{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "pretty-performer",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n",
    "from PIL import Image, ImageDraw, ImageFont\n",
    "from library.analysis import testSets, generators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "engaging-warehouse",
   "metadata": {},
   "source": [
    "# Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "crazy-taxation",
   "metadata": {},
   "outputs": [],
   "source": [
    "kScore = \"cohens kappa score\"\n",
    "f1Score = \"f1 score\"\n",
    "gScore = \"G-mean score\"\n",
    "aScore = \"average precision score\"\n",
    "\n",
    "kScoreSd = kScore + \" - SD\"\n",
    "f1ScoreSd = f1Score + \" - SD\"\n",
    "gScoreSd = gScore + \" - SD\"\n",
    "aScoreSd = aScore + \" - SD\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extensive-future",
   "metadata": {},
   "source": [
    "# Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "warming-department",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Repeater',\n",
       " 'ProWRAS',\n",
       " 'GAN',\n",
       " 'CTGAN',\n",
       " 'CTAB-GAN',\n",
       " 'ConvGeN-majority-5',\n",
       " 'ConvGeN-majority-full',\n",
       " 'ConvGeN-proximity-5',\n",
       " 'ConvGeN-proximity-full']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ignoreSet = [\"ozone_level\", \"yeast_me2\"]\n",
    "\n",
    "gans = [g.replace(\"SimpleGAN\", \"GAN\") for g in generators.keys()]\n",
    "algs = [\"LR\", \"RF\", \"GB\", \"KNN\", \"DoC\"]\n",
    "\n",
    "gans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edf5b592",
   "metadata": {},
   "outputs": [],
   "source": [
    "testSets = [t for t in testSets if t[0:7] == \"folding\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "needed-birmingham",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanupName(name):\n",
    "    return name.replace(\"folding_\", \"\").replace(\"imblearn_\", \"\").replace(\"kaggle_\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8106bddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = [cleanupName(d) for d in testSets]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96a601db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ganName(name):\n",
    "    return name.replace(\n",
    "        \"ConvGeN-majority-5\", \"ConvGeN(5,maj)\").replace(\n",
    "        \"ConvGeN-majority-full\", \"ConvGeN(min,maj)\").replace(\n",
    "        \"ConvGeN-proximity-5\", \"ConvGeN(5,prox)\").replace(\n",
    "        \"ConvGeN-proximity-full\", \"ConvGeN(min,prox)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d35b280b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filterConvGen(name):\n",
    "    return not name.startswith(\"ConvGeN\") or name == \"ConvGeN(min,maj)\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selective-connecticut",
   "metadata": {},
   "source": [
    "# Load data from CSV files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "intended-watts",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadDiagnoseData(ganType, datasetName):\n",
    "    def addAvg(rn, name, avg, items):\n",
    "        avg = float(avg)\n",
    "        rn[name] = avg\n",
    "        rn[name + \" - SD\"] = math.sqrt(sum([(avg - x)*(avg - x) for x in items]) / len(items))\n",
    "        \n",
    "    fileName = f\"data_result/{ganType}/{datasetName}.csv\"\n",
    "    r = {}\n",
    "    try:\n",
    "        f1List = []\n",
    "        kList = []\n",
    "        aList = []\n",
    "        gList = []\n",
    "        indizes = [str(x) for x in range(100)]\n",
    "        with open(fileName) as f:\n",
    "            newBlock = True\n",
    "            n = \"\"\n",
    "            for line in f:\n",
    "                line = line.strip()\n",
    "                if newBlock:\n",
    "                    n = line\n",
    "                    if n == \"GAN\" or n == \"DoG\":\n",
    "                        n = \"DoC\"\n",
    "                    newBlock = False\n",
    "                elif line == \"---\":\n",
    "                    newBlock = True\n",
    "                    f1List = []\n",
    "                    kList = []\n",
    "                    aList = []\n",
    "                    gList = []\n",
    "                else:\n",
    "                    parts = line.split(\";\")\n",
    "                    if parts[0] == \"avg\":\n",
    "                        r[n] = {}\n",
    "                        addAvg(r[n], f1Score, parts[5], f1List)\n",
    "                        addAvg(r[n], kScore, parts[6], kList)\n",
    "                        addAvg(r[n], aScore, parts[7], aList)\n",
    "                        addAvg(r[n], gScore, parts[8], gList)\n",
    "                    elif parts[0] in indizes:\n",
    "                        f1List.append(float(parts[5]))\n",
    "                        kList.append(float(parts[6]))\n",
    "                        aList.append(float(parts[7]))\n",
    "                        gList.append(float(parts[8]))\n",
    "    except FileNotFoundError as e:\n",
    "        print(f\"Missing file: {fileName}\")\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "classical-rescue",
   "metadata": {},
   "outputs": [],
   "source": [
    "statistic = { }\n",
    "\n",
    "for gan in gans:\n",
    "    if ganName(gan) not in statistic:\n",
    "        statistic[ganName(gan)] = {}\n",
    "    \n",
    "    for ds in testSets:\n",
    "        if ds != \"Average\":\n",
    "            statistic[ganName(gan)][cleanupName(ds)] = loadDiagnoseData(gan, ds)\n",
    "            \n",
    "            d = cleanupName(ds)\n",
    "            if d not in dataset:\n",
    "                dataset.append(d)\n",
    "\n",
    "gans = list(statistic.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "unable-entrance",
   "metadata": {},
   "outputs": [],
   "source": [
    "scoreNames = [f1Score, f1ScoreSd, kScore, kScoreSd, aScore, aScoreSd, gScore, gScoreSd]\n",
    "\n",
    "for gan in gans:\n",
    "    sums = { name: { n: 0.0 for n in algs } for name in scoreNames }\n",
    "    c = 0\n",
    "\n",
    "    for ds in dataset:\n",
    "        if ds != \"Average\":\n",
    "            c += 1\n",
    "            for n in algs:\n",
    "                if n in statistic[gan][ds].keys():\n",
    "                    for scoreName in scoreNames:\n",
    "                        sums[scoreName][n] += statistic[gan][ds][n][scoreName]\n",
    "\n",
    "    avg = {}\n",
    "    for n in algs:\n",
    "        avg[n] = { scoreName: sums[scoreName][n] / c for scoreName in scoreNames }\n",
    "    statistic[gan][\"Average\"] = avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "practical-break",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.276, 0.244, 0.471, 0.88]\n",
      "[0.458, 0.409, 0.687, 0.857]\n",
      "[0.106, 0.035, 0.068, 0.603]\n",
      "[0.362, 0.32, 0.366, 0.922]\n",
      "[0.263, 0.21, 0.369, 0.813]\n",
      "[0.358, 0.305, 0.438, 0.868]\n",
      "[0.997, 0.996, 0.997, 1.0]\n",
      "[0.942, 0.941, 0.986, 0.987]\n",
      "[0.65, 0.632, 0.884, 0.969]\n",
      "[0.996, 0.996, 1.0, 1.0]\n",
      "[0.114, 0.057, 0.146, 0.663]\n",
      "[0.236, 0.188, 0.342, 0.826]\n",
      "[0.574, 0.555, 0.686, 0.974]\n",
      "[0.242, 0.21, 0.474, 0.873]\n",
      "[0.4695714285714285, 0.4355714285714285, 0.5652857142857143, 0.8739285714285715]\n"
     ]
    }
   ],
   "source": [
    "def checkValues():\n",
    "    for c in [statistic[\"Repeater\"][x]['LR'] for x in statistic[\"Repeater\"].keys()]:\n",
    "        print([ c[k] for k in [\"f1 score\", \"cohens kappa score\", \"average precision score\", \"G-mean score\"]])\n",
    "\n",
    "checkValues()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "public-collins",
   "metadata": {},
   "source": [
    "# Show Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "extra-taiwan",
   "metadata": {},
   "outputs": [],
   "source": [
    "def drawTransparentRect(img, rect, color, opacity=1.0):\n",
    "    def mix(v, a, b):\n",
    "        return max(0, min(255, int((v * a) + ((1.0 - v) * b))))\n",
    "\n",
    "    def mixPixel(v, a, b):\n",
    "        return (mix(v, a[0], b[0]), mix(v, a[1], b[1]), mix(v, a[2], b[2]))\n",
    "\n",
    "\n",
    "    ((x0, y0), (x1, y1)) = rect\n",
    "    \n",
    "    for y in range(y0, y1):\n",
    "        for x in range(x0, x1):\n",
    "            p = (x, y)\n",
    "            c = mixPixel(opacity, color, img.getpixel(p))\n",
    "            img.putpixel(p, c)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5fec8e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "def drawDiagram(size, rowNames, data, colNames=[], colors=None, border=20, barIndent=10, fontSize=20, markers=[0.25, 0.5, 0.75, 1.00], maxY=1.0):\n",
    "    silver = (204, 204, 204)\n",
    "    black = (0,0,0)\n",
    "    white = (255, 255, 255)\n",
    "    \n",
    "    defaultColors = [ (31,119,180)\n",
    "                    , (255,127,14)\n",
    "                    , (44,160,44)\n",
    "                    , (214,40,40)\n",
    "                    , (148,103,189)\n",
    "                    , (140,86,75)\n",
    "                    , (227,119,194)\n",
    "                    , (127,127,127)\n",
    "                    , (40,40,214)\n",
    "                    ]\n",
    "\n",
    "    defaultColors = [ (209,188,115) # Gold\n",
    "                    , (145,196,223) # bright blue\n",
    "                    , (83,119,202)  # blue\n",
    "                    , (224,138,86)  # orange\n",
    "                    , (131,202,112) # green\n",
    "                    , (199,102,99)  # red\n",
    "                    , (143,110,176) # violet\n",
    "                    , (134,99,66)   # brown\n",
    "                    , (207,131,189) # pink\n",
    "                    ]\n",
    "    \n",
    "    def y(v):\n",
    "        return v / maxY\n",
    "\n",
    "    def med(v):\n",
    "        if type(v) is tuple:\n",
    "            return v[0]\n",
    "        return v\n",
    "\n",
    "    \n",
    "    if colors is None:\n",
    "        colors = defaultColors\n",
    "\n",
    "    print((len(data[0]), len(colNames), len(data), len(rowNames)))\n",
    "\n",
    "    font = ImageFont.truetype(\"FreeSans\", fontSize)\n",
    "    \n",
    "    markerSize = 0\n",
    "    for m in markers:\n",
    "        markerSize = max(markerSize, font.getsize(f\"{m:0.2f}\")[0])\n",
    "\n",
    "    areaTop = 2 * border + markerSize\n",
    "\n",
    "    barStep = (size[0] - border - areaTop) // len(data)\n",
    "    barSize = max(border, barStep - border)\n",
    "    barIndent = min(barIndent, barSize / (1 + len(data[0])))\n",
    "    barIndent = barSize / (2 + len(data[0]))\n",
    "    \n",
    "    print((size[0], barSize, barSize * len(data)))\n",
    "    \n",
    "    # Create new Image\n",
    "    w = max(size[0], size[1])\n",
    "    img = Image.new(\"RGB\", (w,w))\n",
    "    d = ImageDraw.Draw(img)\n",
    "    \n",
    "    # Set background to white.\n",
    "    d.rectangle(((0,0), (w,w)), fill=white)\n",
    "    \n",
    "    # draw row names\n",
    "    height = size[1]\n",
    "    left = w - height\n",
    "    textSize = 0\n",
    "    for (n, name) in enumerate(rowNames):\n",
    "        s = font.getsize(name)\n",
    "        offset = int(border + barSize - s[1] + 1.5) // 2\n",
    "        textSize = max(textSize, s[0])\n",
    "        pos = (left + border, areaTop + offset + (barStep * n))\n",
    "        d.text(pos, name, fill=black, font=font)\n",
    "        \n",
    "        \n",
    "    # Calculate sizes for bar drawing.\n",
    "    barLength = height - (4 * border) - textSize\n",
    "    areaSize = (barLength, barSize)\n",
    "    areaLeft = left + (2 * border) + textSize\n",
    "    \n",
    "    # Draw Lines for bar height comparing.\n",
    "    markerPos = [areaLeft + int(y(v) * barLength) for v in markers]\n",
    "    for p in markerPos:\n",
    "        d.line(((p, border), (p, size[0] - border)), fill=silver)\n",
    "        \n",
    "    # Draw bars.\n",
    "    for (n, row) in enumerate(data):\n",
    "        area = ((areaLeft, areaTop + (n * barStep) + (border // 2)), areaSize)\n",
    "\n",
    "        indices = list(range(len(row)))\n",
    "        indices.sort(key= lambda i: 1.0 - med(row[i]))\n",
    "\n",
    "        for (n, i) in enumerate(indices):\n",
    "            v = y(med(row[i]))\n",
    "            c = colors[i]\n",
    "            offset = barIndent * n\n",
    "            tl = (area[0][0], area[0][1] + offset)\n",
    "            br = (tl[0] + int(v * area[1][0]), area[1][1] + tl[1] - offset)\n",
    "            rect = (tl, br)\n",
    "            d.rectangle(rect, fill=c, outline=black)\n",
    "\n",
    "    for (n, row) in enumerate(data):\n",
    "        area = ((areaLeft, areaTop + (n * barStep) + (border // 2)), areaSize)\n",
    "\n",
    "        indices = list(range(len(row)))\n",
    "        indices.sort(key= lambda i: 1.0 - med(row[i]))\n",
    "\n",
    "        for (n, i) in enumerate(indices):\n",
    "            v = y(med(row[i]))\n",
    "            c = colors[i]\n",
    "            offset = barIndent * n\n",
    "            tl = (area[0][0], area[0][1] + offset)\n",
    "            br = (tl[0] + int(v * area[1][0]), area[1][1] + tl[1] - offset)\n",
    "\n",
    "            if type(row[i]) is tuple and (row[i][0] != 0.0 or row[i][1] != row[i][0] or row[i][2] != row[i][0]):\n",
    "                vLower = y(row[i][1])\n",
    "                vUpper = y(row[i][2])\n",
    "                xCenter = (tl[1] + br[1]) // 2\n",
    "                yUpper = tl[0] + int(vUpper * area[1][0])\n",
    "                yLower = tl[0] + int(vLower * area[1][0])\n",
    "\n",
    "                #d.line(((br[0], tl[1]), (br[0], br[1])), fill=black, width=5)\n",
    "                d.line(((yLower, xCenter), (yUpper, xCenter)), fill=white, width=5)\n",
    "                d.line(((yLower, xCenter - border), (yLower, xCenter + border)), fill=white, width=5)\n",
    "                d.line(((yUpper, xCenter - border), (yUpper, xCenter + border)), fill=white, width=5)\n",
    "\n",
    "                #d.line(((br[0], tl[1]), (br[0], br[1])), fill=c, width=3)\n",
    "                d.line(((yLower, xCenter), (yUpper, xCenter)), fill=c, width=3)\n",
    "                d.line(((yLower, xCenter - border), (yLower, xCenter + border)), fill=c, width=3)\n",
    "                d.line(((yUpper, xCenter - border), (yUpper, xCenter + border)), fill=c, width=3)\n",
    "\n",
    "\n",
    "    # Draw axis.\n",
    "    d.line(((areaLeft, areaTop), (areaLeft, size[0] - border)), fill=black)\n",
    "    d.line(((areaLeft, areaTop), (w - border, areaTop)), fill=black)\n",
    "    \n",
    "    # Draw y-axis text.\n",
    "    img = img.rotate(90)\n",
    "    d = ImageDraw.Draw(img)\n",
    "\n",
    "    for (m, p) in zip(markers, markerPos):\n",
    "        d.text((border, size[1] - (p - left)), f\"{m:0.2f}\", fill=black, font=font)\n",
    "    \n",
    "    # Draw legend.\n",
    "    if len(colNames) > 0:\n",
    "        colNameWidth = 0\n",
    "        colNameHeight = fontSize * len(colNames)\n",
    "        for c in colNames:\n",
    "            colNameWidth = max(colNameWidth, font.getsize(c)[0])\n",
    "        \n",
    "        rWidth = int(fontSize * 0.75)\n",
    "        rHeight = fontSize // 2\n",
    "        rPadd = (fontSize - rHeight) // 2\n",
    "        \n",
    "        tl = (size[0] - (int(2.5 * border) + colNameWidth + rWidth), 0)\n",
    "        br = (size[0], int(1.2 * border) + colNameHeight)\n",
    "        drawTransparentRect(img, (tl, br), white, 0.75)\n",
    "\n",
    "        for (n, c) in enumerate(colNames):\n",
    "            t = border + (fontSize * n)\n",
    "            l = size[0] - border - colNameWidth\n",
    "            d.rectangle(((l - border - rWidth, t + rPadd - 1), (l - border, t + rPadd + rHeight)), fill=colors[n], outline=black)\n",
    "            d.text((l, t), c, fill=black, font=font)\n",
    "    \n",
    "    return img.crop((0, 0, size[0], size[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "038344f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnoseByAlgo(algo, score):\n",
    "    def valueOf(d, g):\n",
    "        d = cleanupName(d)\n",
    "        if d not in statistic[g].keys():\n",
    "            print(f\"Missing '{d}' in '{g}'\")\n",
    "            return 0.0\n",
    "        \n",
    "        if algo in statistic[g][d].keys():\n",
    "            return statistic[g][d][algo][score]\n",
    "        else:\n",
    "            print(f\"Missing '{algo}' in ('{g}', '{d}')\")\n",
    "            return 0.0\n",
    "    \n",
    "    print(f\"{algo}: {score}\")\n",
    "    \n",
    "    data = [[valueOf(d, g) for g in gans] for d in testSets]        \n",
    "    img = drawDiagram((1024, 1024), [cleanupName(d) for d in testSets], data, colNames=gans)\n",
    "    img.save(f\"data_result/statistics/byAlgorithm/statistic-{algo}-{score}.pdf\")\n",
    "\n",
    "def showDiagnoseByAlgoAverage(score):\n",
    "    def valueOf(d, g):\n",
    "        d = cleanupName(d)\n",
    "        if d not in statistic[g].keys():\n",
    "            print(f\"Missing '{d}' in '{g}'\")\n",
    "            return 0.0\n",
    "\n",
    "        s = 0.0\n",
    "        c = 0\n",
    "        for algo in algs:\n",
    "            if algo in statistic[g][d].keys():\n",
    "                s += statistic[g][d][algo][score]\n",
    "                c += 1\n",
    "\n",
    "        if c > 0:\n",
    "            return s / c\n",
    "        else:\n",
    "            return 0.0\n",
    "            \n",
    "    print(f\"showDiagnoseByAlgoAverage({score})\")\n",
    "    \n",
    "    data = [[valueOf(d, g) for g in gans] for d in testSets]        \n",
    "    img = drawDiagram((1024, 1024), [cleanupName(d) for d in testSets], data, colNames=gans)\n",
    "    img.save(f\"data_result/statistics/byAlgorithm/statistic-Average-{score}.pdf\")\n",
    "    plt.imshow(img)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8904b4b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnoseAverage(score, onlyOneBar=False, maxY=0.75):\n",
    "    def valueOf(g, algo):\n",
    "        if algo in statistic[g][\"Average\"].keys():\n",
    "            if True: #not g.startswith(\"ConvGeN\"):\n",
    "                return statistic[g][\"Average\"][algo][score]\n",
    "            else:\n",
    "                a = statistic[g][\"Average\"][algo][score]\n",
    "                sd = statistic[g][\"Average\"][algo][score + \" - SD\"]\n",
    "                return (a, a - sd, a + sd)\n",
    "        else:\n",
    "            return 0.0\n",
    "\n",
    "    print(f\"Average: {score}\")\n",
    "    \n",
    "    data = [[valueOf(g, algo) for g in gans] for algo in algs]\n",
    "    img = drawDiagram((1024, 1024), algs, data, colNames=gans, maxY=maxY)\n",
    "    img.save(f\"data_result/statistics/average/statistic-Algo-Average-{score}.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d1f4961a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnoseDataset(dataset):\n",
    "    print(f\"{dataset}\")\n",
    "    \n",
    "    def valueOf(algo, score, g):\n",
    "        if dataset in statistic[g]:\n",
    "            if algo in statistic[g][dataset]:\n",
    "                if score in statistic[g][dataset][algo]:\n",
    "                    return statistic[g][dataset][algo][score]\n",
    "                    \n",
    "        return 0.0\n",
    "    \n",
    "    scores = [f1Score, kScore]\n",
    "    \n",
    "    for score in scores:\n",
    "        data = [[valueOf(algo, score, g) for algo in algs] for g in gans]\n",
    "        img = drawDiagram((1024, 1024), gans, data, colNames=algs, maxY=0.75)\n",
    "        img.save(f\"data_result/statistics/byDataset/statistic-Classifier-{dataset}-{score}.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "magnetic-romance",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnoseDatasetAverage():\n",
    "    print(\"Average\")\n",
    "    \n",
    "    dataset = \"Average\"\n",
    "    \n",
    "    def valueOf(algo, score, g):\n",
    "        if dataset in statistic[g]:\n",
    "            if algo in statistic[g][dataset]:\n",
    "                if score in statistic[g][dataset][algo]:\n",
    "                    if True: # algo != \"DoC\":\n",
    "                        return statistic[g][dataset][algo][score]\n",
    "                    else:\n",
    "                        a = statistic[g][dataset][algo][score]\n",
    "                        sd = statistic[g][\"Average\"][algo][score + \" - SD\"]\n",
    "                        return (a, a - sd, a + sd)\n",
    "                    \n",
    "        return 0.0\n",
    "    \n",
    "    scores = [f1Score, kScore]\n",
    "    \n",
    "    for score in scores:\n",
    "        data = [[valueOf(algo, score, g) for algo in algs] for g in gans]\n",
    "        img = drawDiagram((1024, 1024), gans, data, colNames=algs, maxY=0.75)\n",
    "        img.save(f\"data_result/statistics/byDataset/statistic-Classifier-{dataset}-{score}.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "57fe8925",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: f1 score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "RF: f1 score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "GB: f1 score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "KNN: f1 score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "DoC: f1 score\n",
      "Missing 'DoC' in ('Repeater', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('ProWRAS', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('CTGAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('Repeater', 'abalone9-18')\n",
      "Missing 'DoC' in ('ProWRAS', 'abalone9-18')\n",
      "Missing 'DoC' in ('GAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('CTGAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('Repeater', 'car_good')\n",
      "Missing 'DoC' in ('ProWRAS', 'car_good')\n",
      "Missing 'DoC' in ('GAN', 'car_good')\n",
      "Missing 'DoC' in ('CTGAN', 'car_good')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'car_good')\n",
      "Missing 'DoC' in ('Repeater', 'car-vgood')\n",
      "Missing 'DoC' in ('ProWRAS', 'car-vgood')\n",
      "Missing 'DoC' in ('GAN', 'car-vgood')\n",
      "Missing 'DoC' in ('CTGAN', 'car-vgood')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'car-vgood')\n",
      "Missing 'DoC' in ('Repeater', 'flare-F')\n",
      "Missing 'DoC' in ('ProWRAS', 'flare-F')\n",
      "Missing 'DoC' in ('GAN', 'flare-F')\n",
      "Missing 'DoC' in ('CTGAN', 'flare-F')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'flare-F')\n",
      "Missing 'DoC' in ('Repeater', 'hypothyroid')\n",
      "Missing 'DoC' in ('ProWRAS', 'hypothyroid')\n",
      "Missing 'DoC' in ('GAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('CTGAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('Repeater', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('ProWRAS', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('CTGAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('Repeater', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('CTGAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('Repeater', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('CTGAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('Repeater', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('ProWRAS', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('CTGAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('Repeater', 'winequality-red-4')\n",
      "Missing 'DoC' in ('ProWRAS', 'winequality-red-4')\n",
      "Missing 'DoC' in ('GAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('CTGAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('Repeater', 'yeast4')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast4')\n",
      "Missing 'DoC' in ('GAN', 'yeast4')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast4')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast4')\n",
      "Missing 'DoC' in ('Repeater', 'yeast5')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast5')\n",
      "Missing 'DoC' in ('GAN', 'yeast5')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast5')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast5')\n",
      "Missing 'DoC' in ('Repeater', 'yeast6')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast6')\n",
      "Missing 'DoC' in ('GAN', 'yeast6')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast6')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast6')\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "showDiagnoseByAlgoAverage(f1 score)\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average: f1 score\n",
      "(6, 6, 5, 5)\n",
      "(1024, 165, 825)\n",
      "LR: cohens kappa score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "RF: cohens kappa score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "GB: cohens kappa score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "KNN: cohens kappa score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "DoC: cohens kappa score\n",
      "Missing 'DoC' in ('Repeater', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('ProWRAS', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('CTGAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('Repeater', 'abalone9-18')\n",
      "Missing 'DoC' in ('ProWRAS', 'abalone9-18')\n",
      "Missing 'DoC' in ('GAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('CTGAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('Repeater', 'car_good')\n",
      "Missing 'DoC' in ('ProWRAS', 'car_good')\n",
      "Missing 'DoC' in ('GAN', 'car_good')\n",
      "Missing 'DoC' in ('CTGAN', 'car_good')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'car_good')\n",
      "Missing 'DoC' in ('Repeater', 'car-vgood')\n",
      "Missing 'DoC' in ('ProWRAS', 'car-vgood')\n",
      "Missing 'DoC' in ('GAN', 'car-vgood')\n",
      "Missing 'DoC' in ('CTGAN', 'car-vgood')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'car-vgood')\n",
      "Missing 'DoC' in ('Repeater', 'flare-F')\n",
      "Missing 'DoC' in ('ProWRAS', 'flare-F')\n",
      "Missing 'DoC' in ('GAN', 'flare-F')\n",
      "Missing 'DoC' in ('CTGAN', 'flare-F')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'flare-F')\n",
      "Missing 'DoC' in ('Repeater', 'hypothyroid')\n",
      "Missing 'DoC' in ('ProWRAS', 'hypothyroid')\n",
      "Missing 'DoC' in ('GAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('CTGAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('Repeater', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('ProWRAS', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('CTGAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('Repeater', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('CTGAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('Repeater', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('CTGAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('Repeater', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('ProWRAS', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('CTGAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('Repeater', 'winequality-red-4')\n",
      "Missing 'DoC' in ('ProWRAS', 'winequality-red-4')\n",
      "Missing 'DoC' in ('GAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('CTGAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('Repeater', 'yeast4')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast4')\n",
      "Missing 'DoC' in ('GAN', 'yeast4')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast4')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast4')\n",
      "Missing 'DoC' in ('Repeater', 'yeast5')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast5')\n",
      "Missing 'DoC' in ('GAN', 'yeast5')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast5')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast5')\n",
      "Missing 'DoC' in ('Repeater', 'yeast6')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast6')\n",
      "Missing 'DoC' in ('GAN', 'yeast6')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast6')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast6')\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "showDiagnoseByAlgoAverage(cohens kappa score)\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n"
     ]
    },
    {
     "data": {
      "image/png": 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96cmny8vLZxzj9/tpaGiYIRjXU1dXd0OZ15tO2vTss8+i6/qCJt5SAqJYmSSu8lv3HSaY555RPBkx8Oi7lsipO48QYk4jKFNbUCw0SkAUKxJNSNaUeSkMzkyE7PNorJAJrHcFajWuQqGYM6oFoljRqC1LlxYlIIoVSzxh8vaVAUTgWhwkHjV5YZsSlcVCCYhixWJaNrGqAPkNJdkyMZ4EXQVBFgsVA1EoFHNGCYhCoZgzqguzzLAsi/Dk5G2Dg5qmUeBgBaZCcSeZs4AIIWqAHwJrABv4npTyL4QQJcB/A+qAK8BXpJTjGZvvAN8ELOCfSSlfn5f3dyH9PT28/X/8b+TfZjLDpD/Ac3/6ZwQCgUXyTKG4kfm0QEzgf5FSHhNCBIFPhRBvAr8DvCWl/DMhxLeBbwN/IoRoBp4HtgBrgYNCiI1SSmt+H+HuwrYsCk0DXcKt2iASoYYwFUvOnAVEStkP9Gf+DgshzgPVwDPAI5nDXgLeAf4kU/6ylDIJdAoh2oFdwJEc6swuIHKKZVmYprliNt62LIuJlMnZz/wGeVU1Nz3OGB/IfralwDTNWytcFollXvPTNJ0+L+SMz2dZluPzPic7yZJ+n4vN1Irf+bIgMRAhRB1wH/AxUJkRF6SU/UKIqZVD1cBH08x6MmWz/b8XgRchvTPXdC5fvszHH3/Mww8/jN/v5+233yYWi1FSUkIqleLAgQP4/deye69EAZFAXmU1hbUbbnpcVM/koViiC37Kz9shAdMyp93QpqMbWkowrenCYzrTK2YKQVrocheeu5mpPWkWgnkLiBAiH/gZ8C+klJO3ULXZ3pj1zEopvwd8D6ClpWXGMcePH6elpYWzZ8/y5JNPsnfvXtra2jh//jwVFRW43e4ZuSI9Hg8+n++OLCS6E3g8Hpw8GKbyRSxVXsxkIuHMT2b6aVmWoyefEAJv5txNoc22fN+yiXzQRd7UXk4xC0+dK2tnGAbCycNDXLtWFM6Zl4AIIdykxePHUsqfZ4oHhRBVmdZHFTCUKe8BprfJ1wF9udapaVr6aSQliUSC48ePs2/fPkpLSxkcHOTq1as0NzfP52MpVhC2abNrJElLQR4Ao5ZgZTwq7g7mMwojgO8D56WUfz7trVeBF4A/y/x+ZVr53woh/px0ELUJ+CTHOtm3bx/Hjh1jx44djIyMsHHjRoqLi2lvbwegoWFxc0HcKSQwNtCPaczepLZDI4vr0DJGcK11oga1F5f5tED2Ad8ATgshTmTK/jVp4fiJEOKbwFXgOQAp5VkhxE+Ac6RHcL41lxGYqqoqnnrqKYAZTeFdu3bdULaSkbbFicMXsFk76/v5WmqRPVIobmQ+ozDvc3PBPzBboZTyu8B351on3Fwg7hbhmI6m+9BdhUhp3/Cezq03WFIoFgM1E3WZE4seofKejhsClp6YDtx8ewOFYjFQArLM8eYl2bBbv2GfWKPn7mtxKVYeK2NyhEKhWJYoAVEoFHNGCYhCoZgzKgaiUCwDpJQcefNNIr09tz2u8YF9bFjAzaHmgxIQhWKZMNR6FO3CGSKWRcryMttaAcOy8FWsUQKicE50JIq3axK3a9oCqIhas3G3IYChgiSxdaX86vJjuPQ+vIGZ59k2TarcyycHjBKQFYCMGzxaVUZR8NrF1D2khnHvRtxujUCeF83jZ9sD91O5fuZqdNNIEfBNLpF3N6IEZAVxN862Vdya5X7OlYCscuLxOJ8eOgS3y4PhctHy6KP4puVaUSiUgKxyouEwAz//KUW2dcu8O8PA5l27lIAoZqAERIFh2fzXSC1GoArJjQv3AHSiPK4WyyuuQwmIAhtJqqABI9+mekP57MdEx1SyjUVHMjbYhWUlsiWmYWCtK15Cn2aiBESRpbA0SN3mBlKRsRveS4ylEEpBFhVpSwaGfkHJhni2zEpZSP3ZpXPqOpSAKGYQvfopLd6PCMeTM2IiExG4lmxfsVi4fDqBIm/2tZky0ZbR3r9KQBQzcGkgNIv3U5MEzwwT1NPLpRK21+E2DorVhBIQxQ1IJJ4CH0+UF7KhID3rsTumOcrCrlhdqNW4CoVizqgWiEKxzLGta7sxWqaNXEaPfSUgCsVyRkLq4GWakmkBMSwbz2eXT0Z+JSAKxTJnjQWPFOQDkDQsJpfRNq1KQO4Qo8PDfPr6r7ndDqRafj77P/80LtetT0UiaXLl8nB2JsZI1HvL4xWKxUAJyB0iNDKC8dab+HUXtxr/7A/kYT75udsKSGgsSmX3OKWBtHBoxuxTzhWKxUQJyB1kImVwpGYjeHyzbg4FIDD5gsP/l+dxUeBzA+AXC7O7ukIxH5SA3EEMwL19Lyc+7iAZjiGsMQxdwLQ9Xsr9anaWYuWiBOROI0ESpKawj91NHVyo8eHJu7Z/vBhQsQzFykUJyCLh0jUCfhfePDfe/GsCYvvUKVCsXFbU1SulJJlMkkwm8XjSN2EymUQIga7r2LaN2+2eYZNMJonH41iWtai+JpNJR2tHbNsmkUhkJwolk8lbJvaZQtqSRCKBNs8hvUQyma37lvXJdH3xeHpl6HSfb2nHHO2kJJE5d5D+XmzbWXcvmUxl7UzTxLYdBJwlpKbVtxQ4vUZTqdS8/BRC4PV6FyRd4ooSEIBjx45x7tw59uzZQ01NDS+99BKf+cxn6O/vZ2Jigi9+8YsUFRVlj3e73Xg8nqzgLBZut9tR/gwhxAz/3G63ozUnaTv3vD+Xx+12diHN5qeDDygQuN3X7AzDdFSfENfOHaQF5fr9gW+G2+3K2gkh0BzeKC73/L/P+aDpzh4GLpdrSf2czooTkK6uLh5//HFOnz5NZWUlfr+fS5cuIYRgy5YtDA4OUlpamj1e0zR0XUfXF3fUwmnLQAiR9TEXOwRo2vw/l2M/YYafuq47TjCk69PtnLaYBPp134vT5+V0P23bnnV/lVmqm2G32EgpHedbWUo/r2f5TGlzSEFBAefOncPr9TI0NERTUxOGYWBZFj09PRQUFCy1iwrFqmFFtUCEEDz66KN0dXVRW1uLYRi43W62bNmCrutMTEywdu3apXZToVg1zFtAhBA60Ar0Sik/L4QoAf4bUAdcAb4ipRzPHPsd4JuABfwzKeXrudbn9/vZNG1bv8LCwuzf07suCoXizrMQXZh/Dpyf9vrbwFtSyibgrcxrhBDNwPPAFuBJ4K8y4qNQKFYo82qBCCHWAU8B3wX+Vab4Ga4lz3wJeAf4k0z5y1LKJNAphGgHdgFH5uODQrHaudx5hbHJyK0PkpJ1ayqoWlO5oHXPtwvzH4A/BoLTyiqllP0AUsp+IURFprwa+GjacT2ZshsQQrwIvAhQW1s72yEKhSLD2ycuMJa37pbH2JbFvaH25SMgQojPA0NSyk+FEI84MZmlbNaZQVLK7wHfA2hpaVGLRRSKW6DpLryBvFvOr7FMAyEWfpLcfFog+4AvCCF+E/ABBUKIvwEGhRBVmdZHFTCUOb4HqJlmvw7om0f9CoViiZlzEFVK+R0p5TopZR3p4OghKeXXgVeBFzKHvQC8kvn7VeB5IYRXCFEPNAGfzNlzhUKx5NyJeSB/BvxECPFN4CrwHICU8qwQ4ifAOcAEviWlXNwFKgrFXcrlU6cZ7QtjmYlZ35e2xboH1y94vQsiIFLKd0iPtiClHAUO3OS475IesVEoFAvIxEicaL/NoxPv4HPdODvCkjYFO0sWvN4VNRNVoVDcHB2TzUGNoOfG29q0bew7sCWmEhCF4i7DniVdwmxlC4ESkNswHgpxuu0y4jarVv0eF/dv2zLv/BwKxXyIGQbtwQmCxb4Z5ZZts9a98Im4lYDchu6+QQ4PGPjyC295nD3UyfbmTUpAFEuKLSXVzcWQrxNJmchMEiZh2eBRXZhFRwiwUikmPn0P1y2+f91OAI8vml8Kxc2QwNFwmERPiOZYutVh2RJZaSx4XUpAHJAMjbLrzGHKvDfPAtVfcOsWikKxmOhunWq/hwNFASC9JeaIe+FvdyUgDhCArmm4btE9cZo2T6G4m1AddoVCMWeUgCgUijmjBEShUMwZFQNxiGVLzidG0X2za24U96zlCsXdjBIQh1jSxr8tQCggGdHs7Pj6FJqhGnOK1YcSkBzQhGDcJZkMull/bhT3tD1OEi61nYRi9aEEZA64khYPerykrGtDtz3SWX5oy7SJj8ayr0XEd4ujFYrljRKQOXJ+xObl2DNonjwAKvIn+bwDu3jvBA+dC1HoTX/1A2Lhl1grlg4pJV3dPURisVvujSylpLa6asa2JCsRJSBzxEaH4Ho0b7rrIjw9juyEhPV5PkoDmT1fjeWxx6li4XjlgxNc+fgj1gxfvekxKcvi0f/x99n32c8uomcLz6oRkEQiwfjo6G2P83g8lJSVLcjO5YrVie7xskaH/XkeTHv2FbAJE7y3aqKsEFaNgFw8eZKz//H/wZKSW+0Knaqu5oXv/umy2bxYsXLpiIcQzR5cs2wonjQsCu6Ckf9VIyDYNgnT4tLep/EGbz5i4k2ML6JTirsZ4RGs2VDMwRNepOaf8Z5hGNTZK//2W/mfIAekphGs30ig6BZ76A5cWjyHFHc9sYTN+fBD6IGZe6hZZgKDvCXyauFYVQICYKaSjB/9Wwp9N8mNYPm4SU5ohWLOSGnDtLSC0ja5Q1kGF5UVJSBSSkKhEN3d3dTW1qLrOt3d3fj9fizLIhKJUFdXh9t9rXMZj8eJRqMkEgmQ6S3+NhYOsXX97B3QjgGbaDSajYHE4wmkg2CXbVtEo9Fs3fF4/JbDeNfsbGKxGJZlZe2cXFjSlsSi0dsfeBti8RjSQYVSSmKxGL5MndFo9IbZuLeyi063c1pf5twBJJNJ7JsEJK8nHk9k7QzDwLYc7B4iITGtvrkipcyey2tlFqb8GSXrktkyM2ViWM9k65NSYlqmozqSyeQMP03ToV0qbSeEwO/3L8hAwYoSEIAPP/wQ0zQJh8Pcc889tLW10d/fj2mabNiwgc2bN+PxXBsa9fv95OXl4fP5ZsROU4aVviCvu5alhLy8vGxqQr/fh7hF0HUKTdPJy8vD5XJl63VghqZpBAIBvF5v1s7JeRWaIJCXRyCQThgTnpzk49f+DvdtttoxXT4efOaf4vWmJ7DF/QFHF5IQgkAgkK3PNAyElrvdVJkjO78/a6fruuN0kX6/75qfponmJCAuwDetvvlwQwBeSkrWJWl84Fp5Km6Tn+ebUZ9Ld3Y7er3emXYuh3Ye74J8vumsOAGZnJzkgQce4PTp0xQUFBAIBGhsbMTtdtPf3093dzeNjY23/T8/fcfm/KBOfsUptGnp7isDG/gCzi7y2Vgqu1g0TN7FQ6y9zfVxJeEhmfwSPp//1gfepj5lNzu3al0lJhNIM92KMhIm0m9n63PSKpvOUn2+61lxAlJXV8fhw4fZuHEjH3zwAa2trdx3331EIhEMwyAYDDr6P3FZjF1YTtMXDFzT9tFw96zcNS1C3P4CUbNblgYpJcFDnbRo6S5uwrDw/+bK35hxRQmIEILdu3ezfft2fD4fUkr27duXvWlM00x3VRSKZUixy0VjfrqJGEuZJB10AZc7K0pA4FrMYIrp/c3pwVOFQnHnWXECstCkIikSocls4DIvsnJFyLQkV0IxonEDw7BnHT0alQF23AXDh4rlgRKQrhBfvBrDn0l53+1duasjoymTiaognZ+M0jMep2uz64aYiNey+dLKbzkrlgmrXkA0oNjvIS8TSB3VVu4aGAnk5XmpKgziKwzifaiUS+9VounXRlw0kqhQqmKhWPUCcjdjJAXS2IvLVZ4t0+lbQo8UdxsqkadCoZgzqgVy1yOxzGR2HYZtL/z+qIrVixKQu5xUfJzyyhD5RUUgQS7A+hmFYop5dWGEEEVCiJ8KIS4IIc4LIfYKIUqEEG8KIS5lfhdPO/47Qoh2IUSbEOKJ+buvuD2SmqZ6GrdvoXHHFtY2rFchVMWCMd8YyF8Av5ZSbgK2A+eBbwNvSSmbgLcyrxFCNAPPA1uAJ4G/EkKs3CEPhUIxdwERQhQADwHfB5BSpqSUIeAZ4KXMYS8Bz2b+fgZ4WUqZlFJ2Au3ArrnWr1Aolp75tEAagGHgB0KI40KI/yKEyAMqpZT9AJnfFZnjq4HuafY9mbIbEEK8KIRoFUK0Dg8Pz8NFhUJxJ5mPgLiA+4H/JKW8D4iS6a7chNm63rNOqpZSfk9K2SKlbCkvL5/tEIVCsQyYj4D0AD1Syo8zr39KWlAGhRBVAJnfQ9OOr5lmvw7UrCaFYiUzZwGRUg4A3UKIezJFB4BzwKvAC5myF4BXMn+/CjwvhPAKIeqBJuCTudavUCiWnvnOA/kj4MdCCA/QAfwuaVH6iRDim8BV4DkAKeVZIcRPSIuMCXxLytvk31MoFMuaeQmIlPIE0DLLW7OmNZdSfhf47nzqVCgUywe1FkahUMwZNZVdoZgFKWX253ZomrZq91JWAqJQzIKUkl9+/8/xTvbe8riUJdn4+W/S1LxtkTxbXigBUShmQUqJd6KHseE2Jnw3X3GRSFmsiUwuomfLCyUgCsVNkMClMi9jZfkUnI3O2k1JGRYONui7a1EColDcAiEE3nGTxyaKcc2yM17EMPDK1TsWoQREoXBAwjQZtuJYtj0jsBo3LUoc7tl7N6IERKFwwISZxLs/jw8H16IV1mW3zDASCbZ7Vu9mZkpAFAqH+H0u8tfew7mLJoaR3tzMSrkxLdWFUSgUTpASIxVA96Y3cJdEEKzevFirVzoVCsW8UQKiUCjmzF3fhbEsi76+PkbGRglbFonJMSajKVLJBDIVJRmxGYnbRM30wuBxLUVfXx9aZshuZHiYRGSCCdPCjgnimk0yCeGUjdTHsWV6mwTDnqCvrw+XK/2VDg0PE7ZsEuFxZGoSw44zGTZJSYvRhI1Fpj7TpL+/H4/Hk7YbGkr7GQ5hpyaxrRjJCYvJqMVoQoKWsUtZ9Pf34/end50bGRoilLSxIiZhQxJFYkwYYEySmBwj6kv7FZ8cp39ggEgkkv4/Y2OETQtbi2AmLOLhBBNJk5RtMZ6QDLnS9Y0lJAMDgySSKQDC4TCThoXUIRaXJMIWIWGQTE4iDbATmd3wjBCDg4OQWXgdj8dJhENETYOUlcJKRQhNGqQsi4mEZMidPm40bqMPDSHcXgBSqRQTKYNELIplurGNFKGwQdKymEiQtRtPSMzhETx56RmkpmkymUyRiMfQIgYh3cRKSGwzihkxiI+mR1CSEZMRzwi9wbSdbduEYini2CQ0i0kLUmGThBHGTqUQchQAacYYHZP09qbtpJREJ0LEk0kmTRs7bCCNScyoQSQhGcqcv7ghSY2NZ+0AQvE44ZSNGTWQxgSJsIFhJYkmYChzvaRMm8h4aIZdZCKEmZjENhNMWBZaxCThsojFJUNW2s60JWMTE/T29qLrOhUVFdlrfD4IJ3P9l5KWlhbZ2to6Z/twOMzExAS2ZWGZJlJ3oUkTSwqkFGiahUuSzZcmEeiZixbSF5Jp2Wi2CZrAzkTfNRtM3Fk7gcTrcc2ws1MpbJcb25IIYaNrElvKmfVJgeb2ZCcp2baNbaSwdTe2LQEbTZNoEoSU2U3Ar7eTUmIbSaQQYKfHCGxdIC0dTRczjnPr2ozXViqFSbpMCNCEhYXEZTOtPtDc3hvsQCIFSE2gAbbUsW1mGHo9+gw7w7IRlomUAlsI3LqNiUS3QZten8uDyFzkU/XZmoaUAinBraf91KbZIQGXZ8bNYaaS2GggQMNGSIkhdYRmI6bdQy7hnmFnGUmsaedb6ALrhs8HbpdA16/ZGaYFtoWQNugC09IRmoUmJfp0P3U3mn4tfmKmUiBtpCaw7PR5k1hotj3DTuoudN01oz7blkgJLq5dp0Iy005zobtc6LpOZWVl9rO2tLTQ2to6p8U8d30LJBgMEgwGl9oNheKuRMVAFArFnFEColAo5sxd34VRLA7hcJhjx46h6zq7du3KBoUVdzeqBaJYENrb2zEMg6qqqgWJ7itWBupMKxaEqqoqurq6OHLkCLFYbKndUSwSqgujWBDGxsYYGBhA13Xi8TgFBQVL7ZJiEVAtEMWC4PF42LRpU3Zim2J1oAREMW8sy+LUqVMkk0mKi4tVDGQVoc60Yt5omsZ9992Hz+fD6/Wi66t3depqQ8VAFPNGCMHIyAh9fX14PB6SyeRSu6RYJFQLRLEgNDQ0sG3bNmKxGPn5+UvtjmKRUAKiWBB6enro6OhASkk4HF5qdxSLhBIQxYJQWVmJ1+tl69atWJaFvYoTDa8mVAxEsSBUVlbyta99DYBjx46xZs0aNRqzCliVAjJbDpTVurfpnWAxvsvp51Cdu6VjVQpIMpnktddeIxKJUFpayhNPPIHb7V5qt1Y0g4ODXLx4kfb2dp588sk7PpRrGAZvvPEGhYWF7NixQ+V8WSJWZRtzfHycRCLB7t27mZiYIBqNLrVLK57BwUE++uijrHAsRvelu7ubt956i5GRkTtel2J2VqWAVFZW0tLSwvDwMHv27KGwsHCpXVrxlJWVUV1dzc6dO6msrLzj9WmaRkVFRTrHaiJxx+tTzM68BEQI8S+FEGeFEGeEEH8nhPAJIUqEEG8KIS5lfhdPO/47Qoh2IUSbEOKJ+bs/N2KxGK+99hodHR2cOnUK0zSXypW7hqKiInw+Hz//+c9JJBKzxpkWEjuzxeTevXvveF2KmzPnGIgQohr4Z0CzlDIuhPgJ8DzQDLwlpfwzIcS3gW8DfyKEaM68vwVYCxwUQmyUMpOuexHJy8vji1/8IjU1NQghmJycpKioSAXj5oFhGNx77708/fTTXL16lfr6ekdxkGQyyeuvv45pmuzbt89x6yUWizE0NISmaRQXF9/eQHFHmG8XxgX4hRAuIAD0Ac8AL2Xefwl4NvP3M8DLUsqklLITaAd2zbP+OSGEoL6+HpfLhaZptLe3q6fYPCkqKqKpqQmPx5POgu9wHkgsFmNycpLNmzeTl5fnuD6/38/WrVuprq6mpqZmrm4r5smcBURK2Qv8e+Aq0A9MSCnfACqllP2ZY/qBioxJNdA97V/0ZMpuQAjxohCiVQjROjw8PFcXb+e/Eo0FwrZtBgcH6e3tpb+/H5/P57g1NyUara2tOQWzLcvixIkT2LatUggsIfPpwhSTblXUAyHg74UQX7+VySxls97BUsrvAd+D9L4wc/XxZiQSCf7xH/+R9vZ27rvvPnbu3Km6L/PAsiwOHjxINBolmUzy3HPPOR7GjUQijI+PU1hYSEdHh6MujG3bvPfee4yNjZFIJKirq6Oqqmq+H0MxB+bThXkM6JRSDkspDeDnwAPAoBCiCiDzeyhzfA8wva25jnSXZ9EZGxvDtm0qKioYHR1V4jFP3G43LS0tQLpr4fF4HH+nU8cPDw9TXT1rg/QGhBDs2rWLhoYGHn30UUZGRlRrcomYj4BcBfYIIQIifbUcAM4DrwIvZI55AXgl8/erwPNCCK8Qoh5oAj6ZR/1zprKykoaGBvbs2cP+/fsZHx9fCjfuKoqKiojH49i2TSAQcGyn6zpVVVWUl5c7thNCUFhYyL333ksoFGLDhg1MTk7O1XXFPJhzF0ZK+bEQ4qfAMcAEjpPuduQDPxFCfJO0yDyXOf5sZqTmXOb4by3FCAykL9qpJ6aUkqNHj1JXV6daIvMgkUjQ19dHc3NzTt+jlJJUKoXX683JTtM0duzYAUBXVxexWEzN51kC5jWVXUr5b4F/e11xknRrZLbjvwt8dz51LgRTF61t29i2jdfrvb2R4pYEAgE2bNhALBbLaSWuZVkMDw9TXFzM5OQkpaWljuyklFiWRTKZRAihsqAtEatyLcz4+Dgvv/xy9qn13HPPqdbHPIlEIlRVVdHT00MqlXI8MpJMJjFNk8HBQQYHB6mvr3dkF4vF+NWvfkVvby8tLS3s3bt3Pu4r5siqnMo+NYRr27YSjgUiGo1y4cIFgsEg8XjckY1lWZw5cwa3200ymaS2ttZxfZqmMTAwwPr16/H7/Sp1wBKxKlsgJSUl/O7v/i62bWNZFl1dXWzdulWJyTwoLy9n+/btdHR0OE5pqGkaW7ZsoaOjg6Kiopzqc7lcrFu3DoC6urocvVUsFKtStoUQBAIB8vPzKSgoUIuxFoD+/n5OnjyJz+djYmLCkY0QAiklkUiEaDTK5cuXcxqOdbvdDA8PEw6HsawlicevelZlCyQcDtPb28vGjRsRQlBWVqZaH/Okrq6O2tpakskkPp/PsV1xcTG7du1ieHiYmpoaotGooxaMrus8/PDD5OXl0dvby+joKBUVFbe1Uywsq1JABgcHOXz4MBcuXKC+vp6tW7cutUsrnkuXLjEyMoLb7SaVSjm20zSNnTt3AnD58mXi8bgjAdE0LXucZVl3tAUipcQwDEzTZGxsjLVr16qYS4ZVKSD5+fns37+f9evX09vbm12Nq5g7DQ0NtLW10dfXl1N2sKlgdiqVQtM0x8OxUkrOnTuHbdsUFxfntBAvV6SU/OhHP2JkZITR0VH++I//mLKysjtW30piVQpIZWUllZWVCCFobGyktbWVz3zmM6obMw+6u7spLi5G13XC4TB5eXmOvs9wOMybb75Jd3c3Dz74IOvXr3dU3+TkJO+88w4FBQVUV1fz2c9+dr4f4aYIIfit3/ot3n33XeLxuOO5KquBVSkgUxe2Wj+xcOTl5XHp0iWi0SimafKVr3zF0QQ9IQTd3d3U1tbi8Xgcdw3y8vIoKSmho6ODffv23VHxF0Lg8/nYsmULiURCPWimsSoFBKC3t5fu7m66urp45JFH1EUxTxobG/nDP/xDAE6cOOFYCDweD+vWrcPj8ThufUB6Alo0GmXr1q2OJ61Nf2DYto2maY7Ou5SSTz/9lI6ODoQQlJSUUFJS4tjXu5lVKyAdHR0cPXqU4uJi3G63EpB5Mn06eS7fpRACr9dLT08P4XCYYDDoSHyEECSTSSzLyqkl+fHHH5NIJLh48SL79+9n8+bNjuwCgQCGYVBVVcXly5eVgGRYtaHkqqoq1q1bx759+1QAdQGYnJzk/Pnz/PrXv6a8vNxxMHRqOPbFF1/EsixCoZAjO7fbTWlpKQMDAzlt5n3q1CmOHTvGww8/TFdXlyPxEULQ0NBAMpnk0qVLauLaNFatgFRUVGBZFj/72c8wTVPFQ+bJpUuXeP311+np6UHTNMddGF3XKSgoQNM0TNN0PBwbj8cxTZOWlhY6Ojoc+/n888/zxS9+kaKiIrZs2cLo6Kgju2g0SigUwuv1MjY25ri+u51V24WJxWLce++9PPvss/T09OS0nF/tinYj9fX1DA4OEgqFchqlsG2bCxcuZOd1OI1nBINBtm7dSnd3N48++qgjGyEEBQUFFBQUAOk4mNN1O1PxkoGBAXXOp7FqBWTNmjWsWbMGKSUjIyOOmqVSSjo7O+ns7GRiYoLdu3c7zqJ1tzP1vQghGB0dza5TuR3j4+McOnSIwsJCGhoacspKVlNTg6ZpOSUwmkrlEIvFkFLi8Xgc2zU1NXHPPffkFOy921mVXRjDMIhEItmWRC5Trz/88ENOnz5NUVERx44du1MuLgm2bRMKhRgbGyMUCuWU16OhoYFAIEBTU1NOk6yCwSD5+fn09fWxfv16x0/3ZDLJr371K1pbW7lw4YLj+hKJBD/72c/40Y9+xIULFygvL3dkNyVSFy9eJBaLOa7vbmdVtkC6u7t59dVX2b17N5s3b84pi9YDDzxAZ2cn4+Pj7N69+w57urikUin+/u//HsMwSCQS/N7v/Z7jAPPVq1cJhULouk4oFGLNmjWO7JLJJJOTk1RUVBAKhRy3XFKpFKZpsm7dupy2ttR1nUgkQmNjI6WlpY5jNV6vl+3bt5NKpVTQfRqrsgWiaRp79+6lsrKSo0ePIqV0LCDDw8MMDAwA3HU72nm9Xnbv3k1hYWHO6QHLyspIJBJMTk7OaVr54OBgTt+ny+Vi7dq1VFVVsWuX8+2FNE1j/fr1jI6OOm59QDpmdvToUcbGxohGoyronmFVtkDWrl2L3+/H5XLR0tKCy+X8a6itraWwsJC1a9fm1PWZC6ZpMjExgZQSXdfv+O55yWQS27YZGxujqKgop9iC2+1m/fr1jIyM5NT1AdiwYQNNTU05xRZ0Xae7u5ve3l6Kiopy2lzKMAxcLhe2bWMYBm63+7Y2LpcLr9fLm2++ycmTJ3n22WfVehhWaQvENE3++q//mvfff59jx445nowkhMjOH7l69WpOAbip/Ku5bGiVSCT4m7/5G37xi1/w0ksv3fG8JV6vN7vHrW3bObUIPB4P1dXVFBYW5hQj8Hg8jI6O8u6775LLJmKmaWa/j6GhodscfQ1d13n88cd5/vnncbvdjmynFu6NjIzwwAMP8OCDD6oEzhlWZQvE7/fzR3/0R9khwytXrjgehQmFQhw9ehTDMNiwYYOjYcfx8XF++MMf4na7cbvd/N7v/Z6jVk9+fj67du2iq6sr56f6XBBCMDQ0RG1tLcPDw8RiMcetkImJCT755BM0TePKlStUVlbeNr4gpSQWi2FZFhs3bsypxSOEQNM0+vv7c07HMCX8U6LuhFQqRSKRoLu7m+bmZketltXAqmyBTGUkm+oODA8PO24VTG0E3dfXx+HDhx3ZFBcX09jYyM6dO/F4PDk92desWUN/fz/BYHBRLtqysjK8Xi979uzJaT7HyMgIO3fupK6ujhMnTjhqhdi2zQcffMDg4CB9fX2O52RMEQwGefrppx2nD5BS0tbWxq9//WvGx8ezXTUndlNdyUgkorKfTWNVCghce/qZppnTtg4bNmygq6uLxsZGx1OahRDs2LGDN954g8rKypzqu3TpEg899BDJZDLnG2wuGIbB+fPnOXv2LIZhOLa75557GB4exu/389BDDzlqTWiaRmlpaXbpfy6jKYZh4PV6ef/99xkcHHRsd/ToUUpLS/nlL39Jb2+vI/ERQnD//fdTUFCQbYUq0qzKLgyk10QcPHiQdevW8bnPfc5RcFIIwfbt2zlx4gSdnZ05NbmPHz/OM888w/Hjx0kkEo5nXG7fvp3vf//7bNmy5Y4mzZmisLCQYDCIZVmMj49TUVHh6LvJy8ujoaGBuro6SkpKHAd7t2/fnt0PN5cgan5+PgcOHGB0dNTRfrpT7N69GyEE69atc7wgbmrhXiQSwe12L4qQrxRWbQvEsiwSiQQjIyMIIXIe3RgcHMwpqLlp0ybeeOMNCgoKcmqBnDp1iscee4zJyclFmcA0FeTVNI0jR444boX09fVx7NgxXn31VaLRqCObqTwbwWAQwzByvjGllJim6fjcCSHYuHEjFRUVBINBtm3b5riukpISampqSKVSOQnWlJ9zGfadssvVdq52c2HVCkhpaSlFRUWUl5fnNByr6zqlpaUEg8GcprH39fVRV1dHKBTKKQZSW1vLRx99hJQSl8vl6KKQUnLhwgVCoZDjm3mK8fFxTNNE0zSefPJJx3GXoaEhysvLsW2b06dPO4oTSClJJpO8/vrr2WxfTpjKUfrGG2/w7rvv5vQZLcvigw8+4OrVq/T09Di203WdtWvXUl1d7XgBnpSSs2fPcuTIEX784x87zlY/Nepz5MgRXnrpJXp7ex3bnTx5kiNHjvCDH/yAzs5OR3bzYdUKSHd3N1u2bOH+++/PaVtEwzCYnJzk0UcfzWkegK7rGIaRc0unr68Pn8/HlStXeOWVVxw/VU6dOsXZs2e5cuWK46BfKpXi+PHj+P1+Kisrcblcjv2trKykv7+fZDKJ1+t15Kdt2xw+fJhQKJRNWuwEKSXvvfceoVCIZDKZU6wmkUhw9epVxsbGOHPmjGM7wzA4c+YMAwMDuN1ux6M34+PjfPLJJwwMDOQ03+jjjz/m2LFj7N69m7a2Nsd2Fy9e5MSJE9TV1XHx4kXHdnNl1QpIfX09J06c4NNPP81piNTtdlNYWMgbb7zB+Pi4YzshBJ2dnfT29mZnvzrB5/NRWlpKVVUVuq47tsvPz+fYsWO8++67HD9+3LGffr8fr9dLb29vTk/2wsJCHnroIYqLi1m7dq2jm0XTNH7jN36D5557jgMHDjie/i6E4MCBA3zlK1/h0UcfdTz9XUpJT08PXq+X7u5uNm7c6MgO0ukDent7SSQSvPfee4TDYUd227ZtY+/evezYsSOnLui+ffuy21w43bFPCMEDDzyAz+djfHycpqYmx/XNlVUrIB0dHezYsQPDMBxfDJB+Sk9MTLBnz56cFnFNTEzw+OOPs2bNGq5cueJYCEpKSpiYmGDfvn184QtfcLx243Of+xx/8Ad/wP33389nPvMZRza6rlNWVsbk5CRer9dxoBfSe+O2trYSi8Uc70wnhMCyLEZHR2lvb3fcNRBCYNs2Q0NDXL16NaeJZIlEApfLxc6dOx13KaaoqKigqqqKvXv3Op5IdunSJU6ePMnQ0FBOgpxIJOjv7+fSpUuObaSUTE5OkkgkSKVSOW2vMVdWpYBYlkVpaSlnzpzBMAzHoylTM0pdLhetra05xUCmhmL37dvHhg0bHHcNiouL0TSN9957z3EOz6lj+vr6mJycZHR01JFg6bqO3++noqKC8fHxnIR1eHiYe+65h4qKCiKRiGO7iYkJDh48SF9fH+fOnXNsF4lEOHjwIL29vZw+fdqRjRCCvLw8TNPE7/fnJJDBYDC7/UQuKQcaGxspKChgYGAgJ4FsaGigqKiIwcFBxzN0hRDU1dVRWlrK8PBwTsPbc2VVDuNOTExw8eJFCgoKsmsinHLq1ClcLhf5+fk52eXn57Nv3z5s26a1tdWxncvl4t5772Xbtm05xWps2+att97C5XJx6NAhnnvuOUd25eXlfPDBBzndYFJK8vLyeP/993POzxEIBCgoKODee++loaHBsZ3P56OwsJAtW7bklGIwHo8zMjKC1+vNKa/p6Ogo8XgcIUROOVHdbjehUIgNGzY4XvoA6fMeDoepr6/PadRO13Wi0Sjr16+/42u1YJW2QEpKSqiurs4uxnLa1JuaUDQ2NkZfXx/9/f2O64xGo1nByeXJ5/P5shdeLgFYTdOoqalhZGSExsZGx3axWIzCwkKEEI6DmqlUirfffpuJiYmcR5k8Hg9NTU38wz/8g+MnNKRvzE2bNvHqq6/m1IXZtGkTW7ZsIRaL5dTELy8vp6CggPHx8ZyEDtLn7cKFCzmvZZqyy3V4W9M02trach6BmwursgUC6YVjDQ0NDA0N5ZxFfGoeQC5Phg8//JBwOExNTQ333XefY7vW1lYGBgaIRqO0tLTktHNbb28vzc3NOe0UNxXomxo9eOSRR24bEPV6vezcuTN70eYyKjIVg3r++edzmkhmmiahUIgvf/nLOQmkpmmUlZVl4wVOGR0dRQjB/v37czrvyWSSvLw8pJQ5TYE3DAOfz0dRUVFOLV3TNHG73RQXF6t5IHcSt9tNOBzG5/PltMZECEE0GkXX9ZxuzIaGBkKhEKdPn8a2bceitX79eqLRKKdPn87piSmEoKmpid7eXs6fP+/YrqysjEAgwFNPPcW2bdscBW2TySRtbW2cOnWKwcFBx0FUSIvPjh07EELkNMzpdru5//77kVLmdP6mRmIGBgYctySklHi9XkZGRjhz5kxOoykFBQXk5eURDAZz6moFAgEKCwvJy8ujvr7esZ3P56OkpAS/38+GDRsc282V214dQoj/KoQYEkKcmVZWIoR4UwhxKfO7eNp73xFCtAsh2oQQT0wr/4wQ4nTmvb8US5iZNhqN0tramm1JOL0xpZRcvnyZoaEh6uvrc1p+PhWw1TQtpyfKuXPnsvvG5vIEs22btra2bODPKaWlpZimyY9//GPef/99R756PB7Wrl3LgQMHaGpqymmtSCQS4fTp0xQXF+fkZzwe59ixY5SXlzve1mEq/+34+Dj79+93PI9HSplNpuzxeHKKRY2Pj9Pf349pmrS3tzu2C4fDdHd3Y1lWTvM5YrEYV65cyZ7/O42TFshfA09eV/Zt4C0pZRPwVuY1Qohm4HlgS8bmr4QQU9/2fwJeBJoyP9f/z0VD0zQ2bdpEKBTC5XLldEEEAgEaGxvp7u7OKbVdc3Nz9omQyxNz8+bNNDY2Zp+CTpi6USoqKrLbJjhlbGwMy7J46qmnHG8ZOTVFfHh42PEK1ylcLhfRaJS33nqLrq4ux3a6rhOPx3nnnXdyujF7e3vxeDy8++672cxyt0PTNOrr6ykpKeHUqVM5xSQKCgooLCzE7XbnNHU+Ly+P0tJSdF3Pqcvr8/moqKhASul4+H4+3FZApJSHges3wngGeCnz90vAs9PKX5ZSJqWUnUA7sEsIUQUUSCmPyHTH7IfTbBYdv99PJBLJBvycBv2EEJSVlWWXnucyzNnW1saGDRtwuVw5PWkvXbpETU0NgUAgpyDcxYsXGRsbo6qqitraWsdbOF65coWamhrWrFnjeIMoKSWJRIL29naKi4tzSrRkmiZDQ0NMTEw4TjE4ZTc2NsbIyIjjtSlCCDZv3pxNRpTLfjKWZTE4OJjT54O0+FRUVNDZ2clHH33kuLU0ZdfV1cWHH37oWLSEEFRUVNDb28uHH354xwOpcw2iVkop+wGklP1CiIpMeTXw0bTjejJlRubv68tnRQjxIunWiuNZeLlyzz334PF4uHr1ak7DXVMBw56eHqqqqhzbbd26lQ8++CDn5fzNzc288847lJSU5DQ8WllZSXt7Oy6Xy/Hks6nFhbFYjEQiQW1trSMBSaVSnDp1KtvisSzLUSvLtm2OHj2aDTLm0hVpbW3F6/Wi63pOO9N5vV7Ky8tpamrKKScqpFM5lJWV5RTjsSyLZDLJE088kR3SdSJ4lmURj8d5/PHH8fv9jI+POxq9s22baDTKZz/7WQoKChgdHb2jq7iFw1R+dcA/Sim3Zl6HpJRF094fl1IWCyH+I3BESvk3mfLvA78ErgL/p5TysUz5fuCPpZRP367ulpYWmcu8iVyY6wZRc7FbzLqm26lNkG5kKc6fECKnczJfu+nczq6lpYXW1tY5XShzbYEMCiGqMq2PKmBqIL4HmJ7ddh3QlylfN0v5kjLXm2sudotZ13zsVgNLdf5ynS4wX7vFYK7DuK8CL2T+fgF4ZVr580IIrxCinnSw9JNMdycshNiTGX357Wk2CoVihXLbFogQ4u+AR4AyIUQP8G+BPwN+IoT4JunuyXMAUsqzQoifAOcAE/iWlHJq7PF/Ij2i4wd+lflRKBQrGEcxkKVECBEG7vyA9vwpA5wn9VxaVoqvK8VPWDm+zubneillbhHlDCthKnublLJlqZ24HUKI1pXgJ6wcX1eKn7ByfF1oP1ftVHaFQjF/lIAoFIo5sxIE5HtL7YBDVoqfsHJ8XSl+wsrxdUH9XPZBVIVCsXxZCS0QhUKxTFEColAo5syyFRAhxJOZnCLtQohvL7EvNUKIt4UQ54UQZ4UQ/zxTnnNelEX0WRdCHBdC/ONy9VUIUSSE+KkQ4kLmu927HP3M1P0vM+f+jBDi74QQvuXiq1jKnD3Tt8FbLj+ADlwGGgAPcBJoXkJ/qoD7M38HgYtAM/B/A9/OlH8b+L8yfzdnfPYC9ZnPoi+yz/8K+FvSiyBZjr6STgXxP2T+9gBFy9TPaqAT8Gde/wT4neXiK/AQcD9wZlpZzr4BnwB7AUF6pvjnblv3Yl7UOXwhe4HXp73+DvCdpfZrmj+vAI+TniFblSmrIj3p7QZ/gdeBvYvo3zrSiZ4enSYgy8pXoCBzU4rrypeVn5m6qoFuoIT05Mt/BH5jOfkK1F0nIDn5ljnmwrTyrwH/3+3qXa5dmKkTNsUt84csJpnUBvcBH3NdXhRgel6UpfT/PwB/DEzPR7jcfG0AhoEfZLpa/0UIkbcM/URK2Qv8e9LrvvqBCSnlG8vR12nk6ls1OeTsmWK5Cshsfa8lH28WQuQDPwP+hZTyVim9l8x/IcTngSEp5adOTWYpWwxfXaSb3f9JSnkfECWTGvMmLOV3Wkw62149sBbIE0J8/VYms5Qt+fWb4Wa+zcnn5SogN8srsmQIIdykxePHUsqfZ4oHM/lQcJgXZTHYB3xBCHEFeBl4VAjxN8vQ1x6gR0r5ceb1T0kLynLzE+AxoFNKOSylNICfAw8sU1+nyNW3OeXsWa4CchRoEkLUCyE8pBM1v7pUzmSi0d8Hzksp/3zaWznlRVkMX6WU35FSrpNS1pH+3g5JKb++3HyVUg4A3UKIezJFB0ingVhWfma4CuwRQgQy18IB4Pwy9XWKxcnZsxhBqDkGhX6T9GjHZeDfLLEvD5Juzp0CTmR+fhMoJR2svJT5XTLN5t9kfG/DQTT7Dvn9CNeCqMvOV2AH0Jr5Xv8BKF6Ofmbq/nfABeAM8CPSoxjLwlfg70jHZqZyD39zLr4BLZnPdxn4f7kuwD3bj5rKrlAo5sxy7cIoFIoVgBIQhUIxZ5SAKBSKOaMERKFQzBklIAqFYs4oAVEoFHNGCYhCoZgz/z90VLA9zT52ygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average: cohens kappa score\n",
      "(6, 6, 5, 5)\n",
      "(1024, 165, 825)\n",
      "LR: average precision score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "RF: average precision score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "GB: average precision score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "KNN: average precision score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "DoC: average precision score\n",
      "Missing 'DoC' in ('Repeater', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('ProWRAS', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('CTGAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('Repeater', 'abalone9-18')\n",
      "Missing 'DoC' in ('ProWRAS', 'abalone9-18')\n",
      "Missing 'DoC' in ('GAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('CTGAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('Repeater', 'car_good')\n",
      "Missing 'DoC' in ('ProWRAS', 'car_good')\n",
      "Missing 'DoC' in ('GAN', 'car_good')\n",
      "Missing 'DoC' in ('CTGAN', 'car_good')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'car_good')\n",
      "Missing 'DoC' in ('Repeater', 'car-vgood')\n",
      "Missing 'DoC' in ('ProWRAS', 'car-vgood')\n",
      "Missing 'DoC' in ('GAN', 'car-vgood')\n",
      "Missing 'DoC' in ('CTGAN', 'car-vgood')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'car-vgood')\n",
      "Missing 'DoC' in ('Repeater', 'flare-F')\n",
      "Missing 'DoC' in ('ProWRAS', 'flare-F')\n",
      "Missing 'DoC' in ('GAN', 'flare-F')\n",
      "Missing 'DoC' in ('CTGAN', 'flare-F')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'flare-F')\n",
      "Missing 'DoC' in ('Repeater', 'hypothyroid')\n",
      "Missing 'DoC' in ('ProWRAS', 'hypothyroid')\n",
      "Missing 'DoC' in ('GAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('CTGAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('Repeater', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('ProWRAS', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('CTGAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('Repeater', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('CTGAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('Repeater', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('CTGAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('Repeater', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('ProWRAS', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('CTGAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('Repeater', 'winequality-red-4')\n",
      "Missing 'DoC' in ('ProWRAS', 'winequality-red-4')\n",
      "Missing 'DoC' in ('GAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('CTGAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('Repeater', 'yeast4')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast4')\n",
      "Missing 'DoC' in ('GAN', 'yeast4')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast4')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast4')\n",
      "Missing 'DoC' in ('Repeater', 'yeast5')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast5')\n",
      "Missing 'DoC' in ('GAN', 'yeast5')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast5')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast5')\n",
      "Missing 'DoC' in ('Repeater', 'yeast6')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast6')\n",
      "Missing 'DoC' in ('GAN', 'yeast6')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast6')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast6')\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "showDiagnoseByAlgoAverage(average precision score)\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average: average precision score\n",
      "(6, 6, 5, 5)\n",
      "(1024, 165, 825)\n",
      "LR: G-mean score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "RF: G-mean score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "GB: G-mean score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "KNN: G-mean score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "DoC: G-mean score\n",
      "Missing 'DoC' in ('Repeater', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('ProWRAS', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('CTGAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('Repeater', 'abalone9-18')\n",
      "Missing 'DoC' in ('ProWRAS', 'abalone9-18')\n",
      "Missing 'DoC' in ('GAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('CTGAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('Repeater', 'car_good')\n",
      "Missing 'DoC' in ('ProWRAS', 'car_good')\n",
      "Missing 'DoC' in ('GAN', 'car_good')\n",
      "Missing 'DoC' in ('CTGAN', 'car_good')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'car_good')\n",
      "Missing 'DoC' in ('Repeater', 'car-vgood')\n",
      "Missing 'DoC' in ('ProWRAS', 'car-vgood')\n",
      "Missing 'DoC' in ('GAN', 'car-vgood')\n",
      "Missing 'DoC' in ('CTGAN', 'car-vgood')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'car-vgood')\n",
      "Missing 'DoC' in ('Repeater', 'flare-F')\n",
      "Missing 'DoC' in ('ProWRAS', 'flare-F')\n",
      "Missing 'DoC' in ('GAN', 'flare-F')\n",
      "Missing 'DoC' in ('CTGAN', 'flare-F')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'flare-F')\n",
      "Missing 'DoC' in ('Repeater', 'hypothyroid')\n",
      "Missing 'DoC' in ('ProWRAS', 'hypothyroid')\n",
      "Missing 'DoC' in ('GAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('CTGAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('Repeater', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('ProWRAS', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('CTGAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('Repeater', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('CTGAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('Repeater', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('CTGAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('Repeater', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('ProWRAS', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('CTGAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('Repeater', 'winequality-red-4')\n",
      "Missing 'DoC' in ('ProWRAS', 'winequality-red-4')\n",
      "Missing 'DoC' in ('GAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('CTGAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('Repeater', 'yeast4')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast4')\n",
      "Missing 'DoC' in ('GAN', 'yeast4')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast4')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast4')\n",
      "Missing 'DoC' in ('Repeater', 'yeast5')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast5')\n",
      "Missing 'DoC' in ('GAN', 'yeast5')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast5')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast5')\n",
      "Missing 'DoC' in ('Repeater', 'yeast6')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast6')\n",
      "Missing 'DoC' in ('GAN', 'yeast6')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast6')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast6')\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "showDiagnoseByAlgoAverage(G-mean score)\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average: G-mean score\n",
      "(6, 6, 5, 5)\n",
      "(1024, 165, 825)\n",
      "abalone_17_vs_7_8_9_10\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "abalone9-18\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "car_good\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "car-vgood\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "flare-F\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "hypothyroid\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "kddcup-guess_passwd_vs_satan\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "kr-vs-k-three_vs_eleven\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "kr-vs-k-zero-one_vs_draw\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "shuttle-2_vs_5\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "winequality-red-4\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "yeast4\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "yeast5\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "yeast6\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "Average\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n"
     ]
    }
   ],
   "source": [
    "gans = [g for g in statistic.keys() if filterConvGen(g)]\n",
    "\n",
    "for (score, maxY) in [(f1Score, 0.75), (kScore, 0.75), (aScore, 0.75), (gScore, 1.0)]:\n",
    "    for a in algs:\n",
    "        showDiagnoseByAlgo(a, score)\n",
    "    \n",
    "    showDiagnoseByAlgoAverage(score)\n",
    "    showDiagnoseAverage(score, maxY=maxY)\n",
    "\n",
    "for t in testSets:\n",
    "    showDiagnoseDataset(cleanupName(t))\n",
    "\n",
    "showDiagnoseDatasetAverage()\n",
    "\n",
    "gans = list(statistic.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "frequent-associate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.276, 0.244, 0.471, 0.88]\n",
      "[0.458, 0.409, 0.687, 0.857]\n",
      "[0.106, 0.035, 0.068, 0.603]\n",
      "[0.362, 0.32, 0.366, 0.922]\n",
      "[0.263, 0.21, 0.369, 0.813]\n",
      "[0.358, 0.305, 0.438, 0.868]\n",
      "[0.997, 0.996, 0.997, 1.0]\n",
      "[0.942, 0.941, 0.986, 0.987]\n",
      "[0.65, 0.632, 0.884, 0.969]\n",
      "[0.996, 0.996, 1.0, 1.0]\n",
      "[0.114, 0.057, 0.146, 0.663]\n",
      "[0.236, 0.188, 0.342, 0.826]\n",
      "[0.574, 0.555, 0.686, 0.974]\n",
      "[0.242, 0.21, 0.474, 0.873]\n",
      "[0.4695714285714285, 0.4355714285714285, 0.5652857142857143, 0.8739285714285715]\n"
     ]
    }
   ],
   "source": [
    "checkValues()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "63841c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getValueOf(gan, dataset, algo, score):\n",
    "    if dataset not in statistic[gan].keys():\n",
    "        #print(f\"Missing '{dataset}' in '{gan}'\")\n",
    "        return None\n",
    "\n",
    "    if algo not in statistic[gan][dataset].keys():\n",
    "        #print(f\"Missing '{algo}' in ('{gan}', '{dataset}')\")\n",
    "        return None\n",
    "    \n",
    "    if score not in statistic[gan][dataset][algo].keys():\n",
    "        #print(f\"Missing '{score}' in ('{gan}', '{dataset}', '{algo}')\")\n",
    "        return None\n",
    "    \n",
    "    return statistic[gan][dataset][algo][score]\n",
    "    \n",
    "    \n",
    "    \n",
    "def calcTable(algo, score, ignore=[]):\n",
    "    table = []\n",
    "    \n",
    "    def calc(gc, g):\n",
    "        n = 0\n",
    "        for d in testSets:\n",
    "            d = cleanupName(d)\n",
    "            if d not in ignore:\n",
    "                vc = getValueOf(gc, d, algo, score)\n",
    "                v = getValueOf(g, d, algo, score)\n",
    "                if vc is not None and v is not None and vc >= v:\n",
    "                    n += 1\n",
    "        return n\n",
    "    \n",
    "    for gc in gans:\n",
    "        table.append([calc(gc, g) for g in gans])\n",
    "    return table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "177774b0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[14, 2, 3, 10, 11, 2, 1, 3, 1],\n",
       " [12, 14, 10, 10, 11, 11, 10, 12, 9],\n",
       " [12, 4, 14, 11, 11, 11, 8, 10, 7],\n",
       " [5, 4, 3, 14, 6, 3, 4, 3, 3],\n",
       " [3, 3, 3, 9, 14, 3, 3, 2, 2],\n",
       " [13, 3, 5, 11, 11, 14, 6, 7, 5],\n",
       " [13, 4, 6, 10, 12, 8, 14, 7, 5],\n",
       " [11, 2, 4, 11, 12, 8, 7, 14, 5],\n",
       " [14, 6, 7, 13, 13, 9, 9, 9, 14]]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tables = {}\n",
    "ignore = [# \"webpage\"\n",
    "         #, \"mammography\"\n",
    "         #, \"protein_homo\"\n",
    "         #, \"ozone_level\"\n",
    "         #, \"creditcard\"\n",
    "         ]\n",
    "for a in algs:\n",
    "    tables[a + \" - \" + f1Score] = calcTable(a, f1Score, ignore)\n",
    "    tables[a + \" - \" + kScore] = calcTable(a, kScore, ignore)\n",
    "    tables[a + \" - \" + aScore] = calcTable(a, aScore, ignore)\n",
    "    tables[a + \" - \" + gScore] = calcTable(a, gScore, ignore)\n",
    "    \n",
    "tables[algs[0] + \" - \" + f1Score]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "compliant-acting",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.276, 0.244, 0.471, 0.88]\n",
      "[0.458, 0.409, 0.687, 0.857]\n",
      "[0.106, 0.035, 0.068, 0.603]\n",
      "[0.362, 0.32, 0.366, 0.922]\n",
      "[0.263, 0.21, 0.369, 0.813]\n",
      "[0.358, 0.305, 0.438, 0.868]\n",
      "[0.997, 0.996, 0.997, 1.0]\n",
      "[0.942, 0.941, 0.986, 0.987]\n",
      "[0.65, 0.632, 0.884, 0.969]\n",
      "[0.996, 0.996, 1.0, 1.0]\n",
      "[0.114, 0.057, 0.146, 0.663]\n",
      "[0.236, 0.188, 0.342, 0.826]\n",
      "[0.574, 0.555, 0.686, 0.974]\n",
      "[0.242, 0.21, 0.474, 0.873]\n",
      "[0.4695714285714285, 0.4355714285714285, 0.5652857142857143, 0.8739285714285715]\n"
     ]
    }
   ],
   "source": [
    "checkValues()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "453f491d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR - f1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR - average precision score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR - G-mean score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF - f1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF - average precision score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RF - G-mean score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB - f1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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thYjYG9gPaJVJxwOrAvdKug8YSam0GhH/As4FPr9kTTYzG5xakwBUOCCnnUuAT+X3nwIu7suHnBx7dxWwrKQDWyskjZP0th72PxfYUdL7CuuWK7yfAOwRESMjYiQp8ZavOwJ8FzgI9+zNrMtUmRwlnQdMATaVdL+kA4BvAbtJugvYLS8vln/59iKXPvcGTpR0FPAccB9wWA/7PytpL+C7kk4EHgKeBI6VNBJYH7ixsP+9kp6QtF3pOI/kkuzhA35SZmYNqnKGnIgoj+NoeWd/j+XkuBgR8QDwkR627ddm3R3Au3s43Ova7N8qw95UWv8l4Ev9aauZ2WDmuVXNzMza8NyqZmZmJe45mpmZFbisamZm1obLqmZmZiXuOZqZmZW452hmZlbga45mZmZtODmamZmVuKxqZmZW4LKq1ecNwCkNxe7Pky6rMLG5p3VFU1/zTA0+qSymNfynf4M/d9PHNPuEuDGTGvra/3XgDuWeo5mZWYl7jmZmZgUuq5qZmbXhsqqZmVmJe45mZmYFknuOZmZmr+Ceo5mZWYEH5JiZmbXhsqqZmVmJe45mZmYl7jmamZkVdNI1xw5p5pKRtI6k8yXdI+k2SVdLekbSDEmPSbo3v78y77+1pJD0rtJx5uf9Zkq6RdIOvcTcWNKlOeb0HHPn0j4XS5pSWndMbttahXVPDcxXwsxscBg2bMlftbaz3nD1kSTgImByRIyKiM2Bw4F3RcRo4BLgyIgYHRG75o9NAK7P/xY9m/fbCvhP4LgeYo4AfgtMyjHHAF8ENizsswqwDbCKpA1Kh3gE+I8lPWczs8Guda/jkrzq1M1l1V2AFyPi5NaKiJjR0845mX4I2A24TtKIiHiuza4rAY/3cJh9gSkRcUkh5hxgTmGfDwK/AR4CPsrLE+3pwH6Svh0Rj/VybmZmHcdl1cFhC/r3cJsdgXsj4h5gMvDuwrbX5LLqHcCpwH/3cIw3AbcsJs4E4Lz8KvdQnyIlyEP70W4zs47RKT3Hbk6O/TUBOD+/P5+XJ65WWXUzYA/grNzT7JWkiyTNkfSrvLw2sBFwfUT8GXhJ0halj30f+JSklXo57kRJ0yRNm9dTH9bMbNCZjqQlftWpm5PjXGBMX3aUNJxU7vy/ku4DfgDsKWnF8r4RMQVYA1hT0jdzj3JGIeY2hX33BvYDVsurxgOrAvfmOCNJpdXi8f8FnAt8vqf2RsSkiBgbEWPXXLUvZ2hmNkgsWIpXjbo5OV4FLCvpwNYKSeMkva3NvrsCMyNivYgYGRFvAH4JfKC8o6TNgOHAoxFxdO5Rjs6bzwV2lPS+wkeWK7yfAOyRY4wkJe+XJcfsu8BBdPc1YTMbimIpXjXq2uQYEQHsDeyWb6uYCxwDPNBm9wmkka1FvwQ+lt+/ptBDvAD4VETMbxPzWWAv4LOS/pJv1/gacKykkcD6wI2F/e8FnpC0Xek4j+T2LNuvkzYzG8yCjuk5dnXPJCIeAD7Sw7b92r0vrLuEdLsHETG8HzHv4OWDeYpe12b/Vhn2ptL6LwFf6mtcM7OOUHMPcEl1dXI0M7NBpuYe4JJycjQzs3q0yqodwMnRzMzq47KqmZlZiXuOZmZmBQ3ckrGknBzNzKw+7jmamZkVeECOmZlZGy6rmpmZlbjnaGZmVuKeo9Xiafr31MqBNKmhuAvV/IC3guljmv0fHqc0d+4a2/C5N/h9b/jb3tz/9WcG6Di+5mhmZtaGk6OZmVlJ073vPnJyNDOzerisamZm1oZ7jmZmZiXuOZqZmRW4rGpmZtaGy6pmZmYl7jmamZkV+JFVZmZmbbjnaGZmVtIhyXFY1QEkrSPpfEn3SLpN0u8kbTKAx19B0k/y8W+VNF3SgX34XEg6obB8hKRjCsuHSfpkfn+MpH9ImpFf7x6o9hfivU/SUfn9wZI+PdAxzMwaFUv5qlGlyVGSgIuAyRExKiI2B74KrD2AYU4FHgc2joitgT2A1frwueeBfSStUd4g6VXA/sC5hdXfi4jR+fW7AWj3y0TEJRHxrbx4OnDIQMcwM2vcgqV49YGkwyXNlTRH0nmSRixJM6vuOe4CvBgRJ7dWRMQM4HpJx+fGz5Y0HkDS2yVNlvQLSXdIOkfJnpIubB0j7/cbSaOAbYGvRcSCfPx5EfHtwr5HSpoqaZakbxTa9hLpuRKHt2n3O4BbIuKlvp5obtM1ki6U9GdJ35K0r6Sb8zmOyvu9V9JNuZd7paS18/r9JP0wn8MzwH2Stu1rfDOzjlBhz1HS60gdi7ERsQUwHPjokjSz6uS4Be0fsrIPMBrYCtgVOF7Sunnb1sBhwObAhsCOwBXA9pKWz/uMBy4A3gTMbCXGMkm7AxuTEuhoYIyknQu7/AjYV9LKpY/u2KbdB+cEe7qkVXs4362AQ4EtgU8Am0TEtqTe7RfzPtcD2+de7vnAl3s41jTgrT1sMzPrPK1JACrsOZLG0rwmVwCXAx5YkqZWfs2xBzsB50XE/Ih4CLgGGJe33RwR9+eENwMYmXtwfwDem0/4PcDF5YNKOjpfE2x9MXbPr1uBW4DNSMkSgIh4AjiLV5Yw1wXmFZZ/AowiJdgHgRNob2pEPBgRzwP3AJfn9bOBkfn964HLJM0GjiQl+HYeBl7bboOkiZKmSZo276kePm1mNhhVmBwj4h/A/wB/I/2u/ndEXN77p9qrOjnOBca0Wd/b00qfL7yfz6IRtRcAHyGVPKdGxJPAbcBWkoYBRMQ3I2I0sFIhznGFa4UbRcRppXgnAgcAyxfWPQssrFNHxEM5kS8AfkrqiS6u7QsKywsK5/ED4IcRsSVwUDFOyYjcjleIiEkRMTYixq65Qg+fNjMbjJaurLpGq2OQXxOLh85VvfcDG5A6F8tL+viSNLPq5HgVsGxx9KikcaQBNOMlDZe0JrAzcPNijjUZ2AY4kJQoiYi7SeXHYyUNz8cfwaLkexmwv6QV8rbXSVqreNCIeAy4kJQgW24HNiq0ed3Ctr2BOYXj/XEx7S5bGfhHfv+pXvbbpBXHzKwrLH1Z9ZFWxyC/JpUi7Arcm8eevAj8CthhSZpaaXKMiCAlk93yrRZzgWNIo0BnATNJCfTLEfHPxRxrPnApsGf+t+UzwOrA3ZKmA1cCX8mfuTzHmpLLmL8AVmxz+BOA4qjV35MSdst38qCaWaRBRq1BPOuSBvb0xzHAzyVdBzxSPs3C+x3zuZiZdY9qb+X4G2l8ynL5bol3kjo7/Vb5JAAR8QCpHFp2ZH4V951M6iG2lg8ubT8YKK97glSe7Cn+ScBJbdavUHj/EOnCbWv5r5IelbRxRNwVEZ/o4fDbkwb1tGv729udV0RcTJvrpaQE/xiApK2BuRFRTp5mZp2twkkAIuImSb8gjTF5iTTepNy77BPPkNOzo0g9w7t62iEifjgQgSR9FtiPNIoXUi/2/wzEsc3MBo0aHlkVEV8Hvr60x3Fy7EFE3AncWVOsk4HivaBX1BHXzKx2nnjczMyspEPmVnVyNDOz+rjnaGZmVlDDNceB4uRoZmb1cXI0MzMrcVnVzMyswGVVMzOzNtxzNDMzK3HP0czMrMBlVavNmmNg4rRmYo/p7cljdcRvrj4zqcfZfOsxpsFzP+WUxkIDoIOaO/eJTX/fJzZz7vO+OXbgDuayqpmZWYl7jmZmZgV9f/RU45wczcysPu45mpmZlTg5mpmZFbisamZm1oZ7jmZmZiVOjmZmZgUuq5qZmbXhnqOZmVmJe45mZmYFHTS36rD+fkDSOpLOl3SPpNsk/U7SJgPVIEkrSPpJPv6tkqZLOrAPnwtJJxSWj5B0TGH5MEmf7Gdb/tSvxi+l/LVcRdIykq6V5D9ezKy7LFiKV436lRwlCbgImBwRoyJic+CrwNoD2KZTgceBjSNia2APYLU+fO55YB9Ja5Q35CSzP3BufxoSETv0Z/+lFRHvjoh/RcQLwB+B8XXGNzOrXCzFq0b97TnuArwYESe3VkTEDOB6ScdLmiNptqTxAJLeLmmypF9IukPSOUr2lHRh6xh5v99IGgVsC3wtIhbk48+LiG8X9j1S0lRJsyR9o9C2l4BJwOFt2v0O4JaIeCkfY7Kk7+Xe2e2Sxkn6laS7JB1biPVUb+dRDtKP4/4694jnSppYWH9fIbn/Gth3Md8PM7PO0SqrdkDPsb9luy2A6W3W7wOMBrYC1gCmSro2b9saeBPwAHADsCNwBXCKpOUj4mlSD+mCvN/MVmIsk7Q7sDEpgQq4RNLOEdGK9SNglqTvlD66Y5t2vxARO0s6FLgYGAM8Btwj6XsR8Whp/3bncX2bZvbluPtHxGOSXpO/Vr9sE28OMK6Hr8NEYCLA+uuv324XM7PBqUMG5PT7mmMPdgLOi4j5EfEQcA2LfrHfHBH354Q3AxiZe3B/AN6bS57vISWSl5F0tKQZkh7Iq3bPr1uBW4DNSMkSgIh4AjgLOKR0qHWBeaV1l+R/ZwNzI+LBiHge+AuwXptzfMV59PC16MtxD5E0E7gxr9u4fJCImA+8IGnFNtsmRcTYiBi75ppr9tAMM7NBqEt7jnOBD7VZ39tTb58vvJ9fiHkB8AVSr2pqRDwp6TZgK0nDImJBRHwT+GarvJnjHBcRvT1u9URS4vxZYd2zwIge2rWg1MYFtP+69HQePe3X9riS3g7sCrwlIp6RNLlN21qWBZ7rYZuZWWfp4tGqVwHLFkePShpHGkAzXtJwSWsCOwM3L+ZYk4FtgANJiZKIuBuYBhwraXg+/ggWJd/LgP0lrZC3vU7SWsWDRsRjwIXAAYXVtwMb9fNc+0TScZL27sdHVgYez4lxM2D7Ho67OjAvIl4ciHaamQ0K3TggJyIC2BvYLd9qMRc4hjQKdBYwk5RAvxwR/1zMseYDlwJ75n9bPgOsDtwtaTpwJfCV/JnLc6wpkmYDvwBeUXYETiBd+2z5PSlhV2FLoNdzLfkDqQc5C/hvUmm1qPUjsAvwu6VvnpnZINKlZVUi4gHgI202HZlfxX0nk3qIreWDS9sPBsrrngAO6iX+ScBJbdavUHj/ELBcYfmvkh6VtHFE3BURb++ljcVtK/ThPF4dEVPafLbH45L+IHiZ3FNeEXgir/oY8J/l/czMOlYHza06UANyOsFRpIE5Ayoi3jVAh5oLnBoRL0paBvh1RNw5QMc2MxscurXn2Klyohm0ySYiNiu8f4E06tbMrLt0yICcIZMczcysYR1UVnVyNDOz+rjnaGZmVuKeo5mZWUEHTQLg5GhmZvVxcjQzMytxWdXMzKzAZVUzM7M23HO0WjwzHab39lCUCk1qJuxCTZ03MPGUZv+Hj5nU3LkzprnQkB9k2hBNavb7HhOb+b5PemYAD+aeo5mZWYHLqmZmZm24rGpmZlbinqOZmVmJe45mZmYFvuZoZmbWhpOjmZlZicuqZmZmBS6rmpmZteGeo5mZWYl7jmZmZgUdVFYdVsVBJa0j6XxJ90i6TdLvJG0ygMdfQdJP8vFvlTRd0oF9+FxIOqGwfISkYwrLh0n6ZH5/jKR/SJqRX+8eqPb3oZ17SfpGXfHMzGoTS/Gq0YAnR0kCLgImR8SoiNgc+Cqw9gCGORV4HNg4IrYG9gBW68Pnngf2kbRGeYOkVwH7A+cWVn8vIkbn1++WtLGShvfzI78F3idpuSWNaWY2KC1YileNqug57gK8GBEnt1ZExAzgeknHS5ojabak8QCS3i5psqRfSLpD0jlK9pR0YesYeb/fSBoFbAt8LSIW5OPPi4hvF/Y9UtJUSbNKPbCXSM+SOLxNu98B3BIRL/X1RHObrpV0Ue4hnyxpWN72lKT/knQT8BZJX8rnPkfSYXmfL0k6Pb/fMm9bLiICmAzs1de2mJkNekvTa+xjz1HSKoV8cruktyxJU6tIjlsA09us3wcYDWwF7AocL2ndvG1r4DBgc2BDYEfgCmB7ScvnfcYDFwBvAma2EmOZpN2BjUkJdDQwRtLOhV1+BOwraeXSR3ds0+6Dc4I9XdKqPZzvtsB/AFsCo/J5AiwPzImI7YBngU8D2wHbAwdK2ho4EdhI0t7Az4CDIqL1cJhpwFt7iGlm1pmq7zmeBPwhIjYj5Zvbl6SZlVxz7MFOwHkRMT8iHgKuAcblbTdHxP054c0ARuYe3B+A9+aS53uAi8sHlXR0vib4QF61e37dCtwCbEZKlgBExBPAWcAhpUOtC8wrLP+ElOxGAw8CJ9DezRHxl4iYD5yXzxNgPvDLwrlfFBFPR8RTwK+At+bz3Q84G7gmIm4oHPdh4LXtAkqaKGmapGnzHu+hVWZmg1GFyVHSSsDOwGkAEfFCRPxrSZpZRXKcS/vHofb2lM7nC+/ns2gU7QXAR0glz6kR8SRwG7BVq3wZEd+MiNHASoU4xxWuFW4UEaeV4p0IHEDq3bU8C4xoLUTEQzmRLwB+SuohtlPu7LeWn8sJs9WmnmwMPMUrE+GI3KZXBoyYFBFjI2Lsmj31Z83MBpvqy6obkjo5P8uDNU8tVB/7pYrkeBWwbHH0qKRxpAE04yUNl7QmKbvfvJhjTQa2AQ4kJUoi4m5SyfHY1kAXSSNYlIAuA/aXtELe9jpJaxUPGhGPAReSEmTL7cBGhTavW9i2NzCncLw/FrZtK2mDnKzHA9e3OY9rgQ9IWi5/o/YGrsul3ZPy12J1SR8qfGaTVkwzs66xdD3HNVpVs/yaWDr6q0g54yd5sObTwFFL0swBv88xIiJfQztR0lHAc8B9pGuKKwAzSX8DfDki/ilps16ONV/SpaTS46cKmz4DHA/cLekxUg/rK/kzl0t6IzAlDZzlKeDjpDJl0QnAwYXl35PKmy3fkTQ6t/U+4KC8fl3SwJ6WKcC3SNccryWN1C2fxy2SzmDRHwOnRsSteTDOjyPiz5IOAK6WdG1EPEwa2PSfPX1tzMw60tLdkvFIRIztZfv9wP0RcVNe/gWDJTkCRMQDpHJo2ZH5Vdx3MqmH2Fo+uLT9YF6exFrXDQ+iBxFxEqlHVl6/QuH9Q8ByheW/SnpU0sYRcVdEfKKHw29PGtTT8kxEjO8tVl7+LvDd0rr9C+//Tu65SlobeE1EzO7pHM3MOk7FkwDkDtffJW0aEXcC7yRdius3z5DzckeReoZ39bRDRPywhnasTxoBa2bWXaq/X/GLwDmSlgH+QrpToN+cHAvyXxp39mP/yRR6vQPYjqkDfUwzs0Gh4plu8n31vZVe+8TJ0czM6tFBc6s6OZqZWX38yCozM7MS9xzNzMwKXFY1MzNrw2VVMzOzEvcczczMStxzNDMzK/A1R6vNcmNgzLRmYo/p7WEjNTiluT9Bp09qLHQysUP+/K7AmInN/dxNK09zXTONber7vtT31C/i5GhmZlbQ90dPNc7J0czM6uOeo5mZWYl7jmZmZgUekGNmZtaGk6OZmVmJy6pmZmYFLquamZm14Z6jmZlZiXuOZmZmBS6rmpmZtdEhZdVhfdlJ0jqSzpd0j6TbJP1O0iYD1QhJK0j6ST7+rZKmSzqwD58LSScUlo+QdExh+TBJn+xnW/7Ur8ZXRNKVklZtuh1mZgNqwVK8arTY5ChJwEXA5IgYFRGbA18F1h7AdpwKPA5sHBFbA3sAq/Xhc88D+0hao7xB0quA/YFz+9OQiNihP/v3RW5Lf50NfH6g22Jm1qhYileN+tJz3AV4MSJObq2IiBnA9ZKOlzRH0mxJ4wEkvV3SZEm/kHSHpHOU7CnpwtYx8n6/kTQK2Bb4WkQsyMefFxHfLux7pKSpkmZJ+kahbS8Bk4DD27T7HcAtEfFSPsZkSd+TdK2k2yWNk/QrSXdJOrYQ66nezqMcJO9zoqQ/5a/Ftnn9MZImSbocOEvSGyT9MZ/DHyWtL2llSXdK2jR/5rxCj/kSYEIfvj9mZp2hdc2xG3qOwBbA9Dbr9wFGA1sBuwLHS1o3b9saOAzYHNgQ2BG4Athe0vJ5n/HABcCbgJmtxFgmaXdgY1ICHQ2MkbRzYZcfAftKWrn00R3btPuFiNgZOBm4GPhCPr/9JK3eJny782hn+dzj/DxwemH9GOD9EfEx4IfAWRHxZuAc4PsR8W/gYOAMSR8FVo2InwJExOPAsj20y8ysM3VRcuzJTsB5ETE/Ih4CrgHG5W03R8T9OeHNAEbmHtwfgPfmMuN7SAnqZSQdLWmGpAfyqt3z61bgFmAzUrIEICKeAM4CDikdal1gXmndJfnf2cDciHgwIp4H/gKs1+YcX3EePXwtzsttuRZYSdIqrXgR8Wx+/xYWlXjPJn39iIgrcnt+BHymdNyHgdeWg0maKGmapGnz5pVP0cxsEOuisupcUg+orLcnjj5feD+fRaNiLwA+Qip5To2IJ4HbgK0kDQOIiG9GxGhgpUKc4yJidH5tFBGnleKdCBwALF9Y9ywwood2LSi1cQHtR+72dB5l5W9ba/npHvZfuE8+7zfm9pavs47I61/+wYhJETE2IsauueaavYQwMxtEuqysehWpvLdw9KikcaQBNOMlDZe0JrAzcPNijjUZ2AY4kJQoiYi7gWnAsZKG5+OPYFHyvQzYX9IKedvrJK1VPGhEPAZcSEqQLbcDG/Xh/PpN0nGS9i6sal1v3Qn4dy6Xlv0J+Gh+vy9wfX5/eG7rBOB0Sa/OxxKwDnDfgJ+AmVlTuqXnGBEB7A3slm+1mAscQyoRzgJmkhLolyPin4s51nzgUmDP/G/LZ4DVgbslTQeuBL6SP3N5jjVF0mzgF8CKbQ5/AlActfp7UsKuwpZA8Vwfz7eAnMzLE3TRIcCnJc0CPgEcmm+H+QzwHxFxHXAt8LW8/xjgxtaAIjOzrtAhPUel3NedJF1EStp3DfBxL4uId+X3k4EjImLaAMc4iXTN8o+97Td27NiYNm1AQ/fdpN4q6zWY2NzP7qRJjYUGYEy7Cx1DxJgGf+6mN/gzBzB2bGORiZi21F/4scsppm265J/XDKZHRC1fhaUZkNMJjiINzBlQrcRYsTmLS4xmZh2nQ8qqXT19XETcCdxZcYy3V3Tcn1ZxXDOzRnluVTMzs4IGeoBLysnRzMzq456jmZlZiZOjmZlZgcuqZmZmbbjnaGZmVuKeo5mZWUFrbtUO4ORoZmb1cXI0MzMr6ZCyalfPrToUSJoH/HUpDrEG8MgANaeTYjcdf6jGbjr+UI29tPHfEBFL/Xy8scsopq2z5J/X3+ubW9U9xw63tD+wkqbV9cM2mGI3HX+oxm46/lCNPRjiL9Qh/TEnRzMzq4+vOZqZmRV4tKp1kCafTNjwUxGH7Ln76z70Yg+G+EmHlFU9IMfMzGox9tWKaasu+ec1zwNyzMysG3VIf8zJ0czM6tFB1xyHNd0Aq5eS9Zpuh5kNUQuW4lUj9xyHmIgISb8GxtQZV9I44O8R8c+8/Engg6QJDI6JiMcqjL1+b9sj4m9VxbbBQ9LywHMRMb/pttRF0qrAa4Fngfsiovl+m8uqNojdKGlcREytMeYpwK4AknYGvgV8ERhNGkX3oQpj/5b0X1KFdQGsCawFDK8qsKQnWfTroBU/SP/3lomISv8PNh0/t2Es8FYW/ZKeA1xZ5R9EOe4w4KPAvsA44Hlg2Tyr1O+ASRFxV4XxRwB78cpz/21EzK0w7srAF4AJwDLAPGAEsLakG4EfR8TVVcXvVQeVVZ0ch6ZdgM9Kug94mvRLMyLizRXGHF74ZTie9Ivpl8AvJc2oMC4RsWVxWdJI4CukZP3/Ko69Yin2isDngYOAi6qM3XR8SfsBhwD3AtOBO0m/pHcCviJpDvB/Kuy5Xw1cCfwnMKfVa5K0Gun/wLckXRQR/zvQgSUdA7wXmAzcBDxMOvdNctwRwH9ExKyBjg38AjgLeGtE/KvUrjHAJyRtGBGnVRB78dxztEFszwZiDpf0qoh4CXgnMLGwrZafQ0kbA0cD2wEnAIdExIs1xV4FOAz4JHAuMC4iHq0jdoPxlwd2jIhne2jTaGBjoKrkuGu772/+I631h9mrK4o9NSKO6WHbdyWtBfRa7l9SEbFbL9umk/5QaU6H9Bw9IGcIioi/AusB78jvn6H6n4XzgGskXUwqL10HIGkj4N9VBpa0haTzSL8QrwS2iIhT60iMktaQdBxwC/ASsHVEfK2uxNhk/Ij4UU+JMW+fERF/rLAJr4HUU2zzWlXS8Kp+BiLitzn2iPI2SWtExMMRMa2K2IU4B5SWh0v6epUxF6tVVu2AATmeBGAIyv9BxgKbRsQmkl4L/Dwidqw47vbAusDlEfF0XrcJsHxE3Fph3PnA30nXHl8xGCMiDqkw9tOkaz4/A55sE/u7VcVuOr6kL0fEdyT9gFcW0wJ4DPjfiLinoviXRsReku7lldecAVYAfhoRX60ifm7DbODAiLgxL38QOC4iNqkqZiH2ucAqwAHA6qSfgWsi4oiqY/dk7DDFtGWW/PN63pMAWLX2BrYm9SaIiAfytahKtX5BtOTRg9uSBg68p8LQ+1d47MU5nkWJofw1ruMv0ybj357/7amHtDrwK2CrKoJHxF753w3abZc0nDRAprLkCHwMOF3SZNKgnNWBd1QYb6GI+Jik8cBsUnVoQkTcUEfsXtXQA8zf22nAP1o/B/3l5Dg0vZBv6QhYmKRqIWkZ4N2kXxp7kEqdJ1cZMyLO7KEtI0iDJqqMfUxP2/LtLZVqMn5E/Cb/2/brn9vwdJVtKMRZlXR9c2GZMyKuBd5YZdyImC3pm8DZpJ77zhFxf5UxW/I19kNJ/8feSBqIc2tEPFNH/LaCugbkHEr642ylJT2Ak+PQdKGkU4BVJB1I6lmdWmVASbuReojvIo0iPBvYNiI+XWXcNu0YDuxeaMt1wM9rjL856faCCaRrrbU+X6+J+JLWJI0O3pyXJ6d3RMQpNcT/DOmX5euBGcD2wBRq6MFJOg0YBbyZNFL1N5J+GBE/qjo28Bvg4Ii4UpKALwFTgTfVELtnFfccJb2eVIn6Jumcl4iT4xAUEf+Tk9UTwKbA/42IKyoOexkpEe0UEfcCSDqp4pgL5XsrP0b6T3MzsCOwQR1/RUt6AykZTSANinkDMDYi7qs69mCID5wDXED62n8W+BTpOmhdDiXd53hjROwiaTPgGzXFngN8JtLgjnvzdfdKrzMXbBsRT0C6Tws4QdIlNcXuWfVl1ROBL/PKywj94uQ4BEn6dkR8BbiizbqqjCH1WK6U9BfgfCq8+b5I0v2k2wV+AhwZEU9KuremxPgnYGXS+X4oIu7Kse+rOvZgiJ+tHhGnSTo0Iq4hjVq+psb4z0XEc5KQtGxE3CFp0zoCR8T3JG0l6a151XURcUCvHxo4K0k6k3Rf6QLgetIfCo2ZDpcJ1liKQ4yQVLyGPSkiFj6KS9JewMMRMV3S25cijpPjELUbqcxVtGebdQMmj0a9lXTz947k2Tsk/R64qPgDXoFfAh8gTT4wP99OUtcw7Xmkct7apBl57qox9mCID9C6XeJBSe8BHshtqsv9+T7PXwNXSHo8t6Fykg4h3dP7q7zqfyVNiogf1BD+Z6R7Wj+clz+e1/V4H2TVImKPikPsCLxP0rtJJfyVJP1vRHy8vwfyrRxDiKTPkWZH2RAoDp9fEbhhSX6AlrI9w0iz1BwYER9e3P5LGUukWVEmkAYErUwa4v7biHiq4tgrk+aRnQBsRBpe/66IuLnKuIMo/l6kkvp6wA9IgyS+ERG1l/gkvY30vf9DRLxQQ7xZwFsKty4tD0ypeDaqVuwZETG6tG5mRFQyOniwyT3HI5Z0tKqT4xCSf0muChwHHFXY9GRUPM9ljv860n2OsyLihTxLyGHAfhHx2qrjF9rxatJI2QnA7hGxNGWe/sZem9SD/SiwXkTU+oSUpuM3JY9WXY9CtSwibqkh7mzSbETP5eURpNlztuz9kwMS+0rgDNIEHJB+3j8dEe+sOvZg4ORoSywnp+LowcqeTiHpMNLUbXcDywInkQYmnAV8JyIerDD2+4HXt0YISrqJNOE4pMFIZ1cVezHtekOeoagRdcWXtAFpkvmRvDw5va/q2Dn+fwP7AX9h0XCQiIg6RqsenmO35rH9AHBGRJxYQ+z1gR8CbyGV0v8EHNrkz1wncXIcgiS9l5SYXkuaEPkNwO0RUdkQb0m3kUaqPpb/095NuufrxsV8dCBi3wB8NCL+npdnkOZ3XR74WZV/SUvaCdgwIs7Ky78AVsubj42Iq6qKPRji55gzgdNIN6MvHKuYB+dUTtKdwJZ1lFFLcYeRbht5jjQoRsC1Vc4GVYg9HDiz7ksl3cQDcoamY0n/aa+MiK0lta7FVem5Vuk2Iv4m6c91JMZsmVZizK6PNLfoozVMgPANUq+pZVNST2J50swsVSenpuND+t5/v4Y4PZlDus76cJ1BI2KBpBMi4i3k2ahqjD1f0pqSlqn7j4Ju4eQ4NL0YEY9KGiZpWERcLenbFcd8vaTiL8i1istR4fympOusC0XEwYXFNSuMC7BSRNxWWL4r0pMRUJoQvGpNxwc4SWk+38tJz1QE6rnmlx0H3Kr0iKxi/DrKupcrzaf6q6i/THcfcEO+t3HhTERR8Xy+3cLJcWj6l6QVSCMIz5H0MOnm8CodWVqu87E5N0k6MCJ+Wlwp6SDShABVWqW4EBH7FBbXrjj2YIgPsCXwCdKMNAuv+VHTHKPAmcC3KZV1a/IlUi99vqTn8rqIiCWe1qwfHsivYSzlDfFDka85DkG5lPgs6T/NvqSh7edEhY8xUnp238wG/npuDTz6NanX0OqtjCENDPpARDxUYezfACdHfoRRYf1ewOciosoJ1xuPn2PdAby5qfKepGsi4m1NxB4MJK1ESsiveCqL9czJcYjKU4ptHGnexeWA4VX+58mzWmxASk43kEbO3Rh5eqs6SHoHi+aVnFvTYJSNgUtJ51tMzDsAe0XEnyuOvxHpUV2NxM9tuAD4YkTUes2vEP+7pD+MLqGBsq6kfUgDcoI0Q86va4o7lnTTf6vX+G9g/1ZZ3Xrn5DgEKU02PhFYLSJG5V/gJ1d9/1NOwtuSfjHvQJrv8p+kCQg+X2XsJklaltRDX5iYgXNb974NgfiTSRNvT6X+a35IurrN6rpu5fgxaeKF1r2G44F7IuILNcSeBXwhIloPFt8J+HEdExB0AyfHISjfyrAtcFNEbJ3Xza7jxuQca3nSaNkdgU8CwyJiwzpi103S5RGxe9PtaFKeleYV6rqVo0mS5gJbtC4n5Ns7Zld521Qh9g1ReoB5u3XWngfkDE3P5xlqAJD0Kiqeb1PSx0i9xdGk3sNU4CbSvY//rDJ2w6oeDdtvkm6JiG3qijfYkmCeFOKfEXFTDeHuBNYHWjferwfMqiEuwM1Kj6Y7j/T/ezwwWdI2UOto4Y7k5Dg0XSPpq8BrlB5d9XnSs9+qNAm4g/Rg42vruNY1SKycrzm1FRG/6mlbhdRAzEXB07RmLwI/iohLG2jCdsCWkl4VEXtWHGt14HZJrVHR44Ap+faKqkvLo/O/Xy+t34F6Rwt3JJdVh6Bc2jmA9NBfkZ61eGqVI0nzjB1bseh646bAg6SHzk6pY3BMEyQ9ClxM+4QUEbF/zU1C0rER8bW64xbiv5Y0x+72Uc9DfxvTU0m5ZbD1qm0RJ8chStIywGakvyDvbGBqrbWBDwGHkx46XMuzHetWdwmzLyStATzaxG01TZG0A6+c2/WsxhrUgJrLyR1vWNMNsPopPVPvHuD7pImJ75ZUaXlJ0pslfVbSWZLuJl1z3Jn0CKPtqozdsE2Vnl/5MpLeKmlU1cElbS9psqRfSdo6zxIzB3hIUqXP1iseX9LKkk6TNEvSufmPo1pIOhv4H9LtFOPya2xd8UttuVLS7/N9pnXbDvia0jNUbTHccxyC8k3Ze0XE3Xl5FOm5hptVGLN4f+OfhsqTASRdBRwWEbNK68cCX4+I91YcfxppDtWVSdd994yIGyVtBpzXGq1cUeyFvWZJp5Ju2/kpsA/wtoj4QFWxS+24Hdh8MPSUh1JJudN5QM7Q9HArMWZ/oeJJmYulRUnLSNoiL94ZES/28LFusFY5MQJExDRJI2uI/6qIuBxA0n+1JnuPiDtao5VrMjYWPXj3e5I+VWPsOcA6pGvcjYqI1pRutdyI73LyknNyHJrmSvodcCHpmuOHgamtUZVVjqBUegDpmaRJkQWsJ+lTEXFtVTEbNqKXba+pIX5xLtFnS9uq7kmtJelLpO/zSpJU6L3VeUlnDeC2PGK0lkkIJO0REX/I71cmPSJuHClRH17llIWFNpwNjAJmAPPz6iA9Q9UWw2XVIUjSz3rZXOkISknTgY9FxJ15eRNSeW9MVTGbJOk84Ko2k54fAOweEeMrjj+f9EQGkZLxM61NwIiIeHWFscu3EPw4IuZJWof0gOtPVhW71I7aJyEYDCXlwVRO7kROjlYrSbPK01e1W9ct8sCTi4AXWFRKGwssA+zd5RMgDFml5DijUFJ+xXKFbfg5cEhENF5O7kQuqw5Bubf2E2DtiNhC0puB90XEsTWEny7pNODsvLwv9T6+qla5fLaD0gOlW9dZf9vkfZ15+r4PkHrwlT2VQ9LHSXO4tn1MVB4Itm5EXF9R/OsjYidJT/LyErKo/rFRg6GkXHs5uZu45zgESbqG9HzFUwpzq86JiC16/+SAxF4W+AJpWL2Aa0nltud7/aAtlXxf67uBjwF7AL8kPYC3spmRJB0K7E/642c6MI90DXYj4G3AI8BREXFXVW1oymAoKQ/lOW0HgpPjECRpakSMk3RrITlWXurJM/PMqiMJW5KnB5wAvAu4GrgA+EFEjKwp/nDSNGU7km5heBa4Hfh9RPytjjbkdqxKmte0OGrTc4taj1xWHZoeySWt1pMCPkQNw9wjYoGkmZLWr/MX4xB3GXAdaYL3ewEknVRX8IiYD1yRX42Q9N/AfqRbllol3krnFm2ypNxwOblrODkOTV8g3RC+maR/APeSrv3VYV3SrSQ3k0ZRAr4OUqExwEeBKyX9BTgfqGWqPkn/t5fNERH/XUc7gI8Ao2qeInF14NY8OrvHknIVgSNip/zviovb13rmsuoQlgdmDCOVusZHxDk1xPR1kIbkaewmAB8k3ft2UURMqjDef7RZvTxp0vvVI2KFqmKX2vFL4HMRUelEF23iNl5Sdjl5yTk5DiGSViL1Gl9HelLElXn5CGBmRLy/wtgjgM+S/nKeDZwWES9VFc8StXnYcr72uxvw0Yj4dE3tWBE4lJQYLwROqCtZ5an6LibdgD9kRm32VE6OCD+qqg+cHIcQSRcDj5MeE/VOYFXS/XaHRsSMimNfQHqG33XAnsBfI+LQKmMaFAddNRR/NeBLpLL9mcBJEfF4zW2YC5xC+qNs4TXAiicBaLykLOlOYMuay8ldw9cch5YNI2JLWDhrxyPA+hHxZA2xNy/EPg24eTH728Bo7GHLko4nzQgzifRL+qmqYi3GIxHx/ZpjPt1m3cKSMlDH9dY5wCpUPG9yt3LPcQhR6dmC5eVujT2UqcGHLUtaQCpjvkSDoyYlfTe34xJeXlat5dpbUyXloVpOHihOjkNIYZ5NePlcm5X/smoy9lDmP0JA0tVtVld+7a3pknIT5eRu4uRo1sUkPU2a4PyG0vq3Ag9ExD01tWMnYOOI+JmkNYAVW/dddqNSSflHTZSUJV0TEW1Hh9viOTmadTE1/LDlHOvrpMnWN42ITZQe+PvziNix4rhN3ojfeEm56XJyp/OAHLPu1vTDlgH2BrYGbsmxH8jX4arW5I34dT6vsietUcrbF9ZVOjNQN3FyNOtuTT9sGeCFiAhJrekKl68jaEScJOmHLLoR/80suhH/EzXeiN9ISTkidqk6RjdzcjTrblMlHRjtH7Zc16PCLpR0CrCKpANJT+r46WI+MyCantu1WFIGfka6r/h/Scm6qpiNPiqsW/iao1kXGywPW85PB9mddM3tsoioPFkNkhvxZ5BLyoUn4FT6cO+h/KiwgeTkaDYElB62PDcafNhyXQbD3K6Sbo6IbVu31OSS8pQqk2OO2/i8rp3OydHMKpFLt6tFxPF5+X5gJVLv8csR8ZMa29LUjfhHABuT5rI9jtSjOzciflB1bFs6To5mVglJU4E9IuLRvHxrRGydJ6G/PCJ2rqENg2Fu11pLyoOhnNwNPCDHzKoyrJUYs58DRMRzkiofKTtY5nbNybDOAUGDYV7Xjueeo5lVQtLdEbFRm/XDgLsjYsOK4zd2I/5gKSk3+aiwTjcYblQ1s+50uaRj26z/L+DyqoNHxLCIeE1ErBgRKxVeK9YwQ81ngdMLy/NyzDVJD5yulKTV8td+FqlCuE1EfMWJse9cVjWzqhwJnCrpbmBmXrcVMA34TJ0NaeBG/MZKyoOlnNzpXFY1s0pJ2hB4U168ra7Jzgvxa5/btcmS8mCY17UbuKxqZlX7HrAicFXdiTHbG3gfeaBKRDyQ21OlxkrKDZeTu4aTo5lV7bvATsBtkn4u6UP5do66vBCpRFbn3K5HAqMk3S3pl/l1N2mWmiNqiA+kcrKkT+f3a0jaoK7Ync5lVTOrRWHWlgNJ9z/W0otp8kb8JkvKTT0qrFs4OZpZ5fIglPcC44FtgEsj4os1xq99btcc92LgAuDiiGh3/2GVsWdQ87yu3cSjVc2sUpIuALYD/gD8CJjc0xMjqtLAjfgt3yX9QXCcpJtJifLSiHiuhtiNPCqsW7jnaGaVkrQHcEV+fFSdcQfFjfg5du0lZc/runScHM2scpJ2AEZSqFZFxFkVx2x8btcct7GSclPl5G7gsqqZVUrS2cAoYAbQ6j0GUGlypOG5XaH5knKD5eSO556jmVVK0u3A5lHzL5um53bNsWovKQ+mcnInc8/RzKo2B1gHeLDmuJdLOjYivlZaX8vcrgAR8QdJO0gaSX0l5c8CexSW50XE61vlZMDJsQ+cHM2samuQJgC4mTStGQAR8b6K4zY+t2tDJeXGy8ndwGVVM6uUpLe1Wx8R19QUv8kb8WsvKQ+GcnI38PRxZlapnATvIM1nuiJwe12JMWtybtdWSblOjT4qrFu452hmlZL0EeB4YDJpUMhbgSMj4hc1xX8b6TaK9wC13ogv6WpgdI5bS0k53+x/KjCONuVkP8Kqb5wczaxSkmYCu7UetCtpTeDKiNiq5nY0cSN+YyXlph8V1ulcVjWzqg0rPYH+UWr+3ZMHonyQNJJzHHBmHXEbLik3/aiwjubkaGZV+4OkyyTtJ2k/4LfA7+oKnm/Ev53Ua/wRMKrGGWo+Qiqpfhj4CHCTpA/VEZvmHxXW0VxWNbNKSNoIWDsibpC0D+kXtYDHgXPq6s00Nbdrjt14SbmpR4V1Ot/naGZVORH4KkBE/Ar4FYCksXnbe+toREM34rc0WlJuM69rLeXkbuDkaGZVGRkRs8orI2JaTlS1aHBuV8glZeC8vDyemkrKTc/r2umcHM2sKr1d36pzppax1H8jfqukfGSppDwFOKemZvwM+FgT5eRu4ORoZlWZKunAiPhpcWWeGHt6je1oYm7XE2m4pNxwObnjeUCOmVVC0trARcALLEqGY4FlgL0j4p81taOJG/HnRMQWPWybHRFbVhW7EKdtOTkiDqk6djdwz9HMKhERDwE7SNoFaCWK30bEVTU35Zia48HgKCnXXk7uJk6OZlapiLgauLrB+NfkXuy4vOrm0gjSKgyGknJTjwrrCi6rmllXa2Ju18FQUm6inNxNnBzNrKs1eSN+qaQ8t86SctOPCut0To5m1tXKA2Dycw1n1jEopmkNlJO7hudWNbNu1+jcrk1peF7Xjueeo5l1pcEyt2tTBsO8rp3MPUcz61YnAk9CuhE/Ir4UEYeTeo0nNtiuujT+qLBO5ls5zKxbDYq5XRvU2Lyu3cDJ0cy61WC4Eb92g2Re147nLraZdaupkg4sr2xgbte6ncjQLicPCA/IMbOuNBhuxG/CYJjXtRu4rGpmXWkQze1atyFZTh5o7jmamXURSecBV/Uwr+vuETG+mZZ1FidHM7MuMlTLyQPNydHMrAs1Oa9rN3ByNDMzK/GtHGZmZiVOjmZmZiVOjmZmZiVOjmZmZiVOjmZmZiX/H+8ihL0nrkfQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB - average precision score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB - G-mean score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN - f1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN - average precision score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN - G-mean score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DoC - f1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DoC - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DoC - average precision score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DoC - G-mean score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = matplotlib.colors.ListedColormap([\n",
    "    (1.0, x / 255.0, 0.0)\n",
    "    for x in range(256)\n",
    "    ] + [\n",
    "    ((255 - x) / 255.0, (255 - x) / 255.0, 1.0) # x / 255.0)\n",
    "    for x in range(256)\n",
    "    ])\n",
    "\n",
    "#cmap.set_extremes(bad=cmap(0.0), under=cmap(0.0), over=cmap(1.0))\n",
    "\n",
    "for k in tables.keys():\n",
    "    print(k)\n",
    "    labels = list(gans)\n",
    "    t = tables[k]\n",
    "    if k[0:3] == \"DoC\":\n",
    "        #continue\n",
    "        labels = labels[-4:]\n",
    "        t = [r[-4:] for r in t[-4:]]\n",
    "        f = plt.figure(figsize=(5, 4))\n",
    "        f.add_axes([0.4, 0.45, 0.6, 0.5])\n",
    "    else:\n",
    "        f = plt.figure(figsize=(7, 6))\n",
    "        f.add_axes([0.27, 0.25, 0.7, 0.74])\n",
    "    p = plt.imshow(t, cmap=cmap)\n",
    "    plt.colorbar(p)\n",
    "    plt.xticks(range(len(labels)), labels, rotation=\"vertical\")\n",
    "    plt.yticks(range(len(labels)), labels)\n",
    "    plt.savefig(f\"data_result/statistics/successCount/statistic-{k}.pdf\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "tired-albert",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.276, 0.244, 0.471, 0.88]\n",
      "[0.458, 0.409, 0.687, 0.857]\n",
      "[0.106, 0.035, 0.068, 0.603]\n",
      "[0.362, 0.32, 0.366, 0.922]\n",
      "[0.263, 0.21, 0.369, 0.813]\n",
      "[0.358, 0.305, 0.438, 0.868]\n",
      "[0.997, 0.996, 0.997, 1.0]\n",
      "[0.942, 0.941, 0.986, 0.987]\n",
      "[0.65, 0.632, 0.884, 0.969]\n",
      "[0.996, 0.996, 1.0, 1.0]\n",
      "[0.114, 0.057, 0.146, 0.663]\n",
      "[0.236, 0.188, 0.342, 0.826]\n",
      "[0.574, 0.555, 0.686, 0.974]\n",
      "[0.242, 0.21, 0.474, 0.873]\n",
      "[0.4695714285714285, 0.4355714285714285, 0.5652857142857143, 0.8739285714285715]\n"
     ]
    }
   ],
   "source": [
    "checkValues()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e4397945",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Table:\n",
    "    def __init__(self, heading):\n",
    "        self.heading = [str(h) for h in heading]\n",
    "        self.sizes = [len(h) for h in self.heading]\n",
    "        self.rows = []\n",
    "        \n",
    "    def add(self, row):\n",
    "        row = [str(r) for r in row]\n",
    "        self.rows.append(row)\n",
    "        self.sizes = [max(a,len(b)) for (a, b) in zip(self.sizes, row)]\n",
    "        \n",
    "    def separator(self):\n",
    "        return \"|\".join([\"-\" * n for n in self.sizes])\n",
    "    \n",
    "    def showRow(self, row):\n",
    "        def pad(n, t):\n",
    "            while len(t) < n:\n",
    "                t += \" \"\n",
    "            return t\n",
    "        \n",
    "        return \"|\".join([pad(n, t) for (n,t) in zip(self.sizes, row)])\n",
    "    \n",
    "    def show(self):\n",
    "        print(self.showRow(self.heading))\n",
    "        print(self.separator())\n",
    "        for row in self.rows:\n",
    "            print(self.showRow(row))\n",
    "            \n",
    "    def showLatex(self, caption, key):\n",
    "        \n",
    "        columnConfig = \"|\".join([\"l\"] + [\"@{\\\\hskip3pt}c@{\\\\hskip3pt}\" for h in self.heading[1:]])\n",
    "\n",
    "        text = \"\\\\begin{table*}[ht]\\\\scriptsize\"\n",
    "        text += \"\\\\caption{\" + caption + \"}\\\\label{\" + key + \"}\"\n",
    "        text += \"\\\\centering\\\\tabularnewline\\n\"\n",
    "\n",
    "        text += \"\\\\begin{tabular}{\" + columnConfig + \"}\\\\hline\\n\"\n",
    "        text += \" & \".join([\"\\\\textbf{\" + h + \"}\" for h in self.heading])\n",
    "        text += \"\\n\\\\tabularnewline\\n\\\\hline\\n\"\n",
    "        \n",
    "        for row in self.rows:\n",
    "            text += \" & \".join(row)\n",
    "            text += \"\\n\\\\tabularnewline\\n\"\n",
    "            \n",
    "        text += \"\\hline\\end{tabular}\\end{table*}\\n\"\n",
    "        \n",
    "        return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d0e2faa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "scoreNames = [f1Score, kScore, aScore, gScore]\n",
    "\n",
    "def tableRow(algo, dataset, myGans):\n",
    "    return [ [ getValueOf(gan, dataset, algo, score) for score in scoreNames ] for gan in myGans ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "generic-juice",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def p(f, bold=False):\n",
    "    if f is None:\n",
    "        text = \"?\"\n",
    "    else:\n",
    "        text = f\"{f:0.3f}\"\n",
    "        \n",
    "    if bold:\n",
    "        return \" \\\\textbf{\" + text + \"} \"\n",
    "    else:\n",
    "        return \" \" + text + \" \"\n",
    "\n",
    "def latex(text):\n",
    "    r = \"\"\n",
    "    for x in text:\n",
    "        if x == \"_\" or x == \"-\":\n",
    "            r += \" \"\n",
    "        else:\n",
    "            r += x\n",
    "    return r\n",
    "\n",
    "def pairMax(row):\n",
    "    best = [0.0 for _ in row[0]]\n",
    "    \n",
    "    for v in row:\n",
    "        for i, x in enumerate(v):\n",
    "            best[i] = max(best[i], x)\n",
    "    \n",
    "    return best\n",
    "\n",
    "def createTable(useConvGeN, fileName, scoreTitle, scoreSelection):    \n",
    "    with open(fileName, \"w\") as latexFile:\n",
    "        for algo in algs:\n",
    "            latexFile.write(\"\\n\")\n",
    "            latexFile.write(\"% ### \" + algo + \"\\n\")\n",
    "            latexFile.write(\"\\n\")\n",
    "            f = \"\"\n",
    "            heading = [f\"dataset ({scoreTitle})\"]\n",
    "\n",
    "            if useConvGeN:\n",
    "                myGans = [x for x in gans if x.startswith(\"ConvGeN\")]\n",
    "            else:\n",
    "                myGans = [x for x in gans if not x.startswith(\"ConvGeN\") or x == 'ConvGeN(min,maj)']\n",
    "                if algo[0:3] == \"DoC\":\n",
    "                    continue\n",
    "                    # myGans = list(gans)[-4:]\n",
    "\n",
    "            for g in myGans:\n",
    "                heading.append(latex(g))\n",
    "            table = Table(heading)\n",
    "\n",
    "            avg = [[0.0, 0.0] for h in heading[1:]]\n",
    "            mx  = [[0.0, 0.0] for h in heading[1:]]\n",
    "            cnt = 0\n",
    "\n",
    "            for d in testSets:\n",
    "                d = cleanupName(d)\n",
    "                if d not in ignore:\n",
    "                    cnt += 1\n",
    "                    row = tableRow(algo, d, myGans)\n",
    "                    line = [latex(d)]\n",
    "                    row = [[r[i] for i in scoreSelection] for r in row]\n",
    "\n",
    "                    #print((algo, d, myGans))\n",
    "                    print(f\"{cnt}: {row[0]}\")\n",
    "\n",
    "                    m = pairMax(row)\n",
    "\n",
    "                    for (n, r) in enumerate(row):\n",
    "                        line.append(f\"{p(r[0], r[0] == m[0])} / {p(r[1], r[1] == m[1])}\")\n",
    "                        avg[n][0] += r[0] or 0.0\n",
    "                        avg[n][1] += r[1] or 0.0\n",
    "                        mx[n][0] = max(mx[n][0], r[0] or 0.0)\n",
    "                        mx[n][1] = max(mx[n][1], r[1] or 0.0)\n",
    "                    table.add(line)\n",
    "\n",
    "            m = pairMax(avg)\n",
    "            table.add([\"\\\\hline Average\"] + [f\"{p(a / cnt, a == m[0])} / {p(b / cnt, b == m[1])}\" for (a,b) in avg])\n",
    "            #table.add([\"maximum\"] + [f\"{p(a)} / {p(b)}\" for (a,b) in mx])\n",
    "\n",
    "            latexFile.write(table.showLatex(algo, \"tab:results:\" + algo + \":A\") + \"\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "consecutive-calculator",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1: [0.276, 0.244]\n",
      "2: [0.458, 0.409]\n",
      "3: [0.106, 0.035]\n",
      "4: [0.362, 0.32]\n",
      "5: [0.263, 0.21]\n",
      "6: [0.358, 0.305]\n",
      "7: [0.997, 0.996]\n",
      "8: [0.942, 0.941]\n",
      "9: [0.65, 0.632]\n",
      "10: [0.996, 0.996]\n",
      "11: [0.114, 0.057]\n",
      "12: [0.236, 0.188]\n",
      "13: [0.574, 0.555]\n",
      "14: [0.242, 0.21]\n",
      "1: [0.195, 0.185]\n",
      "2: [0.293, 0.268]\n",
      "3: [0.911, 0.908]\n",
      "4: [0.985, 0.984]\n",
      "5: [0.222, 0.177]\n",
      "6: [0.795, 0.785]\n",
      "7: [1.0, 1.0]\n",
      "8: [0.996, 0.996]\n",
      "9: [0.948, 0.946]\n",
      "10: [1.0, 1.0]\n",
      "11: [0.026, 0.021]\n",
      "12: [0.304, 0.291]\n",
      "13: [0.725, 0.716]\n",
      "14: [0.511, 0.501]\n",
      "1: [0.331, 0.309]\n",
      "2: [0.386, 0.349]\n",
      "3: [0.839, 0.831]\n",
      "4: [0.938, 0.935]\n",
      "5: [0.287, 0.239]\n",
      "6: [0.781, 0.768]\n",
      "7: [1.0, 1.0]\n",
      "8: [0.995, 0.995]\n",
      "9: [0.944, 0.942]\n",
      "10: [1.0, 1.0]\n",
      "11: [0.122, 0.089]\n",
      "12: [0.377, 0.348]\n",
      "13: [0.714, 0.705]\n",
      "14: [0.468, 0.454]\n",
      "1: [0.315, 0.291]\n",
      "2: [0.342, 0.286]\n",
      "3: [0.357, 0.312]\n",
      "4: [0.413, 0.375]\n",
      "5: [0.245, 0.191]\n",
      "6: [0.568, 0.54]\n",
      "7: [0.991, 0.991]\n",
      "8: [0.901, 0.898]\n",
      "9: [0.84, 0.833]\n",
      "10: [0.951, 0.951]\n",
      "11: [0.069, 0.016]\n",
      "12: [0.329, 0.293]\n",
      "13: [0.67, 0.657]\n",
      "14: [0.409, 0.387]\n"
     ]
    }
   ],
   "source": [
    "createTable(False, \"data_result/statistics/Tables-f1-kappa.tex\", \"$f_1~$score$~/~\\\\kappa~$score\", [0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "exempt-outline",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1: [0.471, 0.88]\n",
      "2: [0.687, 0.857]\n",
      "3: [0.068, 0.603]\n",
      "4: [0.366, 0.922]\n",
      "5: [0.369, 0.813]\n",
      "6: [0.438, 0.868]\n",
      "7: [0.997, 1.0]\n",
      "8: [0.986, 0.987]\n",
      "9: [0.884, 0.969]\n",
      "10: [1.0, 1.0]\n",
      "11: [0.146, 0.663]\n",
      "12: [0.342, 0.826]\n",
      "13: [0.686, 0.974]\n",
      "14: [0.474, 0.873]\n",
      "1: [0.088, 0.322]\n",
      "2: [0.167, 0.444]\n",
      "3: [0.839, 0.953]\n",
      "4: [0.971, 0.996]\n",
      "5: [0.09, 0.565]\n",
      "6: [0.651, 0.868]\n",
      "7: [1.0, 1.0]\n",
      "8: [0.993, 0.996]\n",
      "9: [0.902, 0.972]\n",
      "10: [1.0, 1.0]\n",
      "11: [0.042, 0.049]\n",
      "12: [0.16, 0.449]\n",
      "13: [0.558, 0.853]\n",
      "14: [0.312, 0.662]\n",
      "1: [0.145, 0.664]\n",
      "2: [0.202, 0.613]\n",
      "3: [0.725, 0.992]\n",
      "4: [0.886, 0.997]\n",
      "5: [0.14, 0.758]\n",
      "6: [0.627, 0.934]\n",
      "7: [1.0, 1.0]\n",
      "8: [0.99, 1.0]\n",
      "9: [0.896, 0.988]\n",
      "10: [1.0, 1.0]\n",
      "11: [0.051, 0.321]\n",
      "12: [0.179, 0.718]\n",
      "13: [0.545, 0.851]\n",
      "14: [0.256, 0.736]\n",
      "1: [0.137, 0.704]\n",
      "2: [0.166, 0.701]\n",
      "3: [0.217, 0.919]\n",
      "4: [0.261, 0.942]\n",
      "5: [0.12, 0.761]\n",
      "6: [0.365, 0.875]\n",
      "7: [0.983, 1.0]\n",
      "8: [0.822, 0.997]\n",
      "9: [0.726, 0.988]\n",
      "10: [0.91, 0.999]\n",
      "11: [0.037, 0.368]\n",
      "12: [0.161, 0.778]\n",
      "13: [0.508, 0.975]\n",
      "14: [0.233, 0.882]\n"
     ]
    }
   ],
   "source": [
    "createTable(False, \"data_result/statistics/Tables-a-g.tex\", \"avg.~prec.~score$~/~$G-mean~score\", [2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "rural-memory",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1: [0.294, 0.264]\n",
      "2: [0.524, 0.484]\n",
      "3: [0.108, 0.037]\n",
      "4: [0.375, 0.334]\n",
      "5: [0.272, 0.222]\n",
      "6: [0.36, 0.308]\n",
      "7: [0.993, 0.993]\n",
      "8: [0.943, 0.941]\n",
      "9: [0.704, 0.69]\n",
      "10: [0.996, 0.996]\n",
      "11: [0.126, 0.07]\n",
      "12: [0.246, 0.201]\n",
      "13: [0.585, 0.567]\n",
      "14: [0.295, 0.267]\n",
      "1: [0.229, 0.217]\n",
      "2: [0.323, 0.29]\n",
      "3: [0.665, 0.655]\n",
      "4: [0.902, 0.899]\n",
      "5: [0.07, 0.051]\n",
      "6: [0.787, 0.777]\n",
      "7: [1.0, 1.0]\n",
      "8: [0.994, 0.994]\n",
      "9: [0.945, 0.943]\n",
      "10: [0.996, 0.996]\n",
      "11: [0.075, 0.058]\n",
      "12: [0.278, 0.268]\n",
      "13: [0.731, 0.724]\n",
      "14: [0.502, 0.494]\n",
      "1: [0.317, 0.296]\n",
      "2: [0.348, 0.307]\n",
      "3: [0.746, 0.736]\n",
      "4: [0.95, 0.948]\n",
      "5: [0.166, 0.153]\n",
      "6: [0.784, 0.772]\n",
      "7: [0.995, 0.995]\n",
      "8: [0.995, 0.995]\n",
      "9: [0.961, 0.959]\n",
      "10: [0.994, 0.994]\n",
      "11: [0.141, 0.103]\n",
      "12: [0.277, 0.262]\n",
      "13: [0.729, 0.721]\n",
      "14: [0.479, 0.47]\n",
      "1: [0.296, 0.268]\n",
      "2: [0.363, 0.308]\n",
      "3: [0.5, 0.468]\n",
      "4: [0.682, 0.665]\n",
      "5: [0.273, 0.224]\n",
      "6: [0.574, 0.547]\n",
      "7: [0.986, 0.985]\n",
      "8: [0.889, 0.885]\n",
      "9: [0.862, 0.856]\n",
      "10: [0.959, 0.958]\n",
      "11: [0.067, 0.009]\n",
      "12: [0.324, 0.286]\n",
      "13: [0.643, 0.628]\n",
      "14: [0.354, 0.329]\n",
      "1: [0.325, 0.298]\n",
      "2: [0.486, 0.443]\n",
      "3: [0.796, 0.788]\n",
      "4: [0.865, 0.86]\n",
      "5: [0.27, 0.231]\n",
      "6: [0.704, 0.688]\n",
      "7: [0.991, 0.99]\n",
      "8: [0.994, 0.994]\n",
      "9: [0.966, 0.964]\n",
      "10: [0.998, 0.998]\n",
      "11: [0.13, 0.08]\n",
      "12: [0.332, 0.296]\n",
      "13: [0.643, 0.629]\n",
      "14: [0.403, 0.382]\n",
      "1: [0.465, 0.87]\n",
      "2: [0.688, 0.868]\n",
      "3: [0.065, 0.607]\n",
      "4: [0.37, 0.913]\n",
      "5: [0.345, 0.781]\n",
      "6: [0.441, 0.859]\n",
      "7: [0.997, 1.0]\n",
      "8: [0.983, 0.991]\n",
      "9: [0.9, 0.967]\n",
      "10: [0.993, 1.0]\n",
      "11: [0.16, 0.671]\n",
      "12: [0.38, 0.804]\n",
      "13: [0.668, 0.965]\n",
      "14: [0.507, 0.878]\n",
      "1: [0.107, 0.375]\n",
      "2: [0.17, 0.514]\n",
      "3: [0.511, 0.722]\n",
      "4: [0.835, 0.914]\n",
      "5: [0.056, 0.139]\n",
      "6: [0.637, 0.866]\n",
      "7: [1.0, 1.0]\n",
      "8: [0.989, 0.994]\n",
      "9: [0.899, 0.961]\n",
      "10: [0.993, 1.0]\n",
      "11: [0.05, 0.155]\n",
      "12: [0.168, 0.394]\n",
      "13: [0.573, 0.817]\n",
      "14: [0.327, 0.609]\n",
      "1: [0.128, 0.621]\n",
      "2: [0.17, 0.583]\n",
      "3: [0.589, 0.817]\n",
      "4: [0.91, 0.975]\n",
      "5: [0.1, 0.282]\n",
      "6: [0.63, 0.929]\n",
      "7: [0.991, 1.0]\n",
      "8: [0.99, 0.998]\n",
      "9: [0.927, 0.975]\n",
      "10: [0.99, 1.0]\n",
      "11: [0.054, 0.421]\n",
      "12: [0.136, 0.435]\n",
      "13: [0.561, 0.841]\n",
      "14: [0.286, 0.627]\n",
      "1: [0.141, 0.794]\n",
      "2: [0.188, 0.738]\n",
      "3: [0.328, 0.935]\n",
      "4: [0.517, 0.973]\n",
      "5: [0.134, 0.743]\n",
      "6: [0.369, 0.866]\n",
      "7: [0.973, 0.999]\n",
      "8: [0.803, 0.996]\n",
      "9: [0.758, 0.989]\n",
      "10: [0.923, 0.999]\n",
      "11: [0.036, 0.444]\n",
      "12: [0.163, 0.801]\n",
      "13: [0.475, 0.965]\n",
      "14: [0.195, 0.875]\n",
      "1: [0.169, 0.849]\n",
      "2: [0.29, 0.828]\n",
      "3: [0.655, 0.875]\n",
      "4: [0.765, 0.925]\n",
      "5: [0.114, 0.583]\n",
      "6: [0.518, 0.858]\n",
      "7: [0.982, 1.0]\n",
      "8: [0.988, 1.0]\n",
      "9: [0.936, 0.975]\n",
      "10: [0.996, 1.0]\n",
      "11: [0.053, 0.555]\n",
      "12: [0.162, 0.766]\n",
      "13: [0.457, 0.918]\n",
      "14: [0.22, 0.835]\n"
     ]
    }
   ],
   "source": [
    "createTable(True, \"data_result/statistics/TablesConvGeN-f1-kappa.tex\", \"$f_1~$score$~/~\\\\kappa~$score\", [0,1])\n",
    "createTable(True, \"data_result/statistics/TablesConvGeN-a-g.tex\", \"avg.~prec.~score$~/~$G-mean~score\", [2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "automated-kidney",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% ### LR\n",
      "% ### RF\n",
      "% ### GB\n",
      "% ### KNN\n",
      "% ### DoC\n"
     ]
    }
   ],
   "source": [
    "scoreFileExtensions = [\"f1\", \"kappa\", \"avg-prec\", \"gMean\"]\n",
    "\n",
    "for algo in algs:\n",
    "    print(\"% ### \" + algo)\n",
    "    heading = [\"dataset\"]\n",
    "    for g in gans:\n",
    "        if filterConvGen(g):\n",
    "            heading.append(g)\n",
    "    table = []\n",
    "    \n",
    "    avg = [[0.0 for _ in scoreFileExtensions] for h in heading[1:]]\n",
    "    cnt = 0\n",
    "    \n",
    "    for d in testSets:\n",
    "        d = cleanupName(d)\n",
    "        if d not in ignore:\n",
    "            cnt += 1\n",
    "            row = tableRow(algo, d, heading[1:])\n",
    "            table.append([ [d for _ in scoreFileExtensions] ] + row)\n",
    "\n",
    "            for (n, r) in enumerate(row):\n",
    "                for k in range(len(scoreFileExtensions)):\n",
    "                    avg[n][k] += r[k] or 0.0\n",
    "    \n",
    "    table.append([ [\"Average\" for _ in scoreFileExtensions] ] \n",
    "                 + [[v / cnt for v in a] for a in avg]\n",
    "                )\n",
    "    \n",
    "    for ri, name in enumerate(scoreFileExtensions):\n",
    "        with open(f\"data_result/statistics/{algo}-{name}.csv\", \"w\") as f:\n",
    "            f.write((\";\".join(heading)) + \"\\n\")\n",
    "            for row in table:\n",
    "                f.write((\";\".join([str(x[ri]) for x in row])) + \"\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "vertical-freeze",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----\n",
      "Repeater:\n",
      "f1 score: 0.572625\n",
      "cohens kappa score: 0.5510892857142856\n",
      "average precision score: 0.5174107142857144\n",
      "G-mean score: 0.8183035714285712\n",
      "----\n",
      "ProWRAS:\n",
      "f1 score: 0.5893035714285714\n",
      "cohens kappa score: 0.5715714285714285\n",
      "average precision score: 0.5280357142857143\n",
      "G-mean score: 0.77625\n",
      "----\n",
      "GAN:\n",
      "f1 score: 0.5724107142857141\n",
      "cohens kappa score: 0.5552499999999999\n",
      "average precision score: 0.5134642857142857\n",
      "G-mean score: 0.7599464285714282\n",
      "----\n",
      "CTGAN:\n",
      "f1 score: 0.5259285714285713\n",
      "cohens kappa score: 0.5022321428571428\n",
      "average precision score: 0.45291071428571433\n",
      "G-mean score: 0.7995535714285714\n",
      "----\n",
      "CTAB-GAN:\n",
      "f1 score: 0.5610535714285713\n",
      "cohens kappa score: 0.5405714285714285\n",
      "average precision score: 0.5081964285714287\n",
      "G-mean score: 0.7905\n",
      "----\n",
      "ConvGeN(5,maj):\n",
      "f1 score: -\n",
      "cohens kappa score: -\n",
      "average precision score: -\n",
      "G-mean score: -\n",
      "----\n",
      "ConvGeN(min,maj):\n",
      "f1 score: 0.5972857142857142\n",
      "cohens kappa score: 0.5777285714285716\n",
      "average precision score: 0.5258285714285714\n",
      "G-mean score: 0.8147142857142854\n",
      "----\n",
      "ConvGeN(5,prox):\n",
      "f1 score: -\n",
      "cohens kappa score: -\n",
      "average precision score: -\n",
      "G-mean score: -\n",
      "----\n",
      "ConvGeN(min,prox):\n",
      "f1 score: -\n",
      "cohens kappa score: -\n",
      "average precision score: -\n",
      "G-mean score: -\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for gan in gans:\n",
    "    scoreNames = [f1Score, kScore, aScore, gScore]\n",
    "    sums = {score: 0.0 for score in scoreNames}\n",
    "    counts = {score: 0 for score in scoreNames}\n",
    "\n",
    "    if not gan.startswith(\"ConvGeN\") or gan == 'ConvGeN(min,maj)':\n",
    "        for t in testSets:\n",
    "            for alg in [\"LR\", \"RF\", \"GB\", \"KNN\", \"DoC\"]:\n",
    "                if alg in statistic[gan][cleanupName(t)].keys():\n",
    "                    for score in scoreNames:\n",
    "                        sums[score] += statistic[gan][cleanupName(t)][alg][score]\n",
    "                        counts[score] += 1\n",
    "                    \n",
    "\n",
    "    print(\"----\")\n",
    "    print(f\"{gan}:\")\n",
    "    for score in scoreNames:\n",
    "        if counts[score] > 0:\n",
    "            print(f\"{score}: {sums[score] / counts[score]}\")\n",
    "        else:\n",
    "            print(f\"{score}: -\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "encouraging-carnival",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score\n",
      "['average', 0.5726249999999998, 0.5893035714285714, 0.5724107142857143, 0.5259285714285714, 0.5610535714285715, 0.5972857142857143]\n",
      "f1 score\n",
      "['average', 0.5726249999999998, 0.5893035714285714, 0.5724107142857143, 0.5259285714285714, 0.5610535714285715, 0.585732142857143]\n",
      "cohens kappa score\n",
      "['average', 0.5510892857142857, 0.5715714285714286, 0.55525, 0.502232142857143, 0.5405714285714286, 0.5777285714285715]\n",
      "cohens kappa score\n",
      "['average', 0.5510892857142857, 0.5715714285714286, 0.55525, 0.502232142857143, 0.5405714285714286, 0.5657321428571429]\n",
      "average precision score\n",
      "['average', 0.5174107142857143, 0.5280357142857143, 0.5134642857142858, 0.45291071428571433, 0.5081964285714287, 0.5258285714285715]\n",
      "average precision score\n",
      "['average', 0.5174107142857143, 0.5280357142857143, 0.5134642857142858, 0.45291071428571433, 0.5081964285714287, 0.524625]\n",
      "G-mean score\n",
      "['average', 0.8183035714285715, 0.7762500000000001, 0.7599464285714285, 0.7995535714285714, 0.7905000000000001, 0.8147142857142857]\n",
      "G-mean score\n",
      "['average', 0.8183035714285715, 0.7762500000000001, 0.7599464285714285, 0.7995535714285714, 0.7905000000000001, 0.8077499999999999]\n"
     ]
    }
   ],
   "source": [
    "scoreNames = [f1Score, kScore, aScore, gScore]\n",
    "\n",
    "def csvForScore(f, score, withDoC):\n",
    "    print(score)\n",
    "    myGans = [g for g in gans if filterConvGen(g)]\n",
    "    heading = [\"dataset\"]\n",
    "    heading.extend(myGans)\n",
    "    table = Table([latex(r) for r in heading])\n",
    "    \n",
    "    f.write((\";\".join(heading)) + \"\\n\")\n",
    "    sums = [0.0 for _ in myGans]\n",
    "    counts = [0 for _ in myGans]\n",
    "    for t in testSets:\n",
    "        row = [cleanupName(t)]\n",
    "\n",
    "        for n, gan in enumerate(myGans):\n",
    "            ganTsStat = statistic[gan][cleanupName(t)]\n",
    "            s = 0.0\n",
    "            c = 0\n",
    "\n",
    "            for alg in [\"LR\", \"RF\", \"GB\", \"KNN\", \"DoC\"]:\n",
    "                docOk = withDoC or alg != \"DoC\"\n",
    "                algOk = alg in ganTsStat.keys()\n",
    "                if docOk and algOk and score in ganTsStat[alg].keys():\n",
    "                    s += ganTsStat[alg][score]\n",
    "                    c += 1\n",
    "\n",
    "\n",
    "            counts[n] += 1\n",
    "            s = s / c\n",
    "            sums[n] += s\n",
    "            row.append(s)\n",
    "          \n",
    "        f.write(row[0] + \";\" + (\";\".join([f\"{s:0.3f}\" for s in row[1:]])) + \"\\n\")\n",
    "        \n",
    "        m = max(row[1:])\n",
    "        row[0] = latex(row[0])\n",
    "        for n, s in enumerate(row):\n",
    "            if n == 0:\n",
    "                continue\n",
    "                \n",
    "            if s == m:\n",
    "                row[n] = \"\\\\textbf{\" + f\"{s:0.3f}\" + \"}\"\n",
    "            else:\n",
    "                row[n] = f\"{s:0.3f}\"\n",
    "        table.add(row)\n",
    "        \n",
    "    row = [\"average\"]\n",
    "    row.extend([s/ c for s, c in zip(sums, counts)])\n",
    "    f.write(row[0] + \";\" + (\";\".join([f\"{s:0.3f}\" for s in row[1:]])) + \"\\n\")\n",
    "    print(row)\n",
    "    \n",
    "    m = max(row[1:])\n",
    "    row[0] = latex(row[0])\n",
    "    for n, s in enumerate(row):\n",
    "        if n == 0:\n",
    "            continue\n",
    "\n",
    "        if s == m:\n",
    "            row[n] = \"\\\\textbf{\" + f\"{s:0.3f}\" + \"}\"\n",
    "        else:\n",
    "            row[n] = f\"{s:0.3f}\"\n",
    "    table.add(row)\n",
    "    if withDoC:\n",
    "        ext = \":A\"\n",
    "    else:\n",
    "        ext = \":B\"\n",
    "    return table.showLatex(score, \"tab:results:\" + score + ext)\n",
    "\n",
    "texW = \"\"\n",
    "texWO = \"\"\n",
    "for score in scoreNames:\n",
    "    with open(f\"data_result/statistics/average-with_DoC-{score}.csv\", \"w\") as f:\n",
    "        texW += \"% -------------------------------\\n\"\n",
    "        texW += \"% \" + score + \"\\n\"\n",
    "        texW += \"% -------------------------------\\n\"\n",
    "        texW += csvForScore(f, score, True)\n",
    "        texW += \"\\n\\n\"\n",
    "\n",
    "    with open(f\"data_result/statistics/average-without_DoC-{score}.csv\", \"w\") as f:\n",
    "        texWO += \"% -------------------------------\\n\"\n",
    "        texWO += \"% \" + score + \"\\n\"\n",
    "        texWO += \"% -------------------------------\\n\"\n",
    "        texWO += csvForScore(f, score, False)\n",
    "        texWO += \"\\n\\n\"\n",
    "\n",
    "with open(f\"data_result/statistics/average-by_score-with_DoC.tex\", \"w\") as f:\n",
    "    f.write(texW)\n",
    "\n",
    "with open(f\"data_result/statistics/average-by_score-without_DoC.tex\", \"w\") as f:\n",
    "    f.write(texWO)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "581f1134",
   "metadata": {},
   "source": [
    "def dsWeight(d, score):\n",
    "    w = 0.0\n",
    "    for g in gans:\n",
    "        for a in algs:\n",
    "            x = getValueOf(g, d, a, score)\n",
    "            if x is not None:\n",
    "                w += x\n",
    "    return w\n",
    "\n",
    "\n",
    "\n",
    "dataNames = [cleanupName(d) for d in testSets]\n",
    "dataNames.sort(key=lambda d: dsWeight(d, f1Score) + dsWeight(d, kScore))\n",
    "for d in dataNames:\n",
    "    w = dsWeight(d, f1Score) + dsWeight(d, kScore)\n",
    "    print(f\"{w:0.3f} - {d}\")"
   ]
  }
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