Title: Creating Vectors from Text
# Creating vectors from text
# Introduction
For clustering and classifying documents it is usually necessary to convert the raw text
into vectors that can then be consumed by the clustering [Algorithms](algorithms.html). These approaches are described below.
# From Lucene
*NOTE: Your Lucene index must be created with the same version of Lucene
used in Mahout. As of Mahout 0.9 this is Lucene 4.6.1. If these versions dont match 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](http://lucene.apache.org/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](http://lucene.apache.org/core)
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 [GitHub](http://mahout.apache.org/developers/buildingmahout.html)
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:
#### Generating an output file from a Lucene Index
$MAHOUT_HOME/bin/mahout lucene.vector
--dir (-d) dir The Lucene directory
--idField idField The field in the index
containing the index. If
null, then the Lucene
internal doc id is used
which is prone to error
if the underlying index
changes
--output (-o) output The output file
--delimiter (-l) delimiter The delimiter for
outputting the dictionary
--help (-h) Print out help
--field (-f) field The field in the index
--max (-m) max The maximum number of
vectors to output. If
not specified, then it
will loop over all docs
--dictOut (-t) dictOut The output of the
dictionary
--seqDictOut (-st) seqDictOut The output of the
dictionary as sequence
file
--norm (-n) norm The norm to use,
expressed as either a
double or "INF" if you
want to use the Infinite
norm. Must be greater or
equal to 0. The default
is not to normalize
--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.
--weight (-w) weight The kind of weight to
use. Currently TF or
TFIDF
--minDF (-md) minDF The minimum document
frequency. Default is 1
--maxPercentErrorDocs (-err) mErr The max percentage of
docs that can have a null
term vector. These are
noise document and can
occur if the analyzer
used strips out all terms
in the target field. This
percentage is expressed
as a value between 0 and
1. The default is 0.
#### Example: Create 50 Vectors from an Index
$MAHOUT_HOME/bin/mahout lucene.vector
--dir $WORK_DIR/wikipedia/solr/data/index
--field body
--dictOut $WORK_DIR/solr/wikipedia/dict.txt
--output $WORK_DIR/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.
#### Example: Creating 50 Normalized Vectors from a Lucene Index using the [L_2 Norm](http://en.wikipedia.org/wiki/Lp_space)
$MAHOUT_HOME/bin/mahout lucene.vector
--dir $WORK_DIR/wikipedia/solr/data/index
--field body
--dictOut $WORK_DIR/solr/wikipedia/dict.txt
--output $WORK_DIR/solr/wikipedia/out.txt
--max 50
--norm 2
## From A Directory of Text documents
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://tika.apache.org/) helpful in converting
binary documents to text.
#### Converting directory of documents to SequenceFile format
Mahout has a nifty utility which reads a directory path including its
sub-directories and creates the SequenceFile in a chunked manner for us.
$MAHOUT_HOME/bin/mahout seqdirectory
--input (-i) input Path to job input directory.
--output (-o) output The directory pathname for
output.
--overwrite (-ow) If present, overwrite the
output directory before
running job
--method (-xm) method The execution method to use:
sequential or mapreduce.
Default is mapreduce
--chunkSize (-chunk) chunkSize The chunkSize in MegaBytes.
Defaults to 64
--fileFilterClass (-filter) fFilterClass The name of the class to use
for file parsing. Default:
org.apache.mahout.text.PrefixAdditionFilter
--keyPrefix (-prefix) keyPrefix The prefix to be prepended to
the key
--charset (-c) charset The name of the character
encoding of the input files.
Default to UTF-8 {accepts: cp1252|ascii...}
--method (-xm) method The execution method to use:
sequential or mapreduce.
Default is mapreduce
--overwrite (-ow) If present, overwrite the
output directory before
running job
--help (-h) Print out help
--tempDir tempDir Intermediate output directory
--startPhase startPhase First phase to run
--endPhase endPhase Last phase to run
The output of seqDirectory will be a Sequence file < Text, Text > of all documents (/sub-directory-path/documentFileName, documentText).
#### Creating Vectors from SequenceFile
From the sequence file generated from the above step run the following to
generate vectors.
$MAHOUT_HOME/bin/mahout seq2sparse
--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. Default
Value: 100MB
--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 than 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. Default: 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. Default is false;
true required for running some algorithms
(LDA,Lanczos)
--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
This will create SequenceFiles of tokenized documents < Text, StringTuple > (docID, tokenizedDoc) and vectorized documents < Text, VectorWritable > (docID, TF-IDF Vector).
As well, seq2sparse will create SequenceFiles for: a dictionary (wordIndex, word), a word frequency count (wordIndex, count) and a document frequency count (wordIndex, DFCount) in the output directory.
The --minSupport option 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. These options are helpful in removing high frequency features like stop words.
The vectorized documents can then be used as input to many of Mahout's classification and clustering algorithms.
#### Example: Creating Normalized [TF-IDF](http://en.wikipedia.org/wiki/Tf%E2%80%93idf) Vectors from a directory of text documents using [trigrams](http://en.wikipedia.org/wiki/N-gram) and the [L_2 Norm](http://en.wikipedia.org/wiki/Lp_space)
Create sequence files from the directory of text documents:
$MAHOUT_HOME/bin/mahout seqdirectory
-i $WORK_DIR/reuters
-o $WORK_DIR/reuters-seqdir
-c UTF-8
-chunk 64
-xm sequential
Vectorize the documents using trigrams, L_2 length normalization and a maximum document frequency cutoff of 85%.
$MAHOUT_HOME/bin/mahout seq2sparse
-i $WORK_DIR/reuters-out-seqdir/
-o $WORK_DIR/reuters-out-seqdir-sparse-kmeans
--namedVec
-wt tfidf
-ng 3
-n 2
--maxDFPercent 85
The sequence file in the $WORK_DIR/reuters-out-seqdir-sparse-kmeans/tfidf-vectors directory can now be used as input to the Mahout [k-Means](http://mahout.apache.org/users/clustering/k-means-clustering.html) clustering algorithm.
## Background
* [Discussion on centroid calculations with sparse vectors](http://markmail.org/thread/l5zi3yk446goll3o)
## Converting existing vectors to Mahout's format
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 (called VectorIterable in the example below) and then
reuse the existing VectorWriter classes:
VectorWriter vectorWriter = SequenceFile.createWriter(filesystem,
configuration,
outfile,
LongWritable.class,
SparseVector.class);
long numDocs = vectorWriter.write(new VectorIterable(), Long.MAX_VALUE);