The ORFpy directory contains 3 files: (1) tree.py, (2) ort.py, and (3) orf.py. I will first exlain breifly what they contain.
tree.py contains a simple implementation of a binary tree. It is meant to be for general use. Note that the tree expects exactly 2 or 0 children. Care has been taken in the implementation to ensure that the user cannot create a tree with only one child (either only a left child, or only a right child). The correct way to initialize a tree is either Tree(1) which creates a tree with an integer element 1; or Tree(1,Tree(2),Tree(3) which creates a tree with an integer element 1, left child a tree with integer element 2, and and right child with integer element 3. For more details, read the docstring in python by importing tree.py:
import tree
help(tree)ort.py and orf.py contain respectively the implementations for the on-line random tree and forest by Amir Saffari. Once again, see the docstring for more info. Note that the trees in the forest are independent and so they can be updated in parallel. However, I have not found a simple way of doing so. I have added a FIXME marker to indicate that the update method of the ORF class in orf.py can be reimplemented to be parallelized.
To install, the following command in a terminal:
pip install git+https://github.com/luiarthur/ORFpyTo install locally,
pip install --user git+https://github.com/luiarthur/ORFpyAfter the first install, the library can be updated by using the
previous command and appending --upgrade, as follows:
pip install git+https://github.com/luiarthur/ORFpy --upgradeWhat follows is what I think is a self-explanatory usage example. Note that the ORFpy library does not depend on external packages (i.e. it doesn't use np) but the example uses numpy for creating matrices for simplicity.
import numpy as np
import math
from ORFpy import ORF, dataRange
#from ORFpy import ORT # if you want online random tree
def f(x):
return int(x[0]*x[0] + x[1]*x[1] < 1)
n = 1000
X = np.random.randn(n,2)
y = map(f,X)
# setting parameters for ORF. For more details: >>> help(ORF).
param = {'minSamples': 100, 'minGain': .01, 'numClasses': 2, 'xrng': dataRange(X), 'maxDepth': 4}
orf = ORF(param,numTrees=50)
for i in range(n):
orf.update(X[i,:],y[i])
orf.forest[0].draw()
# _________________________________________________X2 < 1.26_
# _____________X2 < -0.6________________ 0
# _____________X2 < -0.92_ _____________X1 < 0.72___
# __X1 < -0.31_ 1 __X1 < -1.52_ __X1 < 1.74_
# 0 0 0 1 0 0
xtest = np.random.randn(n,2)
ytest = map(f,xtest)
preds = orf.predicts(xtest)
predAcc = sum(map(lambda z: int(z[0] == z[1]), zip(preds,ytest))) / float(len(preds))
conf = orf.confusion(xtest,ytest)
orf.printConfusion(conf)
# y\pred 0 1
# 0 578 52
# 1 1 369
print "Accuracy: " + str(round(predAcc * 100,2)) + "%"
# Accuracy: 94.7%import numpy as np
import math
from ORFpy import ORF, dataRange
def g(x):
return math.sin(x[0]) if x[0]<x[1] else math.cos(x[1]+math.pi/2)
n = 1000
X = np.random.randn(n,2)
y = map(g,X)
param = {'minSamples': 10, 'minGain': 0, 'xrng': dataRange(X), 'maxDepth': 10}
xtest = np.random.randn(n,2)
ytest = map(g,xtest)
orf = ORF(param,numTrees=50)
for i in range(n):
orf.update(X[i,:],y[i])
preds = orf.predicts(xtest)
sse = sum( map(lambda z: (z[0]-z[1])*(z[0]-z[1]) , zip(preds,ytest)) )
rmse = math.sqrt(sse / float(len(preds)))
print "RMSE: " + str(round(rmse,2))
# RMSE: 0.22
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