简介
20 newsgroups数据集18000篇新闻文章，一共涉及到20种话题，所以称作20 newsgroups text dataset，分文两部分：训练集和测试集，通常用来做文本分类.
基本使用
sklearn提供了该数据的接口：sklearn.datasets.fetch_20newsgroups，我们以sklearn的文档来解释下如何使用该数据集。
from sklearn.datasets import fetch_20newsgroups from pprint import pprint newsgroups_train = fetch_20newsgroups(subset='train') pprint(list(newsgroups_train.targernames))
我们可以看到一共有20类：
['alt.atheism', 'comp.graphics', 'comp.os.ms-windows.misc', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc']
我们看下数据newsgroups_train 的一些属性
print(newsgroups_train.filenames.shape) # (11314,) print(newsgroups_train.target.shape) # (11314,)
print(newsgroups_train.target[:10]) # [ 7 4 4 1 14 16 13 3 2 4] print(newsgroups_train['data'][:2]) # 前三篇文章["From: lerxst@wam.umd.edu (where's my thin...
fetch_20newsgroups的参数设置：
fetch_20newsgroups(data_home=None, # 文件下载的路径 subset='train', # 加载那一部分数据集 train/test categories=None, # 选取哪一类数据集[类别列表]，默认20类 shuffle=True, # 将数据集随机排序 random_state=42, # 随机数生成器 remove=(), # ('headers','footers','quotes') 去除部分文本 download_if_missing=True # 如果没有下载过，重新下载 )
将文本转为TF-IDF向量
from sklearn.feature_extraction.text import TfidfVectorizer # 我们选取三类作为实验 categories = ['alt.atheism', 'talk.religion.misc','comp.graphics', 'sci.space'] # 加载数据集 newsgroups_train = fetch_20newsgroups(subset='train',categories=categories) # 提取tfidf特征 vectorizer = TfidfVectorizer() vectors = vectorizer.fit_transform(newsgroups_train.data) print(vectors.shape) print(vectors.nnz / float(vectors.shape[0])) # 输出 (2034, 34118) 159.0132743362832
我们从输出可以看出，提取的TF-IDF 向量是非常稀疏的，超过30000维的特征才有159个非零特征
使用贝叶斯进行分类
from sklearn.feature_extraction.text import TfidfVectorizer # 我们选取三类作为实验 categories = ['alt.atheism', 'talk.religion.misc','comp.graphics', 'sci.space'] # 加载数据集 newsgroups_train = fetch_20newsgroups(subset='train',categories=categories) # 提取tfidf特征 vectorizer = TfidfVectorizer() vectors = vectorizer.fit_transform(newsgroups_train.data) print(vectors.shape) print(vectors.nnz / float(vectors.shape[0])) # MultinomialNB实现文本分类 from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score,f1_score # 加载测试集 newsgroups_test=fetch_20newsgroups(subset='test',categories=categories) # 提取测试集tfidf特征 vectors_test=vectorizer.transform(newsgroups_test.data) # 训练 clf=MultinomialNB(alpha=0.1) clf.fit(vectors,newsgroups_train.target) # 预测 pred=clf.predict(vectors_test) print(f1_score(newsgroups_test.target,pred,average='macro')) print(accuracy_score(newsgroups_test.target,pred)) # 输出 f1_score: 0.8823530044163621 accuracy: 0.8965262379896526
参考
数据集地址：http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.html
sklearn关于20newsgroup的介绍http://scikit-learn.org/stable/datasets/twenty_newsgroups.html
