{
 "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",
    "\n",
    "kScoreSd = kScore + \" - SD\"\n",
    "f1ScoreSd = f1Score + \" - 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",
    "    fileName = f\"data_result/{ganType}/{datasetName}.csv\"\n",
    "    r = {}\n",
    "    try:\n",
    "        f1List = []\n",
    "        kList = []\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",
    "                else:\n",
    "                    parts = line.split(\";\")\n",
    "                    if parts[0] == \"avg\":\n",
    "                        f1Avg = float(parts[5])\n",
    "                        f1Sd = math.sqrt(sum([(f1Avg - x)*(f1Avg - x) for x in f1List]) / len(f1List))\n",
    "                        kAvg = float(parts[6])\n",
    "                        kSd = math.sqrt(sum([(kAvg - x)*(kAvg - x) for x in kList]) / len(kList))\n",
    "                        r[n] = { f1Score: f1Avg, kScore: kAvg, f1ScoreSd: f1Sd, kScoreSd: kSd }\n",
    "                    elif parts[0] in indizes:\n",
    "                        f1List.append(float(parts[5]))\n",
    "                        kList.append(float(parts[6]))\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": [
    "for gan in gans:\n",
    "    f1 = { n: 0.0 for n in algs }\n",
    "    k =  { n: 0.0 for n in algs }\n",
    "    f1Sd = { n: 0.0 for n in algs }\n",
    "    kSd =  { n: 0.0 for n in algs }\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",
    "                    f1[n] += statistic[gan][ds][n][f1Score]\n",
    "                    k[n] += statistic[gan][ds][n][kScore]\n",
    "                    f1Sd[n] += statistic[gan][ds][n][f1ScoreSd]\n",
    "                    kSd[n] += statistic[gan][ds][n][kScoreSd]\n",
    "\n",
    "    avg = {}\n",
    "    for n in algs:\n",
    "        avg[n] = { f1Score: f1[n] / c, f1ScoreSd: f1Sd[n] / c, kScore: k[n] / c, kScoreSd: kSd[n] / c }\n",
    "    statistic[gan][\"Average\"] = avg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "public-collins",
   "metadata": {},
   "source": [
    "# Show Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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": 14,
   "id": "038344f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnose(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}.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8904b4b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnoseAverage(score, onlyOneBar=False):\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=0.75)\n",
    "    img.save(f\"data_result/statistics/average/statistic-Algo-Average-{score}.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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}.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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}.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
      "LR: cohens kappa score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "RF: f1 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: f1 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: f1 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: 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",
      "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",
      "Average: f1 score\n",
      "(6, 6, 5, 5)\n",
      "(1024, 165, 825)\n",
      "Average: cohens kappa 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 a in algs:\n",
    "    showDiagnose(a, f1Score)\n",
    "    showDiagnose(a, kScore)\n",
    "    \n",
    "showDiagnoseAverage(f1Score)\n",
    "showDiagnoseAverage(kScore)\n",
    "\n",
    "for t in testSets:\n",
    "    showDiagnoseDataset(cleanupName(t))\n",
    "\n",
    "showDiagnoseDatasetAverage()\n",
    "\n",
    "gans = list(statistic.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "id": "177774b0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[14, 3, 2, 10, 11, 4, 3, 3, 1],\n",
       " [11, 14, 9, 10, 11, 12, 9, 11, 8],\n",
       " [12, 5, 14, 11, 11, 9, 9, 10, 8],\n",
       " [5, 4, 3, 14, 6, 5, 4, 5, 3],\n",
       " [4, 3, 3, 9, 14, 3, 3, 3, 1],\n",
       " [11, 2, 5, 11, 11, 14, 5, 8, 5],\n",
       " [13, 5, 5, 10, 11, 9, 14, 9, 7],\n",
       " [13, 3, 4, 11, 11, 11, 6, 14, 6],\n",
       " [14, 6, 6, 11, 13, 9, 8, 9, 14]]"
      ]
     },
     "execution_count": 20,
     "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",
    "    \n",
    "tables[algs[0] + \" - \" + f1Score]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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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M4TUihsiwlFWTHBsk6VRJcySdUrZXB9YFLrD9W+CRUnat+zLwTkkrjHRd29NtT7M9bbXlGmt+RMSoS89xfLga2Hwhz39Bb8P2bsBeQK9MugewMnCzpFuAKfSVVm3/DTgeeP+iNTkiYmzqTQKQnuPwOwdYWtI+vR2StpC0/QjnHw9sI2nX2r5lal/vCexke4rtKVSJt/++I8AXgPeSAVMRMc4kOY4Dtg3sBryiPJpxNXAwcNsI598P7AK8T9LvyuManwQOkTQFeDZwce38m4F/SHph33Xuoholu/Sof6iIiA4NS1k1PZP5sH0b8KYRju01YN91wKtGuNwzB5zfK8P+pm//h4EPL0xbIyLGssytGhERMcCwzJCT5BgREa1JzzEiIqImZdWIiIgBUlaNiIjok55jREREn/QcIyIianLPMSIiYoAkx4iIiD4pq0ZERNSkrBrteQC4vqPY13UUdwy44IJu42tbdxbbuw7Jr/5N6Prf/OnzP2Wsa7rnKOkA4D2AgauAd9l+YGGvMyQ5PCIixoMmV+WQ9ExgP2Ca7Y2BSQxe+Wi+0nOMiIhWtFRWXRJ4qqSHqZYMHLiK0vyk5xgREa1ZzCWrVpU0s/bat35t238C/h/wB+B24O+2z1yUdqbnGBERrVnMnuNdtqeNdFDSysBrgecCfwO+L+lttv93YQOl5xgREa1YnF7jAg7keTlws+07bT8MnAJsvShtTc8xIiJa0/A9xz8AW0laBrgfeBkwc1EulOQYERGtaHpAju3fSPoBcBnwCHA5MH1RrpXkGBERrWn6OUfbnwI+tbjXSXKMiIjWZIaciIiIPplbNSIiomaY5lYdkmYuGklrSDpR0k2SrpF0rqR/SZot6R5JN5evzy7nbybJkl7Zd5255bwrJF0macShwZLWk/STEnNWibld3zmnSbqob9/BpW1Pr+27d3S+ExERY0OT08eNajvbDdceSQJOBWbYXsf2RsABwCttT6WawvdA21Ntv7y8bU/ggvJn3f3lvE2B/wIOHSHmZOCnwPQSc3PgP4C1a+esBLwAWEnSc/sucRfwn4v6mSMixrqGn3McNeO5rLoD8LDtI3s7bM8e6eSSTN8AvAL4laTJI8zkvgLw1xEu81bgItuPzZ1vew4wp3bO64EfA3+hmhC3nmiPAfaS9Dnb98zjs0VEDJ2UVceGjYFZC3H+NlQzK9wEzABeVTv21FJWvQ44CvifEa7xPKrna+ZlT+CE8urvod5LlSD3X4h2R0QMjWHpOY7n5Liw9gROLF+fyBMTV6+suiGwE/Dd0tOcJ0mnSpoj6ZSyvTqwLnCB7d8Cj0jauO9tXwbeKWmFeVx3397Eu3fev8CfLyKiY7OQtMivNo3n5Hg1sPmCnChpElW58/9IugX4CrCzpOX7z7V9EbAqsJqkz5Qe5exazBfUzt0N2At4Wtm1B7AycHOJM4W+tcZs/w04Hnj/SO21Pd32NNvTVnvqgnzCiIgx4tHFeLVoPCfHc4ClJe3T2yFpC0nbDzj35cAVtteyPcX2c4AfAq/rP1HShlQLaN5t+xOlRzm1HD4e2EbSrrW3LFP7ek9gpxJjClXyHrQQ5xeA9zK+7wlHxETkxXi1aNwmR9sGdgNeUR6ruBo4mMELX+5JNbK17ofAW8rXT631EE8C3ml77oCY9wO7AO+T9LvyuMYngUMkTQGeDVxcO/9m4B+SXth3nbtKe5ZeqA8dETGWmaHpOY7rnont24A3jXBsr0Ff1/adTvW4B7YnLUTM63jiYJ66Zw44v1eG/U3f/g8DH17QuBERQ6HlHuCiGtfJMSIixpiWe4CLKskxIiLa0SurDoEkx4iIaE/KqhEREX3Sc4yIiKjp4JGMRZXkGBER7UnPMSIioiYDciIiIgZIWTUiIqJPeo4RERF90nOMNtzK5nyUmZ3E/tz1LS+w1u+g7kJff313sQEO6vCz6/Pd/nTz0R3+u3ttd6EBOK2juP89StfJPceIiIgBkhwjIiL6pKwaERFRk7JqRETEAOk5RkRE9EnPMSIioiZl1YiIiAFSVo2IiOiTnmNERERNlqyKiIgYID3HiIiIPkOSHJdoOoCkNSSdKOkmSddI+pmk9Ufx+stJ+ka5/uWSZknaZwHeZ0mH17Y/Iung2vaHJL2jfH2wpD9Jml1erxqt9tfi7SrpY+XrD0p612jHiIjolBfz1aJGk6MkAacCM2yvY3sj4OPA6qMY5ijgr8B6tjcDdgKetgDvexDYXdKq/QckLQnsDRxf2/1F21PL62ej0O4nsH267c+WzWOA/UY7RkRE5x5djFeLmu457gA8bPvI3g7bs4ELJB0maY6kqyTtASDpJZJmSPqBpOskHafKzpJO7l2jnPdjSesAWwKftP1ouf6dtj9XO/dASZdKulLSp2ttewSYDhwwoN0vBS6z/ciCftDSpvMknSzpt5I+K+mtki4pn3Gdct5rJP2m9HLPlrR62b+XpK+Wz/Av4BZJWy5o/IiIoZCeIwAbA7MG7N8dmApsCrwcOEzSmuXYZsCHgI2AtYFtgLOArSQtW87ZAzgJeB5wRS8x9pO0I7AeVQKdCmwuabvaKV8D3ippxb63bjOg3R8sCfYYSSuP8Hk3BfYHNgHeDqxve0uq3u1/lHMuALYqvdwTGXnhpZnAi0c4FhExfHqTAKTnOKJtgRNsz7X9F+A8YIty7BLbt5aENxuYUnpwvwBeU0qer2bAymaSPlHuCd5Wdu1YXpcDlwEbUiVLAGz/A/guTy5hrgncWdv+BrAOVYK9HTicwS61fbvtB4GbgDPL/quAKeXrZwFnSLoKOJAqwQ9yB/CMQQck7StppqSZ999/56BTIiLGpiRHAK4GNh+wf16rlT5Y+3ouj4+oPQl4E1XJ81Lb/wSuATaVtASA7c/YngqsUItzaO1e4bq2j+6L9yXg3cCytX33A5N7G7b/UhL5o8C3qHqi82v7o7XtR2uf4yvAV21vAry3HqfP5NKOJ7E93fY029Oe+tTVRnh7RMQYlLIqAOcAS9dHj0ragmoAzR6SJklaDdgOuGQ+15oBvADYhypRYvtGqvLjIZImletP5vHkewawt6TlyrFnSnp6/aK27wFOpkqQPdcC69bavGbt2G7AnNr1fjmfdvdbEfhT+fqd8zhv/V6ciIhxoYWyqqSVauNWrpX0okVpaqPJ0bapkskryqMWVwMHU40CvRK4giqBHmT7z/O51lzgJ8DO5c+e9wCrADdKmgWcDXy0vOfMEuuiUsb8AbD8gMsfDtRHrf6cKmH3fL4MqrmSapBRbxDPmlQDexbGwcD3Jf0KuKv/Y9a+3qZ8loiI8aP5nuMRwC9sb0g1DuTaRWlm45MA2L6Nqhza78Dyqp87g6qH2Nv+YN/xDwL9+/5BVZ4cKf4RVN+s/v3L1b7+C7BMbfv3ku6WtJ7tG2y/fYTLb0U1qGdQ218y6HPZPo0B90upEvw9AJI2A6623Z88IyKGW4P3DiWtQNWx2QvA9kPAQ4tyrcyQM7KPUfUMbxjpBNtfHY1Akt5H9Ze5e9m1KvD/jca1IyLGjOaXrFqbajDltyVtSvXUwf6271vYC3U1WnXMs3297fNbinWk7U1s31C2z7J9SxuxIyJatXhl1VV7I/XLa9++qy9JNTblG+VxufuoOjoLLT3HiIhoz+L1HO+yPW0ex28FbrX9m7L9AxYxOabnGBER7WlwQE4Z2PlHSRuUXS+jeuRvoaXnGBER7Wj+niNUs5EdJ2kp4HfAIi3ikOQYERHtaTg5lvm751V6XSBJjhER0Z6WZ7pZVEmOERHRjnbKqqMiyTEiItqTnmNERESf9BwjIiJqUlaNtvztb3DaoJlaW/C5kZZpjnHtggu6ja9tu6vLXbDB/M9p0javnddqfw368iheK2XViIiIPuk5RkRE1HSwaPGiSnKMiIj2pOcYERHRJ8kxIiKiJmXViIiIAdJzjIiI6JPkGBERUZOyakRExADpOUZERPRJzzEiIqJmiOZWXWJh3yBpDUknSrpJ0jWSfiZp/dFqkKTlJH2jXP9ySbMk7bMA77Okw2vbH5F0cG37Q5LesZBt+fVCNX4xle/lSpKWknS+pPzyEhHjy6OL8WrRQiVHSQJOBWbYXsf2RsDHgdVHsU1HAX8F1rO9GbAT8LQFeN+DwO6SVu0/UJLM3sDxC9MQ21svzPmLy/arbP/N9kPAL4E92owfEdE4L8arRQvbc9wBeNj2kb0dtmcDF0g6TNIcSVdJ2gNA0kskzZD0A0nXSTpOlZ0lndy7Rjnvx5LWAbYEPmn70XL9O21/rnbugZIulXSlpE/X2vYIMB04YEC7XwpcZvuRco0Zkr5YemfXStpC0imSbpB0SC3WvfP6HP1BFuK6Pyo94qsl7Vvbf0stuf8IeOt8/j4iIoZHr6w6BD3HhS3bbQzMGrB/d2AqsCmwKnCppPPLsc2A5wG3ARcC2wBnAd+UtKzt+6h6SCeV867oJcZ+knYE1qNKoAJOl7Sd7V6srwFXSvp831u3GdDuh2xvJ2l/4DRgc+Ae4CZJX7R9d9/5gz7HoMV7FuS6e9u+R9JTy/fqhwPizQG2GOH7sC+wL8CSSz570CkREWPTkAzIWeh7jiPYFjjB9lzbfwHO4/Ef7JfYvrUkvNnAlNKD+wXwmlLyfDVVInkCSZ+QNFvSbWXXjuV1OXAZsCFVsgTA9j+A7wL79V1qTeDOvn2nlz+vAq62fbvtB4HfAWsN+IxP+hwjfC8W5Lr7SboCuLjsW6//IrbnAg9JWn7Asem2p9meNmnSaiM0IyJiDBqnPcergTcM2D+vFTgfrH09txbzJOADVL2qS23/U9I1wKaSlrD9qO3PAJ/plTdLnENtf3Me8b5ElTi/Xdt3PzB5hHY92tfGRxn8fRnpc4x03sDrSnoJ8HLgRbb/JWnGgLb1LA08MMKxiIjhMo5Hq54DLF0fPSppC6oBNHtImiRpNWA74JL5XGsG8AJgH6pEie0bgZnAIZImletP5vHkewawt6TlyrFnSnp6/aK27wFOBt5d230tsO5CftYFIulQSbstxFtWBP5aEuOGwFYjXHcV4E7bD49GOyMixoTxOCDHtoHdgFeURy2uBg6mGgV6JXAFVQI9yPaf53OtucBPgJ3Lnz3vAVYBbpQ0Czgb+Gh5z5kl1kWSrgJ+ADyp7AgcTnXvs+fnVAm7CZsA8/ysfX5B1YO8EvgfqtJqXe+fwA7Azxa/eRERY8iQlFVV5bvxT9KpVEn7hlG+7hm2XzkK15kE3AGsYfthSacA/2X7+nm9b/LkaZ4yZebihl8k1x00r2r6+HZMx6MKrp/nv4pm7bprd7EBtt22u9gXDBqC16JtNuzm/9y0l8HM2V7s4NOeLc/86KK/Xx9klu1pi9uOBTFaA3KGwceoBuaMqtFIjMXVwFElMS4F/Gh+iTEiYugMSc9xwszAUhLNmE02tjesff0Q1ajbiIjxZUgG5EyY5BgRER3LklUREREDpOcYERHRJz3HiIiImiGaBCDJMSIi2pPkGBER0Sdl1YiIiJqUVSMiIgZIzzHGvSctMtayDed/SmM26DB2x7qcug7AF3Q3baG27fYnu7uavm401wZKzzEiIqImZdWIiIgBUlaNiIjoMyQ9x4m0KkdERHSthcWOJU2SdLmkn8z/7MHSc4yIiHa0d89xf+BaYIVFvUB6jhER0Z6G13OU9Czg1cBRi9PM9BwjIqI9zQ/I+RJwELD84lwkPceIiGhHr6y66D3HVSXNrL32rV9e0i7AHbZnLW5T03OMiIj2LF7P8S7b0+ZxfBtgV0mvAiYDK0j6X9tvW9hA6TlGRER7GrznaPu/bD/L9hTgzcA5i5IYIT3HiIhoyxDNkNNIz1HSGpJOlHSTpGsk/UzS+qN4/eUkfaNc/3JJsyTtswDvs6TDa9sfkXRwbftDkt5Rvj5Y0p8kzS6vV41W+xegnbtI+nRb8SIiWtPCc44AtmfY3mVRmznqyVGSgFOBGbbXsb0R8HFg9VEMcxTwV2A925sBOwFPW4D3PQjsLmnV/gOSlgT2Bo6v7f6i7anl9bNFbaykSQv5lp9S1c2XWdSYERFjUsOPcoyWJnqOOwAP2z6yt8P2bOACSYdJmiPpKkl7AEh6iaQZkn4g6TpJx6mys6STe9co5/1Y0jrAlsAnbT9arn+n7c/Vzj1Q0qWSruzrgT0CTAcOGNDulwKX2X5kQT9oadP5kk4tPeQjJS1Rjt0r6b8l/QZ4kaQPl88+R9KHyjkflnRM+XqTcmwZ2wZmAIv8W09ExJizOL3GludkbSI5bgwMGka7OzAV2BR4OXCYpDXLsc2ADwEbAWtTjTg6C9hK0rLlnD2Ak4DnAVf0EmM/STsC61El0KnA5pK2q53yNeCtklbse+s2A9r9wZJgj5G08gifd0vgP4FNgHXK5wRYFphj+4XA/cC7gBcCWwH7SNqM6nmcdSXtBnwbeK/tf5X3zwRePELMiIjhNIF7jiPZFjjB9lzbfwHOA7Yoxy6xfWtJeLOBKaUH9wvgNaXk+WoGrCAo6RPlnuBtZdeO5XU5cBnVqn/r9c63/Q/gu8B+fZdaE7iztv0NqmQ3FbgdOJzBLrH9O9tzgRPK5wSYC/yw9tlPtX2f7XuBU4AXl8+7F/A94DzbF9auewfwjEEBJe3be85n7tw7B50SETE2TeDkeDWw+YD981qh9MHa13N5fBTtScCbqEqel9r+J3ANsGmvfGn7M7an8vgcegIOrd0rXNf20X3xvgS8m6p313M/1XMxlOv+pSTyR4FvUfUQB+nv7Pe2HygJs9emkawH3MuTE+Hk0qYnB7Sn255me9qkSavN49IREWPIBC+rngMsXR89KmkLqgE0e5TZ0lcDtgMumc+1ZgAvAPahSpTYvpGq5HhIb6CLpMk8noDOAPaWtFw59kxJT69f1PY9wMlUCbLnWmDdWpvXrB3bDZhTu94va8e2lPTckqz3AAat1X0+8DpJy5Qy8W7Ar0pp94jyvVhF0htq71m/FzMiYtwYkp7jqD/naNvlHtqXJH0MeAC4heqe4nLAFVS/Axxk+8+SNpzHteaWJUf2At5ZO/Qe4DDgRkn3UPWwPlrec6akfwMuqgbOci/wNqoyZd3hwAdr2z+nKm/2fF7S1NLWW4D3lv1rUg3s6bkI+CzVPcfzqUbq9n+OyyQdy+O/DBxl+/IyGOfrtn8r6d3AuZLOt30H1cCm/xrpexMRMZQm8mLHtm+jKof2O7C86ufOoOoh9rY/2Hf8gzwxifXuG76XEdg+gqpH1r9/udrXfwGWqW3/XtLdktazfYPtt49w+a2oBvX0/Mv2HvOKVba/AHyhb9/eta//SOm5SlodeKrtq0b6jBERQ2eIJgHIDDlP9DGqnuENI51g+6sttOPZVCNgIyLGlyTH4WP7euD6hTh/BrVe7yi249LRvmZExJgwkcuqERERT5KyakRExADpOUZERPRJzzEiIqImZdWIiIgBUlaNiIjok55jREREn/QcIyIianLPMdry4INw/QJPWzDK+tc6mUCuP73b+Kc9afG29hzd8d/73Rt21/W4YNCyAi3Stl199mmjd6kkx4iIiJoOlp5aVEmOERHRnvQcIyIi+qTnGBERUZMBOREREQMkOUZERPRJWTUiIqImZdWIiIgB0nOMiIjok55jRERETcqqERERAwxJWXWJBTlJ0hqSTpR0k6RrJP1M0vqj1QhJy0n6Rrn+5ZJmSdpnAd5nSYfXtj8i6eDa9ockvWMh2/LrhWp8QySdLWnlrtsRETGqHl2MV4vmmxwlCTgVmGF7HdsbAR8HVh/FdhwF/BVYz/ZmwE7A0xbgfQ8Cu0tatf+ApCWBvYHjF6YhtrdemPMXRGnLwvoe8P7RbktERKe8GK8WLUjPcQfgYdtH9nbYng1cIOkwSXMkXSVpDwBJL5E0Q9IPJF0n6ThVdpZ0cu8a5bwfS1oH2BL4pO1Hy/XvtP252rkHSrpU0pWSPl1r2yPAdOCAAe1+KXCZ7UfKNWZI+qKk8yVdK2kLSadIukHSIbVY987rc/QHKed8SdKvy/diy7L/YEnTJZ0JfFfScyT9snyGX0p6tqQVJV0vaYPynhNqPebTgT0X4O8nImI49O45joeeI7AxMGvA/t2BqcCmwMuBwyStWY5tBnwI2AhYG9gGOAvYStKy5Zw9gJOA5wFX9BJjP0k7AutRJdCpwOaStqud8jXgrZJW7HvrNgPa/ZDt7YAjgdOAD5TPt5ekVQaEH/Q5Blm29DjfDxxT27858FrbbwG+CnzX9vOB44Av2/478EHgWElvBla2/S0A238Flh6hXRERw2kcJceRbAucYHuu7b8A5wFblGOX2L61JLzZwJTSg/sF8JpSZnw1VYJ6AkmfkDRb0m1l147ldTlwGbAhVbIEwPY/gO8C+/Vdak3gzr59vVX4rgKutn277QeB3wFrDfiMT/ocI3wvTihtOR9YQdJKvXi27y9fv4jHS7zfo/r+Yfus0p6vAe/pu+4dwDP6g0naV9JMSTOf/BEjIsawcVRWvZqqB9TvSSXGmgdrX8/l8VGxJwFvoip5Xmr7n8A1wKaSlgCw/RnbU4EVanEOtT21vNa13b/c6peAdwPL1vbdD0weoV2P9rXxUQaP3B3pc/Tr/2vrbd83wvmPnVM+97+V9vbfZ51c9j/xjfZ029NsT4PV5hEiImIMGWdl1XOoynuPjR6VtAXVAJo9JE2StBqwHXDJfK41A3gBsA9VosT2jcBM4BBJk8r1J/N48j0D2FvScuXYMyU9vX5R2/cAJ1MlyJ5rgXUX4PMtNEmHStqttqt3v3Vb4O+lXNrv18Cby9dvBXprih9Q2roncIykp5RrCVgDuGXUP0BERFfGS8/RtoHdgFeURy2uBg6mKhFeCVxBlUAPsv3n+VxrLvATYOfyZ897gFWAGyXNAs4GPlrec2aJdZGkq4AfAMsPuPzhQH3U6s+pEnYTNgHqn/Wv5RGQI3ligq7bD3iXpCuBtwP7l8dh3gP8p+1fAecDnyznbw5c3BtQFBExLgxJz1FV7hufJJ1KlbRvGOXrnmH7leXrGcBHbM8c5RhHUN2z/OW8z5vmquPdPl8wr8r6+PbR07v9f3Pak+7Wt+fo/psaLdtww+5iX3ddd7EBtt22q8jTsGcu9n/4acvIMzdY9PdrNrOq20nNW5wBOcPgY1QDc0ZVLzE2bM78EmNExNAZkrLquJ4+zvb1wPUNx3hJQ9f9VhPXjYjoVIPlUUlrUT29sEaJNN32EYtyrXGdHCMiYgxpvgf4CNUYjsskLQ/MknSW7WsW9kJJjhER0Z4Ge462bwduL1//U9K1wDOpHhlcKEmOERHRnpZGnUqaQjXL2W8W5f1JjhER0Y7FL6uuWs0M9pjptqf3n1Sei/8h8KEyi9pCS3KMiIj2LF7P8a75PcpRJlL5IXCc7VMWNVCSY0REtKfBATllZrGjgWttf2FxrjXen3OMiIixovm5VbehmoHspWUBi9mSXrUoTU3PMSIi2tPsaNULmPeiGAssyTEiItozJDOWJjkOvVl3gX6/GBdYFbhrUd6oxZ/ncZFjj5LFiL/Yv5x2+dkXK/YozO85tJ99iGMvbvznjEoLemXVIZDkOORsL9aCjpJmtjWR71iK3XX8iRq76/gTNfZYiP+Y9BwjIiL6pOcYERFRk7JqDJEnzS4xQWJ3HX+ixu46/kSNPRbiV4akrDquFzuOiIixY9pT5JkrL/r7dWd7ix2n5xgREe0Zkv5YkmNERLRjiO45Zvq4CUaVtbpuR0RMUM1OHzdq0nOcYGxb0o+AzduMK2kL4I+2/1y23wG8Hvg9cLDtexqM/ex5Hbf9h6Zix9ghaVngAdtzu25LWyStDDwDuB+4xXb3/baUVWMMu1jSFrYvbTHmN4GXA0jaDvgs8B/AVKpRdG9oMPZPqf5L1qe1MbAa8HRgUlOBJf2Tx38c9OKb6v/eUrYb/T/YdfzShmnAi3n8h/Qc4OwmfyEqcZcA3gy8FdgCeBBYWtKdwM+o1gK8ocH4k4FdePJn/6ntqxuMuyLwAWBPYCngTmAysLqki4Gv2z63qfjzNERl1STHiWkH4H2SbgHuo/qhadvPbzDmpNoPwz2ofjD9EPihpNkNxsX2JvXtskL4R6mS9f9tOPbyfbGXB94PvBc4tcnYXceXtBewH3AzMAu4nuqH9LbARyXNAf6/Bnvu5wJnA/8FzOn1miQ9jer/wGclnWr7f0c7sKSDgdcAM6hWor+D6rOvX+JOBv7T9pWjHRv4AfBd4MW2/9bXrs2Bt0ta2/bRDcSev/QcYwzbuYOYkyQtafsR4GXAvrVjrfw7lLQe8AnghcDhwH62H24p9krAh4B3AMcDW9i+u43YHcZfFtjG9v0jtGkqsB7QVHJ8+aC/3/JLWu8Xs6c0FPtS2wePcOwLkp4OzLPcv6hsv2Iex2ZR/aLSnSHpOWZAzgRk+/fAWsBLy9f/ovl/CycA50k6jaq89CsASesCf28ysKSNJZ1A9QPxbGBj20e1kRglrSrpUOAy4BFgM9ufbCsxdhnf9tdGSozl+Gzbv2ywCU+Fqqc44LWypElN/Ruw/dMSe3L/MUmr2r7D9swmYtfivLtve5KkTzUZc76aX89x1GQSgAmo/AeZBmxge31JzwC+b3ubhuNuBawJnGn7vrJvfWBZ25c3GHcu8Eeqe49PGoxhe78GY99Hdc/n28A/B8RerNXKx3J8SQfZ/rykr/DkYpqBe4D/tX1TQ/F/YnsXSTfz5HvOAMsB37L98SbilzZcBexj++Ky/XrgUNvrNxWzFvt4YCXg3cAqVP8GzrP9kaZjj2TaEvLMpRb9/XowkwBEs3YDNqPqTWD7tnIvqlG9HxA9ZfTgllQDB17dYOi9G7z2/BzG44mh/3vcxm+mXca/tvw5Ug9pFeAUYNMmgtvepfz53EHHJU2iGiDTWHIE3gIcI2kG1aCcVYCXNhjvMbbfImkP4Cqq6tCeti9sI/Y8DUlZNclxYnqoPNJheCxJtULSUsCrqH5o7ERV6jyyyZi2vzNCWyZTDZpoMvbBIx0rj7c0qsv4tn9c/hz4/S9tuK/JNtTirEx1f/OxMqft84F/azKu7askfQb4HlXPfTvbtzYZs6fcY9+f6v/Yv1ENxLnc9r/aiD+QyYCcGNNOlvRNYCVJ+1D1rI5qMqCkV1D1EF9JNYrwe8CWtt/VZNwB7ZgE7Fhry6+A77cYfyOqxwv2pLrX2ur6el3El7Qa1ejgjXhicnqp7W+2EP89VEniWcBsYCvgIlrowUk6GlgHeD7VSNUfS/qq7a81HRv4MfBB22dLEvBh4FLgeS3EHll6jjFW2f5/JVn9A9gA+D+2z2o47BlUiWhb2zcDSDqi4ZiPKc9WvoWqfHsJsA3w3DZ+i5b0HKpktCfVoJjnANNs39J07LEQHzgOOInqe/8+4J1U90Hbsj/Vc44X295B0obAp1uKPQd4j6vBHTeX++6N3meu2dL2P6B6Tgs4XNLpLcUeWZJjjFWSPmf7o8BZA/Y1ZXOqHsvZkn4HnEiDD9/XSbqV6nGBbwAH2v6npJtbSoy/Blak+rxvsH1DiX1L07HHQvxiFdtHS9rf9nlUo5bPazH+A7YfkISkpW1fJ2mDNgLb/qKkTSW9uOz6le13z/NNo2cFSd+heq70UeACql8UOjMLzhCsuhiXuGvUGjMfSY4T0yuoylx1Ow/YN2rKaNTLqR7+3oYye4eknwOn2m5yrbkfAq+jmnxgbnmcpK07H3dSlfNWp5qR54YWY4+F+AC9xyVul/Rq4LbSprbcWp7z/BFwlqS/ljY0TtJ+VM/0nlJ2/a+k6ba/0kL4b1M90/rGsv22sm/E5yCbZnunrmIvrDzKMYFI+neq2VHWBurD55cHLrT9tpbbswTVLDX72H7j/M5fzFiimhVlT6oBQStSDXH/qe17G469ItU8snsC61INr3+l7UuajDuG4u9CVVJfC/gKsALwadutl/gkbU/1d/8L2w+1EO9K4EW1R5eWBS5qeDaqXuzZtqf27bvCdiOjg8ebJMcJpPyQXBk4FPhY7dA/3fA8lyX+M6mec7zS9kNllpAPAXvZfkbT8WvteArVSNk9gR1tL06ZZ2Fjr07Vg30zsJbtVldI6Tp+V8po1bWoVctsX9ZC3KuoZiN6oGxPppo9Z5N5v3NUYp8NHEs1AQdU/97fZftlTcceD5IcJ7CSnOqjBxtbnULSh6imbrsRWBo4gmpgwneBz9u+vcHYrwWe1RshKOk3VBOOQzUY6XtNxZ5Pu55TZijqRFvxJT2XapL5KTwxOe3adOwS/3+AvYDf8fhwENtuY7TqASV2bx7b1wHH2v5SC7GfDXwVeBFVKf3XwP5d/psbJkmOE5Ck11AlpmdQTYj8HOBa240N8ZZ0DdVI1XvKf9obqZ75ung+bx2N2BcCb7b9x7I9m2p+12WBbzf5m7SkbYG1bX+3bP8AeFo5fIjtc5qKPRbil5hXAEdTPYz+2FjFMjincZKuBzZpo4zaF3cJqsdGHqAaFCPg/CZng6rFngR8p+1bJeNJBuRMTIdQ/ac92/Zmknr34pr0QK90a/sPkn7bRmIsluolxuICV3OL3t3CBAifpuo19WxA1ZNYlmpmlqaTU9fxofq7/3ILcUYyh+o+6x1tBrX9qKTDbb+IMhtVi7HnSlpN0lJt/1IwXiQ5TkwP275b0hKSlrB9rqTPNRzzWZLqPyCfXt92g/ObUt1nfYztD9Y2V2swLsAKtq+pbd/gamUEVE0I3rSu4wMcoWo+3zOp1lQE2rnnVxwKXK5qiax6/DbKumeW+VRPcftluluAC8uzjY/NROSG5/MdL5IcJ6a/SVqOagThcZLuoHo4vEkH9m23uWzObyTtY/tb9Z2S3ks1IUCTVqpv2N69trl6w7HHQnyATYC3U81I89g9P1qaYxT4DvA5+sq6LfkwVS99rqQHyj7bXqGF2LeV1xI8eV7dmI/cc5yASinxfqr/NG+lGtp+nBtcxkjV2n1XdPDbc2/g0Y+oeg293srmVAODXmf7Lw3G/jFwpMsSRrX9uwD/brvJCdc7j19iXQc8v6vynqTzbG/fReyxQNIKVAn5SauyxMiSHCeoMqXYeq7mXVwGmNTkfx5JM4HnUiWnC6lGzl3cm96qDZJeyuPzSl7d0mCU9YCfUH3eemLeGtjF9m8bjr8u1VJdncQvbTgJ+A/brd7zq8X/AtUvRqfTQVlX0u5UA3JMNUPOj1qKO43qof9er/HvwN69snrMW5LjBKRqsvF9gafZXqf8AD+y6eefShLekuoH89ZU813+mWoCgvc3GbtLkpam6qE/lpiB43vPvk2A+DOoJt6+lPbv+SHp3AG723qU4+tUEy/0njXcA7jJ9gdaiH0l8AHbvYXFtwW+3sYEBONBkuMEVB5l2BL4je3Nyr6r2ngwucRalmq07DbAO4AlbK/dRuy2STrT9o5dt6NLZVaaJ2nrUY4uSboa2Lh3O6E83nFVk49N1WJf6L4FzAfti8EyIGdierDMUAOApCVpeL5NSW+h6i1Opeo9XAr8hurZxz83GbtjTY+GXWiSLrP9grbijbUkWCaF+LPt37QQ7nrg2UDvwfu1gCtbiAtwiaql6U6g+v+9BzBD0gug1dHCQynJcWI6T9LHgaeqWrrq/VRrvzVpOnAd1cLG57dxr2uMWLHccxrI9ikjHWuQOoj5ePBqWrOHga/Z/kkHTXghsImkJW3v3HCsVYBrJfVGRW8BXFQer2i6tDy1/Pmpvv1b0+5o4aGUsuoEVEo776Za9FdUay0e1eRI0jJjx6Y8fr9xA+B2qkVnL2pjcEwXJN0NnMbghGTbe7fcJCQdYvuTbcetxX8G1Ry7W7mdRX87M1JJuWes9arjcUmOE5SkpYANqX6DvL6DqbVWB94AHEC16HArazu2re0S5oKQtCpwdxeP1XRF0tY8eW7X73bWoA60XE4eekt03YBon6o19W4Cvkw1MfGNkhotL0l6vqT3SfqupBup7jluR7WE0QubjN2xDVStX/kEkl4saZ2mg0vaStIMSadI2qzMEjMH+IukRtfWq19f0oqSjpZ0paTjyy9HrZD0PeD/UT1OsUV5TWsrfl9bzpb08/KcadteCHxS1RqqMR/pOU5A5aHsXWzfWLbXoVrXcMMGY9afb/z1RFkZQNI5wIdsX9m3fxrwKduvaTj+TKo5VFekuu+7s+2LJW0InNAbrdxQ7Md6zZKOonps51vA7sD2tl/XVOy+dlwLbDQWesoTqaQ87DIgZ2K6o5cYi9/R8KTM9dKipKUkbVw2r7f98AhvGw+e3p8YAWzPlDSlhfhL2j4TQNJ/9yZ7t31db7RyS6b58YV3vyjpnS3GngOsQXWPu1O2e1O6tfIgfsrJiy7JcWK6WtLPgJOp7jm+Ebi0N6qyyRGUkl5CNdflLVSDVNaS9E7b5zcVs2OT53HsqS3Er88len/fsaZ7Uk+X9GGqv+cVJKnWe2vzls6qwDVlxGgrkxBI2sn2L8rXK1ItEbcFVaI+oMkpC2tt+B6wDjAbmFt2m2oN1ZiPlFUnIEnfnsfhRkdQSpoFvMX29WV7fary3uZNxeySpBOAcwZMev5uYEfbezQcfy7VigyiSsb/6h0CJtt+SoOx+x8h+LrtOyWtQbXA9Tuait3XjtYnIRgLJeWxVE4eRkmO0SpJV/ZPXzVo33hRBp6cCjzE46W0acBSwG7jfAKECasvOc6ulZSftN1gG74P7Ge783LyMEpZdQIqvbVvAKvb3ljS84FdbR/SQvhZko4Gvle230q7y1e1qpTPtla1oHTvPutPu3yus0zf9zqqHnxjq3JIehvVHK4Dl4kqA8HWtH1BQ/EvsL2tpH/yxBKyaH7ZqLFQUm69nDyepOc4AUk6j2p9xW/W5ladY3vjeb9zVGIvDXyAali9gPOpym0PzvONsVjKc62vAt4C7AT8kGoB3sZmRpK0P7A31S8/s4A7qe7BrgtsD9wFfMz2DU21oStjoaQ8kee0HQ1JjhOQpEttbyHp8lpybLzUU2bmubKNJByVMj3gnsArgXOBk4Cv2J7SUvxJVNOUbUP1CMP9wLXAz23/oY02lHasTDWvaX3UZuYWjRGlrDox3VVKWr2VAt5AC8PcbT8q6QpJz27zB+MEdwbwK6oJ3m8GkHREW8FtzwXOKq9OSPofYC+qR5Z6Jd5G5xbtsqTccTl53EhynJg+QPVA+IaS/gTcTHXvrw1rUj1KcgnVKEog90EatDnwZuBsSb8DTgRamapP0v+Zx2Hb/p822gG8CVin5SkSVwEuL6OzRywpNxHY9rblz+Xnd26MLGXVCawMzFiCqtS1h+3jWoiZ+yAdKdPY7Qm8nurZt1NtT28w3n8O2L0s1aT3q9herqnYfe34IfDvthud6GJA3M5LyiknL7okxwlE0gpUvcZnUq0UcXbZ/ghwhe3XNhh7MvA+qt+crwKOtv1IU/GiogGLLZd7v68A3mz7XS21Y3lgf6rEeDJweFvJqkzVdxrVA/gTZtTmSOVk21mqagEkOU4gkk4D/kq1TNTLgJWpnrfb3/bshmOfRLWG36+AnYHf296/yZgB9UFXHcV/GvBhqrL9d4AjbP+15TZcDXyT6peyx+4BNjwJQOclZUnXA5u0XE4eN3LPcWJZ2/Ym8NisHXcBz7b9zxZib1SLfTRwyXzOj9HR2WLLkg6jmhFmOtUP6XubijUfd9n+cssx7xuw77GSMtDG/dY5wEo0PG/yeJWe4wSivrUF+7fHa+yJTB0utizpUaoy5iN0OGpS0hdKO07niWXVVu69dVVSnqjl5NGS5DiB1ObZhCfOtdn4D6suY09k+SUEJJ07YHfj9966Lil3UU4eT5IcI8YxSfdRTXB+Yd/+FwO32b6ppXZsC6xn+9uSVgWW7z13OR71lZS/1kVJWdJ5tgeODo/5S3KMGMfU8WLLJdanqCZb38D2+qoW/P2+7W0ajtvlg/idl5S7LicPuwzIiRjful5sGWA3YDPgshL7tnIfrmldPojf5nqVI+mNUt6qtq/RmYHGkyTHiPGt68WWAR6ybUm96QqXbSOo7SMkfZXHH8R/Po8/iP/2Fh/E76SkbHuHpmOMZ0mOEePbpZL28eDFlttaKuxkSd8EVpK0D9VKHd+az3tGRddzu9ZLysC3qZ4r/l+qZN1UzE6XChsvcs8xYhwbK4stl9VBdqS653aG7caT1Rh5EH82paRcWwGn0cW9J/JSYaMpyTFiAuhbbPlqd7jYclvGwtyuki6xvWXvkZpSUr6oyeRY4nY+r+uwS3KMiEaU0u3TbB9Wtm8FVqDqPR5k+xsttqWrB/E/AqxHNZftoVQ9uuNtf6Xp2LF4khwjohGSLgV2sn132b7c9mZlEvozbW/XQhvGwtyurZaUx0I5eTzIgJyIaMoSvcRYfB/A9gOSGh8pO1bmdi3JsM0BQWNhXtehl55jRDRC0o221x2wfwngRttrNxy/swfxx0pJuculwobdWHhQNSLGpzMlHTJg/38DZzYd3PYStp9qe3nbK9Rey7cwQ837gGNq23eWmKtRLTjdKElPK9/7K6kqhC+w/dEkxgWXsmpENOVA4ChJNwJXlH2bAjOB97TZkA4exO+spDxWysnDLmXViGiUpLWB55XNa9qa7LwWv/W5XbssKY+FeV3Hg5RVI6JpXwSWB85pOzEWuwG7Ugaq2L6ttKdJnZWUOy4njxtJjhHRtC8A2wLXSPq+pDeUxzna8pCrElmbc7seCKwj6UZJPyyvG6lmqflIC/GBqpws6V3l61UlPbet2MMuZdWIaEVt1pZ9qJ5/bKUX0+WD+F2WlLtaKmy8SHKMiMaVQSivAfYAXgD8xPZ/tBi/9bldS9zTgJOA02wPev6wydizaXle1/Eko1UjolGSTgJeCPwC+BowY6QVI5rSwYP4PV+g+oXgUEmXUCXKn9h+oIXYnSwVNl6k5xgRjZK0E3BWWT6qzbhj4kH8Erv1knLmdV08SY4R0ThJWwNTqFWrbH+34Zidz+1a4nZWUu6qnDwepKwaEY2S9D1gHWA20Os9Gmg0OdLx3K7QfUm5w3Ly0EvPMSIaJelaYCO3/MOm67ldS6zWS8pjqZw8zNJzjIimzQHWAG5vOe6Zkg6x/cm+/a3M7Qpg+xeStpY0hfZKyu8Ddqpt32n7Wb1yMpDkuACSHCOiaatSTQBwCdW0ZgDY3rXhuJ3P7dpRSbnzcvJ4kLJqRDRK0vaD9ts+r6X4XT6I33pJeSyUk8eDTB8XEY0qSfA6qvlMlweubSsxFl3O7dorKbep06XCxov0HCOiUZLeBBwGzKAaFPJi4EDbP2gp/vZUj1G8Gmj1QXxJ5wJTS9xWSsrlYf+jgC0YUE7OElYLJskxIhol6QrgFb2FdiWtBpxte9OW29HFg/idlZS7Xips2KWsGhFNW6JvBfq7aflnTxmI8nqqkZxbAN9pI27HJeWulwobakmOEdG0X0g6Q9JekvYCfgr8rK3g5UH8a6l6jV8D1mlxhpo3UZVU3wi8CfiNpDe0EZvulwobaimrRkQjJK0LrG77Qkm7U/2gFvBX4Li2ejNdze1aYndeUu5qqbBhl+ccI6IpXwI+DmD7FOAUAEnTyrHXtNGIjh7E7+m0pDxgXtdWysnjQZJjRDRliu0r+3fanlkSVSs6nNsVSkkZOKFs70FLJeWu53UddkmOEdGUed3fanOmlmm0/yB+r6R8YF9J+SLguJaa8W3gLV2Uk8eDJMeIaMqlkvax/a36zjIx9qwW29HF3K5fouOScsfl5KGXATkR0QhJqwOnAg/xeDKcBiwF7Gb7zy21o4sH8efY3niEY1fZ3qSp2LU4A8vJtvdrOvZ4kJ5jRDTC9l+ArSXtAPQSxU9tn9NyUw5uOR6MjZJy6+Xk8STJMSIaZftc4NwO459XerFblF2X9I0gbcJYKCl3tVTYuJCyakSMa13M7ToWSspdlJPHkyTHiBjXunwQv6+kfHWbJeWulwobdkmOETGu9Q+AKesaXtHGoJiudVBOHjcyt2pEjHedzu3alY7ndR166TlGxLg0VuZ27cpYmNd1mKXnGBHj1ZeAf0L1IL7tD9s+gKrX+KUO29WWzpcKG2Z5lCMixqsxMbdrhzqb13U8SHKMiPFqLDyI37oxMq/r0EsXOyLGq0sl7dO/s4O5Xdv2JSZ2OXlUZEBORIxLY+FB/C6MhXldx4OUVSNiXBpDc7u2bUKWk0dbeo4REeOIpBOAc0aY13VH23t007LhkuQYETGOTNRy8mhLcoyIGIe6nNd1PEhyjIiI6JNHOSIiIvokOUZERPRJcoyIiOiT5BgREdEnyTEiIqLP/w+h5/O09yhtxQAAAABJRU5ErkJggg==\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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+ApwAvLHv/d8C9ihLrERETChLMul42+XYJMcF2wSYvQjnPwe4ZCHn7AkcX179z+TcQ5UgD1jQBSTtJ2mWpFm3p/AaEUNkWMqqSY4NknSypHmSflS21wI2AM61/TvgoVJ2rfsS8DZJK490XdszbE+3PX3NFRtrfkTEmEvPcWK4AthqEc/fsrdhe3dgb6BXJt0DeBJwg6QbqVamfkxp1fbfgOOA9yxekyMixqeO5lZdLEmOC/ZrYBlJ+/Z2SNpa0gtHOP84YHtJr6rtW7729Z7ALran2p5KlXj77zsCfB54FxkwFRETTJLjBGDbwO7ATuXRjCuAQ4CbRzj/XmA34N2Sfl8e1/g4cKikqcDTgQtq598A3C3puX3XuYNqlOwyY/6hIiI6NCxl1fRMFsL2zcAbRji294B9VwMvH+FyTxtwfq8Me2Hf/g8CH1yUtkZEjGeZWzUiImKAYZkhJ8kxIiJak55jRERETcqqERERA6SsGhER0Sc9x4iIiD7pOUZERNTknmNERMQASY4RERF9UlaNiIioSVk12rMcsHk3oT83190EHgf22qvb+Dq2u+/9Xpd1FhqAww7rLvbBB3cXG7r7d3f3BdPH7FrpOUZERPRJzzEiIqImZdWIiIgBUlaNiIjok55jRERETReLFi+uJMeIiGhNeo4RERE1GZATERExQMqqERERfdJzjIiI6JOeY0RERM0w3XMckmYuHklPkXSCpOslXSnpLEn/kjRH0l2Sbihfn1nO30KSJb2s7zrzy3lzJV0iabsFxNxQ0mkl5uwSc4e+c06RdH7fvkNK255c23fP2HwnIiLGhylTFv/VajvbDdceSQJOBmbaXt/2s4EDgZfZngacChxse5rtl5a37QmcW/6su7ectznwX8CnR4i5LPBTYEaJuRXwPmC92jmrAlsCq0p6Zt8l7gD+c3E/c0TEeNd71nFxXm2ayGXVHYEHbR/Z22F7zkgnl2T6OmAn4DeSlrV934BTVwb+OsJl3gycb/vUWsx5wLzaOa8FfgLcCryRxybabwF7S/qs7bsW8NkiIoZOyqrjwybA7EU4f3vgBtvXAzOBl9eOLVfKqlcDRwH/PcI1ngNcspA4ewLHl1d/D/UeqgR5wCK0OyJiaAxLz3EiJ8dFtSdwQvn6BB6buHpl1Y2BXYBjSk9zgSSdLGmepB+V7bWADYBzbf8OeEjSJn1v+xLwNkkrL+C6+0maJWnW7X8f9eeLiOjYbCQt9qtNEzk5XgFsNZoTJS1FVe78v5JuBL4M7Cpppf5zbZ8PrAGsKelTpUc5pxZzy9q5uwN7A6uVXXsATwJuKHGmUpVW69f/G3Ac8J6R2mt7hu3ptqevucpoPmFExDjx8BK8WjSRk+OvgWUk7dvbIWlrSS8ccO5Lgbm217U91fYzgB8Cr+4/UdLGwFLAnbY/VnqU08rh44DtJb2q9pbla1/vCexSYkylSt6PSY7F54F3MbHvCUfEZOQleLVowiZH2wZ2B3Yqj1VcARwC3Dzg9D2pRrbW/RB4U/l6uVoP8UTgbbbnD4h5L7Ab8G5Jvy+Pa3wcOFTSVODpwAW1828A7pb03L7r3FHas8wifeiIiPHMDE3PcUL3TGzfDLxhhGN7D/q6tu9Uqsc9sL3UIsS8mscO5ql72oDze2XYC/v2fxD44GjjRkQMhZZ7gItrQifHiIgYZ1ruAS6uJMeIiGhHr6w6BJIcIyKiPSmrRkRE9EnPMSIioqaDRzIWV5JjRES0Z0h6jhP2OceIiBhnWnjOUdKBkq4oU3ceX1ZLWmRJjhER0Z4GZ8iR9DTg/cB025tQzWY2aBayhUpZNSIi2tN8WfUJVLOaPUg1feegWdEWKj3HiIhoz5L1HNforUhUXvs95tL2n4HPAX8EbgH+bvv0xWlmeo7D7k7g2G5Cb75XN3F7dqLlBd5qzti82yF3x8zt7rPr2Mn72Y9ZrB+zY+jgbsKufOcYXWjJJwG4w/b0kQ5KehLw78Azgb8B35f0Ftv/u6iB0nOMiIj2NDsg56VUi9bfbvtB4EfAdovTzCTHiIhoT7NLVv0R2FbS8mVB+pcAVy1OM1NWjYiIdjQ8t6rtCyX9ALgEeAi4FJixONdKcoyIiPY0fMva9ieATyzpdZIcIyKiPUMyQ06SY0REtCNLVkVERAyQiccjIiL6pOcYERFRkyWrIiIiBkjPMSIios+QJMfGZ8iR9BRJJ0i6XtKVkn4maaMxvP6Kkr5ern+ppNmS9h3F+yzp8Nr2QZIOqW1/QNJby9eHSPqzpDnl9fKxan8t3qskfaR8vb+kt491jIiITi3J7Dgtl2MbTY5l+p6TgZm217f9bOCjwFpjGOYo4K/Ahra3AHYBVhvF++4HXiNpjf4Dkp4A7AMcV9v9BdvTyutnY9Dux7B9qu3PlM1vUa1JFhExsTS82PFYabrnuCPwoO0jeztszwHOlXRYWan5ckl7AEh6kaSZkn4g6WpJ31NlV0kn9a5RzvuJpPWBbYCP2364XP9225+tnXuwpIslXSbpk7W2PUQ1rdCBA9r9YuAS2w+N9oOWNp0t6SRJv5P0GUlvlnRR+Yzrl/NeKenC0ss9U9JaZf/ekr5SPsO/gBslbTPa+BERQyE9RwA2AWYP2P8aYBqwOdUs6odJWrsc2wL4APBsYD1ge+AMqslkVyjn7AGcCDwHmNtLjP0k7QxsSJVApwFbSdqhdspXgTdLWqXvrdsPaPf+JcF+qyyLMsjmwAHApsBewEa2t6Hq3b6vnHMusG3p5Z4AfGiEa80CXjDCsYiI4dObBCA9xxE9Hzje9nzbtwJnA1uXYxfZvqkkvDnA1NKD+wXwylLyfAVwSv9FJX2s3BPsrfy8c3ldSjUR7cZUyRIA23cDx/D4EubawO217a8D61Ml2FuAwxnsYtu32L4fuB7orf52OTC1fL0O8EtJl1OtzvacEa51G/DUQQck7ddb7PP2+0d4d0TEeJTkCMAVwFYD9i9otdL6j/v5PDqi9kTgDVQlz4tt/wO4Ethc0hQA25+yPQ1YuRbn07V7hRvYProv3heBdwAr1PbdCyzb27B9a0nkDwPfpOqJLqztD9e2H659ji8DX7G9KfCuepw+y5Z2PI7tGban256+5jIjvDsiYjxKWRWAXwPL1EePStqaagDNHpKWkrQmsANw0UKuNRPYEtiXKlFi+zqq8uOhkpYq11+WR5PvL4F9JK1Yjj1N0pPrF7V9F3ASVYLsuQrYoNbmtWvHdgfm1a73q4W0u98qwJ/L129bwHkb9eJEREwIKatWbJsqmexUHrW4AjiEahToZcBcqgT6Idt/Wci15gOnAbuWP3veCawOXCdpNnAm8OHyntNLrPNLGfMHwEoDLn84UB+1+nOqhN3zP2VQzWVUg4x6g3jWphrYsygOAb4v6TfAHf0fs/b19uWzRERMHEPSc2x8EgDbN1OVQ/sdXF71c2dS9RB72/v3Hd8f6N93N1V5cqT4RwBHDNi/Yu3rW4Hla9t/kHSnpA1tX2t7rxEuvy3VoJ5BbX/RoM9l+xQG3C+lSvB3AUjaArjCdn/yjIgYbkMyCUBmyBnZR6h6hteOdILtr4xFIEnvBvamGsULVS/2/4zFtSMixo0sWTX8bF8DXNNSrCOB+rOgZ7QRNyKidZl4PCIiok96jhEREX3Sc4yIiKjJPceIiIgBkhwjIiL6pKwaERFRk7JqRETEAOk5RkRE9EnPMSIioiZl1WjLg+tsxa2Hzeok9rEHL/ycJs3dbEjqMw3Y6XPdxd7r2O5iA+jY7v7eN9u5s9AAfK6jv/e7L5g+dhcbkv+2SY4REdGe9BwjIiJqOlh6anElOUZERHvSc4yIiOiT5BgREVGTsmpERMQA6TlGRET0SXKMiIioSVk1IiJigPQcIyIi+qTnGBERUTNEc6tOWdQ3SHqKpBMkXS/pSkk/k7TRWDVI0oqSvl6uf6mk2ZL2HcX7LOnw2vZBkg6pbX9A0lsXsS2/XaTGL6HyvVxV0tKSzpGUX14iYmJ5eAleLVqk5ChJwMnATNvr23428FFgrTFs01HAX4ENbW8B7AKsNor33Q+8RtIa/QdKktkHOG5RGmJ7u0U5f0nZfrntv9l+APgVsEeb8SMiGucleLVoUXuOOwIP2j6yt8P2HOBcSYdJmifpckl7AEh6kaSZkn4g6WpJ31NlV0kn9a5RzvuJpPWBbYCP2364XP9225+tnXuwpIslXSbpk7W2PQTMAA4c0O4XA5fYfqhcY6akL5Te2VWStpb0I0nXSjq0FuueBX2O/iCLcN0flx7xFZL2q+2/sZbcfwy8eSF/HxERw6NXVh2CnuOilu02AWYP2P8aYBqwObAGcLGkc8qxLYDnADcD5wHbA2cA35C0gu1/UvWQTiznze0lxn6SdgY2pEqgAk6VtIPtXqyvApdJ+p++t24/oN0P2N5B0gHAKcBWwF3A9ZK+YPvOvvMHfY5zBzRzNNfdx/ZdkpYr36sfDog3D9h6hO/DfsB+AOus8/RBp0REjE9DMiBnke85juD5wPG259u+FTibR3+wX2T7ppLw5gBTSw/uF8ArS8nzFVSJ5DEkfUzSHEk3l107l9elwCXAxlTJEgDbdwPHAO/vu9TawO19+04tf14OXGH7Ftv3A78H1h3wGR/3OUb4Xozmuu+XNBe4oOzbsP8itucDD0haacCxGban256+2mprjtCMiIhxaIL2HK8AXjdg/+NKjDX3176eX4t5IvBeql7Vxbb/IelKYHNJU2w/bPtTwKd65c0S59O2v7GAeF+kSpzfru27F1h2hHY93NfGhxn8fRnpc4x03sDrSnoR8FLgebb/JWnmgLb1LAPcN8KxiIjhMoFHq/4aWKY+elTS1lQDaPaQtJSkNYEdgIsWcq2ZwJbAvlSJEtvXAbOAQyUtVa6/LI8m318C+0hasRx7mqQn1y9q+y7gJOAdtd1XARss4mcdFUmflrT7IrxlFeCvJTFuDGw7wnVXB263/eBYtDMiYlyYiANybBvYHdipPGpxBXAI1SjQy4C5VAn0Q7b/spBrzQdOA3Ytf/a8E1gduE7SbOBM4MPlPaeXWOdLuhz4AfC4siNwONW9z56fUyXsJmwKLPCz9vkFVQ/yMuC/qUqrdb1/AjsCP1vy5kVEjCMTtKyK7ZuBNww4dHB51c+dSdVD7G3v33d8f6B/393AuxYQ/wjgiAH7V6x9fSuwfG37D5LulLSh7Wttv2gBbawfW3EUn+OJts8f8N4Rr0v1C8FjlJ7ySsDdZdebgP/qPy8iYmgN0dyqYzUgZxh8hGpgzpiy/bIxutQVwFG2H5S0NPBj29eM0bUjIsaHidpzHFYl0YzbZGN749rXD1CNuo2ImFgaTnKSVqWaTGYTqn7qPr3q3qKYNMkxIiI61k5Z9QjgF7ZfV6pwyy/sDYMkOUZERHsa7DlKWplq8OXe8EgV7oHFudZkuucYERFdW7JHOdaQNKv22q/v6utRTfjy7bJwxVGSVlicZqbnGBER7VjySQDusD19AcefQPX8/PtsXyjpCKrBmP9nUQOl5xgREe1pdrTqTcBNti8s2z+gSpaLLMkxIiLa0+AMOWXymT9JelbZ9RLgysVpZsqqERHRjnbmVn0f8L0yUvX3wNsX5yJJjhER0Z6GH+Uoawwv6L7kqCQ5Drkn3jSbtQ5e0KIozTnssG7ngVrr2G4+N8AZm3c8B9ZB3YU+5vTuvu8AxxzbXWxd1u3f+04HdfO9X/mmMbzYkKzKkeQYERHtGKIlq5IcIyKiPUMy8XiSY0REtCc9x4iIiD7pOUZERNTknmNERMQASY4RERF9UlaNiIioSVk1IiJigPQcIyIi+qTnGBERUTNEZdVGlqyS9BRJJ0i6XtKVkn4maaMxvP6Kkr5ern+ppNmS9h3F+yzp8Nr2QZIOqW1/QNJby9eHSPqzpDnl9fKxav8o2rmbpE+2FS8iojUNLlk1lsY8OUoScDIw0/b6tp8NfBRYawzDHAX8FdjQ9hbALsBqo3jf/cBrJK3Rf0DSE4B9gONqu79ge1p5/WxxGytpqUV8y0+BV0lafnFjRkSMS80udjxmmug57gg8aPvI3o6yhMi5kg6TNE/S5ZL2AJD0IkkzJf1A0tWSvqfKrpJO6l2jnPcTSesD2wAft/1wuf7ttj9bO/dgSRdLuqyvB/YQMAM4cEC7XwxcYvuh0X7Q0qZzJJ1ceshHSppSjt0j6f9JuhB4nqQPls8+T9IHyjkflPSt8vWm5djytg3MBHYbbVsiIsa9Jek1DnvPEdgEmD1g/2uAacDmwEuBwyStXY5tAXwAeDawHrA9cAawraQVyjl7ACcCzwHm9hJjP0k7AxtSJdBpwFaSdqid8lXgzZJW6Xvr9gPavX9JsN+S9KQRPu82wH8CmwLrl88JsAIwz/ZzgXupFtx8LrAtsK+kLYAvAhtI2h34NvAu2/8q758FvGCEmBERw2kS9xxH8nzgeNvzbd8KnA1sXY5dZPumkvDmAFNLD+4XwCtLyfMVwCn9F5X0sXJP8Oaya+fyuhS4BNiYKlkCYPtu4Bjg/X2XWhu4vbb9dapkNw24BTicwS6y/Xvb84Hjy+cEmA/8sPbZT7b9T9v3AD8CXlA+797AscDZts+rXfc24KmDAkraT9IsSbNuv3+EVkVEjEeTODleAWw1YP+CVums/4ifz6OjaE8E3kBV8rzY9j+AK4HNe+VL25+yPQ1YuRbn07V7hRvYProv3heBd1D17nruBZbtbdi+tSTyh4FvUvUQB+nv7Pe27ysJs9emkWwI3MPjE+GypU2PD2jPsD3d9vQ1l1nAlSMixpNJXlb9NbBMffSopK2pBtDsIWkpSWsCOwAXLeRaM4EtgX2pEiW2r6MqOR7aG+giaVkeTUC/BPaRtGI59jRJT65f1PZdwElUCbLnKmCDWpvXrh3bHZhXu96vase2kfTMkqz3AM4d8DnOAV4taflSJt4d+E0p7R5RvherS3pd7T0b9WJGREwYQ9JzHPPnHG273EP7oqSPAPcBN1LdU1wRmEv1O8CHbP9F0sYLuNZ8SadRlR7fVjv0TuAw4DpJd1H1sD5c3nO6pH8Dzq8GznIP8BaqMmXd4cD+te2fU5U3e/5H0rTS1huBd5X9a1MN7Ok5H/gM1T3Hc6hG6vZ/jkskfYdHfxk4yvalZTDO12z/TtI7gLMknWP7NqqBTf810vcmImIoTeYZcmzfTFUO7XdwedXPnUnVQ+xt7993fH8em8R69w3fxQhsH0HVI+vfv2Lt61uB5Wvbf5B0p6QNbV9re68RLr8t1aCenn/Z3mNBscr254HP9+3bp/b1nyg9V0lrAcvZvnykzxgRMXSGaBKAzJDzWB+h6hleO9IJtr/SQjueTjUCNiJiYklyHD62rwGuWYTzZ1Lr9Y5hOy4e62tGRIwLk7msGhER8Tgpq0ZERAyQnmNERESf9BwjIiJqUlaNiIgYIGXViIiIPuk5RkRE9EnPMSIioib3HKM161DNMtuBgw9e+DlN2muv7n4FnTu3s9AA7DTS5IYt+Nyx3f7qf9BhC1rkpll7XdZZaADU2fd++thdKskxIiKipoOlpxZXkmNERLQnPceIiIg+6TlGRETUZEBORETEAEmOERERfVJWjYiIqElZNSIiYoD0HCMiIvqk5xgREVGTsmpERMQAQ1JWnTKakyQ9RdIJkq6XdKWkn0naaKwaIWlFSV8v179U0mxJ+47ifZZ0eG37IEmH1LY/IOmti9iW3y5S4xsi6UxJT+q6HRERY+rhJXi1aKHJUZKAk4GZtte3/Wzgo8BaY9iOo4C/Ahva3gLYBVhtFO+7H3iNpDX6D0h6ArAPcNyiNMT2doty/miUtiyqY4H3jHVbIiI65SV4tWg0PccdgQdtH9nbYXsOcK6kwyTNk3S5pD0AJL1I0kxJP5B0taTvqbKrpJN61yjn/UTS+sA2wMdtP1yuf7vtz9bOPVjSxZIuk/TJWtseAmYABw5o94uBS2w/VK4xU9IXJJ0j6SpJW0v6kaRrJR1ai3XPgj5Hf5Byzhcl/bZ8L7Yp+w+RNEPS6cAxkp4h6VflM/xK0tMlrSLpGknPKu85vtZjPhXYcxR/PxERw6F3z3Ei9ByBTYDZA/a/BpgGbA68FDhM0trl2BbAB4BnA+sB2wNnANtKWqGcswdwIvAcYG4vMfaTtDOwIVUCnQZsJWmH2ilfBd4saZW+t24/oN0P2N4BOBI4BXhv+Xx7S1p9QPhBn2OQFUqP8z3At2r7twL+3fabgK8Ax9jeDPge8CXbfwf2B74j6Y3Ak2x/E8D2X4FlRmhXRMRwmkDJcSTPB463Pd/2rcDZwNbl2EW2byoJbw4wtfTgfgG8spQZX0GVoB5D0sckzZF0c9m1c3ldClwCbEyVLAGwfTdwDPD+vkutDdzet+/U8uflwBW2b7F9P/B7YN0Bn/Fxn2OE78XxpS3nACtLWrUXz/a95evn8WiJ91iq7x+2zyjt+Srwzr7r3gY8tT+YpP0kzZI06/a7RmhRRMR4NIHKqldQ9YD6LWjF0ftrX8/n0VGxJwJvoCp5Xmz7H8CVwOaSpgDY/pTtacDKtTiftj2tvDawfXRfvC8C7wBWqO27F1h2hHY93NfGhxk8cnekz9Gv/6+tt/3PEc5/5Jzyuf+ttLf/PuuyZf9j32jPsD3d9vQ1R3NnNiJiPJhgZdVfU5X3Hhk9KmlrqgE0e0haStKawA7ARQu51kxgS2BfqkSJ7euAWcChkpYq11+WR5PvL4F9JK1Yjj1N0pPrF7V9F3ASVYLsuQrYYBSfb5FJ+rSk3Wu7evdbnw/8vZRL+/0WeGP5+s3AueXrA0tb9wS+JemJ5VoCngLcOOYfICKiKy30HEteulTSaYvbzIUmR9sGdgd2Ko9aXAEcQlUivAyYS5VAP2T7Lwu51nzgNGDX8mfPO4HVgeskzQbOBD5c3nN6iXW+pMuBHwArDbj84UB91OrPqRJ2EzYF6p/1r+URkCN5bIKuez/wdkmXAXsBB5THYd4J/Kft3wDnAB8v528FXNAbUBQRMSG003M8gKrTsdhG9YiB7ZupyqH9Di6v+rkzqXqIve39+47vTzUIpb7vbuBdC4h/BHDEgP0r1r6+FVi+tv0HSXdK2tD2tbZftIA21o+tOIrP8UTb59e2f2j7v/radkjf9o1U5eR+/1Y754O1/XsBXxtwfkTEcGphhhxJ61CNafkU8MGFnD6iJRmQMww+QjUwZ0zZftlYX3OAebZ/1UKciIj2LFlZdY3eYMTy2m9AhC8CH2IJ0/CEnj7O9jXANQ3HeFFD1/1mE9eNiOjUkvUc77A9faSDknYDbrM9W9KLliTQhE6OERExjjT/SMb2wKskvZxqtP/Kkv7X9lsW9UITvawaERHjSYMDcmz/l+11bE+lejrg14uTGCE9x4iIaFOWrIqIiKhpcaab/icOFlWSY0REtCc9x4iIiD5DsthxkmNERLSjhUkAxkqSY0REtCfJMSIios+QlFVVzSsew0rS7cAfluASawB3jFFzhil21/Ena+yu40/W2Esa/xm211zSBkxfWp71lMV/v/7E7AXNkDOW0nMcckv6D1bSrLb+sY2n2F3Hn6yxu44/WWOPh/iPGJL+WJJjRES0J/ccIyIiajJaNYbIjEkau+v4kzV21/Ena+zxEL8yJGXVDMiJiIhWTH+iPOtJi/9+3Z4BORERMRENSX8syTEiItoxRPccs57jJKPKul23IyImqQbXcxxL6TlOMrYt6cfAVm3GlbQ18CfbfynbbwVeSzWBwSG272ow9tMXdNz2H5uKHeOHpBWA+2zP77otbZH0JOCpwL3Ajba777elrBrj2AWStrZ9cYsxvwG8FEDSDsBngPcB06hG0b2uwdg/pfovqdo+A2sCTwaWaiqwpH/w6I+DXnxT/d9b2naj/we7jl/aMB14AY/+kJ4HnNnkL0Ql7hSq1eDfDGwN3A8sU2aV+hkww/a1DcZfFtiNx3/2n9q+osG4qwDvBfYElgZuB5YF1pJ0AfA122c1FX+BhqismuQ4Oe0IvFvSjcA/qX5o2vZmDcZcqvbDcA+qH0w/BH4oaU6DcbG9aX1b0lTgw1TJ+v9rOPZKfbFXAt4DvAs4ucnYXceXtDfwfuAGYDZwDdUP6ecDH5Y0D/g/DfbczwLOBP4LmNfrNUlajer/wGcknWz7f8c6sKRDgFdSLbZ7IXAb1WffqMRdFvhP25eNdWzgB8AxwAts/62vXVsBe0laz/bRDcReuPQcYxzbtYOYS0l6gu2HgJcA+9WOtfLvUNKGwMeA5wKHA++3/WBLsVcFPgC8FTgO2Nr2nW3E7jD+CsD2tu8doU3TgA2BppLjSwf9/ZZf0nq/mD2xodgX2z5khGOfl/RkYIHl/sVle6cFHJtN9YtKd4ak55gBOZOQ7T8A6wIvLl//i+b/LRwPnC3pFKry0m8AJG0A/L3JwJI2kXQ81Q/EM4FNbB/VRmKUtIakTwOXAA8BW9j+eFuJscv4tr86UmIsx+fY/lWDTVgOqp7igNeTJC3V1L8B2z8tsZftPyZpDdu32Z7VROxanHf0bS8l6RNNxlyoXll1CAbkZBKASaj8B5kOPMv2RpKeCnzf9vYNx90WWBs43fY/y76NgBVsX9pg3PnAn6juPT5uMIbt9zcY+59U93y+DfxjQOzPNxW76/iSPmT7fyR9mccX0wzcBfyv7esbin+a7d0k3cDj7zkDrAh80/ZHm4hf2nA5sK/tC8r2a4FP296oqZi12McBqwLvAFan+jdwtu2Dmo49kulT5FlLL/77dX8mAYhm7Q5sQdWbwPbN5V5Uo3o/IHrK6MFtqAYOvKLB0Ps0eO2FOYxHE0P/97iN30y7jH9V+XOkHtLqwI+AzZsIbnu38uczBx2XtBTVAJnGkiPwJuBbkmZSDcpZHXhxg/EeYftNkvYALqeqDu1p+7w2Yi/QkJRVkxwnpwfKIx2GR5JUKyQtDbyc6ofGLlSlziObjGn7uyO0ZVmqQRNNxj5kpGPl8ZZGdRnf9k/KnwO//6UN/2yyDbU4T6K6v/lImdP2OcC/NRnX9uWSPgUcS9Vz38H2TU3G7Cn32A+g+j/2b1QDcS61/a824g9kMiAnxrWTJH0DWFXSvlQ9q6OaDChpJ6oe4suoRhEeC2xj++1Nxh3QjqWAnWtt+Q3w/RbjP5vq8YI9qe61trq+XhfxJa1JNTr42Tw2Ob3Y9jdaiP9OqiSxDjAH2BY4nxZ6cJKOBtYHNqMaqfoTSV+x/dWmYwM/Afa3faYkAR8ELgae00LskaXnGOOV7c+VZHU38Czg/9o+o+Gwv6RKRM+3fQOApCMajvmI8mzlm6jKtxcB2wPPbOO3aEnPoEpGe1INinkGMN32jU3HHg/xge8BJ1J9798NvI3qPmhbDqB6zvEC2ztK2hj4ZEux5wHvdDW444Zy373R+8w129i+G6rntIDDJZ3aUuyRJTnGeCXps7Y/DJwxYF9TtqLqsZwp6ffACTT48H2dpJuoHhf4OnCw7X9IuqGlxPhbYBWqz/s629eW2Dc2HXs8xC9Wt320pANsn001avnsFuPfZ/s+SUhaxvbVkp7VRmDbX5C0uaQXlF2/sf2OBb5p7Kws6btUz5U+DJxL9YtCZ2bDLwVrLMEl7hizxixEkuPktBNVmatu1wH7xkwZjXop1cPf21Nm75D0c+Bk202uNfdD4NVUkw/ML4+TtHXn43aqct5aVDPyXNti7PEQH6D3uMQtkl4B3Fza1JabynOePwbOkPTX0obGSXo/1TO9Pyq7/lfSDNtfbiH8t6meaX192X5L2Tfic5BNs71LV7EXVR7lmEQk/QfV7CjrAfXh8ysB59l+S8vtmUI1S82+tl+/sPOXMJaoZkXZk2pA0CpUQ9x/avuehmOvQjWP7J7ABlTD619m+6Im446j+LtRldTXBb4MrAx80nbrJT5JL6T6u/+F7QdaiHcZ8Lzao0srAOc3PBtVL/Yc29P69s213cjo4IkmyXESKT8knwR8GvhI7dA/3PA8lyX+06iec7zM9gNllpAPAHvbfmrT8WvteCLVSNk9gZ1tL0mZZ1Fjr0XVg30jsK7tVldI6Tp+V8po1XWpVctsX9JC3MupZiO6r2wvSzV7zqYLfueYxD4T+A7VBBxQ/Xt/u+2XNB17IkhynMRKcqqPHmxsdQpJH6Cauu06YBngCKqBCccA/2P7lgZj/zuwTm+EoKQLqSYch2ow0rFNxV5Iu55RZijqRFvxJT2TapL5qTw2Ob2q6dgl/n8DewO/59HhILbdxmjVA0vs3jy2rwa+Y/uLLcR+OvAV4HlUpfTfAgd0+W9umCQ5TkKSXkmVmJ5KNSHyM4CrbDc2xFvSlVQjVe8q/2mvo3rm64KFvHUsYp8HvNH2n8r2HKr5XVcAvt3kb9KSng+sZ/uYsv0DYLVy+FDbv24q9niIX2LOBY6mehj9kbGKZXBO4yRdA2zaRhm1L+4UqsdG7qMaFCPgnCZng6rFXgr4btu3SiaSDMiZnA6l+k97pu0tJPXuxTXpvl7p1vYfJf2ujcRYLN1LjMW5ruYWvbOFCRA+SdVr6nkWVU9iBaqZWZpOTl3Hh+rv/kstxBnJPKr7rLe1GdT2w5IOt/08ymxULcaeL2lNSUu3/UvBRJHkODk9aPtOSVMkTbF9lqTPNhxzHUn1H5BPrm+7wflNqe6zPsL2/rXNNRuMC7Cy7Str29e6WhkBVROCN63r+ABHqJrP93SqNRWBdu75FZ8GLlW1RFY9fhtl3dPLfKo/cvtluhuB88qzjY/MROSG5/OdKJIcJ6e/SVqRagTh9yTdRvVweJMO7ttuc9mcCyXta/ub9Z2S3kU1IUCTVq1v2H5NbXOthmOPh/gAmwJ7Uc1I88g9P1qaYxT4LvBZ+sq6LfkgVS99vqT7yj7bXrmF2DeX1xQeP69uLETuOU5CpZR4L9V/mjdTDW3/nhtcxkjV2n1zO/jtuTfw6MdUvYZeb2UrqoFBr7Z9a4OxfwIc6bKEUW3/bsB/2G5ywvXO45dYVwObdVXek3S27Rd2EXs8kLQyVUJ+3KosMbIkx0mqTCm2oat5F5cHlmryP4+kWcAzqZLTeVQj5y7oTW/VBkkv5tF5Ja9oaTDKhsBpVJ+3npi3A3az/buG429AtVRXJ/FLG04E3me71Xt+tfifp/rF6FQ6KOtKeg3VgBxTzZDz45biTqd66L/Xa/w7sE+vrB4LluQ4CamabHw/YDXb65cf4Ec2/fxTScLbUP1g3o5qvsu/UE1A8J4mY3dJ0jJUPfRHEjNwXO/Zt0kQfybVxNsX0/49PySdNWB3W49yfI1q4oXes4Z7ANfbfm8LsS8D3mu7t7D484GvtTEBwUSQ5DgJlUcZtgEutL1F2Xd5Gw8ml1grUI2W3R54KzDF9nptxG6bpNNt79x1O7pUZqV5nLYe5eiSpCuATXq3E8rjHZc3+dhULfZ57lvAfNC+GCwDcian+8sMNQBIegINz7cp6U1UvcVpVL2Hi4ELqZ59/EuTsTvW9GjYRSbpEttbthVvvCXBMinEX2xf2EK4a4CnA70H79cFLmshLsBFqpamO57q//cewExJW0Kro4WHUpLj5HS2pI8Cy6lauuo9VGu/NWkGcDXVwsbntHGva5xYpdxzGsj2j0Y61iB1EPPR4NW0Zg8CX7V9WgdNeC6wqaQn2N614VirA1dJ6o2K3ho4vzxe0XRpeVr58xN9+7ej3dHCQyll1UmolHbeQbXor6jWWjyqyZGkZcaOzXn0fuOzgFuoFp09v43BMV2QdCdwCoMTkm3v03KTkHSo7Y+3HbcW/6lUc+xu63YW/e3MSCXlnvHWq45HJTlOUpKWBjam+g3ymg6m1loLeB1wINWiw62s7di2tkuYoyFpDeDOLh6r6Yqk7Xj83K7HdNagDrRcTh56U7puQLRP1Zp61wNfopqY+DpJjZaXJG0m6d2SjpF0HdU9xx2oljB6bpOxO/YsVetXPoakF0hav+ngkraVNFPSjyRtUWaJmQfcKqnRtfXq15e0iqSjJV0m6bjyy1ErJB0LfI7qcYqty2t6W/H72nKmpJ+X50zb9lzg46rWUI2FSM9xEioPZe9m+7qyvT7VuoYbNxiz/nzjbyfLygCSfg18wPZlffunA5+w/cqG48+imkN1Far7vrvavkDSxsDxvdHKDcV+pNcs6Siqx3a+CbwGeKHtVzcVu68dVwHPHg895clUUh52GZAzOd3WS4zF72l4UuZ6aVHS0pI2KZvX2H5whLdNBE/uT4wAtmdJmtpC/CfYPh1A0v/rTfZu++reaOWWTPejC+9+QdLbWow9D3gK1T3uTtnuTenWyoP4KScvviTHyekKST8DTqK65/h64OLeqMomR1BKehHVXJc3Ug1SWVfS22yf01TMji27gGPLtRC/PpfovX3Hmu5JPVnSB6n+nleWpFrvrc1bOmsAV5YRo61MQiBpF9u/KF+vQrVE3NZUifrAJqcsrLXhWGB9YA4wv+w21RqqsRApq05Ckr69gMONjqCUNBt4k+1ryvZGVOW9rZqK2SVJxwO/HjDp+TuAnW3v0XD8+VQrMogqGf+rdwhY1vYTG4zd/wjB12zfLukpVAtcv7Wp2H3taH0SgvFQUh5P5eRhlOQYrZJ0Wf/0VYP2TRRl4MnJwAM8WkqbDiwN7D7BJ0CYtPqS45xaSflx2w224fvA+213Xk4eRimrTkKlt/Z1YC3bm0jaDHiV7UNbCD9b0tHAsWX7zbS7fFWrSvlsO1ULSvfus/60y+c6y/R9r6bqwTe2Koekt1DN4TpwmagyEGxt2+c2FP9c28+X9A8eW0IWzS8bNR5Kyq2XkyeS9BwnIUlnU62v+I3a3KrzbG+y4HeOSexlgPdSDasXcA5Vue3+Bb4xlkh5rvXlwJuAXYAfUi3A29jMSJIOAPah+uVnNnA71T3YDYAXAncAH7F9bVNt6Mp4KClP5jltx0KS4yQk6WLbW0u6tJYcGy/1lJl5LmsjCUelTA+4J/Ay4CzgRODLtqe2FH8pqmnKtqd6hOFe4Crg57b/2EYbSjueRDWvaX3UZuYWjRGlrDo53VFKWr2VAl5HC8PcbT8saa6kp7f5g3GS+yXwG6oJ3m8AkHREW8FtzwfOKK9OSPpvYG+qR5Z6Jd5G5xbtsqTccTl5wkhynJzeS/VA+MaS/gzcQHXvrw1rUz1KchHVKEog90EatBXwRuBMSb8HTgBamapP0v9dwGHb/u822gG8AVi/5SkSVwcuLaOzRywpNxHY9vPLnyst7NwYWcqqk1gZmDGFqtS1h+3vtRAz90E6Uqax2xN4LdWzbyfbntFgvP8csHsFqknvV7e9YlOx+9rxQ+A/bDc60cWAuJ2XlFNOXnxJjpOIpJWpeo1Po1op4syyfRAw1/a/Nxh7WeDdVL85Xw4cbfuhpuJFRQMWWy73fncC3mj77S21YyXgAKrEeBJweFvJqkzVdwrVA/iTZtTmSOVk21mqahSSHCcRSacAf6VaJuolwJOonrc7wPachmOfSLWG32+AXYE/2D6gyZgB9UFXHcVfDfggVdn+u8ARtv/achuuAL5B9UvZI/cAG54EoPOSsqRrgE1bLidPGLnnOLmsZ3tTeGTWjjuAp9v+Rwuxn12LfTRw0ULOj7HR2WLLkg6jmhFmBtUP6XuairUQd9j+Ussx/zlg3yMlZaCN+63zgFVpeN7kiSo9x0lEfWsL9m9P1NiTmTpcbFnSw1RlzIfocNSkpM+XdpzKY8uqrdx766qkPFnLyWMlyXESqc2zCY+da7PxH1Zdxp7M8ksISDprwO7G7711XVLuopw8kSQ5Rkxgkv5JNcH5eX37XwDcbPv6ltrxfGBD29+WtAawUu+5y4mor6T81S5KypLOtj1wdHgsXJJjxASmjhdbLrE+QTXZ+rNsb6Rqwd/v296+4bhdPojfeUm563LysMuAnIiJrevFlgF2B7YALimxby734ZrW5YP4ba5XOZLeKOVta/sanRloIklyjJjYul5sGeAB25bUm65whTaC2j5C0ld49EH8zXj0Qfy9WnwQv5OSsu0dm44xkSU5RkxsF0va14MXW25rqbCTJH0DWFXSvlQrdXxzIe8ZE13P7VovKQPfpnqu+H+pknVTMTtdKmyiyD3HiAlsvCy2XFYH2ZnqntsvbTeerMbJg/hzKCXl2go4jS7uPZmXChtLSY4Rk0DfYstXuMPFltsyHuZ2lXSR7W16j9SUkvL5TSbHErfzeV2HXZJjRDSilG5Xs31Y2b4JWJmq9/gh219vsS1dPYh/ELAh1Vy2n6bq0R1n+8tNx44lk+QYEY2QdDGwi+07y/altrcok9CfbnuHFtowHuZ2bbWkPB7KyRNBBuRERFOm9BJj8X0A2/dJanyk7HiZ27UkwzYHBI2HeV2HXnqOEdEISdfZ3mDA/inAdbbXazh+Zw/ij5eScpdLhQ278fCgakRMTKdLOnTA/v8HnN50cNtTbC9neyXbK9deK7UwQ827gW/Vtm8vMdekWnC6UZJWK9/7y6gqhFva/nAS4+ilrBoRTTkYOErSdcDcsm9zYBbwzjYb0sGD+J2VlMdLOXnYpawaEY2StB7wnLJ5ZVuTndfitz63a5cl5fEwr+tEkLJqRDTtC8BKwK/bTozF7sCrKANVbN9c2tOkzkrKHZeTJ4wkx4ho2ueB5wNXSvq+pNeVxzna8oCrElmbc7seDKwv6TpJPyyv66hmqTmohfhAVU6W9Pby9RqSntlW7GGXsmpEtKI2a8u+VM8/ttKL6fJB/C5Lyl0tFTZRJDlGROPKIJRXAnsAWwKn2X5fi/Fbn9u1xD0FOBE4xfag5w+bjD2Hlud1nUgyWjUiGiXpROC5wC+ArwIzR1oxoikdPIjf83mqXwg+LekiqkR5mu37WojdyVJhE0V6jhHRKEm7AGeU5aPajDsuHsQvsVsvKWde1yWT5BgRjZO0HTCVWrXK9jENx+x8btcSt7OSclfl5IkgZdWIaJSkY4H1gTlAr/dooNHkSMdzu0L3JeUOy8lDLz3HiGiUpKuAZ7vlHzZdz+1aYrVeUh5P5eRhlp5jRDRtHvAU4JaW454u6VDbH+/b38rcrgC2fyFpO0lTaa+k/G5gl9r27bbX6ZWTgSTHUUhyjIimrUE1AcBFVNOaAWD7VQ3H7Xxu145Kyp2XkyeClFUjolGSXjhov+2zW4rf5YP4rZeUx0M5eSLI9HER0aiSBK+mms90JeCqthJj0eXcrr2Scps6XSpsokjPMSIaJekNwGHATKpBIS8ADrb9g5biv5DqMYpXAK0+iC/pLGBaidtKSbk87H8UsDUDyslZwmp0khwjolGS5gI79RbalbQmcKbtzVtuRxcP4ndWUu56qbBhl7JqRDRtSt8K9HfS8s+eMhDltVQjObcGvttG3I5Lyl0vFTbUkhwjomm/kPRLSXtL2hv4KfCztoKXB/Gvouo1fhVYv8UZat5AVVJ9PfAG4EJJr2sjNt0vFTbUUlaNiEZI2gBYy/Z5kl5D9YNawF+B77XVm+lqbtcSu/OScldLhQ27POcYEU35IvBRANs/An4EIGl6OfbKNhrR0YP4PZ2WlAfM69pKOXkiSHKMiKZMtX1Z/07bs0qiakWHc7tCKSkDx5ftPWippNz1vK7DLskxIpqyoPtbbc7UMp32H8TvlZQP7ispnw98r6VmfBt4Uxfl5IkgyTEimnKxpH1tf7O+s0yMPbvFdnQxt+sX6bik3HE5eehlQE5ENELSWsDJwAM8mgynA0sDu9v+S0vt6OJB/Hm2Nxnh2OW2N20qdi3OwHKy7fc3HXsiSM8xIhph+1ZgO0k7Ar1E8VPbv265KYe0HA/GR0m59XLyRJLkGBGNsn0WcFaH8c8uvdity66L+kaQNmE8lJS7WipsQkhZNSImtC7mdh0PJeUuyskTSZJjRExoXT6I31dSvqLNknLXS4UNuyTHiJjQ+gfAlHUN57YxKKZrHZSTJ4zMrRoRE12nc7t2peN5XYdeeo4RMSGNl7lduzIe5nUdZuk5RsRE9UXgH1A9iG/7g7YPpOo1frHDdrWl86XChlke5YiIiWpczO3aoc7mdZ0IkhwjYqIaDw/it26czOs69NLFjoiJ6mJJ+/bv7GBu17Z9kcldTh4TGZATERPSeHgQvwvjYV7XiSBl1YiYkMbR3K5tm5Tl5LGWnmNExAQi6Xjg1yPM67qz7T26adlwSXKMiJhAJms5eawlOUZETEBdzus6ESQ5RkRE9MmjHBEREX2SHCMiIvokOUZERPRJcoyIiOiT5BgREdHn/wcdw0DyitD31gAAAABJRU5ErkJggg==\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": [
      "GB - 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": [
      "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": [
      "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": [
      "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"
    }
   ],
   "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": 22,
   "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": 23,
   "id": "d0e2faa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tableRow(algo, dataset, myGans):\n",
    "    row = []\n",
    "    for gan in myGans:\n",
    "        v = getValueOf(gan, dataset, algo, f1Score)\n",
    "        w = getValueOf(gan, dataset, algo, kScore)\n",
    "        row.append((v, w))\n",
    "    return row"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebffad52",
   "metadata": {
    "scrolled": false
   },
   "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",
    "    a = 0.0\n",
    "    b = 0.0\n",
    "    \n",
    "    for (x,y) in row:\n",
    "        a = max(a, x)\n",
    "        b = max(b, y)\n",
    "    \n",
    "    return (a, b)\n",
    "    \n",
    "with open(\"data_result/statistics/Tables.tex\", \"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 = [\"dataset ($f_1~$score$~/~\\\\kappa~$score)\"]\n",
    "        \n",
    "        myGans = gans\n",
    "        if algo[0:3] == \"DoC\":\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",
    "\n",
    "                print((algo, d, myGans))\n",
    "                print(row)\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",
    "        tableB = None\n",
    "        if len(table.heading) > 5:\n",
    "            heading = [table.heading[0]] + table.heading[6:]\n",
    "            tableB = Table(heading)\n",
    "            tableB.sizes = [table.sizes[0]] + table.sizes[6:]\n",
    "            tableB.rows = [\n",
    "                [r[0]] + r[6:]\n",
    "                for r in table.rows\n",
    "                ]\n",
    "            table.heading = table.heading[0:6]\n",
    "            table.sizes = table.sizes[0:6]\n",
    "            table.rows = [\n",
    "                r[0:6]\n",
    "                for r in table.rows\n",
    "                ]\n",
    "\n",
    "        if tableB is not None:\n",
    "            latexFile.write(table.showLatex(algo + \" (1)\", \"tab:results:\" + algo + \":A\") + \"\\n\")\n",
    "            latexFile.write(\"\\n\")\n",
    "            latexFile.write(tableB.showLatex(algo + \" (2)\", \"tab:results:\" + algo + \":B\") + \"\\n\")\n",
    "        else:\n",
    "            latexFile.write(table.showLatex(algo, \"tab:results:\" + algo + \":A\") + \"\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ad677e",
   "metadata": {},
   "source": [
    "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, 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, heading[1:])\n",
    "            table.append([(d,d)] + row)\n",
    "\n",
    "            for (n, r) in enumerate(row):\n",
    "                avg[n][0] += r[0] or 0.0\n",
    "                avg[n][1] += r[1] or 0.0\n",
    "    \n",
    "    table.append([(\"Average\", \"Average\")] + [(a / cnt, b / cnt) for (a,b) in avg])\n",
    "    \n",
    "    with open(f\"data_result/statistics/{algo}-f1.csv\", \"w\") as f:\n",
    "        f.write((\",\".join(heading)) + \"\\n\")\n",
    "        for row in table:\n",
    "            f.write((\",\".join([str(x[0]) for x in row])) + \"\\n\")\n",
    "\n",
    "    with open(f\"data_result/statistics/{algo}-kappa.csv\", \"w\") as f:\n",
    "        f.write((\",\".join(heading)) + \"\\n\")\n",
    "        for row in table:\n",
    "            f.write((\",\".join([str(x[1]) for x in row])) + \"\\n\")\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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