HMM_Spice_SubmissionBaseline-Normalized-Optimized-V2.1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import sys\n",
    "from collections import defaultdict\n",
    "from math import expm1\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def computeInitialProb(trainDataFile,numOfStates):\n",
    "    trainFile=open(trainDataFile,\"r\")\n",
    "    metaDataLine = trainFile.readline()\n",
    "    headerLine = metaDataLine.split(\" \")\n",
    "    numSequences = int(headerLine[0])\n",
    "    distinctObservations= int(headerLine[1])#Total Number of Distinct Observations\n",
    "    numOfStates=min(numOfStates,distinctObservations)\n",
    "    empiricalCount=np.zeros(shape=numOfStates)\n",
    "    empiricalFreq=defaultdict(int)\n",
    "    for n in range(numSequences):\n",
    "        line = trainFile.readline()#Reading Sequences 1 by 1\n",
    "        l = line.split(\" \")\n",
    "        startState=int(l[1])\n",
    "        empiricalFreq[startState] = empiricalFreq[startState]+1\n",
    "    totalObservations=0\n",
    "    for i in np.arange(numOfStates):\n",
    "        empiricalCount[i]=empiricalFreq[i]\n",
    "        totalObservations=totalObservations+empiricalCount[i]\n",
    "    initialProb=[count/totalObservations for count in empiricalCount]\n",
    "    return (numOfStates,distinctObservations,initialProb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def createRandomMatrixA(numOfStates):\n",
    "    matrixA=np.ndarray(shape=(numOfStates,numOfStates),dtype=float)\n",
    "    prob=1.0/(numOfStates*numOfStates)\n",
    "    matrixA.fill(prob)\n",
    "    return matrixA\n",
    "def createRandomMatrixB(numOfStates,distinctObservations):\n",
    "    matrixB=np.ndarray(shape=(numOfStates,distinctObservations),dtype=float)\n",
    "    prob=1.0/(numOfStates*distinctObservations)\n",
    "    matrixB.fill(prob)\n",
    "    return matrixB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def computeAlpha(observations,a,b,pi,alphaDP):\n",
    "    statesC=a.shape[0]\n",
    "    timePts=observations.shape[0]\n",
    "    if timePts<1:\n",
    "        return\n",
    "    alphaDpScaleTime0=0\n",
    "    bTranspose=b.transpose()\n",
    "    alphaDP[0]=pi*bTranspose[observations[0]]\n",
    "    alphaDpScaleTime0=np.sum(alphaDP[0])\n",
    "    alphaDP[0]/=alphaDpScaleTime0\n",
    "    #print(\"Initial Alpha \",alphaDP[0])\n",
    "    aTranspose=a.transpose()\n",
    "    for t in np.arange(1,timePts):\n",
    "        alphaDpScaleTimeT=0\n",
    "        for i in np.arange(statesC):\n",
    "            alphaDP[t][i]=(np.sum(alphaDP[t-1]*aTranspose[i]))*b[i][observations[t]]\n",
    "            #alphaDP[t][i]*=b[i][observations[t]]\n",
    "        alphaDpScaleTimeT=np.sum(alphaDP[t])\n",
    "        #print(\"Scaling \",alphaDpScaleTimeT)\n",
    "        alphaDP[t]/=alphaDpScaleTimeT\n",
    "    #print(\"Next AlphaDP \",alphaDP[1])\n",
    "    #print(\"alphaDP \",alphaDP)\n",
    "def observationsLikelihood(alphaDP):\n",
    "    timePts=alphaDP.shape[0]\n",
    "    ans=0.0\n",
    "    ans=np.sum(alphaDP[timePts-1])\n",
    "    #print(\"Ob Lik \",ans)\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def computeBeta(observations,a,b,pi,betaDP):\n",
    "    statesC=a.shape[0]\n",
    "    timePts=observations.shape[0]\n",
    "    if timePts<1:\n",
    "        return\n",
    "    betaDP[timePts-1].fill(1)\n",
    "    bTranspose=b.transpose()\n",
    "    for t in np.arange(timePts-2,-1,-1):\n",
    "        betaDpScaleTimeT=0\n",
    "        for i in np.arange(statesC):\n",
    "            betaDP[t][i]=np.sum(a[i]*bTranspose[observations[t+1]]*betaDP[t+1])\n",
    "        betaDpScaleTimeT=np.sum(betaDP[t])\n",
    "        betaDP[t]/=betaDpScaleTimeT\n",
    "    #print(\"BetaDP t-2 \",betaDP[timePts-2])\n",
    "    #print(\"betaDP \",betaDP)\n",
    "    return betaDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def computeDiGammaDP(alphaDP,betaDP,a,b,observations):\n",
    "    observationsC=alphaDP.shape[0]\n",
    "    statesC=alphaDP.shape[1]\n",
    "    diGammaDP=np.zeros(shape=(statesC,statesC),dtype=float)\n",
    "    diGammaDenom=observationsLikelihood(alphaDP)\n",
    "    bTranspose=b.transpose()\n",
    "    for i in np.arange(statesC):\n",
    "        for t in np.arange(observationsC-1):\n",
    "            diGammaDP[i]+=alphaDP[t][i]*a[i]*bTranspose[observations[t+1]]*betaDP[t+1]\n",
    "    diGammaDP/=diGammaDenom\n",
    "    return diGammaDP\n",
    "def computeTransitionProbabilityA(alphaDP,betaDP,a,b,observations):\n",
    "    statesC=alphaDP.shape[1]\n",
    "    newlyComputedTransitionProbA=np.zeros(shape=(statesC,statesC),dtype=float)\n",
    "    diGammaDP=computeDiGammaDP(alphaDP,betaDP,a,b,observations)\n",
    "    diGammaDPSumGrpByJ=np.apply_along_axis(np.sum,1,diGammaDP)\n",
    "    for i in np.arange(statesC):    \n",
    "        if (diGammaDPSumGrpByJ[i]==0):\n",
    "            newlyComputedTransitionProbA[i]=0.0\n",
    "        else:\n",
    "            newlyComputedTransitionProbA[i]=diGammaDP[i]/diGammaDPSumGrpByJ[i]\n",
    "    return newlyComputedTransitionProbA   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#def computeGammaNum(t,j,alphaDP,betaDP):\n",
    "    #return alphaDP[t][j]*betaDP[t][j]\n",
    "def computeGammaDP(alphaDP,betaDP):\n",
    "    gammaDenom=observationsLikelihood(alphaDP)\n",
    "    gammaDP=alphaDP*betaDP#[Time][State]\n",
    "    #gammaDP=gammaDP.transpose()\n",
    "    gammaDP/=gammaDenom\n",
    "    return gammaDP\n",
    "#def customSum(rowObservations,vk):\n",
    "    #return np.sum(rowObservations[np.where(rowObservations==vk)])\n",
    "#vCustomSum=np.vectorize(customSum)\n",
    "def computeTransitionProbabilityB(alphaDP,betaDP,a,b,observations,observationDict):\n",
    "    statesC=a.shape[0]\n",
    "    observationsC=b.shape[1]\n",
    "    newlyComputedObsrProbB=np.zeros(shape=(observationsC,statesC),dtype=float)#Ideal Shape should be transposed\n",
    "    gammaDP=computeGammaDP(alphaDP,betaDP)#[t][state]\n",
    "    #gammaDPTranspose=gammaDP.transpose()\n",
    "    obsrProbDenomVec=np.apply_along_axis(np.sum,0,gammaDP)#Sum Across Rows i.e. Across each state\n",
    "    #for vk in observationDict:\n",
    "            #newlyComputedObsrProbB[vk]=np.apply_along_axis(vCustomSum,1,gammaDP,vk)/obsrProbDenomVec\n",
    "    for vk in observationDict:\n",
    "            newlyComputedObsrProbB[vk]=np.sum(gammaDP[np.where(observations==vk)])/obsrProbDenomVec\n",
    "    #for i in np.arange(statesC):\n",
    "        #obsrProbDenom =np.sum(gammaDP[i])\n",
    "        #for vk in observationDict:\n",
    "            #newlyComputedObsrProbB[i][vk]=np.sum(gammaDP[i][np.where(observations==vk)])/obsrProbDenom\n",
    "    newlyComputedObsrProbB=newlyComputedObsrProbB.transpose()#Back to Shape[i][vk]\n",
    "    return newlyComputedObsrProbB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Change Convergence Criteria to be more reasonable/Useful\n",
    "def isConverged(count,convergenceIters):\n",
    "    if count>=convergenceIters:\n",
    "        return True\n",
    "    return False\n",
    "def Forward_Backward_EM_Algo(observations,A,B,pi,convergenceIters,observationDict):\n",
    "    count=0\n",
    "    updatedA=A\n",
    "    updatedB=B\n",
    "    while isConverged(count,convergenceIters)==False:\n",
    "        #Expectation(E)-Step\n",
    "        alphaDP=np.zeros(shape=(observations.shape[0],updatedA.shape[0]))# Count_of_Observations*Count_of_Hidden_States\n",
    "        betaDP=np.zeros(shape=(observations.shape[0],updatedA.shape[0]))# Count_of_Observations*Count_of_Hidden_States\n",
    "        computeAlpha(observations,updatedA,updatedB,pi,alphaDP)\n",
    "        computeBeta(observations,updatedA,updatedB,pi,betaDP)\n",
    "        #Maximization(M)-Step\n",
    "        newA=computeTransitionProbabilityA(alphaDP,betaDP,updatedA,updatedB,observations)\n",
    "        newB=computeTransitionProbabilityB(alphaDP,betaDP,updatedA,updatedB,observations,observationDict)\n",
    "        updatedA=newA\n",
    "        updatedB=newB\n",
    "        count=count+1\n",
    "    return (updatedA,updatedB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def trainHMM(trainDataFile,A,B,pi,convergenceIters,maxSequences=-1):\n",
    "    trainFile=open(trainDataFile,\"r\")\n",
    "    metaDataLine = trainFile.readline()\n",
    "    headerLine = metaDataLine.split(\" \")\n",
    "    numSequences = int(headerLine[0])\n",
    "    distinctObservations= int(headerLine[1])#Total Number of Distinct Observations\n",
    "    observationDict=np.arange(distinctObservations)\n",
    "    updatedA=np.NaN\n",
    "    updatedB=np.NaN\n",
    "    isAUpdated=False\n",
    "    #for n in range(numSequences):\n",
    "    if(maxSequences==-1):\n",
    "        usedSeqs=numSequences\n",
    "    else:\n",
    "        usedSeqs=min(maxSequences,numSequences)\n",
    "    actuallyUsedSeqs=0\n",
    "    for n in range(usedSeqs):\n",
    "        line = trainFile.readline()#Reading Sequences 1 by 1\n",
    "        l = line.split(\" \")\n",
    "        #print(\"For Sequence \",n,\" =====================================>\")\n",
    "        if(int(l[0])<=1):\n",
    "            #print(\"Skipping \",l)\n",
    "            continue\n",
    "        actuallyUsedSeqs+=1\n",
    "        observations=np.array([int(i) for i in l[1:len(l)]])\n",
    "        learnedParams=Forward_Backward_EM_Algo(observations,A,B,pi,convergenceIters,observationDict)\n",
    "        if isAUpdated==False:\n",
    "            isAUpdated=True\n",
    "            updatedA=learnedParams[0]\n",
    "            updatedB=learnedParams[1]\n",
    "        else:\n",
    "            updatedA+=learnedParams[0]\n",
    "            updatedB+=learnedParams[1]\n",
    "    updatedA=updatedA/actuallyUsedSeqs\n",
    "    updatedB=updatedB/actuallyUsedSeqs\n",
    "    return (updatedA,updatedB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def trainModel(fileLoc,maxNoOfStates,convergenceIters,maxSequences=-1):\n",
    "    start = time.time()\n",
    "    initialProbs=computeInitialProb(fileLoc,maxNoOfStates)\n",
    "    end = time.time()\n",
    "    print(\"Computed Initial Prob. in \", end - start ,\"seconds\")\n",
    "    pi=initialProbs[2]\n",
    "    numOfStates=initialProbs[0]\n",
    "    distinctObservations=initialProbs[1]\n",
    "    #print(initialProbs)\n",
    "    A=createRandomMatrixA(numOfStates)\n",
    "    B=createRandomMatrixB(numOfStates,distinctObservations)\n",
    "    #print(A)\n",
    "    #print(B)\n",
    "    trainedParams=trainHMM(fileLoc,A,B,pi,convergenceIters,maxSequences)\n",
    "    end=time.time()\n",
    "    #print(\"For \",maxSequences,\" Sequences : Total Training Time \",end-start,\" seconds\")\n",
    "    return trainedParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed Initial Prob. in  0.24749040603637695 seconds\n"
     ]
    }
   ],
   "source": [
    "(A,B)=trainModel('Data/1.spice.train.txt',20,7,1)\n",
    "#(A,B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed Initial Prob. in  0.31247663497924805 seconds\n",
      "Computed Initial Prob. in  0.16233348846435547 seconds\n",
      "Computed Initial Prob. in  0.2573864459991455 seconds\n",
      "Computed Initial Prob. in  0.17384719848632812 seconds\n",
      "1 loops, best of 3: 5 s per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit trainModel('Data/1.spice.train.txt',20,7,15)\n",
    "#(A,B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed Initial Prob. in  0.35449671745300293 seconds\n",
      "Computed Initial Prob. in  0.2422492504119873 seconds\n",
      "Computed Initial Prob. in  0.2819344997406006 seconds\n",
      "Computed Initial Prob. in  0.21940302848815918 seconds\n",
      "1 loops, best of 3: 1min 6s per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit trainModel('Data/1.spice.train.txt',20,7,150)\n",
    "#(A,B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%timeit trainModel('Data/1.spice.train.txt',20,7,1500)\n",
    "#(A,B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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