应用scikit-learn做文本分类

文本挖掘的paper没找到统一的benchmark，只好自己跑程序，走过路过的前辈如果知道20newsgroups或者其它好用的公共数据集的分类（最好要所有类分类结果，全部或取部分特征无所谓）麻烦留言告知下现在的benchmark，万谢！

嗯，说正文。20newsgroups官网上给出了3个数据集，这里我们用最原始的20news-19997.tar.gz。

分为以下几个过程：

加载数据集
提feature
分类

Naive Bayes
KNN
SVM

聚类

说明： scipy官网上有参考，但是看着有点乱，而且有bug。本文中我们分块来看。

Environment：Python 2.7 + Scipy （scikit-learn）

1.加载数据集

从20news-19997.tar.gz下载数据集，解压到scikit_learn_data文件夹下，加载数据，详见code注释。

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#first extract the 20 news_group dataset to /scikit_learn_data

from sklearn.datasets import fetch_20newsgroups

#all categories

#newsgroup_train = fetch_20newsgroups(subset='train')

#part categories

categories = ['comp.graphics',

'comp.os.ms-windows.misc',

'comp.sys.ibm.pc.hardware',

'comp.sys.mac.hardware',

'comp.windows.x'];

newsgroup_train = fetch_20newsgroups(subset = 'train',categories = categories);

可以检验是否load好了：

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#print category names

from pprint import pprint

pprint(list(newsgroup_train.target_names))

结果：
['comp.graphics',

'comp.os.ms-windows.misc',

'comp.sys.ibm.pc.hardware',

'comp.sys.mac.hardware',

'comp.windows.x']

2. 提feature：
刚才load进来的newsgroup_train就是一篇篇document，我们要从中提取feature，即词频啊神马的，用fit_transform

Method 1. HashingVectorizer，规定feature个数

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#newsgroup_train.data is the original documents, but we need to extract the

#feature vectors inorder to model the text data

from sklearn.feature_extraction.text import HashingVectorizer

vectorizer = HashingVectorizer(stop_words = 'english',non_negative = True,

n_features = 10000)

fea_train = vectorizer.fit_transform(newsgroup_train.data)

fea_test = vectorizer.fit_transform(newsgroups_test.data);

#return feature vector 'fea_train' [n_samples,n_features]

print 'Size of fea_train:' + repr(fea_train.shape)

print 'Size of fea_train:' + repr(fea_test.shape)

#11314 documents, 130107 vectors for all categories

print 'The average feature sparsity is {0:.3f}%'.format(

fea_train.nnz/float(fea_train.shape[0]*fea_train.shape[1])*100);

结果：
Size of fea_train:(2936, 10000)

Size of fea_train:(1955, 10000)

The average feature sparsity is 1.002%

因为我们只取了10000个词，即10000维feature，稀疏度还不算低。而实际上用TfidfVectorizer统计可得到上万维的feature，我统计的全部样本是13w多维，就是一个相当稀疏的矩阵了。

**************************************************************************************************************************

上面代码注释说TF-IDF在train和test上提取的feature维度不同，那么怎么让它们相同呢？有两种方法：

Method 2. CountVectorizer+TfidfTransformer

让两个CountVectorizer共享vocabulary：

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#----------------------------------------------------

#method 1:CountVectorizer+TfidfTransformer

print '*************************\nCountVectorizer+TfidfTransformer\n*************************'

from sklearn.feature_extraction.text import CountVectorizer,TfidfTransformer

count_v1= CountVectorizer(stop_words = 'english', max_df = 0.5);

counts_train = count_v1.fit_transform(newsgroup_train.data);

print "the shape of train is "+repr(counts_train.shape)

count_v2 = CountVectorizer(vocabulary=count_v1.vocabulary_);

counts_test = count_v2.fit_transform(newsgroups_test.data);

print "the shape of test is "+repr(counts_test.shape)

tfidftransformer = TfidfTransformer();

tfidf_train = tfidftransformer.fit(counts_train).transform(counts_train);

tfidf_test = tfidftransformer.fit(counts_test).transform(counts_test);

结果：
*************************
CountVectorizer+TfidfTransformer

*************************

the shape of train is (2936, 66433)

the shape of test is (1955, 66433)

Method 3. TfidfVectorizer

让两个TfidfVectorizer共享vocabulary：

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#method 2:TfidfVectorizer

print '*************************\nTfidfVectorizer\n*************************'

from sklearn.feature_extraction.text import TfidfVectorizer

tv = TfidfVectorizer(sublinear_tf = True,

max_df = 0.5,

stop_words = 'english');

tfidf_train_2 = tv.fit_transform(newsgroup_train.data);

tv2 = TfidfVectorizer(vocabulary = tv.vocabulary_);

tfidf_test_2 = tv2.fit_transform(newsgroups_test.data);

print "the shape of train is "+repr(tfidf_train_2.shape)

print "the shape of test is "+repr(tfidf_test_2.shape)

analyze = tv.build_analyzer()

tv.get_feature_names()#statistical features/terms

结果：

*************************

TfidfVectorizer

*************************

the shape of train is (2936, 66433)

the shape of test is (1955, 66433)

此外，还有sklearn里封装好的抓feature函数，fetch_20newsgroups_vectorized

Method 4. fetch_20newsgroups_vectorized

但是这种方法不能挑出几个类的feature，只能全部20个类的feature全部弄出来：

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print '*************************\nfetch_20newsgroups_vectorized\n*************************'

from sklearn.datasets import fetch_20newsgroups_vectorized

tfidf_train_3 = fetch_20newsgroups_vectorized(subset = 'train');

tfidf_test_3 = fetch_20newsgroups_vectorized(subset = 'test');

print "the shape of train is "+repr(tfidf_train_3.data.shape)

print "the shape of test is "+repr(tfidf_test_3.data.shape)

结果：

*************************

fetch_20newsgroups_vectorized

*************************

the shape of train is (11314, 130107)

the shape of test is (7532, 130107)

3. 分类
3.1 Multinomial Naive Bayes Classifier
见代码&comment，不解释

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######################################################

#Multinomial Naive Bayes Classifier

print '*************************\nNaive Bayes\n*************************'

from sklearn.naive_bayes import MultinomialNB

from sklearn import metrics

newsgroups_test = fetch_20newsgroups(subset = 'test',

categories = categories);

fea_test = vectorizer.fit_transform(newsgroups_test.data);

#create the Multinomial Naive Bayesian Classifier

clf = MultinomialNB(alpha = 0.01)

clf.fit(fea_train,newsgroup_train.target);

pred = clf.predict(fea_test);

calculate_result(newsgroups_test.target,pred);

#notice here we can see that f1_score is not equal to 2*precision*recall/(precision+recall)

#because the m_precision and m_recall we get is averaged, however, metrics.f1_score() calculates

#weithed average, i.e., takes into the number of each class into consideration.

注意我最后的3行注释，为什么f1≠2*（准确率*召回率）/（准确率+召回率）

其中，函数calculate_result计算f1：

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def calculate_result(actual,pred):

m_precision = metrics.precision_score(actual,pred);

m_recall = metrics.recall_score(actual,pred);

print 'predict info:'

print 'precision:{0:.3f}'.format(m_precision)

print 'recall:{0:0.3f}'.format(m_recall);

print 'f1-score:{0:.3f}'.format(metrics.f1_score(actual,pred));

3.2 KNN：

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######################################################

#KNN Classifier

from sklearn.neighbors import KNeighborsClassifier

print '*************************\nKNN\n*************************'

knnclf = KNeighborsClassifier()#default with k=5

knnclf.fit(fea_train,newsgroup_train.target)

pred = knnclf.predict(fea_test);

calculate_result(newsgroups_test.target,pred);

3.3 SVM:

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######################################################

#SVM Classifier

from sklearn.svm import SVC

print '*************************\nSVM\n*************************'

svclf = SVC(kernel = 'linear')#default with 'rbf'

svclf.fit(fea_train,newsgroup_train.target)

pred = svclf.predict(fea_test);

calculate_result(newsgroups_test.target,pred);

结果：

*************************

Naive Bayes

*************************

predict info:

precision:0.764

recall:0.759

f1-score:0.760

*************************

KNN

*************************

predict info:

precision:0.642

recall:0.635

f1-score:0.636

*************************

SVM

*************************

predict info:

precision:0.777

recall:0.774

f1-score:0.774

4. 聚类

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######################################################

#KMeans Cluster

from sklearn.cluster import KMeans

print '*************************\nKMeans\n*************************'

pred = KMeans(n_clusters=5)

pred.fit(fea_test)

calculate_result(newsgroups_test.target,pred.labels_);

结果：

*************************

KMeans

*************************

predict info:

precision:0.264

recall:0.226

f1-score:0.213

本文全部代码下载：在此

貌似准确率好低……那我们用全部特征吧……结果如下：

*************************

Naive Bayes

*************************

predict info:

precision:0.771

recall:0.770

f1-score:0.769

*************************

KNN

*************************

predict info:

precision:0.652

recall:0.645

f1-score:0.645

*************************

SVM

*************************

predict info:

precision:0.819

recall:0.816

f1-score:0.816

*************************

KMeans

*************************

predict info:

precision:0.289

recall:0.313

f1-score:0.266