weka-porter

Transpile trained decision trees from Weka to C, Java or JavaScript.
It's recommended for limited embedded systems and critical applications where performance matters most.

Benefit

The benefit of the module is to transpile a decision tree from the compact representation by the Weka software to a target programming language:

outlook = sunny
|   humidity <= 75: yes (2.0)
|   humidity > 75: no (3.0)
outlook = overcast: yes (4.0)
outlook = rainy
|   windy = TRUE: no (2.0)
|   windy = FALSE: yes (3.0)

public static String classify(String outlook, boolean windy, double humidity) {
    if (outlook.equals("sunny")) {
        if (humidity <= 75) {
            return "yes";
        }
        else if (humidity > 75) {
            return "no";
        }
    }
    else if (outlook.equals("overcast")) {
        return "yes";
    }
    else if (outlook.equals("rainy")) {
        if (windy == true) {
            return "no";
        }
        else if (windy == false) {
            return "yes";
        }
    }
    return null;
}

Installation

pip install weka-porter

Usage

Either you use the porter as imported module in your application or you use the command-line interface.

Training

First of all a trained decision tree is required.

# download Weka:
wget https://netcologne.dl.sourceforge.net/project/weka/weka-3-8/3.8.2/weka-3-8-2.zip
unzip weka-3-8-2.zip && cd weka-3-8-2

# train decision tree and save the result:
java -cp weka.jar weka.classifiers.trees.J48 -t data/weather.numeric.arff -v > j48.txt

Copy and paste the compact representation from j48.txt to a new file (i.e. j48_tree.txt):

outlook = sunny
|   humidity <= 75: yes (2.0)
|   humidity > 75: no (3.0)
outlook = overcast: yes (4.0)
outlook = rainy
|   windy = TRUE: no (2.0)
|   windy = FALSE: yes (3.0)

Module

Now the saved decision tree can be ported to Java:

from weka_porter import Porter

porter = Porter(language='java')
output = porter.port('j48_tree.txt', method_name='classify')

print(output)

The ported decision tree matches the original version of the estimator.

Command-line interface

This examples shows how you can port a estimator from the command line. The estimator can be ported by using the following command:

python -m weka_porter --input <txt_file> [--output <destination_dir>] [--c] [--java] [--js]
python -m weka_porter -i <txt_file> [-o <destination_dir>] [--c] [--java] [--js]

The target programming language is changeable on the fly:

python -m weka_porter -i j48_tree.txt --c
python -m weka_porter -i j48_tree.txt --java
python -m weka_porter -i j48_tree.txt --js

Finally the following command will display all options:

python -m weka_porter --help
python -m weka_porter -h

Development

Environment

Install the required environment modules by executing the script environment.sh:

bash ./scripts/environment.sh

conda env create -n weka-porter -f environment.yml
source activate weka-porter

Furthermore Node.js (>=6), Java (>=1.6) and GCC (>=4.2) are required for all tests.

Testing

Run all tests by executing the bash script test.sh:

bash ./scripts/test.sh

python -m unittest discover -vp '*Test.py'

The tests cover module functions as well as matching predictions of ported trees.

License

The library is Open Source Software released under the MIT license.

Questions?

Don't be shy and feel free to contact me on Twitter.