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Random forest toolkit
C++ Python C

README.md

rftk (Random Forest Toolkit)

A modular and efficient C++ random forest toolkit with a python wrapper.

Key Features

  • Customizable pipeline for determining best split
  • Pipeline steps can be run per forest, per tree or per node
  • Different forest building strategies including offline (depth first and breadth first) and online (fixed fringe)
  • Factories for common forest configurations (Breiman, Shotton, etc)
  • Lazy evaluation of features allow features to be function of an index and the data
  • Generic indexing allows a datapoint to be a row of a matrix or a pixel in an image
  • Support for different buffer precision (ie 32 bit vs 64 bit) and sparse buffers

Setup

Tested on ubuntu-12.04.03 and ubuntu-13.10

Install dependencies

> sudo apt-get install git
> sudo apt-get install scons swig clang
> sudo apt-get install python-numpy python-scipy python-matplotlib
> sudo apt-get install libboost-all-dev

Clone the project

> git clone https://github.com/david-matheson/rftk.git

Build rftk

> cd /path/to/rftk
> scons

Add rftk to your PYTHONPATH

> PYTHONPATH=/path/to/rftk/
> export PYTHONPATH  

Run unit tests. For now, the debug version of library must be installed for all python unit tests

> scons test-native
Running 113 test cases...
Warning there is another bufferkey conflict of a different type

*** No errors detected
/media/data/projects/rftk-github/build/release/test-cpp
Running 113 test cases...
Warning there is another bufferkey conflict of a different type

*** No errors detected

> scons install-debug 
> python -m unittest discover tests '*.py'
............................................
----------------------------------------------------------------------
Ran 44 tests in 0.105s

OK
> scons install-release

Ideology

Modules that are called within tight loops are combined with templates. Modules that are called at a higher level are combined with inheritance The configuration of the forest is done in python. Templated components are combined with swig (.i files)

How to use the library

import rftk
learner = rftk.learn.create_vanilia_classifier(                         
                        number_of_trees=100,
                        number_of_jobs=5)
forest = learner.fit(x=X_train, classes=Y_train)

Under the hood

The core unit of work is a pipeline step. A pipeline step reads from input buffers and writes to output buffers. Pipeline steps are chained into a pipeline. Below is simplified pipeline where "->"" lines are the steps, "-" lines are the input buffers at each step and "+" lines are the output buffers at each step.

-> sample feature parameters and random split points
    + feature_params_buffer
    + split_points_buffer
-> extract features
    - feature_params_buffer
    + feature_values_buffer
-> estimate sufficient statistics for 
    - split_points_buffer
    - feature_values_buffer
    + statistics_buffer
-> measure information gain  
    - statistics_buffer
    + information_gain_of_splitpoints
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