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rftk

★ 39 Open GitHub ↗

Random forest toolkit

// repository documentation

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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

rftk

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