Getting Started

Installation

rfgb can be installed via the following methods:

  1. Stable builds on PyPi

    pip install rfgb

  2. Development builds on GitHub

    pip install git+git://github.com/hayesall/rfgb.git

  3. Bleeding-edge development builds on the GitHub Development Branch

    pip install git+git://github.com/hayesall/rfgb.git@development

Background

The main function of rfgb is to learn relational dependency networks [1] via gradient tree boosting, based on Natarajan et al. “Boosting Relational Dependency Networks” [2].

../_images/rfgb.svg

This algorithm is implemented as the __main__ method for the rfgb package.

# rfgb.__main__

from .boosting import updateGradients
from .tree import node
from .utils import Utils

# ... class Arguments:

parameters = Arguments().args

for target in parameters.target:

    # Read the training data
    trainData = Utils.readTrainingData(target,
                                       path=parameters.train,
                                       regression=parameters.reg,
                                       advice=parameters.expAdvice)

    # Initialize an empty list for the trees.
    trees = []

    # Learn each tree and update the gradients.
    for i in range(parameters.trees):

        node.setMaxDepth(2)
        node.learnTree(trainData)
        trees.append(node.learnedDecisionTree)
        updateGradients(trainData, trees)

File Structure

File structure follows the structure used by BoostSRL.

Training directories and testing directories are currently used and flat files are read from, converted to a relational internal representation, and then the relationships may be reasoned about.

References