将 Solr 等 data 转换为 Mahout vector
参考:http://mylazycoding.blogspot.com/2012/03/cluster-apache-solr-data-using-apache_13.html
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Lately, I was working on Integration of Apache Mahout algorithms with Apache Solr. I am able to integrate Solr with Mahout Classification and Clustering algorithms. I will post a series of blogs on this integration. This post would guide you to Cluster your Solr data using K-Means Clustering algorithm of Mahout.<field name=”field_name” type=”text” indexed=”true” stored=”true” termVector=”true” />
mahout lucene.vector <PATH OF INDEXES> --output <OUTPUT VECTOR PATH> --field <field_name> --idField id –dicOut <OUTPUT DICTIONARY PATH> --norm 2
mahout kmeans -i <OUTPUT VECTOR PATH> -c <PATH TO CLUSTER CENTROIDS> -o <PATH TO OUTPUT CLUSTERS> -dm org.apache.mahout.common.distance.CosineDistanceMeasure –x 10 –k 20 –ow –clustering
mahout clusterdump -s <PATH TO OUTPUT CLUSTERS> -d <OUTPUT DICTIONARY PATH> -dt text -n 20 -dm org.apache.mahout.common.distance.CosineDistnanceMeasure --pointsDir <PATH OF OUTPUT CLUSTERED POINTS> --output <PATH OF OUTPUT DIR>
In order for Mahout to create vectors from a Lucene index, the first and foremost thing that must be done is that the index must contain Term Vectors.? A term vector is a document centric view of the terms and their frequencies (as opposed to the inverted index, which is a term centric view) and is not on by default.
For this example, I’m going to use Solr’s example, located in <Solr Home>/example
In Solr, storing Term Vectors is as simple as setting termVectors=”true” on on the field in the schema, as in:
<field name=”text” type=”text” indexed=”true” stored=”true” termVectors=”true”/>
For pure Lucene, you will need to set the TermVector option on during Field creation, as in:
Field fld = new Field(“text”, “foo”, Field.Store.NO, Field.Index.ANALYZED, Field.TermVector.YES);
From here, it’s as simple as pointing Mahout’s new shell script (try running <MAHOUT HOME>/bin/mahout for a full listing of it’s capabilities) at the index and letting it rip:
<MAHOUT HOME>/bin/mahout lucene.vector –dir <PATH TO INDEX>/example/solr/data/index/ –output /tmp/foo/part-out.vec –field title-clustering –idField id –dictOut /tmp/foo/dict.out –norm 2
A few things to note about this command:
https://cwiki.apache.org/MAHOUT/creating-vectors-from-text.html
Creating Vectors from Text
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For clustering documents it is usually necessary to convert the raw text into vectors that can then be consumed by the clustering?Algorithms. These approaches are described below.
NOTE: Your Lucene index must be created with the same version of Lucene used in Mahout. Check Mahout's POM file to get the version number, otherwise you will likely get "Exception in thread "main" org.apache.lucene.index.CorruptIndexException: Unknown format version: -11" as an error.
Mahout has utilities that allow one to easily produce Mahout Vector representations from a Lucene (and Solr, since they are they same) index.
For this, we assume you know how to build a Lucene/Solr index. For those who don't, it is probably easiest to get up and running using?Solr?as it can ingest things like PDFs, XML, Office, etc. and create a Lucene index. For those wanting to use just Lucene, see the Lucene?website?or check out?Lucene In Action?by Erik Hatcher, Otis Gospodnetic and Mike McCandless.
To get started, make sure you get a fresh copy of Mahout from?SVN?and are comfortable building it. It defines interfaces and implementations for efficiently iterating over a Data Source (it only supports Lucene currently, but should be extensible to databases, Solr, etc.) and produces a Mahout Vector file and term dictionary which can then be used for clustering. The main code for driving this is the Driver program located in the org.apache.mahout.utils.vectors package. The Driver program offers several input options, which can be displayed by specifying the --help option. Examples of running the Driver are included below:
$MAHOUT_HOME/bin/mahout lucene.vector <PATH TO DIRECTORY CONTAINING LUCENE INDEX> \ --output <PATH TO OUTPUT LOCATION> --field <NAME OF FIELD IN INDEX> --dictOut <PATH TO FILE TO OUTPUT THE DICTIONARY TO] \ <--max <Number of vectors to output>> <--norm {INF|integer >= 0}> <--idField <Name of the idField in the Lucene index>>
$MAHOUT_HOME/bin/mahout lucene.vector --dir <PATH>/wikipedia/solr/data/index --field body \ --dictOut <PATH>/solr/wikipedia/dict.txt --output <PATH>/solr/wikipedia/out.txt --max 50
This uses the index specified by --dir and the body field in it and writes out the info to the output dir and the dictionary to dict.txt. It only outputs 50 vectors. If you don't specify --max, then all the documents in the index are output.
$MAHOUT_HOME/bin/mahout lucene.vector --dir <PATH>/wikipedia/solr/data/index --field body \ --dictOut <PATH>/solr/wikipedia/dict.txt --output <PATH>/solr/wikipedia/out.txt --max 50 --norm 2
Mahout has utilities to generate Vectors from a directory of text documents. Before creating the vectors, you need to convert the documents to SequenceFile format. SequenceFile is a hadoop class which allows us to write arbitary key,value pairs into it. The DocumentVectorizer requires the key to be a Text with a unique document id, and value to be the Text content in UTF-8 format.
You may find Tika (http://lucene.apache.org/tika) helpful in converting binary documents to text.
Mahout has a nifty utility which reads a directory path including its sub-directories and creates the SequenceFile in a chunked manner for us. the document id generated is <PREFIX><RELATIVE PATH FROM PARENT>/document.txt
From the examples directory run
$MAHOUT_HOME/bin/mahout seqdirectory \--input <PARENT DIR WHERE DOCS ARE LOCATED> --output <OUTPUT DIRECTORY> \<-c <CHARSET NAME OF THE INPUT DOCUMENTS> {UTF-8|cp1252|ascii...}> \<-chunk <MAX SIZE OF EACH CHUNK in Megabytes> 64> \<-prefix <PREFIX TO ADD TO THE DOCUMENT ID>>
Mahout_0.3
From the sequence file generated from the above step run the following to generate vectors.
$MAHOUT_HOME/bin/mahout seq2sparse \-i <PATH TO THE SEQUENCEFILES> -o <OUTPUT DIRECTORY WHERE VECTORS AND DICTIONARY IS GENERATED> \<-wt <WEIGHTING METHOD USED> {tf|tfidf}> \<-chunk <MAX SIZE OF DICTIONARY CHUNK IN MB TO KEEP IN MEMORY> 100> \<-a <NAME OF THE LUCENE ANALYZER TO TOKENIZE THE DOCUMENT> org.apache.lucene.analysis.standard.StandardAnalyzer> \<--minSupport <MINIMUM SUPPORT> 2> \<--minDF <MINIMUM DOCUMENT FREQUENCY> 1> \<--maxDFPercent <MAX PERCENTAGE OF DOCS FOR DF. VALUE BETWEEN 0-100> 99> \<--norm <REFER TO L_2 NORM ABOVE>{INF|integer >= 0}>"<-seq <Create SequentialAccessVectors>{false|true required for running some algorithms(LDA,Lanczos)}>"
--minSupport is the min frequency for the word to be considered as a feature. --minDF is the min number of documents the word needs to be in
--maxDFPercent is the max value of the expression (document frequency of a word/total number of document) to be considered as good feature to be in the document. This helps remove high frequency features like stop words
TODO:
If you are in the happy position to already own a document (as in: texts, images or whatever item you wish to treat) processing pipeline, the question arises of how to convert the vectors into the Mahout vector format. Probably the easiest way to go would be to implement your own Iterable<Vector> (called VectorIterable in the example below) and then reuse the existing VectorWriter classes:
long numDocs = vectorWriter.write(new VectorIterable(), Long.MAX_VALUE);