{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "pretty-performer",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-13 18:22:07.600061: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-06-13 18:22:07.600085: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "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\", \"DoC\"]"
   ]
  },
  {
   "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": "6121f46b",
   "metadata": {},
   "outputs": [],
   "source": [
    "statistic = { }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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": 10,
   "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\" or n == \"DoG\":\n",
    "                        n = \"DoC\"\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": 11,
   "id": "classical-rescue",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 12,
   "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",
    "    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": "public-collins",
   "metadata": {},
   "source": [
    "# Show Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "id": "5fec8e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "def drawDiagram(size, rowNames, data, colNames=[], colors=None, border=20, barIndent=10, fontSize=20, markers=[0.25, 0.5, 0.75, 1.00]):\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",
    "    \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": 15,
   "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": 16,
   "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": 17,
   "id": "d1f4961a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnoseDataset(dataset):\n",
    "    print(f\"{dataset}\")\n",
    "    \n",
    "    def valueOf(algo, score, g):\n",
    "        if dataset in statistic[g]:\n",
    "            if algo in statistic[g][dataset]:\n",
    "                if score in statistic[g][dataset][algo]:\n",
    "                    return statistic[g][dataset][algo][score]\n",
    "        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": 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",
    "showDiagnoseDataset(\"Average\")\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, 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": 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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\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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5Yc5nSAnyyMWdQNJhkqZJmjbviT60zsysZk6OhqSLJM2R9Nu8vTqwPnBjRPwFeEXSZk1v+wFwoKTluztvREyKiHERMW5V9zHNrI24rNoZuoCt+3j8Vo2NiNgHOAhopLDxwErA/ZIeAEbTVFqNiH8D5wCf7V+TzcyGJpdVO8c1wFKSDm3skLSNpF26Of4cYEdJ7yvsW7rwfAKwR0SMjojRpMTbfN0R4HvAp/CtNmbWYdxz7AAREcA+wLvyrRldwETgoW6Ofx7YC/i0pL/m2zWOA46XNBpYB7ilcPz9wFOStms6z2OkUbJLDfqHMjOrUbv0HN0z6UFEPATs381rB7XYdxfwnm5Ot1aL4xtl2Fub9n8B+EJf2mpmNpR5hhwzM7MW2mWGHCdHMzOrjHuOZmZmBXUMrOkvJ0czM6uMe45mZmZNnBzNzMwKXFY1MzNrwT1HMzOzJu45mpmZFXgSADMzsxacHK0aXaRVJGswt6ueuA2b1Bi/q+bPrk2jttjRVXNd7IIaY3+jvp87AN+s6Wf//OCdymVVMzOzApdVzczMWnDP0czMrIl7jmZmZgUuq5qZmbXgsqqZmVkT9xzNzMwKPLeqmZlZC+45mpmZNXFyNDMzK3BZ1czMrIV26Tm2STP7R9Iaks6TdJ+kuZKulfScpBmSnpB0f35+dT5+S0kh6d1N55mfj5sp6XZJOywm5gaSLs0xp+eYOzcdc7Gkm5v2TcxtW62w75nB+UmYmQ0Njd5jfx69O7+OltQlaY6kcyWN7E87OzY5ShJwETAlIsZExCbA0cC7I2IscAlwTESMjYjd8tsmADfmP4uez8dtAXwNOKGbmCOBy4BJOebWwOeB9QrHrAhsBawoad2mUzwGfLG/n9nMbChrTALQ30fP59dawBHAuIjYDFgC+HB/2trJZdVdgZcj4tTGjoiY0d3BOZl+CHgXcIOkkRHxQotDlwee7OY0BwA3R8QlhZhzgDmFYz4I/B54hPSXVky0ZwAHSfpORDyxmM9mZtaWKiirvg54g6SXgaWBh/pzko7tOQKbAdP7cPyOwP0RcR8wBXhP4bU35LLqXcBpwP90c45Ngdt7iDMBODc/mnuoz5AS5JF9aLeZWdsos6waEf8E/hf4O/Aw8J+IuLI/7ezk5NhXE4Dz8vPzeG3iapRVNwb2AM7KPc3FknRRrnv/Nm+vDqwP3BgRfwFekbRZ09t+ABwoafnFnPcwSdMkTZvX649nZla36RDq/wNGNX735cdhxbNLWgl4P7Au8EZgGUkf7U9LO7ms2kUqk/ZI0hKkcuf7JB0LCFhF0nIR8XTx2Ii4WdIoYFVJRwLvzfvH5pg7F47dR9I40jcZgPHASsD9ObcuTyqtHld4z78lnQN8trv2RsQkYBLAOKnm1VfNzPpgYL+xHouIcYt5fTdSBXAeQO6Y7AD8uq+BOrnneA2wlKRDGzskbSNplxbH7gbMjIi1I2J0RLwZuBD4QPOBkjYmXeR9PCKOzT3Ksfnlc4AdJb2v8JalC88nAHvkGKOBrWl9sfh7wKfo7C8vZjbcBLBgAI+e/R3YXtLSubr3TuDO/jS1Y5NjRASwD/CufFtFFzCR1hdnJ5BGthZdCHwkP29cc5wBnA8cGBHzW8R8HtgL+LSkv+bbNY4Djpc0GlgHuKVw/P3AU5K2azrPY7k9S/XpQ5uZDXUlJseIuBX4DWnsx2xSjpvUn2Z2dM8kIh4C9u/mtYNaPS/su4R0uwcRsUQfYt7FawfzFK3V4vit8tNbm/Z/AfhCb+OambWFki8ERcQ3gG8M9DwdnRzNzGwIaZRV24CTo5mZVadNhhA6OZqZWXXcczQzMytwWdXMzKwFl1XNzMyauOdoZmZWELjnaGZmtgj3HM3MzJo4OZqZmRW4rGqVWZM0RflwtGl9obu66otdd3xtWu9vt9ivFwv7lWVujbEhrftTh1bLvveXe45mZmZN3HM0MzMr8CQAZmZmLTg5mpmZNXFZ1czMrMBlVTMzsxbcczQzM2vinqOZmVmBy6pmZmYtuKxqZmbWxD1HMzOzJm3ScxxRdgBJa0g6T9J9kuZK+oOkDQfx/MtK+mk+/x2Spks6tBfvC0knFba/JGliYfsoSR/PzydK+qekGfnxnsFqfyHe+yR9NT8/XNInBjuGmVmtGtcc+/uoUKnJUZKAi4ApETEmIjYBvg6sPohhTgOeBDaIiC2BPYCVe/G+F4F9JY1qfkHS64CDgXMKu78fEWPz4w+D0O7XiIhLIuLbefMM4IjBjmFmVjsnRwB2BV6OiFMbOyJiBnCjpBMlzZE0W9J4AElvlzRF0m8k3SXpbCV7SprcOEc+7veSxgDbAsdFxIJ8/nkR8Z3CscdImipplqRvFtr2CjAJOLpFu98B3B4Rr/T2g+Y2XSdpsqS/SPq2pAMk3ZY/45h83N6Sbs293KslrZ73HyTpR/kzPAc8IGnb3sY3M2sLMYBHhcpOjpsB01vs3xcYC2wB7AacKGnN/NqWwFHAJsB6wI7AVcD2kpbJx4wHzictWjSzkRibSdod2ICUQMcCW0vauXDIj4EDJK3Q9NYdW7T78Jxgz5C0UjefdwvgSGBz4GPAhhGxLal3+/l8zI3A9rmXex7w5W7ONQ14WzevmZm1H5dVe7QTcG5EzI+IR4DrgG3ya7dFxIM54c0ARuce3OXA3rnk+V7g4uaTSjo2XxN8KO/aPT/uAG4HNiYlSwAi4ingLBYtYa4JzCts/xQYQ0qwDwMn0drUiHg4Il4E7gOuzPtnA6Pz8zcBV0iaDRxD96sSPgq8sdULkg6TNE3StHnPdfNuM7OhyD1HIC3NuXWL/YtbMfTFwvP5LBxRez6wP6nkOTUingbmAltIGgEQEd+KiLHA8oU4JxSuFa4fEac3xTsZOARYprDveWBkYyMiHsmJfAHwc1JPtKe2LyhsLyh8jh8CP4qIzUnLFI+ktZG5HYuIiEkRMS4ixq26dDfvNjMbitxzBOAaYKni6FFJ25AG0IyXtISkVYGdgdt6ONcUYCvgUFKiJCLuJZUfj5e0RD7/SBYm3yuAgyUtm19bS9JqxZNGxBPAZFKCbLgTWL/Q5jULr+0DzCmc7089tLvZCsA/8/MDF3Pcho04ZmYdwWXVJCKClEzelW+16AImkkaBzgJmkhLolyPiXz2caz5wKbBn/rPhk8AqwL2SpgNXA1/J77kyx7o5lzF/AyzX4vQnAcVRq38kJeyG7+ZBNbNIg4wag3jWJA3s6YuJwAWSbgAea/6Yhec75s9iZtY52qSsWvokABHxEKkc2uyY/CgeO4XUQ2xsH970+uFA876nSOXJ7uKfApzSYv+yheePAEsXtv8m6XFJG0TEPRHxsW5Ovz1pUE+rtr+91eeKiItpcb2UlOCfAJC0JdAVEc3J08ysvXmGnLb3VVLP8J7uDoiIHw1GIEmfBg4ijeKF1Iv9P4NxbjOzIaOGHmB/OTl2IyLuBu6uKNapQPFe0KuqiGtmVjn3HM3MzJo4OZqZmRW4rGpmZtaCe45mZmZN3HM0MzMraEwC0AacHM3MrDpOjmZmZk1cVjUzMytwWdXMzKwF9xytCk++YWsu2GRaLbEvmFhL2IL6/pftt19toQGYPLm+2HV/dl1Q39/7Jl21hQZg4sR64j45fdzgncw9RzMzs4I2KquWvZ6jmZnZQiUvWSVpRUm/kXSXpDslvbU/zXTP0czMqlN+z/EU4PKI+JCkJSksR9gXTo5mZladEi8ZS1qetFD9QQAR8RLwUn/O5bKqmZlVo3HNsb+Pnq0HzAN+IekOSadJWqY/TXVyNDOz6gwsOY6SNK3wOKzp7K8DtgJ+GhFbAs+SFq7vM5dVzcysOgMrqz4WEYu7r+RB4MGIuDVv/4Z+Jkf3HM3MrBoll1Uj4l/APyRtlHe9E5jbn6a652hmZtUpf7Tq54Gz80jVvwKf6M9JnBzNzKw6JU9wFBEzgAFP6ePkaGZm1ejkGXIkrSHpPEn3SZor6Q+SNhysBklaVtJP8/nvkDRd0qG9eF9IOqmw/SVJEwvbR0n6eB/b8uc+NX6A8s9yRUlLSrpekr+8mFlnKXmGnMHSp+QoScBFwJSIGBMRmwBfB1YfxDadBjwJbJCH4u4BrNyL970I7CtpVPMLOckcDJzTl4ZExA59OX6gIuI9EfHvfOPqn4DxVcY3MytV+fc5Dpq+9hx3BV6OiFMbO3J990ZJJ0qaI2m2pPEAkt4uaUphnruzlewp6dV1BfJxv5c0BtgWOC4iFuTzz4uI7xSOPUbSVEmzJH2z0LZXgEnA0S3a/Q7g9oh4JZ9jiqTv597ZnZK2kfRbSfdIOr4Q65nFfY7mIH047+9yj7ireJ+OpAcKyf13wAE9/H2YmbWXNkmOfS3bbQZMb7F/X2AssAUwCpgq6fr82pbApsBDwE3AjsBVwM8kLRMRz5J6SOfn42Y2EmMzSbsDG5ASqIBLJO0cEY1YPwZmSfpu01t3bNHulyJiZ0lHAhcDWwNPAPdJ+n5EPN50fKvPcWOLZvbmvAdHxBOS3pB/Vhe2iDcH2Kabn8NhwGEAo0at0+oQM7OhqU3Wcxys+xx3As6NiPkR8QhwHQt/sd8WEQ/mhDcDGJ17cJcDe+eS53tJieQ1JB0raYakh/Ku3fPjDuB2YGNSsgQgIp4CzgKOaDrVmqQphYouyX/OBroi4uGIeJE09HftFp9xkc/Rzc+iN+c9QtJM4Ja8b4Pmk0TEfOAlScu1eG1SRIyLiHHLL79qN80wMxti2qis2teeYxfwoRb7FykxFrxYeD6/EPN84HOkXtXUiHha0lxgC0kjImJBRHwL+FajvJnjnBARP1tMvJNJifMXhX3PAyO7adeCpjYuoPXPpbvP0d1xLc8r6e3AbsBbI+I5SVNatK1hKeCFbl4zM2s/HdpzvAZYqjh6VNI2pAE04yUtIWlV0qzot/VwrimkOfAOJSVKIuJeYBpwvKQl8vlHsjD5XgEcLGnZ/NpaklYrnjQingAmA4cUdt8JrN/Hz9orkk6QtE8f3rIC8GROjBsD23dz3lWAeRHx8mC008xsSGiTnmOfkmNEBLAP8K58q0UXMJE0CnQWMJOUQL+cp/FZ3LnmA5cCe+Y/Gz4JrALcK2k6cDXwlfyeK3OsmyXNJs2bt0jZETiJdO2z4Y+khF2GzYHFftYml5N6kLOA/yGVVosa36t2Bf4w8OaZmQ0RHVxWJSIeAvZv8dIx+VE8dgqph9jYPrzp9cOB5n1PAZ9aTPxTSItZNu9ftvD8EQoLXEbE3yQ9LmmDiLgnIt6+mDYWX1u2F5/j9RFxc4v3dnte0heC18g95eWAp/KujwBfaz7OzKytdWhZtZ19lTQwZ1BFxLsH6VRdwGkR8XKeE/B3EXH3IJ3bzGxo6NSeY7vKiWbIJpuI2Ljw/CXSqFszs87SJj3HYZMczcysZm00t6qTo5mZVcfJ0czMrInLqmZmZgUuq5qZmbXgnqOZmVkT9xzNzMwKXFY1MzNrwWVVq8JKz09nv7mLWxSlPJtObJN/5SXo6qo3/sSJ9cW+4IL6YgNM7qrn3zuA5tb7b36/xS6AVJ7v9HxI77nnaGZmVhC452hmZrYI9xzNzMyaODmamZkVuKxqZmbWgnuOZmZmTdxzNDMzK/AkAGZmZi04OZqZmTVxWdXMzKzAZVUzM7MW2qTnOKKMk0paQ9J5ku6TNFfSHyRtOIjnX1bST/P575A0XdKhvXhfSDqpsP0lSRML20dJ+nh+PlHSPyXNyI/3DFb7e9HOvSR9s6p4ZmaVWTCAR4UGPTlKEnARMCUixkTEJsDXgdUHMcxpwJPABhGxJbAHsHIv3vcisK+kUc0vSHodcDBwTmH39yNibH78ob+NlbREH99yGfA+SUv3N6aZ2ZDTKKsOx+QI7Aq8HBGnNnZExAzgRkknSpojabak8QCS3i5piqTfSLpL0tlK9pQ0uXGOfNzvJY0BtgWOi4gF+fzzIuI7hWOPkTRV0qymHtgrwCTg6Bbtfgdwe0S80tsPmtt0vaSLcg/5VEkj8mvPSPpvSbcCb5X0hfzZ50g6Kh/zBUln5Oeb59eWjogApgB79bYtZmZtIQbwqFAZyXEzYHqL/fsCY4EtgN2AEyWtmV/bEjgK2ARYD9gRuArYXtIy+ZjxwPnApsDMRmJsJml3YANSAh0LbC1p58IhPwYOkLRC01t3bNHuw3OCPUPSSt183m2BLwKbA2Py5wRYBpgTEdsBzwOfALYDtgcOlbQlcDKwvqR9gF8An4qI5/L7pwFv6yammVl7GsY9x+7sBJwbEfMj4hHgOmCb/NptEfFgTngzgNG5B3c5sHcueb4XuLj5pJKOzdcEH8q7ds+PO4DbgY1JyRKAiHgKOAs4oulUawLzCts/JSW7scDDwEm0dltE/DUi5gPn5s8JMB+4sPDZL4qIZyPiGeC3wNvy5z0I+BVwXUTcVDjvo8AbWwWUdJikaZKmzXuu1RFmZkPUMO45dgFbt9i/uFU6Xyw8n8/CUbTnA/uTSp5TI+JpYC6wRaN8GRHfioixwPKFOCcUrhWuHxGnN8U7GTiE1LtreB4Y2diIiEdyIl8A/JzUQ2yl+a+ssf1CTpiNNnVnA+AZFk2EI3ObFg0YMSkixkXEuFV9VdLM2sUwv+Z4DbBUcfSopG1IA2jGS1pC0qrAzsBtPZxrCrAVcCgpURIR95JKjsc3BrpIGsnCBHQFcLCkZfNra0larXjSiHgCmExKkA13AusX2rxm4bV9gDmF8/2p8Nq2ktbNyXo8cGOLz3E98AFJS+cy8T7ADbm0e0r+Wawi6UOF92zYiGlm1jHaJDkO+n2OERH5GtrJkr4KvAA8QLqmuCwwk/T94csR8S9JGy/mXPMlXUoqPR5YeOmTwInAvZKeIPWwvpLfc6Wk/wJuTgNneQb4KKlMWXQScHhh+4+k8mbDdyWNzW19APhU3r8maWBPw83At0nXHK8njdRt/hy3SzqThV8GTouIO/JgnJ9ExF8kHQJcK+n6iHiUNLDpa939bMzM2lKb3OdYyiQAEfEQqRza7Jj8KB47hdRDbGwf3vT64bw2iTWuG36KbkTEKaQeWfP+ZQvPHwGWLmz/TdLjkjaIiHsi4mPdnH570qCehuciYvziYuXt7wHfa9p3cOH5P8g9V0mrA2+IiNndfUYzs7bjGXLa1ldJPcN7ujsgIn5UQTvWIY2ANTPrLMO559iuIuJu4O4+HD+FQq93ENsxdbDPaWY2JLjnaGZmVuCyqpmZWQsuq5qZmTWpoOeYb/ObBvwzIvo1DWeVM+SYmdlwNpDZcfrW4zySdO96vzk5mplZdUqeBEDSm0jTjZ42kGa6rGpmZtUZWFl1lKRphe1JETGp6ZiTgS8Dyw0kkJOjmZlVY+ATiD8WEeO6e1HSXsCjETFd0tsHEsjJ0czMqlPugJwdSQvFv4e0eMPykn4dER/t64mcHNvdysB+9YSePLnnYzrVppvWG3/ixPpiz51bX2yA/S6oL/bkrvpiA2j/uu6D6Laz1nclfoSI+Bp5Turcc/xSfxIjODmamVlVPAmAmZlZCxUlx4FO7+nkaGZm1fEMOWZmZgUuq5qZmbXgnqOZmVkT9xzNzMwKXFY1MzNrwWVVMzOzJu45mpmZFQx8btXKODmamVl12qTn2Kv1HCWtIek8SfdJmivpD5I2HKxGSFpW0k/z+e+QNF3Sob14X0g6qbD9JUkTC9tHSfp4H9vy5z41viSSrpa0Ut3tMDMbVCWv5zhYekyOkgRcBEyJiDERsQnwdWD1QWzHacCTwAYRsSWwB2lK7Z68COwraVTzC5JeBxwMnNOXhkTEDn05vjdyW/rqV8BnB7stZma1iQE+KtSbnuOuwMsRcWpjR0TMAG6UdKKkOZJmSxoPaSZ0SVMk/UbSXZLOVrKnpFfXccjH/V7SGGBb4LiIWJDPPy8ivlM49hhJUyXNkvTNQtteASYBR7do9zuA2yPilXyOKZK+L+l6SXdK2kbSbyXdI+n4QqxnFvc5moPkY06W9Of8s9g2758oaZKkK4GzJL1Z0p/yZ/iTpHUkrSDpbkkb5fecW+gxXwJM6MXfj5lZ++iUniOwGTC9xf59gbHAFsBuwImS1syvbQkcBWwCrEdaY+sqYHtJy+RjxgPnA5sCMxuJsZmk3YENSAl0LLC1pJ0Lh/wYOEDSCk1v3bFFu1+KiJ2BU4GLgc/lz3eQpFVahG/1OVpZJvc4PwucUdi/NfD+iPgI8CPgrIh4C3A28IOI+A9wOHCmpA8DK0XEzwEi4klgqW7aZWbWnjqo59idnYBzI2J+RDwCXAdsk1+7LSIezAlvBjA69+AuB/bOZcb3khLUa0g6VtIMSQ/lXbvnxx3A7cDGpGQJQEQ8BZwFHNF0qjWBeU37Lsl/zga6IuLhiHgR+CuwdovPuMjn6OZncW5uy/WkxTVXbMSLiOfz87eysMT7K9LPj4i4Krfnx8Anm877KPDG5mCSDpM0TdK0eU900yIzs6GmMQlAh/Qcu0g9oGaLlBgLXiw8n8/CUbHnA/uTSp5TI+JpYC6whaQRABHxrYgYCyxfiHNCRIzNj/Uj4vSmeCcDhwDLFPY9T1oJulW7FjS1cQGtR+529zmaNX+naWw/283xrx6TP/d/5fY2X2cdmfe/9o0RkyJiXESMW7U3V2bNzIaKDkqO15DKe6+OHpW0DWkAzXhJS0haFdgZuK2Hc00BtgIOJSVKIuJeYBpwvKQl8vlHsjD5XgEcLGnZ/NpaklYrnjQingAmkxJkw53A+r34fH0m6QRJ+xR2Na637gT8J5dLm/0Z+HB+fgBwY35+dG7rBOAMSa/P5xKwBvDAoH8AM7O6dEpZNSIC2Ad4V77VoguYSCoRzgJmkhLolyPiXz2caz5wKbBn/rPhk8AqwL2SpgNXA1/J77kyx7pZ0mzgN8ByLU5/ElActfpHUsIuw+ZA8bM+mW8BOZXXJuiiI4BPSJoFfAw4Mt8O80ngixFxA3A9cFw+fmvglsaAIjOzttdGZVWl3NeZJF1EStr3DPJ5r4iId+fnU4AvRcS0QY5xCuma5Z8Wd9y4zRTTJi/uiPJMnNy5/3Z6summ9cbv6qov9ty59cUGmDxxcVd0ynVBV73/5vffv67I44iYNuAf/LilFdM26v/7NYPpETFuoO3ojYEMyGkHXyUNzBlUjcRYsjk9JUYzs7bTJj3Hjp4+LiLuBu4uOcbbSzrvz8s4r5lZbbxklZmZWQttcjXGydHMzKrjnqOZmVkT9xzNzMwKfM3RzMysBSdHMzOzJi6rmpmZFbisamZm1oJ7jmZmZk3cc7QqTO/iMW3K3wZwilHAY/1764CnWhxA7EFRZ/zhGnvA8XVBfbEH+G++nX/ubx6UFrisalWJiFUH8n5J06qayHcoxa47/nCNXXf84Rp7KMR/lcuqZmZmTdxzNDMzK6hh0eL+cnK0ScM0dt3xh2vsuuMP19hDIX7SJj3Hjl7s2MzMho5xr1dMW7H/79dj1S127J6jmZlVw2VVMzOzFtqkrDqi7gZYtZSsXXc7zGyYigE8KuSe4zATESHpd8DWVcaVtA3wj4j4V97+OPBB4G/AxIh4osTY6yzu9Yj4e1mxbeiQtAzwQkTMr7stVZG0EvBG4HnggYiot9/mSQBsiLtF0jYRMbXCmD8DdgOQtDPwbeDzwFjSKLoPlRj7MtJ/y+L0JgGsCqwGLFFWYElPs/A7byN+kP7vLRkRpf4frDt+bsM44G0s/CU9B7i6zC9EOe4I4MPAAcA2wIvAUpLmAX8AJkXEPSXGHwnsxaKf/bKI6Cox7grA54AJwJLAPGAksLqkW4CfRMS1ZcXvkZOjDWG7Ap+W9ADwLOmXZkTEW0qMuUThl+F40i+mC4ELJc0oMS4RsXlxW9Jo4CukZP3/So69XFPs5YDPAp8CLiozdt3xJR0EHAHcD0wH7ib9kt4J+IqkOcD/KbHnfi1wNfA1YE6j1yRpZdL/gW9Luigifj3YgSVNBPYGpgC3Ao+SPvuGOe5I4IsRMWuwYwO/Ac4C3hYR/25q19bAxyStFxGnlxC7Zx6QY0PYnjXEXELS6yLiFeCdwGGF1yr5dyhpA+BYYDvgJOCIiHi5otgrAkcBHwfOAbaJiMeriF1j/GWAHSPi+W7aNBbYACgrOe7W6u83f0lrfDF7fUmxp0bExG5e+56k1YDFlvv7KyLetZjXppO+qNSjjcqqHpAzDEXE34C1gXfk589R/r+Fc4HrJF1MKi/dACBpfeA/ZQaWtJmkc0m/EK8GNouI06pIjJJGSToBuB14BdgyIo6rKjHWGT8iftxdYsyvz4iIP5XYhDdA6im2eKwkaYmy/g1ExGU59sjm1ySNiohHI2JaGbELcQ5p2l5C0jfKjNkrbTIgx5MADEP5P8g4YKOI2FDSG4ELImLHkuNuD6wJXBkRz+Z9GwLLRMQdJcadD/yDdO1xkcEYEXFEibGfJV3z+QXwdIvY3ysrdt3xJX05Ir4r6Ycs+qstgCeAX0fEfSXFvzQi9pJ0P4tecwZYFvh5RHy9jPi5DbOBQyPilrz9QeCEiNiwrJiF2OcAKwKHAKuQ/g1cFxFfKjt2d8aNUEwbQF9dL3kSACvXPsCWpN4EEfFQvhZVqsYviIY8enBb0sCB95YY+uASz92TE1mYGJp/xlV8M60z/p35z+56SKsAvwW2KCN4ROyV/1y31euSliANkCktOQIfAc6QNIU0KGcV4B0lxntVRHxE0nhgNqk6NCEibqoidveNotSyar5N7SxgjRxpUkSc0p9zOTkOTy/lWzoCXk1SlZC0JPAe0i+NPUilzlPLjBkRv+ymLSNJgybKjD2xu9fy7S2lqjN+RPw+/9ny55/b8GyZbSjEWYl0ffPVMmdEXA/8V5lxI2K2pG8BvyL13HeOiAfLjNmQr7EfSfo/9l+kgTh3RMRzVcTvVrlfyV4hDXS6PX/hny7pqoiY29cTOTkOT5Ml/QxYUdKhpJ7VaWUGlPQuUg/x3aRRhL8Cto2IT5QZt0U7lgB2L7TlBmBgy+f2Lf4mpNsLJpCutVa6vl4d8SWtShodvAmvTU7viIifVRD/k6Qk8SZgBrA9cDMV9OAknQ6MAd5CGqn6e0k/iogflx0b+D1weERcLUnAF4CpwKYVxO5eiT3HiHgYeDg/f1rSncBagJOj9Swi/jcnq6eAjYD/GxFXlRz2ClIi2iki7geQ1K9yR3/keys/Qirf3gbsCKxbxbdoSW8mJaMJpG+2bwbGRcQDZcceCvGBs4HzST/7TwMHkq6DVuVI0n2Ot0TErpI2Br5ZUew5wCcjDe64P193L/U6c8G2EfEUpPu0gJMkXVJR7O5VNMwl37K1JelWmj5zchyGJH0nIr4CXNViX1m2JvVYrpb0V+A8Srz5vkjSg6TbBX4KHJO/Ud5fUWL8M7AC6fN+KCLuybEfKDv2UIifrRIRp0s6MiKuI41avq7C+C9ExAuSkLRURNwlaaMqAkfE9yVtIeltedcNEXHIYt80eJaX9EvSfaULgBtJXxRqMx2uEIwawClGSipew54UEYssxSVpWVI5+ajGF4S+cnIcnt5FKnMV7dli36DJo1HvIN38vSN59g5JfwQuavUPfBBdCHyANPnA/Hw7SVXDtOeRynmrk2bkuafC2EMhPkDjdomHJb0XeCi3qSoP5vs8fwdcJenJ3IbSSTqCdE/vb/OuX0uaFBE/rCD8L0j3tO6Xtz+a93V7H2TZImKPsmPke1cvBM6OiN/2dHy35/GtHMOHpM+QZkdZDygOn18OuCkiPlpxe0aQZqk5NCL26+n4AcYSaVaUCaQBQSuQhrhfFhHPlBx7BdI8shOA9UnD698dEbeVGXcIxd+LVFJfG/ghsDzwzYiovMQnaRfS3/3lEfFSBfFmAW8t3Lq0DHBzlDsbVSP2jIgY27RvZkSUMjp4KMj/z38JPBERRw3oXE6Ow0f+JbkScALw1cJLT0fJ81zm+GuR7nOcFREv5VlCjgIOiog3lh2/0I7Xk0bKTgB2j4iBlHn6Gnt1Ug/2w8DaEVHpCil1x69LHq26NoVqWUTcXkHc2aTZiF7I2yNJs+dsvvh3Dkrsq4EzSRNwQPr3/omIeGfZsesiaSfSF7HZLBz68/WI+EOfz+XkOHzl5FQcPVja6hSSjiJN3XYvsBRwCmlgwlnAd/Mos7Jivx94U2OEoKRbSROOQxqM9KuyYvfQrjfnGYpqUVV8SeuSJpkfzWuT0/vKjp3j/w9wEPBXFv7CjIioYrTq0Tl2Yx7bDwBnRsTJFcReB/gR8FZSKf3PwJF1/ptrJ06Ow5CkvUmJ6Y2kCZHfDNwZEaUN8ZY0lzRS9Yn8n/Ze0j1ft/Tw1sGIfRPw4Yj4R96eQZrfdRngF2V+k87fZNeLiLPy9m+AlfPLx0fENWXFHgrxc8yZwOm89ts8eXBO6STdDWxeRRm1Ke4I0m0jL5AGxQi4PkqcDaoQewngl1VfKukkHpAzPB1P+k97dURsKalxLa5MLzRKtxHxd0l/qSIxZks2EmN2Y6S5RR+vYAKEb5J6TQ0bkXoSy5BmZik7OdUdH9Lf/Q8qiNOdOaTrrI9WGTQiFkg6KSLeSp6NqsLY8yWtKmnJqr8UdAonx+Hp5Yh4XNIISSMi4lpJ3yk55pskFX9BrlbcjhLnNyVdZ31VRBxe2Fy1xLgAyzfNznFPpJURUJoQvGx1xwc4RWk+3ytJayoC1Vzzy04A7lBaIqsYv4qy7pVK86n+Nqov0z0A3JTvbXx1JqIoeT7fTuHkODz9O98HdANwtqRHSTeHl+mYpu0ql825VdKhEfHz4k5JnyJNCFCmFYsbEbFvYXP1kmMPhfgAmwMfI81I8+o1PyqaY5Q0evE7NJV1K/IFUi99vqQX8r6IiOUriP1Qfoxg0Xl1rQe+5jgM5VLi86T/NAeQhrafHSUuY6S0dt/MGr49NwYe/Y7Ua2j0VrYmDQz6QEQ8UmLs3wOnRl7CqLB/L+AzEVHmhOu1x8+x7gLeUld5T9J1EbFLHbGHAknLkxLyIquyWPecHIepPKXYBpHmXVwaWKLM/zx5Vot1ScnpJtLIuVv6O3tFP9vwDhbOK9lV0WCUDYBLSZ+3mJh3APaKiL+UHH990lJdtcTPbTgf+HxEVHrNrxD/e6QvRpdQQ1lX0r6kATlBmiHndxXFHUe66b/Ra/wPcHCjrG6L5+Q4DClNNn4YsHJEjMm/wE8t+/6nnIS3Jf1i3oE03+W/SBMQfLbM2HWStBSph/5qYgbOadz7NgziTyFNvD2V6q/5IenaFrurupXjJ6SJFxr3Go4H7ouIz1UQexbwuYhoLCy+E/CTKiYg6AROjsNQvpVhW+DWiNgy75tdxY3JOdYypNGyOwIfB0ZExHpVxK6apCsjYve621GnPCvNIqq6laNOkrqAzRqXE/LtHbPLvG2qEPumaFrAvNU+a80DcoanF/MMNQBIeh0lz7cp6SOk3uJYUu9hKmm2/J0i4l9lxq5Z2aNh+0zS7RGxVVXxhloSzJNC/Csi+rVaQx/dDawDNG68XxuYVUFcgNuUlqY7l/T/ezwwRdJWUOlo4bbk5Dg8XSfp68AblJau+ixp7bcyTQLuIi1sfH0V17qGiBXyNaeWYgATIw+Aaoi5MHia1uxl4McRcWkNTdgO2FzS6yJiz5JjrQLcKakxKnob4OZ8e0XZpeWx+c9vNO3fgWpHC7cll1WHoVzaOYS06K9Iay2eVuZI0jxjxxYsvN64EWlR0ptJEzFXcTN65SQ9DlxM64QUEXFwxU1C0vERcVzVcQvx30iaY3f7qGbR39p0V1JuGGq9alvIyXGYkrQksDHpG+TdNUyttTrwIeBo0qLDlaztWLWqS5i9IWkU8Hgdt9XURdIOLDq361m1NagGFZeT296Iuhtg1VNaU+8+4AekiYnvlVRqeUnSWyR9WtJZku4lXXPcmbSE0XZlxq7ZRkrrV76GpLdJGlN2cEnbS5oi6beStsyzxMwBHpFU6tp6xfNLWkHS6ZJmSTonfzmqhKRfAf9Lup1im/wYV1X8prZcLemP+T7Tqm0HHKe0hqr1wD3HYSjflL1XRNybt8eQ1jXcuMSYxfsb/zxcVgaQdA1pNfJZTfvHAd+IiL1Ljj+NNIfqCqTrvntGxC2SNgbObYxWLin2q71mSaeRbtv5ObAvsEtEfKCs2E3tuBPYZCj0lIdTSbndeUDO8PRoIzFmf6XkSZmLpUVJS0raLG/eHREvd/O2TrBac2IEiIhpkkZXEP91EXElgKT/bkz2HhF3NUYrV2RcLFx49/uSDqww9hxgDdI17lpFRGNKt0puxHc5uf+cHIenLkl/ACaTrjnuB0xtjKoscwSlpLeT5rp8gDRIZW1JB0bE9WXFrNnIxbz2hgriF+cSfb7ptbJ7UqtJ+gLp73l5SSr03qq8pDMKmJtHjFYyCYGkPSLi8vx8BdIScduQEvXRZU5ZWGjDr4AxwAxgft4dpDVUrQcuqw5Dkn6xmJdLHUEpaTrwkYi4O29vSCrvbV1WzDpJOhe4psWk54cAu0fE+JLjzyetyCBSMn6u8RIwMiJeX2Ls5lsIfhIR8yStQVrg+uNlxW5qR+WTEAyFkvJQKie3IydHq5SkWc3TV7Xa1ynywJOLgJdYWEobBywJ7NPhEyAMW03JcUahpLzIdoltuAA4IiJqLye3I5dVh6HcW/spsHpEbCbpLcD7IuL4CsJPl3Q68Ku8fQDVLl9VqVw+20FpQenGddbL6ryvM0/f9wFSD760VTkkfZQ0h2vLZaLyQLA1I+LGkuLfGBE7SXqa15aQRfnLRg2FknLl5eRO4p7jMCTpOtL6ij8rzK06JyI2W/w7ByX2UsDnSMPqBVxPKre9uNg32oDk+1rfA3wE2AO4kLQAb2kzI0k6EjiY9OVnOjCPdA12fWAX4DHgqxFxT1ltqMtQKCkP5zltB4OT4zAkaWpEbCPpjkJyLL3Uk2fmmVVFErYkTw84AXg3cC1wPvDDiBhdUfwlSNOU7Ui6heF54E7gjxHx9yrakNuxEmle0+KoTc8tat1yWXV4eiyXtBorBXyICoa5R8QCSTMlrVPlL8Zh7grgBtIE7/cDSDqlquARMR+4Kj9qIel/gINItyw1Srylzi1aZ0m55nJyx3ByHJ4+R7ohfGNJ/wTuJ137q8KapFtJbiONogR8HaREWwMfBq6W9FfgPKCSqfok/d/FvBwR8T9VtAPYHxhT8RSJqwB35NHZ3ZaUywgcETvlP5fr6Vjrnsuqw1gemDGCVOoaHxFnVxDT10FqkqexmwB8kHTv20URManEeF9ssXsZ0qT3q0TEsmXFbmrHhcBnIqLUiS5axK29pOxycv85OQ4jkpYn9RrXIq0UcXXe/hIwMyLeX2LskcCnSd+cZwOnR8QrZcWzRC0WW87Xft8FfDgiPlFRO5YDjiQlxsnASVUlqzxV38WkG/CHzajN7srJEeGlqnrByXEYkXQx8CRpmah3AiuR7rc7MiJmlBz7fNIafjcAewJ/i4gjy4xpUBx0VVP8lYEvkMr2vwROiYgnK25DF/Az0peyV68BljwJQO0lZUl3A5tXXE7uGL7mOLysFxGbw6uzdjwGrBMRT1cQe5NC7NOB23o43gZHbYstSzqRNCPMJNIv6WfKitWDxyLiBxXHfLbFvldLykAV11vnACtS8rzJnco9x2FETWsLNm93auzhTDUutixpAamM+Qo1jpqU9L3cjkt4bVm1kmtvdZWUh2s5ebA4OQ4jhXk24bVzbZb+y6rO2MOZv4SApGtb7C792lvdJeU6ysmdxMnRrINJepY0wflNTfvfBjwUEfdV1I6dgA0i4heSRgHLNe677ERNJeUf11FSlnRdRLQcHW49c3I062CqebHlHOsbpMnWN4qIDZUW/L0gInYsOW6dN+LXXlKuu5zc7jwgx6yz1b3YMsA+wJbA7Tn2Q/k6XNnqvBG/yvUqu9MYpbx9YV+pMwN1EidHs85W92LLAC9FREhqTFe4TBVBI+IUST9i4Y34b2Hhjfgfq/BG/FpKyhGxa9kxOpmTo1lnmyrp0Gi92HJVS4VNlvQzYEVJh5JW6vh5D+8ZFHXP7VosKQO/IN1X/GtSsi4rZq1LhXUKX3M062BDZbHlvDrI7qRrbldEROnJaojciD+DXFIurIBT6uLew3mpsMHk5Gg2DDQtttwVNS62XJWhMLerpNsiYtvGLTW5pHxzmckxx619Xtd25+RoZqXIpduVI+LEvP0gsDyp9/jliPhphW2p60b8LwEbkOayPYHUozsnIn5YdmwbGCdHMyuFpKnAHhHxeN6+IyK2zJPQXxkRO1fQhqEwt2ulJeWhUE7uBB6QY2ZlGdFIjNkFABHxgqTSR8oOlbldczKsckDQUJjXte2552hmpZB0b0Ss32L/CODeiFiv5Pi13Yg/VErKdS4V1u6Gwo2qZtaZrpR0fIv9/w1cWXbwiBgREW+IiOUiYvnCY7kKZqj5NHBGYXtejrkqacHpUklaOf/sZ5EqhFtFxFecGHvPZVUzK8sxwGmS7gVm5n1bANOAT1bZkBpuxK+tpDxUysntzmVVMyuVpPWATfPm3KomOy/Er3xu1zpLykNhXtdO4LKqmZXt+8BywDVVJ8ZsH+B95IEqEfFQbk+Zaisp11xO7hhOjmZWtu8BOwFzJV0g6UP5do6qvBSpRFbl3K7HAGMk3Svpwvy4lzRLzZcqiA+kcrKkT+TnoyStW1XsdueyqplVojBry6Gk+x8r6cXUeSN+nSXlupYK6xROjmZWujwIZW9gPLAVcGlEfL7C+JXP7ZrjXgycD1wcEa3uPywz9gwqnte1k3i0qpmVStL5wHbA5cCPgSndrRhRlhpuxG/4HukLwQmSbiMlyksj4oUKYteyVFincM/RzEolaQ/gqrx8VJVxh8SN+Dl25SVlz+s6ME6OZlY6STsAoylUqyLirJJj1j63a45bW0m5rnJyJ3BZ1cxKJelXwBhgBtDoPQZQanKk5rldof6Sco3l5LbnnqOZlUrSncAmUfEvm7rnds2xKi8pD6Vycjtzz9HMyjYHWAN4uOK4V0o6PiKOa9pfydyuABFxuaQdJI2mupLyp4E9CtvzIuJNjXIy4OTYC06OZla2UaQJAG4jTWsGQES8r+S4tc/tWlNJufZycidwWdXMSiVpl1b7I+K6iuLXeSN+5SXloVBO7gSePs7MSpWT4F2k+UyXA+6sKjFmdc7t2igpV6nWpcI6hXuOZlYqSfsDJwJTSINC3gYcExG/qSj+LqTbKN4LVHojvqRrgbE5biUl5Xyz/2nANrQoJ3sJq95xcjSzUkmaCbyrsdCupFWBqyNii4rbUceN+LWVlOteKqzduaxqZmUb0bQC/eNU/LsnD0T5IGkk5zbAL6uIW3NJue6lwtqak6OZle1ySVdIOkjSQcBlwB+qCp5vxL+T1Gv8MTCmwhlq9ieVVPcD9gdulfShKmJT/1Jhbc1lVTMrhaT1gdUj4iZJ+5J+UQt4Eji7qt5MXXO75ti1l5TrWiqs3fk+RzMry8nA1wEi4rfAbwEkjcuv7V1FI2q6Eb+h1pJyi3ldKykndwInRzMry+iImNW8MyKm5URViRrndoVcUgbOzdvjqaikXPe8ru3OydHMyrK461tVztQyjupvxG+UlI9pKinfDJxdUTN+AXykjnJyJ3ByNLOyTJV0aET8vLgzT4w9vcJ21DG368nUXFKuuZzc9jwgx8xKIWl14CLgJRYmw3HAksA+EfGvitpRx434cyJis25emx0Rm5cVuxCnZTk5Io4oO3YncM/RzEoREY8AO0jaFWgkissi4pqKmzKx4ngwNErKlZeTO4mTo5mVKiKuBa6tMf51uRe7Td51W9MI0jIMhZJyXUuFdQSXVc2so9Uxt+tQKCnXUU7uJE6OZtbR6rwRv6mk3FVlSbnupcLanZOjmXW05gEweV3DmVUMiqlbDeXkjuG5Vc2s09U6t2tdap7Xte2552hmHWmozO1al6Ewr2s7c8/RzDrVycDTkG7Ej4gvRMTRpF7jyTW2qyq1LxXWznwrh5l1qiExt2uNapvXtRM4OZpZpxoKN+JXbojM69r23MU2s041VdKhzTtrmNu1aiczvMvJg8IDcsysIw2FG/HrMBTmde0ELquaWUcaQnO7Vm1YlpMHm3uOZmYdRNK5wDXdzOu6e0SMr6dl7cXJ0cysgwzXcvJgc3I0M+tAdc7r2gmcHM3MzJr4Vg4zM7MmTo5mZmZNnBzNzMyaODmamZk1cXI0MzNr8v8BaPX5o2uWLIoAAAAASUVORK5CYII=\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": "code",
   "execution_count": 24,
   "id": "8c820b02",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def p(f, bold=False):\n",
    "    if f is None:\n",
    "        text = \"?\"\n",
    "    else:\n",
    "        text = f\"{f:0.3f}\"\n",
    "        \n",
    "    if bold:\n",
    "        return \" \\\\textbf{\" + text + \"} \"\n",
    "    else:\n",
    "        return \" \" + text + \" \"\n",
    "\n",
    "def latex(text):\n",
    "    r = \"\"\n",
    "    for x in text:\n",
    "        if x == \"_\" or x == \"-\":\n",
    "            r += \" \"\n",
    "        else:\n",
    "            r += x\n",
    "    return r\n",
    "\n",
    "def pairMax(row):\n",
    "    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",
    "                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": "code",
   "execution_count": 25,
   "id": "a3792b44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% ### LR\n",
      "% ### RF\n",
      "% ### GB\n",
      "% ### KNN\n",
      "% ### DoC\n"
     ]
    }
   ],
   "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": "code",
   "execution_count": 26,
   "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}\")"
   ]
  }
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