adaboost.py

#!/usr/bin/env python
#coding=utf-8

import sys, math
import numpy
import operator

from abstract_classifier import Classifier

class AdaBoost(Classifier):
  def __init__(self, weak_classifier_type):
    Classifier.__init__(self)
    self.WeakClassifierType = weak_classifier_type

  def train(self, T, k = 1):
    X = self.X
    Y = numpy.array(self.Y)
    N = len(self.Y)
    w = (1.0/N)*numpy.ones(N)
    self.weak_classifier_ensemble = []
    self.alpha = []
    for t in range(T):
      sys.stdout.write('.')
      weak_learner = self.WeakClassifierType()
      weak_learner.set_training_sample(X,Y)
      weak_learner.weights = w
      weak_learner.train()
      Y_pred = weak_learner.predict(X)
      # (Y=-1, Y_pred=1) False Positive
      # (Y=1, Y_pred=-1) Missing  should be assigned more weights
      #ww = numpy.log(k)*(numpy.exp( (Y-Y_pred)>1 ) - 1)/(numpy.exp(1)-1) + 1
      e = sum(0.5*w*abs((Y-Y_pred)))/sum(w)
      #e = sum(0.5*w*abs(Y-Y_pred))
      ee = (1-e)/(e*1.0)
      alpha = 0.5*math.log(ee+0.00001)
      w *= numpy.exp(-alpha*Y*Y_pred) #*ww) # increase weights for wrongly classified
      w /= sum(w)
      self.weak_classifier_ensemble.append(weak_learner)
      self.alpha.append(alpha)
    print "\n"
    self.T = T

  def predict(self,X):
    X = numpy.array(X)
    N, d = X.shape
    Y = numpy.zeros(N)
    for t in range(self.T):
      #sys.stdout.write('.')
      weak_learner = self.weak_classifier_ensemble[t]
      #print Y.shape, self.alpha[t], weak_learner.predict(X).shape
      Y += self.alpha[t]*weak_learner.predict(X)
    return Y

  def test_on_training_set(self, X, Y, T):
    self.set_training_sample(X,Y)
    self.train(T)
    return self.predict(X)