adaboost_test.ipynb · OraYang/AdaBoost

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adaboost_test.ipynb 7.28 KB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total error:  0.2\n",
      "total error:  0.2\n",
      "total error:  0.0\n"
     ]
    }
   ],
   "source": [
    "from numpy import *\n",
    "def loadSimpData():\n",
    "    datMat = matrix([[ 1. ,  2.1],\n",
    "        [ 2. ,  1.1],\n",
    "        [ 1.3,  1. ],\n",
    "        [ 1. ,  1. ],\n",
    "        [ 2. ,  1. ]])\n",
    "    classLabels = [1.0, 1.0, -1.0, -1.0, 1.0]\n",
    "    return datMat,classLabels\n",
    "def stumpClassify(dataMatrix,dimen,threshVal,threshIneq):#just classify the data\n",
    "    retArray = ones((shape(dataMatrix)[0],1))\n",
    "    if threshIneq == 'lt':\n",
    "        retArray[dataMatrix[:,dimen] <= threshVal] = -1.0\n",
    "    else:\n",
    "        retArray[dataMatrix[:,dimen] > threshVal] = -1.0\n",
    "    return retArray\n",
    "    \n",
    "\n",
    "def buildStump(dataArr,classLabels,D):\n",
    "    dataMatrix = mat(dataArr); labelMat = mat(classLabels).T\n",
    "    m,n = shape(dataMatrix)\n",
    "    numSteps = 10.0; bestStump = {}; bestClasEst = mat(zeros((m,1)))\n",
    "    minError = inf #init error sum, to +infinity\n",
    "    for i in range(n):#loop over all dimensions\n",
    "        rangeMin = dataMatrix[:,i].min(); rangeMax = dataMatrix[:,i].max();\n",
    "        stepSize = (rangeMax-rangeMin)/numSteps\n",
    "        for j in range(-1,int(numSteps)+1):#loop over all range in current dimension\n",
    "            for inequal in ['lt', 'gt']: #go over less than and greater than\n",
    "                threshVal = (rangeMin + float(j) * stepSize)\n",
    "                predictedVals = stumpClassify(dataMatrix,i,threshVal,inequal)#call stump classify with i, j, lessThan\n",
    "                errArr = mat(ones((m,1)))\n",
    "                errArr[predictedVals == labelMat] = 0\n",
    "                weightedError = D.T*errArr  #calc total error multiplied by D\n",
    "                #print \"split: dim %d, thresh %.2f, thresh ineqal: %s, the weighted error is %.3f\" % (i, threshVal, inequal, weightedError)\n",
    "                if weightedError < minError:\n",
    "                    minError = weightedError\n",
    "                    bestClasEst = predictedVals.copy()\n",
    "                    bestStump['dim'] = i\n",
    "                    bestStump['thresh'] = threshVal\n",
    "                    bestStump['ineq'] = inequal\n",
    "    return bestStump,minError,bestClasEst\n",
    "\n",
    "\n",
    "def adaBoostTrainDS(dataArr,classLabels,numIt=40):\n",
    "    weakClassArr = []\n",
    "    m = shape(dataArr)[0]\n",
    "    D = mat(ones((m,1))/m)   #init D to all equal\n",
    "    aggClassEst = mat(zeros((m,1)))\n",
    "    for i in range(numIt):\n",
    "        bestStump,error,classEst = buildStump(dataArr,classLabels,D)#build Stump\n",
    "        #print \"D:\",D.T\n",
    "        alpha = float(0.5*log((1.0-error)/max(error,1e-16)))#calc alpha, throw in max(error,eps) to account for error=0\n",
    "        bestStump['alpha'] = alpha  \n",
    "        weakClassArr.append(bestStump)                  #store Stump Params in Array\n",
    "        #print \"classEst: \",classEst.T\n",
    "        expon = multiply(-1*alpha*mat(classLabels).T,classEst) #exponent for D calc, getting messy\n",
    "        D = multiply(D,exp(expon))                              #Calc New D for next iteration\n",
    "        D = D/D.sum()\n",
    "        #calc training error of all classifiers, if this is 0 quit for loop early (use break)\n",
    "        aggClassEst += alpha*classEst\n",
    "        #print \"aggClassEst: \",aggClassEst.T\n",
    "        aggErrors = multiply(sign(aggClassEst) != mat(classLabels).T,ones((m,1)))\n",
    "        errorRate = aggErrors.sum()/m\n",
    "        print(\"total error: \",errorRate)\n",
    "        if errorRate == 0.0: break\n",
    "    return weakClassArr,aggClassEst\n",
    "\n",
    "def adaClassify(datToClass,classifierArr):\n",
    "    dataMatrix = mat(datToClass)#do stuff similar to last aggClassEst in adaBoostTrainDS\n",
    "    m = shape(dataMatrix)[0]\n",
    "    aggClassEst = mat(zeros((m,1)))\n",
    "    for i in range(len(classifierArr)):\n",
    "        classEst = stumpClassify(dataMatrix,classifierArr[i]['dim'],\\\n",
    "                                 classifierArr[i]['thresh'],\\\n",
    "                                 classifierArr[i]['ineq'])#call stump classify\n",
    "        aggClassEst += classifierArr[i]['alpha']*classEst\n",
    "        print(aggClassEst)\n",
    "    return sign(aggClassEst)\n",
    "dataMat,classLabels = loadSimpData()\n",
    "classifierArray,matrix_aggClassEst = adaBoostTrainDS(dataMat,classLabels,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1. ,  2.1],\n",
       "        [ 2. ,  1.1],\n",
       "        [ 1.3,  1. ],\n",
       "        [ 1. ,  1. ],\n",
       "        [ 2. ,  1. ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 1.0, -1.0, -1.0, 1.0]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classLabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'alpha': 0.6931471805599453, 'dim': 0, 'ineq': 'lt', 'thresh': 1.3},\n",
       " {'alpha': 0.9729550745276565, 'dim': 1, 'ineq': 'lt', 'thresh': 1.0},\n",
       " {'alpha': 0.8958797346140273,\n",
       "  'dim': 0,\n",
       "  'ineq': 'lt',\n",
       "  'thresh': 0.90000000000000002}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifierArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.69314718]\n",
      " [-0.69314718]]\n",
      "[[ 1.66610226]\n",
      " [-1.66610226]]\n",
      "[[ 2.56198199]\n",
      " [-2.56198199]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "matrix([[ 1.],\n",
       "        [-1.]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adaClassify([[5,5],[0,0]],classifierArray)"
   ]
  }
 ],
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提交于 2017-10-31 15:25 . AdaBoost

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