Twenty Newsgroups Classification任务之二seq2sparse
seq2sparse对应于mahout中的org.apache.mahout.vectorizer.SparseVectorsFromSequenceFiles，从昨天跑的算法中的任务监控界面可以看到这一步包含了7个Job信息，分别是：（1）DocumentTokenizer（2）WordCount（3）MakePartialVectors（4）MergePartialVectors（5）VectorTfIdf Document Frequency Count（6）MakePartialVectors（7）MergePartialVectors。打印SparseVectorsFromSequenceFiles的参数帮助信息可以看到如下的信息：
Usage:
[--minSupport <minSupport> --analyzerName <analyzerName> --chunkSize
<chunkSize> --output <output> --input <input> --minDF <minDF> --maxDFSigma
<maxDFSigma> --maxDFPercent <maxDFPercent> --weight <weight> --norm <norm>
--minLLR <minLLR> --numReducers <numReducers> --maxNGramSize <ngramSize>
--overwrite --help --sequentialAccessVector --namedVector --logNormalize]
Options
--minSupport (-s) minSupport (Optional) Minimum Support. Default
Value: 2
--analyzerName (-a) analyzerName The class name of the analyzer
--chunkSize (-chunk) chunkSize The chunkSize in MegaBytes. 100-10000 MB
--output (-o) output The directory pathname for output.
--input (-i) input Path to job input directory.
--minDF (-md) minDF The minimum document frequency. Default
is 1
--maxDFSigma (-xs) maxDFSigma What portion of the tf (tf-idf) vectors
to be used, expressed in times the
standard deviation (sigma) of the
document frequencies of these vectors.
Can be used to remove really high
frequency terms. Expressed as a double
value. Good value to be specified is 3.0.
In case the value is less then 0 no
vectors will be filtered out. Default is
-1.0. Overrides maxDFPercent
--maxDFPercent (-x) maxDFPercent The max percentage of docs for the DF.
Can be used to remove really high
frequency terms. Expressed as an integer
between 0 and 100. Default is 99. If
maxDFSigma is also set, it will override
this value.
--weight (-wt) weight The kind of weight to use. Currently TF
or TFIDF
--norm (-n) norm The norm to use, expressed as either a
float or "INF" if you want to use the
Infinite norm. Must be greater or equal
to 0. The default is not to normalize
--minLLR (-ml) minLLR (Optional)The minimum Log Likelihood
Ratio(Float) Default is 1.0
--numReducers (-nr) numReducers (Optional) Number of reduce tasks.
Default Value: 1
--maxNGramSize (-ng) ngramSize (Optional) The maximum size of ngrams to
create (2 = bigrams, 3 = trigrams, etc)
Default Value:1
--overwrite (-ow) If set, overwrite the output directory
--help (-h) Print out help
--sequentialAccessVector (-seq) (Optional) Whether output vectors should
be SequentialAccessVectors. If set true
else false
--namedVector (-nv) (Optional) Whether output vectors should
be NamedVectors. If set true else false
--logNormalize (-lnorm) (Optional) Whether output vectors should
be logNormalize. If set true else false
在昨天算法的终端信息中该步骤的调用命令如下：
./bin/mahout seq2sparse -i /home/mahout/mahout-work-mahout/20news-seq -o /home/mahout/mahout-work-mahout/20news-vectors -lnorm -nv -wt tfidf
我们只看对应的参数，首先是-lnorm 对应的解释为输出向量是否要使用log函数进行归一化（设置则为true），-nv解释为输出向量被设置为named 向量，这里的named是啥意思？（暂时不清楚），-wt tfidf解释为使用权重的算法，具体参考 http://zh.wikipedia.org/wiki/TF-IDF 。
第（1）步在SparseVectorsFromSequenceFiles的253行的：
DocumentProcessor.tokenizeDocuments(inputDir, analyzerClass, tokenizedPath, conf);
这里进入可以看到使用的Mapper是：SequenceFileTokenizerMapper，没有使用Reducer。Mapper的代码如下：
protected void map(Text key, Text value, Context context) throws IOException, InterruptedException {
TokenStream stream = analyzer.reusableTokenStream(key.toString(), new StringReader(value.toString()));
CharTermAttribute termAtt = stream.addAttribute(CharTermAttribute.class);
StringTuple document = new StringTuple();
stream.reset();
while (stream.incrementToken()) {
if (termAtt.length() > 0) {
document.add(new String(termAtt.buffer(), 0, termAtt.length()));
}
}
context.write(key, document);
}
该Mapper的setup函数主要设置Analyzer的，关于Analyzer的api参考： http://lucene.apache.org/core/3_0_3/api/core/org/apache/lucene/analysis/Analyzer.html ，其中在map中用到的函数为 reusableTokenStream( String fieldName, Reader reader) ：Creates a TokenStream that is allowed to be re-used from the previous time that the same thread called this method.
编写下面的测试程序：
package mahout.fansy.test.bayes;
import java.io.IOException;
import java.io.StringReader;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.Text;
import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
import org.apache.mahout.common.ClassUtils;
import org.apache.mahout.common.StringTuple;
import org.apache.mahout.vectorizer.DefaultAnalyzer;
import org.apache.mahout.vectorizer.DocumentProcessor;
public class TestSequenceFileTokenizerMapper {
/**
* @param args
*/
private static Analyzer analyzer = ClassUtils.instantiateAs("org.apache.mahout.vectorizer.DefaultAnalyzer",
Analyzer.class);
public static void main(String[] args) throws IOException {
testMap();
}
public static void testMap() throws IOException{
Text key=new Text("4096");
Text value=new Text("today is also late.what about tomorrow?");
TokenStream stream = analyzer.reusableTokenStream(key.toString(), new StringReader(value.toString()));
CharTermAttribute termAtt = stream.addAttribute(CharTermAttribute.class);
StringTuple document = new StringTuple();
stream.reset();
while (stream.incrementToken()) {
if (termAtt.length() > 0) {
document.add(new String(termAtt.buffer(), 0, termAtt.length()));
}
}
System.out.println("key:"+key.toString()+",document"+document);
}
}
得出的结果如下：
key:4096,document[today, also, late.what, about, tomorrow]
其中，TokenStream有一个stopwords属性，值为：[but, be, with, such, then, for, no, will, not, are, and, their, if, this, on, into, a, or, there, in, that, they, was, is, it, an, the, as, at, these, by, to, of]，所以当遇到这些单词的时候就不进行计算了。
额，又太晚了。哎，早困了，刷个牙线。。。
分享，快乐，成长
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