{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "pretty-performer",
   "metadata": {},
   "outputs": [],
   "source": [
    "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\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extensive-future",
   "metadata": {},
   "source": [
    "# Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "warming-department",
   "metadata": {},
   "outputs": [],
   "source": [
    "ignoreSet = [\"ozone_level\", \"yeast_me2\"]\n",
    "\n",
    "gans = [g.replace(\"SimpleGAN\", \"GAN\") for g in generators.keys()]\n",
    "algs = [\"LR\", \"RF\", \"GB\", \"KNN\", \"DoG\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edf5b592",
   "metadata": {},
   "outputs": [],
   "source": [
    "testSets = [t for t in testSets if t[0:7] == \"folding\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "seasonal-greek",
   "metadata": {},
   "source": [
    "# ProWRAS Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "needed-birmingham",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset  = [\n",
    "    \"abalone9-18\",\n",
    "    \"abalone_17_vs_7_8_9_10\",\n",
    "    \"car-vgood\",\"car_good\",\n",
    "    \"flare-F\",\n",
    "    \"hypothyroid\",\n",
    "    \"kddcup-guess_passwd_vs_satan\",\n",
    "    \"kr-vs-k-three_vs_eleven\",\n",
    "    \"kr-vs-k-zero-one_vs_draw\",\n",
    "    \"shuttle-2_vs_5\",\n",
    "    \"winequality-red-4\",\n",
    "    \"yeast4\",\n",
    "    \"yeast5\",\n",
    "    \"yeast6\",\n",
    "    \"ozone_level\",\n",
    "    \"yeast_me2\",\n",
    "    \"Average\"\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e8179d",
   "metadata": {},
   "source": [
    "knn_ProWRAS_f1 = [0.384,0.347,0.818,0.641,0.301,0.553,1.0,0.94,0.9,1.0,0.141,0.308,0.714,0.545,0.556,0.339,0.538]\n",
    "knn_ProWRAS_k = [0.35,0.328,0.81,0.622,0.263,0.528,1.0,0.938,0.896,1.0,0.093,0.268,0.704,0.531,0.526,0.305,0.515]\n",
    "\n",
    "lr_ProWRAS_f1 = [0.488,0.315,0.407,0.103,0.341,0.446,0.99,0.928,0.853,1.0,0.158,0.308,0.591,0.326,0.347,0.295,0.472]\n",
    "lr_ProWRAS_k = [0.446,0.287,0.371,0.033,0.3,0.407,0.99,0.926,0.847,1.0,0.119,0.268,0.574,0.3,0.319,0.254,0.441]\n",
    "\n",
    "gb_ProWRAS_f1 = [0.385,0.335,0.959,0.863,0.320,0.803,0.998,0.995,0.969,1.0,0.156,0.335,0.735,0.514,0.329,0.225,0.600]\n",
    "gb_ProWRAS_k = [0.341,0.310,0.957,0.857,0.291,0.794,0.998,0.995,0.967,1.0,0.115,0.303,0.726,0.501,0.303,0.328,0.589]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6121f46b",
   "metadata": {},
   "outputs": [],
   "source": [
    "statistic = { }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e825147c",
   "metadata": {},
   "source": [
    "statistic = { \"ProWRAS\": {} }\n",
    "for (n, f1, k) in zip(dataset, lr_ProWRAS_f1, lr_ProWRAS_k):\n",
    "    if n in ignoreSet:\n",
    "        continue\n",
    "    \n",
    "    if n not in statistic[\"ProWRAS\"]:\n",
    "        statistic[\"ProWRAS\"][n] = {}\n",
    "    \n",
    "    statistic[\"ProWRAS\"][n][\"LR\"] = { kScore: k, f1Score: f1 }\n",
    "\n",
    "for (n, f1, k) in zip(dataset, gb_ProWRAS_f1, gb_ProWRAS_k):\n",
    "    if n in ignoreSet:\n",
    "        continue\n",
    "    \n",
    "    if n not in statistic[\"ProWRAS\"]:\n",
    "        statistic[\"ProWRAS\"][n] = {}\n",
    "    \n",
    "    statistic[\"ProWRAS\"][n][\"GB\"] = { kScore: k, f1Score: f1 }\n",
    "\n",
    "    \n",
    "for (n, f1, k) in zip(dataset, knn_ProWRAS_f1, knn_ProWRAS_k):\n",
    "    if n in ignoreSet:\n",
    "        continue\n",
    "    \n",
    "    if n not in statistic[\"ProWRAS\"]:\n",
    "        statistic[\"ProWRAS\"][n] = {}\n",
    "    \n",
    "    statistic[\"ProWRAS\"][n][\"KNN\"] = { kScore: k, f1Score: f1 }\n",
    "    \n",
    "dataset = list(filter(lambda n: n not in ignoreSet, dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selective-connecticut",
   "metadata": {},
   "source": [
    "# Load data from CSV files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a23177bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanupName(name):\n",
    "    return name.replace(\"folding_\", \"\").replace(\"imblearn_\", \"\").replace(\"kaggle_\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "304d69ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = [cleanupName(d) for d in testSets]"
   ]
  },
  {
   "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",
    "        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\":\n",
    "                        n = \"DoG\"\n",
    "                    newBlock = False\n",
    "                elif line == \"---\":\n",
    "                    newBlock = True\n",
    "                else:\n",
    "                    parts = line.split(\";\")\n",
    "                    if parts[0] == \"avg\":\n",
    "                        r[n] = { f1Score: float(parts[5]), kScore: 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": [
    "for gan in gans:\n",
    "    if gan not in statistic:\n",
    "        statistic[gan] = {}\n",
    "    \n",
    "    for ds in testSets:\n",
    "        if ds != \"Average\":\n",
    "            statistic[gan][cleanupName(ds)] = loadDiagnoseData(gan, ds)\n",
    "            \n",
    "            d = cleanupName(ds)\n",
    "            if d not in dataset:\n",
    "                dataset.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "unable-entrance",
   "metadata": {},
   "outputs": [],
   "source": [
    "for gan in statistic.keys():\n",
    "    f1 = { n: 0.0 for n in algs }\n",
    "    k =  { 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",
    "\n",
    "    avg = {}\n",
    "    for n in algs:\n",
    "        avg[n] = { f1Score: f1[n] / c, kScore: k[n] / c }\n",
    "    statistic[gan][\"Average\"] = avg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20ba8039",
   "metadata": {},
   "source": [
    "convGanMax = {}\n",
    "for g in gans:\n",
    "    if g[0:7] == \"convGAN\":\n",
    "        for d in statistic[g].keys():\n",
    "            if d not in convGanMax.keys():\n",
    "                convGanMax[d] = {}\n",
    "            for a in statistic[g][d].keys():\n",
    "                if a not in convGanMax[d].keys():\n",
    "                    convGanMax[d][a] = {}\n",
    "                for s in statistic[g][d][a].keys():\n",
    "                    x = statistic[g][d][a][s]\n",
    "                    if x is None:\n",
    "                        x = 0.0\n",
    "                    if s not in convGanMax[d][a].keys():\n",
    "                        convGanMax[d][a][s] = x\n",
    "                    else:\n",
    "                        convGanMax[d][a][s] = max(x, convGanMax[d][a][s])\n",
    "\n",
    "gs = []\n",
    "for g in list(gans):\n",
    "    if g[0:7] == \"convGAN\":\n",
    "        del statistic[g]\n",
    "    else:\n",
    "        gs.append(g)\n",
    "\n",
    "statistic[\"convGAN\"] = convGanMax\n",
    "gs.append(\"convGAN\")\n",
    "gans = gs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "public-collins",
   "metadata": {},
   "source": [
    "# Show Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "extra-taiwan",
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "def drawTransparentRect(img, rect, color, opacity=1.0):\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]):\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",
    "    \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(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 - row[i])\n",
    "\n",
    "        for (n, i) in enumerate(indices):\n",
    "            v = 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",
    "\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",
    "            return statistic[g][\"Average\"][algo][score] \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)\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",
    "        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)\n",
    "        img.save(f\"data_result/statistics/byDataset/statistic-Classifier-{dataset}-{score}.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
      "DoG: f1 score\n",
      "Missing 'DoG' in ('Repeater', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoG' in ('ProWRAS', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoG' in ('GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoG' in ('ctGAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoG' in ('Repeater', 'abalone9-18')\n",
      "Missing 'DoG' in ('ProWRAS', 'abalone9-18')\n",
      "Missing 'DoG' in ('GAN', 'abalone9-18')\n",
      "Missing 'DoG' in ('ctGAN', 'abalone9-18')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'abalone9-18')\n",
      "Missing 'DoG' in ('Repeater', 'car_good')\n",
      "Missing 'DoG' in ('ProWRAS', 'car_good')\n",
      "Missing 'DoG' in ('GAN', 'car_good')\n",
      "Missing 'DoG' in ('ctGAN', 'car_good')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'car_good')\n",
      "Missing 'DoG' in ('Repeater', 'car-vgood')\n",
      "Missing 'DoG' in ('ProWRAS', 'car-vgood')\n",
      "Missing 'DoG' in ('GAN', 'car-vgood')\n",
      "Missing 'DoG' in ('ctGAN', 'car-vgood')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'car-vgood')\n",
      "Missing 'DoG' in ('Repeater', 'flare-F')\n",
      "Missing 'DoG' in ('ProWRAS', 'flare-F')\n",
      "Missing 'DoG' in ('GAN', 'flare-F')\n",
      "Missing 'DoG' in ('ctGAN', 'flare-F')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'flare-F')\n",
      "Missing 'DoG' in ('Repeater', 'hypothyroid')\n",
      "Missing 'DoG' in ('ProWRAS', 'hypothyroid')\n",
      "Missing 'DoG' in ('GAN', 'hypothyroid')\n",
      "Missing 'DoG' in ('ctGAN', 'hypothyroid')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'hypothyroid')\n",
      "Missing 'DoG' in ('Repeater', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoG' in ('ProWRAS', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoG' in ('GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoG' in ('ctGAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoG' in ('Repeater', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoG' in ('ProWRAS', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoG' in ('GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoG' in ('ctGAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoG' in ('Repeater', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoG' in ('ProWRAS', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoG' in ('GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoG' in ('ctGAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoG' in ('Repeater', 'shuttle-2_vs_5')\n",
      "Missing 'DoG' in ('ProWRAS', 'shuttle-2_vs_5')\n",
      "Missing 'DoG' in ('GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoG' in ('ctGAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoG' in ('Repeater', 'winequality-red-4')\n",
      "Missing 'DoG' in ('ProWRAS', 'winequality-red-4')\n",
      "Missing 'DoG' in ('GAN', 'winequality-red-4')\n",
      "Missing 'DoG' in ('ctGAN', 'winequality-red-4')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'winequality-red-4')\n",
      "Missing 'DoG' in ('Repeater', 'yeast4')\n",
      "Missing 'DoG' in ('ProWRAS', 'yeast4')\n",
      "Missing 'DoG' in ('GAN', 'yeast4')\n",
      "Missing 'DoG' in ('ctGAN', 'yeast4')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'yeast4')\n",
      "Missing 'DoG' in ('Repeater', 'yeast5')\n",
      "Missing 'DoG' in ('ProWRAS', 'yeast5')\n",
      "Missing 'DoG' in ('GAN', 'yeast5')\n",
      "Missing 'DoG' in ('ctGAN', 'yeast5')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'yeast5')\n",
      "Missing 'DoG' in ('Repeater', 'yeast6')\n",
      "Missing 'DoG' in ('ProWRAS', 'yeast6')\n",
      "Missing 'DoG' in ('GAN', 'yeast6')\n",
      "Missing 'DoG' in ('ctGAN', 'yeast6')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'yeast6')\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "DoG: cohens kappa score\n",
      "Missing 'DoG' in ('Repeater', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoG' in ('ProWRAS', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoG' in ('GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoG' in ('ctGAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoG' in ('Repeater', 'abalone9-18')\n",
      "Missing 'DoG' in ('ProWRAS', 'abalone9-18')\n",
      "Missing 'DoG' in ('GAN', 'abalone9-18')\n",
      "Missing 'DoG' in ('ctGAN', 'abalone9-18')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'abalone9-18')\n",
      "Missing 'DoG' in ('Repeater', 'car_good')\n",
      "Missing 'DoG' in ('ProWRAS', 'car_good')\n",
      "Missing 'DoG' in ('GAN', 'car_good')\n",
      "Missing 'DoG' in ('ctGAN', 'car_good')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'car_good')\n",
      "Missing 'DoG' in ('Repeater', 'car-vgood')\n",
      "Missing 'DoG' in ('ProWRAS', 'car-vgood')\n",
      "Missing 'DoG' in ('GAN', 'car-vgood')\n",
      "Missing 'DoG' in ('ctGAN', 'car-vgood')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'car-vgood')\n",
      "Missing 'DoG' in ('Repeater', 'flare-F')\n",
      "Missing 'DoG' in ('ProWRAS', 'flare-F')\n",
      "Missing 'DoG' in ('GAN', 'flare-F')\n",
      "Missing 'DoG' in ('ctGAN', 'flare-F')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'flare-F')\n",
      "Missing 'DoG' in ('Repeater', 'hypothyroid')\n",
      "Missing 'DoG' in ('ProWRAS', 'hypothyroid')\n",
      "Missing 'DoG' in ('GAN', 'hypothyroid')\n",
      "Missing 'DoG' in ('ctGAN', 'hypothyroid')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'hypothyroid')\n",
      "Missing 'DoG' in ('Repeater', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoG' in ('ProWRAS', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoG' in ('GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoG' in ('ctGAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoG' in ('Repeater', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoG' in ('ProWRAS', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoG' in ('GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoG' in ('ctGAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoG' in ('Repeater', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoG' in ('ProWRAS', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoG' in ('GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoG' in ('ctGAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoG' in ('Repeater', 'shuttle-2_vs_5')\n",
      "Missing 'DoG' in ('ProWRAS', 'shuttle-2_vs_5')\n",
      "Missing 'DoG' in ('GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoG' in ('ctGAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoG' in ('Repeater', 'winequality-red-4')\n",
      "Missing 'DoG' in ('ProWRAS', 'winequality-red-4')\n",
      "Missing 'DoG' in ('GAN', 'winequality-red-4')\n",
      "Missing 'DoG' in ('ctGAN', 'winequality-red-4')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'winequality-red-4')\n",
      "Missing 'DoG' in ('Repeater', 'yeast4')\n",
      "Missing 'DoG' in ('ProWRAS', 'yeast4')\n",
      "Missing 'DoG' in ('GAN', 'yeast4')\n",
      "Missing 'DoG' in ('ctGAN', 'yeast4')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'yeast4')\n",
      "Missing 'DoG' in ('Repeater', 'yeast5')\n",
      "Missing 'DoG' in ('ProWRAS', 'yeast5')\n",
      "Missing 'DoG' in ('GAN', 'yeast5')\n",
      "Missing 'DoG' in ('ctGAN', 'yeast5')\n",
      "Missing 'DoG' in ('CTAB-GAN', 'yeast5')\n",
      "Missing 'DoG' in ('Repeater', 'yeast6')\n",
      "Missing 'DoG' in ('ProWRAS', 'yeast6')\n",
      "Missing 'DoG' in ('GAN', 'yeast6')\n",
      "Missing 'DoG' in ('ctGAN', 'yeast6')\n",
      "Missing 'DoG' 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 not g.startswith(\"convGAN\") or g == \"convGAN-majority-full\"]\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",
    "showDiagnoseDataset(\"Average\")\n",
    "\n",
    "gans = list(statistic.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "id": "177774b0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[14, 3, 6, 10, 5, 2, 2, 2, 1],\n",
       " [11, 14, 8, 11, 7, 11, 5, 12, 3],\n",
       " [9, 7, 14, 12, 8, 8, 3, 9, 4],\n",
       " [4, 3, 2, 14, 3, 3, 2, 3, 2],\n",
       " [10, 7, 7, 11, 14, 9, 5, 9, 5],\n",
       " [12, 3, 6, 11, 6, 14, 0, 9, 1],\n",
       " [12, 11, 11, 13, 9, 14, 14, 14, 10],\n",
       " [12, 2, 5, 11, 5, 6, 0, 14, 0],\n",
       " [13, 11, 10, 13, 9, 13, 4, 14, 14]]"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "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": [
      "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": [
      "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": [
      "DoG - 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": [
      "DoG - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT4AAAEdCAYAAACVAAuoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAArGUlEQVR4nO3debRcZZnv8e8vQVTShCmItLYEWXZjkBAgTMrUoVFADMg8qBDapa51FcQl3SLXVrAjcB0YxIs3IgQiEhA1EgYJRgJiNBqSQAiDjNKgQCIqgyCQ/O4f+61kp1I5dU7VyX53Us9nrVpU7drDs084z3nnLduEEEIvGZI7gBBCqFokvhBCz4nEF0LoOZH4Qgg9JxJfCKHnROILIfScSHwhhLWGpEslPSPpnhbffVaSJY1od55IfCGEtclk4IDmjZL+CdgfeLw/J4nEF0JYa9i+HXi2xVfnAf8B9GtGRiS+EMJaTdJ44Enbd/X3mPXWYDxhLSKNMIzMHcZyOw+7M3cIKxuWO4AmL+YOYGV3vsgS25v3Z1/pAMOS1Z1pEfByacMk25NWfy5tAJwBvLffwRKJLyw3EpibO4jl5m6v3CGsbI/cATT5Ve4AVqZf8/v+772EIUN+0/KbZcuGvmx77AAuvQ2wNXCXJIC3AvMk7Wr7qdUdFIkvhFApyay33mstv3vllYGdy/ZC4E0rzq3HgLG2V1ekBKKNL4RQsUbia/Vqf6yuoijv/oukJyT9eycxRIkvhFC5oUOXdnSc7WPbfD+yP+eJxBdCqFRfVd2qROILIVRKcsclvsESiS+EULEo8YUQeozUeRvfYInEF0KoVLTxhRB6ULTxhRB6TJT4Qgg9RyISXwiht9RhOMtaPWVN0q6SZkl6UNI8STdI2r5pn7vSNJfytsmSnpT0+vR5RJrjtyZivETSqE6PkfT5Dq75pXR/C9LroIGeI4Q1p/Mpa4NlrS3xSdoCuAY4zvbstG1PitUaFqbP76RI7ntLGma7vJjPUuAk4OI1Gaftjw5kf0lDm475PPCVDi59nu2vdXBcCGtUHYazrPESn6SPSLo7lbymSNpK0sy0baakt6X9Jku6UNJsSY9IOiJtv7pcYkn7HQ58Eri8kfQAbN9he1rp8scBU4AZwPim0M4HTpXUZ/JP17tY0q0prn3Suv/3SZpc2u9iSXMlLZJ0Zmn7LElj0/tjJS2UdI+kc0v7vCDpLElzgD0ax0g6B3hjKrVdKenLkk4pHTdR0sl9/gOEUDPdLFIwWNZo4pO0HcUigeNs7wCcAlwEXGF7NHAlcGHpkC2BPYGDgXPStqnA0el86wP7ATcC2wHz2oRwNHA1cBXQPLn5ceAO4MP9uJVNgHHAqcB0imWutwO2lzQm7XNGWkdsNLCPpNHlE0j6R+DcdJ4xwC6SDk1fDwPusb2b7Tsax9j+HPCS7TG2jwe+C5yQzjcEOIbiZ9jKJ9Mfl0slbdKPewyhIkUbX6tXVdZ0iW8ccG1jbSzbz1Is6fj99P0UikTXMM32Mtv3AlukbTcB41J73IHA7bZfar6QpDmpFHZB+rwLsNj274GZwE4tEsBXgNNo/3OYbtsUVeinbS+0vQxYxIpli4+SNA+YT5EUm9v1dgFm2V5s+zWKhLV3+m4p8MM2MWD7MeBPknakWHF2vu0/tdj1Yooq/xjgj8DXW51P0sdSKXUuLG53+RAGxTpf4gNE+4d/lL//e9Ox2H4ZmAW8j6IENzV9vwjYaflJ7N2ALwAbpU3HAtumTouHgeHA4Std2H4IWAActfyiRfVxgaQFLeJa1hTjMmA9SVsDnwX2SyXZG4A3NN1nX0sKv2y7v3/uLgFOBCYAl6aYL0sx35ju62nbS1Ny/g6wa6sT2Z5ke2xRUu3XquEhdK3Rxrcul/hmUpSENgOQtCkwm6KKBnA8RXWznakUv+h7ATenbd8CTpT07tJ+G6TrDAGOBEbbHpnW6DqEVau7ABMpkhYAts9IVcsx/bnBZDjFUxD+mjpdDmyxzxyKKvAISUNTLLf149yvSnpd6fOPKR6vtwvpZ2F7Qor5IABJW5b2/yCwyjNIQ8ilDiW+Ndqra3uRpInAbZKWUlQDTwYulXQaRf1qQj9ONQO4ArjO9ivp3E9JOho4V9JbgGconmByFkUV8knbT5bOcTswqikpNGKcR6n02MF93iVpPkUp9BHgl6vu4j9KOh24laL0d6Ptn/Tj9JOAuyXNs3287Vck3Qr8pY9S4v9JbY8GHgM+PvC7CmFNyT9zQ0XTVVhTJC0Extt+dJDON4SiU+dI2w8OxjmL8451nR425N3jYUN9qt/Dhu7s70OCNt74Hd5772+0/G769PH9Pk831tpxfGsDSbcACwcx6Y0Crgd+PJhJL4QqxVzddZzt/Qf5fPcCbx/Mc4ZQtTpMWYvEF0KoWJT4Qgg9pg5T1iLxhRAqVYc2vrV6dZYQwtqo8ylraQrmM5LuKW37qqT70xTNH0vauN15IvGFECrV5QDmyRQD+MtuAd6VZk39Dji93Uki8YUQKtVYgbmTxGf7duDZpm0z0vx3gF8Db213nmjjCyFUag0PZzmJYkWmPkXiCyFUrM/OjRHFakHLTbI9qT9nlXQG0Fj5qE+R+EIIlWoznGVJJ1PWJJ1AsY7nfu7HPNxIfCGESg32cBZJBwD/Cexj+2/9OSYSXwihYp238al4cNi+FFXiJ4AvUvTivh64RRLAr21/oq/zROILAOz89juZe059VkTRUfVaNcg/qM/PBqjd6iwrlvJtr5sSn+1Wa2p+d6DnicQXQqhUTFkLIfScOkxZi8QXQqhYJL4QQo+Jqm4IoedEVTeE0INiBeYQQo+JEl8IoedEG18IoedEiS+E0IOijS+E0GMaC5HmFIkvhFCpqOqGEHpOHR4oXstnbkjaVdIsSQ9KmifpBknbN+1zV1qiprxtsqQnJb0+fR4h6bE1FOMlkkZ1eoykz3dwzW0lLZA0X9I2few3WdIR6f0sSQNe2DGENaerhw0NitolPklbANcAn7f9Dts7AWcD25T2eSdF7HtLGtZ0iqUU6+6vUbY/avve/u4vaWjTMQNOfMChwE9s72j74Q6ODyG7xnCWTh4vOVg6TnySPpKeY3mXpCmStpI0M22bKeltab/Jki6UNFvSI6WSyNWSDiqdb7Kkw4FPApfbnt34zvYdtqeVLn8cMAWYAYxvCu184FRJfVbj0/UulnRrimuf9MzO+yRNLu13saS5khZJOrO0fXlJStKxkhZKukfSuaV9XpB0lqQ5wB6NYySdA7wxld6ulPRlSaeUjpso6eSmeA8CPg18NMU8sunZop+V9KW+7jmEOujy8ZKDoqPEJ2k74AxgnO0dgFOAi4Ar0rMtrwQuLB2yJbAnxZr456RtU4Gj0/nWB/YDbgS2A+a1CeFoiicpXQU0L0z4OHAH8OF+3MomwDjgVGA6cF66/vaSxqR9zkjPABgN7CNpdPkEkv4RODedZwywi6RD09fDgHts72b7jsYxtj8HvGR7jO3jKRZSPCGdbwhwDE0PTLF9I/Bt4Dzb/9qPewuhpjp/oPhg6bTENw641vYSANvPAnsA30/fT6FIdA3TbC9L1bwt0rabgHGpPe5A4HbbLzVfSNKcVAq7IH3eBVhs+/fATGAnSZs0HfYV4LR+3N/09GCShcDTthfaXgYsAkamfY6SNA+YT5EUm9v1dgFm2V6cnu15JbB3+m4p8MM2MWD7MeBPknYE3gvMt/2ndsd1S9LHUml27uLn1vTVQiistSU+QEC7tcHL3/+96VhsvwzMAt5HUYKbmr5fBOy0/CT2bsAXgI3SpmOBbVOnxcPAcODwlS5sPwQsoLQgdqo+LpC0oEVcy5piXAasJ2lr4LMUT24aDdwAvKHpPvtak/xl2/39M3YJcCIwAbg0xXxZivnGFvu/xsr/fs1xtWV7ku2xtsduPnygR4fQmbW5jW8mRUloMwBJmwKzKapoAMdTVDfbmUrxi74XcHPa9i3gREnvLu23QbrOEOBIYLTtkbZHAoewanUXYCJF0gLA9hmpajmmPzeYDAdeBP6aOl0ObLHPHIoq8AhJQ1Mst/Xj3K9Kel3p84+BAyhKkDenmCekmA9qcfzTwJskbZZKzQf3+65CyKgOJb6OxvHZXiRpInCbpKUU1cCTgUslnQYspkho7cwArgCus/1KOvdTko4GzpX0FuAZYAlwFkUV8knbT5bOcTswStKWLWKcR6n02MF93iVpPkUp9BHgl6vu4j9KOh24laL0d6Ptn/Tj9JOAuyXNs3287Vck3Qr8pT+lRNuvSjqLIvE+Ctw/gFsLIaP8A5jVj2fvhhYkLQTG2350kM43hKJT50jbDw7GOQdi7Dby3HPa71eV2j1l7X/iKWt90VHc2d8HgY8aNczf+962Lb/beed5/T5PN2LmRgck3QIsHMSkNwq4HvhxjqQXQpViytpayvb+g3y+e4G3D+Y5Q6ivrh4ofilFe/Yztt+Vtm1KMbxtJPAYcJTtP/d1ntrN3AghrNu67NyYTNEJWPY5YKbtd1B0vH6u3Uki8YUQKtXNcBbbtwPPNm0+BLg8vb+cYmpnn6KqG0Ko1Bpo49vC9h8B0iiLN7U7IBJfCKFifbbxjZA0t/R5ku1Jgx1BJL4QQqXarMC8pIPhLE9L2jKV9rakGPvbp2jjCyFUag3M3LiOtMhH+m/bCQRR4gshVKyr4SxXAftSVImfAL5IseLTNZL+nWJ1piPbnScSXwihUt10bthuNS8fimXt+i0SXwihUvFA8RBCz4kpayGEHpT/KWuR+ELhBWq14of7XN+1evqnmq0WU7Ofz0DEA8VDCL1pWd7LR+ILIVTLFA9OyCgSXwihWpH4Qgg9KW/fRiS+EELFosQXQug5Jkp8IYQeEyW+EEJPihJfCKGnRIkvhNBzoo0vhNBzosQXQuhJkfhCCD2lBlXdteKZG5J2lTRL0oOS5km6QdL2TfvclZalLm+bLOlJSa9Pn0dIeqzC0JF0iaRRA9h/rKQL0/t9Jb27g2sulbQgva4b6PEhrFGNqm6rV0VqX+KTtAVwDXCc7dlp257ANsDC9PmdFEl8b0nDbL9YOsVS4CTg4kGIZT3bA/rnsf3RAe4/F2g8Xm9figWjZg/kHMBLtscM8JgQqrOulPgkfUTS3ankNUXSVpJmpm0zJb0t7TdZ0oWSZkt6RNIRafvVkg4qnW+ypMOBTwKXN5IegO07bE8rXf44YAowAxjfFNr5wKmS+kzy6XrflvQLSb+TdHDafqKkH0iaDsyQtKmkaem+fi1ptKT1JP1W0r7pmLMlTUzvZ0kam96/IOlcSXdK+lmpJPuIpPFpn30lXS9pJPCJFPsCSXtJelTS69J+wyU91vgcwlqjBiW+QUl8krYDzgDG2d4BOAW4CLjC9mjgSuDC0iFbAnsCB1M8IQlgKnB0Ot/6FA8PuRHYDpjXJoSjgauBq4Dmh5E8DtwBfLgftzIS2Ad4P/BtSW9I2/cATrA9DjgTmJ/u6/PpHl8DTgQulrQ/cEDar9kwYJbtnYHngf8G9gc+CJxV3tH2Y8C3gfNsj7H9C2BWig3gGOCHtl9tcZ03SJqbEvOh/bjvEKrTaONr9arIYJX4xgHX2l4CYPtZimTx/fT9FIpE1zDN9jLb9wJbpG03AeNSe9yBwO22X2q+kKQ5ku6TdEH6vAuw2PbvgZnATpI2aTrsK8BptL/fa1JcDwKPANum7bekeyLdx5R0nz8HNpO0ke1Faft04CTbr7Q4/yvAT9P7hcBtKXEtpEi67VwCTEjvJwCXrWa/t6WHMh8HnC9pm1Y7SfpYSpBzF6/ykw5hDVlXSnyAKG6nL+Xv/950LLZfpijRvI+iBDc1fb8I2Gn5SezdgC8AG6VNxwLbpk6Lh4HhwOErXdh+CFgAHLX8otLERgfAamIsfy63GbZa87ux3/bAX1iRzJu9arux7zLSz8H2MvrR3mr7l8BISfsAQ23fI2m3UkfG+LTfH9J/H6H4me64mvNNsj3W9tjN39ju6iEMonWkxDcTOErSZgCSNqVokD8mfX88RXWznakUJZm9gJvTtm8BJzb1bm6QrjOE4uHBo22PtD0SOIRVq7sAE4HPNj7YPiNVIceU9jlS0pBUQno78ECL89ye7ofUprfE9nOSDgM2A/YGLpS0cT/ut53ngQ2btl1BUaW/LN3HnMZ92L5O0iblXmzgPcC9gxBLCIOjixKfpFMlLZJ0j6SrSs1RAzIoiS9V8yYCt0m6C/gGcDIwQdLdFO1rp/TjVDMoEsfPGlVF209RlADPlvSQpNnAERRtiHsDT9p+snSO24FRkrZsEWO7tsIHgNsoqt2fSKXQZl8Cxqb7Ogc4ISWYc4B/t/27FNsF/bjfdqYDH2x0bqRtVwKbUCS/Vt4JzE3/DrcC56QmhRDqocPEJ+ktFHllrO13AUNZUbgaEK2oefU2SZOB621fmzuWvqRe8ENs96ezpt/GbiHPPX4wz9il83IHsDK1bcmpVt2esia4M7UrtzX2HfLcb6zmPONXf56U+H4N7AA8B0wDLrQ9Y6Dx1n4cX1hB0jcpOn4OardvCLXV4Vxd209K+hrFSI2XgBmdJD2IxLec7RNzx9CO7U/ljiGErvU9ZW2EpLmlz5NsTwJIozUOAbam6ET8gaQP2f7eQEOIxBdCqFbfJb4lfVSZ/w141PZiAEk/At4NDDjxrRVzdUMI65jOhrM8DuwuaQNJopjkcF8nl48SXwihWp238c2RdC3F6IzXgPnApE5CiMQXQqhWF8tS2f4i8MVuQ4jEF0KoVqzAHELoSZH4Qgg9pQYrMEfiCyFUK6q6IYSeFCW+EEJPiRJfCKHnRBtfqI1/oFgzO7Tkz9RsNZR/qtdqMa3X512NKPGFEHpSlPhCCD0lSnwhhJ4TiS+E0JOiqhtC6ClR4gsh9JwYzhJC6DlR4gsh9KQo8YUQekqU+EIIPSfa+EIIPSlKfCGEnhJV3RBCz6lBVTf7c3Ul7SpplqQHJc2TdIOk7Zv2uUvSVU3bJkt6UtLr0+cRkh6rMHQkXSJp1AD2HyvpwvR+X0nv7uCaX5W0SNJX+9hnpKR7Ste5fqDXCWGNaZT4Wr0qkrXEJ2kL4BrgONuz07Y9gW2AhenzOykS9N6Shtl+sXSKpcBJwMWDEMt6tgf0o7f90QHuPxeYmz7uC7wAzB7IOYCPA5vb/vsAjwuhPtaGEp+kj0i6O5W8pkjaStLMtG2mpLel/SZLulDSbEmPSDoibb9a0kGl802WdDjwSeDyRtIDsH2H7Wmlyx8HTAFmAOObQjsfOFVSnwk8Xe/bkn4h6XeSDk7bT5T0A0nTgRmSNpU0Ld3XryWNlrSepN9K2jcdc7akien9LElj0/sXJJ0r6U5JPyuVZB+RND7ts6+k6yWNBD6RYl8gaS9Jj0p6XdpvuKTHGp9L93EdMAyYI+nodF9HlL5/oa+fQwi10GWJT9LGkq6VdL+k+yQNeCXJtolP0nbAGcA42zsApwAXAVfYHg1cCVxYOmRLYE/gYOCctG0qcHQ63/rAfsCNwHYUT0Xvy9HA1cBVwLFN3z0O3AF8uN19ACOBfYD3A9+W9Ia0fQ/gBNvjgDOB+em+Pp/u8TXgROBiSfsDB6T9mg0DZtneGXge+G9gf+CDwFnlHW0/BnwbOM/2GNu/AGal2ACOAX5o+9Wm48YDL6Vjru7HPYdQP402vlav/rkA+KntbYEdgPsGGkJ/SnzjgGttLwGw/SxFsvh++n4KRaJrmGZ7me17gS3StpuAcak97kDgdtsvNV9I0pyUwS9In3cBFtv+PTAT2EnSJk2HfQU4rR/3ck2K60HgEWDbtP2WdE+k+5iS7vPnwGaSNrK9KG2fDpxk+5UW538F+Gl6vxC4LSWuhRRJt51LgAnp/QTgsn4c0xVJH5M0V9Lcxc+t6auFkHRR4pM0HNgb+C6A7Vds/2WgIfQn8SmF2pfy9+W2JwHYfpmiRPM+ihLc1PT9ImCn5SexdwO+AGyUNh0LbJs6LR4GhgOHr3Rh+yFgAXDU8otKE1MVcsFqYix/LrcZtlo/u7Hf9sBfWJHMm71qu7HvMtLPwfYy+tGWavuXwEhJ+wBDbd8jabfGfTSqy01eI/0bShKwfrvrNF1zku2xtsduPnwgR4bQpc5LfG8HFgOXSZqfOhiHDfTy/Ul8M4GjJG0GIGlTigb5Y9L3x1NUN9uZSlGS2Qu4OW37FnBiU+/mBuk6Q4AjgdG2R9oeCRzCqtVdgInAZxsfbJ+RqoNjSvscKWmIpG0ofngPtDjP7el+SG16S2w/J+kwYDOKvzQXStq4H/fbzvPAhk3brqCo0l+W7mNO4z5sX9fiHI8BO6f3hwCva7FPCPXSd4lvRKMWkl4fazp6PYrC0sW2d6QouHxuoCH0pySyKDXm3yZpKTAfOBm4VNJpFNl3Ql/nSGZQ/GJf16gq2n5K0tHAuZLeAjwDLKFoE9sbeNL2k6Vz3A6MkrRlixjnUSo9tvAAcBtFie0Ttl8uCkkr+RLFX5K7gb8BJ0gaQdFWuZ/t/5F0EUUbwwn9uOe+TAeulXQI8KnUznclRdvgVX0eucJ3gJ9I+g3FH6gX2+wfQn59D2BeYntsH0c/ATxhe076fC0dJD6tqJ2tuyRNBq63fW3uWPqSemgPsd2fzppBNXYbee457ferzK9yB9DkM7kDWFkNn7J2Z5uEtdzYYfLcbVt/p3m0PY+kXwAftf2ApC8Bw2yfNpBoY+ZGTUj6JkXHz0Ht9g1hrdb9lLVPAVemESKP0L8a50p6IvHZPjF3DO3Y/lTuGEKoRJdT1mwvAPpVulydnkh8IYQaiUUKQgg9KdbjCyH0lCjxhRB6Tg2WpYrEF0KoXpT4Qgg9Jaq6IYSeE1XdEELPiRJfCKEnRYkvhNBTosQXQug5NWjj64nVWUJ7khYDvx+EU42gWFqsLiKevg1WPFvZ3rw/O0r6abpuK0tsHzAI8fQdQyS+MJgkze3v8kRViHj6Vrd4qpL9ubohhFC1SHwhhJ4TiS8Mtkm5A2gS8fStbvFUItr4Qgg9J0p8IYSeE4kvhNBzIvGFEHpOzNwI6wRJfT1TGdvzqoqlFUlbAzsC99q+P2csITo3QockHWD7p+n9RsA3gF2Ae4BTbT9dcTy39vG1bY+rLBhA0jTbh6b3hwDnA7OAdwNn255ccTzTKSaLtWR7fIXhZBeJL3RE0jzbO6X3lwBPAd8BDgP2afzS9ypJ823vmN7PBo63/aikEcBM2ztUHM8+fX1v+7aqYqmDqOqGwTDW9pj0/jxJJ1QdgKTD+vre9o+qiqVxydL79Ww/muJYImlZxbH0XGJrJxJf6NSbJH0GEDBckryi+pCj0+wDfXxnoOrEt4Ok5yh+Pq+X9GbbT0laHxhacSxIWkjfVd3RFYaTXSS+0KnvABum95dTrLaxWNKbgQVVB2N7QtXX7Ivt1SW3DYCPVxlLcnCGa9ZWtPGFdYqk/2q13fZZVcfSTNLBtq/PHUeIcXxhEEmqwy/1i6XXUuBAYGTOgErqkHyfl/Rcer0saWmqkveUqOqGwfSW3AHY/nr5s6SvAddlCqeZcgdge8PyZ0mHArvmiSafKPGFwTQ/dwAtbAC8PdfFJZXb+nK07fXJ9jSg0jGOdRAlvtAVSZ8ErrT9Z9sn1SCecu/lUGBz8lYxH5J0LXCZ7d9kjANYZdjPEGAsffT2rqsi8YVuvRn4raR5wKXAzc7QYyZp6zRWrtx7+RrwtO2cz/QaDRwDXCJpCMXPaKrtXO1q5WE/rwGPAYfkCSWf6NUNXZMk4L3ABIoSxDXAd20/XGEMd9reWdJM2/tVdd2BkLQ3cBWwMXAt8GXbD1V07XNt/6eko2xfU8U16yxKfKFrti3pKYppa68BmwDXSrrF9n9UFMYQSV8E/jkNrG6O8RsVxbGS1Mb3foo/CiOBrwNXAnsBNwL/XFEoB0n638DnKP4w9bRIfKErkk4GTqB4ROElwGm2X03VugeBqhLfMcChFP9Pb9j3rpV6ELgV+Krt2aXt16YSYFV+SvFvNKxp+Ioo/nYNrzCW7KKqG7oi6SyKau0qz+SV9E7b91Ucz4G2b6rymn2RtKftO5q2vcf2LzPF8xPbPdem1yyGs4Rubd2c9CRNAag66aVrLk96NRlQfWGLbd+sPIqknPQk9ew0tqjqhm5tV/6Q2rR2zhRLs2wDqiXtQbH23uZNbY7DybBIwWqcBdThj0PlosQXOiLpdEnPA6NLU6CeB54BfpI5vIacA6rXB/6BFW2OjddzwBEZ4yrLPpMkl2jjC12RdLbt03PH0VAeUJ07FgBJW7Vq/8xF0lDbS9P7XeswqDqHqOqGjkjaNj074getnneR8RkXdRlQfb7tTwMXSVrl+hmXeq/VTJJcosQXOiJpku2PreZZF5U/46KsJgOqd7Z95+qWfM+1IrKkDSmG/kygaOrKPZMki0h8oWNprN4euYZm9EXSDhS/3AdQjKPbHahyQHXt5ZxJklskvtAVSb+yvUfuOBpaDKieVh5QbXubiuM5GPgysBVF01LWAcMtZpJMYcVMkq/YrmomSVbRxhe6NUPS4cCPcrSltTACOKy5Q8H2skzj1s6nePLcwpr8fOoykySrKPGFrqQhLMMoVjt+ifwlmim2P9xuW4Xx3ArsZ7vyJ6u1UreZJLlE4gvrlPLzftPnoRSlrVGZ4tmFoqp7G/D3xvaMiyas9PNZ3bZ1XVR1Q9ckjQca1aRZOR6oI+l04PPAG0uT8AW8AkyqOp6SicALwBsoBjVnsZbMJKlMJL7QFUnnALtQNJADnJKqU5+rMg7bZwNn121ANbCp7ffmDoJVZ5I01GkmSWWiqhu6IuluYEyjDStVLedX/YDqxoDqVoOpId+A6vSH4ee2Z+S4frO6zSTJJRJf6EpKfPvafjZ93pSiult14qvlgOpS58/fgVfJ1PnTmEkiaTotnrGRcSZJFpH4QlckHQucQzFEQhRtfafbnpohltoOqM6trjNJconEF7omaUuKdj4Bc2w/lTGWWgyormvVOxQi8YWOtPnFNvBsjrYkSWcCd5N5QHWNq961mkmSSyS+0JE2v9gAmwF3VT1wuG4DqutG0kPUayZJFpH4whojaUZNhnJkI+nLwJdKa+ANBy6wPSFTPLWaSZJLjOMLXZP0LmAUxSBdAGxfkSvp1WFAdcl6wG8kTaBYK/CbZHzmBsVT726UVIuZJLlEiS90JT3Ldl+KxHcjcCBwh+0sg2JbDKg+Friz6gHVTTH9GzAd+DOwd86lnyTNoJhJshBYXuqzfWaumHKIxBe6ImkhsAPFoOUdJG0BXGL7A5niqcWA6lI8ewMXA98Dtgc2BU6y/YdM8cy1PTbHteskqrqhWy+lJZ9eS+1XzwBvzxzTxsCz6f1GGeMA+BpwpO17ASQdBvwc2DZTPD+T9N66zCTJJRJf6NZcSRsD3wHupKhG5XyWw9nA/NSIv3xAdcZ49rC9NC35bts/Su1rufwv4D8kZZ1JkltUdcOgkTQSGG777sxx1GlA9bsoVjneNMWzGPiI7UW5YgqR+MIgkDSaYhnz5TUI2z+qOIa6DqieDZxh+9b0eV+KJd7fXXEcMZOkJBJf6IqkS4HRwCJW9BLa9kkVx1HXAdV32d6h3bYK4qjlTJJcIvGFrki6N9fqxgOVY0C1pB8D8yiquwAfAsbaPrTKOMLKhuQOIKz1fiWpVolP0rskHSXpI40XQKYB1ScBmwM/Sq8RFE84y0LSl9MQn8bn4ZIuyxVPLtGrG7p1OUXye4piJkCjlzDXuLmWA6qBKzLEMhT4ge1/q/rafajbTJIsIvGFbl0KfJimmQAZHcGKAdUTGgOqcwSShrH8TdJGtv+aI4Zmtk+XNBOYQw1mkuQSiS9063Hb1+UOoqRuA6pfBhZKugV4sbHR9sk5gkkzSS4AzqKYSXKRpGwzSXKJxBe6db+k71PMRS1Peq90OEtJ3QZU35BedVG3mSRZRK9u6MpqGsYrH87SSo0GVK9PkVgMPGD7lYyxDG2aSfKCpM1s/ylXTDlE4gvrnDoMqC7FchDw/4CHKTp+tgY+bvumTPHETBIi8YVBJGme7ZYzAyqMoRYDqkvx3A8c3OhAkLQNcIPtLFXLuswkyS3a+MJgUu4AgN1rNqD6maZe00coOlxyGdZIegC2Z0kaljGeLCLxhcFUh0b8X0ka1Wi8r4FFkm4ErqFo4zsS+G3qVMhRBX9E0hdYeSbJoxXHkF1UdcM6JQ3XmA7UZUB1X7Micsxp3gQ4E9gzbbodONP2n6uMI7dIfKEjkh6lKMEs31T6bNvbVB/V8qeIfYZVl1avfGWWukkzSW6u2UySLKKqGzrVvHz5EOAo4LPA/OrDWa5uA6qXy935U8eZJLlE4gsdaYz7kjSEYsraacAC4P2Z29fqNqC6rA6dP7WaSZJLJL7QEUmvo1h55FSKRQAOsf1w3qgAeCNFwiuvxGKKlVFyq0PnT91mkmQRbXyhI5KeAF4Dzgceb/6+JiWs0EKdZpLkEokvdETSZFbu3Ciry5S1bG1qNe78qdVMklwi8YVBJ2kL20/XII75tnfMdO3NmjaVO3/m2T68+qjqN5Mkl1iBOQwKSRtJOknSzyiWWq+DbG1Ztv+UOoD+DBwM3ArsQdH5kyXpJXWbSZJFlPhCxyS9ERgPHAfsBGwIHArcbrsOi5Jm06Lz5+w6dP5IuhjYipVnkjwA/BJ6p202El/oiKQrKR7WPQOYSrGm20O2t84UT63a1Ora+VO3mSS5xHCW0Kl3UVTj7gPuT4Njc/4VrduA6p9RJN4d0qss2/Aa29kedFQnUeILHZO0LUU192iKdqJtge1tP5UxpuYB1V+p0YIFQK06f7IvI5ZLJL4wKCSNpUiCRwBPVL2+W13b1BokbQQcTvEzeqftt2QOKWuvd26R+MKgkiSKJ3fdVvF1a9emVvfOH0n/bft/544jh0h8oSOStgO2aSwIIOk8YKP09UW2Kx3SUrcB1XXr/Akri86N0KlzgLNLn98HfAHYAPgvipJNZWyfuLrv0rN1q1arzp+69XrnFokvdGpL27NLn5+z/UMASR/PFNNyzW1qQKVtarZ3KHX+/EzSM8CGkt6cqfOnbr3eWUVVN3RE0gO2/2U13/3O9j9niKm2bWq5O39KcdS+17sKMWUtdOoPknZr3ihpd+APVQeT2tR+R7Ec1UUUj5f8s+1ZuZMegO25tj9DMWvi9KqvL+l1qSR+L7AXxTJiH+rFpAdR1Q2d+0/g6tSp0OjI2Bk4gWJcX9Xq1qbWZ+dPhpAeZeVe7x0kLR9Y3StT1Rqiqhs6JulNwCeB7dKmRcC3cg3OrdOAaknTKcYSzk6f72VF58/htg+tOJ7J1KjXO7dIfKErkj4I3Gj77213rlDuNjVJc22PLX3+te3d0/s7bO+5+qOrVZeZJFWKNr7QrfHA7yRNkfR+SbVoPsndpkbRsVKOZ/fSxzdVHMsqarqMWGVq8T9pWHvZnpCmix1IUcL6v5Jusf3RKuOoYZvaHyTtZntOeWOuzp907dX2eueIJ6eo6oZBkZLfAcAEYC/bm1d8/bq1qe0KXA1MpkXnj+3fVBxPzCQpiapu6IqkA1LD+UMU7WmXAFtmCKXlgGrbU4ARVQeTEttuwFDgxPQaAuxeddJLVun1ZvWdHeu8qOqGbp1IUYL4eOYOjtq1qdl+RtJ8YGLuzp8aziTJKkp8oSu2j7E9LfcvNjUbUF1Sm84f2/fb/q804+ZUYArwG0mz2xy6zok2vtAVSYcB51KUqpRetj284jhq1aZWVur8ORrYE6i882d1ci0jllskvtAVSQ8BH7B9Xw1iqdWA6rIadP7Uahmx3CLxha5I+qXt9+SOo6FuA6olHQAcA/wrMIuiVDrD9msVx1GrXu/cIvGFrki6AHgzMA1YnmwyP0VsHMXYtKnAzVUnmaZ4pqY4bsqZjNemmSRViMQXurKaxxVmnftZ5za1XOq4jFhOMZwldKWOjyu0/aqkmyjGqb0ROATIkvjq0vlDDWeS5BQlvtAVSW8Fvgm8hyLR3AGcYvuJTPHUok2tFE8tOn/q3OudQyS+0BVJtwDfpxgTBvAh4Hjb+2eKpxZtaqV4atP5U+de76pF4gtdkbTA9ph223pVDTt/atXrnUvM3AjdWiLpQ5KGpteHgD/lCkbSYZIelPRXSc9Jel7Sc7niAYYDf6NYEv8D6XVwxnhqM5Mkpyjxha5IehvFsk97ULTxzQZOtr3KQ70riqcWbWp1Fr3eUeIL3fsycILtzW2/CTgJ+FLGeJ6uU9KT9FZJP5b0jKSnJf0wdQhlY/tV4CaKttA7KXq9e0okvtCt0bb/3Phg+1lgx4zxzJV0taRjU7X3sDSkJJfLgOuAf6R4tu/0tC2LGi0jllVP1u/DoBoiaZNG8pO0KXn/vyq3qTUYyPUUsc1tlxPdZEmfzhQL1GcZsawi8YVufR2YLelaigRzFDAxVzA1HFC9JHX4XJU+H0vGzh/bx+S6dp1E50bomqRRFPNjBczM+ZDqGg6orlvnT11mkmQViS+sU2o4oPpy4NNNTQFfyzWXOXq9C9G5EdY1m9u+zPZr6TUZqHTtuyZ16/ypVa93LtHGF9Y1tWpTo36dP3MlXU1NZpLkEokvrGtOomhTO48VbWo5Ozxq1flD/Xq9s4g2vrBOqVubWoqhNp0/oRCJL6xTJM23vWO7bb2qbr3euUTnRljXDJG0SeNDDdrU6qZWM0lyif8hwrqmbm1qdVO3mSRZRIkvrFNsXwEcDjwNLAYOsz2l76N6Sq2WEcsl2vhC6CF1m0mSSyS+EHpIHXu9c4iqbgi9pW4zSbKIxBdCb4leb3rwhkPocdHrTbTxhdBzYiZJJL4QQg+KNr4QQs+JxBdC6DmR+EIIPScSXwih50TiCyH0nP8PM1yIE3WoXKkAAAAASUVORK5CYII=\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, 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] == \"DoG\":\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": 21,
   "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",
    "        heading = \"\\\\begin{table*}[ht]\\\\scriptsize\"\n",
    "        heading += \"\\\\caption{\" + caption + \"}\\\\label{\" + key + \"}\"\n",
    "        heading += \"\\\\centering\\\\tabularnewline\"\n",
    "        heading += \"\\\\begin{tabular}{\" + columnConfig + \"}\\hline\"\n",
    "        footer = \"\\hline\\end{tabular}\\end{table*}\"\n",
    "        \n",
    "        print(heading)\n",
    "        print(\" & \".join([\"\\\\textbf{\" + h + \"}\" for h in self.heading]))\n",
    "        print(\"\\\\tabularnewline\")\n",
    "        print(\"\\\\hline\")\n",
    "        for row in self.rows:\n",
    "            print(\" & \".join(row))\n",
    "            print(\"\\\\tabularnewline\")\n",
    "        print(footer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0e2faa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tableRow(algo, dataset):\n",
    "    row = []\n",
    "    for gan in (list(gans)[-4:] if algo[0:3] == \"DoG\" else gans):\n",
    "        v = getValueOf(gan, dataset, algo, f1Score)\n",
    "        w = getValueOf(gan, dataset, algo, kScore)\n",
    "        row.append((v, w))\n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8c820b02",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "% ### LR\n",
      "\n",
      "\\begin{table*}[ht]\\scriptsize\\caption{LR (1)}\\label{tab:results:LR:A}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n",
      "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{Repeater} & \\textbf{ProWRAS} & \\textbf{GAN} & \\textbf{ctGAN} & \\textbf{CTAB GAN}\n",
      "\\tabularnewline\n",
      "\\hline\n",
      "abalone 17 vs 7 8 9 10 &  0.275  /  0.244  &  0.336  /  0.309  &  0.336  /  0.317  &  0.182  /  0.152  &  0.186  /  0.151 \n",
      "\\tabularnewline\n",
      "abalone9 18 &  0.458  /  0.409  &  0.566  /  0.534  &  0.514  /  0.481  &  0.341  /  0.296  &  0.517  /  0.479 \n",
      "\\tabularnewline\n",
      "car good &  0.106  /  0.035  &  0.100  /  0.028  &  \\textbf{0.112}  /  \\textbf{0.043}  &  0.109  /  0.038  &  0.108  /  0.037 \n",
      "\\tabularnewline\n",
      "car vgood &  0.362  /  0.320  &  0.394  /  0.356  &  0.336  /  0.304  &  0.325  /  0.279  &  0.379  /  0.342 \n",
      "\\tabularnewline\n",
      "flare F &  0.263  /  0.210  &  0.278  /  0.228  &  0.322  /  0.287  &  0.321  /  0.277  &  \\textbf{0.342}  /  \\textbf{0.301} \n",
      "\\tabularnewline\n",
      "hypothyroid &  0.358  /  0.305  &  0.400  /  0.353  &  0.297  /  0.275  &  0.294  /  0.234  &  0.223  /  0.175 \n",
      "\\tabularnewline\n",
      "kddcup guess passwd vs satan &  0.997  /  0.996  &  0.996  /  0.996  &  0.997  /  0.996  &  \\textbf{0.998}  /  \\textbf{0.998}  &  0.997  /  0.996 \n",
      "\\tabularnewline\n",
      "kr vs k three vs eleven &  0.942  /  0.941  &  \\textbf{0.965}  /  0.964  &  0.960  /  0.959  &  0.576  /  0.559  &  0.699  /  0.687 \n",
      "\\tabularnewline\n",
      "kr vs k zero one vs draw &  0.650  /  0.632  &  0.744  /  0.732  &  0.796  /  0.788  &  0.398  /  0.361  &  0.735  /  0.723 \n",
      "\\tabularnewline\n",
      "shuttle 2 vs 5 &  \\textbf{0.998}  /  \\textbf{0.998}  &  0.996  /  0.996  &  0.990  /  0.990  &  0.980  /  0.980  &  0.987  /  0.986 \n",
      "\\tabularnewline\n",
      "winequality red 4 &  0.113  /  0.056  &  0.143  /  0.090  &  0.017  /  0.011  &  0.109  /  0.059  &  \\textbf{0.144}  /  \\textbf{0.097} \n",
      "\\tabularnewline\n",
      "yeast4 &  0.236  /  0.188  &  0.247  /  0.202  &  0.271  /  0.256  &  0.230  /  0.188  &  \\textbf{0.284}  /  \\textbf{0.257} \n",
      "\\tabularnewline\n",
      "yeast5 &  0.574  /  0.555  &  0.592  /  0.575  &  0.551  /  0.539  &  0.510  /  0.489  &  0.614  /  0.600 \n",
      "\\tabularnewline\n",
      "yeast6 &  0.242  /  0.210  &  0.292  /  0.263  &  \\textbf{0.393}  /  \\textbf{0.382}  &  0.272  /  0.242  &  0.375  /  0.353 \n",
      "\\tabularnewline\n",
      "\\hline Average &  0.470  /  0.436  &  0.503  /  0.473  &  0.492  /  0.473  &  0.403  /  0.368  &  0.471  /  0.442 \n",
      "\\tabularnewline\n",
      "\\hline\\end{tabular}\\end{table*}\n",
      "\n",
      "\\begin{table*}[ht]\\scriptsize\\caption{LR (2)}\\label{tab:results:LR:B}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n",
      "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{convGAN majority 5} & \\textbf{convGAN majority full} & \\textbf{convGAN proximity 5} & \\textbf{convGAN proximity full}\n",
      "\\tabularnewline\n",
      "\\hline\n",
      "abalone 17 vs 7 8 9 10 &  0.292  /  0.262  &  0.337  /  0.310  &  0.292  /  0.261  &  \\textbf{0.344}  /  \\textbf{0.318} \n",
      "\\tabularnewline\n",
      "abalone9 18 &  0.538  /  0.500  &  \\textbf{0.575}  /  \\textbf{0.544}  &  0.541  /  0.503  &  0.574  /  0.543 \n",
      "\\tabularnewline\n",
      "car good &  0.108  /  0.038  &  0.109  /  0.038  &  0.107  /  0.036  &  0.110  /  0.039 \n",
      "\\tabularnewline\n",
      "car vgood &  0.373  /  0.332  &  \\textbf{0.422}  /  \\textbf{0.387}  &  0.377  /  0.337  &  0.400  /  0.362 \n",
      "\\tabularnewline\n",
      "flare F &  0.273  /  0.223  &  0.323  /  0.279  &  0.276  /  0.226  &  0.317  /  0.274 \n",
      "\\tabularnewline\n",
      "hypothyroid &  0.363  /  0.312  &  0.378  /  0.328  &  0.360  /  0.308  &  \\textbf{0.429}  /  \\textbf{0.386} \n",
      "\\tabularnewline\n",
      "kddcup guess passwd vs satan &  0.989  /  0.989  &  0.995  /  0.995  &  0.994  /  0.994  &  \\textbf{0.998}  /  \\textbf{0.998} \n",
      "\\tabularnewline\n",
      "kr vs k three vs eleven &  0.952  /  0.951  &  \\textbf{0.965}  /  \\textbf{0.965}  &  0.948  /  0.947  &  0.963  /  0.962 \n",
      "\\tabularnewline\n",
      "kr vs k zero one vs draw &  0.700  /  0.686  &  \\textbf{0.836}  /  \\textbf{0.830}  &  0.710  /  0.696  &  0.761  /  0.750 \n",
      "\\tabularnewline\n",
      "shuttle 2 vs 5 &  0.994  /  0.994  &  0.996  /  0.996  &  0.989  /  0.989  &  0.992  /  0.992 \n",
      "\\tabularnewline\n",
      "winequality red 4 &  0.130  /  0.075  &  0.135  /  0.080  &  0.126  /  0.071  &  0.131  /  0.076 \n",
      "\\tabularnewline\n",
      "yeast4 &  0.254  /  0.209  &  0.277  /  0.235  &  0.249  /  0.204  &  0.276  /  0.234 \n",
      "\\tabularnewline\n",
      "yeast5 &  0.582  /  0.564  &  \\textbf{0.620}  /  \\textbf{0.604}  &  0.577  /  0.559  &  0.601  /  0.584 \n",
      "\\tabularnewline\n",
      "yeast6 &  0.295  /  0.266  &  0.353  /  0.328  &  0.290  /  0.261  &  0.316  /  0.289 \n",
      "\\tabularnewline\n",
      "\\hline Average &  0.489  /  0.457  &  \\textbf{0.523}  /  \\textbf{0.494}  &  0.488  /  0.457  &  0.515  /  0.486 \n",
      "\\tabularnewline\n",
      "\\hline\\end{tabular}\\end{table*}\n",
      "\n",
      "% ### RF\n",
      "\n",
      "\\begin{table*}[ht]\\scriptsize\\caption{RF (1)}\\label{tab:results:RF:A}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n",
      "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{Repeater} & \\textbf{ProWRAS} & \\textbf{GAN} & \\textbf{ctGAN} & \\textbf{CTAB GAN}\n",
      "\\tabularnewline\n",
      "\\hline\n",
      "abalone 17 vs 7 8 9 10 &  0.189  /  0.180  &  0.188  /  0.179  &  0.185  /  0.175  &  0.236  /  0.219  &  0.115  /  0.109 \n",
      "\\tabularnewline\n",
      "abalone9 18 &  0.311  /  0.288  &  0.338  /  0.311  &  0.309  /  0.287  &  \\textbf{0.398}  /  \\textbf{0.366}  &  0.252  /  0.235 \n",
      "\\tabularnewline\n",
      "car good &  \\textbf{0.900}  /  \\textbf{0.896}  &  0.557  /  0.548  &  0.672  /  0.664  &  0.533  /  0.504  &  0.669  /  0.660 \n",
      "\\tabularnewline\n",
      "car vgood &  \\textbf{0.982}  /  \\textbf{0.982}  &  0.904  /  0.901  &  0.924  /  0.921  &  0.812  /  0.803  &  0.944  /  0.942 \n",
      "\\tabularnewline\n",
      "flare F &  0.224  /  0.180  &  0.080  /  0.059  &  0.110  /  0.091  &  \\textbf{0.261}  /  \\textbf{0.214}  &  0.091  /  0.071 \n",
      "\\tabularnewline\n",
      "hypothyroid &  0.796  /  0.787  &  0.737  /  0.726  &  0.785  /  0.775  &  0.764  /  0.750  &  0.779  /  0.769 \n",
      "\\tabularnewline\n",
      "kddcup guess passwd vs satan &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  0.993  /  0.992  &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000} \n",
      "\\tabularnewline\n",
      "kr vs k three vs eleven &  \\textbf{0.999}  /  \\textbf{0.999}  &  0.993  /  0.993  &  0.995  /  0.995  &  0.791  /  0.784  &  0.997  /  0.997 \n",
      "\\tabularnewline\n",
      "kr vs k zero one vs draw &  0.945  /  0.943  &  0.939  /  0.936  &  0.938  /  0.935  &  0.703  /  0.688  &  0.938  /  0.936 \n",
      "\\tabularnewline\n",
      "shuttle 2 vs 5 &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000} \n",
      "\\tabularnewline\n",
      "winequality red 4 &  0.014  /  0.009  &  0.000  /  -0.008  &  0.026  /  0.023  &  \\textbf{0.132}  /  \\textbf{0.102}  &  0.007  /  -0.000 \n",
      "\\tabularnewline\n",
      "yeast4 &  0.295  /  0.282  &  0.329  /  0.314  &  0.252  /  0.242  &  \\textbf{0.353}  /  \\textbf{0.325}  &  0.253  /  0.242 \n",
      "\\tabularnewline\n",
      "yeast5 &  0.736  /  0.728  &  0.705  /  0.697  &  0.700  /  0.692  &  0.651  /  0.638  &  0.665  /  0.656 \n",
      "\\tabularnewline\n",
      "yeast6 &  0.516  /  0.507  &  0.551  /  0.543  &  0.478  /  0.470  &  0.381  /  0.362  &  0.459  /  0.452 \n",
      "\\tabularnewline\n",
      "\\hline Average &  \\textbf{0.636}  /  \\textbf{0.627}  &  0.594  /  0.586  &  0.598  /  0.590  &  0.573  /  0.554  &  0.584  /  0.576 \n",
      "\\tabularnewline\n",
      "\\hline\\end{tabular}\\end{table*}\n",
      "\n",
      "\\begin{table*}[ht]\\scriptsize\\caption{RF (2)}\\label{tab:results:RF:B}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n",
      "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{convGAN majority 5} & \\textbf{convGAN majority full} & \\textbf{convGAN proximity 5} & \\textbf{convGAN proximity full}\n",
      "\\tabularnewline\n",
      "\\hline\n",
      "abalone 17 vs 7 8 9 10 &  0.234  /  0.222  &  0.280  /  0.268  &  0.253  /  0.240  &  \\textbf{0.283}  /  \\textbf{0.270} \n",
      "\\tabularnewline\n",
      "abalone9 18 &  0.324  /  0.291  &  0.348  /  0.326  &  0.335  /  0.302  &  0.359  /  0.337 \n",
      "\\tabularnewline\n",
      "car good &  0.634  /  0.624  &  0.659  /  0.650  &  0.655  /  0.646  &  0.647  /  0.638 \n",
      "\\tabularnewline\n",
      "car vgood &  0.910  /  0.907  &  0.929  /  0.927  &  0.904  /  0.901  &  0.921  /  0.918 \n",
      "\\tabularnewline\n",
      "flare F &  0.062  /  0.043  &  0.120  /  0.099  &  0.064  /  0.044  &  0.133  /  0.113 \n",
      "\\tabularnewline\n",
      "hypothyroid &  0.782  /  0.772  &  \\textbf{0.798}  /  \\textbf{0.788}  &  0.791  /  0.781  &  0.796  /  0.786 \n",
      "\\tabularnewline\n",
      "kddcup guess passwd vs satan &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000} \n",
      "\\tabularnewline\n",
      "kr vs k three vs eleven &  0.993  /  0.993  &  0.992  /  0.992  &  0.993  /  0.993  &  0.992  /  0.992 \n",
      "\\tabularnewline\n",
      "kr vs k zero one vs draw &  0.941  /  0.939  &  0.946  /  0.944  &  0.937  /  0.935  &  \\textbf{0.949}  /  \\textbf{0.947} \n",
      "\\tabularnewline\n",
      "shuttle 2 vs 5 &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000} \n",
      "\\tabularnewline\n",
      "winequality red 4 &  0.079  /  0.062  &  0.086  /  0.055  &  0.067  /  0.050  &  0.070  /  0.041 \n",
      "\\tabularnewline\n",
      "yeast4 &  0.251  /  0.240  &  0.321  /  0.304  &  0.238  /  0.228  &  0.314  /  0.298 \n",
      "\\tabularnewline\n",
      "yeast5 &  0.691  /  0.684  &  \\textbf{0.738}  /  \\textbf{0.730}  &  0.705  /  0.698  &  0.727  /  0.718 \n",
      "\\tabularnewline\n",
      "yeast6 &  0.498  /  0.490  &  \\textbf{0.575}  /  \\textbf{0.567}  &  0.510  /  0.503  &  0.510  /  0.501 \n",
      "\\tabularnewline\n",
      "\\hline Average &  0.600  /  0.591  &  0.628  /  0.618  &  0.604  /  0.594  &  0.622  /  0.611 \n",
      "\\tabularnewline\n",
      "\\hline\\end{tabular}\\end{table*}\n",
      "\n",
      "% ### GB\n",
      "\n",
      "\\begin{table*}[ht]\\scriptsize\\caption{GB (1)}\\label{tab:results:GB:A}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n",
      "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{Repeater} & \\textbf{ProWRAS} & \\textbf{GAN} & \\textbf{ctGAN} & \\textbf{CTAB GAN}\n",
      "\\tabularnewline\n",
      "\\hline\n",
      "abalone 17 vs 7 8 9 10 &  0.331  /  0.309  &  \\textbf{0.333}  /  \\textbf{0.315}  &  0.278  /  0.265  &  0.213  /  0.196  &  0.232  /  0.221 \n",
      "\\tabularnewline\n",
      "abalone9 18 &  0.382  /  0.345  &  0.384  /  0.344  &  0.338  /  0.308  &  \\textbf{0.398}  /  \\textbf{0.370}  &  0.389  /  0.361 \n",
      "\\tabularnewline\n",
      "car good &  0.839  /  0.831  &  0.853  /  0.847  &  0.831  /  0.824  &  0.533  /  0.504  &  0.764  /  0.756 \n",
      "\\tabularnewline\n",
      "car vgood &  0.938  /  0.935  &  0.973  /  0.972  &  \\textbf{0.982}  /  0.981  &  0.812  /  0.803  &  0.974  /  0.973 \n",
      "\\tabularnewline\n",
      "flare F &  0.285  /  0.237  &  0.133  /  0.113  &  0.183  /  0.168  &  \\textbf{0.326}  /  \\textbf{0.286}  &  0.166  /  0.149 \n",
      "\\tabularnewline\n",
      "hypothyroid &  0.781  /  0.768  &  0.788  /  0.777  &  0.808  /  0.799  &  0.748  /  0.733  &  0.809  /  0.800 \n",
      "\\tabularnewline\n",
      "kddcup guess passwd vs satan &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  0.987  /  0.986  &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000} \n",
      "\\tabularnewline\n",
      "kr vs k three vs eleven &  \\textbf{0.995}  /  \\textbf{0.995}  &  \\textbf{0.995}  /  \\textbf{0.995}  &  \\textbf{0.995}  /  \\textbf{0.995}  &  0.751  /  0.742  &  0.994  /  0.994 \n",
      "\\tabularnewline\n",
      "kr vs k zero one vs draw &  0.944  /  0.942  &  0.960  /  0.958  &  0.964  /  0.963  &  0.638  /  0.619  &  0.947  /  0.945 \n",
      "\\tabularnewline\n",
      "shuttle 2 vs 5 &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000} \n",
      "\\tabularnewline\n",
      "winequality red 4 &  0.122  /  0.088  &  0.093  /  0.065  &  0.087  /  0.077  &  0.112  /  0.086  &  0.084  /  0.065 \n",
      "\\tabularnewline\n",
      "yeast4 &  \\textbf{0.378}  /  \\textbf{0.350}  &  0.354  /  0.331  &  0.289  /  0.274  &  0.276  /  0.248  &  0.332  /  0.317 \n",
      "\\tabularnewline\n",
      "yeast5 &  0.712  /  0.703  &  0.707  /  0.698  &  \\textbf{0.736}  /  \\textbf{0.728}  &  0.618  /  0.604  &  0.690  /  0.682 \n",
      "\\tabularnewline\n",
      "yeast6 &  0.475  /  0.461  &  0.539  /  0.529  &  0.474  /  0.465  &  0.355  /  0.336  &  0.463  /  0.453 \n",
      "\\tabularnewline\n",
      "\\hline Average &  \\textbf{0.656}  /  0.640  &  0.651  /  0.639  &  0.639  /  0.631  &  0.556  /  0.538  &  0.632  /  0.623 \n",
      "\\tabularnewline\n",
      "\\hline\\end{tabular}\\end{table*}\n",
      "\n",
      "\\begin{table*}[ht]\\scriptsize\\caption{GB (2)}\\label{tab:results:GB:B}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n",
      "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{convGAN majority 5} & \\textbf{convGAN majority full} & \\textbf{convGAN proximity 5} & \\textbf{convGAN proximity full}\n",
      "\\tabularnewline\n",
      "\\hline\n",
      "abalone 17 vs 7 8 9 10 &  0.291  /  0.270  &  0.291  /  0.278  &  0.310  /  0.289  &  0.311  /  0.297 \n",
      "\\tabularnewline\n",
      "abalone9 18 &  0.367  /  0.329  &  0.318  /  0.292  &  0.343  /  0.304  &  0.318  /  0.292 \n",
      "\\tabularnewline\n",
      "car good &  0.714  /  0.705  &  0.864  /  0.858  &  0.733  /  0.723  &  \\textbf{0.869}  /  \\textbf{0.864} \n",
      "\\tabularnewline\n",
      "car vgood &  0.933  /  0.931  &  \\textbf{0.982}  /  \\textbf{0.982}  &  0.951  /  0.949  &  \\textbf{0.982}  /  \\textbf{0.982} \n",
      "\\tabularnewline\n",
      "flare F &  0.139  /  0.125  &  0.181  /  0.164  &  0.162  /  0.147  &  0.156  /  0.138 \n",
      "\\tabularnewline\n",
      "hypothyroid &  0.789  /  0.777  &  0.800  /  0.789  &  0.789  /  0.777  &  \\textbf{0.815}  /  \\textbf{0.806} \n",
      "\\tabularnewline\n",
      "kddcup guess passwd vs satan &  0.995  /  0.995  &  \\textbf{1.000}  /  \\textbf{1.000}  &  0.996  /  0.996  &  \\textbf{1.000}  /  \\textbf{1.000} \n",
      "\\tabularnewline\n",
      "kr vs k three vs eleven &  \\textbf{0.995}  /  \\textbf{0.995}  &  \\textbf{0.995}  /  \\textbf{0.995}  &  0.992  /  0.992  &  \\textbf{0.995}  /  \\textbf{0.995} \n",
      "\\tabularnewline\n",
      "kr vs k zero one vs draw &  0.959  /  0.958  &  \\textbf{0.972}  /  \\textbf{0.971}  &  0.960  /  0.958  &  0.961  /  0.960 \n",
      "\\tabularnewline\n",
      "shuttle 2 vs 5 &  \\textbf{1.000}  /  \\textbf{1.000}  &  \\textbf{1.000}  /  \\textbf{1.000}  &  0.994  /  0.994  &  \\textbf{1.000}  /  \\textbf{1.000} \n",
      "\\tabularnewline\n",
      "winequality red 4 &  \\textbf{0.133}  /  \\textbf{0.095}  &  0.107  /  0.066  &  \\textbf{0.133}  /  \\textbf{0.095}  &  0.102  /  0.063 \n",
      "\\tabularnewline\n",
      "yeast4 &  0.257  /  0.242  &  0.351  /  0.329  &  0.261  /  0.245  &  0.333  /  0.311 \n",
      "\\tabularnewline\n",
      "yeast5 &  0.713  /  0.705  &  0.724  /  0.716  &  0.731  /  0.723  &  0.714  /  0.705 \n",
      "\\tabularnewline\n",
      "yeast6 &  0.490  /  0.481  &  \\textbf{0.564}  /  \\textbf{0.554}  &  0.481  /  0.472  &  0.520  /  0.510 \n",
      "\\tabularnewline\n",
      "\\hline Average &  0.627  /  0.615  &  0.654  /  \\textbf{0.642}  &  0.631  /  0.619  &  0.648  /  0.637 \n",
      "\\tabularnewline\n",
      "\\hline\\end{tabular}\\end{table*}\n",
      "\n",
      "% ### KNN\n",
      "\n",
      "\\begin{table*}[ht]\\scriptsize\\caption{KNN (1)}\\label{tab:results:KNN:A}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n",
      "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{Repeater} & \\textbf{ProWRAS} & \\textbf{GAN} & \\textbf{ctGAN} & \\textbf{CTAB GAN}\n",
      "\\tabularnewline\n",
      "\\hline\n",
      "abalone 17 vs 7 8 9 10 &  0.315  /  0.291  &  \\textbf{0.338}  /  \\textbf{0.315}  &  0.154  /  0.144  &  0.169  /  0.163  &  0.090  /  0.084 \n",
      "\\tabularnewline\n",
      "abalone9 18 &  0.342  /  0.286  &  \\textbf{0.383}  /  \\textbf{0.334}  &  0.161  /  0.144  &  0.212  /  0.197  &  0.153  /  0.140 \n",
      "\\tabularnewline\n",
      "car good &  0.357  /  0.312  &  \\textbf{0.720}  /  \\textbf{0.707}  &  0.465  /  0.439  &  0.220  /  0.160  &  0.375  /  0.332 \n",
      "\\tabularnewline\n",
      "car vgood &  0.413  /  0.375  &  0.805  /  0.797  &  0.718  /  0.707  &  0.297  /  0.249  &  0.618  /  0.597 \n",
      "\\tabularnewline\n",
      "flare F &  0.245  /  0.191  &  0.294  /  0.251  &  0.300  /  0.262  &  0.298  /  0.253  &  0.272  /  0.228 \n",
      "\\tabularnewline\n",
      "hypothyroid &  0.568  /  0.540  &  0.615  /  0.592  &  0.605  /  0.583  &  0.604  /  0.580  &  0.601  /  0.580 \n",
      "\\tabularnewline\n",
      "kddcup guess passwd vs satan &  \\textbf{0.991}  /  \\textbf{0.991}  &  \\textbf{0.991}  /  \\textbf{0.991}  &  \\textbf{0.991}  /  \\textbf{0.991}  &  \\textbf{0.991}  /  \\textbf{0.991}  &  \\textbf{0.991}  /  \\textbf{0.991} \n",
      "\\tabularnewline\n",
      "kr vs k three vs eleven &  0.901  /  0.898  &  0.931  /  0.928  &  0.945  /  0.943  &  0.648  /  0.634  &  0.819  /  0.813 \n",
      "\\tabularnewline\n",
      "kr vs k zero one vs draw &  0.840  /  0.833  &  0.885  /  0.881  &  0.864  /  0.859  &  0.721  /  0.708  &  0.795  /  0.786 \n",
      "\\tabularnewline\n",
      "shuttle 2 vs 5 &  0.951  /  0.951  &  0.956  /  0.955  &  0.931  /  0.930  &  0.947  /  0.946  &  0.914  /  0.913 \n",
      "\\tabularnewline\n",
      "winequality red 4 &  0.069  /  0.016  &  \\textbf{0.074}  /  \\textbf{0.022}  &  0.037  /  0.013  &  0.046  /  0.002  &  0.045  /  -0.005 \n",
      "\\tabularnewline\n",
      "yeast4 &  0.329  /  0.293  &  \\textbf{0.365}  /  0.335  &  0.363  /  \\textbf{0.340}  &  0.319  /  0.290  &  0.254  /  0.218 \n",
      "\\tabularnewline\n",
      "yeast5 &  0.670  /  0.657  &  \\textbf{0.691}  /  \\textbf{0.679}  &  0.677  /  0.667  &  0.583  /  0.565  &  0.678  /  0.667 \n",
      "\\tabularnewline\n",
      "yeast6 &  0.409  /  0.387  &  \\textbf{0.464}  /  \\textbf{0.447}  &  0.406  /  0.386  &  0.397  /  0.375  &  0.424  /  0.405 \n",
      "\\tabularnewline\n",
      "\\hline Average &  0.529  /  0.502  &  \\textbf{0.608}  /  \\textbf{0.588}  &  0.544  /  0.529  &  0.461  /  0.437  &  0.502  /  0.482 \n",
      "\\tabularnewline\n",
      "\\hline\\end{tabular}\\end{table*}\n",
      "\n",
      "\\begin{table*}[ht]\\scriptsize\\caption{KNN (2)}\\label{tab:results:KNN:B}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n",
      "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{convGAN majority 5} & \\textbf{convGAN majority full} & \\textbf{convGAN proximity 5} & \\textbf{convGAN proximity full}\n",
      "\\tabularnewline\n",
      "\\hline\n",
      "abalone 17 vs 7 8 9 10 &  0.290  /  0.261  &  0.315  /  0.296  &  0.273  /  0.243  &  0.311  /  0.291 \n",
      "\\tabularnewline\n",
      "abalone9 18 &  0.361  /  0.305  &  0.136  /  0.120  &  0.347  /  0.290  &  0.165  /  0.149 \n",
      "\\tabularnewline\n",
      "car good &  0.558  /  0.531  &  0.509  /  0.479  &  0.534  /  0.505  &  0.585  /  0.561 \n",
      "\\tabularnewline\n",
      "car vgood &  0.659  /  0.641  &  \\textbf{0.879}  /  \\textbf{0.874}  &  0.700  /  0.684  &  0.794  /  0.784 \n",
      "\\tabularnewline\n",
      "flare F &  0.269  /  0.220  &  \\textbf{0.306}  /  \\textbf{0.265}  &  0.277  /  0.228  &  0.302  /  0.262 \n",
      "\\tabularnewline\n",
      "hypothyroid &  0.576  /  0.548  &  0.606  /  0.582  &  0.578  /  0.551  &  \\textbf{0.646}  /  \\textbf{0.628} \n",
      "\\tabularnewline\n",
      "kddcup guess passwd vs satan &  0.982  /  0.982  &  \\textbf{0.991}  /  \\textbf{0.991}  &  0.985  /  0.985  &  \\textbf{0.991}  /  \\textbf{0.991} \n",
      "\\tabularnewline\n",
      "kr vs k three vs eleven &  0.895  /  0.891  &  \\textbf{0.961}  /  \\textbf{0.960}  &  0.894  /  0.890  &  0.925  /  0.923 \n",
      "\\tabularnewline\n",
      "kr vs k zero one vs draw &  0.864  /  0.858  &  \\textbf{0.897}  /  \\textbf{0.892}  &  0.865  /  0.859  &  0.853  /  0.847 \n",
      "\\tabularnewline\n",
      "shuttle 2 vs 5 &  0.954  /  0.954  &  0.932  /  0.931  &  \\textbf{0.960}  /  \\textbf{0.960}  &  0.943  /  0.942 \n",
      "\\tabularnewline\n",
      "winequality red 4 &  \\textbf{0.074}  /  0.016  &  0.058  /  -0.001  &  \\textbf{0.074}  /  0.016  &  0.059  /  0.000 \n",
      "\\tabularnewline\n",
      "yeast4 &  0.334  /  0.296  &  0.348  /  0.313  &  0.338  /  0.301  &  0.345  /  0.309 \n",
      "\\tabularnewline\n",
      "yeast5 &  0.650  /  0.636  &  0.646  /  0.631  &  0.640  /  0.624  &  0.659  /  0.645 \n",
      "\\tabularnewline\n",
      "yeast6 &  0.337  /  0.311  &  0.426  /  0.406  &  0.334  /  0.308  &  0.390  /  0.367 \n",
      "\\tabularnewline\n",
      "\\hline Average &  0.557  /  0.532  &  0.572  /  0.553  &  0.557  /  0.532  &  0.569  /  0.550 \n",
      "\\tabularnewline\n",
      "\\hline\\end{tabular}\\end{table*}\n",
      "\n",
      "% ### DoG\n",
      "\n",
      "\\begin{table*}[ht]\\scriptsize\\caption{DoG}\\label{tab:results:DoG:A}\\centering\\tabularnewline\\begin{tabular}{l|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}|@{\\hskip3pt}c@{\\hskip3pt}}\\hline\n",
      "\\textbf{dataset ($f_1~$score$~/~\\kappa~$score)} & \\textbf{convGAN majority 5} & \\textbf{convGAN majority full} & \\textbf{convGAN proximity 5} & \\textbf{convGAN proximity full}\n",
      "\\tabularnewline\n",
      "\\hline\n",
      "abalone 17 vs 7 8 9 10 &  0.306  /  0.278  &  \\textbf{0.393}  /  \\textbf{0.372}  &  0.317  /  0.289  &  0.366  /  0.345 \n",
      "\\tabularnewline\n",
      "abalone9 18 &  0.477  /  0.433  &  0.538  /  0.512  &  0.512  /  0.473  &  \\textbf{0.549}  /  \\textbf{0.525} \n",
      "\\tabularnewline\n",
      "car good &  0.741  /  0.729  &  0.659  /  0.646  &  \\textbf{0.759}  /  \\textbf{0.748}  &  0.577  /  0.558 \n",
      "\\tabularnewline\n",
      "car vgood &  0.772  /  0.763  &  0.721  /  0.712  &  \\textbf{0.776}  /  \\textbf{0.766}  &  0.753  /  0.743 \n",
      "\\tabularnewline\n",
      "flare F &  \\textbf{0.321}  /  \\textbf{0.279}  &  0.223  /  0.193  &  0.302  /  0.262  &  0.267  /  0.234 \n",
      "\\tabularnewline\n",
      "hypothyroid &  0.627  /  0.604  &  \\textbf{0.703}  /  \\textbf{0.688}  &  0.625  /  0.602  &  0.690  /  0.674 \n",
      "\\tabularnewline\n",
      "kddcup guess passwd vs satan &  0.991  /  0.991  &  \\textbf{1.000}  /  \\textbf{1.000}  &  0.996  /  0.996  &  \\textbf{1.000}  /  \\textbf{1.000} \n",
      "\\tabularnewline\n",
      "kr vs k three vs eleven &  0.993  /  0.992  &  0.995  /  0.995  &  0.994  /  0.994  &  \\textbf{0.996}  /  \\textbf{0.996} \n",
      "\\tabularnewline\n",
      "kr vs k zero one vs draw &  0.957  /  0.955  &  0.951  /  0.949  &  \\textbf{0.965}  /  \\textbf{0.964}  &  0.941  /  0.939 \n",
      "\\tabularnewline\n",
      "shuttle 2 vs 5 &  \\textbf{1.000}  /  \\textbf{1.000}  &  0.996  /  0.996  &  0.994  /  0.994  &  0.998  /  0.998 \n",
      "\\tabularnewline\n",
      "winequality red 4 &  0.118  /  0.065  &  \\textbf{0.123}  /  \\textbf{0.073}  &  0.115  /  0.063  &  0.105  /  0.052 \n",
      "\\tabularnewline\n",
      "yeast4 &  0.312  /  0.274  &  0.308  /  0.271  &  0.311  /  0.273  &  \\textbf{0.334}  /  \\textbf{0.299} \n",
      "\\tabularnewline\n",
      "yeast5 &  0.578  /  0.560  &  \\textbf{0.652}  /  \\textbf{0.639}  &  0.571  /  0.553  &  0.637  /  0.623 \n",
      "\\tabularnewline\n",
      "yeast6 &  0.366  /  0.342  &  \\textbf{0.491}  /  \\textbf{0.475}  &  0.358  /  0.334  &  0.382  /  0.359 \n",
      "\\tabularnewline\n",
      "\\hline Average &  0.611  /  0.590  &  \\textbf{0.625}  /  \\textbf{0.609}  &  0.614  /  0.594  &  0.614  /  0.596 \n",
      "\\tabularnewline\n",
      "\\hline\\end{tabular}\\end{table*}\n"
     ]
    }
   ],
   "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",
    "for algo in algs:\n",
    "    print()\n",
    "    print(\"% ### \" + algo)\n",
    "    print()\n",
    "    f = \"\"\n",
    "    heading = [\"dataset ($f_1~$score$~/~\\\\kappa~$score)\"]\n",
    "    if algo[0:3] == \"DoG\":\n",
    "        for g in list(gans)[-4:]:\n",
    "            heading.append(latex(g))\n",
    "    else:\n",
    "        for g in gans:\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)\n",
    "            line = [latex(d)]\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",
    "        table.showLatex(algo + \" (1)\", \"tab:results:\" + algo + \":A\")\n",
    "        print()\n",
    "        tableB.showLatex(algo + \" (2)\", \"tab:results:\" + algo + \":B\")\n",
    "    else:\n",
    "        table.showLatex(algo, \"tab:results:\" + algo + \":A\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f1b75f48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.480 - winequality-red-4\n",
      "17.141 - flare-F\n",
      "20.768 - abalone_17_vs_7_8_9_10\n",
      "22.928 - yeast4\n",
      "29.056 - abalone9-18\n",
      "33.003 - yeast6\n",
      "40.735 - car_good\n",
      "49.689 - hypothyroid\n",
      "51.936 - yeast5\n",
      "57.451 - car-vgood\n",
      "68.274 - kr-vs-k-zero-one_vs_draw\n",
      "74.547 - kr-vs-k-three_vs_eleven\n",
      "78.777 - shuttle-2_vs_5\n",
      "79.641 - kddcup-guess_passwd_vs_satan\n"
     ]
    }
   ],
   "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}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}