random_state=5 )
  19.     

  20. #Build a count vectorizer and extract term counts
  21. count_vectorizer = CountVectorizer()
  22. train_tc = count_vectorizer.fit_transform(training_data.data)
  23. print("\nDimensions of training data:", train_tc.shape)

  24. #create the tf-idf transformer
  25. tfidf= TfidfTransformer()
  26. train_tfidf=tfidf.fit_transform(train_tc)

  27. #Define test data
  28. input_data=[
  29.     "You need to be careful with cars when you are driving on slippery roads",
  30.     "A lot of devices can be operated wirelessly",
  31.     "Players need to be careful when they are close to goal posts",
  32.     "Political debates help us understand the perspectives of both sides",
  33.     "Political debates help us understand the perspectives of both slides",
  34.     "Political debates help us understand the perspective of both sides",
  35.     ]

  36. #Train a Multinomial Naive Bayes classifier
  37. classifier = MultinomialNB().fit(train_tfidf, training_data.target)

  38. #Transform input data using count vectorizer
  39. input_tc = count_vectorizer.transform(input_data)

  40. #Transform vectorized data using tfidf transformer
  41. input_tfidf = tfidf.transform(input_tc)

  42. #predict the output categories
  43. predictions=classifier.predict(input_tfidf)

  44. #print the outputs
  45. for sent, category in zip(input_data, predictions):
  46.     print("\nInput:", sent , "\nPredicted category:", \
  47.         category_map[training_data.target_names[category]])

执行结果很有意思:

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  1. In [15]: %cd /usr/local/src/py/py_nlp/usr/local/src/py/py_nlp

  2. In [16]: %run -i /usr/local/src/py/py_nlp/category_predictor.py

  3. Dimensions of training data: (2844, 40321)

  4. Input: You need to be careful with cars when you are driving on slippery roads
  5. Predicted category: Autos

  6. Input: A lot of devices can be operated wirelessly
  7. Predicted category: Electronics

  8. Input: Players need to be careful when they are close to goal posts
  9. Predicted category: Hockey

  10. Input: Political debates help us understand the perspectives of both sides
  11. Predicted category: Politics

  12. Input: Political debates help us understand the perspectives of both slides
  13. Predicted category: Medicine

  14. Input: Political debates help us understand the perspective of both sides
  15. Predicted category: Medicine

后面三句完全是一个letter的差距,category就会变.
sides:方面,
slides: 这个词有滑落的意思,为什么变成slides就会被predict成 Medicine category.
最后一句:
perspectives:复数变单数,也会影响这个句子的归类。Category 类别为Medicine.

3. 结果:
这是个有趣的现象,但是我现在不知道导致这个现象的原因是什么。