decodingCallTypesNaiveBayesPart2Julie.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### decodingCallTypes 2 was used to select the group of units based on pair-wise comparisons that had sufficient data for ensemble analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies \n",
    "import os\n",
    "import glob\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pickle as pk\n",
    "import multiprocessing as mp\n",
    "from scipy.stats import t\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from scipy.special import comb\n",
    "from scipy.stats import norm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some paths\n",
    "rootPath = '/Users/frederictheunissen/Google Drive/My Drive/julie/'\n",
    "pklPath = 'pkl/'\n",
    "\n",
    "\n",
    "# The 6 birds from Julie data set\n",
    "birds = ['BlaBro09xxF', 'GreBlu9508M', 'WhiBlu5396M', 'LblBlu2028M', 'WhiWhi4522M', 'YelBlu6903F']\n",
    "\n",
    "# Color code for call type\n",
    "callColor = {'Be': (0/255.0, 230/255.0, 255/255.0), 'LT': (0/255.0, 95/255.0, 255/255.0), 'Tu': (255/255.0, 200/255.0, 65/255.0), 'Th': (255/255.0, 150/255.0, 40/255.0), \n",
    "             'Di': (255/255.0, 105/255.0, 15/255.0), 'Ag': (255/255.0, 0/255.0, 0/255.0), 'Wh': (255/255.0, 180/255.0, 255/255.0), 'Ne': (255/255.0, 100/255.0, 255/255.0),\n",
    "             'Te': (140/255.0, 100/255.0, 185/255.0), 'DC': (100/255.0, 50/255.0, 200/255.0), 'So': (255/255.0, 255/255.0, 255/255.0)}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data base\n",
    "outPath = rootPath+'JulieDataBase.pkl'\n",
    "fileIn = open(outPath,\"rb\")\n",
    "dfDataBase = pk.load(fileIn)\n",
    "pcKDE = pk.load(fileIn)\n",
    "fileIn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfDataBaseNW = dfDataBase[dfDataBase['call'] != 'Wh']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "609\n"
     ]
    }
   ],
   "source": [
    "# Number of Units\n",
    "unitNames = np.unique(dfDataBaseNW['site']+'_'+ dfDataBaseNW['unit'])\n",
    "nunits = unitNames.shape[0]\n",
    "\n",
    "# Number of Calls\n",
    "callNames = np.unique(dfDataBaseNW['call'])\n",
    "ncalls = callNames.shape[0]\n",
    "\n",
    "# Stims per Units\n",
    "nstimsUnit = dict()\n",
    "unitNamesFull = []\n",
    "for unit in unitNames:\n",
    "    nstimsUnit[unit] = len(np.unique(dfDataBaseNW['stim'][dfDataBaseNW['site']+'_'+ dfDataBaseNW['unit'] == unit]))\n",
    "    # Select only Units with 110 stimuli (for now)\n",
    "    if (nstimsUnit[unit] >= 110):\n",
    "        unitNamesFull.append(unit)\n",
    "    \n",
    "nunitsFull = len(unitNamesFull)\n",
    "\n",
    "print(nunitsFull)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[609. 609. 609. 228. 132. 132.  60.  28.  60. 228. 228. 161.  28. 132.\n",
      " 161.  60.  28. 161. 609. 228. 161.  28.  60. 228. 132.  28. 132.  28.\n",
      " 221. 132.  60. 161. 228. 609. 609. 609. 221.  28.  28. 228. 228. 161.\n",
      " 132.  60. 132.  28. 161. 132. 228.  60. 161. 132.  28.  28. 228.  60.\n",
      "  60. 132. 228. 161. 389. 389. 389. 228. 161. 161. 228. 161. 228. 389.\n",
      " 389. 389. 228. 228. 161. 228. 161. 161. 161. 161. 228. 228. 161. 228.\n",
      " 191. 191. 191. 103.  60. 131.  60.  28.  28.  60. 103.  28. 103. 103.\n",
      "  60.  28. 103.  60.  28.  60.  60. 103.  28. 103.  28. 103.  60.  60.\n",
      "  28. 161. 161. 161. 161. 161. 161. 161. 161. 161. 161. 161. 161. 161.\n",
      " 161. 161. 345. 345. 345. 163. 163. 163. 103. 103. 103. 103. 103. 103.\n",
      " 103. 103. 103. 103. 103. 103. 103. 103. 103. 103. 103.  28.  28.  28.\n",
      " 264. 264. 264. 609. 609. 609. 161.  28. 132. 161.  28. 132.  60. 228.\n",
      "  28. 228.  60. 132. 389.  60. 477. 477. 609.  28. 228. 161.  60. 132.\n",
      " 228. 161.  28.  60. 132.  28.  60. 161. 132. 228. 418. 418. 418. 228.\n",
      " 161.  29. 228. 161.  29.  29.  29.  29.  29.  29.  29.  29.  29.  29.\n",
      "  29.  29.  29.  29.  29.  29. 581. 581. 581. 581. 581. 581. 581. 581.\n",
      " 581. 581. 581. 581. 581. 581. 581. 581. 581. 581. 581. 581. 103. 103.\n",
      " 103. 103. 103. 103. 103. 103. 103. 103. 103. 103. 103. 103. 117.  28.\n",
      "  29.  60.  28.  29.  60.  28.  60.  29. 117.  60.  29.  28.  29.  60.\n",
      "  29.  60.  28.  28.  60.  29.  28.  29.  29.  28.  60.  60.  28.  60.\n",
      "  60.  29.  28.  29.  60.  28.  29.  28.  28.  28.  28.  28.  28.  28.\n",
      "  28.  28.  28.  28.  28.  28.  28.  28.  28.  28.  28. 492. 228. 103.\n",
      " 228. 103. 161. 103. 228. 161. 161. 161. 103. 228. 228. 103. 161. 161.\n",
      " 161. 228. 103. 103. 228. 103. 161. 228. 161. 103. 228. 228. 161. 103.\n",
      " 345. 345. 345.  29. 228. 228.  28.  60.  28. 228.  28.  60.  29.  60.\n",
      "  29. 345. 345. 345.  28. 228.  29. 228.  60.  29.  60.  60.  28. 228.\n",
      "  29.  28. 345. 345. 345.  29.  60.  28. 228.  60.  28. 228.  60.  29.\n",
      "  29.  28. 228. 381.  28. 132. 161. 228.  60. 132.  60. 161.  28. 609.\n",
      "  28. 161. 161.  28.  60. 228. 228. 132.  60.  28.  60. 161. 132. 132.\n",
      " 228. 132. 228.  60.  28. 161.  60. 228. 228.  28. 161. 132.  60. 161.\n",
      " 132.  28.  60.  28. 228.  28.  60. 228. 161. 132. 161. 132.  28. 161.\n",
      " 132. 228.  60. 228.  29. 161. 228.  60. 228.  60. 161.  29. 161.  60.\n",
      "  29. 478. 161. 228. 228. 161.  60.  29. 161.  60.  60.  29.  29. 228.]\n",
      "[ 6864.  7044. 10022.  3933.  4490.  8727. 16145. 10962.  4642.]\n"
     ]
    }
   ],
   "source": [
    "# Generate a list of all stimuli\n",
    "\n",
    "stimNames = np.unique(dfDataBaseNW['stim'])\n",
    "unitNames = np.unique(dfDataBaseNW['site']+'_'+ dfDataBaseNW['unit'])\n",
    "callNames = np.unique(dfDataBaseNW['call'])\n",
    "\n",
    "stimCount = np.zeros(len(stimNames))\n",
    "callCount = np.zeros(len(callNames))\n",
    "\n",
    "for unit in unitNamesFull:\n",
    "    stimsUnit = np.unique(dfDataBaseNW['stim'][dfDataBaseNW['site']+'_'+ dfDataBaseNW['unit'] == unit].values)\n",
    "    for istim, stim in enumerate(stimNames):\n",
    "        if stim in stimsUnit:\n",
    "            stimCount[istim] += 1            \n",
    "            calltype = stim.split('_')[2]\n",
    "            callCount[np.argwhere(callNames == calltype)] += 1\n",
    "            \n",
    "\n",
    "print(stimCount)\n",
    "print(callCount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "279"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(stimCount>100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ag' 'Be' 'DC' 'Di' 'LT' 'Ne' 'So' 'Te' 'Th']\n"
     ]
    }
   ],
   "source": [
    "print(callNames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def naiveBayes(unitChoice, plotFlg = False, usingAllCombos = False, code='PC', verbose = False):\n",
    "    # naiveBayes runs a guassian classifier for call types using the responses in the units in the list unitChoice\n",
    "    \n",
    "    \n",
    "    # Number of units\n",
    "    numUnits = len(unitChoice)\n",
    "    \n",
    "    # Number of PCs (for PC code)\n",
    "    nPCs = dfDataBaseNW['PC'][0].shape[0]\n",
    "    \n",
    "    # Find the unique stims played for the chosen unit\n",
    "    unitIndexDict = dict()\n",
    "    unitIndexLen = []\n",
    "    stimList = []\n",
    "    for unit in unitChoice:   \n",
    "        unitIndex = dfDataBaseNW.index[dfDataBaseNW['site']+'_'+ dfDataBaseNW['unit'] == unit]\n",
    "        unitIndexDict[unit] = unitIndex\n",
    "        unitIndexLen.append(len(unitIndex))\n",
    "        stimList.extend(list(dfDataBaseNW['stim'][unitIndex].array))\n",
    "    \n",
    "    stimNames = np.unique(stimList)\n",
    "    \n",
    "    # Generate ensemble responses  for the units in unitChoice\n",
    "\n",
    "    # Training Trials\n",
    "    rowListList = []\n",
    "    totTrials = 0\n",
    "\n",
    "    # Testing Trials\n",
    "    rowListListTest = []\n",
    "    totTestTrials = 0\n",
    "\n",
    "    for stim in stimNames:\n",
    "        goodStim = 2    # This means that we have at least two trials for each stim for all units\n",
    "        \n",
    "        # Check to see if we have enough trials for all units for this stim\n",
    "        for unit in unitChoice:\n",
    "            unitIndex = unitIndexDict[unit]\n",
    "            unitIndexStim = [ind for ind in unitIndex if dfDataBaseNW['stim'][ind] == stim]\n",
    "            if len(unitIndexStim) == 0:\n",
    "                goodStim = 0\n",
    "                if verbose:\n",
    "                    print('Stim ', stim, 'has no response for unit', unit, '. Skipping')\n",
    "                break\n",
    "            elif len(unitIndexStim) == 1:\n",
    "                goodStim = 1\n",
    "            \n",
    "        # If so continue\n",
    "        if goodStim > 0 :\n",
    "            rowList2Cat = []\n",
    "            rowList2CatLen = []\n",
    "            for unit in unitChoice:\n",
    "                unitIndex = unitIndexDict[unit]\n",
    "                unitIndexStim = [ind for ind in unitIndex if dfDataBaseNW['stim'][ind] == stim]\n",
    "                rowList2Cat.append(unitIndexStim)\n",
    "                rowList2CatLen.append(len(unitIndexStim))\n",
    "            \n",
    "            if goodStim > 1 :  # Use one of the responses for the test set\n",
    "                listTest = []\n",
    "                for iunit, unit in enumerate(unitChoice):\n",
    "                    rowchosen = np.random.choice(rowList2Cat[iunit], 1, replace=False)\n",
    "                    listTest.append(rowchosen[0])\n",
    "                    rowList2Cat[iunit].remove(rowchosen)\n",
    "                    rowList2CatLen[iunit] = len(rowList2Cat[iunit])\n",
    "                rowListListTest.append(listTest)\n",
    "                totTestTrials +=1\n",
    "            \n",
    "            if usingAllCombos:\n",
    "                numSamp = np.prod(rowList2CatLen)\n",
    "                totTrials += numSamp\n",
    "            \n",
    "                list_of_lists = []\n",
    "\n",
    "                indUnit = np.zeros((numUnits,), dtype=int)\n",
    "                indUnit[0] = -1\n",
    "            \n",
    "                for i in range(numSamp):\n",
    "                    for iunit, unit in enumerate(unitChoice):\n",
    "                        indUnit[iunit] += 1\n",
    "                        if (indUnit[iunit] >= len(rowList2Cat[iunit]) ):\n",
    "                            indUnit[iunit] = 0\n",
    "                        else: \n",
    "                            break\n",
    "                    trialList = []\n",
    "                    for iunit, unit in enumerate(unitChoice):\n",
    "                        trialList.append(rowList2Cat[iunit][indUnit[iunit]])\n",
    "                \n",
    "                    list_of_lists.append(trialList)\n",
    "            else:\n",
    "                numSamp = np.max(rowList2CatLen)\n",
    "                totTrials += numSamp\n",
    "                    \n",
    "                list_of_lists = []\n",
    "                for i in range(numSamp):\n",
    "                    trialList = []\n",
    "                    for iunit, unit in enumerate(unitChoice):\n",
    "                        if i < len(rowList2Cat[iunit]):\n",
    "                            trialList.append(rowList2Cat[iunit][i])\n",
    "                        else:\n",
    "                            trialList.append(np.random.choice(rowList2Cat[iunit], 1, replace=False)[0])\n",
    "                    list_of_lists.append(trialList)\n",
    "        \n",
    "            rowListList.extend(list_of_lists)\n",
    "\n",
    "    if verbose:        \n",
    "        print('Total number of trials:', totTrials)\n",
    "        print('Total number of test trials:', totTestTrials)\n",
    "    \n",
    "    # Make X (neural code), Y (call type)\n",
    "    if code == 'z':\n",
    "        Xfit = np.zeros((totTrials, numUnits*1))\n",
    "        Xtest = np.zeros((totTestTrials, numUnits*1))\n",
    "    elif code == 'PC':\n",
    "        Xfit = np.zeros((totTrials, numUnits*(1+nPCs)))\n",
    "        Xtest = np.zeros((totTestTrials, numUnits*(1+nPCs)))\n",
    "        \n",
    "    Yfit = []\n",
    "    for irow,rowList in enumerate(rowListList):\n",
    "        if code == 'z':\n",
    "            Xfit[irow,:] = dfDataBaseNW['z'][rowList].array\n",
    "        elif code == 'PC':\n",
    "            Xfit[irow,:] = np.hstack([np.hstack((dfDataBaseNW['z'][ind], dfDataBaseNW['PC'][ind])) for ind in rowList])\n",
    "            \n",
    "        Yfit.append(np.unique(dfDataBaseNW['call'][rowList].array)[0])\n",
    "    Yfit = np.array(Yfit)\n",
    "\n",
    "    \n",
    "    Ytest = []\n",
    "    for irow,rowList in enumerate(rowListListTest):\n",
    "        if code == 'z':\n",
    "            Xtest[irow,:] = dfDataBaseNW['z'][rowList].array\n",
    "        elif code == 'PC':\n",
    "            Xtest[irow,:] = np.hstack([np.hstack((dfDataBaseNW['z'][ind], dfDataBaseNW['PC'][ind])) for ind in rowList])\n",
    "        Ytest.append(np.unique(dfDataBaseNW['call'][rowList].array)[0])\n",
    "    \n",
    "    Ytest = np.array(Ytest)\n",
    "    \n",
    "    \n",
    "    # Fit and test the classifier clf\n",
    "    nClasses = len(np.unique(Yfit))\n",
    "\n",
    "    clf = GaussianNB(priors=np.ones((nClasses,))/nClasses)\n",
    "    clf.fit(Xfit,Yfit)\n",
    "\n",
    "    probTest = clf.predict_proba(Xtest)\n",
    "\n",
    "    # Make a confusion matrix\n",
    "    confMat = np.zeros((nClasses,nClasses))\n",
    "    testsPerClass = np.zeros((nClasses,))\n",
    "    fitsPerClass = np.zeros((nClasses,))\n",
    "\n",
    "    for y in Yfit:\n",
    "        classID = np.argwhere(clf.classes_ == y)\n",
    "        fitsPerClass[classID] += 1\n",
    "        \n",
    "    for i,y in enumerate(Ytest):\n",
    "        classID = np.argwhere(clf.classes_ == y)\n",
    "        testsPerClass[classID] += 1    \n",
    "        confMat[classID,:] += probTest[i]\n",
    "    \n",
    "    # The confusion matrix is not normalized so that we can average it correctly later on.. \n",
    "    #for i in range(nClasses):\n",
    "    #    confMat[i,:] /= testsPerClass[i]\n",
    "    \n",
    "    # Print percent correct classification by taking average of diagonal of confusion matrix.\n",
    "    sumCorrect = np.trace(confMat)\n",
    "    sumTest = np.sum(testsPerClass)\n",
    "    pcc = 100.0*(sumCorrect/sumTest)\n",
    "    \n",
    "    if verbose:\n",
    "        print('PCC %.0f %%' % (pcc))\n",
    "    \n",
    "    if plotFlg:\n",
    "        # Normalize confMat for display\n",
    "        confMatNorm = np.zeros((nClasses,nClasses))\n",
    "        for i in range(nClasses):\n",
    "            if testsPerClass[i] > 0 :\n",
    "                confMatNorm[i,:] = confMat[i,:]/testsPerClass[i]\n",
    "                \n",
    "        # Display comfusion matrix\n",
    "        cmap='viridis'\n",
    "        fig, ax = plt.subplots()\n",
    "        im_ = ax.imshow(confMatNorm*100.0, interpolation='nearest', cmap=cmap)\n",
    "\n",
    "        fig.colorbar(im_, ax=ax)\n",
    "        ax.set(xticks=np.arange(nClasses),\n",
    "               yticks=np.arange(nClasses),\n",
    "               xticklabels=clf.classes_,\n",
    "               yticklabels=clf.classes_,\n",
    "               ylabel=\"Actual Call Type\",\n",
    "               xlabel=\"Predicted Call Type\",\n",
    "               title='PPC %.0f %%' % (pcc))\n",
    "    \n",
    "    return pcc, confMat, testsPerClass, fitsPerClass\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def naiveBayesFET(unitChoice, plotFlg = False, code='PC', verbose = False, weightedVote = False):\n",
    "    # naiveBayes runs a guassian classifier for call types using the responses in the units in the list unitChoice\n",
    "    # The FET version calculates the parameters of the gaussian for each unit.\n",
    "    # I tried one version with a weighted sum of probabilities that failed.  That code is active when weightedVote is set to true.\n",
    "    # Noted differences with scikit-learn: \n",
    "    # 1. FET uses the unbiased estimate of the variance of the guassian, scikit uses the ML estimate (which is biased)\n",
    "    # 2. scikit adds an epsilon to this variance wich is 1e-9*max of variance\n",
    "    \n",
    "    # Number of units\n",
    "    numUnits = len(unitChoice)    \n",
    "    # Number of PCs (for PC code)\n",
    "    nPCs = dfDataBase['PC'][0].shape[0]\n",
    "    # Number of calls is ncalls with names callNames\n",
    "        \n",
    "    # Find the unique stims played for the chosen unit\n",
    "    unitIndexDict = dict()\n",
    "    unitIndexLen = []\n",
    "    stimList = []\n",
    "    for unit in unitChoice:   \n",
    "        unitIndex = dfDataBase.index[dfDataBase['site']+'_'+ dfDataBase['unit'] == unit]\n",
    "        unitIndexDict[unit] = unitIndex\n",
    "        unitIndexLen.append(len(unitIndex))\n",
    "        stimList.extend(list(dfDataBase['stim'][unitIndex].array))\n",
    "    \n",
    "    stimNames = np.unique(stimList)\n",
    "    \n",
    "    # Find testing and training trials for each stim\n",
    "\n",
    "    # Training data for each unit\n",
    "    unitRowList = [[]] * numUnits\n",
    "\n",
    "    # Testing Trials - One for each stim\n",
    "    rowListListTest = []\n",
    "    totTestTrials = 0\n",
    "\n",
    "    for stim in stimNames:\n",
    "        goodStim = 2    # This means that we have at least two trials for each stim for all units\n",
    "        \n",
    "        # Check to see if we have enough trials for all units for this stim\n",
    "        for unit in unitChoice:\n",
    "            unitIndex = unitIndexDict[unit]\n",
    "            unitIndexStim = [ind for ind in unitIndex if dfDataBase['stim'][ind] == stim]\n",
    "            if len(unitIndexStim) == 0:\n",
    "                goodStim = 0\n",
    "                print('Stim ', stim, 'has no response for unit', unit, '. Skipping')\n",
    "                break\n",
    "            elif len(unitIndexStim) == 1:\n",
    "                goodStim = 1\n",
    "            \n",
    "        # If so continue\n",
    "        if goodStim > 0 :\n",
    "            rowList2Cat = []\n",
    "            for unit in unitChoice:\n",
    "                unitIndex = unitIndexDict[unit]\n",
    "                unitIndexStim = [ind for ind in unitIndex if dfDataBase['stim'][ind] == stim]\n",
    "                rowList2Cat.append(unitIndexStim)\n",
    "                      \n",
    "            listTest = []\n",
    "            for iunit, unit in enumerate(unitChoice):\n",
    "                rowchosen = np.random.choice(rowList2Cat[iunit], 1, replace=False)\n",
    "                listTest.append(rowchosen[0])\n",
    "                rowList2Cat[iunit].remove(rowchosen)\n",
    "                unitRowList[iunit].extend(rowList2Cat[iunit])\n",
    "            \n",
    "            rowListListTest.append(listTest)\n",
    "            totTestTrials +=1\n",
    "    \n",
    "    # For each unit generate mean, variance, and number of trials for each call type.\n",
    "    unitGaussianModelList = [None]*numUnits\n",
    "    for iunit, unit in enumerate(unitChoice):\n",
    "        unitGaussianModelList[iunit] = [None]*ncalls \n",
    "        for icall, call in enumerate(callNames):\n",
    "            unitModel = dict()\n",
    "            unitModel['count'] = 0\n",
    "            unitModel['call'] = call\n",
    "            \n",
    "            # Rows for this unit and call:\n",
    "            rowList = [ind for ind in unitRowList[iunit] if dfDataBase['call'][ind] == call]\n",
    "            \n",
    "            if len(rowList) > 2:\n",
    "                if code == 'z':\n",
    "                    unitModel['means'] = np.mean(dfDataBase['z'][rowList].values, axis = 0)\n",
    "                    unitModel['stds'] = np.std(dfDataBase['z'][rowList].values, axis = 0, ddof = 1)\n",
    "                elif code == 'PC':\n",
    "                    unitModel['means'] = np.hstack((np.mean(dfDataBase['z'][rowList].values, axis = 0), np.mean(dfDataBase['PC'][rowList].values, axis = 0)))\n",
    "                    unitModel['stds'] = np.hstack((np.std(dfDataBase['z'][rowList].values, axis = 0, ddof=1), np.std(dfDataBase['PC'][rowList].values, axis = 0, ddof = 1)))\n",
    "\n",
    "                unitModel['count'] = len(rowList) - 2 \n",
    "            \n",
    "            unitGaussianModelList[iunit][icall] = unitModel\n",
    "  \n",
    "    # Calculate test probabilities  \n",
    "    confMat = np.zeros((ncalls,ncalls))\n",
    "    testsPerClass = np.zeros((ncalls,))\n",
    "    \n",
    "    for irow,rowList in enumerate(rowListListTest):\n",
    "\n",
    "        ytest = np.unique(dfDataBase['call'][rowList].array)[0]\n",
    "        probCall = np.zeros(ncalls)\n",
    "        if verbose:\n",
    "            print('++++++++++++++++++++++++ Call test', ytest, '++++++++++++++++++++++')\n",
    "            print(callNames)\n",
    "\n",
    "        for iunit, unit in enumerate(unitChoice):\n",
    "            unitVotes = 0\n",
    "            probUnit = np.zeros(ncalls)\n",
    "            if code == 'z':\n",
    "                xtest = dfDataBase['z'][rowList[iunit]]\n",
    "            elif code == 'PC':\n",
    "                xtest = np.hstack((dfDataBase['z'][rowList[iunit]], dfDataBase['PC'][rowList[iunit]]))\n",
    "            for icall, call in enumerate(callNames):\n",
    "                unitModel = unitGaussianModelList[iunit][icall]\n",
    "                unitVotes += unitModel['count']\n",
    "                if unitModel['count']:\n",
    "                    probval = 0\n",
    "                    for i, xval in enumerate(xtest):\n",
    "                        probval += norm.logpdf(xval, loc = unitModel['means'][i], scale =  unitModel['stds'][i])\n",
    "                    probUnit[icall] = np.exp(probval)\n",
    "                else:\n",
    "                    probUnit[icall] = np.nan\n",
    "            \n",
    "            # Fix nans\n",
    "            meanProb = np.nanmean(probUnit)\n",
    "            for icall, call in enumerate(callNames):\n",
    "                if np.isnan(probUnit[icall]):\n",
    "                    probUnit[icall] = meanProb\n",
    "                    \n",
    "            # Calculate contribution of this unit\n",
    "            if weightedVote:\n",
    "                sumProb = np.sum(probUnit)\n",
    "                probUnit = probUnit*unitVotes/sumProb\n",
    "                probCall += probUnit\n",
    "            else:\n",
    "                probUnit = np.log(probUnit)\n",
    "                probCall += probUnit\n",
    "            \n",
    "            if verbose:\n",
    "                print('\\n----Adding unit', iunit)\n",
    "                print(probUnit)\n",
    "                print(probCall)\n",
    "\n",
    "                          \n",
    "        # Final normalization\n",
    "        if ~weightedVote:\n",
    "            probCall = np.exp(probCall-np.mean(probCall))\n",
    "        probCall = probCall/np.sum(probCall)\n",
    "\n",
    "        \n",
    "        # Fill in the data\n",
    "        classID = np.argwhere(callNames == ytest)[0]\n",
    "        testsPerClass[classID] += 1    \n",
    "        confMat[classID,:] += probCall\n",
    "        if verbose:\n",
    "            print('\\n------- FINAL')\n",
    "            print(ytest, classID)\n",
    "            print(callNames)\n",
    "            print(probCall)\n",
    "            print(confMat[classID,:])\n",
    "        \n",
    "        \n",
    "    # Print percent correct classification by taking average of diagonal of confusion matrix.\n",
    "    sumCorrect = np.trace(confMat)\n",
    "    sumTest = np.sum(testsPerClass)\n",
    "    pcc = 100.0*(sumCorrect/sumTest)\n",
    "    \n",
    "    if verbose:\n",
    "        print('PCC %.0f %%' % (pcc))\n",
    "    \n",
    "    if plotFlg:\n",
    "        # Normalize confMat for display\n",
    "        confMatNorm = np.zeros((ncalls,ncalls))\n",
    "        for i in range(ncalls):\n",
    "            if testsPerClass[i] > 0 :\n",
    "                confMatNorm[i,:] = confMat[i,:]/testsPerClass[i]\n",
    "                \n",
    "        # Display comfusion matrix\n",
    "        cmap='viridis'\n",
    "        fig, ax = plt.subplots()\n",
    "        im_ = ax.imshow(confMatNorm*100.0, interpolation='nearest', cmap=cmap)\n",
    "\n",
    "        fig.colorbar(im_, ax=ax)\n",
    "        ax.set(xticks=np.arange(ncalls),\n",
    "               yticks=np.arange(ncalls),\n",
    "               xticklabels=callNames,\n",
    "               yticklabels=callNames,\n",
    "               ylabel=\"Actual Call Type\",\n",
    "               xlabel=\"Predicted Call Type\",\n",
    "               title='PPC %.0f %%' % (pcc))\n",
    "    \n",
    "    return pcc, confMat, testsPerClass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the results of Part 1 Runs that includes all neurons\n",
    "\n",
    "# Read the PC and Confusion matrices data base\n",
    "inPath = rootPath+pklPath+'JulieDataBase1UnitPCNW.pkl'\n",
    "fileIn = open(inPath,\"rb\")\n",
    "unitNamesFull = pk.load(fileIn)\n",
    "confMatTot = pk.load(fileIn)\n",
    "pcc = pk.load(fileIn)\n",
    "fileIn.close()\n",
    "\n",
    "# These are the neurons that we will be keeping\n",
    "indGood = np.argwhere(pcc > 13).flatten()           # 13 instead of 12 because only 9 call types\n",
    "nGood = indGood.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Site1_L1400R1400_e7_s0_ss2 14.880418673103216\n",
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      "Site4_L1500R1500_e5_s0_ss1 14.748996610354911\n",
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      "Site4_L2100R2050_e15_s0_ss3 14.737149009106526\n",
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      "Site4_L1500R1900_e10_s0_ss2 13.145098156089993\n",
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      "Site2_L1100R1450_e5_s0_ss1 13.072152167452602\n",
      "Site1_L750R1100_e11_s0_ss1 13.067264381095816\n",
      "Site2_L1500R1450_e26_s0_ss1 13.065300312521618\n",
      "Site2_L1100R1450_e16_s0_ss3 13.049890467692125\n",
      "Site2_L1000R900_e15_s0_ss3 13.040388349621319\n",
      "Site1_L1400R1400_e1_s0_ss1 13.017667897304404\n",
      "Site2_L2000R1600_e16_s1_ss1 13.0105143403681\n",
      "Site : 408\n"
     ]
    }
   ],
   "source": [
    "pccGood = pcc[indGood]\n",
    "unitNamesGood = [unitNamesFull[i] for i in indGood]\n",
    "\n",
    "indSort = np.flip(np.argsort(pccGood))\n",
    "\n",
    "    \n",
    "# Printing the name of the most discriminating units\n",
    "birdName = []\n",
    "for ind in indSort:\n",
    "    print(unitNamesGood[ind], pccGood[ind])\n",
    "    birdName.append(unitNamesGood[ind][0:4])\n",
    "    \n",
    "birdUnique = np.unique(birdName)\n",
    "for bird in birdUnique:\n",
    "    print(bird, \":\", len([x for x in birdName if x == bird]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "408"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nGood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generates the common stimulus set\n",
    "def commonStim(siteUnit, unitList):\n",
    "      \n",
    "    for i, unit in enumerate(unitList):\n",
    "        tempStims = np.unique(dfDataBaseNW['stim'][siteUnit==unit].values)\n",
    "        if i == 0 :\n",
    "            stimSet = set(tempStims)\n",
    "        stimSet = stimSet.intersection(tempStims)\n",
    "                               \n",
    "    return stimSet\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get a new unitList that has pairs of neurons with at least 100 common stim.\n",
    "\n",
    "siteUnit = (dfDataBaseNW['site']+'_'+dfDataBaseNW['unit'])\n",
    "argList = []\n",
    "for i1, ind1 in enumerate(indGood):\n",
    "    unit1 = unitNamesFull[ind1]\n",
    "    for i2, ind2 in enumerate(indGood):\n",
    "        if i2 <= i1:\n",
    "            continue\n",
    "        unit2 = unitNamesFull[ind2]\n",
    "        unitChoice = [unit1, unit2]\n",
    "        stimSet = commonStim(siteUnit, unitChoice)\n",
    "        # print(len(stimSet))\n",
    "        if ( len(stimSet) > 100 ):\n",
    "            argList.append(unitChoice)\n",
    "\n",
    "unitList = np.unique([unit for sublist in argList for unit in sublist])\n",
    "argList2 = argList.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save unitList and argList2 and Confusion matrices data base\n",
    "outPath = rootPath+ pklPath + 'JulieDataBaseGoodUnitList.pkl' \n",
    "fileOut = open(outPath,\"wb\")\n",
    "pk.dump(argList2, fileOut)\n",
    "pk.dump(unitList, fileOut)\n",
    "\n",
    "fileOut.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "numUnits = 2\n",
    "ntests = len(argList2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For 1 and 2 - all permutations are done \n",
    "numUnits = 2\n",
    "\n",
    "nGood = len(unitList)\n",
    "maxTest = 10000\n",
    "\n",
    "\n",
    "ntests = int(comb(nGood,numUnits))\n",
    "if ntests > maxTest:\n",
    "    ntests = maxTest\n",
    "    \n",
    "\n",
    "# Generate the list of arguments to run naiveBayes in parallel\n",
    "argList = []\n",
    "if numUnits == 1:\n",
    "    for i, unit in enumerate(unitList):\n",
    "        if i == ntests:\n",
    "            break\n",
    "        unitChoice = [unit]\n",
    "        argList.append(unitChoice)\n",
    "elif numUnits == 2:  \n",
    "    i = 0\n",
    "    for i1, unit1 in enumerate(unitList):\n",
    "        if i1 == ntests:\n",
    "            break\n",
    "        for i2, unit2 in enumerate(unitList):\n",
    "            if i2 <= i1:\n",
    "                continue\n",
    "            if i2 == ntests:\n",
    "                break\n",
    "            unitChoice = [unit1, unit2]\n",
    "            stimSet = commonStim(unitChoice)\n",
    "            if ( len(stimSet) > 100 ):\n",
    "                argList.append(unitChoice)\n",
    "                i += 1\n",
    "else:\n",
    "    i = 0\n",
    "    while i <ntests:\n",
    "        unitChoice = np.random.choice(unitList, numUnits, replace=False)\n",
    "        stimSet = commonStim(unitChoice)\n",
    "        if ( len(stimSet) > 100 ):\n",
    "            argList.append(unitChoice)\n",
    "            i += 1\n",
    "        else:\n",
    "            print(len(stimSet))\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "code = 'PC'\n",
    "\n",
    "# Run Naive Bayes in chunks of 100 with a pool of 10 workers\n",
    "chunckSize = 100    # A chunk size of 100 is used for memory since it has to keep the results of all\n",
    "poolSize = 30\n",
    "\n",
    "# Allocate memory for results\n",
    "pcc = np.zeros((ntests,))\n",
    "confMatTot = np.zeros((ncalls, ncalls))\n",
    "confMatCount = 0\n",
    "confMatFits = 0\n",
    "\n",
    "# Calculate the number of chunks\n",
    "nchunks = ntests//chunckSize\n",
    "if nchunks % chunckSize:\n",
    "    nchunks += 1\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    for ichunk in range(nchunks):\n",
    "        istart = ichunk*chunckSize\n",
    "        iend = (ichunk+1)*chunckSize\n",
    "        if iend > ntests:\n",
    "            iend = ntests         \n",
    "        \n",
    "        with mp.Pool(poolSize) as p:\n",
    "            results = p.map(naiveBayes, argList[istart:iend])\n",
    "        \n",
    "        # I don't think I need these??\n",
    "        # p.close()\n",
    "        # p.join()\n",
    "        \n",
    "        for i, res in enumerate(results):\n",
    "            pcc[istart + i] = res[0]\n",
    "            confMat = res[1]\n",
    "            testsPerClass = res[2]\n",
    "            fitsPerClass = res[3]\n",
    "            if (confMat.shape == (ncalls,ncalls) ) :\n",
    "                confMatTot = np.add(confMatTot, confMat)\n",
    "                confMatCount += testsPerClass\n",
    "                confMatFits += fitsPerClass\n",
    "                \n",
    "        print('Done with chunk %d/%d' % (ichunk, nchunks))\n",
    "\n",
    "# Calculate the average confusion matrix. \n",
    "for i in range(ncalls):\n",
    "    if confMatCount[i] > 0:\n",
    "        confMatTot[i,:] = confMatTot[i,:]/confMatCount[i]\n",
    "        \n",
    "# save the PC and Confusion matrices data base\n",
    "outPath = rootPath+ pklPath + 'JulieDataBase%dGoodUnitPC.pkl' % numUnits\n",
    "fileOut = open(outPath,\"wb\")\n",
    "pk.dump(argList, fileOut)\n",
    "pk.dump(confMatTot, fileOut)\n",
    "pk.dump(pcc, fileOut)\n",
    "pk.dump(confMatCount, fileOut)\n",
    "pk.dump(confMatFits, fileOut)\n",
    "fileOut.close()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([243732., 267456., 369642., 158688., 175884., 319554., 617175.,\n",
       "       409482., 165303.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confMatCount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,4), dpi=300)\n",
    "ax = fig.subplots()\n",
    "\n",
    "ax.hist(pcc, bins = 30, range = (0,30))\n",
    "ax.set_xlabel('PCC')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title('%d Unit Good PC' % numUnits)\n",
    "\n",
    "plt.savefig('/Users/frederictheunissen/Desktop/pcchist%dUnitGoodPCJulie.eps' % numUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the overall confusion matrix\n",
    "\n",
    "cmap='viridis'\n",
    "fig = plt.figure(figsize=(4,4), dpi=300)\n",
    "ax = fig.subplots()\n",
    "im_ = ax.imshow(confMatTot*100.0, interpolation='nearest', cmap=cmap, extent = (-0.5, ncalls-0.5, ncalls-0.5, -0.5), vmin=5, vmax = 15)\n",
    "\n",
    "fig.colorbar(im_, ax=ax)\n",
    "ax.set(xticks=np.arange(ncalls),\n",
    "               yticks=np.arange(ncalls),\n",
    "               ylim = (ncalls-0.5, -0.5),\n",
    "               xticklabels=callNames,\n",
    "               yticklabels=callNames,\n",
    "               ylabel=\"Actual Call Type\",\n",
    "               xlabel=\"Predicted Call Type\",\n",
    "               title='%d Unit Good PC PCC %.0f %%' % (numUnits, np.mean(pcc)))\n",
    "\n",
    "plt.savefig('/Users/frederictheunissen/Desktop/confMat%dUnitGoodPCJulie.eps' % numUnits)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,4), dpi=300)\n",
    "ax = fig.subplots()\n",
    "\n",
    "x = np.arange(len(callNames))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "rects1 = ax.bar(x - width/2, confMatFits, width, label='Fits')\n",
    "rects2 = ax.bar(x + width/2, confMatCount, width, label='Tests')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Number')\n",
    "ax.set_title('Fitting and Testing Data Size')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(callNames)\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('/Users/frederictheunissen/Desktop/dataSize%dUnitGoodPCJulie.eps' % numUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}