Twenty Newsgroups Classification Example

Introduction

The 20 newsgroups dataset is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering. We will use the Mahout CBayes classifier to create a model that would classify a new document into one of the 20 newsgroups.

Prerequisites

  • Mahout has been downloaded (instructions here)
  • Maven is available
  • Your environment has the following variables:
    • HADOOP_HOME Environment variables refers to where Hadoop lives
    • MAHOUT_HOME Environment variables refers to where Mahout lives

Instructions for running the example

  1. If running Hadoop in cluster mode, start the hadoop daemons by executing the following commands:

        $ cd $HADOOP_HOME/bin
        $ ./start-all.sh
    

    Otherwise:

        $ export MAHOUT_LOCAL=true
    
  2. In the trunk directory of Mahout, compile and install Mahout:

        $ cd $MAHOUT_HOME
        $ mvn -DskipTests clean install
    
  3. Run the 20 newsgroups example script by executing:

        $ ./examples/bin/classify-20newsgroups.sh
    
  4. You will be prompted to select a classification method algorithm:

        1. Complement Naive Bayes
        2. Naive Bayes
        3. Stochastic Gradient Descent
    

Select 1 and the the script will perform the following:

  1. Create a working directory for the dataset and all input/output.
  2. Download and extract the 20news-bydate.tar.gz from the 20 newsgroups dataset to the working directory.
  3. Convert the full 20 newsgroups dataset into a < Text, Text > SequenceFile.
  4. Convert and preprocesses the dataset into a < Text, VectorWritable > SequenceFile containing term frequencies for each document.
  5. Split the preprocessed dataset into training and testing sets.
  6. Train the classifier.
  7. Test the classifier.

Output should look something like:

=======================================================
Confusion Matrix
-------------------------------------------------------
 a  b  c  d  e  f  g  h  i  j  k  l  m  n  o  p  q  r  s  t <--Classified as
381 0  0  0  0  9  1  0  0  0  1  0  0  2  0  1  0  0  3  0 |398 a=rec.motorcycles
 1 284 0  0  0  0  1  0  6  3  11 0  66 3  0  6  0  4  9  0 |395 b=comp.windows.x
 2  0 339 2  0  3  5  1  0  0  0  0  1  1  12 1  7  0  2  0 |376 c=talk.politics.mideast
 4  0  1 327 0  2  2  0  0  2  1  1  0  5  1  4  12 0  2  0 |364 d=talk.politics.guns
 7  0  4  32 27 7  7  2  0  12 0  0  6  0 100 9  7  31 0  0 |251 e=talk.religion.misc
 10 0  0  0  0 359 2  2  0  0  3  0  1  6  0  1  0  0  11 0 |396 f=rec.autos
 0  0  0  0  0  1 383 9  1  0  0  0  0  0  0  0  0  3  0  0 |397 g=rec.sport.baseball
 1  0  0  0  0  0  9 382 0  0  0  0  1  1  1  0  2  0  2  0 |399 h=rec.sport.hockey
 2  0  0  0  0  4  3  0 330 4  4  0  5  12 0  0  2  0  12 7 |385 i=comp.sys.mac.hardware
 0  3  0  0  0  0  1  0  0 368 0  0  10 4  1  3  2  0  2  0 |394 j=sci.space
 0  0  0  0  0  3  1  0  27 2 291 0  11 25 0  0  1  0  13 18|392 k=comp.sys.ibm.pc.hardware
 8  0  1 109 0  6  11 4  1  18 0  98 1  3  11 10 27 1  1  0 |310 l=talk.politics.misc
 0  11 0  0  0  3  6  0  10 6  11 0 299 13 0  2  13 0  7  8 |389 m=comp.graphics
 6  0  1  0  0  4  2  0  5  2  12 0  8 321 0  4  14 0  8  6 |393 n=sci.electronics
 2  0  0  0  0  0  4  1  0  3  1  0  3  1 372 6  0  2  1  2 |398 o=soc.religion.christian
 4  0  0  1  0  2  3  3  0  4  2  0  7  12 6 342 1  0  9  0 |396 p=sci.med
 0  1  0  1  0  1  4  0  3  0  1  0  8  4  0  2 369 0  1  1 |396 q=sci.crypt
 10 0  4  10 1  5  6  2  2  6  2  0  2  1 86 15 14 152 0  1 |319 r=alt.atheism
 4  0  0  0  0  9  1  1  8  1  12 0  3  0  2  0  0  0 341 2 |390 s=misc.forsale
 8  5  0  0  0  1  6  0  8  5  50 0  40 2  1  0  9  0  3 256|394 t=comp.os.ms-windows.misc
=======================================================
Statistics
-------------------------------------------------------
Kappa                                       0.8808
Accuracy                                   90.8596%
Reliability                                86.3632%
Reliability (standard deviation)            0.2131

End to end commands to build a CBayes model for 20 newsgroups

The 20 newsgroups example script issues the following commands as outlined above. We can build a CBayes classifier from the command line by following the process in the script:

Be sure that MAHOUT_HOME/bin and HADOOP_HOME/bin are in your $PATH

  1. Create a working directory for the dataset and all input/output.

        $ export WORK_DIR=/tmp/mahout-work-${USER}
        $ mkdir -p ${WORK_DIR}
    
  2. Download and extract the 20news-bydate.tar.gz from the 20newsgroups dataset to the working directory.

        $ curl http://people.csail.mit.edu/jrennie/20Newsgroups/20news-bydate.tar.gz 
            -o ${WORK_DIR}/20news-bydate.tar.gz
        $ mkdir -p ${WORK_DIR}/20news-bydate
        $ cd ${WORK_DIR}/20news-bydate && tar xzf ../20news-bydate.tar.gz && cd .. && cd ..
        $ mkdir ${WORK_DIR}/20news-all
        $ cp -R ${WORK_DIR}/20news-bydate/*/* ${WORK_DIR}/20news-all
    
    • If you're running on a Hadoop cluster:
      $ hadoop dfs -put ${WORK_DIR}/20news-all ${WORK_DIR}/20news-all
      
  3. Convert the full 20 newsgroups dataset into a < Text, Text > SequenceFile.

        $ mahout seqdirectory 
            -i ${WORK_DIR}/20news-all 
            -o ${WORK_DIR}/20news-seq 
            -ow
    
  4. Convert and preprocesses the dataset into a < Text, VectorWritable > SequenceFile containing term frequencies for each document.

        $ mahout seq2sparse 
            -i ${WORK_DIR}/20news-seq 
            -o ${WORK_DIR}/20news-vectors
            -lnorm 
            -nv 
            -wt tfidf
    

    If we wanted to use different parsing methods or transformations on the term frequency vectors we could supply different options here e.g.: -ng 2 for bigrams or -n 2 for L2 length normalization. See the Creating vectors from text page for a list of all seq2sparse options.

  5. Split the preprocessed dataset into training and testing sets.

        $ mahout split 
            -i ${WORK_DIR}/20news-vectors/tfidf-vectors 
            --trainingOutput ${WORK_DIR}/20news-train-vectors 
            --testOutput ${WORK_DIR}/20news-test-vectors  
            --randomSelectionPct 40 
            --overwrite --sequenceFiles -xm sequential
    
  6. Train the classifier.

        $ mahout trainnb 
            -i ${WORK_DIR}/20news-train-vectors
            -el  
            -o ${WORK_DIR}/model 
            -li ${WORK_DIR}/labelindex 
            -ow 
            -c
    
  7. Test the classifier.

        $ mahout testnb 
            -i ${WORK_DIR}/20news-test-vectors
            -m ${WORK_DIR}/model 
            -l ${WORK_DIR}/labelindex 
            -ow 
            -o ${WORK_DIR}/20news-testing 
            -c