{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "pretty-performer",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-06-16 13:09:04.399231: 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",
      "2022-06-16 13:09:04.399252: 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 math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors\n",
    "from PIL import Image, ImageDraw, ImageFont\n",
    "from library.analysis import testSets, generators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "engaging-warehouse",
   "metadata": {},
   "source": [
    "# Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "crazy-taxation",
   "metadata": {},
   "outputs": [],
   "source": [
    "kScore = \"cohens kappa score\"\n",
    "f1Score = \"f1 score\"\n",
    "\n",
    "kScoreSd = kScore + \" - SD\"\n",
    "f1ScoreSd = f1Score + \" - SD\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extensive-future",
   "metadata": {},
   "source": [
    "# Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "warming-department",
   "metadata": {},
   "outputs": [],
   "source": [
    "ignoreSet = [\"ozone_level\", \"yeast_me2\"]\n",
    "\n",
    "gans = [g.replace(\"SimpleGAN\", \"GAN\") for g in generators.keys()][:-1]\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": "96a601db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ganName(name):\n",
    "    return name.replace(\n",
    "        \"ConvGeN-majority-5\", \"ConvGeN(5,maj)\").replace(\n",
    "        \"ConvGeN-majority-full\", \"ConvGeN(min,maj)\").replace(\n",
    "        \"ConvGeN-proximity-5\", \"ConvGeN(5,prox)\").replace(\n",
    "        \"ConvGeN-proximity-full\", \"ConvGeN(min,prox)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d35b280b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filterConvGen(name):\n",
    "    return not name.startswith(\"ConvGeN\") or name == \"ConvGeN(min,maj)\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selective-connecticut",
   "metadata": {},
   "source": [
    "# Load data from CSV files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "intended-watts",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadDiagnoseData(ganType, datasetName):\n",
    "    fileName = f\"data_result/{ganType}/{datasetName}.csv\"\n",
    "    r = {}\n",
    "    try:\n",
    "        f1List = []\n",
    "        kList = []\n",
    "        indizes = [str(x) for x in range(100)]\n",
    "        with open(fileName) as f:\n",
    "            newBlock = True\n",
    "            n = \"\"\n",
    "            for line in f:\n",
    "                line = line.strip()\n",
    "                if newBlock:\n",
    "                    n = line\n",
    "                    if n == \"GAN\" or n == \"DoG\":\n",
    "                        n = \"DoC\"\n",
    "                    newBlock = False\n",
    "                elif line == \"---\":\n",
    "                    newBlock = True\n",
    "                    f1List = []\n",
    "                    kList = []\n",
    "                else:\n",
    "                    parts = line.split(\";\")\n",
    "                    if parts[0] == \"avg\":\n",
    "                        f1Avg = float(parts[5])\n",
    "                        f1Sd = math.sqrt(sum([(f1Avg - x)*(f1Avg - x) for x in f1List]) / len(f1List))\n",
    "                        kAvg = float(parts[6])\n",
    "                        kSd = math.sqrt(sum([(kAvg - x)*(kAvg - x) for x in kList]) / len(kList))\n",
    "                        r[n] = { f1Score: f1Avg, kScore: kAvg, f1ScoreSd: f1Sd, kScoreSd: kSd }\n",
    "                    elif parts[0] in indizes:\n",
    "                        f1List.append(float(parts[5]))\n",
    "                        kList.append(float(parts[6]))\n",
    "    except FileNotFoundError as e:\n",
    "        print(f\"Missing file: {fileName}\")\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "classical-rescue",
   "metadata": {},
   "outputs": [],
   "source": [
    "statistic = { }\n",
    "\n",
    "for gan in gans:\n",
    "    if ganName(gan) not in statistic:\n",
    "        statistic[ganName(gan)] = {}\n",
    "    \n",
    "    for ds in testSets:\n",
    "        if ds != \"Average\":\n",
    "            statistic[ganName(gan)][cleanupName(ds)] = loadDiagnoseData(gan, ds)\n",
    "            \n",
    "            d = cleanupName(ds)\n",
    "            if d not in dataset:\n",
    "                dataset.append(d)\n",
    "\n",
    "gans = list(statistic.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "unable-entrance",
   "metadata": {},
   "outputs": [],
   "source": [
    "for gan in gans:\n",
    "    f1 = { n: 0.0 for n in algs }\n",
    "    k =  { n: 0.0 for n in algs }\n",
    "    f1Sd = { n: 0.0 for n in algs }\n",
    "    kSd =  { n: 0.0 for n in algs }\n",
    "    c = 0\n",
    "\n",
    "    for ds in dataset:\n",
    "        if ds != \"Average\":\n",
    "            c += 1\n",
    "            for n in algs:\n",
    "                if n in statistic[gan][ds].keys():\n",
    "                    f1[n] += statistic[gan][ds][n][f1Score]\n",
    "                    k[n] += statistic[gan][ds][n][kScore]\n",
    "                    f1Sd[n] += statistic[gan][ds][n][f1ScoreSd]\n",
    "                    kSd[n] += statistic[gan][ds][n][kScoreSd]\n",
    "\n",
    "    avg = {}\n",
    "    for n in algs:\n",
    "        avg[n] = { f1Score: f1[n] / c, f1ScoreSd: f1Sd[n] / c, kScore: k[n] / c, kScoreSd: kSd[n] / c }\n",
    "    statistic[gan][\"Average\"] = avg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "public-collins",
   "metadata": {},
   "source": [
    "# Show Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "extra-taiwan",
   "metadata": {},
   "outputs": [],
   "source": [
    "def drawTransparentRect(img, rect, color, opacity=1.0):\n",
    "    def mix(v, a, b):\n",
    "        return max(0, min(255, int((v * a) + ((1.0 - v) * b))))\n",
    "\n",
    "    def mixPixel(v, a, b):\n",
    "        return (mix(v, a[0], b[0]), mix(v, a[1], b[1]), mix(v, a[2], b[2]))\n",
    "\n",
    "\n",
    "    ((x0, y0), (x1, y1)) = rect\n",
    "    \n",
    "    for y in range(y0, y1):\n",
    "        for x in range(x0, x1):\n",
    "            p = (x, y)\n",
    "            c = mixPixel(opacity, color, img.getpixel(p))\n",
    "            img.putpixel(p, c)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5fec8e83",
   "metadata": {},
   "outputs": [],
   "source": [
    "def drawDiagram(size, rowNames, data, colNames=[], colors=None, border=20, barIndent=10, fontSize=20, markers=[0.25, 0.5, 0.75, 1.00], maxY=1.0):\n",
    "    silver = (204, 204, 204)\n",
    "    black = (0,0,0)\n",
    "    white = (255, 255, 255)\n",
    "    \n",
    "    defaultColors = [ (31,119,180)\n",
    "                    , (255,127,14)\n",
    "                    , (44,160,44)\n",
    "                    , (214,40,40)\n",
    "                    , (148,103,189)\n",
    "                    , (140,86,75)\n",
    "                    , (227,119,194)\n",
    "                    , (127,127,127)\n",
    "                    , (40,40,214)\n",
    "                    ]\n",
    "\n",
    "    defaultColors = [ (209,188,115) # Gold\n",
    "                    , (145,196,223) # bright blue\n",
    "                    , (83,119,202)  # blue\n",
    "                    , (224,138,86)  # orange\n",
    "                    , (131,202,112) # green\n",
    "                    , (199,102,99)  # red\n",
    "                    , (143,110,176) # violet\n",
    "                    , (134,99,66)   # brown\n",
    "                    , (207,131,189) # pink\n",
    "                    ]\n",
    "    \n",
    "    def y(v):\n",
    "        return v / maxY\n",
    "\n",
    "    def med(v):\n",
    "        if type(v) is tuple:\n",
    "            return v[0]\n",
    "        return v\n",
    "\n",
    "    \n",
    "    if colors is None:\n",
    "        colors = defaultColors\n",
    "\n",
    "    print((len(data[0]), len(colNames), len(data), len(rowNames)))\n",
    "\n",
    "    font = ImageFont.truetype(\"FreeSans\", fontSize)\n",
    "    \n",
    "    markerSize = 0\n",
    "    for m in markers:\n",
    "        markerSize = max(markerSize, font.getsize(f\"{m:0.2f}\")[0])\n",
    "\n",
    "    areaTop = 2 * border + markerSize\n",
    "\n",
    "    barStep = (size[0] - border - areaTop) // len(data)\n",
    "    barSize = max(border, barStep - border)\n",
    "    barIndent = min(barIndent, barSize / (1 + len(data[0])))\n",
    "    barIndent = barSize / (2 + len(data[0]))\n",
    "    \n",
    "    print((size[0], barSize, barSize * len(data)))\n",
    "    \n",
    "    # Create new Image\n",
    "    w = max(size[0], size[1])\n",
    "    img = Image.new(\"RGB\", (w,w))\n",
    "    d = ImageDraw.Draw(img)\n",
    "    \n",
    "    # Set background to white.\n",
    "    d.rectangle(((0,0), (w,w)), fill=white)\n",
    "    \n",
    "    # draw row names\n",
    "    height = size[1]\n",
    "    left = w - height\n",
    "    textSize = 0\n",
    "    for (n, name) in enumerate(rowNames):\n",
    "        s = font.getsize(name)\n",
    "        offset = int(border + barSize - s[1] + 1.5) // 2\n",
    "        textSize = max(textSize, s[0])\n",
    "        pos = (left + border, areaTop + offset + (barStep * n))\n",
    "        d.text(pos, name, fill=black, font=font)\n",
    "        \n",
    "        \n",
    "    # Calculate sizes for bar drawing.\n",
    "    barLength = height - (4 * border) - textSize\n",
    "    areaSize = (barLength, barSize)\n",
    "    areaLeft = left + (2 * border) + textSize\n",
    "    \n",
    "    # Draw Lines for bar height comparing.\n",
    "    markerPos = [areaLeft + int(y(v) * barLength) for v in markers]\n",
    "    for p in markerPos:\n",
    "        d.line(((p, border), (p, size[0] - border)), fill=silver)\n",
    "        \n",
    "    # Draw bars.\n",
    "    for (n, row) in enumerate(data):\n",
    "        area = ((areaLeft, areaTop + (n * barStep) + (border // 2)), areaSize)\n",
    "\n",
    "        indices = list(range(len(row)))\n",
    "        indices.sort(key= lambda i: 1.0 - med(row[i]))\n",
    "\n",
    "        for (n, i) in enumerate(indices):\n",
    "            v = y(med(row[i]))\n",
    "            c = colors[i]\n",
    "            offset = barIndent * n\n",
    "            tl = (area[0][0], area[0][1] + offset)\n",
    "            br = (tl[0] + int(v * area[1][0]), area[1][1] + tl[1] - offset)\n",
    "            rect = (tl, br)\n",
    "            d.rectangle(rect, fill=c, outline=black)\n",
    "\n",
    "    for (n, row) in enumerate(data):\n",
    "        area = ((areaLeft, areaTop + (n * barStep) + (border // 2)), areaSize)\n",
    "\n",
    "        indices = list(range(len(row)))\n",
    "        indices.sort(key= lambda i: 1.0 - med(row[i]))\n",
    "\n",
    "        for (n, i) in enumerate(indices):\n",
    "            v = y(med(row[i]))\n",
    "            c = colors[i]\n",
    "            offset = barIndent * n\n",
    "            tl = (area[0][0], area[0][1] + offset)\n",
    "            br = (tl[0] + int(v * area[1][0]), area[1][1] + tl[1] - offset)\n",
    "\n",
    "            if type(row[i]) is tuple and (row[i][0] != 0.0 or row[i][1] != row[i][0] or row[i][2] != row[i][0]):\n",
    "                vLower = y(row[i][1])\n",
    "                vUpper = y(row[i][2])\n",
    "                xCenter = (tl[1] + br[1]) // 2\n",
    "                yUpper = tl[0] + int(vUpper * area[1][0])\n",
    "                yLower = tl[0] + int(vLower * area[1][0])\n",
    "\n",
    "                #d.line(((br[0], tl[1]), (br[0], br[1])), fill=black, width=5)\n",
    "                d.line(((yLower, xCenter), (yUpper, xCenter)), fill=white, width=5)\n",
    "                d.line(((yLower, xCenter - border), (yLower, xCenter + border)), fill=white, width=5)\n",
    "                d.line(((yUpper, xCenter - border), (yUpper, xCenter + border)), fill=white, width=5)\n",
    "\n",
    "                #d.line(((br[0], tl[1]), (br[0], br[1])), fill=c, width=3)\n",
    "                d.line(((yLower, xCenter), (yUpper, xCenter)), fill=c, width=3)\n",
    "                d.line(((yLower, xCenter - border), (yLower, xCenter + border)), fill=c, width=3)\n",
    "                d.line(((yUpper, xCenter - border), (yUpper, xCenter + border)), fill=c, width=3)\n",
    "\n",
    "\n",
    "    # Draw axis.\n",
    "    d.line(((areaLeft, areaTop), (areaLeft, size[0] - border)), fill=black)\n",
    "    d.line(((areaLeft, areaTop), (w - border, areaTop)), fill=black)\n",
    "    \n",
    "    # Draw y-axis text.\n",
    "    img = img.rotate(90)\n",
    "    d = ImageDraw.Draw(img)\n",
    "\n",
    "    for (m, p) in zip(markers, markerPos):\n",
    "        d.text((border, size[1] - (p - left)), f\"{m:0.2f}\", fill=black, font=font)\n",
    "    \n",
    "    # Draw legend.\n",
    "    if len(colNames) > 0:\n",
    "        colNameWidth = 0\n",
    "        colNameHeight = fontSize * len(colNames)\n",
    "        for c in colNames:\n",
    "            colNameWidth = max(colNameWidth, font.getsize(c)[0])\n",
    "        \n",
    "        rWidth = int(fontSize * 0.75)\n",
    "        rHeight = fontSize // 2\n",
    "        rPadd = (fontSize - rHeight) // 2\n",
    "        \n",
    "        tl = (size[0] - (int(2.5 * border) + colNameWidth + rWidth), 0)\n",
    "        br = (size[0], int(1.2 * border) + colNameHeight)\n",
    "        drawTransparentRect(img, (tl, br), white, 0.75)\n",
    "\n",
    "        for (n, c) in enumerate(colNames):\n",
    "            t = border + (fontSize * n)\n",
    "            l = size[0] - border - colNameWidth\n",
    "            d.rectangle(((l - border - rWidth, t + rPadd - 1), (l - border, t + rPadd + rHeight)), fill=colors[n], outline=black)\n",
    "            d.text((l, t), c, fill=black, font=font)\n",
    "    \n",
    "    return img.crop((0, 0, size[0], size[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "038344f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnose(algo, score):\n",
    "    def valueOf(d, g):\n",
    "        d = cleanupName(d)\n",
    "        if d not in statistic[g].keys():\n",
    "            print(f\"Missing '{d}' in '{g}'\")\n",
    "            return 0.0\n",
    "        \n",
    "        if algo in statistic[g][d].keys():\n",
    "            return statistic[g][d][algo][score]\n",
    "        else:\n",
    "            print(f\"Missing '{algo}' in ('{g}', '{d}')\")\n",
    "            return 0.0\n",
    "    \n",
    "    print(f\"{algo}: {score}\")\n",
    "    \n",
    "    data = [[valueOf(d, g) for g in gans] for d in testSets]        \n",
    "    img = drawDiagram((1024, 1024), [cleanupName(d) for d in testSets], data, colNames=gans)\n",
    "    img.save(f\"data_result/statistics/byAlgorithm/statistic-{algo}-{score}.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8904b4b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnoseAverage(score, onlyOneBar=False):\n",
    "    def valueOf(g, algo):\n",
    "        if algo in statistic[g][\"Average\"].keys():\n",
    "            if True: #not g.startswith(\"ConvGeN\"):\n",
    "                return statistic[g][\"Average\"][algo][score]\n",
    "            else:\n",
    "                a = statistic[g][\"Average\"][algo][score]\n",
    "                sd = statistic[g][\"Average\"][algo][score + \" - SD\"]\n",
    "                return (a, a - sd, a + sd)\n",
    "        else:\n",
    "            return 0.0\n",
    "\n",
    "    print(f\"Average: {score}\")\n",
    "    \n",
    "    data = [[valueOf(g, algo) for g in gans] for algo in algs]\n",
    "    img = drawDiagram((1024, 1024), algs, data, colNames=gans, maxY=0.75)\n",
    "    img.save(f\"data_result/statistics/average/statistic-Algo-Average-{score}.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d1f4961a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnoseDataset(dataset):\n",
    "    print(f\"{dataset}\")\n",
    "    \n",
    "    def valueOf(algo, score, g):\n",
    "        if dataset in statistic[g]:\n",
    "            if algo in statistic[g][dataset]:\n",
    "                if score in statistic[g][dataset][algo]:\n",
    "                    return statistic[g][dataset][algo][score]\n",
    "                    \n",
    "        return 0.0\n",
    "    \n",
    "    scores = [f1Score, kScore]\n",
    "    \n",
    "    for score in scores:\n",
    "        data = [[valueOf(algo, score, g) for algo in algs] for g in gans]\n",
    "        img = drawDiagram((1024, 1024), gans, data, colNames=algs, maxY=0.75)\n",
    "        img.save(f\"data_result/statistics/byDataset/statistic-Classifier-{dataset}-{score}.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "magnetic-romance",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showDiagnoseDatasetAverage():\n",
    "    print(\"Average\")\n",
    "    \n",
    "    dataset = \"Average\"\n",
    "    \n",
    "    def valueOf(algo, score, g):\n",
    "        if dataset in statistic[g]:\n",
    "            if algo in statistic[g][dataset]:\n",
    "                if score in statistic[g][dataset][algo]:\n",
    "                    if True: # algo != \"DoC\":\n",
    "                        return statistic[g][dataset][algo][score]\n",
    "                    else:\n",
    "                        a = statistic[g][dataset][algo][score]\n",
    "                        sd = statistic[g][\"Average\"][algo][score + \" - SD\"]\n",
    "                        return (a, a - sd, a + sd)\n",
    "                    \n",
    "        return 0.0\n",
    "    \n",
    "    scores = [f1Score, kScore]\n",
    "    \n",
    "    for score in scores:\n",
    "        data = [[valueOf(algo, score, g) for algo in algs] for g in gans]\n",
    "        img = drawDiagram((1024, 1024), gans, data, colNames=algs, maxY=0.75)\n",
    "        img.save(f\"data_result/statistics/byDataset/statistic-Classifier-{dataset}-{score}.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "57fe8925",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: f1 score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "LR: cohens kappa score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "RF: f1 score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "RF: cohens kappa score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "GB: f1 score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "GB: cohens kappa score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "KNN: f1 score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "KNN: cohens kappa score\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "DoC: f1 score\n",
      "Missing 'DoC' in ('Repeater', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('ProWRAS', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('CTGAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('Repeater', 'abalone9-18')\n",
      "Missing 'DoC' in ('ProWRAS', 'abalone9-18')\n",
      "Missing 'DoC' in ('GAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('CTGAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('Repeater', 'car_good')\n",
      "Missing 'DoC' in ('ProWRAS', 'car_good')\n",
      "Missing 'DoC' in ('GAN', 'car_good')\n",
      "Missing 'DoC' in ('CTGAN', 'car_good')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'car_good')\n",
      "Missing 'DoC' in ('Repeater', 'car-vgood')\n",
      "Missing 'DoC' in ('ProWRAS', 'car-vgood')\n",
      "Missing 'DoC' in ('GAN', 'car-vgood')\n",
      "Missing 'DoC' in ('CTGAN', 'car-vgood')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'car-vgood')\n",
      "Missing 'DoC' in ('Repeater', 'flare-F')\n",
      "Missing 'DoC' in ('ProWRAS', 'flare-F')\n",
      "Missing 'DoC' in ('GAN', 'flare-F')\n",
      "Missing 'DoC' in ('CTGAN', 'flare-F')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'flare-F')\n",
      "Missing 'DoC' in ('Repeater', 'hypothyroid')\n",
      "Missing 'DoC' in ('ProWRAS', 'hypothyroid')\n",
      "Missing 'DoC' in ('GAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('CTGAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('Repeater', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('ProWRAS', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('CTGAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('Repeater', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('CTGAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('Repeater', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('CTGAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('Repeater', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('ProWRAS', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('CTGAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('Repeater', 'winequality-red-4')\n",
      "Missing 'DoC' in ('ProWRAS', 'winequality-red-4')\n",
      "Missing 'DoC' in ('GAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('CTGAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('Repeater', 'yeast4')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast4')\n",
      "Missing 'DoC' in ('GAN', 'yeast4')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast4')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast4')\n",
      "Missing 'DoC' in ('Repeater', 'yeast5')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast5')\n",
      "Missing 'DoC' in ('GAN', 'yeast5')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast5')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast5')\n",
      "Missing 'DoC' in ('Repeater', 'yeast6')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast6')\n",
      "Missing 'DoC' in ('GAN', 'yeast6')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast6')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast6')\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "DoC: cohens kappa score\n",
      "Missing 'DoC' in ('Repeater', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('ProWRAS', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('CTGAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'abalone_17_vs_7_8_9_10')\n",
      "Missing 'DoC' in ('Repeater', 'abalone9-18')\n",
      "Missing 'DoC' in ('ProWRAS', 'abalone9-18')\n",
      "Missing 'DoC' in ('GAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('CTGAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'abalone9-18')\n",
      "Missing 'DoC' in ('Repeater', 'car_good')\n",
      "Missing 'DoC' in ('ProWRAS', 'car_good')\n",
      "Missing 'DoC' in ('GAN', 'car_good')\n",
      "Missing 'DoC' in ('CTGAN', 'car_good')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'car_good')\n",
      "Missing 'DoC' in ('Repeater', 'car-vgood')\n",
      "Missing 'DoC' in ('ProWRAS', 'car-vgood')\n",
      "Missing 'DoC' in ('GAN', 'car-vgood')\n",
      "Missing 'DoC' in ('CTGAN', 'car-vgood')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'car-vgood')\n",
      "Missing 'DoC' in ('Repeater', 'flare-F')\n",
      "Missing 'DoC' in ('ProWRAS', 'flare-F')\n",
      "Missing 'DoC' in ('GAN', 'flare-F')\n",
      "Missing 'DoC' in ('CTGAN', 'flare-F')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'flare-F')\n",
      "Missing 'DoC' in ('Repeater', 'hypothyroid')\n",
      "Missing 'DoC' in ('ProWRAS', 'hypothyroid')\n",
      "Missing 'DoC' in ('GAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('CTGAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'hypothyroid')\n",
      "Missing 'DoC' in ('Repeater', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('ProWRAS', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('CTGAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kddcup-guess_passwd_vs_satan')\n",
      "Missing 'DoC' in ('Repeater', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('CTGAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-three_vs_eleven')\n",
      "Missing 'DoC' in ('Repeater', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('ProWRAS', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('CTGAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'kr-vs-k-zero-one_vs_draw')\n",
      "Missing 'DoC' in ('Repeater', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('ProWRAS', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('CTGAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'shuttle-2_vs_5')\n",
      "Missing 'DoC' in ('Repeater', 'winequality-red-4')\n",
      "Missing 'DoC' in ('ProWRAS', 'winequality-red-4')\n",
      "Missing 'DoC' in ('GAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('CTGAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'winequality-red-4')\n",
      "Missing 'DoC' in ('Repeater', 'yeast4')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast4')\n",
      "Missing 'DoC' in ('GAN', 'yeast4')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast4')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast4')\n",
      "Missing 'DoC' in ('Repeater', 'yeast5')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast5')\n",
      "Missing 'DoC' in ('GAN', 'yeast5')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast5')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast5')\n",
      "Missing 'DoC' in ('Repeater', 'yeast6')\n",
      "Missing 'DoC' in ('ProWRAS', 'yeast6')\n",
      "Missing 'DoC' in ('GAN', 'yeast6')\n",
      "Missing 'DoC' in ('CTGAN', 'yeast6')\n",
      "Missing 'DoC' in ('CTAB-GAN', 'yeast6')\n",
      "(6, 6, 14, 14)\n",
      "(1024, 46, 644)\n",
      "Average: f1 score\n",
      "(6, 6, 5, 5)\n",
      "(1024, 165, 825)\n",
      "Average: cohens kappa score\n",
      "(6, 6, 5, 5)\n",
      "(1024, 165, 825)\n",
      "abalone_17_vs_7_8_9_10\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "abalone9-18\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "car_good\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "car-vgood\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "flare-F\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "hypothyroid\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "kddcup-guess_passwd_vs_satan\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "kr-vs-k-three_vs_eleven\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "kr-vs-k-zero-one_vs_draw\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "shuttle-2_vs_5\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "winequality-red-4\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "yeast4\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "yeast5\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "yeast6\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "Average\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n",
      "(5, 5, 6, 6)\n",
      "(1024, 134, 804)\n"
     ]
    }
   ],
   "source": [
    "gans = [g for g in statistic.keys() if filterConvGen(g)]\n",
    "\n",
    "for a in algs:\n",
    "    showDiagnose(a, f1Score)\n",
    "    showDiagnose(a, kScore)\n",
    "    \n",
    "showDiagnoseAverage(f1Score)\n",
    "showDiagnoseAverage(kScore)\n",
    "\n",
    "for t in testSets:\n",
    "    showDiagnoseDataset(cleanupName(t))\n",
    "\n",
    "showDiagnoseDatasetAverage()\n",
    "\n",
    "gans = list(statistic.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63841c55",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getValueOf(gan, dataset, algo, score):\n",
    "    if dataset not in statistic[gan].keys():\n",
    "        #print(f\"Missing '{dataset}' in '{gan}'\")\n",
    "        return None\n",
    "\n",
    "    if algo not in statistic[gan][dataset].keys():\n",
    "        #print(f\"Missing '{algo}' in ('{gan}', '{dataset}')\")\n",
    "        return None\n",
    "    \n",
    "    if score not in statistic[gan][dataset][algo].keys():\n",
    "        #print(f\"Missing '{score}' in ('{gan}', '{dataset}', '{algo}')\")\n",
    "        return None\n",
    "    \n",
    "    return statistic[gan][dataset][algo][score]\n",
    "    \n",
    "    \n",
    "    \n",
    "def calcTable(algo, score, ignore=[]):\n",
    "    table = []\n",
    "    \n",
    "    def calc(gc, g):\n",
    "        n = 0\n",
    "        for d in testSets:\n",
    "            d = cleanupName(d)\n",
    "            if d not in ignore:\n",
    "                vc = getValueOf(gc, d, algo, score)\n",
    "                v = getValueOf(g, d, algo, score)\n",
    "                if vc is not None and v is not None and vc >= v:\n",
    "                    n += 1\n",
    "        return n\n",
    "    \n",
    "    for gc in gans:\n",
    "        table.append([calc(gc, g) for g in gans])\n",
    "    return table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "177774b0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[14, 3, 6, 10, 5, 2, 2, 2],\n",
       " [11, 14, 8, 11, 7, 11, 5, 12],\n",
       " [9, 7, 14, 12, 8, 8, 3, 9],\n",
       " [4, 3, 2, 14, 3, 3, 2, 3],\n",
       " [10, 7, 7, 11, 14, 9, 5, 9],\n",
       " [12, 3, 6, 11, 6, 14, 0, 9],\n",
       " [12, 11, 11, 13, 9, 14, 14, 14],\n",
       " [12, 2, 5, 11, 5, 6, 0, 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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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB - f1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GB - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN - f1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DoC - f1 score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DoC - cohens kappa score\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = matplotlib.colors.ListedColormap([\n",
    "    (1.0, x / 255.0, 0.0)\n",
    "    for x in range(256)\n",
    "    ] + [\n",
    "    ((255 - x) / 255.0, (255 - x) / 255.0, 1.0) # x / 255.0)\n",
    "    for x in range(256)\n",
    "    ])\n",
    "\n",
    "#cmap.set_extremes(bad=cmap(0.0), under=cmap(0.0), over=cmap(1.0))\n",
    "\n",
    "for k in tables.keys():\n",
    "    print(k)\n",
    "    labels = list(gans)\n",
    "    t = tables[k]\n",
    "    if k[0:3] == \"DoC\":\n",
    "        #continue\n",
    "        labels = labels[-4:]\n",
    "        t = [r[-4:] for r in t[-4:]]\n",
    "        f = plt.figure(figsize=(5, 4))\n",
    "        f.add_axes([0.4, 0.45, 0.6, 0.5])\n",
    "    else:\n",
    "        f = plt.figure(figsize=(7, 6))\n",
    "        f.add_axes([0.27, 0.25, 0.7, 0.74])\n",
    "    p = plt.imshow(t, cmap=cmap)\n",
    "    plt.colorbar(p)\n",
    "    plt.xticks(range(len(labels)), labels, rotation=\"vertical\")\n",
    "    plt.yticks(range(len(labels)), labels)\n",
    "    plt.savefig(f\"data_result/statistics/successCount/statistic-{k}.pdf\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e4397945",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Table:\n",
    "    def __init__(self, heading):\n",
    "        self.heading = [str(h) for h in heading]\n",
    "        self.sizes = [len(h) for h in self.heading]\n",
    "        self.rows = []\n",
    "        \n",
    "    def add(self, row):\n",
    "        row = [str(r) for r in row]\n",
    "        self.rows.append(row)\n",
    "        self.sizes = [max(a,len(b)) for (a, b) in zip(self.sizes, row)]\n",
    "        \n",
    "    def separator(self):\n",
    "        return \"|\".join([\"-\" * n for n in self.sizes])\n",
    "    \n",
    "    def showRow(self, row):\n",
    "        def pad(n, t):\n",
    "            while len(t) < n:\n",
    "                t += \" \"\n",
    "            return t\n",
    "        \n",
    "        return \"|\".join([pad(n, t) for (n,t) in zip(self.sizes, row)])\n",
    "    \n",
    "    def show(self):\n",
    "        print(self.showRow(self.heading))\n",
    "        print(self.separator())\n",
    "        for row in self.rows:\n",
    "            print(self.showRow(row))\n",
    "            \n",
    "    def showLatex(self, caption, key):\n",
    "        \n",
    "        columnConfig = \"|\".join([\"l\"] + [\"@{\\\\hskip3pt}c@{\\\\hskip3pt}\" for h in self.heading[1:]])\n",
    "\n",
    "        text = \"\\\\begin{table*}[ht]\\\\scriptsize\"\n",
    "        text += \"\\\\caption{\" + caption + \"}\\\\label{\" + key + \"}\"\n",
    "        text += \"\\\\centering\\\\tabularnewline\\n\"\n",
    "\n",
    "        text += \"\\\\begin{tabular}{\" + columnConfig + \"}\\\\hline\\n\"\n",
    "        text += \" & \".join([\"\\\\textbf{\" + h + \"}\" for h in self.heading])\n",
    "        text += \"\\n\\\\tabularnewline\\n\\\\hline\\n\"\n",
    "        \n",
    "        for row in self.rows:\n",
    "            text += \" & \".join(row)\n",
    "            text += \"\\n\\\\tabularnewline\\n\"\n",
    "            \n",
    "        text += \"\\hline\\end{tabular}\\end{table*}\\n\"\n",
    "        \n",
    "        return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d0e2faa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tableRow(algo, dataset, myGans):\n",
    "    row = []\n",
    "    for gan in myGans:\n",
    "        v = getValueOf(gan, dataset, algo, f1Score)\n",
    "        w = getValueOf(gan, dataset, algo, kScore)\n",
    "        row.append((v, w))\n",
    "    return row"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebffad52",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "def p(f, bold=False):\n",
    "    if f is None:\n",
    "        text = \"?\"\n",
    "    else:\n",
    "        text = f\"{f:0.3f}\"\n",
    "        \n",
    "    if bold:\n",
    "        return \" \\\\textbf{\" + text + \"} \"\n",
    "    else:\n",
    "        return \" \" + text + \" \"\n",
    "\n",
    "def latex(text):\n",
    "    r = \"\"\n",
    "    for x in text:\n",
    "        if x == \"_\" or x == \"-\":\n",
    "            r += \" \"\n",
    "        else:\n",
    "            r += x\n",
    "    return r\n",
    "\n",
    "def pairMax(row):\n",
    "    a = 0.0\n",
    "    b = 0.0\n",
    "    \n",
    "    for (x,y) in row:\n",
    "        a = max(a, x)\n",
    "        b = max(b, y)\n",
    "    \n",
    "    return (a, b)\n",
    "    \n",
    "with open(\"data_result/statistics/Tables.tex\", \"w\") as latexFile:\n",
    "    for algo in algs:\n",
    "        latexFile.write(\"\\n\")\n",
    "        latexFile.write(\"% ### \" + algo + \"\\n\")\n",
    "        latexFile.write(\"\\n\")\n",
    "        f = \"\"\n",
    "        heading = [\"dataset ($f_1~$score$~/~\\\\kappa~$score)\"]\n",
    "        \n",
    "        myGans = gans\n",
    "        if algo[0:3] == \"DoC\":\n",
    "            myGans = list(gans)[-4:]\n",
    "\n",
    "        for g in myGans:\n",
    "            heading.append(latex(g))\n",
    "        table = Table(heading)\n",
    "        \n",
    "        avg = [[0.0, 0.0] for h in heading[1:]]\n",
    "        mx  = [[0.0, 0.0] for h in heading[1:]]\n",
    "        cnt = 0\n",
    "\n",
    "        for d in testSets:\n",
    "            d = cleanupName(d)\n",
    "            if d not in ignore:\n",
    "                cnt += 1\n",
    "                row = tableRow(algo, d, myGans)\n",
    "                line = [latex(d)]\n",
    "\n",
    "                print((algo, d, myGans))\n",
    "                print(row)\n",
    "                \n",
    "                m = pairMax(row)\n",
    "\n",
    "                for (n, r) in enumerate(row):\n",
    "                    line.append(f\"{p(r[0], r[0] == m[0])} / {p(r[1], r[1] == m[1])}\")\n",
    "                    avg[n][0] += r[0] or 0.0\n",
    "                    avg[n][1] += r[1] or 0.0\n",
    "                    mx[n][0] = max(mx[n][0], r[0] or 0.0)\n",
    "                    mx[n][1] = max(mx[n][1], r[1] or 0.0)\n",
    "                table.add(line)\n",
    "\n",
    "        m = pairMax(avg)\n",
    "        table.add([\"\\\\hline Average\"] + [f\"{p(a / cnt, a == m[0])} / {p(b / cnt, b == m[1])}\" for (a,b) in avg])\n",
    "        #table.add([\"maximum\"] + [f\"{p(a)} / {p(b)}\" for (a,b) in mx])\n",
    "\n",
    "        tableB = None\n",
    "        if len(table.heading) > 5:\n",
    "            heading = [table.heading[0]] + table.heading[6:]\n",
    "            tableB = Table(heading)\n",
    "            tableB.sizes = [table.sizes[0]] + table.sizes[6:]\n",
    "            tableB.rows = [\n",
    "                [r[0]] + r[6:]\n",
    "                for r in table.rows\n",
    "                ]\n",
    "            table.heading = table.heading[0:6]\n",
    "            table.sizes = table.sizes[0:6]\n",
    "            table.rows = [\n",
    "                r[0:6]\n",
    "                for r in table.rows\n",
    "                ]\n",
    "\n",
    "        if tableB is not None:\n",
    "            latexFile.write(table.showLatex(algo + \" (1)\", \"tab:results:\" + algo + \":A\") + \"\\n\")\n",
    "            latexFile.write(\"\\n\")\n",
    "            latexFile.write(tableB.showLatex(algo + \" (2)\", \"tab:results:\" + algo + \":B\") + \"\\n\")\n",
    "        else:\n",
    "            latexFile.write(table.showLatex(algo, \"tab:results:\" + algo + \":A\") + \"\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ad677e",
   "metadata": {},
   "source": [
    "for algo in algs:\n",
    "    print(\"% ### \" + algo)\n",
    "    heading = [\"dataset\"]\n",
    "    for g in gans:\n",
    "        if filterConvGen(g):\n",
    "            heading.append(g)\n",
    "    table = []\n",
    "    \n",
    "    avg = [[0.0, 0.0] for h in heading[1:]]\n",
    "    cnt = 0\n",
    "    \n",
    "    for d in testSets:\n",
    "        d = cleanupName(d)\n",
    "        if d not in ignore:\n",
    "            cnt += 1\n",
    "            row = tableRow(algo, d, heading[1:])\n",
    "            table.append([(d,d)] + row)\n",
    "\n",
    "            for (n, r) in enumerate(row):\n",
    "                avg[n][0] += r[0] or 0.0\n",
    "                avg[n][1] += r[1] or 0.0\n",
    "    \n",
    "    table.append([(\"Average\", \"Average\")] + [(a / cnt, b / cnt) for (a,b) in avg])\n",
    "    \n",
    "    with open(f\"data_result/statistics/{algo}-f1.csv\", \"w\") as f:\n",
    "        f.write((\",\".join(heading)) + \"\\n\")\n",
    "        for row in table:\n",
    "            f.write((\",\".join([str(x[0]) for x in row])) + \"\\n\")\n",
    "\n",
    "    with open(f\"data_result/statistics/{algo}-kappa.csv\", \"w\") as f:\n",
    "        f.write((\",\".join(heading)) + \"\\n\")\n",
    "        for row in table:\n",
    "            f.write((\",\".join([str(x[1]) for x in row])) + \"\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "581f1134",
   "metadata": {},
   "source": [
    "def dsWeight(d, score):\n",
    "    w = 0.0\n",
    "    for g in gans:\n",
    "        for a in algs:\n",
    "            x = getValueOf(g, d, a, score)\n",
    "            if x is not None:\n",
    "                w += x\n",
    "    return w\n",
    "\n",
    "\n",
    "\n",
    "dataNames = [cleanupName(d) for d in testSets]\n",
    "dataNames.sort(key=lambda d: dsWeight(d, f1Score) + dsWeight(d, kScore))\n",
    "for d in dataNames:\n",
    "    w = dsWeight(d, f1Score) + dsWeight(d, kScore)\n",
    "    print(f\"{w:0.3f} - {d}\")"
   ]
  }
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