text: '''
  Created on Nov 28, 2010
  Adaboost is short for Adaptive Boosting
  @author: Peter
  '''
  from numpy import *
  
  
  def loadSimpData():
      datMat = matrix([[1., 2.1],
                       [2., 1.1],
                       [1.3, 1.],
                       [1., 1.],
                       [2., 1.]])
      classLabels = [1.0, 1.0, -1.0, -1.0, 1.0]
      return datMat, classLabels
  
  
  def loadDataSet(fileName):  # general function to parse tab -delimited floats
      numFeat = len(open(fileName).readline().split('\t'))  # get number of fields
      dataMat = [];
      labelMat = []
      fr = open(fileName)
      for line in fr.readlines():
          lineArr = []
          curLine = line.strip().split('\t')
          for i in range(numFeat - 1):
              lineArr.append(float(curLine[i]))
          dataMat.append(lineArr)
          labelMat.append(float(curLine[-1]))
      return dataMat, labelMat
  
  
  def stumpClassify(dataMatrix, dimen, threshVal, threshIneq):  # just classify the data
      retArray = ones((shape(dataMatrix)[0], 1))
      if threshIneq == 'lt':
          retArray[dataMatrix[:, dimen] <= threshVal] = -1.0
      else:
          retArray[dataMatrix[:, dimen] > threshVal] = -1.0
      return retArray
  
  
  def buildStump(dataArr, classLabels, D):
      dataMatrix = mat(dataArr);
      labelMat = mat(classLabels).T
      m, n = shape(dataMatrix)
      numSteps = 10.0;
      bestStump = {};
      bestClasEst = mat(zeros((m, 1)))
      minError = inf  # init error sum, to +infinity
      for i in range(n):  # loop over all dimensions
          rangeMin = dataMatrix[:, i].min();
          rangeMax = dataMatrix[:, i].max();
          stepSize = (rangeMax - rangeMin) / numSteps
          for j in range(-1, int(numSteps) + 1):  # loop over all range in current dimension
              for inequal in ['lt', 'gt']:  # go over less than and greater than
                  threshVal = (rangeMin + float(j) * stepSize)
                  predictedVals = stumpClassify(dataMatrix, i, threshVal,
                                                inequal)  # call stump classify with i, j, lessThan
                  errArr = mat(ones((m, 1)))
                  errArr[predictedVals == labelMat] = 0
                  weightedError = D.T * errArr  # calc total error multiplied by D
                  # print "split: dim %d, thresh %.2f, thresh ineqal: %s, the weighted error is %.3f" % (i, threshVal, inequal, weightedError)
                  if weightedError < minError:
                      minError = weightedError
                      bestClasEst = predictedVals.copy()
                      bestStump['dim'] = i
                      bestStump['thresh'] = threshVal
                      bestStump['ineq'] = inequal
      return bestStump, minError, bestClasEst
  
  
  def adaBoostTrainDS(dataArr, classLabels, numIt=40):
      weakClassArr = []
      m = shape(dataArr)[0]
      D = mat(ones((m, 1)) / m)  # init D to all equal
      aggClassEst = mat(zeros((m, 1)))
      for i in range(numIt):
          bestStump, error, classEst = buildStump(dataArr, classLabels, D)  # build Stump
          # print "D:",D.T
          alpha = float(
              0.5 * log((1.0 - error) / max(error, 1e-16)))  # calc alpha, throw in max(error,eps) to account for error=0
          bestStump['alpha'] = alpha
          weakClassArr.append(bestStump)  # store Stump Params in Array
          # print "classEst: ",classEst.T
          expon = multiply(-1 * alpha * mat(classLabels).T, classEst)  # exponent for D calc, getting messy
          D = multiply(D, exp(expon))  # Calc New D for next iteration
          D = D / D.sum()
          # calc training error of all classifiers, if this is 0 quit for loop early (use break)
          aggClassEst += alpha * classEst
          # print "aggClassEst: ",aggClassEst.T
          aggErrors = multiply(sign(aggClassEst) != mat(classLabels).T, ones((m, 1)))
          errorRate = aggErrors.sum() / m
          print "total error: ", errorRate
          if errorRate == 0.0: break
      return weakClassArr, aggClassEst
  
  
  def adaClassify(datToClass, classifierArr):
      dataMatrix = mat(datToClass)  # do stuff similar to last aggClassEst in adaBoostTrainDS
      m = shape(dataMatrix)[0]
      aggClassEst = mat(zeros((m, 1)))
      for i in range(len(classifierArr)):
          classEst = stumpClassify(dataMatrix, classifierArr[i]['dim'], \
                                   classifierArr[i]['thresh'], \
                                   classifierArr[i]['ineq'])  # call stump classify
          aggClassEst += classifierArr[i]['alpha'] * classEst
          print aggClassEst
      return sign(aggClassEst)
  
  
  def plotROC(predStrengths, classLabels):
      import matplotlib.pyplot as plt
      cur = (1.0, 1.0)  # cursor
      ySum = 0.0  # variable to calculate AUC
      numPosClas = sum(array(classLabels) == 1.0)
      yStep = 1 / float(numPosClas);
      xStep = 1 / float(len(classLabels) - numPosClas)
      sortedIndicies = predStrengths.argsort()  # get sorted index, it's reverse
      fig = plt.figure()
      fig.clf()
      ax = plt.subplot(111)
      # loop through all the values, drawing a line segment at each point
      for index in sortedIndicies.tolist()[0]:
          if classLabels[index] == 1.0:
              delX = 0;
              delY = yStep;
          else:
              delX = xStep;
              delY = 0;
              ySum += cur[1]
          # draw line from cur to (cur[0]-delX,cur[1]-delY)
          ax.plot([cur[0], cur[0] - delX], [cur[1], cur[1] - delY], c='b')
          cur = (cur[0] - delX, cur[1] - delY)
      # ax.plot([0, 1], [0, 1], 'b--')
      # plt.xlabel('False positive rate');
      # plt.ylabel('True positive rate')
      # plt.title('ROC curve for AdaBoost horse colic detection system')
      ax.axis([0, 1, 0, 1])
      plt.show()
      print "the Area Under the Curve is: ", ySum * xStep
  
  
  datArr, labelArr = loadDataSet('horseColicTraining2.txt')
  classifierArray, aggClassEst = adaBoostTrainDS(datArr, labelArr, 10)
  plotROC(aggClassEst.T, labelArr)