Sunday, May 26, 2013

Running Weka's Logistic Regression using Command Line

Running Weka’s algorithms from command line, requires a very simple setup of Weka to be in place. All you need is to download latest release of WEKA. One of useful links working at the time of writing this post is:


Next, you’ll need to unzip this setup, which would give you a directory with name “weka-3-6-9”. We would call it WEKA_HOME for reference in this blog post.

You might want to run Weka’s logistic regression algorithm on two types of input data.
  • One is the sample data files in ARFF format already available in “WEKA_HOME/data”
  • Other is over some data files you already have in CSV format with you. For example, donut.csv file provided by Mahout for running it’s Logistic Regression over it.

Running LR over ARFF files

We would be using the file “WEKA_HOME/data/weather.nominal.arff” for running the algorithm. Cd to WEKA_HOME and run the following command

java -cp ./weka.jar weka.classifiers.functions.Logistic -t WEKA_HOME/weather.nominal.arff -T WEKA_HOME/weather.nominal.arff -d /some_location_on_your_machine/weather.nominal.model.arff

which should generate the trained model at “/some_location_on_your_machine/weather.nominal.model.arff” and the console output should look something like:

Logistic Regression with ridge parameter of 1.0E-8
Coefficients...
                                    Class
Variable                              yes
=========================================
outlook=sunny                    -45.2378
outlook=overcast                  57.5375
outlook=rainy                     -5.9067
temperature=hot                   -8.3327
temperature=mild                  44.8546
temperature=cool                 -45.4929
humidity                         118.1425
windy                             72.9648
Intercept                        -89.2032

Odds Ratios...
                                    Class
Variable                              yes
=========================================
outlook=sunny                           0
outlook=overcast      9.73275593611619E24
outlook=rainy                      0.0027
temperature=hot                    0.0002
temperature=mild     3.020787521374072E19
temperature=cool                        0
humidity            2.0353933107400553E51
windy                4.877521304260806E31

Time taken to build model: 0.12 seconds
Time taken to test model on training data: 0.01 seconds

=== Error on training data ===

Correctly Classified Instances          14              100      %
Incorrectly Classified Instances         0                0      %
Kappa statistic                          1  
Mean absolute error                      0  
Root mean squared error                  0  
Relative absolute error                  0.0002 %
Root relative squared error              0.0008 %
Total Number of Instances               14  

=== Confusion Matrix ===

 a b   <-- classified as
 9 0 | a = yes
 0 5 | b = no

=== Error on test data ===

Correctly Classified Instances          14              100      %
Incorrectly Classified Instances         0                0      %
Kappa statistic                          1  
Mean absolute error                      0  
Root mean squared error                  0  
Relative absolute error                  0.0002 %
Root relative squared error              0.0008 %
Total Number of Instances               14  

=== Confusion Matrix ===

 a b   <-- classified as
 9 0 | a = yes
 0 5 | b = no                                                                                                                                            

Here the three arguments mean:

  • -t <name of training file> : Sets training file.
  • -T <name of test file> : Sets test file. If missing, a cross-validation will be performed on the training data.
  • -d <name of output file> : Sets model output file. In case the filename ends with '.xml', only the options are saved to the XML file, not the model.

For help on all available arguments, try running the following command from WEKA_HOME:

java -cp ./weka.jar weka.classifiers.functions.Logistic -h                                                              

Running LR over CSV files

For running Weka’s LR over a CSV file, you’ll need to convert it into ARFF format using a converter provided by WEKA. Using command line in linux, here are the steps:

Step-I: Convert the data into arff format, for converting from CSV to ARFF, run the following command from WEKA_HOME:
java -cp ./weka.jar weka.core.converters.CSVLoader someCSVFile.csv > outputARFFFile.arff                                                                     

Step-II: Run the NumericToNominal filter over the arff file
java -cp ./weka.jar weka.filters.unsupervised.attribute.NumericToNominal -i outputARFFFile.arff -o outputARFFFile.nominal.arff                                                           

Step-III: Run the classifier over the outputARFFFile.nominal.arff
java -cp ./weka.jar weka.classifiers.functions.Logistic -t outputARFFFile.nominal.arff -T outputARFFFile.nominal.arff -d outputARFFFile.nominal.model.arff                                        

You might encounter an exception stating

"Cannot handle unary class!"                                                                                                               

To resolve this, apply the attribute filter and eliminate the attribute which has same value for all the records in the file using:

java -cp ./weka.jar weka.filters.AttributeFilter -i outputARFFFile.nominal.arff -o outputARFFFile.filtered.nominal.arff -R 8                                        

where the value of “–R” would vary depending upon your input file and the id of attribute to be eliminated in the input arff file.

After this, try running the classifier on the obtained “outputARFFFile.filtered.nominal.arff” file as in:

java -cp ./weka.jar weka.classifiers.functions.Logistic -t outputARFFFile.filtered.nominal.arff -T outputARFFFile.filtered.nominal.arff -d outputARFFFile.nominal.model.arff                                 

The output should appear somewhat like we got when running the classifier over the provided sample data mentioned above.

With these steps, you are ready to play with WEKA. Go for it. Cheers !!!

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