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software:rewinfomap [2010/11/01 14:07] external edit
software:rewinfomap [2010/12/07 11:58] (current)
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 This page is under construction! This page is under construction!
 +==== General ====
   *  **Infomap NLP Software: Not in development any more. The authors recommend to use SemanticVectors instead!!!**   *  **Infomap NLP Software: Not in development any more. The authors recommend to use SemanticVectors instead!!!**
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  --- //​[[eapontep@uos.de|Eduardo Aponte]] 2010/10/31 12:28//  --- //​[[eapontep@uos.de|Eduardo Aponte]] 2010/10/31 12:28//
 +==== Installation ====
 +  * Before installing Infomap you would have to install gdbm libraries in your computer. This could be quite challenging. In the following I document the installation process I followed.
 +  - As a first step, you should download the last version of gdbm.
 +  - Untar the .gz file and go into the created directory.
 +  - Try: <file bash>​./​configure</​file>​This command should try to configure the program to your system specifications. It is highly likely that this process fails. The most likely reason is that a system library called libtool is not version compatible. To check your version of this program (in ubuntu):<​file bash>​apt-cache policy libtool</​file>​. I presuppose you have libtool installed in your computer. You probably have a newer version of libtool as the one presuppose by the gdbm package. The solution I found was to run:<​file bash>​autoconf -f -oconfigure</​file>​
 +  - The last overwrote all the libtool-related files in the directory. Now you can run <file bash>​make</​file>​ safely. If you obtain the following error -which actually is highly unlikely<​file bash>​checking build system type... Invalid configuration `x86_64-unknown-linux-gnu':​ machine `x86_64-unknown'​ not recognized</​file>​you will need to deceive the program. Add before any command:<​file bash>​linux32</​file>​
 +  - You might also have problems with the ANSI c headers. To solve this problem<​file bash>​sudo apt-get install libc6-dev</​file>​
 +==== Testing =====
 +The first step in order to build a model is to choose a directory where the models will be created. This is done by setting an environment variable <file bash>​INFOMAP_WORKING_DIR=/​home/​jrandom/​infomap_models
 +export INFOMAP_WORKING_DIR</​file>​
 +Afterwards run build the model. Informap accepts two formats: a single file where documents are divided by xml markers or as set of files, where every file contains exactly one document. I decided to use this second option. As input, there should be a file specifying the name of file containing a document.<​file bash>​infomap-build -m /​usr/​local/​share/​corpora/​manyNames.txt many_01</​file>​
 +Remember to add Infomap to your PATH variable. The installation includes a manual of all the applications available. ​
 +In corpora directory, you will find a simple py script for building a corpora from a file where every line is a document. Afterwards I used the following command:<​file bash>​infomap-build -m /​net/​data/​CL/​projects/​wordspace/​software_tests/​corpora/​infoCorpus/​directory.txt firstModel</​file>​
 +In order to change the default configuration of the model, you would need to change the file: ??. I ran tests only with the default configuration (including reduction to 100 dimension). '​directory.txt'​ is a file containing the name of every file-document in the directory where the corpus is saved. Although the manual doesn'​t specify what markers should be used, including every file-name in a new line works out. The option '​-m'​ (or '​-sf'​ for single file) specifies the type of corpus. Finally, '​fistModel'​ is the name of the model created in '​INFOMAP_WORKING_DIR'​.
 +Two tests were run and the resulting models are available in the server: firstModel (using approximately 30000 documents -minus corrupted documents- in the Wiki Corpus. Constructing the model took me less than five minutes and the resulting directory occupies 65Mb. 
 +A second test was conducted again with the Wiki-Corpus,​ this time with 200000 documents. Constructing the model take less than 10 minutes. The resulting directory occupies 312Mb
 +In order to access the models, the standard command is<file bash>​associate [<​options>​] <​model>​ <​word></​file>​
 +Among the option, it is possible to obtain a word vector, the nearest neighbors of a word, or the word-document vector. Consider:<​file bash>​associate -m <​pathToTargetModel>​ -d -i d -n 10 document_100.txt
 +This command retrieves the information from the model in <​pathToTargetModel>,​ in particular, the output should be again 10 (-n 10 ) documents (-d ), the input should be a document (- i d ). The input is the document '​document_100.txt'​. (In the server you would find the document in '​./​infoCorpus'​). After performing <file bash>​associate -m <​pathToTargetModel>​ -w -i d -n 10 document_100.txt</​file>​
 +i.e., looking for words instead of document close to '​document_100.txt'​ the result was:<​file bash>​seemingly:​0.731527
 +This turned out to be the entry of Kubricks film "The Clock Work Orange"​ :-). The most related document corresponds to the film  [[http://​www.youtube.com/​watch?​v=tcSMDqXT52s|"​Pretty in Pink"​]]
 +An interesting option provided by Infomap is to install a model. This option is preferred for fina results, which should be available to several users. Following the manual, installing a model is not much more than moving a selected number of files from a non-installed model directory to a directory available system-wide. This option is intended to keep intermediate and final results apart.